binop also

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

@@ -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

.github/workflows/autogen.yml

@@ -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

.github/workflows/benchmark.yml

@@ -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

.github/workflows/mlperf.yml

@@ -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

.github/workflows/test.yml

@@ -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

.gitignore

@@ -38,7 +38,6 @@ extra/huggingface_onnx/models/*
 extra/huggingface_onnx/*.yaml
 extra/weights
 venv
-venv_sd_mlperf
 examples/**/net.*[js,json]
 examples/**/*.safetensors
 node_modules

.pre-commit-config.yaml

@@ -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

.pylintrc

@@ -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

autogen_stubs.sh

@@ -279,9 +279,9 @@ generate_llvm() {
     --clang-args="$(llvm-config-14 --cflags)" \
     -o "$BASE/llvm.py"
 
-  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support, tinygrad.helpers as helpers\g" "$BASE/llvm.py"
+  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support\g" "$BASE/llvm.py"
   sed -i "s\FIXME_STUB\llvm\g" "$BASE/llvm.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH, ctypes.RTLD_GLOBAL if helpers.OSX else ctypes.DEFAULT_MODE)\g" "$BASE/llvm.py"
+  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH)\g" "$BASE/llvm.py"
 
   fixup "$BASE/llvm.py"
 }
@@ -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

examples/mlperf/model_train.py

@@ -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)

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_1xMI300X/dev_beam.sh

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/README.md

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_beam.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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_run.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/README.md

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_beam.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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_run.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/README.md

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_beam.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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_run.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/README.md

@@ -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
-```

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/dev_beam.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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/dev_run.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_green/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/README.md

@@ -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
-```

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/dev_beam.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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/dev_run.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/README.md

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/dev_beam.sh

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/dev_run.sh

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh

@@ -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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_red/dev_beam.sh

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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/retinanet/implementations/tinybox_red/dev_run.sh

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

examples/mlperf/training_submission_v6.0/tinycorp/systems/tinybox_8xMI300X.json

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

examples/mlperf/training_submission_v6.0/tinycorp/systems/tinybox_green.json

@@ -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": ""
-}

examples/mlperf/training_submission_v6.0/tinycorp/systems/tinybox_red.json

@@ -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": ""
-}

examples/openpilot/compile4.py

@@ -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()

examples/stable_diffusion.py

@@ -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():

extra/mesa/lvp_nir_options.sh

@@ -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
-

extra/sqtt/rocprof/install.py

@@ -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)

extra/sqtt/rocprof/rocprof.py

@@ -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')
 
 
 

extra/tinyfs/fetch_file.py

@@ -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()

extra/tinyfs/fetch_raid.py

@@ -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

extra/tinyfs/upload_raid.py

@@ -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)}")

extra/torch_backend/backend.py

@@ -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):

extra/usbgpu/tbgpu/installer/.gitignore

@@ -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

extra/usbgpu/tbgpu/installer/Shared/Assets.xcassets/AccentColor.colorset/Contents.json

@@ -1,11 +0,0 @@
-{
-  "colors" : [
-    {
-      "idiom" : "universal"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

extra/usbgpu/tbgpu/installer/Shared/Assets.xcassets/AppIcon.appiconset/Contents.json

@@ -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
-  }
-}

extra/usbgpu/tbgpu/installer/Shared/Assets.xcassets/Contents.json

@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

extra/usbgpu/tbgpu/installer/Shared/TinyGPUApp.swift

@@ -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()
-        }
-    }
-}

extra/usbgpu/tbgpu/installer/Shared/TinyGPUView.swift

@@ -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()
-    }
-}

extra/usbgpu/tbgpu/installer/Shared/TinyGPUViewModel.swift

@@ -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 = Bundle.main.bundleIdentifier! + ".Driver"
-
-	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

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension.xcodeproj/project.pbxproj

