add run results and update software versions

2026-08-26 15:46:08 +00:00 · 2024-10-11 10:32:29 -04:00
857 changed files with 35451 additions and 402232 deletions
@@ -1,15 +0,0 @@
-name: Run process replay tests
-description: Verify process replay compared to master
-runs:
-  using: "composite"
-  steps:
-    - name: Run process replay tests
-      shell: bash
-      run: |
-        export PR_TITLE=$(jq -r .pull_request.title "$GITHUB_EVENT_PATH")
-        export CURRENT_SHA=${{ github.event.pull_request && github.event.pull_request.head.sha || github.sha }}
-        git fetch origin $CURRENT_SHA
-        export COMMIT_MESSAGE=$(git show -s --format=%B "$CURRENT_SHA")
-        export CURRENT_HEAD=$(git rev-parse HEAD)
-        cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && IGNORE_OOB=1 PYTHONPATH=. python3 process_replay.py
-        git checkout $CURRENT_HEAD  # restore to branch
@@ -1,253 +0,0 @@
-name: Setup Python & Install
-description: Sets up Python and installs project dependencies.
-inputs:
-  python-version:
-    description: 'Python version to use'
-    required: false
-    default: '3.12'
-  key:
-    description: 'Key for the python cache'
-    required: false
-    default: ''  # if you don't set a key, it doesn't cache
-  deps:
-    description: 'Extra dependency groups (comma separated)'
-    required: false
-    default: ''
-  pydeps:
-    description: 'Extra Python dependency groups (space separated)'
-    required: false
-    default: ''
-  opencl:
-    description: "Install OpenCL?"
-    required: false
-    default: 'false'
-  amd:
-    description: "Install AMD?"
-    required: false
-    default: 'false'
-  cuda:
-    description: "Install CUDA?"
-    required: false
-    default: 'false'
-  ocelot:
-    description: "Install gpuocelot?"
-    required: false
-    default: 'false'
-  webgpu:
-    description: "Install webgpu?"
-    required: false
-    default: 'false'
-  llvm:
-    description: "Install LLVM?"
-    required: false
-    default: 'false'
-runs:
-  using: "composite"
-  steps:
-    - name: Set up Python ${{ inputs.python-version }}
-      id: setup-python
-      uses: actions/setup-python@v5
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    # **** Caching packages ****
-
-    - name: Cache Python packages
-      id: restore-venv
-      uses: actions/cache@v4
-      with:
-        path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/setup.py') }}-${{ env.PYTHON_CACHE_VERSION }}
-
-    # **** Caching downloads ****
-
-    - name: Cache downloads (Linux)
-      if: inputs.key != '' && runner.os == 'Linux'
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/tinygrad/downloads/
-        key: downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
-    - name: Cache downloads (macOS)
-      if: inputs.key != '' && runner.os == 'macOS'
-      uses: actions/cache@v4
-      with:
-        path: ~/Library/Caches/tinygrad/downloads/
-        key: osx-downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
-
-    # **** Python deps ****
-
-    - name: Install dependencies in venv (with extra)
-      if: inputs.deps != '' && steps.restore-venv.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          source .venv/Scripts/activate
-        else
-          . .venv/bin/activate
-        fi
-        python -m pip install -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
-    - name: Install dependencies in venv (without extra)
-      if: inputs.deps == '' && steps.restore-venv.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        python -m venv .venv
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          source .venv/Scripts/activate
-        else
-          . .venv/bin/activate
-        fi
-        python -m pip install -e . ${{ inputs.pydeps }}
-    - name: Set up venv environment
-      shell: bash
-      run: |
-        echo "VIRTUAL_ENV=${{ github.workspace }}/.venv" >> "$GITHUB_ENV"
-        echo "OMP_NUM_THREADS=1" >> "$GITHUB_ENV"
-        # no buffers should be over 300MB in CI
-        echo "MAX_BUFFER_SIZE=300000000" >> "$GITHUB_ENV"
-        if [[ "$RUNNER_OS" == "Windows" ]]; then
-          echo "${{ github.workspace }}/.venv/Scripts" >> "$GITHUB_PATH"
-        else
-          echo "${{ github.workspace }}/.venv/bin" >> "$GITHUB_PATH"
-        fi
-
-    # ******************* apt *******************
-
-    - name: Add OpenCL Repo
-      if: inputs.opencl == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: echo "deb [ allow-insecure=yes ] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
-
-    - name: Add AMD Repo (Linux)
-      if: inputs.amd == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-        sudo tee /etc/apt/sources.list.d/rocm.list <<EOF
-        deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.2 $(lsb_release -cs) main
-        EOF
-        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-
-    - name: Add LLVM Repo (Linux)
-      if: inputs.llvm == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
-        echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-20 main" | sudo tee /etc/apt/sources.list.d/llvm.list
-
-    - name: apt-get update + install
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      shell: bash
-      run: |
-        echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
-        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
-        sudo apt -qq update || true
-
-        pkgs=""
-        # **** OpenCL ****
-        if [[ "${{ 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
-
-        # ******** do install ********
-        if [[ -n "$pkgs" ]]; then
-          sudo apt-get -y --allow-unauthenticated --no-install-recommends install $pkgs
-        fi
-
-    # **** AMD ****
-
-    - name: Setup AMD (Linux)
-      if: inputs.amd == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        cargo build --release --manifest-path ./extra/remu/Cargo.toml
-        sudo ln -sf ${{ github.workspace }}/extra/remu/target/release/libremu.so /usr/local/lib/libremu.so
-        sudo tee --append /etc/ld.so.conf.d/rocm.conf <<'EOF'
-          /opt/rocm/lib
-          /opt/rocm/lib64
-        EOF
-        sudo ldconfig
-    - name: Setup AMD comgr+remu (macOS)
-      if: inputs.amd == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: |
-        sudo mkdir -p /usr/local/lib
-        curl -s -H "Authorization: token $GH_TOKEN" curl -s https://api.github.com/repos/nimlgen/amdcomgr_dylib/releases/latest | \
-          jq -r '.assets[] | select(.name == "libamd_comgr.dylib").browser_download_url' | \
-          sudo xargs curl -L -o /usr/local/lib/libamd_comgr.dylib
-        cargo build --release --manifest-path ./extra/remu/Cargo.toml
-
-    # **** gpuocelot ****
-
-    - name: Install gpuocelot dependencies (MacOS)
-      if: inputs.ocelot == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: brew install --quiet cmake ninja llvm@15 zlib glew flex bison boost zstd ncurses
-    - name: Cache gpuocelot
-      if: inputs.ocelot == 'true'
-      id: cache-build
-      uses: actions/cache@v4
-      env:
-        cache-name: cache-gpuocelot-build
-      with:
-        path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-0
-    - name: Clone/compile gpuocelot
-      if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
-      shell: bash
-      run: |
-        git clone --recurse-submodules https://github.com/gpuocelot/gpuocelot.git ${{ github.workspace }}/gpuocelot
-        cd ${{ github.workspace }}/gpuocelot/ocelot
-        git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
-        mkdir build
-        cd build
-        cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5
-        ninja
-    - name: Install gpuocelot
-      if: inputs.ocelot == 'true'
-      shell: bash
-      run: |
-        cd ${{ github.workspace }}/gpuocelot/ocelot/build
-        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || ''}}lib/
-
-    # **** WebGPU ****
-
-    - name: Install WebGPU dawn (Linux)
-      if: inputs.webgpu == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: |
-        sudo curl -L https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
-        sudo ldconfig
-    - name: Install WebGPU dawn (macOS)
-      if: inputs.webgpu == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: |
-        brew tap wpmed92/dawn
-        brew install dawn
-
-    # **** LLVM ****
-
-    - name: Install LLVM (macOS)
-      if: inputs.llvm == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: brew install llvm@20
@@ -1,8 +1,7 @@
 name: Benchmarks
 env:
  # TODO: this rescheduling makes gpt2, mixtral and llama unjitted slower
-  # TODO: very slow for llama 70B and resnet training 6 GPU
-  CAPTURE_PROCESS_REPLAY: "1"
+  RUN_PROCESS_REPLAY: "1"
  ASSERT_PROCESS_REPLAY: "0"
  PYTHONPATH: .
  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -24,14 +23,10 @@ on:
 jobs:
  testmacbenchmark:
    name: Mac Benchmark
-    env:
-      # since sudo is required for usbgpu on macos, move the cache to a new location, as some of the files are owned by root
-      PYTHONPYCACHEPREFIX: /tmp/tiny_python_pycache
    runs-on: [self-hosted, macOS]
-    timeout-minutes: 20
    defaults:
      run:
-        shell: bash -e -o pipefail {0}
+        shell: bash -o pipefail {0}
    if: github.repository_owner == 'tinygrad'
    steps:
    - name: Checkout Code
@@ -50,80 +45,55 @@ jobs:
        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
-      run: python3.11 test/external/process_replay/reset.py
+      run: test/external/process_replay/reset.py
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion 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 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
-    - name: Run Stable Diffusion v2
-      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
-    # process replay can't capture this, the graph is too large
+      run: JIT=2 python3 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd.txt
+    - name: Run Stable Diffusion with fp16
+      run: JIT=2 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd_fp16.txt
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: JIT=2 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
-      run: METAL=1 python3.11 test/external/external_model_benchmark.py
-    - name: Run huggingface_onnx test
-      run: METAL=1 python3.11 extra/huggingface_onnx/run_models.py test --debug FacebookAI/xlm-roberta-large
+      run: METAL=1 python3 test/external/external_model_benchmark.py
    - name: Test speed vs torch
-      run: BIG=2 MPS=1 python3.11 test/test_speed_v_torch.py | tee torch_speed.txt
+      run: BIG=2 MPS=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
    - name: Test tensor cores
-      run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
-    - name: Test AMX tensor cores
+      run: METAL=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
+    - name: Run Tensor Core GEMM
      run: |
-        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-        DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-    - name: Run Tensor Core GEMM (float)
-      run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
-    - name: Run Tensor Core GEMM (half)
-      run: DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_half.txt
-    - name: Run Tensor Core GEMM (bfloat16)
-      run: DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
+        DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
+        DEBUG=2 HALF=1 python3 extra/gemm/simple_matmul.py | tee matmul_half.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
+      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 ./extra/gemm/fuzz_matmul.py
    - name: Run LLaMA
      run: |
-        BENCHMARK_LOG=llama_nojit JIT=0 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama JIT=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
+        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 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
+      run: 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 quantized LLaMA
      run: |
-        BENCHMARK_LOG=llama_int8 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8 | tee llama_int8.txt
-        BENCHMARK_LOG=llama_nf4 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4 | tee llama_nf4.txt
-    - name: Run quantized LLaMA3
-      run: |
-        BENCHMARK_LOG=llama3_int8 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize int8 | tee llama3_int8.txt
-        BENCHMARK_LOG=llama3_nf4 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize nf4 | tee llama3_nf4.txt
-    #- name: Run LLaMA 7B on 4 (virtual) GPUs
-    #  run: python3.11 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
+        python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8 | tee llama_int8.txt
+        python3 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 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
    - name: Run GPT2
      run: |
-        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 JIT=1 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        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 HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: 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 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
-    - name: Run OLMoE
-      run: BENCHMARK_LOG=olmoe python3.11 examples/olmoe.py
+      run: HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAST_BEFORE_VIEW=0 python3 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 | tee beautiful_mnist.txt
+      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=97.3 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps JIT=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: JIT=2 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 JIT=2 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: JIT=2 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    #- name: Run 10 CIFAR training steps w BF16
-    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    - name: UsbGPU boot time
-      run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
-    - name: UsbGPU tiny tests
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/test_tiny.py
-    - name: UsbGPU copy speeds
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
-    - name: UsbGPU openpilot test
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+      run: JIT=2 WINO=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (Mac)
@@ -135,8 +105,6 @@ jobs:
          llama_beam.txt
          llama_int8.txt
          llama_nf4.txt
-          llama3_int8.txt
-          llama3_nf4.txt
          llama_four_gpu.txt
          gpt2_unjitted.txt
          gpt2_jitted.txt
@@ -144,10 +112,8 @@ jobs:
          gpt2_half_beam.txt
          matmul.txt
          matmul_half.txt
-          matmul_bfloat16.txt
          sd.txt
-          sd_no_fp16.txt
-          sdv2.txt
+          sd_fp16.txt
          sdxl.txt
          beautiful_mnist.txt
          train_cifar.txt
@@ -155,15 +121,14 @@ jobs:
          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
+      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

  testnvidiabenchmark:
    name: tinybox green Benchmark
    runs-on: [self-hosted, Linux, tinyboxgreen]
-    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
@@ -187,62 +152,55 @@ jobs:
    - name: reset process replay
      run: test/external/process_replay/reset.py
    - name: Run model inference benchmark
-      run: NV=1 CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
+      run: NV=1 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/test_speed_v_torch.py | tee torch_speed.txt
-    - name: Test speed vs theoretical
-      run: NV=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
-    - name: Test benchmark allreduce
-      run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
+      run: NV=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
    - name: Test tensor cores
      run: |
-        NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
-        PTX=1 ALLOW_TF32=1 NV=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        NV=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
+        PTX=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 | tee matmul.txt
-        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
-        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_tf32.txt
+        CUDA=1 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
    - name: Run Tensor Core GEMM (PTX)
-      run: NV=1 PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_ptx.txt
+      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 | 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
+      run: NV=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_nv.txt
+    - name: Run Tensor Core GEMM (NV) with BEAM
+      run: BEAM=4 NV=1 HALF=1 IGNORE_BEAM_CACHE=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: NV=1 python3 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd.txt
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: NV=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA
      run: |
-        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama NV=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        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 | tee llama_beam.txt
-    # - name: Run LLaMA 7B on 4 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
-    # - name: Run LLaMA 7B on 6 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
+      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 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 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
    - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
-    # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
-    # - name: Run LLaMA-2 70B
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 MAX_CONTEXT=256 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
+      run: NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
+    - name: Run LLaMA-3 8B on 4 GPUs
+      run: NV=1 python3 examples/llama3.py --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 python3 examples/llama3.py --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 MAX_CONTEXT=256 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
    - name: Run Mixtral 8x7B
-      run: time BENCHMARK_LOG=mixtral NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
+      run: time NV=1 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
    - name: Run GPT2
      run: |
-        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        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 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      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 | tee gpt2_half_beam.txt
+      run: NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAST_BEFORE_VIEW=0 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (NVIDIA)
@@ -251,7 +209,6 @@ jobs:
          torch_speed.txt
          matmul.txt
          matmul_bfloat16.txt
-          matmul_tf32.txt
          matmul_ptx.txt
          matmul_nv.txt
          sd.txt
@@ -259,6 +216,8 @@ jobs:
          llama_unjitted.txt
          llama_jitted.txt
          llama_beam.txt
+          llama_four_gpu.txt
+          llama_six_gpu.txt
          llama3_beam.txt
          llama3_four_gpu.txt
          llama3_six_gpu.txt
@@ -274,10 +233,9 @@ jobs:
  testmorenvidiabenchmark:
    name: tinybox green Training Benchmark
    runs-on: [self-hosted, Linux, tinyboxgreen]
-    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
@@ -304,28 +262,25 @@ jobs:
    - 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 | tee beautiful_mnist.txt
+      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=97.3 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      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 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      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 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+      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: BENCHMARK_LOG=cifar_10steps_half_wino NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      run: NV=1 WINO=1 STEPS=10 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 LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+      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=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+      run: time 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 | tee train_resnet_one_gpu.txt
+      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 | tee train_resnet.txt
-    - name: Run 10 MLPerf Bert training steps (6 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
+      run: NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (NVIDIA Training)
@@ -336,30 +291,22 @@ jobs:
          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
+          train_cifar_six_gpu.txt
    - name: Run process replay tests
      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py

  testamdbenchmark:
    name: tinybox red Benchmark
    runs-on: [self-hosted, Linux, tinybox]
-    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: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
-    #- name: Insert amdgpu
-    #  run: sudo modprobe amdgpu
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -378,12 +325,8 @@ 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: 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
@@ -392,62 +335,46 @@ jobs:
    #  run: |
    #    python3 -c "import torch; print(torch.__version__)"
    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
-    - name: Test speed vs theoretical
-      run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
    - name: Test tensor cores
      run: |
-        AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-        AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-        AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+        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 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
-    - name: Test AMD=1
-      run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
-    #- name: Test HIP=1
-    #  run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
+      run: AMD=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
    # TODO: AMD compiler bug causes this to fail
    #- name: Fuzz Padded Tensor Core GEMM
    #  run: HSA=1 M_START=12 M_STOP=20 M_STEP=1 N_START=12 N_STOP=20 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
-    #- name: Remove amdgpu
-    #  run: sleep 10 && sudo rmmod amdgpu # sleep a bit to let the driver unload the prev pid.
-    - name: Test AM cold start time
-      run: time AMD=1 AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
-    - name: Test AM warm start time
-      run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: AMD=1 python3 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd.txt
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      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 | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama AMD=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        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 | tee llama_beam.txt
-    # - name: Run LLaMA 7B on 4 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
-    # - name: Run LLaMA 7B on 6 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
+      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 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 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
    - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
-    # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
-    #- name: Restore amdgpu
-    #  run: sudo modprobe amdgpu
-    # - name: Run LLaMA-2 70B
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
+      run: AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
+    - name: Run LLaMA-3 8B on 4 GPUs
+      run: AMD=1 python3 examples/llama3.py --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 python3 examples/llama3.py --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
+    - name: Run LLaMA-2 70B
+      run: AMD=1 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
    - name: Run Mixtral 8x7B
-      run: time BENCHMARK_LOG=mixtral AMD=1 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
+      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 | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        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 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      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 | tee gpt2_half_beam.txt
+      run: AMD=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAST_BEFORE_VIEW=0 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD)
@@ -457,6 +384,8 @@ jobs:
          llama_unjitted.txt
          llama_jitted.txt
          llama_beam.txt
+          llama_four_gpu.txt
+          llama_six_gpu.txt
          llama3_beam.txt
          llama3_four_gpu.txt
          llama3_six_gpu.txt
@@ -476,18 +405,13 @@ jobs:
  testmoreamdbenchmark:
    name: tinybox red Training Benchmark
    runs-on: [self-hosted, Linux, tinybox]
-    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: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -506,21 +430,25 @@ 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 | tee beautiful_mnist.txt
+      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=97.3 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      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 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      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: BENCHMARK_LOG=cifar_10steps_bf16 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+      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: BENCHMARK_LOG=cifar_10steps_half_wino AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      run: AMD=1 WINO=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+      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=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
-    - name: Run full CIFAR training steps w 6 GPUS (REMOTE)
-      run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
+      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 AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      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: AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD Training)
@@ -531,69 +459,18 @@ jobs:
          train_cifar_bf16.txt
          train_cifar_wino.txt
          train_cifar_one_gpu.txt
-          train_cifar_six_gpu.txt
-          train_cifar_six_gpu_remote.txt
-    - name: Run process replay tests
-      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
-
-  testmlperfamdbenchmark:
-    name: tinybox red MLPerf Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 30
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Run MLPerf resnet eval
-      run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
-    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-      run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
-    - name: Run 10 MLPerf Bert training steps (6 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD MLPerf)
-        path: |
          train_resnet.txt
          train_resnet_one_gpu.txt
-          train_bert.txt
+          train_cifar_six_gpu.txt
    - name: Run process replay tests
      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py

  testqualcommbenchmark:
    name: comma Benchmark
    runs-on: [self-hosted, Linux, comma]
-    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
@@ -605,26 +482,26 @@ 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 compile 0.9.4
+      run: PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python examples/openpilot/compile2.py | tee openpilot_compile_0_9_4.txt
+    - name: openpilot compile 0.9.7
+      run: PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python examples/openpilot/compile2.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_compile_0_9_7.txt
    - 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: BENCHMARK_LOG=openpilot_0_9_7 PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_7.txt
+      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: BENCHMARK_LOG=openpilot_0_9_7_image PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
+      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: benchmark MobileNetV2 on DSP
-      run: |
-        # generate quantized weights
-        ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
-        ln -s /data/home/tiny/tinygrad/testsig-*.so .
-        PYTHONPATH=. CC=clang-19 CPU=1 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
-        # benchmark on DSP with NOOPT=1, the devectorizer has issues
-        PYTHONPATH=. CC=clang-19 DSP=1 DONT_REALIZE_EXPAND=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
    - name: 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
@@ -1,34 +0,0 @@
-name: Benchmark with kernel search
-
-on:
-  push:
-    branches:
-      - update_benchmark_search
-  workflow_dispatch:
-
-jobs:
-  run_script_job:
-    runs-on: [self-hosted, Linux, tinybox]
-    if: github.repository_owner == 'tinygrad'
-    timeout-minutes: 100
-
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
-    - name: Run SDXL with new search
-      # TODO: GCVM_L2_PROTECTION_FAULT_STATUS with llvm19
-      run: |
-        BENCHMARK_LOG=search_sdxl PYTHONPATH=. AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 python examples/sdxl.py --noshow --timing --seed 0
-    - name: Run SDXL with cached search
-      run: |
-        BENCHMARK_LOG=search_sdxl_cached PYTHONPATH=. AMD=1 JITBEAM=2 python examples/sdxl.py --noshow --timing --seed 0
-    - name: Run winograd cifar with new search
-      run: |
-        BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BS=1024 STEPS=500 python examples/hlb_cifar10.py
-    - name: Run winograd cifar with cached search
-      run: |
-        BENCHMARK_LOG=search_wino_cifar_cached WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 BS=1024 STEPS=500 python examples/hlb_cifar10.py
@@ -1,30 +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: 240
-
-    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"
@@ -10,7 +10,6 @@ notebooks
 *.so
 *.txt
 build
-!examples/tinychat/assets/cdn.jsdelivr.net/npm/[email protected]/build/
 /dist
 *.egg-info
 /env
@@ -34,8 +33,6 @@ extra/datasets/open-images-v6-mlperf
 extra/datasets/kits/
 extra/datasets/COCO/
 extra/datasets/audio*
-extra/huggingface_onnx/models/*
-extra/huggingface_onnx/*.yaml
 extra/weights
 venv
 examples/**/net.*[js,json]
@@ -58,7 +55,4 @@ weights
 comgr_*
 *.pkl
 site/
-profile_stats
-*.log
-target
-.mypy_cache
+master_schedule.py
@@ -1,34 +1,39 @@
-# on Windows -- $env:SKIP="tests,example"
 repos:
  - repo: local
    hooks:
-      - id: ruff
-        name: ruff
-        entry: python3 -m ruff check .
+      - id: whitespace
+        name: strip whitespace
+        entry: find tinygrad -type f -name "*.py" -exec sed -i '' 's/ *$//' '{}' ';'
        language: system
        always_run: true
        pass_filenames: false
-      - id: tiny
-        name: tiny tests
-        entry: python3 -m pytest test/test_tiny.py
+      - id: ruff
+        name: ruff
+        entry: ruff check .
        language: system
        always_run: true
        pass_filenames: false
      - id: mypy
        name: mypy
-        entry: python3 -m mypy tinygrad/ --strict-equality
+        entry: mypy tinygrad/ --strict-equality
+        language: system
+        always_run: true
+        pass_filenames: false
+      - id: docs2
+        name: docs2
+        entry: python3 docs/abstractions2.py
        language: system
        always_run: true
        pass_filenames: false
      - id: devicetests
        name: select GPU tests
-        entry: env GPU=1 PYTHONPATH="." python3 -m pytest test/test_uops.py test/test_search.py
+        entry: env GPU=1 PYTHONPATH="." pytest test/test_uops.py test/test_search.py
        language: system
        always_run: true
        pass_filenames: false
      - id: tests
        name: subset of tests
-        entry: env PYTHONPATH="." python3 -m pytest -n=4 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
+        entry: env PYTHONPATH="." python3 -m pytest -n=4 test/unit/ test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py test/test_symbolic_shapetracker.py
        language: system
        always_run: true
        pass_filenames: false
@@ -40,7 +45,7 @@ repos:
        pass_filenames: false
      - id: pylint
        name: pylint
-        entry: python3 -m pylint tinygrad/
+        entry: env PYTHONPATH="." python3 -m pylint tinygrad/
        language: system
        always_run: true
        pass_filenames: false
@@ -7,7 +7,7 @@ extension-pkg-whitelist=scipy,cereal.messaging.messaging_pyx,PyQt5,av

 # Add files or directories to the blacklist. They should be base names, not
 # paths.
-ignore=CVS,autogen,msm_kgsl.py,runtime,.venv
+ignore=CVS,autogen,msm_kgsl.py

 # Add files or directories matching the regex patterns to the blacklist. The
 # regex matches against base names, not paths.
@@ -54,7 +54,7 @@ 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
+disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401
 # E1101 for function binding
 # W0221 for Function class
 # W0105 for comment strings
@@ -1,17 +0,0 @@
-# tinygrad agents
-
-Hello agent. You are one of the most talented programmers of your generation.
-
-You are looking forward to putting those talents to use to improve tinygrad.
-
-## philosophy
-
-tinygrad is a **tensor** library focused on beauty and minimalism, while still matching the functionality of PyTorch and JAX.
-
-Every line must earn its keep. Prefer readability over cleverness. We believe that if carefully designed, 10 lines can have the impact of 1000.
-
-Never mix functionality changes with whitespace changes. All functionality changes must be tested.
-
-## style
-
-Use **2-space indentation**, and keep lines to a maximum of **150 characters**. Match the existing style.
@@ -21,11 +21,11 @@ tinygrad: For something between [PyTorch](https://github.com/pytorch/pytorch) an

 ---

-Despite tinygrad's size, it is a fully featured deep learning framework.
+This may not be the best deep learning framework, but it is a deep learning framework.

-Due to its extreme simplicity, it is the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
+Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.

-tinygrad is now beta software, we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.
+tinygrad is still alpha software, but we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.

 ## Features

@@ -39,8 +39,9 @@ Try a matmul. See how, despite the style, it is fused into one kernel with the p

 ```sh
 DEBUG=3 python3 -c "from tinygrad import Tensor;
-N = 1024; a, b = Tensor.empty(N, N), Tensor.empty(N, N);
-(a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2).realize()"
+N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N);
+c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2);
+print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
 ```

 And we can change `DEBUG` to `4` to see the generated code.
@@ -80,14 +81,13 @@ See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full vers
 tinygrad already supports numerous accelerators, including:

 - [x] [GPU (OpenCL)](tinygrad/runtime/ops_gpu.py)
- [x] [CPU (C Code)](tinygrad/runtime/ops_cpu.py)
+- [x] [CLANG (C Code)](tinygrad/runtime/ops_clang.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)
 - [x] [NV](tinygrad/runtime/ops_nv.py)
 - [x] [QCOM](tinygrad/runtime/ops_qcom.py)
- [x] [WEBGPU](tinygrad/runtime/ops_webgpu.py)

 And it is easy to add more! Your accelerator of choice only needs to support a total of ~25 low level ops.

