Clean up postrange.py by removing comments

seems incorrect, disable by default now

.github/workflows/autogen.yml

@@ -0,0 +1,91 @@
+name: Autogen
+env:
+  # increment this when downloads substantially change to avoid the internet
+  DOWNLOAD_CACHE_VERSION: '12'
+  PYTHON_CACHE_VERSION: '3'
+  APT_CACHE_VERSION: '1'
+  BUILD_CACHE_VERSION: '1'
+  CAPTURE_PROCESS_REPLAY: 1
+  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  PYTHONPATH: ${{ github.workspace }}
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    paths: 
+    - 'tinygrad/runtime/autogen/**/*'
+  workflow_dispatch:
+    paths: 
+    - 'tinygrad/runtime/autogen/**/*'
+
+jobs:
+  autogen:
+    name: Autogen
+    runs-on: ubuntu-24.04
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        opencl: 'true'
+        amd: 'true'
+        cuda: 'true'
+        webgpu: 'true'
+        llvm: 'true'
+    - name: Install autogen support packages
+      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev
+    - name: Verify OpenCL autogen
+      run: |
+        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
+        ./autogen_stubs.sh 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
+        diff /tmp/cuda.py.bak tinygrad/runtime/autogen/cuda.py
+        diff /tmp/nv_gpu.py.bak tinygrad/runtime/autogen/nv_gpu.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
+        diff /tmp/comgr.py.bak tinygrad/runtime/autogen/comgr.py
+        diff /tmp/amd_gpu.py.bak tinygrad/runtime/autogen/amd_gpu.py
+        diff /tmp/sqtt.py.bak tinygrad/runtime/autogen/sqtt.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
+        diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.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
+    - name: Verify LLVM autogen
+      run: |
+        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
+        ./autogen_stubs.sh llvm
+        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py

.github/workflows/benchmark.yml

@@ -65,12 +65,12 @@ jobs:
     - name: Test speed vs torch
       run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
     - name: Test tensor cores
-      run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+      run: METAL=1 python3.11 test/opt/test_tensor_cores.py
     - name: Test AMX tensor cores
       run: |
-        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
+        DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
         DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-        DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
         DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
     - name: Run Tensor Core GEMM (float)
       run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
@@ -122,8 +122,8 @@ jobs:
       run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/test_tiny.py
     - name: UsbGPU copy speeds
       run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
-    - name: UsbGPU openpilot test
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+    #- name: UsbGPU openpilot test
+    #  run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (Mac)
@@ -196,8 +196,8 @@ jobs:
       run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
     - name: Test tensor cores
       run: |
-        NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
-        PTX=1 ALLOW_TF32=1 NV=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
+        PTX=1 ALLOW_TF32=1 NV=1 python3 test/opt/test_tensor_cores.py
     - name: Run Tensor Core GEMM (CUDA)
       run: |
         CUDA=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
@@ -319,10 +319,10 @@ jobs:
       run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
     - 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)
-      run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
-    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-      run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
+    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+    #  run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
+    #- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
+    #  run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
     - name: Run 10 MLPerf Bert training steps (6 gpu)
       # TODO: remove BERT_LAYERS once scheduler is fast
       run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
@@ -396,8 +396,8 @@ jobs:
       run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test tensor cores
       run: |
-        AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-        AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
+        AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
+        AMD=1 AMD_LLVM=1 python3 test/opt/test_tensor_cores.py
         AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (AMD)
       run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
@@ -517,10 +517,10 @@ jobs:
       run: BENCHMARK_LOG=cifar_10steps_half_wino AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
     - name: Run full CIFAR training w 1 GPU
       run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
-    - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
-    - name: Run full CIFAR training steps w 6 GPUS (REMOTE)
-      run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
+    #- name: Run full CIFAR training steps w 6 GPUS
+    #  run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+    #- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
+    #  run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (AMD Training)
@@ -570,10 +570,10 @@ jobs:
       run: test/external/process_replay/reset.py
     - name: Run MLPerf resnet eval
       run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
-    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-      run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
+    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+    #  run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
+    #- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
+    #  run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
     - name: Run 10 MLPerf Bert training steps (6 gpu)
       # TODO: remove BERT_LAYERS once scheduler is fast
       run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
@@ -681,8 +681,8 @@ jobs:
     # Fails on 9070
     # - name: Test tensor cores
     #   run: |
-    #     AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-    #     AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
+    #     AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py test/opt/test_tensor_cores.py
+    #     AMD=1 AMD_LLVM=1 python3 test/test_linearizer.py test/opt/test_tensor_cores.py
     #     AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (AMD)
       run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee am_matmul_amd.txt
@@ -748,7 +748,7 @@ jobs:
     - name: Test driver start time
       run: time DEBUG=3 NV=1 python3 test/test_tiny.py TestTiny.test_plus
     - name: Test tensor cores
-      run: NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+      run: NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
     - name: Test DISK copy time
       run: NV=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
     - name: Test CPU copy time
@@ -759,8 +759,8 @@ jobs:
       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 LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 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 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)
       # TODO: remove BERT_LAYERS once scheduler is fast
       run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt

.github/workflows/test.yml

@@ -89,75 +89,6 @@ jobs:
         clang -O2 recognize.c -lm -o recognize
         cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock
 
-  autogen:
-    name: Autogen
-    runs-on: ubuntu-24.04
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        opencl: 'true'
-        amd: 'true'
-        cuda: 'true'
-        webgpu: 'true'
-        llvm: 'true'
-    - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev
-    - name: Verify OpenCL autogen
-      run: |
-        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
-        ./autogen_stubs.sh 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
-        diff /tmp/cuda.py.bak tinygrad/runtime/autogen/cuda.py
-        diff /tmp/nv_gpu.py.bak tinygrad/runtime/autogen/nv_gpu.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
-        diff /tmp/comgr.py.bak tinygrad/runtime/autogen/comgr.py
-        diff /tmp/amd_gpu.py.bak tinygrad/runtime/autogen/amd_gpu.py
-        diff /tmp/sqtt.py.bak tinygrad/runtime/autogen/sqtt.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
-        diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.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
-    - name: Verify LLVM autogen
-      run: |
-        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
-        ./autogen_stubs.sh llvm
-        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
-
   torchbackend:
     name: Torch Backend Tests
     runs-on: ubuntu-latest
@@ -242,8 +173,7 @@ jobs:
     - name: Test emulated METAL tensor cores
       run: |
         DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
-        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test emulated AMX tensor cores
       run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
     - name: Test emulated AMD tensor cores
@@ -252,37 +182,28 @@ jobs:
         DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
         DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
         DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test emulated AMD MFMA tensor cores
       run: |
         DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test emulated AMD RDNA4 tensor cores
       run: |
         DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
         DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
         DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
         DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test emulated CUDA tensor cores
       run: |
         DEBUG=2 EMULATE=CUDA FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
         DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
         DEBUG=2 EMULATE=CUDA_SM75 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
-        PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test emulated INTEL OpenCL tensor cores
       run: DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
-    - name: Full test tensor cores
-      run: |
-        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+    - name: Test emulated AMX tensor cores
+      run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test device flop counts
       run: |
         DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
@@ -308,7 +229,7 @@ jobs:
     - name: Test dtype with Python emulator
       run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
     - name: Test ops with Python emulator
-      run: DEBUG=2 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py -k "not (test_split or test_simple_cumsum or test_cumsum or test_einsum or test_dot or test_dot_1d or test_big_gemm or test_broadcastdot or test_multidot or test_var_axis or test_std_axis or test_broadcast_full or test_broadcast_partial or test_simple_conv3d or test_dilated_conv_transpose2d or test_simple_conv_transpose3d or test_large_input_conv2d or test_max_pool2d or test_max_pool2d_simple or test_max_pool2d_bigger_stride or test_avg_pool2d or test_cat or test_scaled_product_attention or test_scaled_product_attention_causal or test_slice_fancy_indexing_dim_inject_none or test_slice_fancy_indexing_list_indices or test_slice_fancy_indexing_no_dim_collapse or test_slice_fancy_indexing_tuple_indices or test_slice_fancy_indexing_list_with_tensors or test_slice_fancy_indexing_dim_collapse_int or test_interpolate_bilinear or test_interpolate_bilinear_corners_aligned or test_scaled_dot_product_attention or test_cummax or test_simple_cummax or test_logcumsumexp or test_sort or test_cumprod)" --durations=20
+      run: DEBUG=2 SKIP_SLOW_TEST=1 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py --durations=20
     - name: Test uops with Python emulator
       run: PYTHON=1 python3 -m pytest test/test_uops.py --durations=20
     - name: Test symbolic with Python emulator
@@ -382,7 +303,7 @@ jobs:
         CAPTURE_PROCESS_REPLAY=1 python test/test_tiny.py TestTiny.test_plus
         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
+        #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
 
@@ -478,7 +399,7 @@ jobs:
           llvm: 'true'
       - name: Test openpilot model kernel count and gate usage
         run: |
-          PYTHONPATH="." ALLOWED_KERNEL_COUNT=208 ALLOWED_READ_IMAGE=2134 ALLOWED_GATED_READ_IMAGE=13 FLOAT16=0 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
+          PYTHONPATH="." ALLOWED_KERNEL_COUNT=208 ALLOWED_READ_IMAGE=2175 ALLOWED_GATED_READ_IMAGE=16 FLOAT16=0 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
       - name: Test openpilot alt model correctness (float32)
         run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
       - name: Test openpilot fastvits model correctness (float32)
@@ -542,8 +463,8 @@ jobs:
           opencl: 'true'
       - name: Test ONNX (GPU)
         run: GPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
-      - name: Test Optimization Helpers
-        run: DEBUG=1 python3 extra/optimization/test_helpers.py
+      #- name: Test Optimization Helpers
+      #  run: DEBUG=1 python3 extra/optimization/test_helpers.py
       #- name: Test Action Space
       #  run: DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
       - name: Test Beam Search
@@ -615,11 +536,12 @@ jobs:
         CPU=1 RANGEIFY=1 python3 -m pytest -n auto --durations 20 \
           -k "not test_symbolic_arange_sym_step and not test_threefry_doesnt_use_long" \
           test/test_tiny.py test/test_rangeify.py test/test_ops.py test/test_tensor_variable.py \
-          test/test_outerworld_range.py test/test_sample.py test/test_randomness.py test/test_tensor_data.py
+          test/test_outerworld_range.py test/test_sample.py test/test_randomness.py
     - name: Test CPU=1 RANGEIFY=2
       run: CPU=1 RANGEIFY=2 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
-    - name: Test LLVM=1 RANGEIFY=1 (slow tests)
-      run: LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
+    # slow (and still wrong on beautiful_mnist)
+    #- name: Test LLVM=1 RANGEIFY=1 (slow tests)
+    #  run: LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
 
   testdevectorize:
     name: Linux (devectorize)
@@ -774,7 +696,7 @@ jobs:
           cuda: 'true'
           ocelot: 'true'
       - name: Set env
-        run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nPTX=1' || matrix.backend == 'nv' && 'NV=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nPTX=1' || matrix.backend == 'nv' && 'NV=1\nSKIP_SLOW_TEST=1' }}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
           python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
@@ -808,7 +730,7 @@ jobs:
           opencl: ${{ matrix.backend == 'gpu' && 'true' }}
           llvm: ${{ matrix.backend == 'llvm' && 'true' }}
       - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_COUNT=2' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
           python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
@@ -1032,7 +954,7 @@ jobs:
           pydeps: "capstone"
           llvm: ${{ matrix.backend == 'llvm' && 'true' }}
       - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
           python -c "from tinygrad import Device; assert Device.DEFAULT == '${{ matrix.backend }}'.upper(), Device.DEFAULT"
@@ -1069,10 +991,11 @@ jobs:
           pydeps: ${{ matrix.backend == 'webgpu' && 'dawn-python' || '' }}
       - name: Set env
         shell: bash
-        run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
+        run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_COUNT=2' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
       - name: Run unit tests
         if: matrix.backend=='llvm'
-        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --durations=20
+        # test_newton_schulz hits RecursionError
+        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
       - name: Run pytest (${{ matrix.backend }})
         shell: bash
         run: |

.pre-commit-config.yaml

@@ -20,12 +20,6 @@ repos:
         language: system
         always_run: true
         pass_filenames: false
-      - id: devicetests
-        name: select GPU tests
-        entry: env GPU=1 PYTHONPATH="." python3 -m pytest test/test_uops.py test/test_search.py
-        language: system
-        always_run: true
-        pass_filenames: false
       - id: tests
         name: subset of tests
         entry: env PYTHONPATH="." python3 -m pytest -n=4 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py

extra/backends/clang_graph.py

@@ -10,13 +10,13 @@ from tinygrad.renderer.cstyle import ClangRenderer
 render_dtype = ClangRenderer().render_dtype
 
 class ClangGraph(GraphRunner):
-  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[Variable, int]):
+  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[str, int]):
     super().__init__(jit_cache, input_rawbuffers, var_vals)
     if not all(isinstance(ji.prg, CompiledRunner) for ji in jit_cache): raise GraphException
 
     prgs = '\n'.join(dedup([cast(CompiledRunner, ji.prg).p.src for ji in jit_cache]))
     args = [f"{render_dtype(x.dtype)}* arg{i}" for i,x in enumerate(input_rawbuffers)]
-    args += sorted([f"int {v.expr}" for v in var_vals])
+    args += sorted([f"int {v}" for v in var_vals])
     code = ["void batched("+','.join(args)+") {"]
     for ji in jit_cache:
       args = []
@@ -34,6 +34,6 @@ class ClangGraph(GraphRunner):
     assert compiler is not None
     self._prg = ClangProgram("batched", compiler.compile(prgs+"\n"+"\n".join(code))) # no point in caching the pointers
 
-  def __call__(self, rawbufs: List[Buffer], var_vals: Dict[Variable, int], wait=False):
+  def __call__(self, rawbufs: List[Buffer], var_vals: Dict[str, int], wait=False):
     return cpu_time_execution(
-    lambda: self._prg(*[x._buf for x in rawbufs], *[x[1] for x in sorted(var_vals.items(), key=lambda x: x[0].expr)]), enable=wait)
+    lambda: self._prg(*[x._buf for x in rawbufs], *[x[1] for x in sorted(var_vals.items(), key=lambda x: x[0])]), enable=wait)

extra/backends/hsa_graph.py

@@ -26,7 +26,7 @@ class VirtAQLQueue(AQLQueue):
     self.available_packet_slots -= 1
 
 class HSAGraph(MultiGraphRunner):
-  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[Variable, int]):
+  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[str, int]):
     super().__init__(jit_cache, input_rawbuffers, var_vals)
 
     # Check all jit items are compatible.
@@ -53,7 +53,7 @@ class HSAGraph(MultiGraphRunner):
       self.ji_kargs_structs[j] = ji.prg._prg.args_struct_t.from_address(kernargs_ptrs[ji.prg.dev])
       kernargs_ptrs[ji.prg.dev] += round_up(ctypes.sizeof(ji.prg._prg.args_struct_t), 16)
       for i in range(len(ji.bufs)): self.ji_kargs_structs[j].__setattr__(f'f{i}', cast(Buffer, ji.bufs[i])._buf)
-      for i in range(len(ji.prg.p.vars)): self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[ji.prg.p.vars[i]])
+      for i in range(len(ji.prg.p.vars)): self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[ji.prg.p.vars[i].expr])
 
     # Build queues.
     self.virt_aql_queues: Dict[Compiled, VirtAQLQueue] = {dev:VirtAQLQueue(dev, 2*len(self.jit_cache)+16) for dev in self.devices}
@@ -106,7 +106,7 @@ class HSAGraph(MultiGraphRunner):
     for sig in self.signals_to_reset: hsa.hsa_signal_silent_store_relaxed(sig, 0)
     hsa.hsa_signal_silent_store_relaxed(self.finish_signal, 0)
 
-  def __call__(self, input_rawbuffers: List[Buffer], var_vals: Dict[Variable, int], wait=False) -> Optional[float]:
+  def __call__(self, input_rawbuffers: List[Buffer], var_vals: Dict[str, int], wait=False) -> Optional[float]:
     # Wait and restore signals
     hsa.hsa_signal_wait_scacquire(self.finish_signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
     for sig in self.signals_to_reset: hsa.hsa_signal_silent_store_relaxed(sig, 1)
@@ -123,7 +123,7 @@ class HSAGraph(MultiGraphRunner):
     # Update var_vals
     for j in self.jc_idx_with_updatable_var_vals:
       for i,v in enumerate(cast(CompiledRunner, self.jit_cache[j].prg).p.vars):
-        self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[v])
+        self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[v.expr])
 
     # Update launch dims
     for j in self.jc_idx_with_updatable_launch_dims:

extra/export_model.py

@@ -67,11 +67,12 @@ def export_model_clang(functions:Dict[str,str], statements:Dict[str,Tuple[str,in
   forward_args = ",".join(f"{dtype}{'*' if name not in symbolic_vars.values() else ''} {name}" for name,dtype,_ in (outputs+inputs if wasm else inputs+outputs))
 
   if not wasm:
+    thread_id = 0 # NOTE: export does not support threading, thread_id is always 0
     for name,cl in bufs_to_save.items():
       weight = ''.join(["\\x%02X"%x for x in bytes(to_mv(cl._buf.va_addr, cl._buf.size))])
       cprog.append(f"unsigned char {name}_data[] = \"{weight}\";")
     cprog += [f"{dtype_map[dtype]} {name}[{len}];" if name not in bufs_to_save else f"{dtype_map[dtype]} *{name} = ({dtype_map[dtype]} *){name}_data;" for name,(len,dtype,_key) in bufs.items() if name not in input_names+output_names]
-    cprog += [f"void net({forward_args}) {{"] + [f"{name}({', '.join(args)});" for (name, args, _global_size, _local_size) in statements] + ["}"]
+    cprog += [f"void net({forward_args}) {{"] + [f"{name}({', '.join(args)}, {thread_id});" for (name, args, _global_size, _local_size) in statements] + ["}"]
     return '\n'.join(headers + cprog)
   else:
     if bufs_to_save:
@@ -239,7 +240,9 @@ export default {model_name};
 
 def export_model(model, target:str, *inputs, model_name: Optional[str] = "model", stream_weights=False):
   assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, f"only {', '.join(EXPORT_SUPPORTED_DEVICE)} are supported"
-  with Context(JIT=2): run,special_names = jit_model(model, *inputs)
+
+  # NOTE: CPU_COUNT=1, since export does not support threading
+  with Context(JIT=2, CPU_COUNT=1): run,special_names = jit_model(model, *inputs)
   functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
   state = get_state_dict(model)
   weight_names = {id(x.uop.base.realized): name for name, x in state.items()}

extra/gemm/simple_matmul.py

@@ -2,7 +2,7 @@ import numpy as np
 from tinygrad import dtypes, Tensor
 from tinygrad.helpers import getenv, get_single_element
 from tinygrad.dtype import _to_np_dtype
-from tinygrad.codegen.opt.kernel import OptOps
+from tinygrad.codegen.opt import OptOps
 from tinygrad.engine.realize import lower_schedule
 
 dtype_in = dtypes.half if getenv("HALF") else dtypes.bfloat16 if getenv("BFLOAT16") else dtypes.float

extra/mcts_search.py

@@ -88,7 +88,7 @@ def mcts_search(lin:Kernel, rawbufs:List[Buffer], amt:int) -> Kernel:
     return ret
 
   rawbufs = _ensure_buffer_alloc(rawbufs)
-  var_vals = {k:(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
+  var_vals = {k.expr:(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
   dev = Device[lin.opts.device]
   root = MCTSNode(lin)
 

extra/optimization/helpers.py

@@ -1,6 +1,6 @@
 # stuff needed to unpack a kernel
 from tinygrad import Variable
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
 from tinygrad.dtype import dtypes, PtrDType
 from tinygrad.shape.shapetracker import ShapeTracker
@@ -115,7 +115,7 @@ def time_linearizer(lin:Kernel, rawbufs:list[Buffer], allow_test_size=True, max_
   assert dev.compiler is not None
 
   rawbufs = _ensure_buffer_alloc(rawbufs)
-  var_vals: dict[Variable, int] = {k:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
+  var_vals: dict[str, int] = {k.expr:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
   p = get_program(lin.get_optimized_ast(), lin.opts)
   tms = _time_program(p, dev.compiler.compile(p.src), var_vals, rawbufs,
                       max_global_size=max_global_size if allow_test_size else None, clear_l2=clear_l2, cnt=cnt, name=to_function_name(lin.name))

extra/test_hcopt.py

@@ -1,22 +1,40 @@
-from extra.optimization.helpers import load_worlds, ast_str_to_lin
+import time
+from extra.optimization.helpers import load_worlds, ast_str_to_ast
+from tinygrad import Device
 from tinygrad.codegen.lowerer import pm_lowerer, get_index
 from tinygrad.uop.ops import graph_rewrite
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.codegen.opt.postrange import Scheduler
 from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.helpers import getenv
 
 if __name__ == "__main__":
+  renderer = Device.default.renderer
   ast_strs = load_worlds()
+  if (n:=getenv("N", -1)) != -1: ast_strs = ast_strs[n:n+1]
+  good = 0
   for i, ast_str in enumerate(ast_strs):
-    lin = ast_str_to_lin(ast_str)
-    opt1 = hand_coded_optimizations(lin)
+    ast = ast_str_to_ast(ast_str)
 
-    lowered = graph_rewrite(lin.ast, pm_lowerer, ctx=get_index(lin.ast), bottom_up=True)
-    sch = Scheduler(lowered, lin.opts)
+    st = time.perf_counter()
+    lin = Kernel(ast, renderer)
+    opt1 = hand_coded_optimizations(lin)
+    et_lin = time.perf_counter() - st
+
+    lowered = graph_rewrite(ast, pm_lowerer, ctx=get_index(ast), bottom_up=True)
+    st = time.perf_counter()
+    sch = Scheduler(lowered, renderer)
+    sch.convert_loop_to_global()
+    sch.simplify_merge_adjacent()
     opt2 = hand_coded_optimizations(sch)
+    et_sch = time.perf_counter() - st
 
     if opt1 != opt2:
-      print("*******")
-      print("Kernel:    ", opt1)
-      print("Scheduler: ", opt2)
+      print(f"******* {i:6d}")
+      print("Kernel:    ", lin.colored_shape(), "->", lin.apply_opts(opt1).colored_shape())
+      print("Scheduler: ", sch.colored_shape(), "->", sch.apply_opts(opt2).colored_shape())
+      print(opt1)
+      print(opt2)
     else:
-      print("******* MATCH")
+      good += 1
+      print(f"******* {i:6d} MATCH {good/(i+1)*100:.2f}% -- {et_lin/et_sch:4.2f}x speedup")

extra/test_pyrender.py

@@ -0,0 +1,20 @@
+from extra.optimization.helpers import load_worlds, ast_str_to_ast
+from tinygrad.helpers import tqdm
+from tinygrad.uop.ops import pyrender, UOp, Ops
+from tinygrad import dtypes
+from tinygrad.shape.shapetracker import ShapeTracker, View
+inf, nan = float('inf'), float('nan')
+
+if __name__ == "__main__":
+  ast_strs = load_worlds()
+  for i, ast_str in enumerate(tqdm(ast_strs)):
+    good_ast = ast_str_to_ast(ast_str)
+    code = '\n'.join(pyrender(good_ast))
+    print("\n***************\n\n"+code)
+    exec(code)
+    if str(good_ast) != str(ast):
+      print(code)
+      print("MISMATCH")
+      print(good_ast)
+      print(ast)
+      break

extra/to_movement_ops.py

@@ -2,7 +2,6 @@ import itertools
 from enum import Enum, auto
 from collections import defaultdict
 from typing import List, Tuple, DefaultDict
-from extra.optimization.helpers import load_worlds, ast_str_to_ast
 from tinygrad.helpers import prod, tqdm
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.shape.shapetracker import ShapeTracker
@@ -121,7 +120,7 @@ def st_equivalent(st1: ShapeTracker, st2: ShapeTracker):
     if i > 1000:
       print("WARNING: did not search all possible combinations")
       break
-    var_vals = {k:v for k,v in zip(vs, ranges)}
+    var_vals = {k.expr:v for k,v in zip(vs, ranges)}
     r1 = sym_infer(idx1, var_vals) if sym_infer(valid1, var_vals) else 0
     r2 = sym_infer(idx2, var_vals) if sym_infer(valid2, var_vals) else 0
     if r1 != r2: return False
@@ -147,6 +146,7 @@ def test_rebuild_bufferop_st(ast:UOp):
   for src in ast.src: test_rebuild_bufferop_st(src)
 
 if __name__ == "__main__":
+  from extra.optimization.helpers import load_worlds, ast_str_to_ast
   ast_strs = load_worlds(False, False, True)[:2000]
   for ast_str in tqdm(ast_strs):
     test_rebuild_bufferop_st(ast_str_to_ast(ast_str))

test/device/test_hcq.py

@@ -6,7 +6,7 @@ from tinygrad.runtime.support.hcq import HCQCompiled, HCQBuffer
 from tinygrad.runtime.autogen import libc
 from tinygrad.runtime.support.system import PCIIfaceBase
 from tinygrad.engine.realize import get_runner, CompiledRunner, get_program
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad import Variable
 
 MOCKGPU = getenv("MOCKGPU")
@@ -52,12 +52,12 @@ class TestHCQ(unittest.TestCase):
       with self.subTest(name=str(queue_type)):
         q = queue_type().signal(virt_signal, virt_val)
 
-        var_vals = {virt_signal.base_buf.va_addr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val: TestHCQ.d0.timeline_value}
+        var_vals = {virt_signal.base_buf.va_addr.expr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val.expr: TestHCQ.d0.timeline_value}
         q.submit(TestHCQ.d0, var_vals)
         TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
         TestHCQ.d0.timeline_value += 1
 
-        var_vals = {virt_signal.base_buf.va_addr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val: TestHCQ.d0.timeline_value}
+        var_vals = {virt_signal.base_buf.va_addr.expr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val.expr: TestHCQ.d0.timeline_value}
         q.submit(TestHCQ.d0, var_vals)
         TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
         TestHCQ.d0.timeline_value += 1
@@ -106,7 +106,7 @@ class TestHCQ(unittest.TestCase):
 
         fake_signal.value = 0x30
 
-        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr: fake_signal.base_buf.va_addr, virt_val: fake_signal.value})
+        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr.expr: fake_signal.base_buf.va_addr, virt_val.expr: fake_signal.value})
         TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
         TestHCQ.d0.timeline_value += 1
 
@@ -131,7 +131,7 @@ class TestHCQ(unittest.TestCase):
      .signal(TestHCQ.d0.timeline_signal, virt_val)
 
     for _ in range(100):
-      q.submit(TestHCQ.d0, {virt_val: TestHCQ.d0.timeline_value})
+      q.submit(TestHCQ.d0, {virt_val.expr: TestHCQ.d0.timeline_value})
       TestHCQ.d0.timeline_value += 1
 
     val = TestHCQ.a.uop.buffer.as_buffer().cast("f")[0]
@@ -146,7 +146,7 @@ class TestHCQ(unittest.TestCase):
     q.exec(TestHCQ.runner._prg, TestHCQ.kernargs_ba_ptr, sint_global, sint_local) \
      .signal(TestHCQ.d0.timeline_signal, TestHCQ.d0.timeline_value)
 
-    q.submit(TestHCQ.d0, {sint_global[0]: 1, sint_local[0]: 1})
+    q.submit(TestHCQ.d0, {sint_global[0].expr: 1, sint_local[0].expr: 1})
     TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
     TestHCQ.d0.timeline_value += 1
 
@@ -181,7 +181,7 @@ class TestHCQ(unittest.TestCase):
         for z in range(1, 4):
           ctypes.memset(zt._buf.va_addr, 0, zb.nbytes)
 
-          q.submit(TestHCQ.d0, {virt_val: TestHCQ.d0.timeline_value, virt_local[0]: x, virt_local[1]: y, virt_local[2]: z})
+          q.submit(TestHCQ.d0, {virt_val.expr: TestHCQ.d0.timeline_value, virt_local[0].expr: x, virt_local[1].expr: y, virt_local[2].expr: z})
           TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
           TestHCQ.d0.timeline_value += 1
 
@@ -253,7 +253,7 @@ class TestHCQ(unittest.TestCase):
                                     .copy(virt_dest_addr, virt_src_addr, 8) \
                                     .signal(TestHCQ.d0.timeline_signal, TestHCQ.d0.timeline_value)
 
-    q.submit(TestHCQ.d0, {virt_src_addr: TestHCQ.a.uop.buffer._buf.va_addr, virt_dest_addr: TestHCQ.b.uop.buffer._buf.va_addr})
+    q.submit(TestHCQ.d0, {virt_src_addr.expr: TestHCQ.a.uop.buffer._buf.va_addr, virt_dest_addr.expr: TestHCQ.b.uop.buffer._buf.va_addr})
 
     TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
     TestHCQ.d0.timeline_value += 1
@@ -276,7 +276,7 @@ class TestHCQ(unittest.TestCase):
                                     .copy(virt_dest_addr, virt_src_addr, sz) \
                                     .signal(TestHCQ.d0.timeline_signal, TestHCQ.d0.timeline_value)
 
-    q.submit(TestHCQ.d0, {virt_src_addr: buf2._buf.va_addr, virt_dest_addr: buf1._buf.va_addr})
+    q.submit(TestHCQ.d0, {virt_src_addr.expr: buf2._buf.va_addr, virt_dest_addr.expr: buf1._buf.va_addr})
 
     TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
     TestHCQ.d0.timeline_value += 1
@@ -299,7 +299,7 @@ class TestHCQ(unittest.TestCase):
 
         fake_signal.value = 0x30
 
-        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr: fake_signal.base_buf.va_addr, virt_val: fake_signal.value})
+        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr.expr: fake_signal.base_buf.va_addr, virt_val.expr: fake_signal.value})
         TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
         TestHCQ.d0.timeline_value += 1
 

test/external/external_test_train_gpt2.py

@@ -2,7 +2,7 @@
 import unittest
 
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.codegen.opt.search import Opt, OptOps
+from .search import Opt, OptOps
 from tinygrad.dtype import dtypes
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View

test/external/fuzz_linearizer.py

@@ -22,7 +22,7 @@ if os.getenv("VALIDATE_HCQ", 0) != 0:
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.codegen.opt.search import get_kernel_actions, bufs_from_lin
 from tinygrad.engine.realize import CompiledRunner
 from tinygrad.helpers import getenv, from_mv, prod, colored, Context, DEBUG, Timing
@@ -90,7 +90,7 @@ def get_fuzz_rawbuf_like(old_rawbuf, zero=False, copy=False, size=None, force_de
 
 def run_linearizer(lin: Kernel, rawbufs=None, var_vals=None) -> tuple[str, Any]: # (error msg, run state)
   if rawbufs is None: rawbufs = bufs_from_lin(lin)
-  if var_vals is None: var_vals = {v: v.min for v in lin.vars}
+  if var_vals is None: var_vals = {v.expr: v.min for v in lin.vars}
 
   # TODO: images needs required_optimization
   try:
@@ -129,7 +129,7 @@ def compare_linearizer(lin: Kernel, rawbufs=None, var_vals=None, ground_truth=No
 
   if var_vals is None:
     # TODO: handle symbolic max case
-    var_vals = {v: random.randint(v.vmin, v.vmax) for v in lin.ast.variables()}
+    var_vals = {v.expr: random.randint(v.vmin, v.vmax) for v in lin.ast.variables()}
 
   if ground_truth is None and not has_bf16:
     unoptimized = Kernel(lin.ast)

test/external/process_replay/process_replay.py

@@ -12,7 +12,7 @@ try:
   from tinygrad.renderer import Renderer, ProgramSpec
   from tinygrad.engine.realize import get_program
   from tinygrad.uop.ops import UOp, Ops, KernelInfo
-  from tinygrad.codegen.opt.kernel import Opt
+  from tinygrad.codegen.opt import Opt
   from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm
   from tinygrad.device import Device
 except ImportError as e:

test/models/test_onnx.py

@@ -1,16 +1,9 @@
 #!/usr/bin/env python
-import os
-import time
 import unittest
 import numpy as np
-try:
-  import onnx
-except ModuleNotFoundError:
-  raise unittest.SkipTest("onnx not installed, skipping onnx test")
 from tinygrad.frontend.onnx import OnnxRunner
-from tinygrad.tensor import Tensor
 from tinygrad.device import Device
-from tinygrad.helpers import CI, fetch, temp, Context
+from tinygrad.helpers import fetch, Context
 
 try:
   from extra.onnx_helpers import validate
@@ -27,86 +20,9 @@ def run_onnx_torch(onnx_model, inputs):
     torch_out = torch_model(*[torch.tensor(x) for x in inputs.values()])
   return torch_out
 
-OPENPILOT_MODEL = "https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx"
-
 np.random.seed(1337)
 
 class TestOnnxModel(unittest.TestCase):
-  @unittest.skip("this isn't a test, it can't fail")
-  def test_benchmark_openpilot_model(self):
-    onnx_model = fetch(OPENPILOT_MODEL)
-    run_onnx = OnnxRunner(onnx_model)
-    def get_inputs():
-      np_inputs = {
-        "input_imgs": np.random.randn(*(1, 12, 128, 256)),
-        "big_input_imgs": np.random.randn(*(1, 12, 128, 256)),
-        "desire": np.zeros((1, 100, 8)),
-        "traffic_convention": np.array([[1., 0.]]),
-        "nav_features": np.zeros((1, 256)),
-        "features_buffer": np.zeros((1, 99, 128)),
-    }
-      inputs = {k:Tensor(v.astype(np.float32), requires_grad=False) for k,v in np_inputs.items()}
-      return inputs
-
-    for _ in range(7):
-      inputs = get_inputs()
-      st = time.monotonic()
-      tinygrad_out = run_onnx(inputs)['outputs']
-      mt = time.monotonic()
-      tinygrad_out.realize()
-      mt2 = time.monotonic()
-      tinygrad_out = tinygrad_out.numpy()
-      et = time.monotonic()
-      if not CI:
-        print(f"ran openpilot model in {(et-st)*1000.0:.2f} ms, waited {(mt2-mt)*1000.0:.2f} ms for realize, {(et-mt2)*1000.0:.2f} ms for GPU queue")
-
-    if not CI:
-      import cProfile
-      import pstats
-      inputs = get_inputs()
-      pr = cProfile.Profile(timer=time.perf_counter_ns, timeunit=1e-6)
-      pr.enable()
-    tinygrad_out = run_onnx(inputs)['outputs']
-    tinygrad_out.realize()
-    tinygrad_out = tinygrad_out.numpy()
-    if not CI:
-      pr.disable()
-      stats = pstats.Stats(pr)
-      stats.dump_stats(temp("net.prof"))
-      os.system(f"flameprof {temp('net.prof')} > {temp('prof.svg')}")
-      ps = stats.sort_stats(pstats.SortKey.TIME)
-      ps.print_stats(30)
-
-  def test_openpilot_model(self):
-    onnx_model = fetch(OPENPILOT_MODEL)
-    run_onnx = OnnxRunner(onnx_model)
-    print("got run_onnx")
-    inputs = {
-      "input_imgs": np.random.randn(*(1, 12, 128, 256)),
-      "big_input_imgs": np.random.randn(*(1, 12, 128, 256)),
-      "desire": np.zeros((1, 100, 8)),
-      "traffic_convention": np.array([[1., 0.]]),
-      "nav_features": np.zeros((1, 256)),
-      "features_buffer": np.zeros((1, 99, 128)),
-    }
-    inputs = {k:v.astype(np.float32) for k,v in inputs.items()}
-
-    st = time.monotonic()
-    print("****** run onnx ******")
-    tinygrad_out = run_onnx(inputs)['outputs']
-    mt = time.monotonic()
-    print("****** realize ******")
-    tinygrad_out.realize()
-    mt2 = time.monotonic()
-    tinygrad_out = tinygrad_out.numpy()
-    et = time.monotonic()
-    print(f"ran openpilot model in {(et-st)*1000.0:.2f} ms, waited {(mt2-mt)*1000.0:.2f} ms for realize, {(et-mt2)*1000.0:.2f} ms for GPU queue")
-
-    onnx_model = onnx.load(fetch(OPENPILOT_MODEL))
-    torch_out = run_onnx_torch(onnx_model, inputs).numpy()
-    print(tinygrad_out, torch_out)
-    np.testing.assert_allclose(tinygrad_out, torch_out, atol=1e-4, rtol=1e-2)
-
   @unittest.skip("slow")
   def test_efficientnet(self):
     input_name, input_new = "images:0", True

test/opt/test_gen_float4.py

@@ -176,54 +176,5 @@ class TestFloat4(unittest.TestCase):
       count = TestFloat4.count_half4(program.uops)
       assert count == expected, f"{count=}, {expected=}"
 
-  @unittest.skip("this doesn't happen anymore")
-  def test_float4_acc(self):
-    # from float32 stable diffusion red tinybox
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)),
-        UOp(Ops.ADD, dtypes.float, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
-            UOp(Ops.MUL, dtypes.float, arg=None, src=(
-              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)),
-              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)),
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-            UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),))
-
-    for expected, opts in [
-      (1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]),
-      (4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]),
-    ]:
-      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
-      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)])
-      assert count == expected, f"{count=}, {expected=}"
-
-  @unittest.skip("this doesn't happen anymore")
-  def test_float2_acc(self):
-    # from resnet
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)),
-        UOp(Ops.CAST, dtypes.half, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=(
-            UOp(Ops.CAST, dtypes.float, arg=None, src=(
-              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),))
-    for expected, opts in [
-      (16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]),  # noqa: E501
-      (4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]),
-    ]:
-      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
-      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)])
-      assert count == expected, f"{count=}, {expected=}"
-
 if __name__ == '__main__':
   unittest.main()

test/opt/test_kernel_opts.py

@@ -322,5 +322,33 @@ class TestKernelOpts(unittest.TestCase):
     ]
     helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
 
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
+  def test_arange_opts(self):
+    a = Tensor.arange(128)
+    helper_linearizer_opt(a, [
+      [Opt(OptOps.GROUP, 0, 32)],
+      [Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(op=OptOps.LOCAL, axis=0, arg=8)],
+      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0)],
+      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8)],
+      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8), Opt(op=OptOps.UNROLL, axis=1, arg=4)], # noqa: E501
+    ])
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_threads, "test requires threads")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.global_max is not None and
+                       Device[Device.DEFAULT].renderer.global_max[0] > 1, "test requires multicore")
+  def test_thread_opts(self):
+    a = Tensor.rand(4, 4, 4, 4)
+    b = Tensor.rand(4, 4, 4)
+    r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.THREAD, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.THREAD, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.THREAD, 0, 2), Opt(OptOps.UNROLL, 0, 2)],
+    ] + [[Opt(OptOps.THREAD, 0, 4)] if Device[Device.DEFAULT].renderer.global_max[0] >= 4 else []]
+      + [[Opt(OptOps.THREAD, 0, 8)] if Device[Device.DEFAULT].renderer.global_max[0] >= 8 else []])
+
 if __name__ == '__main__':
   unittest.main()

test/opt/test_tensor_cores.py

@@ -0,0 +1,191 @@
+import numpy as np
+import unittest
+from dataclasses import replace
+
+from tinygrad import Device, Tensor, dtypes
+from tinygrad.tensor import _to_np_dtype
+from tinygrad.uop.ops import Ops
+from tinygrad.dtype import DType
+from tinygrad.device import is_dtype_supported
+from tinygrad.helpers import AMX, CI, AMD_LLVM
+from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
+
+# TODO: write a clean version of this
+from test.test_linearizer import helper_realized_ast, helper_linearizer_opt
+
+def helper_tc_ensure_uops_and_opts_count(N: int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0,
+                                         ensure_triggered:bool=True):
+  a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
+  r = a.matmul(b, dtype=dtype_out)
+  sched = r.schedule()
+  realized_ast = sched[-1].ast
+  opts_to_apply = [Opt(OptOps.TC, axis, (tc_select, tc_opt, 1))]
+
+  if ensure_triggered:
+    program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
+    wmmas = len([uop for uop in program.uops if uop.op is Ops.WMMA])
+    tcs = len([x for x in program.applied_opts if x.op is OptOps.TC])
+    assert wmmas > 0, "tensor core not triggered"
+    assert tcs == 1, "tensor core opt not included"
+  else:
+    try:
+      program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
+      assert False, "OptOps.TC triggered, expected KernelOptError"
+    except KernelOptError: pass
+
+def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0, use_tensor_cores:int=1):
+  a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
+  np_a, np_b = a.numpy(), b.numpy()
+  r = a.matmul(b, dtype=dtype_out)
+  if dtype_in == dtypes.bfloat16: r = r.float()
+  realized_ast, bufs = helper_realized_ast(r)
+  opts = [Opt(op=OptOps.TC, axis=axis, arg=(tc_select, tc_opt, use_tensor_cores))]
+  prg = CompiledRunner(replace(get_program(realized_ast, opts=opts), device=Device.DEFAULT))
+  if use_tensor_cores == 1: assert len([uop for uop in prg.p.uops if uop.op is Ops.WMMA]) > 0, "wmma not triggered"
+  assert len([x for x in prg.p.uops[-1].arg.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
+  prg.exec(bufs)
+  if dtype_in == dtypes.half: tc_atol, tc_rtol = 1e-2, 1e-3
+  elif dtype_in == dtypes.bfloat16: tc_atol, tc_rtol = 1e-2, 1e-2
+  else: tc_atol, tc_rtol = 5e-3, 1e-4
+  c = bufs[0].numpy().reshape((M,N))
+  np.testing.assert_allclose(c, np_a @ np_b, atol=tc_atol, rtol=tc_rtol)
+
+class TestTensorCores(unittest.TestCase):
+  # TODO: don't skip bf16 for real device (METAL, AMD)
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      # for AMX, tc.dims[2] == 1 so reduceop is None thus tensor_cores are not triggered
+      helper_tc_allclose(tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2], tc.dtype_in, tc.dtype_out, axis=0, tc_opt=0)
+
+  @unittest.skipIf(Device.DEFAULT == "PYTHON", "not generated on EMULATED device")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores_codegen(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      n, m, k = tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2]
+      a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in)
+      r = a.matmul(b, dtype=tc.dtype_out)
+      prg = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.TC, axis=0, arg=(-1, 2, 1))])
+      if Device.DEFAULT == "LLVM":
+        assert "0x201000" in prg.src
+      elif Device.DEFAULT == "AMD" and AMD_LLVM:
+        assert "@llvm.amdgcn.wmma" in prg.src
+      elif Device[Device.DEFAULT].renderer.suffix == "PTX":
+        assert "mma.sync.aligned" in prg.src
+      else:
+        assert "__WMMA_" in prg.src
+
+  @unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "broken for AMD")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores_padded(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2)
+
+  # AMD compiler bug: AMD miscompiles non-zero padded tc kernels with -O3, producing wrong results, nans or hang (see #9606)
+  # Internal bug: zero-stride dimensions combined with a mask may produce wrong index/valid for pad == 1 on AMD
+  @unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "test for AMD's tc")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skip("warp elements not duplicated properly across lanes")
+  def test_tensor_cores_padded_amd(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2)
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores_padded_uops(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      pad = 1
+
+      # check that TC is triggered for TC_OPT=2
+      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
+                                           tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=True)
+
+      # check that TC is not triggered for TC_OPT<2
+      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
+                                           tc.dtype_in, tc.dtype_out, tc_opt=1, ensure_triggered=False)
+      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
+                                           tc.dtype_in, tc.dtype_out, tc_opt=0, ensure_triggered=False)
+
+      # check excessive padding doesn't trigger padded TC in TC_OPT=2
+      helper_tc_ensure_uops_and_opts_count(tc.dims[0]//4, tc.dims[1], tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
+      helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1]//4, tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
+      if not AMX: # AMX tc.dims[2] == 1
+        helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1], tc.dims[2]//8, tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
+
+  @unittest.skipIf(Device.DEFAULT == "PYTHON", "not generated on EMULATED device")
+  @unittest.skipIf(CI and Device.DEFAULT in {"AMD"}, "AMD CI is really slow here")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores_multi_reduce(self):
+    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
+      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      if tc.dtype_in is dtypes.bfloat16: continue # <-- broken with numpy
+      # this will be a M=G16, N=G32, M=G16, M=G16, K=R16, K=R16, K=R16 with 9 choices of TC MNK axes
+      golden_result = None
+      for axis in range(9):
+        a = Tensor.rand(16, 16, 29, 29, dtype=tc.dtype_in).realize()
+        b = Tensor.rand(32, 16, 16, 16, dtype=tc.dtype_in).realize()
+        c = a.conv2d(b, padding=1, dtype=tc.dtype_out)
+        realized_ast, real_bufs = helper_realized_ast(c)
+
+        program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=[Opt(OptOps.TC, axis, (-1, 2, 1))])
+        assert len([uop for uop in program.uops if uop.op is Ops.WMMA]) > 0, "tensor core not triggered"
+        assert len([x for x in program.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
+
+        prg = CompiledRunner(program)
+        # TODO: support this even if numpy doesn't
+        if _to_np_dtype(real_bufs[0].dtype) is None: continue
+        real_bufs[0].copyin(np.zeros((real_bufs[0].size, ), dtype=_to_np_dtype(real_bufs[0].dtype)).data) # Zero to check that all values are filled
+        prg.exec(real_bufs)
+        result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype))
+
+        # ensure the results for each choice of axis matches
+        if golden_result is None: golden_result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype))
+        np.testing.assert_allclose(result, golden_result, atol=0.1, rtol=0.2)
+
+  @unittest.skipIf(Device.DEFAULT == "PYTHON", "slow on EMULATED device")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  def test_tensor_cores_unroll_phi(self):
+    tc = Device[Device.DEFAULT].renderer.tensor_cores[0]
+    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    r = x.matmul(y, dtype=tc.dtype_out)
+    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
+    for u in get_program(ast, opts=opts).uops:
+      if u.op is Ops.WMMA:
+        assert u.src[-1].src[0].op != Ops.STORE
+
+  @unittest.skipIf(Device.DEFAULT == "PYTHON", "slow on EMULATED device")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
+  def test_tensor_cores_unroll_casted_phi(self):
+    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
+    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    r = x.matmul(y, dtype=tc.dtype_out)
+    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
+    for u in get_program(ast, opts=opts).uops:
+      if u.op is Ops.WMMA:
+        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
+        assert u.src[-1].src[0].op != Ops.STORE
+
+  @unittest.skipIf(Device.DEFAULT == "PYTHON", "slow on EMULATED device")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
+  def test_tensor_cores_unroll_casted_phi_with_children(self):
+    # all STORE children are outside the loop
+    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
+    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    r = x.matmul(y, dtype=tc.dtype_out).relu()
+    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
+    for u in get_program(ast, opts=opts).uops:
+      if u.op is Ops.WMMA:
+        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
+        assert u.src[-1].src[0].op != Ops.STORE
+
+if __name__ == '__main__':
+  unittest.main()

test/test_arange.py

@@ -1,13 +1,11 @@
-import unittest, contextlib
+import unittest
 import numpy as np
 from tinygrad import Tensor, GlobalCounters, dtypes, nn, Device, Variable
 from tinygrad.helpers import CI, Context, getenv
 from tinygrad.engine.realize import run_schedule
-from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel, KernelOptError
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
-from tinygrad.codegen.opt.search import get_kernel_actions
 from tinygrad.uop.ops import Ops
-from tinygrad.codegen import apply_rewrites, rewrites_for_views
 
 class TestArange(unittest.TestCase):
   def _get_flops(self, N, opts=None):
@@ -49,28 +47,6 @@ class TestArange(unittest.TestCase):
     @unittest.skip("doesn't work yet")
     def test_complexity_w_local_and_padto(self): return self.test_complexity([Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.PADTO, axis=1, arg=32)])
 