@@ -1,589 +0,0 @@
-// !$*UTF8*$!
-{
-	archiveVersion = 1;
-	classes = {
-	};
-	objectVersion = 54;
-	objects = {
-
-/* Begin PBXBuildFile section */
-		0ACB55392E9CB880007029EF /* PCIDriverKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 0ACB55382E9CB880007029EF /* PCIDriverKit.framework */; };
-		54798269286A3512009785F6 /* CoreAudio.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 54798268286A3512009785F6 /* CoreAudio.framework */; };
-		549EB121286A1A37009D38AB /* TinyGPUViewModel.swift in Sources */ = {isa = PBXBuildFile; fileRef = 549EB11F286A1A37009D38AB /* TinyGPUViewModel.swift */; };
-		549EB123286A1D48009D38AB /* org.tinygrad.tinygpu.edriver.dext in Embed System Extensions */ = {isa = PBXBuildFile; fileRef = C5B7D9BC26128AC50089B4C3 /* org.tinygrad.tinygpu.edriver.dext */; settings = {ATTRIBUTES = (RemoveHeadersOnCopy, ); }; };
-		549EB131286A2B98009D38AB /* IOKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549EB130286A2B98009D38AB /* IOKit.framework */; };
-		54E42BC8286A1697000E1E9A /* TinyGPUApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 54E42BB8286A1696000E1E9A /* TinyGPUApp.swift */; };
-		54E42BCA286A1697000E1E9A /* TinyGPUView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 54E42BB9286A1696000E1E9A /* TinyGPUView.swift */; };
-		54E42BCC286A1697000E1E9A /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 54E42BBA286A1697000E1E9A /* Assets.xcassets */; };
-		C5B7D9C326128AC50089B4C3 /* TinyGPUDriver.cpp in Sources */ = {isa = PBXBuildFile; fileRef = C5B7D9C226128AC50089B4C3 /* TinyGPUDriver.cpp */; };
-		C5B7D9C526128AC50089B4C3 /* TinyGPUDriver.iig in Sources */ = {isa = PBXBuildFile; fileRef = C5B7D9C426128AC50089B4C3 /* TinyGPUDriver.iig */; };
-		C5C3BBB32612ACDC003C7BFE /* AudioDriverKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = C5C3BBB12612ACD3003C7BFE /* AudioDriverKit.framework */; };
-		C5C3BBB52612ACEF003C7BFE /* DriverKit.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = C5C3BBB42612ACEF003C7BFE /* DriverKit.framework */; };
-		C5D787AC261667FC006047E5 /* TinyGPUDriverUserClient.iig in Sources */ = {isa = PBXBuildFile; fileRef = C5D787AB261667FC006047E5 /* TinyGPUDriverUserClient.iig */; };
-		C5D787AE26168E59006047E5 /* TinyGPUDriverUserClient.cpp in Sources */ = {isa = PBXBuildFile; fileRef = C5D787AD26168D1E006047E5 /* TinyGPUDriverUserClient.cpp */; };
-/* End PBXBuildFile section */
-
-/* Begin PBXContainerItemProxy section */
-		549EB126286A1D66009D38AB /* PBXContainerItemProxy */ = {
-			isa = PBXContainerItemProxy;
-			containerPortal = C5B7D9B326128AC50089B4C3 /* Project object */;
-			proxyType = 1;
-			remoteGlobalIDString = C5B7D9BB26128AC50089B4C3;
-			remoteInfo = SimpleAudioDriver;
-		};
-/* End PBXContainerItemProxy section */
-
-/* Begin PBXCopyFilesBuildPhase section */
-		549EB122286A1D3A009D38AB /* Embed System Extensions */ = {
-			isa = PBXCopyFilesBuildPhase;
-			buildActionMask = 2147483647;
-			dstPath = "$(SYSTEM_EXTENSIONS_FOLDER_PATH)";
-			dstSubfolderSpec = 16;
-			files = (
-				549EB123286A1D48009D38AB /* org.tinygrad.tinygpu.edriver.dext in Embed System Extensions */,
-			);
-			name = "Embed System Extensions";
-			runOnlyForDeploymentPostprocessing = 0;
-		};
-/* End PBXCopyFilesBuildPhase section */
-
-/* Begin PBXFileReference section */
-		0ACB55382E9CB880007029EF /* PCIDriverKit.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = PCIDriverKit.framework; path = System/DriverKit/System/Library/Frameworks/PCIDriverKit.framework; sourceTree = SDKROOT; };
-		54798268286A3512009785F6 /* CoreAudio.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = CoreAudio.framework; path = Platforms/MacOSX.platform/Developer/SDKs/MacOSX13.0.sdk/System/Library/Frameworks/CoreAudio.framework; sourceTree = DEVELOPER_DIR; };
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-				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 */;
-}