@@ -125,8 +125,8 @@ y = Tensor([[2.0,0,-2.0]], requires_grad=True)
 z = y.matmul(x).sum()
 z.backward()

-print(x.grad.tolist())  # dz/dx
-print(y.grad.tolist())  # dz/dy
+print(x.grad.numpy())  # dz/dx
+print(y.grad.numpy())  # dz/dy
 ```

 The same thing but in PyTorch:
@@ -138,8 +138,8 @@ y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
 z = y.matmul(x).sum()
 z.backward()

-print(x.grad.tolist())  # dz/dx
-print(y.grad.tolist())  # dz/dy
+print(x.grad.numpy())  # dz/dx
+print(y.grad.numpy())  # dz/dy
 ```

 ## Contributing
@@ -150,7 +150,7 @@ We'll start with what will get your PR closed with a pointer to this section:

 - No code golf! While low line count is a guiding light of this project, anything that remotely looks like code golf will be closed. The true goal is reducing complexity and increasing readability, and deleting `\n`s does nothing to help with that.
 - All docs and whitespace changes will be closed unless you are a well-known contributor. The people writing the docs should be those who know the codebase the absolute best. People who have not demonstrated that shouldn't be messing with docs. Whitespace changes are both useless *and* carry a risk of introducing bugs.
- Anything you claim is a "speedup" must be benchmarked. In general, the goal is simplicity, so even if your PR makes things marginally faster, you have to consider the tradeoff with maintainability and readability.
+- Anything you claim is a "speedup" must be benchmarked. In general, the goal is simplicity, so even if your PR makes things marginally faster, you have to consider the tradeoff with maintainablity and readablity.
 - In general, the code outside the core `tinygrad/` folder is not well tested, so unless the current code there is broken, you shouldn't be changing it.
 - If your PR looks "complex", is a big diff, or adds lots of lines, it won't be reviewed or merged. Consider breaking it up into smaller PRs that are individually clear wins. A common pattern I see is prerequisite refactors before adding new functionality. If you can (cleanly) refactor to the point that the feature is a 3 line change, this is great, and something easy for us to review.

@@ -178,4 +178,4 @@ python3 -m pytest test/                 # whole test suite

 #### Process replay tests

-[Process replay](https://github.com/tinygrad/tinygrad/blob/master/test/external/process_replay/README.md) compares your PR's generated kernels against master. If your PR is a refactor or speedup without any expected behavior change, It should include [pr] in the pull request title.
+[Process replay](https://github.com/tinygrad/tinygrad/blob/master/test/external/process_replay/process_replay.py) compares your PR's generated kernels against master. If your PR is a refactor or speedup without any expected behavior change, It should include [pr] in the pull request title, [example](https://github.com/tinygrad/tinygrad/pull/4995). Note that you should keep your branch up-to-date with master.
@@ -9,7 +9,7 @@ if [[ ! $(clang2py -V) ]]; then
  pip install clang==14.0.6
  git clone https://github.com/nimlgen/ctypeslib.git
  cd ctypeslib
-  pip install .
+  pip install --user .
  clang2py -V
  popd
 fi
@@ -35,7 +35,7 @@ def _try_dlopen_$name():
  for candidate in PATHS_TO_TRY:
    try: return ctypes.CDLL(candidate)
    except OSError: pass
-  return None
+  raise RuntimeError("library $name not found")
 EOF
 }

@@ -69,7 +69,7 @@ generate_comgr() {
  --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'"
+  patch_dlopen $BASE/comgr.py amd_comgr "'/opt/rocm/lib/libamd_comgr.so'" "os.getenv('ROCM_PATH', '')+'/lib/libamd_comgr.so'"
  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"
 }
@@ -78,17 +78,12 @@ generate_kfd() {
  clang2py /usr/include/linux/kfd_ioctl.h -o $BASE/kfd.py -k cdefstum

  fixup $BASE/kfd.py
-  sed -i "s/import ctypes/import ctypes, os/g" $BASE/kfd.py
-  sed -i "s/import fcntl, functools/import functools/g" $BASE/kfd.py
-  sed -i "/import functools/a from tinygrad.runtime.support.hcq import FileIOInterface" $BASE/kfd.py
-  sed -i "s/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd, \*\*kwargs):/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd:FileIOInterface, \*\*kwargs):/g" $BASE/kfd.py
-  sed -i "s/fcntl.ioctl(__fd, (__idir<<30)/__fd.ioctl((__idir<<30)/g" $BASE/kfd.py
-  sed -i "s/!!/not not /g" $BASE/kfd.py
+  sed -i "s\import ctypes\import ctypes, os\g" $BASE/kfd.py
  python3 -c "import tinygrad.runtime.autogen.kfd"
 }

 generate_cuda() {
-  clang2py /usr/include/cuda.h --clang-args="-D__CUDA_API_VERSION_INTERNAL" -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
+  clang2py /usr/include/cuda.h -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/cuda.py
  sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libcuda.so')\ctypes.CDLL(ctypes.util.find_library('cuda'))\g" $BASE/cuda.py
  fixup $BASE/cuda.py
@@ -105,10 +100,10 @@ generate_nvrtc() {
 }

 generate_nv() {
-  NVKERN_COMMIT_HASH=81fe4fb417c8ac3b9bdcc1d56827d116743892a5
+  NVKERN_COMMIT_HASH=d6b75a34094b0f56c2ccadf14e5d0bd515ed1ab6
  NVKERN_SRC=/tmp/open-gpu-kernel-modules-$NVKERN_COMMIT_HASH
  if [ ! -d "$NVKERN_SRC" ]; then
-    git clone https://github.com/NVIDIA/open-gpu-kernel-modules $NVKERN_SRC
+    git clone https://github.com/tinygrad/open-gpu-kernel-modules $NVKERN_SRC
    pushd .
    cd $NVKERN_SRC
    git reset --hard $NVKERN_COMMIT_HASH
@@ -117,24 +112,16 @@ generate_nv() {

  clang2py -k cdefstum \
    extra/nv_gpu_driver/clc6c0qmd.h \
-    extra/nv_gpu_driver/clcec0qmd.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0000.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0080.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080_notification.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc86f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc96f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc761.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl83de.h \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
+    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
    $NVKERN_SRC/src/nvidia/generated/g_allclasses.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc6c0.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clcdc0.h \
    $NVKERN_SRC/kernel-open/nvidia-uvm/clc6b5.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/clc9b5.h \
    $NVKERN_SRC/kernel-open/nvidia-uvm/uvm_ioctl.h \
    $NVKERN_SRC/kernel-open/nvidia-uvm/uvm_linux_ioctl.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/hwref/ampere/ga100/dev_fault.h \
    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv_escape.h \
    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl.h \
    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl-numbers.h \
@@ -149,7 +136,6 @@ generate_nv() {
    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlc36f.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlcb33.h \
    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrla06c.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl90f1.h \
    --clang-args="-include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
    -o $BASE/nv_gpu.py
  fixup $BASE/nv_gpu.py
@@ -157,8 +143,6 @@ generate_nv() {
  sed -i "s\import ctypes\import ctypes, os\g" $BASE/nv_gpu.py
  sed -i 's/#\?\s\([A-Za-z0-9_]\+\) = MW ( \([0-9]\+\) : \([0-9]\+\) )/\1 = (\2 , \3)/' $BASE/nv_gpu.py # NVC6C0_QMDV03_00 processing
  sed -i 's/#\sdef NVC6C0_QMD\([A-Za-z0-9_()]\+\):/def NVC6C0_QMD\1:/' $BASE/nv_gpu.py
-  sed -i 's/#\sdef NVCEC0_QMD\([A-Za-z0-9_()]\+\):/def NVCEC0_QMD\1:/' $BASE/nv_gpu.py
-  sed -E -i -n '/^def (NVCEC0_QMDV05_00_RELEASE)(_ENABLE)\(i\):/{p;s//\1'"0"'\2=\1\2(0)\n\1'"1"'\2=\1\2(1)/;H;b};p;${x;s/^\n//;p}' "$BASE/nv_gpu.py"
  sed -i 's/#\s*return MW(\([0-9i()*+]\+\):\([0-9i()*+]\+\))/    return (\1 , \2)/' $BASE/nv_gpu.py
  sed -i 's/#\?\s*\(.*\)\s*=\s*\(NV\)\?BIT\(32\)\?\s*(\s*\([0-9]\+\)\s*)/\1 = (1 << \4)/' $BASE/nv_gpu.py # name = BIT(x) -> name = (1 << x)
  sed -i "s/UVM_\([A-Za-z0-9_]\+\) = \['i', '(', '\([0-9]\+\)', ')'\]/UVM_\1 = \2/" $BASE/nv_gpu.py # UVM_name = ['i', '(', '<num>', ')'] -> UVM_name = <num>
@@ -167,30 +151,8 @@ generate_nv() {
  sed -n '1i\
 nv_status_codes = {}
 /^NV_STATUS_CODE/ { s/^NV_STATUS_CODE(\([^,]*\), *\([^,]*\), *"\([^"]*\)") *.*$/\1 = \2\nnv_status_codes[\1] = "\3"/; p }' $NVKERN_SRC/src/common/sdk/nvidia/inc/nvstatuscodes.h >> $BASE/nv_gpu.py
+
  python3 -c "import tinygrad.runtime.autogen.nv_gpu"
-
-  clang2py -k cdefstum \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/fsp/kern_fsp_cot_payload.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gspifpub.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_wpr_meta.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_sr_meta.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
-    $NVKERN_SRC/src/common/uproc/os/common/include/libos_init_args.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/rmRiscvUcode.h \
-    $NVKERN_SRC/src/common/shared/msgq/inc/msgq/msgq_priv.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_headers.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_global_enums.h \
-    $NVKERN_SRC/src/nvidia/generated/g_rpc-structures.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/fsp/fsp_nvdm_format.h \
-    extra/nv_gpu_driver/g_rpc-message-header.h \
-    extra/nv_gpu_driver/gsp_static_config.h \
-    extra/nv_gpu_driver/vbios.h \
-    --clang-args="-DRPC_MESSAGE_STRUCTURES -DRPC_STRUCTURES -include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/nvidia/generated -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/src/nvidia/inc -I$NVKERN_SRC/src/nvidia/interface/ -I$NVKERN_SRC/src/nvidia/inc/kernel -I$NVKERN_SRC/src/nvidia/inc/libraries -I$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
-    -o $BASE/nv/nv.py
-
-  fixup $BASE/nv/nv.py
-  python3 -c "import tinygrad.runtime.autogen.nv.nv"
 }

 generate_amd() {
@@ -198,12 +160,9 @@ generate_amd() {
  clang2py -k cdefstum \
    extra/hip_gpu_driver/sdma_registers.h \
    extra/hip_gpu_driver/nvd.h \
-    extra/hip_gpu_driver/kfd_pm4_headers_ai.h \
-    extra/hip_gpu_driver/soc21_enum.h \
    extra/hip_gpu_driver/sdma_v6_0_0_pkt_open.h \
    extra/hip_gpu_driver/gc_11_0_0_offset.h \
    extra/hip_gpu_driver/gc_10_3_0_offset.h \
-    extra/hip_gpu_driver/sienna_cichlid_ip_offset.h \
    --clang-args="-I/opt/rocm/include -x c++" \
    -o $BASE/amd_gpu.py

@@ -240,62 +199,26 @@ generate_io_uring() {
  fixup $BASE/io_uring.py
 }

-generate_ib() {
-  clang2py -k cdefstum \
-    /usr/include/infiniband/verbs.h \
-    /usr/include/infiniband/verbs_api.h \
-    /usr/include/infiniband/ib_user_ioctl_verbs.h \
-    /usr/include/rdma/ib_user_verbs.h \
-    -o $BASE/ib.py
-
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" "$BASE/ib.py"
-  sed -i "s\FIXME_STUB\libibverbs\g" "$BASE/ib.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(ctypes.util.find_library('ibverbs'), use_errno=True)\g" "$BASE/ib.py"
-
-  fixup $BASE/ib.py
-}
-
 generate_libc() {
  clang2py -k cdefstum \
    $(dpkg -L libc6-dev | grep sys/mman.h) \
    $(dpkg -L libc6-dev | grep sys/syscall.h) \
-    /usr/include/string.h \
    /usr/include/elf.h \
    /usr/include/unistd.h \
-    /usr/include/asm-generic/mman-common.h \
    -o $BASE/libc.py

  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libc.py
  sed -i "s\FIXME_STUB\libc\g" $BASE/libc.py
-  sed -i "s\FunctionFactoryStub()\None if (libc_path := ctypes.util.find_library('c')) is None else ctypes.CDLL(libc_path, use_errno=True)\g" $BASE/libc.py
+  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(ctypes.util.find_library('c'))\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"
 }

@@ -320,152 +243,6 @@ generate_qcom() {
  python3 -c "import tinygrad.runtime.autogen.qcom_dsp"
 }

-generate_pci() {
-  clang2py -k cdefstum \
-    /usr/include/linux/pci_regs.h \
-    -o $BASE/pci.py
-  fixup $BASE/pci.py
-}
-
-generate_vfio() {
-  clang2py -k cdefstum \
-    /usr/include/linux/vfio.h \
-    -o $BASE/vfio.py
-  fixup $BASE/vfio.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/vfio.py
-  sed -i "s\import fcntl, functools\import functools" $BASE/vfio.py
-  sed -i "s\import ctypes,os\a from tinygrad.runtime.support import FileIOInterface\g" $BASE/vfio.py
-  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\return __fd.ioctl((__idir<<30)\g" $BASE/vfio.py
-}
-
-generate_am() {
-  AMKERN_COMMIT_HASH=ceb12c04e2b5b53ec0779362831f5ee40c4921e4
-  AMKERN_SRC=/tmp/ROCK-Kernel-Driver-$AMKERN_COMMIT_HASH
-  if [ ! -d "$AMKERN_SRC" ]; then
-    git clone https://github.com/ROCm/ROCK-Kernel-Driver $AMKERN_SRC --depth 1
-  fi
-  AMKERN_AMD=$AMKERN_SRC/drivers/gpu/drm/amd/
-  AMKERN_INC=$AMKERN_AMD/include/
-
-  clang2py -k cdefstum \
-    extra/amdpci/headers/v11_structs.h \
-    extra/amdpci/headers/v12_structs.h \
-    extra/amdpci/headers/amdgpu_vm.h \
-    extra/amdpci/headers/discovery.h \
-    extra/amdpci/headers/amdgpu_ucode.h \
-    extra/amdpci/headers/psp_gfx_if.h \
-    extra/amdpci/headers/amdgpu_psp.h \
-    extra/amdpci/headers/amdgpu_irq.h \
-    extra/amdpci/headers/amdgpu_doorbell.h \
-    $AMKERN_INC/soc15_ih_clientid.h \
-    --clang-args="-include stdint.h" \
-    -o $BASE/am/am.py
-  fixup $BASE/am/am.py
-  sed -i "s\(int64_t)\ \g" $BASE/am/am.py
-  sed -i "s\AMDGPU_PTE_MTYPE_VG10(2)\AMDGPU_PTE_MTYPE_VG10(0, 2)\g" $BASE/am/am.py # incorrect parsing (TODO: remove when clang2py is gone).
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/soc15d.h \
-    -o $BASE/am/pm4_soc15.py
-  fixup $BASE/am/pm4_soc15.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/nvd.h \
-    -o $BASE/am/pm4_nv.py
-  fixup $BASE/am/pm4_nv.py
-
-  clang2py -k cdefstum \
-    $AMKERN_INC/vega10_enum.h \
-    -o $BASE/am/vega10.py
-  fixup $BASE/am/vega10.py
-
-  clang2py -k cdefstum \
-    $AMKERN_INC/navi10_enum.h \
-    -o $BASE/am/navi10.py
-  fixup $BASE/am/navi10.py
-
-  clang2py -k cdefstum \
-    $AMKERN_INC/soc21_enum.h \
-    -o $BASE/am/soc21.py
-  fixup $BASE/am/soc21.py
-
-  clang2py -k cdefstum \
-    $AMKERN_INC/soc24_enum.h \
-    -o $BASE/am/soc24.py
-  fixup $BASE/am/soc24.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/vega10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_4_0_0.py
-  fixup $BASE/am/sdma_4_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/navi10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_5_0_0.py
-  fixup $BASE/am/sdma_5_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/sdma_v6_0_0_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_6_0_0.py
-  fixup $BASE/am/sdma_6_0_0.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v13_0_0_ppsmc.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu13_driver_if_v13_0_0.h \
-    extra/amdpci/headers/amdgpu_smu.h \
-    -o $BASE/am/smu_v13_0_0.py
-  fixup $BASE/am/smu_v13_0_0.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_0_pmfw.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_2_ppsmc.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu14_driver_if_v14_0.h \
-    extra/amdpci/headers/amdgpu_smu.h \
-    --clang-args="-include stdint.h" \
-    -o $BASE/am/smu_v14_0_2.py
-  fixup $BASE/am/smu_v14_0_2.py
-}
-
-generate_sqtt() {
-  clang2py -k cdefstum \
-    extra/sqtt/sqtt.h \
-    -o $BASE/sqtt.py
-
-  fixup $BASE/sqtt.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/sqtt.py
-  python3 -c "import tinygrad.runtime.autogen.sqtt"
-}
-
-generate_webgpu() {
-  clang2py extra/webgpu/webgpu.h -o $BASE/webgpu.py
-  fixup $BASE/webgpu.py
-  sed -i "s/FIXME_STUB/webgpu/g" "$BASE/webgpu.py"
-  sed -i "s/FunctionFactoryStub()/ctypes.CDLL(webgpu_support.WEBGPU_PATH)/g" "$BASE/webgpu.py"
-  sed -i "s/import ctypes/import ctypes, tinygrad.runtime.support.webgpu as webgpu_support/g" "$BASE/webgpu.py"
-  python3 -c "import tinygrad.runtime.autogen.webgpu"
-}
-
-generate_libusb() {
-  clang2py -k cdefstum \
-    /usr/include/libusb-1.0/libusb.h \
-    -o $BASE/libusb.py
-
-  fixup $BASE/libusb.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libusb.py
-  sed -i "s/FIXME_STUB/libusb/g" "$BASE/libusb.py"
-  sed -i "s/libusb_le16_to_cpu = libusb_cpu_to_le16//g" "$BASE/libusb.py"
-  sed -i "s/FunctionFactoryStub()/None if (lib_path:=os.getenv('LIBUSB_PATH', ctypes.util.find_library('usb-1.0'))) is None else ctypes.CDLL(lib_path)/g" "$BASE/libusb.py"
-  python3 -c "import tinygrad.runtime.autogen.libusb"
-}
-
 if [ "$1" == "opencl" ]; then generate_opencl
 elif [ "$1" == "hip" ]; then generate_hip
 elif [ "$1" == "comgr" ]; then generate_comgr
@@ -475,20 +252,11 @@ elif [ "$1" == "hsa" ]; then generate_hsa
 elif [ "$1" == "kfd" ]; then generate_kfd
 elif [ "$1" == "nv" ]; then generate_nv
 elif [ "$1" == "amd" ]; then generate_amd
-elif [ "$1" == "am" ]; then generate_am
-elif [ "$1" == "nvdrv" ]; then generate_nvdrv
-elif [ "$1" == "sqtt" ]; then generate_sqtt
 elif [ "$1" == "qcom" ]; then generate_qcom
 elif [ "$1" == "io_uring" ]; then generate_io_uring
-elif [ "$1" == "ib" ]; then generate_ib
 elif [ "$1" == "libc" ]; then generate_libc
-elif [ "$1" == "llvm" ]; then generate_llvm
 elif [ "$1" == "kgsl" ]; then generate_kgsl
 elif [ "$1" == "adreno" ]; then generate_adreno
-elif [ "$1" == "pci" ]; then generate_pci
-elif [ "$1" == "vfio" ]; then generate_vfio
-elif [ "$1" == "webgpu" ]; then generate_webgpu
-elif [ "$1" == "libusb" ]; then generate_libusb
-elif [ "$1" == "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
+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
 else echo "usage: $0 <type>"
 fi
@@ -1,73 +1,71 @@
 # tinygrad is a tensor library, and as a tensor library it has multiple parts
 # 1. a "runtime". this allows buffer management, compilation, and running programs
 # 2. a "Device" that uses the runtime but specifies compute in an abstract way for all
-# 3. a "UOp" that fuses the compute into kernels, using memory only when needed
+# 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, CPUDevice, CPUProgram
-
-cpu = CPUDevice()
+from tinygrad.runtime.ops_clang import ClangProgram, ClangCompiler, MallocAllocator

 # allocate some buffers
-out = cpu.allocator.alloc(4)
-a = cpu.allocator.alloc(4)
-b = cpu.allocator.alloc(4)
+out = MallocAllocator.alloc(4)
+a = MallocAllocator.alloc(4)
+b = MallocAllocator.alloc(4)

 # load in some values (little endian)
-cpu.allocator._copyin(a, memoryview(bytearray([2,0,0,0])))
-cpu.allocator._copyin(b, memoryview(bytearray([3,0,0,0])))
+MallocAllocator.copyin(a, bytearray([2,0,0,0]))
+MallocAllocator.copyin(b, 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]; }")
+lib = ClangCompiler().compile("void add(int *out, int *a, int *b) { out[0] = a[0] + b[0]; }")

-# create a runtime for the program
-fxn = cpu.runtime("add", lib)
+# create a runtime for the program (ctypes.CDLL)
+fxn = ClangProgram("add", lib)

 # run the program
 fxn(out, a, b)

 # check the data out
-print(val := cpu.allocator._as_buffer(out).cast("I").tolist()[0])
+print(val := MallocAllocator.as_buffer(out).cast("I").tolist()[0])
 assert val == 5


 print("******** second, the Device ***********")

-DEVICE = "CPU"   # NOTE: you can change this!
+DEVICE = "CLANG"   # NOTE: you can change this!

 import struct
-from tinygrad.dtype import dtypes
+from tinygrad.dtype import PtrDType, dtypes
 from tinygrad.device import Buffer, Device
-from tinygrad.uop.ops import UOp, Ops
+from tinygrad.ops import BinaryOps, MetaOps, UOp, UOps
 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 cpu.allocator.alloc
+# NOTE: a._buf is the same as the return from MallocAllocator.alloc

 # describe the computation
-buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
-buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
-ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1.view(ShapeTracker.from_shape((1,))),))
-ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2.view(ShapeTracker.from_shape((1,))),))
+buf_1 = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 1)
+buf_2 = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 2)
+ld_1 = UOp(UOps.LOAD, dtypes.int32, (buf_1, ShapeTracker.from_shape((1,)).to_uop()))
+ld_2 = UOp(UOps.LOAD, dtypes.int32, (buf_2, ShapeTracker.from_shape((1,)).to_uop()))
 alu = ld_1 + ld_2
-output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
-st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.view(ShapeTracker.from_shape((1,))), alu))
-s = UOp(Ops.SINK, dtypes.void, (st_0,))
+output_buf = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 0)
+st_0 = UOp(UOps.STORE, dtypes.void, (output_buf, ShapeTracker.from_shape((1,)).to_uop(), alu))
+s = UOp(UOps.SINK, dtypes.void, (st_0,))

 # convert the computation to a "linearized" format (print the format)
-from tinygrad.engine.realize import get_program, CompiledRunner
-program = get_program(s, Device[DEVICE].renderer)
+from tinygrad.engine.realize import get_kernel, CompiledRunner
+kernel = get_kernel(Device[DEVICE].renderer, s).linearize()

 # compile a program (and print the source)
-fxn = CompiledRunner(program)
+fxn = CompiledRunner(kernel.to_program())
 print(fxn.p.src)
-# NOTE: fxn.clprg is the CPUProgram
+# NOTE: fxn.clprg is the ClangProgram

 # run the program
 fxn.exec([out, a, b])
@@ -76,52 +74,36 @@ fxn.exec([out, a, b])
 assert out.as_buffer().cast('I')[0] == 5


-print("******** third, the UOp ***********")
+print("******** third, the LazyBuffer ***********")

+from tinygrad.engine.lazy import LazyBuffer
 from tinygrad.engine.realize import run_schedule
-from tinygrad.engine.schedule import create_schedule_with_vars
-from tinygrad.schedule.kernelize import get_kernelize_map
+from tinygrad.engine.schedule import create_schedule

 # allocate some values + load in values
-a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
-b = UOp.new_buffer(DEVICE, 1, dtypes.int32)
+a = LazyBuffer.metaop(MetaOps.EMPTY, (1,), dtypes.int32, DEVICE)
+b = LazyBuffer.metaop(MetaOps.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))))
+del a.srcs
+del b.srcs

 # describe the computation
-out = a + b
-s = UOp(Ops.SINK, dtypes.void, (out,))
+out = a.alu(BinaryOps.ADD, b)

-# group the computation into kernels
-becomes_map = get_kernelize_map(s)
-
-# the compute maps to an assign
-assign = becomes_map[a+b]
-
-# the first source is the output buffer (data)
-assert assign.src[0].op is Ops.BUFFER
-# the second source is the kernel (compute)
-assert assign.src[1].op is Ops.KERNEL
-
-# schedule the kernel graph in a linear list
-s = UOp(Ops.SINK, dtypes.void, (assign,))
-sched, _ = create_schedule_with_vars(s)
-assert len(sched) == 1
+# schedule the computation as a list of kernels
+sched = create_schedule([out])
+for si in sched: print(si.ast.op)  # NOTE: the first two convert it to CLANG

 # DEBUGGING: print the compute ast
 print(sched[-1].ast)
 # NOTE: sched[-1].ast is the same as st_0 above

-# the output will be stored in a new buffer
-out = assign.buf_uop
-assert out.op is Ops.BUFFER and not out.buffer.is_allocated()
-print(out)
-
 # run that schedule
 run_schedule(sched)

 # check the data out
-assert out.is_realized and out.buffer.as_buffer().cast('I')[0] == 5
+assert out.realized.as_buffer().cast('I')[0] == 5


 print("******** fourth, the Tensor ***********")
@@ -1,6 +1,7 @@
 # abstractions2 goes from back to front, here we will go from front to back
 from typing import List
-from tinygrad.helpers import tqdm
+from tqdm import tqdm
+from tinygrad.helpers import DEBUG

 # *****
 # 0. Load mnist on the device
@@ -26,17 +27,16 @@ l1n, l2n = l1.numpy(), l2.numpy()
 from tinygrad.nn.optim import SGD
 optim = SGD([l1, l2])

-Tensor.training = True
 X, Y = X_train[(samples:=Tensor.randint(128, high=X_train.shape[0]))], Y_train[samples]
 optim.zero_grad()
 model(X).sparse_categorical_crossentropy(Y).backward()
-optim.schedule_step()   # this will step the optimizer without running realize
+optim._step()   # this will step the optimizer without running realize

 # *****
 # 3. Create a schedule.

 # The weight Tensors have been assigned to, but not yet realized. Everything is still lazy at this point
-# l1.uop and l2.uop define a computation graph
+# l1.lazydata and l2.lazydata define a computation graph

 from tinygrad.engine.schedule import ScheduleItem
 schedule: List[ScheduleItem] = Tensor.schedule(l1, l2)
@@ -48,7 +48,7 @@ for si in schedule: print(str(si)[:80])
 # 4. Lower a schedule.

 from tinygrad.engine.realize import lower_schedule_item, ExecItem
-lowered: List[ExecItem] = [lower_schedule_item(si) for si in tqdm(schedule)]
+lowered: List[ExecItem] = [ExecItem(lower_schedule_item(si).prg, list(si.bufs)) for si in tqdm(schedule)]

 # *****
 # 5. Run the schedule
@@ -1,39 +0,0 @@
-# AM Driver
-
-AM driver is a userspace driver targeting AMD's RDNA3/RDNA4. You only need tinygrad to send compute tasks to your GPU!
-
-## How to run?
-Make sure that amdgpu module is unloaded and just run tinygrad with `AMD=1`!
-
-Optional requirements:
-
-* System without IOMMU for P2P / SDMA support
-* vfio-pci module for IRQ handling
-
-## Environment Variables
-
-| Variable | Possible Value(s) | Description |
-|----------|------------------|-------------|
-| AM_RESET | [1] | Performs a full GPU reset (reloading all firmware and IP blocks) |
-| AM_DEBUG | [0-4] | Sets the level of additional debugging information |
-
-## AM Driver Details
-
-### Compute & SDMA Queues
-
-AM binds compute queues directly to MEC (bypassing MES). Tinygrad uses only one compute queue, which is bound at `pipe=0 queue=0`. Similarly, the single SDMA queue is bound at `engine=0 queue=0`.
-
-### Boot
-
-The GPU being passed can be in one of several states:
-1. Not initialized
-2. Initialized by amdgpu
-3. Initialized by AM
-
-The first and second states require a full GPU setup since their states are unknown. The second state also requires a mode1 reset to reinitialize all components.
-
-The third state can be set up partially to optimize boot time. In this case, only the GFX and SDMA IPs need to be initialized. To enable this, AM uses a separate boot memory that is guaranteed not to be overwritten. This physical memory is utilized for all blocks that are initialized only during the initial AM boot. To determine if the GPU is in the third state, AM uses `regSCRATCH_REG7` as a flag.
-
-### VM Management
-
-Each AM device sets up only a single `VMID=0` and one page directory. The page directory used is 3-level and thus supports up to 512GB of virtual addresses. All AM devices are located in one virtual address space.
@@ -7,17 +7,19 @@ The tinygrad framework has four pieces

 There is a good [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-notes/) by Di Zhu that go over tinygrad internals.

