use buf_target in expand_index

* Revert "Revert "merge to fold_divmod_general [p] (#13359)""

This reverts commit 05ccc69248.

* Revert "Revert "actually merge to fold_divmod_general [pr] (#13363)""

This reverts commit 90e5752199.

* Revert "Revert "add cache to fold_divmod_general (#13365)""

This reverts commit 8e17bd6791.

* bring back fold_divmod_general with bugfix and test

.coveragerc

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

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

@@ -61,7 +61,7 @@ runs:
       uses: actions/cache@v4
       with:
         path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/setup.py') }}-${{ env.PYTHON_CACHE_VERSION }}
+        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/pyproject.toml') }}-${{ env.CACHE_VERSION }}
 
     # **** Caching downloads ****
 
@@ -70,13 +70,13 @@ runs:
       uses: actions/cache@v4
       with:
         path: ~/.cache/tinygrad/downloads/
-        key: downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
+        key: downloads-cache-${{ inputs.key }}-${{ env.CACHE_VERSION }}
     - name: Cache downloads (macOS)
       if: inputs.key != '' && runner.os == 'macOS'
       uses: actions/cache@v4
       with:
         path: ~/Library/Caches/tinygrad/downloads/
-        key: osx-downloads-cache-${{ inputs.key }}-${{ env.DOWNLOAD_CACHE_VERSION }}
+        key: osx-downloads-cache-${{ inputs.key }}-${{ env.CACHE_VERSION }}
 
     # **** Python deps ****
 
@@ -187,7 +187,7 @@ runs:
       uses: actions/cache@v4
       with:
         path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.APT_CACHE_VERSION }}
+        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
 
     - name: Run apt Update + Install
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
@@ -247,7 +247,7 @@ runs:
         cache-name: cache-gpuocelot-build-1
       with:
         path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.BUILD_CACHE_VERSION }}
+        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.CACHE_VERSION }}
     - name: Clone/compile gpuocelot
       if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
       shell: bash

.github/workflows/autogen.yml

@@ -1,10 +1,7 @@
 name: Autogen
 env:
   # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
-  APT_CACHE_VERSION: '1'
-  BUILD_CACHE_VERSION: '1'
+  CACHE_VERSION: '13'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
   PYTHONPATH: ${{ github.workspace }}
@@ -14,15 +11,15 @@ on:
     branches:
       - master
   pull_request:
-    paths: 
+    paths:
     - 'tinygrad/runtime/autogen/**/*'
   workflow_dispatch:
-    paths: 
+    paths:
     - 'tinygrad/runtime/autogen/**/*'
 
 jobs:
   autogen:
-    name: Autogen
+    name: In-tree Autogen
     runs-on: ubuntu-24.04
     timeout-minutes: 15
     steps:
@@ -34,64 +31,146 @@ jobs:
         opencl: 'true'
         amd: 'true'
         cuda: 'true'
-        webgpu: 'true'
         llvm: 'true'
+        webgpu: 'true'
+        mesa: '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 libclang-20-dev llvm-20-dev hip-dev libusb-1.0-0-dev
     - name: Verify OpenCL autogen
       run: |
-        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
-        ./autogen_stubs.sh opencl
+        mv tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
+        python3 -c "from tinygrad.runtime.autogen import opencl"
         diff /tmp/opencl.py.bak tinygrad/runtime/autogen/opencl.py
     - name: Verify CUDA autogen
       run: |
-        cp tinygrad/runtime/autogen/cuda.py /tmp/cuda.py.bak
-        cp tinygrad/runtime/autogen/nv_gpu.py /tmp/nv_gpu.py.bak
-        ./autogen_stubs.sh cuda
-        ./autogen_stubs.sh nv
+        mv tinygrad/runtime/autogen/cuda.py /tmp/cuda.py.bak
+        mv tinygrad/runtime/autogen/nvrtc.py /tmp/nvrtc.py.bak
+        mv tinygrad/runtime/autogen/nvjitlink.py /tmp/nvjitlink.py.bak
+        mv tinygrad/runtime/autogen/nv_570.py /tmp/nv_570.py.bak
+        mv tinygrad/runtime/autogen/nv.py /tmp/nv.py.bak
+        python3 -c "from tinygrad.runtime.autogen import cuda, nvrtc, nvjitlink, nv_570, nv"
         diff /tmp/cuda.py.bak tinygrad/runtime/autogen/cuda.py
-        diff /tmp/nv_gpu.py.bak tinygrad/runtime/autogen/nv_gpu.py
+        diff /tmp/nvrtc.py.bak tinygrad/runtime/autogen/nvrtc.py
+        diff /tmp/nvjitlink.py.bak tinygrad/runtime/autogen/nvjitlink.py
+        diff /tmp/nv_570.py.bak tinygrad/runtime/autogen/nv_570.py
+        diff /tmp/nv.py.bak tinygrad/runtime/autogen/nv.py
     - name: Verify AMD autogen
       run: |
-        cp tinygrad/runtime/autogen/hsa.py /tmp/hsa.py.bak
-        cp tinygrad/runtime/autogen/kfd.py /tmp/kfd.py.bak
-        cp tinygrad/runtime/autogen/comgr.py /tmp/comgr.py.bak
-        cp tinygrad/runtime/autogen/amd_gpu.py /tmp/amd_gpu.py.bak
-        cp tinygrad/runtime/autogen/sqtt.py /tmp/sqtt.py.bak
-        ./autogen_stubs.sh hsa
-        ./autogen_stubs.sh kfd
-        ./autogen_stubs.sh comgr
-        ./autogen_stubs.sh amd
-        ./autogen_stubs.sh sqtt
-        diff /tmp/hsa.py.bak tinygrad/runtime/autogen/hsa.py
-        diff /tmp/kfd.py.bak tinygrad/runtime/autogen/kfd.py
+        mv tinygrad/runtime/autogen/comgr.py /tmp/comgr.py.bak
+        mv tinygrad/runtime/autogen/hsa.py /tmp/hsa.py.bak
+        mv tinygrad/runtime/autogen/hip.py /tmp/hip.py.bak
+        mv tinygrad/runtime/autogen/amd_gpu.py /tmp/amd_gpu.py.bak
+        mv tinygrad/runtime/autogen/sqtt.py /tmp/sqtt.py.bak
+        mv tinygrad/runtime/autogen/rocprof.py /tmp/rocprof.py.bak
+        mv tinygrad/runtime/autogen/am/am.py /tmp/am_am.py.bak
+        mv tinygrad/runtime/autogen/am/pm4_soc15.py /tmp/am_pm4_soc15.py.bak
+        mv tinygrad/runtime/autogen/am/pm4_nv.py /tmp/am_pm4_nv.py.bak
+        mv tinygrad/runtime/autogen/am/sdma_4_0_0.py /tmp/am_sdma_4_0_0.py.bak
+        mv tinygrad/runtime/autogen/am/sdma_5_0_0.py /tmp/am_sdma_5_0_0.py.bak
+        mv tinygrad/runtime/autogen/am/sdma_6_0_0.py /tmp/am_sdma_6_0_0.py.bak
+        mv tinygrad/runtime/autogen/am/smu_v13_0_0.py /tmp/am_smu_v13_0_0.py.bak
+        mv tinygrad/runtime/autogen/am/smu_v14_0_2.py /tmp/am_smu_v14_0_2.py.bak
+        python3 -c "from tinygrad.runtime.autogen import comgr, hsa, hip, amd_gpu, sqtt, rocprof; from tinygrad.runtime.autogen.am import am, pm4_soc15, pm4_nv, sdma_4_0_0, sdma_5_0_0, sdma_6_0_0, smu_v13_0_0, smu_v14_0_2"
         diff /tmp/comgr.py.bak tinygrad/runtime/autogen/comgr.py
+        diff /tmp/hsa.py.bak tinygrad/runtime/autogen/hsa.py
+        diff /tmp/hip.py.bak tinygrad/runtime/autogen/hip.py
         diff /tmp/amd_gpu.py.bak tinygrad/runtime/autogen/amd_gpu.py
         diff /tmp/sqtt.py.bak tinygrad/runtime/autogen/sqtt.py
+        diff /tmp/rocprof.py.bak tinygrad/runtime/autogen/rocprof.py
+        diff /tmp/am_am.py.bak tinygrad/runtime/autogen/am/am.py
+        diff /tmp/am_pm4_soc15.py.bak tinygrad/runtime/autogen/am/pm4_soc15.py
+        diff /tmp/am_pm4_nv.py.bak tinygrad/runtime/autogen/am/pm4_nv.py
+        diff /tmp/am_sdma_4_0_0.py.bak tinygrad/runtime/autogen/am/sdma_4_0_0.py
+        diff /tmp/am_sdma_5_0_0.py.bak tinygrad/runtime/autogen/am/sdma_5_0_0.py
+        diff /tmp/am_sdma_6_0_0.py.bak tinygrad/runtime/autogen/am/sdma_6_0_0.py
+        diff /tmp/am_smu_v13_0_0.py.bak tinygrad/runtime/autogen/am/smu_v13_0_0.py
+        diff /tmp/am_smu_v14_0_2.py.bak tinygrad/runtime/autogen/am/smu_v14_0_2.py
     - name: Verify Linux autogen
       run: |
-        cp tinygrad/runtime/autogen/libc.py /tmp/libc.py.bak
-        cp tinygrad/runtime/autogen/io_uring.py /tmp/io_uring.py.bak
-        cp tinygrad/runtime/autogen/ib.py /tmp/ib.py.bak
-        ./autogen_stubs.sh libc
-        ./autogen_stubs.sh io_uring
-        ./autogen_stubs.sh ib
+        mv tinygrad/runtime/autogen/libc.py /tmp/libc.py.bak
+        mv tinygrad/runtime/autogen/kfd.py /tmp/kfd.py.bak
+        mv tinygrad/runtime/autogen/io_uring.py /tmp/io_uring.py.bak
+        mv tinygrad/runtime/autogen/ib.py /tmp/ib.py.bak
+        mv tinygrad/runtime/autogen/pci.py /tmp/pci.py.bak
+        mv tinygrad/runtime/autogen/vfio.py /tmp/vfio.py.bak
+        python3 -c "from tinygrad.runtime.autogen import libc, kfd, io_uring, ib, pci, vfio"
         diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.py
+        diff /tmp/kfd.py.bak tinygrad/runtime/autogen/kfd.py
         diff /tmp/io_uring.py.bak tinygrad/runtime/autogen/io_uring.py
         diff /tmp/ib.py.bak tinygrad/runtime/autogen/ib.py
-    - name: Verify WebGPU autogen
-      run: |
-        cp tinygrad/runtime/autogen/webgpu.py /tmp/webgpu.py.bak
-        ./autogen_stubs.sh webgpu
-        diff /tmp/webgpu.py.bak tinygrad/runtime/autogen/webgpu.py
+        diff /tmp/pci.py.bak tinygrad/runtime/autogen/pci.py
+        diff /tmp/vfio.py.bak tinygrad/runtime/autogen/vfio.py
     - name: Verify LLVM autogen
       run: |
-        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
-        ./autogen_stubs.sh llvm
+        mv tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
+        python3 -c "from tinygrad.runtime.autogen import llvm"
         diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
+    - name: Verify WebGPU autogen
+      run: |
+        mv tinygrad/runtime/autogen/webgpu.py /tmp/webgpu.py.bak
+        python3 -c "from tinygrad.runtime.autogen import webgpu"
+        diff /tmp/webgpu.py.bak tinygrad/runtime/autogen/webgpu.py
+    - name: Verify Qualcomm autogen
+      run: |
+        mv tinygrad/runtime/autogen/kgsl.py /tmp/kgsl.py.bak
+        mv tinygrad/runtime/autogen/adreno.py /tmp/adreno.py.bak
+        mv tinygrad/runtime/autogen/qcom_dsp.py /tmp/qcom_dsp.py.bak
+        python3 -c "from tinygrad.runtime.autogen import kgsl, adreno, qcom_dsp"
+        diff /tmp/kgsl.py.bak tinygrad/runtime/autogen/kgsl.py
+        diff /tmp/adreno.py.bak tinygrad/runtime/autogen/adreno.py
+        diff /tmp/qcom_dsp.py.bak tinygrad/runtime/autogen/qcom_dsp.py
+    - name: Verify libusb autogen
+      run: |
+        mv tinygrad/runtime/autogen/libusb.py /tmp/libusb.py.bak
+        python3 -c "from tinygrad.runtime.autogen import libusb"
+        diff /tmp/libusb.py.bak tinygrad/runtime/autogen/libusb.py
     - name: Verify mesa autogen
       run: |
-        cp tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
-        ./autogen_stubs.sh mesa
+        mv tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
+        python3 -c "from tinygrad.runtime.autogen import mesa"
         diff /tmp/mesa.py.bak tinygrad/runtime/autogen/mesa.py
+    - name: Verify libclang autogen
+      run: |
+        cp tinygrad/runtime/autogen/libclang.py /tmp/libclang.py.bak
+        REGEN=1 python3 -c "from tinygrad.runtime.autogen import libclang"
+        diff /tmp/libclang.py.bak tinygrad/runtime/autogen/libclang.py
+  autogen-mac:
+    name: In-tree Autogen (macos)
+    runs-on: macos-14
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        llvm: 'true'
+    - name: Verify macos autogen
+      run: |
+        mv tinygrad/runtime/autogen/metal.py /tmp/metal.py.bak
+        LIBCLANG_PATH=/opt/homebrew/opt/llvm@20/lib/libclang.dylib python3 -c "from tinygrad.runtime.autogen import metal"
+        diff /tmp/metal.py.bak tinygrad/runtime/autogen/metal.py
+  autogen-comgr-3:
+    name: In-tree Autogen (comgr 3)
+    runs-on: ubuntu-24.04
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+    - name: Install autogen support packages
+      run: |
+        wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
+        sudo tee /etc/apt/sources.list.d/rocm.list <<EOF
+        deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/6.4 $(lsb_release -cs) main
+        EOF
+        echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
+        sudo apt -qq update || true
+        sudo apt-get install -y --no-install-recommends libclang-20-dev comgr
+    - name: Verify comgr (3) autogen
+      run: |
+        mv tinygrad/runtime/autogen/comgr_3.py /tmp/comgr_3.py.bak
+        python3 -c "from tinygrad.runtime.autogen import comgr_3"
+        diff /tmp/comgr_3.py.bak tinygrad/runtime/autogen/comgr_3.py

.github/workflows/benchmark.yml

@@ -54,7 +54,7 @@ jobs:
     - 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
     - name: Run Stable Diffusion v2
@@ -199,7 +199,7 @@ jobs:
     - name: Test speed vs torch
       run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
-      run: NV=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+      run: NV=1 IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test benchmark allreduce
       run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
     - name: Test tensor cores
@@ -320,19 +320,20 @@ jobs:
     #   run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
     - name: Train MNIST
       run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
+    # TODO: too slow
     - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=240 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
-    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=1300 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+    # - name: Run 10 CIFAR training steps w HALF
+    #   run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=240 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+    # - name: Run 10 CIFAR training steps w BF16
+    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
     # TODO: too slow
     # - name: Run 10 CIFAR training steps w winograd
     #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
-    - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+    # - name: Run full CIFAR training w 1 GPU
+    #   run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+    # - name: Run full CIFAR training steps w 6 GPUS
+    #   run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
     - name: Run MLPerf resnet eval on training data
       run: time BENCHMARK_LOG=resnet_eval NV=1 MODEL=resnet python3 examples/mlperf/model_eval.py
     #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
@@ -409,7 +410,7 @@ jobs:
     #    python3 -c "import torch; print(torch.__version__)"
     #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
-      run: AMD=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+      run: AMD=1 IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test tensor cores
       run: |
         AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
@@ -524,17 +525,18 @@ jobs:
       run: test/external/process_replay/reset.py
     - name: Train MNIST
       run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
+    # TODO: too slow
     - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=350 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=2000 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+    # - name: Run 10 CIFAR training steps w HALF
+    #   run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=390 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
     # - name: Run 10 CIFAR training steps w BF16
     #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
     # TODO: too slow
     # - name: Run 10 CIFAR training steps w winograd
     #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+    # - name: Run full CIFAR training w 1 GPU
+    #   run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
     #- name: Run full CIFAR training steps w 6 GPUS
     #  run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
     #- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
@@ -623,32 +625,32 @@ jobs:
         rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
     - name: reset process replay
       run: test/external/process_replay/reset.py
-    - name: openpilot compile3 0.9.9 driving_vision
-      run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
-    - name: openpilot compile3 0.9.9 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
-    - name: openpilot compile3 0.9.9 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
+    # - name: openpilot compile3 0.9.9 driving_vision
+    #   run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+    # - name: openpilot compile3 0.9.9 driving_policy
+    #   run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+    # - name: openpilot compile3 0.9.9 dmonitoring
+    #   run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
     - name: openpilot compile3 0.10.0 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
     - name: openpilot compile3 0.10.0 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=11 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
+    - name: DEBUG=2 openpilot compile3 0.10.1 driving_vision
+      run: PYTHONPATH="." DEBUG=2 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
     - name: openpilot compile3 0.10.1 driving_vision
-      # TODO: ASSERT_MIN_STEP_TIME=17
-      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=25 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=17 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
     - name: openpilot compile3 0.10.1 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
     - name: openpilot compile3 0.10.1 dmonitoring
-      # TODO: ASSERT_MIN_STEP_TIME=10
-      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
-    - name: benchmark MobileNetV2 on DSP
-      run: |
-        # generate quantized weights
-        ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
-        ln -s /data/home/tiny/tinygrad/testsig-*.so .
-        PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
-        # benchmark on DSP with NOOPT=1, the devectorizer has issues
-        PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=10 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
+    # - name: benchmark MobileNetV2 on DSP
+    #   run: |
+    #     # generate quantized weights
+    #     ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
+    #     ln -s /data/home/tiny/tinygrad/testsig-*.so .
+    #     PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
+    #     # benchmark on DSP with NOOPT=1, the devectorizer has issues
+    #     PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
     - name: Run process replay tests
       run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
 
@@ -704,8 +706,9 @@ jobs:
       run: |
         AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
         AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
+    # TODO: too slow
+    # - name: Run full CIFAR training w 1 GPU
+    #   run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
     # TODO: enable
     # - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
     #   run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
@@ -767,8 +770,9 @@ jobs:
         NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
     - name: Test LLAMA-3
       run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
+    # TODO: too slow
+    # - name: Run full CIFAR training w 1 GPU
+    #   run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
     #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
     #  run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
     - name: Run 10 MLPerf Bert training steps (1 gpu)

.github/workflows/benchmark_search.yml

@@ -22,13 +22,13 @@ jobs:
     - name: Run SDXL with new search
       # TODO: GCVM_L2_PROTECTION_FAULT_STATUS with llvm19
       run: |
-        BENCHMARK_LOG=search_sdxl PYTHONPATH=. AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 python examples/sdxl.py --noshow --timing --seed 0
+        BENCHMARK_LOG=search_sdxl PYTHONPATH=. AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CCACHE=0 python examples/sdxl.py --noshow --timing --seed 0
     - name: Run SDXL with cached search
       run: |
         BENCHMARK_LOG=search_sdxl_cached PYTHONPATH=. AMD=1 JITBEAM=2 python examples/sdxl.py --noshow --timing --seed 0
     - name: Run winograd cifar with new search
       run: |
-        BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BS=1024 STEPS=500 python examples/hlb_cifar10.py
+        BENCHMARK_LOG=search_wino_cifar WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 IGNORE_BEAM_CACHE=1 CCACHE=0 BS=1024 STEPS=500 python examples/hlb_cifar10.py
     - name: Run winograd cifar with cached search
       run: |
         BENCHMARK_LOG=search_wino_cifar_cached WINO=1 DEFAULT_FLOAT=HALF JITBEAM=4 BS=1024 STEPS=500 python examples/hlb_cifar10.py

.github/workflows/python-publish.yml

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

.github/workflows/test.yml

@@ -1,10 +1,7 @@
 name: Unit Tests
 env:
   # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
-  APT_CACHE_VERSION: '1'
-  BUILD_CACHE_VERSION: '1'
+  CACHE_VERSION: '13'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
   PYTHONPATH: ${{ github.workspace }}
@@ -290,8 +287,8 @@ jobs:
         python extra/optimization/extract_dataset.py
         gzip -c /tmp/sops > extra/datasets/sops.gz
         #DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
-    - name: Repo line count < 18000 lines
-      run: MAX_LINE_COUNT=18000 python sz.py
+    - name: Repo line count < 19000 lines
+      run: MAX_LINE_COUNT=19000 python sz.py
 
   spec:
     strategy:
@@ -309,6 +306,7 @@ jobs:
       with:
         key: spec-unit
         deps: testing_unit
+        python-version: '3.14'
     - name: Test SPEC=2
       run: IGNORE_OOB=0 SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
 
@@ -344,10 +342,11 @@ jobs:
           key: gpu-image
           deps: testing_minimal
           opencl: 'true'
-      - name: Test CL IMAGE=2 ops + training
+      - name: Test CL IMAGE=2 ops
         run: |
           CL=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
-          CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
+        # TODO: training is broken
+        # CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -392,7 +391,7 @@ jobs:
           llvm: 'true'
       - name: Test openpilot model kernel count and gate usage
         run: |
-          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1452 ALLOWED_GATED_READ_IMAGE=122 FLOAT16=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1397 ALLOWED_GATED_READ_IMAGE=94 FLOAT16=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
       - name: Test openpilot CL compile fp16
         run: FLOAT16=1 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
       - name: Test openpilot CL compile fp32 (test correctness)

.gitignore

@@ -63,3 +63,5 @@ profile_stats
 *.log
 target
 .mypy_cache
+mutants
+.mutmut-cache

README.md

@@ -21,17 +21,38 @@ tinygrad: For something between [PyTorch](https://github.com/pytorch/pytorch) an
 
 ---
 
-Despite tinygrad's size, it is a fully featured deep learning framework.
+tinygrad is an end-to-end deep learning stack:
 
-Due to its extreme simplicity, it is the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
+- **Tensor library** with autograd
+- **IR and compiler** that fuse and lower kernels
+- **JIT + graph execution**
+- **nn / optim / datasets** for real training
 
-tinygrad is now beta software, we [raised some money](https://geohot.github.io/blog/jekyll/update/2023/05/24/the-tiny-corp-raised-5M.html) to make it good. Someday, we will tape out chips.
+It’s inspired by PyTorch (ergonomics), JAX (functional transforms and IR-based AD), and TVM (scheduling and codegen), but stays intentionally tiny and hackable.
 
-## Features
+---
 
-### LLaMA and Stable Diffusion
+## How tinygrad compares
 
-tinygrad can run [LLaMA](/docs/showcase.md#llama) and [Stable Diffusion](/docs/showcase.md#stable-diffusion)!
+**PyTorch**
+
+- ✅ Similar: eager `Tensor` API, autograd, `optim`, basic datasets and layers.
+- ✅ You can write familiar training loops.
+- 🔁 Unlike PyTorch, the entire compiler and IR are visible and hackable.
+
+**JAX**
+
+- ✅ IR-based autodiff over primitives (like JAXPR + XLA).
+- ✅ Function-level JIT (`TinyJit`) that captures and replays kernels.
+- 🔁 Fewer functional transforms (no full `vmap`/`pmap` yet), but far easier to read.
+
+**TVM**
+
+- ✅ Multiple lowering passes, scheduling, and BEAM search over kernels.
+- ✅ Device “graphs” for batched execution.
+- 🔁 tinygrad also ships the **front-end framework** (tensors, nn, optim), not just the compiler.
+
+---
 
 ### Laziness
 

autogen_stubs.sh

@@ -1,568 +0,0 @@
-#!/bin/bash -e
-
-# setup instructions for clang2py
-if [[ ! $(clang2py -V) ]]; then
-  pushd .
-  cd /tmp
-  sudo apt-get install -y --no-install-recommends clang
-  pip install --upgrade pip setuptools
-  pip install clang==14.0.6
-  git clone https://github.com/nimlgen/ctypeslib.git
-  cd ctypeslib
-  pip install .
-  clang2py -V
-  popd
-fi
-
-BASE=tinygrad/runtime/autogen/
-
-fixup() {
-  sed -i '1s/^/# mypy: ignore-errors\n/' $1
-  sed -i 's/ *$//' $1
-  grep FIXME_STUB $1 || true
-}
-
-patch_dlopen() {
-  path=$1; shift
-  name=$1; shift
-  cat <<EOF | sed -i "/import ctypes.*/r /dev/stdin" $path
-PATHS_TO_TRY = [
-$(for p in "$@"; do echo "  $p,"; done)
-]
-def _try_dlopen_$name():
-  library = ctypes.util.find_library("$name")
-  if library:
-    try: return ctypes.CDLL(library)
-    except OSError: pass
-  for candidate in PATHS_TO_TRY:
-    try: return ctypes.CDLL(candidate)
-    except OSError: pass
-  return None
-EOF
-}
-
-generate_opencl() {
-  clang2py /usr/include/CL/cl.h -o $BASE/opencl.py -l /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 -k cdefstum
-  fixup $BASE/opencl.py
-  # hot patches
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/opencl.py
-  sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libOpenCL.so.1')\ctypes.CDLL(ctypes.util.find_library('OpenCL'))\g" $BASE/opencl.py
-  python3 -c "import tinygrad.runtime.autogen.opencl"
-}
-
-generate_hip() {
-  clang2py /opt/rocm/include/hip/hip_ext.h /opt/rocm/include/hip/hiprtc.h \
-  /opt/rocm/include/hip/hip_runtime_api.h /opt/rocm/include/hip/driver_types.h \
-  --clang-args="-D__HIP_PLATFORM_AMD__ -I/opt/rocm/include -x c++" -o $BASE/hip.py -l /opt/rocm/lib/libamdhip64.so
-  echo "hipDeviceProp_t = hipDeviceProp_tR0600" >> $BASE/hip.py
-  echo "hipGetDeviceProperties = hipGetDevicePropertiesR0600" >> $BASE/hip.py
-  fixup $BASE/hip.py
-  # we can trust HIP is always at /opt/rocm/lib
-  #sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/hip.py
-  #sed -i "s\ctypes.CDLL('/opt/rocm/lib/libhiprtc.so')\ctypes.CDLL(ctypes.util.find_library('hiprtc'))\g" $BASE/hip.py
-  #sed -i "s\ctypes.CDLL('/opt/rocm/lib/libamdhip64.so')\ctypes.CDLL(ctypes.util.find_library('amdhip64'))\g" $BASE/hip.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/hip.py
-  sed -i "s\'/opt/rocm/\os.getenv('ROCM_PATH', '/opt/rocm/')+'/\g" $BASE/hip.py
-  python3 -c "import tinygrad.runtime.autogen.hip"
-}
-
-generate_comgr() {
-  clang2py /opt/rocm/include/amd_comgr/amd_comgr.h \
-  --clang-args="-D__HIP_PLATFORM_AMD__ -I/opt/rocm/include -x c++" -o $BASE/comgr.py -l /opt/rocm/lib/libamd_comgr.so
-  fixup $BASE/comgr.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/comgr.py
-  patch_dlopen $BASE/comgr.py amd_comgr "'/opt/rocm/lib/libamd_comgr.so'" "os.getenv('ROCM_PATH', '')+'/lib/libamd_comgr.so'" "'/usr/local/lib/libamd_comgr.dylib'" "'/opt/homebrew/lib/libamd_comgr.dylib'"
-  sed -i "s\ctypes.CDLL('/opt/rocm/lib/libamd_comgr.so')\_try_dlopen_amd_comgr()\g" $BASE/comgr.py
-  python3 -c "import tinygrad.runtime.autogen.comgr"
-}
-
-generate_kfd() {
-  clang2py /usr/include/linux/kfd_ioctl.h -o $BASE/kfd.py -k cdefstum
-
-  fixup $BASE/kfd.py
-  sed -i "s/import ctypes/import ctypes, os/g" $BASE/kfd.py
-  sed -i "s/import fcntl, functools/import functools/g" $BASE/kfd.py
-  sed -i "/import functools/a from tinygrad.runtime.support.hcq import FileIOInterface" $BASE/kfd.py
-  sed -i "s/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd, \*\*kwargs):/def _do_ioctl(__idir, __base, __nr, __user_struct, __fd:FileIOInterface, \*\*kwargs):/g" $BASE/kfd.py
-  sed -i "s/fcntl.ioctl(__fd, (__idir<<30)/__fd.ioctl((__idir<<30)/g" $BASE/kfd.py
-  sed -i "s/!!/not not /g" $BASE/kfd.py
-  python3 -c "import tinygrad.runtime.autogen.kfd"
-}
-
-generate_cuda() {
-  clang2py /usr/include/cuda.h --clang-args="-D__CUDA_API_VERSION_INTERNAL" -o $BASE/cuda.py -l /usr/lib/x86_64-linux-gnu/libcuda.so
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/cuda.py
-  sed -i "s\ctypes.CDLL('/usr/lib/x86_64-linux-gnu/libcuda.so')\ctypes.CDLL(ctypes.util.find_library('cuda'))\g" $BASE/cuda.py
-  fixup $BASE/cuda.py
-  python3 -c "import tinygrad.runtime.autogen.cuda"
-}
-
-generate_nvrtc() {
-  clang2py /usr/local/cuda/include/nvrtc.h /usr/local/cuda/include/nvJitLink.h -o $BASE/nvrtc.py -l /usr/local/cuda/lib64/libnvrtc.so -l /usr/local/cuda/lib64/libnvJitLink.so
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" $BASE/nvrtc.py
-  sed -i "s\ctypes.CDLL('/usr/local/cuda/lib64/libnvrtc.so')\ctypes.CDLL(ctypes.util.find_library('nvrtc'))\g" $BASE/nvrtc.py
-  sed -i "s\ctypes.CDLL('/usr/local/cuda/lib64/libnvJitLink.so')\ctypes.CDLL(ctypes.util.find_library('nvJitLink'))\g" $BASE/nvrtc.py
-  fixup $BASE/nvrtc.py
-  python3 -c "import tinygrad.runtime.autogen.nvrtc"
-}
-
-generate_nv() {
-  NVKERN_COMMIT_HASH=81fe4fb417c8ac3b9bdcc1d56827d116743892a5
-  NVKERN_SRC=/tmp/open-gpu-kernel-modules-$NVKERN_COMMIT_HASH
-  if [ ! -d "$NVKERN_SRC" ]; then
-    git clone https://github.com/NVIDIA/open-gpu-kernel-modules $NVKERN_SRC
-    pushd .
-    cd $NVKERN_SRC
-    git reset --hard $NVKERN_COMMIT_HASH
-    popd
-  fi
-
-  clang2py -k cdefstum \
-    extra/nv_gpu_driver/clc6c0qmd.h \
-    extra/nv_gpu_driver/clcec0qmd.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0000.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl0080.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl2080_notification.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc56f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc86f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc96f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc761.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/cl83de.h \
-    $NVKERN_SRC/src/nvidia/generated/g_allclasses.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clc6c0.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/class/clcdc0.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/clc6b5.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/clc9b5.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/uvm_ioctl.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/uvm_linux_ioctl.h \
-    $NVKERN_SRC/kernel-open/nvidia-uvm/hwref/ampere/ga100/dev_fault.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv_escape.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl-numbers.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-ioctl-numa.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include/nv-unix-nvos-params-wrappers.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/alloc/alloc_channel.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/nvos.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl0000/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl0080/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl2080/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl83de/*.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlc36f.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrlcb33.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrla06c.h \
-    $NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl/ctrl90f1.h \
-    --clang-args="-include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
-    -o $BASE/nv_gpu.py
-  fixup $BASE/nv_gpu.py
-  sed -i "s\(0000000001)\1\g" $BASE/nv_gpu.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/nv_gpu.py
-  sed -i 's/#\?\s\([A-Za-z0-9_]\+\) = MW ( \([0-9]\+\) : \([0-9]\+\) )/\1 = (\2 , \3)/' $BASE/nv_gpu.py # NVC6C0_QMDV03_00 processing
-  sed -i 's/#\sdef NVC6C0_QMD\([A-Za-z0-9_()]\+\):/def NVC6C0_QMD\1:/' $BASE/nv_gpu.py
-  sed -i 's/#\sdef NVCEC0_QMD\([A-Za-z0-9_()]\+\):/def NVCEC0_QMD\1:/' $BASE/nv_gpu.py
-  sed -E -i -n '/^def (NVCEC0_QMDV05_00_RELEASE)(_ENABLE)\(i\):/{p;s//\1'"0"'\2=\1\2(0)\n\1'"1"'\2=\1\2(1)/;H;b};p;${x;s/^\n//;p}' "$BASE/nv_gpu.py"
-  sed -i 's/#\s*return MW(\([0-9i()*+]\+\):\([0-9i()*+]\+\))/    return (\1 , \2)/' $BASE/nv_gpu.py
-  sed -i 's/#\?\s*\(.*\)\s*=\s*\(NV\)\?BIT\(32\)\?\s*(\s*\([0-9]\+\)\s*)/\1 = (1 << \4)/' $BASE/nv_gpu.py # name = BIT(x) -> name = (1 << x)
-  sed -i "s/UVM_\([A-Za-z0-9_]\+\) = \['i', '(', '\([0-9]\+\)', ')'\]/UVM_\1 = \2/" $BASE/nv_gpu.py # UVM_name = ['i', '(', '<num>', ')'] -> UVM_name = <num>
-
-  # Parse status codes
-  sed -n '1i\
-nv_status_codes = {}
-/^NV_STATUS_CODE/ { s/^NV_STATUS_CODE(\([^,]*\), *\([^,]*\), *"\([^"]*\)") *.*$/\1 = \2\nnv_status_codes[\1] = "\3"/; p }' $NVKERN_SRC/src/common/sdk/nvidia/inc/nvstatuscodes.h >> $BASE/nv_gpu.py
-  python3 -c "import tinygrad.runtime.autogen.nv_gpu"
-
-  clang2py -k cdefstum \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/fsp/kern_fsp_cot_payload.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gspifpub.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_wpr_meta.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/gsp/gsp_fw_sr_meta.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/gpu/gsp/gsp_init_args.h \
-    $NVKERN_SRC/src/common/uproc/os/common/include/libos_init_args.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/rmRiscvUcode.h \
-    $NVKERN_SRC/src/common/shared/msgq/inc/msgq/msgq_priv.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_headers.h \
-    $NVKERN_SRC/src/nvidia/inc/kernel/vgpu/rpc_global_enums.h \
-    $NVKERN_SRC/src/nvidia/generated/g_rpc-structures.h \
-    $NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc/fsp/fsp_nvdm_format.h \
-    extra/nv_gpu_driver/g_rpc-message-header.h \
-    extra/nv_gpu_driver/gsp_static_config.h \
-    extra/nv_gpu_driver/vbios.h \
-    --clang-args="-DRPC_MESSAGE_STRUCTURES -DRPC_STRUCTURES -include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/nvidia/generated -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/src/nvidia/inc -I$NVKERN_SRC/src/nvidia/interface/ -I$NVKERN_SRC/src/nvidia/inc/kernel -I$NVKERN_SRC/src/nvidia/inc/libraries -I$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
-    -o $BASE/nv/nv.py
-
-  fixup $BASE/nv/nv.py
-  python3 -c "import tinygrad.runtime.autogen.nv.nv"
-}
-
-generate_amd() {
-  # clang2py broken when pass -x c++ to prev headers
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    extra/hip_gpu_driver/nvd.h \
-    extra/hip_gpu_driver/gc_11_0_0_offset.h \
-    extra/hip_gpu_driver/sienna_cichlid_ip_offset.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/amd_gpu.py
-
-  fixup $BASE/amd_gpu.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/amd_gpu.py
-  python3 -c "import tinygrad.runtime.autogen.amd_gpu"
-}
-
-generate_hsa() {
-  clang2py \
-    /opt/rocm/include/hsa/hsa.h \
-    /opt/rocm/include/hsa/hsa_ext_amd.h \
-    /opt/rocm/include/hsa/amd_hsa_signal.h \
-    /opt/rocm/include/hsa/amd_hsa_queue.h \
-    /opt/rocm/include/hsa/amd_hsa_kernel_code.h \
-    /opt/rocm/include/hsa/hsa_ext_finalize.h /opt/rocm/include/hsa/hsa_ext_image.h \
-    /opt/rocm/include/hsa/hsa_ven_amd_aqlprofile.h \
-    --clang-args="-I/opt/rocm/include" \
-    -o $BASE/hsa.py -l /opt/rocm/lib/libhsa-runtime64.so
-
-  fixup $BASE/hsa.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/hsa.py
-  sed -i "s\ctypes.CDLL('/opt/rocm/lib/libhsa-runtime64.so')\ctypes.CDLL(os.getenv('ROCM_PATH')+'/lib/libhsa-runtime64.so' if os.getenv('ROCM_PATH') else ctypes.util.find_library('hsa-runtime64'))\g" $BASE/hsa.py
-  python3 -c "import tinygrad.runtime.autogen.hsa"
-}
-
-generate_io_uring() {
-  clang2py -k cdefstum \
-    /usr/include/liburing.h \
-    /usr/include/linux/io_uring.h \
-    -o $BASE/io_uring.py
-
-  sed -r '/^#define __NR_io_uring/ s/^#define __(NR_io_uring[^ ]+) (.*)$/\1 = \2/; t; d' /usr/include/asm-generic/unistd.h >> $BASE/io_uring.py # io_uring syscalls numbers
-  fixup $BASE/io_uring.py
-}
-
-generate_ib() {
-  clang2py -k cdefstum \
-    /usr/include/infiniband/verbs.h \
-    /usr/include/infiniband/verbs_api.h \
-    /usr/include/infiniband/ib_user_ioctl_verbs.h \
-    /usr/include/rdma/ib_user_verbs.h \
-    -o $BASE/ib.py
-
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" "$BASE/ib.py"
-  sed -i "s\FIXME_STUB\libibverbs\g" "$BASE/ib.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(ctypes.util.find_library('ibverbs'), use_errno=True)\g" "$BASE/ib.py"
-
-  fixup $BASE/ib.py
-}
-
-generate_libc() {
-  clang2py -k cdefstum \
-    $(dpkg -L libc6-dev | grep sys/mman.h) \
-    $(dpkg -L libc6-dev | grep sys/syscall.h) \
-    /usr/include/string.h \
-    /usr/include/elf.h \
-    /usr/include/unistd.h \
-    /usr/include/asm-generic/mman-common.h \
-    -o $BASE/libc.py
-
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libc.py
-  sed -i "s\FIXME_STUB\libc\g" $BASE/libc.py
-  sed -i "s\FunctionFactoryStub()\None if (libc_path := ctypes.util.find_library('c')) is None else ctypes.CDLL(libc_path, use_errno=True)\g" $BASE/libc.py
-
-  fixup $BASE/libc.py
-}
-
-generate_llvm() {
-  INC="$(llvm-config-14 --includedir)"
-  clang2py -k cdefstum \
-    $(find "$INC/llvm-c/" -type f -name '*.h' | sort) \
-    "$INC/llvm/Config/Targets.def" \
-    "$INC/llvm/Config/AsmPrinters.def" \
-    "$INC/llvm/Config/AsmParsers.def" \
-    "$INC/llvm/Config/Disassemblers.def" \
-    --clang-args="$(llvm-config-14 --cflags)" \
-    -o "$BASE/llvm.py"
-
-  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support\g" "$BASE/llvm.py"
-  sed -i "s\FIXME_STUB\llvm\g" "$BASE/llvm.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH)\g" "$BASE/llvm.py"
-
-  fixup "$BASE/llvm.py"
-}
-
-generate_kgsl() {
-  clang2py extra/qcom_gpu_driver/msm_kgsl.h -o $BASE/kgsl.py -k cdefstum
-  fixup $BASE/kgsl.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/kgsl.py
-  sed -nE 's/#define ([A-Za-z0-9_]+)_SHIFT\s*[^\S\r\n]*[0-9]*$/def \1(val): return (val << \1_SHIFT) \& \1_MASK/p' extra/qcom_gpu_driver/msm_kgsl.h >> $BASE/kgsl.py
-  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\__fd.ioctl((__idir<<30)\g" $BASE/kgsl.py
-  python3 -c "import tinygrad.runtime.autogen.kgsl"
-}
-
-generate_adreno() {
-  clang2py extra/qcom_gpu_driver/a6xx.xml.h -o $BASE/adreno.py -k cestum
-  sed -nE 's/#define ([A-Za-z0-9_]+)__SHIFT\s*[^\S\r\n]*[0-9]*$/def \1(val): return (val << \1__SHIFT) \& \1__MASK/p' extra/qcom_gpu_driver/a6xx.xml.h >> $BASE/adreno.py
-  fixup $BASE/adreno.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/adreno.py
-  python3 -c "import tinygrad.runtime.autogen.adreno"
-}
-
-generate_qcom() {
-  clang2py -k cdefstum \
-    extra/dsp/include/ion.h \
-    extra/dsp/include/msm_ion.h \
-    extra/dsp/include/adsprpc_shared.h \
-    extra/dsp/include/remote_default.h \
-    extra/dsp/include/apps_std.h \
-    -o $BASE/qcom_dsp.py
-
-  fixup $BASE/qcom_dsp.py
-  python3 -c "import tinygrad.runtime.autogen.qcom_dsp"
-}
-
-generate_pci() {
-  clang2py -k cdefstum \
-    /usr/include/linux/pci_regs.h \
-    -o $BASE/pci.py
-  fixup $BASE/pci.py
-}
-
-generate_vfio() {
-  clang2py -k cdefstum \
-    /usr/include/linux/vfio.h \
-    -o $BASE/vfio.py
-  fixup $BASE/vfio.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/vfio.py
-  sed -i "s\import fcntl, functools\import functools" $BASE/vfio.py
-  sed -i "s\import ctypes,os\a from tinygrad.runtime.support import FileIOInterface\g" $BASE/vfio.py
-  sed -i "s\fcntl.ioctl(__fd, (__idir<<30)\return __fd.ioctl((__idir<<30)\g" $BASE/vfio.py
-}
-
-generate_am() {
-  AMKERN_COMMIT_HASH=ceb12c04e2b5b53ec0779362831f5ee40c4921e4
-  AMKERN_SRC=/tmp/ROCK-Kernel-Driver-$AMKERN_COMMIT_HASH
-  if [ ! -d "$AMKERN_SRC" ]; then
-    git clone https://github.com/ROCm/ROCK-Kernel-Driver $AMKERN_SRC --depth 1
-  fi
-  AMKERN_AMD=$AMKERN_SRC/drivers/gpu/drm/amd/
-  AMKERN_INC=$AMKERN_AMD/include/
-
-  clang2py -k cdefstum \
-    extra/amdpci/headers/v11_structs.h \
-    extra/amdpci/headers/v12_structs.h \
-    extra/amdpci/headers/amdgpu_vm.h \
-    extra/amdpci/headers/discovery.h \
-    extra/amdpci/headers/amdgpu_ucode.h \
-    extra/amdpci/headers/psp_gfx_if.h \
-    extra/amdpci/headers/amdgpu_psp.h \
-    extra/amdpci/headers/amdgpu_irq.h \
-    extra/amdpci/headers/amdgpu_doorbell.h \
-    $AMKERN_INC/soc15_ih_clientid.h \
-    --clang-args="-include stdint.h" \
-    -o $BASE/am/am.py
-  fixup $BASE/am/am.py
-  sed -i "s\(int64_t)\ \g" $BASE/am/am.py
-  sed -i "s\AMDGPU_PTE_MTYPE_VG10(2)\AMDGPU_PTE_MTYPE_VG10(0, 2)\g" $BASE/am/am.py # incorrect parsing (TODO: remove when clang2py is gone).
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/soc15d.h \
-    -o $BASE/am/pm4_soc15.py
-  fixup $BASE/am/pm4_soc15.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/amdkfd/kfd_pm4_headers_ai.h \
-    $AMKERN_AMD/amdgpu/nvd.h \
-    -o $BASE/am/pm4_nv.py
-  fixup $BASE/am/pm4_nv.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/vega10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_4_0_0.py
-  fixup $BASE/am/sdma_4_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/navi10_sdma_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_5_0_0.py
-  fixup $BASE/am/sdma_5_0_0.py
-
-  clang2py -k cdefstum \
-    extra/hip_gpu_driver/sdma_registers.h \
-    $AMKERN_AMD/amdgpu/sdma_v6_0_0_pkt_open.h \
-    --clang-args="-I/opt/rocm/include -x c++" \
-    -o $BASE/am/sdma_6_0_0.py
-  fixup $BASE/am/sdma_6_0_0.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v13_0_0_ppsmc.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu13_driver_if_v13_0_0.h \
-    extra/amdpci/headers/amdgpu_smu.h \
-    -o $BASE/am/smu_v13_0_0.py
-  fixup $BASE/am/smu_v13_0_0.py
-
-  clang2py -k cdefstum \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_0_pmfw.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu_v14_0_2_ppsmc.h \
-    $AMKERN_AMD/pm/swsmu/inc/pmfw_if/smu14_driver_if_v14_0.h \
-    extra/amdpci/headers/amdgpu_smu.h \
-    --clang-args="-include stdint.h" \
-    -o $BASE/am/smu_v14_0_2.py
-  fixup $BASE/am/smu_v14_0_2.py
-}
-
-generate_sqtt() {
-  clang2py -k cdefstum \
-    extra/sqtt/sqtt.h \
-    -o $BASE/sqtt.py
-  fixup $BASE/sqtt.py
-  sed -i "s\import ctypes\import ctypes, os\g" $BASE/sqtt.py
-  python3 -c "import tinygrad.runtime.autogen.sqtt"
-
-  ROCPROF_COMMIT_HASH=dd0485100971522cc4cd8ae136bdda431061a04d
-  ROCPROF_SRC=/tmp/rocprof-trace-decoder-$ROCPROF_COMMIT_HASH
-  if [ ! -d "$ROCPROF_SRC" ]; then
-    git clone https://github.com/ROCm/rocprof-trace-decoder $ROCPROF_SRC
-    pushd .
-    cd $ROCPROF_SRC
-    git reset --hard $ROCPROF_COMMIT_HASH
-    popd
-  fi
-
-  clang2py -k cdefstum \
-    $ROCPROF_SRC/include/rocprof_trace_decoder.h \
-    $ROCPROF_SRC/include/trace_decoder_instrument.h \
-    $ROCPROF_SRC/include/trace_decoder_types.h \
-    -o $BASE/rocprof.py
-  fixup $BASE/rocprof.py
-  sed -i '1s/^/# pylint: skip-file\n/' $BASE/rocprof.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util/g" $BASE/rocprof.py
-  patch_dlopen $BASE/rocprof.py rocprof-trace-decoder "'/usr/local/lib/librocprof-trace-decoder.so'" "'/usr/local/lib/librocprof-trace-decoder.dylib'"
-  sed -i "s/def _try_dlopen_rocprof-trace-decoder():/def _try_dlopen_rocprof_trace_decoder():/g" $BASE/rocprof.py
-  sed -i "s|FunctionFactoryStub()|_try_dlopen_rocprof_trace_decoder()|g" $BASE/rocprof.py
-}
-
-generate_webgpu() {
-  clang2py extra/webgpu/webgpu.h -o $BASE/webgpu.py
-  fixup $BASE/webgpu.py
-  sed -i "s/FIXME_STUB/webgpu/g" "$BASE/webgpu.py"
-  sed -i "s/FunctionFactoryStub()/ctypes.CDLL(webgpu_support.WEBGPU_PATH)/g" "$BASE/webgpu.py"
-  sed -i "s/import ctypes/import ctypes, tinygrad.runtime.support.webgpu as webgpu_support/g" "$BASE/webgpu.py"
-  python3 -c "import tinygrad.runtime.autogen.webgpu"
-}
-
-generate_libusb() {
-  clang2py -k cdefstum \
-    /usr/include/libusb-1.0/libusb.h \
-    -o $BASE/libusb.py
-
-  fixup $BASE/libusb.py
-  sed -i "s\import ctypes\import ctypes, ctypes.util, os\g" $BASE/libusb.py
-  sed -i "s/FIXME_STUB/libusb/g" "$BASE/libusb.py"
-  sed -i "s/libusb_le16_to_cpu = libusb_cpu_to_le16//g" "$BASE/libusb.py"
-  sed -i "s/FunctionFactoryStub()/None if (lib_path:=os.getenv('LIBUSB_PATH', ctypes.util.find_library('usb-1.0'))) is None else ctypes.CDLL(lib_path)/g" "$BASE/libusb.py"
-  python3 -c "import tinygrad.runtime.autogen.libusb"
-}
-
-generate_mesa() {
-  MESA_TAG="mesa-25.2.4"
-  MESA_SRC=/tmp/mesa-$MESA_TAG
-  TINYMESA_TAG=tinymesa-32dc66c
-  TINYMESA_DIR=/tmp/tinymesa-$MESA_TAG-$TINYMESA_TAG/
-  TINYMESA_SO=$TINYMESA_DIR/libtinymesa_cpu.so
-  if [ ! -d "$MESA_SRC" ]; then
-    git clone --depth 1 --branch $MESA_TAG https://gitlab.freedesktop.org/mesa/mesa.git $MESA_SRC
-    pushd .
-    cd $MESA_SRC
-    git reset --hard $MESA_COMMIT_HASH
-    # clang 14 doesn't support packed enums
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/headers/nv_device_info.h
-    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/compiler/nak.h
-    sed -i "s/nir_instr_type \(\w\+\);/uint8_t \1;/" $MESA_SRC/src/compiler/nir/nir.h
-    mkdir -p gen/util/format
-    python3 src/util/format/u_format_table.py src/util/format/u_format.yaml --enums > gen/util/format/u_format_gen.h
-    python3 src/compiler/nir/nir_opcodes_h.py > gen/nir_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/nir/nir_intrinsics_indices_h.py --outdir gen
-    python3 src/compiler/nir/nir_builder_opcodes_h.py > gen/nir_builder_opcodes.h
-    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
-    python3 src/compiler/builtin_types_h.py gen/builtin_types.h
-    popd
-  fi
-
-  if [ ! -d "$TINYMESA_DIR" ]; then
-    mkdir $TINYMESA_DIR
-    curl -L https://github.com/sirhcm/tinymesa/releases/download/$TINYMESA_TAG/libtinymesa_cpu-$MESA_TAG-linux-amd64.so -o $TINYMESA_SO
-  fi
-
-  clang2py -k cdefstu \
-    $MESA_SRC/src/compiler/nir/nir.h \
-    $MESA_SRC/src/compiler/nir/nir_builder.h \
-    $MESA_SRC/src/compiler/nir/nir_shader_compiler_options.h \
-    $MESA_SRC/src/compiler/nir/nir_serialize.h \
-    $MESA_SRC/gen/nir_intrinsics.h \
-    $MESA_SRC/src/nouveau/headers/nv_device_info.h \
-    $MESA_SRC/src/nouveau/compiler/nak.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_passmgr.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_misc.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_type.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_init.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_nir.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_struct.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_jit_types.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_flow.h \
-    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_const.h \
-    $MESA_SRC/src/compiler/glsl_types.h \
-    $MESA_SRC/src/util/blob.h \
-    $MESA_SRC/src/util/ralloc.h \
-    --clang-args="-DHAVE_ENDIAN_H -DHAVE_STRUCT_TIMESPEC -DHAVE_PTHREAD -I$MESA_SRC/src -I$MESA_SRC/include -I$MESA_SRC/gen -I$MESA_SRC/src/compiler/nir -I$MESA_SRC/src/gallium/auxiliary -I$MESA_SRC/src/gallium/include -I$(llvm-config-20 --includedir)" \
-    -l $TINYMESA_SO \
-    -o $BASE/mesa.py
-
-  LVP_NIR_OPTIONS=$(./extra/mesa/lvp_nir_options.sh $MESA_SRC)
-
-  fixup $BASE/mesa.py
-  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "'/opt/homebrew/lib/libtinymesa_cpu.dylib'" "'/opt/homebrew/lib/libtinymesa.dylib'"
-  echo "lvp_nir_options = gzip.decompress(base64.b64decode('$LVP_NIR_OPTIONS'))" >> $BASE/mesa.py
-  sed -i "/in_dll/s/.*/try: &\nexcept (AttributeError, ValueError): pass/" $BASE/mesa.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers/" $BASE/mesa.py
-  sed -i "s/ctypes.CDLL('.\+')/(dll := _try_dlopen_tinymesa_cpu())/" $BASE/mesa.py
-  echo "def __getattr__(nm): raise AttributeError('LLVMpipe requires tinymesa_cpu' if 'tinymesa_cpu' not in dll._name else f'attribute {nm} not found') if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
-  sed -i "s/ctypes.glsl_base_type/glsl_base_type/" $BASE/mesa.py
-  # bitfield bug in clang2py
-  sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py
-  sed -i "s/('\(\w\+\)', pipe_shader_type, 8)/('\1', ctypes.c_ubyte)/" $BASE/mesa.py
-  sed -i "s/\([0-9]\+\)()/\1/" $BASE/mesa.py
-  sed -i '/struct_nir_builder._pack_ = 1 # source:False/d' "$BASE/mesa.py"
-  python3 -c "import tinygrad.runtime.autogen.mesa"
-}
-
-if [ "$1" == "opencl" ]; then generate_opencl
-elif [ "$1" == "hip" ]; then generate_hip
-elif [ "$1" == "comgr" ]; then generate_comgr
-elif [ "$1" == "cuda" ]; then generate_cuda
-elif [ "$1" == "nvrtc" ]; then generate_nvrtc
-elif [ "$1" == "hsa" ]; then generate_hsa
-elif [ "$1" == "kfd" ]; then generate_kfd
-elif [ "$1" == "nv" ]; then generate_nv
-elif [ "$1" == "amd" ]; then generate_amd
-elif [ "$1" == "am" ]; then generate_am
-elif [ "$1" == "nvdrv" ]; then generate_nvdrv
-elif [ "$1" == "sqtt" ]; then generate_sqtt
-elif [ "$1" == "qcom" ]; then generate_qcom
-elif [ "$1" == "io_uring" ]; then generate_io_uring
-elif [ "$1" == "ib" ]; then generate_ib
-elif [ "$1" == "libc" ]; then generate_libc
-elif [ "$1" == "llvm" ]; then generate_llvm
-elif [ "$1" == "kgsl" ]; then generate_kgsl
-elif [ "$1" == "adreno" ]; then generate_adreno
-elif [ "$1" == "pci" ]; then generate_pci
-elif [ "$1" == "vfio" ]; then generate_vfio
-elif [ "$1" == "webgpu" ]; then generate_webgpu
-elif [ "$1" == "libusb" ]; then generate_libusb
-elif [ "$1" == "mesa" ]; then generate_mesa
-elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu; generate_mesa
-else echo "usage: $0 <type>"
-fi