-  def test_all_opts(self, opts=None, exclude=None):
-    k = Kernel(apply_rewrites(Tensor.arange(256).schedule()[-1].ast, rewrites_for_views))
-    if opts is not None:
-      for o in opts: k.apply_opt(o)
-    all_opts_256 = [kk.applied_opts for kk in get_kernel_actions(k, include_0=False).values()]
-    k = Kernel(apply_rewrites(Tensor.arange(2560).schedule()[-1].ast, rewrites_for_views))
-    if opts is not None:
-      for o in opts: k.apply_opt(o)
-    all_opts_2560 = [kk.applied_opts for kk in get_kernel_actions(k, include_0=False).values()]
-    all_opts = [x for x in all_opts_256 if x in all_opts_2560]
-    for opts in all_opts:
-      if exclude is not None and opts[-1] in exclude: continue
-      print(opts)
-      self.test_complexity(opts)
-  def test_all_opts_w_local(self):
-    with contextlib.suppress(KernelOptError):
-      return self.test_all_opts([Opt(OptOps.LOCAL, 0, 16)], [Opt(op=OptOps.PADTO, axis=1, arg=32)])
-  def test_all_opts_w_upcast(self): return self.test_all_opts([Opt(OptOps.UPCAST, 0, 4)])
-  def test_all_opts_w_unroll(self): return self.test_all_opts([Opt(OptOps.UNROLL, 0, 4)], [Opt(op=OptOps.GROUP, axis=0, arg=0)])
-  def test_all_opts_w_upcast_and_unroll(self):
-    return self.test_all_opts([Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)], [Opt(op=OptOps.GROUP, axis=0, arg=0)])
-
 class TestRand(unittest.TestCase):
   def test_fused_rand_less_ops(self, noopt=1):
     GlobalCounters.reset()

test/test_dtype.py

@@ -297,7 +297,6 @@ class TestUint8DType(TestDType):
   def test_uint8_to_int8_overflow(self):
     _test_op(lambda: Tensor([255, 254, 253, 252], dtype=dtypes.uint8).cast(dtypes.int8), dtypes.int8, [-1, -2, -3, -4])
 
-@unittest.skipIf(Device.DEFAULT == "WEBGL", "No bitcast on WebGL")
 class TestBitCast(unittest.TestCase):
   @given(strat.sampled_from(dtype_ints + dtype_floats), strat.sampled_from(dtype_ints + dtype_floats))
   def test_shape_change_bitcast(self, dt1, dt2):

test/test_dtype_alu.py

@@ -1,7 +1,7 @@
 import unittest, operator, math
 from tinygrad import Tensor, dtypes, Device
 from tinygrad.dtype import DType
-from tinygrad.helpers import CI, getenv, AMD_LLVM
+from tinygrad.helpers import CI, getenv
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
 from tinygrad.runtime.ops_python import from_storage_scalar
@@ -20,21 +20,14 @@ dtypes_int = (dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint
 dtypes_bool = (dtypes.bool,)
 binary_operations = [operator.add, operator.sub, operator.mul, operator.lt, operator.eq]
 
-# TODO: LLVM comparing with nan is incorrect
-if (Device.DEFAULT == "LLVM") or (Device.DEFAULT == "AMD" and AMD_LLVM):
-  binary_operations.remove(operator.lt)
-
 integer_binary_operations = binary_operations + [(Tensor.bitwise_xor, np.bitwise_xor), (Tensor.bitwise_and, np.bitwise_and),
-                                                 (Tensor.bitwise_or, np.bitwise_or), operator.mod]
+                                                 (Tensor.bitwise_or, np.bitwise_or), (Tensor.maximum, np.maximum), operator.mod]
 unary_operations = [(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin),
                     (Tensor.sqrt, np.sqrt), (Tensor.reciprocal, np.reciprocal), (Tensor.cos, np.cos)]
 
 # TODO: enable this (this is a dtype issue)
 #binary_operations.append(operator.truediv)
 
-# TODO: (a+b)/2 in tensor.py's maximum can overflow. This requires a new implementation of maximum that can be backpropagated
-#binary_operations += [(Tensor.maximum, np.maximum)]
-
 # TODO: CI CUDA segfaults on sin, WEBGPU sin is not precise enough for large numbers
 if (getenv("MOCKGPU") and Device.DEFAULT in {"NV", "CUDA"}) or Device.DEFAULT == "WEBGPU":
   unary_operations.remove((Tensor.sin, np.sin))
@@ -56,10 +49,10 @@ class ht:
 ht.bfloat16 = ht.uint16
 
 def universal_test(a, b, dtype, op):
-  # The 'nan' cases only fail with Vulkan WebGPU backend (CI)
-  if (math.isnan(a) or math.isnan(b)) and Device.DEFAULT == "WEBGPU" and CI: return
   if not isinstance(op, tuple): op = (op, op)
   if op[0] == operator.mod and b == 0: return
+  # lt and max with nan is undefined in tinygrad
+  if op[0] in (operator.lt, Tensor.maximum) and (math.isnan(a) or math.isnan(b)): return
   ta, tb = Tensor([a], dtype=dtype), Tensor([b], dtype=dtype)
   tensor_value = (op[0](ta, tb)).numpy()
   numpy_value = op[1](ta.numpy(), tb.numpy())
@@ -72,7 +65,7 @@ def universal_test_unary(a, dtype, op):
   if not isinstance(op, tuple): op = (op, op)
   ta = Tensor([a], dtype=dtype)
   # TODO: cos does not match for large input
-  if op[0] == Tensor.cos and abs(a) > 100: return
+  if op[0] == Tensor.cos and abs(a) > 30: return
   if op[0] == Tensor.log and a <= 0: return
   out: Tensor = op[0](ta)
   tensor_value = out.numpy()
@@ -91,6 +84,9 @@ def universal_test_cast(a, in_dtype, dtype):
 def universal_test_midcast(a, b, c, op1, op2, d1:DType, d2:DType):
   if not isinstance(op1, tuple): op1 = (op1, op1)
   if not isinstance(op2, tuple): op2 = (op2, op2)
+  # lt and max with nan is undefined in tinygrad
+  if op1[0] in (operator.lt, Tensor.maximum) and (math.isnan(a) or math.isnan(b)): return
+  if op2[0] in (operator.lt, Tensor.maximum) and math.isnan(c): return
   at, bt, ct = Tensor([a], dtype=d1), Tensor([b], dtype=d1), Tensor([c], dtype=d2)
   an, bn, cn = np.array([a]).astype(_to_np_dtype(d1)), np.array([b]).astype(_to_np_dtype(d1)), np.array([c]).astype(_to_np_dtype(d2))
   tensor_value = op2[0](op1[0](at, bt).cast(d2), ct).numpy()

test/test_edgecases.py

@@ -26,6 +26,10 @@ import unittest
 import numpy as np
 import torch
 from tinygrad import Tensor, dtypes, nn
+from tinygrad.device import is_dtype_supported
+from tinygrad.helpers import getenv
+
+MOCKGPU = getenv("MOCKGPU")
 
 class TestNaNEdgeCases(unittest.TestCase):
   # we don't need more of these. it's unclear if torch's behavior is desired here
@@ -167,34 +171,6 @@ class TestZeroFolding(unittest.TestCase):
     with self.assertRaises(RuntimeError):
       (x % x).numpy()
 
-class TestArangeUOpValidationIssue(unittest.TestCase):
-  # these fail with UOp verification error.
-  # we don't need more of these involving arange
-
-  @unittest.expectedFailure
-  def test_large_arange_sum(self):
-    # Summing a huge arange should either succeed or raise a MemoryError.
-    n = 2**31 + 3
-    expected = (n - 1) * n // 2
-    out = Tensor.arange(n).sum().item()
-    self.assertEqual(out, expected)
-
-  @unittest.expectedFailure
-  def test_large_arange_index(self):
-    # Indexing a huge arange should return the correct value instead of failing
-    # with a UOp verification error.
-    n = 2**31 + 3
-    out = Tensor.arange(n)[0].item()
-    self.assertEqual(out, 0)
-
-  @unittest.expectedFailure
-  def test_large_arange_permute(self):
-    # Permuting a huge tensor should not trigger UOp verification failures.
-    n = 2**31 + 3
-    out = Tensor.arange(n).reshape(n, 1).permute(1, 0)
-    self.assertEqual(out.shape, (1, n))
-    out.realize()
-
 class TestAssignIssues(unittest.TestCase):
   # these are good failures. i'm not sure we need more, but we need to fix these.
 
@@ -230,10 +206,8 @@ class TestUOpValidationIssue(unittest.TestCase):
   # these fail with UOp verification error.
   # we want more of these with diverse errors!
 
-  @unittest.expectedFailure
+  @unittest.skipIf((not is_dtype_supported(dtypes.long)) or MOCKGPU, "hangs gpuocelot")
   def test_tensor_index_overflow(self):
-    # Advanced indexing on tensors expanded past int32 should not error, but
-    # tinygrad fails with a UOp verification error.
     val = Tensor([1])
     big = val.expand(2**31 + 3)
     idx = Tensor([0, 2**31 + 2])
@@ -273,4 +247,4 @@ class TestEdgeCases(unittest.TestCase):
 
 
 if __name__ == "__main__":
-  unittest.main()
+  unittest.main()

test/test_linearizer.py

@@ -2,69 +2,18 @@ import numpy as np
 import unittest
 from dataclasses import replace
 
-from tinygrad.codegen.opt.kernel import Opt, OptOps, KernelOptError, Kernel, AxisType
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.codegen.gpudims import get_grouped_dims
-from tinygrad.uop.ops import UOp, Ops, GroupOp, KernelInfo
+from tinygrad.uop.ops import UOp, Ops, GroupOp
 from tinygrad.device import Device, Buffer, is_dtype_supported
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
 from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM, TC_SELECT, TC_OPT
+from tinygrad.helpers import Context, getenv, flatten, dedup, TC_SELECT, TC_OPT
 from tinygrad.dtype import DType, dtypes, PtrDType, AddrSpace
 from tinygrad.codegen import apply_rewrites, rewrites_for_views
 
-def push_views(ast): return apply_rewrites(ast, rewrites_for_views)
-
-def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
-  if isinstance(r, Tensor): r = [r]
-  s = Tensor.schedule(*r)
-  run_schedule(s[:-1])  # run all kernels except the last one
-  assert s[-1].ast.op is Ops.SINK, f"helper_realized_ast expects a SINK {s[-1]}"
-  # 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)]
-  return push_views(s[-1].ast), bufs
-
-def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0, use_tensor_cores:int=1):
-  a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
-  np_a, np_b = a.numpy(), b.numpy()
-  r = a.matmul(b, dtype=dtype_out)
-  if dtype_in == dtypes.bfloat16: r = r.float()
-  realized_ast, bufs = helper_realized_ast(r)
-  opts = [Opt(op=OptOps.TC, axis=axis, arg=(tc_select, tc_opt, use_tensor_cores))]
-  prg = CompiledRunner(replace(get_program(realized_ast, opts=opts), device=Device.DEFAULT))
-  if use_tensor_cores == 1: assert len([uop for uop in prg.p.uops if uop.op is Ops.WMMA]) > 0, "wmma not triggered"
-  assert len([x for x in prg.p.uops[-1].arg.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
-  prg.exec(bufs)
-  if dtype_in == dtypes.half: tc_atol, tc_rtol = 1e-2, 1e-3
-  elif dtype_in == dtypes.bfloat16: tc_atol, tc_rtol = 1e-2, 1e-2
-  else: tc_atol, tc_rtol = 5e-3, 1e-4
-  c = bufs[0].numpy().reshape((M,N))
-  np.testing.assert_allclose(c, np_a @ np_b, atol=tc_atol, rtol=tc_rtol)
-
-def helper_tc_ensure_uops_and_opts_count(N: int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0,
-                                         ensure_triggered:bool=True):
-  a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
-  r = a.matmul(b, dtype=dtype_out)
-  sched = r.schedule()
-  realized_ast = sched[-1].ast
-  opts_to_apply = [Opt(OptOps.TC, axis, (tc_select, tc_opt, 1))]
-  realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
-
-  if ensure_triggered:
-    program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
-    wmmas = len([uop for uop in program.uops if uop.op is Ops.WMMA])
-    tcs = len([x for x in program.applied_opts if x.op is OptOps.TC])
-    assert wmmas > 0, "tensor core not triggered"
-    assert tcs == 1, "tensor core opt not included"
-  else:
-    try:
-      program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
-      assert False, "OptOps.TC triggered, expected KernelOptError"
-    except KernelOptError: pass
-
 class TestLinearizer(unittest.TestCase):
   def test_arg_dedup(self):
     # NOTE: this realize exists because Tensor.numpy calls .contiguous() internally
@@ -100,9 +49,8 @@ class TestLinearizer(unittest.TestCase):
 
     a_t = Tensor.full(st.shape, 2).contiguous().realize()
     b_t = Tensor.full(st.shape, 3).contiguous().realize()
-    lin = helper_linearizer_ast(sink, [a_t, b_t], wanna_output=[a_t.numpy()+b_t.numpy(), a_t.numpy()*b_t.numpy()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
-
+    helper_linearizer_ast(sink, [a_t, b_t], wanna_output=[a_t.numpy()+b_t.numpy(), a_t.numpy()*b_t.numpy()])
+    uops = get_program(sink, opts=[]).uops
     stores = [u for u in uops if u.op is Ops.STORE]
     mutable_bufs = dedup(flatten([[x for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL] for u in stores]))
     assert len(mutable_bufs) == len(stores) == 2
@@ -116,43 +64,27 @@ class TestLinearizer(unittest.TestCase):
         if skip and i in skip: continue
         assert ranges[i-1] != u, f"multireduce nested the ranges! {ranges[i-1], {u}}"
 
-  @unittest.skip("broken. should not depends on push_views and implementation details of getitem")
-  @unittest.skipIf(CI and Device.DEFAULT in {"PTX", "AMD", "NV"}, "very slow")
-  def test_indexing_multireduce(self):
-    dataset = Tensor.rand(16384, 256).realize()
-    idxs = Tensor([0,3,5,6]).realize()
-    with Context(FUSE_ARANGE=1):
-      sink = dataset[idxs].contiguous().kernelize().uop.base.src[1].arg.ast
-    real_index = dataset.numpy()[idxs.numpy()].reshape(4, 256, 1, 1)
-    helper_linearizer_ast(push_views(sink), [dataset, idxs], wanna_output=[real_index])
-
   def test_two_nested_range(self):
     a = Tensor.randn(2, ).realize()
     out = a.reshape(2, 1).expand(2, 3).sum()
-    lin = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)).sum()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
+    ast = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)).sum()])
+    uops = get_program(ast, opts=[]).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
-    # RANGE -> LOAD -> RANGE -> STORE
-    #assert any(x.op is Ops.LOAD for x in uops[ranges[0]:ranges[1]])
 
   def test_three_nested_range(self):
     a = Tensor.randn(2, ).realize()
     out = a.reshape(2, 1).expand(2, 3).expand(2, 2, 3).sum()
-    lin = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)), (2, 2, 3)).sum()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
+    ast = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)), (2, 2, 3)).sum()])
+    uops = get_program(ast, opts=[]).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
-    # RANGE -> RANGE -> LOAD -> RANGE -> STORE
-    # NOTE: nothing should toposort between the first two ranges
-    #assert ranges[0]+1 == ranges[1]
-    #assert any(x.op is Ops.LOAD for x in uops[ranges[1]:ranges[2]])
 
   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()
-    lin = helper_linearizer_opt(out, wanna_output=[24])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
+    ast = helper_linearizer_opt(out, wanna_output=[24])
+    uops = get_program(ast, opts=[]).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     # RANGE -> ALU -> RANGE -> ALU + LOAD -> STORE
     assert any(x.op in GroupOp.ALU for x in uops[ranges[0]:ranges[1]])
@@ -163,8 +95,8 @@ class TestLinearizer(unittest.TestCase):
     a = Tensor.randn(4, 1).realize()
     b = Tensor.randn(1, 1).realize()
     out = (a + b[0]).sum() + b[0]
-    lin = helper_linearizer_opt(out, wanna_output=[(a.numpy()+b.numpy()[0]).sum()+b.numpy()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
+    ast = helper_linearizer_opt(out, wanna_output=[(a.numpy()+b.numpy()[0]).sum()+b.numpy()])
+    uops = get_program(ast, opts=[]).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     # LOAD -> RANGE -> LOAD -> STORE
     assert len([x for x in uops[:ranges[0]] if x.op is Ops.LOAD]) == 1
@@ -173,42 +105,10 @@ class TestLinearizer(unittest.TestCase):
     a = Tensor.randn(2, ).realize()
     b = Tensor.randn(1, 1).realize()
     out = (a.reshape(2, 1).expand(2, 3) + b[0]).sum() + b[0]
-    lin = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)) + b.numpy()[0]).sum() + b.numpy()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
+    ast = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)) + b.numpy()[0]).sum() + b.numpy()])
+    uops = get_program(ast, opts=[]).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
-    #if getenv("PTX"):
-    # LOAD -> RANGE -> CAST -> ALU -> ALU -> LOAD -> ALU -> RANGE -> ALU -> STORE
-    #  assert uops[ranges[0]-2].op is Ops.LOAD
-    #  assert ranges[1] == ranges[0]+6
-    #  assert [x.op for x in uops[ranges[1]-2:ranges[1]]] == [Ops.LOAD, Ops.ALU]
-    # LOAD -> RANGE -> LOAD -> ALU -> RANGE -> STORE
-    #else:
-    #  assert uops[ranges[0]-2].op is Ops.LOAD
-    #  assert ranges[1] == ranges[0]+3
-    #  assert [x.op for x in uops[ranges[1]-2:ranges[1]]] == [Ops.LOAD, Ops.ALU]
-
-  @unittest.skip("fragile crap")
-  def test_range_outer_op_after_phi(self):
-    a = Tensor.randn(4, 1).realize()
-    out = a.sum() * a.sum()
-    lin = helper_linearizer_opt(out, wanna_output=[a.numpy().sum()*a.numpy().sum()])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
-    # RANGE -> LOAD -> STORE -> ALU
-    end = max(i for i,u in enumerate(uops) if u.op is Ops.ENDRANGE)
-    # the INDEX can be first
-    assert uops[end+1].op in GroupOp.ALU or uops[end+2].op in GroupOp.ALU
-
-  @unittest.skip("fragile crap")
-  def test_range_outer_op_after_phi_nested_range(self):
-    a = Tensor.randn(2, ).realize()
-    out = a.reshape(2, 1).expand(2, 3).sum() + a.reshape(2, 1).expand(2, 3).sum()
-    lin = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3))).sum()*2])[0]
-    uops = get_program(lin.get_optimized_ast(), lin.opts).uops
-    # RANGE -> LOAD -> STORE -> ALU
-    end = max(i for i,u in enumerate(uops) if u.op is Ops.ENDRANGE)
-    # the INDEX can be first
-    assert uops[end+1].op in GroupOp.ALU or uops[end+2].op in GroupOp.ALU
 
   def test_load_dedup(self):
     # for different leaves in the AST, the same loads may occur.
@@ -312,149 +212,14 @@ class TestLinearizer(unittest.TestCase):
         d, w = Tensor.rand(4, 8, 8, 8, dtype=tensor_dtype), Tensor.rand(8, 8, 2, 2, dtype=tensor_dtype)
         helper_arg_acc_dtype(d.conv2d(w, dtype=acc_dtype), expected_dtype)
 
-  # TODO: don't skip bf16 for real device (METAL, AMD)
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
-      # for AMX, tc.dims[2] == 1 so reduceop is None thus tensor_cores are not triggered
-      helper_tc_allclose(tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2], tc.dtype_in, tc.dtype_out, axis=0, tc_opt=0)
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores_codegen(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
-      n, m, k = tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2]
-      a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in)
-      r = a.matmul(b, dtype=tc.dtype_out)
-      prg = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.TC, axis=0, arg=(-1, 2, 1))])
-      if Device.DEFAULT == "LLVM":
-        assert "0x201000" in prg.src
-      elif Device.DEFAULT == "AMD" and AMD_LLVM:
-        assert "@llvm.amdgcn.wmma" in prg.src
-      elif Device[Device.DEFAULT].renderer.suffix == "PTX":
-        assert "mma.sync.aligned" in prg.src
-      else:
-        assert "__WMMA_" in prg.src
-
-  @unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "broken for AMD")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores_padded(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
-      helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2)
-
-  # AMD compiler bug: AMD miscompiles non-zero padded tc kernels with -O3, producing wrong results, nans or hang (see #9606)
-  # Internal bug: zero-stride dimensions combined with a mask may produce wrong index/valid for pad == 1 on AMD
-  @unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "test for AMD's tc")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skip("warp elements not duplicated properly across lanes")
-  def test_tensor_cores_padded_amd(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
-      helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2)
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores_padded_uops(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      pad = 1
-
-      # check that TC is triggered for TC_OPT=2
-      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
-                                           tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=True)
-
-      # check that TC is not triggered for TC_OPT<2
-      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
-                                           tc.dtype_in, tc.dtype_out, tc_opt=1, ensure_triggered=False)
-      helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad,
-                                           tc.dtype_in, tc.dtype_out, tc_opt=0, ensure_triggered=False)
-
-      # check excessive padding doesn't trigger padded TC in TC_OPT=2
-      helper_tc_ensure_uops_and_opts_count(tc.dims[0]//4, tc.dims[1], tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
-      helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1]//4, tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
-      if not AMX: # AMX tc.dims[2] == 1
-        helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1], tc.dims[2]//4, tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False)
-
-  @unittest.skipIf(CI and Device.DEFAULT in {"AMD"}, "AMD CI is really slow here")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores_multi_reduce(self):
-    for tc in Device[Device.DEFAULT].renderer.tensor_cores:
-      if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
-      if tc.dtype_in is dtypes.bfloat16: continue # <-- broken with numpy
-      # this will be a M=G16, N=G32, M=G16, M=G16, K=R16, K=R16, K=R16 with 9 choices of TC MNK axes
-      golden_result = None
-      for axis in range(9):
-        a = Tensor.rand(16, 16, 29, 29, dtype=tc.dtype_in).realize()
-        b = Tensor.rand(32, 16, 16, 16, dtype=tc.dtype_in).realize()
-        c = a.conv2d(b, padding=1, dtype=tc.dtype_out)
-        realized_ast, real_bufs = helper_realized_ast(c)
-
-        opts_to_apply = [Opt(OptOps.TC, axis, (-1, 2, 1))]
-        realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
-        program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
-        assert len([uop for uop in program.uops if uop.op is Ops.WMMA]) > 0, "tensor core not triggered"
-        assert len([x for x in program.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
-
-        prg = CompiledRunner(program)
-        # TODO: support this even if numpy doesn't
-        if _to_np_dtype(real_bufs[0].dtype) is None: continue
-        real_bufs[0].copyin(np.zeros((real_bufs[0].size, ), dtype=_to_np_dtype(real_bufs[0].dtype)).data) # Zero to check that all values are filled
-        prg.exec(real_bufs)
-        result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype))
-
-        # ensure the results for each choice of axis matches
-        if golden_result is None: golden_result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype))
-        np.testing.assert_allclose(result, golden_result, atol=0.1, rtol=0.2)
-
-      # check that get_kernel_actions produces all 9 options
-      from tinygrad.codegen.opt.search import get_kernel_actions
-      tc_actions = [k for i, k in get_kernel_actions(Kernel(realized_ast), False).items() if k.applied_opts[0].op == OptOps.TC]
-
-      available_tc = len([x for x in Device[Device.DEFAULT].renderer.tensor_cores if x.dtype_in == tc.dtype_in and x.dtype_out == tc.dtype_out])
-      assert len(tc_actions) == 9 * available_tc, f"should contain 9 possible TC actions for every available TC, got {len(tc_actions)}"
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_tensor_cores_unroll_phi(self):
-    tc = Device[Device.DEFAULT].renderer.tensor_cores[0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
-    r = x.matmul(y, dtype=tc.dtype_out)
-    k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
-      if u.op is Ops.WMMA:
-        assert u.src[-1].src[0].op != Ops.STORE
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
-  def test_tensor_cores_unroll_casted_phi(self):
-    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
-    r = x.matmul(y, dtype=tc.dtype_out)
-    k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
-      if u.op is Ops.WMMA:
-        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
-        assert u.src[-1].src[0].op != Ops.STORE
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
-  def test_tensor_cores_unroll_casted_phi_with_children(self):
-    # all STORE children are outside the loop
-    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
-    r = x.matmul(y, dtype=tc.dtype_out).relu()
-    k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
-      if u.op is Ops.WMMA:
-        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
-        assert u.src[-1].src[0].op != Ops.STORE
-
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
   def test_simple_unroll_no_between_phi_dependencies(self):
     x, y = Tensor.rand(128, 128), Tensor.rand(128, 128)
     r = (x@y).relu()
-    k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4)]])[-1]
+    opt = [Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4)]
+    ast = helper_linearizer_opt(r, [opt])
     # the uops graph is DEFINE_REG -> 4x STORE 0.0 -> RANGE -> 4x ALU -> 4x STORE -> ENDRANGE
-    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+    uops = get_program(ast, opts=opt).uops
     begin_range = [i for i, x in enumerate(uops) if x.op is Ops.RANGE][-1]
     end_range = [i for i, x in enumerate(uops) if x.op is Ops.ENDRANGE][0]
     for i,u in enumerate(uops): print(i, u.op, [uops.index(s) for s in u.src], u.arg, u.dtype)
@@ -544,8 +309,8 @@ class TestLinearizer(unittest.TestCase):
   def test_default_global_reversed(self):
     # shrink so that the dims do not collapse
     t = Tensor.ones(5, 6, 7).contiguous().realize().shrink(((0, 4), (0, 5), (0, 6)))
-    k = helper_linearizer_opt(t+1)[0]
-    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+    ast = helper_linearizer_opt(t+1)
+    uops = get_program(ast, opts=[]).uops
     idxs = dedup([uop for uop in uops if uop.op is Ops.SPECIAL])
     idxs = sorted(idxs, key=lambda uop: uop.arg)
     assert (idxs[0].arg, idxs[0].src[0].arg) == ('gidx0', 6), idxs[0]
@@ -577,15 +342,13 @@ class TestLinearizer(unittest.TestCase):
     sched_copy = sched[:]
     run_schedule(sched)
     np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
-    realized_ast = sched_copy[-1].ast
-    realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple()))
-    program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
+    program = get_program(sched_copy[-1].ast, opts=())
     assert not any(u.op == Ops.WHERE for u in program.uops), "found where where where should be folded"
 
   def test_phi_simplification(self):
     def helper(t, max_ops=0):
-      k = helper_linearizer_opt(t)[-1]
-      uops = get_program(k.ast, k.opts, k.applied_opts).uops
+      ast = helper_linearizer_opt(t)
+      uops = get_program(ast).uops
       # ignore kernel optimized IF statements for now
       if if_op:=next((u for u in uops if u.op is Ops.IF), None):
         uops = uops[:uops.index(if_op)]
@@ -617,33 +380,19 @@ class TestLinearizer(unittest.TestCase):
     x, y = Tensor.randn(64,64), Tensor.randn(64,64)
     out = x.matmul(y)
     with Context(TC=0):
-      k = helper_linearizer_opt(out)[-1]
-    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+      ast = helper_linearizer_opt(out)
+      uops = get_program(ast).uops
     # check that the float4 cast collapses
     store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
     for val in store_vals:
       assert val.dtype == dtypes.float.vec(4) # and val.op is not Ops.VECTORIZE
 
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  def test_arange_opts(self):
-    a = Tensor.arange(128)
-    helper_linearizer_opt(a, [
-      [Opt(OptOps.GROUP, 0, 32)],
-      [Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(op=OptOps.LOCAL, axis=0, arg=8)],
-      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0)],
-      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8)],
-      [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8), Opt(op=OptOps.UNROLL, axis=1, arg=4)], # noqa: E501
-    ])
-
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
   def test_grouped_store_values(self):
     x = Tensor.randn((4,3,6,6)).realize()
     out = x.flip((0,1)).contiguous()
-    k = helper_linearizer_opt(out)[-1]
-    store_val = [u.src[1] for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
+    ast = helper_linearizer_opt(out)
+    store_val = [u.src[1] for u in get_program(ast).uops if u.op is Ops.STORE][0]
     assert store_val.dtype == dtypes.float.vec(4) and store_val.op is not Ops.VECTORIZE
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
@@ -654,9 +403,9 @@ class TestLinearizer(unittest.TestCase):
     out = x@y
     opt = [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8),
             Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 2)] # upcast accs in both reduces
-    k = helper_linearizer_opt(out, opts=[opt])[-1]
+    ast = helper_linearizer_opt(out, opts=[opt])
     def get_recursive(uop): return set.union(set(uop.src), [uop], *[get_recursive(v) for v in uop.src])
-    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+    uops = get_program(ast, opts=opt).uops
     local_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_LOCAL for x in get_recursive(u.src[0]))]
     global_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_GLOBAL for x in get_recursive(u.src[0]))]
     barrier = [u for u in uops if u.op is Ops.BARRIER][0]
@@ -675,8 +424,8 @@ class TestLinearizer(unittest.TestCase):
   def test_grouped_store_local_only(self):
     x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
     r = (x@y).relu()
-    k = helper_linearizer_opt(r)[-1]
-    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+    ast = helper_linearizer_opt(r)
+    uops = get_program(ast).uops
     stores = [u for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
 
     # the float4 value stores directly in lds and we skip upcast
@@ -690,179 +439,96 @@ class TestLinearizer(unittest.TestCase):
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
   def test_skip_unmatching_upcasts(self):
     Tensor.manual_seed(0)
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(9600), arg=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(40, 1, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=0, src=()),)),
-        UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.VIEW, dtypes.float.ptr(9600), arg=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(1, 240, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-            UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=1, src=()),)),)),)),))
-    opt = [
-        Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=16),
-        Opt(op=OptOps.LOCAL, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=3, arg=2)
-    ]
-    k = helper_linearizer_ast(ast, [Tensor.randn(240*40).realize()], opts=[opt])[-1]
-    out = [u for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(40, 1, 0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(1, 240, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c4 = c3.load()
+    c5 = c1.store(c4)
+    ast = c5.sink()
+    opt = [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=16),
+           Opt(op=OptOps.LOCAL, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=3, arg=2)]
+    helper_linearizer_ast(ast, [Tensor.randn(240*40).realize()], opts=[opt])
+    out = [u for u in get_program(ast, opts=opt).uops if u.op is Ops.STORE][0]
     assert out.src[1].op is Ops.VECTORIZE and out.src[1].dtype == dtypes.float.vec(4)
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
   def test_skip_unmatching_upcasts_with_gep(self):
     Tensor.manual_seed(0)
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(32, 1, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=0, src=()),)),
-        UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(1, 8, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-            UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=1, src=()),)),)),)),))
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(32, 1, 0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(1, 8, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c4 = c3.load()
+    c5 = c1.store(c4)
+    ast = c5.sink()
     opt = [Opt(op=OptOps.LOCAL, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=1, arg=8),
             Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8),
             Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=2)]
-    k = helper_linearizer_ast(ast, [Tensor.randn(8*32).realize()], opts=[opt])[-1]
-    out = [u for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
+    helper_linearizer_ast(ast, [Tensor.randn(8*32).realize()], opts=[opt])
+    out = [u for u in get_program(ast).uops if u.op is Ops.STORE][0]
     assert out.src[1].op is Ops.VECTORIZE and out.src[1].dtype.count != 1
 
-class TestHandCodedOpts(unittest.TestCase):
-  def test_masked_upcast(self):
-    layer_1 = Tensor.cat(*[Tensor.empty(5) for _ in range(4)])
-    layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.empty(6, 20))
+# *** helpers ***
 
-    s = layer_2.schedule()[-1]
-    k = Kernel(push_views(s.ast))
-    k.apply_opts(hand_coded_optimizations(k))
-    assert len(k.bufs) == 6  # make sure all ops are done in one kernel
-    # masked upcast should upcast masked axis of size 7
-    # masked upcast should not upcast large (20) last axis
-    # float4/other hcopt shouldn't upcast last axis, since we already have 7 upcast, and the last axis is not very contiguous
-    assert k.upcasted == 1 and k.full_shape[-1] == 7
+def push_views(ast): return apply_rewrites(ast, rewrites_for_views)
 
-  @unittest.skipIf(Device.DEFAULT in {"METAL", "WEBGPU"}, "METAL/WEBGPU split this kernel since it has 37 buffers")
-  def test_masked_upcast_wino(self):
-    monster = Tensor.stack(*[Tensor.stack(*[Tensor.empty(16) for _ in range(6)]) for _ in range(6)])
-
-    s = monster.schedule()[-1]
-    k = Kernel(push_views(s.ast))
-    k.apply_opts(hand_coded_optimizations(k))
-    assert len(k.bufs) == 37  # make sure all ops are done in one kernel
-    # should upcast the two Tensor.stacks
-    assert k.upcasted >= 2 and k.full_shape[k.shape_len-k.upcasted:k.shape_len].count(6) == 2
-
-  def test_masked_upcast_wino_full(self):
-    with Context(WINO=1):
-      x,w = Tensor.rand(1,4,8,8, requires_grad=True).realize(), Tensor.rand(4,4,3,3, requires_grad=True).realize()
-      out = Tensor.conv2d(x,w, padding=1)
-      out.mean().backward()
-
-      upcasts = []
-      wino_schedule = out.schedule()
-      # collect upcasts of tile transform kernels
-      for i, si in enumerate(wino_schedule):
-        k = Kernel(push_views(si.ast))
-        k.apply_opts(hand_coded_optimizations(k))
-        if k.reduceop is not None: continue  # not a tile transform kernel (there is a gemm reduce kernel)
-        if len(k.bufs) < 22: continue  # not a tile transform kernel (there's a permute kernel at the end)
-        upcasts.append(tuple(k.full_shape[k.shape_len - k.upcasted:k.shape_len]))
-      assert len(upcasts) == 3  # 3 transformation matrices
-      assert len(wino_schedule) <= 4  # 4 kernels
-      # this test case's inputs are too small, so one of the 4-stacks became a local, which is fine i guess
-      assert upcasts.count((6, 6)) == 2 #and upcasts.count((4, 4)) == 1
-
-      backward_schedule = Tensor.schedule(x.grad, w.grad)
-      for si in backward_schedule:
-        k = Kernel(push_views(si.ast))
-        k.apply_opts(hand_coded_optimizations(k))
-        if len(k.bufs) < 20: continue  # not a tile transform kernel
-        # heuristic number to make sure that at least some upcasts but not too many upcasts are being done
-        assert 6 <= prod(k.full_shape[k.shape_len - k.upcasted:k.shape_len]) <= 216
-      assert len(backward_schedule) <= 13  # just the current number, but it could be better
-
-  def test_masked_upcast_many(self):
-    layer_1 = Tensor.cat(Tensor.rand(3, 4), Tensor.rand(4, 4))
-    layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 7, 4))
-    layer_3 = Tensor.cat(layer_2.unsqueeze(0), Tensor.rand(6, 7, 7, 4))
-
-    k = helper_linearizer_opt(layer_3)[-1]
-    assert len(k.bufs) == 5  # make sure all ops are done in one kernel
-    # check that we don't do too many upcasts
-    assert prod(k.full_shape[k.shape_len-k.upcasted:k.shape_len]) <= 49
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  def test_matvec(self):
-    N = 128
-    a = Tensor.rand(1, N).realize()
-    b = Tensor.rand(N, N).realize()
-    c = a @ b
-
-    k = helper_linearizer_opt(c)[-1]
-
-    assert k.group_for_reduces == 1
-    assert k.axis_types.count(AxisType.LOCAL) == 1
-    assert k.upcasted == 1
+def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
+  if isinstance(r, Tensor): r = [r]
+  s = Tensor.schedule(*r)
+  run_schedule(s[:-1])  # run all kernels except the last one
+  assert s[-1].ast.op is Ops.SINK, f"helper_realized_ast expects a SINK {s[-1]}"
+  # 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)]
+  return push_views(s[-1].ast), bufs
 
 def helper_linearizer_ast(ast:UOp, inputs:list[Tensor], *args, **kwargs):
   assert isinstance(ast, UOp), "ast must be UOp"
   inbufs = [x.uop.base.buffer for x in inputs]
-  outbufs = [Buffer(inbufs[-1].device if inbufs else Device.DEFAULT, out.st_arg.size, out.src[1].dtype).allocate() \
-      for out in ast.src]
-  return _helper_linearizer_opt_ast(ast, outbufs+inbufs, *args, **kwargs)
+  outbufs = [Buffer(inbufs[-1].device if inbufs else Device.DEFAULT, out.st_arg.size, out.src[1].dtype).allocate() for out in ast.src]
+  _helper_linearizer_opt_ast(ast, outbufs+inbufs, *args, **kwargs)
 
 def helper_linearizer_opt(r:Tensor|list[Tensor], *args, **kwargs):
   realized_ast, real_bufs = helper_realized_ast(r)
-  return _helper_linearizer_opt_ast(realized_ast, real_bufs, *args, **kwargs)
+  _helper_linearizer_opt_ast(realized_ast, real_bufs, *args, **kwargs)
+  return realized_ast
 
-def copyout_outputs(lin:Kernel, outbufs:list[Buffer]) -> list[np.ndarray]:
-  ret = []
-  for i,x in enumerate(outbufs):
-    shape: tuple[int, ...] = lin.ast.src[i].st_arg.shape
-    ret.append(np.frombuffer(x.as_buffer(), _to_np_dtype(x.dtype)).reshape(shape))
-  return ret
+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
 
 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=[]) -> list[Kernel]:
-  lins: list[Kernel] = []
+                               apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[], wanna_output=[]):
   outbufs = [real_bufs[x.src[0].base.arg] for x in realized_ast.src]
   device = real_bufs[0].device
+  wanna_output = [np.array(x).flatten() for x in wanna_output]
 
-  def get_prg(k:Kernel): return CompiledRunner(replace(get_program(k.ast, k.opts, k.applied_opts), device=device))
+  def get_prg(opts): return CompiledRunner(replace(get_program(realized_ast, opts=opts), device=device))
 
-  def check_opt(opts, create_k, expected_color_size):
-    k = create_k()
-    lins.append(k)
-    if apply_tc: k.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, 1)))
-    k.apply_opts(opts)
-    if expected_color_size is not None:
-      cs = list(zip(k.colors(), k.full_shape))
-      assert cs == expected_color_size, f"expected={expected_color_size} got={cs}"
-    prg = get_prg(k)
+  def check_opt(opts):
+    prg = get_prg(opts=opts)
     reset_bufs(outbufs)
     prg.exec(real_bufs)
-
-    for x,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(x, want, atol=atol, rtol=rtol)
+    for x,want in zip(copyout_outputs(outbufs), wanna_output): np.testing.assert_allclose(x, want, atol=atol, rtol=rtol)
 
   # Get baseline if it is not provided, which is not optimized at all.
-  k = Kernel(realized_ast)
-  lins.append(k)
-  prg = get_prg(k)
+  prg = get_prg(opts=())
   prg.exec(real_bufs)
-  if len(wanna_output) == 0: wanna_output = copyout_outputs(k, outbufs)
+  if len(wanna_output) == 0: wanna_output = copyout_outputs(outbufs)
   else:
-    for buf,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
+    for buf,want in zip(copyout_outputs(outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
 
   # Check correctness of handcoded optimiztions.
-  k = Kernel(realized_ast)
-  k.apply_opts(hand_coded_optimizations(k))
-  lins.append(k)
-  prg = get_prg(k)
+  prg = get_prg(opts=None)
   reset_bufs(outbufs)
   prg.exec(real_bufs)
-  for buf,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
-  for i,x in enumerate(opts): # Check custom transformations if any.
-    check_opt(x, lambda: Kernel(realized_ast), color_sizes[i] if i < len(color_sizes) else None)
-  return lins
+  for buf,want in zip(copyout_outputs(outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
+  for x in opts: # Check custom transformations if any.
+    check_opt(([Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, 1))] if apply_tc else [])+x)
 
 if __name__ == '__main__':
   unittest.main()

test/test_linearizer_dumb.py

@@ -5,34 +5,33 @@
 import unittest
 from tinygrad import Device, dtypes
 from tinygrad.device import is_dtype_supported
-from tinygrad.uop.ops import UOp, Ops
+from tinygrad.uop.ops import UOp, Ops, AxisType, KernelInfo
 from tinygrad.helpers import getenv
 from tinygrad.shape.shapetracker import ShapeTracker, View
 from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad.engine.realize import get_program
 