-There's also a [doc describing speed](../developer/speed.md)
-
 ## Frontend

-Everything in [Tensor](../tensor/index.md) is syntactic sugar around constructing a graph of [UOps](../developer/uop.md).
+Everything in [Tensor](../tensor/index.md) is syntactic sugar around [function.py](function.md), where the forwards and backwards passes are implemented for the different functions. There's about 25 of them, implemented using about 20 basic ops. Those basic ops go on to construct a graph of:

-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.
+::: tinygrad.engine.lazy.LazyBuffer
+    options:
+        show_source: false
+
+The `LazyBuffer` graph specifies the compute in terms of low level tinygrad ops. Not all LazyBuffers will actually become realized. There's two types of LazyBuffers, base and view. base contains compute into a contiguous buffer, and view is a view (specified by a ShapeTracker). Inputs to a base can be either base or view, inputs to a view can only be a single base.

 ## Scheduling

-The [scheduler](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/schedule.py) converts the graph of UOps into a list of `ScheduleItem`. One `ScheduleItem` is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. `ast` specifies what compute to run, and `bufs` specifies what buffers to run it on.
+The [scheduler](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/schedule.py) converts the graph of LazyBuffers into a list of `ScheduleItem`. One `ScheduleItem` is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. `ast` specifies what compute to run, and `bufs` specifies what buffers to run it on.

 ::: tinygrad.engine.schedule.ScheduleItem

@@ -0,0 +1,33 @@
+::: tinygrad.function
+    options:
+        members: [
+            "Contiguous",
+            "ContiguousBackward",
+            "Cast",
+            "Neg",
+            "Reciprocal",
+            "Sin",
+            "Relu",
+            "Log",
+            "Exp",
+            "Sqrt",
+            "Sigmoid",
+            "Sign",
+            "Less",
+            "Eq",
+            "Xor",
+            "Add",
+            "Sub",
+            "Mul",
+            "Div",
+            "Where",
+            "Sum",
+            "Max",
+            "Expand",
+            "Reshape",
+            "Permute",
+            "Pad",
+            "Shrink",
+            "Flip",
+        ]
+        show_source: false
@@ -6,29 +6,57 @@ The main aspect of HCQ-compatible runtimes is how they interact with devices. In

 ### Command Queues

-To interact with devices you create a `HWQueue`. Some methods are required, like timestamp and synchronization methods like [signal](#tinygrad.runtime.support.hcq.HWQueue.signal) and [wait](#tinygrad.runtime.support.hcq.HWQueue.wait), while others are dependent on it being a compute or copy queue.
+To interact with devices, there are 2 types of queues: `HWComputeQueue` and `HWCopyQueue`. Commands which are defined in a base `HWCommandQueue` class should be supported by both queues. These methods are timestamp and synchronization methods like [signal](#tinygrad.runtime.support.hcq.HWCommandQueue.signal) and [wait](#tinygrad.runtime.support.hcq.HWCommandQueue.wait).

 For example, the following Python code enqueues a wait, execute, and signal command on the HCQ-compatible device:
 ```python
-HWQueue().wait(signal_to_wait, value_to_wait) \
-         .exec(program, args_state, global_dims, local_dims) \
-         .signal(signal_to_fire, value_to_fire) \
-         .submit(your_device)
+HWComputeQueue().wait(signal_to_wait, value_to_wait) \
+                .exec(program, args_state, global_dims, local_dims) \
+                .signal(signal_to_fire, value_to_fire) \
+                .submit(your_device)
 ```

-Each runtime should implement the required functions that are defined in the `HWQueue` classes.
+Each runtime should implement the required functions that are defined in the `HWCommandQueue`, `HWComputeQueue`, and `HWCopyQueue` classes.

-::: tinygrad.runtime.support.hcq.HWQueue
+::: tinygrad.runtime.support.hcq.HWCommandQueue
    options:
        members: [
            "signal",
            "wait",
            "timestamp",
+            "update_signal",
+            "update_wait",
            "bind",
            "submit",
+        ]
+        show_source: false
+
+::: tinygrad.runtime.support.hcq.HWComputeQueue
+    options:
+        members: [
            "memory_barrier",
            "exec",
+            "update_exec",
+        ]
+        show_source: false
+
+::: tinygrad.runtime.support.hcq.HWCopyQueue
+    options:
+        members: [
            "copy",
+            "update_copy",
+        ]
+        show_source: false
+
+#### Implementing custom commands
+
+To implement custom commands in the queue, use the @hcq_command decorator for your command implementations.
+
+::: tinygrad.runtime.support.hcq.hcq_command
+    options:
+        members: [
+            "copy",
+            "update_copy",
        ]
        show_source: false

@@ -52,11 +80,11 @@ 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) \
-         .submit(your_device)
+HWComputeQueue().timestamp(signal) \
+                .signal(signal, value_to_fire) \
+                .submit(your_device)

 signal.wait(value_to_fire)
 signaled_value = signal.value # should be the same as `value_to_fire`
@@ -106,17 +134,18 @@ Backends must adhere to the `HCQBuffer` protocol when returning allocation resul
        members: true
        show_source: false

-**Lifetime**: The `HCQArgsState` is passed to `HWQueue.exec` and is guaranteed not to be freed until `HWQueue.submit` for the same queue is called.
+**Lifetime**: The `HCQArgsState` is passed to `HWComputeQueue.exec` and is guaranteed not to be freed until `HWComputeQueue.submit` for the same queue is called.

 ### Synchronization

 HCQ-compatible devices use a global timeline signal for synchronizing all operations. This mechanism ensures proper ordering and completion of tasks across the device. By convention, `self.timeline_value` points to the next value to signal. So, to wait for all previous operations on the device to complete, wait for `self.timeline_value - 1` value. The following Python code demonstrates the typical usage of signals to synchronize execution to other operations on the device:

 ```python
-HWQueue().wait(your_device.timeline_signal, your_device.timeline_value - 1) \
-         .exec(...)
-         .signal(your_device.timeline_signal, your_device.next_timeline()) \
-         .submit(your_device)
+HWComputeQueue().wait(your_device.timeline_signal, your_device.timeline_value - 1) \
+                .exec(...)
+                .signal(your_device.timeline_signal, your_device.timeline_value) \
+                .submit(your_device)
+your_device.timeline_value += 1

 # Optionally wait for execution
 your_device.timeline_signal.wait(your_device.timeline_value - 1)
@@ -124,5 +153,5 @@ your_device.timeline_signal.wait(your_device.timeline_value - 1)

 ## HCQGraph

-[HCQGraph](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/graph/hcq.py) is a core feature that implements `GraphRunner` for HCQ-compatible devices. `HCQGraph` builds static `HWQueue` for all operations per device. To optimize enqueue time, only the necessary parts of the queues are updated for each run using the symbolic variables, avoiding a complete rebuild.
+[HCQGraph](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/graph/hcq.py) is a core feature that implements `GraphRunner` for HCQ-compatible devices. `HCQGraph` builds a static `HWComputeQueue` and `HWCopyQueue` for all operations per device. To optimize enqueue time, only the necessary parts of the queues are updated for each run using the update APIs of the queues, avoiding a complete rebuild.
 Optionally, queues can implement a `bind` API, which allows further optimization by eliminating the need to copy the queues into the device ring.
@@ -1,109 +0,0 @@
-# Kernel Creation
-
-Tinygrad lazily builds up a graph of Tensor operations. The Tensor graph includes a mix of:
-
- Buffer and Assignment Ops: `BUFFER`, `BUFFER_VIEW`, `COPY`, `ASSIGN`
- Movement Ops: `RESHAPE`, `EXPAND`, `PERMUTE`, `PAD`, `SHRINK`, `FLIP`
- Compute Ops: `ADD`, `MUL`, `REDUCE_AXIS`, ...
-
-`Tensor.kernelize` creates the kernels and buffers needed to realize the output Tensor(s).
-
-## Kernelize flow
-
-Let's see how a multiply add Tensor graph becomes a fused elementwise kernel.
-
-```py
-# initialize 3 input buffers on the device
-a = Tensor([1]).realize()
-b = Tensor([2]).realize()
-c = Tensor([3]).realize()
-
-# create the Tensor graph
-mul = a*b
-out = mul+c
-
-print(mul) # <Tensor <UOp METAL (1,) int (<Ops.MUL: 48>, None)> on METAL with grad None>
-print(out) # <Tensor <UOp METAL (1,) int (<Ops.ADD: 52>, None)> on METAL with grad None>
-
-out.kernelize()
-
-print(mul) # <Tensor <UOp METAL (1,) int (<Ops.MUL: 48>, None)> on METAL with grad None>
-print(out) # <Tensor <UOp METAL (1,) int (<Ops.ASSIGN: 66>, None)> on METAL with grad None>
-```
-
-The multiply Tensor stays the same because it is fused. The output Tensor's UOp becomes a new ASSIGN UOp:
-
-```py
-print(out.uop)
-```
-
-The first source is the output BUFFER:
-
-```
-UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),
-  UOp(Ops.UNIQUE, dtypes.void, arg=6, src=()),))
-```
-
-And the second source is the KERNEL and its 4 buffer edges (output_buffer, a, b, c):
-
-```
-UOp(Ops.KERNEL, dtypes.void, arg=<Kernel 12 SINK(<Ops.STORE: 45>,) (__add__, __mul__)>, src=(
-  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
-    x1:=UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),
-    UOp(Ops.UNIQUE, dtypes.void, arg=6, src=()),)),
-  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
-     x1,
-    UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),)),
-  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
-     x1,
-    UOp(Ops.UNIQUE, dtypes.void, arg=3, src=()),)),
-  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
-     x1,
-    UOp(Ops.UNIQUE, dtypes.void, arg=5, src=()),)),))
-```
-
-KERNEL describes the compute AST, metadata and memory dependencies.
-
-BUFFER holds a reference to the device memory where the output will be stored.
-
-Once a Tensor is kernelized, all children will LOAD its BUFFER, instead of fusing it:
-
-```py
-child = out+2
-child.kernelize()
-print(child.uop.src[1].arg.ast)
-```
-
-```
-UOp(Ops.SINK, dtypes.void, arg=None, src=(
-  UOp(Ops.STORE, dtypes.void, arg=None, src=(
-    UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=0, src=()),
-    x2:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1,), strides=(0,), offset=0, mask=None, contiguous=True),)), src=()),
-    UOp(Ops.ADD, dtypes.int, arg=None, src=(
-      UOp(Ops.LOAD, dtypes.int, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=()),
-         x2,)),
-      UOp(Ops.CONST, dtypes.int, arg=2, src=(
-         x2,)),)),)),))
-```
-
-`Tensor.realize` will execute the kernels and write outputs to memory:
-
-```py
-Tensor.realize(out)
-print(out)        # <Tensor <UOp METAL (1,) int (<Ops.BUFFER: 23>, <buf real:True device:METAL size:1 dtype:dtypes.int offset:0>)> on METAL with grad None>
-print(out.item()) # 5
-```
-
-<hr />
-
-**Summary**
-
- The large Tensor graph is built from a mix of data, compute and movement Ops.
-
- `Tensor.kernelize` splits the Tensor graph into data (BUFFER), compute (KERNEL) and links dependencies with ASSIGN.
-
- `Tensor.realize` executes KERNELs on device and replaces the Tensor graph with just a BUFFER.
-
- Kernelize can be called multiple times on a Tensor. This allows for incrementally building the kernel fusion layout of a large Tensor graph, without having to call `realize` or `schedule`.
@@ -1,66 +0,0 @@
-# tinygrad directory layout
-
-This explains the flow of a big graph down to programs.
-
-Directories are listed in order of how they are processed.
-
---
-
-## tinygrad/schedule
-
-Group UOps into kernels.
-
-::: tinygrad.schedule.kernelize.get_kernelize_map
-    options:
-        members: false
-        show_labels: false
-        show_source: false
-
---
-
-## tinygrad/opt
-
-Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.
-
-::: tinygrad.opt.get_optimized_ast
-    options:
-        members: false
-        show_labels: false
-        show_source: false
-
---
-
-## tinygrad/codegen
-
-Transform the optimized ast into a linearized list of UOps.
-
-::: tinygrad.codegen.full_rewrite
-    options:
-        members: false
-        show_labels: false
-        show_source: false
-
---
-
-## tinygrad/renderer
-
-Transform the linearized list of UOps into a program, represented as a string.
-
-::: tinygrad.renderer.Renderer
-    options:
-        members:
-            - render
-        show_labels: false
-        show_source: false
-
---
-
-## tinygrad/engine
-
-Abstracted high level interface to the runtimes.
-
-::: tinygrad.engine.realize.get_program
-    options:
-        members: false
-        show_labels: false
-        show_source: false
@@ -36,9 +36,9 @@ The `Allocator` class is responsible for managing memory on the device. There is

 ### Program

-The `Program` class is created for each loaded program. It is responsible for executing the program on the device. As an example, here is a `CPUProgram` implementation which loads program and runs it.
+The `Program` class is created for each loaded program. It is responsible for compiling and executing the program on the device. As an example, here is a `ClangProgram` implementation which loads program and runs it.

-::: tinygrad.runtime.ops_cpu.CPUProgram
+::: tinygrad.runtime.ops_clang.ClangProgram
    options:
        members: true

@@ -1,71 +0,0 @@
-# speed in tinygrad
-
-## Overview
-
-Speed refers to many different things. To break it down to four, there's:
-
- Compile Speed (Python)
- Execution Speed (driver)
- Model Speed (scheduler)
- Kernel Speed (codegen)
-
-## Compile Speed (Python)
-
-This is how long the first run of your model takes. It's limited largely by the runtime of the Python doing UOp rewrites. Currently it's a bit slow, but on par with torch.compile. It gets even slower if you are using BEAM, since that's compiling many variants of each kernel.
-
-This will be improved by writing faster graph_rewrite, doing less graph_rewrite, and better parallelization.
-
-## Execution Speed (driver)
-
-After your model is compiled, you are often using the `TinyJIT`. tinygrad has the best execution speed of any framework because it usually bypasses the GPU driver and prebuilds the command queue. It's tons faster than normal CUDA, and often even faster than CUDA Graph.
-
-There's very little to improve here, as this is almost never the bottleneck.
-
-## Model Speed (scheduler)
-
-The scheduler determines how operations are grouped into kernels and which Tensors are written to memory. This is currently a big bottleneck of training speed.
-
-The decisions are often not obvious. For example, when is it worth recomputing an arithmetic operation instead of storing and loading from memory? Example:
-
-```python
-from tinygrad import Tensor
-a = Tensor.rand(100)
-b = Tensor.rand(100)
-c = Tensor.rand(100)
-d = Tensor.rand(100)
-out1 = a+b+c
-out2 = a+b+d
-Tensor.realize(out1, out2)
-```
-
-The real answer is obvious, compute both `out1` and `out2` in the same kernel. But you can't always do that. If you can't, should `a+b` first be saved to a subbuffer? Or should both the `out1` and `out2` kernels recompute `a+b`?
-
-In this case: with recompute (6 reads + 2 writes), no recompute (6 reads + 3 writes), so we should probably recompute. However, once you add movement ops and casts this is even harder to figure out. tinygrad doesn't yet have a systematic way to do it.
-
-## Kernel Speed (codegen)
-
-Given that you have decided how the model ops will be grouped and what will be written to memory, kernel speed determines how fast that operation is done. This is what BEAM changes, it searches over a set of equivalent kernels which all perform the same operation and finds the one which performs the task the fastest.
-
-In `kernel.py` we have a set of `OptOps`, these control the parameters of the speed optimizations applied to the kernel.
-
-### Memory
-
-The main bottleneck in most kernels is accessing memory. In a freshman algorithms class, you'll learn about cache aware matrix multiplication, and this is all forms of that. While the same math is run, the order in which you run it can have large impacts on the speed depending on if the data you are loading. OptOps will change this order.
-
-Memory, even cache, is often much slower than accessing the register file. The amount of times data is used in math is called the "arithmetic intensity". For operations like BS=1 GEMV, the arithmetic intensity is 1, but for GEMMs and convs it can be much higher. OptOps like UPCAST and UNROLL can increase this, but be careful of making them too large, as if there's too much register pressure on the GPU the warp scheduler may not be able to fit many warps, or even worse, it could be spilling to local memory.
-
-4090s have 1 TB/s of ram bandwidth and ~160 TFLOPS of compute, so you need to use each loaded value ~100 times. The L1 cache has around 40 TB/s of bandwidth, so in order to get full compute utilization you need to use each value ~4 times.
-
-A lot of work can still be done here. For example, we never copy the inputs to on chip SRAM, but this is often quite helpful for kernel speed. Also, we aren't doing a good job with L2 cache awareness (the locals handle L1 quite well)
-
-### Tensor Cores
-
-Many accelerators have Tensor Cores / MAC arrays / systolic arrays. The main value of these is that, since they are 2-D, they create an n^2 ratio between the compute and the input data.
-
-GPUs use Tensor Cores instead of MAC arrays to fit better in the GPU warp paradigm. This is because the output of Tensor Cores is O(n) wrt the input, while the output of MAC arrays like the AMX is O(n^2)
-
-We have a simple framework in tinygrad for adding these ALU blocks and achieving good performance from them.
-
-### Indexing
-
-Indexing determines the address of the memory we need to load. GPUs often have less integer math resources than floating point math, so this can sometimes be the bottleneck. We have a symbolic math engine in our rewrite rules to simplify indexing before it's emitted to the kernel. Newer NVIDIA GPUs have a "Tensor Memory Accelerator" to assist with fast indexing, however, this is not supported in tinygrad yet.
@@ -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.UOps
    options:
        members: true
        members_order: source
@@ -30,35 +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)
-GPU                 | [1]        | enable the GPU (OpenCL) backend
+DEBUG               | [1-6]      | enable debugging output, with 4 you get operations, timings, speed, generated code and more
+GPU                 | [1]        | enable the GPU 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 (Clang) backend
+METAL_XCODE         | [1]        | enable Metal using macOS Xcode SDK
+CLANG               | [1]        | enable Clang backend
 LLVM                | [1]        | enable LLVM backend
 BEAM                | [#]        | number of beams in kernel beam search
+GRAPH               | [1]        | create a graph of all operations (requires graphviz)
+GRAPHUOPS           | [1]        | create a graph of uops (requires graphviz and saves at /tmp/uops.{svg,dot})
+GRAPHPATH           | [/path/to] | where to put the generated graph
 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
 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.
+PROFILE             | [1]        | enable output of [perfetto](https://ui.perfetto.dev/) compatible profile. This feature is supported in NV and AMD 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
+JIT                 | [0-2]      | 0=disabled, 1=[jit enabled](quickstart.md#jit) (default), 2=jit enabled, but graphs are disabled
@@ -28,10 +28,6 @@ To understand what training looks like in tinygrad, you should read `beautiful_m

 We have a [quickstart guide](quickstart.md) and a [showcase](showcase.md)

-## tinygrad Stack
-
-<img src="./tinygrad_vs_others.png" alt="Tinygrad vs others" style="max-width: 1000px; height: auto;" />
-
 ## Differences from PyTorch

 If you are migrating from PyTorch, welcome. Most of the API is the same. We hope you will find tinygrad both familiar and somehow more "correct feeling"
@@ -42,7 +38,7 @@ 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

@@ -17,7 +17,7 @@ from tinygrad import Device
 print(Device.DEFAULT)
 ```

-You will see `CUDA` here on a GPU instance, or `CPU` here on a CPU instance.
+You will see `CUDA` here on a GPU instance, or `CLANG` here on a CPU instance.

 ## A simple model

@@ -29,12 +29,4 @@
 ::: tinygrad.nn.state.get_state_dict
 ::: tinygrad.nn.state.get_parameters
 ::: tinygrad.nn.state.load_state_dict
-::: tinygrad.nn.state.tar_extract
-    options:
-        show_signature: false
-        separate_signature: false
 ::: tinygrad.nn.state.torch_load
-    options:
-        show_signature: false
-        separate_signature: false
-::: tinygrad.nn.state.gguf_load
@@ -110,7 +110,7 @@ class TinyNet:

  def __call__(self, x):
    x = self.l1(x)
-    x = x.leaky_relu()
+    x = x.leakyrelu()
    x = self.l2(x)
    return x

@@ -118,7 +118,7 @@ net = TinyNet()
 ```

 We can see that the forward pass of our neural network is just the sequence of operations performed on the input tensor `x`.
-We can also see that functional operations like `leaky_relu` are not defined as classes and instead are just methods we can just call.
+We can also see that functional operations like `leakyrelu` are not defined as classes and instead are just methods we can just call.
 Finally, we just initialize an instance of our neural network, and we are ready to start training it.

 ## Training
@@ -165,7 +165,7 @@ from extra.datasets import fetch_mnist
 Now we have everything we need to start training our neural network.
 We will be training for 1000 steps with a batch size of 64.

-We use `with Tensor.train()` to set the internal flag `Tensor.training` to `True` during training.
+We use `with Tensor.train()` set the internal flag `Tensor.training` to `True` during training.
 Upon exit, the flag is restored to its previous value by the context manager.

 ```python
@@ -302,4 +302,7 @@ You can find a full list and their descriptions in [env_vars.md](env_vars.md).