examples/llama3.py

@@ -1,8 +1,6 @@
 from pathlib import Path
 from typing import List
 import json, argparse, random, time, os
-import tiktoken
-from tiktoken.load import load_tiktoken_bpe
 from extra.models.llama import Transformer, convert_from_huggingface, convert_from_gguf, fix_bf16
 from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters, gguf_load
 from tinygrad import Tensor, dtypes, nn, Context, Device, GlobalCounters
@@ -12,6 +10,8 @@ from extra.bench_log import BenchEvent, WallTimeEvent
 class Tokenizer:
   pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
   def __init__(self, model_path: str):
+    import tiktoken
+    from tiktoken.load import load_tiktoken_bpe
     mergeable_ranks = load_tiktoken_bpe(model_path)
     self.num_base_tokens = len(mergeable_ranks)
     special_tokens = [

examples/openpilot/compile3.py

@@ -1,11 +1,10 @@
 import os, sys, pickle, time, re
 import numpy as np
+if "JIT_BATCH_SIZE" not in os.environ: os.environ["JIT_BATCH_SIZE"] = "0"
 
 from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters, Device, dtypes
 from tinygrad.helpers import DEBUG, getenv
 from tinygrad.engine.realize import CompiledRunner
-
-import onnx
 from tinygrad.nn.onnx import OnnxRunner
 
 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"
@@ -40,7 +39,7 @@ def compile(onnx_file):
   np.testing.assert_equal(test_val, ret, "JIT run failed")
   print("jit run validated")
 
-  # checks from compile2
+  # check gated read_image usage
   kernel_count = 0
   read_image_count = 0
   gated_read_image_count = 0
@@ -96,6 +95,7 @@ def test_vs_compile(run, inputs, test_val=None):
   return val
 
 def test_vs_onnx(new_inputs, test_val, onnx_file, tol):
+  import onnx
   import onnxruntime as ort
   
   onnx_inputs = {k:v.numpy() for k,v in new_inputs.items()}

examples/whisper.py

@@ -3,7 +3,7 @@
 import sys, base64, multiprocessing, itertools, collections
 from typing import Optional, Union, Literal, List
 
-from tinygrad import Tensor, TinyJit, Variable, nn
+from tinygrad import Tensor, TinyJit, Variable, nn, dtypes
 from tinygrad.nn.state import torch_load, load_state_dict
 from tinygrad.helpers import getenv, fetch
 
@@ -244,15 +244,16 @@ def transcribe_waveform(model: Whisper, enc, waveforms, truncate=False):
 
   log_spec = prep_audio(waveforms, model.batch_size, truncate)
   nsample = model.decoder.max_tokens_to_sample
+  nctx = model.decoder.max_self_attn_cache_len
 
   def inferloop(ctx: Union[np.ndarray, List[np.ndarray]], encoded_audio):
     pos, next_tokens = 0, ctx
-    for i in range((nsample-len(start_tokens))*2):
-      next_tokens = model.decoder(Tensor(next_tokens), pos, encoded_audio)[:, -1].argmax(axis=-1).numpy().astype(np.int32).reshape(-1, 1)
+    for i in range(nsample):
+      next_tokens = model.decoder(Tensor(next_tokens, dtype=dtypes.int32), pos, encoded_audio)[:, -1].argmax(axis=-1).numpy().astype(np.int32).reshape(-1, 1)
       next_tokens[ctx[:, -1] == eot] = eot
       ctx = np.concatenate((ctx, next_tokens), axis=1)
       pos = ctx.shape[-1] - 1
-      if (next_tokens == eot).all(): break
+      if (next_tokens == eot).all() or pos == nctx: break
     return ctx
 
   def gettexttoks(line): return [tok for tok in line if tok < eot or tok > enc._special_tokens["<|notimestamps|>"]][-nsample+len(start_tokens):]

extra/bandwidth_test.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+from tinygrad import Tensor, Device, GlobalCounters, Context, dtypes
+from tinygrad.helpers import getenv, colored
+
+SZ = 8_000_000_000
+GPUS = getenv("GPUS", 4) # TODO: expose a way in tinygrad to access this
+
+if __name__ == "__main__":
+  # create tensors
+  tens = [Tensor.ones(SZ, dtype=dtypes.uint8, device=f"{Device.DEFAULT}:{i}").contiguous() for i in range(GPUS)]
+  Tensor.realize(*tens)
+
+  bw = [[0.0]*GPUS for _ in range(GPUS)]
+  for i in range(GPUS):
+    for j in range(GPUS):
+      GlobalCounters.reset()
+      with Context(DEBUG=2):
+        if i == j:
+          # this copy would be optimized out, just add 1
+          (tens[i]+1).realize()
+        else:
+          tens[i].to(f"{Device.DEFAULT}:{j}").realize()
+      t = max(GlobalCounters.time_sum_s, 1e-9)
+      bw[i][j] = SZ / t / 1e9  # GB/s
+
+  def fmt(x):
+    c = "green" if x > 50 else "yellow" if x > 20 else "red"
+    return colored(f"{x:6.1f}", c)
+
+  # header
+  print(" " * 8 + " ".join(f"{'d'+str(j):>6}" for j in range(GPUS)))
+  # rows
+  for i in range(GPUS):
+    print(f"{'s'+str(i):>6} -> " + " ".join(fmt(x) for x in bw[i]))

extra/gemm/amd_uop_matmul.py

@@ -4,9 +4,9 @@ from tinygrad.engine.realize import ExecItem, get_runner
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import getenv
 
-N = 4096
+N = getenv("N", 4096)
 M = K = N
-run_count = 5
+run_count = getenv("CNT", 5)
 
 # ---------------------------
 # launch/config constants
@@ -155,14 +155,15 @@ def test_matmul(sink:UOp, N=N):
       ets.append(ei.run(wait=True))
   print(f"REAL TFLOPS {N * N * N * 2 / min(ets) * 1e-12:.2f}")
 
-  GlobalCounters.reset()
-  with Context(DEBUG=2):
-    tc = (a @ b).realize()
-  with Context(DEBUG=0):
-    err = (hc - tc).square().mean().item()
-  print(f"mean squared error {err}")
-  if err > 1e-06:
-    raise RuntimeError("matmul is wrong!")
+  if getenv("VERIFY", 1):
+    GlobalCounters.reset()
+    with Context(DEBUG=2):
+      tc = (a @ b).realize()
+    with Context(DEBUG=0):
+      err = (hc - tc).square().mean().item()
+    print(f"mean squared error {err}")
+    if err > 1e-06:
+      raise RuntimeError("matmul is wrong!")
 
 if __name__ == "__main__":
   test_matmul(hand_spec_kernel3(), N=N)

extra/gemm/mi350x_uop_matmul_2.py

@@ -0,0 +1,141 @@
+import os
+import numpy as np
+np.set_printoptions(linewidth=1000000)
+os.environ["AMD_LLVM"] = "0"
+
+from tinygrad import Tensor, Context, dtypes, UOp, GlobalCounters
+from tinygrad.helpers import DEBUG, getenv
+from tinygrad.dtype import AddrSpace
+from tinygrad.uop.ops import sint, AxisType, KernelInfo, Ops
+
+WARP_SIZE = 64
+
+# Reg tile sizes (tensor cores)
+TC_M = 16
+TC_N = 16
+TC_K = 32
+
+N,M,K = 4096,4096,4096
+
+# Threadblock tile sizes (block-level tile of C that a block computes)
+BLOCK_M = 64
+BLOCK_N = 64
+BLOCK_K = 64
+
+WARPGROUP_SIZE = 1
+BLOCK_M = BLOCK_M * WARPGROUP_SIZE
+
+TID_SIZE = WARPGROUP_SIZE*WARP_SIZE
+
+def copy(dest:UOp, src:UOp, rng:int, set=False, upcast=()):
+  assert dest.shape == src.shape
+  rngs = [UOp.range(s, rng+i, AxisType.UPCAST if i in upcast else AxisType.LOOP) for i,s in enumerate(src.shape)]
+  copy = dest[*rngs].store(src[*rngs]).end(*rngs)
+  return dest.after(copy) if set else copy
+
+def compute_on_locals(acc:UOp, Asl:UOp, Bsl:UOp, rng:int, afters:tuple[UOp, ...], warpgroup, warp) -> UOp:
+  K_inner_loop = UOp.range(BLOCK_K//TC_K, rng, AxisType.REDUCE)
+
+  # load from locals into registers
+  Ar = UOp.placeholder((BLOCK_M//TC_M//WARPGROUP_SIZE,), dtypes.half.vec(8), slot=1, addrspace=AddrSpace.REG)
+  Br = UOp.placeholder((BLOCK_N//TC_N,), dtypes.half.vec(8), slot=2, addrspace=AddrSpace.REG)
+
+  M_load_loop = UOp.range(BLOCK_M//TC_M//WARPGROUP_SIZE, rng+10)
+  Asl = Asl.reshape(BLOCK_K//TC_K, TC_K, BLOCK_M//TC_M//WARPGROUP_SIZE, WARPGROUP_SIZE, TC_M)
+  load_rng = UOp.range(8, rng+11, axis_type=AxisType.UPCAST)
+  A_in = Asl[K_inner_loop, (warp//16)*8+load_rng, M_load_loop, warpgroup, warp%16].contract(load_rng)
+  Ar = Ar[M_load_loop].set(A_in, end=M_load_loop)
+
+  N_load_loop = UOp.range(BLOCK_N//TC_N, rng+20)
+  Bsl = Bsl.reshape(BLOCK_K//TC_K, TC_K, BLOCK_N//TC_N, TC_N)
+  load_rng = UOp.range(8, rng+21, axis_type=AxisType.UPCAST)
+  B_in = Bsl[K_inner_loop, (warp//16)*8+load_rng, N_load_loop, warp%16].contract(load_rng)
+  Br = Br[N_load_loop].set(B_in, end=N_load_loop)
+
+  M_inner_loop = UOp.range(BLOCK_M//TC_M//WARPGROUP_SIZE, rng+30)
+  N_inner_loop = UOp.range(BLOCK_N//TC_N, rng+31)
+
+  # load values
+  acc_after = acc.after(*afters, M_inner_loop, N_inner_loop, K_inner_loop)
+  acc_load = acc_after[N_inner_loop, M_inner_loop]
+
+  # do WMMA
+  wmma_arg = ('WMMA_16_16_32_half_float', (16, 16, 32), dtypes.half, dtypes.float, 'AMD', 64, ((), (), ((3, 2), (2, 2))), ())
+  out = UOp(Ops.WMMA, dtypes.float.vec(4), (Ar[M_inner_loop], Br[N_inner_loop], acc_load), arg=wmma_arg)
+
+  # store back the acc
+  acc_store = acc[N_inner_loop, M_inner_loop].store(out)
+  return acc_store.end(M_inner_loop, N_inner_loop, K_inner_loop)
+
+def custom_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
+  gx, gy = UOp.special(M//BLOCK_M, "gidx0"), UOp.special(N//BLOCK_N, "gidx1")
+  K_outer_loop = UOp.range(K//BLOCK_K, 0, AxisType.REDUCE)
+
+  # split out the globals into blocks
+  C = C.src[0].cast(dtypes.float.vec(4).ptr(C.ptrdtype.size)).reshape((M//BLOCK_M, BLOCK_M, N//BLOCK_N, BLOCK_N))
+  A = A.reshape((M//BLOCK_M, BLOCK_M, K//BLOCK_K, BLOCK_K))[gx, :, K_outer_loop, :]
+  B = B.reshape((K//BLOCK_K, BLOCK_K, N//BLOCK_N, BLOCK_N))[K_outer_loop, :, gy, :]
+
+  # ---------------------------
+  # GLOBAL -> LOCAL (As, Bs)
+  # ---------------------------
+  tid = UOp.special(TID_SIZE, "lidx0")
+  warpgroup, warp = tid//WARP_SIZE, tid%WARP_SIZE
+
+  A_view = A.reshape(-1, TID_SIZE, 8)
+  B_view = B.reshape(-1, TID_SIZE, 8)
+
+  # A: read BM x BK tiles (permute on store into locals)
+  As = UOp.placeholder((BLOCK_K, BLOCK_M), dtypes.half, slot=0, addrspace=AddrSpace.LOCAL).shrink_to(BLOCK_K, BLOCK_M)
+  As_view = As.reshape(-1, TID_SIZE, 8)
+
+  Bs = UOp.placeholder((BLOCK_K, BLOCK_N+4), dtypes.half, slot=1, addrspace=AddrSpace.LOCAL).shrink_to(BLOCK_K, BLOCK_N)
+  Bs_view = Bs.reshape(-1, TID_SIZE, 8)
+
+  outer_copy = UOp.range(A_view.shape[0], 100, AxisType.UPCAST)
+  inner_copy = UOp.range(A_view.shape[2], 101, AxisType.UPCAST)
+  As_store = As_view[outer_copy, tid, inner_copy].store(A_view[outer_copy, tid, inner_copy])
+  Bs_store = Bs_view[outer_copy, tid, inner_copy].store(B_view[outer_copy, tid, inner_copy])
+
+  if getenv("NOLOAD"):
+    As_store = As[0,0].store(0)
+    Bs_store = Bs[0,0].store(0)
+
+  # TODO: can we automate barrier?
+  barrier = UOp.barrier(UOp.group(As_store, Bs_store).end(outer_copy, inner_copy))
+
+  if getenv("COMPUTE"):
+    As, Bs = As.after(barrier), Bs.after(barrier)
+
+    acc = UOp.placeholder((BLOCK_N//TC_N, BLOCK_M//TC_M//WARPGROUP_SIZE), dtypes.float.vec(4), 0, AddrSpace.REG)
+
+    sink = compute_on_locals(acc, As, Bs, 200, afters=(barrier,), warpgroup=warpgroup, warp=warp)
+    sink = sink.end(K_outer_loop)
+
+    C_view = C[gx, :, gy, :].reshape(BLOCK_M//TC_M//WARPGROUP_SIZE, WARPGROUP_SIZE, TC_M, BLOCK_N//TC_N, TC_N)[:, warpgroup, warp%16, :, (warp//16)*4]
+    sink = copy(C_view, acc.after(sink), rng=300)
+  else:
+    sink = C.after(barrier.end(K_outer_loop))[0,0,0,0].store(As[0,0]+Bs[0,0])
+
+  return sink.sink(arg=KernelInfo(name="custom_gemm", opts_to_apply=())).simplify()
+
+if __name__ == "__main__":
+  a = Tensor.randn(M, K, dtype=dtypes.half)
+  b = Tensor.randn(K, N, dtype=dtypes.half)
+  c = Tensor.empty(M, N, dtype=dtypes.float)
+  with Context(DEBUG=0): Tensor.realize(a,b)
+
+
+  GlobalCounters.reset()
+  with Context(DEBUG=max(2, DEBUG.value), DEVECTORIZE=2):
+    tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm)[0]
+    tst.realize()
+  print(f"{(N*M*K*2 / GlobalCounters.time_sum_s)*1e-12:.2f} REAL TFLOPS")
+
+
+  with Context(DEBUG=0):
+    ref = a.dot(b, dtype=dtypes.float)
+    ref.realize()
+    #print(ref.numpy())
+    #print(tst.numpy())
+    assert Tensor.isclose(ref, tst, atol=1e-2).all().item(), "matrix not close"

extra/gemm/torch_gemm.py

@@ -12,7 +12,7 @@ MPS = getenv("MPS", 0)
 if getenv("FP16_ACC"): torch.backends.cuda.matmul.allow_fp16_accumulation = True
 
 for dtype in [torch.float32, torch.float16, torch.bfloat16]:
-  for N in [256, 512, 1024, 2048, 4096]:
+  for N in [256, 512, 1024, 2048, 4096] + ([6144, 8192] if getenv("BIG") else []):
     FLOPS = N*N*N*2
 
     b = torch.rand((N,N), dtype=dtype)

extra/gpuburn.py

@@ -0,0 +1,16 @@
+from tinygrad import Tensor, Device, TinyJit, dtypes
+from tinygrad.helpers import getenv
+
+GPUS = getenv("GPUS", 4) # TODO: expose a way in tinygrad to access this
+N = 6144
+
+@TinyJit
+def many_matmul(A, B):
+  out = A
+  for _ in range(8): out = out@B
+  return out
+
+if __name__ == "__main__":
+  A = Tensor.ones(GPUS, N, N, dtype=dtypes.half).shard(devices=tuple([f"{Device.DEFAULT}:{i}" for i in range(GPUS)]), axis=0).contiguous()
+  B = Tensor.ones(GPUS, N, N, dtype=dtypes.half).shard(devices=tuple([f"{Device.DEFAULT}:{i}" for i in range(GPUS)]), axis=0).contiguous()
+  while 1: many_matmul(A, B)

extra/hcqfuzz/tools.py

@@ -51,11 +51,15 @@ def create_report(dev, test, result, stdout, stderr):
   dmesg_output = subprocess.check_output(["sudo", "dmesg", "--ctime", "--color=never"], text=True)
   with open(dmesg_path, "w") as f: f.write(dmesg_output)
 
+  env_vars = " ".join(f"{k}={v}" for k, v in test.env.items())
+  reproduce_cmd = f"{env_vars} {test.cmd}"
+
   summary_path = os.path.join(report_path, "summary.txt")
   with open(summary_path, "w") as f:
     f.write(f"Test: {test.name()}\n")
     f.write(f"Dev params: {vars(dev)}\n")
     f.write(f"Test params: {vars(test)}\n")
+    f.write(f"Reproduce cmd: {reproduce_cmd}\n")
     f.write(f"Exit Code: {result}\n")
 
   print(f"Crash report saved to {report_path}")

extra/mesa/lvp_nir_options.sh

@@ -19,5 +19,6 @@ trap 'rm -f "$TMP"' EXIT
 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
+) | cc -x c -o $TMP - -I$1/src/compiler/nir -I$1/src -I$1/include || exit 1
 
+printf 'lvp_nir_options = gzip.decompress(base64.b64decode("%s"))' $("$TMP" | gzip | base64 -w0)

extra/mmapeak/mmapeak.py

@@ -1,8 +1,10 @@
-import pathlib
+import os, pathlib
+
+# TODO: there is a timing bug without this
+os.environ["AMD_AQL"] = "1"
+
 from tinygrad.device import Device
 from tinygrad.runtime.ops_amd import AMDProgram, HIPCompiler
-import time
-import os
 
 NUM_WORKGROUPS = 96
 WAVE_SIZE = 32
@@ -32,7 +34,7 @@ def launchBenchmark(instruction, vgprIndices, dense=True, accum=False, extra="")
   src = src.replace("DIRECTIVE", DIRECTIVE)
   lib = COMPILER.compile(src)
   fxn = AMDProgram(DEV, "matmul", lib)
-  elapsed = fxn(global_size=(NUM_WORKGROUPS,1,1), local_size=(WAVE_SIZE*NUM_WAVES,1,1), wait=True)
+  elapsed = min([fxn(global_size=(NUM_WORKGROUPS,1,1), local_size=(WAVE_SIZE*NUM_WAVES,1,1), wait=True) for _ in range(2)])
   FLOPs = FLOPS_PER_MATMUL * NUM_WAVES * NUM_WORKGROUPS * INTERNAL_LOOP * INSTRUCTIONS_PER_LOOP
   print(f"{instruction:<29} : {FLOPs/elapsed/10**12:.2f} T(FL)OPS")
 
@@ -44,9 +46,9 @@ if __name__=="__main__":
     raise RuntimeError("Error while initiating AMD device")
 
   COMPILER = HIPCompiler(DEV.arch)
-  if DEV.arch in {'gfx1100', 'gfx1103'}:
-    if DEV.arch == 'gfx1103':
-      NUM_WORKGROUPS = 8
+  if DEV.arch in {'gfx1100', 'gfx1103', 'gfx1151'}:
+    if DEV.arch == 'gfx1103': NUM_WORKGROUPS = 8
+    if DEV.arch == 'gfx1151': NUM_WORKGROUPS = 40
     launchBenchmark("v_wmma_bf16_16x16x16_bf16", (7,8,15))
     launchBenchmark("v_wmma_f16_16x16x16_f16", (7,8,15))
     launchBenchmark("v_wmma_f32_16x16x16_bf16", (7,8,15))

extra/mmapeak/template.s

@@ -3,14 +3,14 @@
     .p2align 8
     .type matmul,@function
 matmul:
-    s_mov_b32 s1, INTERNAL_LOOP
-    s_mov_b32 s2, 0
-    inner_loop:
-        INSTRUCTION
-        s_sub_u32 s1, s1, 1
-        s_cmp_lg_i32 s1, s2
-        s_cbranch_scc1 inner_loop
-    s_endpgm
+  s_mov_b32 s1, INTERNAL_LOOP
+  s_mov_b32 s2, 0
+  inner_loop:
+    INSTRUCTION
+    s_sub_u32 s1, s1, 1
+    s_cmp_lg_i32 s1, s2
+    s_cbranch_scc1 inner_loop
+  s_endpgm
 
 .rodata
 .p2align 6

extra/models/llama.py

@@ -89,6 +89,20 @@ class Attention:
     keys, values = repeat_kv(keys, self.n_rep), repeat_kv(values, self.n_rep)
     xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
     attn = xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2)
+    if getenv("STUB_ATTENTION"):
+      # TODO: do we need mask?
+      from tinygrad.uop.ops import UOp, KernelInfo
+      def fa_custom_forward(attn:UOp, q:UOp, k:UOp, v:UOp) -> UOp:
+        return UOp.sink(arg=KernelInfo(name="fa_custom_forward"))
+      def fa_custom_backward(out_q:UOp, out_k:UOp, out_v:UOp, grad:UOp, q:UOp, k:UOp, v:UOp) -> UOp:
+        return UOp.sink(arg=KernelInfo(name="fa_custom_backward"))
+      def fa_backward(grad:UOp, kernel:UOp) -> tuple[None, UOp, UOp, UOp]:
+        grad_q = Tensor.empty_like(q:=Tensor(kernel.src[1]))
+        grad_k = Tensor.empty_like(k:=Tensor(kernel.src[2]))
+        grad_v = Tensor.empty_like(v:=Tensor(kernel.src[3]))
+        ck = Tensor.custom_kernel(grad_q, grad_k, grad_v, Tensor(grad), q, k, v, fxn=fa_custom_backward)[:3]
+        return (None, ck[0].uop, ck[1].uop, ck[2].uop)
+      attn = Tensor.empty_like(attn).custom_kernel(xq, keys, values, fxn=fa_custom_forward, grad_fxn=fa_backward)[0]
     attn = attn.reshape(bsz, seqlen, -1)
     return self.wo(attn)
 

extra/nvJitLink.h

@@ -0,0 +1,53 @@
+/*
+ * NVIDIA_COPYRIGHT_BEGIN
+ *
+ * Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * NVIDIA CORPORATION and its licensors retain all intellectual property
+ * and proprietary rights in and to this software, related documentation
+ * and any modifications thereto.  Any use, reproduction, disclosure or
+ * distribution of this software and related documentation without an express
+ * license agreement from NVIDIA CORPORATION is strictly prohibited.
+ *
+ * NVIDIA_COPYRIGHT_END
+ */
+
+#include <stdint.h>
+#include <stdlib.h>
+
+typedef enum {
+  NVJITLINK_SUCCESS = 0,
+  NVJITLINK_ERROR_UNRECOGNIZED_OPTION,
+  NVJITLINK_ERROR_MISSING_ARCH,
+  NVJITLINK_ERROR_INVALID_INPUT,
+  NVJITLINK_ERROR_PTX_COMPILE,
+  NVJITLINK_ERROR_NVVM_COMPILE,
+  NVJITLINK_ERROR_INTERNAL
+} nvJitLinkResult;
+
+typedef enum {
+  NVJITLINK_INPUT_NONE = 0,
+  NVJITLINK_INPUT_CUBIN = 1,
+  NVJITLINK_INPUT_PTX,
+  NVJITLINK_INPUT_LTOIR,
+  NVJITLINK_INPUT_FATBIN,
+  NVJITLINK_INPUT_OBJECT,
+  NVJITLINK_INPUT_LIBRARY
+} nvJitLinkInputType;
+
+typedef struct nvJitLink* nvJitLinkHandle;
+
+nvJitLinkResult nvJitLinkCreate(nvJitLinkHandle *handle, uint32_t numOptions, const char **options);
+nvJitLinkResult nvJitLinkDestroy(nvJitLinkHandle *handle);
+nvJitLinkResult nvJitLinkAddData(nvJitLinkHandle handle, nvJitLinkInputType inputType, const void *data, size_t size, const char *name);
+nvJitLinkResult nvJitLinkAddFile(nvJitLinkHandle handle, nvJitLinkInputType inputType, const char *fileName);
+nvJitLinkResult nvJitLinkComplete(nvJitLinkHandle handle);
+nvJitLinkResult nvJitLinkGetLinkedCubinSize(nvJitLinkHandle handle, size_t *size);
+nvJitLinkResult nvJitLinkGetLinkedCubin(nvJitLinkHandle handle, void *cubin);
+nvJitLinkResult nvJitLinkGetLinkedPtxSize(nvJitLinkHandle handle, size_t *size);
+nvJitLinkResult nvJitLinkGetLinkedPtx(nvJitLinkHandle handle, char *ptx);
+nvJitLinkResult nvJitLinkGetErrorLogSize(nvJitLinkHandle handle, size_t *size);
+nvJitLinkResult nvJitLinkGetErrorLog(nvJitLinkHandle handle, char *log);
+nvJitLinkResult nvJitLinkGetInfoLogSize(nvJitLinkHandle handle, size_t *size);
+nvJitLinkResult nvJitLinkGetInfoLog(nvJitLinkHandle handle, char *log);
+nvJitLinkResult nvJitLinkVersion(unsigned int *major, unsigned int *minor);

extra/nv_gpu_driver/clcec0qmd.h

@@ -65,6 +65,8 @@
 #define NVCEC0_QMDV05_00_GRID_HEIGHT_RESUME                        MW(271:256)
 #define NVCEC0_QMDV05_00_GRID_DEPTH_RESUME                         MW(287:272)
 #define NVCEC0_QMDV05_00_RELEASE_ENABLE(i)                         MW((288+(i)*16):(288+(i)*16))
+#define NVCEC0_QMDV05_00_RELEASE0_ENABLE                           NVCEC0_QMDV05_00_RELEASE_ENABLE(0)
+#define NVCEC0_QMDV05_00_RELEASE1_ENABLE                           NVCEC0_QMDV05_00_RELEASE_ENABLE(1)
 #define NVCEC0_QMDV05_00_RELEASE_ENABLE_FALSE                      0x00000000
 #define NVCEC0_QMDV05_00_RELEASE_ENABLE_TRUE                       0x00000001
 #define NVCEC0_QMDV05_00_RELEASE_STRUCTURE_SIZE(i)                 MW((290+(i)*16):(289+(i)*16))

extra/nv_gpu_driver/nv_ioctl.py

@@ -58,20 +58,23 @@ def install_hook(c_function, python_function):
   return orig_func
 
 # *** ioctl lib end ***
-import tinygrad.runtime.autogen.nv_gpu as nv_gpu
+from tinygrad.runtime.autogen import nv_570 as nv_gpu
 nvescs = {getattr(nv_gpu, x):x for x in dir(nv_gpu) if x.startswith("NV_ESC")}
 nvcmds = {getattr(nv_gpu, x):(x, getattr(nv_gpu, "struct_"+x+"_PARAMS", getattr(nv_gpu, "struct_"+x.replace("_CMD_", "_")+"_PARAMS", None))) for x in dir(nv_gpu) if \
           x.startswith("NV") and x[6:].startswith("_CTRL_") and isinstance(getattr(nv_gpu, x), int)}
 
 def get_classes():
-  hdrpy = (pathlib.Path(__file__).parent.parent.parent / "tinygrad/runtime/autogen/nv_gpu.py").read_text()
-  clss = re.search(r'NV01_ROOT.*?NV_SEMAPHORE_SURFACE = \(0x000000da\) # macro', hdrpy, re.DOTALL).group()
-  pattern = r'([0-9a-zA-Z_]*) = +\((0x[0-9a-fA-F]+)\)'
-  matches = re.findall(pattern, clss, re.MULTILINE)
-  return {int(num, base=16):name for name, num in matches}
+  res = {}
+  known_classes = {"NV01_DEVICE_0", "NV01_ROOT", "NV1_MEMORY_SYSTEM", "NV01_MEMORY_VIRTUAL", "NV1_MEMORY_USER", "NV50_MEMORY_VIRTUAL", "NV_FERMI_VASPACE_A",
+                   "NV20_SUBDEVICE_0"}
+  for nm,val in nv_gpu.__dict__.items():
+    if not isinstance(val, int): continue
+    if 0x3000 < val < 0xffff: res[val] = nm
+    if nm in known_classes: res[val] = nm
+  return res
 nvclasses = get_classes()
 nvuvms = {getattr(nv_gpu, x):x for x in dir(nv_gpu) if x.startswith("UVM_") and nv_gpu.__dict__.get(x+"_PARAMS")}
-nvqcmds = {int(getattr(nv_gpu, x)):x for x in dir(nv_gpu) if x[:7] in {"NVC6C0_", "NVC56F_", "NVC6B5_"} and isinstance(getattr(nv_gpu, x), int)}
+nvqcmds = {int(getattr(nv_gpu, x)):x for x in dir(nv_gpu) if x[:7] in {"NVC9B0_", "NVC6C0_", "NVC56F_", "NVC6B5_"} and isinstance(getattr(nv_gpu, x), int)}
 
 global_ioctl_id = 0
 gpus_user_modes = []
@@ -272,4 +275,4 @@ def compare_launch_state(states, good_states):
 
   return True, "PASS"
 
-# IOCTL=1 CUDA=1 CUDA_PTX=1 python3 test/test_ops.py TestOps.test_tiny_add
+# IOCTL=1 CUDA=1 CUDA_PTX=1 python3 test/test_ops.py TestOps.test_tiny_add

extra/nv_gpu_driver/pci_exp_table.h

@@ -0,0 +1,134 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 1993-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the "Software"),
+ * to deal in the Software without restriction, including without limitation
+ * the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ * and/or sell copies of the Software, and to permit persons to whom the
+ * Software is furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+ * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+ * DEALINGS IN THE SOFTWARE.
+ */
+
+#ifndef PCIEXPTBL_H
+#define PCIEXPTBL_H
+
+#define NV_BCRT_HASH_INFO_BASE_CODE_TYPE_VBIOS_BASE   0x00
+#define NV_BCRT_HASH_INFO_BASE_CODE_TYPE_VBIOS_EXT    0xE0
+
+//
+// The VBIOS object comes from walking the PCI expansion code block
+// The following structure holds the expansion code format.
+//
+#define PCI_EXP_ROM_SIGNATURE     0xaa55
+#define PCI_EXP_ROM_SIGNATURE_NV  0x4e56 // "VN" in word format
+#define PCI_EXP_ROM_SIGNATURE_NV2 0xbb77
+#define IS_VALID_PCI_ROM_SIG(sig) ((sig == PCI_EXP_ROM_SIGNATURE) ||    \
+                                   (sig == PCI_EXP_ROM_SIGNATURE_NV) || \
+                                   (sig == PCI_EXP_ROM_SIGNATURE_NV2))
+
+#define OFFSETOF_PCI_EXP_ROM_SIG                    0x0
+#define OFFSETOF_PCI_EXP_ROM_NBSI_DATA_OFFSET       0x16
+#define OFFSETOF_PCI_EXP_ROM_PCI_DATA_STRUCT_PTR    0x18
+
+#pragma pack(1)
+typedef struct _PCI_EXP_ROM_STANDARD
+{
+    NvU16       sig;                //  00h: ROM Signature 0xaa55
+    NvU8        reserved [0x16];    //  02h: Reserved (processor architecture unique data)
+    NvU16       pciDataStrucPtr;    //  18h: Pointer to PCI Data Structure
+    NvU32       sizeOfBlock;        //  1Ah: <NBSI-specific appendage>
+} PCI_EXP_ROM_STANDARD, *PPCI_EXP_ROM_STANDARD;
+#pragma pack()
+
+#pragma pack(1)
+typedef struct _PCI_EXP_ROM_NBSI
+{
+    NvU16       sig;                //  00h: ROM Signature 0xaa55
+    NvU8        reserved [0x14];    //  02h: Reserved (processor architecture unique data)
+    NvU16       nbsiDataOffset;     //  16h: Offset from header to NBSI image
+    NvU16       pciDataStrucPtr;    //  18h: Pointer to PCI Data Structure
+    NvU32       sizeOfBlock;        //  1Ah: <NBSI-specific appendage>
+} PCI_EXP_ROM_NBSI, *PPCI_EXP_ROM_NBSI;
+#pragma pack()
+
+typedef union _PCI_EXP_ROM {
+    PCI_EXP_ROM_STANDARD standard;
+    PCI_EXP_ROM_NBSI nbsi;
+} PCI_EXP_ROM, *PPCI_EXP_ROM;
+
+#define PCI_DATA_STRUCT_SIGNATURE     0x52494350 // "PCIR" in dword format
+#define PCI_DATA_STRUCT_SIGNATURE_NV  0x5344504E // "NPDS" in dword format
+#define PCI_DATA_STRUCT_SIGNATURE_NV2 0x53494752 // "RGIS" in dword format
+#define IS_VALID_PCI_DATA_SIG(sig) ((sig == PCI_DATA_STRUCT_SIGNATURE) ||    \
+                                    (sig == PCI_DATA_STRUCT_SIGNATURE_NV) || \
+                                    (sig == PCI_DATA_STRUCT_SIGNATURE_NV2))
+
+#define PCI_LAST_IMAGE NVBIT(7)
+#define PCI_ROM_IMAGE_BLOCK_SIZE 512U
+
+#define OFFSETOF_PCI_DATA_STRUCT_SIG        0x0
+#define OFFSETOF_PCI_DATA_STRUCT_VENDOR_ID  0x4
+#define OFFSETOF_PCI_DATA_STRUCT_LEN        0xa
+#define OFFSETOF_PCI_DATA_STRUCT_CLASS_CODE 0xd
+#define OFFSETOF_PCI_DATA_STRUCT_CODE_TYPE  0x14
+#define OFFSETOF_PCI_DATA_STRUCT_IMAGE_LEN  0x10
+#define OFFSETOF_PCI_DATA_STRUCT_LAST_IMAGE 0x15
+
+#pragma pack(1)
+typedef struct _PCI_DATA_STRUCT
+{
+    NvU32       sig;                //  00h: Signature, the string "PCIR" or NVIDIA's alternate "NPDS"
+    NvU16       vendorID;           //  04h: Vendor Identification
+    NvU16       deviceID;           //  06h: Device Identification
+    NvU16       deviceListPtr;      //  08h: Device List Pointer
+    NvU16       pciDataStructLen;   //  0Ah: PCI Data Structure Length
+    NvU8        pciDataStructRev;   //  0Ch: PCI Data Structure Revision
+    NvU8        classCode[3];       //  0Dh: Class Code
+    NvU16       imageLen;           //  10h: Image Length (units of 512 bytes)
+    NvU16       vendorRomRev;       //  12h: Revision Level of the Vendor's ROM
+    NvU8        codeType;           //  14h: holds NBSI_OBJ_CODE_TYPE (0x70) and others
+    NvU8        lastImage;          //  15h: Last Image Indicator: bit7=1 is lastImage
+    NvU16       maxRunTimeImageLen; //  16h: Maximum Run-time Image Length (units of 512 bytes)
+} PCI_DATA_STRUCT, *PPCI_DATA_STRUCT;
+#pragma pack()
+
+#define NV_PCI_DATA_EXT_SIG 0x4544504E // "NPDE" in dword format
+#define NV_PCI_DATA_EXT_REV_10 0x100      // 1.0
+#define NV_PCI_DATA_EXT_REV_11 0x101      // 1.1
+
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_SIG            0x0
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_LEN            0x6
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_REV            0x4
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_SUBIMAGE_LEN   0x8
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_LAST_IMAGE     0xa
+#define OFFSETOF_PCI_DATA_EXT_STRUCT_FLAGS          0xb
+
+#define PCI_DATA_EXT_STRUCT_FLAGS_CHECKSUM_DISABLED 0x04
+
+#pragma pack(1)
+typedef struct _NV_PCI_DATA_EXT_STRUCT
+{
+    NvU32   signature;          //  00h: Signature, the string "NPDE"
+    NvU16   nvPciDataExtRev;    //  04h: NVIDIA PCI Data Extension Revision
+    NvU16   nvPciDataExtLen;    //  06h: NVIDIA PCI Data Extension Length
+    NvU16   subimageLen;        //  08h: Sub-image Length
+    NvU8    privLastImage;      //  0Ah: Private Last Image Indicator
+    NvU8    flags;              //  0Bh: Private images enabled if bit0=1
+} NV_PCI_DATA_EXT_STRUCT, *PNV_PCI_DATA_EXT_STRUCT;
+#pragma pack()
+
+#endif // PCIEXPTBL_H
+
+

extra/self_tokenize.py

@@ -2,7 +2,8 @@ import os, pathlib, argparse
 from examples.llama3 import Tokenizer
 from tabulate import tabulate
 from tinygrad import fetch
-from tinygrad.helpers import flatten
+from tinygrad.helpers import flatten, getenv
+from sz import NONCORE_DIRS
 
