binop also

improve typing
no need for Self yet
2026-08-15 07:58:26 +00:00 · 2025-10-11 16:04:05 +08:00 · 2025-10-11 15:57:37 +08:00 · 2025-10-11 15:53:47 +08:00 · 2025-10-11 15:46:39 +08:00
178 changed files with 3595 additions and 25570 deletions
@@ -41,10 +41,6 @@ inputs:
    description: "Install LLVM?"
    required: false
    default: 'false'
-  mesa:
-    description: "Install mesa"
-    required: false
-    default: 'false'
 runs:
  using: "composite"
  steps:
@@ -293,13 +289,3 @@ runs:
      if: inputs.llvm == 'true' && runner.os == 'macOS'
      shell: bash
      run: brew install llvm@20
-
-    # **** mesa ****
-    - name: Install mesa (linux)
-      if: inputs.mesa == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: sudo curl -L https://github.com/sirhcm/tinymesa/releases/download/tinymesa-32dc66c/libtinymesa_cpu-mesa-25.2.4-linux-amd64.so -o /usr/lib/libtinymesa_cpu.so
-    - name: Install mesa (macOS)
-      if: inputs.mesa == 'true' && runner.os == 'macOS'
-      shell: bash
-      run: brew install sirhcm/tinymesa/tinymesa
@@ -36,9 +36,8 @@ jobs:
        cuda: 'true'
        webgpu: 'true'
        llvm: 'true'
-        pydeps: 'pyyaml mako'
    - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev llvm-20-dev
+      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev
    - name: Verify OpenCL autogen
      run: |
        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
@@ -90,8 +89,3 @@ jobs:
        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
        ./autogen_stubs.sh llvm
        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
-    - name: Verify mesa autogen
-      run: |
-        cp tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
-        ./autogen_stubs.sh mesa
-        diff /tmp/mesa.py.bak tinygrad/runtime/autogen/mesa.py
@@ -51,18 +51,17 @@ jobs:
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
      run: python3.11 test/external/process_replay/reset.py
-    - name: Print macOS version
-      run: sw_vers
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
+      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=900 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
    - name: Run Stable Diffusion v2
      # TODO: very slow step time
-      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=4500 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
+      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=10000 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
    # process replay can't capture this, the graph is too large
-    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+    # TODO: too slow
+    # - name: Run SDXL
+    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
      run: METAL=1 python3.11 test/external/external_model_benchmark.py
    - name: Test speed vs torch
@@ -633,7 +632,7 @@ jobs:
      run: PYTHONPATH="." ASSERT_MIN_STEP_TIME=12 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 Space Lab policy + vision
      run: |
-        PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
+        PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
        PYTHONPATH="." ASSERT_MIN_STEP_TIME=26 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/c824f68646a3b94f117f01c70dc8316fb466e05fbd42ccdba440b8a8dc86914b
    - name: benchmark MobileNetV2 on DSP
      run: |
@@ -642,7 +641,7 @@ jobs:
        ln -s /data/home/tiny/tinygrad/testsig-*.so .
        PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
        # benchmark on DSP with NOOPT=1, the devectorizer has issues
-        PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+        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
@@ -12,7 +12,7 @@ jobs:
  run_script_job:
    runs-on: [self-hosted, Linux, tinybox]
    if: github.repository_owner == 'tinygrad'
-    timeout-minutes: 720
+    timeout-minutes: 360

    steps:
    - name: Checkout Code
@@ -89,65 +89,64 @@ jobs:
        clang -O2 recognize.c -lm -o recognize
        cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock

-  # TODO: fix the torch backend and reenable
-  # torchbackend:
-  #   name: Torch Backend Tests
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       pydeps: "pillow torchvision expecttest"
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Lint with ruff
-  #     run: |
-  #       pip3 install --upgrade --force-reinstall ruff==0.11.0
-  #       python3 -m ruff check extra/torch_backend/backend.py
-  #   - name: Test one op
-  #     run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
-  #   - name: Test ResNet-18
-  #     run: DEBUG=2 python3 extra/torch_backend/example.py
-  #   - name: My (custom) tests
-  #     run: python3 extra/torch_backend/test.py
-  #   - name: Test one op in torch tests
-  #     run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
-  #   - name: Test Ops with TINY_BACKEND
-  #     run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
-  #   - name: Test in-place operations on views
-  #     run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
-  #   - name: Test multi-gpu
-  #     run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+  torchbackend:
+    name: Torch Backend Tests
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: torch-backend-pillow-torchvision-et-pt
+        deps: testing_minimal
+        pydeps: "pillow torchvision expecttest"
+        llvm: 'true'
+    - name: Install ninja
+      run: |
+        sudo apt update || true
+        sudo apt install -y --no-install-recommends ninja-build
+    - name: Lint with ruff
+      run: |
+        pip3 install --upgrade --force-reinstall ruff==0.11.0
+        python3 -m ruff check extra/torch_backend/backend.py
+    - name: Test one op
+      run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
+    - name: Test ResNet-18
+      run: DEBUG=2 python3 extra/torch_backend/example.py
+    - name: My (custom) tests
+      run: python3 extra/torch_backend/test.py
+    - name: Test one op in torch tests
+      run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
+    - name: Test Ops with TINY_BACKEND
+      run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
+    - name: Test in-place operations on views
+      run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
+    - name: Test multi-gpu
+      run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py

-  # torchbackendmore:
-  #   name: Torch Backend Tests More
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Test beautiful_mnist in torch with TINY_BACKEND
-  #     run: STEPS=20 CPU=1 TARGET_EVAL_ACC_PCT=90.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
-  #   - name: Test some torch tests (expect failure)
-  #     run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
+  torchbackendmore:
+    name: Torch Backend Tests More
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: torch-backend-pillow-torchvision-et-pt
+        deps: testing_minimal
+        llvm: 'true'
+    - name: Install ninja
+      run: |
+        sudo apt update || true
+        sudo apt install -y --no-install-recommends ninja-build
+    - name: Test beautiful_mnist in torch with TINY_BACKEND
+      run: CPU=1 CPU_LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+    - name: Test some torch tests (expect failure)
+      run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true

  bepython:
    name: Python Backend
@@ -239,6 +238,8 @@ jobs:
        pip3 install --upgrade --force-reinstall ruff==0.11.0
        python3 -m ruff check .
        python3 -m ruff check examples/mlperf/ --ignore E501
+    - name: Lint tinygrad with pylint
+      run: python -m pylint tinygrad/
    - name: Run mypy
      run: |
        python -m mypy --strict-equality --lineprecision-report .
@@ -273,8 +274,6 @@ jobs:
    #   run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
    - name: Run Clip tests for SD MLPerf on NULL backend
      run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
-    - name: Run AMD emulated BERT training on NULL backend
-      run: EMULATE=AMD_RDNA4 NULL=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
    # TODO: support fake weights
    #- name: Run LLaMA 7B on 4 fake devices
    #  run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
@@ -310,6 +309,10 @@ jobs:
      run: python test/external/fuzz_symbolic.py
    - name: Fuzz Test fast idiv
      run: python test/external/fuzz_fast_idiv.py
+    - name: Fuzz Test shapetracker
+      run: |
+        python test/external/fuzz_shapetracker.py
+        python test/external/fuzz_shapetracker_math.py
    - name: Fuzz Test shape ops
      run: python test/external/fuzz_shape_ops.py

@@ -374,7 +377,7 @@ jobs:
          llvm: 'true'
      - name: Test openpilot model kernel count and gate usage
        run: |
-          ALLOWED_KERNEL_COUNT=190 ALLOWED_READ_IMAGE=2081 ALLOWED_GATED_READ_IMAGE=28 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
+          ALLOWED_KERNEL_COUNT=190 ALLOWED_READ_IMAGE=2041 ALLOWED_GATED_READ_IMAGE=41 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
      - name: Test openpilot alt model correctness (float32)
        run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
      - name: Test openpilot fastvits model correctness (float32)
@@ -383,6 +386,8 @@ jobs:
      #   run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
      - name: Test openpilot LLVM compile
        run: CPU=1 CPU_LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+      - name: Test openpilot compile4
+        run: NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 DEBUG=2 python3 examples/openpilot/compile4.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -672,7 +677,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        backend: [llvm, cpu, opencl, lvp]
+        backend: [llvm, cpu, opencl]

    name: Linux (${{ matrix.backend }})
    runs-on: ubuntu-22.04
@@ -686,10 +691,9 @@ jobs:
          key: ${{ matrix.backend }}-minimal
          deps: testing_minimal
          opencl: ${{ matrix.backend == 'opencl' && 'true' }}
-          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
-          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
+          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' }}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
@@ -891,7 +895,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        backend: [metal, llvm, cpu, lvp]
+        backend: [metal, llvm, cpu]
    name: MacOS (${{ matrix.backend }})
    runs-on: macos-15
    timeout-minutes: 20
@@ -904,13 +908,12 @@ jobs:
          key: macos-${{ matrix.backend }}-minimal
          deps: testing_minimal
          pydeps: "capstone"
-          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
-          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
+          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU','LVP':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
      - name: Run pytest (${{ matrix.backend }})
        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
@@ -38,7 +38,6 @@ extra/huggingface_onnx/models/*
 extra/huggingface_onnx/*.yaml
 extra/weights
 venv
-venv_sd_mlperf
 examples/**/net.*[js,json]
 examples/**/*.safetensors
 node_modules
@@ -20,15 +20,21 @@ repos:
        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
+        language: system
+        always_run: true
+        pass_filenames: false
      - id: example
-        name: test all devices
+        name: multi device tests
        entry: python3 test/external/external_test_example.py
        language: system
        always_run: true
        pass_filenames: false
-      - id: tests
-        name: subset of tests
-        entry: env PYTHONPATH="." python3 -m pytest -n=8 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
+      - id: pylint
+        name: pylint
+        entry: python3 -m pylint tinygrad/
        language: system
        always_run: true
        pass_filenames: false
@@ -30,6 +30,10 @@ persistent=yes
 # Specify a configuration file.
 #rcfile=

+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages
+suggestion-mode=yes
+
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
 unsafe-load-any-extension=no
@@ -435,8 +435,8 @@ generate_sqtt() {
    -o extra/sqtt/rocprof/rocprof.py
  fixup extra/sqtt/rocprof/rocprof.py
  sed -i '1s/^/# pylint: skip-file\n/' extra/sqtt/rocprof/rocprof.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util/g" extra/sqtt/rocprof/rocprof.py
-  sed -i "s|FunctionFactoryStub()|ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder'))|g" extra/sqtt/rocprof/rocprof.py
+  sed -i "s/import ctypes/import ctypes\nfrom tinygrad.helpers import fetch/g" extra/sqtt/rocprof/rocprof.py
+  sed -i "s|FunctionFactoryStub()|ctypes.CDLL(str(fetch('https://github.com/ROCm/rocprof-trace-decoder/raw/5420409ad0963b2d76450add067b9058493ccbd0/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so')))|g" extra/sqtt/rocprof/rocprof.py
 }

 generate_webgpu() {
@@ -461,85 +461,6 @@ generate_libusb() {
  python3 -c "import tinygrad.runtime.autogen.libusb"
 }

-generate_mesa() {
-  MESA_TAG="mesa-25.2.4"
-  MESA_SRC=/tmp/mesa-$MESA_TAG
-  TINYMESA_TAG=tinymesa-32dc66c
-  TINYMESA_DIR=/tmp/tinymesa-$MESA_TAG-$TINYMESA_TAG/
-  TINYMESA_SO=$TINYMESA_DIR/libtinymesa_cpu.so
-  if [ ! -d "$MESA_SRC" ]; then
-    git clone --depth 1 --branch $MESA_TAG https://gitlab.freedesktop.org/mesa/mesa.git $MESA_SRC
-    pushd .
-    cd $MESA_SRC
-    git reset --hard $MESA_COMMIT_HASH
-    # clang 14 doesn't support packed enums
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/headers/nv_device_info.h
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/compiler/nak.h
-    sed -i "s/nir_instr_type \(\w\+\);/uint8_t \1;/" $MESA_SRC/src/compiler/nir/nir.h
-    mkdir -p gen/util/format
-    python3 src/util/format/u_format_table.py src/util/format/u_format.yaml --enums > gen/util/format/u_format_gen.h
-    python3 src/compiler/nir/nir_opcodes_h.py > gen/nir_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/nir/nir_intrinsics_indices_h.py --outdir gen
-    python3 src/compiler/nir/nir_builder_opcodes_h.py > gen/nir_builder_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/builtin_types_h.py gen/builtin_types.h
-    popd
-  fi
-
-  if [ ! -d "$TINYMESA_DIR" ]; then
-    mkdir $TINYMESA_DIR
-    curl -L https://github.com/sirhcm/tinymesa/releases/download/$TINYMESA_TAG/libtinymesa_cpu-$MESA_TAG-linux-amd64.so -o $TINYMESA_SO
-  fi
-
-  clang2py -k cdefstu \
-    $MESA_SRC/src/compiler/nir/nir.h \
-    $MESA_SRC/src/compiler/nir/nir_builder.h \
-    $MESA_SRC/src/compiler/nir/nir_shader_compiler_options.h \
-    $MESA_SRC/src/compiler/nir/nir_serialize.h \
-    $MESA_SRC/gen/nir_intrinsics.h \
-    $MESA_SRC/src/nouveau/headers/nv_device_info.h \
-    $MESA_SRC/src/nouveau/compiler/nak.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_passmgr.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_misc.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_type.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_init.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_nir.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_struct.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_jit_types.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_flow.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_const.h \
-    $MESA_SRC/src/compiler/glsl_types.h \
-    $MESA_SRC/src/util/blob.h \
-    $MESA_SRC/src/util/ralloc.h \
-    --clang-args="-DHAVE_ENDIAN_H -DHAVE_STRUCT_TIMESPEC -DHAVE_PTHREAD -I$MESA_SRC/src -I$MESA_SRC/include -I$MESA_SRC/gen -I$MESA_SRC/src/compiler/nir -I$MESA_SRC/src/gallium/auxiliary -I$MESA_SRC/src/gallium/include -I$(llvm-config-20 --includedir)" \
-    -l $TINYMESA_SO \
-    -o $BASE/mesa.py
-
-  LVP_NIR_OPTIONS=$(./extra/mesa/lvp_nir_options.sh $MESA_SRC)
-
-  fixup $BASE/mesa.py
-  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "f'{brew_prefix()}/lib/libtinymesa_cpu.dylib'"
-  echo "lvp_nir_options = gzip.decompress(base64.b64decode('$LVP_NIR_OPTIONS'))" >> $BASE/mesa.py
-  cat <<EOF | sed -i "/import ctypes.*/r /dev/stdin" $BASE/mesa.py
-def brew_prefix():
-  try: return subprocess.check_output(['brew', '--prefix', 'tinymesa']).decode().strip()
-  except Exception: return ''
-EOF
-  sed -i "/in_dll/s/.*/try: &\nexcept AttributeError: pass/" $BASE/mesa.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers/" $BASE/mesa.py
-  sed -i "s/ctypes.CDLL('.\+')/(dll := _try_dlopen_tinymesa_cpu())/" $BASE/mesa.py
-  echo "def __getattr__(nm): raise AttributeError() if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
-  sed -i "s/ctypes.glsl_base_type/glsl_base_type/" $BASE/mesa.py
-  # bitfield bug in clang2py
-  sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py
-  sed -i "s/('\(\w\+\)', pipe_shader_type, 8)/('\1', ctypes.c_ubyte)/" $BASE/mesa.py
-  sed -i "s/\([0-9]\+\)()/\1/" $BASE/mesa.py
-  sed -i "s/\(struct_nir_builder._pack_\) = 1/\1 = 0/" $BASE/mesa.py
-  python3 -c "import tinygrad.runtime.autogen.mesa"
-}
-
 if [ "$1" == "opencl" ]; then generate_opencl
 elif [ "$1" == "hip" ]; then generate_hip
 elif [ "$1" == "comgr" ]; then generate_comgr
@@ -563,7 +484,6 @@ elif [ "$1" == "pci" ]; then generate_pci
 elif [ "$1" == "vfio" ]; then generate_vfio
 elif [ "$1" == "webgpu" ]; then generate_webgpu
 elif [ "$1" == "libusb" ]; then generate_libusb
-elif [ "$1" == "mesa" ]; then generate_mesa
-elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu; generate_mesa
+elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu
 else echo "usage: $0 <type>"
 fi
@@ -232,7 +232,7 @@ if __name__ == "__main__":
  gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith("gpt2_gguf_") else GPT2.build(args.model_size)

  if args.benchmark != -1:
-    gpt2.model(Tensor.randint(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
+    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
  else:
    texts = gpt2.generate(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)
    if not args.noshow:
@@ -1188,9 +1188,7 @@ def train_bert():
      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", 0) and train_step_bert.captured is not None:
-        # TODO: FREE_INTERMEDIATE nan'ed after jit step 2
-        train_step_bert.captured.free_intermediates()
+      elif getenv("FREE_INTERMEDIATE", 1) and train_step_bert.captured is not None: train_step_bert.captured.free_intermediates()
      eval_lm_losses = []
      eval_clsf_losses = []
      eval_lm_accs = []
@@ -1224,7 +1222,7 @@ def train_bert():
          return

      if getenv("RESET_STEP"): eval_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 0) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()
+      elif getenv("FREE_INTERMEDIATE", 1) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()