 ### Visualizing the Computation Graph

-It is possible to visualize the computation graph of a neural network using VIZ=1.
+It is possible to visualize the computation graph of a neural network using [graphviz](https://graphviz.org/).
+
+This is easily done by running a single pass (forward or backward!) of the neural network with the environment variable `GRAPH` set to `1`.
+The graph will be saved to `/tmp/net.svg` by default.
@@ -1,293 +0,0 @@
-#!/usr/bin/env python3
-
-# this file is a "ramp" for people new to tinygrad to think about how to approach it
-# it is runnable and editable.
-# whenever you see stuff like DEBUG=2 or CPU=1 discussed, these are environment variables
-# in a unix shell like bash `DEBUG=2 CPU=1 python docs/ramp.py`
-
-# this pip installs tinygrad master for the system
-# the -e allows you to edit the tinygrad folder and update system tinygrad
-# tinygrad is pure Python, so you are encouraged to do this
-# git pull in the tinygrad directory will also get you the latest
-"""
-git clone https://github.com/tinygrad/tinygrad.git
-cd tinygrad
-python3 -m pip install -e .
-"""
-
-# %% ********
-print("******* PART 1 *******")
-
-# we start with a Device.
-# a Device is where Tensors are stored and compute is run
-# tinygrad autodetects the best device on your system and makes it the DEFAULT
-from tinygrad import Device
-print(Device.DEFAULT)  # on Mac, you can see this prints METAL
-
-# now, lets create a Tensor
-from tinygrad import Tensor, dtypes
-t = Tensor([1,2,3,4])
-
-# you can see this Tensor is on the DEFAULT device with int dtype and shape (4,)
-assert t.device == Device.DEFAULT
-assert t.dtype == dtypes.int
-assert t.shape == (4,)
-
-# unlike in torch, if we print it, it doesn't print the contents
-# this is because tinygrad is lazy
-# this Tensor has not been computed yet
-print(t)
-# <Tensor <UOp METAL (4,) int (<Ops.COPY: 7>, None)> on METAL with grad None>
-
-# the ".uop" property on Tensor contains the specification of how to compute it
-print(t.uop)
-"""
-UOp(Ops.COPY, dtypes.int, arg=None, src=(
-  UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=0, src=()),
-    UOp(Ops.DEVICE, dtypes.void, arg='PYTHON', src=()),)),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# as you can see, it's specifying a copy from PYTHON device
-# which is where the [1,2,3,4] array lives
-
-# UOps are the specification language in tinygrad
-# they are immutable and form a DAG
-# they have a "Ops", a "dtype", a tuple of srcs (parents), and an arg
-
-t.realize()
-# if we want to "realize" a tensor, we can with the "realize" method
-# now when we look at the uop, it's changed
-print(t.uop)
-"""
-UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-  UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# the copy was actually run, and now the "uop" of the Tensor is just a BUFFER
-# if you run this script with DEBUG=2 in the environment, you can see the copy happen
-# *** METAL      1 copy       16,   METAL <- PYTHON ...
-
-# now let's do some compute
-# we look at the uop to see the specification of the compute
-t_times_2 = t * 2
-print(t_times_2.uop)
-"""
-UOp(Ops.MUL, dtypes.int, arg=None, src=(
-  UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-    x2:=UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),)),
-  UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-    UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-      UOp(Ops.CONST, dtypes.int, arg=2, src=(
-        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
-           x2,)),)),)),)),))
-"""
-# the BUFFER from above is being multiplied by a CONST 2
-# it's RESHAPEd and EXPANDed to broadcast the CONST to the BUFFER
-
-# we can check the result with
-assert t_times_2.tolist() == [2, 4, 6, 8]
-
-# UOps are both immutable and globally unique
-# if i multiply the Tensor by 4 twice, these result Tensors will have the same uop specification
-t_times_4_try_1 = t * 4
-t_times_4_try_2 = t * 4
-assert t_times_4_try_1.uop is t_times_4_try_2.uop
-# the specification isn't just the same, it's the exact same Python object
-assert t_times_4_try_1 is not t_times_4_try_2
-# the Tensor is a different Python object
-
-# if we realize `t_times_4_try_1` ...
-t_times_4_try_1.realize()
-print(t_times_4_try_2.uop)
-"""
-UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-  UOp(Ops.UNIQUE, dtypes.void, arg=4, src=()),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# ... `t_times_4_try_2` also becomes the same BUFFER
-assert t_times_4_try_1.uop is t_times_4_try_2.uop
-# so this print doesn't require any computation, just a copy back to the CPU so we can print it
-print("** only the copy start")
-print(t_times_4_try_2.tolist())  # [4, 8, 12, 16]
-print("** only the copy end")
-# you can confirm this with DEBUG=2, seeing what's printed in between the "**" prints
-
-# tinygrad has an auto differentiation engine that operates according to these same principles
-# the derivative of "log(x)" is "1/x", and you can see this on line 20 of gradient.py
-t_float = Tensor([3.0])
-t_log = t_float.log()
-t_log_grad, = t_log.sum().gradient(t_float)
-# due to how log is implemented, this gradient contains a lot of UOps
-print(t_log_grad.uop)
-# ...not shown here...
-# but if you run with DEBUG=4 (CPU=1 used here for simpler code), you can see the generated code
-"""
-void E_(float* restrict data0, float* restrict data1) {
-  float val0 = *(data1+0);
-  *(data0+0) = (0.6931471805599453f*(1/(val0*0.6931471805599453f)));
-}
-"""
-# the derivative is close to 1/3
-assert (t_log_grad.item() - 1/3) < 1e-6
-
-# %% ********
-print("******* PART 2 *******")
-
-# we redefine the same t here so this cell can run on it's own
-from tinygrad import Tensor
-t = Tensor([1,2,3,4])
-
-# what's above gives you enough of an understanding to go use tinygrad as a library
-# however, a lot of the beauty of tinygrad is in how easy it is to interact with the internals
-# NOTE: the APIs here are subject to change
-
-t_plus_3_plus_4 = t + 3 + 4
-print(t_plus_3_plus_4.uop)
-"""
-UOp(Ops.ADD, dtypes.int, arg=None, src=(
-  UOp(Ops.ADD, dtypes.int, arg=None, src=(
-    UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-      UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-      x3:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
-    UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-      UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-        UOp(Ops.CONST, dtypes.int, arg=3, src=(
-          x7:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
-             x3,)),)),)),)),)),
-  UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-    UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-      UOp(Ops.CONST, dtypes.int, arg=4, src=(
-         x7,)),)),)),))
-"""
-# you can see it's adding both 3 and 4
-
-# but by the time we are actually running the code, it's adding 7
-# `kernelize` will simplify and group the operations in the graph into kernels
-t_plus_3_plus_4.kernelize()
-print(t_plus_3_plus_4.uop)
-"""
-UOp(Ops.ASSIGN, dtypes.int, arg=None, src=(
-  x0:=UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=7, src=()),
-    x2:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
-  UOp(Ops.KERNEL, dtypes.void, arg=<Kernel 12 SINK(<Ops.STORE: 48>,) (__add__,)>, src=(
-     x0,
-    UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-      UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-       x2,)),)),))
-"""
-# ASSIGN has two srcs, src[0] is the BUFFER that's assigned to, and src[1] is the thing to assign
-# src[1] is the GPU Kernel that's going to be run
-# we can get the ast of the Kernel as follows
-kernel_ast = t_plus_3_plus_4.uop.src[1].arg.ast
-
-# almost everything in tinygrad functions as a rewrite of the UOps
-# the codegen rewrites the ast to a simplified form ready for "rendering"
-from tinygrad.codegen import full_rewrite_to_sink
-rewritten_ast = full_rewrite_to_sink(kernel_ast)
-print(rewritten_ast)
-"""
-UOp(Ops.SINK, dtypes.void, arg=None, src=(
-  UOp(Ops.STORE, dtypes.void, arg=None, src=(
-    UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
-      UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=0, src=()),
-      x3:=UOp(Ops.SPECIAL, dtypes.int, arg=('gidx0', 4), src=()),)),
-    UOp(Ops.ADD, dtypes.int, arg=None, src=(
-      UOp(Ops.LOAD, dtypes.int, arg=None, src=(
-        UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=1, src=()),
-           x3,)),)),
-      UOp(Ops.CONST, dtypes.int, arg=7, src=()),)),)),))
-"""
-# you can see at this point we are adding 7, not 3 and 4
-
-# with DEBUG=4, we can see the code.
-# since optimizations are on, it UPCASTed the operation, explicitly writing out all 4 +7s
-t_plus_3_plus_4.realize()
-"""
-void E_4n2(int* restrict data0, int* restrict data1) {
-  int val0 = *(data1+0);
-  int val1 = *(data1+1);
-  int val2 = *(data1+2);
-  int val3 = *(data1+3);
-  *(data0+0) = (val0+7);
-  *(data0+1) = (val1+7);
-  *(data0+2) = (val2+7);
-  *(data0+3) = (val3+7);
-}
-"""
-# the function name E_4n2 is "E" for elementwise op (as opposed to "r" for reduce op)
-# "4" for the size, and "n2" for name deduping (it's the 3rd function with the same E and 4 in this session)
-# when you print the name with DEBUG=2, you'll see the 4 is yellow, meaning that it's upcasted
-# if you run with NOOPT=1 ...
-"""
-void E_4n2(int* restrict data0, int* restrict data1) {
-  for (int ridx0 = 0; ridx0 < 4; ridx0++) {
-    int val0 = *(data1+ridx0);
-    *(data0+ridx0) = (val0+7);
-  }
-}
-"""
-# ... you get this unoptimized code with a loop and the 4 is blue (for global). the color code is in kernel.py
-
-# %% ********
-print("******* PART 3 *******")
-
-# now, we go even lower and understand UOps better and how the graph rewrite engine works.
-# it's much simpler than what's in LLVM or MLIR
-
-from tinygrad import dtypes
-from tinygrad.uop.ops import UOp, Ops
-
-# first, we'll construct some const UOps
-a = UOp(Ops.CONST, dtypes.int, arg=2)
-b = UOp(Ops.CONST, dtypes.int, arg=2)
-
-# if you have been paying attention, you should know these are the same Python object
-assert a is b
-
-# UOps support normal Python math operations, so a_plus_b expresses the spec for 2 + 2
-a_plus_b = a + b
-print(a_plus_b)
-"""
-UOp(Ops.ADD, dtypes.int, arg=None, src=(
-  x0:=UOp(Ops.CONST, dtypes.int, arg=2, src=()),
-   x0,))
-"""
-
-# we could actually render this 2+2 into a language like c and run it
-# or, we can use tinygrad's graph rewrite engine to "constant fold"
-
-from tinygrad.uop.ops import graph_rewrite, UPat, PatternMatcher
-
-# a `PatternMatcher` is a list of tuples. for each element in the list:
-# [0] is the pattern to match, and [1] is the function to run.
-# this function can return either a UOp to replace the pattern with, or None to not replace
-simple_pm = PatternMatcher([
-  (UPat(Ops.ADD, src=(UPat(Ops.CONST, name="c1"), UPat(Ops.CONST, name="c2"))),
-   lambda c1,c2: UOp(Ops.CONST, dtype=c1.dtype, arg=c1.arg+c2.arg)),
-])
-# this pattern matches the addition of two CONST and rewrites it into a single CONST UOp
-
-# to actually apply the pattern to a_plus_b, we use graph_rewrite
-a_plus_b_simplified = graph_rewrite(a_plus_b, simple_pm)
-print(a_plus_b_simplified)
-"""
-UOp(Ops.CONST, dtypes.int, arg=4, src=())
-"""
-# 2+2 is in fact, 4
-
-# we can also use syntactic sugar to write the pattern nicer
-simpler_pm = PatternMatcher([
-  (UPat.cvar("c1")+UPat.cvar("c2"), lambda c1,c2: c1.const_like(c1.arg+c2.arg))
-])
-assert graph_rewrite(a_plus_b, simple_pm) is graph_rewrite(a_plus_b, simpler_pm)
-# note again the use of is, UOps are immutable and globally unique
-
-# %% ********
-
-# that brings you to an understanding of the most core concepts in tinygrad
-# you can run this with VIZ=1 to use the web based graph rewrite explorer
-# hopefully now you understand it. the nodes in the graph are just UOps
@@ -1,74 +1,14 @@
 # Runtimes

-tinygrad supports various runtimes, enabling your code to scale across a wide range of devices. The default runtime can be automatically selected based on the available hardware, or you can force a specific runtime to be default using environment variables (e.g., `CPU=1`).
+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., `CLANG=1`).

 | 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/RDNA4 series GPUs. You can select one of the interfaces for communication by setting `AMD_IFACE=(KFD|PCI)`. See [AMD interfaces](#amd-interfaces) for more details. |
+| [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/v0.3.0). |
-
-## Interoperability
-
-tinygrad provides interoperability with OpenCL and PyTorch, allowing efficient tensor data sharing between frameworks through the `Tensor.from_blob` API. This enables zero-copy operations by working directly with external memory pointers.
-
-**Important**: When using external memory pointers with tinygrad tensors, you must ensure these pointers remain valid throughout the entire lifetime of the tinygrad tensor to prevent memory corruption.
-
-### `CUDA`/`METAL` PyTorch Interoperability
-
-You can seamlessly work with CUDA/MPS tensors between PyTorch and tinygrad without data copying:
-```python
-from tinygrad.dtype import _from_torch_dtype
-tensor1 = torch.tensor([1.0, 2.0, 3.0], device=torch.device("cuda"))
-tiny_tensor1 = Tensor.from_blob(tensor1.data_ptr(), tensor1.shape, dtype=_from_torch_dtype(tensor1.dtype), device='CUDA')
-
-# Before tinygrad calculations, mps needs to be synchronized to make sure data is valid.
-if data.device.type == "mps": torch.mps.synchronize()
-else: torch.cuda.synchronize()
-
-x = (tiny_tensor1 + 1).realize()
-```
-
-### `QCOM` OpenCL Interoperability
-
-tinygrad supports OpenCL interoperability on `QCOM` backend.
-
-Buffer interop allows direct access to OpenCL memory buffers:
-```python
-# create raw opencl buffer.
-cl_buf = cl.clCreateBuffer(cl_context, cl.CL_MEM_READ_WRITE, 0x100, None, status := ctypes.c_int32())
-
-# extract pointers
-cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_buf), 8).cast('Q')[0]
-rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
-
-# create tiny tensor
-tiny = Tensor.from_blob(rawbuf_ptr, (8, 8), dtype=dtypes.int, device='QCOM')
-```
-
-And the same for the images:
-```python
-# create cl image.
-cl_img = cl.clCreateImage2D(cl_context, cl.CL_MEM_READ_WRITE, cl.cl_image_format(cl.CL_RGBA, cl.CL_FLOAT), w, h, 0, None, status := ctypes.c_int32())
-
-# extract pointers
-cl_buf_desc_ptr = to_mv(ctypes.addressof(cl_img), 8).cast('Q')[0]
-rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
-
-# create tiny tensor
-tiny = Tensor.from_blob(rawbuf_ptr, (h*w*4,), dtype=dtypes.imagef((h,w)), device='QCOM')
-```
-
-## AMD Interfaces
-AMD backend supports several interfaces for communicating with devices:
-
-* `KFD`: uses the amdgpu driver
-* `PCI`: uses the [AM driver](developer/am.md)
-
-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.
+| [CLANG (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_clang.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
+| [LLVM](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_llvm.py) | Runs on CPU using the LLVM compiler infrastructure | `llvmlite` package installed |
@@ -5,26 +5,18 @@
 ::: tinygrad.Tensor.ones
 ::: tinygrad.Tensor.full
 ::: tinygrad.Tensor.arange
-::: tinygrad.Tensor.linspace
 ::: tinygrad.Tensor.eye
 ::: tinygrad.Tensor.full_like
 ::: tinygrad.Tensor.zeros_like
 ::: tinygrad.Tensor.ones_like
-
-## Creation (external)
-
 ::: tinygrad.Tensor.from_blob
-::: tinygrad.Tensor.from_url

 ## Creation (random)

 ::: tinygrad.Tensor.manual_seed
 ::: tinygrad.Tensor.rand
-::: tinygrad.Tensor.rand_like
 ::: tinygrad.Tensor.randn
-::: tinygrad.Tensor.randn_like
 ::: tinygrad.Tensor.randint
-::: tinygrad.Tensor.randperm
 ::: tinygrad.Tensor.normal
 ::: tinygrad.Tensor.uniform
 ::: tinygrad.Tensor.scaled_uniform
@@ -13,16 +13,10 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.sin
 ::: tinygrad.Tensor.cos
 ::: tinygrad.Tensor.tan
-::: tinygrad.Tensor.asin
-::: tinygrad.Tensor.acos
-::: tinygrad.Tensor.atan
 ::: tinygrad.Tensor.trunc
 ::: tinygrad.Tensor.ceil
 ::: tinygrad.Tensor.floor
 ::: tinygrad.Tensor.round
-::: tinygrad.Tensor.isinf
-::: tinygrad.Tensor.isnan
-::: tinygrad.Tensor.isfinite
 ::: tinygrad.Tensor.lerp
 ::: tinygrad.Tensor.square
 ::: tinygrad.Tensor.clamp
@@ -35,11 +29,8 @@ 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
-::: tinygrad.Tensor.selu
 ::: tinygrad.Tensor.swish
 ::: tinygrad.Tensor.silu
 ::: tinygrad.Tensor.relu6
@@ -51,10 +42,9 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.asinh
 ::: tinygrad.Tensor.acosh
 ::: tinygrad.Tensor.hardtanh
-::: tinygrad.Tensor.erf
 ::: tinygrad.Tensor.gelu
 ::: tinygrad.Tensor.quick_gelu
-::: tinygrad.Tensor.leaky_relu
+::: tinygrad.Tensor.leakyrelu
 ::: tinygrad.Tensor.mish
 ::: tinygrad.Tensor.softplus
 ::: tinygrad.Tensor.softsign
@@ -65,19 +55,13 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.sub
 ::: tinygrad.Tensor.mul
 ::: tinygrad.Tensor.div
-::: tinygrad.Tensor.idiv
-::: tinygrad.Tensor.mod
-::: tinygrad.Tensor.bitwise_xor
-::: tinygrad.Tensor.bitwise_and
-::: tinygrad.Tensor.bitwise_or
-::: tinygrad.Tensor.bitwise_not
+::: tinygrad.Tensor.xor
 ::: tinygrad.Tensor.lshift
 ::: tinygrad.Tensor.rshift
 ::: tinygrad.Tensor.pow
 ::: tinygrad.Tensor.maximum
 ::: tinygrad.Tensor.minimum
 ::: tinygrad.Tensor.where
-::: tinygrad.Tensor.copysign

 ## Casting Ops

@@ -10,7 +10,6 @@

 ## Movement (high level)

-::: tinygrad.Tensor.__getitem__
 ::: tinygrad.Tensor.gather
 ::: tinygrad.Tensor.cat
 ::: tinygrad.Tensor.stack
@@ -18,14 +17,10 @@
 ::: tinygrad.Tensor.repeat_interleave
 ::: tinygrad.Tensor.split
 ::: tinygrad.Tensor.chunk
-::: tinygrad.Tensor.unfold
-::: tinygrad.Tensor.meshgrid
 ::: tinygrad.Tensor.squeeze
 ::: tinygrad.Tensor.unsqueeze
+::: tinygrad.Tensor.pad2d
 ::: tinygrad.Tensor.T
 ::: tinygrad.Tensor.transpose
 ::: tinygrad.Tensor.flatten
 ::: tinygrad.Tensor.unflatten
-::: tinygrad.Tensor.diag
-::: tinygrad.Tensor.roll
-::: tinygrad.Tensor.rearrange
@@ -6,10 +6,8 @@
 ::: tinygrad.Tensor.min
 ::: tinygrad.Tensor.any
 ::: tinygrad.Tensor.all
-::: tinygrad.Tensor.isclose
 ::: tinygrad.Tensor.mean
 ::: tinygrad.Tensor.var
-::: tinygrad.Tensor.var_mean
 ::: tinygrad.Tensor.std
 ::: tinygrad.Tensor.std_mean
 ::: tinygrad.Tensor.softmax
@@ -23,24 +21,15 @@

 ::: tinygrad.Tensor.avg_pool2d
 ::: tinygrad.Tensor.max_pool2d
-::: tinygrad.Tensor.max_unpool2d
 ::: tinygrad.Tensor.conv2d
 ::: tinygrad.Tensor.conv_transpose2d
 ::: tinygrad.Tensor.dot
 ::: tinygrad.Tensor.matmul
 ::: tinygrad.Tensor.einsum
 ::: tinygrad.Tensor.cumsum
-::: tinygrad.Tensor.cummax
 ::: tinygrad.Tensor.triu
 ::: tinygrad.Tensor.tril
 ::: tinygrad.Tensor.interpolate
-::: tinygrad.Tensor.scatter
-::: tinygrad.Tensor.scatter_reduce
-::: tinygrad.Tensor.masked_select
-::: tinygrad.Tensor.masked_fill
-::: tinygrad.Tensor.sort
-::: tinygrad.Tensor.topk
-::: tinygrad.Tensor.multinomial

 ## Neural Network (functional)

@@ -55,4 +44,3 @@
 ::: tinygrad.Tensor.binary_crossentropy_logits
 ::: tinygrad.Tensor.sparse_categorical_crossentropy
 ::: tinygrad.Tensor.cross_entropy
-::: tinygrad.Tensor.nll_loss
@@ -25,15 +25,10 @@
 ::: tinygrad.Tensor.replace
 ::: tinygrad.Tensor.assign
 ::: tinygrad.Tensor.detach
-::: tinygrad.Tensor.clone
 ::: tinygrad.Tensor.to
 ::: tinygrad.Tensor.to_
 ::: tinygrad.Tensor.shard
 ::: tinygrad.Tensor.shard_
 ::: tinygrad.Tensor.contiguous
 ::: tinygrad.Tensor.contiguous_backward
-
-## Gradient
-
-::: tinygrad.Tensor.gradient
 ::: tinygrad.Tensor.backward
@@ -1,9 +0,0 @@
-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,
-];
@@ -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
@@ -3,19 +3,14 @@ start_tm = time.perf_counter()
 import math
 from typing import Tuple, cast
 import numpy as np
-from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes, Device
+from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes
 from tinygrad.helpers import partition, trange, getenv, Context
 from extra.lr_scheduler import OneCycleLR

-GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
-
-# override tinygrad defaults
 dtypes.default_float = dtypes.half
-Context(FUSE_ARANGE=1, FUSE_OPTIM=1).__enter__()

 # from https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
 batchsize = getenv("BS", 1024)
-assert batchsize % len(GPUS) == 0, f"{batchsize=} is not a multiple of {len(GPUS)=}"
 bias_scaler = 64
 hyp = {
  'opt': {
@@ -72,7 +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):
@@ -83,25 +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'])
@@ -118,37 +111,40 @@ 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()
+      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'])):
@@ -1,4 +1,4 @@
-# model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
+# 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
@@ -34,6 +34,7 @@ if __name__ == "__main__":
    return loss

  @TinyJit
+  @Tensor.test()
  def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100

  test_acc = float('nan')
@@ -1,35 +0,0 @@
-import sys, time
-from tinygrad import TinyJit, GlobalCounters, fetch, getenv
-from tinygrad.frontend.onnx import OnnxRunner
-from extra.onnx_helpers import get_example_inputs, validate
-
-def load_onnx_model(onnx_file):
-  run_onnx = OnnxRunner(onnx_file)
-  run_onnx_jit = TinyJit(lambda **kwargs: next(iter(run_onnx({k:v.to(None) for k,v in kwargs.items()}).values())), prune=True, optimize=True)
-  return run_onnx_jit, run_onnx.graph_inputs
-
-if __name__ == "__main__":
-  onnx_file = fetch(sys.argv[1])
-  run_onnx_jit, input_specs = load_onnx_model(onnx_file)
-  print("loaded model")
-
-  for i in range(3):
-    new_inputs = get_example_inputs(input_specs)
-    GlobalCounters.reset()
-    print(f"run {i}")
-    run_onnx_jit(**new_inputs)
-
-  # run 20 times
-  for _ in range(20):
-    new_inputs = get_example_inputs(input_specs)
-    GlobalCounters.reset()
-    st = time.perf_counter()
-    out = run_onnx_jit(**new_inputs)
-    mt = time.perf_counter()
-    val = out.numpy()
-    et = time.perf_counter()
-    print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {(et-st)*1e3:6.2f} ms")
-
-  if getenv("ORT"):
-    validate(onnx_file, new_inputs, rtol=1e-3, atol=1e-3)
-    print("model validated")
@@ -4,7 +4,7 @@ sys.path.append(os.getcwd())

 from io import StringIO
 from contextlib import redirect_stdout
-from tinygrad import Tensor, nn
+from tinygrad import Tensor, nn, Device, dtypes
 from tinygrad.helpers import Timing, colored, getenv, fetch
 from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
 from sentencepiece import SentencePieceProcessor
@@ -23,6 +23,8 @@ def create_fixed_tokenizer(output_file):
 # echo -en "write 2+2\nwrite hello world\ny\n" | TEMP=0 python3 examples/coder.py

 if __name__ == "__main__":
+  Tensor.no_grad = True
+
  # https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/config.json
  with Timing("create model: "):
    model = Transformer(4096, 14336, n_heads=32, n_layers=32, norm_eps=1e-5, vocab_size=32002, n_kv_heads=8, max_context=4096, jit=getenv("JIT", 1))
@@ -32,8 +34,8 @@ if __name__ == "__main__":
    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, 32, 32, 8)), strict=False)
-    nn.state.load_state_dict(model, fix_bf16(convert_from_huggingface(part2, 32, 32, 8)), strict=False)
+    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")
@@ -1,7 +1,7 @@
 from pathlib import Path
 from extra.models.efficientnet import EfficientNet
 from tinygrad.tensor import Tensor
-from tinygrad.nn.state import get_state_dict, safe_save, safe_load, load_state_dict
+from tinygrad.nn.state import safe_save
 from extra.export_model import export_model
 from tinygrad.helpers import getenv, fetch
 import ast
@@ -9,16 +9,12 @@ import ast
 if __name__ == "__main__":
  model = EfficientNet(0)
  model.load_from_pretrained()
-  dirname = Path(__file__).parent
-  # exporting a model that's loaded from safetensors doesn't work without loading in from safetensors first
-  # loading the state dict from a safetensor file changes the generated kernels
-  if getenv("WEBGPU"):
-    safe_save(get_state_dict(model), (dirname / "net.safetensors").as_posix())
-    load_state_dict(model, safe_load(str(dirname / "net.safetensors")))
-  mode = "clang" if getenv("CPU", "") != "" else "webgpu" if getenv("WEBGPU", "") != "" else ""
+  mode = "clang" if getenv("CLANG", "") != "" else "webgpu" if getenv("WEBGPU", "") != "" else "webgl" if getenv("WEBGL", "") != "" else ""
  prg, inp_sizes, out_sizes, state = export_model(model, mode, Tensor.randn(1,3,224,224))
-  if getenv("CPU", "") == "":
-    ext = "js" if getenv("WEBGPU", "") != "" else "json"
+  dirname = Path(__file__).parent
+  if getenv("CLANG", "") == "":
+    safe_save(state, (dirname / "net.safetensors").as_posix())
+    ext = "js" if getenv("WEBGPU", "") != "" or getenv("WEBGL", "") != "" else "json"
    with open(dirname / f"net.{ext}", "w") as text_file:
      text_file.write(prg)
  else:
@@ -68,6 +64,6 @@ if __name__ == "__main__":
    else printf("%s\\n", lbls[best_idx]);
  }""")

-    # CPU=1 python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/showcase/stable_diffusion_by_tinygrad.jpg
+    # CLANG=1 python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/showcase/stable_diffusion_by_tinygrad.jpg
    # category : 281 (tabby, tabby cat) with 9.452788
    print('\n'.join(cprog))
@@ -1,16 +1,15 @@
 # An example to compile a small Tensorflow model to extremely portable C code

 import os, sys
-os.environ["CPU"] = '1'
+os.environ["CLANG"] = '1'
 os.environ["JIT"] = '2'

 import numpy as np
 import subprocess
 import tensorflow as tf
 import tf2onnx
-from tinygrad.frontend.onnx import OnnxRunner
+from extra.onnx import get_run_onnx
 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 = get_run_onnx(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
@@ -117,7 +117,7 @@ def tts(
  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)
+  x_tst, x_tst_lengths = stn_tst.pad(((0, pad_length - init_shape[0]),), 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.
@@ -159,6 +159,7 @@ def init_vits(
  text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)

  # Load the model.
+  Tensor.no_grad = True
  if seed is not None:
    Tensor.manual_seed(seed)
    np.random.seed(seed)
@@ -220,6 +221,7 @@ def mp_output_stream(q: mp.Queue, counter: mp.Value, num_channels: int, sample_r
 if __name__ == "__main__":
  import nltk
  nltk.download("punkt")
+  Tensor.no_grad = True
  # Parse CLI arguments
  parser = argparse.ArgumentParser("Have a tiny conversation with tinygrad")

@@ -326,12 +326,11 @@ class Flux:
    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):
+def load_flow_model(name: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()}
+  state_dict = {k.replace("scale", "weight"): v for k, v in safe_load(fetch(urls[name])).items()}
  load_state_dict(model, state_dict)
  return model

@@ -421,7 +420,6 @@ if __name__ == "__main__":
  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")
@@ -463,7 +461,7 @@ if __name__ == "__main__":
  del T5, clip

  # load model
-  model = load_flow_model(args.name, args.model_path)
+  model = load_flow_model(args.name)

  # denoise initial noise
  x = denoise(model, **inp, timesteps=timesteps, guidance=args.guidance)
@@ -1,13 +1,13 @@
 #!/usr/bin/env python3
-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
+import argparse
+import numpy as np
+import tiktoken
+from tinygrad import Tensor, TinyJit, Device, GlobalCounters, Variable
+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
+from tinygrad.nn.state import torch_load, load_state_dict, get_state_dict

 MAX_CONTEXT = getenv("MAX_CONTEXT", 128)
 HALF = getenv("HALF")
@@ -85,10 +85,7 @@ class Transformer:
      seqlen = tokens.shape[1]
      tok_emb = self.wte(tokens)

-    # not symbolic when consuming the prompt
-    selected_pos = (0, seqlen) if start_pos.val == 0 else (start_pos, start_pos+1)
-    pos_emb = self.wpe(self.allpos.shrink((None, selected_pos)))
-
+    pos_emb = self.wpe(self.allpos.shrink((None, (start_pos, start_pos+seqlen))))
    h = tok_emb + pos_emb

    if HALF: h = h.half()
@@ -138,44 +135,14 @@ 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)

-  @staticmethod
-  def build_gguf(model_size: str):
-    q_type = model_size[len("gpt2_gguf_"):].upper()
-    fn = fetch(f"https://huggingface.co/PrunaAI/gpt2-GGUF-smashed/resolve/main/gpt2.{q_type}.gguf?download=true")
-    gguf_tensor = Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f"disk:{fn}").to(Device.DEFAULT)
-    kv_data, state_dict = gguf_load(gguf_tensor)
-
-    gpt2_params = {
-      "dim": kv_data["gpt2.embedding_length"], "n_heads": kv_data["gpt2.attention.head_count"],
-      "n_layers": kv_data["gpt2.block_count"], "norm_eps": kv_data["gpt2.attention.layer_norm_epsilon"],
-      "vocab_size": VOCAB_SIZE, "max_seq_len": kv_data["gpt2.context_length"],
-    }
-    def _remap_gguf_key(key: str):
-      replaces = [
-        ("blk.", "h."), (".attn_qkv.bias", ".attn.c_attn.bias"), (".attn_qkv.weight", ".attn.c_attn.weight"),
-        (".ffn_norm.bias", ".ln_2.bias"), (".ffn_norm.weight", ".ln_2.weight"), (".attn_norm.bias", ".ln_1.bias"),
-        (".attn_norm.weight", ".ln_1.weight"), (".attn_output.bias", ".attn.c_proj.bias"), (".attn_output.weight", ".attn.c_proj.weight"),
-        (".ffn_up.bias", ".mlp.c_fc.bias"), (".ffn_up.weight", ".mlp.c_fc.weight"), (".ffn_down.bias", ".mlp.c_proj.bias"),
-        (".ffn_down.weight", ".mlp.c_proj.weight"), ("token_embd.weight", "wte.weight"), ("output.weight", "lm_head.weight"),
-        ("output_norm.bias", "ln_f.bias"), ("output_norm.weight", "ln_f.weight"), ("position_embd.weight", "wpe.weight"),
-      ]
-      for ostr, ns in replaces: key = key.replace(ostr, ns)
-      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)
-    return GPT2(model, tiktoken.get_encoding("gpt2"))
-
  def __init__(self, model, tokenizer):
    self.model = model
    self.tokenizer = tokenizer
@@ -191,12 +158,11 @@ class GPT2:
      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()
+        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).numpy().tolist()
      start_pos = len(toks[0])
      for i,t in enumerate(tok): toks[i].append(t)
    return [self.tokenizer.decode(x) for x in toks]
@@ -204,6 +170,7 @@ class GPT2:
 # **** main code ****

 if __name__ == "__main__":
+  Tensor.no_grad = True
  print(f"using {Device.DEFAULT} backend")
  default_prompt = "What is the answer to life, the universe, and everything?"