-class TestLinearizerDumb(unittest.TestCase):
+class TestLinearizerFailure(unittest.TestCase):
+  @unittest.expectedFailure
   @unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
-  def test_unmerged_ifs(self):
-    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=0, src=())
-    c1 = c0.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(25088, 0, 49, 7, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)))
-    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=1, src=())
-    c3 = c2.view(ShapeTracker(views=(View(shape=(1, 64, 1, 512, 4, 9, 4, 9), strides=(0, 25088, 0, 49, 0, 7, 0, 1), offset=-8, mask=((0, 1), (0, 64), (0, 1), (0, 512), (0, 4), (1, 8), (0, 4), (1, 8)), contiguous=False), View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(663552, 0, 0, 36, 1, 1296, 360, 10), offset=0, mask=None, contiguous=False))))
-    c4 = c3.load()
-    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(2359296), arg=2, src=())
-    c6 = c5.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(0, 0, 4608, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)))
-    c7 = c6.load()
-    c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
-    c9 = c1.store(((c4*c7).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (5, 6, 7))).cast(dtypes.half)*UOp.const(dtypes.half, 0.9999950000374996, src=c8)).alu(Ops.MAX, UOp.const(dtypes.half, 0.0, src=c8)))
-    ast = c9.sink()
-    opts = [Opt(op=OptOps.TC, axis=2, arg=(-1, 2, 1)), Opt(op=OptOps.UPCAST, axis=2, arg=0), Opt(op=OptOps.UNROLL, axis=1, arg=0)]
-    prg = get_program(ast, Device["METAL"].renderer, opts)
-    print(prg.src)
-    Device[Device.DEFAULT].compiler.compile_cached(prg.src)
-    gate_count = len([x for x in prg.src.splitlines() if "if" in x])
-    assert gate_count == 1, f"must have only one gate {gate_count} != 1"
-    assert len([u for u in prg.uops if u.op is Ops.IF]) == 1, "must have a single IF"
+  def test_failure_beam_mnist(self):
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(4014080), arg=0, src=())
+    c1 = UOp.range(UOp.const(dtypes.int, 512), 0, AxisType.GLOBAL)
+    c2 = UOp.range(UOp.const(dtypes.int, 784), 1, AxisType.GLOBAL)
+    c3 = UOp.range(UOp.const(dtypes.int, 10), 3, AxisType.GLOBAL)
+    c4 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(512), arg=1, src=())
+    c5 = c4.index(c1, UOp.const(dtypes.bool, True)).load()
+    c6 = UOp.range(UOp.const(dtypes.int, 6000), 1004, AxisType.REDUCE)
+    c7 = UOp.range(UOp.const(dtypes.int, 3750), 2006, AxisType.REDUCE)
+    c8 = UOp.range(UOp.const(dtypes.int, 16), 2007, AxisType.GROUP_REDUCE)
+    c9 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(47040000), arg=2, src=())
+    c10 = c9.index((((c3*UOp.const(dtypes.int, 4704000))+c2)+(c6*UOp.const(dtypes.int, 784))), UOp.const(dtypes.bool, True)).load()
+    c11 = c5.alu(Ops.CMPNE, ((((c3*UOp.const(dtypes.int, 6000))+c6)+((c7*UOp.const(dtypes.int, 16))+c8)).alu(Ops.CMPLT, UOp.const(dtypes.int, 59999)).where(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1)).reduce(c7, c8, arg=Ops.ADD)+UOp.const(dtypes.int, -1))).where(UOp.const(dtypes.uchar, 0), c10).reduce(c6, arg=Ops.ADD)
+    c12 = c0.index((((c1*UOp.const(dtypes.int, 7840))+(c2*UOp.const(dtypes.int, 10)))+c3), UOp.const(dtypes.bool, True)).store(c11, c1, c2, c3)
+    ast = c12.sink(arg=KernelInfo(name='test', axis_types=(), dont_use_locals=False, applied_opts=(Opt(op=OptOps.GROUP, axis=1, arg=16),), opts_to_apply=None))
+    _ = get_program(ast, Device["METAL"].renderer)
 
+class TestLinearizerDumb(unittest.TestCase):
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
   def test_max_simplify_and_cancel(self):
     c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=())

test/test_linearizer_overflows.py

@@ -1,165 +0,0 @@
-# ruff: noqa: E501
-import unittest
-from tinygrad import dtypes
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.search import Opt, OptOps, bufs_from_lin
-from extra.optimization.helpers import time_linearizer
-
-# stuff needed to unpack a kernel
-from tinygrad.uop.ops import UOp, Ops
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import View
-
-def _test_overflow(ast, opts):
-  lin = Kernel(ast)
-  lin.apply_opts(opts)
-  bufs = bufs_from_lin(lin)
-  print(bufs)
-  time_linearizer(lin, bufs)
-
-# NOTE: if you want these to trigger, set launch bounds on HIP kernels
-@unittest.skip("unneeded without launch bounds")
-class TestLinearizerOverflow(unittest.TestCase):
-  def test_overflow_1(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(51380224), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(64, 1, 64, 112, 112, 1, 1, 1), strides=(802816, 0, 12544, 112, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.MAX, dtypes.float, arg=None, src=(
-          UOp(Ops.ADD, dtypes.float, arg=None, src=(
-            UOp(Ops.MUL, dtypes.float, arg=None, src=(
-              UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                  UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-                    UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                      UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9633792), arg=1, src=()),
-                        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 64, 1, 3, 8, 230, 8, 230), strides=(0, 150528, 0, 50176, 0, 224, 0, 1), offset=-675, mask=((0, 1), (0, 64), (0, 1), (0, 3), (0, 8), (3, 227), (0, 8), (3, 227)), contiguous=False), View(shape=(64, 1, 64, 112, 112, 3, 7, 7), strides=(10156800, 0, 0, 3680, 2, 3385600, 425040, 231), offset=0, mask=None, contiguous=False))), src=()),)),
-                      UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9408), arg=2, src=()),
-                        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(64, 1, 64, 112, 112, 3, 7, 7), strides=(0, 0, 147, 0, 0, 49, 7, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),
-                  x16:=UOp(Ops.CONST, dtypes.float, arg=0.0, src=(
-                    x17:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 64, 112, 112, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-                UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=3, src=()),
-                  x20:=UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(64, 1, 64, 112, 112, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-              UOp(Ops.SQRT, dtypes.float, arg=None, src=(
-                UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                  x23:=UOp(Ops.CONST, dtypes.float, arg=1.0, src=(
-                     x17,)),
-                  UOp(Ops.RECIP, dtypes.float, arg=None, src=(
-                    UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                       x23,
-                      UOp(Ops.CONST, dtypes.float, arg=1e-05, src=(
-                         x17,)),)),)),)),)),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=4, src=()),
-               x20,)),)),
-           x16,)),)),))
-    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.LOCAL, axis=2, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=2, arg=0)]
-    _test_overflow(ast, opts)
-
-  # From BEAM on hlb_cifar.py
-  def test_overflow_2(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(512, 1, 64, 32, 32, 1, 1, 1), strides=(65536, 0, 1024, 32, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(16777216), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 512, 1, 32, 4, 34, 4, 34), strides=(0, 32768, 0, 1024, 0, 32, 0, 1), offset=-33, mask=((0, 1), (0, 512), (0, 1), (0, 32), (0, 4), (1, 33), (0, 4), (1, 33)), contiguous=False), View(shape=(512, 1, 64, 32, 32, 32, 3, 3), strides=(591872, 0, 0, 136, 1, 18496, 4760, 35), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18432), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(512, 1, 64, 32, 32, 32, 3, 3), strides=(0, 0, 288, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.LOCAL, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=2, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=0)]
-    _test_overflow(ast, opts)
-
-  # from BEAM on default simple_conv.py (which is quite large):
-  def test_overflow_3(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(16, 1, 128, 128, 128, 1, 1, 1), strides=(2097152, 0, 16384, 128, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 16, 1, 128, 4, 130, 4, 130), strides=(0, 2097152, 0, 16384, 0, 128, 0, 1), offset=-129, mask=((0, 1), (0, 16), (0, 1), (0, 128), (0, 4), (1, 129), (0, 4), (1, 129)), contiguous=False), View(shape=(16, 1, 128, 128, 128, 128, 3, 3), strides=(34611200, 0, 0, 520, 1, 270400, 68120, 131), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(147456), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(16, 1, 128, 128, 128, 128, 3, 3), strides=(0, 0, 1152, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=2, arg=2)]
-    _test_overflow(ast, opts)
-
-  # from BEAM on BS=4 simple_conv.py:
-  def test_overflow_4(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(8388608), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(4, 1, 128, 128, 128, 1, 1, 1), strides=(2097152, 0, 16384, 128, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(8388608), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 4, 1, 128, 4, 130, 4, 130), strides=(0, 2097152, 0, 16384, 0, 128, 0, 1), offset=-129, mask=((0, 1), (0, 4), (0, 1), (0, 128), (0, 4), (1, 129), (0, 4), (1, 129)), contiguous=False), View(shape=(4, 1, 128, 128, 128, 128, 3, 3), strides=(34611200, 0, 0, 520, 1, 270400, 68120, 131), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(147456), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(4, 1, 128, 128, 128, 128, 3, 3), strides=(0, 0, 1152, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.UPCAST, axis=3, arg=4), Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=2, arg=4)]
-    _test_overflow(ast, opts)
-
-  # from BEAM on BS=2 simple_conv.py:
-  def test_overflow_5(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(4194304), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(2, 1, 128, 128, 128, 1, 1, 1), strides=(2097152, 0, 16384, 128, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(4194304), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 2, 1, 128, 4, 130, 4, 130), strides=(0, 2097152, 0, 16384, 0, 128, 0, 1), offset=-129, mask=((0, 1), (0, 2), (0, 1), (0, 128), (0, 4), (1, 129), (0, 4), (1, 129)), contiguous=False), View(shape=(2, 1, 128, 128, 128, 128, 3, 3), strides=(34611200, 0, 0, 520, 1, 270400, 68120, 131), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(147456), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(2, 1, 128, 128, 128, 128, 3, 3), strides=(0, 0, 1152, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.LOCAL, axis=2, arg=2), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=2, arg=2)]
-    _test_overflow(ast, opts)
-
-  # from BEAM on BS=3 simple_conv.py:
-  def test_overflow_6(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(6291456), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(3, 1, 128, 128, 128, 1, 1, 1), strides=(2097152, 0, 16384, 128, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(6291456), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 3, 1, 128, 4, 130, 4, 130), strides=(0, 2097152, 0, 16384, 0, 128, 0, 1), offset=-129, mask=((0, 1), (0, 3), (0, 1), (0, 128), (0, 4), (1, 129), (0, 4), (1, 129)), contiguous=False), View(shape=(3, 1, 128, 128, 128, 128, 3, 3), strides=(34611200, 0, 0, 520, 1, 270400, 68120, 131), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(147456), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(3, 1, 128, 128, 128, 128, 3, 3), strides=(0, 0, 1152, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=3, arg=2)]
-    _test_overflow(ast, opts)
-
-  # from BEAM on BS=3 simple_conv.py: (alt)
-  def test_overflow_7(self):
-    ast = UOp(Ops.SINK, None, arg=None, src=(
-      UOp(Ops.STORE, None, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(6291456), arg=0, src=()),
-        UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(3, 1, 128, 128, 128, 1, 1, 1), strides=(2097152, 0, 16384, 128, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (7, 6, 5)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(6291456), arg=1, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(1, 3, 1, 128, 4, 130, 4, 130), strides=(0, 2097152, 0, 16384, 0, 128, 0, 1), offset=-129, mask=((0, 1), (0, 3), (0, 1), (0, 128), (0, 4), (1, 129), (0, 4), (1, 129)), contiguous=False), View(shape=(3, 1, 128, 128, 128, 128, 3, 3), strides=(34611200, 0, 0, 520, 1, 270400, 68120, 131), offset=0, mask=None, contiguous=False))), src=()),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(147456), arg=2, src=()),
-              UOp(Ops.VIEW, None, arg=ShapeTracker(views=(View(shape=(3, 1, 128, 128, 128, 128, 3, 3), strides=(0, 0, 1152, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.UPCAST, axis=3, arg=4), Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=2, arg=4)]
-    _test_overflow(ast, opts)
-
-if __name__ == '__main__':
-  unittest.main()

test/test_ops.py

@@ -17,6 +17,9 @@ if CI:
 FORWARD_ONLY = getenv("FORWARD_ONLY", 0)
 PRINT_TENSORS = getenv("PRINT_TENSORS", 0)
 
+def slow_test(test_func):
+  return unittest.skipIf(getenv("SKIP_SLOW_TEST"), "Skipping slow test")(test_func)
+
 def helper_test_op(shps, torch_fxn, tinygrad_fxn=None, atol=1e-6, rtol=1e-3, grad_atol=1e-4, grad_rtol=1e-3,
                    forward_only=False, vals=None, low=-2, high=2):
   if tinygrad_fxn is None: tinygrad_fxn = torch_fxn
@@ -1009,9 +1012,11 @@ class TestOps(unittest.TestCase):
 
   def test_small_cumsum(self):
     helper_test_op([(10)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
+  @slow_test
   def test_simple_cumsum(self):
     helper_test_op([(512)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
     helper_test_op([(1022)], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
+  @slow_test
   def test_cumsum(self):
     helper_test_op([()], lambda x: torch.cumsum(x, dim=0), lambda x: Tensor.cumsum(x, axis=0))
     self.helper_test_exception([()], lambda x: torch.cumsum(x, dim=1), lambda x: Tensor.cumsum(x, axis=1), expected=IndexError)
@@ -1029,9 +1034,11 @@ class TestOps(unittest.TestCase):
 
   def test_small_cumprod(self):
     helper_test_op([(10)],lambda x: torch.cumprod(x, dim=0),lambda x: Tensor.cumprod(x, axis=0))
+  @slow_test
   def test_simple_cumprod(self):
     helper_test_op([(512)],lambda x: torch.cumprod(x, dim=0),lambda x: Tensor.cumprod(x, axis=0))
     helper_test_op([(1022)],lambda x: torch.cumprod(x, dim=0),lambda x: Tensor.cumprod(x, axis=0))
+  @slow_test
   def test_cumprod(self):
     helper_test_op([()],lambda x: torch.cumprod(x, dim=0),lambda x: Tensor.cumprod(x, axis=0))
     self.helper_test_exception([()],lambda x: torch.cumprod(x, dim=1),lambda x: Tensor.cumprod(x, axis=1),expected=IndexError)
@@ -1049,9 +1056,11 @@ class TestOps(unittest.TestCase):
 
   def test_small_cummax(self):
     helper_test_op([(10)], lambda x: torch.cummax(x, dim=0).values, lambda x: Tensor.cummax(x, axis=0))
+  @slow_test
   def test_simple_cummax(self):
     helper_test_op([(512)], lambda x: torch.cummax(x, dim=0).values, lambda x: Tensor.cummax(x, axis=0))
     helper_test_op([(1022)], lambda x: torch.cummax(x, dim=0).values, lambda x: Tensor.cummax(x, axis=0))
+  @slow_test
   def test_cummax(self):
     helper_test_op([()], lambda x: torch.cummax(x, dim=0).values, lambda x: Tensor.cummax(x, axis=0))
     # TODO: torch allows this?
@@ -1128,12 +1137,12 @@ class TestOps(unittest.TestCase):
                                   lambda x: x.argsort(dim, descending), forward_only=True)
 
   def test_topk(self):
-    helper_test_op([(10)], lambda x: x.topk(3).values, lambda x: x.topk(3)[0], forward_only=True)
-    helper_test_op([(10)], lambda x: x.topk(3).indices.type(torch.int32), lambda x: x.topk(3)[1], forward_only=True)
+    helper_test_op([(8)], lambda x: x.topk(3).values, lambda x: x.topk(3)[0], forward_only=True)
+    helper_test_op([(8)], lambda x: x.topk(3).indices.type(torch.int32), lambda x: x.topk(3)[1], forward_only=True)
     for dim in [0, 1, -1]:
       for largest in [True, False]:
         for sorted_ in [True]: # TODO support False
-          helper_test_op([(6,5,4)],
+          helper_test_op([(5,5,4)],
                           lambda x: x.topk(4, dim, largest, sorted_).values,
                           lambda x: x.topk(4, dim, largest, sorted_)[0], forward_only=True)
           helper_test_op([(5,5,4)],
@@ -1148,53 +1157,55 @@ class TestOps(unittest.TestCase):
     np.testing.assert_equal(indices.numpy(), [2, 4, 6])
     self.helper_test_exception([(4)], lambda x: x.topk(5), expected=(RuntimeError, ValueError))
 
+  @slow_test
   def test_einsum(self):
     # matrix transpose
-    helper_test_op([(150,150)], lambda a: torch.einsum('ij->ji', a), lambda a: Tensor.einsum('ij->ji', a))
-    helper_test_op([(150,150)], lambda a: torch.einsum('ij -> ji', a), lambda a: Tensor.einsum('ij -> ji', a))
-    helper_test_op([(150,150)], lambda a: torch.einsum('ji', a), lambda a: Tensor.einsum('ji', a))
-    helper_test_op([(20,30,40)], lambda a: torch.einsum('jki', a), lambda a: Tensor.einsum('jki', a))
-    helper_test_op([(20,30,40)], lambda a: torch.einsum('dog', a), lambda a: Tensor.einsum('dog', a))
+    helper_test_op([(10,10)], lambda a: torch.einsum('ij->ji', a), lambda a: Tensor.einsum('ij->ji', a))
+    helper_test_op([(10,10)], lambda a: torch.einsum('ij -> ji', a), lambda a: Tensor.einsum('ij -> ji', a))
+    helper_test_op([(10,10)], lambda a: torch.einsum('ji', a), lambda a: Tensor.einsum('ji', a))
+    helper_test_op([(4,6,8)], lambda a: torch.einsum('jki', a), lambda a: Tensor.einsum('jki', a))
+    helper_test_op([(4,6,8)], lambda a: torch.einsum('dog', a), lambda a: Tensor.einsum('dog', a))
     # no -> and empty rhs
-    helper_test_op([(20,30),(30,40)], lambda a, b: torch.einsum('ij,jk', a, b), lambda a, b: Tensor.einsum('ij,jk', a, b))
+    helper_test_op([(4,6),(6,8)], lambda a, b: torch.einsum('ij,jk', a, b), lambda a, b: Tensor.einsum('ij,jk', a, b))
     # sum all elements
-    helper_test_op([(20,30,40)], lambda a: torch.einsum('ijk->', a), lambda a: Tensor.einsum('ijk->', a))
+    helper_test_op([(4,6,8)], lambda a: torch.einsum('ijk->', a), lambda a: Tensor.einsum('ijk->', a))
     # column sum
-    helper_test_op([(50,50)], lambda a: torch.einsum('ij->j', a), lambda a: Tensor.einsum('ij->j', a))
+    helper_test_op([(5,5)], lambda a: torch.einsum('ij->j', a), lambda a: Tensor.einsum('ij->j', a))
     # row sum
-    helper_test_op([(15,15)], lambda a: torch.einsum('ij->i', a), lambda a: Tensor.einsum('ij->i', a))
+    helper_test_op([(5,5)], lambda a: torch.einsum('ij->i', a), lambda a: Tensor.einsum('ij->i', a))
     # matrix-vector multiplication
-    helper_test_op([(15,20), (20,)], lambda a,b: torch.einsum('ik,k->i', a,b), lambda a,b: Tensor.einsum('ik,k->i', a, b))
+    helper_test_op([(3,4), (4,)], lambda a,b: torch.einsum('ik,k->i', a,b), lambda a,b: Tensor.einsum('ik,k->i', a, b))
     # matrix-matrix multiplication
-    helper_test_op([(15,20), (20,30)], lambda a,b: torch.einsum('ik,kj->ij', a,b), lambda a,b: Tensor.einsum('ik,kj->ij', a, b))
+    helper_test_op([(3,4), (4,5)], lambda a,b: torch.einsum('ik,kj->ij', a,b), lambda a,b: Tensor.einsum('ik,kj->ij', a, b))
     # matrix-matrix multiplication, different letter order
-    helper_test_op([(15,20), (20,30)], lambda a,b: torch.einsum('jk,ki->ji', a,b), lambda a,b: Tensor.einsum('jk,ki->ji', a, b))
+    helper_test_op([(3,4), (4,5)], lambda a,b: torch.einsum('jk,ki->ji', a,b), lambda a,b: Tensor.einsum('jk,ki->ji', a, b))
     # dot product
-    helper_test_op([(30),(30)], lambda a,b: torch.einsum('i,i->i', [a,b]), lambda a,b: Tensor.einsum('i,i->i', [a,b]))
+    helper_test_op([(5),(5)], lambda a,b: torch.einsum('i,i->i', [a,b]), lambda a,b: Tensor.einsum('i,i->i', [a,b]))
     # hadamard product
-    helper_test_op([(30,40),(30,40)], lambda a,b: torch.einsum('ij,ij->ij', a,b), lambda a,b: Tensor.einsum('ij,ij->ij', a,b))
+    helper_test_op([(5,6),(5,6)], lambda a,b: torch.einsum('ij,ij->ij', a,b), lambda a,b: Tensor.einsum('ij,ij->ij', a,b))
     # outer product
-    helper_test_op([(15,), (15,)], lambda a,b: torch.einsum('i,j->ij', a,b), lambda a,b: Tensor.einsum('i,j->ij',a,b))
+    helper_test_op([(5,), (5,)], lambda a,b: torch.einsum('i,j->ij', a,b), lambda a,b: Tensor.einsum('i,j->ij',a,b))
     # batch matrix multiplication
-    helper_test_op([(10,20,30),(10,30,40)], lambda a,b: torch.einsum('ijk,ikl->ijl', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->ijl', [a, b]))
+    helper_test_op([(2,4,6),(2,6,8)], lambda a,b: torch.einsum('ijk,ikl->ijl', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->ijl', [a, b]))
     # batch matrix multiplication, result permuted
-    helper_test_op([(10,20,25),(10,25,32)], lambda a,b: torch.einsum('ijk,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->jil', [a, b]))
+    helper_test_op([(2,4,5),(2,5,7)], lambda a,b: torch.einsum('ijk,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('ijk,ikl->jil', [a, b]))
     # batch matrix multiplication, result & input permuted
-    helper_test_op([(20,10,25),(10,25,32)], lambda a,b: torch.einsum('jik,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('jik,ikl->jil', [a, b]))
+    helper_test_op([(4,2,5),(2,5,7)], lambda a,b: torch.einsum('jik,ikl->jil', [a, b]), lambda a,b: Tensor.einsum('jik,ikl->jil', [a, b]))
     # batch matrix multiplication, result with different letters
-    helper_test_op([(10,20,30),(10,30,40)], lambda a,b: torch.einsum('ijk,ika->ija', [a, b]), lambda a,b: Tensor.einsum('ijk,ika->ija', [a, b]))
+    helper_test_op([(2,4,6),(2,6,8)], lambda a,b: torch.einsum('ijk,ika->ija', [a, b]), lambda a,b: Tensor.einsum('ijk,ika->ija', [a, b]))
     # tensor contraction
-    helper_test_op([(3,5,8,10),(11,13,5,16,8)], lambda a,b: torch.einsum('pqrs,tuqvr->pstuv', a,b),
+    helper_test_op([(3,5,8,10),(11,7,5,13,8)], lambda a,b: torch.einsum('pqrs,tuqvr->pstuv', a,b),
                                                 lambda a,b: Tensor.einsum('pqrs,tuqvr->pstuv', a,b), atol=1e-5)
     # tensor contraction, input permuted
-    helper_test_op([(3,8,10,5),(11,5,13,16,8)], lambda a,b: torch.einsum('prsq,tquvr->pstuv', a,b),
+    helper_test_op([(3,8,10,5),(11,5,7,13,8)], lambda a,b: torch.einsum('prsq,tquvr->pstuv', a,b),
                                                 lambda a,b: Tensor.einsum('prsq,tquvr->pstuv', a,b), atol=1e-5)
     # tensor contraction, result with different letters
-    helper_test_op([(3,5,8,10),(11,13,5,16,8)], lambda a,b: torch.einsum('zqrs,tuqvr->zstuv', a,b),
+    helper_test_op([(3,5,8,10),(11,7,5,13,8)], lambda a,b: torch.einsum('zqrs,tuqvr->zstuv', a,b),
                                                 lambda a,b: Tensor.einsum('zqrs,tuqvr->zstuv', a,b), atol=1e-5)
     # bilinear transformation
     helper_test_op([(2,3),(5,3,7),(2,7)], lambda a,b,c: torch.einsum('ik,jkl,il->ij', [a,b,c]), lambda a,b,c: Tensor.einsum('ik,jkl,il->ij', [a,b,c]))
 
+  @slow_test
   def test_einsum_ellipsis(self):
     """The expected behavior for einsum is described in the PyTorch docs: https://pytorch.org/docs/stable/generated/torch.einsum.html"""
     # test ellipsis
@@ -1238,6 +1249,7 @@ class TestOps(unittest.TestCase):
     self.helper_test_exception([(4), (1,2)], lambda x, y: x.matmul(y), Tensor.dot, expected=RuntimeError)
     self.helper_test_exception([(2,1), (4)], lambda x, y: x.matmul(y), Tensor.dot, expected=RuntimeError)
     self.helper_test_exception([(1), (4)], lambda x, y: x.matmul(y), Tensor.dot, expected=RuntimeError)
+  @slow_test
   def test_dot(self):
     helper_test_op([(45,65), (65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-5)
     helper_test_op([(8,45,65), (8,65,100)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-5)
@@ -1298,6 +1310,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(64,64), (64,64)], lambda x,y: x.half().matmul(y.half()), atol=5e-3, rtol=5e-3)
   def test_gemm(self):
     helper_test_op([(64,64), (64,64)], lambda x,y: x.matmul(y))
+  @slow_test
   def test_big_gemm(self):
     helper_test_op([(256,256), (256,256)], lambda x,y: x.matmul(y), atol=1e-4)
   @unittest.skipIf(IMAGE>0, "no 0 in shape matmul on images")
@@ -1309,12 +1322,14 @@ class TestOps(unittest.TestCase):
     helper_test_op([(0,0), (0,0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
     helper_test_op([(0), (0,8)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
     helper_test_op([(0), (0)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-7)
+  @slow_test
   def test_broadcastdot(self):
     helper_test_op([(10,45,65), (65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
     with self.assertRaises(RuntimeError):
       a = Tensor(3.14)
       b = Tensor.ones(3,3)
       a @ b
+  @slow_test
   def test_multidot(self):
     helper_test_op([(10,45,65), (10,65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
     helper_test_op([(3,3,45,65), (3,3,65,45)], lambda x,y: x @ y, Tensor.dot, atol=1e-4)
@@ -1449,6 +1464,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(15, 25, 35)], lambda x: x.var(correction=5))
     # TODO: fix this
     # helper_test_op([(10, 2)], lambda x: x.var(correction=50))
+  @slow_test
   def test_var_axis(self):
     helper_test_op([(15, 25, 35)], lambda x: x.var(0))
     helper_test_op([(15, 25, 35)], lambda x: x.var(2))
@@ -1481,6 +1497,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(15, 25, 35)], lambda x: x.std())
     helper_test_op([(15, 25, 35)], lambda x: x.std(correction=0))
     helper_test_op([(15, 25, 35)], lambda x: x.std(correction=5))
+  @slow_test
   def test_std_axis(self):
     helper_test_op([(15, 25, 35)], lambda x: x.std(0))
     helper_test_op([(15, 25, 35)], lambda x: x.std(2))
@@ -1557,6 +1574,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([()], lambda x: torch.logsumexp(x, dim=0), lambda x: x.logsumexp(0), atol=1e-7, grad_atol=1e-7)
     helper_test_op([()], lambda x: torch.logsumexp(x, dim=-1), lambda x: x.logsumexp(-1), atol=1e-7, grad_atol=1e-7)
 
+  @slow_test
   def test_logcumsumexp(self):
     helper_test_op([(45,65)], lambda x: torch.logcumsumexp(x, dim=0), lambda x: x.logcumsumexp(0), atol=1e-7, grad_atol=1e-7)
     helper_test_op([(45,65)], lambda x: torch.logcumsumexp(x, dim=1), lambda x: x.logcumsumexp(1), atol=1e-7, grad_atol=1e-7)
@@ -1626,6 +1644,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,65), (45,1)], lambda x,y: x/y)
     helper_test_op([(45,65), ()], lambda x,y: x/y)
 
+  @slow_test
   def test_broadcast_partial(self):
     for torch_op, tinygrad_op in [(torch.add, Tensor.add), (torch.sub, Tensor.sub), (torch.mul, Tensor.mul),
                                   (torch.div, Tensor.div), (torch.pow, Tensor.pow)]:
@@ -2048,6 +2067,7 @@ class TestOps(unittest.TestCase):
       lambda x,w: torch.nn.functional.conv2d(x,w),
       lambda x,w: Tensor.conv2d(x,w), grad_rtol=1e-5)
 
+  @slow_test
   def test_nested_conv2d(self):
     helper_test_op([(1,32,9,9), (32,32,3,3), (32,32,3,3)],
       lambda x,w1,w2: torch.nn.functional.conv2d(torch.nn.functional.conv2d(x,w1).relu(), w2),
@@ -2108,6 +2128,7 @@ class TestOps(unittest.TestCase):
         lambda x,w,b: torch.nn.functional.conv_transpose2d(x,w,b,output_padding=output_padding,stride=stride),
         lambda x,w,b: Tensor.conv_transpose2d(x,w,b,output_padding=output_padding,stride=stride), grad_rtol=1e-5)
 
+  @slow_test
   @unittest.skipIf(IMAGE>0, "no conv3d on images")
   def test_simple_conv_transpose3d(self):
     helper_test_op([(2,4,9,9,9), (4,4,3,3,3)],
@@ -2177,6 +2198,7 @@ class TestOps(unittest.TestCase):
     self.helper_test_exception([(2,16,2,2), (32,16,3,3)], lambda x,w:torch.nn.functional.conv2d(x,w,padding=(1,1,1)),
                                lambda x,w: Tensor.conv2d(x,w,padding=(1,1,1)), expected=(RuntimeError, ValueError))
 
+  @slow_test
   def test_large_input_conv2d(self):
     bs = 4
     cin = 16
@@ -2329,6 +2351,7 @@ class TestOps(unittest.TestCase):
       lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
       lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
 
+  @slow_test
   def test_max_pool2d(self):
     for ksz in [(2,2), (3,3), 2, 3, (3,2), (5,5), (5,1)]:
       with self.subTest(kernel_size=ksz):
@@ -2336,41 +2359,45 @@ class TestOps(unittest.TestCase):
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
           lambda x: Tensor.max_pool2d(x, kernel_size=ksz))
 
+  @slow_test
   def test_max_pool2d_padding(self):
     for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
       for p in [1, (1,0), (0,1)]:
         with self.subTest(kernel_size=ksz, padding=p):
-          helper_test_op([(32,2,11,28)],
+          helper_test_op([(4,2,11,28)],
             lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz, padding=p),
             lambda x: Tensor.max_pool2d(x, kernel_size=ksz, padding=p))
-    self.helper_test_exception([(32,2,110,28)], lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), padding=(1,1,1)),
+    self.helper_test_exception([(4,2,110,28)], lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), padding=(1,1,1)),
                    lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), padding=(1,1,1)), expected=(RuntimeError, ValueError))
 
+  @slow_test
   def test_max_pool2d_asymmetric_padding(self):
-    shape = (32,2,111,28)
     for p in [(0,1,0,1), (2,1,2,1), (2,0,2,1)]:
       with self.subTest(padding=p):
-        helper_test_op([shape],
+        helper_test_op([(4,2,111,28)],
           lambda x: torch.nn.functional.max_pool2d(torch.nn.functional.pad(x, p, value=float("-inf")), kernel_size=(5,5)),
           lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), padding=p))
 
+  @slow_test
   def test_max_pool2d_padding_int(self):
     ksz = (2,2)
-    helper_test_op([(32,2,11,28)],
+    helper_test_op([(4,2,11,28)],
       lambda x: torch.nn.functional.max_pool2d(x.int(), kernel_size=ksz, padding=1),
       lambda x: Tensor.max_pool2d(x.int(), kernel_size=ksz, padding=1), forward_only=True)
 
+  @slow_test
   def test_max_pool2d_bigger_stride(self):
     for stride in [(2,3), (3,2), 2, 3]:
       with self.subTest(stride=stride):
-        helper_test_op([(32,2,11,28)],
+        helper_test_op([(4,2,11,28)],
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), stride=stride),
           lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), stride=stride))
 
+  @slow_test
   def test_max_pool2d_bigger_stride_dilation(self):
     for stride, dilation in zip([(2,3), (3,2), 2, 3, 4], [(3,2), (2,3), 2, 3, 6]):
       with self.subTest(stride=stride):
-        helper_test_op([(32,2,11,28)],
+        helper_test_op([(4,2,11,28)],
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(2,2), stride=stride, dilation=dilation),
           lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), stride=stride, dilation=dilation))
 
@@ -2380,6 +2407,7 @@ class TestOps(unittest.TestCase):
       lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(5,5), stride=1),
       lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), stride=1))
 
+  @slow_test
   def test_max_pool2d_smaller_stride(self):
     for stride in [(2,3), (3,2), 2, 3]:
       with self.subTest(stride=stride):
@@ -2387,6 +2415,7 @@ class TestOps(unittest.TestCase):
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=(5,5), stride=stride),
           lambda x: Tensor.max_pool2d(x, kernel_size=(5,5), stride=stride))
 
+  @slow_test
   def test_max_pool2d_dilation(self):
     for dilation in [(2, 3), (3, 2), 2, 3]:
       helper_test_op([(3, 2, 17, 14)],
@@ -2441,6 +2470,7 @@ class TestOps(unittest.TestCase):
       lambda x: Tensor.max_pool2d(x, kernel_size=(2,2), stride=1, return_indices=True)[1],
       vals=[[[[[1,2]*3]*6]]], forward_only=True)  # Tensor([1,2,1,2,1,2]).expand(1,1,6,6)
 
+  @slow_test
   def test_max_unpool2d(self):
     args = {"kernel_size":(5,5), "stride":(6,5)}
     helper_test_op([(8,3,50,50)],
@@ -2484,6 +2514,7 @@ class TestOps(unittest.TestCase):
       lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(1,2), padding=(0,1), stride=(5,1)),
       lambda x: Tensor.avg_pool2d(x, kernel_size=(1,2), padding=(0,1), stride=(5,1)), rtol=1e-5)
 
+  @slow_test
   def test_avg_pool2d_padding(self):
     shape = (32,2,111,28)
     for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
@@ -2505,6 +2536,7 @@ class TestOps(unittest.TestCase):
     self.helper_test_exception([shape], lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(2,2), padding=(1,1,1)),
                                lambda x: Tensor.avg_pool2d(x, kernel_size=(2,2), padding=(1,1,1)), expected=(RuntimeError, ValueError))
 
+  @slow_test
   def test_avg_pool2d_padding_not_counted(self):
     shape = (32,2,111,28)
     for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
@@ -2586,12 +2618,14 @@ class TestOps(unittest.TestCase):
 
   def test_interpolate_nearest_exact(self): self.test_interpolate_nearest("nearest-exact")
 
+  @slow_test
   def test_interpolate_bilinear(self):
     for in_sz, out_sz in [((12,20),(9,31)), ((12,9),(31,20)), ((9,31),(20,12))]:
       helper_test_op([(2,3)+in_sz],
         lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="bilinear"),
         lambda x: Tensor.interpolate(x, size=out_sz, mode="linear"), atol=1e-4)
 
+  @slow_test
   def test_interpolate_bilinear_corners_aligned(self):
     for in_sz, out_sz in [((12,20),(9,31)), ((12,9),(31,20)), ((9,31),(20,12))]:
       helper_test_op([(2,3)+in_sz],
@@ -2610,6 +2644,7 @@ class TestOps(unittest.TestCase):
         lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="trilinear", align_corners=True),
         lambda x: Tensor.interpolate(x, size=out_sz, mode="linear", align_corners=True), atol=1e-4)
 
+  @slow_test
   def test_cat(self):
     for dim in range(-2, 3):
       helper_test_op([(45,65,9), (45,65,9), (45,65,9)], lambda x,y,z: torch.cat((x,y,z), dim), lambda x,y,z: x.cat(y, z, dim=dim))
@@ -2708,6 +2743,7 @@ class TestOps(unittest.TestCase):
     data = [math.inf, -math.inf, math.nan]
     helper_test_op((), lambda: torch.tensor(data)[torch.tensor([0, 1, 2])], lambda: Tensor(data)[Tensor([0, 1, 2])])
 
+  @slow_test
   def test_slice_fancy_indexing_no_dim_collapse(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # no dim collapse from int or dim injection from None
@@ -2717,6 +2753,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,...,e], lambda x: x[i,...,p])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[...,c,:,e], lambda x: x[...,k,:,p])
 
+  @slow_test
   def test_slice_fancy_indexing_dim_collapse_int(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim collapse from int
@@ -2726,6 +2763,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,2,2,2,e], lambda x: x[i,2,2,2,p])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,:,3:11:2,d,0:2], lambda x: x[1,:,3:11:2,o,0:2])
 
+  @slow_test
   def test_slice_fancy_indexing_dim_inject_none(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim injection from None
@@ -2759,6 +2797,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,1,-1],[-1,-2,0]]), torch.tensor([2,1,-1])],
                             lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
 
+  @slow_test
   def test_slice_fancy_indexing_list_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
@@ -2769,6 +2808,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[-2,1,0],e], lambda x: x[i,[2,1,0],k,[-2,1,0],p])
 
+  @slow_test
   def test_slice_fancy_indexing_tuple_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[(((0,),),)], lambda x: x[(((0,),),)])
@@ -2778,6 +2818,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,((2,),(1,),(0,)),c,(2,1,0)], lambda x: x[i,((2,),(1,),(0,)),k,(2,1,0)])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,(2,1,0),None,c,(2,1,0),e], lambda x: x[1,(2,1,0),None,k,(2,1,0),p])
 
+  @slow_test
   def test_slice_fancy_indexing_list_with_tensors(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]])
@@ -2884,6 +2925,7 @@ class TestOps(unittest.TestCase):
     with self.assertRaises(TypeError):
       Tensor.ones(4).scatter(dim=1, index=Tensor([0]), src=Tensor.ones(4), reduce="add")
 
+  @slow_test
   def test_scatter_reduce(self):
     b = torch.randint(3, size=[3,4,5], dtype=torch.int64, requires_grad=False)
     a = Tensor(b.detach().cpu().numpy().astype(np.int32), dtype=dtypes.int32, requires_grad=False)
@@ -2919,15 +2961,18 @@ class TestOps(unittest.TestCase):
       lambda x,src: x.half().scatter_reduce(dim=0, index=a, src=src, reduce="sum"),
       RuntimeError)
 
+  @slow_test
   def test_scaled_dot_product_attention(self):
     helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64)], torch.nn.functional.scaled_dot_product_attention, Tensor.scaled_dot_product_attention)
     helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64), (32,8,16,16)],
                    lambda x,y,z,m: torch.nn.functional.scaled_dot_product_attention(x,y,z,attn_mask=m),
                    lambda x,y,z,m: Tensor.scaled_dot_product_attention(x,y,z,attn_mask=m))
 
+  @slow_test
   def test_scaled_dot_product_attention_mismatch_ls(self):
     helper_test_op([(32,8,4,64), (32,8,16,64), (32,8,16,64)], torch.nn.functional.scaled_dot_product_attention, Tensor.scaled_dot_product_attention)
 
+  @slow_test
   def test_scaled_dot_product_attention_causal(self):
     helper_test_op([(32,8,16,64), (32,8,16,64), (32,8,16,64)],
                    lambda x,y,z: torch.nn.functional.scaled_dot_product_attention(x,y,z,is_causal=True),
@@ -2938,6 +2983,7 @@ class TestOps(unittest.TestCase):
       lambda x,y,z,m: Tensor.scaled_dot_product_attention(x,y,z,is_causal=True,attn_mask=m),
       expected=RuntimeError)
 
+  @slow_test
   def test_scaled_dot_product_attention_gqa(self):
     helper_test_op([(32,32,16,64), (32,8,16,64), (32,8,16,64)],
                    lambda x,y,z: torch.nn.functional.scaled_dot_product_attention(x,y,z,enable_gqa=True),

test/test_opt_gemm.py

@@ -2,7 +2,7 @@ import numpy as np
 import unittest
 from tinygrad import Tensor
 from tinygrad.helpers import get_single_element
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 
 class TestOptGemm(unittest.TestCase):

test/test_opts.py

@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor, Device
 from tinygrad.helpers import RANGEIFY
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import get_program
 
 @unittest.skipIf(RANGEIFY>0, "arg is partial contig in rangeify")

test/test_quantize_onnx.py

@@ -3,11 +3,9 @@ import numpy as np
 import unittest
 from dataclasses import replace
 from tinygrad import Tensor, Context, Device, dtypes
-from tinygrad.uop.ops import Ops, UOp # noqa: F401 # pylint: disable=unused-import
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.uop.ops import Ops
+from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, lower_schedule_item, get_program
-from tinygrad.codegen.opt.search import bufs_from_lin
-from tinygrad.shape.shapetracker import ShapeTracker, View # noqa: F401 # pylint: disable=unused-import
 
 N = 512
 
@@ -236,129 +234,5 @@ class TestQuantizeOnnx(unittest.TestCase):
     opts = [Opt(op=OptOps.UPCAST, axis=0, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
     sexec(out, opts)
 