      del eval_data
      avg_lm_loss = sum(eval_lm_losses) / len(eval_lm_losses)
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
-
-export IGNORE_OOB=1
-
-export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-# export BEAM_LOG_SURPASS_MAX=1
-# export BASEDIR="/raid/datasets/wiki"
-
-export RESET_STEP=1
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py
@@ -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
-```
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-export BENCHMARK=10 BERT_LAYERS=2
-
-python3 examples/mlperf/model_train.py
@@ -1,20 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-
-# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-export TRAIN_STEPS=3900
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-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
@@ -1,31 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export SUBMISSION_PLATFORM="tinybox_8xMI300X"
-export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
-
-# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
-export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
-export TRAIN_STEPS=3900
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
-export BASEDIR="/raid/datasets/wiki"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="bert_8xMI300x_${DATETIME}_${SEED}.log"
-
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -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
-```
@@ -1,17 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BEAM_LOG_SURPASS_MAX=1
-export BASEDIR="/raid/datasets/wiki"
-
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/wiki"
-
-export WANDB=1 PARALLEL=0
-
-RUNMLPERF=1 python3 examples/mlperf/model_train.py
@@ -1,28 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." NV=1
-export MODEL="bert"
-export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/wiki"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="bert_green_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -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
-```
@@ -1,18 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BEAM_LOG_SURPASS_MAX=1
-export BASEDIR="/raid/datasets/wiki"
-
-export RESET_STEP=1
-export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
-
-python3 examples/mlperf/model_train.py
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/wiki"
-
-export WANDB=1 PARALLEL=0
-
-RUNMLPERF=1 python3 examples/mlperf/model_train.py
@@ -1,31 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." AMD=1
-export MODEL="bert"
-export SUBMISSION_PLATFORM="tinybox_red"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export IGNORE_OOB=1
-export REWRITE_STACK_LIMIT=500000
-
-export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/wiki"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="bert_red_${DATETIME}_${SEED}.log"
-
-export HCQDEV_WAIT_TIMEOUT_MS=100000  # prevents hang?
-
-# init
-sleep 5 && sudo rmmod amdgpu || true
-BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -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="." NV=1
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
-
-export BENCHMARK=10 DEBUG=2
-
-python3 examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
-
-export EVAL_START_EPOCH=3 EVAL_FREQ=4
-
-export WANDB=1 PARALLEL=0
-
-python3 examples/mlperf/model_train.py
@@ -1,25 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." NV=1
-export MODEL="resnet"
-export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=${LOGMLPERF:-1}
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -1,50 +0,0 @@
-# 1. Problem
-
-This problem uses the ResNet-50 CNN to do image classification.
-
-## Requirements
-
-Install tinygrad and mlperf-logging from master.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-### tinybox_red
-Disable cwsr
-This is the default on production tinybox red.
-```
-sudo vi /etc/modprobe.d/amdgpu.conf
-cat <<EOF > /etc/modprobe.d/amdgpu.conf
-options amdgpu cwsr_enable=0
-EOF
-sudo update-initramfs -u
-sudo reboot
-
-# validate
-sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
-```
-
-# 2. Directions
-
-## Steps to download and verify data
-
-```
-IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
-```
-
-## Steps for one time setup
-
-### tinybox_red
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
-```
-
-## Steps to run benchmark
-```
-examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
-```
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export BENCHMARK=10 DEBUG=${DEBUG:-2}
-
-python3 examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="resnet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export EVAL_START_EPOCH=3 EVAL_FREQ=4
-
-export WANDB=1 PARALLEL=0
-
-python3 examples/mlperf/model_train.py
@@ -1,26 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." AMD=1
-export MODEL="resnet"
-export SUBMISSION_PLATFORM="tinybox_red"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
-
-export RESET_STEP=0
-
-export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=${LOGMLPERF:-1}
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
-
-# init
-sleep 5 && sudo rmmod amdgpu || true
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -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
@@ -1,38 +0,0 @@
-# 1. Problem
-
-This problem uses RetinaNet for SSD.
-
-## Requirements
-
-Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
-```
-git clone https://github.com/tinygrad/tinygrad.git
-python3 -m pip install -e ".[mlperf]"
-```
-
-Also install the following dependencies:
-```
-pip install tqdm numpy pycocotools boto3 pandas torch torchvision
-```
-
-### tinybox_green
-Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
-This is the default on production tinybox green.
-
-# 2. Directions
-
-## Steps to download data
-
-Run the following:
-```
-BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
-```
-
-## Running
-
-### tinybox_green
-
-#### Steps to run benchmark
-```
-examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
-```
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export BENCHMARK=5 DEBUG=2
-
-python examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export WANDB=1 PARALLEL=0
-export RUNMLPERF=1
-
-python examples/mlperf/model_train.py
@@ -1,25 +0,0 @@
-#!/bin/bash
-set -e  # Exit on any error
-set -o pipefail  # Make pipeline fail if any command fails
-
-export PYTHONPATH="." NV=1
-export MODEL="retinanet"
-export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-
-export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-export IGNORE_JIT_FIRST_BEAM=1
-export BASEDIR="/raid/datasets/openimages"
-
-# pip install -e ".[mlperf]"
-export LOGMLPERF=1
-
-export SEED=$RANDOM
-DATETIME=$(date "+%m%d%H%M")
-LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
-
-# init
-BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
-
-# run
-PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export BENCHMARK=5 DEBUG=2
-
-python examples/mlperf/model_train.py
@@ -1,15 +0,0 @@
-#!/bin/bash
-
-export PYTHONPATH="." AMD=1
-export MODEL="retinanet"
-export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
-export BASEDIR="/raid/datasets/openimages"
-
-# export RESET_STEP=0
-
-export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
-
-export WANDB=1 PARALLEL=0
-export RUNMLPERF=1
-
-python examples/mlperf/model_train.py
@@ -1,38 +0,0 @@
-{
-    "submitter": "tinycorp",
-    "division": "closed",
-    "status": "Available on-premise",
-    "system_name": "tinybox 8xMI300X",
-    "number_of_nodes": "1",
-    "host_processors_per_node": "2",
-    "host_processor_model_name": "AMD EPYC 9354",
-    "host_processor_core_count": "32",
-    "host_processor_vcpu_count": "64",
-    "host_processor_frequency": "",
-    "host_processor_caches": "",
-    "host_processor_interconnect": "",
-    "host_memory_capacity": "2304GB",
-    "host_storage_type": "NVMe SSD",
-    "host_storage_capacity": "3x 4TB raid array",
-    "host_networking": "",
-    "host_networking_topology": "",
-    "host_memory_configuration": "24x 96GB DDR5",
-    "accelerators_per_node": "8",
-    "accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
-    "accelerator_host_interconnect": "PCIe 5.0 x16",
-    "accelerator_frequency": "",
-    "accelerator_on-chip_memories": "",
-    "accelerator_memory_configuration": "HBM3",
-    "accelerator_memory_capacity": "192GB",
-    "accelerator_interconnect": "",
-    "accelerator_interconnect_topology": "",
-    "cooling": "air",
-    "hw_notes": "",
-    "framework": "tinygrad, branch mlperf_training_v5.0",
-    "other_software_stack": {
-        "python": "3.10.16",
-        "ROCm": "3.0.0+94441cb"
-    },
-    "operating_system": "Ubuntu 24.04.1 LTS",
-    "sw_notes": ""
-  }
@@ -1,38 +0,0 @@
-{
-  "submitter": "tinycorp",
-  "division": "closed",
-  "status": "Available on-premise",
-  "system_name": "tinybox green",
-  "number_of_nodes": "1",
-  "host_processors_per_node": "1",
-  "host_processor_model_name": "AMD EPYC 7532",
-  "host_processor_core_count": "32",
-  "host_processor_vcpu_count": "64",
-  "host_processor_frequency": "",
-  "host_processor_caches": "",
-  "host_processor_interconnect": "",
-  "host_memory_capacity": "128GB",
-  "host_storage_type": "NVMe SSD",
-  "host_storage_capacity": "4 TB raid array + 1 TB boot",
-  "host_networking": "",
-  "host_networking_topology": "",
-  "host_memory_configuration": "8x 16GB DDR4",
-  "accelerators_per_node": "6",
-  "accelerator_model_name": "NVIDIA GeForce RTX 4090",
-  "accelerator_host_interconnect": "PCIe 4.0 x16",
-  "accelerator_frequency": "",
-  "accelerator_on-chip_memories": "",
-  "accelerator_memory_configuration": "GDDR6X",
-  "accelerator_memory_capacity": "24GB",
-  "accelerator_interconnect": "",
-  "accelerator_interconnect_topology": "",
-  "cooling": "air",
-  "hw_notes": "",
-  "framework": "tinygrad, branch mlperf_training_v5.0",
-  "other_software_stack": {
-    "python": "3.10.12",
-    "CUDA": "12.4"
-  },
-  "operating_system": "Ubuntu 22.04.4",
-  "sw_notes": ""
-}
@@ -1,37 +0,0 @@
-{
-  "submitter": "tinycorp",
-  "division": "closed",
-  "status": "Available on-premise",
-  "system_name": "tinybox red",
-  "number_of_nodes": "1",
-  "host_processors_per_node": "1",
-  "host_processor_model_name": "AMD EPYC 7532",
-  "host_processor_core_count": "32",
-  "host_processor_vcpu_count": "64",
-  "host_processor_frequency": "",
-  "host_processor_caches": "",
-  "host_processor_interconnect": "",
-  "host_memory_capacity": "128GB",
-  "host_storage_type": "NVMe SSD",
-  "host_storage_capacity": "4 TB raid array + 1 TB boot",
-  "host_networking": "",
-  "host_networking_topology": "",
-  "host_memory_configuration": "8x 16GB DDR4",
-  "accelerators_per_node": "6",
-  "accelerator_model_name": "AMD Radeon RX 7900 XTX",
-  "accelerator_host_interconnect": "PCIe 4.0 x16",
-  "accelerator_frequency": "",
-  "accelerator_on-chip_memories": "",
-  "accelerator_memory_configuration": "GDDR6",
-  "accelerator_memory_capacity": "24GB",
-  "accelerator_interconnect": "",
-  "accelerator_interconnect_topology": "",
-  "cooling": "air",
-  "hw_notes": "",
-  "framework": "tinygrad, branch mlperf_training_v5.0",
-  "other_software_stack": {
-    "python": "3.10.12"
-  },
-  "operating_system": "Ubuntu 22.04.4",
-  "sw_notes": ""
-}
@@ -0,0 +1,47 @@
+import sys
+from tinygrad import Tensor, fetch, GlobalCounters, dtypes
+from tinygrad.uop.ops import UOp
+from tinygrad.nn.onnx import OnnxRunner
+from tinygrad.schedule.rangeify import get_rangeify_map
+from tinygrad.engine.schedule import create_schedule_with_vars
+from tinygrad.engine.realize import run_schedule
+
+# NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py
+
+OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
+OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
+
+if __name__ == "__main__":
+  onnx_file = fetch(OPENPILOT_MODEL)
+  run_onnx = OnnxRunner(onnx_file)
+
+  inputs = run_onnx.get_empty_input_data("npy", dtypes.float32)
+  out: Tensor = next(iter(run_onnx({k:v.to(None) for k,v in inputs.items()}).values())).to('cpu')
+  root = out.uop
+  targets = [x.uop for x in inputs.values()]
+  print(targets)
+
+  # TODO: abstract this from gradient?
+
+  # compute the target path (top down)
+  in_target_path: dict[UOp, bool] = {}
+  for u in root.toposort(): in_target_path[u] = any(x in targets or in_target_path[x] for x in u.src)
+  independent_set = {}
+  for u in root.toposort():
+    if in_target_path[u]:
+      for s in u.src:
+        if not in_target_path[s]:
+          independent_set[s] = None
+  independent = UOp.sink(*independent_set.keys())
+  kernelized = get_rangeify_map(independent)
+  independent = independent.substitute(kernelized)
+  schedule, var_vals = create_schedule_with_vars(independent)
+  run_schedule(schedule)
+
+  print("**** real ****")
+  GlobalCounters.reset()
+  out.uop = root.substitute(kernelized)
+  out.kernelize()
+
+  # realize
+  out.realize()
@@ -269,8 +269,7 @@ if __name__ == "__main__":

  # load in weights
  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    model_bin = fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt')
-    load_state_dict(model, torch_load(model_bin)['state_dict'], verbose=False, strict=False, realize=False)
+    load_state_dict(model, torch_load(fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt'))['state_dict'], verbose=False, strict=False, realize=False)

    if args.fp16:
      for k,v in get_state_dict(model).items():
@@ -19,8 +19,8 @@ from tinygrad.helpers import fetch, getenv

 # QUANT=1 python3 examples/test_onnx_imagenet.py
 #   https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx
-# python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
-# VIZ=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx
+# DONT_REALIZE_EXPAND=1 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+# VIZ=1 DONT_REALIZE_EXPAND=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx

 def imagenet_dataloader(cnt=0):
  input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
@@ -1,23 +0,0 @@
-#!/bin/sh
-
-if [ "$#" -ne 1 ] || ! [ -d $1 ]; then
-  echo "usage: $0 MESA_PREFIX"
-  exit 1
-fi
-
-TMP=$(mktemp)
-trap 'rm -f "$TMP"' EXIT
-
-(
-  cat <<EOF
-#define HAVE_ENDIAN_H
-#define HAVE_STRUCT_TIMESPEC
-#define HAVE_PTHREAD
-#include <unistd.h>
-#include "nir_shader_compiler_options.h"
-#include "compiler/shader_enums.h"
-EOF
-  sed -n '/struct nir_shader_compiler_options/,/^}/{p;/^}/q}' $1/src/gallium/drivers/llvmpipe/lp_screen.c
-  echo "int main(void) { write(1, &gallivm_nir_options, sizeof(gallivm_nir_options)); }"
-) | cc -x c -o $TMP - -I$1/src/compiler/nir -I$1/src -I$1/include && $TMP | gzip | base64 -w0
-
@@ -3,21 +3,8 @@ from tinygrad.tensor import _to_np_dtype
 from tinygrad.nn.onnx import OnnxRunner, OnnxValue
 import numpy as np
 import onnxruntime as ort
-ort_options = ort.SessionOptions()
-ort_options.log_severity_level = 3

 def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
-  """
-  Generate example input tensors based on the provided ONNX graph input specifications.
-
-  NOTE: This is not guaranteed to be reliable. It's a best-effort helper
-  that uses heuristics to guess input shapes and values.
-
-  Example:
-    from tinygrad.nn.onnx import OnnxRunner
-    from extra.onnx_helpers import get_example_inputs
-    inputs = get_example_inputs(OnnxRunner(model_path).graph_inputs)
-  """
  def _get_shape(onnx_shape: tuple[str|int]):
    shape = []
    for onnx_dim in onnx_shape:
@@ -57,9 +44,11 @@ def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
    ret.update({name:value})
  return ret

-def _get_tinygrad_and_ort_np_outputs(onnx_file, inputs):
+def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
  run_onnx = OnnxRunner(onnx_file)

+  ort_options = ort.SessionOptions()
+  ort_options.log_severity_level = 3
  ort_sess = ort.InferenceSession(onnx_file, ort_options, ["CPUExecutionProvider"])
  np_inputs = {k:v.numpy() if isinstance(v, Tensor) else v for k,v in inputs.items()}
  out_names = list(run_onnx.graph_outputs)
@@ -67,101 +56,9 @@ def _get_tinygrad_and_ort_np_outputs(onnx_file, inputs):
  ort_out = dict(zip(out_names, out_values))

  tinygrad_out = run_onnx(inputs)
-  Tensor.realize(*(x for x in tinygrad_out.values() if x is not None))
-  tinygrad_out = {k:v.numpy() if v is not None else None for k,v in tinygrad_out.items()}
-  return tinygrad_out, ort_out
-
-def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
-  """
-  Compares the final output tensors of an onnx model run in tinygrad and onnxruntime.
-  """
-  tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(onnx_file, inputs)

  assert tinygrad_out.keys() == ort_out.keys()
  for k in tinygrad_out.keys():
    tiny_v, onnx_v = tinygrad_out[k], ort_out[k]
    if tiny_v is None: assert onnx_v is None, f"{k}: {tiny_v=}, {onnx_v=}"
-    else: np.testing.assert_allclose(tiny_v, onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
-
-def validate_all_intermediates(onnx_file, inputs, rtol=1e-5, atol=1e-5):
-  """
-  Compares all intermediate node output of an onnx model run in tinygrad and onnxruntime.
-  """
-  report = generate_node_output_report(onnx_file, inputs)
-  for i, node in enumerate(report):
-    node_name = node["node"]
-    op = node["op"]
-    outputs = node["outputs"]
-    for output in outputs:
-      output_name = output["name"]
-      tinygrad_out = output["tinygrad"]
-      ort_out = output["onnxruntime"]
-      try:
-        if tinygrad_out is None: assert ort_out is None, f"None outputs are not equal {tinygrad_out=} {ort_out=}"
-        else: np.testing.assert_allclose(tinygrad_out, ort_out, rtol=rtol, atol=atol)
-        print(f"Validated {i}: {op=} {node_name=} {output_name=}")
-      except AssertionError as e:
-        print(f"FAILED {i}: {op=} {node_name=} {output_name=}")
-        print(str(e).strip() + "\n")
-
-def generate_node_output_report(onnx_file, inputs):
-  """
-  Build a report of all ONNX node outputs from tinygrad and onnxruntime
-
-  Returns:
-    A list of dictionaries, where each entry corresponds to one
-    node in the ONNX graph. The structure is as follows:
-    [
-      {
-        "node": str,  # The name of the ONNX node.
-        "op": str,    # The operation type of the ONNX node.
-        "outputs": [
-          {
-            "name": str,                       # The name of the output tensor.
-            "tinygrad": np.ndarray | None,     # The output value from tinygrad.
-            "onnxruntime": np.ndarray | None,  # The output value from onnxruntime.
-          },
-          ...
-        ]
-      },
-      ...
-    ]
-  """
-  import onnx_graphsurgeon as gs
-  import onnx
-  import tempfile
-
-  # rewrite the model to output all the node outputs
-  # `infer_shapes` here tries to fill the shapes and dtypes of intermediate values which graphsurgeon requires when assigning them as outputs
-  inferred_model = onnx.shape_inference.infer_shapes(onnx.load(onnx_file))
-  model = gs.import_onnx(inferred_model)
-  model_nodes = model.nodes
-  node_outputs = [n.outputs for n in model.nodes]
-  model.outputs = [
-      each_output for outputs in node_outputs for each_output in outputs
-      if not (each_output.dtype is None and each_output.shape is None)  # output with None dtype and None shape is likely a `None` value
-  ]
-  rewritten_model = gs.export_onnx(model)
-
-  # TODO: remove this once ORT supports 1.18.0
-  if getattr(rewritten_model, "ir_version", 0) > 10:
-    rewritten_model.ir_version = 10
-
-  with tempfile.NamedTemporaryFile(suffix=".onnx") as f:
-    onnx.save(rewritten_model, f.name)
-    rewritten_model_path = f.name
-    tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(rewritten_model_path, inputs)
-
-  report = []
-  for node in model_nodes:
-    outputs = []
-    for each_output in node.outputs:
-      if each_output.dtype is None and each_output.shape is None:
-        continue
-      name = each_output.name
-      tinygrad_output = tinygrad_out[name]
-      ort_output = ort_out[name]
-      outputs.append({"name": name, "tinygrad": tinygrad_output, "onnxruntime": ort_output})
-    report.append({"node": node.name, "op": node.op, "outputs": outputs})
-
-  return report
+    else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
@@ -1,18 +0,0 @@
-#!/usr/bin/env python3
-import os, shutil
-from pathlib import Path
-from tinygrad.helpers import fetch, OSX
-
-DEST = Path("/usr/local/lib")
-DEST.mkdir(exist_ok=True)
-
-if __name__ == "__main__":
-  if OSX:
-    fp = fetch("https://github.com/ROCm/rocprof-trace-decoder/releases/download/0.1.4/rocprof-trace-decoder-macos-arm64-0.1.4-Darwin.sh")
-    lib = fp.parent/"rocprof-trace-decoder-macos-arm64-0.1.4-Darwin"/"lib"/"librocprof-trace-decoder.dylib"
-    os.chmod(fp, 0o755)
-    os.system(f"sudo {fp} --prefix={fp.parent} --include-subdir")
-  else:
-    lib = fetch("https://github.com/ROCm/rocprof-trace-decoder/raw/5420409ad0963b2d76450add067b9058493ccbd0/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so", name="librocprof-trace-decoder.so")
-  shutil.copy2(lib, DEST)
-  print(f"Installed {lib.name} to", DEST)
@@ -7,7 +7,8 @@
 # POINTER_SIZE is: 8
 # LONGDOUBLE_SIZE is: 16
 #
-import ctypes, ctypes.util
+import ctypes
+from tinygrad.helpers import fetch


 class AsDictMixin:
@@ -155,7 +156,7 @@ class FunctionFactoryStub:
 # You can either re-run clan2py with -l /path/to/library.so
 # Or manually fix this by comment the ctypes.CDLL loading
 _libraries = {}
-_libraries['FIXME_STUB'] = ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder')) #  ctypes.CDLL('FIXME_STUB')
+_libraries['FIXME_STUB'] = ctypes.CDLL(str(fetch('https://github.com/ROCm/rocprof-trace-decoder/raw/5420409ad0963b2d76450add067b9058493ccbd0/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so'))) #  ctypes.CDLL('FIXME_STUB')



@@ -1,11 +0,0 @@
-from tinygrad.tensor import Tensor
-import argparse
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser()
-  parser.add_argument("hash", type=str, required=True, help="file hash to fetch")
-  parser.add_argument("len", type=int, required=True, help="file length to fetch")
-  parser.add_argument("dest", type=str, required=True, help="destination path to save the file")
-  args = parser.parse_args()
-
-  Tensor(bytes.fromhex(args.hash), device="CPU").load(args.len).to(f"disk:{args.dest}").realize()
@@ -1,39 +0,0 @@
-import json, multiprocessing
-from pathlib import Path
-
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import tqdm, getenv
-
-raid_root = Path(getenv("RAID_ROOT", "/raid"))
-
-def fetch_file(item):
-  path, info = item
-  h, size = info["hash"], info["size"]
-
-  path = raid_root / Path(path)
-  path.parent.mkdir(parents=True, exist_ok=True)
-
-  try:
-    pt = Tensor(bytes.fromhex(h), device="CPU").load(size).to(f"disk:{path.as_posix()}").realize()
-  except Exception as e:
-    print(f"error fetching {path}, {h}, {size}: {e}")
-    raise
-
-  pt.uop.buffer.deallocate()
-
-def fetch_mapping():
-  mapping_tensor = Tensor(bytes.fromhex("d734f5e3be9f1e9d863bfaa4fc6c1ef2")).load(175866113).realize()
-  mapping = mapping_tensor.data().tobytes().decode()
-  mapping = json.loads(mapping)
-  mapped_files = mapping.items()
-  return list(mapped_files)
-
-if __name__ == "__main__":
-  with multiprocessing.Pool(processes=1) as pool:
-    mapped_files = pool.apply(fetch_mapping)
-
-  print(f"fetched mapping for {len(mapped_files)} files")
-
-  with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
-    for _ in tqdm(pool.imap_unordered(fetch_file, mapped_files), total=len(mapped_files)):
-      pass
@@ -1,31 +0,0 @@
-from pathlib import Path
-import multiprocessing, json
-
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import tqdm
-
-raid_root = Path("/raid")
-
-def upload_file(path: Path):
-  pt = Tensor(path).realize()
-  h = pt.store().realize()
-  pt.uop.realized.deallocate()
-  return h.data().hex(), path, pt.nbytes()
-
-if __name__ == "__main__":
-  raid_files = sorted([p for p in raid_root.rglob("*") if p.is_file()])
-  print(f"found {len(raid_files)} files in /raid")
-
-  mapping = {}
-  with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
-    for h, p, s in tqdm(pool.imap_unordered(upload_file, raid_files), total=len(raid_files)):
-      mapping[p.relative_to(raid_root).as_posix()] = {"hash": h, "size": s}
-
-  # sort the mapping by key
-  mapping = dict(sorted(mapping.items()))
-
-  mapping = json.dumps(mapping).encode()
-  mapping_tensor = Tensor(mapping, device="CPU")
-  h = mapping_tensor.store().realize()
-
-  print(f"final hash: {h.data().hex()}, size: {len(mapping)}")
@@ -155,14 +155,16 @@ def index_tensor(x, y):
 def zero_(x):
  if TORCH_DEBUG: print(f"zero_ {x.shape}")
  tt = unwrap(x)
-  tt.assign(tt.zeros_like())
+  # NOTE: unconditional contiguous covers if x is contiguous (match it) or if x is view (realize for inplace)
+  # TODO: consolidate
+  tt.assign(tt.zeros_like().contiguous())

@torch.library.impl("aten::fill_.Scalar", "privateuseone")
@inplace_fn("x")
 def fill_scalar(x, y):
  if TORCH_DEBUG: print(f"fill_.Scalar {x.shape} {y}")
  tt = unwrap(x)
-  tt.assign(tt.full_like(y))
+  tt.assign(tt.full_like(y).contiguous())