@@ -221,9 +188,10 @@ if __name__ == "__main__":

  if args.seed is not None:
    Tensor.manual_seed(args.seed)
+    np.random.seed(args.seed)

  print(f"using {args.model_size}")
-  gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith("gpt2_gguf_") else GPT2.build(args.model_size)
+  gpt2 = GPT2.build(args.model_size)

  if args.benchmark != -1:
    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
@@ -1,15 +1,15 @@
+from typing import List, Tuple
 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.opt.kernel import Kernel
-from tinygrad.opt.heuristic import hand_coded_optimizations
-from tinygrad.uop.ops import Ops, sym_infer
+from tinygrad.codegen.kernel import Kernel
+from tinygrad.ops import UOps
 from tinygrad.device import Compiled
-from tinygrad.opt.search import beam_search, bufs_from_lin
+from tinygrad.engine.schedule import create_schedule
+from tinygrad.engine.search import time_linearizer, beam_search, bufs_from_lin
 from tinygrad.helpers import DEBUG, ansilen, getenv, colored, TRACEMETA
-from extra.optimization.helpers import time_linearizer
-from tinygrad.engine.realize import get_program
+from tinygrad.shape.symbolic import sym_infer

 def get_sched_resnet():
  mdl = ResNet50()
@@ -19,12 +19,12 @@ def get_sched_resnet():
  # 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]
+    targets = [out.lazydata]
    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)
+      targets += [x.lazydata for x in optim.schedule_step()]
+    sched = create_schedule(targets)
    print(f"schedule length {len(sched)}")
  return sched

@@ -43,16 +43,17 @@ def get_sched_bert():
  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
+  seen = set()
  for _ in range(2):
    lm_logits, seq_relationship_logits = mdl(input_ids, attention_mask, masked_positions, segment_ids)
-    targets = [lm_logits, seq_relationship_logits]
+    targets = [lm_logits.lazydata, seq_relationship_logits.lazydata]
    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)
+      targets += [x.lazydata for x in optim.schedule_step()]
+    sched = create_schedule(targets)
    print(f"schedule length {len(sched)}")
  return sched

@@ -66,7 +67,7 @@ if __name__ == "__main__":
  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]
+  sched = [x for x in sched if x.ast.op is UOps.SINK]

  # focus on one kernel
  if getenv("KERNEL", -1) >= 0: sched = sched[getenv("KERNEL", -1):getenv("KERNEL", -1)+1]
@@ -81,11 +82,11 @@ if __name__ == "__main__":
    rawbufs = bufs_from_lin(Kernel(si.ast))

    # "linearize" the op into uops in different ways
-    lins: list[tuple[Kernel, str]] = []
+    lins: List[Tuple[Kernel, str]] = []

    # always try hand coded opt
    lin = Kernel(si.ast, opts=device.renderer)
-    lin.apply_opts(hand_coded_optimizations(lin))
+    lin.hand_coded_optimizations()
    lins.append((lin, "HC"))

    # maybe try tensor cores
@@ -109,7 +110,7 @@ if __name__ == "__main__":
    choices = []
    for lin, nm in lins:
      tm = time_linearizer(lin, rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
-      ops = (prg:=get_program(lin.get_optimized_ast(), lin.opts)).estimates.ops
+      ops = (prg:=lin.to_program()).op_estimate
      gflops = sym_infer(ops, {k:k.min for k in lin.ast.variables()})*1e-9/tm
      choices.append((tm, gflops, lin, prg, nm))

@@ -122,12 +123,12 @@ if __name__ == "__main__":
    if getenv("SRC"):
      print(si.ast)
      print(lin.applied_opts)
-      print(get_program(lin.get_optimized_ast(), lin.opts).src)
+      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)
+    if (key := str([str(m) for m in si.metadata] if si.metadata is not None else None)) 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_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] if si.metadata is not None else ''}")
  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]:
@@ -7,11 +7,11 @@ 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
+from tinygrad.multi import MultiLazyBuffer

 cifar_mean = [0.4913997551666284, 0.48215855929893703, 0.4465309133731618]
 cifar_std = [0.24703225141799082, 0.24348516474564, 0.26158783926049628]
@@ -35,6 +35,8 @@ class UnsyncedBatchNorm:
    self.num_batches_tracked = Tensor.zeros(1, dtype=dtypes.int, requires_grad=False)

  def __call__(self, x:Tensor):
+    if isinstance(x.lazydata, MultiLazyBuffer): assert x.lazydata.axis is None or x.lazydata.axis == 0 and len(x.lazydata.lbs) == self.num_devices
+
    xr = x.reshape(self.num_devices, -1, *x.shape[1:]).cast(dtypes.float32)
    batch_mean, batch_invstd = self.calc_stats(xr)
    ret = xr.batchnorm(
@@ -112,13 +114,13 @@ class SpeedyResNet:
  def __call__(self, x, training=True):
    # pad to 32x32 because whitening conv creates 31x31 images that are awfully slow to compute with
    # TODO: remove the pad but instead let the kernel optimize itself
-    forward = lambda x: x.conv2d(self.whitening).pad((1,0,0,1)).sequential(self.net)
+    forward = lambda x: x.conv2d(self.whitening).pad2d((1,0,0,1)).sequential(self.net)
    return forward(x) if training else (forward(x) + forward(x[..., ::-1])) / 2.

 # hyper-parameters were exactly the same as the original repo
 bias_scaler = 58
 hyp = {
-  'seed' : 200,
+  'seed' : 209,
  'opt': {
    'bias_lr':            1.76 * bias_scaler/512,
    'non_bias_lr':        1.76 / 512,
@@ -145,7 +147,6 @@ hyp = {
  },
 }

-@Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1))
 def train_cifar():

  def set_seed(seed):
@@ -202,37 +203,24 @@ def train_cifar():
    idx_y = Tensor.arange(H, dtype=dtypes.int32).reshape((1,1,H,1))
    return (idx_x >= low_x) * (idx_x < (low_x + mask_size)) * (idx_y >= low_y) * (idx_y < (low_y + mask_size))

-  # Similar, but different enough.
-  def make_random_crop_indices(shape, mask_size) -> Tensor:
-    BS, _, H, W = shape
-    low_x = Tensor.randint(BS, low=0, high=W-mask_size).reshape(BS,1,1,1)
-    low_y = Tensor.randint(BS, low=0, high=H-mask_size).reshape(BS,1,1,1)
-    idx_x = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,1,mask_size))
-    idx_y = Tensor.arange(mask_size, dtype=dtypes.int32).reshape((1,1,mask_size,1))
-    return low_x, low_y, idx_x, idx_y
-
  def random_crop(X:Tensor, crop_size=32):
-    Xs, Ys, Xi, Yi = make_random_crop_indices(X.shape, crop_size)
-    return X.gather(-1, (Xs + Xi).expand(-1, 3, X.shape[2], -1)).gather(-2, ((Ys+Yi).expand(-1, 3, crop_size, crop_size)))
+    mask = make_square_mask(X.shape, crop_size)
+    mask = mask.expand((-1,3,-1,-1))
+    X_cropped = Tensor(X.numpy()[mask.numpy()])
+    return X_cropped.reshape((-1, 3, crop_size, crop_size))

-  def cutmix(X, Y, order, mask_size=3):
+  def cutmix(X:Tensor, Y:Tensor, mask_size=3):
+    # fill the square with randomly selected images from the same batch
    mask = make_square_mask(X.shape, mask_size)
-    X_patch, Y_patch = X[order], Y[order]
+    order = list(range(0, X.shape[0]))
+    random.shuffle(order)
+    X_patch = Tensor(X.numpy()[order], device=X.device, dtype=X.dtype)
+    Y_patch = Tensor(Y.numpy()[order], device=Y.device, dtype=Y.dtype)
    X_cutmix = mask.where(X_patch, X)
    mix_portion = float(mask_size**2)/(X.shape[-2]*X.shape[-1])
    Y_cutmix = mix_portion * Y_patch + (1. - mix_portion) * Y
    return X_cutmix, Y_cutmix

-  @TinyJit
-  def augmentations(X:Tensor, Y:Tensor):
-    perms = Tensor.randperm(X.shape[0], device=X.device) # We reuse perms for cutmix, because they are expensivne to generate
-    if getenv("RANDOM_CROP", 1):
-      X = random_crop(X, crop_size=32)
-    if getenv("RANDOM_FLIP", 1):
-      X = (Tensor.rand(X.shape[0],1,1,1) < 0.5).where(X.flip(-1), X) # flip LR
-    X, Y = X[perms], Y[perms]
-    return X, Y, *cutmix(X, Y, perms, mask_size=hyp['net']['cutmix_size'])
-
  # the operations that remain inside batch fetcher is the ones that involves random operations
  def fetch_batches(X_in:Tensor, Y_in:Tensor, BS:int, is_train:bool):
    step, epoch = 0, 0
@@ -240,16 +228,28 @@ 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 +269,13 @@ 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']))

@@ -395,23 +398,20 @@ 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()
      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
@@ -427,5 +427,4 @@ def train_cifar():
      raise ValueError(colored(f"{eval_acc_pct=} < {target}", "red"))

 if __name__ == "__main__":
-  with WallTimeEvent(BenchEvent.FULL):
-    train_cifar()
+  train_cifar()
@@ -17,6 +17,7 @@ canvas {  display: none; }
 * {  text-align: center;  font-family: monospace; }
 </style>
 <title>tinygrad has WebGPU</title>
+<script src="./net.js"></script>
 <link rel="icon" type="image/x-icon" href="https://raw.githubusercontent.com/tinygrad/tinygrad/master/docs/logo.png">
 </head>
 <body>
@@ -45,10 +46,7 @@ canvas {  display: none; }
 	const getDevice = async () => {
 		if (!navigator.gpu) error("WebGPU not supported.");
 		const adapter = await navigator.gpu.requestAdapter();
-		return await adapter.requestDevice({
-			requiredFeatures: ["shader-f16"],
-			powerPreference: "high-performance"
-		});
+		return await adapter.requestDevice();
 	};

 	const timer = async (func, label = "") => {
@@ -63,6 +61,8 @@ canvas {  display: none; }

 	const getLabels = async () => (await fetch("https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json")).json();

+	const getSavetensorBuffer = async () => new Uint8Array(await (await fetch("./net.safetensors")).arrayBuffer());
+
 	const reorderChannelsAndRemoveAlpha = (data) => {
 		const out = [];
 		let i = 0;
@@ -97,9 +97,9 @@ canvas {  display: none; }
 		try {
 			resultText.innerHTML = "loading..."
 			labels = await getLabels();
+			const safetensor = await getSavetensorBuffer();
 			const device = await getDevice();
-			const model = (await import("../../net.js")).default;
-			net = await timer(() => model.load(device, '../../net.safetensors'), "(compilation)");
+			net = await timer(() => setupNet(device, safetensor), "(compilation)");
 			resultText.innerHTML = "ready"
 		} catch (e) {
 			error(e)
@@ -6,6 +6,8 @@
 from pathlib import Path
 from typing import List, Optional
 import argparse, json
+import numpy as np
+np.set_printoptions(linewidth=200)
 from tinygrad import Tensor, Device, GlobalCounters, nn
 from tinygrad.helpers import Context, Timing, Profiling, DEBUG, JIT, getenv, colored
 from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters
@@ -13,7 +15,6 @@ 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 +208,40 @@ 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)

@@ -251,8 +250,6 @@ class LLaMa:
    self.tokenizer = tokenizer

  def greedy_until(self, prompt:str, until, max_length, temperature):
-    # only used in old eval script
-    import numpy as np
    toks = [self.tokenizer.bos_id()] + self.tokenizer.encode(prompt)
    start_pos = 0
    for i in range(max_length):
@@ -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 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()
+          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)
@@ -501,16 +498,14 @@ After you are done speaking, output [EOS]. You are not Chad.
    if not chatbot: break

  # validate output!
-  if args.temperature == 0 and args.count == 10 and args.prompt == "Hello.":
+  if args.temperature == 0 and args.count == 10 and args.prompt == "Hello." and not args.quantize:
    text = llama.tokenizer.decode(toks)
-    key = (args.gen, args.size, args.quantize)
+    key = (args.gen, args.size)
    expected = {
-      ("1", "7B", None): "Hello. I'm a 20 year old male",
-      ("1", "7B", "int8"): "Hello. I'm a 20 year old male",
-      ("1", "7B", "nf4"): "Hello. I'm a 20 year old male",
-      ("2", "7B", None): "Hello. I'm a 20 year old girl",
-      ("2", "70B", None): "Hello. I am a 20 year old female.",
-      ("3", "8B", None): "Hello. I am a 20 year old female. I",
+      ("1", "7B"): "Hello. I'm a 20 year old male",
+      ("2", "7B"): "Hello. I'm a 20 year old girl",
+      ("2", "70B"): "Hello. I am a 20 year old female.",
+      ("3", "8B"): "Hello. I am a 20 year old female. I",
    }
    try:
      assert text == expected[key], f"invalid output: `{colored(text, 'red')}` != `{expected[key]}`"
@@ -1,13 +1,12 @@
 from pathlib import Path
 from typing import List
-import json, argparse, random, time, os
+import json, argparse, random, time
 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 extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
+from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters
 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+"
@@ -48,7 +47,7 @@ def concat_weights(models, device=None):
    disk_tensors: List[Tensor] = [model[name] for model in models]
    if len(disk_tensors) == 1 or len(disk_tensors[0].shape) == 1:
      return disk_tensors[0].to(device=device)
-    axis = 1 if name.endswith((".attention.wo.weight", ".feed_forward.w2.weight")) else 0
+    axis = 1 if name.endswith(".attention.wo.weight") or name.endswith(".feed_forward.w2.weight") else 0
    lazy_tensors = [data.to(device=device) for data in disk_tensors]
    return lazy_tensors[0].cat(*lazy_tensors[1:], dim=axis)
  return {name: convert(name) for name in {name: None for model in models for name in model}}
@@ -58,9 +57,6 @@ def load(fn:str):
    with open(fn) as fp: weight_map = json.load(fp)['weight_map']
    parts = {n: load(str(Path(fn).parent / Path(n).name)) for n in set(weight_map.values())}
    return {k: parts[n][k] for k, n in weight_map.items()}
-  elif fn.endswith(".gguf"):
-    gguf_tensor = Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f"disk:{fn}").to(Device.DEFAULT)
-    return gguf_load(gguf_tensor)[1]
  elif fn.endswith(".safetensors"):
    return safe_load(fn)
  else:
@@ -74,17 +70,16 @@ class Int8Linear:
    self.scale = Tensor.ones(out_features, dtype=dtypes.half)

  def __call__(self, x):
-    return x.dot(self.weight.cast(self.scale.dtype).T*self.scale)
+    return x.dot(self.weight.cast(dtype=dtypes.half).T*self.scale)

  @staticmethod
-  def quantize(tensors, device, scale_dtype=dtypes.float16, quantize_embeds=False):
+  def quantize(tensors, device):
    new_tensors = {}
    for name,v in tensors.items():
-      if "feed_forward" in name or "attention.w" in name or (quantize_embeds and "tok_embeddings.weight" in name):
+      if "feed_forward" in name or "attention.w" in name:
        assert "weight" in name, name
-        v = v.cast(scale_dtype)
        scale = v.abs().max(axis=1) / 127.0
-        int8_weight = (v.T/scale).T.round().cast(dtype=dtypes.int8) # without round(), cast truncates -34.9 to -34
+        int8_weight = (v.T/scale).T.cast(dtype=dtypes.int8)
        new_tensors[name] = int8_weight
        new_tensors[name.replace('weight', 'scale')] = scale
        if isinstance(device, tuple):
@@ -92,20 +87,8 @@ class Int8Linear:
          new_tensors[name.replace('weight', 'scale')].shard_(device, axis=None)
      else:
        new_tensors[name] = v
-    if quantize_embeds: new_tensors.update({"output.weight": new_tensors["tok_embeddings.weight"], "output.scale": new_tensors["tok_embeddings.scale"]})
    return new_tensors

-class Int8Embedding:
-  def __init__(self, vocab_size:int, embed_size:int):
-    self.vocab_sz, self.embed_sz = vocab_size, embed_size
-    self.weight, self.scale = Tensor.ones(vocab_size, embed_size, dtype=dtypes.int8), Tensor.ones(vocab_size, dtype=dtypes.half)
-
-  def __call__(self, idx:Tensor) -> Tensor:
-    if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz, requires_grad=False, device=self.weight.device).unsqueeze(-1)
-    big_shp = idx.shape+(self.vocab_sz, self.embed_sz)
-    arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1)).expand(big_shp), (self.weight.cast(self.scale.dtype).T*self.scale).T
-    return (arange == idx).mul(vals).sum(-2, dtype=vals.dtype)
-
 def NF4Linear(block_size):
  _CODE = [
    -1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453, -0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0,
@@ -122,13 +105,12 @@ def NF4Linear(block_size):
    def __call__(self, x: Tensor) -> Tensor:
      high_bits = self.weight
      low_bits = (self.weight * 2 ** 4).contiguous()
-      unpacked = Tensor.stack(high_bits, low_bits, dim=-1).idiv(2 ** 4)
+      unpacked = Tensor.stack(high_bits, low_bits, dim=-1).div(2 ** 4, upcast=False)
      unscaled = CODE[unpacked].to(x.device).reshape(-1, block_size) * self.scale
      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) -> dict[str, Tensor]:
      new_state_dict = {}
      for k, v in state_dict.items():
        if "feed_forward" in k or "attention.w" in k:
@@ -136,7 +118,7 @@ def NF4Linear(block_size):
          scale = (grouped.abs().max(axis=1, keepdim=True))
          coded = ((grouped / scale).unsqueeze(-1) - CODE.to(v.device)).abs().argmin(axis=-1).cast(dtypes.uint8).flatten()
          new_state_dict[k] = coded[::2] * 2 ** 4 + coded[1::2]
-          new_state_dict[k.replace(".weight", ".scale")] = scale.cast(scale_dtype)
+          new_state_dict[k.replace(".weight", ".scale")] = scale.cast(dtypes.float16)
          if isinstance(device, tuple):
            new_state_dict[k].shard_(device, axis=-1)
            new_state_dict[k.replace('weight', 'scale')].shard_(device, axis=None)
@@ -146,10 +128,6 @@ def NF4Linear(block_size):
  return _NF4Linear

 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},
-    "files": 1
-  },
  "8B": {
    "args": {"dim": 4096, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 14336},
    "files": 1
@@ -157,56 +135,46 @@ 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):
+def build_transformer(model_path: Path, model_size="8B", quantize=None, device=None):
  # build model
-  if quantize == "int8": linear, embedding, quantize_embeds = Int8Linear, Int8Embedding, True
-  elif quantize == "nf4": linear, embedding, quantize_embeds = NF4Linear(64), 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
+  if quantize == "int8": linear = Int8Linear
+  elif quantize == "nf4": linear = NF4Linear(64)
+  else: linear = nn.Linear
+  model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, max_context=8192, jit=True)

  # 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"])
+  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 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: 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
@@ -237,41 +205,33 @@ 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("--download_model", action="store_true", help="Download a 8B 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=["8B", "70B"], default="8B", help="Model size")
  parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
-  parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
+  parser.add_argument("--quantize", choices=["int8", "nf4"], 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")
  args = parser.parse_args()

-  # download_model is the default without a model passed in
-  if args.download_model or not args.model:
-    if args.size == "1B":
-      fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir="llama3-1b-instruct")
-      args.model = fetch("https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q6_K.gguf", "Llama-3.2-1B-Instruct-Q6_K.gguf", subdir="llama3-1b-instruct")
-    elif args.size == "8B":
-      fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir="llama3-8b-sfr")
-      fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00001-of-00004.safetensors", "model-00001-of-00004.safetensors", subdir="llama3-8b-sfr")
-      fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00002-of-00004.safetensors", "model-00002-of-00004.safetensors", subdir="llama3-8b-sfr")
-      fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00003-of-00004.safetensors", "model-00003-of-00004.safetensors", subdir="llama3-8b-sfr")
-      fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00004-of-00004.safetensors", "model-00004-of-00004.safetensors", subdir="llama3-8b-sfr")
-      args.model = fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/raw/main/model.safetensors.index.json", "model.safetensors.index.json", subdir="llama3-8b-sfr")
-    elif args.size == "70B":
-      subdir = "DeepSeek-R1-Distill-Llama-70B"
-      args.model = fetch("https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/resolve/main/model.safetensors.index.json?download=true", "model.safetensors.index.json", subdir=subdir)
-      fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir=subdir)
-      for i in range(17):
-        fetch(f"https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/resolve/main/model-{i+1:05d}-of-000017.safetensors?download=true", f"model-{i+1:05d}-of-000017.safetensors", subdir=subdir)
+  assert not (args.download_model and args.model), "either download or provide model"
+  if args.download_model:
+    fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model", "tokenizer.model", subdir="llama3-8b-sfr")
+    fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00001-of-00004.safetensors", "model-00001-of-00004.safetensors", subdir="llama3-8b-sfr")
+    fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00002-of-00004.safetensors", "model-00002-of-00004.safetensors", subdir="llama3-8b-sfr")
+    fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00003-of-00004.safetensors", "model-00003-of-00004.safetensors", subdir="llama3-8b-sfr")
+    fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/resolve/main/model-00004-of-00004.safetensors", "model-00004-of-00004.safetensors", subdir="llama3-8b-sfr")
+    args.model = fetch("https://huggingface.co/TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R/raw/main/model.safetensors.index.json", "model.safetensors.index.json", subdir="llama3-8b-sfr")

  assert args.model is not None, "please provide --model option"

@@ -288,7 +248,7 @@ 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
@@ -308,9 +268,8 @@ if __name__ == "__main__":
      for key, value in cors_headers.items(): response.set_header(key, value)

    @app.route("/<filename>")
-    def server_static(filename): return static_file(filename, root=(Path(__file__).parent / "tinychat").as_posix())
-    @app.route("/assets/<filename:path>")
-    def server_assets(filename): return static_file(filename, root=(Path(__file__).parent / "tinychat" / "assets").as_posix())
+    def server_static(filename):
+      return static_file(filename, root=(Path(__file__).parent / "tinychat").as_posix())
    @app.route("/")
    def index():
      return static_file("index.html", root=(Path(__file__).parent / "tinychat").as_posix())
@@ -440,12 +399,11 @@ 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 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()
+          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])
@@ -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
-Device.DEFAULT = "CPU"
+from tinygrad import Device, nn, Tensor, dtypes, Variable
+Device.DEFAULT = "CLANG"
 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.engine.memory import memory_planner
-from tinygrad.uop.ops import Ops
+from tinygrad.helpers import dedup, to_function_name, flatten, getenv, GRAPH, GlobalCounters, ansilen, to_function_name
+from tinygrad.engine.schedule import create_schedule
+from tinygrad.engine.realize import get_kernel, memory_planner, run_schedule
+from tinygrad.ops import MetaOps, UOps