 # llama 3 tokenizer
 tokenizer = Tokenizer(fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model").as_posix())
@@ -10,19 +11,15 @@ tokenizer = Tokenizer(fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/
 def read_code(base_path):
   ret = []
   for path, _, files in os.walk(os.path.join(base_path, "tinygrad")):
+    if not getenv("CORE") and any(path.split("./")[1].startswith(x) for x in NONCORE_DIRS): continue
     for name in files:
       if not name.endswith(".py"): continue
       if 'tinygrad/runtime/autogen' in path.replace('\\', '/'): continue
       fullpath = os.path.join(path, name)
       code = pathlib.Path(fullpath).read_text()
-      ret.append(("### " + fullpath.split("tinygrad/", 1)[1], code))
+      ret.append((fullpath.split("tinygrad/", 1)[1], code))
   return ret
 
-def write_code_to_file(filename, code_list):
-  """Writes the combined code to a specified file."""
-  with open(filename, 'w') as f:
-    f.write('\n'.join(flatten(code_list)))
-
 if __name__ == "__main__":
   parser = argparse.ArgumentParser(description="Analyze and optionally save tinygrad code.")
   parser.add_argument("--output", help="Output file to write the combined code to.")
@@ -32,10 +29,11 @@ if __name__ == "__main__":
 
   table = []
   for name,code in ret:
-    table.append([name, len(tokenizer.encode(name+"\x00"+code))])
+    table.append([name, len(tokenizer.encode(code))])
   print(tabulate([["name", "llm tokens"]]+sorted(table, key=lambda x: -x[1]), headers="firstrow"))
 
-  code_str = '\x00'.join(flatten(ret))
+  banner = "#"*40
+  code_str = ''.join([f"{banner}\n# {name}\n{banner}\n\n{code}\n" for name,code in ret])
   print(f"code has {len(code_str)} chars")
   newline_count = code_str.count('\n')
   print(f"code has {newline_count} newlines")
@@ -44,5 +42,5 @@ if __name__ == "__main__":
   print(f"code has {len(encoded)} tokens")
 
   if args.output:
-    write_code_to_file(args.output, ret)
-    print(f"Combined code written to {args.output}")
+    with open(args.output, 'w') as f: f.write(code_str)
+    print(f"Combined code written to {args.output}")

extra/sqtt/active_sqtt_parse.py

@@ -0,0 +1,131 @@
+import os
+os.environ["PYTHONPATH"] = "."
+os.environ["SQTT"] = "1"
+if "DEV" not in os.environ: os.environ["DEV"] = "AMD"
+os.environ["PROFILE"] = "1"
+os.environ["AMD_LLVM"] = "0"
+
+from dataclasses import replace
+import atexit, contextlib
+from tinygrad import Tensor
+from tinygrad.helpers import system, OSX
+from tinygrad.runtime.ops_amd import AMDProgram
+from extra.sqtt.roc import decode, WaveExec, ProfileSQTTEvent
+from tinygrad.device import Device, ProfileDeviceEvent
+
+from extra.sqtt.attempt_sqtt_parse import parse_sqtt_print_packets
+
+# TODO: should really check for AM driver / USB
+if not OSX:
+  def set_power(x): system(f"sudo /opt/rocm/bin/amd-smi set -l {x}")
+  @atexit.register
+  def reset_power(): set_power("auto")
+  set_power("stable_std")
+
+dev = Device["AMD"]
+
+@contextlib.contextmanager
+def save_sqtt():
+  # clear the old traces
+  dev.profile_events.clear()
+  sqtt:dict[str, list[WaveExec]] = {}
+  yield sqtt
+  events = dev.profile_events+[ProfileDeviceEvent("AMD", props=dev.device_props())]
+
+  rctx = decode(events)
+  assert len(rctx.inst_execs) > 0, "empty sqtt output"
+  sqtt.update(rctx.inst_execs)
+
+  for e in events:
+    if isinstance(e, ProfileSQTTEvent):
+      print(replace(e, blob=b''))
+      if e.se == 0:
+        parse_sqtt_print_packets(e.blob)
+
+template = """.text
+.globl matmul
+.p2align 8
+.type matmul,@function
+matmul:
+  INSTRUCTION
+  s_endpgm
+
+.rodata
+.p2align 6
+.amdhsa_kernel matmul
+  .amdhsa_user_sgpr_kernarg_segment_ptr 1
+  .amdhsa_next_free_vgpr .amdgcn.next_free_vgpr
+  .amdhsa_next_free_sgpr .amdgcn.next_free_sgpr
+  .amdhsa_wavefront_size32 1
+.end_amdhsa_kernel
+
+.amdgpu_metadata
+---
+amdhsa.version:
+  - 1
+  - 0
+amdhsa.kernels:
+  - .name: matmul
+    .symbol: matmul.kd
+    .group_segment_fixed_size: 0
+    .private_segment_fixed_size: 0
+    .wavefront_size: 32
+    .sgpr_count: 8
+    .vgpr_count: 32
+    .max_flat_workgroup_size: 1024
+    .kernarg_segment_align: 8
+    .kernarg_segment_size: 8
+    .args:
+      - .address_space:  global
+        .name:           a
+        .offset:         0
+        .size:           8
+        .type_name:      'float*'
+        .value_kind:     global_buffer
+...
+.end_amdgpu_metadata
+"""
+
+def run_asm(src):
+  NUM_WORKGROUPS = 1
+  WAVE_SIZE = 32
+  NUM_WAVES = 1
+  t = Tensor.empty(0x1000).realize()
+  buf = t.uop.buffer.ensure_allocated()
+  lib = dev.compiler.compile(template.replace("INSTRUCTION", '\n'.join(src)))
+  dev.compiler.disassemble(lib)
+  fxn = AMDProgram(dev, "matmul", lib)
+  fxn(buf._buf, global_size=(NUM_WORKGROUPS,1,1), local_size=(WAVE_SIZE*NUM_WAVES,1,1), wait=True)
+
+if __name__ == "__main__":
+  with save_sqtt() as sqtt:
+    #(Tensor.empty(16,16) @ Tensor.empty(16,16)).elu().realize()
+    Tensor.empty(1).elu().realize()
+  exit(0)
+
+  with save_sqtt() as sqtt:
+    # what's in v0?
+    run_asm([
+      "v_mov_b32_e32 v0, 0",
+      "v_mov_b32_e32 v1, 0",
+      "s_clause 0x1",
+      "s_load_b64 s[0:1], s[0:1], null",
+      "s_waitcnt lgkmcnt(0)",
+    ]+[
+      "global_load_b32 v1, v0, s[0:1]",
+    ]*10+[
+      "global_load_b32 v10, v1, s[0:1]",
+      "s_waitcnt vmcnt(0)",
+
+      #"v_rcp_f32 v1, v0"
+      #"v_add_f32_e32 v1 v0 v0",
+      #"v_add_f32_e32 v5 v4 v4",
+      #"v_add_f32_e32 v7 v6 v6",
+      #"v_add_f32_e32 v1 v0 v0",
+      #"v_add_f32_e32 v2 v1 v1",
+      #"s_nop 1"
+    ]*5+[
+      "v_add_f32_e32 v3 v2 v2",
+    ]*5+[
+      "v_mul_f32_e32 v3 v2 v2",
+    ]*7)

extra/sqtt/attempt_sqtt_parse.py

@@ -0,0 +1,543 @@
+import pickle
+from tinygrad.helpers import getenv
+from extra.sqtt.roc import decode, ProfileSQTTEvent
+
+# Instruction packets (one per ISA op)
+# NOTE: these are bad guesses and may be wrong! feel free to update if you know better
+# some names were taken from SQ_TT_TOKEN_MASK_TOKEN_EXCLUDE_SHIFT
+
+OPCODE_NAMES = {
+  # gated by SQ_TT_TOKEN_EXCLUDE_VMEMEXEC_SHIFT
+  0x02: "VMEMEXEC",
+  # gated by SQ_TT_TOKEN_EXCLUDE_ALUEXEC_SHIFT
+  0x03: "ALUEXEC",
+  # gated by SQ_TT_TOKEN_EXCLUDE_VALUINST_SHIFT (but others must be enabled for it to show)
+  0x01: "VALUINST",
+  # gated by SQ_TT_TOKEN_EXCLUDE_WAVERDY_SHIFT
+  0x06: "WAVERDY",
+  # gated by SQ_TT_TOKEN_EXCLUDE_WAVESTARTEND_SHIFT
+  0x08: "WAVEEND",
+  0x09: "WAVESTART",
+  # gated by SQ_TT_TOKEN_EXCLUDE_IMMEDIATE_SHIFT
+  0x04: "IMMEDIATE_4",
+  0x05: "IMMEDIATE_5",
+  # some gated by SQ_TT_TOKEN_EXCLUDE_REG_SHIFT, some always there
+  0x14: "REG",
+  # gated by SQ_TT_TOKEN_EXCLUDE_EVENT_SHIFT
+  0x12: "EVENT",
+  # gated by SQ_TT_TOKEN_EXCLUDE_INST_SHIFT
+  0x18: "INST",
+  # gated by SQ_TT_TOKEN_EXCLUDE_UTILCTR_SHIFT
+  0x19: "UTILCTR",
+
+  # ------------------------------------------------------------------------
+  # 0x07–0x0F: pure timestamp-ish deltas
+  # ------------------------------------------------------------------------
+  0x07: "TS_DELTA_S8_W3",           # shift=8,  width=3  (small delta)
+  0x0A: "TS_DELTA_S5_W2_A",         # shift=5,  width=2
+  0x0B: "TS_DELTA_S5_W3_A",         # shift=5,  width=3
+  0x0C: "TS_DELTA_S5_W3_B",         # shift=5,  width=3 (different consumer)
+  0x0D: "TS_DELTA_S5_W3_C",         # shift=5,  width=3
+  0x0E: "TS_DELTA_S7_W2",           # shift=7,  width=2
+  0x0F: "TS_DELTA_SHORT_PLUS4",     # short delta; ROCm adds +4 before accumulate
+
+  # ------------------------------------------------------------------------
+  # 0x10–0x19: timestamps, layout headers, events, perf
+  # ------------------------------------------------------------------------
+  0x10: "PSEUDO_NEED_MORE_BITS",    # not a real packet; decoder refill hint
+
+  0x11: "TS_WAVE_STATE_SAMPLE",     # wave stall/termination sample (byte at +10)
+  0x13: "EVT_SMALL_GENERIC",        # same structural family as 0x08/0x12/0x19
+
+  0x15: "PERFCOUNTER_SNAPSHOT",     # small delta + 50-ish bits of snapshot
+  0x16: "TS_DELTA36_OR_MARK",       # 36-bit long delta or 36-bit marker
+  0x17: "LAYOUT_MODE_HEADER",       # layout/mode/group + selectors A/B
+}
+
+# these tables are from rocprof trace decoder
+# rocprof_trace_decoder_parse_data-0x11c6a0
+# parse_sqtt_180 = b *rocprof_trace_decoder_parse_data-0x11c6a0+0x110040
+
+# ---------- 1. local_138: 256-byte state->token table ----------
+
+STATE_TO_TOKEN: bytes = bytes([
+  0x10, 0x16, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x17, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x07, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x19, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x00, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x11, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x12, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x15, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x16, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x17, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x07, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x19, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x00, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x11, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+  0x10, 0x13, 0x18, 0x01, 0x05, 0x0b, 0x0c, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x09, 0x04, 0x03, 0x02,
+  0x10, 0x15, 0x18, 0x01, 0x06, 0x08, 0x0d, 0x00, 0x0f, 0x14, 0x18, 0x01, 0x0a, 0x04, 0x03, 0x02,
+])
+
+
+# ---------- 2. DAT_0012e280: nibble budget per opcode&0x1F ----------
+
+NIBBLE_BUDGET = [
+  0x08, 0x0C, 0x08, 0x08, 0x0C, 0x18, 0x18, 0x40,
+  0x14, 0x20, 0x30, 0x14, 0x34, 0x1C, 0x30, 0x08,
+  0x04, 0x18, 0x18, 0x20, 0x40, 0x40, 0x30, 0x40,
+  0x14, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+]
+assert len(NIBBLE_BUDGET) == 32
+
+
+# ---------- 3. delta_map from your hash nodes ----------
+
+# opcode -> (shift, width)
+DELTA_MAP_DEFAULT = {
+  0x01: (3,  3),   # shift=3,  end=6
+  0x02: (4,  2),   # shift=4,  end=6
+  0x03: (4,  2),   # shift=4,  end=6
+  0x04: (4,  3),   # shift=4,  end=7
+  0x05: (5,  3),   # shift=5,  end=8
+  0x06: (5,  3),   # shift=5,  end=8
+  0x07: (8,  3),   # shift=8,  end=11
+  0x08: (5,  3),   # shift=5,  end=8
+  0x09: (5,  2),   # shift=5,  end=7
+  0x0A: (5,  2),   # shift=5,  end=7
+  0x0B: (5,  3),   # shift=5,  end=8
+  0x0C: (5,  3),   # shift=5,  end=8
+  0x0D: (5,  3),   # shift=5,  end=8
+  0x0E: (7,  2),   # shift=7,  end=9
+  0x0F: (4,  4),   # shift=4,  end=8
+  0x10: (0,  0),   # shift=0,  end=0  (no delta)
+  0x11: (7,  9),   # shift=7,  end=16
+  0x12: (8,  3),   # shift=8,  end=11
+  0x13: (8,  3),   # shift=8,  end=11
+  0x14: (4,  3),   # shift=4,  end=7
+  0x15: (7,  3),   # shift=7,  end=10
+  0x16: (12, 36),  # shift=12, end=48 (36-bit field, matches the 0x16 special-case)
+  0x17: (0,  0),   # shift=0,  end=0  (no delta)
+  0x18: (4,  3),   # shift=4,  end=7
+  0x19: (7,  2),   # shift=7,  end=9
+}
+
+# ---------- 4. One-line-per-packet parser ----------
+
+def decode_packet_fields(opcode: int, reg: int, delta: int) -> str:
+  """
+  Decode packet payloads conservatively, using:
+    - NIBBLE_BUDGET[opcode & 0x1F] to mask reg down to true width.
+    - DELTA_MAP_DEFAULT[opcode] to expose the "primary" field (often delta).
+    - Per-opcode layouts derived from rocprof's decompiled consumers.
+  """
+  # --- 0. Restrict to real packet bits ---------------------------------
+  nb_bits = NIBBLE_BUDGET[opcode & 0x1F]
+  if nb_bits <= 0 or nb_bits >= 64:
+    pkt = reg & ((1 << 64) - 1)
+  else:
+    pkt = reg & ((1 << nb_bits) - 1)
+
+  fields: list[str] = []
+
+  shift, width = DELTA_MAP_DEFAULT.get(opcode, (0, 0))
+  if width:
+    field_mask = (1 << width) - 1
+    shaped_field = (pkt >> shift) & field_mask
+  else:
+    field_mask = 0
+    shaped_field = 0
+
+  # =====================================================================
+  # 1. Timestamp-centric opcodes (actually drive 'time')
+  # =====================================================================
+
+  if opcode == 0x0F:  # TS_DELTA_SHORT_PLUS4
+    # In the caller, delta already has +4 applied.
+    raw_delta = shaped_field
+    fields.append(f"raw_delta={raw_delta}")
+    fields.append(f"ts_short_plus4={delta}")
+    return ", ".join(fields)
+
+  if opcode == 0x11:  # TS_WAVE_STATE_SAMPLE
+    # DELTA_MAP_DEFAULT: shift=7, width=9 -> small delta.
+    raw_delta = shaped_field
+    coarse    = (pkt >> (shift + width)) & 0xFF  # matches byte at +10 in C
+    fields.append(f"raw_delta={raw_delta}")
+    if coarse:
+      fields.append(f"coarse_state=0x{coarse:02x}")
+    # From decomp:
+    #  - when layout<3 and coarse&1, it sets a "has interesting wave" flag
+    #  - when coarse&8, it marks all live waves as "terminated"
+    if coarse & 0x01:
+      fields.append("flag_wave_interest=1")
+    if coarse & 0x08:
+      fields.append("flag_terminate_all=1")
+    return ", ".join(fields)
+
+  if opcode == 0x16:  # TS_DELTA36_OR_MARK
+    # Bits:
+    #   bit8  -> 0x100
+    #   bit9  -> 0x200
+    #   bits 12..47 -> 36-bit field used as delta or marker
+    bit8 = bool(pkt & 0x100)
+    bit9 = bool(pkt & 0x200)
+    if not bit9:
+      mode = "delta"
+    elif not bit8:
+      mode = "marker"
+    else:
+      mode = "other"
+    val36 = (pkt >> 12) & ((1 << 36) - 1)
+    fields.append(f"mode={mode}")
+    if mode != "delta":
+      fields.append(f"val36=0x{val36:x}")
+    return ", ".join(fields)
+
+  # For 0x07, 0x0A–0x0E, we know they drive time (via DELTA_MAP_DEFAULT),
+  # but we don't see any other fields used in the decomp.
+  if opcode in (0x07, 0x0A, 0x0B, 0x0C, 0x0D, 0x0E):
+    if width:
+      raw_delta = shaped_field
+      leftover  = pkt & ~(field_mask << shift)
+      fields.append(f"raw_delta={raw_delta}")
+      if leftover:
+        fields.append(f"payload=0x{leftover:x}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 2. Small "meta + tiny delta" packets (0x01–0x06)
+  # =====================================================================
+
+  if opcode == 0x01:  # META_ID12_TS_SMALL
+    id12 = pkt & 0xFFF
+    fields.append(f"id12=0x{id12:03x}")
+    if width:
+      fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    return ", ".join(fields)
+
+  if opcode == 0x02:  # META_FLAG8_TS_SMALL
+    flag8 = pkt & 0xFF
+    fields.append(f"flag8=0x{flag8:02x}")
+    if width:
+      fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    return ", ".join(fields)
+
+  if opcode == 0x03:  # META_SUBEVENT8_TS_SMALL
+    sub8 = pkt & 0xFF
+    fields.append(f"subevent8=0x{sub8:02x}")
+    if width:
+      fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    return ", ".join(fields)
+
+  if opcode == 0x04:  # META_BASE_INDEX12_TS
+    idx12 = pkt & 0xFFF
+    fields.append(f"base_index12=0x{idx12:03x}")
+    if width:
+      fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    return ", ".join(fields)
+
+  if opcode in (0x05, 0x06):  # META_DESC24_TS_A/B
+    desc24 = pkt & 0xFFFFFF
+    fields.append(f"desc24=0x{desc24:06x}")
+    if width:
+      fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 3. Opcode 0x14: exec/config record (+ COR marker)
+  # =====================================================================
+
+  if opcode == 0x14:  # INST_EXEC_OR_CFG
+    subop   = (pkt >> 16) & 0xFFFF       # (short)(w >> 0x10)
+    val32   = (pkt >> 32) & 0xFFFFFFFF   # (uint)(w >> 0x20)
+    slot    = (pkt >> 7) & 0x7           # index in local_168[...] tables
+    hi_byte = (pkt >> 8) & 0xFF          # determines config vs marker
+
+    fields.append(f"subop=0x{subop:04x}")
+    fields.append(f"slot={slot}")
+    fields.append(f"val32=0x{val32:08x}")
+
+    if hi_byte & 0x80:
+      # Config flavour: writes config words into per-slot state arrays.
+      fields.append("kind=config")
+      if subop == 0x000C:
+        fields.append("cfg_target=local_168[slot].lo")
+      elif subop == 0x000D:
+        fields.append("cfg_target=local_168[slot].hi")
+    else:
+      # COR marker: subop 0xC342, payload "COR\0" → start of a COR region.
+      if subop == 0xC342:
+        fields.append("kind=cor_stream")
+        if val32 == 0x434F5200:
+          fields.append("cor_magic='COR\\0'")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 4. Opcode 0x17: layout / mode header
+  # =====================================================================
+
+  if opcode == 0x17:  # LAYOUT_MODE_HEADER
+    # From decomp (two sites with identical logic):
+    #   layout = (w >> 7) & 0x3f
+    #   mode   = (w >> 0xd) & 3
+    #   group  = (w >> 0xf) & 7
+    #   sel_a  = (w >> 0x1c) & 0xf
+    #   sel_b  = (w >> 0x21) & 7
+    #   flag4  = (w >> 0x3b) & 1  (only meaningful when layout == 4)
+    layout = (pkt >> 7)  & 0x3F
+    mode   = (pkt >> 13) & 0x3
+    group  = (pkt >> 15) & 0x7
+    sel_a  = (pkt >> 0x1C) & 0xF
+    sel_b  = (pkt >> 0x21) & 0x7
+    flag4  = (pkt >> 0x3B) & 0x1
+
+    fields.append(f"layout={layout}")
+    fields.append(f"group={group}")
+    fields.append(f"mode={mode}")
+    fields.append(f"sel_a={sel_a}")
+    fields.append(f"sel_b={sel_b}")
+    if layout == 4:
+      fields.append(f"layout4_flag={flag4}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 5. Opcode 0x09: state / route config record
+  # =====================================================================
+
+  if opcode == 0x09:  # PERF_ROUTE_CONFIG
+    # From case 9 in multiple consumers:
+    #   flag7  = (w >> 7) & 1        (low bit of uVar41)
+    #   cls2   = (w >> 8) & 3        (class / group)
+    #   slot4  = (w >> 10) & 0xf     (slot / group index)
+    #   idx_lo = (w >> 0xd) & 0x1f   (low index, layout<4 path)
+    #   idx_hi = (w >> 0xf) & 0x1f   (high index, layout>=4 path)
+    #   id7    = (w >> 0x19) & 0x7f  (7-bit id)
+    flag7   = (pkt >> 7) & 0x1
+    cls2    = (pkt >> 8) & 0x3
+    slot4   = (pkt >> 10) & 0xF
+    idx_lo  = (pkt >> 13) & 0x1F
+    idx_hi  = (pkt >> 15) & 0x1F
+    id7     = (pkt >> 0x19) & 0x7F
+
+    fields.append(f"flag7={flag7}")
+    fields.append(f"cls2={cls2}")
+    fields.append(f"slot4=0x{slot4:x}")
+    fields.append(f"idx_lo5=0x{idx_lo:x}")
+    fields.append(f"idx_hi5=0x{idx_hi:x}")
+    fields.append(f"id7=0x{id7:x}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 6. Opcode 0x18: perf/event selector (FUN_0010aba0)
+  # =====================================================================
+
+  if opcode == 0x18:  # PERF_EVENT_SELECT
+    # From case 0x18:
+    #   low3   = w & 7
+    #   grp3   = (w >> 3) or (w >> 4) & 7   (layout-dependent)
+    #   flags  = bits 6 (B6) and 7 (B7)
+    #   hi8    = (w >> 0xc) & 0xff   (layout 4 path)
+    #   hi7    = (w >> 0xd) & 0x7f   (other layouts)
+    #   idx5   = (w >> 7) or (w >> 8) & 0x1f, used as wave index
+    low3     = pkt & 0x7
+    grp3_a   = (pkt >> 3) & 0x7
+    grp3_b   = (pkt >> 4) & 0x7
+    flag_b6  = (pkt >> 6) & 0x1
+    flag_b7  = (pkt >> 7) & 0x1
+    idx5_a   = (pkt >> 7) & 0x1F
+    idx5_b   = (pkt >> 8) & 0x1F
+    hi8      = (pkt >> 12) & 0xFF
+    hi7      = (pkt >> 13) & 0x7F
+
+    fields.append(f"low3=0x{low3:x}")
+    fields.append(f"grp3_a=0x{grp3_a:x}")
+    fields.append(f"grp3_b=0x{grp3_b:x}")
+    fields.append(f"flag_b6={flag_b6}")
+    fields.append(f"flag_b7={flag_b7}")
+    fields.append(f"idx5_a=0x{idx5_a:x}")
+    fields.append(f"idx5_b=0x{idx5_b:x}")
+    fields.append(f"hi8=0x{hi8:02x}")
+    fields.append(f"hi7=0x{hi7:02x}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 7. Opcode 0x15: perfcounter snapshot
+  # =====================================================================
+
+  if opcode == 0x15:  # PERFCOUNTER_SNAPSHOT
+    # NIBBLE_BUDGET gives full 64 bits here.
+    # DELTA_MAP_DEFAULT: shift=7, width=3 → tiny delta field.
+    raw_delta = shaped_field if width else 0
+    # low bits below the delta field
+    snap_low  = pkt & ((1 << shift) - 1) if shift else 0
+    # everything above delta field
+    snap_hi   = pkt >> (shift + width) if width else (pkt >> shift)
+
+    fields.append(f"raw_delta={raw_delta}")
+    fields.append(f"snap_low_s{shift}=0x{snap_low:x}")
+    fields.append(f"snap_hi=0x{snap_hi:x}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 8. Small event-ish packets (0x08 / 0x12 / 0x13 / 0x19)
+  # =====================================================================
+
+  if opcode in (0x08, 0x12, 0x13, 0x19):
+    # These are all "small event / metric" style tokens. The exact semantics
+    # depend on layout (0x17) and accumulated state (local_500 etc), so we
+    # expose:
+    #   - low 8 bits as kind byte
+    #   - rest as opaque payload.
+    kind    = pkt & 0xFF
+    payload = pkt >> 8
+    fields.append(f"kind_byte=0x{kind:02x}")
+    if payload:
+      fields.append(f"payload=0x{payload:x}")
+    return ", ".join(fields)
+
+  # =====================================================================
+  # 9. Pseudo opcode 0x10: never a "real" packet
+  # =====================================================================
+
+  if opcode == 0x10:  # PSEUDO_NEED_MORE_BITS
+    # The main loop never prints these; they're just a control token.
+    return ""
+
+  # =====================================================================
+  # 10. Generic fallback: expose the DELTA_MAP_DEFAULT field + leftover
+  # =====================================================================
+
+  if width:
+    fields.append(f"field_s{shift}_w{width}={shaped_field}")
+    leftover = pkt & ~(field_mask << shift)
+    if leftover:
+      fields.append(f"payload=0x{leftover:x}")
+
+  return ", ".join(fields)
+
+# 0xb is time something
+# 0xd is time something
+# 0xf is small time advance
+# 0x11 is time advance
+# 0x16 is big time advance + markers
+# 0x14 is REG
+DEFAULT_FILTER = (0xb, 0xd, 0xf, 0x11, 0x16, 0x14) if getenv("FILTER", 1) else None
+
+def parse_sqtt_print_packets(data: bytes, max_tokens: int = 100000, filter=DEFAULT_FILTER) -> None:
+  """
+  Minimal debug: print ONE LINE per decoded token (packet).
+
+  Now prints only the actual nibbles that belong to each packet, instead of
+  the full 64-bit shift register.
+  """
+  n = len(data)
+  time = 0
+  reg = 0          # shift register
+  offset = 0       # bit offset, in steps of 4 (one nibble)
+  nib_budget = 0x40
+  flags = 0
+  token_index = 0
+
+  while (offset >> 3) < n and token_index < max_tokens:
+    # Remember where we started refilling for this step (bit offset),
+    # but the *logical* start of the current packet is last_real_offset.
+    refill_start = offset
+
+    # 1) Fill register with nibbles according to nib_budget
+    if nib_budget != 0:
+      target = refill_start + 4 + ((nib_budget - 1) & ~3)
+      cur = refill_start
+      while cur != target and (cur >> 3) < n:
+        byte_index = cur >> 3
+        byte = data[byte_index]
+        shift = 4 if (cur & 4) else 0  # low then high nibble
+        nib = (byte >> shift) & 0xF
+        reg = ((reg >> 4) | (nib << 60)) & ((1 << 64) - 1)
+        cur += 4
+      offset = cur
+
+    # 2) Decode token from low 8 bits
+    state = reg & 0xFF
+    opcode = STATE_TO_TOKEN[state]
+
+    # 3) Handle pseudo-token 0x10: need more bits, don't print. Looks like a NOP.
+    if opcode == 0x10:
+      # "need more bits" pseudo-token: adjust nibble budget and continue
+      nib_budget = 4
+      if (offset >> 3) >= n:
+        break
+      # Do NOT count this as a real packet; do not update last_real_offset.
+      continue
+
+    # 4) Set next nibble budget
+    nb_index = opcode & 0x1F
+    nib_budget = NIBBLE_BUDGET[nb_index]
+    time_before = time
+    note = ""
+    # 5) Special opcode 0x16 (timestamp / marker)
+    if opcode == 0x16:
+      two_bits = (reg >> 8) & 0x3
+      if two_bits == 1:
+        flags |= 0x01
+
+      # Common 36-bit field at bits [12..47]
+
+      if (reg & 0x200) == 0:
+        # delta mode: add 36-bit delta to time
+        delta = (reg >> 12) & ((1 << 36) - 1)
+        time += delta
+      else:
+        # marker / other modes: no time advance
+        if (reg & 0x100) == 0:
+          # real marker: bit9=1, bit8=0, non-zero payload
+          # "other" 0x16 variants, ignored for timing
+          delta = 0
+    else:
+      # 6) Generic opcode (including 0x0F)
+      shift, width = DELTA_MAP_DEFAULT[opcode]
+      mask = (1 << width) - 1
+      delta = (reg >> shift) & mask
+
+      # TODO: add more opcode parsers here that add notes to other opcodes
+      if opcode == 0x0F:
+        delta_with_fix = delta + 4
+        time += delta_with_fix
+        delta = delta_with_fix
+      else:
+        time += delta
+
+    # Append extra decoded fields into the note string
+    note = decode_packet_fields(opcode, reg, delta)
+
+    if filter is None or opcode not in filter:
+      my_reg = reg
+      my_reg &= (1 << nib_budget) - 1
+      print(
+        f"{token_index:4d}  "
+        f"off={offset//4:5d}  "
+        f"op=0x{opcode:02x} "
+        f"{OPCODE_NAMES[opcode]:24s} "
+        f" time={time_before:8d}+{delta:8d}  "
+        f"{my_reg:16X} "
+        f"{note}"
+      )
+
+    token_index += 1
+
+  # Optional summary at the end
+  print(f"# done: tokens={token_index}, final_time={time}, flags=0x{flags:02x}")
+
+def parse(fn:str):
+  dat = pickle.load(open(fn, "rb"))
+  ctx = decode(dat)
+  dat_sqtt = [x for x in dat if isinstance(x, ProfileSQTTEvent)]
+  print(f"got {len(dat_sqtt)} SQTT events in {fn}")
+  return dat_sqtt
+
+if __name__ == "__main__":
+  #dat_sqtt = parse("extra/sqtt/examples/profile_empty_run_0.pkl")
+  #dat_sqtt = parse("extra/sqtt/examples/profile_plus_run_0.pkl")
+  dat_sqtt = parse("extra/sqtt/examples/profile_gemm_run_0.pkl")
+  blob_0 = dat_sqtt[0].blob
+  parse_sqtt_print_packets(blob_0[8:])

extra/sqtt/install_sqtt_decoder.py

@@ -12,7 +12,9 @@ if __name__ == "__main__":
     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")
+    shutil.copy2(lib, DEST)
   else:
-    lib = fetch("https://github.com/ROCm/rocprof-trace-decoder/raw/43bf0fef74a83c3c25badfc5a09c0bd39ed8c6f9/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so", name="librocprof-trace-decoder.so")
-  shutil.copy2(lib, DEST)
+    lib = DEST/"librocprof-trace-decoder.so"
+    os.system("sudo curl -L https://github.com/ROCm/rocprof-trace-decoder/raw/43bf0fef74a83c3c25badfc5a09c0bd39ed8c6f9/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so -o"+str(lib))
+    os.system("sudo ldconfig")
   print(f"Installed {lib.name} to", DEST)

extra/sqtt/rgptool.py

@@ -4,7 +4,7 @@ import argparse, ctypes, struct, hashlib, pickle, code, typing, functools
 import tinygrad.runtime.autogen.sqtt as sqtt
 from tinygrad.device import ProfileEvent, ProfileDeviceEvent, ProfileProgramEvent
 from tinygrad.runtime.ops_amd import ProfileSQTTEvent
-from tinygrad.helpers import round_up, flatten, all_same
+from tinygrad.helpers import round_up, flatten, all_same, temp
 from dataclasses import dataclass
 
 CHUNK_CLASSES = {
@@ -185,9 +185,7 @@ class RGP:
       magic_number=sqtt.SQTT_FILE_MAGIC_NUMBER,
       version_major=sqtt.SQTT_FILE_VERSION_MAJOR,
       version_minor=sqtt.SQTT_FILE_VERSION_MINOR,
-      flags=sqtt.struct_sqtt_file_header_flags(
-        _0=sqtt.union_sqtt_file_header_flags_0(value=1),
-      ),
+      flags=sqtt.struct_sqtt_file_header_flags(value=1,),
       chunk_offset=ctypes.sizeof(sqtt.struct_sqtt_file_header),
     )
     chunks = [
@@ -210,7 +208,7 @@ class RGP:
         flags=0,
         trace_shader_core_clock=0x93f05080,
         trace_memory_clock=0x4a723a40,
-        device_id={110000: 0x744c, 110003: 0x7480, 120001: 0x7550}[device_props['gfx_target_version']],
+        device_id={110000: 0x744c, 110003: 0x7480, 120001: 0x7550, 120000: 0x7550}[device_props['gfx_target_version']],
         device_revision_id=0xc8,
         vgprs_per_simd=1536,
         sgprs_per_simd=128*16,
@@ -265,7 +263,7 @@ class RGP:
         profiling_mode=sqtt.SQTT_PROFILING_MODE_PRESENT,
         instruction_trace_mode=sqtt.SQTT_INSTRUCTION_TRACE_FULL_FRAME if sqtt_itrace_enabled else sqtt.SQTT_INSTRUCTION_TRACE_DISABLED,
         instruction_trace_data=sqtt.union_sqtt_instruction_trace_data(
-          shader_engine_filter=sqtt.struct_sqtt_instruction_trace_data_shader_engine_filter(mask=sqtt_itrace_se_mask),
+          shader_engine_filter=sqtt.union_sqtt_instruction_trace_data_shader_engine_filter(mask=sqtt_itrace_se_mask),
         ),
       )),
       *flatten([(
@@ -276,13 +274,11 @@ class RGP:
           ),
           shader_engine_index=sqtt_event.se,
           sqtt_version={11: sqtt.SQTT_VERSION_3_2, 12: sqtt.SQTT_VERSION_3_3}.get(gfx_ver),
-          _0=sqtt.union_sqtt_file_chunk_sqtt_desc_0(
-            v1=sqtt.struct_sqtt_file_chunk_sqtt_desc_0_v1(
-              instrumentation_spec_version=1,
-              instrumentation_api_version=0,
-              compute_unit_index=0,
-            )
-          ),
+          v1=sqtt.struct_sqtt_file_chunk_sqtt_desc_0_v1(
+            instrumentation_spec_version=1,
+            instrumentation_api_version=0,
+            compute_unit_index=0,
+          )
         )),
         RGPChunk(sqtt.struct_sqtt_file_chunk_sqtt_data(
           header=sqtt.struct_sqtt_file_chunk_header(
@@ -324,7 +320,7 @@ class RGP:
 if __name__ == '__main__':
   parser = argparse.ArgumentParser(prog='rgptool', description='A tool to create (from pickled tinygrad profile), inspect and modify Radeon GPU Profiler files')
   parser.add_argument('command')
-  parser.add_argument('input')
+  parser.add_argument('input', nargs='?', default=temp("profile.pkl", append_user=True))
   parser.add_argument('-d', '--device')
   parser.add_argument('-o', '--output')
   args = parser.parse_args()
@@ -346,3 +342,4 @@ if __name__ == '__main__':
 
   if args.output is not None:
     with open(args.output, 'wb+') as fd: fd.write(rgp.to_bytes())
+    print(f"Saved to {args.output}")

extra/sqtt/roc.py

@@ -1,4 +1,4 @@
-import ctypes, pathlib, argparse, pickle, re, functools, dataclasses, itertools
+import ctypes, pathlib, argparse, pickle, re, functools, dataclasses, itertools, threading
 from tinygrad.helpers import temp, unwrap, DEBUG
 from tinygrad.device import ProfileEvent, ProfileDeviceEvent, ProfileProgramEvent
 from tinygrad.runtime.ops_amd import ProfileSQTTEvent, ProfilePMCEvent
@@ -28,18 +28,6 @@ def llvm_disasm(arch:str, lib:bytes) -> dict[int, tuple[str, int]]:
     cur_off += instr_sz
   return addr_table
 
-@dataclasses.dataclass
-class InstInfo:
-  typ:str=""
-  inst:str=""
-  hit:int=0
-  lat:int=0
-  stall:int=0
-  def __str__(self): return f"{self.inst:>20} hits:{self.typ:>6} hits:{self.hit:>6} latency:{self.lat:>6} stall:{self.stall:>6}"
-
-  def on_ev(self, ev):
-    self.hit, self.lat, self.stall = self.hit + 1, self.lat + ev.duration, self.stall + ev.stall
-
 @dataclasses.dataclass(frozen=True)
 class InstExec:
   typ:str
@@ -49,19 +37,20 @@ class InstExec:
   time:int
 
 @dataclasses.dataclass(frozen=True)
-class PrgExec:
-  name:str
-  wave:int
+class WaveExec:
+  wave_id:int
   cu:int
   simd:int
-  def __str__(self): return f"{self.name},{self.wave},{self.cu},{self.simd}"
+  se:int
+  begin_time:int
+  end_time:int
+  insts:list[InstExec]
 
 class _ROCParseCtx:
   def __init__(self, dev_evs:dict[str, ProfileDeviceEvent], sqtt_evs:list[ProfileSQTTEvent], prog_evs:list[ProfileProgramEvent]):
     self.dev_evs, self.sqtt_evs, self.prog_evs = dev_evs, iter(sqtt_evs), prog_evs
-    self.wave_events:dict[PrgExec, dict[int, InstInfo]] = {}
-    self.disasms:dict[int, tuple[str, int]] = {}
-    self.inst_execs:dict[PrgExec, list[InstExec]] = {}
+    self.disasms:dict[tuple[str, int], tuple[str, int]] = {}
+    self.inst_execs:dict[str, list[WaveExec]] = {}
 
     for prog in prog_evs:
       arch = "gfx%d%x%x" % ((trgt:=unwrap(dev_evs[prog.device].props)['gfx_target_version']) // 10000, (trgt // 100) % 100, trgt % 100)
@@ -72,27 +61,26 @@ class _ROCParseCtx:
     x = next(self.sqtt_evs, None)
     self.active_kern = x.kern if x is not None else None
     self.active_se = x.se if x is not None else None
-    return x
+    self.active_blob = (ctypes.c_ubyte * len(x.blob)).from_buffer_copy(x.blob) if x is not None else None
+    return self.active_blob
 
-  def on_occupancy_ev(self, ev):
+  def on_occupancy_ev(self, ev:rocprof.rocprofiler_thread_trace_decoder_occupancy_t):
     if DEBUG >= 5: print("OCC", ev.time, self.active_se, ev.cu, ev.simd, ev.wave_id, ev.start)
 
-  def on_wave_ev(self, ev):
+  def on_wave_ev(self, ev:rocprof.rocprofiler_thread_trace_decoder_wave_t):
     if DEBUG >= 5: print("WAVE", ev.wave_id, self.active_se, ev.cu, ev.simd, ev.contexts, ev.begin_time, ev.end_time)
 
-    asm:dict[int, InstInfo] = {}
     inst_execs:list[InstExec] = []
     for j in range(ev.instructions_size):
       inst_ev = ev.instructions_array[j]
-      inst_typ = rocprof.rocprofiler_thread_trace_decoder_inst_category_t__enumvalues[inst_ev.category]
-      inst_disasm = self.disasms[(self.active_kern, inst_ev.pc.address)][0]
-      asm.setdefault(inst_ev.pc.address, InstInfo(typ=inst_typ, inst=inst_disasm))
-      asm[inst_ev.pc.address].on_ev(inst_ev)
+      inst_typ = rocprof.enum_rocprofiler_thread_trace_decoder_inst_category_t.get(inst_ev.category)
+      inst_disasm = self.disasms[(unwrap(self.active_kern), unwrap(inst_ev.pc.address))][0]
       inst_execs.append(InstExec(inst_typ, inst_disasm, inst_ev.stall, inst_ev.duration, inst_ev.time))
+      if DEBUG >= 8: print(inst_execs[-1])
 
     if ev.instructions_size > 0:
-      self.wave_events[key:=PrgExec(self.active_kern, ev.wave_id, ev.cu, ev.simd)] = asm
-      self.inst_execs[key] = inst_execs
+      self.inst_execs.setdefault(unwrap(self.active_kern), []).append(WaveExec(ev.wave_id, ev.cu, ev.simd, unwrap(self.active_se), ev.begin_time,
+                                                                               ev.end_time, inst_execs))
 
 def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:
   dev_events:dict[str, ProfileDeviceEvent] = {}
@@ -106,26 +94,26 @@ def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:
   ROCParseCtx = _ROCParseCtx(dev_events, sqtt_events, prog_events)
 
   @rocprof.rocprof_trace_decoder_se_data_callback_t
-  def copy_cb(buf, buf_size, data_ptr):
-    if (prof:=ROCParseCtx.next_sqtt()) is None: return 0
-    buf[0] = ctypes.cast((ctypes.c_ubyte * len(prof.blob)).from_buffer_copy(prof.blob), ctypes.POINTER(ctypes.c_ubyte))
-    buf_size[0] = len(prof.blob)
-    return len(prof.blob)
+  def copy_cb(buf, buf_size, _):
+    if (prof_info:=ROCParseCtx.next_sqtt()) is None: return 0
+    buf[0] = ctypes.cast(prof_info, ctypes.POINTER(ctypes.c_ubyte))
+    buf_size[0] = len(prof_info)
+    return len(prof_info)
 