@torch.library.impl("aten::_local_scalar_dense", "privateuseone")
 def _local_scalar_dense(tensor): return unwrap(tensor).item()
@@ -642,11 +644,10 @@ def get_real_tinygrad_buffers():
 torch.nn.modules.module.register_module_buffer_registration_hook(register_torch_buffer)

 from torch.nn.modules import Module
-def param_hook(_grad):
-  if _grad is not None and _grad.is_tiny: Tensor.realize(unwrap(_grad))
-def module_hook(module:Module, _name, _submodule):
-  for param in _submodule.parameters(recurse=False):
-    if param.requires_grad: param.register_hook(param_hook)
+def backward_hook(model:Module, _grad_input, _grad_out):
+  grads_to_realize = [unwrap(p.grad) for p in model.parameters() if p.grad is not None]
+  if len(grads_to_realize): Tensor.realize(*grads_to_realize)
+def module_hook(module:Module, _name, _submodule): module.register_backward_hook(backward_hook)
 torch.nn.modules.module.register_module_module_registration_hook(module_hook)

 def realize_optimizer_step(optimizer: torch.optim.Optimizer, *args, **kwargs):
@@ -1,13 +0,0 @@
-xcuserdata/
-
-**/*.xcodeproj/project.xcworkspace/*
-!**/*.xcodeproj/project.xcworkspace/xcshareddata
-
-**/*.xcodeproj/project.xcworkspace/xcshareddata/*
-!**/*.xcodeproj/project.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
-
-**/*.playground/playground.xcworkspace/*
-!**/*.playground/playground.xcworkspace/xcshareddata
-
-**/*.playground/playground.xcworkspace/xcshareddata/*
-!**/*.playground/playground.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings
@@ -1,11 +0,0 @@
-{
-  "colors" : [
-    {
-      "idiom" : "universal"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}
@@ -1,148 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "iphone",
-      "scale" : "2x",
-      "size" : "20x20"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "3x",
-      "size" : "20x20"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "2x",
-      "size" : "29x29"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "3x",
-      "size" : "29x29"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "2x",
-      "size" : "40x40"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "3x",
-      "size" : "40x40"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "2x",
-      "size" : "60x60"
-    },
-    {
-      "idiom" : "iphone",
-      "scale" : "3x",
-      "size" : "60x60"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "1x",
-      "size" : "20x20"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "2x",
-      "size" : "20x20"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "1x",
-      "size" : "29x29"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "2x",
-      "size" : "29x29"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "1x",
-      "size" : "40x40"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "2x",
-      "size" : "40x40"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "1x",
-      "size" : "76x76"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "2x",
-      "size" : "76x76"
-    },
-    {
-      "idiom" : "ipad",
-      "scale" : "2x",
-      "size" : "83.5x83.5"
-    },
-    {
-      "idiom" : "ios-marketing",
-      "scale" : "1x",
-      "size" : "1024x1024"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "1x",
-      "size" : "16x16"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "2x",
-      "size" : "16x16"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "1x",
-      "size" : "32x32"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "2x",
-      "size" : "32x32"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "1x",
-      "size" : "128x128"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "2x",
-      "size" : "128x128"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "1x",
-      "size" : "256x256"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "2x",
-      "size" : "256x256"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "1x",
-      "size" : "512x512"
-    },
-    {
-      "idiom" : "mac",
-      "scale" : "2x",
-      "size" : "512x512"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}
@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}
@@ -1,19 +0,0 @@
-import AppKit
-import SwiftUI
-
-final class AppDelegate: NSObject, NSApplicationDelegate {
-	func applicationShouldTerminateAfterLastWindowClosed(_ sender: NSApplication) -> Bool {
-		true
-	}
-}
-
-@main
-struct TinyGPUApp: App {
-	@NSApplicationDelegateAdaptor(AppDelegate.self) var appDelegate
-
-    var body: some Scene {
-        WindowGroup {
-			TinyGPUView()
-        }
-    }
-}
@@ -1,33 +0,0 @@
-import SwiftUI
-
-struct TinyGPUView: View {
-	@ObservedObject var viewModel = TinyGPUViewModel()
-
-	var body: some View {
-#if os(macOS)
-		VStack(alignment: .center) {
-			Text("TinyGPU Intsaller")
-				.padding()
-				.font(.title)
-			Text(self.viewModel.dextLoadingState)
-				.multilineTextAlignment(.center)
-			HStack {
-				Button(
-					action: {
-						self.viewModel.activateMyDext()
-					}, label: {
-						Text("Install extension")
-					}
-				)
-			}
-		}
-		.frame(width: 500, height: 200, alignment: .center)
-#endif
-	}
-}
-
-struct TinyGPUView_Previews: PreviewProvider {
-    static var previews: some View {
-		TinyGPUView()
-    }
-}
@@ -1,149 +0,0 @@
-import Foundation
-import os.log
-import SystemExtensions
-
-class TinyGPUDriverLoadingStateMachine {
-  enum State { case unloaded, activating, needsApproval, activated, activationError }
-}
-
-class TinyGPUViewModel: NSObject {
-
-	@Published private var state: TinyGPUDriverLoadingStateMachine.State = .unloaded
-	
-	override init() {
-		super.init()
-		refreshInitialDextState()
-	}
-	
-	private func refreshInitialDextState() {
-#if os(macOS)
-		Task.detached { [dextIdentifier] in
-		  let newState = Self.queryDextState(bundleID: dextIdentifier)
-		  await MainActor.run { self.state = newState }
-		}
-#endif
-	}
-
-#if os(macOS)
-	private static func queryDextState(bundleID: String) -> TinyGPUDriverLoadingStateMachine.State {
-		let tool = "/usr/bin/systemextensionsctl"
-		let p = Process()
-		p.executableURL = URL(fileURLWithPath: tool)
-		p.arguments = ["list"]
-
-		let pipe = Pipe()
-		p.standardOutput = pipe
-		p.standardError = Pipe()
-
-		do {
-			try p.run()
-			p.waitUntilExit()
-			let data = pipe.fileHandleForReading.readDataToEndOfFile()
-			guard let output = String(data: data, encoding: .utf8) else { return .unloaded }
-
-			// Look for our bundle id line
-			if let line = output.split(separator: "\n").first(where: { $0.contains(bundleID) }) {
-				if line.contains("[activated enabled]") { return .activated }
-				if line.contains("[activated waiting for user]") { return .needsApproval }
-				if line.contains("terminated waiting to uninstall") { return .unloaded }
-				return .activating
-			} else {
-				return .unloaded
-			}
-		} catch {
-			return .unloaded
-		}
-	}
-#endif
-
-	private let dextIdentifier: String = "org.tinygrad.tinygpu.edriver"
-
-	public var dextLoadingState: String {
-		switch state {
-		case .unloaded:
-			return "TinyGPUDriver isn't loaded."
-		case .activating:
-			return "Activating TinyGPUDriver, please wait."
-		case .needsApproval:
-			return "Please follow the prompt to approve TinyGPUDriver."
-		case .activated:
-			return "TinyGPUDriver has been activated and is ready to use. You can close the installer."
-		case .activationError:
-			return "TinyGPUDriver has experienced an error during activation.\nPlease check the logs to find the error."
-		}
-	}
-}
-
-extension TinyGPUViewModel: ObservableObject {
-
-#if os(macOS)
-	func activateMyDext() {
-		activateExtension(dextIdentifier)
-	}
-	
-	func deactivateMyDext() {
-		deactivateExtension(dextIdentifier)
-	}
-
-	func activateExtension(_ dextIdentifier: String) {
-
-		let request = OSSystemExtensionRequest
-			.activationRequest(forExtensionWithIdentifier: dextIdentifier,
-							   queue: .main)
-		request.delegate = self
-		OSSystemExtensionManager.shared.submitRequest(request)
-
-		self.state = .activating
-	}
-	
-	func deactivateExtension(_ dextIdentifier: String) {
-
-		let request = OSSystemExtensionRequest.deactivationRequest(forExtensionWithIdentifier: dextIdentifier, queue: .main)
-		request.delegate = self
-		OSSystemExtensionManager.shared.submitRequest(request)
-
-		self.state = .unloaded
-	}
-#endif
-}
-
-#if os(macOS)
-extension TinyGPUViewModel: OSSystemExtensionRequestDelegate {
-
-	func request(
-		_ request: OSSystemExtensionRequest,
-		actionForReplacingExtension existing: OSSystemExtensionProperties,
-		withExtension ext: OSSystemExtensionProperties) -> OSSystemExtensionRequest.ReplacementAction {
-
-		var replacementAction: OSSystemExtensionRequest.ReplacementAction
-
-		os_log("sysex actionForReplacingExtension: %@ %@", existing, ext)
-
-		// Add appropriate logic here to determine whether to replace the extension
-		// with the new extension. Common things to check for include
-		// testing whether the new extension's version number is newer than
-		// the current version number, or whether the bundleIdentifier is different.
-		// For simplicity, this sample always replaces the current extension
-		// with the new one.
-		replacementAction = .replace
-
-		self.state = .activating
-		return replacementAction
-	}
-
-	func requestNeedsUserApproval(_ request: OSSystemExtensionRequest) {
-		os_log("sysex requestNeedsUserApproval")
-		self.state = .needsApproval
-	}
-
-	func request(_ request: OSSystemExtensionRequest, didFinishWithResult result: OSSystemExtensionRequest.Result) {
-		os_log("sysex didFinishWithResult: %d", result.rawValue)
-		self.state = .activated
-	}
-
-	func request(_ request: OSSystemExtensionRequest, didFailWithError error: Error) {
-		os_log("sysex didFailWithError: %@", error.localizedDescription)
-		self.state = .activationError
-	}
-}
-#endif
@@ -1,589 +0,0 @@
-// !$*UTF8*$!
-{
-	archiveVersion = 1;
-	classes = {
-	};
-	objectVersion = 54;
-	objects = {
-
-/* Begin PBXBuildFile section */
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-				PRODUCT_BUNDLE_IDENTIFIER = org.tinygrad.tinygpu.installer;
-				PRODUCT_NAME = TinyGPU;
-				PROVISIONING_PROFILE_SPECIFIER = "";
-				SDKROOT = macosx;
-				SWIFT_COMPILATION_MODE = wholemodule;
-				SWIFT_EMIT_LOC_STRINGS = YES;
-				SWIFT_OPTIMIZATION_LEVEL = "-O";
-				SWIFT_VERSION = 5.0;
-			};
-			name = Release;
-		};
-		C5B7D9C726128AC50089B4C3 /* Debug */ = {
-			isa = XCBuildConfiguration;
-			buildSettings = {
-				ALWAYS_SEARCH_USER_PATHS = NO;
-				CLANG_ANALYZER_NONNULL = YES;
-				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
-				CLANG_CXX_LANGUAGE_STANDARD = "gnu++17";
-				CLANG_CXX_LIBRARY = "libc++";
-				CLANG_ENABLE_MODULES = YES;
-				CLANG_ENABLE_OBJC_ARC = YES;
-				CLANG_ENABLE_OBJC_WEAK = YES;
-				CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
-				CLANG_WARN_BOOL_CONVERSION = YES;
-				CLANG_WARN_COMMA = YES;
-				CLANG_WARN_CONSTANT_CONVERSION = YES;
-				CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
-				CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
-				CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
-				CLANG_WARN_EMPTY_BODY = YES;
-				CLANG_WARN_ENUM_CONVERSION = YES;
-				CLANG_WARN_INFINITE_RECURSION = YES;
-				CLANG_WARN_INT_CONVERSION = YES;
-				CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
-				CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
-				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
-				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
-				CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
-				CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
-				CLANG_WARN_STRICT_PROTOTYPES = YES;
-				CLANG_WARN_SUSPICIOUS_MOVE = YES;
-				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
-				CLANG_WARN_UNREACHABLE_CODE = YES;
-				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
-				COPY_PHASE_STRIP = NO;
-				DEBUG_INFORMATION_FORMAT = dwarf;
-				DRIVERKIT_DEPLOYMENT_TARGET = 21.0;
-				ENABLE_STRICT_OBJC_MSGSEND = YES;
-				ENABLE_TESTABILITY = YES;
-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				GCC_DYNAMIC_NO_PIC = NO;
-				GCC_NO_COMMON_BLOCKS = YES;
-				GCC_OPTIMIZATION_LEVEL = 0;
-				GCC_PREPROCESSOR_DEFINITIONS = (
-					"DEBUG=1",
-					"$(inherited)",
-				);
-				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
-				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
-				GCC_WARN_UNDECLARED_SELECTOR = YES;
-				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
-				GCC_WARN_UNUSED_FUNCTION = YES;
-				GCC_WARN_UNUSED_VARIABLE = YES;
-				MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
-				MTL_FAST_MATH = YES;
-				ONLY_ACTIVE_ARCH = YES;
-				SDKROOT = driverkit;
-			};
-			name = Debug;
-		};
-		C5B7D9C826128AC50089B4C3 /* Release */ = {
-			isa = XCBuildConfiguration;
-			buildSettings = {
-				ALWAYS_SEARCH_USER_PATHS = NO;
-				CLANG_ANALYZER_NONNULL = YES;
-				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
-				CLANG_CXX_LANGUAGE_STANDARD = "gnu++17";
-				CLANG_CXX_LIBRARY = "libc++";
-				CLANG_ENABLE_MODULES = YES;
-				CLANG_ENABLE_OBJC_ARC = YES;
-				CLANG_ENABLE_OBJC_WEAK = YES;
-				CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
-				CLANG_WARN_BOOL_CONVERSION = YES;
-				CLANG_WARN_COMMA = YES;
-				CLANG_WARN_CONSTANT_CONVERSION = YES;
-				CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
-				CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
-				CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
-				CLANG_WARN_EMPTY_BODY = YES;
-				CLANG_WARN_ENUM_CONVERSION = YES;
-				CLANG_WARN_INFINITE_RECURSION = YES;
-				CLANG_WARN_INT_CONVERSION = YES;
-				CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
-				CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
-				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
-				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
-				CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
-				CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
-				CLANG_WARN_STRICT_PROTOTYPES = YES;
-				CLANG_WARN_SUSPICIOUS_MOVE = YES;
-				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
-				CLANG_WARN_UNREACHABLE_CODE = YES;
-				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
-				COPY_PHASE_STRIP = NO;
-				DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
-				DRIVERKIT_DEPLOYMENT_TARGET = 21.0;
-				ENABLE_NS_ASSERTIONS = NO;
-				ENABLE_STRICT_OBJC_MSGSEND = YES;
-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				GCC_NO_COMMON_BLOCKS = YES;
-				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
-				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
-				GCC_WARN_UNDECLARED_SELECTOR = YES;
-				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
-				GCC_WARN_UNUSED_FUNCTION = YES;
-				GCC_WARN_UNUSED_VARIABLE = YES;
-				MTL_ENABLE_DEBUG_INFO = NO;
-				MTL_FAST_MATH = YES;
-				SDKROOT = driverkit;
-				SWIFT_COMPILATION_MODE = wholemodule;
-			};
-			name = Release;
-		};
-		C5B7D9CA26128AC50089B4C3 /* Debug */ = {
-			isa = XCBuildConfiguration;
-			buildSettings = {
-				AD_HOC_CODE_SIGNING_ALLOWED = YES;
-				CODE_SIGN_ENTITLEMENTS = TinyGPUDriverExtension/TinyGPUDriver.entitlements;
-				CODE_SIGN_IDENTITY = "Apple Development";
-				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_TEAM = 9YG3G8543N;
-				DRIVERKIT_DEPLOYMENT_TARGET = 21.0;
-				ENABLE_USER_SCRIPT_SANDBOXING = YES;
-				FRAMEWORK_SEARCH_PATHS = (
-					"$(inherited)",
-					"$(SDKROOT)/System/DriverKit/System/Library/Frameworks",
-				);
-				GENERATE_INFOPLIST_FILE = YES;
-				INFOPLIST_FILE = TinyGPUDriverExtension/Info.plist;
-				INFOPLIST_KEY_OSBundleUsageDescription = "Sample Code Audio Driver Kit Extension";
-				MARKETING_VERSION = 1.0;
-				PRODUCT_BUNDLE_IDENTIFIER = org.tinygrad.tinygpu.edriver;
-				PRODUCT_NAME = "$(inherited)";
-				PROVISIONING_PROFILE_SPECIFIER = "";
-				RUN_CLANG_STATIC_ANALYZER = YES;
-				SDKROOT = driverkit;
-				SKIP_INSTALL = YES;
-			};
-			name = Debug;
-		};
-		C5B7D9CB26128AC50089B4C3 /* Release */ = {
-			isa = XCBuildConfiguration;
-			buildSettings = {
-				AD_HOC_CODE_SIGNING_ALLOWED = YES;
-				CODE_SIGN_ENTITLEMENTS = TinyGPUDriverExtension/TinyGPUDriver.entitlements;
-				CODE_SIGN_IDENTITY = "Apple Development";
-				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_TEAM = 9YG3G8543N;
-				DRIVERKIT_DEPLOYMENT_TARGET = 21.0;
-				ENABLE_USER_SCRIPT_SANDBOXING = YES;
-				FRAMEWORK_SEARCH_PATHS = (
-					"$(inherited)",
-					"$(SDKROOT)/System/DriverKit/System/Library/Frameworks",
-				);
-				GENERATE_INFOPLIST_FILE = YES;
-				INFOPLIST_FILE = TinyGPUDriverExtension/Info.plist;
-				INFOPLIST_KEY_OSBundleUsageDescription = "Sample Code Audio Driver Kit Extension";
-				MARKETING_VERSION = 1.0;
-				PRODUCT_BUNDLE_IDENTIFIER = org.tinygrad.tinygpu.edriver;
-				PRODUCT_NAME = "$(inherited)";
-				PROVISIONING_PROFILE_SPECIFIER = "";
-				RUN_CLANG_STATIC_ANALYZER = YES;
-				SDKROOT = driverkit;
-				SKIP_INSTALL = YES;
-			};
-			name = Release;
-		};
-/* End XCBuildConfiguration section */
-
-/* Begin XCConfigurationList section */
-		54E42BD2286A1697000E1E9A /* Build configuration list for PBXNativeTarget "TinyGPU Installer (macOS)" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				54E42BCF286A1697000E1E9A /* Debug */,
-				54E42BD0286A1697000E1E9A /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-		C5B7D9B626128AC50089B4C3 /* Build configuration list for PBXProject "TinyGPUDriverExtension" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				C5B7D9C726128AC50089B4C3 /* Debug */,
-				C5B7D9C826128AC50089B4C3 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-		C5B7D9C926128AC50089B4C3 /* Build configuration list for PBXNativeTarget "TinyGPUDriver" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				C5B7D9CA26128AC50089B4C3 /* Debug */,
-				C5B7D9CB26128AC50089B4C3 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-/* End XCConfigurationList section */
-	};
-	rootObject = C5B7D9B326128AC50089B4C3 /* Project object */;
-}
@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>BuildSystemType</key>
-	<string>Latest</string>
-	<key>DerivedDataLocationStyle</key>
-	<string>Default</string>
-</dict>
-</plist>
@@ -1,37 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IOKitPersonalities</key>
-	<dict>
-		<key>TinyGPUDriver</key>
-		<dict>
-			<key>CFBundleIdentifier</key>
-			<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-			<key>IOClass</key>
-			<string>IOUserService</string>
-			<key>IOMatchCategory</key>
-			<string>TinyGPUDriver</string>
-			<key>IOPCIClassMatch</key>
-			<string>0x03000000</string>
-			<key>IOPCITunnelCompatible</key>
-			<true/>
-			<key>IOProviderClass</key>
-			<string>IOPCIDevice</string>
-			<key>IOResourceMatch</key>
-			<string>IOKit</string>
-			<key>IOUserClass</key>
-			<string>TinyGPUDriver</string>
-			<key>IOUserServerName</key>
-			<string>org.tinygrad.tinygpu.Driver</string>
-			<key>TinyGPUDriverUserClientProperties</key>
-			<dict>
-				<key>IOClass</key>
-				<string>IOUserUserClient</string>
-				<key>IOUserClass</key>
-				<string>TinyGPUDriverUserClient</string>
-			</dict>
-		</dict>
-	</dict>
-</dict>
-</plist>
@@ -1,259 +0,0 @@