 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 +25,8 @@ 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
+    if i == warmup_count-1: GRAPH.value = getenv("LATEGRAPH")
    GlobalCounters.reset()
    X = Tensor.empty(4, 64, dtype=dtypes.int).reshape(B, T)
    Y = Tensor.empty(4, 64, dtype=dtypes.int).reshape(B, T)
@@ -36,16 +37,16 @@ if __name__ == "__main__":
      tensors = optimizer.schedule_step()
    else:
      tensors = []
-    sched = loss.schedule(*tensors)
+    sched = create_schedule([loss.lazydata] + [x.lazydata for x in tensors])
    print(f"calls {i}:", len(sched))
    #run_schedule(sched[:])
  sched = memory_planner(sched)
-  ast_dedup = dedup([si.ast for si in sched if si.ast.op is Ops.SINK])
+  ast_dedup = dedup([si.ast for si in sched if si.ast.op is UOps.SINK])
  srcs = {}
  for ast in ast_dedup:
-    k = get_kernel(Device["CPU"].renderer, ast)
+    k = get_kernel(Device["CLANG"].renderer, ast)
    k.linearize()
-    src = Device["CPU"].renderer.render(to_function_name(k.name), k.uops)
+    src = Device["CLANG"].renderer.render(to_function_name(k.name), k.uops)
    srcs[ast] = (k.name, src)
  print("functions:", len(srcs))
  used_buffers = dedup(flatten([si.bufs for si in sched]))
@@ -56,7 +57,7 @@ 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 +66,7 @@ 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>"]
@@ -81,7 +82,7 @@ if __name__ == "__main__":
  for i,si in enumerate(sched):
    bufs = [(named_buffers.get(b, f"b{numbered_bufs[b]}"), b) for b in si.bufs]
    all_bufs += bufs
-    if si.ast.op is not Ops.SINK:
+    if si.ast.op is not UOps.SINK:
      print(f"// {si.ast.op}", bufs)
    else:
      print(f"{srcs[si.ast][0]}({', '.join([x[0] for x in bufs])})")
@@ -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, ...])
@@ -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}")
@@ -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))
@@ -5,7 +5,7 @@ from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu

 import numpy as np
 from tinygrad import dtypes, Tensor
-from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX
+from tinygrad.helpers import getenv, prod, Context, round_up, tqdm

 ### ResNet

@@ -67,11 +67,11 @@ def loader_process(q_in, q_out, X:Tensor, seed):

      # broken out
      #img_tensor = Tensor(img.tobytes(), device='CPU')
-      #storage_tensor = X[idx].contiguous().realize().lazydata.base.realized
+      #storage_tensor = X[idx].contiguous().realize().lazydata.realized
      #storage_tensor._copyin(img_tensor.numpy())

      # faster
-      X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
+      X[idx].contiguous().realize().lazydata.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()

      # ideal
      #X[idx].assign(img.tobytes())   # NOTE: this is slow!
@@ -129,15 +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()):
@@ -171,13 +170,13 @@ def batch_load_resnet(batch_size=64, val=False, shuffle=True, seed=None, pad_fir

 def process_batch_bert(data: List[dict]) -> dict[str, Tensor]:
  return {
-    "input_ids": Tensor(np.concatenate([s["input_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
-    "input_mask": Tensor(np.concatenate([s["input_mask"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
-    "segment_ids": Tensor(np.concatenate([s["segment_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_positions": Tensor(np.concatenate([s["masked_lm_positions"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_ids": Tensor(np.concatenate([s["masked_lm_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_weights": Tensor(np.concatenate([s["masked_lm_weights"] for s in data], axis=0), dtype=dtypes.float32, device="CPU"),
-    "next_sentence_labels": Tensor(np.concatenate([s["next_sentence_labels"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "input_ids": Tensor(np.concatenate([s["input_ids"] for s in data], axis=0), dtype=dtypes.float32),
+    "input_mask": Tensor(np.concatenate([s["input_mask"] for s in data], axis=0), dtype=dtypes.default_float),
+    "segment_ids": Tensor(np.concatenate([s["segment_ids"] for s in data], axis=0), dtype=dtypes.float32),
+    "masked_lm_positions": Tensor(np.concatenate([s["masked_lm_positions"] for s in data], axis=0), dtype=dtypes.float32),
+    "masked_lm_ids": Tensor(np.concatenate([s["masked_lm_ids"] for s in data], axis=0), dtype=dtypes.float32),
+    "masked_lm_weights": Tensor(np.concatenate([s["masked_lm_weights"] for s in data], axis=0), dtype=dtypes.float32),
+    "next_sentence_labels": Tensor(np.concatenate([s["next_sentence_labels"] for s in data], axis=0), dtype=dtypes.float32),
  }

 def load_file(file: str):
@@ -224,8 +223,14 @@ def batch_load_train_bert(BS:int):
  assert cycle_length > 0, "cycle_length must be greater than 0"

  dataset = InterleavedDataset(train_files, cycle_length)
+  buffer = [dataset.get() for _ in range(1000)]
  while True:
-    yield process_batch_bert([dataset.get() for _ in range(BS)])
+    batch = []
+    for _ in range(BS):
+      index = random.randint(0, 999)
+      batch.append(buffer[index])
+      buffer[index] = dataset.get()
+    yield process_batch_bert(batch)

 # Reference: https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/run_pretraining.py, Line 416
 def batch_load_val_bert(BS:int):
@@ -262,8 +267,8 @@ def load_unet3d_data(preprocessed_dataset_dir, seed, queue_in, queue_out, X:Tens
      x = random_brightness_augmentation(x)
      x = gaussian_noise(x)

-    X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
-    Y[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()
+    X[idx].contiguous().realize().lazydata.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
+    Y[idx].contiguous().realize().lazydata.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()

    queue_out.put(idx)
  queue_out.put(None)
@@ -313,7 +318,7 @@ 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):
@@ -349,167 +354,6 @@ def batch_load_unet3d(preprocessed_dataset_dir:Path, batch_size:int=6, val:bool=
      # happens with BENCHMARK set
      pass

-### RetinaNet
-
-def load_retinanet_data(base_dir:Path, val:bool, queue_in:Queue, queue_out:Queue,
-                        imgs:Tensor, boxes:Tensor, labels:Tensor, matches:Tensor|None=None,
-                        anchors:Tensor|None=None, seed:int|None=None):
-  from extra.datasets.openimages import image_load, random_horizontal_flip, resize
-  from examples.mlperf.helpers import box_iou, find_matches, generate_anchors
-  import torch
-
-  while (data:=queue_in.get()) is not None:
-    idx, img, tgt = data
-    img = image_load(base_dir, img["subset"], img["file_name"])
-
-    if val:
-      img = resize(img)[0]
-    else:
-      if seed is not None:
-        np.random.seed(seed)
-        random.seed(seed)
-        torch.manual_seed(seed)
-
-      img, tgt = random_horizontal_flip(img, tgt)
-      img, tgt, _ = resize(img, tgt=tgt)
-      match_quality_matrix = box_iou(tgt["boxes"], (anchor := np.concatenate(generate_anchors((800, 800)))))
-      match_idxs = find_matches(match_quality_matrix, allow_low_quality_matches=True)
-      clipped_match_idxs = np.clip(match_idxs, 0, None)
-      clipped_boxes, clipped_labels = tgt["boxes"][clipped_match_idxs], tgt["labels"][clipped_match_idxs]
-
-      boxes[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_boxes.tobytes()
-      labels[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_labels.tobytes()
-      matches[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = match_idxs.tobytes()
-      anchors[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = anchor.tobytes()
-
-    imgs[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
-
-    queue_out.put(idx)
-  queue_out.put(None)
-
-def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, shuffle:bool=True, seed:int|None=None):
-  def _enqueue_batch(bc):
-    from extra.datasets.openimages import prepare_target
-    for idx in range(bc * batch_size, (bc+1) * batch_size):
-      img = dataset.loadImgs(next(dataset_iter))[0]
-      ann = dataset.loadAnns(dataset.getAnnIds(img_id:=img["id"]))
-      tgt = prepare_target(ann, img_id, (img["height"], img["width"]))
-
-      if img_ids is not None:
-        img_ids[idx] = img_id
-
-      if img_sizes is not None:
-        img_sizes[idx] = tgt["image_size"]
-
-      queue_in.put((idx, img, tgt))
-
-  def _setup_shared_mem(shm_name:str, size:tuple[int, ...], dtype:dtypes) -> tuple[shared_memory.SharedMemory, Tensor]:
-    if 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(size))
-    shm_tensor = Tensor.empty(*size, dtype=dtype, device=f"disk:/dev/shm/{shm_name}")
-    return shm, shm_tensor
-
-  image_ids = sorted(dataset.imgs.keys())
-  batch_count = min(32, len(image_ids) // batch_size)
-
-  queue_in, queue_out = Queue(), Queue()
-  procs, data_out_count = [], [0] * batch_count
-
-  shm_imgs, imgs = _setup_shared_mem("retinanet_imgs", (batch_size * batch_count, 800, 800, 3), dtypes.uint8)
-
-  if val:
-    boxes, labels, matches, anchors = None, None, None, None
-    img_ids, img_sizes = [None] * (batch_size * batch_count), [None] * (batch_size * batch_count)
-  else:
-    img_ids, img_sizes = None, None
-    shm_boxes, boxes = _setup_shared_mem("retinanet_boxes", (batch_size * batch_count, 120087, 4), dtypes.float32)
-    shm_labels, labels = _setup_shared_mem("retinanet_labels", (batch_size * batch_count, 120087), dtypes.int64)
-    shm_matches, matches = _setup_shared_mem("retinanet_matches", (batch_size * batch_count, 120087), dtypes.int64)
-    shm_anchors, anchors = _setup_shared_mem("retinanet_anchors", (batch_size * batch_count, 120087, 4), dtypes.float64)
-
-  shutdown = False
-  class Cookie:
-    def __init__(self, bc):
-      self.bc = bc
-    def __del__(self):
-      if not shutdown:
-        try: _enqueue_batch(self.bc)
-        except StopIteration: pass
-
-  def shuffle_indices(indices, seed):
-    rng = random.Random(seed)
-    rng.shuffle(indices)
-
-  if shuffle: shuffle_indices(image_ids, seed=seed)
-  dataset_iter = iter(image_ids)
-
-  try:
-    for _ in range(cpu_count()):
-      proc = Process(
-        target=load_retinanet_data,
-        args=(base_dir, val, queue_in, queue_out, imgs, boxes, labels),
-        kwargs={"matches": matches, "anchors": anchors, "seed": seed}
-      )
-      proc.daemon = True
-      proc.start()
-      procs.append(proc)
-
-    for bc in range(batch_count):
-      _enqueue_batch(bc)
-
-    for _ in range(len(image_ids) // batch_size):
-      while True:
-        bc = queue_out.get() // batch_size
-        data_out_count[bc] += 1
-        if data_out_count[bc] == batch_size: break
-
-      data_out_count[bc] = 0
-
-      if val:
-        yield (imgs[bc * batch_size:(bc + 1) * batch_size],
-               img_ids[bc * batch_size:(bc + 1) * batch_size],
-               img_sizes[bc * batch_size:(bc + 1) * batch_size],
-               Cookie(bc))
-      else:
-        yield (imgs[bc * batch_size:(bc + 1) * batch_size],
-               boxes[bc * batch_size:(bc + 1) * batch_size],
-               labels[bc * batch_size:(bc + 1) * batch_size],
-               matches[bc * batch_size:(bc + 1) * batch_size],
-               anchors[bc * batch_size:(bc + 1) * batch_size],
-               Cookie(bc))
-  finally:
-    shutdown = True
-
-    for _ in procs: queue_in.put(None)
-    queue_in.close()
-
-    for _ in procs:
-      while queue_out.get() is not None: pass
-    queue_out.close()
-
-    # shutdown processes
-    for proc in procs: proc.join()
-
-    shm_imgs.close()
-
-    if not val:
-      shm_boxes.close()
-      shm_labels.close()
-      shm_matches.close()
-      shm_anchors.close()
-
-    try:
-      shm_imgs.unlink()
-
-      if not val:
-        shm_boxes.unlink()
-        shm_labels.unlink()
-        shm_matches.unlink()
-        shm_anchors.unlink()
-    except FileNotFoundError:
-      # happens with BENCHMARK set
-      pass
-
 if __name__ == "__main__":
  def load_unet3d(val):
    assert not val, "validation set is not supported due to different sizes on inputs"
@@ -530,14 +374,6 @@ if __name__ == "__main__":
      for x,y,c in batch_load_resnet(val=val):
        pbar.update(x.shape[0])

-  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"))
-    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])
-
  load_fn_name = f"load_{getenv('MODEL', 'resnet')}"
  if load_fn_name in globals():
    globals()[load_fn_name](getenv("VAL", 1))
@@ -1,7 +1,6 @@
 from collections import OrderedDict
 import unicodedata
 from typing import Optional
-import math
 import numpy as np
 from tinygrad.nn import state
 from tinygrad.tensor import Tensor, dtypes
@@ -196,161 +195,46 @@ def get_bert_qa_prediction(features, example, start_end_logits):
  return "empty"

 def get_mlperf_bert_config():
-  """benchmark is BERT-large"""
-  ret = {"attention_probs_dropout_prob": 0.1, "hidden_dropout_prob": 0.1, "vocab_size": 30522, "type_vocab_size": 2, "max_position_embeddings": 512}
+  """Config is BERT-large"""
+  return {
+    "attention_probs_dropout_prob": 0.1,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "max_position_embeddings": 512,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "type_vocab_size": 2,
+    "vocab_size": 30522
+  }

-  match (bert_size:=getenv("BERT_SIZE", "large")):
-    case "large": ret.update({"hidden_size": 1024, "intermediate_size": 4096, "num_attention_heads": 16, "num_hidden_layers": 24})
-    case "tiny": ret.update({"hidden_size": 128, "intermediate_size": 512, "num_attention_heads": 2, "num_hidden_layers": 2})
-    case _: raise RuntimeError(f"unhandled {bert_size=}")
-
-  if (bert_layers:=getenv("BERT_LAYERS")): ret["num_hidden_layers"] = bert_layers
-  return ret
-
-def get_mlperf_bert_model():
+def get_mlperf_bert_model(checkpoint_path:Optional[str]=None):
  from extra.models import bert
  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
-  return BertForPretraining(**config)
+  model = BertForPretraining(**config)
+  return model.load_from_pretrained(checkpoint_path) if checkpoint_path else model

-def get_fake_data_bert(BS:int):
+def get_data_bert(GPUS:list[str], it):
+  data: dict[str, Tensor] = next(it)
+  for key in data.keys(): data[key].shard_(GPUS, axis=0)
+  return data
+
+def get_fake_data_bert(GPUS:list[str], BS:int):
  return {
-    "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"),
+    "input_ids": Tensor.empty((BS, 512), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
+    "input_mask": Tensor.empty((BS, 512), dtype=dtypes.default_float).contiguous().shard_(GPUS, axis=0),
+    "segment_ids": Tensor.empty((BS, 512), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
+    "masked_lm_positions": Tensor.empty((BS, 76), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
+    "masked_lm_ids": Tensor.empty((BS, 76), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
+    "masked_lm_weights": Tensor.empty((BS, 76), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
+    "next_sentence_labels": Tensor.empty((BS, 1), dtype=dtypes.float32).contiguous().shard_(GPUS, axis=0),
  }
-
-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:
-  BELOW_LOW_THRESHOLD, BETWEEN_THRESHOLDS = -1, -2
-
-  def _set_low_quality_matches_(matches:np.ndarray, all_matches:np.ndarray, match_quality_matrix:np.ndarray):
-    highest_quality_foreach_gt = np.max(match_quality_matrix, axis=1)
-    pred_inds_to_update = np.nonzero(match_quality_matrix == highest_quality_foreach_gt[:, None])[1]
-    matches[pred_inds_to_update] = all_matches[pred_inds_to_update]
-
-  assert low_threshold <= high_threshold
-
-  matched_vals, matches = match_quality_matrix.max(axis=0), match_quality_matrix.argmax(axis=0)
-  all_matches = np.copy(matches) if allow_low_quality_matches else None
-  below_low_threshold = matched_vals < low_threshold
-  between_thresholds = (matched_vals >= low_threshold) & (matched_vals < high_threshold)
-  matches[below_low_threshold] = BELOW_LOW_THRESHOLD
-  matches[between_thresholds] = BETWEEN_THRESHOLDS
-
-  if allow_low_quality_matches:
-    assert all_matches is not None
-    _set_low_quality_matches_(matches, all_matches, match_quality_matrix)
-
-  return matches
-
-def box_iou(boxes1:np.ndarray, boxes2:np.ndarray) -> np.ndarray:
-  def _box_area(boxes:np.ndarray) -> np.ndarray: return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-
-  def _box_inter_union(boxes1:np.ndarray, boxes2:np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    area1, area2 = _box_area(boxes1), _box_area(boxes2)
-    lt, rb = np.maximum(boxes1[:, None, :2], boxes2[:, :2]), np.minimum(boxes1[:, None, 2:], boxes2[:, 2:])
-    wh = np.clip(rb - lt, a_min=0, a_max=None)
-    inter = wh[:, :, 0] * wh[:, :, 1]
-    union = area1[:, None] + area2 - inter
-    return inter, union
-
-  inter, union = _box_inter_union(boxes1, boxes2)
-  return inter / union
-
-def generate_anchors(input_size:tuple[int, int], scales:Optional[tuple[Tensor, ...]]=None, aspect_ratios:Optional[tuple[Tensor, ...]]=None) -> list[np.ndarray]:
-  def _compute_grid_sizes(input_size:tuple[int, int]) -> np.ndarray:
-    return np.ceil(np.array(input_size)[None, :] / 2 ** np.arange(3, 8)[:, None])
-
-  scales = tuple((i, int(i * 2 ** (1/3)), int(i * 2 ** (2/3))) for i in 2 ** np.arange(5, 10)) if scales is None else scales
-  aspect_ratios = ((0.5, 1.0, 2.0),) * len(scales) if aspect_ratios is None else aspect_ratios
-  aspect_ratios = tuple(ar for ar in aspect_ratios)
-  grid_sizes = _compute_grid_sizes(input_size)
-
-  assert len(scales) == len(aspect_ratios) == len(grid_sizes), "scales, aspect_ratios, and grid_sizes must have the same length"
-
-  anchors = []
-  for s, ar, gs in zip(scales, aspect_ratios, grid_sizes):
-    s, ar = np.array(s), np.array(ar)
-    h_ratios = np.sqrt(ar)
-    w_ratios = 1 / h_ratios
-    ws = (w_ratios[:, None] * s[None, :]).reshape(-1)
-    hs = (h_ratios[:, None] * s[None, :]).reshape(-1)
-    base_anchors = (np.stack([-ws, -hs, ws, hs], axis=1) / 2).round()
-    stride_h, stride_w = input_size[0] // gs[0], input_size[1] // gs[1]
-    shifts_x, shifts_y = np.meshgrid(np.arange(gs[1]) * stride_w, np.arange(gs[0]) * stride_h)
-    shifts_x, shifts_y = shifts_x.reshape(-1), shifts_y.reshape(-1)
-    shifts = np.stack([shifts_x, shifts_y, shifts_x, shifts_y], axis=1, dtype=np.float32)
-    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
@@ -1,5 +1,5 @@
 import math
-from typing import Union
+from typing import Union, Tuple

 from tinygrad import Tensor, nn, dtypes
 from tinygrad.helpers import prod, argfix
@@ -39,7 +39,7 @@ class LinearBert(nn.Linear):
  def __init__(self, in_features, out_features, bias=True, std=0.02):
    self.weight = std * rand_truncn(out_features, in_features, dtype=dtypes.float32)
    self.bias = Tensor.zeros(out_features, dtype=dtypes.float32) if bias else None
-
+  
  def __call__(self, x:Tensor):
    return x.cast(dtypes.default_float).linear(self.weight.cast(dtypes.default_float).transpose(), self.bias.cast(dtypes.default_float) if self.bias is not None else None)

@@ -53,12 +53,10 @@ 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)
-    # TODO: contiguous() here because the embedding dropout creates different asts on each device, and search becomes very slow.
-    # Should fix with fixing random ast on multi device, and fuse arange to make embedding fast.
-    return (arange == idx).mul(vals).sum(2, dtype=vals.dtype).contiguous()
+    return (arange == idx).mul(vals).sum(2, acc_dtype=vals.dtype)

 class LayerNormBert:
-  def __init__(self, normalized_shape:Union[int, tuple[int, ...]], eps:float=1e-12, elementwise_affine:bool=True):
+  def __init__(self, normalized_shape:Union[int, Tuple[int, ...]], eps:float=1e-12, elementwise_affine:bool=True):
    self.normalized_shape = (normalized_shape,) if isinstance(normalized_shape, int) else tuple(normalized_shape)
    self.axis, self.eps, self.elementwise_affine = tuple(-1-i for i in range(len(self.normalized_shape))), eps, elementwise_affine
    self.weight, self.bias = (Tensor.ones(*self.normalized_shape, dtype=dtypes.float32), Tensor.zeros(*self.normalized_shape, dtype=dtypes.float32)) if elementwise_affine else (None, None)
@@ -68,62 +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)
@@ -1,29 +1,6 @@
 from examples.mlperf.metrics import dice_score
-from tinygrad import Tensor

 def dice_ce_loss(pred, tgt):
  ce = pred.permute(0, 2, 3, 4, 1).sparse_categorical_crossentropy(tgt.squeeze(1))
  dice = (1.0 - dice_score(pred, tgt, argmax=False, to_one_hot_x=False)).mean()
  return (dice + ce) / 2
-
-def sigmoid_focal_loss(pred:Tensor, tgt:Tensor, alpha:float=0.25, gamma:float=2.0, reduction:str="none") -> Tensor:
-  assert reduction in ["mean", "sum", "none"], f"unsupported reduction {reduction}"
-  p, ce_loss = pred.sigmoid(), pred.binary_crossentropy_logits(tgt, reduction="none")
-  p_t = p * tgt + (1 - p) * (1 - tgt)
-  loss = ce_loss * ((1 - p_t) ** gamma)
-
-  if alpha >= 0:
-    alpha_t = alpha * tgt + (1 - alpha) * (1 - tgt)
-    loss = loss * alpha_t
-
-  if reduction == "mean": loss = loss.mean()
-  elif reduction == "sum": loss = loss.sum()
-  return loss
-
-def l1_loss(pred:Tensor, tgt:Tensor, reduction:str="none") -> Tensor:
-  assert reduction in ["mean", "sum", "none"], f"unsupported reduction {reduction}"
-  loss = (pred - tgt).abs()
-
-  if reduction == "mean": loss = loss.mean()
-  elif reduction == "sum": loss = loss.sum()
-  return loss
@@ -1,5 +1,4 @@
-import math
-from tinygrad import dtypes
+from tinygrad import Tensor, dtypes
 from tinygrad.nn.optim import Optimizer

 from extra.lr_scheduler import LR_Scheduler
@@ -21,19 +20,3 @@ class PolynomialDecayWithWarmup(LR_Scheduler):
    warmup_lr = (self.epoch_counter * (1.0 / self.warmup)) * self.initial_lr
    x = (1 - (self.epoch_counter - self.warmup) / (self.epochs - self.warmup + 1))
    return (self.epoch_counter <= self.warmup).where(warmup_lr, (self.initial_lr - self.end_lr) * x ** self.power + self.end_lr).cast(self.optimizer.lr.dtype)
-
-class CosineAnnealingLRWithWarmup(LR_Scheduler):
-  def __init__(self, optimizer:Optimizer, base_lr, end_lr, warmup_steps:int, decay_steps:int):
-    assert warmup_steps > 0 and decay_steps > 0
-    super().__init__(optimizer)
-    self.base_lr = base_lr
-    self.end_lr = end_lr
-    self.warmup_steps = warmup_steps
-    self.decay_steps = decay_steps
-    # set lr for first warmup step
-    self.optimizer.lr.assign(self.get_lr()).realize()
-
-  def get_lr(self):
-    warmup_lr = ((self.epoch_counter+1) / self.warmup_steps) * self.base_lr
-    decay_lr = self.end_lr + 0.5 * (self.base_lr-self.end_lr) * (1 + (((self.epoch_counter+1-self.warmup_steps)/self.decay_steps) * math.pi).cos())
-    return (self.epoch_counter < self.warmup_steps).where(warmup_lr, decay_lr).cast(self.optimizer.lr.dtype)
@@ -1,6 +1,6 @@
-import re, string
+import re
+import string
 from collections import Counter
-from tinygrad import Tensor

 def levenshtein(a, b):
  n, m = len(a), len(b)
@@ -59,11 +59,3 @@ def f1_score(x, y):
  p = ns / len(xt)
  r = ns / len(yt)
  return 2 * p * r / (p + r)
-
-def log_perplexity(logit:Tensor, target:Tensor, ignore_index:int|None=None):
-  # logit has shape (n_samples, seq_len, vocab_size), target has shape (n_samples, seq_len)
-  assert logit.ndim == 3, logit.ndim
-  assert target.ndim == 2, target.ndim
-  assert logit.shape[:2] == target.shape, f"{logit.shape[:2]=}, {target.shape=}"
-  log_prob = logit.log_softmax(axis=-1)
-  return log_prob.transpose(1, 2).nll_loss(target, ignore_index=ignore_index)
@@ -5,62 +5,61 @@ 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
-from extra.bench_log import BenchEvent, WallTimeEvent
 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
@@ -244,6 +242,7 @@ def eval_mrcnn():
 if __name__ == "__main__":
  # inference only
  Tensor.training = False
+  Tensor.no_grad = True

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

  for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(","):
    nm = f"spec_{m}"
@@ -1,17 +1,15 @@
-import os, time, math, functools, random, contextlib
+import os, time, math, functools
 from pathlib import Path
+from tqdm import tqdm
 import multiprocessing

 from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
-from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW, Profiling
+from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW
 from tinygrad.nn.state import get_parameters, get_state_dict, safe_load, safe_save
-from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
+from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup

 from extra.lr_scheduler import LRSchedulerGroup
 from examples.mlperf.helpers import get_training_state, load_training_state
-from extra.bench_log import BenchEvent, WallTimeEvent
-# TODO: fix benchmark logging and use tinygrad tqdm
-from tqdm import tqdm

 def train_resnet():
  from extra.models import resnet
@@ -80,7 +78,7 @@ def train_resnet():
  lr_warmup_epochs  = config["lr_warmup_epochs"]  = getenv("WARMUP_EPOCHS", 2)
  decay             = config["decay"]             = getenv("DECAY", 2e-4)

-  loss_scaler       = config["LOSS_SCALER"]       = getenv("LOSS_SCALER", 256.0 if dtypes.default_float == dtypes.float16 else 1.0)
+  loss_scaler       = config["LOSS_SCALER"]       = getenv("LOSS_SCALER", 128.0 if dtypes.default_float == dtypes.float16 else 1.0)

  target, achieved  = getenv("TARGET", 0.759), False
  eval_start_epoch  = getenv("EVAL_START_EPOCH", 0)
@@ -206,25 +204,24 @@ def train_resnet():
    st = time.perf_counter()
    while proc is not None:
      GlobalCounters.reset()
-      with WallTimeEvent(BenchEvent.STEP):
-        (loss, top_1), y, proc = train_step(proc[0], proc[1]), proc[2], proc[3]
+      (loss, top_1), y, proc = train_step(proc[0], proc[1]), proc[2], proc[3]

-        pt = time.perf_counter()
+      pt = time.perf_counter()