   @rocprof.rocprof_trace_decoder_trace_callback_t
-  def trace_cb(record_type, events_ptr, n, data_ptr):
+  def trace_cb(record_type, events_ptr, n, _):
     match record_type:
       case rocprof.ROCPROFILER_THREAD_TRACE_DECODER_RECORD_OCCUPANCY:
         for ev in (rocprof.rocprofiler_thread_trace_decoder_occupancy_t * n).from_address(events_ptr): ROCParseCtx.on_occupancy_ev(ev)
       case rocprof.ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE:
         for ev in (rocprof.rocprofiler_thread_trace_decoder_wave_t * n).from_address(events_ptr): ROCParseCtx.on_wave_ev(ev)
       case _:
-        if DEBUG >= 5: print(rocprof.rocprofiler_thread_trace_decoder_record_type_t__enumvalues[record_type], events_ptr, n)
+        if DEBUG >= 5: print(rocprof.enum_rocprofiler_thread_trace_decoder_record_type_t.get(record_type), events_ptr, n)
     return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS
 
   @rocprof.rocprof_trace_decoder_isa_callback_t
-  def isa_cb(instr_ptr, mem_size_ptr, size_ptr, pc, data_ptr):
-    instr, mem_size_ptr[0] = ROCParseCtx.disasms[(ROCParseCtx.active_kern, pc.address)]
+  def isa_cb(instr_ptr, mem_size_ptr, size_ptr, pc, _):
+    instr, mem_size_ptr[0] = ROCParseCtx.disasms[(unwrap(ROCParseCtx.active_kern), pc.address)]
 
     # this is the number of bytes to next instruction, set to 0 for end_pgm
     if instr == "s_endpgm": mem_size_ptr[0] = 0
@@ -138,9 +126,11 @@ def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:
 
     return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS
 
-  try:
-    rocprof.rocprof_trace_decoder_parse_data(copy_cb, trace_cb, isa_cb, None)
-  except AttributeError as e: raise RuntimeError("Failed to find rocprof-trace-decoder. Run ./extra/sqtt/install_sqtt_decoder.py to install") from e
+  def worker():
+    try: rocprof.rocprof_trace_decoder_parse_data(copy_cb, trace_cb, isa_cb, None)
+    except AttributeError as e: raise RuntimeError("Failed to find rocprof-trace-decoder. Run sudo ./extra/sqtt/install_sqtt_decoder.py to install") from e
+  (t:=threading.Thread(target=worker, daemon=True)).start()
+  t.join()
   return ROCParseCtx
 
 if __name__ == "__main__":
@@ -150,7 +140,7 @@ if __name__ == "__main__":
 
   with args.profile.open("rb") as f: profile = pickle.load(f)
   rctx = decode(profile)
-  print('SQTT:', rctx.wave_events.keys())
+  print('SQTT:', rctx.inst_execs.keys())
 
   for ev in profile:
     if not isinstance(ev, ProfilePMCEvent): continue

extra/sqtt/test_timing.py

@@ -1,32 +1,32 @@
 import os
 os.environ["PYTHONPATH"] = "."
 os.environ["SQTT"] = "1"
-os.environ["AMD"] = "1"
+if "DEV" not in os.environ: os.environ["DEV"] = "AMD"
 os.environ["VIZ"] = "1"
 os.environ["AMD_LLVM"] = "0"
 
 import unittest
 import sys, contextlib
-from tinygrad import Tensor
-from tinygrad.dtype import dtypes
-from tinygrad.renderer import ProgramSpec
+from tinygrad import Tensor, dtypes
+from tinygrad.helpers import getenv
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
-from tinygrad.engine.realize import CompiledRunner
 from tinygrad.device import Device, ProfileDeviceEvent
 
-from extra.sqtt.roc import decode, InstExec, PrgExec
+from extra.sqtt.roc import decode, WaveExec
 
-dev = Device["AMD"]
+dev = Device[os.environ["DEV"]]
+
+def custom(arg:str, s:UOp|None=None) -> UOp: return UOp(Ops.CUSTOM, src=(s,) if s is not None else (), arg=arg)
 
 def asm_kernel(instrs:list[str], l:int=1, g:int=1) -> Tensor:
   name = sys._getframe(1).f_code.co_name
   def fxn(_):
     L = UOp.special(l, "lidx0")
     G = UOp.special(g, "gidx0")
-    ops:list[str] = [UOp(Ops.CUSTOM, arg="asm volatile (")]
-    for inst in instrs: ops.append(UOp(Ops.CUSTOM, src=(ops[-1],), arg=f'  "{inst}\\n\\t"'))
-    ops.append(UOp(Ops.CUSTOM, src=(ops[-1],), arg=");"))
-    return UOp.sink(*ops, L, G, arg=KernelInfo(name=name))
+    op = custom("asm volatile (")
+    for inst in instrs: op = custom(f'  "{inst}\\n\\t"', op)
+    op = custom(");", op)
+    return UOp.sink(op, L, G, arg=KernelInfo(name=name))
   k = Tensor.custom_kernel(Tensor.empty(1), fxn=fxn)[0]
   return k
 
@@ -34,9 +34,10 @@ def asm_kernel(instrs:list[str], l:int=1, g:int=1) -> Tensor:
 def save_sqtt():
   # clear the old traces
   dev.profile_events.clear()
-  sqtt:dict[PrgExec, list[InstExec]] = {}
+  sqtt:dict[str, list[WaveExec]] = {}
   yield sqtt
   # decode sqtt
+  if os.environ["DEV"] != "AMD": return
   rctx = decode(dev.profile_events+[ProfileDeviceEvent("AMD", props=dev.device_props())])
   assert len(rctx.inst_execs) > 0, "empty sqtt output"
   sqtt.update(rctx.inst_execs)
@@ -60,45 +61,73 @@ class TestTiming(unittest.TestCase):
     assert all(s.stall == 0 for s in wave)
 
   def test_multi_cycle_inst(self):
+    def custom_vrcp(A, B):
+      op = custom("float a = 0.0;")
+      op = custom("float b = (*(data1_1+0));", op)
+      #op = custom('asm volatile("v_mul_f32_e32 %2 %2 %1" : "+v"(a) : "v"(b));', op)
+      op = custom('asm volatile("v_rcp_f32_e32 %2 %1" : "+v"(a) : "v"(b));', op)
+      op = custom('asm volatile("v_add_f32_e64 %1 %1 1.0" : "+v"(a));', op)
+      op = custom("*(data0_1+0) = a;", op)
+      return UOp.sink(op, A, B, arg=KernelInfo(name="custom_vrcp"))
+    out = Tensor([0.]).realize()
+    inp = Tensor([-2.0]).realize()
     with save_sqtt() as sqtt:
-      asm_kernel([
-        "v_mov_b32_e32 v4 0x3f800000",
-        "v_rcp_f32_e32 v5 v4",
-        "v_mul_f32_e32 v6 v5 v4",
-      ]).realize()
-    w = list(sqtt.values())[0]
-    rcp, mul = w[1], w[2]
-    self.assertGreater(rcp.dur, 1) # 4 cycles on gfx11
-    self.assertEqual(mul.dur, 1)
-    # mul depends on v5, how can it run before rcp is done?
-    self.assertGreaterEqual(mul.time, rcp.time+rcp.dur)
+      Tensor.custom_kernel(out, inp, fxn=custom_vrcp)[0].realize()
+    wave = list(sqtt.values())[0][0]
+    for i in range(len(wave.insts)):
+      if wave.insts[i].inst.startswith("global_store"):
+        print(f"store diff {wave.insts[i].time-(wave.insts[i-1].time)}")
+    self.assertEqual(out.item(), 0.5)
 
   def test_wmma(self):
     with save_sqtt() as sqtt:
-      asm_kernel([
-        "v_wmma_f32_16x16x16_f16 v[16:23], v[0:7], v[8:15], v[16:23]",
-        "v_add_f32_e32 v0 v16 v0",
-      ], l=32*4).realize()
-    assert len(sqtt) == 2, f"expected two waves, got {len(sqtt)} {list(sqtt.keys())}"
-    wmma = list(sqtt.values())[0][0]
-    self.assertGreater(wmma.dur, 1) # rgp says 32 clocks
+      for tc in dev.renderer.get_tensor_cores(dev.arch):
+        M, K, N = tc.dims
+        s = 32
+        a = Tensor.empty(M*s, K*s, dtype=tc.dtype_in)@Tensor.empty(K*s, N*s, dtype=tc.dtype_in)
+        a.realize()
+        print(a)
+    for p,waves in sqtt.items():
+      for e in waves[0].insts:
+        if (e.inst.startswith("v_wmma")):
+          instruction = e.inst.split(" ")[0]
+          print(f"{instruction:<29} : {e.dur} cycles")
 
   def test_sleep(self):
     n = 1
     def sleep_kernel(data0):
       assert data0.dtype.base == dtypes.ulong
-      ops:list[UOp] = []
-      ops.append(UOp(Ops.CUSTOM, arg="unsigned long long t0 = __builtin_readcyclecounter();"))
-      ops.append(UOp(Ops.CUSTOM, arg=f"__builtin_amdgcn_s_sleep({n});", src=(ops[-1],)))
-      ops.append(UOp(Ops.CUSTOM, arg="unsigned long long t1 = __builtin_readcyclecounter();", src=(ops[-1],)))
-      ops.append(UOp(Ops.CUSTOM, arg=f"data0_{data0.size}[0] = t1 - t0;", src=(ops[-1],)))
-      return UOp.sink(data0, *ops, arg=KernelInfo(name=f"sleep_{n}"))
+      op = custom("unsigned long long t0 = __builtin_readcyclecounter();")
+      op = custom(f"__builtin_amdgcn_s_sleep({n});", op)
+      op = custom("unsigned long long t1 = __builtin_readcyclecounter();", op)
+      op = custom(f"data0_{data0.size}[0] = t1 - t0;", op)
+      return UOp.sink(data0, op, arg=KernelInfo(name=f"sleep_{n}"))
     diff_hw_reg = Tensor.empty(1, dtype=dtypes.ulong)
     diff_hw_reg = Tensor.custom_kernel(diff_hw_reg, fxn=sleep_kernel)[0]
     with save_sqtt() as sqtt:
       diff_hw_reg.realize()
-    diff_sqtt = list(sqtt.values())[0][2]
-    self.assertEqual(diff_sqtt.dur, diff_hw_reg.item()-1) # 1 cycle for reading the counter register
+    sleep = next((e for e in sqtt[f"sleep_{n}"][0].insts if e.inst.startswith("s_sleep")))
+    # cycles = sleep dur + overhead of storing hi/lo REG_SHADER_CYCLES
+    self.assertGreaterEqual(diff_hw_reg.item(), sleep.dur)
+
+  def test_nop(self):
+    with save_sqtt() as sqtt:
+      asm_kernel(["s_nop 1"]*10).realize()
+    wave = list(sqtt.values())[0][0]
+    for e in wave.insts:
+      print(f"{e.inst} {e.dur=} {e.stall=}")
+
+  def test_wave_sched(self):
+    num_waves = getenv("NUM_WAVES", 16)
+    num_wgps = getenv("NUM_WGPS", 2)
+    num_vgpr = getenv("NUM_VGPR", 256)
+    with save_sqtt() as sqtt:
+      # 1 cycle decode, no stall
+      asm_kernel([f"v_mov_b32_e32 v{i} {i}" for i in range(num_vgpr)], l=32*num_waves, g=num_wgps).realize()
+    waves = list(sqtt.values())[0]
+    print(len(waves), "waves decoded")
+    for w in waves:
+      print(f"{w.wave_id:<2} {w.simd=} {w.cu=} {w.se=} @ clk {w.begin_time}")
 
 if __name__ == "__main__":
   unittest.main()

extra/thunder/tiny/tk/__init__.py

@@ -0,0 +1,6 @@
+from tinygrad.device import Device
+
+if Device.DEFAULT == "AMD":
+  WARP_THREADS = 64
+else:
+  WARP_THREADS = 32

extra/thunder/tiny/tk/group.py

@@ -0,0 +1,290 @@
+import math, functools
+from typing import cast, Callable
+from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
+from tinygrad.uop.ops import AxisType, UOp, KernelInfo, Ops
+from tinygrad.engine.realize import ExecItem, get_runner
+from tinygrad.dtype import AddrSpace, PtrDType
+from tinygrad.helpers import getenv, prod
+
+from extra.thunder.tiny.tk import WARP_THREADS
+from extra.thunder.tiny.tk.tiles import ALL_TILES, GL, ST, RT, RV
+
+class Group:
+  def __init__(self, warps:int, ker):
+    self.warps = warps
+    self.group_threads = warps * WARP_THREADS
+    self.threadIdx_x = ker.threadIdx_x
+    self.ker = ker
+
+  # helpers
+  @property
+  def laneid(self): return self.threadIdx_x % self.group_threads
+  @property
+  def warpid(self): return self.laneid // WARP_THREADS
+  @property
+  def groupid(self): return self.threadIdx_x // self.group_threads
+
+  # ops that only work on a single warp
+
+  clear_rid = 1000
+  def clear(self, reg:ALL_TILES, value:float=0):
+    reg = cast(UOp, reg)
+    assert self.warps == 1
+
+    rngs_for_shape = tuple(UOp.range(dim, Group.clear_rid + i) for i, dim in enumerate(reg.shape))
+    Group.clear_rid += len(reg.shape)
+
+    reg_store = reg[*rngs_for_shape].store(value).end(*rngs_for_shape)
+
+    self.ker.push_store(reg_store, reg)
+    return reg.after(reg_store).reshape(reg.shape)
+
+  def zero(self, reg:ALL_TILES): return self.clear(reg, 0)
+  def neg_inf(self, reg:ALL_TILES): return self.clear(reg, -math.inf)
+
+  copy_rid = 300
+  def copy(self, dst:ALL_TILES, src:ALL_TILES):
+    dst, src = cast(UOp, dst), cast(UOp, src)
+    assert self.warps == 1
+    assert dst.shape == src.shape
+
+    rngs_for_shape = tuple(UOp.range(dim, Group.copy_rid + i) for i, dim in enumerate(dst.shape))
+    Group.copy_rid += len(dst.shape)
+
+    dst_store = dst[*rngs_for_shape].store(src[*rngs_for_shape].cast(dst.dtype.base)).end(*rngs_for_shape)
+
+    self.ker.push_store(dst_store, dst)
+    return dst.after(dst_store).reshape(dst.shape)
+
+  def mma_AB(self, c:UOp|RT, a:UOp|RT, b:UOp|RT):
+    c, a, b = cast(UOp, c), cast(UOp, a), cast(UOp, b)
+    assert self.warps == 1
+
+    for height in self.ker.range(c.shape[-3], track=False):
+      for width in self.ker.range(c.shape[-2], track=False):
+        for inner in self.ker.range(a.shape[-2], AxisType.REDUCE, track=False):
+          wmma_arg = ("WMMA_8_16_16_bfloat16_float", (8, 16, 16), dtypes.bfloat16, dtypes.float, "CUDA", 32, (((4, 2), (3, 2), (8, 2)), ((4, 2), (3, 2)), ((4, 2), (3, 2))), ())
+
+          a_in = UOp.vectorize(*[a[height, inner, i] for i in range(8)])
+          b_in1 = UOp.vectorize(*([b[inner, width, i] for i in range(2)] + [b[inner, width, 4+i] for i in range(2)]))
+          c_out1 = UOp.vectorize(*[c[height, width, i] for i in range(4)])
+          b_in2 = UOp.vectorize(*([b[inner, width, 2+i] for i in range(2)] + [b[inner, width, 6+i] for i in range(2)]))
+          c_out2 = UOp.vectorize(*[c[height, width, 4+i] for i in range(4)])
+
+          out1 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in1, c_out1), arg=wmma_arg)
+          out2 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in2, c_out2), arg=wmma_arg)
+          c_i = [c[height, width, i].store(out1.gep(i)) for i in range(4)] + [c[height, width, 4+i].store(out2.gep(i)) for i in range(4)]
+          c_store = UOp.group(*c_i).end(height, width, inner)
+
+    self.ker.push_store(c_store, c)
+    return c.after(c_store).reshape(c.shape)
+
+  def mma_ABt(self, c:UOp|RT, a:UOp|RT, b:UOp|RT):
+    c, a, b = cast(UOp, c), cast(UOp, a), cast(UOp, b)
+    assert self.warps == 1
+
+    for height in self.ker.range(c.shape[-3], track=False):
+      for width in self.ker.range(c.shape[-2], track=False):
+        for inner in self.ker.range(a.shape[-2], AxisType.REDUCE, track=False):
+          wmma_arg = ("WMMA_8_16_16_bfloat16_float", (8, 16, 16), dtypes.bfloat16, dtypes.float, "CUDA", 32, (((4, 2), (3, 2), (8, 2)), ((4, 2), (3, 2)), ((4, 2), (3, 2))), ())
+
+          a_in = UOp.vectorize(*[a[height, inner, i] for i in range(8)])
+          b_in1 = UOp.vectorize(*([b[width, inner, i] for i in range(2)] + [b[width, inner, 4+i] for i in range(2)]))
+          c_out1 = UOp.vectorize(*[c[height, width, i] for i in range(4)])
+          b_in2 = UOp.vectorize(*([b[width, inner, 2+i] for i in range(2)] + [b[width, inner, 6+i] for i in range(2)]))
+          c_out2 = UOp.vectorize(*[c[height, width, 4+i] for i in range(4)])
+
+          out1 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in1, c_out1), arg=wmma_arg)
+          out2 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in2, c_out2), arg=wmma_arg)
+          c_i = [c[height, width, i].store(out1.gep(i)) for i in range(4)] + [c[height, width, 4+i].store(out2.gep(i)) for i in range(4)]
+          c_store = UOp.group(*c_i).end(height, width, inner)
+
+    self.ker.push_store(c_store, c)
+    return c.after(c_store).reshape(c.shape)
+
+  map_rid = 400
+  def map(self, a:ALL_TILES, op:Callable[[UOp], UOp]|Callable[[UOp, tuple], UOp]):
+    a = cast(UOp, a)
+    assert self.warps == 1
+
+    rngs_for_shape = tuple(UOp.range(dim, Group.map_rid + i) for i, dim in enumerate(a.shape))
+    Group.map_rid += len(a.shape)
+
+    if op.__code__.co_argcount == 1:
+      to_store = op(a[*rngs_for_shape])
+    else:
+      to_store = op(a[*rngs_for_shape], rngs_for_shape)
+
+    a_store = a[*rngs_for_shape].store(to_store).end(*rngs_for_shape)
+
+    self.ker.push_store(a_store, a)
+    return a.after(a_store).reshape(a.shape)
+
+  def row_reduce(self, vec:UOp|RV, src:UOp|RT, op:Callable[[UOp, UOp], UOp]):
+    vec, src = cast(UOp, vec), cast(UOp, src)
+    assert self.warps == 1
+
+    red_local = self.ker.alloc((self.group_threads, 2), src.dtype.base, AddrSpace.LOCAL)
+    red_reg = self.ker.alloc((2,), src.dtype.base, AddrSpace.REG)
+
+    for height in self.ker.range(src.shape[-3], track=False):
+      i = UOp.range(red_reg.size, Group.clear_rid)
+      Group.clear_rid += 1
+      red_reg = red_reg.after(height, *[tkr._rng for tkr in self.ker.range_stack])
+      reg_store = red_reg.flatten()[i].store(0.).end(i)
+      red_reg = red_reg.after(reg_store).reshape(red_reg.shape)
+
+      for outer in self.ker.range(2, track=False):
+        for width in self.ker.range(src.shape[-2], AxisType.REDUCE, track=False):
+          for inner in self.ker.range(4, AxisType.REDUCE, track=False):
+            elem_index = inner + 2 * (inner // 2) + outer * 2
+            reg_store = red_reg[outer].store(op(red_reg[outer], src[height, width, elem_index])).end(inner, width, outer)
+            red_reg = red_reg.after(reg_store).reshape(red_reg.shape)
+
+      # store to shared memory
+      for outer in self.ker.range(2, track=False):
+        red_local_store = red_local[self.laneid, outer].store(red_reg[outer]).end(outer)
+        red_local = red_local.after(red_local_store.barrier()).reshape(red_local.shape)
+
+      # reduce from shared memory
+      for outer in self.ker.range(2, track=False):
+        for inner in self.ker.range(3, AxisType.REDUCE, track=False):
+          offset = (self.laneid // 4) * 4 + ((self.laneid + inner + 1) % 4)
+          reg_store = red_reg[outer].store(op(red_reg[outer], red_local[offset, outer])).end(inner, outer)
+          red_reg = red_reg.after(reg_store).reshape(red_reg.shape)
+
+      # reduce with vec
+      for outer in self.ker.range(2, track=False):
+        vec_store = vec[height, 0, outer].store(op(vec[height, 0, outer], red_reg[outer])).end(outer, height)
+
+    self.ker.push_store(vec_store, vec)
+    return vec.after(vec_store).reshape(vec.shape)
+
+  # ops that can work across multiple warps
+
+  LOAD_INNER = 4
+  def load(self, dst:ALL_TILES, src:ALL_TILES, dst_idxs:tuple[UOp|int,...]=(), idxs:tuple[UOp|int,...]=(), axis:int=0, transpose:bool=False):
+    dst, src = cast(UOp, dst), cast(UOp, src)
+    assert isinstance(dst.dtype, PtrDType) and isinstance(src.dtype, PtrDType)
+    dst_dtype, src_dtype = cast(PtrDType, dst.dtype), cast(PtrDType, src.dtype)
+    if dst_dtype.addrspace == AddrSpace.REG and src_dtype.addrspace == AddrSpace.LOCAL:
+      srcf = src.flatten(-2)
+
+      if self.warps % 4 == 0: local_warpid = (self.warpid // 4) + (self.warpid % 4) * (self.warps // 4)
+      else: local_warpid = self.warpid
+      warp_laneid = self.threadIdx_x % WARP_THREADS
+
+      for height in self.ker.range(dst.shape[-3], track=False):
+        for width in self.ker.range(dst.shape[-2], track=False):
+          for inner in self.ker.range(RT.BASE_TILE_NEPT, track=False):
+            base_row = (local_warpid * dst.shape[-3] + height) * RT.BASE_TILE_ROWS
+            base_col = width * RT.BASE_TILE_COLS
+
+            if not transpose:
+              row = base_row + (warp_laneid // 4)
+              col = base_col + 2 * (warp_laneid % 4)
+
+              row_offset = ((inner % 4) // 2) * 8
+              col_offset = (inner % 2) + (inner // 4) * 8
+            else:
+              row = base_row + 2 * (warp_laneid % 4)
+              col = base_col + (warp_laneid // 4)
+
+              row_offset = (inner % 2) + (inner // 4) * 8
+              col_offset = ((inner % 4) // 2) * 8
+
+            src_i_last = (row + row_offset) * src.shape[-1] + col + col_offset
+
+            dst_store = dst[*dst_idxs, height, width, inner].store(srcf[*idxs[:-2], src_i_last])
+            dst_store = dst_store.end(height, width, inner)
+    elif dst_dtype.addrspace == AddrSpace.LOCAL and src_dtype.addrspace == AddrSpace.GLOBAL:
+      dstf = dst.flatten(-2)
+
+      srcf = src.flatten()
+      row_stride = prod(src.shape[axis+1:])
+
+      idxs = tuple(idx * dst.shape[-2] if i == axis else idx for i, idx in enumerate(idxs))
+      idxs = tuple(idx * dst.shape[-1] if i == 3 else idx for i, idx in enumerate(idxs))
+      src_i = ((idxs[0] * src.shape[-3] + idxs[1]) * src.shape[-2] + idxs[2]) * src.shape[-1] + idxs[3]
+
+      memcpy_per_row = dst.shape[-1] // Group.LOAD_INNER
+      total_calls = prod(dst.shape[-2:]) // (self.group_threads * Group.LOAD_INNER)
+
+      for outer in self.ker.range(total_calls, track=False):
+        for inner in self.ker.range(Group.LOAD_INNER, track=False):
+          load_idx = outer * self.group_threads + self.laneid
+          row = load_idx // memcpy_per_row
+          col = (load_idx * Group.LOAD_INNER) % dst.shape[-1]
+
+          dst_i = row * dst.shape[-1] + col + inner
+          src_i += row * row_stride + col + inner
+
+          dst_store = dstf[*dst_idxs, dst_i].store(srcf[src_i]).end(outer, inner)
+    else:
+      raise NotImplementedError(f"load from {src_dtype.addrspace} to {dst_dtype.addrspace} not implemented")
+
+    return dst.after(dst_store.barrier()).reshape(dst.shape)
+
+  STORE_INNER = 4
+  def store(self, dst:ALL_TILES, src:ALL_TILES, idxs:tuple[UOp|int,...]=(), src_idxs:tuple[UOp|int,...]=(), axis:int=0, transpose:bool=False):
+    dst, src = cast(UOp, dst), cast(UOp, src)
+    assert isinstance(dst.dtype, PtrDType) and isinstance(src.dtype, PtrDType)
+    dst_dtype, src_dtype = cast(PtrDType, dst.dtype), cast(PtrDType, src.dtype)
+    if src_dtype.addrspace == AddrSpace.REG and dst_dtype.addrspace == AddrSpace.LOCAL:
+      dstf = dst.flatten(-2)
+
+      if self.warps % 4 == 0: local_warpid = (self.warpid // 4) + (self.warpid % 4) * (self.warps // 4)
+      else: local_warpid = self.warpid
+      warp_laneid = self.threadIdx_x % WARP_THREADS
+
+      for height in self.ker.range(src.shape[-3], track=False):
+        for width in self.ker.range(src.shape[-2], track=False):
+          for inner in self.ker.range(RT.BASE_TILE_NEPT, track=False):
+            base_row = (local_warpid * src.shape[-3] + height) * RT.BASE_TILE_ROWS
+            base_col = width * RT.BASE_TILE_COLS
+
+            if not transpose:
+              row = base_row + (warp_laneid // 4)
+              col = base_col + 2 * (warp_laneid % 4)
+
+              row_offset = ((inner % 4) // 2) * 8
+              col_offset = (inner % 2) + (inner // 4) * 8
+            else:
+              row = base_row + 2 * (warp_laneid % 4)
+              col = base_col + (warp_laneid // 4)
+
+              row_offset = (inner % 2) + (inner // 4) * 8
+              col_offset = ((inner % 4) // 2) * 8
+
+            dst_i_last = (row + row_offset) * dst.shape[-1] + col + col_offset
+
+            dst_store = dstf[*idxs[:-2], dst_i_last].store(src[*src_idxs, height, width, inner])
+            dst_store = dst_store.end(height, width, inner)
+    elif src_dtype.addrspace == AddrSpace.LOCAL and dst_dtype.addrspace == AddrSpace.GLOBAL:
+      dstf = dst.flatten()
+      row_stride = prod(dst.shape[axis+1:])
+
+      idxs = tuple(idx * src.shape[-2] if i == axis else idx for i, idx in enumerate(idxs))
+      idxs = tuple(idx * src.shape[-1] if i == 3 else idx for i, idx in enumerate(idxs))
+      dst_i = ((idxs[0] * dst.shape[-3] + idxs[1]) * dst.shape[-2] + idxs[2]) * dst.shape[-1] + idxs[3]
+
+      srcf = src.flatten(-2)
+
+      memcpy_per_row = src.shape[-1] // Group.STORE_INNER
+      total_calls = prod(src.shape[-2:]) // (self.group_threads * Group.STORE_INNER)
+
+      for outer in self.ker.range(total_calls, track=False):
+        for inner in self.ker.range(Group.STORE_INNER, track=False):
+          load_idx = outer * self.group_threads + self.laneid
+          row = load_idx // memcpy_per_row
+          col = (load_idx * Group.STORE_INNER) % src.shape[-1]
+
+          src_i = row * src.shape[-1] + col + inner
+          dst_i += row * row_stride + col + inner
+
+          dst_store = dstf[dst_i].store(srcf[*src_idxs, src_i]).end(outer, inner)
+    else:
+      raise NotImplementedError(f"store from {src_dtype.addrspace} to {dst_dtype.addrspace} not implemented")
+
+    self.ker.push_store(dst_store, dst)
+    return dst.after(dst_store.barrier()).reshape(dst.shape)

extra/thunder/tiny/tk/kernel.py

@@ -0,0 +1,92 @@
+from contextlib import AbstractContextManager
+from tinygrad.uop.ops import UOp, KernelInfo, AxisType, AddrSpace
+from extra.thunder.tiny.tk import WARP_THREADS
+from extra.thunder.tiny.tk.group import Group
+from extra.thunder.tiny.tk.tiles import GL, ST, RT, RV
+
+class _tk_range:
+  user_rid = 0
+  def __init__(self, end:int, axis_type:AxisType): self.end, self.axis_type, self.done = end, axis_type, False
+  def __iter__(self): return self
+  def __next__(self):
+    if not self.done:
+      self.done = True
+      _tk_range.user_rid += 1
+      self._rng = UOp.range(self.end, _tk_range.user_rid-1, axis_type=self.axis_type)
+      return self._rng
+    raise StopIteration
+
+class Kernel(AbstractContextManager):
+  def __init__(self, grid_size:tuple[int, int, int], block_size:int):
+    self.blockIdx_x = UOp.special(grid_size[0], "gidx0")
+    self.blockIdx_y = UOp.special(grid_size[1], "gidx1")
+    self.blockIdx_z = UOp.special(grid_size[2], "gidx2")
+    self.threadIdx_x = UOp.special(block_size, "lidx0")
+
+    self.range_stack = []
+    self.store_stack = []
+
+    self.global_slot = 0
+    self.shared_slot = 0
+    self.register_slot = 0
+    self.allocs = {}
+
+  @property
+  def warpid(self): return self.threadIdx_x // WARP_THREADS
+
+  def __enter__(self): return self
+  def __exit__(self, exc_type, exc_value, traceback): pass
+
+  def group(self, size:int): return Group(size, self)
+  @property
+  def warp(self): return self.group(1)
+  @property
+  def warpgroup(self): return self.group(4)
+
+  def range(self, end:int, axis_type:AxisType=AxisType.LOOP, track:bool=True):
+    rng = _tk_range(end, axis_type)
+    if track: self.range_stack.append(rng)
+    return rng
+
+  def alloc(self, shape, dtype, addrspace:AddrSpace, name:str|None=None):
+    match addrspace:
+      case AddrSpace.GLOBAL:
+        slot = self.global_slot
+        self.global_slot += 1
+      case AddrSpace.LOCAL:
+        slot = self.shared_slot
+        self.shared_slot += 1
+      case AddrSpace.REG:
+        slot = self.register_slot
+        self.register_slot += 1
+
+    uop = UOp.placeholder(shape, dtype, slot=slot, addrspace=addrspace)
+
+    if name:
+      if (name, shape) in self.allocs: return self.allocs[(name, shape)]
+      self.allocs[(name, shape)] = uop
+
+    return uop
+
+  def gl(self, shape, dtype): return GL.create(shape, dtype, self)
+  def st(self, shape, dtype): return ST.create(shape, dtype, self)
+  def rt(self, shape, dtype): return RT.create(shape, dtype, self)
+  def rv(self, length, dtype, layout="naive"): return RV.create(length, dtype, layout, self)
+
+  def push_store(self, store:UOp, uop:UOp): self.store_stack.append((store, uop))
+
+  def finish(self):
+    # end all ranges
+    rngs = []
+    while self.range_stack: rngs.append(self.range_stack.pop(0)._rng)
+
+    last_store = self.store_stack.pop()[0]
+    if hasattr(last_store, '_uop'): uop = last_store._uop
+    else: uop = last_store
+
+    return uop.end(*rngs).sink(arg=KernelInfo(opts_to_apply=())).simplify()
+
+  def endrange(self):
+    last_store = self.store_stack.pop()
+    last_range = self.range_stack.pop()
+    return last_store[1].after(last_store[0].barrier().end(last_range._rng)).reshape(last_store[1].shape)

extra/thunder/tiny/tk/tiles.py

@@ -0,0 +1,155 @@
+import functools
+from tinygrad.dtype import AddrSpace
+from tinygrad.mixin import MathMixin
+from tinygrad.uop.ops import UOp, Ops
+
+from extra.thunder.tiny.tk import WARP_THREADS
+
+def unwrap(x):
+  if hasattr(x, "_uop"): return x._uop
+  if isinstance(x, (list, tuple)): return type(x)(unwrap(y) for y in x)
+  if isinstance(x, dict): return {k: unwrap(v) for k,v in x.items()}
+  return x
+
+def wrap(x, s):
+  if isinstance(x, UOp): return s.ruop(x)
+  if isinstance(x, (list, tuple)): return type(x)(wrap(y, s) for y in x)
+  return x
+
+def autowrap(source_cls, blacklist=None):
+  if blacklist is None:
+    blacklist = {
+      "__init__", "__new__", "__str__", "__del__", "__repr__", "__dict__", "__getattribute__",
+      "__setattr__", "__delattr__", "__weakref__", "__slots__", "__class__",
+      "__reduce__", "__reduce_ex__", "__getstate__", "__setstate__", "__hash__"
+    }
+
+  def decorator(cls):
+    def __getattr__(self, name):
+      uop = object.__getattribute__(self, "_uop")
+      val = getattr(uop, name)
+      if callable(val):
+        @functools.wraps(val)
+        def proxy(*args, **kwargs):
+          return wrap(val(*unwrap(args), **unwrap(kwargs)), self)
+        return proxy
+      if name in UOp.__slots__: return val
+      return wrap(val, self)
+    cls.__getattr__ = __getattr__
+
+    for name in dir(source_cls):
+      if name in blacklist or not name.startswith("__"): continue
+
+      for base in cls.mro():
+        if base is source_cls: break
+        if name in base.__dict__: break
+      else:
+        original = getattr(source_cls, name)
+        if callable(original):
+          def make_proxy(_, func):
+            def proxy(self, *args, **kwargs):
+              return wrap(func(self._uop, *unwrap(args), **unwrap(kwargs)), self)
+            return proxy
+          setattr(cls, name, make_proxy(name, original))
+
+    return cls
+  return decorator
+
+class TileMathMixin(MathMixin):
+  def alu(self, op, *src, inner_op=lambda x:x):
+    assert isinstance(self, (RT, RV))
+    if len(src) == 0:
+      if self._uop._shape is None: uop = UOp.alu(self._uop, op)
+      else: uop = self.ker.warp.map(self._uop, lambda x: UOp.alu(x, op))
+    elif len(src) == 1:
+      if self._uop._shape is None: uop = UOp.alu(self._uop, op, inner_op(self._uop.ufix(src[0])))
+      elif isinstance(src[0], (int,float,bool)): uop = self.ker.warp.map(self._uop, lambda x: UOp.alu(x, op, inner_op(x.ufix(src[0]))))
+      elif src[0]._shape is None: uop = UOp.alu(self._uop, op, inner_op(self._uop.ufix(src[0])))
+      else:
+        if isinstance(self, RT) and isinstance(src[0], RV): uop = self.ker.warp.map(self._uop, lambda x, idx: UOp.alu(x, op, inner_op(src[0]._uop[idx[0], 0, (idx[2]%4)//2])))
+        else: uop = self.ker.warp.map(self._uop, lambda x, idx: UOp.alu(x, op, inner_op(src[0]._uop[*idx])))
+    else: raise NotImplementedError
+    return self.ruop(uop)
+  def const_like(self, b): return b
+
+  # override ops that do compute on the src uop
+  def sub(self, x, reverse=False):
+    return self.ufix(x).alu(Ops.ADD, self, inner_op=lambda y: -y) if reverse else self.alu(Ops.ADD, self.ufix(x), inner_op=lambda y: -y)
+  def div(self, x, reverse=False):
+    return self.ufix(x).alu(Ops.MUL, self, inner_op=lambda y: 1/y) if reverse else self.alu(Ops.MUL, self.ufix(x), inner_op=lambda y: 1/y)
+
+@autowrap(UOp)
+class GL:
+  def __init__(self, uop, ker):
+    self._uop, self.ker = uop, ker
+
+  def ruop(self, uop):
+    return GL(uop, self.ker)
+
+  @classmethod
+  def create(cls, shape, dtype, ker):
+    uop = ker.alloc(shape, dtype, AddrSpace.GLOBAL)
+    return cls(uop, ker)
+
+@autowrap(UOp)
+class ST:
+  def __init__(self, uop, ker):
+    self._uop, self.ker = uop, ker
+
+  def ruop(self, uop):
+    return ST(uop, self.ker)
+
+  @classmethod
+  def create(cls, shape, dtype, ker):
+    uop = ker.alloc(shape, dtype, AddrSpace.LOCAL)
+    return cls(uop, ker)
+
+@autowrap(UOp)
+class RT(TileMathMixin):
+  BASE_TILE_ROWS, BASE_TILE_COLS = 16, 16
+  BASE_TILE_NE = BASE_TILE_ROWS * BASE_TILE_COLS
+  BASE_TILE_NEPT = BASE_TILE_NE // WARP_THREADS
+
+  def __init__(self, uop, ker):
+    self._uop, self.ker = uop, ker
+
+  def ruop(self, uop):
+    return RT(uop, self.ker)
+
+  @classmethod
+  def create(cls, shape, dtype, ker):
+    assert len(shape) == 2
+    assert shape[0] % RT.BASE_TILE_ROWS == 0
+    assert shape[1] % RT.BASE_TILE_COLS == 0
+
+    height = shape[0] // RT.BASE_TILE_ROWS
+    width = shape[1] // RT.BASE_TILE_COLS
+
+    uop = ker.alloc((height, width, RT.BASE_TILE_NEPT), dtype, AddrSpace.REG)
+    return cls(uop, ker)
+
+@autowrap(UOp)
+class RV(TileMathMixin):
+  def __init__(self, uop, layout, ker):
+    self._uop, self.layout, self.ker = uop, layout, ker
+
+  def ruop(self, uop):
+    return RV(uop, self.layout, self.ker)
+
+  @classmethod
+  def create(cls, length, dtype, layout, ker):
+    tiles = length // RT.BASE_TILE_ROWS
+
+    match layout:
+      case "naive":
+        inner_dim = 1
+        outer_dim = (tiles + 1) // 2
+      case "ortho":
+        inner_dim = 1
+        outer_dim = tiles
+      case _: raise NotImplementedError(f"rv layout {layout} not implemented")
+
+    uop = ker.alloc((outer_dim, inner_dim, 2), dtype, AddrSpace.REG)
+    return RV(uop, layout, ker)
+
+ALL_TILES = UOp | GL | ST | RT | RV

extra/usbgpu/patch.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 import sys, os, zlib, struct, hashlib
-from hexdump import hexdump
 from tinygrad.helpers import DEBUG, getenv, fetch
 from tinygrad.runtime.support.usb import USB3
 

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

@@ -119,14 +119,7 @@ extension TinyGPUViewModel: OSSystemExtensionRequestDelegate {
 
 		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
 	}

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

@@ -7,30 +7,48 @@
 struct TinyGPUDriverUserClient_IVars
 {
 	OSSharedPtr<TinyGPUDriver> provider = nullptr;
+
+	TinyGPUCreateDMAResp *dmas = nullptr;
+	size_t dmaCount = 0;
+	size_t dmaCap = 0;
+
+	int ensureDMACap(size_t need)
+	{
+		// not thread-safe
+		if (need <= dmaCap) return 0;
+
+		size_t newCap = dmaCap ? dmaCap * 2 : 16;
+		while (newCap < need) newCap *= 2;
+
+		auto *newArr = IONewZero(TinyGPUCreateDMAResp, newCap);
+		if (!newArr) return -kIOReturnNoMemory;
+
+		if (dmas && dmaCount) {
+			memcpy(newArr, dmas, dmaCount * sizeof(TinyGPUCreateDMAResp));
+		}
+
+		IOSafeDeleteNULL(dmas, TinyGPUCreateDMAResp, dmaCap);
+		dmas = newArr;
+		dmaCap = newCap;
+		return 0;
+	}
 };
 
 bool TinyGPUDriverUserClient::init()
 {
-	auto theAnswer = super::init();
-	if (!theAnswer) {
-		return false;
-	}
+	auto ok = super::init();
+	if (!ok) return false;
 
 	ivars = IONewZero(TinyGPUDriverUserClient_IVars, 1);
-	if (ivars == nullptr) {
-		return false;
-	}
-
+	if (!ivars) return false;
 	return true;
 }
 
 void TinyGPUDriverUserClient::free()
 {
-	if (ivars != nullptr) {
-		ivars->provider.reset();
+	if (ivars) {
+		IOSafeDeleteNULL(ivars, TinyGPUDriverUserClient_IVars, 1);
 	}
-
-	IOSafeDeleteNULL(ivars, TinyGPUDriverUserClient_IVars, 1);
 	super::free();
 }
 
@@ -59,6 +77,22 @@ error:
 
 kern_return_t TinyGPUDriverUserClient::Stop_Impl(IOService* in_provider)
 {
+	// release all DMA allocations for this client
+	if (ivars) {
+		for (size_t i = 0; i < ivars->dmaCount; i++) {
+			auto &d = ivars->dmas[i];
+			if (d.dmaCmd) {
+				d.dmaCmd->CompleteDMA(kIODMACommandCompleteDMANoOptions);
+				d.dmaCmd->release();
+				d.dmaCmd = nullptr;
+			}
+		}
+		ivars->dmaCount = 0;
+		IOSafeDeleteNULL(ivars->dmas, TinyGPUCreateDMAResp, ivars->dmaCap);
+		ivars->dmas = nullptr;
+		ivars->provider.reset();
+	}
+
 	return Stop(in_provider, SUPERDISPATCH);
 }
 
@@ -102,26 +136,26 @@ kern_return_t TinyGPUDriverUserClient::ExternalMethod(uint64_t selector, IOUserC
 
 kern_return_t IMPL(TinyGPUDriverUserClient, CopyClientMemoryForType)
 {
-	if (!memory) {
-		return kIOReturnBadArgument;
-	}
-
-	if (ivars->provider.get() == nullptr) {
-		return kIOReturnNotAttached;
-	}
+	if (!memory) return kIOReturnBadArgument;
+	if (!ivars->provider.get()) return kIOReturnNotAttached;
 
+	// bar handling, type is bar num
 	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;
+	// dma handling, type is size
+	if (ivars->ensureDMACap(ivars->dmaCount + 1)) {
+		os_log(OS_LOG_DEFAULT, "tinygpu: cannot grow dma array");
+		return kIOReturnNoMemory;
 	}
 
+	TinyGPUCreateDMAResp buf{};
+	kern_return_t err = ivars->provider->CreateDMA(type, &buf);
+	if (err) return err;
+
+	ivars->dmas[ivars->dmaCount++] = buf;
 	*memory = buf.sharedBuf;
 	return 0;
 }

extra/weekly_commits_table.py

@@ -1,7 +1,7 @@
 # extra/weekly_commits_table.py
 import os, subprocess, datetime as dt
 