-#include "TinyGPUDriver.h"
-#include "TinyGPUDriverUserClient.h"
-#include <AudioDriverKit/AudioDriverKit.h>
-#include <DriverKit/IOUserServer.h>
-#include <DriverKit/IOLib.h>
-#include <DriverKit/OSString.h>
-#include <DriverKit/IOMemoryMap.h>
-#include <DriverKit/IODMACommand.h>
-#include <DriverKit/IODispatchQueue.h>
-#include <PCIDriverKit/PCIDriverKit.h>
-#include <DriverKit/OSAction.h>
-
-struct TinyGPUDriver_IVars
-{
-	IOPCIDevice *pci = nullptr;
-};
-
-bool TinyGPUDriver::init()
-{
-	os_log(OS_LOG_DEFAULT, "tinygpu: init");
-
-	auto answer = super::init();
-	if (!answer) {
-		return false;
-	}
-
-	ivars = new TinyGPUDriver_IVars();
-	if (ivars == nullptr) {
-		return false;
-	}
-
-	return true;
-}
-
-void TinyGPUDriver::free()
-{
-	if (ivars != nullptr) {
-		
-	}
-	IOSafeDeleteNULL(ivars, TinyGPUDriver_IVars, 1);
-	super::free();
-}
-
-kern_return_t TinyGPUDriver::Start_Impl(IOService* in_provider)
-{
-	IOServiceName service_name;
-	os_log(OS_LOG_DEFAULT, "tinygpu: on gpu detected");
-
-	kern_return_t err = Start(in_provider, SUPERDISPATCH);
-	if (err) return err;
-
-	ivars->pci = OSDynamicCast(IOPCIDevice, in_provider);
-	if (!ivars->pci) return kIOReturnNoDevice;
-
-	err = ivars->pci->Open(this, 0);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: Open() failed 0x%08x", err);
-		ivars->pci = nullptr;
-		return err;
-	}
-
-	uint16_t ven = 0, dev = 0;
-	ivars->pci->ConfigurationRead16(kIOPCIConfigurationOffsetVendorID, &ven);
-	ivars->pci->ConfigurationRead16(kIOPCIConfigurationOffsetDeviceID, &dev);
-	os_log(OS_LOG_DEFAULT, "tinygpu: opened device ven=0x%04x dev=0x%04x", ven, dev);
-
-#if 0
-	uint32_t off = 0x100;
-	while (off) {
-		uint32_t hdr = 0, next = 0, cap_id = 0;
-		ivars->pci->ConfigurationRead32(off, &hdr);
-		cap_id = hdr & 0xFFFFu;
-		next = (hdr >> 20) & 0xFFCu;
-		os_log(OS_LOG_DEFAULT, "tinygpu: cap: %u", cap_id);
-		if (cap_id == 0x15) {
-			uint32_t cap = 0, ctrl = 0;
-			ivars->pci->ConfigurationRead32(off+0x4, &cap);
-			ivars->pci->ConfigurationRead32(off+0x8, &ctrl);
-
-			uint32_t new_bar_size = 31 - __builtin_clz(cap >> 4);
-			uint32_t new_ctrl = (ctrl & ~0x1f00) | (new_bar_size << 8);
-			ivars->pci->ConfigurationWrite32(off+0x8, new_ctrl);
-
-			os_log(OS_LOG_DEFAULT, "tinygpu: rebar: cap=%u ctrl=%u new_bar_size=%u new_ctrl=%u", cap, ctrl, new_bar_size, new_ctrl);
-			ivars->pci->Reset(0);
-			break;
-		}
-		off = next;
-	}
-	ivars->pci->Reset(kIOPCIDeviceResetTypeHotReset);
-#endif
-
-	uint16_t commandRegister;
-	ivars->pci->ConfigurationRead16(kIOPCIConfigurationOffsetCommand, &commandRegister);
-	commandRegister |= (kIOPCICommandIOSpace | kIOPCICommandBusMaster | kIOPCICommandMemorySpace);
-	ivars->pci->ConfigurationWrite16(kIOPCIConfigurationOffsetCommand, commandRegister);
-
-	memcpy((void*)service_name, (void*)"tinygpu\0", 8);
-	SetName(service_name);
-
-	os_log(OS_LOG_DEFAULT, "tinygpu: will register service %s", service_name);
-	RegisterService();
-
-	os_log(OS_LOG_DEFAULT, "tinygpu: service started %s", service_name);
-	return 0;
-}
-
-kern_return_t TinyGPUDriver::Stop_Impl(IOService* in_provider)
-{
-	ivars->pci->Close(this, 0);
-	return 0;
-}
-
-kern_return_t TinyGPUDriver::NewUserClient_Impl(uint32_t in_type, IOUserClient** out_user_client)
-{
-	kern_return_t err = 0;
-
-	IOService* user_client_service = nullptr;
-	err = Create(this, "TinyGPUDriverUserClientProperties", &user_client_service);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: failed to create NewUserClient");
-		goto error;
-	}
-	*out_user_client = OSDynamicCast(IOUserClient, user_client_service);
-	os_log(OS_LOG_DEFAULT, "tinygpu: NewUserClient created");
-
-error:
-	return err;
-}
-
-kern_return_t TinyGPUDriver::MapBar(uint32_t bar, IOMemoryDescriptor** memory)
-{
-	kern_return_t err = 0;
-	uint8_t barMemoryIndex, barMemoryType;
-	uint64_t barMemorySize;
-	err = ivars->pci->GetBARInfo(bar, &barMemoryIndex, &barMemorySize, &barMemoryType);
-	if (err) return err;
-
-	os_log(OS_LOG_DEFAULT, "tinygpu: requested bar mapping %d, %d", bar, (uint32_t)barMemoryIndex);
-	err = ivars->pci->_CopyDeviceMemoryWithIndex(barMemoryIndex, memory, this);
-	return err;
-}
-
-kern_return_t TinyGPUDriver::CreateDMA(size_t size, TinyGPUCreateDMAResp* dmaDesc)
-{
-	kern_return_t err = 0;
-	IOMemoryMap* memoryMap = nullptr;
-	IOBufferMemoryDescriptor* sharedBuf = nullptr;
-	IODMACommand* dmaCmd = nullptr;
-	uint64_t flags = kIOMemoryDirectionInOut;
-	uint32_t segCount = 32;
-	IOAddressSegment segments[32];
-	IODMACommandSpecification dmaSpec = {
-		.options = 0,
-		.maxAddressBits = 40,
-	};
-
-	err = IOBufferMemoryDescriptor::Create(kIOMemoryDirectionInOut, size, IOVMPageSize, &sharedBuf);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: failed to alloc user buffer, err=%d", err);
-		goto error;
-	}
-	
-	err = IODMACommand::Create(ivars->pci, kIODMACommandCreateNoOptions, &dmaSpec, &dmaCmd);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: failed to create dma command, err=%d", err);
-		goto error;
-	}
-
-	err = dmaCmd->PrepareForDMA(kIODMACommandPrepareForDMANoOptions, sharedBuf, 0, size,
-								&flags, &segCount, segments);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: failed to prepare for dma, err=%d", err);
-		goto error;
-	}
-
-	// pass addresses to userland
-	{
-		// debug
-		for (int i = 0; i < segCount; i++) {
-			os_log(OS_LOG_DEFAULT, "tinygpu: new dma mapping (sz=0x%zx) %d 0x%llx 0x%llx", size, i, segments[i].address, segments[i].length);
-		}
-
-		err = sharedBuf->CreateMapping(0, 0, 0, IOVMPageSize, IOVMPageSize, &memoryMap); // one page should be fine
-		if (err) {
-			os_log(OS_LOG_DEFAULT, "tinygpu: failed to map memory, err=%d", err);
-			goto error;
-		}
-
-		// Send back gpu addresses
-		uint64_t* addr = (uint64_t*)memoryMap->GetAddress();
-		for (int i = 0; i < segCount; i++) {
-			addr[i * 2] = segments[i].address;
-			addr[i * 2 + 1] = segments[i].length;
-		}
-		addr[segCount * 2] = 0;
-		addr[segCount * 2 + 1] = 0;
-
-		// free memoryMap
-		memoryMap->release();
-		memoryMap = nullptr;
-	}
-
-	dmaDesc->sharedBuf = sharedBuf;
-	dmaDesc->dmaCmd = dmaCmd;
-	return 0;
-
-error:
-	if (memoryMap) {
-		memoryMap->release();
-		memoryMap = nullptr;
-	}
-	if (dmaCmd) {
-		dmaCmd->CompleteDMA(kIODMACommandCompleteDMANoOptions);
-		dmaCmd->release();
-		dmaCmd = nullptr;
-	}
-	if (sharedBuf) {
-		sharedBuf->release();
-		sharedBuf = nullptr;
-	}
-	return err;
-}
-
-kern_return_t TinyGPUDriver::CfgRead(uint32_t off, uint32_t size, uint32_t* outVal)
-{
-  if (!ivars->pci || !outVal) return kIOReturnNotReady;
-
-  if (size == 1) {
-	uint8_t v8 = 0;
-	ivars->pci->ConfigurationRead8(off, &v8);
-	*outVal = v8;
-  } else if (size == 2) {
-	uint16_t v16 = 0;
-	ivars->pci->ConfigurationRead16(off, &v16);
-	*outVal = v16;
-  } else if (size == 4) {
-	uint32_t v32 = 0;
-	ivars->pci->ConfigurationRead32(off, &v32);
-	*outVal = v32;
-  }
-  return 0;
-}
-
-kern_return_t TinyGPUDriver::CfgWrite(uint32_t off, uint32_t size, uint32_t val)
-{
-  if (!ivars->pci) return kIOReturnNotReady;
-  if (size == 1) ivars->pci->ConfigurationWrite8 (off, (uint8_t)val);
-  else if (size == 2) ivars->pci->ConfigurationWrite16(off, (uint16_t)val);
-  else if (size == 4) ivars->pci->ConfigurationWrite32(off, (uint32_t)val);
-  return 0;
-}
-
-kern_return_t TinyGPUDriver::ResetDevice()
-{
-	if (!ivars->pci) return kIOReturnNotReady;
-	ivars->pci->Reset(kIOPCIDeviceResetTypeFunctionReset);
-	return 0;
-}
@@ -1,21 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>com.apple.developer.driverkit.transport.pci</key>
-	<array>
-		<dict>
-			<key>IOPCIMatch</key>
-			<string>0x00001002&amp;0x0000FFFF</string>
-		</dict>
-		<dict>
-			<key>IOPCIMatch</key>
-			<string>0x000010de&amp;0x0000FFFF</string>
-		</dict>
-	</array>
-	<key>com.apple.developer.driverkit.allow-any-userclient-access</key>
-	<true/>
-	<key>com.apple.developer.driverkit</key>
-	<true/>
-</dict>
-</plist>
@@ -1,38 +0,0 @@
-#ifndef TinyGPUDriver_h
-#define TinyGPUDriver_h
-
-#include <Availability.h>
-#include <DriverKit/IOService.iig>
-#include <PCIDriverKit/IOPCIDevice.iig>
-#include <DriverKit/IOMemoryMap.iig>
-#include <DriverKit/IODMACommand.iig>
-
-struct TinyGPUCreateDMAResp
-{
-	IOBufferMemoryDescriptor* sharedBuf;
-	IODMACommand* dmaCmd;
-};
-
-class TinyGPUDriver: public IOService
-{
-public:
-	virtual bool init() override;
-
-	virtual void free() override;
-
-	virtual kern_return_t Start(IOService * provider) override;
-
-	virtual kern_return_t Stop(IOService * provider) override;
-
-	virtual kern_return_t NewUserClient(uint32_t in_type, IOUserClient** out_user_client) override;
-
-	kern_return_t MapBar(uint32_t bar, IOMemoryDescriptor** memory) LOCALONLY;
-	kern_return_t CreateDMA(size_t size, TinyGPUCreateDMAResp* dmaDesc) LOCALONLY;
-
-	kern_return_t CfgRead(uint32_t off, uint32_t size, uint32_t* val) LOCALONLY;
-	kern_return_t CfgWrite(uint32_t off, uint32_t size, uint32_t val) LOCALONLY;
-	kern_return_t ResetDevice() LOCALONLY;
-	kern_return_t BarInfo() LOCALONLY;
-};
-
-#endif /* TinyGPUDriver_h */
@@ -1,127 +0,0 @@
-#include "TinyGPUDriverUserClient.h"
-#include "TinyGPUDriver.h"
-#include <DriverKit/DriverKit.h>
-#include <DriverKit/OSSharedPtr.h>
-#include <PCIDriverKit/PCIDriverKit.h>
-
-struct TinyGPUDriverUserClient_IVars
-{
-	OSSharedPtr<TinyGPUDriver> provider = nullptr;
-};
-
-bool TinyGPUDriverUserClient::init()
-{
-	auto theAnswer = super::init();
-	if (!theAnswer) {
-		return false;
-	}
-
-	ivars = IONewZero(TinyGPUDriverUserClient_IVars, 1);
-	if (ivars == nullptr) {
-		return false;
-	}
-
-	return true;
-}
-
-void TinyGPUDriverUserClient::free()
-{
-	if (ivars != nullptr) {
-		ivars->provider.reset();
-	}
-
-	IOSafeDeleteNULL(ivars, TinyGPUDriverUserClient_IVars, 1);
-	super::free();
-}
-
-kern_return_t TinyGPUDriverUserClient::Start_Impl(IOService* in_provider)
-{
-	kern_return_t err = kIOReturnSuccess;
-	if (!in_provider) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: provider is null");
-		err = kIOReturnBadArgument;
-		goto error;
-	}
-
-	err = Start(in_provider, SUPERDISPATCH);
-	if (err) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: failed to start super (%d)", err);
-		goto error;
-	}
-
-	ivars->provider = OSSharedPtr(OSDynamicCast(TinyGPUDriver, in_provider), OSRetain);
-	return 0;
-
-error:
-	ivars->provider.reset();
-	return err;
-}
-
-kern_return_t TinyGPUDriverUserClient::Stop_Impl(IOService* in_provider)
-{
-	return Stop(in_provider, SUPERDISPATCH);
-}
-
-kern_return_t TinyGPUDriverUserClient::ExternalMethod(uint64_t selector, IOUserClientMethodArguments* args, const IOUserClientMethodDispatch* in_dispatch, OSObject* in_target, void* in_reference)
-{
-	kern_return_t err = 0;
-
-	os_log(OS_LOG_DEFAULT, "tinygpu: rpc (%llu) in:%d, out:%d", selector, args->scalarInputCount, args->scalarOutputCount);
-
-	if (selector == TinyGPURPC::ReadCfg) {
-		if (args->scalarInputCount != 2 or args->scalarOutputCount < 1) return kIOReturnBadArgument;
-
-		uint32_t off = uint32_t(args->scalarInput[0]);
-		uint32_t size = uint32_t(args->scalarInput[1]);
-
-		uint32_t val = 0;
-		err = ivars->provider->CfgRead(off, size, &val);
-		os_log(OS_LOG_DEFAULT, "tinygpu: read cfg off:%x sz:%d, val:%x", off, size, val);
-
-		if (!err) {
-			args->scalarOutput[0] = val;
-			args->scalarOutputCount = 1;
-		}
-		return err;
-	} else if (selector == TinyGPURPC::WriteCfg) {
-		if (args->scalarInputCount != 3) return kIOReturnBadArgument;
-
-		uint32_t off = uint32_t(args->scalarInput[0]);
-		uint32_t size = uint32_t(args->scalarInput[1]);
-		uint32_t val = uint32_t(args->scalarInput[2]);
-
-		os_log(OS_LOG_DEFAULT, "tinygpu: wr cfg off:%x sz:%d, val:%x", off, size, val);
-		return ivars->provider->CfgWrite(off, size, val);
-	} else if (selector == TinyGPURPC::Reset) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: reset");
-		return ivars->provider->ResetDevice();
-	}
-
-	return kIOReturnUnsupported;
-}
-
-kern_return_t IMPL(TinyGPUDriverUserClient, CopyClientMemoryForType)
-{
-	if (!memory) {
-		return kIOReturnBadArgument;
-	}
-
-	if (ivars->provider.get() == nullptr) {
-		return kIOReturnNotAttached;
-	}
-
-	if (type < 6) {
-		uint32_t bar = (uint32_t)type;
-		return ivars->provider->MapBar(bar, memory);
-	}
-
-	// dma page buffer
-	TinyGPUCreateDMAResp buf;
-	kern_return_t err = ivars->provider->CreateDMA(type, &buf);
-	if (err) {
-		return err;
-	}
-
-	*memory = buf.sharedBuf;
-	return 0;
-}
@@ -1,28 +0,0 @@
-#ifndef TinyGPUDriverUserClient_h
-#define TinyGPUDriverUserClient_h
-
-#include <DriverKit/IOUserClient.iig>
-
-enum TinyGPURPC
-{
-	ReadCfg,
-	WriteCfg,
-	Reset
-};
-
-class TinyGPUDriverUserClient : public IOUserClient
-{
-public:
-	virtual bool init() final;
-	virtual void free() final;
-
-	virtual kern_return_t Start(IOService* in_provider) final;
-	virtual kern_return_t Stop(IOService* in_provider) final;
-
-	virtual kern_return_t ExternalMethod(uint64_t in_selector, IOUserClientMethodArguments* in_arguments, const IOUserClientMethodDispatch* in_dispatch, OSObject* in_target, void* in_reference) final;
-
-	virtual kern_return_t CopyClientMemoryForType(
-		uint64_t type, uint64_t *options, IOMemoryDescriptor **memory) final;
-};
-
-#endif /* TinyGPUDriverUserClient_h */
@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>com.apple.security.app-sandbox</key>
-	<false/>
-	<key>com.apple.security.files.user-selected.read-only</key>
-	<true/>
-	<key>com.apple.developer.system-extension.install</key>
-	<true/>
-</dict>
-</plist>
@@ -1,39 +0,0 @@
-#include <CoreFoundation/CoreFoundation.h>
-#include <IOKit/IOKitLib.h>
-#include <mach/mach.h>
-#include <stdio.h>
-#include <inttypes.h>
-
-static io_connect_t open_uc_by_name(const char *svc_name) {
-  io_connect_t conn = IO_OBJECT_NULL;
-  io_service_t service = IOServiceGetMatchingService(kIOMasterPortDefault, IOServiceNameMatching(svc_name));
-  if (!service) { fprintf(stderr, "service not found: %s\n", svc_name); return IO_OBJECT_NULL; }
-  kern_return_t kr = IOServiceOpen(service, mach_task_self(), /*type*/0, &conn);
-  IOObjectRelease(service);
-  if (kr) { fprintf(stderr, "IOServiceOpen 0x%x\n", kr); return IO_OBJECT_NULL; }
-  return conn;
-}
-
-int main(int argc, char **argv) {
-  uint32_t bar = (argc > 1) ? (uint32_t)strtoul(argv[1], NULL, 0) : 0;  // pick BAR index
-  io_connect_t conn = open_uc_by_name("tinygpu");
-  if (!conn) return 2;
-
-  mach_vm_address_t addr = 0;
-  mach_vm_size_t    size = 0;
-  kern_return_t kr = IOConnectMapMemory64(conn, bar, mach_task_self(), &addr, &size, kIOMapAnywhere);
-  if (kr) { fprintf(stderr, "Map BAR%u failed 0x%x\n", bar, kr); IOServiceClose(conn); return 3; }
-
-  printf("BAR%u mapped at 0x%llx, size 0x%llx\n", bar, (unsigned long long)addr, (unsigned long long)size);
-
-  // example: read a 32-bit register at offset 0x0 (make sure it’s safe!)
-  volatile uint32_t *mmio = (volatile uint32_t*)(uintptr_t)addr;
-  uint32_t v = mmio[0];
-  printf("mmio[0]=0x%08x\n", v);
-
-  kr = IOConnectUnmapMemory64(conn, bar, mach_task_self(), addr);
-  if (kr) fprintf(stderr, "Unmap failed 0x%x\n", kr);
-
-  IOServiceClose(conn);
-  return 0;
-}
@@ -1,82 +0,0 @@
-import ctypes, ctypes.util, sys
-
-cf = ctypes.CDLL(ctypes.util.find_library("CoreFoundation"))
-iokit = ctypes.CDLL(ctypes.util.find_library("IOKit"))
-libsys = ctypes.CDLL(ctypes.util.find_library("System"))
-
-kern_return_t = ctypes.c_int
-mach_port_t = ctypes.c_uint
-io_object_t = mach_port_t
-io_service_t = io_object_t
-io_connect_t = mach_port_t
-CFMutableDictionaryRef = ctypes.c_void_p
-CFStringRef = ctypes.c_void_p
-
-kIOMasterPortDefault = mach_port_t(0)
-
-libsys.mach_task_self_.restype = mach_port_t
-
-iokit.IOServiceNameMatching.argtypes = [ctypes.c_char_p]
-iokit.IOServiceNameMatching.restype  = CFMutableDictionaryRef
-
-iokit.IOServiceGetMatchingService.argtypes = [mach_port_t, CFMutableDictionaryRef]
-iokit.IOServiceGetMatchingService.restype  = io_service_t
-
-iokit.IOObjectRelease.argtypes = [io_object_t]
-iokit.IOObjectRelease.restype  = kern_return_t
-
-iokit.IOServiceOpen.argtypes = [io_service_t, mach_port_t, ctypes.c_uint32, ctypes.POINTER(io_connect_t)]
-iokit.IOServiceOpen.restype  = kern_return_t
-
-iokit.IOConnectCallMethod.argtypes = [io_connect_t, ctypes.c_uint32, ctypes.POINTER(ctypes.c_uint64), ctypes.c_uint32, ctypes.c_void_p,
-  ctypes.c_size_t, ctypes.POINTER(ctypes.c_uint64), ctypes.POINTER(ctypes.c_uint32), ctypes.c_void_p, ctypes.POINTER(ctypes.c_size_t)]
-iokit.IOConnectCallMethod.restype = kern_return_t
-
-def open_userclient_by_name(name: str, uc_type: int = 0) -> io_connect_t:
-  mdict = iokit.IOServiceNameMatching(name.encode("utf-8"))
-  if not mdict: raise RuntimeError("IOServiceNameMatching returned NULL")
-
-  # Grab the first matching service
-  service = iokit.IOServiceGetMatchingService(kIOMasterPortDefault, mdict)
-  if not service: raise RuntimeError(f'service "{name}" not found')
-
-  # print("lol", service)
-  # print(libsys.mach_task_self_)
-  # cast libsys.mach_task_self_ to uint and print
-  # print("lol", ctypes.cast(libsys.mach_task_self_, ctypes.POINTER(ctypes.c_uint)).contents.value)
-
-  try:
-    # Open user client (type -> passed to NewUserClient_Impl)
-    conn = io_connect_t(0)
-    # print("lol", libsys.mach_task_self_)
-    kr = iokit.IOServiceOpen(service, ctypes.cast(libsys.mach_task_self_, ctypes.POINTER(ctypes.c_uint)).contents.value,
-                             ctypes.c_uint32(uc_type), ctypes.byref(conn))
-    if kr != 0: raise OSError(kr, f"IOServiceOpen failed (0x{kr:08x})")
-    return conn
-  finally: iokit.IOObjectRelease(service)
-
-def external_method(conn: io_connect_t, selector: int = 0) -> int:
-  # no scalars in/out, no struct in/out — just ping selector 0
-  in_scalars = ctypes.POINTER(ctypes.c_uint64)()  # NULL
-  out_scalars = (ctypes.c_uint64 * 1)()       # space if driver returns something
-  out_scalars_cnt = ctypes.c_uint32(0)      # driver can set this
-
-  return iokit.IOConnectCallMethod(conn, ctypes.c_uint32(selector), in_scalars, ctypes.c_uint32(0), None, ctypes.c_size_t(0),
-    out_scalars, ctypes.byref(out_scalars_cnt), None, ctypes.byref(ctypes.c_size_t(0)))
-
-def close_userclient(conn: io_connect_t) -> None:
-  # IOServiceClose is a macro; exported symbol is IOServiceClose in IOKit
-  iokit.IOServiceClose.argtypes = [io_connect_t]
-  iokit.IOServiceClose.restype  = kern_return_t
-  iokit.IOServiceClose(conn)
-
-if __name__ == "__main__":
-  try:
-    conn = open_userclient_by_name("tinygpu", uc_type=0)
-    kr = external_method(conn, selector=0)
-    print(f"ExternalMethod(0) -> 0x{kr:08x}")
-  except Exception as e:
-    print(e)
-    sys.exit(1)
-  finally:
-    if 'conn' in locals() and conn.value: close_userclient(conn)
@@ -42,6 +42,7 @@ setup(name='tinygrad',
        'tinygrad.runtime.support.am',
        'tinygrad.runtime.support.nv',
        'tinygrad.schedule',
+        'tinygrad.shape',
        'tinygrad.uop',
        'tinygrad.viz',
      ],
@@ -1,4 +1,4 @@
-from tinygrad import Tensor, dtypes, GlobalCounters
+from tinygrad import Tensor, dtypes, Context, GlobalCounters
 dtypes.default_float = dtypes.float16
 from tinygrad.dtype import to_dtype
 from tinygrad.helpers import getenv
@@ -13,5 +13,6 @@ if __name__ == "__main__":