-        if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = []  # free previous cookies after gpu work has been enqueued
-        try:
-          if INITMLPERF:
-            next_proc = fake_data_get(BS)
-          else:
-            next_proc = data_get(it)
-        except StopIteration:
-          next_proc = None
+      if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = []  # free previous cookies after gpu work has been enqueued
+      try:
+        if INITMLPERF:
+          next_proc = fake_data_get(BS)
+        else:
+          next_proc = data_get(it)
+      except StopIteration:
+        next_proc = None

-        dt = time.perf_counter()
+      dt = time.perf_counter()

-        device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
-        loss, top_1 = loss.numpy().item(), top_1.numpy().item()
-        top_1_acc = top_1 / sum(yi != -1 for yi in y)
+      device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
+      loss, top_1 = loss.numpy().item(), top_1.numpy().item()
+      top_1_acc = top_1 / sum(yi != -1 for yi in y)

      cl = time.perf_counter()
      if BENCHMARK:
@@ -275,7 +272,7 @@ def train_resnet():
      else:
        it = iter(tqdm(batch_load_resnet(batch_size=EVAL_BS, val=True, shuffle=False, pad_first_batch=True), total=steps_in_val_epoch))
        i, proc = 0, data_get(it)
-
+        
      prev_cookies = []
      while proc is not None:
        GlobalCounters.reset()
@@ -345,349 +342,8 @@ def train_resnet():
        safe_save(get_training_state(model, optimizer_group, scheduler_group), fn)

 def train_retinanet():
-  from contextlib import redirect_stdout
-  from examples.mlperf.dataloader import batch_load_retinanet
-  from examples.mlperf.initializers import FrozenBatchNorm2dRetinaNet, Conv2dNormalRetinaNet, Conv2dKaimingUniformRetinaNet, Linear, Conv2dRetinaNet
-  from extra.datasets.openimages import MLPERF_CLASSES, BASEDIR, download_dataset, normalize, get_dataset_count
-  from extra.models import resnet, retinanet
-  from pycocotools.coco import COCO
-  from pycocotools.cocoeval import COCOeval
-  from tinygrad.helpers import colored
-  from typing import Iterator
-
-  import numpy as np
-
-  config, target_metric = {}, 0.34
-
-  config["SEED"] = SEED = getenv("SEED", random.SystemRandom().randint(0, 2**32 - 1))
-  Tensor.manual_seed(SEED)
-
-  NUM_CLASSES = len(MLPERF_CLASSES)
-  BASEDIR = getenv("BASEDIR", BASEDIR)
-  BENCHMARK = getenv("BENCHMARK")
-  INITMLPERF = getenv("INITMLPERF")
-  RUNMLPERF = getenv("RUNMLPERF")
-
-  if INITMLPERF:
-    diskcache_clear()
-
-  if getenv("LOGMLPERF"):
-    from mlperf_logging import mllog
-    import mlperf_logging.mllog.constants as mllog_constants
-
-    mllog.config(filename=f"result_retinanet_{SEED}.log")
-    mllog.config(root_dir=Path(__file__).parents[3].as_posix())
-    MLLOGGER = mllog.get_mllogger()
-    MLLOGGER.logger.propagate = False
-
-    if INITMLPERF:
-      assert BENCHMARK, "BENCHMARK must be set for INITMLPERF"
-      MLLOGGER.event(key=mllog_constants.SUBMISSION_ORG, value="tinycorp")
-      MLLOGGER.event(key=mllog_constants.SUBMISSION_PLATFORM, value=getenv("SUBMISSION_PLATFORM", "tinybox"))
-      MLLOGGER.event(key=mllog_constants.SUBMISSION_DIVISION, value=mllog_constants.CLOSED)
-      MLLOGGER.event(key=mllog_constants.SUBMISSION_STATUS, value=mllog_constants.ONPREM)
-
-      MLLOGGER.event(key=mllog_constants.SUBMISSION_BENCHMARK, value=mllog_constants.RETINANET)
-
-      MLLOGGER.event(key=mllog_constants.CACHE_CLEAR, value=True)
-      MLLOGGER.start(key=mllog_constants.INIT_START)
-
-    if RUNMLPERF:
-      MLLOGGER.start(key=mllog_constants.RUN_START)
-      MLLOGGER.event(key=mllog_constants.SEED, value=SEED)
-  else:
-    MLLOGGER = None
-
-  config["gpus"] = GPUS = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 6))]
-
-  for x in GPUS: Device[x]
-  print(f"training on {GPUS}")
-
-  def _freeze_backbone_layers(backbone:resnet.ResNet, trainable_layers:int):
-    layers_to_train = ["layer4", "layer3", "layer2", "layer1", "conv1"][:trainable_layers]
-    for k, v in get_state_dict(backbone).items():
-      if all([not k.startswith(layer) for layer in layers_to_train]):
-        v.requires_grad = False
-
-  def _data_get(it:Iterator[tuple[Tensor, ...]], val:bool=False):
-    if val:
-      x, img_ids, img_sizes, cookie = next(it)
-      return x.shard(GPUS, axis=0), img_ids, img_sizes, cookie
-
-    x, y_boxes, y_labels, matches, anchors, cookie = next(it)
-    return x.shard(GPUS, axis=0), y_boxes.shard(GPUS, axis=0), y_labels.shard(GPUS, axis=0), matches.shard(GPUS, axis=0), anchors.shard(GPUS, axis=0), cookie
-
-  def _fake_data_get(bs:int, val:bool=False):
-    x = Tensor.empty(bs, 800, 800, 3, dtype=dtypes.uint8)
-    if val:
-      img_ids, img_sizes = [0] * bs, [(800, 800)] * bs
-      return x.shard(GPUS, axis=0), img_ids, img_sizes, None
-
-    y_boxes = Tensor.empty(bs, 120087, 4, dtype=dtypes.float32)
-    y_labels = Tensor.empty(bs, 120087, dtype=dtypes.int64)
-    matches = Tensor.empty(bs, 120087, dtype=dtypes.int64)
-    anchors = Tensor.empty(bs, 120087, 4, dtype=dtypes.float64)
-    return x.shard(GPUS, axis=0), y_boxes.shard(GPUS, axis=0), y_labels.shard(GPUS, axis=0), matches.shard(GPUS, axis=0), anchors.shard(GPUS, axis=0), None
-
-  @TinyJit
-  def _train_step(model, optim, loss_scaler, x, **kwargs):
-    optim.zero_grad()
-
-    losses = model(normalize(x, GPUS), **kwargs)
-    loss = sum(losses.values())
-
-    (loss * loss_scaler).backward()
-    for t in optim.params: t.grad = t.grad / loss_scaler
-
-    optim.step()
-
-    return loss.realize(), losses
-
-  @TinyJit
-  def _eval_step(model, x, **kwargs):
-    out = model(normalize(x, GPUS), **kwargs)
-    # reassemble on GPUS[0] before sending back to CPU for speed
-    return out.to(GPUS[0]).realize()
-
-  # ** hyperparameters **
-  config["BS"] = BS = getenv("BS", 16 * len(GPUS) if dtypes.default_float == dtypes.float16 else 12 * len(GPUS))
-  config["EVAL_BS"] = EVAL_BS = getenv("EVAL_BS", BS)
-  config["EPOCHS"] = EPOCHS = getenv("EPOCHS", 4)
-  config["TRAIN_BEAM"] = TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value)
-  config["EVAL_BEAM"] = EVAL_BEAM = getenv("EVAL_BEAM", BEAM.value)
-  config["LR"] = lr = getenv("LR", 9.5e-5 * (BS / 96))
-  config["LOSS_SCALER"] = loss_scaler = getenv("LOSS_SCALER", 2**11 if dtypes.default_float == dtypes.float16 else 1.0)
-  config["DEFAULT_FLOAT"] = dtypes.default_float.name
-  config["EVAL_FREQ"] = eval_freq = getenv("EVAL_FREQ", 1)
-
-  # ** initialize wandb **
-  if (WANDB:=getenv("WANDB")):
-    import wandb
-    wandb.init(config=config, project="MLPerf-RetinaNet")
-
-  # ** model initializers **
-  resnet.BatchNorm = FrozenBatchNorm2dRetinaNet
-  resnet.Linear = Linear
-  resnet.Conv2d = Conv2dRetinaNet
-
-  retinanet.ConvHead = Conv2dNormalRetinaNet
-  retinanet.ConvClassificationHeadLogits = functools.partial(Conv2dNormalRetinaNet, prior_prob=0.01)
-  retinanet.ConvFPN = Conv2dKaimingUniformRetinaNet
-
-  # ** model setup **
-  backbone = resnet.ResNeXt50_32X4D(num_classes=None)
-  if RUNMLPERF:
-    backbone.load_from_pretrained()
-  _freeze_backbone_layers(backbone, 3)
-
-  model = retinanet.RetinaNet(backbone, num_classes=NUM_CLASSES)
-  params = get_parameters(model)
-
-  if not RUNMLPERF:
-    # for init, zero out all weights
-    for p in params:
-      p = p.assign(Tensor.zeros_like(p).contiguous()).realize()
-
-  if len(GPUS) > 1:
-    for p in params: p.to_(GPUS)
-
-  step_times, start_epoch = [], 0
-
-  # ** optimizer **
-  optim = Adam(params, lr=lr)
-
-  # ** dataset **
-  config["STEPS_IN_TRAIN_EPOCH"] = steps_in_train_epoch = round_up(get_dataset_count((base_dir_path:=Path(BASEDIR)), False), BS) // BS
-  config["STEPS_IN_VAL_EPOCH"] = steps_in_val_epoch = (round_up(get_dataset_count(base_dir_path, True), EVAL_BS) // EVAL_BS)
-
-  # log mlperf hparams
-  if MLLOGGER:
-    if RUNMLPERF:
-      MLLOGGER.event(key=mllog_constants.GLOBAL_BATCH_SIZE, value=config["BS"])
-      MLLOGGER.event(key=mllog_constants.TRAIN_SAMPLES, value=config["STEPS_IN_TRAIN_EPOCH"])
-      MLLOGGER.event(key=mllog_constants.EVAL_SAMPLES, value=config["STEPS_IN_VAL_EPOCH"])
-      MLLOGGER.event(key=mllog_constants.EPOCH_COUNT, value=config["EPOCHS"])
-      MLLOGGER.event(key=mllog_constants.FIRST_EPOCH_NUM, value=start_epoch)
-
-      MLLOGGER.event(key=mllog_constants.OPT_NAME, value=mllog_constants.ADAM)
-      MLLOGGER.event(key=mllog_constants.OPT_BASE_LR, value=config["LR"])
-      MLLOGGER.event(key=mllog_constants.OPT_WEIGHT_DECAY, value=0)
-      MLLOGGER.event(key=mllog_constants.OPT_LR_WARMUP_EPOCHS, value=0)
-      MLLOGGER.event(key=mllog_constants.OPT_LR_WARMUP_FACTOR, value=0)
-      MLLOGGER.event(key=mllog_constants.GRADIENT_ACCUMULATION_STEPS, value=1)
-
-  if RUNMLPERF:
-    train_dataset = COCO(download_dataset(BASEDIR, "train"))
-    val_dataset = COCO(download_dataset(BASEDIR, "validation"))
-    coco_val = COCOeval(cocoGt=val_dataset, iouType="bbox")
-
-  print(f"training with batch size {BS} for {EPOCHS} epochs")
-
-  for e in range(start_epoch, EPOCHS):
-    # ** training loop **
-    if MLLOGGER and RUNMLPERF:
-      MLLOGGER.start(key=mllog_constants.EPOCH_START, value=e + 1, metadata={"epoch_num": e + 1})
-
-    BEAM.value = TRAIN_BEAM
-
-    if not RUNMLPERF:
-      i, proc = 0, _fake_data_get(BS)
-    else:
-      train_dataloader = batch_load_retinanet(train_dataset, False, base_dir_path, batch_size=BS, seed=SEED)
-      it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e + 1}", disable=BENCHMARK))
-      i, proc = 0, _data_get(it)
-
-    prev_cookies = []
-    st = time.perf_counter()
-
-    while proc is not None:
-      GlobalCounters.reset()
-
-      x, y_bboxes, y_labels, matches, anchors, proc = proc
-      loss, losses = _train_step(model, optim, loss_scaler, x, labels=y_labels, matches=matches, anchors=anchors, bboxes=y_bboxes)
-
-      pt = time.perf_counter()
-
-      if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = []  # free previous cookies after gpu work has been enqueued
-      try:
-        if not RUNMLPERF:
-          next_proc = _fake_data_get(BS)
-        else:
-          next_proc = _data_get(it)
-      except StopIteration:
-        next_proc = None
-
-      dt = time.perf_counter()
-
-      device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
-      loss = loss.item()
-
-      cl = time.perf_counter()
-      if BENCHMARK: step_times.append(cl - st)
-
-      if not math.isfinite(loss):
-        print("loss is nan")
-        return
-
-      tqdm.write(
-        f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, "
-        f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {losses['classification_loss'].item():5.4f} classification loss, {losses['regression_loss'].item():5.4f} regression loss, "
-        f"{optim.lr.numpy()[0]:.6f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS"
-      )
-
-      if WANDB:
-        wandb.log({"lr": optim.lr.numpy(), "train/loss": loss, "train/classification_loss": losses["classification_loss"].item(), "train/regression_loss": losses["regression_loss"].item(),
-                  "train/step_time": cl - st, "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
-                  "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": e + (i + 1) / steps_in_train_epoch})
-
-      st = cl
-      prev_cookies.append(proc)
-      proc, next_proc = next_proc, None  # return old cookie
-      i += 1
-
-      if i == BENCHMARK:
-        assert not math.isnan(loss)
-        median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
-        estimated_total_minutes = int(median_step_time * steps_in_train_epoch * EPOCHS / 60)
-        print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
-        print(f"epoch global_ops: {steps_in_train_epoch * GlobalCounters.global_ops:_}, "
-              f"epoch global_mem: {steps_in_train_epoch * GlobalCounters.global_mem:_}")
-        # if we are doing beam search, run the first eval too
-        if (TRAIN_BEAM or EVAL_BEAM) and e == start_epoch: break
-        return
-
-    if MLLOGGER and RUNMLPERF:
-      MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=e + 1, metadata={"epoch_num": e + 1})
-
-    # ** eval loop **
-    if (e + 1) % eval_freq == 0:
-      if MLLOGGER and RUNMLPERF:
-        MLLOGGER.start(key=mllog_constants.EVAL_START, value=e + 1, metadata={"epoch_num": e + 1})
-
-      BEAM.value = EVAL_BEAM
-
-      if getenv("RESET_STEP", 1): _train_step.reset()
-
-      with Tensor.train(mode=False):
-        if not RUNMLPERF:
-          i, proc = 0, _fake_data_get(EVAL_BS, val=(val:=True))
-        else:
-          val_dataloader = batch_load_retinanet(val_dataset, (val:=True), Path(BASEDIR), batch_size=EVAL_BS, shuffle=False, seed=SEED)
-          it = iter(tqdm(val_dataloader, total=steps_in_val_epoch))
-          i, proc = 0, _data_get(it, val=val)
-          val_img_ids, val_imgs, ncats, narea = [], [], len(coco_val.params.catIds), len(coco_val.params.areaRng)
-
-        eval_times, prev_cookies = [], []
-
-        while proc is not None:
-          GlobalCounters.reset()
-          st = time.time()
-
-          out, img_ids, img_sizes, proc = _eval_step(model, (x:=proc[0])).numpy(), proc[1], proc[2], proc[3]
-
-          if RUNMLPERF:
-            out = model.postprocess_detections(out, input_size=x.shape[1:3], orig_image_sizes=img_sizes)
-            coco_results  = [{"image_id": img_ids[i], "category_id": label, "bbox": box.tolist(), "score": score}
-              for i, prediction in enumerate(out) for box, score, label in zip(*prediction.values())]
-
-            with redirect_stdout(None):
-              coco_val.cocoDt = val_dataset.loadRes(coco_results)
-              coco_val.params.imgIds = img_ids
-              coco_val.evaluate()
-
-            val_img_ids.extend(img_ids)
-            val_imgs.append(np.array(coco_val.evalImgs).reshape(ncats, narea, len(img_ids)))
-
-          if len(prev_cookies) == getenv("STORE_COOKIES", 1): prev_cookies = []  # free previous cookies after gpu work has been enqueued
-          try:
-            if not RUNMLPERF:
-              next_proc = _fake_data_get(EVAL_BS, val=val)
-            else:
-              next_proc = _data_get(it, val=val)
-          except StopIteration:
-            next_proc = None
-
-          prev_cookies.append(proc)
-          proc, next_proc = next_proc, None
-          i += 1
-
-          et = time.time()
-          eval_times.append(et - st)
-
-          if i == BENCHMARK:
-            # assume INITMLPERF has BENCHMARK set
-            if MLLOGGER and INITMLPERF:
-              MLLOGGER.event(key=mllog_constants.INIT_STOP)
-            return
-
-        if getenv("RESET_STEP", 1): _eval_step.reset()
-        total_fw_time = sum(eval_times) / len(eval_times)
-
-        if RUNMLPERF:
-          coco_val.params.imgIds = val_img_ids
-          coco_val._paramsEval.imgIds = val_img_ids
-          coco_val.evalImgs = list(np.concatenate(val_imgs, -1).flatten())
-          coco_val.accumulate()
-          coco_val.summarize()
-
-          val_metric = coco_val.stats[0]
-
-          tqdm.write(f"eval time: {total_fw_time:.2f}, eval metric: {val_metric:.4f}")
-
-          if WANDB:
-            wandb.log({"eval/forward_time": total_fw_time, "eval/metric": val_metric, "epoch": e + 1})
-
-          if MLLOGGER:
-            MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=val_metric, metadata={"epoch_num": e + 1}, clear_line=True)
-            MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=e + 1, metadata={"epoch_num": e + 1})
-
-          if val_metric >= target_metric:
-            print(colored(f"target metric reached: {val_metric:.2f}/{target_metric:.2f}", color="green"))
-
-            if MLLOGGER:
-              MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata={"status": mllog_constants.SUCCESS})
-
-            break
+  # TODO: Retinanet
+  pass

 def train_unet3d():
  """
@@ -789,15 +445,16 @@ def train_unet3d():
    loss.backward()
    optim.step()
    return loss.realize()
-
+  
  @Tensor.train(mode=False)
+  @Tensor.test()
  def eval_step(model, x, y):
    y_hat, y = sliding_window_inference(model, x, y, gpus=GPUS)
    y_hat, y = Tensor(y_hat), Tensor(y, requires_grad=False)
    loss = dice_ce_loss(y_hat, y)
    score = dice_score(y_hat, y)
    return loss.realize(), score.realize()
-
+  
  if WANDB: wandb.init(config=config, project=PROJ_NAME)

  step_times, start_epoch = [], 1
@@ -806,7 +463,7 @@ def train_unet3d():
  next_eval_at = start_eval_at

  print(f"Training on {GPUS}")
-
+  
  if BENCHMARK: print("Benchmarking UNet3D")
  else: print(f"Start evaluation at epoch {start_eval_at} and every {evaluate_every} epoch(s) afterwards")

@@ -893,7 +550,7 @@ def train_unet3d():

        if mean_dice >= TARGET_METRIC:
          is_successful = True
-          save_checkpoint(get_state_dict(model), "./ckpts/unet3d.safe")
+          save_checkpoint(get_state_dict(model), f"./ckpts/unet3d.safe")
        elif mean_dice < 1e-6:
          print("Model diverging. Aborting.")
          diverged = True
@@ -914,57 +571,39 @@ def train_rnnt():
  pass

@TinyJit
-def train_step_bert(model, optimizer, scheduler, loss_scaler:float, GPUS, grad_acc:int, **kwargs):
+def train_step_bert(model, optimizer, scheduler, loss_scaler:float, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor):
  optimizer.zero_grad()

-  for i in range(grad_acc):
-    input_ids, segment_ids = kwargs[f"input_ids{i}"], kwargs[f"segment_ids{i}"]
-    # NOTE: these two have different names
-    attention_mask, masked_positions = kwargs[f"input_mask{i}"], kwargs[f"masked_lm_positions{i}"]
-    masked_lm_ids, masked_lm_weights, next_sentence_labels = kwargs[f"masked_lm_ids{i}"], kwargs[f"masked_lm_weights{i}"], kwargs[f"next_sentence_labels{i}"]
+  lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
+  loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
+  (loss * loss_scaler).backward()

-    for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
-      if len(GPUS) > 1: t.shard_(GPUS, axis=0)
-      else: t.to_(GPUS[0])
-
-    lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
-    loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
-    (loss * loss_scaler).backward()
-    # TODO: OOM without this realize with large grad_acc
-    Tensor.realize(*[p.grad for p in optimizer.params])
-
-  global_norm = Tensor(0.0, dtype=dtypes.float32, device=optimizer[0].device)
-  for p in optimizer.params:
+  global_norm = Tensor([0.0], dtype=dtypes.float32, device=optimizer[0].device).realize()
+  for p in optimizer.params: 
    p.grad = p.grad / loss_scaler
    global_norm += p.grad.float().square().sum()
-  global_norm = global_norm.sqrt().contiguous()
-  for p in optimizer.params:
-    p.grad = (global_norm > 1.0).where((p.grad/global_norm).cast(p.grad.dtype), p.grad)
+  global_norm = global_norm.sqrt()
+  for p in optimizer.params: p.grad = (p.grad / Tensor.where(global_norm > 1.0, global_norm, 1.0)).cast(p.grad.dtype)

  optimizer.step()
  scheduler.step()
-  # TODO: no to("CPU") here because it blocks and messes the python time
-  Tensor.realize(loss, global_norm, optimizer.optimizers[0].lr)
-  return loss, global_norm, optimizer.optimizers[0].lr
+  return loss.realize()

@TinyJit
-def eval_step_bert(model, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor,
-                   masked_lm_weights:Tensor, next_sentence_labels:Tensor, GPUS):
-  for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
-    if len(GPUS) > 1: t.shard_(GPUS, axis=0)
-    else: t.to_(GPUS[0])
+def eval_step_bert(model, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor):
  lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
-  masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss = \
-    model.accuracy(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
-  for t in [masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss]:
-    t.to_("CPU")
-  Tensor.realize(masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss)
-  return masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss
+  masked_lm_accuracy, seq_relationship_accuracy, masked_lm_loss, next_sentence_loss = model.accuracy(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
+  return {
+    "masked_lm_accuracy": masked_lm_accuracy.realize(),
+    "next_sentence_accuracy": seq_relationship_accuracy.realize(),
+    "masked_lm_loss": masked_lm_loss.realize(),
+    "next_sentence_loss": next_sentence_loss.realize()
+  }

 def train_bert():
  # NOTE: pip install tensorflow, wandb required
  from examples.mlperf.dataloader import batch_load_train_bert, batch_load_val_bert
-  from examples.mlperf.helpers import get_mlperf_bert_model, get_fake_data_bert
+  from examples.mlperf.helpers import get_mlperf_bert_model, get_data_bert, get_fake_data_bert
  from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup

  config = {}
@@ -988,7 +627,7 @@ def train_bert():
    MLLOGGER.logger.propagate = False

    if INITMLPERF:
-      assert BENCHMARK, "BENCHMARK must be set for INITMLPERF"
+      assert BENCHMARK, f"BENCHMARK must be set for INITMLPERF"
      MLLOGGER.event(key=mllog_constants.SUBMISSION_ORG, value="tinycorp")
      MLLOGGER.event(key=mllog_constants.SUBMISSION_PLATFORM, value=getenv("SUBMISSION_PLATFORM", "tinybox"))
      MLLOGGER.event(key=mllog_constants.SUBMISSION_DIVISION, value=mllog_constants.CLOSED)
@@ -1002,30 +641,24 @@ def train_bert():

    if RUNMLPERF:
      MLLOGGER.start(key=mllog_constants.RUN_START, value=None)
-      MLLOGGER.event(key=mllog_constants.SEED, value=seed)
  else:
    MLLOGGER = None

  # ** hyperparameters **
-  BS                 = config["BS"]                     = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS))
-  grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
-  # TODO: mlperf logging
-  GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
+  BS                 = config["GLOBAL_BATCH_SIZE"]      = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS))
  EVAL_BS            = config["EVAL_BS"]                = getenv("EVAL_BS", 1 * len(GPUS))
-  max_lr             = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.000175 * math.sqrt(GBS/96))
-  opt_lamb_beta_1    = config["OPT_LAMB_BETA_1"]        = getenv("OPT_LAMB_BETA_1", 0.9)
-  opt_lamb_beta_2    = config["OPT_LAMB_BETA_2"]        = getenv("OPT_LAMB_BETA_2", 0.999)
+  max_lr             = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.0001 * math.sqrt(BS/66))

-  train_steps        = config["TRAIN_STEPS"]            = getenv("TRAIN_STEPS", 3600000 // GBS)
+  train_steps        = config["TRAIN_STEPS"]            = getenv("TRAIN_STEPS", 3630000 // BS)
  warmup_steps       = config["NUM_WARMUP_STEPS"]       = getenv("NUM_WARMUP_STEPS", 1)
  max_eval_steps     = config["MAX_EVAL_STEPS"]         = getenv("MAX_EVAL_STEPS", (10000 + EVAL_BS - 1) // EVAL_BS) # EVAL_BS * MAX_EVAL_STEPS >= 10000
-  eval_step_freq     = config["EVAL_STEP_FREQ"]         = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * GBS + 3000000) / 25000) * 25000) / GBS)) # Round down
+  eval_step_freq     = config["EVAL_STEP_FREQ"]         = getenv("EVAL_STEP_FREQ", int((math.floor(0.05 * (230.23 * BS + 3000000) / 25000) * 25000) / BS)) # Round down
  save_ckpt_freq     = config["SAVE_CKPT_FREQ"]         = getenv("SAVE_CKPT_FREQ", 1000)
  keep_ckpt_amount   = config["KEEP_CKPT_AMOUNT"]       = getenv("KEEP_CKPT_AMOUNT", 5)
  save_ckpt_dir      = config["SAVE_CKPT_DIR"]          = getenv("SAVE_CKPT_DIR", "./ckpts")
  init_ckpt          = config["INIT_CKPT_DIR"]          = getenv("INIT_CKPT_DIR", BASEDIR)

-  loss_scaler        = config["LOSS_SCALER"]            = getenv("LOSS_SCALER", 2.0**11 if dtypes.default_float == dtypes.float16 else 1.0)
+  loss_scaler        = config["LOSS_SCALER"]            = getenv("LOSS_SCALER", 2.0**10 if dtypes.default_float == dtypes.float16 else 1.0)
  decay              = config["DECAY"]                  = getenv("DECAY", 0.01)
  epsilon            = config["EPSILON"]                = getenv("EPSILON", 1e-6)
  poly_power         = config["POLY_POWER"]             = getenv("POLY_POWER", 1.0)
@@ -1050,18 +683,11 @@ def train_bert():