-@unittest.skipIf(Device.DEFAULT != "DSP", "only tests for DSP")
-class TestDSPCache(unittest.TestCase):
-  def test_cache_speed(self):
-    # string becuase this breaks Python language server for syntax highlight for some reason
-    ast = eval("""UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.uchar.ptr(25088), arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 896, 32, 1, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(25088), arg=0, src=()),)),
-        UOp(Ops.CAST, dtypes.uchar, arg=None, src=(
-          UOp(Ops.XOR, dtypes.int, arg=None, src=(
-            UOp(Ops.MAX, dtypes.int, arg=None, src=(
-              UOp(Ops.XOR, dtypes.int, arg=None, src=(
-                UOp(Ops.MAX, dtypes.int, arg=None, src=(
-                  UOp(Ops.CAST, dtypes.int, arg=None, src=(
-                    UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                      UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                        UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                          UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                            UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4,)), src=(
-                              UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                                UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                                  UOp(Ops.CAST, dtypes.float, arg=None, src=(
-                                    UOp(Ops.CAST, dtypes.int, arg=None, src=(
-                                      UOp(Ops.LOAD, dtypes.uchar, arg=None, src=(
-                                        UOp(Ops.VIEW, dtypes.uchar.ptr(150528), arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 192), strides=(0, 5376, 192, 0, 1), offset=0, mask=None, contiguous=False),)), src=(
-                                          UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(150528), arg=1, src=()),)),)),)),)),
-                                  UOp(Ops.CONST, dtypes.float, arg=0.012368360534310341, src=(
-                                    x22:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 192), strides=(0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-                                UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                                  UOp(Ops.CAST, dtypes.float, arg=None, src=(
-                                    UOp(Ops.CAST, dtypes.int, arg=None, src=(
-                                      UOp(Ops.LOAD, dtypes.char, arg=None, src=(
-                                        UOp(Ops.VIEW, dtypes.char.ptr(6144), arg=ShapeTracker(views=(View(shape=(32, 48, 4), strides=(4, 128, 1), offset=0, mask=None, contiguous=False), View(shape=(1, 28, 28, 32, 192), strides=(0, 0, 0, 192, 1), offset=0, mask=None, contiguous=False))), src=(
-                                          UOp(Ops.DEFINE_GLOBAL, dtypes.char.ptr(6144), arg=2, src=()),)),)),)),)),
-                                  UOp(Ops.CONST, dtypes.float, arg=0.007441135589033365, src=(
-                                    x22,)),)),)),)),
-                            UOp(Ops.MUL, dtypes.float, arg=None, src=(
-                              UOp(Ops.CAST, dtypes.float, arg=None, src=(
-                                UOp(Ops.LOAD, dtypes.int, arg=None, src=(
-                                  UOp(Ops.VIEW, dtypes.int.ptr(32), arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 0, 0, 1, 0), offset=0, mask=None, contiguous=False),)), src=(
-                                    UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(32), arg=3, src=()),)),)),)),
-                              UOp(Ops.CONST, dtypes.float, arg=9.203465015161783e-05, src=(
-                                x36:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 28, 28, 32, 1), strides=(0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),
-                          UOp(Ops.CONST, dtypes.float, arg=33.812857328652136, src=(
-                            x36,)),)),
-                        UOp(Ops.CONST, dtypes.float, arg=0.4999999, src=(
-                          x36,)),)),
-                      UOp(Ops.CONST, dtypes.float, arg=136.0, src=(
-                        x36,)),)),)),
-                  UOp(Ops.CONST, dtypes.int, arg=0, src=(
-                    x36,)),)),
-                x41:=UOp(Ops.CONST, dtypes.int, arg=-1, src=(
-                  x36,)),)),
-              UOp(Ops.CONST, dtypes.int, arg=-256, src=(
-                x36,)),)),
-            x41,)),)),)),))""")
-    opts = [Opt(op=OptOps.UNROLL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=32), Opt(op=OptOps.UPCAST, axis=0, arg=4)]
-    with Context(DEVECTORIZE=0, QUANTIZE=1):
-      prg = get_program(ast, opts=opts)
-
-    new_src = """
-typedef int int32 __attribute__((aligned(128),vector_size(128)));
-typedef signed char signed_char128 __attribute__((aligned(128),vector_size(128)));
-typedef unsigned char unsigned_char8 __attribute__((aligned(8),vector_size(8)));
-typedef unsigned char unsigned_char4 __attribute__((aligned(4),vector_size(4)));
-typedef unsigned char unsigned_char128 __attribute__((aligned(128),vector_size(128)));
-__attribute__((noinline)) void r_196_32_4_24_8(unsigned char* restrict __attribute__((align_value(128))) data0, unsigned char* restrict __attribute__((align_value(128))) data1, signed char* restrict __attribute__((align_value(
-128))) data2, int* restrict __attribute__((align_value(128))) data3) {
-  int32 cast0 = (int32){0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
-  int32 val0 = *((int32*)((data3+0)));
-  for (int ridx0 = 0; ridx0 < 196; ridx0++) {
-    int32 acc0 = cast0;
-    int32 acc1 = cast0;
-    int32 acc2 = cast0;
-    int32 acc3 = cast0;
-    __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768);
-    __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+192);
-    __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+384);
-    __builtin_HEXAGON_Y2_dcfetch(data1+ridx0*768+576);
-    for (int ridx1 = 0; ridx1 < 24; ridx1++) {
-      signed_char128 val1 = *((signed_char128*)((data2+(ridx1<<8))));
-      signed_char128 val2 = *((signed_char128*)((data2+((1+(ridx1<<1))<<7))));
-
-      int alu0 = ((ridx0*768)+(ridx1<<3));
-
-      unsigned_char8 val3 = *((unsigned_char8*)((data1+alu0)));
-      __builtin_HEXAGON_Y2_dcfetch(((data1+alu0)+16));
-      unsigned_char8 val4 = *((unsigned_char8*)((data1+(alu0+192))));
-      __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+192))+16));
-      unsigned_char8 val5 = *((unsigned_char8*)((data1+(alu0+384))));
-      __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+384))+16));
-      unsigned_char8 val6 = *((unsigned_char8*)((data1+(alu0+576))));
-      __builtin_HEXAGON_Y2_dcfetch(((data1+(alu0+576))+16));
-
-      unsigned_char4 alu5 = __builtin_shufflevector(val3, val3,    0,   1,   2,   3);
-      unsigned_char4 alu6 = __builtin_shufflevector(val4, val4,    0,   1,   2,   3);
-      unsigned_char4 alu7 = __builtin_shufflevector(val5, val5,    0,   1,   2,   3);
-      unsigned_char4 alu8 = __builtin_shufflevector(val6, val6,    0,   1,   2,   3);
-      acc0 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc0, val1, (*((unsigned int*)&alu5)));
-      acc1 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc1, val1, (*((unsigned int*)&alu6)));
-      acc2 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc2, val1, (*((unsigned int*)&alu7)));
-      acc3 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc3, val1, (*((unsigned int*)&alu8)));
-
-      unsigned_char4 alu9 = __builtin_shufflevector(val3, val3,    4,   5,   6,   7);
-      unsigned_char4 alu10 = __builtin_shufflevector(val4, val4,    4,   5,   6,   7);
-      unsigned_char4 alu11 = __builtin_shufflevector(val5, val5,    4,   5,   6,   7);
-      unsigned_char4 alu12 = __builtin_shufflevector(val6, val6,    4,   5,   6,   7);
-      acc0 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc0, val2, (*((unsigned int*)&alu9)));
-      acc1 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc1, val2, (*((unsigned int*)&alu10)));
-      acc2 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc2, val2, (*((unsigned int*)&alu11)));
-      acc3 = __builtin_HEXAGON_V6_vrmpybus_acc_128B(acc3, val2, (*((unsigned int*)&alu12)));
-    }
-    unsigned_char128 alu18 = __builtin_HEXAGON_V6_vpackhub_sat_128B(__builtin_HEXAGON_V6_vpackwh_sat_128B((((((acc3+val0)*203)+32767)/65536)+136), (((((acc2+val0)*203)+32767)/65536)+136)), __builtin_HEXAGON_V6_vpackwh_sat_128B((((((acc1+val0)*203)+32767)/65536)+136), (((((acc0+val0)*203)+32767)/65536)+136)));
-    *((unsigned_char128*)((data0+(ridx0<<7)))) = alu18;
-  }
-}
-"""
-    prg = replace(prg, src=new_src+prg.src.split("/* DSP boilerplate */ ")[1])
-    rt = CompiledRunner(prg)
-    #Device.default.compiler.disassemble(rt.lib)
-    ei = ExecItem(rt, bufs_from_lin(Kernel(ast)))
-    tm = ei.run(wait=True)
-    print(f"final time {tm*1e6:.2f} us")
-
 if __name__ == "__main__":
   unittest.main()

test/test_randomness.py

@@ -323,9 +323,9 @@ class TestRandomness(unittest.TestCase):
         torch_res = torch_res.unsqueeze(0)
       for i in range(torch_res.shape[0]):
         self.assertTrue(equal_distribution(lambda *_: tiny_res[i], lambda _: torch_res[i]))
-    _check_with_torch(w=[0.231, 0., 1., 0.5], num_samples=2000, replacement=True)
-    _check_with_torch(w=[[0.2, 0.8]], num_samples=2000, replacement=True)  # 2D but only 1 row
-    _check_with_torch(w=[[0.453, 0., 1., 0.81], [0.1, 0.8, 0., 0.1]], num_samples=2000, replacement=True)
+    _check_with_torch(w=[0.231, 0., 1., 0.5], num_samples=300, replacement=True)
+    _check_with_torch(w=[[0.2, 0.8]], num_samples=300, replacement=True)  # 2D but only 1 row
+    _check_with_torch(w=[[0.453, 0., 1., 0.81], [0.1, 0.8, 0., 0.1]], num_samples=300, replacement=True)
     # no-replacement isn't supported, unless taking only one sample
     w = [0.1, 0.9]
     self.assertRaises(AssertionError, lambda: Tensor(w).multinomial(100, replacement=False))

test/test_search.py

@@ -1,79 +0,0 @@
-import unittest
-
-from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel
-from tinygrad.codegen.opt.search import bufs_from_lin, actions, beam_search
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import Context, GlobalCounters
-from tinygrad.engine.realize import capturing
-
-class TestBEAM(unittest.TestCase):
-  def test_dynamic_beam(self):
-    # TODO: make this infra globally usable
-    class Capture:
-      def __init__(self): self.captured = []
-      def add(self, x): self.captured.append(x)
-
-    capturing.append(Capture())
-    kernel_count = GlobalCounters.kernel_count
-    with Context(BEAM=1): Tensor.zeros(16).contiguous().realize()
-    assert GlobalCounters.kernel_count == kernel_count + 1
-    k_beam_1 = capturing[0].captured
-    capturing.clear()
-
-    capturing.append(Capture())
-    kernel_count = GlobalCounters.kernel_count
-    with Context(BEAM=0): Tensor.zeros(16).contiguous().realize()
-    assert GlobalCounters.kernel_count == kernel_count + 1
-    k_beam_0 = capturing[0].captured
-    capturing.clear()
-    self.assertNotEqual(k_beam_0[-1].prg.p.src, k_beam_1[-1].prg.p.src)
-
-  def test_get_kernel_actions_dedup(self):
-    from test.test_linearizer import helper_realized_ast
-    from tinygrad.codegen.opt.search import get_kernel_actions
-    a = Tensor.empty(4, 3)
-    b = Tensor.empty(3)
-    realized_ast, _ = helper_realized_ast(a @ b)
-    candidates = [
-      Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=4),
-      Opt(op=OptOps.LOCAL, axis=0, arg=0), Opt(op=OptOps.LOCAL, axis=0, arg=4),
-      Opt(op=OptOps.UNROLL, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=3),
-      Opt(op=OptOps.GROUP, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=3),
-      Opt(op=OptOps.GROUPTOP, axis=0, arg=0), Opt(op=OptOps.GROUPTOP, axis=0, arg=3),
-    ]
-    lins = get_kernel_actions(Kernel(realized_ast), include_0=False, candidates=candidates).values()
-
-    # ensure amt=0 are not duplicated
-    assert all(len(x.applied_opts) == 1 for x in lins)
-    kernel_actions = [x.applied_opts[0] for x in lins]
-    assert Opt(OptOps.UPCAST, axis=0, arg=4) not in kernel_actions, "did not de-dup UPCAST"
-    assert Opt(OptOps.LOCAL, axis=0, arg=4) not in kernel_actions, "did not de-dup LOCAL"
-    assert Opt(OptOps.UNROLL, axis=0, arg=3) not in kernel_actions, "did not de-dup UNROLL"
-    assert Opt(OptOps.GROUP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUP"
-    assert Opt(OptOps.GROUPTOP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUPTOP"
-
-  def test_get_kernel_actions_preserves_actions_state(self):
-    from test.test_linearizer import helper_realized_ast
-    from tinygrad.codegen.opt.search import get_kernel_actions
-    a = Tensor.rand(16, 16)
-    b = Tensor.rand(16, 16)
-    realized_ast, _ = helper_realized_ast(a @ b)
-    actions_before = actions.copy()
-    get_kernel_actions(Kernel(realized_ast))
-    actions_after = actions.copy()
-    assert actions_after == actions_before, "actions state was not preserved"
-
-  def test_beam_unnamed_kernels(self):
-    from test.test_linearizer import push_views
-    a = Tensor.rand(100)
-    b = Tensor.rand(100)
-    si = (a+b).schedule()[-1]
-    lin = Kernel(push_views(si.ast))
-    bufs = bufs_from_lin(lin)
-    # TODO: beam should have better instrumentation so we don't have to check this indirect thing
-    kcount = len(Kernel.kernel_cnt)
-    beam_search(lin, bufs, 3, disable_cache=True)
-    self.assertEqual(kcount, len(Kernel.kernel_cnt))
-
-if __name__ == '__main__':
-  unittest.main()

test/test_symbolic_jit.py

@@ -97,8 +97,8 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     a = Tensor.rand(10, 3)
     b = Tensor.rand(10, 3)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = jf(a[:vi], b[:vj]).reshape(i+j, 3).numpy()
@@ -111,8 +111,8 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     a = Tensor.rand(3, 10)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = jf(a[:, :vi], b[:, :vj]).reshape(3, i+j).numpy()
@@ -125,8 +125,8 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     a = Tensor.rand(10, 3)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = jf(a[:vi, :], b[:, :vj]).reshape(i, j).numpy()
@@ -139,8 +139,8 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     a = Tensor.rand(10, 3)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = jf(a[:vj, :], b[:, :vi]).reshape(j, i).numpy()
@@ -245,8 +245,8 @@ class TestSymbolicJit(unittest.TestCase):
     a = Tensor.rand(10, 10)
     b = Tensor.rand(10, 10)
     c = Tensor.rand(10, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         # axis = None
@@ -297,8 +297,8 @@ class TestSymbolicJit(unittest.TestCase):
     a = Tensor.rand(10, 10)
     b = Tensor.rand(10, 10)
     c = Tensor.rand(10, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         # axis = None

test/test_symbolic_ops.py

@@ -103,8 +103,8 @@ class TestSymbolicOps(unittest.TestCase):
     def f(a, b): return a.cat(b, dim=0).realize()
     a = Tensor.rand(10, 3)
     b = Tensor.rand(10, 3)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = f(a[:vi, :], b[:vj, :]).reshape(i+j, 3).numpy()
@@ -115,8 +115,8 @@ class TestSymbolicOps(unittest.TestCase):
     def f(a, b): return a.cat(b, dim=1).realize()
     a = Tensor.rand(3, 10)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = f(a[:, :vi], b[:, :vj]).reshape(3, i+j).numpy()
@@ -127,8 +127,8 @@ class TestSymbolicOps(unittest.TestCase):
     def f(a, b): return (a@b+1).realize()
     a = Tensor.rand(10, 3)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = f(a[:vi, :], b[:, :vj]).reshape(i, j).numpy()
@@ -140,8 +140,8 @@ class TestSymbolicOps(unittest.TestCase):
     def f(a, b): return (a@b+1).realize()
     a = Tensor.rand(10, 3)
     b = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         symbolic = f(a[:vj, :], b[:, :vi]).reshape(j, i).numpy()
@@ -225,8 +225,8 @@ class TestSymbolicOps(unittest.TestCase):
 
   def test_mean_2d(self):
     a = Tensor.rand(10, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         for axis in [None, 0, 1]:
@@ -245,8 +245,8 @@ class TestSymbolicOps(unittest.TestCase):
 
   def test_var_2d(self):
     a = Tensor.rand(10, 10)
-    for i in range(1, 5):
-      for j in range(1, 5):
+    for i in range(2, 5):
+      for j in range(2, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         for axis in [None, 0, 1]:

test/test_tensor.py

@@ -928,6 +928,7 @@ class TestIdxUpcast(unittest.TestCase):
       self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
 
   @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
+  @unittest.expectedFailure  # bug in gpu dims limiting
   def test_int64_unsupported_overflow(self):
     with self.assertRaises(KeyError):
       self.do_op_then_assert(dtypes.long, 2048, 2048, 2048)

test/test_tiny.py

@@ -99,7 +99,7 @@ class TestTiny(unittest.TestCase):
   # *** a model ***
 
   # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
-  @unittest.skipIf(IMAGE>0 or (CI and Device.DEFAULT == "DSP"), "failing because of make things that can't be images not images")
+  @unittest.skipIf(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
   def test_mnist(self):
     layers = [
       nn.Conv2d(1, 32, 5), Tensor.relu,
@@ -118,7 +118,7 @@ class TestTiny(unittest.TestCase):
     self.assertEqual(len(probs[0]), 10)
 
   # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
-  @unittest.skipIf(IMAGE>0 or (CI and Device.DEFAULT == "DSP"), "failing because of make things that can't be images not images")
+  @unittest.skipIf(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
   def test_mnist_backward(self):
     # NOTE: we don't have the whole model here for speed
     layers = [

test/test_uop_graph.py

@@ -3,7 +3,7 @@ import unittest, pytest
 from tinygrad import dtypes, Variable
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import DEBUG, Context
-from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, track_rewrites, graph_rewrite, GroupOp
+from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, track_rewrites, graph_rewrite, GroupOp, KernelInfo
 from tinygrad.uop.symbolic import sym
 from tinygrad.codegen import full_rewrite, full_rewrite_to_sink
 from tinygrad.codegen.late.expander import expander
@@ -17,7 +17,7 @@ simple_pm = PatternMatcher([
 
 def to_uops_list(u:List[UOp]) -> List[UOp]:
   # we strip the SINK here for legacy reasons
-  ret = full_rewrite(UOp.sink(*u))
+  ret = full_rewrite(UOp.sink(*u, arg=KernelInfo(opts_to_apply=())))
   assert ret[-1].op is Ops.SINK
   return ret[:-1]
 
@@ -212,7 +212,7 @@ class TestUOpGraph(unittest.TestCase):
 
   def test_where_same_fold(self):
     v = UOp.variable('tmp', 0, 1)
-    c0 = UOp(Ops.CONST, dtypes.int, arg=0)
+    c0 = UOp(Ops.CONST, dtypes.index, arg=0)
     vc = UOp(Ops.CMPNE, dtypes.bool, (v, c0))
     c1 = UOp(Ops.CONST, dtypes.float, arg=1.0)
     out = UOp(Ops.WHERE, dtypes.float, (vc, c1, c1))
@@ -398,7 +398,7 @@ class TestUOpGraph(unittest.TestCase):
     self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 1)
 
   def test_depth_2_const_fold(self):
-    v = UOp.variable("tmp", 0, 1)
+    v = UOp.variable("tmp", 0, 1, dtypes.int)
     c2 = UOp(Ops.CONST, dtypes.int, arg=2)
     c4 = UOp(Ops.CONST, dtypes.int, arg=4)
     vc = UOp(Ops.ADD, dtypes.int, (v, c2))
@@ -417,6 +417,17 @@ class TestUOpGraph(unittest.TestCase):
       uops = to_uops_list([v.bitcast(dt)])
       self.assertEqual(len([x for x in uops if x.op is Ops.BITCAST]), 0, f"dtype = {dt}")
 
+  def test_load_idx_becomes_int(self):
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
+    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 1)
+    l0 = UOp(Ops.LOAD, dtypes.long, (d0.index(UOp.const(dtypes.int, 0)),))
+    idx = l0 * 600
+    valid = (l0<-1).ne(True)&(l0<3000)
+    l1 = UOp(Ops.LOAD, dtypes.long, (d1.index(idx, valid),))
+    uops = to_uops_list([l1])
+    for u in uops:
+      if u.op is Ops.INDEX: self.assertEqual(u.src[1].dtype, dtypes.int)
+
   def test_in_out_of_bounds_access(self):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
@@ -445,7 +456,7 @@ class TestUOpGraph(unittest.TestCase):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), src=(), arg=0)
       v = Variable("v", 0, 20)
-      st0 = UOp(Ops.STORE, dtypes.void, src=(glbl0.index(v), UOp.const(dtypes.int, 0), UOp(Ops.IF, src=(v<16,))))
+      st0 = UOp(Ops.STORE, dtypes.void, src=(glbl0.index(v, v<16), UOp.const(dtypes.int, 0)))
       to_uops_list([st0])
 
       st1 = UOp(Ops.STORE, dtypes.void, (glbl0.index(v), v, v<20))
@@ -463,7 +474,7 @@ class TestUOpGraph(unittest.TestCase):
 
       gate = (gidx<400) & (lidx<8)
 
-      local_store = UOp(Ops.STORE, dtypes.void, (sbuf.index(lidx), UOp.const(dtypes.uint, 1), UOp(Ops.IF, src=(lidx<8,))))
+      local_store = UOp(Ops.STORE, dtypes.void, (sbuf.index(lidx, lidx<8), UOp.const(dtypes.uint, 1)))
 
       barrier = UOp(Ops.BARRIER, dtypes.void, (local_store,))
       if_barrier = UOp(Ops.IF, dtypes.void, (gate, barrier))
@@ -512,7 +523,7 @@ class TestUOpGraph(unittest.TestCase):
   def test_in_out_bounds_access_with_mask(self):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
-      gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
+      gidx0 = UOp(Ops.SPECIAL, dtypes.index, (UOp.const(dtypes.index, 42),), "gidx0")
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, (5<gidx0)&(gidx0<16)),))
       ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<16),))
       to_uops_list([ld0, ld1])
@@ -536,9 +547,9 @@ class TestUOpGraph(unittest.TestCase):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
       glbl1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(64), (), 0)
-      gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<8),))
-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<32)),))
+      gidx0 = UOp(Ops.SPECIAL, dtypes.index, (UOp.const(dtypes.index, 42),), "gidx0")
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<8),)).cast(dtypes.index)
+      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<32)),)).cast(dtypes.index)
       to_uops_list([ld1])
 
       ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<64)),))

test/test_uops.py

@@ -14,7 +14,7 @@ from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.symbolic import sym
 from tinygrad.device import is_dtype_supported
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt import Opt, OptOps
 
 def to_uops_list(u:list[UOp], opts=None, skip_check=False) -> list[UOp]: return full_rewrite(UOp.sink(*u), opts)
 
@@ -476,7 +476,7 @@ class TestUOpMethod(unittest.TestCase):
     st_var = Tensor.empty((2, 10))[:, :a.bind(1)]
     _, var_vals = (uop_var+st_var).schedule_with_vars()
     self.assertEqual(len(var_vals), 1)
-    self.assertEqual(list(var_vals)[0], a)
+    self.assertEqual(list(var_vals)[0], a.expr)
 
   def test_const_factor(self):
     gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 8),), 'gidx0')

test/test_uops_stats.py

@@ -1,12 +1,12 @@
 import unittest
 from tinygrad import Tensor
-from tinygrad.helpers import getenv, GlobalCounters
+from tinygrad.helpers import getenv, GlobalCounters, EMULATE
 from tinygrad.engine.realize import lower_schedule_item, ProgramSpec, get_program
 from tinygrad.renderer import Estimates
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.dtype import dtypes
-from tinygrad.codegen.opt.kernel import Opt, OptOps, KernelOptError
+from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.device import Device
 
 def flops_mem(uops, ignore_indexing=False):
@@ -87,6 +87,7 @@ class TestUOpsStatsMatmulHalf(unittest.TestCase):
     expected_ops = N ** 3 * 2
     self.assertEqual(expected_ops, GlobalCounters.global_ops)
 
+  @unittest.skipIf(EMULATE.value=="INTEL", "intel gets 524288 != 524352")
   def test_bigger_matmul_half(self): self.test_simple_matmul_half(64)
 
   def test_batched_matmul_half(self, N=16):

test/unit/test_graph_rewrite.py

@@ -97,19 +97,22 @@ class TestFoldingAndReduction(unittest.TestCase):
 
 class TestModuloAndDivisionFolding(unittest.TestCase):
   def test_full_graph_rewrite_modulo_folding_with_define_var(self):
-    x_var_uop = UOp.variable('x', 0, 100)
+    # index dtype because div-mod rules only work on index
+    x_var_uop = UOp.variable('x', 0, 100).cast(dtypes.index)
     optimized_mod_uop = apply_rewrite(((x_var_uop * 4) + 2) % 4)
     self.assertEqual(optimized_mod_uop.op, Ops.CONST)
     self.assertEqual(optimized_mod_uop.arg, 2)
 
   def test_full_graph_rewrite_division_folding_with_define_var(self):
-    n_var_uop = UOp.variable('n', 1, 1000)
+    # index dtype because div-mod rules only work on index
+    n_var_uop = UOp.variable('n', 1, 1000).cast(dtypes.index)
     optimized_div_uop = apply_rewrite((n_var_uop * 6) // 3)
     self.assertEqual(optimized_div_uop.op, Ops.MUL)
     self.assertEqual(optimized_div_uop.src[1].arg, 2)
 
   def test_full_graph_rewrite_complex_mod_div_folding(self):
-    k_var_uop = UOp.variable('k', 0, 50)
+    # index dtype because div-mod rules only work on index
+    k_var_uop = UOp.variable('k', 0, 50).cast(dtypes.index)
     optimized_div_uop = apply_rewrite(((k_var_uop * 12 + 8) % 6) // 2)
     self.assertEqual(optimized_div_uop.op, Ops.CONST)
     self.assertEqual(optimized_div_uop.arg, 1)
@@ -126,8 +129,9 @@ class TestModuloAndDivisionFolding(unittest.TestCase):
     if opt.op is Ops.VECTORIZE: self.assertFalse(all_same(opt.src))
 
   def test_full_graph_rewrite_modulo_large_divisor(self):
+    # index dtype because div-mod rules only work on index
     x_var_uop = UOp.variable('x', 1, 5)
-    self.assertIs(apply_rewrite(x_var_uop % 10), x_var_uop)
+    self.assertIs(apply_rewrite(x_var_uop.cast(dtypes.index) % 10).render(simplify=False), x_var_uop.render(simplify=False))
 
   def test_full_graph_rewrite_division_with_remainder(self):
     x_var_uop = UOp.variable('x', 7, 9)

test/unit/test_rewrite_map.py

@@ -46,8 +46,8 @@ class TestRewriteMap(unittest.TestCase):
 
   def test_add_zero(self):
     # Build a small graph: add(0, add(const=0, const=5))
-    zero_node = UOp.const(dtypes.int, 0)
-    five_node = UOp.const(dtypes.int, 5)
+    zero_node = UOp.const(dtypes.index, 0)
+    five_node = UOp.const(dtypes.index, 5)
     inner_add = zero_node + five_node
     root_add = zero_node + inner_add
 
@@ -67,7 +67,7 @@ class TestRewriteMap(unittest.TestCase):
     Test rewriting neg(neg(5)) => 5 using symbolic.
     """
     # In some versions of TinyGrad, you might do: (-(-five_node))
-    five_node = UOp.const(dtypes.int, 5)
+    five_node = UOp.const(dtypes.index, 5)
     # If your code allows UOp(...), do that; else you might do something like:
     # double_neg_five = -(-five_node)
     # But let's be explicit:
@@ -85,8 +85,8 @@ class TestRewriteMap(unittest.TestCase):
     """
     Combine both rewrites: add(0, neg(neg(5))) => add(0, 5) => 5
     """
-    zero_node = UOp.const(dtypes.int, 0)
-    five_node = UOp.const(dtypes.int, 5)
+    zero_node = UOp.const(dtypes.index, 0)
+    five_node = UOp.const(dtypes.index, 5)
     neg_five = -five_node
     double_neg_five = -neg_five
     root_add = zero_node + double_neg_five
@@ -103,7 +103,7 @@ class TestRewriteMap(unittest.TestCase):
   def test_multi_var_rewrites(self):
     x_var = UOp.variable('x', 0, 10)
     y_var = UOp.variable('y', -5, 5)
-    zero_node = UOp.const(dtypes.int, 0)
+    zero_node = UOp.const(dtypes.index, 0)
 
     sum_with_zero = y_var + zero_node    # (y + 0)
     combined = x_var + sum_with_zero     # x + (y + 0)
@@ -155,8 +155,8 @@ class TestRewriteMap(unittest.TestCase):
     x_var = UOp.variable('x', 1, 10)
     y_var = UOp.variable('y', -5, 5)
     z_var = UOp.variable('z', 0, 5)
-    zero_node = UOp.const(dtypes.int, 0)
-    one_node = UOp.const(dtypes.int, 1)
+    zero_node = UOp.const(dtypes.index, 0)
+    one_node = UOp.const(dtypes.index, 1)
 
     # Build sub-expressions
     yz_sum = y_var + z_var              # (y + z)

test/unit/test_search.py

@@ -1,56 +0,0 @@
-import unittest
-from tinygrad import Tensor, Device
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.device import Buffer
-from tinygrad.codegen.opt.search import get_test_global_size, bufs_from_lin
-from tinygrad.helpers import GlobalCounters
-from extra.optimization.helpers import time_linearizer
-from test.test_linearizer import push_views
-
-class TestSearchUtil(unittest.TestCase):
-  def test_get_test_global_size(self):
-    self.assertEqual(get_test_global_size([256, 256, 256], 65536, {}), ([256, 16, 16], 256.0))
-    self.assertEqual(get_test_global_size([65536, 1, 1], 256, {}), ([256, 1, 1], 256.0))
-    self.assertEqual(get_test_global_size([77, 1, 1], 16, {}), ([9, 1, 1], 77/9))
-
-  def test_bufs_from_lin(self):
-    a = Tensor([1,2,3,4]).realize()
-    si = (a+1).schedule()[0]
-    rawbufs = bufs_from_lin(Kernel(si.ast))
-    assert len(rawbufs) == 2
-    assert all(r is not None for r in rawbufs)
-    assert all(isinstance(r, Buffer) for r in rawbufs)
-    assert all(r.size > 0 for r in rawbufs)
-
-  def test_bufs_from_lin_alt(self):
-    a = Tensor.randn(4, 4).realize()
-    b = a+a[0]
-    si = b.schedule()[0]
-    rawbufs = bufs_from_lin(Kernel(push_views(si.ast)))
-    assert len(rawbufs) == 2
-    assert all(r is not None for r in rawbufs)
-    assert all(isinstance(r, Buffer) for r in rawbufs)
-    assert all(r.size > 0 for r in rawbufs)
-
-class TestTimeLinearizer(unittest.TestCase):
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WebGPU timestamps are low precision, tm is 0")
-  def test_reasonable_time(self):
-    a = Tensor([1,2,3,4]).realize()
-    si = (a+1).schedule()[0]
-    # create fresh empty buffers
-    rawbufs = [Buffer(b.device, b.size, b.dtype).allocate() for b in si.bufs]
-    tm = time_linearizer(Kernel(push_views(si.ast)), rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
-    assert tm > 0 and tm != float('inf')
-
-  # Ensure that the kernel count is not incremented by time_linearizer when clearing l2
-  def test_kernel_count(self):
-    ast = Tensor.zeros(16).contiguous().kernelize().uop.src[1].arg.ast
-    lin = Kernel(push_views(ast))
-    bufs = bufs_from_lin(lin)
-
-    kernel_count = GlobalCounters.kernel_count
-    time_linearizer(lin, bufs, allow_test_size=False, cnt=2, disable_cache=True, clear_l2=True)
-    assert GlobalCounters.kernel_count == kernel_count, "kernel count was incremented by time_linearizer"
-
-if __name__ == "__main__":
-  unittest.main()

test/unit/test_simplify_valid_idx.py

@@ -18,7 +18,7 @@ def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UO
     UOp(Ops.VECTORIZE, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
   ))
 
-def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int32, (UOp.const(dtypes.int, nmax),), expr)
+def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.index, (UOp.const(dtypes.index, nmax),), expr)
 def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
 def Range(n, nmax): return UOp.range(nmax, n)
 

test/unit/test_symbolic_shapetracker.py

@@ -73,19 +73,19 @@ class TestSymbolicVarVals(unittest.TestCase):
 
   def test_var_vals_shape(self):
     x = Variable("x", 1, 100).bind(3)
-    assert ShapeTracker.from_shape((x, 3)).var_vals == {Variable("x", 1, 100): 3}
+    assert ShapeTracker.from_shape((x, 3)).var_vals == {"x": 3}
 
   def test_var_vals_offset(self):
     x = Variable("x", 1, 100).bind(3)
     st = ShapeTracker.from_shape((4, 3)).shrink(((x, x+1), (0, 3)))
     self.assert_equal(st.views[-1].offset, x * 3)
-    assert st.var_vals == {Variable("x", 1, 100): 3}
+    assert st.var_vals == {"x": 3}
 
   def test_var_vals_mask(self):
     x = Variable("x", 1, 100).bind(3)
     view = View.create(shape=(3,4), strides=(4,1), offset=0, mask=((0, x), (0, 4)))
     st = ShapeTracker(views=(view,))
-    assert st.var_vals == {Variable("x", 1, 100): 3}
+    assert st.var_vals == {"x": 3}
 
   def test_var_vals_complex(self):
     x = Variable("x", 1, 100).bind(3)
@@ -93,13 +93,13 @@ class TestSymbolicVarVals(unittest.TestCase):
     z = Variable("z", 1, 100).bind(5)
     st = ShapeTracker.from_shape((x, 5, y)).shrink(((0, x), (z, z+1), (0, 3)))
     self.assert_equal(st.views[-1].offset, y * z)
-    assert st.var_vals == {Variable("x", 1, 100): 3, Variable("y", 1, 100):4, Variable("z", 1, 100): 5}
+    assert st.var_vals == {"x": 3, "y": 4, "z": 5}
 
   def test_shrink_reshape(self):
     x = Variable("x", 1, 100).bind(3)
     st = ShapeTracker.from_shape((10, 10, 10)).shrink(((x, x+3), (3, 7), (2, 5)))
     st = st.reshape((3*4*3,))
-    assert st.var_vals == {Variable("x", 1, 100): 3}
+    assert st.var_vals == {"x": 3}
 
 class TestShapeTrackerUnbind(unittest.TestCase):
   def test_view_unbind(self):

test/unit/test_uop_symbolic.py

@@ -2,22 +2,20 @@
 import unittest, pickle, functools, math
 import z3
 
-from tinygrad.dtype import dtypes, ConstType
+from tinygrad.dtype import dtypes, ConstType, DType
 from tinygrad.codegen import full_rewrite
-from tinygrad.codegen.late.devectorizer import sym
 from tinygrad.helpers import Context
-from tinygrad.uop.ops import UOp, Ops, graph_rewrite, sym_infer
-from tinygrad import Variable
+from tinygrad.uop.ops import UOp, Ops, graph_rewrite, sym_infer, track_rewrites
+from tinygrad.uop.symbolic import sym
 from tinygrad.uop.spec import uops_to_z3
 
-def render(self) -> tuple[str, ConstType, ConstType]:
-  # NOTE: we need STORE so the ALU op has children
-  glbl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), arg=0)
-  uops = full_rewrite(UOp(Ops.STORE, dtypes.void, (glbl.index(UOp.const(dtypes.int, 0)), self)).sink())
-  rewritten_uop = [uop for uop in uops if uop.op is Ops.STORE][0].src[1]
-  return rewritten_uop.render(simplify=False), rewritten_uop.vmin, rewritten_uop.vmax
+@track_rewrites(name="simplify symbolic uop")
+def render(v) -> UOp:
+  v_simplified = graph_rewrite(v, sym)
+  return v_simplified
 
-def uconst(val): return UOp.const(dtypes.int, val)
+def Variable(name: str, min_val: ConstType, max_val: ConstType, dtype: DType=dtypes.index): return UOp.variable(name,min_val,max_val,dtype)
+def uconst(val): return UOp.const(dtypes.index, val)
 def usum(ops): return functools.reduce(lambda x,y: x+y, ops)
 def uand(ops): return functools.reduce(lambda x,y: x*y, ops)
 
@@ -30,11 +28,12 @@ class TestSymbolicPickle(unittest.TestCase):
 
 class TestSymbolic(unittest.TestCase):
   def helper_test_variable(self, v, n, m, s, test_z3:bool=True):
+    v_simplified = render(v)
     if test_z3:
       solver = z3.Solver()
-      expr, expr_simplified = uops_to_z3(solver, v, v.simplify())
+      expr, expr_simplified = uops_to_z3(solver, v, v_simplified)
       self.assertEqual(solver.check(expr != expr_simplified), z3.unsat, "simplified expression not equal to original")
-    rendered, nmin, nmax = render(v)
+    rendered, nmin, nmax = v_simplified.render(simplify=False), v_simplified.vmin, v_simplified.vmax
     if isinstance(s, tuple): self.assertIn(rendered, s)
     else: self.assertEqual(rendered, s)
     self.assertEqual(nmin, n)
@@ -111,7 +110,7 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(-Variable("a", 0, 8), -8, 0, "(a*-1)")
 
   def test_xor_0(self):
-    self.helper_test_variable(Variable("a", 0, 8) ^ 0, 0, 8, "a")
+    self.helper_test_variable(Variable("a", 0, 8, dtypes.int) ^ 0, 0, 8, "a")
 
   def test_add_1(self):
     self.helper_test_variable(Variable("a", 0, 8)+1, 1, 9, "(a+1)")
@@ -209,12 +208,12 @@ class TestSymbolic(unittest.TestCase):
     self.assertEqual((Variable("x", -10, 0)%Variable("y", 1, 10))._min_max, (-9, 0))
 
   def test_range_div_its_symbolic_bound(self):
-    a = Variable("a", 1, 10)
+    a = Variable("a", 1, 10, dtypes.index)
     ridx0 = UOp.range(a+2, 0)
     self.helper_test_variable(ridx0//(a+2), 0, 0, "0")
 
   def test_range_mod_its_symbolic_bound(self):
-    a = Variable("a", 1, 10)
+    a = Variable("a", 1, 10, dtypes.index)
     ridx = UOp.range(a+2, 0)
     self.helper_test_variable(ridx%(a+2), 0, 11, "ridx0")
 
@@ -463,8 +462,8 @@ class TestSymbolic(unittest.TestCase):
       self.helper_test_variable((Variable("idx", 0, 9)*-10)//11, -8, 0, "(((idx*10)//11)*-1)")
 
   def test_nest_div_negative_factor(self):
-    ridx0=UOp.variable("ridx0", 0, 9)
-    ridx1=UOp.variable("ridx1", 0, 6)
+    ridx0=Variable("ridx0", 0, 9)
+    ridx1=Variable("ridx1", 0, 6)
     self.helper_test_variable(((((ridx0*-7)+ridx1)+63)//35), 0, 1, "(((ridx0//5)*-1)+1)")
 
   def test_div_into_mod(self):
@@ -533,8 +532,8 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(x//y, 2, 2, "2")
     self.helper_test_variable(x%y, 0, 7, "(x+(y*-2))")
     # ensure all 4 corners are checked
-    x = Variable("x", -10, 10)
-    y = Variable("y", -8, 9)
+    x = Variable("x", -10, 10, dtypes.int)
+    y = Variable("y", -8, 9, dtypes.int)
     self.helper_test_variable(x//y, -2147483648, 2147483647, "(x//y)")
     self.helper_test_variable(x%y, -2147483648, 2147483647, "(x%y)")
 
@@ -560,8 +559,13 @@ class TestSymbolic(unittest.TestCase):
 
   def test_div_mod_recombine(self):
     gidx = Variable("gidx", 0, 124)
+    lidx = Variable("lidx", 0, 124)
     self.helper_test_variable(gidx%4+(gidx//4)*4, 0, 124, "gidx")
     self.helper_test_variable((gidx//4)*4+gidx%4, 0, 124, "gidx")
+    self.helper_test_variable(lidx+gidx%4+(gidx//4)*4, 0, 248, "(gidx+lidx)")
+    self.helper_test_variable(lidx+(gidx//4)*4+gidx%4, 0, 248, "(gidx+lidx)")
+    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_folded_mod(self):
     a = Variable("a", 0, 2)
@@ -582,6 +586,12 @@ class TestSymbolic(unittest.TestCase):
     unrolled_div = (gidx+2561)//4+(gidx+2562)//4+(gidx+2560)//4+(gidx+2559)//4
     self.helper_test_variable(unrolled_div, 2559, 5118, "(gidx+2559)")
 
+  def test_arange_unrolled4_with_cast(self):
+    gidx = Variable("gidx", 0, 2559, dtypes.index)
+    dt = dtypes.int
+    unrolled_div = ((gidx+2561)//4 + 2).cast(dt)+((gidx+2562)//4).cast(dt)+((gidx+2560)//4).cast(dt)+((gidx+2559)//4).cast(dt)
+    self.helper_test_variable(unrolled_div, 2561, 5120, "((int)(gidx)+2561)")
+
   def test_arange_unrolled4_mul(self):
     gidx = Variable("gidx", 0, 2559)
     unrolled_div = 2*((gidx+2561)//4)+2*((gidx+2562)//4)+2*((gidx+2560)//4)+2*((gidx+2559)//4)
@@ -683,10 +693,10 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(-a<-b, False, True, "(b<a)")
 
   def test_where_cast(self):
-    s = Variable("s", 0, 3)
+    s = Variable("s", 0, 3, dtypes.int)
     cond = s < 2
-    a = Variable("a", 0, 3)
-    b = Variable("b", 0, 3)
+    a = Variable("a", 0, 3, dtypes.int)
+    b = Variable("b", 0, 3, dtypes.int)
     expr = cond.where(a, b).cast(dtypes.half)
 
     # TODO: copied from render, render does not support cast
@@ -733,8 +743,8 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(a.trunc(), 1, 10, "a", test_z3=False)
 
   def test_do_math_in_int32(self):
-    a = Variable("a", 1, 10)
-    b = Variable("b", 1, 10)
+    a = Variable("a", 1, 10, dtypes.int)
+    b = Variable("b", 1, 10, dtypes.int)
     self.helper_test_variable(a.cast(dtypes.long)+b.cast(dtypes.long), 2, 20, "(long)((a+b))")
     self.helper_test_variable(a.cast(dtypes.long)*b.cast(dtypes.long), 1, 100, "(long)((a*b))")
 
@@ -799,7 +809,7 @@ class TestSymInfer(unittest.TestCase):
     a = Variable("a", 0, 10)
     b = Variable("b", 0, 10)
     c = Variable("c", 0, 10)
-    var_vals = {a: 2, b: 3, c: 4}
+    var_vals = {a.expr: 2, b.expr: 3, c.expr: 4}
     assert sym_infer(5, var_vals) == 5
     assert sym_infer(4.2, var_vals) == 4.2
     assert sym_infer(a, var_vals) == 2
@@ -812,7 +822,7 @@ class TestSymInfer(unittest.TestCase):
   def test_sym_infer_cdiv_cmod(self):
     a = Variable("a", -1000, 1)
     b = Variable("b", -1000, 1)
-    var_vals = {a: 1, b: -1000}
+    var_vals = {a.expr: 1, b.expr: -1000}
     assert sym_infer(a%b, var_vals) == 1
     assert sym_infer(a//b, var_vals) == 0
 

test/unit/test_viz.py

@@ -1,5 +1,6 @@
 import unittest, decimal, json, struct
 from dataclasses import dataclass
+from typing import Generator
 
 from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher
 from tinygrad.uop.ops import graph_rewrite, track_rewrites, TRACK_MATCH_STATS
@@ -19,6 +20,10 @@ from tinygrad.uop.ops import tracked_keys, tracked_ctxs, uop_fields, active_rewr
 traces = [(tracked_keys, tracked_ctxs, uop_fields)]
 from tinygrad.viz.serve import get_metadata, uop_to_json, get_details
 def get_viz_list(): return get_metadata(traces)
+def get_viz_details(rewrite_idx:int, step:int) -> Generator[dict, None, None]:
+  lst = get_viz_list()
+  assert len(lst) > rewrite_idx, "only loaded {len(lst)} traces, expecting at least {idx}"
+  return get_details(tracked_ctxs[rewrite_idx][step])
 
 class BaseTestViz(unittest.TestCase):
   def setUp(self):
@@ -65,7 +70,6 @@ class TestViz(BaseTestViz):
 
   def test_rewrite_location(self):
     def inner(sink): return graph_rewrite(sink, PatternMatcher([]))
-    @track_rewrites(name=True)
     def outer(sink): return inner(sink)
     outer(UOp.variable("a", 1, 10))
     lst = get_viz_list()
@@ -129,7 +133,7 @@ class TestViz(BaseTestViz):
       (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
     ])
     with self.assertRaises(RuntimeError): exec_rewrite(a, [pm])
-    graphs = flatten(x["graph"].values() for x in get_details(tracked_ctxs[0][0]))
+    graphs = flatten(x["graph"].values() for x in get_viz_details(0, 0))
     self.assertEqual(graphs[0], uop_to_json(a)[id(a)])
     self.assertEqual(graphs[1], uop_to_json(b)[id(b)])
     # fallback to NOOP with the error message
@@ -138,12 +142,12 @@ class TestViz(BaseTestViz):
 
   def test_const_node_visibility(self):
     a = UOp.variable("a", 0, 10)
-    z = UOp.const(dtypes.int, 0)
+    z = UOp.const(dtypes.index, 0)
     alu = a*z
     exec_rewrite(alu, [sym])
     lst = get_viz_list()
     self.assertEqual(len(lst), 1)
-    graphs = [x["graph"] for x in get_details(tracked_ctxs[0][0])]
+    graphs = [x["graph"] for x in get_viz_details(0, 0)]
     # embed const in the parent node when possible
     self.assertEqual(list(graphs[0]), [id(a), id(alu)])
     self.assertEqual(list(graphs[1]), [id(z)])
@@ -176,7 +180,6 @@ class TestVizTree(BaseTestViz):
     c = UOp.variable("c",0,10)
     d = UOp.variable("d",0,10)
     sink = UOp.sink(a+b, c+d)
-    @track_rewrites()
     def tree_rewrite(): return graph_rewrite(sink, root, name="root")
     tree_rewrite()
     lst = get_viz_list()
@@ -247,9 +250,46 @@ class TestVizIntegration(BaseTestViz):
       b = Tensor.empty(1)
       metadata = (alu:=a+b).uop.metadata
       alu.kernelize()
-      graph = next(get_details(tracked_ctxs[0][0]))["graph"]
+      graph = next(get_viz_details(0, 0))["graph"]
     self.assertEqual(len([n for n in graph.values() if repr(metadata) in n["label"]]), 1)
 
+  # tracing also works without a track_rewrites context
+  # all graph_rewrites get put into the default group
+  def test_default_tracing(self):
+    def test(root):
+      return graph_rewrite(root, sym)
+    test(c:=UOp.const(dtypes.int, 1))
+    test(c+1)
+    ls = get_viz_list()
+    self.assertEqual(len(ls), 1)
+    self.assertEqual(ls[0]["name"], "default graph_rewrite")
+
+  # using @track_rewrites organizes function calls into groups
+  # and nicely counts function calls.
+  def test_group_traces(self):
+    @track_rewrites()
+    def test(root):
+      return graph_rewrite(root, sym)
+    test(c:=UOp.const(dtypes.int, 1))
+    test(c+1)
+    ls = get_viz_list()
+    self.assertEqual(len(ls), 2)
+    for i in range(2): self.assertEqual(ls[i]["name"], f"test n{i+1}")
+
+  # @track_rewrites always starts a new group.
+  def test_group_combined(self):
+    def default_test(root): return graph_rewrite(root, sym)
+    tracked_test = track_rewrites()(default_test)
+    c = UOp.const(dtypes.int, 1)
+    default_test(c+1) # goes to the default group
+    tracked_test(c)   # all rewrites after this go inside the second group.
+    default_test(c+2)
+    ls = get_viz_list()
+    self.assertEqual(len(ls), 2)
+    self.assertEqual(list(next(get_viz_details(0, 0))["graph"]), [id(c+1)])
+    self.assertEqual(list(next(get_viz_details(1, 0))["graph"]), [id(c)])
+    self.assertEqual(list(next(get_viz_details(1, 1))["graph"]), [id(c+2)])
+
 from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent, ProfileGraphEntry
 from tinygrad.viz.serve import get_profile
 

test/unit/test_winograd.py

@@ -1,8 +1,9 @@
-import unittest
+import unittest, sys
 import numpy as np
 from tinygrad import Tensor, GlobalCounters, dtypes, Context, nn
 from tinygrad.helpers import CI, Profiling, WINO
 