  # test single kernel softmax
  GlobalCounters.reset()
-  single_kernel_softmax(t, -1, acc_dtype).realize()
+  with Context(DONT_GROUP_REDUCES=1):
+    single_kernel_softmax(t, -1, acc_dtype).realize()

@@ -0,0 +1,46 @@
+# ruff: noqa: E501
+from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.dtype import dtypes
+from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.uop.ops import UOp, Ops
+from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.shape.view import View
+
+ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+  UOp(Ops.STORE, dtypes.void, arg=None, src=(
+    UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=0, src=()),
+    UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 1, 1, 1), strides=(81920, 0, 64, 8, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
+    UOp(Ops.ADD, dtypes.half, arg=None, src=(
+      UOp(Ops.ADD, dtypes.half, arg=None, src=(
+        UOp(Ops.CAST, dtypes.half, arg=None, src=(
+          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
+            UOp(Ops.CAST, dtypes.float, arg=None, src=(
+              UOp(Ops.MUL, dtypes.half, arg=None, src=(
+                UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=1, src=()),
+                  UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 2, 1, 2560, 4, 10, 4, 10), strides=(0, 163840, 0, 64, 0, 8, 0, 1), offset=-9, mask=((0, 1), (0, 2), (0, 1), (0, 2560), (0, 4), (1, 9), (0, 4), (1, 9)), contiguous=False), View(shape=(2, 1, 1280, 8, 8, 2560, 3, 3), strides=(4096000, 0, 0, 40, 1, 1600, 440, 11), offset=0, mask=None, contiguous=False))), src=()),)),
+                UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=2, src=()),
+                  UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 2560, 3, 3), strides=(0, 0, 23040, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),)),
+        UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=3, src=()),
+          x17:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
+      UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+        UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=4, src=()),
+         x17,)),)),)),))
+opts = [Opt(op=OptOps.UPCAST, axis=3, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=2, arg=0), Opt(op=OptOps.UNROLL, axis=1, arg=0), Opt(op=OptOps.LOCAL, axis=1, arg=8), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.LOCAL, axis=2, arg=2)]
+
+k = Kernel(ast)
+k.apply_opts(opts)
+bufs = bufs_from_lin(k)
+
+prg = CompiledRunner(get_program(k.ast, k.opts, k.applied_opts))
+
+for i in range(10):
+  speed = prg(bufs, var_vals={}, wait=True)
+  print(f"kernel time: {speed*1e3:.2f} ms")
+
+# on M1 Max
+# 11ms before block 9b0859d71780fef5cf3831e317f74e53f2483229
+# 15ms after block cbcc1c20eb09a1342f6581cfbb99632bade982a8
@@ -1,39 +0,0 @@
-import subprocess, unittest, os, sys
-from tinygrad.device import Device
-
-class TestTinygradSlow(unittest.TestCase):
-  def test_env_overwrite_default_device(self):
-    subprocess.run([f'{Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                    shell=True, check=True)
-    subprocess.run([f'DISK=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                    shell=True, check=True)
-    subprocess.run([f'NPY=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                    shell=True, check=True)
-
-    if Device.DEFAULT != "CPU":
-      # setting multiple devices fail
-      with self.assertRaises(subprocess.CalledProcessError):
-        subprocess.run([f'{Device.DEFAULT}=1 CPU=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                        shell=True, check=True)
-
-      # setting device via DEV
-      subprocess.run([f'DEV={Device.DEFAULT.capitalize()} python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                      shell=True, check=True)
-      subprocess.run([f'DEV={Device.DEFAULT.lower()} python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                      shell=True, check=True)
-      subprocess.run([f'DEV={Device.DEFAULT.upper()} python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                      shell=True, check=True)
-
-      with self.assertRaises(subprocess.CalledProcessError):
-        subprocess.run([f'DEV={Device.DEFAULT} CPU=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
-                        shell=True, check=True)
-
-class TestRunAsModule(unittest.TestCase):
-  def test_module_runs(self):
-    p = subprocess.run([sys.executable, "-m", "tinygrad.device"],stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-      env={**os.environ, "DEBUG": "1"}, timeout=40,)
-    out = (p.stdout + p.stderr).decode()
-    self.assertEqual(p.returncode, 0, msg=out)
-
-if __name__ == '__main__':
-  unittest.main()
@@ -58,8 +58,8 @@ class TestExample(unittest.TestCase):
      print(f"WARNING: {device} test isn't running")
      return

-    x = Tensor.eye(8, device=device, requires_grad=True)
-    y = Tensor.eye(8, device=device, requires_grad=True)
+    x = Tensor.eye(64, device=device, requires_grad=True)
+    y = Tensor.eye(64, device=device, requires_grad=True)
    z = y.matmul(x).sum()
    z.backward()

@@ -1,50 +1,41 @@
-import functools, multiprocessing
 from transformers import AutoTokenizer
 from datasets import load_dataset
-from tinygrad.apps.llm import SimpleTokenizer
+from tinygrad.apps.llm import SimpleTokenizer, gpt2_decode_vocab, get_llama_re
 from tinygrad.helpers import tqdm, getenv, partition

-@functools.cache
-def get_tokenizers():
-  print("getting tokenizers")
-  base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
-  special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()), lambda e: e[1] in base_tokenizer.all_special_ids)
-  simple_tokenizer = SimpleTokenizer(dict(normal_tokens), dict(special_tokens))
-  return base_tokenizer, simple_tokenizer
-
-def test_tokenize(samp) -> bool:
-  base_tokenizer, simple_tokenizer = get_tokenizers()
-  idx, txt = samp
-  try: simple_tokens = tuple(simple_tokenizer.encode(txt))
-  except RuntimeError: simple_tokens = ()
-  base_tokens = tuple(base_tokenizer.encode(txt, add_special_tokens=False))
-  if simple_tokens != base_tokens:
-    print(f"tokens mismatch at index: {idx}.\n")
-    color_codes = [91, 92, 94, 93, 95]
-    def color_tokens(tids):
-      return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
-    print("simple:  ", color_tokens(simple_tokens))
-    print("official:", color_tokens(base_tokens) + "\n")
-    return False
-  if simple_tokenizer.decode(simple_tokens) != txt:
-    print(f"decode mismatch at {idx}")
-    return False
-  return True
-
 # use ALLOW_FAILED=-1 to go over the entire dataset without printing.
 if __name__ == "__main__":
-  print("loading datasets")
-  ds = load_dataset("OpenAssistant/oasst1")
-  loaded_ds = [(idx, el["text"]) for idx, el in enumerate(ds["train"])]
-  print(f"loaded {len(loaded_ds)}")
+  base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
+  special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()),
+    lambda e: e[1] in base_tokenizer.all_special_ids)
+  inv_vocab = { tid: word for word, tid in base_tokenizer.get_vocab().items() }
+  simple_tokenizer = SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))

+  color_codes = [ 91, 92, 94, 93, 95 ]
+  def color_tokens(tids):
+    return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
+
+  ds = load_dataset("OpenAssistant/oasst1")
  allow_failed = getenv("ALLOW_FAILED", 10)
+
  fail_count, total = 0, 0
-  with multiprocessing.Pool(16) as pool:
-    for good in tqdm(pool.imap_unordered(test_tokenize, loaded_ds), total=len(loaded_ds)):
-      total += 1
-      if not good:
-        fail_count += 1
-        allow_failed -= 1
-        if allow_failed == 0: break
-    print(f"{fail_count}/{total} samples are inconsistent with the official tokenizer.")
+
+  for idx, el in enumerate(tqdm(ds["train"])):
+    total += 1
+
+    try: simple_tokens = tuple(simple_tokenizer.encode(el["text"]))
+    except RuntimeError: simple_tokens = ()
+    base_tokens = tuple(base_tokenizer.encode(el["text"], add_special_tokens=False))
+
+    if simple_tokens != base_tokens:
+      fail_count += 1
+      allow_failed -= 1
+
+      if allow_failed >= 0:
+        print(f"tokens mismatch at index: {idx}.\n")
+
+        print("simple:  ", color_tokens(simple_tokens))
+        print("official:", color_tokens(base_tokens) + "\n")
+
+      if allow_failed == 0: break
+  print(f"{fail_count}/{total} samples are inconsistent with the official tokenizer.")
@@ -0,0 +1,55 @@
+# ruff: noqa: E501
+import unittest
+
+from tinygrad.uop.ops import UOp, Ops
+from .search import Opt, OptOps
+from tinygrad.dtype import dtypes
+from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.shape.view import View
+from tinygrad.codegen.opt.kernel import Kernel
+
+from test.external.fuzz_linearizer import run_linearizer
+
+class TestTrainGpt2Kernel(unittest.TestCase):
+  def test_1(self):
+    # kernel 244
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(206045184), arg=0, src=()),
+        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 1), strides=(51511296, 50304, 1, 0), offset=0, mask=None, contiguous=True),)), src=()),
+        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
+        UOp(Ops.MUL, dtypes.float, arg=None, src=(
+          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(3145728), arg=1, src=()),
+          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(786432, 768, 0, 1), offset=0, mask=None, contiguous=False),)), src=()),)),
+          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(38633472), arg=2, src=()),
+          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(0, 0, 768, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
+
+    opts = [Opt(op=OptOps.LOCAL, axis=0, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=3), Opt(op=OptOps.LOCAL, axis=0, arg=2)]
+    kernel = Kernel(ast)
+    kernel.apply_opts(opts)
+    run_linearizer(kernel)
+
+  def test_2(self):
+    # kernel 254
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(3145728), arg=0, src=()),
+        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 1, 768), strides=(786432, 768, 0, 1), offset=0, mask=None, contiguous=True),)), src=()),
+        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (2,)), src=(
+        UOp(Ops.MUL, dtypes.float, arg=None, src=(
+          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(38633472), arg=1, src=()),
+          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(0, 0, 768, 1), offset=0, mask=None, contiguous=False),)), src=()),)),
+          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(205852672), arg=2, src=()),
+          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(51463168, 50257, 1, 0), offset=0, mask=((0, 4), (0, 1024), (0, 50257), (0, 768)), contiguous=False),)), src=()),)),)),)),)),))
+
+    opts = [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=3, arg=4)]
+    kernel = Kernel(ast)
+    kernel.apply_opts(opts)
+    run_linearizer(kernel)
+
+if __name__ == "__main__":
+  unittest.main()
@@ -1,5 +1,6 @@
 import gc
 from tinygrad import Tensor, UOp, Device, nn
+from tinygrad.shape.shapetracker import views_to_valid_uop
 from tinygrad.engine.realize import method_cache, get_program
 from tinygrad.schedule.indexing import apply_movement_op
 from test.test_tiny import TestTiny
@@ -68,6 +69,7 @@ if __name__ == "__main__":

    # these caches will keep uops alive
    method_cache.clear()
+    views_to_valid_uop.cache_clear()
    apply_movement_op.cache_clear()
    Tensor._device_seeds.clear()
    Tensor._device_rng_counters.clear()
@@ -42,13 +42,13 @@ class ProcessReplayWarning(Warning): pass

 # *** replay the function and convert return values to string

-def replay_get_rangeify_map(ret:dict[UOp, UOp], big_sink:UOp) -> tuple[str, str, tuple[Any, ...]]:
+def replay_kernelize(ret:dict[UOp, UOp], big_sink:UOp) -> tuple[str, str, tuple[Any, ...]]:
  UOp.unique_num = itertools.count(max([u.arg for u in big_sink.toposort() if u.op is Ops.UNIQUE], default=0)+1)
  new_sink = big_sink.substitute(get_rangeify_map(big_sink))
  def to_str(ret:UOp) -> str:
    asts = [repr(u.arg.ast) for u in ret.toposort() if u.op is Ops.KERNEL]
    return "\n".join([f"{len(asts)} kernels", *asts])
-  return to_str(new_sink), to_str(big_sink.substitute(ret)), (big_sink,)
+  return to_str(new_sink), to_str(ret[big_sink]), (big_sink,)

 def replay_get_program(p:ProgramSpec, ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None) -> tuple[str, str, tuple[Any, ...]]:
  # NOTE: this always uses the opts_to_apply path
@@ -65,7 +65,7 @@ def replay_get_program(p:ProgramSpec, ast:UOp, renderer:Renderer|None=None, opts
  ast_repr = codecs.decode(str(input_ast), "unicode_escape")
  return to_str(p2), to_str(p), (ast_repr, renderer)

-replayers: dict[str, Callable[..., tuple[str, str, tuple[Any, ...]]]] = {"get_rangeify_map":replay_get_rangeify_map, "get_program":replay_get_program}
+replayers: dict[str, Callable[..., tuple[str, str, tuple[Any, ...]]]] = {"get_kernelize_map":replay_kernelize, "get_program":replay_get_program}

 # *** run replayers on captured rows and print diffs

@@ -2,6 +2,7 @@ import unittest
 from tinygrad import Device, Tensor, dtypes
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.codegen.opt import Opt, OptOps
+from tinygrad.shape.shapetracker import ShapeTracker, View
 from tinygrad.engine.realize import get_program
 from tinygrad.helpers import AMX

@@ -148,5 +149,33 @@ class TestFloat4(unittest.TestCase):

    assert TestFloat4.count_float4(uops) == (1, 1)

+  @unittest.skip("Ops.VIEW no longer exists")
+  def test_half4_load_unrolled(self):
+    # from llama 7B shard 4 gpus
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)),
+        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
+          UOp(Ops.CAST, dtypes.float, arg=None, src=(
+            UOp(Ops.MUL, dtypes.half, arg=None, src=(
+              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)),
+              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),))
+
+    # TODO: fix this, expected might change but should be positive
+    for expected, opts in [
+      ((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+      ((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+      ((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+    ]:
+      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
+
+      count = TestFloat4.count_half4(program.uops)
+      assert count == expected, f"{count=}, {expected=}"
+
 if __name__ == '__main__':
  unittest.main()
@@ -1,5 +1,6 @@
 import unittest
 from tinygrad import Device, Tensor, dtypes
+from tinygrad.helpers import CI
 from tinygrad.codegen.opt import Opt, OptOps, KernelOptError

 # TODO: write a clean version of this
@@ -176,7 +177,9 @@ class TestKernelOpts(unittest.TestCase):
    ], apply_tc=True, atol=atol, rtol=rtol)

  def test_padto_matmul(self):
-    N = 17
+    if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]):
+      self.skipTest("super slow on CUDA and AMD because of the big grid dims")
+    N = 17 * 17
    Tensor.manual_seed(289)
    a = Tensor.rand(N, N)
    b = Tensor.rand(N, N)
@@ -210,7 +213,7 @@ class TestKernelOpts(unittest.TestCase):
      helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]])

  def test_padto_sum_ok(self):
-    N = 18
+    N = 18 * 18
    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
    a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100
    b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17)))
@@ -241,7 +244,7 @@ class TestKernelOpts(unittest.TestCase):
    helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])

  def test_padto_sum_not_ok(self):
-    N = 18
+    N = 18 * 18
    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
    a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp()
    # exp is not safe to pad
@@ -258,7 +261,7 @@ class TestKernelOpts(unittest.TestCase):
      helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],])

  def test_padto_max(self):
-    N = 18
+    N = 18 * 18
    # NOTE: this setup prevents 17 * 17 contiguous merged into one axis
    a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100

@@ -279,7 +282,7 @@ class TestKernelOpts(unittest.TestCase):

  def test_padto_where(self):
    Tensor.manual_seed(0)
-    N = 17
+    N = 17 * 17
    a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0)
    helper_linearizer_opt(a.max(0), [
      [Opt(OptOps.PADTO, 0, 32)],
@@ -288,7 +291,7 @@ class TestKernelOpts(unittest.TestCase):

  def test_padto_where_multioutput(self):
    Tensor.manual_seed(0)
-    N = 17
+    N = 17 * 17
    r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1
    a0 = r.where(1, 0)
    a1 = r.where(2, 0)
@@ -131,7 +131,8 @@ class TestIndexing(unittest.TestCase):
    # llama3 is 128256
    vocab_size, embed_size = (10, 3) if CI else (32000, 4096)
    emb = nn.Embedding(vocab_size, embed_size)
-    emb_w = emb.weight.numpy()
+    # TODO: why is a new realize needed here
+    emb_w = emb.weight.realize().numpy()
    x = Tensor([1,2,3,4])
    with Context(NOOPT=noopt):
      GlobalCounters.reset()
@@ -129,7 +129,6 @@ class TestAssign(unittest.TestCase):
  @unittest.expectedFailure
  def test_assign_changes_realized_alt(self): return self.test_assign_changes_alt(realize=True)

-  @unittest.skip("assign to contiguous shouldn't change the base buffer")
  def test_assign_changes_buffer_alt(self):
    a, b = [Tensor(Tensor(0).contiguous().realize().uop.as_buf()) for _ in range(2)]
    Tensor.realize(a.contiguous().assign(1), b.contiguous().assign(2))
@@ -1,7 +1,7 @@
 import unittest, io
 from contextlib import redirect_stdout
 from tinygrad import Tensor, dtypes, Device
-from tinygrad.helpers import OSX, CPU_LLVM, CPU_LVP
+from tinygrad.helpers import OSX, CPU_LLVM
 from tinygrad.engine.realize import lower_schedule
 from tinygrad.device import is_dtype_supported
 from tinygrad.engine.realize import get_program
@@ -19,7 +19,7 @@ class TestCompileFailures(unittest.TestCase):

 class TestDisassembly(unittest.TestCase):
  # TODO: fails on llvm. llvm.LLVMGetHostCPUName() returns "generic"
-  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and not (CPU_LLVM or CPU_LVP) and OSX, "m series cpus support fp16 arithmetic")
+  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and not CPU_LLVM and OSX, "m series cpus support fp16 arithmetic")
  def test_float16_alu(self):
    c = Tensor([1], dtype=dtypes.float16) + Tensor([1], dtype=dtypes.float16)
    s = c.schedule()[-1]
@@ -6,7 +6,6 @@ from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv, DEBUG, CI
 from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8, _to_np_dtype, _to_torch_dtype, truncate
 from tinygrad.renderer.ptx import PTXRenderer
-from tinygrad.renderer.nir import NIRRenderer
 from tinygrad import Device, Tensor, dtypes
 from hypothesis import given, settings, strategies as strat
 from test.helpers import rand_for_dtype
@@ -103,7 +102,7 @@ class TestDType(unittest.TestCase):
    ))

  @unittest.skipIf(Device.DEFAULT == "PYTHON", "skip for now")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)), "skip for now")
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "skip for now")
  def test_uint_overflow(self):
    if not dtypes.is_unsigned(self.DTYPE): raise unittest.SkipTest("only for unsigned")
    v = dtypes.max(self.DTYPE)
@@ -262,7 +261,7 @@ class TestFloatDType(TestDType):
 class TestDoubleDType(TestDType):
  DTYPE = dtypes.double
  @unittest.skipIf((CI and Device.DEFAULT in {"CUDA", "NV"}) or \
-   isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)), "conversion not supported on CI CUDA, PTX, and NIR")  # TODO: why not?
+                    isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "conversion not supported on CI CUDA and PTX")  # TODO: why not?
  def test_float64_increased_precision(self):
    for func in [
      lambda t: t.exp(),
@@ -6,7 +6,6 @@ from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
 from tinygrad.runtime.ops_python import from_storage_scalar
 from tinygrad.renderer.ptx import PTXRenderer
-from tinygrad.renderer.nir import NIRRenderer
 import numpy as np
 import pytest
 from hypothesis import assume, given, strategies as strat, settings, HealthCheck
@@ -30,8 +29,8 @@ unary_operations = [(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.
 # TODO: enable this (this is a dtype issue)
 #binary_operations.append(operator.truediv)

-# TODO: CI CUDA segfaults on sin, WEBGPU and NIR sines are not precise enough for large numbers
-if (getenv("MOCKGPU") and Device.DEFAULT in {"NV", "CUDA"}) or Device.DEFAULT == "WEBGPU" or isinstance(Device[Device.DEFAULT].renderer, NIRRenderer):
+# TODO: CI CUDA segfaults on sin, WEBGPU sin is not precise enough for large numbers
+if (getenv("MOCKGPU") and Device.DEFAULT in {"NV", "CUDA"}) or Device.DEFAULT == "WEBGPU":
  unary_operations.remove((Tensor.sin, np.sin))
  unary_operations.remove((Tensor.cos, np.cos))

@@ -185,8 +184,8 @@ class TestDTypeALU(unittest.TestCase):
  @given(ht.int32, ht.int32, ht.float32, strat.sampled_from(integer_binary_operations), strat.sampled_from(binary_operations))
  def test_int32_midcast_float(self, a, b, c, op1, op2): universal_test_midcast(a, b, c, op1, op2, dtypes.int32, dtypes.float32)

-  # Metal and CUDA and HIP and NIR behave differently than numpy in CI for overflows
-  skip_overflow = (CI and Device.DEFAULT in {"AMD", "NV", "CUDA"}) or isinstance(Device[Device.DEFAULT].renderer, NIRRenderer)
+  # Metal and CUDA and HIP behave differently than numpy in CI for overflows
+  skip_overflow = CI and Device.DEFAULT in {"AMD", "NV", "CUDA"}
  @given(strat.floats(width=32, min_value=0, max_value=10.0) if skip_overflow else ht.float32,
         strat.floats(width=32, min_value=0, max_value=10.0) if skip_overflow else ht.float32,
         ht.int32, strat.sampled_from(binary_operations), strat.sampled_from(integer_binary_operations))
@@ -26,9 +26,8 @@ import unittest
 import numpy as np
 import torch
 from tinygrad import Tensor, dtypes, nn
-from tinygrad.device import Device, is_dtype_supported
+from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv
-from tinygrad.renderer.nir import NIRRenderer

 MOCKGPU = getenv("MOCKGPU")

@@ -207,8 +206,7 @@ class TestUOpValidationIssue(unittest.TestCase):
  # these fail with UOp verification error.
  # we want more of these with diverse errors!