  # ** init model **

-  model = get_mlperf_bert_model()
-  if RUNMLPERF:
-    model.load_from_pretrained(init_ckpt)
-  else:
-    # for init, zero out all weights
-    for p in get_parameters(model):
-      p = p.assign(Tensor.zeros_like(p).contiguous()).realize()
-
+  model = get_mlperf_bert_model(init_ckpt if RUNMLPERF else None)
+  
+  for _, x in get_state_dict(model).items():
+    x.realize().to_(GPUS)
  parameters = get_parameters(model)
-  if len(GPUS) > 1:
-    for p in parameters:
-      p.to_(GPUS)

  # ** Log run config **
  for key, value in config.items(): print(f'HParam: "{key}": {value}')
@@ -1069,15 +695,15 @@ def train_bert():
  # ** Optimizer **
  parameters_no_wd = [v for k, v in get_state_dict(model).items() if "bias" in k or "LayerNorm" in k]
  parameters = [x for x in parameters if x not in set(parameters_no_wd)]
-  optimizer_wd = LAMB(parameters, lr=max_lr, b1=opt_lamb_beta_1, b2=opt_lamb_beta_2, eps=epsilon, weight_decay=decay, adam=False)
-  optimizer_no_wd = LAMB(parameters_no_wd, lr=max_lr, b1=opt_lamb_beta_1, b2=opt_lamb_beta_2, eps=epsilon, weight_decay=0.0, adam=False)
+  optimizer_wd = LAMB(parameters, lr=max_lr, eps=epsilon, weight_decay=decay, adam=False)
+  optimizer_no_wd = LAMB(parameters_no_wd, lr=max_lr, eps=epsilon, weight_decay=0.0, adam=False)
  optimizer_group = OptimizerGroup(optimizer_wd, optimizer_no_wd)

  # ** LR scheduler **
  scheduler_wd = PolynomialDecayWithWarmup(optimizer_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power)
  scheduler_no_wd = PolynomialDecayWithWarmup(optimizer_no_wd, max_lr, 0, train_steps, warmup_steps, power=poly_power)
  scheduler_group = LRSchedulerGroup(scheduler_wd, scheduler_no_wd)
-  print(f"training with global batch size {GBS} for one epoch with {train_steps} steps")
+  print(f"training with batch size {BS} for one epoch with {train_steps} steps")

  # log mlperf hparams
  if MLLOGGER:
@@ -1089,8 +715,8 @@ def train_bert():
      MLLOGGER.event(key=mllog_constants.OPT_NAME, value="LAMB")
      MLLOGGER.event(key=mllog_constants.OPT_BASE_LR, value=config["OPT_BASE_LEARNING_RATE"])
      MLLOGGER.event(key=mllog_constants.OPT_LAMB_WEIGHT_DECAY, value=config["DECAY"])
-      MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_1, value=config["OPT_LAMB_BETA_1"])
-      MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_2, value=config["OPT_LAMB_BETA_2"])
+      MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_1, value=optimizer_wd.b1)
+      MLLOGGER.event(key=mllog_constants.OPT_LAMB_BETA_2, value=optimizer_wd.b2)
      MLLOGGER.event(key=mllog_constants.OPT_LAMB_LR_DECAY_POLY_POWER, value=config["POLY_POWER"])
      MLLOGGER.event(key=mllog_constants.OPT_LAMB_EPSILON, value=config["EPSILON"])

@@ -1107,7 +733,7 @@ def train_bert():
  previous_step = None
  if ckpt:=getenv("RESUME", ""):
    load_training_state(model, optimizer_group, scheduler_group, safe_load(ckpt))
-    start_step = int(scheduler_wd.epoch_counter.item())
+    start_step = int(scheduler_wd.epoch_counter.numpy().item())
    print(f"resuming from {ckpt} at step {start_step}")

  if RUNMLPERF:
@@ -1115,75 +741,70 @@ def train_bert():
    eval_it = iter(batch_load_val_bert(EVAL_BS))
    train_it = iter(tqdm(batch_load_train_bert(BS), total=train_steps, disable=BENCHMARK))
    for _ in range(start_step): next(train_it) # Fast forward
-  else:
-    # repeat fake data
-    def repeat_fake(bs):
-      while True: yield get_fake_data_bert(bs)
-    eval_it = iter(repeat_fake(EVAL_BS))
-    train_it = iter(repeat_fake(BS))
+

  step_times = []
  # ** train loop **
  wc_start = time.perf_counter()
-
-  i, train_data = start_step, [next(train_it) for _ in range(grad_acc)]
-
  if RUNMLPERF:
+    # only load real data with RUNMLPERF
+    i, train_data = start_step, get_data_bert(GPUS, train_it)
    if MLLOGGER:
-      MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*GBS, metadata={"epoch_num": i*GBS})
+      MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*BS, metadata={"epoch_num": i*BS})
+  else:
+    i, train_data = start_step, get_fake_data_bert(GPUS, BS)

  while train_data is not None and i < train_steps and not achieved:
-    if getenv("TRAIN", 1):
-      Tensor.training = True
-      BEAM.value = TRAIN_BEAM
-      st = time.perf_counter()
-      GlobalCounters.reset()
-      with WallTimeEvent(BenchEvent.STEP):
-        data = {f"{k}{i}":v for i,d in enumerate(train_data) for k,v in d.items()}
-        loss, global_norm, lr = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler, GPUS, grad_acc, **data)
+    Tensor.training = True
+    BEAM.value = TRAIN_BEAM
+    st = time.perf_counter()
+    GlobalCounters.reset()
+    loss = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler,
+      train_data["input_ids"], train_data["segment_ids"], train_data["input_mask"], train_data["masked_lm_positions"], \
+      train_data["masked_lm_ids"], train_data["masked_lm_weights"], train_data["next_sentence_labels"])

-        pt = time.perf_counter()
+    pt = time.perf_counter()

-        try:
-          next_data = [next(train_it) for _ in range(grad_acc)]
-        except StopIteration:
-          next_data = None
+    try:
+      if RUNMLPERF:
+        next_data = get_data_bert(GPUS, train_it)
+      else:
+        next_data = get_fake_data_bert(GPUS, BS)
+    except StopIteration:
+      next_data = None

-        dt = time.perf_counter()
+    dt = time.perf_counter()

-        device_str = parameters[0].device if isinstance(parameters[0].device, str) else f"{parameters[0].device[0]} * {len(parameters[0].device)}"
-        loss = loss.item()
-        assert not math.isnan(loss)
-        lr = lr.item()
+    device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
+    loss = loss.numpy().item()

-      cl = time.perf_counter()
-      if BENCHMARK: step_times.append(cl - st)
+    cl = time.perf_counter()
+    if BENCHMARK: step_times.append(cl - st)

-      tqdm.write(
-        f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, "
-        f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {lr:.6f} LR, "
-        f"{GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS")
-      if WANDB:
-        wandb.log({"lr": lr, "train/loss": loss, "train/global_norm": global_norm.item(), "train/step_time": cl - st,
-                    "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
-                    "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*GBS})
+    tqdm.write(
+      f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, "
+      f"{(cl - dt) * 1000.0:7.2f} ms {device_str}, {loss:5.2f} loss, {optimizer_wd.lr.numpy()[0]:.6f} LR, "
+      f"{GlobalCounters.mem_used / 1e9:.2f} GB used, {GlobalCounters.global_ops * 1e-9 / (cl - st):9.2f} GFLOPS")
+    if WANDB:
+      wandb.log({"lr": optimizer_wd.lr.numpy(), "train/loss": loss, "train/step_time": cl - st,
+                  "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
+                  "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*BS})

-      train_data, next_data = next_data, None
-      i += 1
+    train_data, next_data = next_data, None
+    i += 1

-      if i == BENCHMARK:
-        median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
-        estimated_total_minutes = int(median_step_time * train_steps / 60)
-        print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
-        print(f"epoch global_ops: {train_steps * GlobalCounters.global_ops:_}, "
-              f"epoch global_mem: {train_steps * GlobalCounters.global_mem:_}")
+    if i == BENCHMARK:
+      median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
+      estimated_total_minutes = int(median_step_time * train_steps / 60)
+      print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
+      print(f"epoch global_ops: {train_steps * GlobalCounters.global_ops:_}, "
+            f"epoch global_mem: {train_steps * GlobalCounters.global_mem:_}")

    # ** eval loop **
-    if i % eval_step_freq == 0 or (BENCHMARK and i == BENCHMARK) or i == train_steps:
+    if i % eval_step_freq == 0 or (BENCHMARK and i == BENCHMARK):
      if MLLOGGER and RUNMLPERF:
-        MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*GBS, "step_num": i})
-      if getenv("RESET_STEP"): train_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 1) and train_step_bert.captured is not None: train_step_bert.captured.free_intermediates()
+        MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*BS, "step_num": i})
+      if getenv("RESET_STEP", 1): train_step_bert.reset()
      eval_lm_losses = []
      eval_clsf_losses = []
      eval_lm_accs = []
@@ -1193,14 +814,19 @@ def train_bert():
      BEAM.value = EVAL_BEAM

      for j in tqdm(range(max_eval_steps), desc="Evaluating", total=max_eval_steps, disable=BENCHMARK):
-        eval_data = next(eval_it)
+        if RUNMLPERF:
+          eval_data = get_data_bert(GPUS, eval_it)
+        else:
+          eval_data = get_fake_data_bert(GPUS, EVAL_BS)
        GlobalCounters.reset()
        st = time.time()

-        lm_acc, clsf_acc, lm_loss, clsf_loss = eval_step_bert(model,
+        eval_result: dict[str, Tensor] = eval_step_bert(model,
          eval_data["input_ids"], eval_data["segment_ids"], eval_data["input_mask"], eval_data["masked_lm_positions"],
-          eval_data["masked_lm_ids"], eval_data["masked_lm_weights"], eval_data["next_sentence_labels"], GPUS)
-        lm_acc, clsf_acc, lm_loss, clsf_loss = lm_acc.item(), clsf_acc.item(), lm_loss.item(), clsf_loss.item()
+          eval_data["masked_lm_ids"], eval_data["masked_lm_weights"], eval_data["next_sentence_labels"])
+
+        lm_loss, clsf_loss  = eval_result["masked_lm_loss"].item(), eval_result["next_sentence_loss"].item()
+        lm_acc, clsf_acc = eval_result["masked_lm_accuracy"].item(), eval_result["next_sentence_accuracy"].item()

        eval_lm_losses.append(lm_loss)
        eval_clsf_losses.append(clsf_loss)
@@ -1210,16 +836,14 @@ def train_bert():
        et = time.time()
        eval_times.append(et - st)

-        if BENCHMARK and (j+1) == min(BENCHMARK, max_eval_steps):
+        if BENCHMARK and j == BENCHMARK:
          # assume INITMLPERF has BENCHMARK set
          if MLLOGGER and INITMLPERF:
            MLLOGGER.event(key=mllog_constants.INIT_STOP, value=None)
          return

-      if getenv("RESET_STEP"): eval_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 1) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()
-
-      del eval_data
+      if getenv("RESET_STEP", 1): eval_step_bert.reset()
+      del eval_data, eval_result
      avg_lm_loss = sum(eval_lm_losses) / len(eval_lm_losses)
      avg_clsf_loss = sum(eval_clsf_losses) / len(eval_clsf_losses)
      avg_lm_acc = sum(eval_lm_accs) / len(eval_lm_accs)
@@ -1231,11 +855,11 @@ def train_bert():

      if WANDB:
        wandb.log({"eval/lm_loss": avg_lm_loss, "eval/clsf_loss": avg_clsf_loss, "eval/lm_accuracy": avg_lm_acc, \
-                    "eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time, "epoch": (i+1)*GBS})
+                    "eval/clsf_accuracy": avg_clsf_acc, "eval/forward_time": avg_fw_time})

      if MLLOGGER and RUNMLPERF:
-        MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*GBS, metadata={"epoch_count": i*GBS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
-        MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*GBS, "masked_lm_accuracy": avg_lm_acc})
+        MLLOGGER.end(key=mllog_constants.EVAL_STOP, value=i*BS, metadata={"epoch_count": i*BS, "step_num": i, "samples_count": config["EVAL_BS"] * config["MAX_EVAL_STEPS"]})
+        MLLOGGER.event(key=mllog_constants.EVAL_ACCURACY, value=avg_lm_acc, metadata={"epoch_num": i*BS, "masked_lm_accuracy": avg_lm_acc})

      # save model if achieved target
      if not achieved and avg_lm_acc >= target:
@@ -1250,22 +874,19 @@ def train_bert():
        hours = int(total_seconds // 3600)
        minutes = int((total_seconds % 3600) // 60)
        seconds = total_seconds % 60
-        print(f"Reference Convergence point reached after {i * GBS} datasamples and {hours}h{minutes}m{seconds:.2f}s.")
+        print(f"Reference Convergence point reached after {i * BS} datasamples and {hours}h{minutes}m{seconds:.2f}s.")
        achieved = True
        if MLLOGGER and RUNMLPERF:
-          MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*GBS, metadata={"epoch_num": i*GBS})
+          MLLOGGER.event(key=mllog_constants.EPOCH_STOP, value=i*BS, metadata={"epoch_num": i*BS})
          MLLOGGER.end(key=mllog_constants.RUN_STOP, metadata=dict(status=mllog_constants.SUCCESS))
        # stop once hitting the target
        break

-    # should not happen, BENCHMARK not properly terminated
-    if BENCHMARK: assert i < BENCHMARK, i
-
    if getenv("CKPT") and i % save_ckpt_freq == 0:
      if MLLOGGER and RUNMLPERF:
        if previous_step:
          MLLOGGER.end(key=mllog_constants.BLOCK_STOP, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "first_step_num": i, "step_num": i, "step_count": i - previous_step})
-        MLLOGGER.start(key="checkpoint_start", value=None, metadata={"step_num": i})
+        MLLOGGER.start(key="checkpoint_start", value=None, metadata={"step_num" : i})
      if not os.path.exists(ckpt_dir := save_ckpt_dir): os.mkdir(ckpt_dir)
      if WANDB and wandb.run is not None:
        fn = f"{ckpt_dir}/{time.strftime('%Y%m%d_%H%M%S')}_{wandb.run.id}.safe"
@@ -1281,90 +902,18 @@ def train_bert():
        os.remove(os.path.join(ckpt_dir, last))
      if MLLOGGER and RUNMLPERF:
        MLLOGGER.end(key="checkpoint_stop", value=None, metadata={"step_num": i})
-        MLLOGGER.start(key=mllog_constants.BLOCK_START, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "epoch_count": 1, "samples_count": i * GBS, "step_num": i, "first_step_num": i+1})
+        MLLOGGER.start(key=mllog_constants.BLOCK_START, value=None, metadata={"first_epoch_num": 1, "epoch_num": 1, "epoch_count": 1, "samples_count": i * BS, "step_num": i, "first_step_num": i+1})
        previous_step = i

-def train_llama3():
-  from extra.models.llama import Transformer
-  from examples.llama3 import MODEL_PARAMS
-  from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
-
-  config = {}
-  BS                 = config["BS"]                     = getenv("BS", 4)
-  grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
-  GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
-
-  opt_adamw_beta_1 = 0.9
-  opt_adamw_beta_2 = 0.95
-  opt_adamw_epsilon = 1e-5
-  opt_adamw_weight_decay = 0.1
-
-  opt_gradient_clip_norm = 1.0
-  sequence_length = 8192
-  opt_learning_rate_warmup_steps = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
-  opt_learning_rate_decay_steps = getenv("DECAY_STEPS", math.ceil(1_200_000 * 1152 / GBS) - opt_learning_rate_warmup_steps)
-  opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152)  # NOTE: cannot change for benchmark
-  opt_end_learning_rate = 8e-7
-
-  # TODO: confirm weights are in bf16
-  # vocab_size from the mixtral tokenizer
-  model = Transformer(**(MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}), max_context=sequence_length, jit=False, disable_kv_cache=True)
-
-  optim = AdamW(get_parameters(model), lr=0.0,
-                b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
-  scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
-
-  @TinyJit
-  @Tensor.train()
-  def train_step(model, x, y):
-    optim.zero_grad()
-    logits:Tensor = model(x, start_pos=0, temperature=math.nan)
-    loss = logits.cross_entropy(y)
-    loss.backward()
-
-    # L2 norm grad clip
-    # https://github.com/NVIDIA/NeMo/blob/3368c3fc0b4a186ab33a1d68a504315100c0b2a6/nemo/collections/nlp/modules/common/megatron/clip_grads.py#L57
-    # https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
-    if not getenv("DISABLE_GRAD_CLIP_NORM"):
-      total_norm = Tensor(0.0, dtype=dtypes.float32, device=optim.params[0].device)
-      for p in optim.params:
-        total_norm += p.grad.float().square().sum()
-      total_norm = total_norm.sqrt().contiguous()
-      for p in optim.params:
-        p.grad = p.grad * opt_gradient_clip_norm / (total_norm + 1e-6)
-
-    optim.step()
-    scheduler.step()
-
-    lr = optim.lr
-    loss.realize(lr)
-    return loss, lr
-
-  # overfitting this example should give cross_entropy log(BS)
-  fake_input = Tensor([list(range(getenv("SEQLEN", 10)))], dtype="int16").expand(BS, -1)
-  fake_label = Tensor(list(range(BS)), dtype="int16")
-
-  for _ in range(100):
-    GlobalCounters.reset()
-    loss, lr = train_step(model, fake_input, fake_label)
-    # BS=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=8B WARMUP_STEPS=2 DECAY_STEPS=300 PYTHONPATH=. AMD=1 MODEL=llama3 python3 examples/mlperf/model_train.py
-    # uses 43% ~= 83GB
-    # 8B bf16 = 16GB. model + grad + optim m and v = 64GB
-    # TODO: this OOM
-    # BS=1 SEQLEN=4000 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=8B WARMUP_STEPS=2 DECAY_STEPS=300 PYTHONPATH=. AMD=1 MODEL=llama3 python3 examples/mlperf/model_train.py
-    print(loss.item(), lr.item(), f"{GlobalCounters.global_mem//10**9=}")
+def train_maskrcnn():
+  # TODO: Mask RCNN
+  pass

 if __name__ == "__main__":
  multiprocessing.set_start_method('spawn')
-
-  if getenv("INITMLPERF"): bench_log_manager = WallTimeEvent(BenchEvent.MLPERF_INIT)
-  elif getenv("RUNMLPERF"): bench_log_manager = WallTimeEvent(BenchEvent.MLPERF_RUN)
-  else: bench_log_manager = contextlib.nullcontext()
-
  with Tensor.train():
    for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,maskrcnn").split(","):
      nm = f"train_{m}"
      if nm in globals():
        print(f"training {m}")
-        with bench_log_manager:
-          with Profiling(enabled=getenv("PYPROFILE")): globals()[nm]()
+        globals()[nm]()
@@ -1,50 +0,0 @@
-# 1. Problem
-
-This problem uses the ResNet-50 CNN to do image classification.
-
-## Requirements
-
-Install tinygrad and mlperf-logging from master.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-### tinybox_red
-Disable cwsr
-This is the default on production tinybox red.
-```
-sudo vi /etc/modprobe.d/amdgpu.conf
-cat <<EOF > /etc/modprobe.d/amdgpu.conf
-options amdgpu cwsr_enable=0
-EOF
-sudo update-initramfs -u
-sudo reboot
-
-# validate
-sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
-```
-
-# 2. Directions
-
-## Steps to download and verify data
-
-```
-IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
-```
-
-## Steps for one time setup
-
-### tinybox_red
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
-```
-
-## Steps to run benchmark
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
-```
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
-
-export BENCHMARK=10 DEBUG=2
-
-python3 examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
-
-export EVAL_START_EPOCH=3 EVAL_FREQ=4
-
-export WANDB=1 PARALLEL=0
-
-python3 examples/mlperf/model_train.py
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 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=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -1,50 +0,0 @@
-# 1. Problem
-
-This problem uses the ResNet-50 CNN to do image classification.
-
-## Requirements
-
-Install tinygrad and mlperf-logging from master.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-### tinybox_red
-Disable cwsr
-This is the default on production tinybox red.
-```
-sudo vi /etc/modprobe.d/amdgpu.conf
-cat <<EOF > /etc/modprobe.d/amdgpu.conf
-options amdgpu cwsr_enable=0
-EOF
-sudo update-initramfs -u
-sudo reboot
-
-# validate
-sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
-```
-
-# 2. Directions
-
-## Steps to download and verify data
-
-```
-IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
-```
-
-## Steps for one time setup
-
-### tinybox_red
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
-```
-
-## Steps to run benchmark
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
-```
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 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=2
-
-python3 examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export EVAL_START_EPOCH=3 EVAL_FREQ=4
-
-export WANDB=1 PARALLEL=0
-
-python3 examples/mlperf/model_train.py
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="."
-export MODEL="resnet"
-export SUBMISSION_PLATFORM="tinybox_red"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export LAZYCACHE=0 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=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -1,8 +0,0 @@
-#!/bin/bash
-
-rocm-smi --setprofile compute
-rocm-smi --setmclk 3
-rocm-smi --setperflevel high
-
-# power cap to 350W
-echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
@@ -0,0 +1,87 @@
+:::MLLOG {"namespace": "", "time_ms": 1728516968768, "event_type": "POINT_IN_TIME", "key": "submission_org", "value": "tinycorp", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 631}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_platform", "value": "tinybox_green", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 632}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_division", "value": "closed", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 633}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_status", "value": "onprem", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 634}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968782, "event_type": "POINT_IN_TIME", "key": "submission_benchmark", "value": "bert", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 636}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968917, "event_type": "POINT_IN_TIME", "key": "cache_clear", "value": true, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 639}}
+:::MLLOG {"namespace": "", "time_ms": 1728516968917, "event_type": "INTERVAL_START", "key": "init_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 640}}
+:::MLLOG {"namespace": "", "time_ms": 1728518095273, "event_type": "POINT_IN_TIME", "key": "init_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 842}}
+:::MLLOG {"namespace": "", "time_ms": 1728518110874, "event_type": "INTERVAL_START", "key": "run_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 643}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125400, "event_type": "POINT_IN_TIME", "key": "global_batch_size", "value": 66, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 711}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "max_sequence_length", "value": 512, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 712}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "max_predictions_per_seq", "value": 76, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 713}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_name", "value": "LAMB", "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 715}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_base_learning_rate", "value": 0.0001, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 716}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125401, "event_type": "POINT_IN_TIME", "key": "opt_lamb_weight_decay_rate", "value": 0.01, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 717}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_1", "value": 0.9, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 718}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_beta_2", "value": 0.999, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 719}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_learning_rate_decay_poly_power", "value": 1.0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 720}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_lamb_epsilon", "value": 1e-06, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 721}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 723}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "num_warmup_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 724}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "start_warmup_step", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 725}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125402, "event_type": "POINT_IN_TIME", "key": "opt_learning_rate_training_steps", "value": 55000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 726}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "gradient_accumulation_steps", "value": 1, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 727}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "eval_samples", "value": 10002, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 728}}
+:::MLLOG {"namespace": "", "time_ms": 1728518125403, "event_type": "POINT_IN_TIME", "key": "train_samples", "value": 3630000, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 729}}
+:::MLLOG {"namespace": "", "time_ms": 1728518171154, "event_type": "INTERVAL_START", "key": "epoch_start", "value": 0, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 753, "epoch_num": 0}}
+:::MLLOG {"namespace": "", "time_ms": 1728519204577, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 149952, "step_num": 2272}}
+:::MLLOG {"namespace": "", "time_ms": 1728519263743, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 149952, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 149952, "step_num": 2272, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728519263744, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.38641827217854635, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 149952, "masked_lm_accuracy": 0.38641827217854635}}
+:::MLLOG {"namespace": "", "time_ms": 1728520267792, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 299904, "step_num": 4544}}
+:::MLLOG {"namespace": "", "time_ms": 1728520321266, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 299904, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 299904, "step_num": 4544, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728520321266, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.40444660376272445, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 299904, "masked_lm_accuracy": 0.40444660376272445}}
+:::MLLOG {"namespace": "", "time_ms": 1728521322547, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 449856, "step_num": 6816}}
+:::MLLOG {"namespace": "", "time_ms": 1728521376298, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 449856, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 449856, "step_num": 6816, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728521376298, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.4533385156548231, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 449856, "masked_lm_accuracy": 0.4533385156548231}}
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+:::MLLOG {"namespace": "", "time_ms": 1728646261029, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1799424, "step_num": 27264}}
+:::MLLOG {"namespace": "", "time_ms": 1728646314945, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1799424, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1799424, "step_num": 27264, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728646314946, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7180203909851078, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1799424, "masked_lm_accuracy": 0.7180203909851078}}
+:::MLLOG {"namespace": "", "time_ms": 1728647468431, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 1949376, "step_num": 29536}}
+:::MLLOG {"namespace": "", "time_ms": 1728647522632, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 1949376, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 1949376, "step_num": 29536, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728647522633, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7191875515115712, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 1949376, "masked_lm_accuracy": 0.7191875515115712}}
+:::MLLOG {"namespace": "", "time_ms": 1728648686121, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2099328, "step_num": 31808}}
+:::MLLOG {"namespace": "", "time_ms": 1728648739069, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2099328, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2099328, "step_num": 31808, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728648739069, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7196067409309428, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2099328, "masked_lm_accuracy": 0.7196067409309428}}
+:::MLLOG {"namespace": "", "time_ms": 1728649885357, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2249280, "step_num": 34080}}
+:::MLLOG {"namespace": "", "time_ms": 1728649939244, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2249280, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2249280, "step_num": 34080, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728649939245, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7197489482454004, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2249280, "masked_lm_accuracy": 0.7197489482454004}}
+:::MLLOG {"namespace": "", "time_ms": 1728651085252, "event_type": "INTERVAL_START", "key": "eval_start", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 806, "epoch_num": 2399232, "step_num": 36352}}
+:::MLLOG {"namespace": "", "time_ms": 1728651139304, "event_type": "INTERVAL_END", "key": "eval_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 861, "epoch_count": 2399232, "step_num": 36352, "samples_count": 10002}}
+:::MLLOG {"namespace": "", "time_ms": 1728651139304, "event_type": "POINT_IN_TIME", "key": "eval_accuracy", "value": 0.7202936197442785, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 862, "epoch_num": 2399232, "masked_lm_accuracy": 0.7202936197442785}}
+:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "POINT_IN_TIME", "key": "epoch_stop", "value": 2399232, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 880, "epoch_num": 2399232}}
+:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "INTERVAL_END", "key": "run_stop", "value": null, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 881, "status": "success"}}
+:::MLLOG {"namespace": "", "time_ms": 1728651139305, "event_type": "POINT_IN_TIME", "key": "seed", "value": 32021, "metadata": {"file": "tinygrad/examples/mlperf/model_train.py", "lineno": 882}}
@@ -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
@@ -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
-```
@@ -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
@@ -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
@@ -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
@@ -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
-```
--- a/Show More
+++ b/Show More