+@unittest.skipIf(sys.platform.startswith("win"), "flaky on Windows")
 class TestWinogradClose(unittest.TestCase):
   def test_close(self):
     inp = Tensor.rand(1, 16, 16, 16)
@@ -18,6 +19,7 @@ class TestWinogradClose(unittest.TestCase):
       test = conv(inp).realize()
     np.testing.assert_allclose(cmp.numpy(), test.numpy(), atol=1e-5)
 
+@unittest.skipIf(sys.platform.startswith("win"), "flaky on Windows")
 class TestWinograd(unittest.TestCase):
   def setUp(self):
     self.old = WINO.value
@@ -28,15 +30,19 @@ class TestWinograd(unittest.TestCase):
   def test_profile(self):
     x,w = Tensor.rand(1,4,9,9).realize(), Tensor.rand(4,4,3,3).realize()
     with Profiling(enabled=not CI, sort='time'):
-      out = Tensor.conv2d(x,w).realize()
-    out.numpy()
+      Tensor.conv2d(x,w).realize()
 
-  def test_four_kernels(self):
+  def test_forward_kernels(self):
     x,w = Tensor.rand(1,4,9,9).realize(), Tensor.rand(4,4,3,3).realize()
-    GlobalCounters.reset()
-    out = Tensor.conv2d(x,w).realize()
-    assert GlobalCounters.kernel_count == 4
-    out.numpy()
+    out = Tensor.conv2d(x,w)
+    self.assertEqual(len(out.schedule()), 4)
+
+  def test_backward_kernels(self):
+    x,w = Tensor.empty(1,4,9,9,requires_grad=True).realize(), Tensor.empty(4,4,3,3,requires_grad=True).realize()
+    out = Tensor.conv2d(x,w, padding=1)
+    out.mean().backward()
+    backward_schedule = Tensor.schedule(x.grad, w.grad)
+    self.assertEqual(len(backward_schedule), 9)
 
   def test_counters(self):
     IC, OC, X, Y = 4,4,9,9

tinygrad/codegen/__init__.py

@@ -1,8 +1,8 @@
 from typing import Any, Callable
 import functools
 from dataclasses import dataclass
-from tinygrad.helpers import QUANTIZE, DEVECTORIZE, TRANSCENDENTAL, RANGEIFY, POSTOPT
-from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp
+from tinygrad.helpers import QUANTIZE, DEVECTORIZE, TRANSCENDENTAL
+from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, pm_lower_index_dtype
 from tinygrad.uop.spec import type_verify
 from tinygrad.renderer import Renderer
 
@@ -10,15 +10,15 @@ from tinygrad.renderer import Renderer
 from tinygrad.codegen.lowerer import pm_lowerer, get_index
 from tinygrad.codegen.quantize import pm_quant
 from tinygrad.codegen.gpudims import pm_add_gpudims
-from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing
+from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing, cast_folding
 from tinygrad.uop.decompositions import get_late_rewrite_patterns
 from tinygrad.codegen.late.expander import migrate_indexing, expander, pm_pre_expander
 from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize, pm_reduce, \
   ReduceContext, correct_load_store, pm_render
 from tinygrad.codegen.late.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
-from tinygrad.codegen.opt.kernel import pm_get_optimization, pm_do_optimize
 from tinygrad.codegen.opt.swizzler import view_left, view_right, fix_kernel_ops
 from tinygrad.codegen.opt.postrange import pm_postrange_opt
+from tinygrad.codegen.simplify import pm_simplify_ranges
 from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen
 
 @dataclass
@@ -44,29 +44,32 @@ rewrites_for_linearizer = [
   RewriteStep(block_merge, name="Linearizer: Merge Blocks"),
   RewriteStep(pm_finalize, name="Linearizer: Finalize")]
 
-def get_rewrites_for_renderer(opts:Renderer, linearizer:bool=True) -> list[RewriteStep]:
+def get_rewrites_for_renderer(opts:Renderer, optimize:bool=True, linearizer:bool=True) -> list[RewriteStep]:
   # cache with the values of the context vars
-  return _get_rewrites_for_renderer(opts, linearizer, QUANTIZE.value, DEVECTORIZE.value, TRANSCENDENTAL.value, RANGEIFY.value, POSTOPT.value)
+  return _get_rewrites_for_renderer(opts, optimize, linearizer, QUANTIZE.value, DEVECTORIZE.value, TRANSCENDENTAL.value)
 
 @functools.cache
-def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVECTORIZE, _TRANSCENDENTAL, _RANGEIFY, _POSTOPT) -> list[RewriteStep]:
+def _get_rewrites_for_renderer(opts:Renderer, optimize:bool, linearizer:bool, _QUANTIZE, _DEVECTORIZE, _TRANSCENDENTAL) -> list[RewriteStep]:
   # ** lowerer (rewrite_shapetracker_with_index) **
   ret: list[RewriteStep] = []
 
-  # view pushing
-  ret.extend(rewrites_for_views)
+  if optimize:
+    # view pushing
+    ret.extend(rewrites_for_views)
 
-  # this is kernel.py
-  if _POSTOPT <= 1 and not _RANGEIFY: ret.append(RewriteStep(pm_get_optimization, ctx=lambda _: opts, name="get optimization"))
-  if not _POSTOPT and not _RANGEIFY: ret.append(RewriteStep(pm_do_optimize, ctx=lambda _: opts, name="optimize ast"))
+    # lowerer first
+    if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
+    ret.append(RewriteStep(pm_lowerer, get_index, name="lowerer", bottom_up=True))
 
-  if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
-  ret.append(RewriteStep(pm_lowerer, get_index, name="lowerer", bottom_up=True))
+    # symbolic (NOTE: this is a requirement for pm_simplify_ranges to be correct)
+    ret.append(RewriteStep(sym, name="initial symbolic"))
 
-  if _POSTOPT or _RANGEIFY: ret.append(RewriteStep(pm_postrange_opt, ctx=lambda _: opts, name="post optimize ast"))
+    # optimize (schedule) the AST
+    ret.append(RewriteStep(pm_simplify_ranges, name="simplify ranges"))
+    ret.append(RewriteStep(pm_postrange_opt, ctx=lambda _: opts, name="post optimize ast"))
 
   # ** expander (expand_rewrite) **
-  ret.append(RewriteStep(sym+migrate_indexing, name="initial symbolic"))
+  ret.append(RewriteStep(sym+migrate_indexing, name="postopt symbolic"))
 
   # expand
   ret.append(RewriteStep(sym+pm_pre_expander+expander, name="expander"))
@@ -90,6 +93,9 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
   supported_ops = tuple(opts.code_for_op.keys())
   extra_matcher = opts.extra_matcher if opts.extra_matcher is not None else PatternMatcher([])
 
+  # lower the index dtype to a concrete int
+  ret.append(RewriteStep(pm_lower_index_dtype+cast_folding+load_store_indexing, lambda _: opts.device, name="lower all index dtypes"))
+
   # optional pre matcher
   if opts.pre_matcher is not None: ret.append(RewriteStep(opts.pre_matcher, name="pre_matcher"))
 
@@ -104,8 +110,8 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
   # return the list (with optional linearizer)
   return ret + (rewrites_for_linearizer if linearizer else [])
 
-def full_rewrite_to_sink(sink:UOp, opts:Renderer|None=None, linearizer:bool=False) -> UOp:
-  return apply_rewrites(sink, get_rewrites_for_renderer(opts if opts is not None else Renderer(), linearizer))
+def full_rewrite_to_sink(sink:UOp, opts:Renderer|None=None, optimize:bool=True, linearizer:bool=False) -> UOp:
+  return apply_rewrites(sink, get_rewrites_for_renderer(opts if opts is not None else Renderer(), optimize, linearizer))
 
 def full_rewrite(sink:UOp, opts:Renderer|None=None) -> list[UOp]:
   """
@@ -119,6 +125,6 @@ def full_rewrite(sink:UOp, opts:Renderer|None=None) -> list[UOp]:
     Linear program in UOps.
   """
 
-  lst = list(full_rewrite_to_sink(sink, opts, linearizer=True).arg.lst)
+  lst = list(full_rewrite_to_sink(sink, opts, optimize=sink.tag is None, linearizer=True).arg.lst)
   if __debug__: type_verify(lst)
   return lst

tinygrad/codegen/gpudims.py

@@ -34,7 +34,7 @@ def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|No
   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.int, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
+  ret = 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=}")
@@ -59,8 +59,8 @@ def add_gpudims(ctx:Renderer, s:UOp):
   all_ranges = {x.arg[0:-1]:x for x in s_topo if x.op is Ops.RANGE}
 
   # extract global/local dims
-  global_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] is AxisType.GLOBAL]))
-  local_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]))
+  global_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] in (AxisType.GLOBAL, AxisType.THREAD)]))
+  local_dims = sorted(dedup([x.arg[0:-1] for x in all_ranges.values() if x.arg[-1] in (AxisType.WARP, AxisType.LOCAL, AxisType.GROUP_REDUCE)]))
   if not global_dims and not local_dims: return None
 
   # get global and local shape

tinygrad/codegen/late/devectorizer.py

@@ -4,7 +4,7 @@ from collections import defaultdict
 from dataclasses import dataclass
 from tinygrad.dtype import dtypes, ImageDType, DType, AddrSpace
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, graph_rewrite, GroupOp, identity_element
-from tinygrad.uop.symbolic import uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
+from tinygrad.uop.symbolic import uop_given_valid, parse_valid, sym, symbolic_flat, cast_folding
 from tinygrad.helpers import getenv, flatten, AMX, prod, partition
 from tinygrad.renderer import Renderer
 
@@ -51,12 +51,13 @@ def delete_redundant_gates(store:UOp, buf:UOp, idx:UOp, val:UOp, store_gate:UOp,
   return UOp.store(buf.index(idx).cast(cast.dtype) if cast is not None else buf.index(idx), val, *store.src[2:])
 
 load_store_indexing = PatternMatcher([
-  # simplify valid
-  (UPat(Ops.AND, name="valid"), simplify_valid),
   # image load valid idx simplification
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("start_idx"), UPat.var("valid"))), simplify_valid_load),
   # index True is just Index
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("start_idx"), UPat(Ops.CONST, arg=True))), lambda buf,start_idx: buf.index(start_idx)),
+  # remove hanging cast
+  (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.int).cast()),), lambda buf,idx: buf.index(idx)),
+  (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.int).cast(), UPat.var("valid"))), lambda buf,idx,valid: buf.index(idx, valid)),
   # delete_redundant_gates (after expand)
   (UPat(Ops.STORE, src=(UPat.any(stidx:=UPat.var("buf").index(UPat.var("idx"), UPat.var("store_gate")), stidx.cast().named("cast")),
                                   UPat.var("val")), name="store", allow_any_len=True), delete_redundant_gates),
@@ -316,12 +317,12 @@ def no_range(u:UOp) -> bool: return not any(x.op is Ops.RANGE for x in u.sparent
 
 pm_reduce_collapse = PatternMatcher([
   # lift x+y out of reduce on lt
-  ((UPat.var("x")+UPat.var("y")) < UPat.var("c"), lambda x,y,c: (x < (c-y)) if no_range(y) and no_range(c) else None),
+  ((UPat.var("x")+UPat.var("y")).or_casted() < UPat.var("c"), lambda x,y,c: (x < (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
   # lift x*y out of reduce
   ((UPat.var("x")*UPat.var("y")) < UPat.var("c"),
    lambda x,y,c: (x < ((c+y-1) // y)) if no_range(y) and no_range(c) and y.vmin > 0 else None),
   # lift x+y out of reduce on ne
-  ((UPat.var("x")+UPat.var("y")) != UPat.var("c"), lambda x,y,c: (x != (c-y)) if no_range(y) and no_range(c) else None),
+  ((UPat.var("x")+UPat.var("y")).or_casted() != UPat.var("c"), lambda x,y,c: (x != (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
   # fold the range
   ((UPat(Ops.RANGE, name="r") < UPat.var("cut")).where(0, UPat.cvar("val")).reduce(arg=Ops.ADD, allow_any_len=True),
    lambda r,cut,val: (r.src[0]-cut).maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
@@ -351,7 +352,7 @@ pm_reduce_collapse = PatternMatcher([
   (UPat(Ops.VECTORIZE, name="inp").reduce(name="red", allow_any_len=True), no_vectorized_reduce),
   # index/load/where. TODO: this is more aggressive than needed
   (UPat((Ops.INDEX, Ops.LOAD, Ops.WHERE), name="alu"), no_vectorized_alu),
-])+sym
+])+sym+cast_folding
 
 def reduce_collapse(red:UOp):
   included, not_included = partition(red.parents, lambda x: any(y in x.sparents for y in red.src[1:]))

tinygrad/codegen/late/expander.py

@@ -1,5 +1,4 @@
 # this converts a lowerer program into a vectorized program
-
 import functools, itertools, operator
 from tinygrad.dtype import dtypes, PtrDType, AddrSpace
 from tinygrad.helpers import AMX, dedup, flatten, all_same, prod, partition
@@ -152,7 +151,7 @@ def fix_group_for_reduce(x:UOp):
 pm_pre_expander = PatternMatcher([
   # rewrite UPCAST/UNROLL range to something to be expanded
   (UPat(Ops.RANGE, name="r"),
-   lambda r: UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s:=r.vmax+1), tuple(range(s))),), ((r.arg[0],s),)) \
+   lambda r: UOp(Ops.UNROLL, r.dtype, (UOp.const(r.dtype.vec(s:=r.vmax+1), tuple(range(s))),), ((r.arg[0],s),)) \
     if r.arg[1] in {AxisType.UNROLL, AxisType.UPCAST} else None),
   # fix REDUCEs with UNROLLs
   (UPat(Ops.REDUCE, name="x"), fix_reduce_unroll),

tinygrad/codegen/opt/__init__.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass
 from tinygrad.uop.ops import AxisType
 
 class OptOps(Enum):
-  TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
+  TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto(); THREAD = auto() # noqa: E702
   GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
   def __lt__(self, x:OptOps): return self.value < x.value
 
@@ -16,10 +16,10 @@ class Opt:
   arg: int|tuple|None = None
   def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
 
-axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
+axis_letters = {AxisType.GLOBAL: "g", AxisType.THREAD: "t", AxisType.LOCAL: "l", AxisType.WARP: "w", AxisType.LOOP: "L", AxisType.UPCAST: "u",
                 AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
-axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
-               AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
+axis_colors = {AxisType.GLOBAL: "blue", AxisType.THREAD: "BLUE", AxisType.LOCAL: "cyan", AxisType.WARP: "CYAN", AxisType.LOOP: "WHITE",
+               AxisType.UPCAST: "yellow", AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
 
 class KernelOptError(Exception): pass
 def check(cond:bool, msg:str=""):

tinygrad/codegen/opt/heuristic.py

@@ -3,12 +3,9 @@ from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS, TC_OPT, TC_SELECT, USE_TC, AMX
 from tinygrad.dtype import ImageDType
 from tinygrad.uop.ops import Ops, resolve, AxisType
-
-# both versions
-from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.codegen.opt.postrange import Scheduler
 
-def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
+def hand_coded_optimizations(k:Scheduler) -> Scheduler:
   # first try the tensor cores
   """ Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
   Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
@@ -27,23 +24,26 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
     1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
     2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
   """
-  if USE_TC > 0:
+  # NOTE: unless TC_OPT is > 0, we only trigger tensor cores if there's only one reduce axis
+  if USE_TC > 0 and (len(k.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)) == 1 or (TC_OPT.value >= 1)):
+    good_tc_opt = False
     try: # check TC first and apply hand-coded opts if successful
       tk = k.copy()
-      tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
-
-      # skip hand-coded TC opts if AMX, upcasting will make kernel slower
-      if isinstance(k, Kernel) and (tc_opts:=tk.tensor_core_opts) is not None and not AMX:
-        # hand-coded TC opts
-        for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
-          szs = [sz for sz in [5,4,3,2] if tk.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
-          if szs: tk.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
-
-        if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if tk.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
-          tk.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
-      return tk.applied_opts
+      rngs = tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
+      good_tc_opt = True
     except KernelOptError:
       pass
+    if good_tc_opt:
+      # skip hand-coded TC opts if AMX, upcasting will make kernel slower
+      if rngs is not None and not AMX:
+        for tc_dim in [1,0]: # attempt to upcast M and N
+          szs = [sz for sz in [5,4,3,2] if rngs[tc_dim].src[0].divides(sz) is not None]
+          if szs:
+            # set it to the replaced range
+            rngs[tc_dim] = tk.apply_opt(Opt(OptOps.UPCAST, tk.rngs.index(rngs[tc_dim]), szs[0]))[0]
+        if (szs := [sz for sz in [4,2] if rngs[0].src[0].divides(sz) is not None]): # attempt to local N
+          tk.apply_opt(Opt(OptOps.LOCAL, tk.rngs.index(rngs[0]), szs[0]))
+      return tk
 
   # make a copy so it does not mutate the input
   k = k.copy()
@@ -53,20 +53,17 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
   if k.opts.has_local and getenv("MV",1) != 0 and (MV_BLOCKSIZE > 1 or MV_THREADS_PER_ROW > 1 or MV_ROWS_PER_THREAD > 1) and  \
     k.reduceop is not None and k.reduceop.arg[0] is Ops.ADD and len(k.full_shape) >= 2 and k.opts.has_shared and \
     (mulop:=k.reduceop.src[0]).op is Ops.MUL and mulop.src[0].op is Ops.LOAD and mulop.src[1].op is Ops.LOAD:
-    if isinstance(k, Kernel):
-      st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
-      strides0, strides1 = st0.real_strides(), st1.real_strides()
-      def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
-      if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
-        not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
-        for global_idx in k.axes_of(AxisType.GLOBAL):
-          if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
-            if DEBUG >= 3:
-              print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
-            if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
-            if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
-            if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
-            return k.applied_opts
+    idx0, idx1 = mulop.src[0].src[0].src[1], mulop.src[1].src[0].src[1]
+    first_reduce_rng = k.ranges_of(AxisType.REDUCE)[0]
+    if any(u is first_reduce_rng for u in idx0.split_uop(Ops.ADD)) and all(r in idx1.ranges for r in idx0.ranges):
+      for global_idx in k.axes_of(AxisType.GLOBAL):
+        if first_reduce_rng.src[0].divides(MV_THREADS_PER_ROW) is not None and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
+          if DEBUG >= 3:
+            print(f"MATVEC: {k.full_shape=} {first_reduce_rng.render()} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
+          if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
+          if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
+          if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
+          return k
 
   # are we grouping? (requires local shape support)
   if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= 2048, False):
@@ -79,11 +76,8 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
   # upcast float4 images
   for buf_index,buf in enumerate(k.bufs):
     if isinstance(buf.src[0].dtype, ImageDType):
-      if hasattr(k, "sts"):
-        unit_stride_axes_mul_4 = [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]
-      else:
-        # part of real_strides
-        unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].split_uop(Ops.ADD) if c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
+      # part of real_strides
+      unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].split_uop(Ops.ADD) if c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
       if len(unit_stride_axes_mul_4):
         if (axis:=unit_stride_axes_mul_4[0]) in k.upcastable_dims:
           k.apply_opt(Opt(OptOps.UPCAST, axis, 4))
@@ -91,7 +85,7 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
           k.apply_opt(Opt(OptOps.UNROLL, k.unrollable_dims.index(axis), 4))
 
   # no more opt if we are grouping
-  if k.group_for_reduces: return k.applied_opts
+  if k.group_for_reduces: return k
 
   # **** below this line need to be optional and benchmarked ****
 
@@ -99,8 +93,9 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
   to_upcast: list[int] = []
   # upcast leading axes first (hack-ish for winograd; we actually want to upcast masked axes with low stride first)
   for axis in k.upcastable_dims:
-    if isinstance(k, Kernel): is_masked = any(st.axis_is_masked(axis) for st in k.sts)
-    else: is_masked = any(len(st.src) > 2 and k.rngs[axis] in st.src[2].parents for st in k.bufs)
+    # for Schedule, we check if the range is used in INDEX gates or WHERE gates
+    is_masked = any(len(st.src) > 2 and k.rngs[axis] in st.src[2].parents for st in k.bufs) or \
+                any(any(o is k.rngs[axis] for o in u.src[0].parents) for u in k.ast.parents if u.op is Ops.WHERE)
     if k.full_shape[axis] <= 7 and is_masked and prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
       if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
       to_upcast.append(axis)
@@ -115,24 +110,16 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
     for axis, upcast_amount in itertools.product(k.upcastable_dims, ([128] if not len(upcasted_axis) else []) if is_dsp else [3,4]):
       # if we haven't upcasted it, it mods, and buffer has stride 0 on axis while having no stride 0 in the upcasted axis already
       if axis in upcasted_axis or k.full_shape[axis]%upcast_amount != 0: continue
-      if isinstance(k, Kernel):
-        # must have stride 0 on a view
-        # must have all non stride 0 on what's upcasted before
-        if any(st.views[-1].strides[axis] == 0 and \
-              all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
-          xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
-                            sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
-      else:
-        rng = k.rngs[axis]
-        if any(rng not in b.src[1].parents and all(r2 in b.src[1].parents for r2 in k.ranges_of(AxisType.UPCAST, AxisType.UNROLL)) for b in k.bufs):
-          num_strides, sum_strides = 0, 0
-          for b in k.bufs:
-            if rng in b.src[1].parents: num_strides += 1
-            for c in b.src[1].split_uop(Ops.ADD):
-              if c is rng: sum_strides += 1
-              if c.op is Ops.MUL and c.src[0] is rng and c.src[1].op is Ops.CONST: sum_strides += c.src[1].arg
-              if c.op is Ops.MUL and c.src[1] is rng and c.src[0].op is Ops.CONST: sum_strides += c.src[0].arg
-          xb_choices.append((num_strides, sum_strides, axis, upcast_amount))
+      rng = k.rngs[axis]
+      if any(rng not in b.src[1].parents and all(r2 in b.src[1].parents for r2 in k.ranges_of(AxisType.UPCAST, AxisType.UNROLL)) for b in k.bufs):
+        num_strides, sum_strides = 0, 0
+        for b in k.bufs:
+          if rng in b.src[1].parents: num_strides += 1
+          for c in b.src[1].split_uop(Ops.ADD):
+            if c is rng: sum_strides += 1
+            if c.op is Ops.MUL and c.src[0] is rng and c.src[1].op is Ops.CONST: sum_strides += c.src[1].arg
+            if c.op is Ops.MUL and c.src[1] is rng and c.src[0].op is Ops.CONST: sum_strides += c.src[0].arg
+        xb_choices.append((num_strides, sum_strides, axis, upcast_amount))
     if xb_choices:
       xb_choices = sorted(xb_choices)
       if DEBUG >= 4: print(f"more upcast axis : {xb_choices}")
@@ -170,11 +157,8 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
       k.apply_opt(Opt(OptOps.NOLOCALS))
     else:
       # prioritize making expand axes local
-      if isinstance(k, Kernel):
-        local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
-      else:
-        local_axis_ranking = [(any(k.rngs[axis] not in b.src[1].parents for b in k.bufs), axis) \
-                                for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP) if k.rngs[axis].src[0].op is Ops.CONST]
+      local_axis_ranking = [(any(k.rngs[axis] not in b.src[1].parents for b in k.bufs), axis) \
+                              for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP) if k.rngs[axis].src[0].op is Ops.CONST]
       to_local: list[tuple[int, int]] = []
       for _, axis in sorted(local_axis_ranking, key=lambda x: (-x[0], -x[1])):
         local_size = prod(sz for _, sz in to_local)
@@ -187,4 +171,16 @@ def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
         k.apply_opt(Opt(OptOps.LOCAL, axis, local_sz))
         if will_delete_shape: deleted_shape += 1
 
-  return k.applied_opts
+  # **** threading ****
+
+  if k.opts.has_threads and k.opts.global_max is not None:
+    for threads in [32,16,12,8,6,5,4,3,2]:
+      # Skip is too many threads. Heuristic: use about 128K ops per thread
+      if threads > k.opts.global_max[0] or resolve(prod(k.full_shape) // (128 << 10) < threads): continue
+      for axis in k.axes_of(AxisType.LOOP):
+        if k.full_shape[axis] % threads == 0:
+          k.apply_opt(Opt(OptOps.THREAD, axis, threads))
+          break
+      if k.applied_opts and k.applied_opts[-1].op is OptOps.THREAD: break
+
+  return k

tinygrad/codegen/opt/kernel.py

@@ -1,479 +0,0 @@
-from __future__ import annotations
-import itertools, functools, math
-from dataclasses import dataclass
-from collections import defaultdict
-from typing import cast, Final, Callable, Sequence
-from tinygrad.codegen.opt import OptOps, Opt, KernelOptError, check, axis_letters, axis_colors
-from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType, PatternMatcher, UPat
-from tinygrad.uop.spec import type_verify, ast_spec
-from tinygrad.device import Device
-from tinygrad.codegen.opt.tc import TensorCore
-from tinygrad.renderer import Renderer
-from tinygrad.dtype import ImageDType
-from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, NOOPT, BEAM, getenv, POSTOPT
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import strides_for_shape, get_contraction
-from tinygrad.codegen.opt.swizzler import view_left, view_left_through_load
-
-@dataclass
-class TensorCoreOptions:
-  axes: tuple[int, ...] # the location of the original N and M axes if still in the shape
-  axes_exist: tuple[bool, ...] # true if the original N and M axes are still in the shape
-  axis_pads: tuple[tuple[int, int], ...]
-  def fix_axes(self, removed_axis:int): # adjust the TC axes if necessary when a dimension is removed
-    axes, axes_exist = list(self.axes), list(self.axes_exist)
-    for tc_dim in [i for i in range(2) if axes_exist[i]]:
-      if removed_axis < axes[tc_dim]: axes[tc_dim] -= 1
-      elif removed_axis == axes[tc_dim]: axes_exist[tc_dim] = False
-    self.axes, self.axes_exist = tuple(axes), tuple(axes_exist)
-
-class Kernel:
-  def __init__(self, ast:UOp, opts:Renderer|None=None):
-    assert ast.op is Ops.SINK, ast.op
-    self.ast = ast
-
-    self.opts = opts if opts is not None else Device[Device.DEFAULT].renderer
-    # verify AST matches the spec
-    if __debug__: type_verify(list(self.ast.toposort()), ast_spec)
-
-    self.vars: list[Variable] = self.ast.variables()
-    # NOTE: this requires a specific order with the [::-1], this is likely a bug
-    self.bufs: list[UOp] = [x for x in self.ast.toposort() if x.op in GroupOp.Buffer and x.st is not None][::-1]
-
-    # create new shapetrackers inside this kernel, we will permute them
-    self.sts: list[ShapeTracker] = [x.st_arg for x in self.bufs]
-
-    # add the shapetrackers for each reduce
-    # we use this to track which axes are reduced in each reduce
-    self.reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE_AXIS]
-    for x in self.reduceops:
-      self.sts.append(unwrap(x.st))
-      self.sts.append(unwrap(x.src[0].st))
-
-    # add a shapetracker to the end to track the full shape, with 0 strides so it can merge
-    full_shape = ast.full_shape
-    self.sts.append(ShapeTracker.from_shape(full_shape, (0,)*len(full_shape)))
-
-    # parameters for optimization
-    self.tensor_core: TensorCore|None = None
-    self.tensor_core_opts: TensorCoreOptions|None = None
-    self.use_tensor_cores: int = 0
-    self.applied_opts: list[Opt] = []
-    self.dont_use_locals = False
-    self.finalized: bool = False
-
-    # group simplifies
-    self.simplify_ones()
-    self.simplify_merge_adjacent()
-
-    # axis types
-    global_loops = AxisType.GLOBAL if self.opts.has_local else AxisType.LOOP
-    self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.output_shape, self.full_shape)]
-
-    # confirm all reduce axes are at the end
-    if (final_reduces := [x for x in self.axis_types if x == AxisType.REDUCE]) and final_reduces != self.axis_types[-len(final_reduces):]:
-      raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
-
-  def copy(self):
-    ret = type(self).__new__(type(self))
-
-    # base linearizer params
-    ret.opts, ret.ast = self.opts, self.ast
-
-    # things downstream of the AST
-    ret.reduceops, ret.vars, ret.bufs = self.reduceops, self.vars, self.bufs
-    ret.sts = self.sts[:]
-    ret.axis_types = self.axis_types[:]
-
-    # parameters for optimizations
-    ret.applied_opts, ret.dont_use_locals = self.applied_opts[:], self.dont_use_locals
-    ret.tensor_core, ret.tensor_core_opts, ret.use_tensor_cores = self.tensor_core, self.tensor_core_opts, self.use_tensor_cores
-    ret.finalized = self.finalized
-
-    return ret
-
-  @property
-  def reduceop(self) -> UOp|None: return self.reduceops[0] if len(self.reduceops) > 0 else None
-  @property
-  def full_shape(self) -> tuple[sint, ...]: return self.sts[-1].shape
-
-  @property
-  def output_shape(self) -> tuple[sint, ...]: return self.sts[0].shape
-  @property
-  def shape_len(self) -> int: return len(self.full_shape)
-
-  def axes_of(self, *axis_type:AxisType) -> list[int]: return [i for i,t in enumerate(self.axis_types) if t in argfix(axis_type)]
-  @property
-  def upcasted(self) -> int: return len(self.axes_of(AxisType.UPCAST, AxisType.UNROLL))
-  @property
-  def group_for_reduces(self) -> int: return len(self.axes_of(AxisType.GROUP_REDUCE))
-
-  # heuristic helpers
-  @property
-  def upcastable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP) \
-                                                  if isinstance(s:=self.full_shape[i], int) and s > 1]
-  @property
-  def unrollable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE) \
-                                                  if isinstance(s:=self.full_shape[i], int) and s > 1]
-
-  # ******************** colors and names ********************
-
-  def colors(self) -> list[str]:
-    assert len(self.axis_types) == self.shape_len, "colors size mismatch"
-    return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
-
-  def colored_shape(self, pad:int|None=None, dense=False) -> str:
-    shape_strs = [(s if dense else f"{s:4d}") if isinstance(s, int) else s.render() for s in self.full_shape]
-    ret = ' '.join(colored(s, color) for s,color in zip(shape_strs, self.colors()))
-    if pad: ret += ' '*(pad-ansilen(ret))
-    return ret
-
-  kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
-  @functools.cached_property
-  def name(self) -> str:
-    # kernel name (before late upcast)
-    kernel_type = "r" if self.reduceop is not None else ("C" if all(x.op is Ops.SINK or x.op in GroupOp.Buffer for x in self.ast.toposort()) else "E")
-    suffix = colored('_', 'BLACK').join([colored(x.render() if isinstance(x, UOp) else str(x), c) for x,c in zip(self.full_shape, self.colors())])
-    name = kernel_type + (f"{len(self.ast.src)}" if len(self.ast.src) > 1 else "") + "_" + suffix
-
-    # name the function something unique
-    Kernel.kernel_cnt[(function_name := to_function_name(name))] += 1
-    num = f"n{Kernel.kernel_cnt[function_name]-1}" if Kernel.kernel_cnt[function_name] > 1 else ""
-    return name + colored(num, 'BLACK')
-
-  # ******************** base simplifiers ********************
-
-  # apply reshape and permute to all shapetrackers
-  def reshape(self, new_shape_fxn:Callable[[tuple[sint, ...]], Sequence[sint]]):
-    self.sts = [st.reshape(tuple(new_shape_fxn(st.shape))) for st in self.sts]
-  def permute(self, new_axes:Sequence[int]): self.sts = [st.permute(tuple(new_axes)) for st in self.sts]
-
-  # axis : the axis to pull from
-  # amount : the amount to take
-  # top : if you want to pull that amount from the top
-  # insert_at : place to insert the new stuff
-  def shift_to(self, axis:int, amount:int, new_type:AxisType, top:bool=False, insert_at:int|None=None) -> int:
-    if insert_at is None: insert_at = self.shape_len
-    self.axis_types.insert(insert_at, new_type)
-    move_axis = axis if top else axis+1
-    if move_axis < insert_at: insert_at += 1
-    def new_shape_fxn(x): return x[0:axis] + (((amount,x[axis]//amount) if top else (x[axis]//amount,amount)) if x[axis] > 1 else (1,1)) + x[axis+1:]
-    new_axes = [i for i in range(insert_at) if i != move_axis]+[move_axis]+[i for i in range(insert_at, self.shape_len+1) if i != move_axis]
-    self.reshape(new_shape_fxn)
-    self.permute(new_axes)
-    return insert_at
-
-  # ******************** complex simplifiers ********************
-
-  def simplify_ones(self) -> bool:
-    # remove places where the shape is all ones
-    if any(all_ones:=[s==1 for s in self.full_shape]):
-      if hasattr(self, 'axis_types'):
-        self.axis_types = [x for i,x in enumerate(self.axis_types) if not all_ones[i]]
-      self.reshape(lambda shape: [x for i,x in enumerate(shape) if not all_ones[i]])
-      return True
-    return False
-
-  def simplify_merge_adjacent(self):
-    assert not hasattr(self, 'axis_types'), "don't call this after init"
-    if self.shape_len == 0: return
-    shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts]
-    # NOTE: we can't use self.first_reduce yet
-    first_reduce = [resolve(x!=y) for x,y in zip(self.output_shape+(0,), self.full_shape+(1,))].index(True)
-
-    # if it's an image, insert fake strides such that this fusion doesn't happen across image axes
-    # TODO: remove membufs
-    membufs = dedup([x.src[0].base for x in self.bufs if x.op in {Ops.LOAD, Ops.STORE}])
-    if isinstance(membufs[0].base.dtype, ImageDType):
-      base_shape = membufs[0].base.dtype.shape
-      if shape_idx_groups := get_contraction(self.output_shape, base_shape):
-        special_strides: tuple[sint, ...] = tuple()
-        for i,g in enumerate(shape_idx_groups):
-          shape_piece = tuple(self.output_shape[x] for x in g)
-          assert prod(shape_piece) == base_shape[i], f"get_contraction was wrong? {shape_piece} != {base_shape[i]}"
-          special_strides += strides_for_shape(shape_piece)
-        # adding the fake image shape
-        shapes.append(self.output_shape)
-        strides.append(special_strides)
-
-    # merge dimensions if we can, multi _merge_dims
-    # NOTE: this does not always preserve the reduce dimension
-    # TODO: move this into shapetracker, with tests!
-    # TODO: how does this work with multi-reduce?
-    rets = [[(s[0], st[0])] for s,st in zip(shapes, strides)]
-    for i in range(1, len(shapes[0])):
-      can_merge = []
-      for s,st,ret in zip(shapes, strides, rets):
-        # TODO: added the always mergeability of 1s, is this right? if so, add to shapetracker in the 1 case
-        si, sti, last_st = s[i], st[i], ret[-1][1]
-        can_merge.append((sti is not None) and ((sti != 0 and last_st == si*sti) or (sti == 0 and last_st == 0)))
-      # more can merge than this
-      mergeable = all(can_merge) and i != first_reduce
-      for j,(s,st) in enumerate(zip(shapes, strides)):
-        if mergeable: rets[j][-1] = (rets[j][-1][0] * s[i], st[i])
-        else: rets[j].append((s[i], st[i]))
-
-    # do the reshapes
-    for i,x in enumerate(rets[:len(self.sts)]): self.sts[i] = self.sts[i].reshape(tuple([y[0] for y in x]))
-
-  # ******************** apply optimizations ********************
-
-  def real_axis(self, op:OptOps, axis:int|None):
-    try:
-      if axis is None: return -1
-      if op is OptOps.UNROLL: return self.unrollable_dims[axis]
-      if op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.axes_of(AxisType.REDUCE)[axis]
-      check(axis < self.shape_len, f"invalid axis on {axis=} {op=} {self.shape_len=}")
-      return axis
-    except IndexError as e: raise KernelOptError from e
-
-  def apply_opt(self, opt:Opt, append_opt:bool=True) -> int|None:
-    if self.finalized: raise RuntimeError("can't optimize Kernel after it's finalized")
-    if self.dont_use_locals: check(opt.op not in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}, "not using locals")
-
-    if opt.op is OptOps.TC:
-      check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: things like PADTO might be fine
-      check(len(self.opts.tensor_cores) > 0, "must have tensor cores")
-      check(opt.axis is not None, "tensor core opts must have an axis")
-      check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg")
-      check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
-      check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
-      check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
-      check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
-      self.applied_opts.append(opt)
-      return None
-
-    axis = self.real_axis(opt.op, opt.axis)
-
-    if opt.op is OptOps.SWAP: amt = self.real_axis(opt.op, cast(int, opt.arg))  # arg is an axis in the SWAPs
-    elif opt.arg is not None:
-      check(isinstance(opt.arg, int), "arg should be int")
-      amt = arg if (arg:=cast(int, opt.arg)) != 0 else self.full_shape[axis]
-      check(isinstance(amt, int) and amt != 1, f"shift/padto of {amt=}, 1 or symbolic amount is meaningless")
-      if opt.op is not OptOps.PADTO:
-        # we check both the full_shape and each shape
-        check(self.full_shape[axis] % amt == 0, f"no longer valid shift {self.full_shape[axis]=}, {amt=}")
-        for st in self.sts: check(st.shape[axis] == 1 or st.shape[axis] % amt == 0, f"no longer valid shift {st.shape[axis]=}, {amt=}")
-    else: amt = -1
-
-    if self.reduceop is not None and (opt.op in {OptOps.GROUP, OptOps.GROUPTOP} or \
-                                      (self.group_for_reduces and opt.op not in {OptOps.NOLOCALS, OptOps.PADTO})):
-      acc_sz = self.reduceop.dtype.itemsize
-      upcast_sz = prod([self.full_shape[a] for a in self.axes_of(AxisType.UPCAST)])
-      local_sz = prod([self.full_shape[a] for a in self.axes_of(AxisType.LOCAL)])
-      smem_sz = amt*acc_sz*upcast_sz*local_sz
-      check(smem_sz <= self.opts.shared_max, f"exceeds maximum shared memory size: needs {smem_sz}, max {self.opts.shared_max}")
-
-    new_axis = None
-    if opt.op is OptOps.LOCAL:    # cyan
-      # NOTE: LLVM/CPU can use locals too, but they are treated the same as globals (still helpful for L1 cache)
-      # it's disabled for now since it makes BEAM slow for little gain
-      check(self.opts.has_local, "target does not support local")
-      check(self.axis_types[axis] is AxisType.GLOBAL, "local is for globals")
-      new_axis = self.shift_to(axis, amt, AxisType.LOCAL, insert_at=max(self.axes_of(AxisType.GLOBAL, AxisType.LOCAL))+1)
-    elif opt.op in {OptOps.GROUP, OptOps.GROUPTOP}:   # green
-      check(self.opts.has_local and self.opts.has_shared, "target does not support local or shared mem")
-      check(self.axis_types[axis] is AxisType.REDUCE, "must be reduce axis to group")
-      check(not self.tensor_core, "can't group with tensor cores")
-      check(len(reduce_axes:=[i for r in self.reduceops for i in r.axis_arg]) == len(set(reduce_axes)), "can't group with parallel reduces")
-      new_axis = self.shift_to(axis, amt, AxisType.GROUP_REDUCE, top=(opt.op is OptOps.GROUPTOP), insert_at=min(self.axes_of(AxisType.REDUCE)))
-    elif opt.op is OptOps.UNROLL:                     # purple
-      check(self.axis_types[axis] not in (AxisType.UPCAST, AxisType.UNROLL), "can't upcasted already upcasted")
-      check(amt <= 32, "don't unroll more than 32")
-      new_axis = self.shift_to(axis, amt, AxisType.UNROLL, insert_at=None)
-    elif opt.op is OptOps.UPCAST:                     # yellow
-      check(axis in self.upcastable_dims, f"{axis=} not in {self.upcastable_dims=}")
-      # NOTE: assume the first get_local_axes() LOCAL are for TC
-      check(not (self.tensor_core and axis in self.axes_of(AxisType.LOCAL)[:len(self.tensor_core.get_local_axes())]), "can't upcast TC locals")
-      check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
-      new_axis = self.shift_to(axis, amt, AxisType.UPCAST,
-                               insert_at=max(self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP, AxisType.UPCAST))+1)
-    elif opt.op is OptOps.NOLOCALS:
-      check(self.opts.has_local and not self.dont_use_locals, "NOLOCALS is meaningless if target does not support local or already not using locals")
-      check(AxisType.LOCAL not in self.axis_types and self.group_for_reduces == 0, "can't have no locals with locals")
-      self.dont_use_locals = True
-    elif opt.op is OptOps.SWAP:
-      check(axis < amt, f"swap is only for axis < amt, getting {amt=}, {axis=}")
-      check(self.axis_types[axis]==self.axis_types[amt]==AxisType.GLOBAL, f"swap is for globals {self.axis_types[axis]=}, {self.axis_types[amt]=}")
-      permute = list(range(self.shape_len))
-      permute[axis], permute[amt] = permute[amt], permute[axis]
-      self.permute(tuple(permute))
-    elif opt.op is OptOps.PADTO:
-      check(not self.vars, "does not work with symbolic shape")
-      check(self.axis_types[axis] not in (AxisType.UPCAST, AxisType.UNROLL), "cannot pad upcasted")
-      # ok to pad SUM if all parent ALU ops have f(0) = 0
-      if (r:=self.reduceop) is not None and self.axis_types[axis] in (AxisType.GROUP_REDUCE, AxisType.REDUCE):
-        check(r.arg[0] is Ops.ADD and can_pad(r, {}), f"cannot pad {r}")
-      padded = False
-      for i,st in enumerate(self.sts):
-        if (s:=st.shape[axis]) == 1: continue  # reduced
-        check(s > amt//4, f"pad adds more than quadruple the work {st.shape[axis]=} > {amt//4=}")
-        if (ru := round_up(cast(int, s), amt) - s):
-          # pad right seems to be faster
-          self.sts[i] = st.pad(((0,0),) * axis + ((0,ru),) + ((0,0),) * (len(st.shape)-axis-1))
-          padded = True
-      check(padded, "nothing was padded")
-
-    if append_opt: self.applied_opts.append(opt)
-    if self.simplify_ones() and self.tensor_core_opts:
-      self.tensor_core_opts.fix_axes(axis) # fix up axes in TC opts if required after simplify_ones()
-    return new_axis
-
-  def apply_opts(self, opts:Sequence[Opt]) -> Kernel:
-    for opt in opts: self.apply_opt(opt)
-    return self
-
-  # **** kernel outputs, mostly tensor cores ****
-
-  def _create_tc_opts(self, reduceop:UOp, tc:TensorCore, axis:int, opt_level:int) -> TensorCoreOptions|None:
-    has_cast = tc.dtype_in != tc.dtype_out
-    if has_cast and not (reduceop.src[0].op is Ops.CAST and reduceop.src[0].dtype == tc.dtype_out): return None
-
-    mul_op = reduceop.src[0].src[0] if has_cast else reduceop.src[0]
-    if mul_op.op is not Ops.MUL: return None
-
-    def buf_index(src:UOp) -> int|None:
-      # TODO: apply tc even if the sources are not from LOAD
-      if src.op is Ops.LOAD and src.dtype == tc.dtype_in: return self.bufs.index(src)
-      try:
-        if opt_level >= 1 and src.op is Ops.CAST and src.dtype == tc.dtype_in: return self.bufs.index(src.src[0])
-      except ValueError: return None
-      return None
-    if (buf0:=buf_index(mul_op.src[0])) is None or (buf1:=buf_index(mul_op.src[1])) is None: return None
-
-    buf0_strides, buf1_strides = self.sts[buf0].real_strides(), self.sts[buf1].real_strides()
-    axis_buf0 = [(i,self.full_shape[i],buf1_strides[i]) for i in self.upcastable_dims if buf0_strides[i] == 0]
-    axis_buf1 = [(i,self.full_shape[i],buf0_strides[i]) for i in self.upcastable_dims if buf1_strides[i] == 0]
-    if not (axis_buf0 and axis_buf1 and (len(self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)) == 1 or (opt_level >= 1))): return None
-
-    axis_choices = list(itertools.product(axis_buf0, axis_buf1, self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE)))
-    if not (axis < len(axis_choices)): return None
-
-    s0, s1, s2 = axis_choices[-(axis+1)][0][0], axis_choices[-(axis+1)][1][0], axis_choices[-(axis+1)][2]  # s0 is n, s1 is m, s2 is k
-    axis_pads = tuple((x, tc.dims[i]) for i, x in enumerate([s0, s1, s2]) if resolve(self.full_shape[x]%tc.dims[i] != 0))
-    if axis_pads and (opt_level < 2): return None
-    if DEBUG >= 3: print("TENSOR CORES", axis_buf0, axis_buf1, tc)
-    return TensorCoreOptions(axes=(s0, s1, s2), axes_exist=(True, True), axis_pads=axis_pads)
-
-  def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool:
-    if use_tensor_cores and self.reduceop is not None and self.reduceop.arg[0] is Ops.ADD:
-      tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
-      for tc in tensor_cores:
-        tensor_core_opts = [self._create_tc_opts(reduceop, tc, axis, opt_level) for reduceop in self.reduceops]
-        if tensor_core_opts[0] is None: continue
-        # can only fuse reduces with the same tc options
-        assert all_same(tensor_core_opts)
-        self.tensor_core_opts = tc_opts = tensor_core_opts[0]
-
-        # attempt to pad the tensor axes that require it
-        try:
-          for axis, dim in tc_opts.axis_pads: self.apply_opt(Opt(OptOps.PADTO, axis, dim), append_opt=False) # PADTO might fail
-        except KernelOptError: continue
-        # tensor core -- unroll the reduce dim (K), upcast and local the inner and outer dims (N, M)
-        for opt in tc.opts: self.apply_opt(Opt({"u":OptOps.UPCAST, "l":OptOps.LOCAL}[opt[0]], tc_opts.axes[int(opt[1])], 2), append_opt=False)
-        for dim, amt in tc.get_reduce_axes(): self.apply_opt(Opt(OptOps.UNROLL, 0, amt), append_opt=False) # TODO: this should be the reduce, not 0
-        self.tensor_core = tc
-        self.use_tensor_cores = use_tensor_cores  # TC=2 will do the shape ops without the WMMA
-        return True
-    return False
-
-  # strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
-  def shape_str(self) -> list[str]:
-    ret: list[str] = []
-    cnt: dict[AxisType, int] = {}
-    for x in self.axis_types:
-      cnt[x] = (cnt[x] + 1) if x in cnt else 0
-      ret.append(f"{axis_letters[x]}{cnt[x]}")
-    return ret
-  def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms])
-
-  def get_optimized_ast(self, name_override:str|None=None) -> UOp:
-    @functools.cache
-    def fixup_ast(op:UOp) -> UOp:
-      ret = op.replace(src=tuple(fixup_ast(x) for x in op.src)) # noqa: F821
-      if op.op in GroupOp.Buffer and op in self.bufs:
-        st = self.sts[self.bufs.index(op)]
-        # replace the VIEW source
-        return ret.replace(src=(ret.src[0].replace(arg=st),)+ret.src[1:])
-      if op.op is Ops.SINK:
-        # NOTE: should group_for_reduces be added to the local_dims?
-        # TODO: arg.name should be able to be None
-        kernel_name = ret.arg.name if ret.arg is not None and ret.arg.name != "test" else self.name if name_override is None else name_override
-        return ret.replace(arg=KernelInfo(kernel_name, tuple(self.axis_types), self.dont_use_locals, tuple(self.applied_opts)))
-      if op.op is Ops.REDUCE_AXIS:
-        reduce_idx = len(self.bufs) + self.reduceops.index(op) * 2
-        changed = tuple(i for i in range(self.shape_len) if resolve(self.sts[reduce_idx].shape[i] != self.sts[reduce_idx + 1].shape[i]))
-        axes = tuple(i for i in self.axes_of(AxisType.REDUCE, AxisType.GROUP_REDUCE, AxisType.UNROLL) if i in changed)
-        if (tc := self.tensor_core) and self.use_tensor_cores == 1:
-          # get reduce/upcast axes for the tensor cores
-          tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
-          base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
-          tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
-
-          # permute the srcs
-          srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src)
-          for i, (src, permaxis) in enumerate(zip(srcs, tc.permutes_for_shape_str(self.shape_str()))):
-            src_st = (src if src.op is Ops.LOAD else src.src[0]).st_arg
-            srcs[i] = src.view(ShapeTracker.from_shape(src_st.shape).permute(permaxis))
-
-          # construct the op
-          wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, tc_reduce_axes)
-          wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
-            UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0]),
-            UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1]),
-            UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg)
-          tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2])
-
-          # preserve any other reduce
-          return ret.replace(src=(tc_uop,), arg=(Ops.ADD, new_axes)) if (new_axes := tuple(i for i in axes if i not in tc_reduce_axes)) else tc_uop
-
-        ret = ret.replace(arg = (op.arg[0], axes))
-      return ret
-    self.finalized = True
-    fixed_ast = fixup_ast(self.ast)
-    del fixup_ast
-    return graph_rewrite(fixed_ast, view_left+view_left_through_load, name="fixup optimized AST")
-
-def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp|None:
-  """
-  Optimize an AST based on heuristics or BEAM search.
-
-  Args:
-    ast: The Ops.SINK rooted AST
-    renderer: The renderer used to generate the code
-
-  Returns:
-    The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
-  """
-
-  # no shape, no opt
-  if ast.src[0].st is None: return None
-  new_arg = ast.arg
-  if new_arg is None:
-    k = Kernel(ast, opts=renderer)
-    if not NOOPT:
-      from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-      k.apply_opts(hand_coded_optimizations(k))
-      if not POSTOPT and BEAM >= 1:
-        from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
-        kb = Kernel(ast, opts=renderer)
-        rawbufs = bufs_from_lin(kb, allocate=False)
-        k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
-    new_arg = KernelInfo(opts_to_apply=tuple(k.applied_opts))
-  elif len(new_arg.applied_opts): return None
-  return Kernel(ast.replace(arg=None), opts=renderer).get_optimized_ast().replace(arg=new_arg)
-
-pm_get_optimization = PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx)),
-])
-
-def apply_opt(ast:UOp, renderer:Renderer):
-  k = Kernel(ast, opts=renderer)
-  k.apply_opts(ast.arg.opts_to_apply)
-  ret = k.get_optimized_ast()
-  if __debug__: type_verify(list(ret.toposort()))
-  return ret
-
-pm_do_optimize = PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
-])