-  @unittest.skipIf((not is_dtype_supported(dtypes.long)) or MOCKGPU or isinstance(Device[Device.DEFAULT].renderer, NIRRenderer),
-                   "hangs gpuocelot, NIR cannot render")
+  @unittest.skipIf((not is_dtype_supported(dtypes.long)) or MOCKGPU, "hangs gpuocelot")
  def test_tensor_index_overflow(self):
    val = Tensor([1])
    big = val.expand(2**31 + 3)
@@ -8,11 +8,9 @@ from tinygrad.uop.ops import UOp, Ops, GroupOp
 from tinygrad.device import Device, Buffer, is_dtype_supported
 from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
-from tinygrad.helpers import Context, flatten, dedup, TC_SELECT, TC_OPT, getenv
+from tinygrad.helpers import Context, flatten, dedup, TC_SELECT, TC_OPT
 from tinygrad.dtype import DType, dtypes, PtrDType, AddrSpace
 from tinygrad.renderer.ptx import PTXRenderer
-from tinygrad.renderer.cstyle import CUDARenderer
-MOCKGPU = getenv("MOCKGPU")

 class TestLinearizer(unittest.TestCase):
  def test_arg_dedup(self):
@@ -70,8 +68,7 @@ class TestLinearizer(unittest.TestCase):
    ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
    # RANGE -> ALU -> RANGE -> ALU + LOAD -> STORE
    assert any(x.op in GroupOp.ALU for x in uops[ranges[0]:ranges[1]])
-    # the index of the load doesnt depend on the second range
-    assert any(x.op is Ops.LOAD for x in uops[ranges[0]:ranges[1]])
+    assert not any(x.op is Ops.LOAD for x in uops[ranges[0]:ranges[1]])
    assert any(x.op in {*GroupOp.ALU, Ops.LOAD} for x in uops[ranges[1]:])

  def test_range_outer_op_before_phi(self):
@@ -317,7 +314,7 @@ class TestLinearizer(unittest.TestCase):
    a.realize()
    np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])

-  @unittest.skipIf(MOCKGPU and isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, CUDARenderer)), "PTX indexes differently. might be ok?")
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX indexes differently. might be ok?")
  def test_where_fold(self):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.shrink(((1, 2), None)).pad(((1, 2), None))
@@ -4,9 +4,12 @@

 import unittest
 from tinygrad import Device, dtypes
+from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import UOp, Ops, AxisType, KernelInfo
+from tinygrad.shape.shapetracker import ShapeTracker, View
 from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad.engine.realize import get_program
+from tinygrad.renderer.ptx import PTXRenderer

 class TestLinearizerFailure(unittest.TestCase):
  @unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
@@ -27,5 +30,91 @@ class TestLinearizerFailure(unittest.TestCase):
    ast = c12.sink(arg=KernelInfo(name='test', axis_types=(), dont_use_locals=False, applied_opts=(Opt(op=OptOps.GROUP, axis=1, arg=16),), opts_to_apply=None))
    _ = get_program(ast, Device["METAL"].renderer)

+class TestLinearizerDumb(unittest.TestCase):
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
+  @unittest.skip("Ops.VALID no longer exists")
+  def test_max_simplify_and_cancel(self):
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1000), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=2, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c9 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1001, 1999), strides=(0, 0), offset=0, mask=((0, 1001), (999, 1999)), contiguous=False), View(shape=(1000, 1000), strides=(1, 2000), offset=0, mask=None, contiguous=False))), src=())
+    c10 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c11 = c1.store((c4.alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c8)).cast(dtypes.int)*(c9.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, -1, src=c10), UOp.const(dtypes.int, 0, src=c10)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,)))+UOp.const(dtypes.int, 1000, src=c8))))
+    ast = c11.sink()
+    #opts = [Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8)]
+    opts = [Opt(op=OptOps.LOCAL, axis=0, arg=8)]
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
+    print(prg.src)
+    assert prg.uops is not None and not any(uop.op is Ops.MAX for uop in prg.uops), "leftover MAX"
+
+  # this was a bug in embedding, someday we should fold this anyway
+  @unittest.skipUnless(is_dtype_supported(dtypes.half), f"half dtype not supported on {Device.DEFAULT}")
+  @unittest.skip("UOp.view is no longer supported")
+  def test_llama_embedding(self):
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(4096), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(4096, 1, 1), strides=(1, 0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32001, 63999), strides=(0, 0), offset=0, mask=((0, 32001), (31999, 63999)), contiguous=False), View(shape=(4096, 32000, 32000), strides=(0, 1, 64000), offset=0, mask=None, contiguous=False))), src=())
+    c3 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 32000), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c4 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(131072000), arg=2, src=())
+    c9 = c8.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(1, 4096, 0), offset=0, mask=None, contiguous=False),)))
+    c10 = c9.load()
+    c11 = c1.store(((c2.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, 1, src=c3), UOp.const(dtypes.int, 0, src=c3)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (2,)))+UOp.const(dtypes.int, -1, src=c4)).alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c4)).cast(dtypes.half)*c10).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,))).cast(dtypes.half))
+    ast = c11.sink()
+    prg = get_program(ast, Device[Device.DEFAULT].renderer)
+    print(prg.src)
+
+  @unittest.expectedFailure
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
+  def test_unrolled_float4_align(self):
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(1, 1), strides=(0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(18), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(3, 6), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c6 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18), arg=2, src=())
+    c7 = c6.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
+    c8 = c7.load()
+    c9 = c1.store(c4.alu(Ops.CMPNE, UOp.const(dtypes.long, -1, src=c5)).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c5)).where(UOp.const(dtypes.float, 0.0, src=c5), c8).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (0, 1))))
+    ast = c9.sink()
+    opts = [Opt(op=OptOps.UNROLL, axis=0, arg=0)]
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
+    print(prg.src)
+    load_idxs = [x.src[1] for x in prg.uops if x.op is Ops.LOAD and x.src[0].arg == 2]
+    assert load_idxs[0] < load_idxs[1], f"first loaded idx {load_idxs[0].arg} then {load_idxs[1].arg}!"
+
+  @unittest.expectedFailure
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "this is somehow correct in PTX")
+  def test_upcasted_stores_out_of_order(self):
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(0, 0, 0, 0, 0, 0, 1, 0, 4, 48, 16), offset=0, mask=None, contiguous=False),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1040), arg=2, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(260, 13, 1, 0, 0, 0, 65, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = c1.store((c4*c7).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (6,))))
+    ast = c8.sink()
+    opts = [Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=0)]
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
+    print(prg.src)
+    store_idxs = [x.src[1] for x in prg.uops if x.op is Ops.STORE]
+    for i in range(len(store_idxs) - 1):
+      first_bounds = store_idxs[i].vmin+store_idxs[i].vmax
+      next_bounds = store_idxs[i+1].vmin+store_idxs[i+1].vmax
+      assert first_bounds < next_bounds, f"first stored (max) idx {first_bounds} then {next_bounds}!"
+
 if __name__ == '__main__':
  unittest.main()
@@ -390,6 +390,7 @@ class TestMultiTensor(unittest.TestCase):

  # NOTE: this is failing on LLVM CI, no idea why. Works locally.
  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "CPU", "AMD"), "slow, and flaky on CPU")
+  @unittest.skip("TODO: pm_rangeify hangs")
  def test_data_parallel_resnet(self):
    from extra.models.resnet import ResNet18

@@ -658,7 +659,7 @@ class TestMultiTensor(unittest.TestCase):

  # it doesn't work like this anymore
  # NOTE: this never failed in assign_multi, it failed tensor spec because MULTI was never pushed in the graph
-  @unittest.skip("this test is broken")
+  @unittest.expectedFailure
  def test_mlb_assign_change_axis(self):
    t_none = Tensor.zeros((16, 16)).shard(devices_2).contiguous().realize()
    t_zero = Tensor.ones((16, 16)).shard(devices_2, axis=0)
@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
 import numpy as np
 from typing import List, Callable
 import torch
-from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, CPU_LLVM, CPU_LVP, AMD_LLVM
+from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, CPU_LLVM, AMD_LLVM
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
@@ -698,8 +698,8 @@ class TestOps(unittest.TestCase):

  def test_pow_zero_tensor(self):
    helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [0.0]])
-    # TODO: fix WEBGPU and LVP
-    if Device.DEFAULT != "WEBGPU" and not CPU_LVP:
+    # TODO: fix WEBGPU
+    if Device.DEFAULT != "WEBGPU":
      helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [0.3]])
      helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [-0.3]])
  def test_pow_zero_const(self):
@@ -830,7 +830,6 @@ class TestOps(unittest.TestCase):
    self.assertEqual(a, b)
    self.assertEqual(Tensor(-1).contiguous().idiv(4).item(), 0)  # NOTE this is trunc-div behaviour

-  @unittest.skipIf(getenv("NV_NAK"), "MUFU.SIN is not accurate enough")
  def test_sin(self):
    helper_test_op([(45,65)], lambda x: x.sin())
    helper_test_op([()], lambda x: x.sin())
@@ -840,7 +839,6 @@ class TestOps(unittest.TestCase):
      helper_test_op(None, lambda x: x.sin(), vals=[[1e1, 1e2, 1e3, 1e4, 1e5, 1e6, -1e1, -1e2, -1e3, -1e4, -1e5, -1e6]],
                    atol=3e-3, rtol=3e-3, grad_atol=3e-3, grad_rtol=3e-3)
  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and platform.system() == "Windows", "Not accurate enough with DirectX backend")
-  @unittest.skipIf(getenv("NV_NAK"), "MUFU.SIN is not accurate enough")
  def test_cos(self):
    helper_test_op([(45,65)], lambda x: x.cos())
    helper_test_op([()], lambda x: x.cos())
@@ -849,7 +847,6 @@ class TestOps(unittest.TestCase):
      helper_test_op(None, lambda x: x.cos(), vals=[[1e1, 1e2, 1e3, 1e4, 1e5, 1e6, -1e1, -1e2, -1e3, -1e4, -1e5, -1e6]],
                    atol=3e-3, rtol=3e-3, grad_atol=3e-3, grad_rtol=3e-3)
  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and platform.system() == "Windows", "Not accurate enough with DirectX backend")
-  @unittest.skipIf(getenv("NV_NAK"), "MUFU.SIN is not accurate enough")
  def test_tan(self):
    # NOTE: backward has much higher diff with input close to pi/2 and -pi/2
    helper_test_op([(45,65)], lambda x: x.tan(), low=-1.5, high=1.5)
@@ -3180,7 +3177,6 @@ class TestOps(unittest.TestCase):
  def test_bitcast(self):
    helper_test_op([(3, 3)], lambda x: x.view(torch.int32), lambda x: x.bitcast(dtypes.int32), forward_only=True)

-  @unittest.skip("we have test_linalg, no need to test here. TODO: should be in torch backend tests")
  def test_svd(self):
    # test for tiny backend. real svd tests are in test_linalg
    A = torch.randn(5, 5)
@@ -1,6 +1,6 @@
 import unittest
 from tinygrad import Tensor, Device
-from tinygrad.helpers import CPU_LLVM, CPU_LVP
+from tinygrad.helpers import CPU_LLVM
 from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import get_program

@@ -12,7 +12,7 @@ class TestOpts(unittest.TestCase):
    out = (a+b).contiguous(arg=opts)
    s = out.schedule()
    self.assertEqual(s[-1].ast.arg.opts_to_apply, opts)
-    if Device.DEFAULT in {"CPU", "CL", "METAL"} and not CPU_LLVM and not CPU_LVP:
+    if Device.DEFAULT in {"CPU", "CL", "METAL"} and not CPU_LLVM:
      prg = get_program(s[-1].ast)
      self.assertIn('float4', prg.src)

@@ -217,9 +217,9 @@ class TestProfiler(unittest.TestCase):
        Tensor.realize(a, b)
    profile, _ = helper_profile_filter_device(profile, TestProfiler.d0.device)
    exec_points = [e for e in profile if isinstance(e, ProfilePointEvent) and e.name == "exec"]
-    range_events = [e for e in profile if isinstance(e, ProfileRangeEvent) and not e.is_copy]
+    range_events = [e for e in profile if isinstance(e, ProfileRangeEvent)]
    self.assertEqual(len(exec_points), len(range_events), 2)
-    self.assertEqual(len(dedup(e.arg['name'] for e in exec_points)), 1)
+    self.assertEqual(len(dedup(e.key for e in exec_points)), 1)
    self.assertEqual(len(dedup(e.arg['metadata'] for e in exec_points)), 1)

 if __name__ == "__main__":
@@ -72,7 +72,7 @@ class TestQuantizeOnnxCPU(unittest.TestCase):
    out_file = get_quantized_model(sz)
    run_onnx = OnnxRunner(out_file)
    inp = Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32))
-    with Context(QUANTIZE=1):
+    with Context(DONT_REALIZE_EXPAND=1, QUANTIZE=1):
      sched = run_onnx({"input":inp})["output"].schedule()
      ei = lower_schedule_item(sched[-2])
      daccs = [u for u in ei.prg.p.uops if u.op is Ops.DEFINE_REG]
@@ -86,7 +86,8 @@ class TestQuantizeOnnx(unittest.TestCase):
    # divide is ~1500-2000 without reduce_range, 750-900 with it
    out_file = get_quantized_model(sz)
    run_onnx_jit, _ = load_onnx_model(out_file)
-    run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)))
+    with Context(DONT_REALIZE_EXPAND=1):
+      run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)))

  def test_prequant_conv2d_1x1(self):
    X = Tensor(np.random.uniform(0, 255, size=(1, 32, 128, 128)).astype(np.uint8))
@@ -108,10 +109,11 @@ class TestQuantizeOnnx(unittest.TestCase):
    N = 512
    X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(xi))
    W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(wi))
-    # this divide is interesting and forces the accumulator to actually be an int
-    out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8")
-    opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
-    sexec(out, opts)
+    with Context(DONT_REALIZE_EXPAND=1):
+      # this divide is interesting and forces the accumulator to actually be an int
+      out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8")
+      opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
+      sexec(out, opts)

  def test_prequant_gemm_handcode(self):
    src = """typedef int int128 __attribute__((aligned(512),vector_size(512)));
@@ -201,12 +203,14 @@ class TestQuantizeOnnx(unittest.TestCase):
  def test_prequant_gemm_intacc(self, xi=np.uint8, wi=np.uint8, replace_src=None, N=512, clip=True, opts=None):
    X = Tensor(m1:=(np.random.uniform(0, 255, size=(N,N)).astype(xi))).realize()
    W = Tensor(m2:=(np.random.uniform(0, 255, size=(N,N)).astype(wi))).realize()
+    # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant
    tg_dtype = dtypes.int8 if xi == np.int8 else dtypes.uint8
-    out = (X.int().matmul(W.int())//1000)
-    if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
-    out = out.cast(tg_dtype)
-    opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
-    sexec(out, opts, replace_src, run_count=1)
+    with Context(DONT_REALIZE_EXPAND=1):
+      out = (X.int().matmul(W.int())//1000)
+      if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
+      out = out.cast(tg_dtype)
+      opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
+      sexec(out, opts, replace_src, run_count=1)
    tout = out.numpy()
    mout = ((m1.astype(np.int32) @ m2.astype(np.int32)) // 1000)
    if clip: mout = mout.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
@@ -221,6 +225,7 @@ class TestQuantizeOnnx(unittest.TestCase):

  def test_prequant_gemv(self):
    N = 2048
+    # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant
    X = Tensor(np.random.uniform(0, 255, size=(1,N)).astype(np.uint8)).realize()
    W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)).realize()
    #out = X.cast(dtypes.int) @ W.cast(dtypes.int)
@@ -6,7 +6,6 @@ from tinygrad.helpers import getenv, CI, OSX
 from tinygrad.device import is_dtype_supported
 from tinygrad.engine.realize import lower_schedule, CompiledRunner
 from tinygrad.renderer.ptx import PTXRenderer
-from tinygrad.renderer.nir import NIRRenderer
 from test.helpers import not_support_multi_device

 import numpy as np
@@ -101,7 +100,7 @@ class TestRandomness(unittest.TestCase):

    np.testing.assert_allclose(jr, r)

-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (NIRRenderer, PTXRenderer)), "PTX and NIR use pointer arithmetic")
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "fails with PTX")
  def test_threefry_doesnt_use_long(self):
    for (_,ei) in lower_schedule(Tensor.rand(20).schedule()):
      if isinstance(ei.prg, CompiledRunner):
@@ -1,6 +1,6 @@
 import unittest
 from tinygrad import Tensor, nn
-from tinygrad.helpers import Context, GlobalCounters, CI, CPU_LVP, getenv
+from tinygrad.helpers import Context, GlobalCounters
 from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops

 class TestRangeifyAssign(unittest.TestCase):
@@ -17,89 +17,8 @@ class TestRangeifyAssign(unittest.TestCase):
    self.assertListEqual(lst, lst3)
    self.assertListEqual(lst2, B.permute(1, 0).tolist())

-class TestRangeifyEdgeCase(unittest.TestCase):
-  def test_matmul_relu_cat(self):
-    a = Tensor.ones(100, 512).contiguous().realize()
-    c = Tensor.ones(1, 512).contiguous().realize()
-    cm = Tensor.ones(512, 512)
-    c = c @ cm
-    c = c.relu()
-
-    res = Tensor.cat(a, c, dim=0)
-    self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
-
-if getenv("BIG") > 2:
-  # llama 8B (8192)
-  BS, HEADS, SEQLEN, EMB = 4, 32, 8192, 128
-elif getenv("BIG") > 1:
-  # llama 8B
-  BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
-elif getenv("BIG") > 0:
-  # bigger
-  BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
-else:
-  BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
-
-@unittest.skipIf(CPU_LVP, "broken in LVP")
-class TestPcontig(unittest.TestCase):
-  def test_flash_attention_bw(self):
-    def fa_bw():
-      Tensor.manual_seed(1337)
-      with Context(DEBUG=0):
-        q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize().requires_grad_() for _ in range(3)]
-        attn_output = nn.Linear(HEADS*EMB, HEADS*EMB, bias=False)
-        attn_output.weight.requires_grad_().realize()
-        target = Tensor.rand(BS, SEQLEN, HEADS*EMB).contiguous().realize()
-
-      GlobalCounters.reset()
-      attn = q.scaled_dot_product_attention(k, v).contiguous().contiguous_backward()
-      attn = attn.transpose(1, 2).reshape(BS, SEQLEN, -1)
-      out = attn_output(attn)
-      loss = (out - target).square().mean()
-      loss.backward()
-      #ret = [out, Tensor.stack(q.grad, k.grad, v.grad)]
-      ret = [out, q.grad, k.grad, v.grad]
-      Tensor.realize(*ret)
-      return ret
-
-    with Context(PCONTIG=2, REAL_SUBSTITUTE=1, DEBUG=2):
-      grads = fa_bw()
-      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
-
-    with Context(DEBUG=2):
-      cmp_grads = fa_bw()
-      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
-
-    with Context(DEBUG=0):
-      mses = [((x-y)**2).sum().item() for x,y in zip(grads, cmp_grads)]
-    mse = sum(mses)
-    print(f"mse: {mse}")
-    self.assertLessEqual(mse, 1e-6)
-
-  def test_flash_attention(self):
-    def fa():
-      Tensor.manual_seed(1337)
-      with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
-      GlobalCounters.reset()
-      return q.scaled_dot_product_attention(k, v).realize()
-
-    with Context(PCONTIG=2, DEBUG=2):
-      ret = fa()
-      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
-    with Context(DEBUG=2):
-      cmp = fa()
-      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
-    with Context(DEBUG=0):
-      mse = ((cmp-ret)**2).sum().item()
-    print(f"mse: {mse}")
-    self.assertLessEqual(mse, 1e-6)
-
-
-# *** non CI rangeify tests below this line ***
-
 N = 256

-@unittest.skipIf(CI, "useless in CI, doesn't test anything")
 class TestRangeifyOpt(unittest.TestCase):
  def test_randperm(self):
    Tensor.randperm(10000).realize()
@@ -135,7 +54,6 @@ class TestRangeifyOpt(unittest.TestCase):
    A = Tensor.empty(8,8,8,8).permute(1,0,3,2).flatten()
    A.sum().realize()

-@unittest.skipIf(CI, "useless in CI, doesn't test anything")
 class TestRangeify(unittest.TestCase):
  def test_groupnorm(self):
    # ranges 1 and 3 are merging
@@ -282,6 +200,33 @@ class TestRangeify(unittest.TestCase):
    out = blk._feed_forward(x)
    out.realize()

+  @unittest.skip("RANGEIFY=0 does nothing")
+  def test_flash_attention(self):
+    BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
+
+    # bigger
+    #BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
+
+    # llama 8B
+    #BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
+
+    def fa():
+      Tensor.manual_seed(1337)
+      with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
+      return q.scaled_dot_product_attention(k, v).realize()
+
+    with Context(DEBUG=4):
+      GlobalCounters.reset()
+      ret = fa()
+    with Context(RANGEIFY=0):
+      with Context(DEBUG=2):
+        GlobalCounters.reset()
+        cmp = fa()
+      with Context(DEBUG=0):
+        mse = ((cmp-ret)**2).sum().item()
+    print(f"mse: {mse}")
+    self.assertLessEqual(mse, 1e-6)
+
 # contiguous + reduce can support ranges?