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension.xcodeproj/project.xcworkspace/xcshareddata/WorkspaceSettings.xcsettings

@@ -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>

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/Info.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>IOPCIPrimaryMatch</key>
-			<string>0x70001002&amp;0xF000FFFF</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>

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.cpp

@@ -1,223 +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(0);
-#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;
-}

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.entitlements

@@ -1,17 +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>0x70001002&amp;0xF000FFFF</string>
-		</dict>
-	</array>
-	<key>com.apple.developer.driverkit.allow-any-userclient-access</key>
-	<true/>
-	<key>com.apple.developer.driverkit</key>
-	<true/>
-</dict>
-</plist>

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.iig

@@ -1,33 +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;
-};
-
-#endif /* TinyGPUDriver_h */

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriverUserClient.cpp

@@ -1,94 +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 in_selector, IOUserClientMethodArguments* in_arguments, const IOUserClientMethodDispatch* in_dispatch, OSObject* in_target, void* in_reference)
-{
-	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;
-}

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriverUserClient.iig

@@ -1,21 +0,0 @@
-#ifndef TinyGPUDriverUserClient_h
-#define TinyGPUDriverUserClient_h
-
-#include <DriverKit/IOUserClient.iig>
-
-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 */

extra/usbgpu/tbgpu/installer/macOS/macOS.entitlements

@@ -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>

extra/usbgpu/tbgpu/main.cpp

@@ -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;
-}

extra/usbgpu/tbgpu/main.py

@@ -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)

setup.py

@@ -42,6 +42,7 @@ setup(name='tinygrad',
         'tinygrad.runtime.support.am',
         'tinygrad.runtime.support.nv',
         'tinygrad.schedule',
+        'tinygrad.shape',
         'tinygrad.uop',
         'tinygrad.viz',
       ],

test/external/external_test_dev_var.py

@@ -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()

test/external/external_test_example.py

@@ -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()
 

test/external/external_test_simple_tokenizer.py

@@ -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.")

test/external/external_uop_gc.py

@@ -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()

test/external/process_replay/process_replay.py

@@ -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
 

test/opt/test_gen_float4.py

@@ -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()

test/opt/test_kernel_opts.py

@@ -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)

test/test_arange.py

@@ -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()

test/test_assign.py

@@ -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))

test/test_compile_failures.py

@@ -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]

test/test_dtype.py

@@ -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(),

test/test_dtype_alu.py

@@ -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))

test/test_edgecases.py

@@ -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)

test/test_linearizer.py

@@ -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))

test/test_linearizer_dumb.py

@@ -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()

test/test_multitensor.py

@@ -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)

test/test_ops.py

@@ -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)

test/test_opts.py

@@ -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)
 

test/test_profiler.py

@@ -217,7 +217,7 @@ 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.key for e in exec_points)), 1)
     self.assertEqual(len(dedup(e.arg['metadata'] for e in exec_points)), 1)

test/test_randomness.py

@@ -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):

test/test_schedule.py

@@ -11,6 +11,7 @@ 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
@@ -2159,8 +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()
@@ -2250,7 +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

test/test_setitem.py

@@ -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")

test/test_symbolic_ops.py

@@ -1,5 +1,6 @@
 import unittest
 from tinygrad import Tensor, Variable, GlobalCounters
+from tinygrad.shape.shapetracker import View
 from tinygrad.uop.ops import sym_infer
 from tinygrad.dtype import dtypes
 from tinygrad.device import is_dtype_supported
@@ -63,6 +64,14 @@ class TestSymbolicOps(unittest.TestCase):
     self.test_attention(imin=4, imax=5, use_symbolic=False)
     self.test_attention(imin=4, imax=5, use_symbolic=True)
 