-NAMES = ["chenyu","George Hotz","nimlgen","qazal","Sieds Lykles","wozeparrot"]
+NAMES = ["chenyu","George Hotz","nimlgen","qazal","wozeparrot"]
 REPO  = os.environ.get("REPO_PATH",".")
 today = dt.date.today()
 days  = [(today - dt.timedelta(i)).strftime("%Y-%m-%d") for i in range(6,-1,-1)]
@@ -40,4 +40,4 @@ for d in days:
 print("** Commits by day (last 7) **")
 print("```")
 print("\n".join([header, rule] + rows))
-print("```")
+print("```")

mypy.ini

@@ -1,10 +0,0 @@
-[mypy]
-warn_unused_configs = True
-files = tinygrad
-ignore_missing_imports = True
-check_untyped_defs = True
-explicit_package_bases = True
-warn_unreachable = True
-warn_redundant_casts = True
-# NOTE: had to comment this out to make mypy pass on both CI and OSX
-#warn_unused_ignores = True

pyproject.toml

@@ -0,0 +1,232 @@
+[project]
+name = "tinygrad"
+version = "0.11.0"
+description = "You like pytorch? You like micrograd? You love tinygrad! <3"
+authors = [{ name = "George Hotz" }]
+
+classifiers = ["Programming Language :: Python :: 3"]
+
+license = 'MIT'
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = []
+
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+include-package-data = true
+packages = [
+  'tinygrad',
+  'tinygrad.apps',
+  'tinygrad.codegen',
+  'tinygrad.codegen.opt',
+  'tinygrad.codegen.late',
+  'tinygrad.engine',
+  'tinygrad.mixin',
+  'tinygrad.nn',
+  'tinygrad.renderer',
+  'tinygrad.runtime',
+  'tinygrad.runtime.autogen',
+  'tinygrad.runtime.autogen.am',
+  'tinygrad.runtime.graph',
+  'tinygrad.runtime.support',
+  'tinygrad.runtime.support.am',
+  'tinygrad.runtime.support.nv',
+  'tinygrad.schedule',
+  'tinygrad.uop',
+  'tinygrad.viz',
+]
+
+[tool.setuptools.package-data]
+tinygrad = ["py.typed"]
+"tinygrad.viz" = ["index.html", "assets/**/*", "js/*"]
+
+
+[project.optional-dependencies]
+arm = ["unicorn"]
+triton = ["triton-nightly>=2.1.0.dev20231014192330"]
+linting = [
+  "pylint",
+  "mypy==1.18.1",
+  "typing-extensions",
+  "pre-commit",
+  "ruff",
+  "numpy",
+  "typeguard",
+]
+# mlperf = [
+#   "mlperf-logging @ git+https://github.com/mlperf/[email protected]",
+# ]
+testing_minimal = [
+  "numpy",
+  "torch==2.9.0",
+  "pytest",
+  "pytest-xdist",
+  "pytest-timeout",
+  "pytest-split",
+  "hypothesis",
+  "z3-solver",
+]
+testing_unit = ["tinygrad[testing_minimal]", "tqdm", "safetensors", "tabulate"]
+testing = [
+  "tinygrad[testing_minimal]",
+  "pillow",
+  "onnx==1.18.0",
+  "onnx2torch",
+  "onnxruntime",
+  "opencv-python",
+  "tabulate",
+  "tqdm",
+  "safetensors",
+  "transformers",
+  "sentencepiece",
+  "tiktoken",
+  "blobfile",
+  "librosa",
+  # librosa needs numba but uv ignores python upper bounds and some numba versions require <python3.10
+  "numba>=0.55",
+  "networkx",
+  "nibabel",
+  "bottle",
+  "ggml-python",
+  "capstone",
+  "pycocotools",
+  "boto3",
+  "pandas",
+  "influxdb3-python",
+]
+docs = [
+  "mkdocs",
+  "mkdocs-material",
+  "mkdocstrings[python]",
+  "markdown-callouts",
+  "markdown-exec[ansi]",
+  "black",
+  "numpy",
+]
+
+
+[tool.mutmut]
+paths_to_mutate = ["tinygrad/"]
+do_not_mutate = [
+  "tinygrad/apps/*",
+  "tinygrad/codegen/*",
+  "tinygrad/engine/*",
+  "tinygrad/nn/*",
+  "tinygrad/renderer/*",
+  "tinygrad/runtime/*",
+  "tinygrad/schedule/*",
+  "tinygrad/uop/*",
+  "tinygrad/viz/*",
+  "tinygrad/device.py",
+  "tinygrad/dtype.py",
+  "tinygrad/gradient.py",
+  "tinygrad/helpers.py",
+  "tinygrad/tensor.py",
+]
+tests_dir = ["test/test_tiny.py", "test/test_ops.py"]
+debug = true
+
+
+[tool.mypy]
+warn_unused_configs = true
+files = ["tinygrad"]
+ignore_missing_imports = true
+check_untyped_defs = true
+explicit_package_bases = true
+warn_unreachable = true
+warn_redundant_casts = true
+# NOTE: had to comment this out to make mypy pass on both CI and OSX
+#warn_unused_ignores = true
+
+[tool.pytest.ini_options]
+norecursedirs = [
+  "extra",
+  ".hypothesis",
+  ".git",
+]
+timeout = 300
+timeout_method = "thread"
+timeout_func_only = true
+testpaths = ["test"]
+
+[tool.ruff]
+preview = true
+target-version = "py311"
+line-length = 150
+indent-width = 2
+exclude = [
+  ".git/",
+  "docs/",
+  "extra/",
+  "test/external/mlperf_resnet",
+  "test/external/mlperf_unet3d",
+]
+
+[tool.ruff.lint]
+select = [
+  "F",  # Pyflakes
+  "W6",
+  "E71",
+  "E72",
+  "E112",   # no-indented-block
+  "E113",   # unexpected-indentation
+  # "E124",
+  "E203",   # whitespace-before-punctuation
+  "E272",   # multiple-spaces-before-keyword
+  "E275",   # missing-whitespace-after-keyword
+  "E303",   # too-many-blank-lines
+  "E304",   # blank-line-after-decorator
+  "E501",   # line-too-long
+  # "E502",
+  "E702",   # multiple-statements-on-one-line-semicolon
+  "E703",   # useless-semicolon
+  "E731",   # lambda-assignment
+  "W191",   # tab-indentation
+  "W291",   # trailing-whitespace
+  "W293",   # blank-line-with-whitespace
+  "UP039",  # unnecessary-class-parentheses
+  "C416",   # unnecessary-comprehension
+  "RET506", # superfluous-else-raise
+  "RET507", # superfluous-else-continue
+  "A",      # builtin-variable-shadowing, builtin-argument-shadowing, builtin-attribute-shadowing
+  "FURB110",# if-exp-instead-of-or-operator
+  "RUF018", # assignment-in-assert
+]
+
+# detect unused imports in examples
+[tool.ruff.lint.per-file-ignores]
+"examples/**/*.py" = [
+  "W6",
+  "E71",
+  "E72",
+  "E112",
+  "E113",
+  "E203",
+  "E272",
+  "E275",
+  "E303",
+  "E304",
+  "E501",
+  "E702",
+  "E703",
+  "E731",
+  "W191",
+  "W291",
+  "W293",
+  "UP039",
+  "C416",
+  "RET506",
+  "RET507",
+  "A",
+  "FURB110",
+  "RUF018",
+  "F541",
+  "F841",
+]
+"tinygrad/runtime/autogen/**/*.py" = ["E501", "F401", "E722", "E731", "F821", "A006"]
+
+[tool.ruff.format]
+exclude = ["*"]

pytest.ini

@@ -1,9 +0,0 @@
-[pytest]
-norecursedirs =
-    extra
-    .hypothesis
-    .git
-timeout = 300
-timeout_method = thread
-timeout_func_only = true
-testpaths = test

ruff.toml

@@ -1,56 +0,0 @@
-indent-width = 2
-preview = true
-target-version = "py310"
-
-lint.select = [
-  "F",  # Pyflakes
-  "W6",
-  "E71",
-  "E72",
-  "E112",   # no-indented-block
-  "E113",   # unexpected-indentation
-  # "E124",
-  "E203",   # whitespace-before-punctuation
-  "E272",   # multiple-spaces-before-keyword
-  "E275",   # missing-whitespace-after-keyword
-  "E303",   # too-many-blank-lines
-  "E304",   # blank-line-after-decorator
-  "E501",   # line-too-long
-  # "E502",
-  "E702",   # multiple-statements-on-one-line-semicolon
-  "E703",   # useless-semicolon
-  "E731",   # lambda-assignment
-  "W191",   # tab-indentation
-  "W291",   # trailing-whitespace
-  "W293",   # blank-line-with-whitespace
-  "UP039",  # unnecessary-class-parentheses
-  "C416",   # unnecessary-comprehension
-  "RET506", # superfluous-else-raise
-  "RET507", # superfluous-else-continue
-  "A",      # builtin-variable-shadowing, builtin-argument-shadowing, builtin-attribute-shadowing
-  "FURB110",# if-exp-instead-of-or-operator
-  "RUF018", # assignment-in-assert
-]
-
-line-length = 150
-
-exclude = [
-  ".git/",
-  "docs/",
-  "extra/",
-  "tinygrad/runtime/autogen",
-  "test/external/mlperf_resnet",
-  "test/external/mlperf_unet3d",
-]
-
-# detect unused imports in examples
-[lint.per-file-ignores]
-"examples/**/*.py" = [
-  "W6", "E71", "E72", "E112", "E113", "E203", "E272", "E275",
-  "E303", "E304", "E501", "E702", "E703", "E731", "W191",
-  "W291", "W293", "UP039", "C416", "RET506", "RET507", "A",
-  "FURB110", "RUF018", "F541", "F841"
-]
-
-[format]
-exclude = ["*"]

setup.py

@@ -1,111 +0,0 @@
-#!/usr/bin/env python3
-
-from pathlib import Path
-from setuptools import setup
-
-directory = Path(__file__).resolve().parent
-with open(directory / 'README.md', encoding='utf-8') as f:
-  long_description = f.read()
-
-testing_minimal = [
-  "numpy",
-  "torch==2.9.0",
-  "pytest",
-  "pytest-xdist",
-  "pytest-timeout",
-  "pytest-split",
-  "hypothesis",
-  "z3-solver",
-]
-
-setup(name='tinygrad',
-      version='0.11.0',
-      description='You like pytorch? You like micrograd? You love tinygrad! <3',
-      author='George Hotz',
-      license='MIT',
-      long_description=long_description,
-      long_description_content_type='text/markdown',
-      packages = [
-        'tinygrad',
-        'tinygrad.apps',
-        'tinygrad.codegen',
-        'tinygrad.codegen.opt',
-        'tinygrad.codegen.late',
-        'tinygrad.engine',
-        'tinygrad.mixin',
-        'tinygrad.nn',
-        'tinygrad.renderer',
-        'tinygrad.runtime',
-        'tinygrad.runtime.autogen',
-        'tinygrad.runtime.autogen.am',
-        'tinygrad.runtime.autogen.nv',
-        'tinygrad.runtime.graph',
-        'tinygrad.runtime.support',
-        'tinygrad.runtime.support.am',
-        'tinygrad.runtime.support.nv',
-        'tinygrad.schedule',
-        'tinygrad.uop',
-        'tinygrad.viz',
-      ],
-      package_data = {'tinygrad': ['py.typed'], 'tinygrad.viz': ['index.html', 'assets/**/*', 'js/*']},
-      classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License"
-      ],
-      install_requires=[],
-      python_requires='>=3.11',
-      extras_require={
-        'arm': ["unicorn"],
-        'triton': ["triton-nightly>=2.1.0.dev20231014192330"],
-        'linting': [
-            "pylint",
-            "mypy==1.18.1",
-            "typing-extensions",
-            "pre-commit",
-            "ruff",
-            "numpy",
-            "typeguard",
-        ],
-        #'mlperf': ["mlperf-logging @ git+https://github.com/mlperf/[email protected]"],
-        'testing_minimal': testing_minimal,
-        'testing_unit': testing_minimal + [
-            "tqdm",
-            "safetensors",
-            "tabulate",  # for sz.py
-        ],
-        'testing': testing_minimal + [
-            "pillow",
-            "onnx==1.18.0",
-            "onnx2torch",
-            "onnxruntime",
-            "opencv-python",
-            "tabulate",
-            "tqdm",
-            "safetensors",
-            "transformers",
-            "sentencepiece",
-            "tiktoken",
-            "blobfile",
-            "librosa",
-            "numba>=0.55",  # librosa needs numba but uv ignores python upper bounds and some numba versions require <python3.10
-            "networkx",
-            "nibabel",
-            "bottle",
-            "ggml-python",
-            "capstone",
-            "pycocotools",
-            "boto3",
-            "pandas",
-            "influxdb3-python"
-        ],
-        'docs': [
-            "mkdocs",
-            "mkdocs-material",
-            "mkdocstrings[python]",
-            "markdown-callouts",
-            "markdown-exec[ansi]",
-            "black",
-            "numpy",
-        ],
-      },
-      include_package_data=True)

test/external/external_benchmark_op_conv.py

@@ -1,237 +1,90 @@
-# ruff: noqa: E501
+# ruff: noqa: E501 E712 F401
 from tinygrad import dtypes, Device
-from tinygrad.uop.ops import UOp, AxisType, Ops
+from tinygrad.uop.ops import UOp, AxisType, Ops, KernelInfo
 from tinygrad.codegen import full_rewrite
+from tinygrad.codegen.opt import Opt, OptOps # pylint: disable=unused-import
 from tinygrad.renderer import ProgramSpec
 from tinygrad.engine.realize import CompiledRunner
-from tinygrad.helpers import dedup
+from tinygrad.helpers import dedup, getenv
 from tinygrad.device import Buffer
-from tinygrad.dtype import ImageDType
+from tinygrad.dtype import ImageDType, Invalid
 
-# PYTHONPATH="." DEBUG=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
-# kernel 672
-# faster on d59d4cd, 50% slower with the new linearizer
+# PYTHONPATH="." DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
 
-""" d59d4cd
-c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), arg=0, src=())
-c1 = UOp.range(UOp.const(dtypes.index, 64), 3, AxisType.LOOP)
-c2 = UOp.range(UOp.const(dtypes.index, 64), 4, AxisType.LOOP)
-c3 = UOp.range(UOp.const(dtypes.index, 32), 2, AxisType.LOOP)
-c4 = (((c1*UOp.const(dtypes.index, 64))+c2)+(c3*UOp.const(dtypes.index, 4096)))
-c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), arg=1, src=())
-c6 = c5.index(c4).load()
-c7 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 3072, 4)), arg=2, src=())
-c8 = UOp.range(UOp.const(dtypes.index, 48), 0, AxisType.REDUCE)
-c9 = UOp.range(UOp.const(dtypes.index, 4), 1, AxisType.REDUCE)
-c10 = c7.index(((((c8*UOp.const(dtypes.index, 4))+c9)+(c1*UOp.const(dtypes.index, 192)))+(c3*UOp.const(dtypes.index, 12288)))).load()
-c11 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 192, 4)), arg=3, src=())
-c12 = c11.index(((((c9*UOp.const(dtypes.index, 4))+(c2%UOp.const(dtypes.index, 4)))+(c8*UOp.const(dtypes.index, 16)))+((c2//UOp.const(dtypes.index, 4))*UOp.const(dtypes.index, 768)))).load()
-c13 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), arg=4, src=())
-c14 = c13.index(c2).load()
-c15 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), arg=5, src=())
-c16 = c15.index(c2).load()
-c17 = (c6+(((c10*c12.cast(dtypes.float)).cast(dtypes.float).reduce(c8, c9, arg=Ops.ADD)+c14.cast(dtypes.float))*c16.cast(dtypes.float)))
-c18 = c0.index(c4).store(c17, c3, c1, c2)
-ast = c18.sink()
-more upcast axis : [(3, 320, 0, 4)]
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-__kernel void r_512_16_4_4_48_4(write_only image2d_t data0_131072, read_only image2d_t data1_131072, read_only image2d_t data2_393216, read_only image2d_t data3_12288, __global half* data4_64, __global half* data5_64) {
-const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
-  float acc0[16];
-  int idx0 = get_global_id(0); /* 16 */
-  int idx1 = get_global_id(1); /* 512 */
-  int alu0 = (idx1>>4);
-  *(acc0+0) = 0.0f;
-  *(acc0+1) = 0.0f;
-  *(acc0+2) = 0.0f;
-  *(acc0+3) = 0.0f;
-  *(acc0+4) = 0.0f;
-  *(acc0+5) = 0.0f;
-  *(acc0+6) = 0.0f;
-  *(acc0+7) = 0.0f;
-  *(acc0+8) = 0.0f;
-  *(acc0+9) = 0.0f;
-  *(acc0+10) = 0.0f;
-  *(acc0+11) = 0.0f;
-  *(acc0+12) = 0.0f;
-  *(acc0+13) = 0.0f;
-  *(acc0+14) = 0.0f;
-  *(acc0+15) = 0.0f;
-  for (int Ridx0 = 0; Ridx0 < 48; Ridx0++) {
-    int alu17 = ((idx1*192)+Ridx0);
-    int alu18 = (alu17+48);
-    int alu19 = (alu17+96);
-    int alu20 = (alu17+144);
-    int alu21 = (Ridx0<<2);
-    float4 val0 = read_imagef(data3_12288, smp, (int2)(alu21,idx0));
-    float4 val1 = read_imagef(data3_12288, smp, (int2)((alu21+1),idx0));
-    float4 val2 = read_imagef(data3_12288, smp, (int2)((alu21+2),idx0));
-    float4 val3 = read_imagef(data3_12288, smp, (int2)((alu21+3),idx0));
-    float4 val4 = read_imagef(data2_393216, smp, (int2)((alu18-(3072*(((alu18>>10)*43)>>7))),alu0));
-    float4 val5 = read_imagef(data2_393216, smp, (int2)((alu19-(3072*(((alu19>>10)*43)>>7))),alu0));
-    float4 val6 = read_imagef(data2_393216, smp, (int2)((alu20-(3072*(((alu20>>10)*43)>>7))),alu0));
-    float4 val7 = read_imagef(data2_393216, smp, (int2)((alu17-(3072*(((alu17>>10)*43)>>7))),alu0));
-    *(acc0+1) = ((*(acc0+1))+(val4.x*val0.x)+(val4.y*val1.x)+(val4.z*val2.x)+(val4.w*val3.x));
-    *(acc0+5) = ((*(acc0+5))+(val4.x*val0.y)+(val4.y*val1.y)+(val4.z*val2.y)+(val4.w*val3.y));
-    *(acc0+9) = ((*(acc0+9))+(val4.x*val0.z)+(val4.y*val1.z)+(val4.z*val2.z)+(val4.w*val3.z));
-    *(acc0+13) = ((*(acc0+13))+(val4.x*val0.w)+(val4.y*val1.w)+(val4.z*val2.w)+(val4.w*val3.w));
-    *(acc0+2) = ((*(acc0+2))+(val5.x*val0.x)+(val5.y*val1.x)+(val5.z*val2.x)+(val5.w*val3.x));
-    *(acc0+6) = ((*(acc0+6))+(val5.x*val0.y)+(val5.y*val1.y)+(val5.z*val2.y)+(val5.w*val3.y));
-    *(acc0+10) = ((*(acc0+10))+(val5.x*val0.z)+(val5.y*val1.z)+(val5.z*val2.z)+(val5.w*val3.z));
-    *(acc0+14) = ((*(acc0+14))+(val5.x*val0.w)+(val5.y*val1.w)+(val5.z*val2.w)+(val5.w*val3.w));
-    *(acc0+3) = ((*(acc0+3))+(val6.x*val0.x)+(val6.y*val1.x)+(val6.z*val2.x)+(val6.w*val3.x));
-    *(acc0+7) = ((*(acc0+7))+(val6.x*val0.y)+(val6.y*val1.y)+(val6.z*val2.y)+(val6.w*val3.y));
-    *(acc0+11) = ((*(acc0+11))+(val6.x*val0.z)+(val6.y*val1.z)+(val6.z*val2.z)+(val6.w*val3.z));
-    *(acc0+15) = ((*(acc0+15))+(val6.x*val0.w)+(val6.y*val1.w)+(val6.z*val2.w)+(val6.w*val3.w));
-    *(acc0+0) = ((*(acc0+0))+(val7.x*val0.x)+(val7.y*val1.x)+(val7.z*val2.x)+(val7.w*val3.x));
-    *(acc0+4) = ((*(acc0+4))+(val7.x*val0.y)+(val7.y*val1.y)+(val7.z*val2.y)+(val7.w*val3.y));
-    *(acc0+8) = ((*(acc0+8))+(val7.x*val0.z)+(val7.y*val1.z)+(val7.z*val2.z)+(val7.w*val3.z));
-    *(acc0+12) = ((*(acc0+12))+(val7.x*val0.w)+(val7.y*val1.w)+(val7.z*val2.w)+(val7.w*val3.w));
-  }
-  int alu39 = (idx0<<2);
-  half4 val8 = (*((__global half4*)((data4_64+alu39))));
-  half4 val9 = (*((__global half4*)((data5_64+alu39))));
-  int alu40 = (idx0+(idx1<<6));
-  int2 cast0 = (int2)((alu40&1023),alu0);
-  float4 val10 = read_imagef(data1_131072, smp, cast0);
-  int2 cast1 = (int2)(((alu40+16)&1023),alu0);
-  float4 val11 = read_imagef(data1_131072, smp, cast1);
-  int2 cast2 = (int2)(((alu40+32)&1023),alu0);
-  float4 val12 = read_imagef(data1_131072, smp, cast2);
-  int2 cast3 = (int2)(((alu40+48)&1023),alu0);
-  float4 val13 = read_imagef(data1_131072, smp, cast3);
-  float cast4 = ((float)(val8.x));
-  float cast5 = ((float)(val9.x));
-  float cast6 = ((float)(val8.y));
-  float cast7 = ((float)(val9.y));
-  float cast8 = ((float)(val8.z));
-  float cast9 = ((float)(val9.z));
-  float cast10 = ((float)(val8.w));
-  float cast11 = ((float)(val9.w));
-  write_imagef(data0_131072, cast0, (float4)((val10.x+(((*(acc0+0))+cast4)*cast5)),(val10.y+(((*(acc0+4))+cast6)*cast7)),(val10.z+(((*(acc0+8))+cast8)*cast9)),(val10.w+(((*(acc0+12))+cast10)*cast11))));
-  write_imagef(data0_131072, cast1, (float4)((val11.x+(((*(acc0+1))+cast4)*cast5)),(val11.y+(((*(acc0+5))+cast6)*cast7)),(val11.z+(((*(acc0+9))+cast8)*cast9)),(val11.w+(((*(acc0+13))+cast10)*cast11))));
-  write_imagef(data0_131072, cast2, (float4)((val12.x+(((*(acc0+2))+cast4)*cast5)),(val12.y+(((*(acc0+6))+cast6)*cast7)),(val12.z+(((*(acc0+10))+cast8)*cast9)),(val12.w+(((*(acc0+14))+cast10)*cast11))));
-  write_imagef(data0_131072, cast3, (float4)((val13.x+(((*(acc0+3))+cast4)*cast5)),(val13.y+(((*(acc0+7))+cast6)*cast7)),(val13.z+(((*(acc0+11))+cast8)*cast9)),(val13.w+(((*(acc0+15))+cast10)*cast11))));
-}
-*** QCOM     672 r_512_16_4_4_48_4                              arg  6 mem   0.10 GB tm    322.55us/    77.83ms (    157 GFLOPS    4|160    GB/s) ['mul', '__add__', 'conv2d']
-"""
+def vision_conv_143():
+  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 1024, 4)), (), 0)
+  c2 = UOp.range(32, 3, AxisType.LOOP)
+  c5 = UOp.range(128, 4, AxisType.LOOP)
+  c8 = UOp.range(16, 2, AxisType.LOOP)
+  c16 = UOp.range(7, 0, AxisType.REDUCE)
+  c17 = c8*2+c16
+  c24 = ((c17<3)!=True)&(c17<35)
+  c26 = UOp.range(7, 1, AxisType.REDUCE)
+  c27 = c2*2+c26
+  c32 = ((c27<3)!=True)&(c27<67)
+  c34 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 1)
+  c38 = c5//2
+  c45 = (c32&c24).where((c27*64+c38+c17*4096+-12480), UOp.const(dtypes.index, Invalid))
+  c48 = (c24&c32).where(c34.index(c45), UOp.const(dtypes.float, 0.0))
+  c49 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((64, 49, 4)), (), 2)
+  c61 = c48*c49.index((c26*4+c5%2+c16*28+c38*196))
+  c63 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), (), 3)
+  c65 = c61.reduce(c16, c26, arg=Ops.ADD)+c63.index(c5)
+  c67 = c0.index((c2*128+c5+c8*4096), ptr=True).store(c65).end(c8, c2, c5)
 
-""" master 99e76f33a0f4ec84c79c1271dbc955fe6b5a7778
-c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 0)
-c2 = UOp.range(64, 3, AxisType.LOOP)
-c4 = UOp.range(64, 4, AxisType.LOOP)
-c7 = UOp.range(32, 2, AxisType.LOOP)
-c10 = (((c2*64)+c4)+(c7*4096))
-c12 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 1)
-c14 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 3072, 4)), (), 2)
-c16 = UOp.range(48, 0, AxisType.REDUCE)
-c19 = UOp.range(4, 1, AxisType.REDUCE)
-c28 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 192, 4)), (), 3)
-c40 = (c14.index(((((c16*4)+c19)+(c2*192))+(c7*12288)))*c28.index(((((c19*4)+(c4%4))+(c16*16))+((c4//4)*768))))
-c42 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), (), 4)
-c46 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), (), 5)
-c50 = (c12.index(c10)+((c40.reduce(c16, c19, arg=Ops.ADD)+c42.index(c4).cast(dtypes.float))*c46.index(c4).cast(dtypes.float)))
-c52 = c0.index(c10, ptr=True).store(c50).end(c7, c2, c4)
-ast = c52.sink()
-more upcast axis : [(3, 320, 0, 4)]
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-__kernel void r_512_16_4_4_48_4(write_only image2d_t data0_131072, read_only image2d_t data1_131072, read_only image2d_t data2_393216, read_only image2d_t data3_12288, __global half* data4_64, __global half* data5_64) {
-const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
-  float acc0[16];
-  int idx0 = get_global_id(0); /* 16 */
-  int idx1 = get_global_id(1); /* 512 */
-  *(acc0+0) = 0.0f;
-  *(acc0+1) = 0.0f;
-  *(acc0+2) = 0.0f;
-  *(acc0+3) = 0.0f;
-  *(acc0+4) = 0.0f;
-  *(acc0+5) = 0.0f;
-  *(acc0+6) = 0.0f;
-  *(acc0+7) = 0.0f;
-  *(acc0+8) = 0.0f;
-  *(acc0+9) = 0.0f;
-  *(acc0+10) = 0.0f;
-  *(acc0+11) = 0.0f;
-  *(acc0+12) = 0.0f;
-  *(acc0+13) = 0.0f;
-  *(acc0+14) = 0.0f;
-  *(acc0+15) = 0.0f;
-  int alu16 = (idx0<<2);
-  half4 val0 = (*((__global half4*)((data4_64+alu16))));
-  half4 val1 = (*((__global half4*)((data5_64+alu16))));
-  int alu17 = (idx0+(idx1<<6));
-  int alu18 = (idx1>>4);
-  int2 cast0 = (int2)((alu17&1023),alu18);
-  float4 val2 = read_imagef(data1_131072, smp, cast0);
-  int2 cast1 = (int2)(((alu17+16)&1023),alu18);
-  float4 val3 = read_imagef(data1_131072, smp, cast1);
-  int2 cast2 = (int2)(((alu17+32)&1023),alu18);
-  float4 val4 = read_imagef(data1_131072, smp, cast2);
-  int2 cast3 = (int2)(((alu17+48)&1023),alu18);
-  float4 val5 = read_imagef(data1_131072, smp, cast3);
-  for (int Ridx0 = 0; Ridx0 < 48; Ridx0++) {
-    int alu19 = ((idx1*192)+Ridx0);
-    int alu20 = (alu19+48);
-    int alu21 = (alu19+96);
-    int alu22 = (alu19+144);
-    int alu23 = (Ridx0<<2);
-    float4 val6 = read_imagef(data3_12288, smp, (int2)(alu23,idx0));
-    float4 val7 = read_imagef(data3_12288, smp, (int2)((alu23+1),idx0));
-    float4 val8 = read_imagef(data3_12288, smp, (int2)((alu23+2),idx0));
-    float4 val9 = read_imagef(data3_12288, smp, (int2)((alu23+3),idx0));
-    float4 val10 = read_imagef(data2_393216, smp, (int2)((alu20-(3072*(((alu20>>10)*43)>>7))),alu18));
-    *(acc0+1) = ((*(acc0+1))+(val10.x*val6.x)+(val10.y*val7.x)+(val10.z*val8.x)+(val10.w*val9.x));
-    *(acc0+5) = ((*(acc0+5))+(val10.x*val6.y)+(val10.y*val7.y)+(val10.z*val8.y)+(val10.w*val9.y));
-    *(acc0+9) = ((*(acc0+9))+(val10.x*val6.z)+(val10.y*val7.z)+(val10.z*val8.z)+(val10.w*val9.z));
-    *(acc0+13) = ((*(acc0+13))+(val10.x*val6.w)+(val10.y*val7.w)+(val10.z*val8.w)+(val10.w*val9.w));
-    float4 val11 = read_imagef(data2_393216, smp, (int2)((alu21-(3072*(((alu21>>10)*43)>>7))),alu18));
-    *(acc0+2) = ((*(acc0+2))+(val11.x*val6.x)+(val11.y*val7.x)+(val11.z*val8.x)+(val11.w*val9.x));
-    *(acc0+6) = ((*(acc0+6))+(val11.x*val6.y)+(val11.y*val7.y)+(val11.z*val8.y)+(val11.w*val9.y));
-    *(acc0+10) = ((*(acc0+10))+(val11.x*val6.z)+(val11.y*val7.z)+(val11.z*val8.z)+(val11.w*val9.z));
-    *(acc0+14) = ((*(acc0+14))+(val11.x*val6.w)+(val11.y*val7.w)+(val11.z*val8.w)+(val11.w*val9.w));
-    float4 val12 = read_imagef(data2_393216, smp, (int2)((alu22-(3072*(((alu22>>10)*43)>>7))),alu18));
-    *(acc0+3) = ((*(acc0+3))+(val12.x*val6.x)+(val12.y*val7.x)+(val12.z*val8.x)+(val12.w*val9.x));
-    *(acc0+7) = ((*(acc0+7))+(val12.x*val6.y)+(val12.y*val7.y)+(val12.z*val8.y)+(val12.w*val9.y));
-    *(acc0+11) = ((*(acc0+11))+(val12.x*val6.z)+(val12.y*val7.z)+(val12.z*val8.z)+(val12.w*val9.z));
-    *(acc0+15) = ((*(acc0+15))+(val12.x*val6.w)+(val12.y*val7.w)+(val12.z*val8.w)+(val12.w*val9.w));
-    float4 val13 = read_imagef(data2_393216, smp, (int2)((alu19-(3072*(((alu19>>10)*43)>>7))),alu18));
-    *(acc0+0) = ((*(acc0+0))+(val13.x*val6.x)+(val13.y*val7.x)+(val13.z*val8.x)+(val13.w*val9.x));
-    *(acc0+4) = ((*(acc0+4))+(val13.x*val6.y)+(val13.y*val7.y)+(val13.z*val8.y)+(val13.w*val9.y));
-    *(acc0+8) = ((*(acc0+8))+(val13.x*val6.z)+(val13.y*val7.z)+(val13.z*val8.z)+(val13.w*val9.z));
-    *(acc0+12) = ((*(acc0+12))+(val13.x*val6.w)+(val13.y*val7.w)+(val13.z*val8.w)+(val13.w*val9.w));
-  }
-  float cast4 = ((float)(val0.x));
-  float cast5 = ((float)(val1.x));
-  float cast6 = ((float)(val0.y));
-  float cast7 = ((float)(val1.y));
-  float cast8 = ((float)(val0.z));
-  float cast9 = ((float)(val1.z));
-  float cast10 = ((float)(val0.w));
-  float cast11 = ((float)(val1.w));
-  write_imagef(data0_131072, cast0, (float4)((val2.x+(((*(acc0+0))+cast4)*cast5)),(val2.y+(((*(acc0+4))+cast6)*cast7)),(val2.z+(((*(acc0+8))+cast8)*cast9)),(val2.w+(((*(acc0+12))+cast10)*cast11))));
-  write_imagef(data0_131072, cast1, (float4)((val3.x+(((*(acc0+1))+cast4)*cast5)),(val3.y+(((*(acc0+5))+cast6)*cast7)),(val3.z+(((*(acc0+9))+cast8)*cast9)),(val3.w+(((*(acc0+13))+cast10)*cast11))));
-  write_imagef(data0_131072, cast2, (float4)((val4.x+(((*(acc0+2))+cast4)*cast5)),(val4.y+(((*(acc0+6))+cast6)*cast7)),(val4.z+(((*(acc0+10))+cast8)*cast9)),(val4.w+(((*(acc0+14))+cast10)*cast11))));
-  write_imagef(data0_131072, cast3, (float4)((val5.x+(((*(acc0+3))+cast4)*cast5)),(val5.y+(((*(acc0+7))+cast6)*cast7)),(val5.z+(((*(acc0+11))+cast8)*cast9)),(val5.w+(((*(acc0+15))+cast10)*cast11))));
-}
-*** QCOM     672 r_512_16_4_4_48_4                              arg  6 mem   0.10 GB tm    527.97us/    78.94ms (     96 GFLOPS    3|98     GB/s) ['conv2d', 'mul', '__add__']
-"""
+  opts = None
+  # JITBEAM=2
+  # (Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.NOLOCALS, axis=None, arg=None), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.SWAP, axis=1, arg=2))
+  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))
 
-c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 0)
-c2 = UOp.range(64, 3, AxisType.LOOP)
-c4 = UOp.range(64, 4, AxisType.LOOP)
-c7 = UOp.range(32, 2, AxisType.LOOP)
-c10 = (((c2*64)+c4)+(c7*4096))
-c12 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 1)
-c14 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 3072, 4)), (), 2)
-c16 = UOp.range(48, 0, AxisType.REDUCE)
-c19 = UOp.range(4, 1, AxisType.REDUCE)
-c28 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 192, 4)), (), 3)
-c40 = (c14.index(((((c16*4)+c19)+(c2*192))+(c7*12288)))*c28.index(((((c19*4)+(c4%4))+(c16*16))+((c4//4)*768))))
-c42 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), (), 4)
-c46 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(64), (), 5)
-c50 = (c12.index(c10)+((c40.reduce(c16, c19, arg=Ops.ADD)+c42.index(c4).cast(dtypes.float))*c46.index(c4).cast(dtypes.float)))
-c52 = c0.index(c10, ptr=True).store(c50).end(c7, c2, c4)
-ast = c52.sink()
+def vision_conv_153():
+  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((8, 1024, 4)), (), 0)
+  c2 = UOp.range(16, 3, AxisType.LOOP)
+  c5 = UOp.range(256, 4, AxisType.LOOP)
+  c8 = UOp.range(8, 2, AxisType.LOOP)
+  c16 = UOp.range(7, 0, AxisType.REDUCE)
+  c17 = c8*2+c16
+  c24 = ((c17<3)!=True)&(c17<19)
+  c26 = UOp.range(7, 1, AxisType.REDUCE)
+  c27 = c2*2+c26
+  c32 = ((c27<3)!=True)&(c27<35)
+  c34 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 1024, 4)), (), 1)
+  c38 = c5//2
+  c45 = (c32&c24).where((c27*128+c38+c17*4096+-12672), UOp.const(dtypes.index, Invalid))
+  c48 = (c24&c32).where(c34.index(c45), UOp.const(dtypes.float, 0.0))
+  c49 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((128, 49, 4)), (), 2)
+  c61 = c48*c49.index((c26*4+c5%2+c16*28+c38*196))
+  c63 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), (), 3)
+  c65 = c61.reduce(c16, c26, arg=Ops.ADD)+c63.index(c5)
+  c67 = c0.index((c2*256+c5+c8*4096), ptr=True).store(c65).end(c8, c2, c5)
+
+  opts = None
+  # JITBEAM=2
+  # (Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.NOLOCALS, axis=None, arg=None), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.SWAP, axis=1, arg=2))
+  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))
+
+def dm_conv_172():
+  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 240, 4)), (), 0)
+  c2 = UOp.range(960, 4, AxisType.LOOP)
+  c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((8, 384, 4)), (), 1)
+  c7 = UOp.range(32, 0, AxisType.REDUCE)
+  c10 = UOp.range(4, 1, AxisType.REDUCE)
+  c13 = UOp.range(12, 3, AxisType.REDUCE)
+  c18 = UOp.range(8, 2, AxisType.REDUCE)
+  c23 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((240, 128, 4)), (), 2)
+  c35 = c5.index((c7*4+c10+c13*128+c18*1536))*c23.index((c10*4+c2%4+c7*16+c2//4*512))
+  c37 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(960), (), 3)
+  c39 = c35.reduce(c7, c10, arg=Ops.ADD)+c37.index(c2)
+  c50 = (1.0+((c39+0.044708251953125*(c39*(c39*c39)))*-2.3021129851685216).exp2()).reciprocal()*c39
+  c53 = c50.reduce(c18, c13, arg=Ops.ADD)*0.010416666666666666
+  c55 = c0.index(c2, ptr=True).store(c53).end(c2)
+
+  opts = None
+  # JITBEAM=2
+  # (Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.GROUPTOP, axis=1, arg=32), Opt(op=OptOps.UNROLL, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.GROUP, axis=1, arg=0))
+  return c55.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))
+
+ast = {143: vision_conv_143, 153: vision_conv_153, 172: dm_conv_172}[getenv("NUM", 143)]()
 
 compiler = Device.default.compiler
 renderer = Device.default.renderer
@@ -240,20 +93,13 @@ allocator = Device.default.allocator
 uops = full_rewrite(ast, renderer)
 src = renderer.render(uops)
 
-# NOLOCALS=1 IMAGE=2 DEV=CL
 lib = compiler.compile(src)
-# r_64_8_16_4_4_48_4
-# NOLOCALS: r_512_16_4_4_48_4
-ps = ProgramSpec("r_512_16_4_4_48_4", src, Device.DEFAULT, ast, uops)
-print(ps.src)
-print(ps.applied_opts)
-# (Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.NOLOCALS, axis=None, arg=None))
+ps = ProgramSpec("conv", src, Device.DEFAULT, ast, uops)
 cr = CompiledRunner(ps, precompiled=lib)
 
 gs = sorted(dedup([u for u in ast.toposort() if u.op is Ops.DEFINE_GLOBAL]), key=lambda u: u.arg)
-print(len(gs))
-print([g.dtype for g in gs])
-
+# print(len(gs))
+# print([g.dtype for g in gs])
 bufs = [Buffer(ps.device, g.size, g.dtype if isinstance(g.dtype, ImageDType) else g.dtype._base).ensure_allocated() for g in gs]
 
 t = cr(bufs, wait=True)

test/external/external_openpilot_image_warp.py

@@ -0,0 +1,55 @@
+import time
+from tinygrad.tensor import Tensor, Device
+
+MODEL_WIDTH = 512
+MODEL_HEIGHT = 256
+MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 // 2
+IMG_INPUT_SHAPE = (1, 12, 128, 256)
+
+def tensor_arange(end): return Tensor([float(i) for i in range(end)])
+def tensor_round(tensor:Tensor): return (tensor + 0.5).floor()
+
+h_src, w_src = 1208, 1928
+h_dst, w_dst = MODEL_HEIGHT, MODEL_WIDTH
+x = tensor_arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst)
+y = tensor_arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst)
+ones = Tensor.ones_like(x)
+dst_coords = x.reshape((1,-1)).cat(y.reshape((1,-1))).cat(ones.reshape((1,-1)))
+
+def warp_perspective_tinygrad(src:Tensor, M_inv:Tensor) -> Tensor:
+  src_coords = M_inv @ dst_coords
+  src_coords = src_coords / src_coords[2:3, :]
+
+  x_src = src_coords[0].reshape(h_dst, w_dst)
+  y_src = src_coords[1].reshape(h_dst, w_dst)
+
+  x_nearest = tensor_round(x_src).clip(0, w_src - 1).cast('int')
+  y_nearest = tensor_round(y_src).clip(0, h_src - 1).cast('int')
+
+  # TODO: make 2d indexing fast
+  idx = y_nearest*src.shape[1] + x_nearest
+  dst = src.flatten()[idx]
+  return dst.reshape(h_dst, w_dst)
+
+if __name__ == "__main__":
+  from tinygrad.engine.jit import TinyJit
+  update_img_jit = TinyJit(warp_perspective_tinygrad, prune=True)
+
+  step_times = []
+  for _ in range(10):
+    # regenerate inputs
+    inputs = [Tensor.randn(1928,1208), Tensor.randn(3,3)]
+    Tensor.realize(*inputs)
+    Device.default.synchronize()
+
+    # do the warp
+    st = time.perf_counter()
+    out = update_img_jit(*inputs)
+    mt = time.perf_counter()
+    val = out.contiguous().realize()
+    Device.default.synchronize()
+    et = time.perf_counter()
+
+    # measure the time
+    step_times.append((et-st)*1e3)
+    print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {step_times[-1]:6.2f} ms")

test/external/external_test_example.py

@@ -1,13 +1,14 @@
 import unittest
 from tinygrad import Device
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import getenv, CI
+from tinygrad.helpers import getenv, CI, OSX
 
 def multidevice_test(fxn):
   exclude_devices = getenv("EXCLUDE_DEVICES", "").split(",")
   def ret(self):
     for device in Device._devices:
-      if device in ["REMOTE", "DISK", "NPY", "FAKE", "DSP", "NULL"]: continue
+      # broken on OSX USB AMD, why?
+      if device in ["REMOTE", "DISK", "NPY", "FAKE", "DSP", "NULL"] or (OSX and device in ["AMD"]): continue
       if not CI: print(device)
       if device in exclude_devices:
         if not CI: print(f"WARNING: {device} test is excluded")

test/external/external_test_onnx_backend.py

@@ -112,7 +112,6 @@ backend_test.exclude('test_dequantizelinear_e5m2_cpu')
 backend_test.exclude('test_dequantizelinear_float4e2m1_cpu')
 
 # we don't support indexes
-backend_test.exclude('test_nonzero_*')
 
 # no support for int pow
 backend_test.exclude('test_pow_types_int32_int32_cpu')

test/external/external_uop_gc.py

@@ -2,6 +2,7 @@ import gc
 from tinygrad import Tensor, UOp, Device, nn
 from tinygrad.engine.realize import method_cache, get_program
 from tinygrad.schedule.indexing import apply_movement_op
+from tinygrad.uop.divandmod import fold_divmod_general
 from test.test_tiny import TestTiny
 
 def uops_allocated(): return sum([isinstance(x, UOp) for x in gc.get_objects()])
@@ -69,6 +70,7 @@ if __name__ == "__main__":
     # these caches will keep uops alive
     method_cache.clear()
     apply_movement_op.cache_clear()
+    fold_divmod_general.cache_clear()
     Tensor._device_seeds.clear()
     Tensor._device_rng_counters.clear()
 

test/external/process_replay/process_replay.py

@@ -36,7 +36,9 @@ def trunc_log(x):
   logging.info("\n".join(lines))
 
 # user config
-SKIP_PROCESS_REPLAY = (k:="[skip_process_replay]") in os.getenv("COMMIT_MESSAGE", "") or k in os.getenv("PR_TITLE", "")
+# NOTE: process replay is slow so it's now disabled by default. add [pr] to enable it
+#SKIP_PROCESS_REPLAY = (k:="[skip_process_replay]") in os.getenv("COMMIT_MESSAGE", "") or k in os.getenv("PR_TITLE", "")
+SKIP_PROCESS_REPLAY = not ASSERT_DIFF and not ((k:="[p]") in os.getenv("COMMIT_MESSAGE", "") or k in os.getenv("PR_TITLE", ""))
 if REF == "master": SKIP_PROCESS_REPLAY = True
 class ProcessReplayWarning(Warning): pass
 

test/mockgpu/amd/amdgpu.py

@@ -33,7 +33,7 @@ remu = _try_dlopen_remu()
 def create_sdma_packets():
   # TODO: clean up this, if we want to keep it
   structs = {}
-  for name,pkt in [(name,s) for name,s in amd_gpu.__dict__.items() if name.startswith("struct_SDMA_PKT_") and name.endswith("_TAG")]:
+  for name,pkt in [(name,s) for name,s in amd_gpu.__dict__.items() if name.startswith("rocr_AMD_SDMA_PKT_") and name.endswith("_TAG")]:
     names = set()
     fields = []
     for pkt_fields in pkt._fields_:
@@ -47,7 +47,7 @@ def create_sdma_packets():
           # merge together 64-bit fields, otherwise just append them
           if fname.endswith("_63_32") and fields[-1][0].endswith("_31_0"): fields[-1] = tuple([fname[:-6], ctypes.c_ulong, 64])
           else: fields.append(tuple([fname, *union_fields[1:]]))
-    new_name = name[16:-4].lower()
+    new_name = name[18:-4].lower()
     structs[new_name] = init_c_struct_t(tuple(fields))
     assert ctypes.sizeof(structs[new_name]) == ctypes.sizeof(pkt), f"{ctypes.sizeof(structs[new_name])} != {ctypes.sizeof(pkt)}"
   return type("SDMA_PKTS", (object, ), structs)
@@ -124,6 +124,7 @@ class PM4Executor(AMDQueue):
     elif mem_data_sel == 3:
       if mem_event_type == CACHE_FLUSH_AND_INV_TS_EVENT: ptr.cast('Q')[0] = int(time.perf_counter() * 1e8)
       else: raise RuntimeError(f"Unknown {mem_data_sel=} {mem_event_type=}")
+    elif mem_data_sel == 0: pass # no write
     else: raise RuntimeError(f"Unknown {mem_data_sel=}")
 
   def _exec_copy_data(self, n):

test/mockgpu/cuda/cuda.py

@@ -164,7 +164,7 @@ def cuStreamWaitEvent(stream: Any, event, flags: int) -> int: return orig_cuda.C
 def cuCtxSynchronize() -> int: return orig_cuda.CUDA_SUCCESS
 
 def cuGetErrorString(error: int, pStr) -> int:
-  error_str = orig_cuda.cudaError_enum__enumvalues.get(error, "Unknown CUDA error").encode()
+  error_str = orig_cuda.enum_cudaError_enum.get(error, "Unknown CUDA error").encode()
   buf = ctypes.create_string_buffer(error_str)
   # Set the pointer to point to our error string buffer
   pStr._obj.value = ctypes.cast(buf, ctypes.POINTER(ctypes.c_char))

test/mockgpu/nv/nvdriver.py

@@ -1,5 +1,5 @@
 import ctypes, mmap, collections, functools, os
-import tinygrad.runtime.autogen.nv_gpu as nv_gpu
+from tinygrad.runtime.autogen import nv_570 as nv_gpu
 from typing import Any
 from tinygrad.helpers import to_mv
 from test.mockgpu.driver import VirtDriver, VirtFileDesc, VirtFile
@@ -153,8 +153,10 @@ class NVDriver(VirtDriver):
                  51059, 51069, 51071, 51632, 51639, 51639, 51706, 52019, 222, 50287, 50273, 50031, 50017] # from ada102
       params.numClasses = len(classes)
       if struct.cmd == nv_gpu.NV0080_CTRL_CMD_GPU_GET_CLASSLIST:
-        clslist = to_mv(params.classList, params.numClasses * 4).cast('I')
-        for i,c in enumerate(classes): clslist[i] = c
+        if params.classList and params.numClasses > 0:
+          clslist = to_mv(params.classList, params.numClasses * 4).cast('I')
+          for i,c in enumerate(classes): clslist[i] = c
+        else: params.numClasses = len(classes)
       else:
         for i,c in enumerate(classes): params.classList[i] = c
     elif struct.cmd == nv_gpu.NV2080_CTRL_CMD_GR_GET_INFO:
@@ -192,6 +194,9 @@ class NVDriver(VirtDriver):
       params.mmuFaultInfoList[0].faultAddress = int(os.environ['MOCKGPU_EMU_FAULTADDR'], base=16)
       params.mmuFaultInfoList[0].faultType = 1
       params.mmuFaultInfoList[0].accessType = 1
+    elif struct.cmd == nv_gpu.NV0000_CTRL_CMD_SYSTEM_GET_BUILD_VERSION_V2:
+      params = nv_gpu.NV0000_CTRL_SYSTEM_GET_BUILD_VERSION_V2_PARAMS.from_address(params_ptr)
+      params.driverVersionBuffer = b"570.00.00\0"
     else: raise RuntimeError(f"Unknown {struct.cmd} to rm_control")
     return 0
 