@unittest.skip("pm_rangeify no longer exists. test this in a different way")
@@ -335,5 +280,16 @@ class TestRangeifyPM(unittest.TestCase):
    b = self.base.pad(((0,1),(0,0))).pad(((0,0),(0,1)))
    self.assert_same(a, b)

+class TestRangeifyEdgeCase(unittest.TestCase):
+  def test_matmul_relu_cat(self):
+    a = Tensor.ones(100, 512).contiguous().realize()
+    c = Tensor.ones(1, 512).contiguous().realize()
+    cm = Tensor.ones(512, 512)
+    c = c @ cm
+    c = c.relu()
+
+    res = Tensor.cat(a, c, dim=0)
+    self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
+
 if __name__ == '__main__':
  unittest.main()
@@ -2,14 +2,16 @@
 # schedule confirms the right things are capable of fusing
 # NOTE: this has overlap with external_test_opt.py

-import unittest, functools
+import unittest
 import numpy as np
+import functools
 from typing import cast
 from hypothesis import assume, given, settings, strategies as strat

 from tinygrad import nn, dtypes, Device, Tensor, Variable
 from tinygrad.device import is_dtype_supported
 from tinygrad.dtype import DType, ImageDType
+from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.uop.ops import UOp, Ops, GroupOp, UPat
 from tinygrad.helpers import CI, DEBUG, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp
 from tinygrad.schedule.rangeify import get_rangeify_map, Kernel
@@ -30,6 +32,7 @@ def check_schedule(t:Tensor|list[Tensor]|UOp, allowed:int, to_prerealize:list[Te
  # test lowering all the ScheduleItems to ExecItems
  kernel_cnt = len([si for si,ei in lower_schedule(sched.copy()) if isinstance(ei.prg, CompiledRunner) or not filter_sink])
  if kernel_cnt != allowed:
+    return sched # allow different kernel count, TODO: fix the asserts
    print(f"SCHEDULE ISSUE, expecting {allowed} got {len(sched)}")
    if DEBUG >= 3:
      for i,s in enumerate(sched):
@@ -115,7 +118,8 @@ class TestSchedule(unittest.TestCase):
    c = a+b
    with self.assertRaisesRegex(RuntimeError, "all buffers must be on the same device"): check_schedule(c, 2)

-  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
+  @unittest.skipUnless(is_dtype_supported(dtypes.half) and getenv("CAST_AFTER_EXPAND"), "need half and CAST_AFTER_EXPAND=1")
+  @unittest.skip("CAST_AFTER_EXPAND is not supported")
  def test_expand_buffer_before_cast(self):
    a = Tensor.randn(4, 2, 1).realize().permute((1, 0, 2))
    b = a.cast(dtypes.half).expand((2, 4, 4))+2
@@ -125,7 +129,7 @@ class TestSchedule(unittest.TestCase):
  def test_indexing_scalars_simple(self):
    X = Tensor.randn(2, 2).realize()
    xt = X[Tensor(1)][Tensor(0)]
-    run_schedule(check_schedule(xt, 1))
+    run_schedule(check_schedule(xt, 2))
    np.testing.assert_equal(xt.numpy(), X.numpy()[1][0])

  @unittest.skipIf(CI and Device.DEFAULT == "NV", "crashes on NV CI")
@@ -145,30 +149,31 @@ class TestSchedule(unittest.TestCase):
    assume(a<x and b<y)
    X = Tensor.randn(x, y).realize()
    xt = X[Tensor(a)][Tensor(b)]
-    run_schedule(check_schedule(xt, 1))
+    run_schedule(check_schedule(xt, 2))
    np.testing.assert_equal(xt.numpy(), X.numpy()[a][b])

  def test_push_pads_elementwise(self):
    x = Tensor.full((4,4), 2.).contiguous().realize()
    y = Tensor.full((4,4), 4.).contiguous().realize()
    z = (x.reciprocal()*y).pad((None, (0,1),)).sum()
-    run_schedule(check_schedule(z, 1))
+    run_schedule(check_schedule(z, 2))
    self.assertEqual(z.item(), 32)

  def test_push_pads_contiguous(self):
    x = Tensor.full((4,1), 2.).contiguous()
    y = Tensor.full((4,4), 4.).contiguous()
    z = (x.reciprocal().expand(4,4)*y).pad((None, (0,1),)).sum()
-    run_schedule(check_schedule(z, 1, [x,y]))
+    run_schedule(check_schedule(z, 2, [x,y]))
    self.assertEqual(z.item(), 32)

  def test_rand(self):
    x = Tensor.rand(32)
-    check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
+    check_schedule(x, 4, [Tensor._device_rng_counters[x.device]])

  def test_rand_recompute_arange(self):
    x = Tensor.rand(32)
-    check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
+    with Context(DONT_GROUP_REDUCES=1):
+      check_schedule(x, 3, [Tensor._device_rng_counters[x.device]])

  def test_empty_is_not_realized(self):
    a = Tensor.empty(10)
@@ -185,7 +190,10 @@ class TestSchedule(unittest.TestCase):

  def test_simplify_padded_const(self):
    a = Tensor.empty(1022).cummax(axis=0)
-    check_schedule(a, 3)
+    check_schedule(a, 5)
+    # TODO: what is this testing?
+    #ast = sched[0].ast
+    #self.assertLessEqual(len([u for u in ast.toposort() if u.op is Ops.WHERE]), 6)

  def test_basic_binop_fusion(self):
    a = Tensor.empty(10)
@@ -258,17 +266,18 @@ class TestSchedule(unittest.TestCase):
    c = a.sum(axis=0) + b
    check_schedule(c, 1)

+  # not pushing permutes through reduces
  def test_reduce_permute_binop_fusion(self):
    a = Tensor.empty(10,10,10)
    b = Tensor.empty(10,10,1)
    c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
-    check_schedule(c, 1)
+    check_schedule(c, 2)

  def test_allow_push_permutes(self):
    a = Tensor.randn(10,10,10).realize()
    b = Tensor.randn(10,10,1).realize()
    c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
-    run_schedule(check_schedule(c, 1))
+    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(c, 1))
    np.testing.assert_allclose(c.numpy(), np.sum(a.numpy(), axis=0, keepdims=True).transpose(2,1,0)+b.numpy())

  def test_binop_early_reshape_reduce_fusion(self):
@@ -333,7 +342,7 @@ class TestSchedule(unittest.TestCase):
    r1 = (x - r0).sum(axis=0).div(2)
    out0 = r0 + y
    out1 = r1 + y
-    schedule = check_schedule([out0, out1], 3)
+    schedule = check_schedule([out0, out1], 2)
    reduceops = [x for si in schedule for x in si.ast.toposort() if x.op in {Ops.REDUCE_AXIS, Ops.REDUCE}]
    self.assertEqual(len(reduceops), 2) # why is RANGEIFY different?

@@ -366,7 +375,7 @@ class TestSchedule(unittest.TestCase):
    b = Tensor.full((4,), 2.).contiguous()
    first = a.assign(b)
    second = a.assign(b)
-    check_schedule([first, second], 2) # TODO: 1?
+    check_schedule([first, second], 1)

  # NOTE: this is causing "LAZYCACHE=1 incorrectly reuses contiguous const" #4562
  # should contiguous dedup?
@@ -446,7 +455,7 @@ class TestSchedule(unittest.TestCase):
  @unittest.skipUnless(is_dtype_supported(dtypes.ulong), "Needs ulong")
  def test_fold_conv_batchnorm_optim(self):
    # this is too high
-    for optim, cnt in [(nn.optim.Adam, 30), (nn.optim.SGD, 13)]:
+    for optim, cnt in [(nn.optim.Adam, 30), (nn.optim.SGD, 11)]:
      with self.subTest(optim=optim.__name__):
        with Tensor.train():
          img = Tensor.ones(1,3,4,4)
@@ -467,7 +476,7 @@ class TestSchedule(unittest.TestCase):
      fw = bn(x).contiguous_backward().relu().contiguous()
      fw.sum().backward()
      # TODO: this is too many
-      check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 9)
+      check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 10)

  def test_fold_conv_relu(self):
    c1 = nn.Conv2d(3,16,3)
@@ -510,8 +519,9 @@ class TestSchedule(unittest.TestCase):
    img = Tensor.empty(64,64)
    x = (img.sum(0) + img.sum(1))
    out = x.relu()
-    check_schedule(out, 1)
+    check_schedule(out, 2)

+  #@unittest.skip("failing in old lazy")
  def test_push_permute_through_reshape(self):
    a = Tensor.empty(16,16)
    b = Tensor.empty(16,16)
@@ -545,7 +555,7 @@ class TestSchedule(unittest.TestCase):
    c = a+b
    d = a.reshape(10,1)+b.reshape(10,1)
    out = c.sum() + d.sum()
-    check_schedule(out, 1)
+    check_schedule(out, 2)

  def test_children_dont_push(self):
    a = Tensor.empty(10, 10, 1)
@@ -553,7 +563,7 @@ class TestSchedule(unittest.TestCase):
    d = (a+b).expand(10, 10, 10)
    e = (a+b).permute(2,1,0)
    f = d+e
-    check_schedule(f, 1)
+    check_schedule(f, 2)

  # failing in new lazy
  @unittest.skip("always fusing elementwise")
@@ -592,13 +602,13 @@ class TestSchedule(unittest.TestCase):
    e = c[0] * d
    check_schedule(e, 1)

-  def test_expand_fuse(self):
+  def test_expand_nofuse(self):
    a = Tensor.empty(1, 16)
    b = Tensor.empty(1, 16)
    c = a * b
    d = Tensor.empty(8192, 16)
    e = c * d
-    check_schedule(e, 1)
+    check_schedule(e, 2)

  # this is the failing case in openpilot...it's very simple like this
  def test_image_conv_fusion(self):
@@ -616,7 +626,7 @@ class TestSchedule(unittest.TestCase):

    # NOOP, 3 convs, contiguous
    #check_schedule(x, 5)
-    check_schedule(x, 7)
+    check_schedule(x, 8)

  def test_image_conv_fusion_minimal(self):
    b1 = Tensor.empty(16)
@@ -799,13 +809,13 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.empty(32, 32, 32)
    y = Tensor.empty(32, 32)
    out = x.sum(axis=2).T+y
-    check_schedule(out, 1)
+    check_schedule(out, 2)

  def test_two_elus_sum(self):
    x = Tensor.empty(32, 32)
    y = Tensor.empty(32, 32)
    out = x.sum(1).relu().elu() + y.sum(1).relu().elu()
-    check_schedule(out, 1)
+    check_schedule(out, 2)

  @unittest.skipUnless(SPLIT_REDUCEOP, "Testing split reducop requires SPLIT_REDUCEOP")
  def test_preserve_multistage_reduce(self):
@@ -818,7 +828,7 @@ class TestSchedule(unittest.TestCase):
  def test_multistage_reduce(self):
    x = Tensor.empty(32, 32, 32)
    out = x.sum(2).relu().sum(1)
-    check_schedule(out, 1)
+    check_schedule(out, 2)

  def test_multistage_reduce_fork(self):
    x = Tensor.empty(32, 32, 32)
@@ -834,7 +844,7 @@ class TestSchedule(unittest.TestCase):
    z = y.matmul(x).sum()
    z.backward()
    out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.ones((64,64)))

  def test_example_matmul_contig(self):
@@ -843,7 +853,7 @@ class TestSchedule(unittest.TestCase):
    z = y.matmul(x).sum()
    z.backward()
    out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.ones((64,64)))

  def test_example_matmul_same(self):
@@ -851,7 +861,7 @@ class TestSchedule(unittest.TestCase):
    z = x.matmul(x).sum()
    z.backward()
    out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    # NOTE: the gradient flows twice
    np.testing.assert_allclose(out.numpy(), 2*np.ones((64,64)))

@@ -874,7 +884,8 @@ class TestSchedule(unittest.TestCase):
    x = x.sum(1)
    x = x[:16]
    out = x + y
-    check_schedule(out, 1)
+    # NOTE: this could be 1 kernel if we mask the store?
+    check_schedule(out, 2)

  def test_multireduce_shrink(self):
    Tensor.manual_seed(0)
@@ -886,7 +897,8 @@ class TestSchedule(unittest.TestCase):
    b_out = b.sum(1)
    b_out = b_out[:16]
    out = a_out + b_out + c
-    run_schedule(check_schedule(out, 1))
+    # run_schedule(check_schedule(out, 2))  # TODO: this should be 1 (can we make it 1 with the new linearizer?)
+    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), a.numpy().sum(axis=1)[:16] + b.numpy().sum(axis=1)[:16] + c.numpy(), atol=1e-4, rtol=1e-4)

  # broken due to const folding and two contiguous are different kernels
@@ -903,7 +915,7 @@ class TestSchedule(unittest.TestCase):
    out0 = a.sum() + 2
    out1 = a.sum() + 4
    out2 = out0 * out1
-    run_schedule(check_schedule([out0, out1, out2], 3)) # TODO: 1?
+    run_schedule(check_schedule([out0, out1, out2], 1))
    np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out1.numpy(), out1_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out2.numpy(), out0_np*out1_np, atol=1e-4, rtol=1e-6)
@@ -914,7 +926,7 @@ class TestSchedule(unittest.TestCase):
    out0 = a.sum().exp2()
    # out1 has two paths to a.sum()
    out1 = a.sum() + out0
-    run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
+    run_schedule(check_schedule([out0, out1], 1))
    np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+out0_np, atol=1e-4, rtol=1e-6)

@@ -927,7 +939,7 @@ class TestSchedule(unittest.TestCase):
    out2 = b.sum().exp2()
    out3 = b.sum() + out2
    # run_schedule(check_schedule([out0, out1, out2, out3], 1))
-    run_schedule(check_schedule([out0, out1, out2, out3], 4))
+    run_schedule(check_schedule([out0, out1, out2, out3], 6))
    np.testing.assert_allclose(out0.numpy(), np_out0:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), np_out1:=a.numpy().sum()+np_out0, atol=1e-4, rtol=1e-4)
    np_b = (a.numpy() + np_out0 + np_out1)
@@ -942,7 +954,7 @@ class TestSchedule(unittest.TestCase):
    out0 = a.sum() + b.sum() + 2
    out1 = a.sum() + b.sum() + 4
    # run_schedule(check_schedule([out0, out1], 1))
-    run_schedule(check_schedule([out0, out1], 2))
+    run_schedule(check_schedule([out0, out1], 4))
    np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+b.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy().sum()+4, atol=1e-4, rtol=1e-4)

@@ -969,7 +981,7 @@ class TestSchedule(unittest.TestCase):
    out1 = b.max() + out0*2
    out2 = a.sum() + out1
    # run_schedule(check_schedule([out0, out1, out2], 1))
-    run_schedule(check_schedule([out0, out1, out2], 3))
+    run_schedule(check_schedule([out0, out1, out2], 4))
    np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out1.numpy(), out1_np:=b.numpy().max() + out0_np*2, atol=1e-4, rtol=1e-6)
    np.testing.assert_allclose(out2.numpy(), a.numpy().sum() + out1_np, atol=1e-4, rtol=1e-6)
@@ -1006,7 +1018,7 @@ class TestSchedule(unittest.TestCase):
    b = Tensor.empty(10,)
    c = a.sum() + b[0]
    d = a.sum() + 2
-    check_schedule([c, d], 2) # TODO: 1?
+    check_schedule([c, d], 1)

  def test_reduce_multiple_paths_midshrink(self):
    a = Tensor.empty(4, 4)
@@ -1035,7 +1047,7 @@ class TestSchedule(unittest.TestCase):
    k = Tensor.randn(32,8,16,8).realize()
    v = Tensor.randn(32,8,16,8).realize()
    out = Tensor.scaled_dot_product_attention(q,k,v)
-    run_schedule(check_schedule(out, 4))
+    run_schedule(check_schedule(out, 5))
    if getenv("CHECK", 1):
      import torch
      compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
@@ -1043,7 +1055,7 @@ class TestSchedule(unittest.TestCase):

    with Context(FUSE_ATTENTION=1):
      out = Tensor.scaled_dot_product_attention(q,k,v)
-      run_schedule(check_schedule(out, 4)) # TODO: should be 1?
+      run_schedule(check_schedule(out, 1))
      if getenv("CHECK", 1):
        import torch
        compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
@@ -1056,7 +1068,7 @@ class TestSchedule(unittest.TestCase):
    c = Tensor.randn(4, 32).realize()
    out = (c * a.sum(-1, keepdim=True)).sum(-1) + (b * a.sum(-1, keepdim=True)).sum(-1) # a.sum has >1 children but should still fuse
    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), \
      (c.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1) + (b.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1), atol=1e-4, rtol=1e-4)

@@ -1101,7 +1113,8 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = y.sum(axis=-1) + x.sum(axis=-1)
-    run_schedule(check_schedule(out, 1))
+    # run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), y.numpy().sum(axis=-1) + x.numpy().sum(axis=-1), atol=1e-4, rtol=1e-4)

  def test_multireduce_fusion_sequential(self):
@@ -1118,7 +1131,7 @@ class TestSchedule(unittest.TestCase):
    y = Tensor.randn(4, 32).realize()
    out = x.std(-1) + y.std(-1)
    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 3))
+    run_schedule(check_schedule(out, 4))
    np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1) + y.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)

  def test_multireduce_diffops_sequential(self):
@@ -1134,7 +1147,8 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(4, 32).realize()
    y = Tensor.randn(4, 32).realize()
    out = x.sum(-1) + y.max(-1)
-    run_schedule(check_schedule(out, 1))
+    # run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), x.numpy().sum(axis=-1) + y.numpy().max(axis=-1), atol=1e-4, rtol=1e-4)

  def test_multireduce_fusion_sequential_and_parallel(self):
@@ -1146,7 +1160,7 @@ class TestSchedule(unittest.TestCase):
    np_mu = (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True) + \
      (y.numpy() - y.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True)
    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 5))
+    run_schedule(check_schedule(out, 6))
    np.testing.assert_allclose(out[0].numpy(), np.sqrt(np.square(x.numpy() - np_mu).sum(-1)/x.shape[-1]), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out[1].numpy(), np.sqrt(np.square(y.numpy() - np_mu).sum(-1)/y.shape[-1]), atol=1e-4, rtol=1e-4)

@@ -1155,7 +1169,8 @@ class TestSchedule(unittest.TestCase):
    a,b = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
    c,d = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
    out = a@b + c@d
-    run_schedule(check_schedule(out, 1))
+    # run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), a.numpy()@b.numpy() + c.numpy()@d.numpy(), atol=1e-4, rtol=1e-4)

  def test_softmax_fusion(self):
@@ -1166,15 +1181,17 @@ class TestSchedule(unittest.TestCase):
    expected = (x_exp:=np.exp(x.numpy()-x.numpy().max(-1, keepdims=True)))/x_exp.sum(-1, keepdims=True)
    np.testing.assert_allclose(out.numpy(), expected, atol=1e-4, rtol=1e-4)

+  # TODO: rangeify stores the output in float32
  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
+  @unittest.expectedFailure
  def test_softmax_upcast(self):
    # input half, softmax in float
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 12, 64, 64, dtype=dtypes.half).realize()
    out = x.softmax(dtype=dtypes.float)
    sched = out.schedule()
-    self.assertEqual(len(sched), 3)
-    self.assertEqual(sched[0].bufs[0].dtype, dtypes.float)
+    self.assertEqual(len(sched), 2)
+    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)

    # input float, softmax in float
    Tensor.manual_seed(0)
@@ -1206,12 +1223,12 @@ class TestSchedule(unittest.TestCase):
  def test_scaled_dot_product_attention_fusion(self):
    x, y, z, m = (Tensor.empty(32, 8, 16, 16) for _ in range(4))
    out = Tensor.scaled_dot_product_attention(x, y, z, attn_mask=m)
-    check_schedule(out, 4)
+    check_schedule(out, 5)

  def test_scaled_dot_product_attention_causal_fusion(self):
    x, y, z = (Tensor.empty(32, 8, 16, 16) for _ in range(3))
    out = Tensor.scaled_dot_product_attention(x, y, z, is_causal=True)
-    check_schedule(out, 4)
+    check_schedule(out, 5)

  def test_adam_step_fusion(self):
    with Tensor.train():
@@ -1241,7 +1258,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 18)
+      check_schedule(opt.schedule_step(), 20)

  def test_sgd_conv_fuse(self):
    with Tensor.train():
@@ -1251,7 +1268,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.SGD(nn.state.get_parameters(c1))
      opt.zero_grad()
      c1(img).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 5) # TODO: 3?
+      check_schedule(opt.schedule_step(), 3)

  def test_sgd_2convs_fuse(self):
    with Tensor.train():
@@ -1274,7 +1291,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2]), nesterov=True, momentum=0.9, weight_decay=0.1)
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 15)
+      check_schedule(opt.schedule_step(), 13)

  def test_sgd_4convs_fuse(self):
    with Tensor.train():
@@ -1287,7 +1304,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
      opt.zero_grad()
      c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 15)
+      check_schedule(opt.schedule_step(), 17)

  def test_sgd_4convs_fuse_conv_bw(self):
    with Tensor.train():
@@ -1300,7 +1317,50 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
      opt.zero_grad()
      c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 15)
+      check_schedule(opt.schedule_step(), 14)
+
+  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
+  @unittest.expectedFailure
+  def test_prefer_half_buffer(self):
+    x = Tensor.ones(4).contiguous().realize()
+    # y = Tensor.ones(4).contiguous().realize()
+    z = Tensor.ones(4, 4).contiguous().realize()
+
+    # should not create extra kernel if output will be realized anyways
+    dummy = x.sum().half().float()
+    check_schedule(dummy, 1)
+    dummy = x.sum().half().float().contiguous() + 1
+    check_schedule(dummy, 2)
+
+    # shared between two outputs
+    shared = x.sum().half().float()
+    a = shared * 2
+    b = shared * 3
+    sched = check_schedule([a, b], 3)
+    # store reduceop in half
+    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)
+    # fuse cast with the child kernel
+    self.assertEqual(sched[1].bufs[0].dtype, dtypes.float)
+    self.assertEqual(sched[2].bufs[0].dtype, dtypes.float)
+
+    # reduce
+    a = z.sum(axis=0).half().float().sum(axis=0)
+    sched = check_schedule(a, 2)
+    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)
+    self.assertEqual(sched[1].bufs[0].dtype, dtypes.float)
+
+    # expand
+    # expand will realize just after the .float(), so requires change to realize-before-expand
+    # normal = (x.sum().half().float().reshape(1) * y).sum()
+    # sched = check_schedule(normal, 2)
+    # for si in sched[:-1]: assert all(out.dtype == dtypes.half for out in si.outputs[:-1])
+
+    # parallel reduce
+    # a = x.sum().half().float() * y.sum().half().float()
+    # b = a + 1
+    # c = a + 2
+    # sched = check_schedule([b, c], 4)
+    # doesn't store either in half because it doesn't chase

  def test_reduce_simple_chase(self):
    a = Tensor.empty(4, 4, 4)
@@ -1349,7 +1409,7 @@ class TestSchedule(unittest.TestCase):
    c = Tensor.empty(16, )
    r = a.sum(1) + c
    d = r[:4] * b
-    check_schedule(d, 1)
+    check_schedule(d, 2)

  def test_multireduce_push_shrink_chase(self):
    Tensor.manual_seed(0)
@@ -1359,20 +1419,22 @@ class TestSchedule(unittest.TestCase):
    d = Tensor.randn(16, 16).realize()
    r = a.sum(1) + c
    out = r[:4] * b + d.sum(1)[:4]
-    schedule = check_schedule(out, 1)
+    # schedule = check_schedule(out, 2)
+    schedule = check_schedule(out, 3)
    run_schedule(schedule)
    np.testing.assert_allclose(out.numpy(), (a.numpy().sum(1) + c.numpy())[:4] * b.numpy() + d.numpy().sum(1)[:4], atol=1e-4, rtol=1e-4)

  def test_midreduce_nochase(self):
    a = Tensor.empty(16, 16)
    b = (a.sum(0) + a.max(1)) + 2
-    check_schedule(b, 1)
+    check_schedule(b, 2)

  def test_multireduce_midreduce_nochase(self):
    Tensor.manual_seed(0)
    a = Tensor.randn(16, 16).realize()
    b = (a.sum(0)+a.max(0) + a.max(1)+a.sum(1)) + 2
-    schedule = check_schedule(b, 1)
+    # schedule = check_schedule(b, 2)
+    schedule = check_schedule(b, 4)
    run_schedule(schedule)
    np.testing.assert_allclose(b.numpy(), a.numpy().sum(0)+a.numpy().max(0) + a.numpy().max(1)+a.numpy().sum(1)+2, atol=1e-4, rtol=1e-4)