+  # until this works, symbolic single kernel softmax won't
+  @unittest.expectedFailure
+  def test_attention_simple_view(self):
+    i = Variable("i", 2, 10)
+    v1 = View.create((2,4,1,i,i), ((i*4),i,0,0,1))
+    v2 = View.create((2,4,1,i,i,i), (((i*i)*4),(i*i),0,0,i,1))
+    self.assertIsNotNone(v1+v2)
+
   def test_attention_training(self):
     with Tensor.train():
       self.test_attention(dropout_p=0.0)

test/test_tensor.py

@@ -1,3 +1,4 @@
+import subprocess
 import numpy as np
 import torch
 import unittest, copy, mmap, random, math, array
@@ -9,7 +10,6 @@ from hypothesis import given, settings, strategies as strat
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.renderer.ptx import PTXRenderer
-from tinygrad.renderer.nir import NIRRenderer
 from tinygrad.codegen import full_rewrite
 from tinygrad.dtype import DType
 
@@ -515,6 +515,32 @@ class TestTinygrad(unittest.TestCase):
     print(a)
     print(c)
 
+  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)
+
   def test_no_attributeerror_after_apply_uop_exception(self):
     try:
       Tensor.arange(4).reshape(3,2)
@@ -527,7 +553,6 @@ class TestTinygrad(unittest.TestCase):
     self.assertListEqual(t.shrink_to(16).tolist(), list(range(16)))
     t = t.reshape(4, 8).contiguous().realize()
     self.assertListEqual(t.shrink_to(2, 2).tolist(), [[0, 1], [8, 9]])
-    self.assertListEqual(t.shrink_to(None, 2).tolist(), t.shrink_to(4, 2).tolist())
     with self.assertRaises(ValueError): t.shrink_to(2)
     with self.assertRaises(ValueError): t.shrink_to(2, 2, 2)
 
@@ -570,6 +595,22 @@ class TestMoveTensor(unittest.TestCase):
     np.testing.assert_equal(x.grad.numpy(), [[2,2,2],[0,0,0],[-2,-2,-2]])
 
 class TestZeroShapeTensor(unittest.TestCase):
+  def test_shape_is_expanded(self):
+    t = Tensor.empty(3, 2, 0)
+    assert t.shape == (3, 2, 0)
+    # numpy has stride 0, 0, 0; torch has stride 2, 1, 1
+    assert t.uop.st.is_expanded() == (True, True, True)
+
+    t = Tensor.empty(3, 0, 2)
+    assert t.shape == (3, 0, 2)
+    # numpy has stride 0, 0, 0; torch has stride 2, 2, 1
+    assert t.uop.st.is_expanded() == (True, True, True)
+
+    t = Tensor.empty(0, 0, 0)
+    assert t.shape == (0, 0, 0)
+    # numpy has stride 0, 0, 0; torch has stride 1, 1, 1
+    assert t.uop.st.is_expanded() == (True, True, True)
+
   def test_rand(self):
     t = Tensor.rand(3, 2, 0)
     assert t.shape == (3, 2, 0)
@@ -621,10 +662,8 @@ class TestZeroShapeTensor(unittest.TestCase):
 
     np.testing.assert_equal(Tensor([1, 2]).pad_to(4).numpy(), [1, 2, 0, 0])
     np.testing.assert_equal(Tensor([[1, 2]]).pad_to(2, 3).numpy(), [[1, 2, 0], [0, 0, 0]])
-    np.testing.assert_equal(Tensor([[1, 2]]).pad_to(1, 3).numpy(), [[1, 2, 0]])
-    np.testing.assert_equal(Tensor([[1, 2]]).pad_to(None, 3).numpy(), [[1, 2, 0]])
-    with self.assertRaises(ValueError): Tensor([1, 2]).pad_to(2, 3)
-    with self.assertRaises(ValueError): Tensor([[1, 2]]).pad_to(3)
+    with self.assertRaises(TypeError): Tensor([1, 2]).pad_to(2, 3)
+    with self.assertRaises(TypeError): Tensor([[1, 2]]).pad_to(3)
 
   def test_shrink_into_zero(self):
     t = Tensor.rand(3, 4).realize()
@@ -821,6 +860,7 @@ class TestTensorMetadata(unittest.TestCase):
     self.assertEqual(len(si.metadata), 3)
     self.assertEqual(set(m.name for m in si.metadata), {"relu", "sigmoid", "__mul__"})
 