tinygrad/codegen/opt/postrange.py

@@ -1,32 +1,19 @@
+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, _substitute, AxisType
-from tinygrad.uop.symbolic import symbolic
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, AxisType, ssimplify, can_pad
 from tinygrad.device import Buffer
-from tinygrad.dtype import AddrSpace, dtypes
-from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up
+from tinygrad.dtype import AddrSpace, dtypes, ImageDType
+from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up, prod
 from tinygrad.codegen.opt import axis_colors, Opt, OptOps, KernelOptError, check, axis_letters
+from tinygrad.codegen.simplify import pm_flatten_range
 from tinygrad.renderer import Renderer
 from tinygrad.schedule.rangeify import remove_tags
 
 # NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
-axis_to_pos = {AxisType.LOOP: -1, AxisType.GLOBAL: 0, AxisType.LOCAL: 1, AxisType.UPCAST: 2,
-               AxisType.GROUP_REDUCE: 1, AxisType.REDUCE: 3, AxisType.UNROLL: 4}
-
-def flatten_range(r:UOp):
-  off = 2 if r.op is Ops.STORE else 1
-  rngs = r.src[off:]
-  if not len(rngs): return None
-  new_rngs = [x for x in UOp.sink(*rngs).toposort() if x.op is Ops.RANGE]
-  return r.replace(src=r.src[:off]+tuple(new_rngs))
-
-pm_flatten_range = PatternMatcher([
-  # real ranges only
-  (UPat((Ops.REDUCE, Ops.STORE), name="r"), flatten_range),
-])
-
-def count_divmod(x:UOp): return len([u for u in x.toposort() if u.op in {Ops.IDIV, Ops.MOD}])
+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, opts:Renderer):
@@ -41,7 +28,7 @@ class Scheduler:
   @property
   def shape_len(self): return len(self.rngs)
   @property
-  def full_shape(self): return [x.vmax+1 for x in self.rngs]
+  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
@@ -57,31 +44,25 @@ class Scheduler:
     return ret
   def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms])
 
-  @property
-  def termination(self):
-    terminators = [u for u in self.ast.parents if u.op in {Ops.REDUCE, Ops.STORE}]
-    termination = {}
-    for t in terminators:
-      # works without pm_flatten_range
-      for u in UOp.sink(*t.src[1 if t.op is Ops.REDUCE else 2:]).parents:
-        if u.op is Ops.RANGE: termination[u] = t
-    return termination
-
-  def copy(self): return Scheduler(self.get_optimized_ast(), self.opts)
+  def copy(self):
+    ret = Scheduler(self.ast, self.opts)
+    ret.dont_use_locals = self.dont_use_locals
+    ret.applied_opts = self.applied_opts[:]
+    return ret
 
   kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
   def get_optimized_ast(self, name_override:str|None=None):
     if name_override is not None: name = name_override
     else:
-      name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
+      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())])
       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')
     self.ast = graph_rewrite(self.ast, pm_flatten_range, name="flatten range")
     return self.ast.replace(arg=KernelInfo(name=name, applied_opts=tuple(self.applied_opts), dont_use_locals=self.dont_use_locals), tag=1)
 
-  def convert_loop_to_global(self):
-    if not self.opts.has_local: return None
+  def _globalizable_rngs(self) -> list[UOp]:
     store_rngs = self.ast.src[0].src[2:]
 
     # filter any not in local stores
@@ -89,54 +70,42 @@ class Scheduler:
                         or (x.op is Ops.BUFFERIZE and x.arg == AddrSpace.LOCAL)]
     for ls in local_store_rngs: store_rngs = tuple([x for x in store_rngs if x in ls])
 
-    store_rng = [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE] if store_rngs else []
-    rng = [x.replace(arg=(x.arg[0], AxisType.GLOBAL)) if x.arg[1] == AxisType.LOOP and x in store_rng else x for x in self.rngs]
+    return [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE and x.arg[1] == AxisType.LOOP] if store_rngs else []
+
+  def convert_loop_to_global(self):
+    if not self.opts.has_local: return None
+
+    globalizible_rngs = self._globalizable_rngs()
+    rng = [x.replace(arg=(x.arg[0], AxisType.GLOBAL)) if x in globalizible_rngs else x for x in self.rngs]
 
     self.ast = self.ast.substitute(dict(zip(self.rngs, rng)))
 
-  def simplify_merge_adjacent(self):
-    i = 0
-    while i < len(self.rngs)-1:
-      r0, r1 = self.rngs[i], self.rngs[i+1]
-      # same axistype and same termination
-      termination = self.termination
-      if r0.arg[1] == r1.arg[1] and r0 in termination and r1 in termination and termination[r0] == termination[r1]:
-        s0, s1 = r0.src[0], r1.src[0]
-        new_range = r0.replace(src=(s0*s1,)).simplify()
-        # this checks the legality of a merge
-        oidx = self.ast.simplify()
-        nidx = graph_rewrite(oidx, _substitute+symbolic+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1}, name=f"check_merge_{i}_{i+1}")
-        # it simplifies
-        if count_divmod(nidx) <= count_divmod(oidx):
-          # it is correct
-          midx = graph_rewrite(nidx, _substitute+symbolic+pm_flatten_range, ctx={new_range:r0*s1+r1}, name=f"correct_merge_{i}_{i+1}")
-          if oidx is midx:
-            self.ast = nidx
-            continue
-      i += 1
-
   def colors(self) -> list[str]: return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
-  def colored_shape(self) -> str: return ' '.join([colored(f'{x.src[0].render():4s}', color) for x,color in zip(self.rngs, self.colors())])
+  def colored_shape(self) -> str: return ' '.join([colored(f'{x.src[0].render():>4s}', color) for x,color in zip(self.rngs, self.colors())])
 
-  def shift_to(self, rng:UOp, amount:int, new_type:AxisType, top:bool=False):
+  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)
+    new_rng = UOp.range(amount, self.maxarg+1, 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[0]} {amount}")
+    self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[0]} {amount} {str(new_type).split('.')[1].lower()}")
     return replaced_rng, new_rng
 
   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]
+
+  # copied from kernel.py
   @property
-  def upcastable_dims(self): return self.axes_of(AxisType.GLOBAL, AxisType.LOCAL)
+  def upcastable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP) \
+                                                  if isinstance(s:=self.full_shape[i], int) and s > 1]
   @property
-  def unrollable_dims(self): return self.axes_of(AxisType.REDUCE, AxisType.GROUP_REDUCE)
+  def unrollable_dims(self) -> list[int]: return [i for i in self.axes_of(AxisType.GROUP_REDUCE, AxisType.REDUCE) \
+                                                  if isinstance(s:=self.full_shape[i], int) and s > 1]
 
   def real_axis(self, op:OptOps, axis:int|None):
     try:
-      if axis is None: return -1
+      if axis is None or op is OptOps.TC: return -1
       if op is OptOps.UNROLL: return self.unrollable_dims[axis]
       if op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.axes_of(AxisType.REDUCE)[axis]
       check(axis < self.shape_len, f"invalid axis on {axis=} {op=} {self.shape_len=}")
@@ -145,36 +114,51 @@ class Scheduler:
 
   def apply_opt(self, opt:Opt, append_opt:bool=True):
     if opt.op is OptOps.NOLOCALS:
-      check(all(x not in {AxisType.LOCAL, AxisType.GROUP_REDUCE} for x in self.axis_types), "no locals can't have locals")
+      check(all(x not in {AxisType.WARP, AxisType.LOCAL, AxisType.GROUP_REDUCE} for x in self.axis_types), "no locals can't have locals")
+      if append_opt: self.applied_opts.append(opt)
       self.dont_use_locals = True
-      self.applied_opts.append(opt)
       return
 
     if opt.op in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}:
       check(self.opts.has_local, "locals needed for opt")
 
-    rng = self.rngs[self.real_axis(opt.op, opt.axis)]
+    rng = self.rngs[real_axis] if (real_axis:=self.real_axis(opt.op, opt.axis)) >= 0 else UOp(Ops.NOOP)
 
     opt_to_at = {
       OptOps.LOCAL: AxisType.LOCAL, OptOps.UPCAST: AxisType.UPCAST,
       OptOps.UNROLL: AxisType.UNROLL, OptOps.GROUP: AxisType.GROUP_REDUCE,
-      OptOps.GROUPTOP: AxisType.GROUP_REDUCE}
+      OptOps.GROUPTOP: AxisType.GROUP_REDUCE, OptOps.THREAD: AxisType.THREAD}
 
+    ret = None
     if opt.op in opt_to_at:
-      amt:int = (rng.vmax+1) if opt.arg == 0 else cast(int, opt.arg)
+      amt:int = int(rng.vmax+1) if opt.arg == 0 else cast(int, opt.arg)
+
+      # copied from kernel.py. prevents METAL compiler hangs
+      if self.reduceop is not None and (opt.op in {OptOps.GROUP, OptOps.GROUPTOP} or \
+                                        (self.group_for_reduces and opt.op not in {OptOps.NOLOCALS, OptOps.PADTO})):
+        upcast_local_sz = prod([self.full_shape[a] for a in self.axes_of(AxisType.UPCAST, AxisType.WARP, AxisType.LOCAL, AxisType.GROUP_REDUCE)])
+        smem_sz = amt*upcast_local_sz*self.reduceop.dtype.itemsize
+        check(smem_sz <= self.opts.shared_max, f"exceeds maximum shared memory size: needs {smem_sz}, max {self.opts.shared_max}")
+
       if opt.op is OptOps.UNROLL:
         check(amt <= 32, "don't unroll more than 32")
         check(rng.arg[-1] in {AxisType.GROUP_REDUCE, AxisType.REDUCE}, "unroll is for GROUP_REDUCE/REDUCE")
       if opt.op is OptOps.UPCAST:
         check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
-        check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP}, "upcast is for GLOBAL/LOCAL/LOOP")
+        check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP}, f"upcast is for GLOBAL/LOCAL/LOOP, not {rng.arg[-1]}")
       if opt.op is OptOps.LOCAL:
         check(not self.dont_use_locals, "can't use locals")
         check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOOP}, "local is for globals")
+      if opt.op is OptOps.THREAD:
+        check(self.opts is not None and self.opts.has_threads, "target does not support threads")
+        check(self.opts is not None and self.opts.global_max is not None and amt <= self.opts.global_max[0], "too many threads")
+        check(all(x is not AxisType.THREAD for x in self.axis_types), "already threaded")
+        check(rng in self._globalizable_rngs(), "can't apply range to this dim")
       if opt.op in {OptOps.GROUP, OptOps.GROUPTOP}:
+        check(all(x.op is not OptOps.TC for x in self.applied_opts), "no grouping with tensor cores")  # TODO: why is this wrong?
         check(not self.dont_use_locals, "can't use locals")
         check(rng.arg[-1] == AxisType.REDUCE, "group is for reduce")
-      self.shift_to(rng, amt, opt_to_at[opt.op], top=opt.op==OptOps.GROUPTOP)
+      ret = self.shift_to(rng, amt, opt_to_at[opt.op], top=opt.op in {OptOps.GROUPTOP, OptOps.THREAD})
     elif opt.op is OptOps.TC:
       check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: remove the need for this by having warps
       check(opt.axis is not None, "tensor core opts must have an axis")
@@ -182,10 +166,18 @@ class Scheduler:
       check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
       check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
       check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
-      check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
+      ret = self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt)
+      check(ret is not None, "no tensor core available")
     elif opt.op is OptOps.PADTO:
-      check(rng.src[0].op is Ops.CONST, "only pad const")
-      replaced_rng = UOp.range(round_up(rng.vmax+1, cast(int, opt.arg)), *rng.arg)
+      check(rng.src[0].op is Ops.CONST, "only pad const axes")
+      check(rng.arg[-1] not in {AxisType.UPCAST, AxisType.UNROLL}, "cannot pad upcasted") # TODO: why is this wrong?
+      check(rng.arg[-1] is not AxisType.THREAD, "cannot pad thread")
+      # ok to pad SUM if all parent ALU ops have f(0) = 0
+      if (r:=self.reduceop) is not None and rng.arg[-1] in (AxisType.GROUP_REDUCE, AxisType.REDUCE):
+        check(r.arg[0] is Ops.ADD and can_pad(r, {}), f"cannot pad {r}")
+      new_sz = round_up(int(rng.vmax+1), cast(int, opt.arg))
+      check(rng.vmax+1 > new_sz//4, "pad adds more than quadruple the work")
+      replaced_rng = UOp.range(new_sz, *rng.arg)
       replaces = {rng:replaced_rng}
       for b in self.bufs:
         if rng in b.src[1].sparents:
@@ -204,16 +196,17 @@ class Scheduler:
       self.ast = graph_rewrite(self.ast, remove_tags)
     else:
       raise KernelOptError(f"unsupported opt {opt.op}")
-    if append_opt:
-      self.applied_opts.append(opt)
 
-  def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool:
+    if append_opt: self.applied_opts.append(opt)
+    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")
     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]
-      if mul.op is not Ops.MUL: return False
+      if mul.op is not Ops.MUL: return None
       in0, in1 = mul.src
       try:
         tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
@@ -222,9 +215,9 @@ 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])
+          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])
           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]}")
@@ -239,16 +232,26 @@ class Scheduler:
           # do optimizations and save the ranges
           try:
             for i,a in enumerate(axes):
-              # apply_opt should return the updated range?
               idx = self.rngs.index(a)
-              self.apply_opt(Opt(OptOps.PADTO, idx, tc.dims[i]), append_opt=False) # PADTO might fail
-              axes[i] = self.rngs[idx]
+              if (a.vmax+1) % tc.dims[i] != 0:
+                if opt_level < 2: raise KernelOptError("tc padding requires opt_level >= 2")
+                # apply_opt should return the updated range?
+                self.apply_opt(Opt(OptOps.PADTO, idx, tc.dims[i]), append_opt=False) # PADTO might fail
+                axes[i] = self.rngs[idx]
           except KernelOptError: continue
 
+          # we create the warp as a whole thing, in case some of these ranges are moved/removed later
+          warp = UOp.range(tc.threads, -1, AxisType.WARP)
           ne: list[UOp] = []
           for opt in tc.opts:
-            axes[int(opt[1])], new_range = self.shift_to(axes[int(opt[1])], 2, {"u":AxisType.UPCAST, "l":AxisType.LOCAL}[opt[0]])
+            if opt[0] == "l":
+              axes[int(opt[1])], new_range = self.shift_to(axes[int(opt[1])], 2, AxisType.LOCAL, input_new_rng=warp%2)
+              warp //= 2
+            elif opt[0] == "u":
+              axes[int(opt[1])], new_range = self.shift_to(axes[int(opt[1])], 2, AxisType.UPCAST)
+            else: raise RuntimeError(f"unsupported opt {opt[0]} in tensor cores")
             ne.append(new_range)
+
           for _, amt in tc.get_reduce_axes():
             axes[2], new_range = self.shift_to(axes[2], amt, AxisType.UNROLL)
             ne.append(new_range)
@@ -285,8 +288,8 @@ class Scheduler:
             reduce_ranges = [x for x in UOp.sink(*reduceop.src[1:]).toposort() if x.op is Ops.RANGE and x.arg[0] not in tc_reduce_axes]
             if len(reduce_ranges): tc_uop = UOp(Ops.REDUCE, tc_uop.dtype, (tc_uop,)+tuple(reduce_ranges), Ops.ADD)
             self.ast = self.ast.substitute({reduceop: tc_uop})
-          return True
-    return False
+          return axes
+    return None
 
   # helpers for hand_coded_optimizations
   @property
@@ -306,25 +309,23 @@ class Scheduler:
 
 def bufs_from_ast(ast:UOp, dname:str) -> list[Buffer]:
   glbls = sorted([x for x in ast.parents if x.op is Ops.DEFINE_GLOBAL], key=lambda x: x.arg)
-  return [Buffer(dname, x.ptrdtype.size, x.dtype.base) for x in glbls]
+  return [Buffer(dname, x.ptrdtype.size, x.dtype.base if not isinstance(x.dtype, ImageDType) else x.dtype) for x in glbls]
 
 def apply_opts(ctx:Renderer, ast:UOp):
   if ast.tag is not None: return None
   k = Scheduler(ast, ctx)
   k.convert_loop_to_global()
   if BEAM >= 1:
-    k.simplify_merge_adjacent()
     from tinygrad.codegen.opt.search import beam_search
     rawbufs = bufs_from_ast(ast, ctx.device)
     k = beam_search(k, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
   elif ast.arg is not None and ast.arg.opts_to_apply is not None:
     for opt in ast.arg.opts_to_apply: k.apply_opt(opt)
-  elif not NOOPT:
-    k.simplify_merge_adjacent()
+  elif not NOOPT and (ast.arg is None or ast.arg.applied_opts == ()):
     from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
     # NOTE: hand_coded_optimizations doesn't support multiblock opts yet
     if all(len(u.src) == 1 for u in ast.parents if u.op is Ops.LOAD):
-      for opt in hand_coded_optimizations(k): k.apply_opt(opt)
+      k = hand_coded_optimizations(k)
   return k.get_optimized_ast(name_override=ast.arg.name if ast.arg is not None and ast.arg.name != "test" else None)
 
 pm_postrange_opt = PatternMatcher([

tinygrad/codegen/opt/search.py

@@ -1,19 +1,14 @@
 from typing import cast
 import functools, math, time, multiprocessing, traceback, signal, atexit
-from collections import defaultdict
 from dataclasses import replace
-from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType, pyrender
+from tinygrad.uop.ops import sym_infer, AxisType, pyrender
 from tinygrad.device import Device, Buffer, Compiler
 from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
 from tinygrad.helpers import IGNORE_BEAM_CACHE
-from tinygrad.dtype import ImageDType, PtrDType
 from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.tensor import Tensor
 from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.renderer import ProgramSpec
-
-# both versions
-from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.codegen.opt.postrange import Scheduler
 
 actions = [Opt(op=OptOps.UPCAST, axis=axis, arg=amt) for amt in [0,2,3,4,5,7] for axis in range(8)]
@@ -21,12 +16,13 @@ actions += [Opt(op=OptOps.UNROLL, axis=axis, arg=amt) for amt in [0,4,7] for axi
 actions += [Opt(op=OptOps.LOCAL, axis=axis, arg=amt) for amt in [2,3,4,8,13,16,29] for axis in range(6)]
 actions += [Opt(op=OptOps.GROUPTOP, axis=axis, arg=amt) for amt in [13,16,28,29,32,49,64,256] for axis in range(3)]
 actions += [Opt(op=OptOps.GROUP, axis=axis, arg=amt) for amt in [0,4,8,16] for axis in range(3)]
-if getenv("BEAM_PADTO", 1): actions += [Opt(op=OptOps.PADTO, axis=axis, arg=amt) for amt in [32] for axis in range(7)]
+if getenv("BEAM_PADTO", 0): actions += [Opt(op=OptOps.PADTO, axis=axis, arg=amt) for amt in [32] for axis in range(7)]
 actions += [Opt(op=OptOps.LOCAL, axis=0, arg=32), Opt(op=OptOps.LOCAL, axis=6, arg=2)]
 actions += [Opt(op=OptOps.TC, axis=0, arg=(-1, 0, getenv("TC", 1)))]
 # covers resnet kernels (3 global * 3 reduce)
 actions += [Opt(op=OptOps.TC, axis=axis, arg=(-1, getenv("TC_OPT", 2), getenv("TC", 1))) for axis in range(9)]
 actions += [Opt(op=OptOps.SWAP, axis=axis_0, arg=axis_1) for axis_0 in range(5) for axis_1 in range(axis_0+1, 5)]
+actions += [Opt(op=OptOps.THREAD, axis=axis, arg=amt) for amt in [2,3,4,5,8,12,16,24,32,64] for axis in range(3)]
 if getenv("NOLOCALS"): actions += [Opt(op=OptOps.NOLOCALS)]
 
 def get_test_global_size(global_size, max_global_size, var_vals):
@@ -39,7 +35,7 @@ def get_test_global_size(global_size, max_global_size, var_vals):
         break
   return test_global_size, input_size / prod(test_global_size)
 
-def _time_program(p:ProgramSpec, lib:bytes, var_vals:dict[Variable, int], rawbufs:list[Buffer], early_stop:float|None=None,
+def _time_program(p:ProgramSpec, lib:bytes, var_vals:dict[str, int], rawbufs:list[Buffer], early_stop:float|None=None,
                   allow_test_size:int=True, max_global_size:int|None=65536, clear_l2=False, cnt=3, name="test") -> list[float]:
   factor = 1
   if allow_test_size and p.global_size is not None and max_global_size is not None:
@@ -63,7 +59,7 @@ def timeout_handler(signum, frame):
   if DEBUG >= 2: print("*** BEAM COMPILE TIMEOUT")
   raise TimeoutException()
 
-def _try_compile_linearized_w_idx(x:tuple[int,Kernel], compiler:Compiler) -> tuple[int, tuple[ProgramSpec, bytes, float]|None]:
+def _try_compile_linearized_w_idx(x:tuple[int,Scheduler], compiler:Compiler) -> tuple[int, tuple[ProgramSpec, bytes, float]|None]:
   if hasattr(signal, "alarm"):
     signal.signal(getattr(signal, 'SIGALRM'), timeout_handler)
     # set timeout
@@ -96,26 +92,8 @@ def _ensure_buffer_alloc(bufs:list[Buffer]) -> list[Buffer]: return [buf.ensure_
 
 # *** external API ***
 
-# get (scrap) buffers for timing the linearizer
-# NOTE: there's also bufs_from_ast in postrange
-def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
-  bufsts: defaultdict[int, list[UOp]] = defaultdict(list)
-  for x in lin.bufs:
-    if x.src[0].base.op is Ops.DEFINE_GLOBAL: bufsts[x.src[0].base.arg].append(x)
-  # TODO: Nones are staying in here if buffers are optimized out!
-  # TODO: add a test for this
-  rawbufs: list[Buffer|None] = [None]*(max(bufsts)+1)
-  for k,lx in bufsts.items():
-    buf_size = prod(dtype.shape) if isinstance(dtype:=lx[0].src[0].dtype, ImageDType) else max(y.st_arg.real_size() for y in lx)
-    assert isinstance(dtype, (PtrDType, ImageDType))
-    if buf_size == 0: buf_size = 1  # create a size 1 buffer if no cell is accessed in kernel. # TODO: remove from kernel input in this case.
-    buf_dtype = dtype if isinstance(dtype, ImageDType) else dtype.base
-    rawbufs[k] = Buffer(lin.opts.device, buf_size, buf_dtype).allocate() if allocate else Buffer(lin.opts.device, buf_size, buf_dtype)
-  #assert all(r is not None for r in rawbufs)
-  return cast(list[Buffer], rawbufs)
-
 # get dictionary of all possible actions
-def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel|Scheduler]:
+def get_kernel_actions(lin:Scheduler, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Scheduler]:
   acted_lins, max_up, max_lcl = {0:lin} if include_0 else {}, getenv("BEAM_UPCAST_MAX", 256), getenv("BEAM_LOCAL_MAX", 1024)
   kernel_actions = (actions if candidates is None else candidates).copy()
 
@@ -130,7 +108,7 @@ def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt
       up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if hasattr(lin2, 'tensor_core') and (tc:=lin2.tensor_core) else 1
       for s,c in zip(lin2.full_shape, lin2.axis_types):
         if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
-        elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
+        elif c in (AxisType.WARP, AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
       if up//tc_up > max_up or lcl > max_lcl:
         if getenv("BEAM_LOG_SURPASS_MAX"): print(f"too many upcast/local. {up//tc_up=}, {max_up=}, {lcl=}, {max_lcl=}")
         continue
@@ -139,7 +117,7 @@ def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt
   return acted_lins
 
 beam_pool, BEAM_DEBUG = None, getenv("BEAM_DEBUG")
-def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
+def beam_search(lin:Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
   global beam_pool
   key = {"ast": lin.ast.key, "amt": amt, "allow_test_size": allow_test_size, "device": lin.opts.device, "suffix": lin.opts.suffix}
   if not disable_cache and CACHELEVEL >= 1 and (val:=diskcache_get("beam_search", key)) is not None:
@@ -147,7 +125,7 @@ def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_
     for o in val[len(lin.applied_opts):]: ret.apply_opt(o)
     return ret
 
-  beam: list[tuple[Kernel|Scheduler, float]] = [(lin, float("inf"))]
+  beam: list[tuple[Scheduler, float]] = [(lin, float("inf"))]
   seen_libs = set()
 
   default_parallel = multiprocessing.cpu_count() if lin.opts.device in {"CUDA", "AMD", "NV", "METAL", "HIP"} else 0
@@ -164,12 +142,12 @@ def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_
 
   try:
     rawbufs = _ensure_buffer_alloc(rawbufs)
-    var_vals: dict[Variable, int] = {k:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
+    var_vals: dict[str, int] = {k.expr:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
     exiting, st = False, time.perf_counter()
     dev = Device[lin.opts.device]
     while not exiting:
-      acted_lins: list[Kernel|Scheduler] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
-      timed_lins: list[tuple[Kernel|Scheduler, float]] = []
+      acted_lins: list[Scheduler] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
+      timed_lins: list[tuple[Scheduler, float]] = []
       _compile_fn = functools.partial(_try_compile_linearized_w_idx, compiler=dev.compiler)
       least_compute_ops = math.inf
       for i,proc in (map(_compile_fn, enumerate(acted_lins)) if beam_pool is None else beam_pool.imap_unordered(_compile_fn, enumerate(acted_lins))):

tinygrad/codegen/simplify.py

@@ -0,0 +1,37 @@
+from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, graph_rewrite, _substitute
+from tinygrad.uop.symbolic import symbolic_flat
+
+def flatten_range(r:UOp):
+  off = 2 if r.op is Ops.STORE else 1
+  rngs = r.src[off:]
+  if not len(rngs): return None
+  new_rngs = [x for x in UOp.sink(*rngs).toposort() if x.op is Ops.RANGE]
+  return r.replace(src=r.src[:off]+tuple(new_rngs))
+
+pm_flatten_range = PatternMatcher([
+  # real ranges only
+  (UPat((Ops.REDUCE, Ops.STORE), name="r"), flatten_range),
+])
+
+def count_divmod(x:UOp): return len([u for u in x.toposort() if u.op in {Ops.IDIV, Ops.MOD}])
+def simplify_merge_adjacent(u:UOp) -> UOp|None:
+  i = 2 if u.op is Ops.STORE else 1
+  while i < len(u.src)-1:
+    r0, r1 = u.src[i], u.src[i+1]
+    # check same type
+    if r0.arg[-1] == r1.arg[-1]:
+      s0, s1 = r0.src[0], r1.src[0]
+      # do the merge
+      new_range = r0.replace(src=(s0*s1,))
+      nidx = graph_rewrite(u, _substitute+symbolic_flat+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1},
+                           name=f"check_merge_{r0.arg[0]}_{r1.arg[0]}")
+      # check if it simplifies
+      if count_divmod(nidx) <= count_divmod(u):
+        u = nidx
+        continue
+    i += 1
+  return u
+
+pm_simplify_ranges = PatternMatcher([
+  (UPat((Ops.STORE, Ops.REDUCE), name="u"), simplify_merge_adjacent),
+])

tinygrad/device.py

@@ -298,6 +298,7 @@ class Compiled:
 
 # TODO: move this to each Device
 def is_dtype_supported(dtype:DType, device:str|None=None) -> bool:
+  if dtype == dtypes.index: return False
   if device is None: device = Device.DEFAULT
   if dtype == dtypes.bfloat16:
     if device == "METAL": return not CI

tinygrad/dtype.py

@@ -89,7 +89,7 @@ class dtypes:
   def is_float(x: DType) -> bool: return x.scalar() in dtypes.floats or isinstance(x, ImageDType)
   @staticmethod # static methods on top, or bool in the type info will refer to dtypes.bool
   @functools.cache
-  def is_int(x: DType) -> bool: return x.scalar() in dtypes.ints
+  def is_int(x: DType) -> bool: return x.scalar() in dtypes.ints + (dtypes.index,)
   @staticmethod
   @functools.cache
   def is_unsigned(x: DType) -> bool: return x.scalar() in dtypes.uints
@@ -128,6 +128,7 @@ class dtypes:
   @staticmethod
   def fields() -> dict[str, DType]: return DTYPES_DICT
   void: Final[DType] = DType.new(-1, 0, "void", None)
+  index: Final[DType] = DType.new(-1,100, "index", None)
   bool: Final[DType] = DType.new(0, 1, "bool", '?')
   int8: Final[DType] = DType.new(1, 1, "signed char", 'b')
   uint8: Final[DType] = DType.new(2, 1, "unsigned char", 'B')
@@ -164,7 +165,7 @@ class dtypes:
   uints = (uint8, uint16, uint32, uint64)
   sints = (int8, int16, int32, int64)
   ints = uints + sints
-  all = floats + ints + (bool,)
+  all = floats + ints + (bool, index)
 
 if (env_default_float := getenv("DEFAULT_FLOAT", "")):
   dtypes.default_float = getattr(dtypes, env_default_float.lower())
@@ -186,11 +187,12 @@ def _get_recursive_parents(dtype:DType) -> set[DType]:
   return set.union(*[_get_recursive_parents(d) for d in promo_lattice[dtype]], {dtype}) if dtype != dtypes.float64 else {dtypes.float64}
 @functools.cache
 def least_upper_dtype(*ds:DType) -> DType:
-  return min(set.intersection(*[_get_recursive_parents(d) for d in ds])) if not (images:=[d for d in ds if isinstance(d, ImageDType)]) else images[0]
+  return min(set.intersection(*[_get_recursive_parents(d.scalar()) for d in ds])) \
+      if not (images:=[d for d in ds if isinstance(d, ImageDType)]) else images[0]
 def least_upper_float(dt:DType) -> DType: return dt if dtypes.is_float(dt) else least_upper_dtype(dt, dtypes.default_float)
 
-DTYPES_DICT = {k: v for k, v in dtypes.__dict__.items() if isinstance(v, DType) and not k.startswith(("default", "void"))}
-INVERSE_DTYPES_DICT = {**{v.name:k for k,v in DTYPES_DICT.items()}, "void": "void"}
+DTYPES_DICT = {k: v for k, v in dtypes.__dict__.items() if isinstance(v, DType) and not k.startswith(("default", "void", "index"))}
+INVERSE_DTYPES_DICT = {**{v.name:k for k,v in DTYPES_DICT.items()}, "void": "void", "index":"index"}
 
 @functools.cache
 def can_safe_cast(dt0:DType, dt1:DType) -> bool:
@@ -198,6 +200,7 @@ def can_safe_cast(dt0:DType, dt1:DType) -> bool:
   # https://numpy.org/doc/stable/reference/generated/numpy.can_cast.html
   if dt0 == dt1 or dt0 == dtypes.bool: return True
   match dt1:
+    case dtypes.index: return dt0 in dtypes.ints
     case dtypes.double: return dt0 in (dtypes.float, dtypes.half, dtypes.bfloat16,
       dtypes.uint32, dtypes.uint16, dtypes.uint8, dtypes.int32, dtypes.int16, dtypes.int8)
     case dtypes.float: return dt0 in (dtypes.half, dtypes.bfloat16, dtypes.uint16, dtypes.uint8, dtypes.int16, dtypes.int8)
@@ -315,4 +318,4 @@ def _to_torch_dtype(dtype:DType) -> 'torch.dtype'|None:  # type: ignore [name-de
   except TypeError: return None
 @functools.cache
 def _from_torch_dtype(torchdtype:'torch.dtype') -> DType: # type: ignore [name-defined] # noqa: F821
-  return {v:k for k in dtypes.all if (v:=_to_torch_dtype(k)) is not None}[torchdtype]
+  return {v:k for k in DTYPES_DICT.values() if (v:=_to_torch_dtype(k)) is not None}[torchdtype]

tinygrad/engine/jit.py

@@ -16,7 +16,7 @@ class GraphException(Exception): pass
 
 def graph_class(dev): return dev.graph.func if isinstance(dev.graph, functools.partial) else dev.graph
 
-def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[Variable, int], max_batch_size=0) -> list[ExecItem]:
+def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[str, int], max_batch_size=0) -> list[ExecItem]:
   # Split JIT cache into batches for faster graph execution.
   # This allows the accelerator to run some batches while subsequent graphs are still being updated.
   graphed_jit_cache: list[ExecItem] = []
@@ -73,7 +73,7 @@ def get_input_replace(jit_cache: list[ExecItem], input_rawbuffers:list[Buffer])
   return input_replace
 
 class GraphRunner(Runner):
-  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[Variable, int]):
+  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[str, int]):
     self.jit_cache = jit_cache  # NOTE: this is not used, but you have to keep these objects alive for the Graph
     self.input_replace:dict[tuple[int, int], int] = get_input_replace(jit_cache, input_rawbuffers)
     self.var_vals_replace:dict[int, list[tuple[int, int]]] = {}
@@ -82,7 +82,7 @@ class GraphRunner(Runner):
 
     def is_sym_dim(dim) -> bool: return not all(isinstance(d, (int, float)) for d in dim)
 
-    self.vars = sorted(var_vals.keys(), key=lambda v: v.expr)
+    self.vars = sorted(var_vals.keys())
     self.symbolic_dims = dedup([tuple(d) for ji in jit_cache if isinstance(ji.prg, CompiledRunner) and (d:=ji.prg.p.local_size) and is_sym_dim(d)] +
                                [tuple(d) for ji in jit_cache if isinstance(ji.prg, CompiledRunner) and (d:=ji.prg.p.global_size) and is_sym_dim(d)])
     def find_symbolic_dim(dim): return self.symbolic_dims.index(tuple(dim)) if dim is not None and tuple(dim) in self.symbolic_dims else None
@@ -91,7 +91,7 @@ class GraphRunner(Runner):
     for j,ji in enumerate(jit_cache):
       estimates += ji.prg.estimates
       if isinstance(ji.prg, CompiledRunner):
-        if ji.prg.p.vars: self.var_vals_replace[j] = [(i, self.vars.index(v)) for i, v in enumerate(ji.prg.p.vars) if v not in ji.fixedvars]
+        if ji.prg.p.vars: self.var_vals_replace[j] = [(i, self.vars.index(v.expr)) for i, v in enumerate(ji.prg.p.vars) if v.expr not in ji.fixedvars]
 
         global_dim_idx, local_dim_idx = find_symbolic_dim(ji.prg.p.global_size), find_symbolic_dim(ji.prg.p.local_size)
         if global_dim_idx is not None or local_dim_idx is not None:
@@ -105,12 +105,12 @@ class GraphRunner(Runner):
 
     super().__init__(colored(f"<batched {len(jit_cache)}>", "cyan"), jit_cache[0].prg.device.split(":")[0], estimates.simplify())
 
-  def updated_vars(self, var_vals: dict[Variable, int]):
+  def updated_vars(self, var_vals: dict[str, int]):
     vals = [var_vals[v] for v in self.vars]
     for j, vidxs in self.var_vals_replace.items():
       for i, v in vidxs: yield j, i, vals[v]
 
-  def updated_launch_dims(self, var_vals: dict[Variable, int]):
+  def updated_launch_dims(self, var_vals: dict[str, int]):
     dims = [tuple(sym_infer(s, var_vals) for s in dim) for dim in self.symbolic_dims]
     for j, (gl, lc) in self.launch_dims_replace.items():
       yield j, (dims[gl] if gl is not None else self.launch_dims_base[j][0]), (dims[lc] if lc is not None else self.launch_dims_base[j][1])
@@ -192,7 +192,7 @@ class CapturedJit(Generic[ReturnType]):
     self.__post_init__()
 
   # jit exec
-  def __call__(self, input_buffers:list[Buffer], var_vals:dict[Variable, int]) -> ReturnType:
+  def __call__(self, input_buffers:list[Buffer], var_vals:dict[str, int]) -> ReturnType:
     # assign inputs
     for idx, offset, device, size, dtype in self.extra_view_inputs:
       input_buffers.append(Buffer(device, size, dtype, base=input_buffers[idx], offset=offset).ensure_allocated())
@@ -225,7 +225,8 @@ def _prepare_jit_inputs(args, kwargs):
                                          for lb in lbs if lb.base.realized is not None])
   assert len(set(input_buffers)) == len(input_buffers), "duplicate inputs to JIT"
   st_varval_dtype_device = [(*unwrap(lb.st).unbind(), lb.dtype, lb.device) for lb in lbs]
-  var_vals = merge_dicts([x[1] for x in st_varval_dtype_device] + [dict(v.unbind() for v in (args + tuple(kwargs.values())) if isinstance(v, UOp))])
+  _var_vals = merge_dicts([x[1] for x in st_varval_dtype_device] + [dict(v.unbind() for v in (args + tuple(kwargs.values())) if isinstance(v, UOp))])
+  var_vals = {k.expr:v for k,v in _var_vals.items()}
   st_vars_dtype_device = [(x[0], tuple(sorted(x[1].keys(), key=lambda v: v.expr)), x[2], x[3]) for x in st_varval_dtype_device]
   return input_buffers, var_vals, names, st_vars_dtype_device
 
@@ -317,7 +318,7 @@ class TinyJit(Generic[ReturnType]):
 
       # memory planning (optional)
       # Exclude buffers involved in transfer ops to preserve parallelism.
-      noopt_buffers = {b for ji in jit_cache if isinstance(ji.prg, BufferXfer) for b in ji.bufs}
+      noopt_buffers = {b for ji in jit_cache if isinstance(ji.prg, (BufferXfer, BufferCopy)) for b in ji.bufs}
       assigned = _internal_memory_planner([cast(list[Buffer], item.bufs) for item in jit_cache], noopt_buffers, debug_prefix="JIT ")
       jit_cache = [ExecItem(item.prg, [assigned.get(b,b).ensure_allocated() for b in item.bufs if b is not None],
                             item.metadata, item.fixedvars) for item in jit_cache]

tinygrad/engine/realize.py

@@ -2,13 +2,13 @@ from typing import cast, Generator, Callable
 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
-from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo, pyrender
+from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile, PROFILE, ProfilePointEvent, cpu_events, prod, Context
+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
 from tinygrad.engine.schedule import ScheduleItem
 from tinygrad.codegen import full_rewrite
-from tinygrad.codegen.opt.kernel import Opt
+from tinygrad.codegen.opt import Opt
 