@@ -1384,7 +1446,7 @@ class TestSchedule(unittest.TestCase):
    c = a.sum() + 2
    d = (a.sum() - b.sum()) * 4
    # run_schedule(check_schedule([c, d], 1))
-    run_schedule(check_schedule([c, d], 2))
+    run_schedule(check_schedule([c, d], 3))
    np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), (a.numpy().sum() - b.numpy().sum()) * 4, atol=1e-4, rtol=1e-4)

@@ -1410,7 +1472,7 @@ class TestSchedule(unittest.TestCase):
    e = c * d
    f = b.sum() - e
    # run_schedule(check_schedule([c, d, e, f], 1))
-    run_schedule(check_schedule([c, d, e, f], 4))
+    run_schedule(check_schedule([c, d, e, f], 2))
    np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
@@ -1425,7 +1487,7 @@ class TestSchedule(unittest.TestCase):
    e = c * d
    f = (b - d).sum() - e
    # run_schedule(check_schedule([c, d, e, f], 1))
-    run_schedule(check_schedule([c, d, e, f], 4))
+    run_schedule(check_schedule([c, d, e, f], 5))
    np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
@@ -1444,7 +1506,8 @@ class TestSchedule(unittest.TestCase):
    a = Tensor.randn(3, 4, 5).realize()
    b = Tensor.randn(3, 4, 5).realize()
    out = (a.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum(keepdim=True)+b.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()).contiguous()
-    run_schedule(check_schedule(out, 1))
+    # run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.pad(a.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(keepdims=True) + \
                                                   np.pad(b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-4, rtol=1e-4)

@@ -1452,7 +1515,7 @@ class TestSchedule(unittest.TestCase):
    Tensor.manual_seed(0)
    a = Tensor.rand(3, 4, 5).realize()
    out = a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-5, rtol=1e-6)

  def test_multireduce_pad_reduce_unsafe(self):
@@ -1461,7 +1524,7 @@ class TestSchedule(unittest.TestCase):
    b = Tensor.randn(3, 4, 5).abs().realize()
    out = (a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()+b).abs().log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 4))
    np.testing.assert_allclose(out.numpy(), np.pad(np.log2(np.abs(np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum() + \
                                                   b.numpy())), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=3e-4, rtol=1e-5)

@@ -1475,7 +1538,7 @@ class TestSchedule(unittest.TestCase):
  def test_shrink_pad_unsafe(self):
    a = Tensor.ones((3, )).contiguous().realize()
    out = a.exp2().shrink(((0, 1),)).pad(((0, 1),)).contiguous()
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    np.testing.assert_equal(out.numpy(), [2, 0])

  def test_base_change_shrink_pad(self):
@@ -1483,7 +1546,7 @@ class TestSchedule(unittest.TestCase):
    b = a.exp2()
    c = b[:-1, :-1]
    d = c.pad(((0, 1), (0, 1))) * 2
-    run_schedule(check_schedule(d, 1))
+    run_schedule(check_schedule(d, 2))
    np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:-1, :-1], ((0, 1), (0, 1)))*2)

  def test_base_change_expand_pad(self):
@@ -1491,14 +1554,14 @@ class TestSchedule(unittest.TestCase):
    b = a.exp2()
    c = b[:, None, :]
    d = c.pad(((0, 0), (1, 1), (0, 0))) * 2
-    run_schedule(check_schedule(d, 1))
+    run_schedule(check_schedule(d, 2))
    np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:, None, :], ((0, 0), (1, 1), (0, 0)))*2)

  def test_fuse_arange_pad_replicate_mode(self):
    x = Tensor.empty(3,3,3,3, requires_grad=True)
    y = x.pad((-1,2,2,-1), mode="replicate")
    dx = y.sum().gradient(x)[0]
-    sched = check_schedule(dx, 1)
+    sched = check_schedule(dx, 3)
    run_schedule(sched)
    np.testing.assert_allclose(dx.numpy(), [[[[0.,3.,9.],[0,1.,3.],[0.,0.,0.]]]*3]*3)

@@ -1508,7 +1571,7 @@ class TestSchedule(unittest.TestCase):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.cast(dtypes.half).expand(2, 4, 4)
    c = b.cast(dtypes.int).expand(2, 2, 4, 4)
-    run_schedule(check_schedule(c, 1))
+    run_schedule(check_schedule(c, 2))
    np.testing.assert_equal(c.numpy(), np.ones(((2, 2, 4, 4)), dtype=np.int32))

  def test_base_change_pad_expand(self):
@@ -1516,7 +1579,7 @@ class TestSchedule(unittest.TestCase):
    b = Tensor.full((4, 4), 2.).contiguous().realize()
    c = (a + b).pad(((1, 1), (1, 1)))
    d = c.cast(dtypes.int).expand((2, 6, 6)) * 4
-    run_schedule(check_schedule(d, 1))
+    run_schedule(check_schedule(d, 2))
    c_np = np.pad((np.full((4, 4), 2., dtype=np.float32) + np.full((4, 4), 1., dtype=np.float32)), ((1, 1), (1, 1)), constant_values=0.0)
    np.testing.assert_equal(d.numpy(), np.broadcast_to(c_np.astype(np.half), (2, *c_np.shape)) * 4)

@@ -1615,7 +1678,7 @@ class TestSchedule(unittest.TestCase):
    self._test_fusion([(4, 4), (1, 4)], lambda a,b:a.sum(1).reshape(b.shape)+b, 1)

  def test_late_fusion_post_permute(self):
-    self._test_fusion([(4, 6, 4), (4, 4, 1)], lambda a,b:a.sum(1, keepdim=True).permute((2, 0, 1))+b, 1)
+    self._test_fusion([(4, 6, 4), (4, 4, 1)], lambda a,b:a.sum(1, keepdim=True).permute((2, 0, 1))+b, 2)

  def test_late_fusion_double_transpose(self):
    self._test_fusion([(32, 16, 1)],
@@ -1653,7 +1716,6 @@ class TestSchedule(unittest.TestCase):
    self.assertListEqual(realized_const_view.tolist(), [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])

  @given(strat.sampled_from(dtypes.all), strat.sampled_from(dtypes.all))
-  @unittest.skip("kernel count depends on input")
  def test_cast_padded_const(self, dt1, dt2):
    assume(is_dtype_supported(dt1) and is_dtype_supported(dt2))
    a = Tensor(1, dtype=dt1).reshape(1, 1).pad(((1, 1), None))
@@ -1667,7 +1729,7 @@ class TestSchedule(unittest.TestCase):
    X = Tensor.randn(10, 10).realize()
    idxs = Tensor([0, 2]).realize()
    xt = X[idxs]
-    run_schedule(check_schedule(xt, 1))
+    run_schedule(check_schedule(xt, 2))
    np.testing.assert_equal(xt.numpy(), X.numpy()[idxs.numpy()])

  def test_simple_indexing_alt(self):
@@ -1685,7 +1747,7 @@ class TestSchedule(unittest.TestCase):
  def test_advanced_indexing_alt(self):
    X = Tensor.arange(6).reshape(3, 2)+1
    xt = X[[Tensor([2]), Tensor([1])]]
-    run_schedule(check_schedule(xt, 1))
+    run_schedule(check_schedule(xt, 3))
    np.testing.assert_equal(xt.numpy(), 6)

  def test_advanced_simple_indexing_combined(self):
@@ -1733,7 +1795,7 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.full((2,2), 16)
    y = x.idiv(Tensor.linspace(2, 8, steps=4, dtype=dtypes.int).reshape(2,2)).pad(((1,1), (1,1)))
    out = y.sum(axis=1)
-    run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2))
    self.assertListEqual(out.tolist(), [0, 12, 4, 0])

  def test_arange_transposed_descendants(self):
@@ -1766,7 +1828,7 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(5, 2).realize()
    a = Tensor.arange(10).contiguous()
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)

  def test_arange_index_child(self):
@@ -1782,7 +1844,7 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(5, 2).realize()
    a = (Tensor.arange(10)+1).contiguous()
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3))
    np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)

  @unittest.skip("BUFFER_VIEW no longer supported on non-disk devices")
@@ -1797,10 +1859,10 @@ class TestSchedule(unittest.TestCase):
    from extra.models.llama import precompute_freqs_cis
    args = {"dim":32 if CI else 128, "end":2048 if CI else 8192, "theta":10000}
    fused = precompute_freqs_cis(**args)
-    run_schedule(check_schedule(fused, 1))
+    run_schedule(check_schedule(fused, 3))
    if getenv("CHECK", 1):
      ref = precompute_freqs_cis(**args)
-      run_schedule(check_schedule(ref, 1))
+      run_schedule(check_schedule(ref, 3))
      np.testing.assert_equal(fused.numpy(), ref.numpy())

  def test_fuse_assign_contiguous(self):
@@ -1842,7 +1904,7 @@ class TestSchedule(unittest.TestCase):
    X = Tensor([[0, 2, 3], [1, 2, 3]]).realize()
    Y = Tensor([1, 2]).realize()
    loss = X.sparse_categorical_crossentropy(Y)
-    run_schedule(check_schedule(loss, 3))
+    run_schedule(check_schedule(loss, 4))
    np.testing.assert_allclose(loss.item(), 0.878309, atol=1e-5, rtol=1e-6)

  def test_const_folding_alt(self):
@@ -1863,7 +1925,7 @@ class TestSchedule(unittest.TestCase):
    yt = Tensor.randn(BS, 10).realize()
    with Context(SPLIT_REDUCEOP=0):
      loss = yt.sparse_categorical_crossentropy(Y_train[samples])
-      run_schedule(check_schedule(loss, 5))
+      run_schedule(check_schedule(loss, 6))
      loss_fused = loss.numpy()
    loss_ref = torch.nn.CrossEntropyLoss()(torch.tensor(yt.numpy()), torch.tensor(Y_train.numpy())[torch.tensor(samples.numpy())])
    np.testing.assert_allclose(loss_fused, loss_ref.numpy(), atol=1e-6, rtol=1e-6)
@@ -1873,7 +1935,7 @@ class TestSchedule(unittest.TestCase):
    r = (X+Tensor.arange(16).reshape(4, 4)).sum()
    out0 = r+2
    out1 = r+3
-    run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
+    run_schedule(check_schedule([out0, out1], 1))
    r_ref = (X.numpy()+np.arange(16).reshape(4, 4)).sum()
    np.testing.assert_allclose(out0.numpy(), r_ref+2, rtol=2e-7)
    np.testing.assert_allclose(out1.numpy(), r_ref+3, rtol=2e-7)
@@ -1915,7 +1977,8 @@ class TestSwizzle(unittest.TestCase):
      a = Tensor.randint(32, 32).realize()
    r = (a+a).sum(1).sum(0)
    # double reduce collapses to a single reduce
-    run_schedule(check_schedule(r, 1))
+    with Context(DONT_GROUP_REDUCES=1):
+      run_schedule(check_schedule(r, 1))
    self.assertEqual(r.numpy(), (a.numpy()+a.numpy()).sum(1).sum(0))

  def test_single_swizzle(self):
@@ -1935,29 +1998,33 @@ class TestSwizzle(unittest.TestCase):
      b = Tensor.randint(4,).realize()
    # parallel reduce!
    add = a.sum(0)+b.sum(0)
-    run_schedule(check_schedule(add, 1))
+    with Context(DONT_GROUP_REDUCES=1):
+      run_schedule(check_schedule(add, 1))
    self.assertEqual(add.numpy(), a.numpy().sum(0)+b.numpy().sum(0))

+  @unittest.skip("TODO: how do we express the norm")
  def test_softmax_one_kernel(self):
    Tensor.manual_seed(0)
    with Context(DEBUG=0, TRACK_MATCH_STATS=0):
      a = Tensor.randn(32, 32).realize()
    t = a.softmax()
-    check_schedule(t, 3) # TODO: 1?
+    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1):
+      check_schedule(t, 1)

  def test_argmax_one_kernel(self):
    Tensor.manual_seed(0)
    with Context(DEBUG=0, TRACK_MATCH_STATS=0):
      a = Tensor.randn(10, 20).realize()
    t = a.argmax(0)
-    check_schedule(t, 2) # TODO: 1?
+    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): t.realize()

  def test_swizzle_reduceop(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4,4).realize()
    y = Tensor.randn(4,4,4).realize()
    out = x.reshape(4,4,1).expand(4,4,4).sum(axis=(1,))+y
-    run_schedule(check_schedule(out, 2)) # TODO: 1?
+    with Context(DONT_REALIZE_EXPAND=1, DONT_GROUP_REDUCES=1):
+      run_schedule(check_schedule(out, 1))
    np.testing.assert_allclose(out.numpy(), np.tile(x.numpy().reshape(4,4,1), (1,1,4)).sum(axis=1)+y.numpy())

  def test_permute_rewrite(self):
@@ -1965,7 +2032,7 @@ class TestSwizzle(unittest.TestCase):
    y = Tensor.randn(4, 1, 16).realize()
    z = Tensor.randn(4, 4, 1).realize()
    t = (x*y).sum(axis=(0, 2)).reshape(1, 4, 1).permute(0, 2, 1)+z
-    run_schedule(check_schedule(t, 2)) # TODO: 1?
+    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): run_schedule(check_schedule(t, 1))
    t_np = (x.numpy()*y.numpy()).sum(axis=(0, 2)).reshape(1, 4, 1).transpose(0, 2, 1)+z.numpy()
    np.testing.assert_allclose(t.numpy(), t_np, atol=1e-6, rtol=1e-3)

@@ -1976,14 +2043,14 @@ class TestSwizzle(unittest.TestCase):
    a_reduce = a.sum(axis=(2,), keepdim=True).sum(axis=(1,))
    b_reduce = b.sum(axis=(0,))
    t = a_reduce+b_reduce
-    run_schedule(check_schedule(t, 1))
+    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): run_schedule(check_schedule(t, 1))

  def test_parallel_reduce_possible(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 2, 2).realize()
    y = Tensor.randn(4, 2, 2).realize()
    t = x.sum(axis=1)+y.sum(axis=1)
-    run_schedule(check_schedule(t, 1))
+    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(t, 1))
    np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)

  # kernels can only have 1 or n in each dim
@@ -1992,7 +2059,7 @@ class TestSwizzle(unittest.TestCase):
    x = Tensor.randn(4, 2, 2).realize()
    y = Tensor.randn(4, 3, 2).realize()
    t = x.sum(axis=1)+y.sum(axis=1)
-    run_schedule(check_schedule(t, 1))
+    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(t, 1))
    np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)

  def test_unsafe_pad(self):
@@ -2085,7 +2152,7 @@ class TestCopyFolding(unittest.TestCase):
    a = Tensor.arange(3).realize()
    zeros = Tensor.zeros(3).realize()
    b = (a*zeros).to("CPU")
-    run_schedule(check_schedule(b, 2, filter_sink=False)) # TODO: 0?
+    run_schedule(check_schedule(b, 0, filter_sink=False))
    self.assertListEqual(b.tolist(), [0, 0, 0])
    self.assertEqual(b.device, "CPU")

@@ -2093,8 +2160,8 @@ class TestCopyFolding(unittest.TestCase):
    a = Tensor.ones((4,)).to("CPU")
    b = Tensor.empty(4, device="CPU")
    add = a+b
-    assert all_same([x.device for x in add.uop.src]), f"ALU has different devices! {[x.device for x in add.src]}"
    add.kernelize()
+    assert all_same([x.device for x in add.uop.src]), f"ALU has different devices! {[x.device for x in add.src]}"

  def test_alu_before_copy(self):
    buf = Tensor.ones(1).contiguous().realize()
@@ -2105,12 +2172,12 @@ class TestCopyFolding(unittest.TestCase):
  def test_copy_to_same_device(self):
    a = Tensor.empty(4).uop
    b = a.copy_to_device(a.device)
-    check_schedule(b, 1, filter_sink=False) # TODO: 0?
+    check_schedule(b, 0, filter_sink=False)

  def test_copy_to_same_device_alt(self):
    a = Tensor.empty(4, 4).uop
    b = a.copy_to_device(a.device)
-    check_schedule(b, 1, filter_sink=False) # TODO: 0?
+    check_schedule(b, 0, filter_sink=False)

  def test_copy_to_same_device_sched(self):
    a = Tensor.ones(4).contiguous().realize().uop.as_buf()
@@ -2125,11 +2192,13 @@ class TestCopyFolding(unittest.TestCase):
    a = Tensor.empty(4)
    check_schedule(a.clone(), 1, filter_sink=False)

+  # NOTE: moving copy before view might change this
  def test_shrink_copy(self):
    a = Tensor.arange(4)
    view = a.shrink(((0, 2),))
    b = view.clone()
-    run_schedule(check_schedule(b, 1, filter_sink=False))
+    # NOTE: this was sort of a bug making this 2
+    run_schedule(check_schedule(b, 2, filter_sink=False))
    self.assertEqual(b.uop.base.buffer.size, 2)
    self.assertEqual(b.uop.size, 2)
    self.assertListEqual(b.tolist(), [0, 1])
@@ -2138,7 +2207,7 @@ class TestCopyFolding(unittest.TestCase):
    a = Tensor.arange(2)
    view = a.reshape(2, 1).expand(2, 2)
    b = view.clone()
-    run_schedule(check_schedule(b, 1, filter_sink=False))
+    run_schedule(check_schedule(b, 2, filter_sink=False))
    self.assertEqual(b.uop.base.buffer.size, 4)
    self.assertEqual(b.uop.size, 4)
    self.assertListEqual(b.tolist(), [[0, 0], [1, 1]])
@@ -2182,7 +2251,7 @@ class TestBufferUOp(unittest.TestCase):
  def test_buffer_has_buffer(self):
    buf = Tensor.empty(10)
    self.assertIsNotNone(buf.uop.buffer)
-    self.assertEqual(buf.uop.shape, (10,))
+    self.assertEqual(buf.uop.st, ShapeTracker.from_shape((10,)))
    # the device Buffer remains unallocated until it's we run the schedule
    self.assertFalse(buf.uop.buffer.is_allocated())
    add = buf+1
@@ -2261,7 +2330,7 @@ class TestContiguous(unittest.TestCase):
  def test_double_contiguous_realizes_once(self):
    a = Tensor.empty(4, 1)
    b = a.expand((4, 4)).contiguous().contiguous()
-    check_schedule(b, 2) # TODO: should be 1?
+    check_schedule(b, 1)

  def test_view_does_not_realize(self):
    a = Tensor.empty(4)
@@ -2397,6 +2466,10 @@ class TestUOpBecome(unittest.TestCase):
    c = (a.reshape(1, 1, 4, 4)+0).shrink(((0, 1), (0, 1), (0, 3), (0, 3)))+0
    check_schedule([b, c], 0)
    assert all_same([x.uop.base.realized for x in [a,b,c]])
+    # these movement ops result in the same ShapeTracker
+    assert b.uop.st == c.uop.st
+    assert b.uop is c.uop
+    assert UPat(Ops.VIEW, src=(UPat(Ops.BUFFER),)).match(c.uop, {})

  def test_setitem_becomes_subbuffer(self):
    a = Tensor.full((4,), 2.).contiguous().realize()
@@ -52,6 +52,7 @@ class TestSetitem(unittest.TestCase):

  def test_setitem_into_noncontiguous(self):
    t = Tensor.ones(4)
+    self.assertFalse(t.uop.st.contiguous)
    with self.assertRaises(RuntimeError): t[1] = 5

  @unittest.skip("TODO: flaky")
@@ -165,7 +165,8 @@ class TestSoftmaxFusion(unittest.TestCase):
      sout.realize()

    print("*** single kernel softmax ***")
-    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2)):
+    # NOTE: DONT_GROUP_REDUCES is required here
+    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2), DONT_GROUP_REDUCES=1):
      out = single_kernel_softmax(self.test)
      out.realize()

@@ -185,6 +186,7 @@ class TestSoftmaxFusion(unittest.TestCase):

    np.testing.assert_allclose(sout.numpy(), out.numpy(), atol=3e-7)

+  @unittest.skip("recursion error no longer raised")
  def test_softmax_bw(self):
    print("*** softmax bw ***")
    self.test.requires_grad_()
@@ -195,11 +197,14 @@ class TestSoftmaxFusion(unittest.TestCase):
    self.test.grad = None

    print("*** single kernel softmax bw ***")
-    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2)):
-      single_kernel_softmax(self.test).sum().backward()
-      g = self.test.grad.realize()
+    # NOTE: DONT_GROUP_REDUCES is required here
+    # TODO: fix RecursionError with DONT_GROUP_REDUCES
+    with self.assertRaises(RecursionError):
+      with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2), DONT_GROUP_REDUCES=1):
+        single_kernel_softmax(self.test).sum().backward()
+        g = self.test.grad.realize()

-    np.testing.assert_allclose(sg.numpy(), g.numpy(), atol=1e-7)
+      np.testing.assert_allclose(sg.numpy(), g.numpy(), atol=1e-7)

 if __name__ == '__main__':
  unittest.main()
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
geohot	dbf8e641ac	binop also	2025-10-11 16:04:05 +08:00
geohot	ac53c8866e	improve typing	2025-10-11 15:57:37 +08:00
geohot	c260482d84	no need for Self yet	2025-10-11 15:53:47 +08:00
geohot	26d54530d6	improve typing and bump to 3.11	2025-10-11 15:46:39 +08:00