+  @unittest.skip("not accurate")
   def test_complex_backward(self):
     x = Tensor.rand(3, requires_grad=True).realize()
     y = Tensor.rand(3, requires_grad=True).realize()
@@ -832,12 +872,11 @@ class TestTensorMetadata(unittest.TestCase):
     self.assertEqual(y.grad.uop.metadata[0].name, "sigmoid")
     self.assertTrue(y.grad.uop.metadata[0].backward)
     si = Tensor.schedule(out, x.grad, y.grad)[-1]
-    self.assertEqual(len(si.metadata), 4, f"failed with {si.metadata}")
-    self.assertSetEqual(set(m.name for m in si.metadata), {"__mul__", "sigmoid", "relu"})
+    self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
+    self.assertSetEqual(set(m.name for m in si.metadata), {"sigmoid", "relu"})
     bw = [m for m in si.metadata if m.backward]
-    self.assertEqual(len(bw), 2)
-    self.assertEqual(bw[0].name, "__mul__")
-    self.assertEqual(bw[1].name, "sigmoid")
+    self.assertEqual(len(bw), 1)
+    self.assertEqual(bw[0].name, "sigmoid")
 
 class TestIdxUpcast(unittest.TestCase):
   def _find_op(self, ast: UOp, op: Ops):
@@ -859,8 +898,7 @@ class TestIdxUpcast(unittest.TestCase):
     store = next(uop for uop in uops if uop.op is Ops.STORE)
     assert store.op is Ops.STORE
     idx = self._find_op(store, Ops.INDEX)
-    # PTX and NIR turn Ops.INDEX into pointer arithmetic earlier than cstyle, plus it's already cast to int64
-    if not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)):
+    if idx is not None: # PTX turns Ops.INDEX into pointer arithmetic earlier than cstyle, plus it's already cast to int64
       assert idx.op is Ops.INDEX
       idx_val = idx.src[1]
       assert idx_val.dtype is dtype
@@ -884,7 +922,7 @@ class TestIdxUpcast(unittest.TestCase):
   def test_regular_sym(self):
     self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 1, 64).bind(32))
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)), "PTX and NIR always converts Ops.INDEX to int64")
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX always convert Ops.INDEX to int64")
   def test_symfold(self):
     # This would cause an overflow, but after sym fold it's within int32
     a = Tensor.arange(65535)

test/test_tensor_uop.py

@@ -11,7 +11,8 @@ class TestTensorUOp(unittest.TestCase):
     def helper(a: np.ndarray):
       print(a.shape, a.strides, a.flags.c_contiguous)
       b = Tensor(a).uop
-      assert b.shape == a.shape
+      #assert b.st.contiguous == a.flags.c_contiguous
+      assert b.st.shape == a.shape
       np.testing.assert_equal(a, Tensor(b).numpy())
 
     for ndims in range(1, 4):

test/test_tensor_variable.py

@@ -93,7 +93,7 @@ class TestTensorVariable(unittest.TestCase):
     vb = v.bind(3)
     t = Tensor.empty(3, vb)
     assert t.uop.base.buffer.size == 30
-    assert t.uop.shape == (3, vb)
+    assert t.uop.st.shape == (3, vb)
 
 
 if __name__ == '__main__':

test/test_tiny.py

@@ -134,8 +134,8 @@ class TestTiny(unittest.TestCase):
   def test_mnist_backward(self):
     # NOTE: we don't have the whole model here for speed
     layers = [
-      nn.Conv2d(1, 8, 5), Tensor.relu,
-      nn.Conv2d(8, 8, 5), Tensor.relu]
+      nn.Conv2d(1, 32, 5), Tensor.relu,
+      nn.Conv2d(32, 32, 5), Tensor.relu]
 
     # replace random weights with ones
     # TODO: there's a bug here where it's tying two of the biases together. we need UNIQUE const
@@ -144,7 +144,7 @@ class TestTiny(unittest.TestCase):
 