@@ -254,4 +259,4 @@ class NVDriver(VirtDriver):
       for gpu in self.gpus.values():
         for q in gpu.queues:
           if q.ctrl.GPGet != q.ctrl.GPPut:
-            any_progress |= q.execute()
+            any_progress |= q.execute()

test/mockgpu/nv/nvgpu.py

@@ -1,5 +1,5 @@
 import ctypes, time
-import tinygrad.runtime.autogen.nv_gpu as nv_gpu
+from tinygrad.runtime.autogen import nv_570 as nv_gpu
 from enum import Enum, auto
 from test.mockgpu.gpu import VirtGPU
 from test.mockgpu.helpers import _try_dlopen_gpuocelot

test/test_arange.py

@@ -5,26 +5,40 @@ from tinygrad.helpers import CI, Context, getenv
 from tinygrad.engine.realize import run_schedule
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.uop.ops import Ops
+from tinygrad.renderer import Estimates
+from tinygrad.renderer.ptx import PTXRenderer
 
 class TestArange(unittest.TestCase):
-  def _get_flops(self, N):
+  def _get_flops(self, tensor, desired):
     GlobalCounters.reset()
-    tt = Tensor.arange(N)
-    sched = tt.schedule()
+    sched = tensor.schedule()
     self.assertEqual(len(sched), 1)
     p = get_program(sched[-1].ast)
-    ExecItem(CompiledRunner(p), [tt.uop.buffer]).run()
-    np.testing.assert_equal(tt.numpy(), np.arange(N))
+    ExecItem(CompiledRunner(p), [tensor.uop.buffer]).run()
+    np.testing.assert_equal(tensor.numpy(), desired)
     return p.estimates.ops
 
-  def test_complexity(self):
-    self.assertEqual(self._get_flops(256), 0)
-    self.assertEqual(self._get_flops(2560), 0)
+  def test_arange_complexity(self):
+    self.assertEqual(self._get_flops(Tensor.arange(256), np.arange(256)), 0)
+    self.assertEqual(self._get_flops(Tensor.arange(2560), np.arange(2560)), 0)
 
   def test_arange_cat(self):
     t = Tensor.arange(2, dtype=dtypes.int)+Tensor([3])
     self.assertEqual(t.cat(t).tolist(), [3, 4, 3, 4])
 
+  def test_eye_complexity(self):
+    with Context(NOOPT=1):
+      # NOTE: not every backend supports CMPEQ
+      self.assertLessEqual(self._get_flops(Tensor.eye(2560).contiguous(), np.eye(2560)), 2*2560*2560)
+
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX indexing is weird")
+  def test_tri_complexity(self):
+    with Context(NOOPT=1):
+      t = Tensor.ones(256, 256).contiguous().realize()
+      sched = t.triu().schedule()
+      p = get_program(sched[-1].ast)
+      self.assertLessEqual(Estimates.from_uops(p.uops).ops, 4 * 256 * 256)
+
 DSET, DDIM = 2048, 32
 
 class TestIndexing(unittest.TestCase):

test/test_custom_kernel.py

@@ -9,6 +9,11 @@ def custom_arange_kernel(C:UOp) -> UOp:
   i = UOp.range(C.size, 0)
   return C[i].store(i.cast(C.dtype.base)).end(i).sink(arg=KernelInfo(name=f"custom_arange_{C.size}"))
 
+def custom_eye_kernel(C:UOp) -> UOp:
+  i = UOp.range(C.shape[0], 0)
+  j = UOp.range(C.shape[1], 1)
+  return C[i, j].store((i.eq(j)).cast(C.dtype.base)).end(i, j).sink(arg=KernelInfo(name=f"custom_eye_{C.size}"))
+
 def custom_add_one_kernel(B:UOp, A:UOp) -> UOp:
   A,B = A.flatten(), B.flatten()
   assert B.size == A.size
@@ -97,6 +102,11 @@ def backward_gemm_custom(gradient:UOp, kernel:UOp) -> tuple[UOp, UOp]:
 # **** tests ****
 
 class TestCustomKernel(unittest.TestCase):
+  def test_empty(self):
+    a = Tensor.empty(1)
+    a = Tensor.custom_kernel(a, fxn=lambda _: UOp.sink())[0]
+    a.realize()
+
   def test_simple(self):
     a = Tensor.ones(16, 16).contiguous()
     b = Tensor.ones(16, 16).contiguous()
@@ -125,6 +135,12 @@ class TestCustomKernel(unittest.TestCase):
     tst = tst.custom_kernel(fxn=custom_arange_kernel)[0]
     self.assertTrue((ref == tst).all().item())
 
+  def test_eye(self):
+    ref = Tensor.eye(1024).contiguous().realize()
+    tst = Tensor.empty_like(ref)
+    tst = tst.custom_kernel(fxn=custom_eye_kernel)[0]
+    self.assertTrue((ref == tst).all().item())
+
   def test_flip_contract(self):
     a = Tensor.randn(10,4)
     b = Tensor.empty_like(a)

test/test_linearizer.py

@@ -4,7 +4,7 @@ from dataclasses import replace
 
 from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.codegen.gpudims import get_grouped_dims
-from tinygrad.uop.ops import UOp, Ops, GroupOp
+from tinygrad.uop.ops import UOp, Ops, GroupOp, AxisType, PatternMatcher, graph_rewrite, UPat
 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
@@ -14,6 +14,8 @@ from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.cstyle import CUDARenderer
 MOCKGPU = getenv("MOCKGPU")
 
+from tinygrad.uop.ops import print_uops # noqa: F401 # pylint: disable=unused-import
+
 class TestLinearizer(unittest.TestCase):
   def test_arg_dedup(self):
     # NOTE: this realize exists because Tensor.numpy calls .contiguous() internally
@@ -38,6 +40,38 @@ class TestLinearizer(unittest.TestCase):
     np.testing.assert_equal(a.numpy(), ta)
     np.testing.assert_equal(b.numpy(), tb)
 
+  @unittest.skip("TODO: some backends insert more casts")
+  def test_cast_there_and_back(self):
+    tst = Tensor.ones(16, dtype=dtypes.int).contiguous().realize()
+    out = tst.neg().cast(dtypes.char).cast(dtypes.int).cast(dtypes.char) * 2
+    ast = helper_linearizer_opt(out)
+    uops = get_program(ast, opts=[]).uops
+    self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 1)
+
+  @unittest.expectedFailure
+  def test_cast_back_and_there(self):
+    tst = Tensor.ones(16, dtype=dtypes.int).contiguous().realize()
+    out = tst.neg().cast(dtypes.char).cast(dtypes.int) * 2
+    ast = helper_linearizer_opt(out)
+    uops = get_program(ast, opts=[]).uops
+    self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 0)
+
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken on ptx")
+  def test_late_bias_load(self):
+    img = Tensor.empty(1, 3, 16, 16)
+    w = Tensor.empty(16, 3, 3, 3)
+    b = Tensor.empty(16)
+    out = img.conv2d(w, b)
+    ast = helper_linearizer_opt(out)
+    uops = get_program(ast, opts=[]).uops
+    # slice at the last loop end
+    uslice = [i for i,u in enumerate(uops) if u.op == Ops.END][-1]
+    # only valid test if outermost range is the reduce
+    if uops[uslice].src[-1].arg[-1] == AxisType.REDUCE:
+      load_types = [u.src[0].dtype for u in uops[uslice+1:] if u.op == Ops.LOAD]
+      # assert that there is a global load after the reduce ends
+      assert any(dt.addrspace == AddrSpace.GLOBAL for dt in load_types)
+
   def _test_no_nested_ranges(self, lins, skip=None):
     for l in lins:
       range_in_acc = flatten([[x for x in u.src if x.op is Ops.RANGE] for u in l.uops if u.op is Ops.DEFINE_REG])
@@ -62,6 +96,7 @@ class TestLinearizer(unittest.TestCase):
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
 
+  @unittest.expectedFailure # TODO: investigate
   def test_two_nested_range_alt_indexing(self):
     a = Tensor([2, 2]).realize()
     out = a.reshape(2, 1).pad(((1, 1), (1, 1)), value=2).sum()
@@ -262,6 +297,8 @@ class TestLinearizer(unittest.TestCase):
     _assert_grouped_dims("gidx", (65536,), (16,16,256), False, [16,16,256], False)
     #                              2             ->             3
     _assert_grouped_dims("gidx", (128,128), (16,16,256), False, [16,16,64], False)
+    #                              2             ->             2
+    _assert_grouped_dims("gidx", (65536,2), (65535,65535,65535), False, [32768,4], False)
     # test when the only divisor is the square root of dim
     _assert_grouped_dims("gidx", (121,), (12,12,12), False, [11,11], False)
 
@@ -286,6 +323,27 @@ class TestLinearizer(unittest.TestCase):
     with self.assertRaises(RuntimeError):
       get_grouped_dims("gidx", (2,3,4,5,6), (16,16,16))
 
+    # TODO: In the above cases we only test if the shape after reshape is correct, never the indices.
+    # We should check if the returned indices are correct, for all cases.
+    # (65536, 2) -> (32768, 4)
+    dims, expected_limited_dims = (65536,2), (32768, 4)
+    idxs = get_grouped_dims("gidx", dims, (65535,65535,65535))
+    def match_div(): raise RuntimeError("match_div")
+    def match_mod(): raise RuntimeError("match_mod")
+    flat_idx_pattern = UPat(Ops.SPECIAL, arg='gidx0')*expected_limited_dims[1]+UPat(Ops.SPECIAL, arg='gidx1')
+    pm = PatternMatcher([
+      (flat_idx_pattern//dims[1], match_div),
+      (flat_idx_pattern%dims[1], match_mod)
+    ])
+
+    with self.assertRaises(RuntimeError) as error:
+      graph_rewrite(idxs[0], pm)
+    self.assertIn("match_div", str(error.exception))
+
+    with self.assertRaises(RuntimeError) as error:
+      graph_rewrite(idxs[1], pm)
+    self.assertIn("match_mod", str(error.exception))
+
     # # variable too large
     # with self.assertRaises(AssertionError):
     #   get_grouped_dims("gidx", (Variable("start_pos",0,16),3,4), (16,16,16), False,)
@@ -432,6 +490,8 @@ def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
   # now all input buffers in s[-1] should be realized
   # create fresh buffers for the outputs
   bufs = [Buffer(x.device, x.size, x.dtype).allocate() if i < len(s[-1].ast.src) else x for i,x in enumerate(s[-1].bufs)]
+  # ensure buffers are allocated
+  for b in bufs: b.ensure_allocated()
   return s[-1].ast, bufs
 
 def helper_linearizer_ast(ast:UOp, inputs:list[Tensor], *args, **kwargs):
@@ -449,7 +509,7 @@ def copyout_outputs(outbufs:list[Buffer]) -> list[np.ndarray]:
   return [np.frombuffer(x.as_buffer(), _to_np_dtype(x.dtype)) for x in outbufs]
 
 def reset_bufs(bufs:list[Buffer]):
-  for buf in bufs: buf.copyin(np.zeros((buf.size, ), dtype=_to_np_dtype(buf.dtype)).data) # Zero to check that all values are filled
+  for buf in bufs: buf.copyin(np.zeros((buf.size*buf.dtype.itemsize,), dtype=np.uint8).data)
 
 def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[],
                                apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[], wanna_output=[]):

test/test_outerworld.py

@@ -1,5 +1,6 @@
 import unittest
-from tinygrad import Tensor, UOp
+import numpy as np
+from tinygrad import Tensor, UOp, nn
 from tinygrad.uop.ops import AxisType, Ops
 
 class TestOuterworldReduce(unittest.TestCase):
@@ -11,6 +12,81 @@ class TestOuterworldReduce(unittest.TestCase):
     t = Tensor(UOp(Ops.REDUCE, dtype=out.uop.dtype, src=(out.uop, a), arg=Ops.ADD))
     self.assertListEqual(t.tolist(), [5.,5.,5.,5.,5.])
 
+# TODO: delete test_outerworld_range?
+class TestOuterRange(unittest.TestCase):
+  def test_simple_range(self):
+    a = Tensor.ones(10).contiguous()
+    acc = Tensor.zeros().contiguous()
+    Tensor.realize(a, acc)
+
+    # this is fold
+    i = UOp.range(10, -100, AxisType.OUTER)
+    acc_i = acc.uop.after(i)
+    vi = UOp.variable("i", i.vmin, i.vmax).bind(i)
+    out = Tensor(acc.uop.after(acc_i.store(acc_i + a[vi].uop).end(i)))
+    out.realize()
+    assert out.item() == 10.0
+
+  def test_inner_range(self):
+    a = Tensor.ones(10, 10).contiguous()
+    acc = Tensor.zeros(10).contiguous()
+    Tensor.realize(a, acc)
+
+    # this is fold
+    i = UOp.range(10, -100, AxisType.OUTER)
+    acc_i = acc.uop.after(i)
+    vi = UOp.variable("i", i.vmin, i.vmax).bind(i)
+    out = Tensor(acc.uop.after(acc_i.store(acc_i + a[:, vi].uop).end(i)))
+    out.realize()
+    assert all(x == 10.0 for x in out.tolist())
+
+  def test_range_matmul(self):
+    vec = Tensor.randn(1, 10).realize()
+    mats = Tensor.randn(3, 10, 10).realize()
+
+    # 3 matmuls in "scan"
+    ref = ((vec @ mats[0]) @ mats[1]) @ mats[2]
+    ref.realize()
+
+    # 3 matmuls with outer world range
+    i = UOp.range(3, -100, AxisType.OUTER)
+    vec_i = Tensor(vec.uop.after(i))
+    comp = vec_i.contiguous() @ mats[i]
+    store = vec_i.uop.store(comp.uop).end(i)
+    out = Tensor(vec.uop.after(store))
+    out.realize()
+
+    # TODO: testing allclose
+    assert Tensor.allclose(ref, out, atol=1e-6), f"{ref.numpy()=}, {out.numpy()=}"
+
+class TestOuterScan(unittest.TestCase):
+  def _test_scan(self):
+    vec = Tensor.randn(1, 10).realize()
+    mats = Tensor.randn(3, 10, 10).realize()
+
+    # 3 matmuls in "scan"
+    vec1 = vec @ mats[0]
+    vec2 = vec1 @ mats[1]
+    vec3 = vec2 @ mats[2]
+    ref = Tensor.stack(vec1, vec2, vec3)
+    ref.realize()
+    return vec, mats, ref
+
+  def test_uop_scan_matmul(self):
+    vec, mats, ref = self._test_scan()
+
+    # 3 matmuls with SCAN
+    i = UOp.range(3, -100, AxisType.OUTER)
+    out = Tensor.empty(3, 1, 10)
+    phi = Tensor(i.eq(0).where(vec.uop, out[(i-1).maximum(0)].uop))
+    comp = phi @ mats[i]
+    store = out[i].uop.store(comp.uop).end(i)
+    out = Tensor(out.uop.after(store))
+    out.realize()
+
+    # TODO: testing allclose
+    assert Tensor.allclose(ref, out, atol=1e-6), f"{ref.numpy()=}, {out.numpy()=}"
+
 class TestOuterworld(unittest.TestCase):
   def test_range_plus_1(self):
     t = Tensor.arange(100).reshape(10,10).realize()
@@ -70,5 +146,85 @@ class TestOuterworld(unittest.TestCase):
     out = out.reshape(1, 3).expand(a, 3).contiguous().realize()
     self.assertListEqual([[0,4,8],[4,8,12],[8,12,16]], out.tolist())
 
+class TestVmap(unittest.TestCase):
+  def test_vmap_inner(self, axis_type=AxisType.LOOP, fuse=False, grad=False):
+    x = Tensor.ones(1, 10).contiguous().requires_grad_()
+    mats = Tensor.ones(3, 10, 10).contiguous().requires_grad_()
+
+    ref = x @ mats
+    if fuse: ref = ref * 2
+
+    # vmap across axis 0
+    a = UOp.range(3, -1, axis_type)
+    out = x @ mats[a]
+    out = out.reshape(1, 10).pad(((a,(3-a)-1), None))
+    out = Tensor(out.uop.reduce(a, arg=Ops.ADD))
+    if fuse: out = out * 2
+    if grad:
+      out.mean().backward()
+      np.testing.assert_allclose(mats.grad.numpy(), (2./30) if fuse else (1./30))
+    out.realize()
+
+    # TODO: testing allclose
+    assert Tensor.allclose(ref, out, atol=1e-6), f"{ref.numpy()=}, {out.numpy()=}"
+  def test_vmap_inner_fuse(self): self.test_vmap_inner(fuse=True)
+  def test_vmap_outer(self): self.test_vmap_inner(AxisType.OUTER)
+  def test_vmap_outer_fuse(self): self.test_vmap_inner(AxisType.OUTER, fuse=True)
+
+  def test_vmap_inner_grad(self): self.test_vmap_inner(grad=True)
+  def test_vmap_inner_fuse_grad(self): self.test_vmap_inner(fuse=True, grad=True)
+  def test_vmap_outer_grad(self): self.test_vmap_inner(AxisType.OUTER, grad=True)
+
+  def test_vmap_convs(self):
+    layers = [
+      nn.Conv2d(1, 8, 3), Tensor.relu,
+      nn.Conv2d(8, 8, 3), Tensor.relu]
+    img = Tensor.randn(4, 1, 16, 16).realize(*nn.state.get_parameters(layers))
+    a = UOp.range(4, -1, AxisType.OUTER)
+    out = img[a:a+1].sequential(layers)
+    out = out.pad(((a,(4-a)-1), None, None, None))
+    out = Tensor(out.uop.reduce(a, arg=Ops.ADD))
+    out.realize()
+    np.testing.assert_allclose(out.numpy(), img.sequential(layers).numpy(), atol=1e-6)
+
+  def test_vmap_gemm(self):
+    layers = [
+      nn.Linear(16, 16, bias=False), Tensor.relu,
+      nn.Linear(16, 16, bias=False), Tensor.relu]
+    img = Tensor.randn(4, 16).realize(*nn.state.get_parameters(layers))
+    a = UOp.range(4, -1, AxisType.OUTER)
+    out = img[a:a+1].sequential(layers)
+    out = out.pad(((a,(4-a)-1), None))
+    out = Tensor(out.uop.reduce(a, arg=Ops.ADD))
+    out.realize()
+    np.testing.assert_allclose(out.numpy(), img.sequential(layers).numpy(), atol=1e-6)
+
+  @unittest.skip("this is broken, we need to lower the outer reduce in the outer graph")
+  def test_vmap_gemm_grad(self):
+    layers = [
+      nn.Linear(16, 16, bias=False), Tensor.relu,
+      nn.Linear(16, 16, bias=False), Tensor.relu]
+    layer_tensors = nn.state.get_parameters(layers)
+    img = Tensor.randn(4, 16).realize(*layer_tensors)
+    for l in layer_tensors: l.requires_grad_()
+    a = UOp.range(4, -1, AxisType.OUTER)
+    out = img[a:a+1].sequential(layers)
+    out = out.pad(((a,(4-a)-1), None))
+    out = Tensor(out.uop.reduce(a, arg=Ops.ADD))
+    out.mean().backward()
+    grads = [l.grad for l in layer_tensors]
+    out.realize(*grads)
+    out_grads = [x.numpy() for x in grads]
+
+    # compute reference grads
+    for l in layer_tensors: l.grad = None
+    img.sequential(layers).mean().backward()
+    grads = [l.grad for l in layer_tensors]
+    out.realize(*grads)
+    ref_grads = [x.numpy() for x in grads]
+
+    # compare
+    for o,r in zip(out_grads, ref_grads): np.testing.assert_allclose(o, r, atol=1e-6)
+
 if __name__ == '__main__':
   unittest.main()

test/test_profiler.py

@@ -17,7 +17,7 @@ def helper_collect_profile(*devs):
   cpu_events.clear()
 
   profile_list = []
-  with Context(VIZ=1):
+  with Context(VIZ=1, PROFILE=1):
     yield profile_list
     for dev in devs: dev.synchronize()
     for dev in devs: dev._at_profile_finalize()

test/test_rangeify.py

@@ -300,6 +300,12 @@ class TestRangeify(unittest.TestCase):
     w2 = Tensor.empty(12, 8, 3, 3)
     x.conv2d(w1).conv2d(w2).realize()
 
+  def test_resnet_conv2d(self):
+    x = Tensor.empty(1, 8, 32, 32)
+    w1 = Tensor.empty(8, 8, 3, 3)
+    w2 = Tensor.empty(8, 8, 1, 1)
+    x.conv2d(w1).conv2d(w2).realize()
+
   def test_xception_conv2d(self):
     # NOTE: this fusion is bad, it's recomputing the inner many times
     x = Tensor.empty(1, 4, 32, 32)

test/test_schedule.py

@@ -1573,6 +1573,13 @@ class TestSchedule(unittest.TestCase):
   def test_conv2d(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
   def test_conv2d_fused(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
 
+  def test_resnet_conv2d(self):
+    x = Tensor.empty(1, 8, 32, 32)
+    w1 = Tensor.empty(8, 8, 3, 3)
+    w2 = Tensor.empty(8, 8, 1, 1)
+    out = x.conv2d(w1).conv2d(w2)
+    check_schedule(out, 2)
+
   @unittest.skipUnless(is_dtype_supported(dtypes.half) and is_dtype_supported(dtypes.ulong), "need half and ulong")
   def test_conv2d_half(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4, dtype=dtypes.half)
   @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")

test/test_tensor.py

@@ -3,7 +3,7 @@ import torch
 import unittest, copy, mmap, random, math, array
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _METADATA
-from tinygrad.helpers import getenv, temp, mv_address
+from tinygrad.helpers import Context, getenv, temp, mv_address
 from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
 from hypothesis import given, settings, strategies as strat
 from tinygrad.device import is_dtype_supported
@@ -846,6 +846,16 @@ class TestTensorMetadata(unittest.TestCase):
     #self.assertEqual(len(bw), 1)
     #self.assertEqual(bw[0].name, "sigmoid")
 
+  def test_tracemeta_0(self):
+    with Context(TRACEMETA=0):
+      x = Tensor.rand(3, requires_grad=True)
+      y = Tensor.rand(3, requires_grad=True)
+      out = (x.relu() * y.sigmoid()).sum()
+      self.assertIsNone(out.uop.metadata)
+      self.assertIsNone(out.uop.src[0].metadata)
+      si = out.schedule()[-1]
+      self.assertEqual(si.metadata, ())
+
 class TestIdxUpcast(unittest.TestCase):
   def _find_op(self, ast: UOp, op: Ops):
     if ast.op is op: return ast

test/test_tiny.py

@@ -32,8 +32,8 @@ class TestTiny(unittest.TestCase):
     self.assertListEqual(out.tolist(), [2]*16)
 
   def test_cat(self):
-    out = Tensor.cat(Tensor.ones(8).contiguous(), Tensor.ones(8).contiguous())
-    self.assertListEqual(out.tolist(), [1]*16)
+    out = Tensor.cat(Tensor.ones(8).contiguous(), Tensor.zeros(8).contiguous())
+    self.assertListEqual(out.tolist(), [1]*8+[0]*8)
 
   def test_sum(self):
     out = Tensor.ones(256).contiguous().sum()
@@ -62,7 +62,7 @@ class TestTiny(unittest.TestCase):
     out = Tensor.rand(10)
     for x in out.tolist():
       self.assertGreaterEqual(x, 0.0)
-      self.assertLessEqual(x, 1.0)
+      self.assertLess(x, 1.0)
 
   # *** JIT (for Python speed) ***
 
@@ -138,9 +138,7 @@ class TestTiny(unittest.TestCase):
       nn.Conv2d(8, 8, 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
-    #Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
-    for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))
+    Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
 
     # realize gradients
     for x in nn.state.get_parameters(layers): x.requires_grad_()

test/test_uops.py

@@ -517,7 +517,7 @@ class TestUOpStr(unittest.TestCase):
 
 class TestUPatHelpers(unittest.TestCase):
   def test_location(self):
-    self.assertEqual(sym.patterns[-1][0].location[0].replace("\\", "/").split("/")[-1], "math.py")
+    self.assertEqual(sym.patterns[-1][0].location[0].replace("\\", "/").split("/")[-1], "symbolic.py")
     self.assertEqual(shared_spec.patterns[0][0].location[0].replace("\\", "/").split("/")[-1], "spec.py")
     test_upat = UPat(Ops.CONST, dtypes.bool)
     self.assertEqual(test_upat.location[0].split("/")[-1], __file__.replace("\\", "/").split("/")[-1])

test/testextra/test_tk.py

@@ -0,0 +1,443 @@
+import unittest, math
+
+from tinygrad import Tensor, Device, dtypes, Context
+from tinygrad.engine.realize import ExecItem, get_runner
+from tinygrad.helpers import CI
+from tinygrad.renderer.ptx import PTXRenderer
+import numpy as np
+
+from extra.thunder.tiny.tk import WARP_THREADS
+from extra.thunder.tiny.tk.kernel import Kernel
+
+@unittest.skipIf(CI and Device.DEFAULT not in ["CUDA", "NV"], "only cuda")
+@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "no ptx")
+class TestTK(unittest.TestCase):
+  @unittest.skipIf(CI, "no wmma in ci")
+  def test_simple_matmul(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      c = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.bfloat16)
+      b = ker.gl((1, 1, N, N), dtypes.bfloat16)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      c_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      c_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      col, row = ker.blockIdx_x, ker.blockIdx_y
+
+      c_reg = warp.zero(c_reg)
+      for tile in ker.range(N // BLOCK_SIZE):
+        a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
+        b_smem = warp.load(b_smem, b, (), (0, 0, tile, col), axis=2)
+
+        a_reg = warp.load(a_reg, a_smem)
+        b_reg = warp.load(b_reg, b_smem, transpose=True)
+
+        c_reg = warp.mma_AB(c_reg, a_reg, b_reg)
+      c_reg = ker.endrange()
+
+      c_smem = warp.store(c_smem, c_reg)
+      c = warp.store(c, c_smem, (0, 0, row, col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
+      b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
+      c = Tensor.empty(1, 1, N, N, dtype="float32")
+      Tensor.realize(a, b, c)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (c, a, b)])
+    for _ in range(5): ei.run(wait=True)
+    c = c.float()
+
+    ref = a.matmul(b, dtype=dtypes.float32).float()
+
+    np.testing.assert_allclose(c.numpy(), ref.numpy())
+
+  @unittest.skipIf(CI, "no wmma in ci")
+  def test_simple_matmul_transposed(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      c = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.bfloat16)
+      b = ker.gl((1, 1, N, N), dtypes.bfloat16)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      c_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
+      c_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      col, row = ker.blockIdx_x, ker.blockIdx_y
+
+      c_reg = warp.zero(c_reg)
+      for tile in ker.range(N // BLOCK_SIZE):
+        a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
+        b_smem = warp.load(b_smem, b, (), (0, 0, col, tile), axis=2)
+
+        a_reg = warp.load(a_reg, a_smem)
+        b_reg = warp.load(b_reg, b_smem)
+
+        c_reg = warp.mma_ABt(c_reg, a_reg, b_reg)
+      c_reg = ker.endrange()
+
+      c_smem = warp.store(c_smem, c_reg)
+      c = warp.store(c, c_smem, (0, 0, row, col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
+      b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
+      c = Tensor.empty(1, 1, N, N, dtype="float32")
+      Tensor.realize(a, b, c)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (c, a, b)])
+    for _ in range(5): ei.run(wait=True)
+    c = c.float()
+
+    ref = a.matmul(b.transpose(2, 3), dtype=dtypes.float32).float()
+
+    np.testing.assert_allclose(c.numpy(), ref.numpy())
+
+  def test_load_store(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      col, row = ker.blockIdx_x, ker.blockIdx_y
+
+      a_smem = warp.load(a_smem, a, (), (0, 0, row, col), axis=2)
+      a_reg = warp.load(a_reg, a_smem)
+      b_reg = warp.copy(b_reg, a_reg)
+      b_smem = warp.store(b_smem, b_reg)
+      b = warp.store(b, b_smem, (0, 0, row, col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
+      b = Tensor.empty(1, 1, N, N, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float()
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy())
+
+  def test_add(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      for tile_row in ker.range(N // BLOCK_SIZE):
+        for tile_col in ker.range(N // BLOCK_SIZE):
+          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
+          a_reg = warp.load(a_reg, a_smem)
+
+          a_reg += 1
+
+          a_smem = warp.store(a_smem, a_reg)
+          b = warp.store(b, a_smem, (0, 0, tile_row, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+      with Context(DEBUG=0):
+        a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
+        b = Tensor.empty(1, 1, N, N, dtype="float32")
+        Tensor.realize(a, b)
+
+      ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+      for _ in range(5): ei.run(wait=True)
+      b = b.float()
+
+      ref = a.float() + 1
+
+      np.testing.assert_allclose(b.numpy(), ref.numpy())
+
+  def test_max(self):
+    N = 16
+    BLOCK_SIZE = 16
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      max_reg = ker.rv(BLOCK_SIZE, dtypes.float32, "ortho")
+
+      for tile_row in ker.range(N // BLOCK_SIZE):
+        max_reg = warp.neg_inf(max_reg.after(tile_row))
+
+        for tile_col in ker.range(N // BLOCK_SIZE):
+          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
+          a_reg = warp.load(a_reg, a_smem)
+          max_reg = warp.row_reduce(max_reg, a_reg, lambda a, b: a.maximum(b))
+        max_reg = ker.endrange()
+
+        b_reg = warp.map(b_reg, lambda _, idx: max_reg[idx[0], 0, (idx[2]%4)//2])
+        b_smem = warp.store(b_smem, b_reg)
+
+        for tile_col in ker.range(N // BLOCK_SIZE):
+          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
+      b = Tensor.empty(1, 1, N, N, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float().max(axis=3, keepdim=True).expand(a.shape)
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy())
+
+  def test_max_nonsquare(self):
+    N, M = 16, 64
+    BLOCK_N, BLOCK_M = 16, 64
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, M), dtypes.float32)
+      a = ker.gl((1, 1, N, M), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
+      b_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32)
+      b_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32)
+
+      max_reg = ker.rv(BLOCK_N, dtypes.float32, "ortho")
+
+      for tile_row in ker.range(N // BLOCK_N):
+        max_reg = warp.neg_inf(max_reg.after(tile_row))
+
+        for tile_col in ker.range(M // BLOCK_M):
+          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
+          a_reg = warp.load(a_reg, a_smem)
+          max_reg = warp.row_reduce(max_reg, a_reg, lambda a, b: a.maximum(b))
+        max_reg = ker.endrange()
+
+        b_reg = warp.map(b_reg, lambda _, idx: max_reg[idx[0], 0, (idx[2]%4)//2])
+        b_smem = warp.store(b_smem, b_reg)
+
+        for tile_col in ker.range(M // BLOCK_M):
+          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
+      b = Tensor.empty(1, 1, N, M, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float().max(axis=3, keepdim=True).expand(a.shape)
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy())
+
+  def test_sum(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, N), dtypes.float32)
+      a = ker.gl((1, 1, N, N), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+      b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      sum_reg = ker.rv(BLOCK_SIZE, dtypes.float32, "ortho")
+
+      for tile_row in ker.range(N // BLOCK_SIZE):
+        sum_reg = warp.zero(sum_reg.after(tile_row))
+
+        for tile_col in ker.range(N // BLOCK_SIZE):
+          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
+          a_reg = warp.load(a_reg, a_smem)
+          sum_reg = warp.row_reduce(sum_reg, a_reg, lambda a, b: a + b)
+        sum_reg = ker.endrange()
+
+        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
+        b_smem = warp.store(b_smem, b_reg)
+
+        for tile_col in ker.range(N // BLOCK_SIZE):
+          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
+      b = Tensor.empty(1, 1, N, N, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float().sum(axis=3, keepdim=True).expand(a.shape)
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
+
+  def test_sum_nonsquare(self):
+    N, M = 16, 64
+    BLOCK_N, BLOCK_M = 16, 64
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, N, M), dtypes.float32)
+      a = ker.gl((1, 1, N, M), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
+      b_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32)
+      b_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32)
+
+      sum_reg = ker.rv(BLOCK_N, dtypes.float32, "ortho")
+
+      for tile_row in ker.range(N // BLOCK_N):
+        sum_reg = warp.zero(sum_reg.after(tile_row))
+
+        for tile_col in ker.range(M // BLOCK_M):
+          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
+          a_reg = warp.load(a_reg, a_smem)
+          sum_reg = warp.row_reduce(sum_reg, a_reg, lambda a, b: a + b)
+        sum_reg = ker.endrange()
+
+        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
+        b_smem = warp.store(b_smem, b_reg)
+
+        for tile_col in ker.range(M // BLOCK_M):
+          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
+      b = Tensor.empty(1, 1, N, M, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float().sum(axis=3, keepdim=True).expand(a.shape)
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
+
+  @unittest.skip("fake range not ended")
+  def test_softmax(self):
+    N = 32
+    BLOCK_SIZE = 16
+    with Kernel((1, 1, 1), WARP_THREADS) as ker:
+      warp = ker.warp
+
+      b = ker.gl((1, 1, BLOCK_SIZE, N), dtypes.float32)
+      a = ker.gl((1, 1, BLOCK_SIZE, N), dtypes.float32)
+
+      a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
+
+      max_vec_last = ker.rv(BLOCK_SIZE, dtypes.float32, "ortho")
+      max_vec = ker.rv(BLOCK_SIZE, dtypes.float32, "ortho")
+      norm_vec = ker.rv(BLOCK_SIZE, dtypes.float32, "ortho")
+
+      max_vec = warp.neg_inf(max_vec)
+      norm_vec = warp.zero(norm_vec)
+
+      for tile_col in ker.range(N // BLOCK_SIZE):
+        a_smem = warp.load(a_smem, a, (), (0, 0, 0, tile_col), axis=2)
+        a_reg = warp.load(a_reg, a_smem)
+
+        a_reg *= 1.0 / math.log(2)
+
+        max_vec_last = warp.copy(max_vec_last.after(tile_col), max_vec)
+        max_vec = warp.row_reduce(max_vec, a_reg, lambda a, b: a.maximum(b))
+        a_reg = (a_reg - max_vec).exp2()
+        max_vec_last = (max_vec_last - max_vec).exp2()
+        norm_vec *= max_vec_last
+        norm_vec = warp.row_reduce(norm_vec, a_reg, lambda a, b: a + b)
+      norm_vec = ker.endrange()
+
+      for tile_col in ker.range(N // BLOCK_SIZE):
+        a_smem = warp.load(a_smem, a, (), (0, 0, 0, tile_col), axis=2)
+        a_reg = warp.load(a_reg, a_smem)
+
+        a_reg *= 1.0 / math.log(2)
+        a_reg = (a_reg - max_vec).exp2()
+        a_reg /= norm_vec
+
+        a_smem = warp.store(a_smem, a_reg)
+        b = warp.store(b, a_smem, (0, 0, 0, tile_col), (), axis=2)
+
+      sink = ker.finish()
+
+    with Context(DEBUG=0):
+      a = Tensor.rand(1, 1, BLOCK_SIZE, N, dtype="float32")
+      b = Tensor.empty(1, 1, BLOCK_SIZE, N, dtype="float32")
+      Tensor.realize(a, b)
+
+    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
+    for _ in range(5): ei.run(wait=True)
+    b = b.float()
+
+    ref = a.float().softmax(axis=3)
+
+    np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
+
+if __name__ == "__main__":
+  unittest.main()

test/unit/test_autogen.py

@@ -0,0 +1,85 @@
+import ctypes, subprocess, tempfile, unittest
+from tinygrad.helpers import WIN
+from tinygrad.runtime.support.c import Struct
+
+class TestAutogen(unittest.TestCase):
+  def test_packed_struct_sizeof(self):
+    layout = [('a', ctypes.c_char), ('b', ctypes.c_int, 5), ('c', ctypes.c_char)]
+    class Y(ctypes.Structure): _fields_, _pack_, _layout_ = layout, 1, 'ms'
+    class Z(Struct): pass
+    Z._packed_, Z._fields_ = True, layout
+    self.assertEqual(ctypes.sizeof(Y), 6)
+    self.assertEqual(ctypes.sizeof(Z), 3)
+    layout = [('a', ctypes.c_int, 31), ('b', ctypes.c_int, 31), ('c', ctypes.c_int, 1), ('d', ctypes.c_int, 1)]
+    class Foo(ctypes.Structure): _fields_, _layout_ = layout, 'gcc-sysv'
+    class Bar(ctypes.Structure): _fields_, _pack_, _layout_ = layout, 1, 'ms'
+    class Baz(Struct): pass
+    Baz._packed_, Baz._fields_ = True, layout
+    self.assertEqual(ctypes.sizeof(Foo), 12)
+    self.assertEqual(ctypes.sizeof(Bar), 12)
+    self.assertEqual(ctypes.sizeof(Baz), 8)
+
+  @unittest.skipIf(WIN, "doesn't compile on windows")
+  def test_packed_struct_interop(self):
+    class Baz(Struct): pass
+    Baz._packed_ = True
+    Baz._fields_ = [('a', ctypes.c_int, 30), ('b', ctypes.c_int, 30), ('c', ctypes.c_int, 2), ('d', ctypes.c_int, 2)]
+    src = '''
+      struct __attribute__((packed)) baz {
+        int a:30;
+        int b:30;
+        int c:2;
+        int d:2;
+      };
+
+      int test(struct baz x) {
+        return x.a + x.b + x.c + x.d;
+      }
+    '''
+    args = ('-x', 'c', '-fPIC', '-shared')
+    with tempfile.NamedTemporaryFile(suffix=".so") as f:
+      subprocess.check_output(('clang',) + args + ('-', '-o', f.name), input=src.encode('utf-8'))
+      b = Baz(0xAA000, 0x00BB0, 0, 1)
+      test = ctypes.CDLL(f.name).test
+      test.argtypes = [Baz]
+      self.assertEqual(test(b), b.a + b.b + b.c + b.d)
+
+  @unittest.skipIf(WIN, "doesn't compile on windows")
+  def test_packed_structs(self):
+    NvU32 = ctypes.c_uint32
+    NvU64 = ctypes.c_uint64
+    class FWSECLIC_READ_VBIOS_DESC(Struct): pass
+    FWSECLIC_READ_VBIOS_DESC._packed_ = True
+    FWSECLIC_READ_VBIOS_DESC._fields_ = [
+      ('version', NvU32),
+      ('size', NvU32),
+      ('gfwImageOffset', NvU64),
+      ('gfwImageSize', NvU32),
+      ('flags', NvU32),
+    ]
+    class FWSECLIC_FRTS_REGION_DESC(Struct): pass
+    FWSECLIC_FRTS_REGION_DESC._packed_ = True
+    FWSECLIC_FRTS_REGION_DESC._fields_ = [
+      ('version', NvU32),
+      ('size', NvU32),
+      ('frtsRegionOffset4K', NvU32),
+      ('frtsRegionSize', NvU32),
+      ('frtsRegionMediaType', NvU32),
+    ]
+    class FWSECLIC_FRTS_CMD(Struct): pass
+    FWSECLIC_FRTS_CMD._packed_ = True
+    FWSECLIC_FRTS_CMD._fields_ = [
+      ('readVbiosDesc', FWSECLIC_READ_VBIOS_DESC),
+      ('frtsRegionDesc', FWSECLIC_FRTS_REGION_DESC),
+    ]
+    read_vbios_desc = FWSECLIC_READ_VBIOS_DESC(version=0x1, size=ctypes.sizeof(FWSECLIC_READ_VBIOS_DESC), flags=2)
+    frst_reg_desc = FWSECLIC_FRTS_REGION_DESC(version=0x1, size=ctypes.sizeof(FWSECLIC_FRTS_REGION_DESC),
+      frtsRegionOffset4K=0xdead, frtsRegionSize=0x100, frtsRegionMediaType=2)
+    frts_cmd = FWSECLIC_FRTS_CMD(readVbiosDesc=read_vbios_desc, frtsRegionDesc=frst_reg_desc)
+    assert int.from_bytes(frts_cmd, 'little') == 0x2000001000000dead0000001400000001000000020000000000000000000000000000001800000001
+    assert int.from_bytes(frts_cmd.readVbiosDesc, 'little') == int.from_bytes(read_vbios_desc, 'little')
+    assert int.from_bytes(frts_cmd.frtsRegionDesc, 'little') == int.from_bytes(frst_reg_desc, 'little')
+    assert frts_cmd.readVbiosDesc.__class__ is FWSECLIC_READ_VBIOS_DESC
+    assert frts_cmd.frtsRegionDesc.__class__ is FWSECLIC_FRTS_REGION_DESC
+
+if __name__ == "__main__": unittest.main()

test/unit/test_conv.py

@@ -62,6 +62,7 @@ class TestConv(unittest.TestCase):
     np.testing.assert_allclose(r1.numpy(), np.maximum(out.numpy(), 0), atol=1e-5)
     np.testing.assert_allclose(r2.numpy(), np.where(out.numpy() > 0, out.numpy(), (np.exp(out.numpy()) - 1)), atol=1e-5)
 
+  @unittest.skip("this test is flaky")
   def test_two_overlapping_binops_no_rerun_wino(self):
     with Context(WINO=1):
       x = Tensor.randn(1,4,16,16)

test/unit/test_device.py

@@ -30,7 +30,7 @@ class TestDevice(unittest.TestCase):
 