 # **************** Program Creation ****************
 
@@ -47,7 +47,8 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
   src = renderer.render(uops)
 
   return ProgramSpec(uops[-1].arg.name if uops[-1].arg is not None else "test", src, renderer.device, ast, uops,
-                     global_size=[1,1,1] if renderer.has_local else None, local_size=[1,1,1] if renderer.has_local else None)
+                     global_size=[1,1,1] if renderer.has_local or renderer.has_threads else None,
+                     local_size=[1,1,1] if renderer.has_local else None)
 
 # **************** Runners ****************
 
@@ -56,9 +57,9 @@ class Runner:
     self.first_run, self.display_name, self.device, self.estimates = True, display_name, device, estimates
   @property
   def dev(self): return Device[self.device]
-  def exec(self, rawbufs:list[Buffer], var_vals:dict[Variable, int]|None=None) -> float|None:
+  def exec(self, rawbufs:list[Buffer], var_vals:dict[str, int]|None=None) -> float|None:
     return self(rawbufs, {} if var_vals is None else var_vals)
-  def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False) -> float|None:
+  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int], wait=False) -> float|None:
     raise NotImplementedError("override this")
 
 def optimize_local_size(_prg:Callable, global_size:list[int], rawbufs:list[Buffer]) -> list[int]:
@@ -89,9 +90,10 @@ class CompiledRunner(Runner):
 
   def __reduce__(self): return self.__class__, (self.p, self.lib)
 
-  def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False) -> float|None:
+  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int], wait=False) -> float|None:
+    has_local = Device[self.p.device].renderer.has_local
     global_size, local_size = self.p.launch_dims(var_vals)
-    if global_size is not None and local_size is None and all_int(self.p.global_size): # type: ignore[arg-type]
+    if has_local and global_size is not None and local_size is None and all_int(self.p.global_size): # type: ignore[arg-type]
       local_size = optimize_local_size(self._prg, global_size, rawbufs)
       global_size = [g//l if g%l == 0 else g/l for g,l in zip(global_size, local_size)]
       self.p = replace(self.p, global_size=global_size, local_size=local_size)
@@ -102,11 +104,11 @@ class CompiledRunner(Runner):
     if local_size:
       lra['local_size'] = tuple(local_size)
       assert len(local_size) == 3, "local size must have len 3"
-    return self._prg(*[x._buf for x in rawbufs], **lra, vals=tuple(var_vals[k] for k in self.p.vars), wait=wait)
+    return self._prg(*[x._buf for x in rawbufs], **lra, vals=tuple(var_vals[k.expr] for k in self.p.vars), wait=wait)
 
 class ViewOp(Runner):
   def __init__(self, buf:Buffer): super().__init__(colored(f"view {buf.nbytes:8d} @ {buf.offset:<10d}", "yellow"), buf.device)
-  def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False):
+  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int], wait=False):
     assert rawbufs[0]._base is not None and rawbufs[0]._base == rawbufs[1].base, f"must be base {rawbufs}"
 
 class BufferCopy(Runner):
@@ -124,7 +126,7 @@ class BufferCopy(Runner):
       src.allocator._copyout(dest.allocator._as_buffer(dest._buf), src._buf)
     else:
       dest.copyin(src.as_buffer(allow_zero_copy=True))  # may allocate a CPU buffer depending on allow_zero_copy
-  def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False):
+  def __call__(self, rawbufs:list[Buffer], var_vals:dict[str, int], wait=False):
     dest, src = rawbufs[0:2]
     assert dest.size == src.size and dest.dtype == src.dtype, f"buffer copy mismatch, {dest.size} != {src.size}, {dest.dtype} != {src.dtype}"
     st = time.perf_counter()
@@ -159,8 +161,8 @@ class ExecItem:
   prg: Runner
   bufs: list[Buffer|None]
   metadata: tuple[Metadata, ...]|None = None
-  fixedvars: dict[Variable, int] = field(default_factory=dict)
-  def run(self, _var_vals:dict[Variable, int]|None=None, wait=False, jit=False, do_update_stats=True) -> float|None:
+  fixedvars: dict[str, int] = field(default_factory=dict)
+  def run(self, _var_vals:dict[str, int]|None=None, wait=False, jit=False, do_update_stats=True) -> float|None:
     var_vals = self.fixedvars if _var_vals is None else (_var_vals|self.fixedvars)
     bufs = [cast(Buffer, x) for x in self.bufs] if jit else [cast(Buffer, x).ensure_allocated() for x in self.bufs]
     if PROFILE: cpu_events.append(ProfilePointEvent(self.prg.device, "exec", self.prg.display_name, {"metadata":self.metadata, "var_vals":var_vals}))
@@ -211,7 +213,7 @@ def lower_schedule(schedule:list[ScheduleItem]) -> Generator[tuple[ScheduleItem,
 
 capturing: list = []  # put classes with an add method in here
 
-def run_schedule(schedule:list[ScheduleItem], var_vals:dict[Variable, int]|None=None, do_update_stats=True):
+def run_schedule(schedule:list[ScheduleItem], var_vals:dict[str, int]|None=None, do_update_stats=True):
   for si, ei in lower_schedule(schedule):
     if len(capturing) and CAPTURING: capturing[0].add(ei)
     if VALIDATE_WITH_CPU and si.ast.op is Ops.SINK:
@@ -224,9 +226,8 @@ def run_schedule(schedule:list[ScheduleItem], var_vals:dict[Variable, int]|None=
       ei.run(var_vals, do_update_stats=do_update_stats)
 
       # validate the output buffers match (NOTE: this is assuming the output is buffer 0)
-      lower_schedule_item(ScheduleItem(si.ast, nb, si.metadata, si.fixedvars)).run(var_vals, do_update_stats=do_update_stats)
+      with Context(BEAM=0): lower_schedule_item(ScheduleItem(si.ast, nb, si.metadata, si.fixedvars)).run(var_vals, do_update_stats=do_update_stats)
       import numpy as np
       np.testing.assert_allclose(si.bufs[0].numpy(), nb[0].numpy(), rtol=1e-3, atol=1e-3)
     else:
       ei.run(var_vals, do_update_stats=do_update_stats)
-

tinygrad/engine/schedule.py

@@ -1,7 +1,7 @@
 from typing import cast
 from dataclasses import dataclass, field
 from collections import deque, defaultdict
-from tinygrad.uop.ops import UOp, Variable, Ops, buffers
+from tinygrad.uop.ops import UOp, Ops, buffers
 from tinygrad.device import Device, Buffer, MultiBuffer
 from tinygrad.helpers import Metadata, all_same
 
@@ -12,15 +12,15 @@ class ScheduleItem:
   ast: UOp
   bufs: tuple[Buffer, ...]
   metadata: tuple[Metadata, ...] = ()
-  fixedvars: dict[Variable, int] = field(default_factory=dict)
+  fixedvars: dict[str, int] = field(default_factory=dict)
 
 # **** schedule linearizer
 
-def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[Variable, int]]:
+def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[str, int]]:
   # construct the KERNEL children graph based on assigns
   children: defaultdict[UOp, list[UOp]] = defaultdict(list)
   in_degree: dict[UOp, int] = {}
-  var_vals: dict[Variable, int] = {}
+  var_vals: dict[str, int] = {}
   for u in sched_sink.toposort():
     if u.op is not Ops.ASSIGN: continue  # anything that's not an ASSIGN doesn't write a kernel, so we can skip
     k = u.src[1]
@@ -40,8 +40,8 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
         pass  # a BUFFER is already realized, nothing to do here
       elif s.op is Ops.BIND:
         var, val = s.unbind()
-        assert var not in var_vals or var_vals[var] == val, f"bind mismatch on {var}, {var_vals[var]} != {val}"
-        var_vals[var] = val
+        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 ASSIGN or BUFFER, not {s.op}")
 
@@ -72,7 +72,7 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
       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]:i} if len(dnums) else {}))
+        schedule.append(ScheduleItem(ast, bufs, k.arg.metadata, {dnums[0].expr:i} if len(dnums) else {}))
     else:
       # ONE -> ONE
       schedule.append(ScheduleItem(ast, cast(tuple[Buffer, ...], ubufs), k.arg.metadata))

tinygrad/helpers.py

@@ -10,11 +10,12 @@ U = TypeVar("U")
 def prod(x:Iterable[T]) -> T|int: return functools.reduce(operator.mul, x, 1)
 
 # NOTE: helpers is not allowed to import from anything else in tinygrad
-OSX = platform.system() == "Darwin"
+OSX, WIN = platform.system() == "Darwin", sys.platform == "win32"
 CI = os.getenv("CI", "") != ""
+ARCH_X86 = any(x in platform.processor() for x in ("Intel", "i386", "x86_64"))
 
 # fix colors on Windows, https://stackoverflow.com/questions/12492810/python-how-can-i-make-the-ansi-escape-codes-to-work-also-in-windows
-if sys.platform == "win32": os.system("")
+if WIN: os.system("")
 
 def dedup(x:Iterable[T]): return list(dict.fromkeys(x))   # retains list order
 def argfix(*x):
@@ -126,8 +127,7 @@ class ContextVar:
   def __lt__(self, x): return self.value < x
 
 DEBUG, IMAGE, BEAM, NOOPT = ContextVar("DEBUG", 0), ContextVar("IMAGE", 0), ContextVar("BEAM", 0), ContextVar("NOOPT", 0)
-JIT = ContextVar("JIT", 2 if platform.system() == 'Darwin' and ('Intel' in platform.processor() or 'i386' in platform.processor()) else 1)
-JIT_BATCH_SIZE = ContextVar("JIT_BATCH_SIZE", 32)
+JIT, JIT_BATCH_SIZE = ContextVar("JIT", 2 if OSX and ARCH_X86 else 1), ContextVar("JIT_BATCH_SIZE", 32)
 WINO, CAPTURING, TRACEMETA = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1)
 USE_TC, TC_SELECT, TC_OPT, AMX = ContextVar("TC", 1), ContextVar("TC_SELECT", -1), ContextVar("TC_OPT", 0), ContextVar("AMX", 0)
 TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS", 0)
@@ -140,8 +140,9 @@ DONT_REALIZE_EXPAND, DONT_GROUP_REDUCES = ContextVar("DONT_REALIZE_EXPAND", 0),
 QUANTIZE, VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("QUANTIZE", 0), 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, AMD_LLVM = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0), ContextVar("AMD_LLVM", 1)
-RANGEIFY, POSTOPT, FUSE_ATTENTION = ContextVar("RANGEIFY", 0), ContextVar("POSTOPT", 0), ContextVar("FUSE_ATTENTION", 0)
+RANGEIFY, FUSE_ATTENTION = ContextVar("RANGEIFY", 0), ContextVar("FUSE_ATTENTION", 0)
 EMULATE = ContextVar("EMULATE", "")
+CPU_COUNT = ContextVar("CPU_COUNT", max(1, (os.cpu_count() or 1) // (4 if ARCH_X86 else 2))) # take 1/2 of the cores, accounting HT
 
 @dataclass(frozen=True)
 class Metadata:

tinygrad/renderer/__init__.py

@@ -82,9 +82,9 @@ class ProgramSpec:
         if u.op is Ops.LOAD: self.ins.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
         if u.op is Ops.SPECIAL:
           # NOTE: you have to set local_size and global_size to the base [1,1,1] outside this
+          if u.arg[0] == 'i': self.local_size = None
           special_size = self.local_size if u.arg[0] == 'l' else self.global_size
-          assert special_size is not None, f"special_size is None but found SPECIAL in uops {u}"
-          special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify())  # TODO: the type here should be sint
+          if special_size is not None: special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify())
       self.vars = sorted(self.vars, key=lambda v: v.arg)
       self.outs = sorted(dedup(self.outs))
       self.ins = sorted(dedup(self.ins))
@@ -101,7 +101,7 @@ class ProgramSpec:
   def applied_opts(self) -> tuple[Opt, ...]|None: return self.uops[-1].arg.applied_opts if \
     self.uops is not None and self.uops[-1].op is Ops.SINK and self.uops[-1].arg is not None else None
 
-  def launch_dims(self, var_vals:dict[Variable, int]):
+  def launch_dims(self, var_vals:dict[str, int]):
     global_size = [sym_infer(sz, var_vals) for sz in self.global_size] if self.global_size is not None else None
     local_size = [sym_infer(sz, var_vals) for sz in self.local_size] if self.local_size is not None else None
     return global_size, local_size
@@ -112,6 +112,7 @@ class Renderer:
   # TODO: make this generic with a list of supported types
   supports_float4: bool = True
   has_local: bool = True
+  has_threads: bool = False
   has_shared: bool = True
   # NOTE: these two should be in (x,y,z) order to match the max_sizes argument in get_grouped_dims
   global_max: tuple[int, ...]|None = (0x8FFFFFFF,) * (3) # TODO: Ops.SPECIAL int32 indexes right now

tinygrad/renderer/cstyle.py

@@ -3,7 +3,7 @@ import os, math, sys
 from collections import defaultdict, Counter
 from tinygrad.codegen.opt import tc
 from tinygrad.uop.ops import GroupOp, Ops, UOp, PatternMatcher, UPat, sint_to_uop
-from tinygrad.helpers import strip_parens, getenv, prod, dedup, AMX
+from tinygrad.helpers import strip_parens, getenv, prod, dedup, AMX, CPU_COUNT
 from tinygrad.dtype import ImageDType, dtypes, DType, PtrDType, AddrSpace, truncate
 from tinygrad.renderer import Renderer
 from tinygrad.codegen.late.devectorizer import no_vectorized_alu
@@ -192,9 +192,12 @@ class ClangRenderer(CStyleLanguage):
   float4_style = ('{', '}')
   gep_arr_threshold = 0
   has_local = False
-  global_max = None
+  has_threads = True
+  global_max = (CPU_COUNT.value, 0, 0)
   infinity = "__builtin_inff()"
   nan = '__builtin_nanf("")'
+  code_for_workitem = {"g": lambda _: "core_id"}
+  extra_args = ['int core_id']
   if AMX: tensor_cores = tc.amx
 
   # language options

tinygrad/runtime/graph/cuda.py

@@ -4,12 +4,11 @@ import tinygrad.runtime.autogen.cuda as cuda
 from tinygrad.helpers import init_c_var, dedup
 from tinygrad.device import Buffer, Device
 from tinygrad.runtime.ops_cuda import CUDADevice, check, encode_args, cu_time_execution
-from tinygrad.uop.ops import Variable
 from tinygrad.engine.realize import ExecItem, BufferXfer, CompiledRunner
 from tinygrad.engine.jit import MultiGraphRunner, GraphException
 
 class CUDAGraph(MultiGraphRunner):
-  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[Variable, int]):
+  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[str, int]):
     super().__init__(jit_cache, input_rawbuffers, var_vals)
 
     # Check all jit items are compatible.
@@ -28,7 +27,7 @@ class CUDAGraph(MultiGraphRunner):
         deps = self._access_resources([x.base for x in ji.bufs if x is not None], ji.prg.p.outs, new_dependency=new_node)
         c_deps = (cuda.CUgraphNode*len(deps))(*deps) if deps else None
 
-        c_args, vargs = encode_args([cast(Buffer, x)._buf for x in ji.bufs], [var_vals.get(x, ji.fixedvars.get(x)) for x in ji.prg.p.vars])
+        c_args, vargs = encode_args([cast(Buffer, x)._buf for x in ji.bufs], [var_vals.get(x.expr, ji.fixedvars.get(x.expr)) for x in ji.prg.p.vars])
         kern_params = cuda.CUDA_KERNEL_NODE_PARAMS_v1(ji.prg._prg.prg, *global_size, *local_size, 0, None, vargs)
         check(cuda.cuGraphAddKernelNode(ctypes.byref(new_node), self.graph, c_deps, len(deps), ctypes.byref(kern_params)))
 
@@ -48,7 +47,7 @@ class CUDAGraph(MultiGraphRunner):
 
     self.instance = init_c_var(cuda.CUgraphExec(), lambda x: check(cuda.cuGraphInstantiate_v2(ctypes.byref(x), self.graph, None, None, 0)))
 
-  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[Variable, int], wait=False) -> float|None:
+  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[str, int], wait=False) -> float|None:
     # Update rawbuffers in the c_args struct.
     for (j,i),input_idx in self.input_replace.items():
       if not self.updatable_nodes[j][3]: setattr(self.updatable_nodes[j][2], f'f{i}', input_rawbuffers[input_idx]._buf)

tinygrad/runtime/graph/hcq.py

@@ -9,7 +9,7 @@ from tinygrad.engine.realize import ExecItem, BufferXfer, CompiledRunner, Buffer
 from tinygrad.engine.jit import MultiGraphRunner
 
 class HCQGraph(MultiGraphRunner):
-  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[Variable, int]):
+  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[str, int]):
     super().__init__(jit_cache, input_rawbuffers, var_vals)
     self.devices = list(set(cast(HCQCompiled, d) for ji in jit_cache for d in [Device[cast(Buffer, x).device] for x in ji.bufs]))
 
@@ -69,7 +69,7 @@ class HCQGraph(MultiGraphRunner):
     for dev, queue in self.comp_queues.items(): dev_access[queue].add(dev)
 
     self.input_replace_map: dict[HCQCompiled, set[int]] = collections.defaultdict(set)
-    self.fixedvars: dict[HCQCompiled, dict[Variable, int]] = {}
+    self.fixedvars: dict[HCQCompiled, dict[str, int]] = {}
 
     for j,ji in enumerate(jit_cache):
       if is_exec_prg:=isinstance(ji.prg, CompiledRunner): enqueue_dev: HCQCompiled = ji.prg.dev
@@ -183,7 +183,7 @@ class HCQGraph(MultiGraphRunner):
     self.last_timeline: dict[HCQCompiled, tuple[HCQSignal, int]] = {dev: (dev.timeline_signal, 0) for dev in self.devices}
     self.queue_signals_to_reset = [self.signals[q] for q in list(self.comp_queues.values()) + list(self.copy_queues.values()) if q in self.signals]
 
-  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[Variable, int], wait=False) -> float|None:
+  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[str, int], wait=False) -> float|None:
     # Wait and restore signals
     self.kickoff_value += 1
     for dev in self.devices: self.last_timeline[dev][0].wait(self.last_timeline[dev][1])
@@ -195,12 +195,13 @@ class HCQGraph(MultiGraphRunner):
 
     if PROFILE and self.kickoff_value > 1: self.collect_timestamps()
 
-    hcq_var_vals = {self.kickoff_var: self.kickoff_value, **var_vals,
-                    **{var: dev.timeline_value - 1 for dev, var in self.virt_timeline_vals.items()},
-                    **{sig.base_buf.va_addr: dev.timeline_signal.base_buf.va_addr for dev, sig in self.virt_timeline_signals.items()}}
+    hcq_var_vals = {self.kickoff_var.expr: self.kickoff_value, **var_vals,
+                    **{var.expr: dev.timeline_value - 1 for dev, var in self.virt_timeline_vals.items()},
+                    **{sig.base_buf.va_addr.expr: dev.timeline_signal.base_buf.va_addr for dev, sig in self.virt_timeline_signals.items()}}
 
     # Update rawbuffers
-    for (j,i),input_idx in self.input_replace.items(): hcq_var_vals[self.input_replace_to_var.get((j,i))] = input_rawbuffers[input_idx]._buf.va_addr
+    for (j,i),input_idx in self.input_replace.items():
+      hcq_var_vals[self.input_replace_to_var[(j,i)].expr] = input_rawbuffers[input_idx]._buf.va_addr
 
     for dev in self.devices:
       self.comp_queues[dev].submit(dev, hcq_var_vals_local:=hcq_var_vals|self.fixedvars.get(dev, {}))

tinygrad/runtime/graph/metal.py

@@ -5,7 +5,6 @@ from tinygrad.helpers import dedup, getenv, merge_dicts, PROFILE
 from tinygrad.device import Buffer, ProfileGraphEntry, ProfileGraphEvent
 from tinygrad.engine.realize import ExecItem, CompiledRunner
 from tinygrad.engine.jit import GraphRunner, GraphException
-from tinygrad.uop.ops import Variable
 from tinygrad.runtime.ops_metal import wait_check, msg, libobjc, to_struct, objc_instance,\
   MTLResourceOptions, cmdbuf_st_time, cmdbuf_en_time, objc_id, to_ns_str
 
@@ -17,7 +16,7 @@ class MTLResourceUsage:
   MTLResourceUsageWrite = 0b10
 
 class MetalGraph(GraphRunner):
-  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[Variable, int]):
+  def __init__(self, jit_cache: list[ExecItem], input_rawbuffers: list[Buffer], var_vals: dict[str, int]):
     super().__init__(jit_cache, input_rawbuffers, var_vals)
     if not all(isinstance(ji.prg, CompiledRunner) for ji in jit_cache): raise GraphException
 
@@ -48,7 +47,8 @@ class MetalGraph(GraphRunner):
         if b is not None and b not in input_rawbuffers:
           msg("setKernelBuffer:offset:atIndex:")(icb_command, b._buf.buf, b._buf.offset, i)
           all_resources.append(b._buf.buf)
-      for i,v in enumerate(prg.p.vars): msg("setKernelBuffer:offset:atIndex:")(icb_command, self.int_buf.buf, self.varlist.index(v)*4, len(ji.bufs)+i)
+      for i,v in enumerate(prg.p.vars):
+        msg("setKernelBuffer:offset:atIndex:")(icb_command, self.int_buf.buf, self.varlist.index(v.expr)*4, len(ji.bufs)+i)
 
       global_size, local_size = prg.p.launch_dims(var_vals)
       msg("concurrentDispatchThreadgroups:threadsPerThreadgroup:")(icb_command, to_struct(*global_size), to_struct(*local_size))
@@ -61,7 +61,7 @@ class MetalGraph(GraphRunner):
     for var in self.fixedvars: self.int_buf_view[self.varlist.index(var)] = self.fixedvars[var]
     self.range = to_struct(0, len(jit_cache))
 
-  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[Variable, int], wait=False) -> float|None:
+  def __call__(self, input_rawbuffers: list[Buffer], var_vals: dict[str, int], wait=False) -> float|None:
     if self.command_buffer is not None and self.command_buffer in self.dev.mtl_buffers_in_flight: wait_check(self.command_buffer)
     # NOTE: old command buffer may not be inflight anymore
     if self.command_buffer is not None and PROFILE: self.collect_timestamps()

tinygrad/runtime/graph/remote.py

@@ -1,5 +1,4 @@
 import time, itertools
-from tinygrad.uop.ops import Variable
 from tinygrad.engine.jit import MultiGraphRunner
 from tinygrad.engine.realize import CompiledRunner, BufferXfer, ExecItem
 from tinygrad.device import Device, Compiled, Buffer
@@ -18,7 +17,7 @@ def dev_key(dev:RemoteDevice): return dev.conn if dev.properties.graph_supports_
 def map_rawbuf(rawbuf:Buffer): return (cast(RemoteDevice, Device[rawbuf.device]).session, rawbuf._buf)
 
 class RemoteGraph(MultiGraphRunner):
-  def __init__(self, jit_cache: list[ExecItem], rawbufs: list[Buffer], var_vals: dict[Variable, int]):
+  def __init__(self, jit_cache: list[ExecItem], rawbufs: list[Buffer], var_vals: dict[str, int]):
     super().__init__(jit_cache, rawbufs, var_vals)
     devices = dedup(flatten([[Device[unwrap(buf).device] for buf in ji.bufs] for ji in jit_cache]))
     c2d = {device.conn: device for device in devices}
@@ -93,7 +92,7 @@ class RemoteGraph(MultiGraphRunner):
     for req in self.template:
       match req:
         case GraphExec(): RemoteConnection(unwrap(req.session).host).q(GraphFree(req.graph_num, session=req.session))
-  def __call__(self, rawbufs: list[Buffer], var_vals: dict[Variable, int], wait=False):
+  def __call__(self, rawbufs: list[Buffer], var_vals: dict[str, int], wait=False):
     if wait: st = time.perf_counter()
     rmap = {orig: map_rawbuf(rawbufs[replace_idx]) for orig,replace_idx in self.handle_indexes.items()}
     for req in self.template:

tinygrad/runtime/ops_cpu.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import platform, subprocess, sys, ctypes, functools, time, mmap, threading, queue
-from tinygrad.helpers import capstone_flatdump, getenv, from_mv, to_mv, OSX, mv_address, wait_cond, cpu_profile
+from tinygrad.helpers import capstone_flatdump, getenv, from_mv, to_mv, OSX, WIN, mv_address, wait_cond, cpu_profile, suppress_finalizing
 from tinygrad.device import Compiler, BufferSpec, DMACPURef
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocatorBase, HCQBuffer, HWQueue, HCQArgsState, HCQSignal, HCQProgram, MMIOInterface
 from tinygrad.runtime.support.elf import jit_loader
@@ -30,29 +30,38 @@ class ClangJITCompiler(Compiler):
 class CPUWorker(threading.Thread):
   def __init__(self, dev, tasks, thread_id):
     super().__init__()
-    self.dev, self.tasks, self.thread_id, self.daemon = dev, tasks, thread_id, True
+    self.dev, self.tasks, self.thread_id, self.pool, self.daemon = dev, tasks, thread_id, [], True
+
+  def push_task(self, tid, cmd, args):
+    if len(self.pool) <= tid:
+      self.pool.append(queue.Queue())
+      CPUWorker(self, self.pool[tid], thread_id=tid+1).start()
+    self.pool[tid].put([cmd, 1, len(args)] + args)
 
   def run(self):
     while True:
       cmd_iter = iter(self.tasks.get())
       for cmd in cmd_iter:
-        args_cnt = next(cmd_iter)
-        cmd(*[next(cmd_iter) for _ in range(args_cnt)])
+        threads, args_cnt = next(cmd_iter), next(cmd_iter)
+        args = [next(cmd_iter) for _ in range(args_cnt)]
+        for th in range(threads - 1): self.push_task(th, cmd, args)
+        cmd(self.thread_id, *args)
+        for th in range(threads - 1): self.pool[th].join()
       self.tasks.task_done()
 
 class CPUComputeQueue(HWQueue):
-  def _exec(self, prg, bufs, *args):
-    prg.fxn(*map(ctypes.c_uint64, args[:bufs]), *map(ctypes.c_int64 if platform.machine() == "arm64" else ctypes.c_int32, args[bufs:]))
-  def _signal(self, signal_addr, value): to_mv(signal_addr, 4).cast('I')[0] = value
-  def _wait(self, signal_addr, value): wait_cond(lambda: to_mv(signal_addr, 4).cast('I')[0] >= value, timeout_ms=60000)
-  def _timestamp(self, timestamp_addr): to_mv(timestamp_addr, 8).cast('Q')[0] = time.perf_counter_ns()
-  def cmd(self, cmd, *args):
-    self.q(cmd, len(args), *args)
+  def _exec(self, tid, prg, bufs, *args):
+    prg.fxn(*map(ctypes.c_uint64, args[:bufs]), *map(ctypes.c_int64 if platform.machine() == "arm64" else ctypes.c_int32, args[bufs:]), tid)
+  def _signal(self, tid, signal_addr, value): to_mv(signal_addr, 4).cast('I')[0] = value
+  def _wait(self, tid, signal_addr, value): wait_cond(lambda: to_mv(signal_addr, 4).cast('I')[0] >= value, timeout_ms=60000)
+  def _timestamp(self, tid, timestamp_addr): to_mv(timestamp_addr, 8).cast('Q')[0] = time.perf_counter_ns()
+  def cmd(self, cmd, *args, threads=1):
+    self.q(cmd, threads, len(args), *args)
     return self
 
   def memory_barrier(self): return self
   def exec(self, prg:CPUProgram, args_state:HCQArgsState, global_size, local_size):
-    return self.cmd(self._exec, prg, len(args_state.bufs), *[x.va_addr for x in args_state.bufs], *args_state.vals)
+    return self.cmd(self._exec, prg, len(args_state.bufs), *[x.va_addr for x in args_state.bufs], *args_state.vals, threads=(global_size or (1,))[0])
   def wait(self, signal, value=0): return self.cmd(self._wait, signal.value_addr, value)
   def timestamp(self, signal): return self.cmd(self._timestamp, signal.timestamp_addr)
   def signal(self, signal, value:sint=0): return self.cmd(self._signal, signal.value_addr, value)
@@ -62,10 +71,10 @@ class CPUComputeQueue(HWQueue):
 MAP_JIT = 0x0800
 
 class CPUProgram(HCQProgram):
-  rt_lib = ctypes.CDLL(ctypes.util.find_library('System' if OSX else 'kernel32') if OSX or sys.platform == "win32" else 'libgcc_s.so.1')
+  rt_lib = ctypes.CDLL(ctypes.util.find_library('System' if OSX else 'kernel32') if OSX or WIN else 'libgcc_s.so.1')
 
   def __init__(self, dev, name:str, lib:bytes):
-    if sys.platform == "win32":
+    if sys.platform == "win32": # mypy doesn't understand when WIN is used here
       PAGE_EXECUTE_READWRITE, MEM_COMMIT, MEM_RESERVE = 0x40, 0x1000, 0x2000
       ctypes.windll.kernel32.VirtualAlloc.restype = ctypes.c_void_p
       self.mem = ctypes.windll.kernel32.VirtualAlloc(ctypes.c_void_p(0), ctypes.c_size_t(len(lib)), MEM_COMMIT | MEM_RESERVE, PAGE_EXECUTE_READWRITE)
@@ -93,14 +102,14 @@ class CPUProgram(HCQProgram):
 
     super().__init__(HCQArgsState, dev, name, kernargs_alloc_size=0)
 
+  @suppress_finalizing
   def __del__(self):
-    if getattr(sys, 'is_finalizing', lambda: True)(): return
     if sys.platform == 'win32': ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(self.mem), ctypes.c_size_t(0), 0x8000) #0x8000 - MEM_RELEASE
 
 class CPUAllocator(HCQAllocatorBase):
   def _alloc(self, size:int, options:BufferSpec) -> HCQBuffer:
     if options.external_ptr: addr, buf = options.external_ptr, None
-    elif sys.platform == "win32": addr = mv_address(buf:=mmap.mmap(-1, size, access=mmap.ACCESS_WRITE))
+    elif WIN: addr = mv_address(buf:=mmap.mmap(-1, size, access=mmap.ACCESS_WRITE))
     else: addr = mv_address(buf:=mmap.mmap(-1, size, mmap.MAP_ANON | mmap.MAP_PRIVATE, mmap.PROT_READ | mmap.PROT_WRITE))
     return HCQBuffer(va:=addr, sz:=size, meta=buf, view=MMIOInterface(va, sz, fmt='B'), owner=self.dev)
   def _as_buffer(self, src) -> memoryview:

tinygrad/runtime/ops_dsp.py

@@ -36,8 +36,10 @@ dsp_string = PatternMatcher([
 class DSPRenderer(ClangRenderer):
   device = "DSP"
   supports_float4 = True
+  has_threads = False
   buffer_suffix = " restrict __attribute__((align_value(128)))"
   kernel_typedef = "__attribute__((noinline)) void"
+  extra_args = []
   pre_matcher = dsp_pm
   extra_matcher = dsp_pm_late+ClangRenderer.extra_matcher
   string_rewrite = dsp_string+ClangRenderer.string_rewrite

tinygrad/runtime/ops_remote.py

@@ -100,7 +100,7 @@ class GraphComputeItem:
   datahash: str
   bufs: tuple[int, ...]
   vars: tuple[Variable, ...]
-  fixedvars: dict[Variable, int]
+  fixedvars: dict[str, int]
   ins: tuple[int, ...]
   outs: tuple[int, ...]
   global_size: tuple[sint, ...]|None
@@ -111,7 +111,7 @@ class GraphAlloc(RemoteRequest):
   graph_num: int
   jit_cache: tuple[GraphComputeItem|Transfer, ...]
   bufs: tuple[tuple[SessionKey, int], ...]
-  var_vals: dict[Variable, int]
+  var_vals: dict[str, int]
 
 @dataclass(frozen=True)
 class GraphFree(RemoteRequest):
@@ -121,7 +121,7 @@ class GraphFree(RemoteRequest):
 class GraphExec(RemoteRequest):
   graph_num: int
   bufs: tuple[tuple[SessionKey, int], ...]
-  var_vals: dict[Variable, int]
+  var_vals: dict[str, int]
   wait: bool
 
 # for safe deserialization

tinygrad/runtime/support/hcq.py

@@ -6,7 +6,7 @@ except ImportError: fcntl = None #type:ignore[assignment]
 from tinygrad.helpers import PROFILE, getenv, to_mv, round_up, ProfileRangeEvent
 from tinygrad.renderer import Renderer
 from tinygrad.device import BufferSpec, Compiler, Compiled, LRUAllocator, ProfileDeviceEvent, ProfileProgramEvent
-from tinygrad.uop.ops import sym_infer, sint, Variable, UOp
+from tinygrad.uop.ops import sym_infer, sint, UOp
 from tinygrad.runtime.autogen import libc
 
 class MMIOInterface:
@@ -28,10 +28,7 @@ class FileIOInterface:
   def __del__(self):
     if hasattr(self, 'fd'): os.close(self.fd)
   def ioctl(self, request, arg): return fcntl.ioctl(self.fd, request, arg)
-  def mmap(self, start, sz, prot, flags, offset):
-    x = libc.mmap(start, sz, prot, flags, self.fd, offset)
-    if x == 0xffffffffffffffff: raise OSError(f"Failed to mmap {sz} bytes at {hex(start)}: {os.strerror(ctypes.get_errno())}")
-    return x
+  def mmap(self, start, sz, prot, flags, offset): return FileIOInterface._mmap(start, sz, prot, flags, self.fd, offset)
   def read(self, size=None, binary=False, offset=None):
     if offset is not None: self.seek(offset)
     with open(self.fd, "rb" if binary else "r", closefd=False) as file: return file.read(size)
@@ -41,11 +38,13 @@ class FileIOInterface:
   def listdir(self): return os.listdir(self.path)
   def seek(self, offset): os.lseek(self.fd, offset, os.SEEK_SET)
   @staticmethod
-  def anon_mmap(start, sz, prot, flags, offset):
-    x = libc.mmap(start, sz, prot, flags, -1, offset)
+  def _mmap(start, sz, prot, flags, fd, offset):
+    x = libc.mmap(start, sz, prot, flags, fd, offset)
     if x == 0xffffffffffffffff: raise OSError(f"Failed to mmap {sz} bytes at {hex(start)}: {os.strerror(ctypes.get_errno())}")
     return x
   @staticmethod
+  def anon_mmap(start, sz, prot, flags, offset): return FileIOInterface._mmap(start, sz, prot, flags, -1, offset)
+  @staticmethod
   def munmap(buf, sz): return libc.munmap(buf, sz)
   @staticmethod
   def exists(path): return os.path.exists(path)
@@ -192,7 +191,7 @@ class HWQueue(Generic[SignalType, HCQDeviceType, ProgramType, ArgsStateType]):
       if isinstance(val, int): mv[i] = val if mask is None else ((mv[i] & ~mask) | val)
       else: self.mv_sints.append((mv, i, self._new_sym(val), mask))
 
-  def _apply_var_vals(self, var_vals:dict[Variable, int]):
+  def _apply_var_vals(self, var_vals:dict[str, int]):
     resolved_syms = [sym_infer(sym, var_vals) for sym in self.syms]
 
     for off, sym_idx in self.q_sints:
@@ -205,7 +204,7 @@ class HWQueue(Generic[SignalType, HCQDeviceType, ProgramType, ArgsStateType]):
 
     self._prev_resolved_syms = cast(list[int|None], resolved_syms)
 
-  def submit(self, dev:HCQDeviceType, var_vals:dict[Variable, int]|None=None):
+  def submit(self, dev:HCQDeviceType, var_vals:dict[str, int]|None=None):
     """
     Submits the command queue to a specific device for execution.
 

tinygrad/schedule/rangeify.py

@@ -119,7 +119,7 @@ def map_reshape(idx:UOp, r:UOp):
   for s,src in list(zip(idx.shape, idx.src[1:]))[::-1]:
     to_sum.append(acc*src)
     acc *= s
-  mish = sum(to_sum, start=UOp.const(dtypes.int, 0))
+  mish = sum(to_sum, start=UOp.const(dtypes.index, 0))
   ret:list[UOp] = []
   for s in r.src[0].shape[::-1]:
     ret.append(mish % s) # NOTE: simplify will turn this to CONST
@@ -186,7 +186,7 @@ def map_partial_contiguous(ctx:RangeifyContext, x:UOp, idx:UOp):
       ranges.append(idx.src[1+i])
       continue
     passthrough_idx.append(idx.src[1+i])
-    ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.int, 0))
+    ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.index, 0))
     new_ranges.append(ranges[-1])
   ret = x.src[0].index(*ranges).bufferize(*[x for x in new_ranges if x.op is not Ops.CONST], arg=x.device)
   return ret.index(*passthrough_idx)
@@ -195,7 +195,7 @@ def map_contiguous(ctx:RangeifyContext, x:UOp):
   if x.arg is not None: return None
   ranges = []
   for s in x.shape[len(x.src)-1:]:
-    ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.int, 0))
+    ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.index, 0))
   ret = x.src[0].index(*ranges).bufferize(*x.src[1:], *[x for x in ranges if x.op is not Ops.CONST], arg=x.device)
   return ret.shrink(((0, prod(x.shape)),)).forced_reshape(x.shape)
 

tinygrad/shape/shapetracker.py

@@ -5,22 +5,8 @@ import functools
 from typing import Callable
 from tinygrad.helpers import merge_dicts, getenv
 from tinygrad.shape.view import View, unravel
-from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context, PatternMatcher, UPat, GroupOp
 from tinygrad.uop.symbolic import symbolic_flat, uop_given_valid, simplify_valid
-
-# If a node overflow, its srcs need to be checked to see if this overflow is the result of an ALU operation,
-# or that the node simply inherits the dtype from srcs. Upcast is either `Ops.CAST`+`replace` or just `replace`.
-def handle_upcast(u: UOp) -> UOp|None:
-  dtype = dtypes.int64.vec(u.dtype.count) if u.dtype.count > 1 else dtypes.int64
-  # check for overflow, upcast this to int64
-  if u.vmax > dtypes.max(dtypes.int) or u.vmin < dtypes.min(dtypes.int):
-    return u.replace(dtype=dtype, src=tuple([x.cast(dtype) for x in u.src]))
-  # if any inputs are int64 and this *doesn't* overflow, cast back to int
-  if any(x.dtype == dtypes.int64 for x in u.src):
-    return u.replace(dtype=dtype, src=tuple([x.cast(dtype) for x in u.src])).cast(u.dtype)
-  return None
-pm_upcast = PatternMatcher([(UPat(GroupOp.ALU, dtype=dtypes.int, name="u"), handle_upcast),])
+from tinygrad.uop.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context
 
 @functools.cache
 def views_to_indexed_uops(views: tuple[View, ...], _idxs:tuple[UOp, ...]|None=None) -> tuple[UOp, UOp]:
@@ -34,8 +20,8 @@ def views_to_indexed_uops(views: tuple[View, ...], _idxs:tuple[UOp, ...]|None=No
     # simplify
     if (newvalid:=simplify_valid(valid)) is not None: valid = newvalid
     if (newidx:=uop_given_valid(valid, idx)) is not None: idx = newidx
-    # symbolic again, upcast if needed
-    return graph_rewrite(UOp.sink(idx, valid), symbolic_flat+pm_upcast, name="indexing sym @ 2").src
+    # symbolic again
+    return graph_rewrite(UOp.sink(idx, valid), symbolic_flat, name="indexing sym @ 2").src
 
 @functools.cache
 def views_to_real_strides(views: tuple[View, ...], ignore_valid=False) -> tuple[sint|None, ...]:
@@ -97,7 +83,7 @@ class ShapeTracker:
   def vars(self) -> set[Variable]: return set().union(*[v.vars() for v in self.views])
 
   @property
-  def var_vals(self) -> dict[Variable, int]: return merge_dicts([dict([v.unbind()]) for v in self.vars()])
+  def var_vals(self) -> dict[str, int]: return merge_dicts([{(vu:=v.unbind())[0].expr:vu[1]} for v in self.vars()])
 
   def unbind(self) -> tuple[ShapeTracker, dict[Variable, int]]:
     unbound_views, var_vals = zip(*[v.unbind() for v in self.views])

tinygrad/shape/view.py

@@ -204,15 +204,15 @@ class View:
 
     # Merge dimensions in vm2 if required.
     # NB: Merging too many dimensions can make it difficult to project vm2's mask, hence only combining when required.
-    idxs: list[UOp] = [UOp.variable(f"idx{i}", 0, s-1) for i,s in enumerate(vm1.shape)]
-    merged_size, merged_term = 1, UOp.const(dtypes.int, 0)
+    idxs: list[UOp] = [UOp.variable(f"idx{i}", 0, s-1, dtypes.index) for i,s in enumerate(vm1.shape)]
+    merged_size, merged_term = 1, UOp.const(dtypes.index, 0)
     extents: list[tuple[sint, UOp]] = []
     for term, s, o in zip(reversed(terms), reversed(vm2.shape), reversed(origin)):
       merged_term += (sum([idxs[d1] * s1 for d1, s1 in term]) + o) * merged_size
       merged_size *= s
       if resolve(merged_term < merged_size, False) and resolve(0 <= merged_term, False):
         extents.append((merged_size, merged_term))
-        merged_size, merged_term = 1, UOp.const(dtypes.int, 0)
+        merged_size, merged_term = 1, UOp.const(dtypes.index, 0)
     if resolve(merged_term != 0): return None
     if (vm2_shape := tuple(s for s,_ in reversed(extents))) != vm2.shape:
       if (reshaped_vm2 := vm2.reshape(vm2_shape)) is None: return None

tinygrad/tensor.py

@@ -8,7 +8,7 @@ from tinygrad.dtype import _from_np_dtype, _to_np_dtype
 from tinygrad.helpers import argfix, make_tuple, flatten, prod, all_int, round_up, merge_dicts, argsort, getenv, all_same, fully_flatten, dedup
 from tinygrad.helpers import IMAGE, WINO, Metadata, TRACEMETA, ceildiv, fetch, polyN, unwrap, DEBUG, is_numpy_ndarray, RANGEIFY, FUSE_ATTENTION
 from tinygrad.gradient import compute_gradient
-from tinygrad.uop.ops import smax, smin, resolve, UOp, Ops, sint, Variable, MathTrait, identity_element, all_metadata
+from tinygrad.uop.ops import smax, smin, resolve, UOp, Ops, sint, MathTrait, identity_element, all_metadata, index_to_concrete_int
 from tinygrad.uop.spec import tensor_uop_spec, type_verify
 from tinygrad.device import Device, Buffer
 from tinygrad.engine.realize import run_schedule
@@ -139,11 +139,13 @@ class Tensor(MathTrait):
     # create a UOp from the different types of inputs
     if isinstance(data, UOp):
       assert dtype is None or dtype==data.dtype, "dtype doesn't match, and casting isn't supported"
-      if data.op is Ops.BIND:
-        var, val = data.unbind()
+      # if data is dtype.index that means that this is a symbolic int and we need to lower it to something we can make a Tensor out of
+      if data.dtype==dtypes.index: data = index_to_concrete_int(data)
+      if data.op is Ops.BIND:  # type: ignore  # mypy type narrowing is bugged here
+        var, val = data.unbind()  # type: ignore
         # give the bound constant a device
         const = UOp.const(var.dtype, val, device, ())
-        data = data.replace(src=(var.replace(src=const.src), const))
+        data = data.replace(src=(var.replace(src=const.src), const))  # type: ignore
     elif data is None: data = UOp.const(dtype or dtypes.default_float, 0, device, ())
     elif isinstance(data, get_args(ConstType)): data = UOp.const(dtype or dtypes.from_py(data), data, device, ())
     elif isinstance(data, bytes): data = _frompy(data, dtypes.uint8 if dtype is None else dtype)
@@ -175,7 +177,9 @@ class Tensor(MathTrait):
 
     # add to all_tensors after construction succeeds
     all_tensors[weakref.ref(self)] = None
-  def __del__(self): all_tensors.pop(weakref.ref(self), None)
+  def __del__(self):
+    try: all_tensors.pop(weakref.ref(self), None)
+    except Exception: pass
 
   def _apply_uop(self, fxn:Callable, *x:Tensor, extra_args=(), **kwargs) -> Tensor:
     new_uop: UOp = fxn(*[t.uop for t in (self,)+x], *extra_args, **kwargs)
@@ -239,7 +243,7 @@ class Tensor(MathTrait):
     _apply_map_to_tensors(becomes_map, name="Apply Kernelize Map")
     return self
 
-  def schedule_with_vars(self, *lst:Tensor) -> tuple[list[ScheduleItem], dict[Variable, int]]:
+  def schedule_with_vars(self, *lst:Tensor) -> tuple[list[ScheduleItem], dict[str, int]]:
     """
     Creates the schedule needed to realize these Tensor(s), with Variables.
 

tinygrad/uop/ops.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 from enum import Enum, auto
 from tinygrad.uop import Ops, GroupOp
 from tinygrad.uop.mathtraits import MathTrait
-from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType
+from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType, least_upper_dtype
 from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten, TRACEMETA
 from tinygrad.helpers import PICKLE_BUFFERS, PROFILE, dedup, cdiv, cmod, diskcache_put, to_function_name, cpu_profile, TracingKey
 if TYPE_CHECKING:
@@ -13,7 +13,8 @@ if TYPE_CHECKING:
   from tinygrad.device import Buffer, MultiBuffer
 
 class AxisType(Enum):
-  GLOBAL = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto()  # noqa: E702
+  GLOBAL = auto(); WARP = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto() # noqa: E702
+  THREAD = auto()
 
 # https://en.wikipedia.org/wiki/Identity_element
 def identity_element(op:Ops, dt:DType) -> ConstType: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dtypes.min(dt)}[op], dt)
@@ -37,7 +38,7 @@ def smin(*lst): return _suop(argfix(*lst), UOp.minimum, min)
 def srender(x) -> str: return x.render() if isinstance(x, UOp) else str(x)
 
 def ssimplify(uop): return uop.ssimplify() if isinstance(uop, UOp) else uop
-def sym_infer(uop: UOp|int, var_vals: dict[UOp, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop
+def sym_infer(uop: UOp|int, var_vals: dict[str, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop
 
 # used for UOp and UPat
 def pretty_print(x:Any, rep:Callable, srcfn=lambda x: x.src, cache=None, d=0)->str:
@@ -307,7 +308,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def range(end:sint, *arg):
     if len(arg) == 0: raise RuntimeError("range needs an arg")
     if len(arg) == 1: arg = arg+(AxisType.LOOP,)
-    return UOp(Ops.RANGE, dtype=dtypes.int, src=(sint_to_uop(end),), arg=arg)
+    return UOp(Ops.RANGE, dtype=dtypes.index, src=(sint_to_uop(end),), arg=arg)
   def r(self, op:Ops, axis:tuple[int, ...]):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
     if len(axis) == 0: return self
@@ -482,11 +483,11 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   # *** uop Variable stuff ***
 
   @staticmethod
-  def variable(name:str, min_val:ConstType, max_val:ConstType, dtype:DType=dtypes.int) -> UOp:
+  def variable(name:str, min_val:ConstType, max_val:ConstType, dtype:DType=dtypes.index) -> UOp:
     assert not isinstance(min_val, UOp) and not isinstance(max_val, UOp), f"can't create Variable {name} with {min_val}/{max_val}"
     return UOp(Ops.DEFINE_VAR, dtype, arg=(name, min_val, max_val))
   @property
-  def expr(self):
+  def expr(self) -> str:
     assert self.op is Ops.DEFINE_VAR, f"op is {self.op}, need DEFINE_VAR"
     return self.arg[0]
   def bind(self, val:int|UOp):
@@ -573,7 +574,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.VCONST: return (min(self.arg), max(self.arg))
     if self.op is Ops.GEP: return self.src[0]._min_max
     # TODO: CAST to bool/unsigned is not monotone, still some case can be simplified
-    if self.op is Ops.CAST and self.dtype in (dtypes.floats+dtypes.sints):
+    if self.op is Ops.CAST and self.dtype in dtypes.floats+dtypes.sints+(dtypes.index,):
       return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))
     return dtypes.min(self.dtype), dtypes.max(self.dtype)
 
@@ -584,9 +585,9 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     # TODO: sanitize varnames, or don't use naked eval while staying fast
     return eval("lambda "+','.join(varnames)+": "+sself.render(pm=renderer_infer)), varnames  # pylint: disable=eval-used
 
-  def sym_infer(self, var_vals:dict[UOp, int]):
+  def sym_infer(self, var_vals:dict[str, int]):
     fxn, varnames = self._sym_fxn
-    return fxn(**{k.arg[0]:v for k,v in var_vals.items() if k.arg[0] in varnames})
+    return fxn(**{k:v for k,v in var_vals.items() if k in varnames})
 
   def render(self, simplify=True, pm:PatternMatcher|None=None) -> str:
     with Context(TRACK_MATCH_STATS=0):
@@ -842,13 +843,15 @@ if getenv("CAPTURE_PROCESS_REPLAY"):
   def save_to_diskcache():
     for k,v in replay_capture.items(): diskcache_put("process_replay", k, v, prepickled=True)
 
+def add_trace_group(kt:TracingKey) -> None:
+  tracked_keys.append(kt)
+  tracked_ctxs.append([])
+
 def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=False):
   def _decorator(func):
     def __wrapper(*args, **kwargs):
       fn = key = func.__name__
-      if TRACK_MATCH_STATS >= 2:
-        tracked_keys.append(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,)))
-        tracked_ctxs.append([])
+      if TRACK_MATCH_STATS >= 2: add_trace_group(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,)))
       with cpu_profile(key, "TINY") as e:
         ret = func(*args, **kwargs)
       if TRACK_MATCH_STATS >= 2 and callable(name):
@@ -872,9 +875,10 @@ def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=Fal
 active_rewrites:list[TrackedGraphRewrite] = []
 def track_matches(func):
   def _track_func(*args, **kwargs):
-    if tracking:=(TRACK_MATCH_STATS >= 2 and tracked_ctxs):
+    if tracking:=(TRACK_MATCH_STATS >= 2):
       loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)
       depth = len(active_rewrites)
+      if not tracked_ctxs: add_trace_group(TracingKey(f"default {func.__name__}"))
       tracked_ctxs[-1].append(ctx:=TrackedGraphRewrite(loc, track_uop(args[0]), [], kwargs.get("name", None), depth, kwargs.get("bottom_up", False)))
       active_rewrites.append(ctx)
     with cpu_profile(kwargs.get("name", "<unnamed>"), "TINY", display=tracking):
@@ -1022,7 +1026,30 @@ def graph_rewrite_map(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, na
     for k,v in input_map.items(): new_map[k] = new_map.get(v,v)
   return new_map
 
-def sint_to_uop(x:sint, dtype:DType=dtypes.int) -> UOp: return UOp.const(dtype, x) if isinstance(x, int) else x
+def sint_to_uop(x:sint) -> UOp: return UOp.const(dtypes.index, x) if isinstance(x, int) else x.cast(dtypes.index)
+
+def select_dtype(u): return (dtypes.long if u.overflows(dtypes.int32) else dtypes.int).vec(u.dtype.count)
+pm_lower_index_dtype = PatternMatcher([
+  # There are no Unary ops at this point in symbolic, those are introduced later
+  (UPat(GroupOp.Binary, dtypes.index, name="u", src=(UPat.var("x"), UPat.var("y"))), lambda u,x,y:
+    x.cast(dt:=least_upper_dtype(select_dtype(u), x.dtype, y.dtype)).alu(u.op, y.cast(dt))),
+  # comparison ops might now have different dtypes in their sources
+  (UPat(GroupOp.Comparison, name="u", src=(UPat.var("x",dtypes.ints), UPat.var("y", dtypes.ints))), lambda u,x,y:
+    x.cast(dt:=least_upper_dtype(x.dtype, y.dtype)).alu(u.op, y.cast(dt)) if x.dtype!=y.dtype else None),
+  (UPat(Ops.WHERE, dtype=dtypes.index, src=(UPat.var("cond"), UPat.var("x"), UPat.var("y")), name="u"), lambda cond,u,x,y:
+    cond.where(x.cast(dt:=least_upper_dtype(x.dtype, y.dtype)), y.cast(dt))),
+  (UPat((Ops.CONST, Ops.VCONST), dtype=dtypes.index, name="u"), lambda u: u.replace(dtype=select_dtype(u))),
+  (UPat((Ops.RANGE,), dtype=dtypes.index, src=(UPat.var("end")), name="r"), lambda ctx,r,end:
+    r.replace(dtype=(dt:=select_dtype(r)), src=(end.cast(dt),))),
+  (UPat(Ops.CAST, dtype=dtypes.index, src=(UPat.var("x", dtypes.ints),), name="u"), lambda u,x: x),
+  (UPat(Ops.VECTORIZE, dtype=dtypes.index, name="u"), lambda u: u.replace(
+    dtype=(dt:=least_upper_dtype(*[x.dtype for x in u.src])).vec(u.dtype.count), src=tuple(x.cast(dt) for x in u.src))),
+  (UPat(Ops.VECTORIZE, dtype=dtypes.index, name="u"), lambda u: u.replace(dtype=(dt:=(dtypes.long if any(v.overflows(dtypes.int) for v in u.src)
+    else dtypes.long)).vec(u.dtype.count),src=tuple(x.cast(dt) for x in u.src))),
+  (UPat((Ops.SPECIAL,Ops.DEFINE_VAR), dtypes.index, name="u"), lambda u: u.replace(dtype=dtypes.int)),
+  (UPat((Ops.BIND), dtypes.index, name="u"), lambda u: u.replace(dtype=u.src[0].dtype)),
+])
+def index_to_concrete_int(u:UOp): return graph_rewrite(u, pm_lower_index_dtype)
 
 _substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get(x,None))])
 
@@ -1053,13 +1080,13 @@ renderer_infer = PatternMatcher([
 ])
 
 sugar = { Ops.SINK: "sink", Ops.STORE: "store", Ops.LOAD: "load", Ops.SQRT: "sqrt", Ops.INDEX: "index", Ops.REDUCE: "reduce",
-          Ops.WHERE: "where", Ops.RECIP: "reciprocal", Ops.EXP2: "exp2", Ops.LOG2: "log2"}
+          Ops.WHERE: "where", Ops.RECIP: "reciprocal", Ops.EXP2: "exp2", Ops.LOG2: "log2", Ops.SIN: "sin"}
 pm_pyrender = PatternMatcher([
   (UPat(Ops.CONST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg}, src={x.src[0].arg})")),
   (UPat(Ops.CONST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg})")),
   (UPat(Ops.CAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.cast({x.dtype})")),
   (UPat(Ops.BITCAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.bitcast({x.dtype})")),
-  (UPat({Ops.MAX, Ops.THREEFRY, Ops.CMPLT, Ops.CMPNE}, src=UPat(Ops.NOOP), name="x"),
+  (UPat({Ops.MAX, Ops.THREEFRY, Ops.CMPLT, Ops.CMPNE, Ops.POW}, src=UPat(Ops.NOOP), name="x"),
    lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.alu({x.op}, {x.src[1].arg})")),
   (UPat(Ops.RANGE, src=(UPat(Ops.NOOP),), name="x"), lambda x:
     UOp(Ops.NOOP, arg=f"UOp.range({x.src[0].arg}, {str(x.arg[0])}, {str(x.arg[1])})")),

tinygrad/uop/spec.py

@@ -5,6 +5,8 @@ from tinygrad.helpers import all_same, prod, DEBUG, ContextVar, Context
 from tinygrad.shape.shapetracker import ShapeTracker
 try:
   import z3
+  # older versions of z3 dont have some operators like & overloaded
+  if z3.get_version() < (4, 12, 4, 0): raise ImportError
 
   # IDIV is truncated division but z3 does euclidian division (floor if b>0 ceil otherwise); mod by power of two sometimes uses Ops.AND
   def z3_cdiv(a, b):return z3.If((a<0), z3.If(0<b, (a+(b-1))/b, (a-(b+1))/b), a/b)
@@ -26,17 +28,17 @@ try:
     # float loads only become a variable when they get cast to int/bool
     (UPat(Ops.LOAD, dtypes.ints, name="x"),
       lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0],create_bounded(f"load{ctx[1].setdefault(x, len(ctx[1]))}", x.dtype.min, x.dtype.max, ctx[0])))),
-    (UPat(Ops.CONST, dtype=dtypes.ints+(dtypes.bool,), name="x"),
+    (UPat(Ops.CONST, dtype=dtypes.ints+(dtypes.bool,dtypes.index), name="x"),
       lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0],(z3.BoolVal if dtypes.is_bool(x.dtype) else z3.IntVal)(x.arg, ctx=ctx[0].ctx)))),
     # z3 can cast from bool to int automatically
-    (UPat(Ops.CAST, dtype=dtypes.ints, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
+    (UPat(Ops.CAST, dtype=dtypes.ints+(dtypes.index,), src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
     (UPat(Ops.CAST, dtype=dtypes.bool, src=UPat(Ops.NOOP), name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0], x.src[0].arg[1]!=0))),
     # if the source of the cast is not a noop it means that it is a float and so we create a new variable
-    (UPat(Ops.CAST, dtype=dtypes.ints, name="x"), lambda x,ctx:
+    (UPat(Ops.CAST, dtype=dtypes.ints+(dtypes.index,), name="x"), lambda x,ctx:
       UOp(Ops.NOOP, arg=(ctx[0], create_bounded(f"cast{ctx[1].setdefault(x, len(ctx[1]))}", x.dtype.min, x.dtype.max, ctx[0])))),
     (UPat(Ops.CAST, dtype=dtypes.bool, name="x"), lambda x,ctx:
       UOp(Ops.NOOP, arg=(ctx[0], z3.Bool(f"cast{ctx[1].setdefault(x, len(ctx[1]))}",ctx=ctx[0].ctx)))),
-    (UPat(Ops.XOR, src=UPat(Ops.NOOP), name="x"),
+    (UPat(Ops.XOR, dtype=dtypes.ints+(dtypes.bool, ), src=UPat(Ops.NOOP), name="x"),
       lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0], z3.BV2Int(z3_alu[x.op](*(z3.Int2BV(s.arg[1], x.dtype.itemsize*8) for s in x.src)))))),
     (UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(ctx[0], z3_alu[x.op](*(s.arg[1] for s in x.src))))),
     # A comparison between floats introduces a new bool variable
@@ -93,7 +95,7 @@ tensor_uop_spec = buffer_spec+assign_spec+PatternMatcher([
   (UPat(Ops.VIEW, src=(UPat.var("x"),)), lambda x: x.base.op in {Ops.BUFFER, Ops.BUFFER_VIEW, Ops.ASSIGN, Ops.CONST, Ops.DEVICE}),
 
   # Tensor variable bindings
-  (UPat(Ops.BIND, dtypes.int, (UPat(Ops.DEFINE_VAR), UPat.cvar(dtype=dtypes.int)), arg=None), lambda: True),
+  (UPat(Ops.BIND, (dtypes.int,dtypes.index,), (UPat(Ops.DEFINE_VAR), UPat.cvar(dtype=(dtypes.int,dtypes.index,))), arg=None), lambda: True),
 
   # Tensor const has a device and an unmasked ShapeTracker of stride 0
   # NOTE: variables in shape can cause multiple views in this ShapeTracker and other issues, see TestSymbolicJit.test_ones_sum
@@ -118,6 +120,7 @@ tensor_uop_spec = buffer_spec+assign_spec+PatternMatcher([
 # ***** uop type spec *****
 
 def validate_index(idx:UOp, gate:UOp=UOp.const(dtypes.bool, True)):
+  # TODO: check for overflow
   if IGNORE_OOB or isinstance(idx.dtype, ImageDType) or (sz := idx.src[0].ptrdtype.size) == -1: return True
   # We can use UOp min/max to do a faster check, but it can give false positive since its not an exact bound and doesn't consider the mask
   if 0<=idx.src[1].vmin and idx.src[1].vmax<sz: return True
@@ -126,7 +129,7 @@ def validate_index(idx:UOp, gate:UOp=UOp.const(dtypes.bool, True)):
   # WEBGPU has a BITCAST in the index. TODO: fix
   if any(x.op is Ops.BITCAST for x in idx.toposort()): return True
 
-  if not z3_imported: raise ImportError("z3 is required for bounds checking, try IGNORE_OOB=0 or \"pip install z3-solver\"")
+  if not z3_imported: raise ImportError("z3 >= 4.12.4 is required for bounds checking, try IGNORE_OOB=0 or \"pip install 'z3-solver>=4.12.4\"")
   solver = z3.Solver(ctx=z3.Context())
   z3_idx, z3_mask = uops_to_z3(solver, idx.src[1], mask)
   solver.add(z3_mask)
@@ -173,6 +176,9 @@ spec = PatternMatcher([
 
   # **** new style load/store ****
 
+  # make sure all index dtypes have been lowered
+  (UPat(GroupOp.All, dtype=dtypes.index), lambda: False),
+
   # INDEX is used in new style load/store
   # INDEX takes a <buf, alu, gate?>
   (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat())), lambda: True),

tinygrad/uop/symbolic.py

@@ -26,27 +26,34 @@ symbolic_simple = PatternMatcher([
   # ** self folding **
   (UPat.var("x") + 0, lambda x: x),    # x+0 -> x
   (UPat.var("x") * 1, lambda x: x),    # x*1 -> x
-  (UPat.var("x", dtype=dtypes.ints) ^ 0, lambda x: x), # x^0 -> x
+  (UPat.var("x", dtype=dtypes.ints+(dtypes.bool, dtypes.index)) ^ 0, lambda x: x), # x^0 -> x
   (UPat.var("x") // UPat.var("x"), lambda x: x.const_like(1)), # x//x -> 1
   (UPat.var("x") // 1, lambda x: x),   # x//1 -> x
   (UPat.var("x") // -1, lambda x: -x), # x//-1 -> -x
   (UPat.var("x") / UPat.var("x"), lambda x: x.const_like(1)), # x/x -> 1
   ((UPat.var("x") * UPat.var("x2")) / UPat.var("x2"), lambda x,x2: x), # (x*x2)/x2 -> x
   ((UPat.var() % UPat.var("y")).named("base") % UPat.var("y"), lambda base,y: base),  # (x%y)%y = -> x%y (rewritten with base for speed)
+  # 4 variations of (x%c)+(x//c)*c = x   TODO: add sorting to remove some variations
   (UPat.var("x")%UPat.cvar("c")+(UPat.var("x")//UPat.cvar("c"))*UPat.cvar("c"), lambda x,c: x), # (x%c)+(x//c)*c = x
   ((UPat.var("x")//UPat.cvar("c1"))*UPat.cvar("c3")+UPat.var("x")%UPat.cvar("c1")*UPat.cvar("c2"),
     lambda x,c1,c2,c3: x*c2 if c1.arg*c2.arg==c3.arg else None), # (x%c1)*c2+(x//c1)*c3 = x*c2 if c1*c2==c3
+  ((UPat.var("y")+(UPat.var("x")//UPat.cvar("c"))*UPat.cvar("c"))+UPat.var("x")%UPat.cvar("c"), lambda y,x,c: y+x),
+  ((UPat.var("y")+UPat.var("x")%UPat.cvar("c"))+(UPat.var("x")//UPat.cvar("c"))*UPat.cvar("c"), lambda y,x,c: y+x),
+  ((UPat.var("y")+(UPat.var("x")//UPat.cvar("c1"))*UPat.cvar("c3"))+UPat.var("x")%UPat.cvar("c1")*UPat.cvar("c2"),
+    lambda y,x,c1,c2,c3: y+x*c2 if c1.arg*c2.arg==c3.arg else None),
+  ((UPat.var("y")+UPat.var("x")%UPat.cvar("c1")*UPat.cvar("c2"))+(UPat.var("x")//UPat.cvar("c1"))*UPat.cvar("c3"),
+    lambda y,x,c1,c2,c3: y+x*c2 if c1.arg*c2.arg==c3.arg else None),
   (UPat.var("x", dtype=dtypes.bool) & UPat.cvar("c", vec=False), lambda x,c: x if c.arg else c),
   (UPat.var("x", dtype=dtypes.bool) | UPat.cvar("c", vec=False), lambda x,c: c if c.arg else x),
   (UPat(GroupOp.Idempotent, src=(UPat.var("x"), UPat.var("x"))), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).logical_not().logical_not(), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).where(UPat.const(dtypes.bool, True), UPat.const(dtypes.bool, False)), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).where(UPat.const(dtypes.bool, False), UPat.const(dtypes.bool, True)), lambda x: x.logical_not()),
-  (UPat.var("x", dtype=dtypes.ints+(dtypes.bool,)).trunc(), lambda x: x),
+  (UPat.var("x", dtype=dtypes.ints+(dtypes.bool, dtypes.index)).trunc(), lambda x: x),
   # ** zero folding **
   (UPat.var("x") < UPat.var("x"), lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x < x -> False
   (UPat.var("x") % UPat.var("x"), lambda x: x.const_like(0)), # x%x -> 0
-  (UPat.var("x", dtype=dtypes.ints) != UPat.var("x", dtype=dtypes.ints),
+  (UPat.var("x", dtype=dtypes.ints+(dtypes.bool, dtypes.index)) != UPat.var("x"),
    lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x != x -> False (only ints)
   # x*0 -> 0 or 0*x -> 0
   # if x is nan or inf it should render the nan value.
@@ -101,6 +108,7 @@ def fold_unrolled_divs(divs:UOp, denominator: int, fac=1) -> UOp|None:
     if fac!=1:
       if u.op is not Ops.MUL or u.src[1].op is not Ops.CONST or u.src[1].arg != fac: return None
       u = u.src[0]
+    if u.op is Ops.CAST and u.src[0].dtype == dtypes.index: u = u.src[0]
     if not (u.op is Ops.IDIV and u.src[1].op is Ops.CONST): return None
     if denominator != u.src[1].arg: return None
     if (s0:=u.src[0]).vmin < 0: return None
@@ -116,7 +124,7 @@ def fold_unrolled_divs(divs:UOp, denominator: int, fac=1) -> UOp|None:
   for i in range(denominator-len(seen_const)):
     if ans is not None and 0 <= ans.vmin and ans.vmax + i < denominator: seen_const.append(i)
   if sorted(seen_const)==list(range(denominator)):
-    return fac*ans
+    return (fac*ans).cast(divs.dtype)
   return None
 
 def lt_folding(x:UOp, c:int) -> UOp|None:
@@ -263,10 +271,19 @@ gep_pushing = PatternMatcher([
   (UPat(Ops.WMMA, name="wmma").f(Ops.GEP, name="gep"), gep_through_wmma),
 ])
 
+cast_folding = PatternMatcher([
+  (UPat.var('x', dtypes.ints+(dtypes.index,)).cast(dtypes.ints+(dtypes.index,), name="a").cast(name="b"),
+    lambda x,a,b: x.cast(b.dtype) if a.dtype.min<=x.vmin and x.vmax<=a.dtype.max else None),
+  # try to do math in int instead of long
+  (UPat(GroupOp.Binary, src=(UPat.var("x", dtypes.long), UPat.var("y", dtypes.long)), name="u"), lambda u,x,y:
+    x.cast(dtypes.int).alu(u.op, y.cast(dtypes.int)).cast(u.dtype) if not any(v.overflows(dtypes.int) for v in (u,x,y)) else None),
+  ((UPat.var("x", dtypes.index) + UPat.cvar("c")).cast(dtypes.sints, name="cast"), lambda x,c,cast:x.cast(cast.dtype)+c.cast(cast.dtype)),
+])
+
 commutative = PatternMatcher([
-  # ** COMMUTATIVE flipping (only for ints) **
+  # ** COMMUTATIVE flipping (only for index) **
   # NOTE: this can break merging vector math by only flipping some of them
-  (UPat(GroupOp.Commutative, dtype=dtypes.int, name='x'), lambda x: x.replace(src=x.src[::-1]) if x.src[1].tuplize < x.src[0].tuplize else None),
+  (UPat(GroupOp.Commutative, dtype=dtypes.index, name='x'), lambda x: x.replace(src=x.src[::-1]) if x.src[1].tuplize < x.src[0].tuplize else None),
 ])
 
 symbolic = symbolic_simple+commutative+PatternMatcher([
@@ -282,10 +299,6 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
   ((UPat.var("y") + UPat.var("x")) + UPat.var("x"), lambda y,x: y+x*2),
   ((UPat.var("x") / UPat.var("x2")) / UPat.var("x3"), lambda x,x2,x3: x/(x2*x3) if x2 is not x3 else None), # (x/x2)/x3 -> x/(x2*x3)
   (-1 * (UPat.var("x") + UPat.cvar("c")), lambda x,c: (-x)+(-c)),  # -(x+c) -> -x + -c
-  (UPat.var('x', dtypes.ints).cast(dtypes.ints, name="a").cast(name="b"),
-    lambda x,a,b: x.cast(b.dtype) if a.dtype.min<=x.vmin and x.vmax<=a.dtype.max else None),
-  (UPat(GroupOp.Binary, src=(UPat.var("x",dtypes.long), UPat.var("y", dtypes.long)), name="u"), lambda u,x,y:
-    x.cast(dtypes.int).alu(u.op, y.cast(dtypes.int)).cast(u.dtype) if not any(v.overflows(dtypes.int) for v in (u,x,y)) else None),
   # a conditional with the same results either way is a noop, also fold const conditionals
   (UPat.var().where(UPat.var("val"), UPat.var("val")), lambda val: val),
   (UPat.cvar("gate", vec=False).where(UPat.var("c0"), UPat.var("c1")), lambda gate, c0, c1: c0 if gate.arg else c1),
@@ -310,27 +323,27 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
   ((UPat.var("x") // UPat.cvar("c1")) // UPat.cvar("c2"), lambda x,c1,c2: x//(c1*c2)), # (x//c1)//c2 -> x//(c1*c2)
   # ** lt **
   # c0*x<c1 for positive int c0,c1
-  ((UPat.cvar("c0", vec=False)*UPat.var("x", dtype=dtypes.ints))<UPat.cvar("c1", vec=False),
+  ((UPat.cvar("c0", vec=False)*UPat.var("x", dtype=dtypes.index))<UPat.cvar("c1", vec=False),
    lambda x,c0,c1: x<math.ceil(c1.arg/c0.arg) if c0.arg > 0 and c1.arg > 0 else None),
   # c0*x<c1 for negative int c0 and non-positive c1
-  ((UPat.cvar("c0", vec=False)*UPat.var("x", dtype=dtypes.ints))<UPat.cvar("c1", vec=False),
+  ((UPat.cvar("c0", vec=False)*UPat.var("x", dtype=dtypes.index))<UPat.cvar("c1", vec=False),
    lambda x,c0,c1: (-x)<(-(math.floor(-c1.arg/-c0.arg))) if c0.arg < 0 and c0.arg != -1 and c1.arg <= 0 else None),
   # x//d<c
-  ((UPat.var("x", dtype=dtypes.ints)//UPat.cvar("d", vec=False))<UPat.cvar("c", vec=False),
+  ((UPat.var("x", dtype=dtypes.index)//UPat.cvar("d", vec=False))<UPat.cvar("c", vec=False),
    lambda x,d,c: (x<(c.arg*d.arg) if c.arg > 0 else x<(c.arg*d.arg-(d.arg-1))) if d.arg > 0 else None),
   # ** move add/mul consts to end (NOTE: this is still happening before constant folding) **
   ((UPat.var("x") + UPat.cvar("c1")) + UPat.var("y"), lambda x,c1,y: (x+y)+c1),
   ((UPat.var("x") * UPat.cvar("c1")) * UPat.var("y"), lambda x,c1,y: (x*y)*c1),
   # *** rules from symbolic ***
   # unrolled arange div folding
-  ((UPat() + UPat()//UPat.cvar("d", vec=False)).named("divs"), lambda divs,d: fold_unrolled_divs(divs, d.arg)),
-  ((UPat() + (UPat()//UPat.cvar("d", vec=False))*UPat.cvar("c")).named("divs"), lambda divs,d,c: fold_unrolled_divs(divs, d.arg, c.arg)),
+  ((UPat()+(UPat()//UPat.cvar("d", vec=False)).or_casted()).named("divs"), lambda divs,d: fold_unrolled_divs(divs, d.arg)),
+  ((UPat()+((UPat()//UPat.cvar("d", vec=False)).or_casted()*UPat.cvar("c"))).named("divs"), lambda divs,d,c: fold_unrolled_divs(divs, d.arg, c.arg)),
   # generic lt folding
-  (UPat.var("x", dtypes.sints)<UPat.cvar("c", vec=False), lambda x,c: lt_folding(x, c.arg) if 0 < c.arg else None),
-  (UPat.var("x", dtypes.sints)*-1 < UPat.var("y", dtypes.sints)*-1, lambda x,y: y<x),
+  (UPat.var("x", dtypes.index)<UPat.cvar("c", vec=False), lambda x,c: lt_folding(x, c.arg) if 0 < c.arg else None),
+  (UPat.var("x", dtypes.index)*-1 < UPat.var("y")*-1, lambda x,y: y<x),
   # canonicalize a simplex with positive coefficients > 0
   # not x < 1 -> X > 0
-  ((UPat.var("x", dtypes.ints)<1).ne(True), lambda x: (newx<1).ne(True) if (newx:=canonicalize_simplex(x)) is not None else None),
+  ((UPat.var("x", dtypes.index)<1).ne(True), lambda x: (newx<1).ne(True) if (newx:=canonicalize_simplex(x)) is not None else None),
   # ** div **
   # div folding
   ((UPat.var("x")//UPat.cvar("c") + UPat.cvar("a"))//UPat.cvar("d"), lambda x,c,a,d: (x+a*c)//(c*d)
@@ -338,28 +351,28 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
   # a range mod its own upper bound is just the range
   (UPat(Ops.RANGE, src=UPat.var("end"), name="r")%UPat.var("end"), lambda r,end: r),
   (UPat(Ops.RANGE, src=UPat.var("end"), name="r")//UPat.var("end"), lambda r,end: r.const_like(0)),
-  (UPat((Ops.IDIV, Ops.MOD), dtypes.sints, name="d", src=(UPat.var("x"), UPat.var("y"))), cancel_divmod),
-  (UPat((Ops.IDIV, Ops.MOD), dtypes.sints, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), fold_binary_numerator),
-  (UPat((Ops.IDIV, Ops.MOD), dtypes.sints, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), fold_divmod_congruence),
-  (UPat((Ops.IDIV, Ops.MOD), dtypes.sints, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), divide_by_gcd),
-  (UPat(Ops.MOD, dtypes.sints, name="m", src=(UPat.var("x"), UPat.cvar("y", vec=False))), remove_nested_mod),
-  (UPat((Ops.IDIV), dtypes.sints, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), nest_div_by_smallest_factor),
-  (UPat((Ops.IDIV, Ops.MOD), dtypes.sints, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), simplify_remainder),
+  (UPat((Ops.IDIV, Ops.MOD), dtypes.index, name="d", src=(UPat.var("x"), UPat.var("y"))), cancel_divmod),
+  (UPat((Ops.IDIV, Ops.MOD), dtypes.index, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), fold_binary_numerator),
+  (UPat((Ops.IDIV, Ops.MOD), dtypes.index, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), fold_divmod_congruence),
+  (UPat((Ops.IDIV, Ops.MOD), dtypes.index, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), divide_by_gcd),
+  (UPat(Ops.MOD, dtypes.index, name="m", src=(UPat.var("x"), UPat.cvar("y", vec=False))), remove_nested_mod),
+  (UPat((Ops.IDIV), dtypes.index, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), nest_div_by_smallest_factor),
+  (UPat((Ops.IDIV, Ops.MOD), dtypes.index, name="d", src=(UPat.var("x"), UPat.cvar("y", vec=False))), simplify_remainder),
   (UPat.var("x") // UPat.var("d"), lambda x,d: -(x//(-d)) if d.vmax < 0 else None),
   (UPat.var("x") // UPat.var("d"), lambda x,d: -((-x)//d) if x.vmax <=0 else None),
-  ((UPat.var("x", dtypes.sints)+UPat.cvar("c", vec=False)).named("n")//UPat.cvar("d", vec=False),
+  ((UPat.var("x", dtypes.index)+UPat.cvar("c", vec=False)).named("n")//UPat.cvar("d", vec=False),
     lambda x,c,n,d: (-(-(c.arg%d.arg + x - (d.arg-1))//d) + c.arg//d.arg) if x.vmax<=0 and n.vmin>=0 and d.arg>0 else None),
   # ** mod **
   # mod folding
   (UPat.var("x") % UPat.var("d"), lambda x,d: -((-x)%d) if x.vmax <= 0 else None),
   (UPat.var("x") % UPat.var("d"), lambda x,d: (x%(-d)) if d.vmax <  0 else None),
-])+gep_pushing
+])+gep_pushing+cast_folding
 
 symbolic_flat = symbolic+PatternMatcher([
   # ** combine terms (opinionated) **
   (-1 * (UPat.var("x") + UPat.var("y")), lambda x,y: (-x)+(-y)),  # -(x+y) -> -x + -y
   # (x+y)*c -> x*c+y*c. only for int, float has inf*0=nan issue
-  ((UPat.var("x", dtypes.ints) + UPat.var("y")) * UPat.cvar("c"), lambda x,y,c: x*c+y*c),
+  ((UPat.var("x", dtypes.index) + UPat.var("y")) * UPat.cvar("c"), lambda x,y,c: x*c+y*c),
 ])
 
 # ******** we take a small aside to "simplify_valid" to rewrite valids ********
@@ -391,6 +404,7 @@ def uop_given_valid(valid:UOp, uop:UOp) -> UOp|None:
   # simplify uop given that valid is True
   for expr,v in bounds.items():
     v0, v1 = (expr.vmin if v[0] is None else v[0], expr.vmax if v[1] is None else v[1])
+    expr = expr.substitute(load_subs)  # make sure expr appears in same form in the uop
     # some expr has lower bound > upper bound -> valid is an empty set and we return None
     if v0 > v1: return None
     # whole node became a const
@@ -450,6 +464,8 @@ def reduce_mul_chain(r:UOp):
 REMOVE_FROM_SINK = {Ops.SINK, Ops.UNROLL, Ops.PTRCAT, Ops.CAT, Ops.NOOP}
 REMOVE_FROM_BARRIER = {Ops.VECTORIZE, Ops.SINK, Ops.CAT, Ops.PTRCAT, Ops.NOOP}
 sym = symbolic_flat+PatternMatcher([
+  # simplify valid
+  (UPat(Ops.AND, name="valid"), simplify_valid),
   # LOAD/STORE -> NOOP
   (UPat.var('x').store(UPat.var('x').load(), allow_any_len=True), lambda x: None if x.dtype.addrspace != AddrSpace.REG else x.src[0].src[0]),
   (UPat(Ops.LOAD, src=(UPat.cvar('c'))), lambda c: c),

tinygrad/viz/index.html

@@ -244,15 +244,15 @@
     z-index: 4;
     background-color: #1e2029;
     padding: 4px 8px;
+    max-width: 164px;
     border-radius: 4px;
     pointer-events: none;
     display: none;
     font-size: 10px;
-    white-space: pre;
   }
   #device-list > div {
     min-height: 32px;
-    max-width: 132px;
+    width: 132px;
     overflow-x: auto;
     overflow-y: hidden;
     white-space: nowrap;
@@ -261,6 +261,9 @@
   #device-list > div:hover {
     background-color: rgba(20, 23, 35, 0.3);
   }
+  #device-list {
+    height: fit-content;
+  }
   .raw-text {
     padding: 0 8px;
     width: 100%;
@@ -363,7 +366,7 @@
     </div>
     <div class="container metadata-parent"><div class="metadata"></div></div>
   </div>
-  <div id="tooltip"></div>
+  <div id="tooltip" class="wrap"></div>
   <script src="/js/index.js"></script>
 </body>
 </html>

tinygrad/viz/js/index.js

@@ -207,7 +207,7 @@ async function renderProfiler() {
     if (eventType === EventTypes.TIMELINE) {
       const levelHeight = baseHeight-padding;
       const levels = [];
-      data.tracks.set(k, { shapes, offsetY });
+      data.tracks.set(k, { shapes, visible:[], offsetY });
       let colorKey, ref;
       for (let j=0; j<eventsLen; j++) {
         const e = {name:strings[u32()], ref:optional(u32()), st:u32(), dur:f32(), info:strings[u32()] || null};
@@ -228,15 +228,14 @@ async function renderProfiler() {
           const stepIdx = ctxs[ref.ctx+1].steps.findIndex((s, i) => i >= start && s.name == e.name);
           ref = stepIdx === -1 ? null : {ctx:ref.ctx, step:stepIdx};
         }
-        const arg = { tooltipText:formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
+        const htmlLabel = label.map(({color, st}) => `<span style="color:${color}">${st}</span>`).join('');
+        const arg = { tooltipText:htmlLabel+"\n"+formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
         // offset y by depth
         shapes.push({x:e.st, y:levelHeight*depth, width:e.dur, height:levelHeight, arg, label, fillColor });
       }
       div.style("height", levelHeight*levels.length+padding+"px").style("pointerEvents", "none");
     } else {
       const peak = u64();
-      const height = heightScale(peak);
-      const yscale = d3.scaleLinear().domain([0, peak]).range([height, 0]);
       let x = 0, y = 0;
       const buf_shapes = new Map(), temp = new Map();
       const timestamps = [];
@@ -267,12 +266,14 @@ async function renderProfiler() {
         v.y.push(v.y.at(-1));
       }
       timestamps.push(dur);
+      const height = heightScale(peak);
+      const yscale = d3.scaleLinear().domain([0, peak]).range([height, 0]);
       for (const [_, {dtype, sz, nbytes, y, x:steps}] of buf_shapes) {
         const x = steps.map(s => timestamps[s]);
         const arg = {tooltipText:`${dtype} len:${formatUnit(sz)}\n${formatUnit(nbytes, "B")}`};
         shapes.push({ x, y0:y.map(yscale), y1:y.map(y0 => yscale(y0+nbytes)), arg, fillColor:cycleColors(colorScheme.BUFFER, shapes.length) });
       }
-      data.tracks.set(k, { shapes, offsetY, height, peak, scaleFactor:maxheight*4/height });
+      data.tracks.set(k, { shapes, visible:[], offsetY, height, peak, scaleFactor:maxheight*4/height });
       div.style("height", height+padding+"px").style("cursor", "pointer").on("click", (e) => {
         const newFocus = e.currentTarget.id === focusedDevice ? null : e.currentTarget.id;
         let offset = 0;
@@ -291,18 +292,16 @@ async function renderProfiler() {
   // draw events on a timeline
   const dpr = window.devicePixelRatio || 1;
   const ellipsisWidth = ctx.measureText("...").width;
-  const rectLst = [];
   function render(transform) {
     zoomLevel = transform;
-    rectLst.length = 0;
     ctx.clearRect(0, 0, canvas.clientWidth, canvas.clientHeight);
     // rescale to match current zoom
     const xscale = d3.scaleLinear().domain([0, dur]).range([0, canvas.clientWidth]);
     const visibleX = xscale.range().map(zoomLevel.invertX, zoomLevel).map(xscale.invert, xscale);
     xscale.domain(visibleX);
-    const yscale = data.axes.y != null ? d3.scaleLinear().domain(data.axes.y.domain).range(data.axes.y.range) : null;
     // draw shapes
-    for (const [_, { offsetY, shapes }] of data.tracks) {
+    for (const [_, { offsetY, shapes, visible }] of data.tracks) {
+      visible.length = 0;
       for (const e of shapes) {
         if (e.width == null) { start = e.x[0]; end = end = e.x[e.x.length-1]; }
         else { start = e.x; end = e.x+e.width; }
@@ -318,12 +317,8 @@ async function renderProfiler() {
           p.closePath();
           ctx.fill(p);
           // NOTE: y coordinates are in reverse order
-          for (let i = 0; i < x.length - 1; i++) {
-            let tooltipText = e.arg.tooltipText;
-            if (yscale != null && ((yaxisVal=yscale.invert(offsetY+e.y1[i]))>0)) {
-              tooltipText += `\nTotal: ${formatUnit(yaxisVal, data.axes.y.fmt)}`;
-            }
-            rectLst.push({ x0:x[i], x1:x[i+1], y0:offsetY+e.y1[i], y1:offsetY+e.y0[i], arg:{...e.arg, tooltipText} });
+          for (let i = 0; i<x.length-1; i++) {
+            visible.push({ x0:x[i], x1:x[i+1], y0:offsetY+e.y1[i], y1:offsetY+e.y0[i], arg:e.arg });
           }
           continue;
         }
@@ -331,7 +326,7 @@ async function renderProfiler() {
         const x = xscale(start);
         const width = xscale(end)-x;
         ctx.fillRect(x, offsetY+e.y, width, e.height);
-        rectLst.push({ y0:offsetY+e.y, y1:offsetY+e.y+e.height, x0:x, x1:x+width, arg:e.arg });
+        visible.push({ y0:offsetY+e.y, y1:offsetY+e.y+e.height, x0:x, x1:x+width, arg:e.arg });
         // add label
         if (e.label == null) continue;
         ctx.textAlign = "left";
@@ -360,8 +355,9 @@ async function renderProfiler() {
       ctx.textBaseline = "top";
       ctx.fillText(formatTime(tick, dur), x+ctx.lineWidth+2, tickSize);
     }
-    if (yscale != null) {
-      drawLine(ctx, [0, 0], yscale.range());
+    if (data.axes.y != null) {
+      drawLine(ctx, [0, 0], data.axes.y.range);
+      const yscale = d3.scaleLinear().domain(data.axes.y.domain).range(data.axes.y.range);
       for (const tick of yscale.ticks()) {
         const y = yscale(tick);
         drawLine(ctx, [0, tickSize], [y, y]);
@@ -379,9 +375,9 @@ async function renderProfiler() {
 
   function resize() {
     const profiler = document.querySelector(".profiler");
-    // NOTE: use clientWidth to account for the scrollbar
-    let [width, height] = [profiler.clientWidth, profiler.scrollHeight];
-    width -= rect("#device-list").width+padding;
+    const sideRect = rect("#device-list");
+    const width = profiler.clientWidth-(sideRect.width+padding), height = Math.round(sideRect.height);
+    if (canvas.width === width*dpr && canvas.height === height*dpr) return;
     canvas.width = width*dpr;
     canvas.height = height*dpr;
     canvas.style.height = `${height}px`;
@@ -394,14 +390,19 @@ async function renderProfiler() {
   d3.select(canvas).call(canvasZoom);
   document.addEventListener("contextmenu", e => e.ctrlKey && e.preventDefault());
 
-  resize();
-  window.addEventListener("resize", resize);
+  new ResizeObserver(([e]) => e.contentRect.width > 0 && resize()).observe(profiler.node());
 
   function findRectAtPosition(x, y) {
+    let tid = null;
+    for (const k of data.tracks.keys()) {
+      const r = rect(document.getElementById(k));
+      if (y >= r.y && y <= r.y+r.height) { tid = k; break; }
+    }
+    if (tid == null) return;
     const { top, left, width, height } = rect(canvas);
     const X = ((x-left) * (canvas.width/width))/dpr;
     const Y = ((y-top) * (canvas.height/height))/dpr;
-    for (const r of rectLst) {
+    for (const r of data.tracks.get(tid).visible) {
       if (Y>=r.y0 && Y<=r.y1 && X>=r.x0 && X<=r.x1) return r.arg;
     }
   }
@@ -419,7 +420,7 @@ async function renderProfiler() {
       tooltip.style.display = "block";
       tooltip.style.left = (e.pageX+10)+"px";
       tooltip.style.top = (e.pageY)+"px";
-      tooltip.innerText = foundRect.tooltipText;
+      tooltip.innerHTML = foundRect.tooltipText;
     } else tooltip.style.display = "none";
   });
   canvas.addEventListener("mouseleave", () => document.getElementById("tooltip").style.display = "none");

tinygrad/viz/serve.py

@@ -11,7 +11,7 @@ from tinygrad.uop.ops import TrackedGraphRewrite, UOp, Ops, printable, GroupOp,
 from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent, ProfileGraphEntry, Device
 from tinygrad.renderer import ProgramSpec
 from tinygrad.dtype import dtypes
-from tinygrad.codegen.opt.kernel import axis_colors
+from tinygrad.codegen.opt import axis_colors
 
 uops_colors = {Ops.LOAD: "#ffc0c0", Ops.STORE: "#87CEEB", Ops.CONST: "#e0e0e0", Ops.VCONST: "#e0e0e0", Ops.REDUCE: "#FF5B5B",
                Ops.DEFINE_GLOBAL: "#ffe0b0", Ops.DEFINE_LOCAL: "#ffe0d0", Ops.DEFINE_REG: "#f0ffe0", Ops.REDUCE_AXIS: "#FF6B6B",