     # realize gradients
     for x in nn.state.get_parameters(layers): x.requires_grad_()
-    Tensor.empty(4, 1, 14, 14).sequential(layers).sum().backward()
+    Tensor.empty(4, 1, 28, 28).sequential(layers).sum().backward()
     Tensor.realize(*[x.grad for x in nn.state.get_parameters(layers) if x.grad is not None])
 
   # *** image ***

test/test_transcendental.py

@@ -149,7 +149,6 @@ class TestTranscendentalVectorized(unittest.TestCase):
     for vec_size in [1,2,3,4,5,127,128]: self._test_vectorized_op(Tensor.log2, np.log2, (0.001, 200), vec_size)
 
   @unittest.skipIf(getenv("DSP"), "requires int division")
-  @unittest.skipIf(getenv("NV_NAK"), "MUFU.SIN is not accurate enough")
   def test_sin_vectorized(self):
     for vec_size in [1,2,3,4,5,127,128]: self._test_vectorized_op(Tensor.sin, np.sin, (-100, 100), vec_size)
 

test/unit/test_device.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
-import unittest, os, subprocess
+import unittest, os, subprocess, sys
 from tinygrad import Tensor
-from tinygrad.device import Device, Compiler, enumerate_devices_str
+from tinygrad.device import Device, Compiler
 from tinygrad.helpers import diskcache_get, diskcache_put, getenv, Context, WIN, CI
 
 class TestDevice(unittest.TestCase):
@@ -100,7 +100,10 @@ class TestCompiler(unittest.TestCase):
 
 class TestRunAsModule(unittest.TestCase):
   def test_module_runs(self):
-    out = '\n'.join(enumerate_devices_str())
+    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)
     self.assertIn("CPU", out) # for sanity check
 
 if __name__ == "__main__":

test/unit/test_disk_tensor.py

@@ -418,32 +418,5 @@ class TestPathTensor(unittest.TestCase):
       Tensor(pathlib.Path(test_file)).tolist()
     os.chmod(test_file, 0o644)
     assert Tensor(pathlib.Path(test_file)).tolist(), list(range(10))
-
-class TestDiskTensorMovement(unittest.TestCase):
-  def setUp(self):
-    self.fn = pathlib.Path(temp("custom_disk_range"))
-    self.fn.unlink(missing_ok=True)
-    Tensor.arange(100, dtype=dtypes.uint8).to(f"disk:{str(self.fn)}").realize()
-
-  def test_simple_read(self):
-    t = Tensor(self.fn)
-    self.assertTrue(Tensor.all(t.to(None) == Tensor.arange(100, dtype=dtypes.uint8)).item())
-
-  def test_slice_read(self):
-    t = Tensor(self.fn)
-    self.assertListEqual(t[16:18].tolist(), [16,17])
-
-  # TODO: fix this! at least assert on it
-  @unittest.expectedFailure
-  def test_slice_read_cat(self):
-    t = Tensor(self.fn)
-    self.assertListEqual(Tensor.cat(t[16:18], t[20:22]).tolist(), [16,17,20,21])
-
-  # TODO: fix this! at least assert on it
-  @unittest.expectedFailure
-  def test_slice_sum(self):
-    t = Tensor(self.fn)
-    self.assertListEqual((t[16:18]+t[20:22]).tolist(), [16+20,17+21])
-
 if __name__ == "__main__":
   unittest.main()

test/unit/test_elf.py

@@ -24,15 +24,6 @@ class TestElfLoader(unittest.TestCase):
     '''
     with self.assertRaisesRegex(RuntimeError, 'evil_external_function'):
       ClangJITCompiler().compile(src)
-  def test_link(self):
-    src = '''
-      float powf(float, float); // from libm
-      float test(float x, float y) { return powf(x, y); }
-    '''
-    args = ('-x', 'c', '-c', '-target', f'{platform.machine()}-none-unknown-elf', '-march=native', '-fPIC', '-O2', '-ffreestanding', '-nostdlib')
-    obj = subprocess.check_output(('clang',) + args + ('-', '-o', '-'), input=src.encode())
-    with self.assertRaisesRegex(RuntimeError, 'powf'): elf_loader(obj)
-    elf_loader(obj, link_libs=['m'])
 
 if __name__ == '__main__':
   unittest.main()