   @unittest.skipIf(WIN and CI, "skipping windows test") # TODO: subproccess causes memory violation?
   def test_env_overwrite_default_compiler(self):
-    expect_failure = "\ntry: assert Device[Device.DEFAULT].compiler is None;\nexcept RuntimeError: pass"
+    expect_failure = "\ntry: assert Device[Device.DEFAULT].compiler is None;\nexcept Exception: pass"
 
     if Device.DEFAULT == "CPU":
       from tinygrad.runtime.support.compiler_cpu import CPULLVMCompiler, ClangJITCompiler
@@ -81,20 +81,20 @@ class TestCompiler(unittest.TestCase):
   def test_compile_cached(self):
     diskcache_put("key", "123", None) # clear cache
     getenv.cache_clear()
-    with Context(DISABLE_COMPILER_CACHE=0):
+    with Context(CCACHE=1):
       self.assertEqual(MockCompiler("key").compile_cached("123"), str.encode("123"))
       self.assertEqual(diskcache_get("key", "123"), str.encode("123"))
 
   def test_compile_cached_disabled(self):
     diskcache_put("disabled_key", "123", None) # clear cache
     getenv.cache_clear()
-    with Context(DISABLE_COMPILER_CACHE=1):
+    with Context(CCACHE=0):
       self.assertEqual(MockCompiler("disabled_key").compile_cached("123"), str.encode("123"))
       self.assertIsNone(diskcache_get("disabled_key", "123"))
 
   def test_device_compile(self):
     getenv.cache_clear()
-    with Context(DISABLE_COMPILER_CACHE=1):
+    with Context(CCACHE=0):
       a = Tensor([0.,1.], device=Device.DEFAULT).realize()
       (a + 1).realize()
 

test/unit/test_microbenchmarks.py

@@ -1,4 +1,5 @@
 import unittest, time
+from tinygrad.helpers import Profiling
 from tinygrad.uop.ops import UOp
 from tinygrad.dtype import dtypes
 
@@ -38,6 +39,14 @@ class TestMicrobenchmarks(unittest.TestCase):
     a = UOp.const(dtypes.int, 2)
     for _ in range(N): (a+a).simplify()
 
+class TestMicroprofile(unittest.TestCase):
+  def test_uop_simplify_complex(self):
+    x = UOp.variable("x", 0, 10)
+    y = UOp.variable("y", 0, 10)
+    expr = (x*2)+5+(x*4)+(y*2)+y
+    with Profiling():
+      for _ in range(1000): expr.simplify()
+
 if __name__ == '__main__':
   unittest.main()
 

test/unit/test_symbolic_failures.py

@@ -159,3 +159,38 @@ class TestFuzzFailure(unittest.TestCase):
     num = expr.simplify().substitute({v1:v1_val, v2:v2_val, v3:v3_val}).ssimplify()
     rn = expr.substitute({v1:v1_val, v2:v2_val, v3:v3_val}).ssimplify()
     self.assertEqual(num, rn)
+
+  def test_fuzz_failure11(self):
+    v1=Variable("v1", 0, 16)
+    v2=Variable("v2", 0, 128)
+    v3=Variable("v3", 0, 5)
+    expr = UOp(Ops.MOD, dtypes.index, arg=None, src=(
+      UOp(Ops.ADD, dtypes.index, arg=None, src=(
+        UOp(Ops.MOD, dtypes.index, arg=None, src=(
+          UOp(Ops.ADD, dtypes.index, arg=None, src=(
+            UOp(Ops.MAX, dtypes.index, arg=None, src=(
+              UOp(Ops.MUL, dtypes.index, arg=None, src=(
+                x5:=UOp(Ops.DEFINE_VAR, dtypes.index, arg=('v2', 0, 128), src=()),
+                UOp(Ops.CONST, dtypes.index, arg=0, src=()),)),
+              UOp(Ops.CONST, dtypes.index, arg=8, src=()),)),
+            UOp(Ops.MUL, dtypes.index, arg=None, src=(
+              x5,
+              UOp(Ops.CONST, dtypes.index, arg=-2, src=()),)),)),
+          x10:=UOp(Ops.CONST, dtypes.index, arg=5, src=()),)),
+        UOp(Ops.ADD, dtypes.index, arg=None, src=(
+          UOp(Ops.ADD, dtypes.index, arg=None, src=(
+            UOp(Ops.IDIV, dtypes.index, arg=None, src=(
+              x14:=UOp(Ops.DEFINE_VAR, dtypes.index, arg=('v1', 0, 16), src=()),
+              UOp(Ops.CONST, dtypes.index, arg=6, src=()),)),
+            UOp(Ops.CONST, dtypes.index, arg=4, src=()),)),
+          UOp(Ops.ADD, dtypes.index, arg=None, src=(
+            x14,
+            UOp(Ops.CONST, dtypes.index, arg=1, src=()),)),)),)),
+      x10,))
+    v1_val, v2_val, v3_val = UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 7),UOp.const(dtypes.int, 0)
+    num = expr.simplify().substitute({v1:v1_val, v2:v2_val, v3:v3_val}).ssimplify()
+    rn = expr.substitute({v1:v1_val, v2:v2_val, v3:v3_val}).ssimplify()
+    self.assertEqual(num, rn)
+
+if __name__ == '__main__':
+  unittest.main()

test/unit/test_tqdm.py

@@ -66,6 +66,7 @@ class TestProgressBar(unittest.TestCase):
       tqdm_output = tqdm.format_meter(n=total, total=total, elapsed=elapsed, ncols=ncols, prefix="Test")
       self._compare_bars(tinytqdm_output, tqdm_output)
 
+  @unittest.skip("this is flaky")
   @patch('sys.stderr', new_callable=StringIO)
   @patch('shutil.get_terminal_size')
   def test_unit_scale(self, mock_terminal_size, mock_stderr):
@@ -127,6 +128,7 @@ class TestProgressBar(unittest.TestCase):
         self._compare_bars(tinytqdm_output, tqdm_output)
         if n > 5: break
 
+  @unittest.skip("this is flaky")
   @patch('sys.stderr', new_callable=StringIO)
   @patch('shutil.get_terminal_size')
   def test_set_description(self, mock_terminal_size, mock_stderr):

test/unit/test_uop_symbolic.py

@@ -643,6 +643,10 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(lidx+(gidx//4)*8+2*(gidx%4), 0, 372, "(lidx+(gidx*2))")
     self.helper_test_variable(lidx+2*(gidx%4)+(gidx//4)*8, 0, 372, "(lidx+(gidx*2))")
 
+  def test_div_mod_recombine_partial(self):
+    gidx = Variable("gidx", 0, 15)
+    self.helper_test_variable((gidx//2)%4+(gidx//8)*4, 0, 7, "gidx//2")
+
   def test_div_mod_recombine_folded_mod(self):
     a = Variable("a", 0, 2)
     b = Variable("b", 0, 100)

test/unit/test_viz.py

@@ -6,6 +6,7 @@ from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatch
 from tinygrad.uop.symbolic import sym
 from tinygrad.dtype import dtypes
 from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context, cpu_events, profile_marker
+from tinygrad.helpers import VIZ
 from tinygrad.device import Buffer
 
 @track_rewrites(name=True)
@@ -33,11 +34,14 @@ class BaseTestViz(unittest.TestCase):
     cpu_events.clear()
     self.tms = TRACK_MATCH_STATS.value
     self.profile = PROFILE.value
+    self.viz = VIZ.value
     TRACK_MATCH_STATS.value = 2
     PROFILE.value = 1
+    VIZ.value = 1
   def tearDown(self):
     TRACK_MATCH_STATS.value = self.tms
     PROFILE.value = self.profile
+    VIZ.value = self.viz
 
 class TestViz(BaseTestViz):
   def test_simple(self):
@@ -366,8 +370,8 @@ def load_profile(lst:list[ProfileEvent]) -> dict:
         else: v["events"].append({"event":"free", "ts":ts, "key":key, "arg": {"users":[u("<IIBB") for _ in range(u("<I")[0])]}})
   return {"dur":total_dur, "peak":global_peak, "layout":layout, "markers":markers}
 
-class TestVizProfiler(unittest.TestCase):
-  def test_perfetto_node(self):
+class TestVizProfiler(BaseTestViz):
+  def test_node(self):
     prof = [ProfileRangeEvent(device='NV', name='E_2', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=False),
             ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100))]
 
@@ -381,7 +385,7 @@ class TestVizProfiler(unittest.TestCase):
     self.assertEqual(event['dur'], 10)
     assert event['ref'] is None
 
-  def test_perfetto_copy_node(self):
+  def test_copy_node(self):
     prof = [ProfileRangeEvent(device='NV', name='COPYxx', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=True),
             ProfileRangeEvent(device='NV:2', name='COPYxx', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=True),
             ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100)),
@@ -399,7 +403,7 @@ class TestVizProfiler(unittest.TestCase):
 
     self.assertEqual(j["dur"], (event2["st"]+event2["dur"])-event["st"])
 
-  def test_perfetto_graph(self):
+  def test_graph(self):
     prof = [ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100)),
             ProfileDeviceEvent(device='NV:1', comp_tdiff=decimal.Decimal(-500), copy_tdiff=decimal.Decimal(-50)),
             ProfileGraphEvent(ents=[ProfileGraphEntry(device='NV', name='E_25_4n2', st_id=0, en_id=1, is_copy=False),
@@ -436,6 +440,12 @@ class TestVizProfiler(unittest.TestCase):
     sz = len(get_profile(prof))
     self.assertLessEqual(sz/n_events, 26)
 
+  def test_calltrace(self):
+    def fxn(): return Tensor.empty(10).mul(2).realize()
+    fxn()
+    trace = get_viz_list()[0]["steps"][0]["trace"]
+    assert any(fxn.__code__.co_filename == f and fxn.__code__.co_firstlineno == l for f,l,*_ in trace), str(trace)
+
   # can pack up to 1hr 11 min of trace events
   def test_trace_duration(self):
     dur_mins = 72

tinygrad/codegen/__init__.py

@@ -136,7 +136,7 @@ def full_rewrite(sink:UOp, ren:Renderer|None=None) -> list[UOp]:
   """
 
   full_sink = full_rewrite_to_sink(sink, ren, optimize=sink.tag is None)
-  assert len(full_sink.ranges) == 0, "all ranges must end by the sink"
+  assert len(full_sink.ranges) == 0, f"all ranges must end by the sink, {full_sink.ranges}"
   lst = line_rewrite(linearize(full_sink), pm_linearize_cleanups)
   if SPEC: type_verify(lst, program_spec)
   return lst

tinygrad/codegen/gpudims.py

@@ -26,28 +26,35 @@ def _split_dims(dims, max_sizes):
   return tuple(_dims[:2] if _dims[2] == 1 else _dims[0] if _dims[1:3] == [1,1] else _dims)
 
 def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|None, reverse=False) -> list[UOp]:
-  if reverse: dims = dims[::-1]
-  # try to group first: (a, b, c, d) -> (ab, c, d)
-  limited = (grouped if (grouped := _group_dims(dims, max_sizes)) else dims) if max_sizes is not None else dims
-  # check if grouping failed
-  if max_sizes is not None and len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
-  # try to split up dims: (a,) -> (b, c)
-  if limited == dims: limited = _split_dims(dims, max_sizes) if max_sizes is not None else dims
-  ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.index, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
+  if reverse: return get_grouped_dims(prefix, dims[::-1], max_sizes)[::-1]
+  if max_sizes is None: limited = dims
+  else:
+    # try to group first: (a, b, c, d) -> (ab, c, d)
+    limited = grouped if (grouped := _group_dims(dims, max_sizes)) else dims
+    # check if grouping failed
+    if len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
+    # try to split up dims: (a,) -> (b, c)
+    if limited == dims: limited = _split_dims(dims, max_sizes)
+  raw_idxs = [UOp(Ops.SPECIAL, dtypes.index, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
   if len(limited) < len(dims):
     ret = []
-    if (contraction:=get_contraction(dims, limited)) is None: raise AssertionError(f"get_contraction should not be None {dims=} {limited=}")
+    if (contraction:=get_contraction(dims, limited)) is None: raise RuntimeError(f"get_contraction should not be None {dims=} {limited=}")
     for idx, contraction_group in zip(raw_idxs, contraction):
       for c in contraction_group[:-1]:
         ret.append(idx % dims[c])
         idx //= dims[c]
       ret.append(idx)
-  elif len(limited) > len(dims):
-    a, b = len(limited), len(dims)
-    if a == 2 and b == 1: ret = [raw_idxs[0] * limited[1] + raw_idxs[1]]
-    if a == 3 and b == 1: ret = [raw_idxs[0] * (limited[1] * limited[2]) + raw_idxs[1] * limited[2] + raw_idxs[2]]
-    if a == 3 and b == 2: ret = [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
-  return ret[::-1] if reverse else ret
+    return ret
+  elif (a:=len(limited)) > (b:=len(dims)):
+    if a == 2 and b == 1: return [raw_idxs[0] * limited[1] + raw_idxs[1]]
+    if a == 3 and b == 1: return [(raw_idxs[0] * limited[1] + raw_idxs[1]) * limited[2] + raw_idxs[2]]
+    if a == 3 and b == 2: return [raw_idxs[0] * limited[1] + raw_idxs[1], raw_idxs[2]]
+  elif limited != dims:
+    # Convert to 1D
+    flat = raw_idxs[0]*limited[1]+raw_idxs[1] if len(dims) == 2 else raw_idxs[0]*(limited[1]*limited[2])+raw_idxs[1]*limited[2]+raw_idxs[2]
+    # Get back original indices from 1D
+    return [flat//dims[1], flat%dims[1]] if len(dims) == 2 else [flat//(dims[2]*dims[1]), (flat//dims[2])%dims[1], flat%dims[2]]
+  return raw_idxs
 
 def add_gpudims(ctx:Renderer, s:UOp):
   if s.arg is None: return None

tinygrad/codegen/late/devectorizer.py

@@ -60,7 +60,9 @@ load_store_indexing = PatternMatcher([
 def expand_index(buf:UOp, vec:UOp):
   if getenv("UNSAFE_DISABLE_MASK", 0): vec = vec.get_idx()
   # generate the individual indexes
-  midx = graph_rewrite(UOp.sink(*[buf.index(vec.gep(i), ptr=True) for i in range(vec.dtype.count)]),
+  # we use `.buf_target()` here to avoid traversing into the AFTER
+  buf_target = buf.buf_target().rtag() if buf.op is Ops.AFTER else buf
+  midx = graph_rewrite(UOp.sink(*[buf_target.index(vec.gep(i), ptr=True) for i in range(vec.dtype.count)]),
                        symbolic+load_store_indexing, name=f"index_buf_{buf.arg}")
   # extract all the relevant offsets
   offsets_rootsrc: defaultdict[Any, dict[int, list[int]]] = defaultdict(dict)
@@ -93,7 +95,7 @@ def expand_index(buf:UOp, vec:UOp):
   assert None not in idxs, f"some idxs are missing {idxs}"
   # this base thing is for image, we want the CAT to be a normal pointer
   post_cat = UOp(Ops.PTRCAT, buf.ptrdtype.base.ptr(size=buf.ptrdtype.size, addrspace=buf.ptrdtype.addrspace).vec(global_offset), tuple(ret))
-  return post_cat.gep(tuple(cast(list[int], idxs)))
+  return post_cat.gep(tuple(cast(list[int], idxs))).substitute({buf_target:buf})
 
 def cat_after_store(cat:UOp, data:UOp, sto:UOp):
   # TODO: this is written in many places

tinygrad/codegen/late/linearizer.py

@@ -1,48 +1,59 @@
 import heapq
+from typing import Any
 from collections import defaultdict
-from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat
-from tinygrad.helpers import prod
+from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat, multirange_str
+from tinygrad.helpers import prod, getenv, TUPLE_ORDER
 
-def linearize(u:UOp) -> list[UOp]:
+def linearize(sink:UOp) -> list[UOp]:
   # this is a toposort with priority
-  lst = list(u.toposort())
+  lst = list(sink.toposort())
   consumers: defaultdict[UOp, list[UOp]] = defaultdict(list)
   in_degree:dict[UOp, int] = {}
-  priorities:dict[UOp, tuple[int, int]] = {}
+  out_degree:dict[UOp, int] = {}
+  priorities:dict[UOp, tuple[int, int, Any]] = {}
 
   # get consumers and assign priorities
   # NOTE: this requires the lst be locally toposorted
   for u in reversed(lst):
     for s in u.src: consumers[s].append(u)
     in_degree[u] = len(u.src)
+    out_degree[u] = len(consumers[u])
 
     # we place UOps with higher run_counts later
-    # this will cause ranges to be placed late and ends to be placed early
     run_count = prod([int(r.vmax)+1 for r in u.ranges])
 
-    # put loads in the beginning of the block and prevent priority inversion. hack for BARRIER grouping too
-    priority = [0] + [priorities[x][1] for x in consumers[u]]
-    if u.op is Ops.LOAD: priority.append(-1000)
-    if u.op is Ops.BARRIER: priority.append(-1500)
-    # ranges are scheduled as late as possible so anything that can be outside is
-    # if u.op is Ops.RANGE: priority = [2000]
-    if u.op is Ops.END: priority = [-1000]
-    # move defines and consts to the top
-    if u.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.CONST}: priority.append(-2000)
-    priorities[u] = (run_count, min(priority))
+    # simple priority override. this is all bottom up now, smaller numbers will be closer to the top
+    extra = None
+    match u.op:
+      # the order and placement of these defines is important
+      case Ops.DEFINE_GLOBAL: priority, extra = -20, u.arg
+      case Ops.DEFINE_VAR: priority, extra = -19, u.arg
+      case Ops.DEFINE_LOCAL: priority = -18
+      case Ops.DEFINE_REG: priority = -17
+      case Ops.CONST: priority = -10  # early consts
+      case Ops.LOAD: priority = -1    # place loads early
+      case Ops.STORE: priority = 1    # place stores late
+      case Ops.RANGE: priority = 5    # placing RANGE is good
+      case Ops.END: priority = -5     # placing END is bad
+      case _: priority = 0            # everything else has priority 0
+    priorities[u] = (run_count, priority, extra)
 
   # number the uops in "ideal" order
-  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: (priorities[x],)+x.tuplize))}
+  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: priorities[x]+(x.tuplize if TUPLE_ORDER else ())))}
 
-  # then force then to be toposorted in as close to the ideal order as possible
-  heapq.heapify(heap:=[(nkey[u],u) for u in lst if in_degree[u] == 0])
+  # then force them to be toposorted in as close to the ideal order as possible
+  heap = [(-nkey[sink], sink)]
   newlst = []
   while heap:
     newlst.append(u:=heapq.heappop(heap)[1])
-    for v in consumers[u]:
-      in_degree[v] -= 1
-      if in_degree[v] == 0: heapq.heappush(heap, (nkey[v],v))
-  assert len(newlst) == len(lst), f"len mismatch {len(newlst)} != {len(lst)}"
+    for v in u.src:
+      out_degree[v] -= 1
+      if out_degree[v] == 0: heapq.heappush(heap, (-nkey[v],v))
+  newlst = newlst[::-1]
+
+  if getenv("DEBUG_LINEARIZE"):
+    for i,u in enumerate(newlst):
+      print(f"{i:4d} {str(u.op):20s} {multirange_str(u.ranges, color=True, pad=10)} {priorities[u]}")
   return newlst
 
 class CFGContext:

tinygrad/codegen/opt/heuristic.py

@@ -81,10 +81,10 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
             return k
 
   # are we grouping? (requires local shape support)
-  if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= 2048, False):
-    for sz in [16]:
+  if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= (240 if NOLOCALS else 2048), False):
+    for axis, sz in itertools.product((0, 1, 2), (16,)):
       try:
-        k.apply_opt(Opt(OptOps.GROUPTOP, 0, sz))
+        k.apply_opt(Opt(OptOps.GROUPTOP, axis, sz))
         break
       except KernelOptError: pass
 
@@ -107,7 +107,7 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
   # potentially do more upcasts of non reduce axes based on a heuristic
   is_dsp = k.ren is not None and k.ren.device == "DSP"
   upcasted_axis: set[int] = set()
-  while resolve(prod(k.output_shape[i] for i in k.upcastable_dims) >= 1024):
+  while resolve(prod(k.output_shape[i] for i in k.upcastable_dims) >= 1024) and (k.upcast_size() < 32):
     xb_choices = []
     # consider all upcastable axes with 3 or 4 upcast (128 on the DSP)
     for axis, upcast_amount in itertools.product(k.upcastable_dims, ([128] if not len(upcasted_axis) else []) if is_dsp else [3,4]):
@@ -135,8 +135,7 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
   # if last reduce dim is small(ish), loop unroll the reduce
   # NOTE: this can fail on multireduce with mismatching dimensions, this is okay
   try:
-    upcast_size = prod(k.full_shape[a] for a in k.axes_of(AxisType.UPCAST, AxisType.UNROLL))
-    if k.unrollable_dims and (upcast_size <= 4 or not k.axes_of(AxisType.UNROLL)) and (upcast_size < 64):
+    if k.unrollable_dims and (k.upcast_size() <= 4 or not k.axes_of(AxisType.UNROLL)) and (k.upcast_size() < 64):
       if (s:=k.full_shape[k.unrollable_dims[-1]]) <= 32:
         k.apply_opt(Opt(OptOps.UNROLL, len(k.unrollable_dims)-1, 0))
         # if it's small, upcast a second reduce dimension too

tinygrad/codegen/opt/postrange.py

@@ -2,7 +2,8 @@ from __future__ import annotations
 import math, itertools
 from collections import defaultdict
 from typing import cast, Final
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, AxisType, ssimplify, GroupOp, axis_letters, axis_colors
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, AxisType, ssimplify, GroupOp
+from tinygrad.uop.ops import axis_letters, axis_colors, axis_to_pos
 from tinygrad.device import Buffer
 from tinygrad.dtype import dtypes, ImageDType
 from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up, prod, merge_dicts, get_single_element, flatten
@@ -12,15 +13,12 @@ from tinygrad.renderer import Renderer
 
 remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
 
-# NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
-axis_to_pos = {AxisType.LOOP: -1, AxisType.THREAD: 0, AxisType.GLOBAL: 0, AxisType.WARP: 1, AxisType.LOCAL: 2, AxisType.UPCAST: 3,
-               AxisType.GROUP_REDUCE: 2, AxisType.REDUCE: 4, AxisType.UNROLL: 5}
-
 class Scheduler:
   def __init__(self, ast:UOp, ren:Renderer):
     self.ast, self.ren = ast, ren
     self.dont_use_locals = self.ast.arg.dont_use_locals if self.ast.arg is not None else False
     self.applied_opts = list(self.ast.arg.applied_opts) if self.ast.arg is not None else []
+    self.opt_range = itertools.count(start=max([x.arg[0] for x in self.rngs], default=0)+1)
 
   @property
   def rngs(self):
@@ -32,8 +30,6 @@ class Scheduler:
   def full_shape(self): return [ssimplify(x.src[0]) for x in self.rngs]
   @property
   def axis_types(self): return [x.arg[-1] for x in self.rngs]
-  @property
-  def maxarg(self): return max([x.arg[0] for x in self.rngs], default=0)
 
   # strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
   def shape_str(self) -> list[str]:
@@ -55,8 +51,10 @@ class Scheduler:
   def get_optimized_ast(self, name_override:str|None=None):
     if name_override is not None: name = name_override
     else:
-      kernel_type = "r" if self.reduceop is not None else "E"
-      name = kernel_type + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
+      k_type = "r" if self.reduceop is not None else "E"
+      special_uops = sorted([x for x in self.ast.toposort() if x.op is Ops.SPECIAL], key=lambda x: x.arg)
+      special_ops = [colored(str(x.vmax+1), "blue" if x.arg[0] == "g" else "cyan") for x in special_uops]
+      name = k_type + colored('_', 'BLACK').join(['']+special_ops+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
       Scheduler.kernel_cnt[(function_name := to_function_name(name))] += 1
       num = f"n{Scheduler.kernel_cnt[function_name]-1}" if Scheduler.kernel_cnt[function_name] > 1 else ""
       name += colored(num, 'BLACK')
@@ -96,7 +94,7 @@ class Scheduler:
   def shift_to(self, rng:UOp, amount:int, new_type:AxisType, top:bool=False, input_new_rng=None):
     if (old_sz:=rng.src[0].divides(amount)) is None:
       raise KernelOptError(f"{amount} can't divide {rng.src[0]} in {self.colored_shape()}")
-    new_rng = UOp.range(amount, self.maxarg+1, new_type) if input_new_rng is None else input_new_rng
+    new_rng = UOp.range(amount, next(self.opt_range), new_type) if input_new_rng is None else input_new_rng
     replaced_rng = rng.replace(src=(UOp.const(dtypes.int, old_sz),))
     sub_axis = (new_rng * old_sz + replaced_rng) if top else (replaced_rng * amount + new_rng)
     self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[:-1]} {amount} {str(new_type).split('.')[1].lower()}")
@@ -105,6 +103,8 @@ class Scheduler:
   def ranges_of(self, *axis_type:AxisType) -> list[UOp]: return [r for r in self.rngs if r.arg[-1] in axis_type]
   def axes_of(self, *axis_type:AxisType) -> list[int]: return [i for i,t in enumerate(self.axis_types) if t in axis_type]
 
+  def upcast_size(self) -> int: return prod(self.full_shape[a] for a in self.axes_of(AxisType.UPCAST, AxisType.UNROLL))
+
   # copied from kernel.py
   @property
   def upcastable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP) \
@@ -217,8 +217,7 @@ class Scheduler:
     return ret
 
   def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> None|list[UOp]:
-    reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE]
-    if not len(reduceops): raise KernelOptError("no reduce ops for TensorCore")
+    if not (reduceops := self.reduceops): raise KernelOptError("no reduce ops for TensorCore")
     reduceop = reduceops[0]
     if use_tensor_cores and reduceop is not None and reduceop.arg is Ops.ADD:
       mul = reduceop.src[0] if reduceop.src[0].op is not Ops.CAST else reduceop.src[0].src[0]
@@ -231,9 +230,9 @@ class Scheduler:
       for tc in tensor_cores:
         if tc.dtype_in == in0.dtype.scalar() and tc.dtype_in == in1.dtype.scalar() and tc.dtype_out == reduceop.dtype.scalar():
           # tensor cores have three ranges. X, Y, and REDUCE
-          in0_ranges = sorted([u for u in in0.ranges if u not in in1.ranges], key=lambda x: -x.arg[0])
-          in1_ranges = sorted([u for u in in1.ranges if u not in in0.ranges], key=lambda x: -x.arg[0])
-          red_ranges = sorted(reduceop.src[1:], key=lambda x: -x.arg[0])
+          in0_ranges = sorted([u for u in in0.ranges if u not in in1.ranges], key=lambda x: x.arg[0], reverse=True)
+          in1_ranges = sorted([u for u in in1.ranges if u not in in0.ranges], key=lambda x: x.arg[0], reverse=True)
+          red_ranges = sorted(reduceop.src[1:], key=lambda x: x.arg[0], reverse=True)
           if DEBUG >= 3:
             print(f"TC({axis}): {[(x.arg[0],x.vmax+1) for x in in0_ranges]}",
                               f"{[(x.arg[0],x.vmax+1) for x in in1_ranges]} {[(x.arg[0],x.vmax+1) for x in red_ranges]}")
@@ -312,9 +311,10 @@ class Scheduler:
 
   # helpers for hand_coded_optimizations
   @property
+  def reduceops(self) -> list[UOp]: return [x for x in self.ast.backward_slice if x.op is Ops.REDUCE]
+  @property
   def reduceop(self) -> UOp|None:
-    red = [x for x in self.ast.backward_slice if x.op is Ops.REDUCE]
-    if not len(red): return None
+    if not (red := self.reduceops): return None
     return UOp(Ops.REDUCE_AXIS, red[0].dtype, red[0].src, (red[0].arg, ()))
   @property
   def bufs(self) -> list[UOp]: return [x for x in self.ast.toposort() if x.op is Ops.INDEX][::-1]

tinygrad/codegen/simplify.py

@@ -34,7 +34,6 @@ def simplify_merge_adjacent(u:UOp) -> UOp|None:
         # check if it simplifies
         if count_divmod(nidx) <= count_divmod(u):
           u = nidx
-          continue
   return u
 
 pm_simplify_ranges = PatternMatcher([

tinygrad/device.py

@@ -4,8 +4,8 @@ from collections import defaultdict
 from typing import Any, Generic, TypeVar, Iterator, Sequence, cast, Generator
 import importlib, inspect, functools, pathlib, os, platform, contextlib, sys, re, atexit, pickle, decimal
 from tinygrad.helpers import CI, OSX, LRU, getenv, diskcache_get, diskcache_put, DEBUG, GlobalCounters, flat_mv, PROFILE, temp, colored, CPU_LLVM
-from tinygrad.helpers import Context, DISABLE_COMPILER_CACHE, ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, cpu_events, ProfileEvent, ProfilePointEvent, dedup
-from tinygrad.helpers import unwrap_class_type, suppress_finalizing, AMD_LLVM
+from tinygrad.helpers import Context, CCACHE, ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, cpu_events, ProfileEvent, ProfilePointEvent, dedup
+from tinygrad.helpers import unwrap_class_type, suppress_finalizing, AMD_LLVM, select_first_inited, VIZ
 from tinygrad.dtype import DType, ImageDType, PtrDType, dtypes, _to_np_dtype
 from tinygrad.renderer import Renderer
 
@@ -266,7 +266,7 @@ class LRUAllocator(Allocator, Generic[DeviceType]):
 class CompileError(Exception): pass
 
 class Compiler:
-  def __init__(self, cachekey:str|None=None): self.cachekey = None if DISABLE_COMPILER_CACHE else cachekey
+  def __init__(self, cachekey:str|None=None): self.cachekey = cachekey if CCACHE else None
   def compile(self, src:str) -> bytes: return src.encode()   # NOTE: empty compiler is the default
   def compile_cached(self, src:str) -> bytes:
     if self.cachekey is None or (lib := diskcache_get(self.cachekey, src)) is None:
@@ -291,8 +291,8 @@ class Compiled:
     if len(enable_comps) > 1: raise RuntimeError(f"{self.device}: multiple compilers set in env {enable_comps}")
     for _, comp_pair in disable_comps: self.compilers.remove(comp_pair)
 
-    try: self.renderer, self.compiler = next(self._get_available_compilers([list(enable_comps)[0][1]] if len(enable_comps) == 1 else self.compilers))
-    except StopIteration as exc: raise RuntimeError(f"no usable compilers for {self.device}") from exc
+    self.renderer, self.compiler = select_first_inited([list(enable_comps)[0][1]] if len(enable_comps) == 1 else self.compilers,
+                                                       f"No compiler for {self.device} is available")
 
     if DEBUG >= 1: print(f"{self.device}: using {self.compiler.__class__.__name__}")
 
@@ -300,10 +300,6 @@ class Compiled:
     compiler_name = f"{unwrap_class_type(c).__name__.upper().removesuffix('COMPILER').removeprefix(devname:=self.device.split(':')[0].upper())}"
     return f"{devname}_{compiler_name if len(compiler_name) > 0 else unwrap_class_type(c).__name__.upper()}"
 
-  def _get_available_compilers(self, compilers) -> Iterator[tuple[Renderer, Compiler]]:
-    for renderer, compiler in compilers:
-      with contextlib.suppress(Exception): yield renderer(), compiler()
-
   def synchronize(self):
     """
     Synchronize all pending operations on the device.
@@ -343,6 +339,7 @@ def is_dtype_supported(dtype:DType, device:str|None=None) -> bool:
   # PYTHON supports half memoryview in 3.12+ https://github.com/python/cpython/issues/90751
   if dtype == dtypes.half:
     if device == "CL": return not CI and not OSX
+    if device == "QCOM": return False # QCOM compiler is flaky with half
     if device in ["CUDA", "NV"]: return not CI
     if device == "CPU" and CPU_LLVM: return OSX
     if device == "PYTHON": return sys.version_info >= (3, 12)
@@ -358,8 +355,9 @@ if PROFILE:
 
     with open(fn:=temp("profile.pkl", append_user=True), "wb") as f: pickle.dump(cpu_events+Compiled.profile_events+Buffer.profile_events, f)
 
-    from tinygrad.uop.ops import launch_viz
-    launch_viz("PROFILE", fn)
+    if VIZ:
+      from tinygrad.uop.ops import launch_viz
+      launch_viz("PROFILE", fn)
 
 def enumerate_devices_str() -> Generator[str, None, None]:
   from tinygrad import Tensor, Device

tinygrad/engine/realize.py

@@ -3,7 +3,7 @@ import time, pprint, random, itertools, math
 from dataclasses import dataclass, replace, field
 from tinygrad.helpers import all_same, colored, DEBUG, GlobalCounters, ansilen, BEAM, NOOPT, all_int, CAPTURING, Metadata, TRACEMETA, TracingKey
 from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile, PROFILE, ProfilePointEvent, cpu_events, prod, Context
-from tinygrad.helpers import unwrap
+from tinygrad.helpers import unwrap, disable_gc
 from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo, pyrender
 from tinygrad.device import Device, Buffer
 from tinygrad.renderer import Renderer, ProgramSpec, Estimates
@@ -13,6 +13,7 @@ from tinygrad.codegen.opt import Opt
 
 # **************** Program Creation ****************
 
+@disable_gc()
 @track_rewrites(name=lambda *args,ret,**kwargs: TracingKey(ret.name, (ret.function_name, ret.ast), ret=ret), replay=True)
 def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None) -> ProgramSpec:
   """

tinygrad/engine/schedule.py

@@ -1,5 +1,5 @@
 from typing import cast
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, replace
 from collections import deque, defaultdict
 from tinygrad.uop.ops import UOp, Ops, buffers
 from tinygrad.device import Device, Buffer, MultiBuffer
@@ -13,6 +13,7 @@ class ScheduleItem:
   bufs: tuple[Buffer, ...]
   metadata: tuple[Metadata, ...] = ()
   fixedvars: dict[str, int] = field(default_factory=dict)
+  bound_ranges: tuple[UOp, ...] = ()
 
 # **** schedule linearizer
 
@@ -22,10 +23,13 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
   in_degree: dict[UOp, int] = {}
   var_vals: dict[str, int] = {}
   for u in sched_sink.toposort():
-    if u.op is not Ops.AFTER: continue  # anything that's not an ASSIGN doesn't write a kernel, so we can skip
+    if u.op is Ops.RANGE:
+      in_degree.setdefault(u, 0)
+      continue
+    if u.op is not Ops.AFTER or u.src[1].op is Ops.RANGE: continue
     k = u.src[1]
     in_degree.setdefault(k, 0)
-    for s in k.src:
+    for s in k.src[0].src if k.op is Ops.END else k.src:
       if s.op is Ops.AFTER:
         children[s.src[1]].append(k)
         in_degree[k] += 1
@@ -39,16 +43,19 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
       elif s.op is Ops.BUFFER:
         pass  # a BUFFER is already realized, nothing to do here
       elif s.op is Ops.BIND:
-        var, val = s.unbind()
-        assert var.expr not in var_vals or var_vals[var.expr] == val, f"bind mismatch on {var}, {var_vals[var.expr]} != {val}"
-        var_vals[var.expr] = val
+        # for RANGE this is in fixedvars
+        if s.src[1].op is not Ops.RANGE:
+          var, val = s.unbind()
+          assert var.expr not in var_vals or var_vals[var.expr] == val, f"bind mismatch on {var}, {var_vals[var.expr]} != {val}"
+          var_vals[var.expr] = val
       else:
         raise RuntimeError(f"input to kernel must be AFTER or BUFFER, not {s.op}")
 
   # linearize KERNEL UOps into ScheduleItems in BFS order
 
   def _heuristic(k: UOp):
-    if k.arg.ast.op is Ops.COPY and not all_same([Device[cast(Buffer, s.buf_uop.buffer).device].group_id for s in k.src]): return 1000
+    if k.op is Ops.KERNEL and k.arg.ast.op is Ops.COPY and not all_same([Device[cast(Buffer, s.buf_uop.buffer).device].group_id for s in k.src]):
+      return 1000
     return 0
 
   last_heuristic: int = 0
@@ -57,27 +64,53 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
   for k,v in in_degree.items():
     if v == 0: queues[_heuristic(k)].append(k)
 
-  schedule: list[ScheduleItem] = []
+  schedule: list[ScheduleItem|UOp] = []
   while last_queue or any(queues.values()):
     if not last_queue: last_heuristic, last_queue = min((it for it in queues.items() if it[1]), key=lambda x: abs(x[0]-last_heuristic))
-    k = last_queue.popleft()
-    ast = k.arg.ast
-    # create subbuffers if needed
-    if ast.op is Ops.BUFFER_VIEW:
-      base = k.src[1].buf_uop.buffer
-      assert isinstance(base, Buffer), "base can't be MultiBuffer"
-      buffers[k.src[0]] = base.view(k.size, ast.dtype, ast.arg[1]*base.dtype.itemsize)
-    ubufs = tuple(s.buf_uop.buffer for s in k.src if s.op is not Ops.BIND)
-    if any(isinstance(x, MultiBuffer) for x in ubufs):
-      assert all(isinstance(x, MultiBuffer) for x in ubufs), "kernel must all be multibuffer"
-      dnums = [x for x in ast.variables() if x.arg[0] == '_device_num']
-      for i,bufs in enumerate(zip(*[x.bufs for x in cast(tuple[MultiBuffer, ...], ubufs)])):
-        schedule.append(ScheduleItem(ast, bufs, k.arg.metadata, {dnums[0].expr:i} if len(dnums) else {}))
+    k = rk = last_queue.popleft()
+    if k.op is Ops.END: k = k.src[0]
+    if k.op is Ops.RANGE: schedule.append(k)
+    elif k.op is Ops.KERNEL:
+      ast = k.arg.ast
+      # create subbuffers if needed
+      if ast.op is Ops.BUFFER_VIEW:
+        base = k.src[1].buf_uop.buffer
+        assert isinstance(base, Buffer), "base can't be MultiBuffer"
+        buffers[k.src[0]] = base.view(k.size, ast.dtype, ast.arg[1]*base.dtype.itemsize)
+      ubufs = tuple(s.buf_uop.buffer for s in k.src if s.op is not Ops.BIND)
+      bound_ranges = tuple(s for s in k.src if s.op is Ops.BIND and s.src[1].op is Ops.RANGE)
+      if any(isinstance(x, MultiBuffer) for x in ubufs):
+        assert all(isinstance(x, MultiBuffer) for x in ubufs), "kernel must all be multibuffer"
+        dnums = [x for x in ast.variables() if x.arg[0] == '_device_num']
+        for i,bufs in enumerate(zip(*[x.bufs for x in cast(tuple[MultiBuffer, ...], ubufs)])):
+          schedule.append(ScheduleItem(ast, bufs, k.arg.metadata, {dnums[0].expr:i} if len(dnums) else {}, bound_ranges=bound_ranges))
+      else:
+        # ONE -> ONE
+        schedule.append(ScheduleItem(ast, cast(tuple[Buffer, ...], ubufs), k.arg.metadata, bound_ranges=bound_ranges))
+      if rk.op is Ops.END: schedule.append(rk)
     else:
-      # ONE -> ONE
-      schedule.append(ScheduleItem(ast, cast(tuple[Buffer, ...], ubufs), k.arg.metadata))
-    for x in children[k]:
+      raise RuntimeError(f"can't schedule {k.op}")
+    for x in children[rk]:
       in_degree[x] -= 1
       if in_degree[x] == 0: queues[_heuristic(x)].append(x)
 
-  return schedule, var_vals
+  # expand the ranges in the schedule
+  real_schedule: list[ScheduleItem] = []
+  sched_ptr = 0
+  in_ranges = {}
+  range_ptrs = {}
+  while sched_ptr < len(schedule):
+    si = schedule[sched_ptr]
+    if isinstance(si, UOp):
+      if si.op is Ops.RANGE:
+        in_ranges[si] = 0
+        range_ptrs[si] = sched_ptr + 1
+      elif si.op is Ops.END:
+        if in_ranges[si.src[1]] < si.src[1].vmax:
+          in_ranges[si.src[1]] += 1
+          sched_ptr = range_ptrs[si.src[1]]
+          continue
+    else:
+      real_schedule.append(replace(si, fixedvars=si.fixedvars | {s.src[0].arg[0]:in_ranges[s.src[1]] for s in si.bound_ranges}, bound_ranges=()))
+    sched_ptr += 1
+  return real_schedule, var_vals

tinygrad/gradient.py

@@ -3,14 +3,15 @@ import math, dataclasses
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata
 from tinygrad.helpers import argsort
 
-def reduce_gradient(ctx:UOp, ret:UOp):
+def reduce_gradient(ctx:UOp, ret:UOp, op:Ops):
   def broadcast_to_input(x): return x.reshape(x.shape+(1,)*(len(ret.src[0].shape)-len(x.shape))).expand(ret.src[0].shape)
-  if ret.arg[0] == Ops.ADD: return (broadcast_to_input(ctx),)
-  if ret.arg[0] == Ops.MAX:
+  if op == Ops.ADD: return (broadcast_to_input(ctx),)
+  if op == Ops.MAX:
+    assert ret.op is Ops.REDUCE_AXIS, "only works on REDUCE_AXIS"
     mask = ret.src[0].eq(broadcast_to_input(ret)).cast(ctx.dtype)
     count = mask.r(Ops.ADD, ret.arg[1])
     return ((mask/broadcast_to_input(count)) * broadcast_to_input(ctx),)
-  if ret.arg[0] == Ops.MUL: return (broadcast_to_input(ctx * ret) / ret.src[0],)
+  if op == Ops.MUL: return (broadcast_to_input(ctx * ret) / ret.src[0],)
 
 # ctx is grad_output
 pm_gradient = PatternMatcher([
@@ -28,7 +29,8 @@ pm_gradient = PatternMatcher([
     ((x>y).where(ctx, (x.eq(y)).where(ctx * 0.5, 0)), (x<y).where(ctx, (x.eq(y)).where(ctx * 0.5, 0)))),
   (UPat(Ops.MUL, name="ret"), lambda ctx, ret: (ret.src[1]*ctx, ret.src[0]*ctx)),
   (UPat(Ops.WHERE, name="ret"), lambda ctx, ret: (None, ret.src[0].where(ctx, ctx.const_like(0)), ret.src[0].where(ctx.const_like(0), ctx))),
-  (UPat(Ops.REDUCE_AXIS, name="ret"), reduce_gradient),
+  (UPat(Ops.REDUCE_AXIS, name="ret"), lambda ctx, ret: reduce_gradient(ctx, ret, ret.arg[0])),
+  (UPat(Ops.REDUCE, name="ret"), lambda ctx, ret: reduce_gradient(ctx, ret, ret.arg) + (None,)*(len(ret.src)-1)),
   (UPat(Ops.CONTIGUOUS), lambda ctx: (ctx,)),
   (UPat(Ops.CONTIGUOUS_BACKWARD), lambda ctx: (ctx.contiguous(),)),
   (UPat(Ops.RESHAPE, name="ret"), lambda ctx, ret: (ctx.reshape(ret.src[0].shape), None)),
@@ -68,4 +70,8 @@ def compute_gradient(root:UOp, root_grad:UOp, targets:set[UOp]) -> dict[UOp, UOp
         # we add the backward metadata to everything new in the graph
         for bw_uop in v.toposort(lambda x: x not in (t0, *t0.src, grads[t0])):
           all_metadata[bw_uop] = all_metadata.get(bw_uop, ())+backward_metadata
+  # end any ranges on grads with a reduce sum
+  for k,v in grads.items():
+    if len(v.ranges):
+      grads[k] = v.reduce(*v.ranges, arg=Ops.ADD)
   return grads

tinygrad/helpers.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-import os, functools, platform, time, re, contextlib, operator, hashlib, pickle, sqlite3, tempfile, pathlib, string, ctypes, sys, gzip, getpass
+import os, functools, platform, time, re, contextlib, operator, hashlib, pickle, sqlite3, tempfile, pathlib, string, ctypes, sys, gzip, getpass, gc
 import urllib.request, subprocess, shutil, math, types, copyreg, inspect, importlib, decimal, itertools
 from dataclasses import dataclass, field
 from typing import ClassVar, Iterable, Any, TypeVar, Callable, Sequence, TypeGuard, Iterator, Generic, Generator, cast, overload
@@ -114,6 +114,13 @@ def suppress_finalizing(func):
       if not getattr(sys, 'is_finalizing', lambda: True)(): raise # re-raise if not finalizing
   return wrapper
 
+def select_first_inited(candidates:Sequence[Callable[...,T]|Sequence[Callable[...,T]]], err_msg: str) -> tuple[T,...]|T:
+  excs = []
+  for typ in candidates:
+    try: return tuple([cast(Callable, t)() for t in typ]) if isinstance(typ, Sequence) else cast(Callable, typ)()
+    except Exception as e: excs.append(e)
+  raise ExceptionGroup(err_msg, excs)
+
 def unwrap_class_type(cls_t): return cls_t.func if isinstance(cls_t, functools.partial) else cls_t
 
 def pluralize(st:str, cnt:int): return f"{cnt} {st}"+('' if cnt == 1 else 's')
@@ -166,19 +173,24 @@ TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS
 SPLIT_REDUCEOP, NO_MEMORY_PLANNER, RING = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("RING", 1)
 PICKLE_BUFFERS, LRU = ContextVar("PICKLE_BUFFERS", 1), ContextVar("LRU", 1)
 CACHELEVEL, IGNORE_BEAM_CACHE, DEVECTORIZE = ContextVar("CACHELEVEL", 2), ContextVar("IGNORE_BEAM_CACHE", 0), ContextVar("DEVECTORIZE", 1)
-DISABLE_COMPILER_CACHE = ContextVar("DISABLE_COMPILER_CACHE", 0)
 VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("VALIDATE_WITH_CPU", 0), ContextVar("DISABLE_FAST_IDIV", 0)
 CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)
 ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0)
 EMULATE = ContextVar("EMULATE", "")
 CPU_COUNT = ContextVar("CPU_COUNT", max(1, len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else (os.cpu_count() or 1)))
-CPU_LLVM, CPU_LVP, AMD_LLVM = ContextVar("CPU_LLVM", 0), ContextVar("CPU_LVP", 0), ContextVar("AMD_LLVM", 1)
-VIZ = PROFILE = ContextVar("VIZ", 0)
+CPU_LLVM, CPU_LVP, AMD_LLVM = ContextVar("CPU_LLVM", 0), ContextVar("CPU_LVP", 0), ContextVar("AMD_LLVM", 0)
+# VIZ implies PROFILE, but you can run PROFILE without VIZ
+VIZ = ContextVar("VIZ", 0)
+PROFILE = ContextVar("PROFILE", VIZ.value)
 SPEC = ContextVar("SPEC", 1)
 # TODO: disable by default due to speed
 IGNORE_OOB = ContextVar("IGNORE_OOB", 1)
 PCONTIG = ContextVar("PCONTIG", 0)  # partial contiguous in rangeify
 DEBUG_RANGEIFY = ContextVar("DEBUG_RANGEIFY", 0)
+# set to 1, this uses tuplize in the linearizer sort order
+TUPLE_ORDER = ContextVar("TUPLE_ORDER", 1)
+# set to 0 to disable the compiler cache
+CCACHE = ContextVar("CCACHE", 1)
 
 @dataclass(frozen=True)
 class Metadata:
@@ -231,11 +243,29 @@ class Profiling(contextlib.ContextDecorator):
 
 def perf_counter_us() -> decimal.Decimal: return decimal.Decimal(time.perf_counter_ns())/1000
 
+@functools.cache
+def lines(fn) -> list[str]:
+  try:
+    with open(fn, encoding="utf-8") as f: return f.readlines()
+  except (FileNotFoundError, OSError): return []
+
+def printable(loc:tuple[str, int]) -> str:
+  try: return lines(loc[0])[loc[1]-1].strip()
+  except IndexError: return "<missing>"
+
+def get_stacktrace(frm, max_frames=30) -> tuple[tuple, ...]:
+  ret:list[tuple] = []
+  for i in range(max_frames):
+    if (frm:=frm.f_back) is None: break
+    ret.append(((fc:=frm.f_code).co_filename, frm.f_lineno, fc.co_name, printable((fc.co_filename, frm.f_lineno))))
+  return tuple(ret)
+
 @dataclass(frozen=True)
 class TracingKey:
   display_name:str                       # display name of this trace event
   keys:tuple[Any, ...]=()                # optional keys to search for related traces
   ret:Any=None
+  tb:tuple[tuple, ...]|None=field(default_factory=lambda: get_stacktrace(sys._getframe(1)) if VIZ else None)
 
 class ProfileEvent: pass
 
@@ -352,10 +382,12 @@ def fetch(url:str, name:pathlib.Path|str|None=None, subdir:str|None=None, gunzip
 
 # *** Exec helpers
 
+def system(cmd, **kwargs): return subprocess.check_output(cmd.split(), **kwargs).decode().strip()
+
 def cpu_objdump(lib, objdump_tool='objdump'):
   with tempfile.NamedTemporaryFile(delete=True) as f:
     pathlib.Path(f.name).write_bytes(lib)
-    print(subprocess.check_output([objdump_tool, '-d', f.name]).decode('utf-8'))
+    print(system(f"{objdump_tool} -d {f.name}"))
 
 def capstone_flatdump(lib: bytes):
   try: import capstone
@@ -386,6 +418,7 @@ def to_mv(ptr:int, sz:int) -> memoryview: return memoryview((ctypes.c_uint8 * sz
 def mv_address(mv): return ctypes.addressof(ctypes.c_char.from_buffer(mv))
 def to_char_p_p(options: list[bytes], to_type=ctypes.c_char):
   return (ctypes.POINTER(to_type) * len(options))(*[ctypes.cast(ctypes.create_string_buffer(o), ctypes.POINTER(to_type)) for o in options])
+def charptr(s:str|bytes): return ctypes.cast(ctypes.c_char_p(s if isinstance(s, bytes) else s.encode()), ctypes.POINTER(ctypes.c_char))
 @functools.cache
 def init_c_struct_t(fields: tuple[tuple[str, type[ctypes._SimpleCData]], ...]):
   class CStruct(ctypes.Structure):
@@ -433,6 +466,13 @@ class tqdm(Generic[T]):
 class trange(tqdm):
   def __init__(self, n:int, **kwargs): super().__init__(iterable=range(n), total=n, **kwargs)
 
+class disable_gc(contextlib.ContextDecorator):
+  def __enter__(self):
+    self._was_enabled = gc.isenabled()
+    if self._was_enabled: gc.disable()
+  def __exit__(self, *exc):
+    if self._was_enabled: gc.enable()
+
 # *** universal support for code object pickling
 
 def _reconstruct_code(*args): return types.CodeType(*args)

tinygrad/mixin/__init__.py

@@ -1,4 +1,6 @@
 from tinygrad.mixin.math import MathMixin
 from tinygrad.mixin.movement import MovementMixin
 
-class OpMixin(MathMixin, MovementMixin): pass
+
+class OpMixin(MathMixin, MovementMixin):
+  pass

tinygrad/mixin/math.py

@@ -2,24 +2,38 @@ from typing import Self
 from tinygrad.uop import Ops
 from tinygrad.dtype import dtypes, ConstType
 
+
 class MathMixin:
   # required to implement
-  def alu(self, op:Ops, *src:Self) -> Self: raise NotImplementedError
-  def const_like(self, b:ConstType) -> Self: raise NotImplementedError
+  def alu(self, op: Ops, *src: Self) -> Self:
+    raise NotImplementedError
+
+  def const_like(self, b: ConstType) -> Self:
+    raise NotImplementedError
 
   # great functions you get!
-  def ufix(self, x:Self|ConstType) -> Self: return self.const_like(x) if not isinstance(x, MathMixin) else x
-  def _binop(self, op:Ops, x:Self|ConstType, reverse:bool) -> Self:
+  def ufix(self, x: Self | ConstType) -> Self:
+    return self.const_like(x) if not isinstance(x, MathMixin) else x
+
+  def _binop(self, op: Ops, x: Self | ConstType, reverse: bool) -> Self:
     return self.ufix(x).alu(op, self) if reverse else self.alu(op, self.ufix(x))
-  def logical_not(self): return self.ne(True)
+
+  def logical_not(self):
+    return self.ne(True)
+
   def neg(self):
-    if (dtype:=getattr(self, 'dtype')) is None: raise TypeError(f"MathTraits __neg__ requires a dtype, {self=}")
-    return self.logical_not() if dtype.scalar() == dtypes.bool else self*(-1)
+    if (dtype := getattr(self, "dtype")) is None:
+      raise TypeError(f"MathTraits __neg__ requires a dtype, {self=}")
+    return self.logical_not() if dtype.scalar() == dtypes.bool else self * (-1)
+
   def _check_dtype(self):
-    if (dtype:=getattr(self, 'dtype')) is not None:
-      if isinstance(dtype, tuple): dtype = dtype[0]
-      if not (dtypes.is_bool(dtype) or dtypes.is_int(dtype)): raise RuntimeError(f"{dtype} is not supported")
-  def add(self, x:Self|ConstType, reverse:bool=False):
+    if (dtype := getattr(self, "dtype")) is not None:
+      if isinstance(dtype, tuple):
+        dtype = dtype[0]
+      if not (dtypes.is_bool(dtype) or dtypes.is_int(dtype)):
+        raise RuntimeError(f"{dtype} is not supported")
+
+  def add(self, x: Self | ConstType, reverse: bool = False):
     """
     Adds `self` and `x`.
     Equivalent to `self + x`.
@@ -37,7 +51,8 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.ADD, x, reverse)
-  def mul(self, x:Self|ConstType, reverse:bool=False):
+
+  def mul(self, x: Self | ConstType, reverse: bool = False):
     """
     Multiplies `self` and `x`.
     Equivalent to `self * x`.
@@ -56,7 +71,8 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.MUL, x, reverse)
-  def bitwise_and(self, x:Self|ConstType, reverse:bool=False):
+
+  def bitwise_and(self, x: Self | ConstType, reverse: bool = False):
     """
     Computes the bitwise AND of `self` and `x`.
     Equivalent to `self & x`.
@@ -70,7 +86,8 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.AND, x, reverse)
-  def bitwise_or(self, x:Self|ConstType, reverse:bool=False):
+
+  def bitwise_or(self, x: Self | ConstType, reverse: bool = False):
     """
     Computes the bitwise OR of `self` and `x`.
     Equivalent to `self | x`.
@@ -84,7 +101,8 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.OR, x, reverse)
-  def bitwise_xor(self, x:Self|ConstType, reverse:bool=False):
+
+  def bitwise_xor(self, x: Self | ConstType, reverse: bool = False):
     """
     Computes bitwise xor of `self` and `x`.
     Equivalent to `self ^ x`.
@@ -99,7 +117,8 @@ class MathMixin:
     """
     self._check_dtype()
     return self._binop(Ops.XOR, x, reverse)
-  def idiv(self, x:Self|ConstType, reverse:bool=False):
+
+  def idiv(self, x: Self | ConstType, reverse: bool = False):
     """
     Divides `self` by `x`.
     Equivalent to `self // x`.
@@ -111,62 +130,150 @@ class MathMixin:
     ```
     """
     return self._binop(Ops.IDIV, x, reverse)
-  def mod(self, x:Self|ConstType, reverse:bool=False): return self._binop(Ops.MOD, x, reverse)
-  def sub(self, x:Self|ConstType, reverse:bool=False): return self.ufix(x).alu(Ops.ADD, -self) if reverse else self.alu(Ops.ADD, self.ufix(-x))
-  def div(self, x:Self|ConstType, reverse:bool=False):
-    return (self.ufix(x)*self.alu(Ops.RECIPROCAL)) if reverse else (self*self.ufix(x).alu(Ops.RECIPROCAL))
 
-  def __neg__(self): return self.neg()
+  def mod(self, x: Self | ConstType, reverse: bool = False):
+    return self._binop(Ops.MOD, x, reverse)
 
-  def __add__(self, x:Self|ConstType): return self.add(x)
-  def __sub__(self, x:Self|ConstType): return self.sub(x)
-  def __mul__(self, x:Self|ConstType): return self.mul(x)
-  def __truediv__(self, x:Self|ConstType): return self.div(x)
-  def __floordiv__(self, x:Self|ConstType): return self.idiv(x)  # TODO: idiv is trunc div, not floordiv
-  def __mod__(self, x:Self|ConstType): return self.mod(x)
-  def __and__(self, x:Self|ConstType): return self.bitwise_and(x)
-  def __or__(self, x:Self|ConstType): return self.bitwise_or(x)
-  def __xor__(self, x:Self|ConstType): return self.bitwise_xor(x)
+  def sub(self, x: Self | ConstType, reverse: bool = False):
+    return self.ufix(x).alu(Ops.ADD, -self) if reverse else self.alu(Ops.ADD, self.ufix(-x))
 
-  def __radd__(self, x:Self|ConstType): return self.add(x, True)
-  def __rsub__(self, x:Self|ConstType): return self.sub(x, True)
-  def __rmul__(self, x:Self|ConstType): return self.mul(x, True)
-  def __rtruediv__(self, x:Self|ConstType): return self.div(x, True)
-  def __rfloordiv__(self, x:Self|ConstType): return self.idiv(x, True)
-  def __rand__(self, x:Self|ConstType): return self.bitwise_and(x, True)
-  def __ror__(self, x:Self|ConstType): return self.bitwise_or(x, True)
-  def __rxor__(self, x:Self|ConstType): return self.bitwise_xor(x, True)
-  def __rmod__(self, x:Self|ConstType): return self.mod(x, True)
+  def div(self, x: Self | ConstType, reverse: bool = False):
+    return (self.ufix(x) * self.alu(Ops.RECIPROCAL)) if reverse else (self * self.ufix(x).alu(Ops.RECIPROCAL))
 
-  def __lt__(self, x:Self|ConstType): return self.alu(Ops.CMPLT, self.ufix(x))
-  def __gt__(self, x:Self|ConstType): return self.ufix(x).alu(Ops.CMPLT, self)
-  def __ge__(self, x:Self|ConstType): return (self < x).logical_not()
-  def __le__(self, x:Self|ConstType): return (self > x).logical_not()
+  def __neg__(self):
+    return self.neg()
+
+  def __add__(self, x: Self | ConstType):
+    return self.add(x)
+
+  def __sub__(self, x: Self | ConstType):
+    return self.sub(x)
+
+  def __mul__(self, x: Self | ConstType):
+    return self.mul(x)
+
+  def __truediv__(self, x: Self | ConstType):
+    return self.div(x)
+
+  def __floordiv__(self, x: Self | ConstType):
+    return self.idiv(x)  # TODO: idiv is trunc div, not floordiv
+
+  def __mod__(self, x: Self | ConstType):
+    return self.mod(x)
+
+  def __and__(self, x: Self | ConstType):
+    return self.bitwise_and(x)
+
+  def __or__(self, x: Self | ConstType):
+    return self.bitwise_or(x)
+
+  def __xor__(self, x: Self | ConstType):
+    return self.bitwise_xor(x)
+
+  def __radd__(self, x: Self | ConstType):
+    return self.add(x, True)
+
+  def __rsub__(self, x: Self | ConstType):
+    return self.sub(x, True)
+
+  def __rmul__(self, x: Self | ConstType):
+    return self.mul(x, True)
+
+  def __rtruediv__(self, x: Self | ConstType):
+    return self.div(x, True)
+
+  def __rfloordiv__(self, x: Self | ConstType):
+    return self.idiv(x, True)
+
+  def __rand__(self, x: Self | ConstType):
+    return self.bitwise_and(x, True)
+
+  def __ror__(self, x: Self | ConstType):
+    return self.bitwise_or(x, True)
+
+  def __rxor__(self, x: Self | ConstType):
+    return self.bitwise_xor(x, True)
+
+  def __rmod__(self, x: Self | ConstType):
+    return self.mod(x, True)
+
+  def __lt__(self, x: Self | ConstType):
+    return self.alu(Ops.CMPLT, self.ufix(x))
+
+  def __gt__(self, x: Self | ConstType):
+    return self.ufix(x).alu(Ops.CMPLT, self)
+
+  def __ge__(self, x: Self | ConstType):
+    return (self < x).logical_not()
+
+  def __le__(self, x: Self | ConstType):
+    return (self > x).logical_not()
+
+  def ne(self, x: Self | ConstType):
+    return self.alu(Ops.CMPNE, self.ufix(x))
+
+  def eq(self, x: Self | ConstType):
+    return self.ne(x).logical_not()
+
+  def __ne__(self, x: Self | ConstType):  # type: ignore[override]
+    return self.ne(x)
 
-  def ne(self, x:Self|ConstType): return self.alu(Ops.CMPNE, self.ufix(x))
-  def eq(self, x:Self|ConstType): return self.ne(x).logical_not()
-  def __ne__(self, x:Self|ConstType): return self.ne(x)  # type: ignore[override]
   # NOTE: __eq__ isn't overridden, and means the same thing as is by default
 
-  def lshift(self, x:Self|int, reverse:bool=False): return self._binop(Ops.SHL, x, reverse)
-  def rshift(self, x:Self|int, reverse:bool=False): return self._binop(Ops.SHR, x, reverse)
-  def __lshift__(self, x:Self|int): return self.lshift(x)
-  def __rshift__(self, x:Self|int): return self.rshift(x)
-  def __rlshift__(self, x:Self|int): return self.lshift(x, True)
-  def __rrshift__(self, x:Self|int): return self.rshift(x, True)
+  def lshift(self, x: Self | int, reverse: bool = False):
+    return self._binop(Ops.SHL, x, reverse)
 
-  def maximum(self, x:Self|ConstType): return self.alu(Ops.MAX, self.ufix(x))
-  def minimum(self, x:Self|ConstType): return -(-self).maximum(-x)
-  def where(self, x:Self|ConstType, y:Self|ConstType):
-    if isinstance(x, type(self)): return self.alu(Ops.WHERE, x, x.ufix(y))
-    if isinstance(y, type(self)): return self.alu(Ops.WHERE, y.ufix(x), y)
+  def rshift(self, x: Self | int, reverse: bool = False):
+    return self._binop(Ops.SHR, x, reverse)
+
+  def __lshift__(self, x: Self | int):
+    return self.lshift(x)
+
+  def __rshift__(self, x: Self | int):
+    return self.rshift(x)
+
+  def __rlshift__(self, x: Self | int):
+    return self.lshift(x, True)
+
+  def __rrshift__(self, x: Self | int):
+    return self.rshift(x, True)
+
+  def maximum(self, x: Self | ConstType):
+    return self.alu(Ops.MAX, self.ufix(x))
+
+  def minimum(self, x: Self | ConstType):
+    return -(-self).maximum(-x)
+
+  def where(self, x: Self | ConstType, y: Self | ConstType):
+    if isinstance(x, type(self)):
+      return self.alu(Ops.WHERE, x, x.ufix(y))
+    if isinstance(y, type(self)):
+      return self.alu(Ops.WHERE, y.ufix(x), y)
     raise RuntimeError("where needs at least one UOp arg")
-  def threefry(self, seed:Self): return self.alu(Ops.THREEFRY, seed)
-  def reciprocal(self): return self.alu(Ops.RECIPROCAL)
-  def trunc(self): return self.alu(Ops.TRUNC)
-  def sqrt(self): return self.alu(Ops.SQRT)
-  def sin(self): return self.alu(Ops.SIN)
-  def log2(self): return self.alu(Ops.LOG2)
-  def exp2(self): return self.alu(Ops.EXP2)
-  def pow(self, x:Self|ConstType): return self.alu(Ops.POW, self.ufix(x))
-  def __pow__(self, x:Self|ConstType): return self.pow(x)
+
+  def threefry(self, seed: Self):
+    return self.alu(Ops.THREEFRY, seed)
+
+  def reciprocal(self):
+    return self.alu(Ops.RECIPROCAL)
+
+  def trunc(self):
+    return self.alu(Ops.TRUNC)
+
+  def sqrt(self):
+    return self.alu(Ops.SQRT)
+
+  def sin(self):
+    return self.alu(Ops.SIN)
+
+  def log2(self):
+    return self.alu(Ops.LOG2)
+
+  def exp2(self):
+    return self.alu(Ops.EXP2)
+
+  def pow(self, x: Self | ConstType):
+    return self.alu(Ops.POW, self.ufix(x))
+
+  def __pow__(self, x: Self | ConstType):
+    return self.pow(x)

tinygrad/mixin/movement.py

@@ -2,16 +2,28 @@
 import functools
 from typing import TypeAlias, TYPE_CHECKING, Self
 from tinygrad.uop import Ops
-from tinygrad.helpers import prod, argfix, flatten, dedup
+from tinygrad.helpers import prod, argfix, flatten, dedup, make_tuple, ceildiv
+from tinygrad.uop.ops import resolve, smax
+
 if TYPE_CHECKING:
   from tinygrad.uop.ops import UOp
-  sint:TypeAlias = UOp|int
+sint: TypeAlias = "UOp | int"
+
+
+def _align_left(*shapes: tuple[sint, ...]) -> tuple[tuple[sint, ...], ...]:
+  # unsqueeze left to make every shape same length
+  max_dim = max(len(shape) for shape in shapes)
+  return tuple((1,) * (max_dim - len(shape)) + shape for shape in shapes)
+
 
 class MovementMixin:
   # required to implement
-  def _mop(self, op:Ops, arg) -> Self: raise NotImplementedError
+  def _mop(self, op: Ops, arg) -> Self:
+    raise NotImplementedError
+
   @property
-  def shape(self) -> tuple["sint", ...]: raise NotImplementedError
+  def shape(self) -> tuple[sint, ...]:
+    raise NotImplementedError
 
   # great functions you get!
   @property
@@ -26,7 +38,7 @@ class MovementMixin:
     """
     return len(self.shape)
 
-  def numel(self) -> "sint":
+  def numel(self) -> sint:
     """
     Returns the total number of elements in the tensor.
 
@@ -37,11 +49,41 @@ class MovementMixin:
     """
     return prod(self.shape)
 
-  def _resolve_dim(self, dim:int, *, extra:bool=False) -> int:
+  def _resolve_dim(self, dim: int, *, extra: bool = False) -> int:
     total = self.ndim + int(extra)
-    if not -max(1, total) <= dim <= max(1, total)-1: raise IndexError(f"{dim=} out of range {[-max(1, total), max(1, total)-1]}")
+    if not -max(1, total) <= dim <= max(1, total) - 1:
+      raise IndexError(f"{dim=} out of range {[-max(1, total), max(1, total) - 1]}")
     return dim + total if dim < 0 else dim
 
+  def _broadcast_to(self, new_shape: tuple[sint, ...]) -> Self:
+    if self.shape == new_shape:
+      return self
+    if self.ndim > len(new_shape):
+      raise ValueError(f"cannot broadcast tensor to fewer dimensions. shape={self.shape} to {new_shape=}")
+    # first unsqueeze left with 1s https://data-apis.org/array-api/latest/API_specification/broadcasting.html
+    shape, _ = _align_left(self.shape, new_shape)
+    # for each dimension, check either dim is 1, or it does not change
+    if not all(s == ns or s == 1 for s, ns in zip(shape, new_shape)):
+      raise ValueError(f"cannot broadcast {self.shape} to {new_shape=}")
+    reshaped = self.reshape(shape)
+    ret = reshaped._mop(Ops.EXPAND, arg=new_shape)
+    return reshaped if ret.shape == reshaped.shape else ret
+
+  def expand(self, shape, *args) -> Self:
+    """
+    Returns a tensor that is expanded to the shape that is specified.
+    Expand can also increase the number of dimensions that a tensor has.
+
+    Passing a `-1` or `None` to a dimension means that its size will not be changed.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([1, 2, 3])
+    print(t.expand(4, -1).numpy())
+    ```
+    """
+    new_shape = tuple(from_ if to == -1 or to is None else to for from_, to in zip(*(_align_left(self.shape, argfix(shape, *args)))))
+    return self._broadcast_to(new_shape)
+
   def reshape(self, shape, *args) -> Self:
     """
     Returns a tensor with the same data as the original tensor but with a different shape.
@@ -53,15 +95,18 @@ class MovementMixin:
     ```
     """
     # resolve None and args
-    new_shape = tuple([s if s is not None else self.shape[i] for i,s in enumerate(argfix(shape, *args))])
+    new_shape = tuple([s if s is not None else self.shape[i] for i, s in enumerate(argfix(shape, *args))])
     # resolve -1
-    if (c := new_shape.count(-1)) > 1: raise RuntimeError(f"only one dimension can be inferred using -1, getting {new_shape}")
-    if c: new_shape = tuple([-prod(self.shape) // prod(new_shape) if s == -1 else s for s in new_shape])
-    if prod(self.shape) != prod(new_shape): raise ValueError(f"size mismatch, can't reshape ({self.shape}) -> ({new_shape})")
+    if (c := new_shape.count(-1)) > 1:
+      raise RuntimeError(f"only one dimension can be inferred using -1, getting {new_shape}")
+    if c:
+      new_shape = tuple([-prod(self.shape) // prod(new_shape) if s == -1 else s for s in new_shape])
+    if prod(self.shape) != prod(new_shape):
+      raise ValueError(f"size mismatch, can't reshape ({self.shape}) -> ({new_shape})")
     ret = self._mop(Ops.RESHAPE, arg=new_shape)
     return self if ret.shape == self.shape else ret
 
-  def shrink(self, arg:tuple[tuple["sint", "sint"]|None, ...]) -> Self:
+  def shrink(self, arg: tuple[tuple[sint, sint] | None, ...]) -> Self:
     """
     Returns a tensor that shrinks the each axis based on input arg.
     `arg` must have the same length as `self.ndim`.
@@ -78,8 +123,9 @@ class MovementMixin:
     print(t.shrink((((0, 2), (0, 2)))).numpy())
     ```
     """
-    if self.ndim != len(arg): raise ValueError(f"{self.ndim=} != {len(arg)=}")
-    ret = self._mop(Ops.SHRINK, arg=[x if x is not None else (0,s) for x,s in zip(arg, self.shape)])
+    if self.ndim != len(arg):
+      raise ValueError(f"{self.ndim=} != {len(arg)=}")
+    ret = self._mop(Ops.SHRINK, arg=[x if x is not None else (0, s) for x, s in zip(arg, self.shape)])
     return self if ret.shape == self.shape else ret
 
   def permute(self, order, *args) -> Self:
@@ -97,7 +143,8 @@ class MovementMixin:
     ```
     """
     order_arg = tuple(self._resolve_dim(x) for x in argfix(order, *args))
-    if sorted(order_arg) != list(range(self.ndim)): raise RuntimeError(f"order is not a valid permutation, getting {order_arg}")
+    if sorted(order_arg) != list(range(self.ndim)):
+      raise RuntimeError(f"order is not a valid permutation, getting {order_arg}")
     return self._mop(Ops.PERMUTE, arg=order_arg) if order_arg != tuple(range(self.ndim)) else self
 
   def flip(self, axis, *args) -> Self:
@@ -118,17 +165,21 @@ class MovementMixin:
     """
     axis_arg = tuple(self._resolve_dim(x) for x in argfix(axis, *args))
     assert all(not isinstance(x, bool) and x >= 0 and x < self.ndim for x in axis_arg), f"flip args must be axis ints {axis_arg}"
-    if len(axis_arg) != len(dedup(axis_arg)): raise RuntimeError(f"dim can appear at most once, getting {axis_arg}")
+    if len(axis_arg) != len(dedup(axis_arg)):
+      raise RuntimeError(f"dim can appear at most once, getting {axis_arg}")
     flip_arg = tuple([i in axis_arg for i in range(len(self.shape))])
     return self._mop(Ops.FLIP, arg=flip_arg) if any(flip_arg) else self
 
   # **** high level ****
 
+  def shrink_to(self, shape, *args) -> Self:
+    return self.shrink(tuple([None if ns is None else (0, ns) for ns in argfix(shape, *args)]))
+
   def view(self, shape, *args) -> Self:
     """`.view` is an alias for `.reshape`."""
     return self.reshape(shape, *args)
 
-  def squeeze(self, dim:int|None=None) -> Self:
+  def squeeze(self, dim: int | None = None) -> Self:
     """
     Returns a tensor with specified dimensions of input of size 1 removed.
     If `dim` is not specified, all dimensions with size 1 are removed.
@@ -144,11 +195,12 @@ class MovementMixin:
     print(t.squeeze(1).shape)
     ```
     """
-    if dim is None: return self.reshape(tuple(dim for dim in self.shape if dim != 1))
+    if dim is None:
+      return self.reshape(tuple(dim for dim in self.shape if dim != 1))
     dim = self._resolve_dim(dim)
-    return self if not self.ndim or self.shape[dim] != 1 else self.reshape(self.shape[:dim] + self.shape[dim+1:])
+    return self if not self.ndim or self.shape[dim] != 1 else self.reshape(self.shape[:dim] + self.shape[dim + 1 :])
 
-  def unsqueeze(self, dim:int) -> Self:
+  def unsqueeze(self, dim: int) -> Self:
     """
     Returns a tensor with a new dimension of size 1 inserted at the specified `dim`.
 
@@ -199,9 +251,9 @@ class MovementMixin:
     ```
     """
     start_dim, end_dim = self._resolve_dim(start_dim), self._resolve_dim(end_dim)
-    return self.reshape(self.shape[:start_dim] + (prod(self.shape[start_dim:end_dim+1]), ) + self.shape[end_dim+1:])
+    return self.reshape(self.shape[:start_dim] + (prod(self.shape[start_dim : end_dim + 1]),) + self.shape[end_dim + 1 :])
 
-  def unflatten(self, dim:int, sizes:tuple[int,...]) -> Self:
+  def unflatten(self, dim: int, sizes: tuple[int, ...]) -> Self:
     """
     Unflattens dimension `dim` of the tensor into multiple dimensions specified by `sizes`. `Tensor.flatten()` is the inverse of this function.
 
@@ -216,9 +268,9 @@ class MovementMixin:
     ```
     """
     dim = self._resolve_dim(dim)
-    return self.reshape(self.shape[:dim] + sizes + self.shape[dim+1:])
+    return self.reshape(self.shape[:dim] + sizes + self.shape[dim + 1 :])
 
-  def rearrange(self, formula:str, **sizes) -> Self:
+  def rearrange(self, formula: str, **sizes) -> Self:
     """
     Rearranges input according to formula
 
@@ -229,31 +281,96 @@ class MovementMixin:
     print(Tensor.rearrange(x, "batch channel -> (batch channel)").numpy())
     ```
     """
+
     def parse_formula(formula: str):
       tokens = f" {formula} ".replace("…", "...").replace("(", " ( ").replace(")", " ) ").replace(" ", "  ").replace(" 1 ", " ( ) ").split()
       lparens, rparens = map(lambda x: [i for i, ch in enumerate(tokens) if ch == x], ("(", ")"))
       pairs = list(zip(lparens, rparens))
       assert len(lparens) == len(rparens) and sorted(flatten(pairs)) == flatten(pairs), "bracket mismatch"
-      return [name for name in tokens if name not in ("(", ")")], [(s - 2*i, e - 1 - 2*i) for i, (s, e) in enumerate(pairs)]
+      return [name for name in tokens if name not in ("(", ")")], [(s - 2 * i, e - 1 - 2 * i) for i, (s, e) in enumerate(pairs)]
 
     assert formula.count("->") == 1, 'need exactly one "->" in formula'
 
     (lhs, unflatten_dims), (rhs, flatten_dims) = map(parse_formula, formula.split("->"))
 
-    for name in sizes: assert name in lhs, f"axis {name} is not used in transform"
+    for name in sizes:
+      assert name in lhs, f"axis {name} is not used in transform"
     assert sorted(lhs) == sorted(rhs) and len(lhs) == len(set(lhs)), f"name mismatch in {formula}"
-    for name in flatten((lhs, rhs)): assert name == "..." or (name.isidentifier() and "_" not in (name[0], name[-1])), f"invalid axis name {name}"
+    for name in flatten((lhs, rhs)):
+      assert name == "..." or (name.isidentifier() and "_" not in (name[0], name[-1])), f"invalid axis name {name}"
     assert "..." not in flatten([lhs[s:e] for s, e in unflatten_dims]), f"cannot have collapsed ellipsis (...) in lhs of {formula}"
     assert lhs.count("...") <= 1, f"too many ellipses in {formula}"
 
     # resolve ellipsis
-    if "..." in lhs: ell_len = len(self.shape) - len(lhs) + 1 + sum(e - s - 1 for s, e in unflatten_dims)
-    lhs, rhs = map(lambda l: l[:(i:=l.index("..."))] + [f"...{j}" for j in range(ell_len)] + l[i + 1:] if "..." in l else l, (lhs, rhs))
+    if "..." in lhs:
+      ell_len = len(self.shape) - len(lhs) + 1 + sum(e - s - 1 for s, e in unflatten_dims)
+    lhs, rhs = map(lambda l: l[: (i := l.index("..."))] + [f"...{j}" for j in range(ell_len)] + l[i + 1 :] if "..." in l else l, (lhs, rhs))
     unflatten_dims = [(s + (ell_len - 1 if "...0" in lhs[:s] else 0), e + (ell_len - 1 if "...0" in lhs[:e] else 0)) for s, e in unflatten_dims]
     flatten_dims = [(s + (ell_len - 1 if "...0" in rhs[:s] else 0), e + (ell_len - 1 if "...0" in rhs[:e] else 0)) for s, e in flatten_dims]
 
     # apply movement ops in order unflatten -> permute -> flatten/unsqueeze
     t = functools.reduce(lambda x, dims: x.unflatten(dims[0], tuple(sizes.get(lhs[d], -1) for d in range(*dims))), unflatten_dims, self)
-    for i, name in enumerate(lhs): assert (name not in sizes) or sizes[name] == t.shape[i], f"size provided for dimension {name} incorrect"
+    for i, name in enumerate(lhs):
+      assert (name not in sizes) or sizes[name] == t.shape[i], f"size provided for dimension {name} incorrect"
     t = t.permute([lhs.index(name) for name in rhs])
-    return functools.reduce(lambda x, dims: x.flatten(dims[0], dims[1] - 1) if dims[0]<dims[1] else x.unsqueeze(dims[0]), reversed(flatten_dims), t)
+    return functools.reduce(lambda x, dims: x.flatten(dims[0], dims[1] - 1) if dims[0] < dims[1] else x.unsqueeze(dims[0]), reversed(flatten_dims), t)
+
+  # *** movement ops with expand ***
+
+  def repeat_interleave(self, repeats: int, dim: int | None = None) -> Self:
+    """
+    Repeats elements of a tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([1, 2, 3])
+    print(t.repeat_interleave(2).numpy())
+    ```
+    """
+    x, dim = (self.flatten(), 0) if dim is None else (self, self._resolve_dim(dim))
+    shp = x.shape
+    x = x.reshape(*shp[: dim + 1], 1, *shp[dim + 1 :])
+    x = x.expand(*shp[: dim + 1], repeats, *shp[dim + 1 :])
+    x = x.reshape(*shp[:dim], shp[dim] * repeats, *shp[dim + 1 :])
+    return x
+
+  def repeat(self, repeats, *args) -> Self:
+    """
+    Repeats tensor number of times along each dimension specified by `repeats`.
+    `repeats` can be passed as a tuple or as separate arguments.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([1, 2, 3])
+    print(t.repeat(4, 2).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.repeat(4, 2, 1).shape)
+    ```
+    """
+    repeats = argfix(repeats, *args)
+    base_shape = _align_left(self.shape, repeats)[0]
+    unsqueezed_shape = flatten([[s] if r == 1 else [1, s] for r, s in zip(repeats, base_shape)])
+    expanded_shape = flatten([[s] if r == 1 else [r, s] for r, s in zip(repeats, base_shape)])
+    final_shape = [r * s for r, s in zip(repeats, base_shape)]
+    return self.reshape(unsqueezed_shape).expand(expanded_shape).reshape(final_shape)
+
+  # **** pool level ****
+
+  def _pool(self, k_: tuple[sint, ...], stride: int | tuple[int, ...] = 1, dilation: int | tuple[int, ...] = 1) -> Self:
+    assert len(self.shape) >= len(k_), f"can't pool {self.shape} with {k_}"
+    s_, d_ = make_tuple(stride, len(k_)), make_tuple(dilation, len(k_))
+    assert len(k_) == len(s_) == len(d_), f"stride/dilation mismatch kernel:{k_} stride:{s_} dilation:{d_}"
+    noop, i_ = [None] * (self.ndim - len(k_)), self.shape[-len(k_) :]
+    assert all(resolve(d * (k - 1) + 1 <= i) for k, d, i in zip(k_, d_, i_)), "kernel size cannot be greater than actual input size"
+    o_ = [ceildiv(i - d * (k - 1), s) for i, d, k, s in zip(i_, d_, k_, s_)]
+    # input size scaling factor to make sure shrink for stride is possible
+    f_ = [smax(1, ceildiv(o * s - d, i)) for o, s, i, d in zip(o_, s_, i_, d_)]
+    # repeats such that we don't need padding
+    x = self.repeat([1] * len(noop) + [ceildiv(k * (i * f + d), i) for k, i, d, f in zip(k_, i_, d_, f_)])
+    # handle dilation
+    x = x.shrink_to(noop + [k * (i * f + d) for k, i, d, f in zip(k_, i_, d_, f_)])
+    x = x.reshape(noop + flatten((k, (i * f + d)) for k, i, d, f in zip(k_, i_, d_, f_)))
+    # handle stride
+    x = x.shrink_to(noop + flatten((k, o * s) for k, o, s in zip(k_, o_, s_))).reshape(noop + flatten((k, o, s) for k, o, s in zip(k_, o_, s_)))
+    x = x.shrink_to(noop + flatten((k, o, 1) for k, o in zip(k_, o_))).reshape(noop + flatten((k, o) for k, o in zip(k_, o_)))
+    # permute to move reduce to the end
+    return x.permute(*range(len(noop)), *[len(noop) + i * 2 + 1 for i in range(len(i_))], *[len(noop) + i * 2 for i in range(len(i_))])

tinygrad/nn/onnx.py

@@ -1124,6 +1124,16 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     return output.flatten(start_dim=2) if len(original_input_shape) == 3 else output.permute(0, 2, 1, 3)
 
   # ***** Indexing Ops *****
+  def NonZero(x:Tensor):
+    mask = (x!=0).flatten()
+    flat_idx = Tensor.arange(mask.numel(), dtype=dtypes.int64, device=x.device).masked_select(mask)
+    if flat_idx.ndim == 0: flat_idx = flat_idx.reshape(1)
+    if x.ndim == 0:
+      return Tensor.zeros((0, flat_idx.shape[0]), dtype=dtypes.int64, device=x.device, requires_grad=False)
+    strides = [prod(int(s) for s in x.shape[i+1:]) if i+1 < x.ndim else 1 for i in range(x.ndim)]
+    coords = [((flat_idx // stride) % int(dim)) for stride, dim in zip(strides, x.shape)]
+    return Tensor.stack(*coords, dim=0)
+
   def ArrayFeatureExtractor(x:Tensor, indices:Tensor): return x[..., indices]
 
   def Gather(x:Tensor, indices:Tensor, axis:int=0):

tinygrad/nn/state.py

@@ -194,6 +194,10 @@ def torch_load(t:Tensor) -> dict[str, Tensor]:
   """
   offsets: dict[str|int, int] = {}
   lens: dict[str|int, int] = {}
+
+  def _rebuild_tensor(storage, storage_offset, size, stride):
+    return _rebuild_tensor_v2(storage, storage_offset, size, stride)
+
   def _rebuild_tensor_v2(storage, storage_offset, size, stride, requires_grad=None, backward_hooks=None, metadata=None):
     #print(storage, storage_offset, size, stride, requires_grad, backward_hooks, metadata)
     lens[storage[2]] = storage[4] * storage[1].itemsize
@@ -220,7 +224,8 @@ def torch_load(t:Tensor) -> dict[str, Tensor]:
   deserialized_objects: dict[str, Any] = {}
   intercept = {"HalfStorage": dtypes.float16, "FloatStorage": dtypes.float32, "BFloat16Storage": dtypes.bfloat16,
                "IntStorage": dtypes.int32, "BoolStorage": dtypes.bool,
-               "LongStorage": dtypes.int64, "_rebuild_tensor_v2": _rebuild_tensor_v2, "FloatTensor": None, "Parameter": Parameter}
+               "LongStorage": dtypes.int64, "_rebuild_tensor": _rebuild_tensor, "_rebuild_tensor_v2": _rebuild_tensor_v2,
+               "FloatTensor": None, "Parameter": Parameter}
   whitelist = {"torch", "collections", "numpy", "_codecs"}  # NOTE: this is not for security, only speed
   class Dummy: pass
   class TorchPickle(pickle.Unpickler):

tinygrad/renderer/cstyle.py

@@ -450,16 +450,9 @@ class AMDRenderer(CStyleLanguage):
       ]) + base_rewrite
   def __reduce__(self): return self.__class__, (self.arch,)
 
-  # language options
-  ockl = [(f"__ockl_get_{name}", "unsigned int", "size_t", "const") for name in ["local_id", "group_id", "local_size"]]
-  ocml = [(f"__ocml_{name}_f{n}", f"{dt}, {dt}" if "fmax" == name else dt, dt, atr)
-            for dt, n in [(dtype.name, dtype.itemsize * 8) for dtype in [dtypes.float, dtypes.double, dtypes.half]]
-            for name, atr in [("fmax", "const"), ("exp2", "pure"), ("log2", "pure"), ("sqrt", "const"), ("sin", ""), ("trunc", "")]]
-
-  kernel_typedef = "\n".join(f'extern "C" __attribute__((device{f", {atr}" if atr else ""})) {dto} {meth}({dti});' for meth,dti,dto,atr in ockl+ocml)
   # https://clang.llvm.org/docs/AttributeReference.html#amdgpu-flat-work-group-size
   # NOTE: this makes hlb_cifar10 twice as fast, there may be more gains in tweaking these parameters
-  kernel_typedef += '\nextern "C" __attribute__((global)) void __attribute__((amdgpu_flat_work_group_size(1, {launch_bounds})))'
+  kernel_typedef = 'extern "C" __attribute__((global)) void __attribute__((amdgpu_flat_work_group_size(1, {launch_bounds})))'
   code_for_workitem = {"g": lambda x: f"__ockl_get_group_id({x})", "l": lambda x: f"__ockl_get_local_id({x})",
                        "i": lambda x: f"(__ockl_get_group_id({x})*__ockl_get_local_size({x})+__ockl_get_local_id({x}))"}
   code_for_op = { **CStyleLanguage.code_for_op,
@@ -490,15 +483,25 @@ class AMDRenderer(CStyleLanguage):
            f"{vec} make_{vec}({', '.join([f'{scal} {x}' for x in _nms[:dtype.count]])}) {{ return {{ {', '.join(_nms[:dtype.count])} }}; }}"
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
-    prefix = ["#define INFINITY (__builtin_inff())","#define NAN (__builtin_nanf(\"\"))","typedef long unsigned int size_t;","#define half _Float16"]
+    prefix, ockl = [], []
     type_map = { dtypes.bfloat16: "bf16", dtypes.float: "f32", dtypes.half: "f16", dtypes.fp8e4m3: "_fp8_fp8", dtypes.fp8e5m2: "_bf8_bf8" }
     used_dtypes = uops_to_dtypes(uops)
+    if any(u.op is Ops.CONST and not math.isfinite(u.arg) for u in uops):
+      prefix += ["#define INFINITY (__builtin_inff())", "#define NAN (__builtin_nanf(\"\"))"]
+    if any(u.op is Ops.SPECIAL for u in uops):
+      prefix.append("typedef long unsigned int size_t;")
+      ockl = [(f"__ockl_get_{name}", "unsigned int", "size_t", "const") for name in ["local_id", "group_id", "local_size"]]
+    ocml_ops = {Ops.EXP2: ("exp2", "pure"), Ops.LOG2: ("log2", "pure"), Ops.SQRT: ("sqrt", "const"), Ops.SIN: ("sin", ""), Ops.TRUNC: ("trunc", "")}
+    ocml = [(f"__ocml_{ocml_ops[op][0]}_f{dt.itemsize * 8}", dt.name, dt.name, ocml_ops[op][1])
+      for op, dt in dedup((u.op, u.dtype.scalar()) for u in uops) if op in ocml_ops and dt in (dtypes.half, dtypes.float, dtypes.double)]
     if any(dt.scalar() == dtypes.bfloat16 for dt in used_dtypes): prefix.append("typedef unsigned short hip_bfloat16;")
+    if any(dt.scalar() == dtypes.half for dt in used_dtypes): prefix.append("#define half _Float16")
     if any(dt.scalar() in dtypes.fp8s for dt in used_dtypes):
       prefix += ["typedef unsigned char hip_bf8;", "typedef unsigned char hip_fp8;"]
       prefix.append("""static inline __attribute__((device)) unsigned char f32_to_fp8(float v, int is_bf8) {
   v = (((*(unsigned*)&v)&0x7F800000)!=0x7F800000)?__builtin_amdgcn_fmed3f(v,is_bf8?57344.0f:448.0f,is_bf8?-57344.0f:-448.0f) : v;
   return (unsigned char)(is_bf8?__builtin_amdgcn_cvt_pk_bf8_f32(v,v,0,false):__builtin_amdgcn_cvt_pk_fp8_f32(v,v,0,false));\n}""")
+    prefix += [f'extern "C" __attribute__((device{f", {atr}" if atr else ""})) {dto} {meth}({dti});' for meth,dti,dto,atr in ockl+ocml]
     prefix += [self.render_vector_prefix(dt) for dt in used_dtypes if dt.count > 1]
 
     for name, (N, M, K), dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper