not a part of regen

fix torch frontend
imports
2025-09-05 15:41:34 -07:00 · 2025-09-05 15:39:52 -07:00 · 2025-09-05 15:34:49 -07:00 · 2025-09-05 15:31:39 -07:00 · 2025-09-05 15:15:05 -07:00 · 2025-09-05 15:13:36 -07:00
166 changed files with 1806 additions and 2164 deletions
@@ -253,13 +253,8 @@ runs:
        git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
        mkdir build
        cd build
-
-        CMAKE_ARGS="-Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5"
-        if [[ "${{ runner.os }}" == "macOS" ]]; then
-          CMAKE_ARGS="$CMAKE_ARGS -DBoost_INCLUDE_DIR=$(brew --prefix boost)/include -DBoost_LIBRARY_DIR=$(brew --prefix boost)/lib"
-        fi
-
-        cmake .. $CMAKE_ARGS
+        cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF \
+          -DBoost_INCLUDE_DIR=$(brew --prefix boost)/include -DBoost_LIBRARY_DIR=$(brew --prefix boost)/lib -DCMAKE_POLICY_VERSION_MINIMUM=3.5
        ninja
    - name: Install gpuocelot
      if: inputs.ocelot == 'true'
@@ -1,91 +0,0 @@
-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
@@ -52,14 +52,14 @@ jobs:
    - name: reset process replay
      run: python3.11 test/external/process_replay/reset.py
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=500 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion JIT=1 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=700 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
+      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
    - name: Run Stable Diffusion v2
-      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=1600 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
+      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
    # process replay can't capture this, the graph is too large
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
      run: METAL=1 python3.11 test/external/external_model_benchmark.py
    - name: Test speed vs torch
@@ -68,10 +68,10 @@ jobs:
      run: METAL=1 python3.11 test/opt/test_tensor_cores.py
    - name: Test AMX tensor cores
      run: |
-        DEBUG=2 CPU=1 CPU_LLVM=0 AMX=1 python3.11 test/opt/test_tensor_cores.py
-        DEBUG=2 CPU=1 CPU_LLVM=1 AMX=1 python3.11 test/opt/test_tensor_cores.py
-        DEBUG=2 CPU=1 CPU_LLVM=0 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-        DEBUG=2 CPU=1 CPU_LLVM=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 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/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
    - name: Run Tensor Core GEMM (half)
@@ -99,7 +99,7 @@ jobs:
    - name: Run GPT2
      run: |
        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=8 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2 JIT=1 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
    - name: Run GPT2 w HALF
      run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
    - name: Run GPT2 w HALF/BEAM
@@ -109,13 +109,13 @@ jobs:
    - name: Train MNIST
      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=320 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: BENCHMARK_LOG=cifar_10steps JIT=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=385 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half JIT=2 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
    #- name: Run 10 CIFAR training steps w BF16
    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - name: UsbGPU boot time
      run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
    - name: UsbGPU tiny tests
@@ -197,14 +197,14 @@ jobs:
    - name: Test tensor cores
      run: |
        NV=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-        NV=1 NV_PTX=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
        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_tf32.txt
    - name: Run Tensor Core GEMM (PTX)
-      run: NV=1 NV_PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_ptx.txt
+      run: NV=1 PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_ptx.txt
    - name: Run Tensor Core GEMM (NV)
      run: NV=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_nv.txt
    - name: Test NV=1
@@ -214,7 +214,7 @@ jobs:
    - name: Run Stable Diffusion
      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA
      run: |
        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
@@ -238,9 +238,9 @@ jobs:
    - name: Run GPT2
      run: |
        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2 NV=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
    - name: Run GPT2 w HALF/BEAM
      run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
    - uses: actions/upload-artifact@v4
@@ -302,17 +302,17 @@ jobs:
    - name: Fuzz Padded Tensor Core GEMM (NV)
      run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
    - name: Fuzz Padded Tensor Core GEMM (PTX)
-      run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
+      run: NV=1 PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
    - name: Train MNIST
      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: BENCHMARK_LOG=cifar_10steps NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=68 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=75 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+      run: BENCHMARK_LOG=cifar_10steps_bf16 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=35 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      run: BENCHMARK_LOG=cifar_10steps_half_wino NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - name: Run full CIFAR training w 1 GPU
      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
    - name: Run full CIFAR training steps w 6 GPUS
@@ -415,9 +415,9 @@ jobs:
    - name: Test AM warm start time
      run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=450 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=1400 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA 7B
      run: |
        BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
@@ -443,9 +443,9 @@ jobs:
    - name: Run GPT2
      run: |
        BENCHMARK_LOG=gpt2_nojit AMD=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half AMD=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half AMD=1 HALF=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
    - name: Run GPT2 w HALF/BEAM
      run: BENCHMARK_LOG=gpt2_half_beam AMD=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
    - uses: actions/upload-artifact@v4
@@ -508,13 +508,13 @@ jobs:
    - name: Train MNIST
      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: BENCHMARK_LOG=cifar_10steps AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=188 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+      run: BENCHMARK_LOG=cifar_10steps_bf16 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      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
@@ -606,11 +606,11 @@ jobs:
    - name: reset process replay
      run: test/external/process_replay/reset.py
    - name: benchmark openpilot 0.9.9 driving_vision
-      run: BENCHMARK_LOG=openpilot_0_9_9_vision ASSERT_MIN_STEP_TIME=30 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
    - name: benchmark openpilot 0.9.9 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_9_9_policy ASSERT_MIN_STEP_TIME=45 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
    - name: benchmark openpilot 0.9.9 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring ASSERT_MIN_STEP_TIME=70 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 0.9.9 driving_vision
      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
    - name: openpilot compile3 0.9.9 driving_policy
@@ -626,7 +626,7 @@ jobs:
        # generate quantized weights
        ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
        ln -s /data/home/tiny/tinygrad/testsig-*.so .
-        PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
+        PYTHONPATH=. CC=clang-19 CPU=1 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
        # benchmark on DSP with NOOPT=1, the devectorizer has issues
        PYTHONPATH=. CC=clang-19 DSP=1 DONT_REALIZE_EXPAND=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
    - name: Run process replay tests
@@ -31,11 +31,11 @@ jobs:
        deps: testing_minimal
        llvm: 'true'
    - name: External Benchmark Schedule
-      run: python3 test/external/external_benchmark_schedule.py
+      run: PYTHONPATH="." python3 test/external/external_benchmark_schedule.py
    - name: Speed Test
-      run: CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: LLVM=1 python3 test/speed/external_test_speed_v_torch.py
    - name: Speed Test (BEAM=2)
-      run: BEAM=2 CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: BEAM=2 LLVM=1 python3 test/speed/external_test_speed_v_torch.py

  docs:
    name: Docs
@@ -72,7 +72,7 @@ jobs:
        source venv/bin/activate
        pip install $GITHUB_WORKSPACE
        cp $GITHUB_WORKSPACE/examples/beautiful_mnist.py .
-        BS=2 STEPS=10 python beautiful_mnist.py
+        PYTHONPATH=$GITHUB_WORKSPACE BS=2 STEPS=10 python beautiful_mnist.py
    - name: Test Docs Build
      run: python -m mkdocs build --strict
    - name: Test Docs
@@ -85,14 +85,85 @@ jobs:
      run: DEBUG=100 python3 -c "from tinygrad import Tensor; N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N); c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2); print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
    - name: Compile EfficientNet to C and test it
      run: |
-        CPU=1 CPU_LLVM=0 python examples/compile_efficientnet.py > recognize.c
+        CPU=1 PYTHONPATH="." python examples/compile_efficientnet.py > recognize.c
        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
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
@@ -120,16 +191,18 @@ jobs:
    - name: Test one op in torch tests
      run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
    - name: Test Ops with TINY_BACKEND
-      run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
+      run: LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
    - name: Test in-place operations on views
      run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
    - name: Test multi-gpu
-      run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+      run: LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py

  torchbackendmore:
    name: Torch Backend Tests More
    runs-on: ubuntu-latest
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
@@ -144,32 +217,24 @@ jobs:
        sudo apt update || true
        sudo apt install -y --no-install-recommends ninja-build
    - name: Test beautiful_mnist in torch with TINY_BACKEND
-      run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 CPU=1 CPU_LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+      run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
    - name: Test some torch tests (expect failure)
      run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true

-  bepython:
-    name: Python Backend
+  tc:
+    name: Tensor Core tests
    runs-on: ubuntu-latest
-    timeout-minutes: 15
+    timeout-minutes: 10
+    env:
+      IGNORE_OOB: 0
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
    - name: Setup Environment
      uses: ./.github/actions/setup-tinygrad
      with:
-        key: be-minimal
+        key: uops-minimal
        deps: testing_minimal
-    - 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 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
-      run: PYTHON=1 python3 test/test_symbolic_ops.py
-    - name: test_renderer_failures with Python emulator
-      run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
    - name: Test IMAGE=2 support
      run: |
        IMAGE=2 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
@@ -203,7 +268,7 @@ jobs:
        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
-        DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
+        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: Test emulated AMX tensor cores
@@ -216,6 +281,31 @@ jobs:
        DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
        DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul

+  bepython:
+    name: Python Backend
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    env:
+      IGNORE_OOB: 0
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: be-minimal
+        deps: testing_minimal
+    - 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 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
+      run: PYTHON=1 python3 test/test_symbolic_ops.py
+    - name: test_renderer_failures with Python emulator
+      run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
+
  linter:
    name: Linters
    runs-on: ubuntu-latest
@@ -264,16 +354,16 @@ jobs:
    - name: Test README
      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py &&  python README.py
    - name: Run unit tests
-      run: python -m pytest -n=auto test/unit/ --durations=20
+      run: PYTHONPATH="." python -m pytest -n=auto test/unit/ --durations=20
    - name: Run targetted tests on NULL backend
-      run: NULL=1 python3 test/test_multitensor.py TestMultiTensor.test_data_parallel_resnet_train_step
+      run: PYTHONPATH="." NULL=1 python3 test/test_multitensor.py TestMultiTensor.test_data_parallel_resnet_train_step
    - name: Run SDXL on NULL backend
-      run: MAX_BUFFER_SIZE=0 NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
+      run: MAX_BUFFER_SIZE=0 PYTHONPATH="." NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
    # TODO: support fake weights
    #- name: Run LLaMA 7B on 4 fake devices
    #  run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
    - name: Run GC tests
-      run: python test/external/external_uop_gc.py
+      run: PYTHONPATH="." python test/external/external_uop_gc.py
    - name: Run process replay tests
      uses: ./.github/actions/process-replay
    - name: Regen dataset on test_tiny
@@ -304,15 +394,17 @@ jobs:
      run: python test/external/fuzz_fast_idiv.py
    - name: Fuzz Test shapetracker
      run: |
-        python test/external/fuzz_shapetracker.py
-        python test/external/fuzz_shapetracker_math.py
+        PYTHONPATH="." python test/external/fuzz_shapetracker.py
+        PYTHONPATH="." python test/external/fuzz_shapetracker_math.py
    - name: Fuzz Test shape ops
      run: python test/external/fuzz_shape_ops.py

-  testopenclimage:
-    name: 'CL IMAGE Tests'
+  testgpuimage:
+    name: 'GPU IMAGE Tests'
    runs-on: ubuntu-22.04
    timeout-minutes: 10
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -322,17 +414,25 @@ jobs:
          key: gpu-image
          deps: testing_minimal
          opencl: 'true'
-      - name: Test CL IMAGE=2 ops + training
+      - name: Run Kernel Count Test
+        run: PYTHONPATH="." GPU=1 python -m pytest -n=auto test/external/external_test_opt.py
+      - name: Test WINO=1
+        run: GPU=1 DEBUG=2 WINO=1 python3 test/test_ops.py TestOps.test_simple_conv2d
+      - name: Test GPU IMAGE=2 ops + training
        run: |
-          CL=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
-          CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
+          PYTHONPATH="." GPU=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
+          PYTHONPATH="." GPU=1 IMAGE=2 python3 test/models/test_end2end.py TestEnd2End.test_linear_mnist
+      - name: Run fused optimizer tests
+        run: PYTHONPATH="." GPU=1 FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-  testgpumisc:
-    name: 'CL Misc tests'
+  testgendataset:
+    name: 'GPU Generate Kernel Dataset'
    runs-on: ubuntu-22.04
    timeout-minutes: 10
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -343,11 +443,7 @@ jobs:
          deps: testing_minimal
          opencl: 'true'
      - name: Generate Dataset
-        run: CL=1 extra/optimization/generate_dataset.sh
-      - name: Run Kernel Count Test
-        run: CL=1 python -m pytest -n=auto test/external/external_test_opt.py
-      - name: Run fused optimizer tests
-        run: CL=1 FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py
+        run: PYTHONPATH="." extra/optimization/generate_dataset.sh
      - name: Upload artifact
        uses: actions/upload-artifact@v4
        with:
@@ -358,6 +454,8 @@ jobs:
    name: 'openpilot Compile Tests'
    runs-on: ubuntu-22.04
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -370,26 +468,26 @@ jobs:
          llvm: 'true'
      - name: Test openpilot model kernel count and gate usage
        run: |
-          ALLOWED_KERNEL_COUNT=208 ALLOWED_READ_IMAGE=2175 ALLOWED_GATED_READ_IMAGE=16 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
+          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: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
+        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)
-        run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+        run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
      # - name: Test openpilot simple_plan vision model correctness (float32)
-      #   run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
+      #   run: PYTHONPATH="." FLOAT16=0 DEBUGCL=1 GPU=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
      - name: Test openpilot LLVM compile
-        run: CPU=1 CPU_LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+        run: PYTHONPATH="." LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
      - name: Test openpilot compile4
-        run: NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 DEBUG=2 python3 examples/openpilot/compile4.py
+        run: PYTHONPATH="." NOLOCALS=1 GPU=1 IMAGE=2 FLOAT16=1 DEBUG=2 python3 examples/openpilot/compile4.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-# ****** ONNX Tests ******
-
  testonnxcpu:
    name: 'ONNX (CPU) Tests'
    runs-on: ubuntu-22.04
    timeout-minutes: 20
+    env:
+      IGNORE_OOB: 0

    steps:
      - name: Checkout Code
@@ -402,15 +500,15 @@ jobs:
          python-version: '3.11'
          llvm: 'true'
      - name: Test ONNX (CPU)
-        run: CPU=1 CPU_LLVM=0 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
+        run: CPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
      - name: Test ONNX (LLVM)
-        run: CPU=1 CPU_LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
+        run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
      - name: Test ONNX Runner (CPU)
-        run: CPU=1 CPU_LLVM=0 python3 test/external/external_test_onnx_runner.py
+        run: CPU=1 python3 test/external/external_test_onnx_runner.py
      - name: Test Additional ONNX Ops (CPU)
-        run: CPU=1 CPU_LLVM=0 python3 test/external/external_test_onnx_ops.py
+        run: CPU=1 python3 test/external/external_test_onnx_ops.py
      - name: Test Quantize ONNX
-        run: CPU=1 CPU_LLVM=0 python3 test/test_quantize_onnx.py
+        run: CPU=1 python3 test/test_quantize_onnx.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -418,6 +516,9 @@ jobs:
    name: 'ONNX (GPU)+Optimization Tests'
    runs-on: ubuntu-22.04
    timeout-minutes: 20
+    env:
+      IGNORE_OOB: 0
+
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -429,16 +530,16 @@ jobs:
          pydeps: "tensorflow==2.15.1 tensorflow_addons"
          python-version: '3.11'
          opencl: 'true'
-      - name: Test ONNX (CL)
-        run: CL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
+      - 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 Action Space
-      #  run: DEBUG=1 CL=1 python3 extra/optimization/get_action_space.py
+      #  run: DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
      - name: Test Beam Search
-        run: CL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
+        run: GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
      - name: Test MLPerf stuff
-        run: CL=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
+        run: GPU=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
      - name: Test llama 3 training
        run: MAX_BUFFER_SIZE=0 DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
      - name: Run process replay tests
@@ -458,12 +559,12 @@ jobs:
      - name: Test 1B LLM
        run: echo "What's a male chicken called? Answer with only one word." | MAX_BUFFER_SIZE=0 python3 -m tinygrad.apps.llm | grep -i rooster

-# ****** Models Tests ******
-
  testmodels:
    name: Models (llvm+cpu+gpu)
    runs-on: ubuntu-22.04
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -475,34 +576,14 @@ jobs:
          opencl: 'true'
          llvm: 'true'
      - name: Test models (llvm)
-        run: CPU=1 CPU_LLVM=1 python -m pytest -n=auto test/models --durations=20
-      - name: Test models (opencl)
-        run: CL=1 python -m pytest -n=auto test/models --durations=20
+        run: LLVM=1 python -m pytest -n=auto test/models --durations=20
+      - name: Test models (gpu)
+        run: GPU=1 python -m pytest -n=auto test/models --durations=20
      - name: Test models (cpu)
-        run: CPU=1 CPU_LLVM=0 python -m pytest -n=auto test/models --durations=20
+        run: CPU=1 python -m pytest -n=auto test/models --durations=20
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-  testmetalmodels:
-    name: Models (metal)
-    runs-on: macos-14
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: metal
-        deps: testing
-        python-version: '3.11'
-    - name: Test models (Metal)
-      run: METAL=1 python -m pytest -n=auto test/models --durations=20
-    - name: Test LLaMA compile speed
-      run: METAL=1 python test/external/external_test_speed_llama.py
-
-# ****** Feature Tests ******
-
  testrangeify:
    name: Linux (rangeify)
    runs-on: ubuntu-24.04
@@ -515,31 +596,27 @@ jobs:
      with:
        key: rangeify-minimal-llvm
        deps: testing_minimal
-        opencl: 'true'
        llvm: "true"
    - name: Test CPU=1 RANGEIFY=1
      # TODO: add more passing tests here
      # test_symbolic_arange_sym_step is passing now
      # test_threefry_doesnt_use_long is because there's a contig after the long now
      run: |
-        CPU=1 CPU_LLVM=0 RANGEIFY=1 python3 -m pytest -n auto --durations 20 \
+        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
-    - name: Test multitensor
-      run: RANGEIFY=1 PYTHONPATH="." python3 test/test_multitensor.py TestMultiTensor.test_matmul_shard_1_1 TestMultiTensor.test_simple_add_W
-    - name: Test GPU=1 RANGEIFY=1
-      run: GPU=1 RANGEIFY=1 pytest -n auto test/test_ops.py
    - name: Test CPU=1 RANGEIFY=2
-      run: CPU=1 CPU_LLVM=0 RANGEIFY=2 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
-    # slow (and still wrong on beautiful_mnist)
-    #- name: Test LLVM=1 RANGEIFY=1 (slow tests)
-    #  run: CPU=1 CPU_LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
+      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

  testdevectorize:
    name: Linux (devectorize)
    runs-on: ubuntu-24.04
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
@@ -551,16 +628,18 @@ jobs:
        pydeps: "pillow"
        llvm: "true"
    - name: Test LLVM=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+      run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
    - name: Test LLVM=1 DEVECTORIZE=0 for model
-      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
+      run: LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
    - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+      run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"

  testdsp:
    name: Linux (DSP)
    runs-on: ubuntu-24.04
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
@@ -627,6 +706,7 @@ jobs:
    runs-on: ubuntu-22.04
    timeout-minutes: 20
    env:
+      IGNORE_OOB: 0
      AMD: 1
      MOCKGPU: 1
      FORWARD_ONLY: 1
@@ -684,7 +764,7 @@ jobs:
          cuda: 'true'
          ocelot: 'true'
      - name: Set env
-        run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nCUDA_PTX=1' || matrix.backend == 'nv' && 'NV=1\nSKIP_SLOW_TEST=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"
@@ -695,15 +775,18 @@ jobs:
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-  testcpuopencl:
+  tests:
    strategy:
      fail-fast: false
      matrix:
-        backend: [llvm, cpu, opencl]
+        backend: [llvm, cpu, gpu]

    name: Linux (${{ matrix.backend }})
    runs-on: ubuntu-22.04
    timeout-minutes: 20
+    env:
+      IGNORE_OOB: 0
+
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -712,124 +795,65 @@ jobs:
        with:
          key: ${{ matrix.backend }}-minimal
          deps: testing_minimal
-          opencl: ${{ matrix.backend == 'opencl' && 'true' }}
+          opencl: ${{ matrix.backend == 'gpu' && 'true' }}
          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
-          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
+          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
-      - name: Run pytest (${{ matrix.backend }})
+      - name: Run pytest (not cuda)
        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
      - name: Run TRANSCENDENTAL math
        run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-  amdremote:
-    name: Linux (remote)
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    env:
-      REMOTE: 1
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: linux-remote
-          deps: testing_minimal
-          amd: 'true'
-          llvm: 'true'
-          opencl: 'true'
-      - name: Start remote server
-        run: |
-          start_server() {
-            systemd-run --user \
-              --unit="$1" \
-              --setenv=REMOTEDEV="$2" \
-              --setenv=MOCKGPU=1 \
-              --setenv=PYTHONPATH=. \
-              --setenv=PORT="$3" \
-              --working-directory="$(pwd)" \
-              python tinygrad/runtime/ops_remote.py
-          }
-
-          start_server "remote-server-amd-1" "AMD" 6667
-          start_server "remote-server-amd-2" "AMD" 6668
-          start_server "remote-server-gpu" "CL" 7667
-          start_server "remote-server-cpu" "CPU" 8667
-      - name: Check Device.DEFAULT and print some source
-        env:
-          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'AMD', Device.default.properties.real_device"
-          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-      - name: Run REMOTE=1 Test (AMD)
-        env:
-          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py --durations 20
-      - name: Run REMOTE=1 Test (CL)
-        env:
-          HOST: 127.0.0.1:7667*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py --durations 20
-          IMAGE=2 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
-      - name: Run REMOTE=1 Test (CPU)
-        env:
-          HOST: 127.0.0.1:8667*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py --durations 20
-      - name: Show remote server logs
-        if: always()
-        run: |
-          journalctl --user -u remote-server-amd-1 --no-pager
-          journalctl --user -u remote-server-amd-2 --no-pager
-          journalctl --user -u remote-server-gpu --no-pager
-          journalctl --user -u remote-server-cpu --no-pager
-
 # ****** OSX Tests ******

-  testmetal:
+  testmetal2:
    name: MacOS (unit)
    runs-on: macos-14
    timeout-minutes: 20
+    env:
+      IGNORE_OOB: 0
+
    steps:
    - name: Checkout Code
      uses: actions/checkout@v4
    - name: Setup Environment
      uses: ./.github/actions/setup-tinygrad
      with:
-        key: metal
+        key: metal2
        deps: testing
        python-version: '3.11'
        amd: 'true'
        cuda: 'true'
        ocelot: 'true'
        llvm: 'true'
-    - name: Run unit tests
-      run: METAL=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Run real world test
+      run: METAL=1 python -m pytest -n=auto test/models/test_real_world.py --durations=20
+    - name: Test models (Metal)
+      run: METAL=1 python -m pytest -n=auto test/models -v --durations=20
    - name: Run ONNX
      run: METAL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
    - name: Test tensor core ops (fake)
-      run: METAL=1 DEBUG=3 TC=2 python test/test_ops.py TestOps.test_gemm
+      run: TC=2 METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_gemm
    - name: Test tensor core ops (real)
      run: METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_big_gemm
+    - name: Test LLaMA compile speed
+      run: METAL=1 python test/external/external_test_speed_llama.py
    - name: Test Beam Search
      run: METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
    #- name: Fuzz Test linearizer
    #  run: METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
    - name: Run TRANSCENDENTAL math
-      run: METAL=1 TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
+      run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
    - name: Run pytest (amd)
      env:
        MOCKGPU: 1
        AMD: 1
-        AMD_LLVM: 0
        FORWARD_ONLY: 1
      run: |
        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
@@ -837,14 +861,13 @@ jobs:
      env:
        MOCKGPU: 1
        AMD: 1
-        AMD_LLVM: 1
        FORWARD_ONLY: 1
      run: |
        python -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py test/device/test_amd_llvm.py --durations=20
    - name: Run pytest (ptx)
      env:
        MOCKGPU: 1
-        NV_PTX: 1
+        PTX: 1
        NV: 1
        FORWARD_ONLY: 1
      run: |
@@ -913,6 +936,71 @@ jobs:
       run: |
         python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py

+  amdremote:
+    name: Linux (remote)
+    runs-on: ubuntu-22.04
+    timeout-minutes: 20
+    env:
+      REMOTE: 1
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: linux-remote
+          deps: testing_minimal
+          amd: 'true'
+          llvm: 'true'
+          opencl: 'true'
+      - name: Start remote server
+        run: |
+          start_server() {
+            systemd-run --user \
+              --unit="$1" \
+              --setenv=REMOTEDEV="$2" \
+              --setenv=MOCKGPU=1 \
+              --setenv=PYTHONPATH=. \
+              --setenv=PORT="$3" \
+              --working-directory="$(pwd)" \
+              python tinygrad/runtime/ops_remote.py
+          }
+
+          start_server "remote-server-amd-1" "AMD" 6667
+          start_server "remote-server-amd-2" "AMD" 6668
+          start_server "remote-server-gpu" "GPU" 7667
+          start_server "remote-server-cpu" "CPU" 8667
+      - name: Check Device.DEFAULT and print some source
+        env:
+          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
+        run: |
+          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'AMD', Device.default.properties.real_device"
+          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
+      - name: Run REMOTE=1 Test (AMD)
+        env:
+          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
+        run: |
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py --durations 20
+      - name: Run REMOTE=1 Test (GPU)
+        env:
+          HOST: 127.0.0.1:7667*6
+        run: |
+          python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py --durations 20
+          IMAGE=2 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
+      - name: Run REMOTE=1 Test (CPU)
+        env:
+          HOST: 127.0.0.1:8667*6
+        run: |
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py --durations 20
+      - name: Show remote server logs
+        if: always()
+        run: |
+          journalctl --user -u remote-server-amd-1 --no-pager
+          journalctl --user -u remote-server-amd-2 --no-pager
+          journalctl --user -u remote-server-gpu --no-pager
+          journalctl --user -u remote-server-cpu --no-pager
+
  osxtests:
    strategy:
      fail-fast: false
@@ -921,6 +1009,8 @@ jobs:
    name: MacOS (${{ matrix.backend }})
    runs-on: macos-15
    timeout-minutes: 20
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -932,10 +1022,10 @@ jobs:
          pydeps: "capstone"
          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.DEFAULT == '${{ matrix.backend }}'.upper(), Device.DEFAULT"
          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
      - name: Run pytest (${{ matrix.backend }})
        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
@@ -956,6 +1046,8 @@ jobs:
    name: Windows (${{ matrix.backend }})
    runs-on: windows-latest
    timeout-minutes: 15
+    env:
+      IGNORE_OOB: 0
    steps:
      - name: Checkout Code
        uses: actions/checkout@v4
@@ -967,7 +1059,7 @@ jobs:
          pydeps: ${{ matrix.backend == 'webgpu' && 'dawn-python' || '' }}
      - name: Set env
        shell: bash
-        run:  printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
+        run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
      - name: Run unit tests
        if: matrix.backend=='llvm'
        # test_newton_schulz hits RecursionError
@@ -975,5 +1067,5 @@ jobs:
      - name: Run pytest (${{ matrix.backend }})
        shell: bash
        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.DEFAULT == '${{ matrix.backend }}'.upper(), Device.DEFAULT"
          python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20
@@ -79,8 +79,9 @@ See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full vers

 tinygrad already supports numerous accelerators, including:

- [x] [OpenCL](tinygrad/runtime/ops_cl.py)
- [x] [CPU](tinygrad/runtime/ops_cpu.py)
+- [x] [GPU (OpenCL)](tinygrad/runtime/ops_gpu.py)
+- [x] [CPU (C Code)](tinygrad/runtime/ops_cpu.py)
+- [x] [LLVM](tinygrad/runtime/ops_llvm.py)
 - [x] [METAL](tinygrad/runtime/ops_metal.py)
 - [x] [CUDA](tinygrad/runtime/ops_cuda.py)
 - [x] [AMD](tinygrad/runtime/ops_amd.py)
@@ -3,7 +3,7 @@
 This is a list of environment variable that control the runtime behavior of tinygrad and its examples.
 Most of these are self-explanatory, and are usually used to set an option at runtime.

-Example: `CL=1 DEBUG=4 python3 -m pytest`
+Example: `GPU=1 DEBUG=4 python3 -m pytest`

 However you can also decorate a function to set a value only inside that function.

@@ -31,12 +31,13 @@ These control the behavior of core tinygrad even when used as a library.
 Variable | Possible Value(s) | Description
 ---|---|---
 DEBUG               | [1-7]      | enable debugging output (operations, timings, speed, generated code and more)
-CL                  | [1]        | enable OpenCL backend
+GPU                 | [1]        | enable the GPU (OpenCL) backend
 CUDA                | [1]        | enable CUDA backend
 AMD                 | [1]        | enable AMD backend
 NV                  | [1]        | enable NV backend
 METAL               | [1]        | enable Metal backend (for Mac M1 and after)
-CPU                 | [1]        | enable CPU backend
+CPU                 | [1]        | enable CPU (Clang) backend
+LLVM                | [1]        | enable LLVM backend
 BEAM                | [#]        | number of beams in kernel beam search
 DEFAULT_FLOAT       | [HALF, ...]| specify the default float dtype (FLOAT32, HALF, BFLOAT16, FLOAT64, ...), default to FLOAT32
 IMAGE               | [1-2]      | enable 2d specific optimizations
@@ -9,7 +9,7 @@ tinygrad supports various runtimes, enabling your code to scale across a wide ra
 | [QCOM](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_qcom.py) | Provides acceleration for QCOM GPUs | 6xx series GPUs |
 | [METAL](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_metal.py) | Utilizes Metal for acceleration on Apple devices | M1+ Macs; Metal 3.0+ for `bfloat` support |
 | [CUDA](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cuda.py) | Utilizes CUDA for acceleration on NVIDIA GPUs | NVIDIA GPU with CUDA support |
-| [OpenCL](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cl.py) | Accelerates computations using OpenCL on GPUs | OpenCL 2.0 compatible device |
+| [GPU (OpenCL)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_gpu.py) | Accelerates computations using OpenCL on GPUs | OpenCL 2.0 compatible device |
 | [CPU (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cpu.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
 | [LLVM (LLVM IR)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_llvm.py) | Runs on CPU using the LLVM compiler infrastructure | llvm libraries installed and findable |
 | [WEBGPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_webgpu.py) | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | Dawn library installed and findable. Download binaries [here](https://github.com/wpmed92/pydawn/releases/tag/v0.3.0). |
@@ -181,7 +181,6 @@ class GPT2:
    self.tokenizer = tokenizer

  def generate(self, prompt:str, max_length:int, temperature:float, timing:bool=False, batch_size:int=1):
-    step_times = []
    prompt_tokens = self.tokenizer.encode(prompt, allowed_special={"<|endoftext|>"})
    toks = [prompt_tokens[:] for _ in range(batch_size)]
    start_pos = 0
@@ -189,7 +188,7 @@ class GPT2:
      GlobalCounters.reset()
      if timing: print("")
      st = GlobalCounters.time_sum_s
-      with Timing("ran model in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+      with Timing("ran model in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                  f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                  (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=timing):
        with WallTimeEvent(BenchEvent.STEP):
@@ -198,13 +197,8 @@ class GPT2:
          else:
            tokens = Tensor([x[start_pos:] for x in toks])
          tok = self.model(tokens, Variable("start_pos", 1 if start_pos else 0, MAX_CONTEXT-1).bind(start_pos), temperature).tolist()
-      step_times.append((GlobalCounters.time_sum_s-st)*1e3)
      start_pos = len(toks[0])
      for i,t in enumerate(tok): toks[i].append(t)
-
-    if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-      min_time = min(step_times)
-      assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
    return [self.tokenizer.decode(x) for x in toks]

 # **** main code ****
@@ -355,7 +355,7 @@ def train_cifar():

  # https://www.anandtech.com/show/16727/nvidia-announces-geforce-rtx-3080-ti-3070-ti-upgraded-cards-coming-in-june
  # 136 TFLOPS is the theoretical max w float16 on 3080 Ti
-  step_times = []
+
  model_ema: Optional[modelEMA] = None
  projected_ema_decay_val = hyp['ema']['decay_base'] ** hyp['ema']['every_n_steps']
  i = 0
@@ -413,17 +413,12 @@ def train_cifar():
          model_ema.update(model, Tensor([projected_ema_decay_val*(i/STEPS)**hyp['ema']['decay_pow']]))

      cl = time.monotonic()
-      step_times.append((cl-st)*1000.0)
      device_str = loss.device if isinstance(loss.device, str) else f"{loss.device[0]} * {len(loss.device)}"
      #  53  221.74 ms run,    2.22 ms python,  219.52 ms CL,  803.39 loss, 0.000807 LR, 4.66 GB used,   3042.49 GFLOPS,    674.65 GOPS
      print(f"{i:3d} {(cl-st)*1000.0:7.2f} ms run, {(et-st)*1000.0:7.2f} ms python, {(cl-et)*1000.0:7.2f} ms {device_str}, {loss_cpu:7.2f} loss, {opt_non_bias.lr.numpy()[0]:.6f} LR, {GlobalCounters.mem_used/1e9:.2f} GB used, {GlobalCounters.global_ops*1e-9/(cl-st):9.2f} GFLOPS, {GlobalCounters.global_ops*1e-9:9.2f} GOPS")
      st = cl
      i += 1

-  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-    min_time = min(step_times)
-    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
-
  # verify eval acc
  if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
    if eval_acc_pct >= target:
@@ -478,7 +478,7 @@ After you are done speaking, output [EOS]. You are not Chad.
      with Profiling(enabled=args.profile):
        with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
          with WallTimeEvent(BenchEvent.STEP):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                        f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                        (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
              tok_tensor = llama.model(next_tok, start_pos, args.temperature)
@@ -441,7 +441,7 @@ if __name__ == "__main__":
      with Profiling(enabled=args.profile):
        with Timing("total ", on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
          with WallTimeEvent(BenchEvent.STEP):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                        f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                        (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None):
              tok = model(Tensor([[last_tok]], device=device), start_pos, TEMPERATURE, TOP_K, TOP_P, ALPHA_F, ALPHA_P)
@@ -479,7 +479,7 @@ if __name__ == "__main__":
        st = GlobalCounters.time_sum_s
        with Profiling(enabled=args.profile):
          with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
-            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "")+
+            with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "")+
                        f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB"+
                        (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):

@@ -279,15 +279,9 @@ def generate(model, tokenizer, prompt: str, n_tokens_to_gen: int = 10, temp: boo
  # Loading in the prompt tokens
  logits = model.forward(Tensor([tks]))[:, -1, :]
  for _ in tqdm(range(n_tokens_to_gen), desc="Speed Gen"):
+    # TODO: topk
    if sample:
-      scaled_logits = logits / temp
-      if top_k is not None:
-        topk_values, topk_indices = scaled_logits.topk(top_k)
-        filtered_logits = Tensor.full_like(scaled_logits, -float("inf"))
-        filtered_logits = filtered_logits.scatter(dim=-1, index=topk_indices, src=topk_values)
-        tok_Tens = filtered_logits.softmax().multinomial()
-      else:
-        tok_Tens = scaled_logits.softmax().multinomial()
+      tok_Tens = (logits/temp).softmax().multinomial()
    else:
      tok_Tens = logits.argmax(axis=-1).unsqueeze(0)
    tok = tok_Tens.item()
@@ -304,7 +298,6 @@ if __name__ == "__main__":
  parser.add_argument("--size", type=str, default="370m",
                      help=f"Size of model to use [{', '.join([k for k in MODELS.keys()])}]")
  parser.add_argument("--n_tokens", type=int, default=10, help="Number of tokens to generate")
-  parser.add_argument("--top_k", type=int, help="Limit sampling to the top k most likely tokens")
  parser.add_argument("--sample", dest="sample", action="store_true", help="Sample flag")
  parser.add_argument("--temp", type=float, default=1.0, help="Sampling temp has to be <=1.0")
  args = parser.parse_args()
@@ -315,9 +308,8 @@ if __name__ == "__main__":
  num_toks = args.n_tokens
  sample = args.sample
  temp = args.temp
-  top_k = args.top_k
  s = time.time()
-  tinyoutput = generate(model, tokenizer, prompt, n_tokens_to_gen=num_toks, sample=sample, temp=temp, top_k=top_k)
+  tinyoutput = generate(model, tokenizer, prompt, n_tokens_to_gen=num_toks, sample=sample, temp=temp)
  print(tinyoutput)
  print('TIME: ', time.time() - s)
  TORCHOUTPUT = "Why is gravity \nso important?\nBecause it's the only"
@@ -252,10 +252,6 @@ def train_resnet():
        print(f"epoch global_ops: {steps_in_train_epoch * GlobalCounters.global_ops:_}, "
              f"epoch global_mem: {steps_in_train_epoch * GlobalCounters.global_mem:_}")
        # if we are doing beam search, run the first eval too
-        if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-          min_time = min(step_times)
-          assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
-
        if (TRAIN_BEAM or EVAL_BEAM) and e == start_epoch: break
        return
    if MLLOGGER and RUNMLPERF:
@@ -348,8 +344,6 @@ def train_resnet():
        print(f"saving ckpt to {fn}")
        safe_save(get_training_state(model, optimizer_group, scheduler_group), fn)

-
-
 def train_retinanet():
  from contextlib import redirect_stdout
  from examples.mlperf.dataloader import batch_load_retinanet
@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-# adapted from https://github.com/mlcommons/training/blob/4bdf5c8ed218ad76565a2ba1ac27c919ccc6d689/stable_diffusion/README.md
-
-# setup dirs
-
-DATA=/raid/datasets/stable_diffusion
-
-LAION=$DATA/laion-400m/webdataset-moments-filtered 
-COCO=$DATA/coco2014
-mkdir -p $LAION $COCO
-
-CKPT=/raid/weights/stable_diffusion
-mkdir -p $CKPT/clip $CKPT/sd $CKPT/inception
-
-# download data
-
-# if rclone isn't installed system-wide / in your PATH, put the executable path in quotes below
-#RCLONE=""
-RCLONE="rclone"
-
-## VAE-encoded image latents, from 6.1M image subset of laion-400m
-## about 1 TB for whole download
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/laion-400m/moments-webdataset-filtered/ ${LAION} --include="*.tar" -P
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/laion-400m/moments-webdataset-filtered/sha512sums.txt ${LAION} -P
-cd $LAION && grep -E '\.tar$' sha512sums.txt | sha512sum -c --quiet - && \
-  echo "All .tar files verified" || { echo "Checksum failure when validating downloaded Laion moments"; exit 1; }
-
-## prompts and FID statistics from 30k image subset of coco2014
-## 33 MB
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/coco2014/val2014_30k.tsv ${COCO} -P
-
-$RCLONE config create mlc-training s3 provider=Cloudflare access_key_id=76ea42eadb867e854061a1806220ee1e secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-$RCLONE copy mlc-training:mlcommons-training-wg-public/stable_diffusion/datasets/coco2014/val2014_30k_stats.npz ${COCO} -P
-
-# download checkpoints
-
-## clip (needed for text and vision encoders for validation)
-CLIP_WEIGHTS_URL="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin"
-CLIP_WEIGHTS_SHA256="9a78ef8e8c73fd0df621682e7a8e8eb36c6916cb3c16b291a082ecd52ab79cc4"
-CLIP_CONFIG_URL="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/raw/main/open_clip_config.json"
-wget -N -P ${CKPT}/clip ${CLIP_WEIGHTS_URL}
-wget -N -P ${CKPT}/clip ${CLIP_CONFIG_URL}
-echo "${CLIP_WEIGHTS_SHA256}  ${CKPT}/clip/open_clip_pytorch_model.bin"                    | sha256sum -c
-
-## sd (needed for latent->image decoder for validation, also has clip text encoder for training)
-SD_WEIGHTS_URL='https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt'
-SD_WEIGHTS_SHA256="d635794c1fedfdfa261e065370bea59c651fc9bfa65dc6d67ad29e11869a1824"
-wget -N -P ${CKPT}/sd ${SD_WEIGHTS_URL}
-echo "${SD_WEIGHTS_SHA256}  ${CKPT}/sd/512-base-ema.ckpt"                    | sha256sum -c
-
-## inception (needed for validation)
-FID_WEIGHTS_URL='https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-FID_WEIGHTS_SHA1="bd836944fd6db519dfd8d924aa457f5b3c8357ff"
-wget -N -P ${CKPT}/inception ${FID_WEIGHTS_URL}
-echo "${FID_WEIGHTS_SHA1}  ${CKPT}/inception/pt_inception-2015-12-05-6726825d.pth"                    | sha1sum -c
@@ -6,7 +6,7 @@ from tinygrad.schedule.kernelize import get_kernelize_map
 from tinygrad.engine.schedule import create_schedule_with_vars
 from tinygrad.engine.realize import run_schedule

-# NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py
+# NOLOCALS=1 GPU=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py

 OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
 OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
@@ -8,7 +8,7 @@ from typing import Dict, Union

 from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
 from examples.llama3 import load
-from tinygrad import nn, Tensor, Device
+from tinygrad import nn, Tensor
 from tinygrad.helpers import fetch, colored, GlobalCounters, Timing, DEBUG
 from tinygrad.nn.state import load_state_dict, get_parameters

@@ -80,7 +80,7 @@ if __name__ == "__main__":
    st = GlobalCounters.time_sum_s
    next_tok = Tensor([toks[start_pos:]]) if tok_tensor is None or (len(toks)-start_pos) > 1 else tok_tensor.reshape(1, 1)
    with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
-      with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on {Device.DEFAULT}" if DEBUG>=2 else "") +
+      with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "") +
                  f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB" +
                  (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
        tok_tensor = transformer(next_tok, start_pos, args.temperature)
@@ -6,7 +6,7 @@
 from tinygrad import Tensor, TinyJit, dtypes, GlobalCounters
 from tinygrad.nn import Conv2d, GroupNorm
 from tinygrad.nn.state import safe_load, load_state_dict
-from tinygrad.helpers import fetch, trange, colored, Timing, getenv
+from tinygrad.helpers import fetch, trange, colored, Timing
 from extra.models.clip import Embedder, FrozenClosedClipEmbedder, FrozenOpenClipEmbedder
 from extra.models.unet import UNetModel, Upsample, Downsample, timestep_embedding
 from extra.bench_log import BenchEvent, WallTimeEvent
@@ -14,7 +14,7 @@ from examples.stable_diffusion import ResnetBlock, Mid
 import numpy as np

 from typing import Dict, List, Callable, Optional, Any, Set, Tuple, Union, Type
-import argparse, tempfile, time
+import argparse, tempfile
 from abc import ABC, abstractmethod
 from pathlib import Path
 from PIL import Image
@@ -342,13 +342,11 @@ class DPMPP2MSampler:
    sigmas = self.discretization(num_steps).to(x.device)
    x *= Tensor.sqrt(1.0 + sigmas[0] ** 2.0)
    num_sigmas = len(sigmas)
-    step_times = []

    old_denoised = None
    for i in trange(num_sigmas - 1):
      with Timing("step in ", enabled=timing, on_exit=lambda _: f", using {GlobalCounters.mem_used/1e9:.2f} GB"):
        GlobalCounters.reset()
-        st = time.perf_counter_ns()
        with WallTimeEvent(BenchEvent.STEP):
          x, old_denoised = self.sampler_step(
            old_denoised=old_denoised,
@@ -360,13 +358,8 @@ class DPMPP2MSampler:
            c=c,
            uc=uc,
          )
-          step_times.append(t:=(time.perf_counter_ns() - st)*1e-6)
          x.realize(old_denoised)

-    if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-      min_time = min(step_times)
-      assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
-
    return x


@@ -437,8 +430,8 @@ if __name__ == "__main__":
    im.show()

  # validation!
-  is_default = args.prompt == default_prompt and args.steps == 10 and args.seed == 0 and args.guidance == 6.0 and args.width == args.height == 1024
-  if is_default and not args.weights and not args.fakeweights:
+  if args.prompt == default_prompt and args.steps == 10 and args.seed == 0 and args.guidance == 6.0 and args.width == args.height == 1024 \
+    and not args.weights:
    ref_image = Tensor(np.array(Image.open(Path(__file__).parent / "sdxl_seed0.png")))
    distance = (((x.cast(dtypes.float) - ref_image.cast(dtypes.float)) / ref_image.max())**2).mean().item()
    assert distance < 4e-3, colored(f"validation failed with {distance=}", "red")
@@ -2,7 +2,7 @@
 # https://github.com/ekagra-ranjan/huggingface-blog/blob/main/stable_diffusion.md
 import tempfile
 from pathlib import Path
-import argparse, time
+import argparse
 from collections import namedtuple
 from typing import Dict, Any

@@ -266,23 +266,17 @@ if __name__ == "__main__":
  def run(model, *x): return model(*x).realize()

  # this is diffusion
-  step_times = []
  with Context(BEAM=getenv("LATEBEAM")):
    for index, timestep in (t:=tqdm(list(enumerate(timesteps))[::-1])):
      GlobalCounters.reset()
-      st = time.perf_counter_ns()
      t.set_description("%3d %3d" % (index, timestep))
      with Timing("step in ", enabled=args.timing, on_exit=lambda _: f", using {GlobalCounters.mem_used/1e9:.2f} GB"):
        with WallTimeEvent(BenchEvent.STEP):
          tid = Tensor([index])
          latent = run(model, unconditional_context, context, latent, Tensor([timestep]), alphas[tid], alphas_prev[tid], Tensor([args.guidance]))
          if args.timing: Device[Device.DEFAULT].synchronize()
-      step_times.append((time.perf_counter_ns() - st)*1e-6)
    del run

-  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-    min_time = min(step_times)
-    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
  # upsample latent space to image with autoencoder
  x = model.decode(latent)
  print(x.shape)
@@ -1,7 +1,7 @@
 # copying the kernels from https://github.com/microsoft/ArchProbe into Python
 import numpy as np
 import pickle
-from tinygrad.runtime.ops_cl import CLProgram, CLBuffer
+from tinygrad.runtime.ops_gpu import CLProgram, CLBuffer
 from tinygrad import dtypes
 from tqdm import trange, tqdm
 from matplotlib import pyplot as plt
@@ -4,7 +4,7 @@ from tinygrad import dtypes
 from tinygrad.codegen.assembly import AssemblyCodegen, Register
 from tinygrad.codegen.opt.kernel import Ops
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
-from tinygrad.runtime.ops_cl import ROCM_LLVM_PATH
+from tinygrad.runtime.ops_gpu import ROCM_LLVM_PATH

 # ugh, is this really needed?
 from extra.helpers import enable_early_exec
@@ -5,7 +5,7 @@ from tinygrad.helpers import colored
 from extra.helpers import enable_early_exec
 early_exec = enable_early_exec()

-from tinygrad.runtime.ops_cl import CLProgram, CLBuffer, ROCM_LLVM_PATH
+from tinygrad.runtime.ops_gpu import CLProgram, CLBuffer, ROCM_LLVM_PATH

 ENABLE_NON_ASM = False

@@ -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[str, int]):
+  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[Variable, 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}" for v in var_vals])
+    args += sorted([f"int {v.expr}" 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[str, int], wait=False):
+  def __call__(self, rawbufs: List[Buffer], var_vals: Dict[Variable, 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])]), 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].expr)]), enable=wait)
@@ -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[str, int]):
+  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[Variable, 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].expr])
+      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]])

    # 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[str, int], wait=False) -> Optional[float]:
+  def __call__(self, input_rawbuffers: List[Buffer], var_vals: Dict[Variable, 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.expr])
+        self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[v])

    # Update launch dims
    for j in self.jc_idx_with_updatable_launch_dims:
@@ -10,7 +10,7 @@ from tinygrad.uop.ops import Ops
 import json
 from collections import OrderedDict

-EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CPU", "CUDA", "CL"]
+EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CPU", "CUDA", "GPU"]

 def compile_net(run:TinyJit, special_names:Dict[int,str]) -> Tuple[Dict[str,str],List[Tuple[str,List[str],List[int]]],Dict[str,Tuple[int,DType,int]],Dict[str,Tensor]]:
  functions, bufs, bufs_to_save, statements, bufnum = {}, {}, {}, [], 0
@@ -67,12 +67,11 @@ 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)}, {thread_id});" for (name, args, _global_size, _local_size) in statements] + ["}"]
+    cprog += [f"void net({forward_args}) {{"] + [f"{name}({', '.join(args)});" for (name, args, _global_size, _local_size) in statements] + ["}"]
    return '\n'.join(headers + cprog)
  else:
    if bufs_to_save:
@@ -240,9 +239,7 @@ 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"
-
-  # NOTE: CPU_COUNT=1, since export does not support threading
-  with Context(JIT=2, CPU_COUNT=1): run,special_names = jit_model(model, *inputs)
+  with Context(JIT=2): 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()}
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 import numpy as np
-from tinygrad.runtime.ops_cl import CLProgram, CLCompiler
+from tinygrad.runtime.ops_gpu import CLProgram, CLCompiler
 from tinygrad import Device, dtypes
 from tinygrad.device import Buffer
 from hexdump import hexdump
@@ -11,7 +11,7 @@ from hexdump import hexdump
 # https://registry.khronos.org/OpenCL/extensions/intel/cl_intel_subgroup_split_matrix_multiply_accumulate.html
 # https://hc34.hotchips.org/assets/program/conference/day1/GPU%20HPC/Intel_s%20Ponte%20Vecchio%20GPU%20-%20Architecture%20Systems%20and%20Software%20FINAL.pdf

-device = Device["CL"]
+device = Device["GPU"]

 # NOTE: only the subgroup type 8 ones work
 prog = CLProgram(device, "test", CLCompiler(device, "test").compile(f"""
@@ -26,9 +26,9 @@ __kernel void test(__global float* data0, const __global int* data1, const __glo
 """))
 #with open("/tmp/test.elf", "wb") as f: f.write(prog.lib)

-a = Buffer("CL", 8, dtypes.float32).allocate()
-b = Buffer("CL", 0x10, dtypes.float16).allocate()
-c = Buffer("CL", 8*0x10, dtypes.float16).allocate()
+a = Buffer("GPU", 8, dtypes.float32).allocate()
+b = Buffer("GPU", 0x10, dtypes.float16).allocate()
+c = Buffer("GPU", 8*0x10, dtypes.float16).allocate()

 row = np.array([1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8], np.float16)
 mat = np.random.random((8, 0x10)).astype(np.float16)
@@ -75,7 +75,7 @@ if __name__ == "__main__":

    if GEMM_VARIATION == "max" and (M%64)==0 and (N%128)==0 and (K%64)==0 and DTYPE_IN == dtypes.half and DTYPE_OUT == dtypes.float and DTYPE_ACC == dtypes.float:
      print("Using CUDA and triton-generated kernel")
-      # See nv_triton_gemm.annotated.ptx for PTX code which was generated from `PYTHONPATH=. DEBUG=6 CUDA=1 CUDA_PTX=1 python3 extra/gemm/triton_nv_matmul.py`
+      # See nv_triton_gemm.annotated.ptx for PTX code which was generated from `PYTHONPATH=. DEBUG=6 CUDA=1 PTX=1 python3 extra/gemm/triton_nv_matmul.py`
      # this kernel with M=N=K=4096 does 162TFLOPS, vs torch at 144TFLOPS and BEAM=8 tinygrad at 138TFLOPS.  theo max is 165TFLOPS.

      # WMMA element size is (M, N, K) = (16, 8, 16)
@@ -43,7 +43,7 @@ def matmul_kernel(c_ptr, a_ptr, b_ptr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N:
  c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
  tl.store(c_ptrs, c)

-# CUDA=1 CUDA_PTX=1 python3 extra/gemm/triton_nv_matmul.py
+# CUDA=1 PTX=1 python3 extra/gemm/triton_nv_matmul.py
 if __name__ == "__main__":
  BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K = 64, 128, 64
  M, N, K = 4096, 4096, 4096
@@ -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.expr:(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
+  var_vals = {k:(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
  dev = Device[lin.opts.device]
  root = MCTSNode(lin)

@@ -270,10 +270,8 @@ class FidInceptionV3:
    self.Mixed_7b = inception.Mixed_7b
    self.Mixed_7c = inception.Mixed_7c

-  def load_from_pretrained(self, path=None):
-    if path is None:
-      path = fetch("https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth", "pt_inception-2015-12-05-6726825d.pth")
-    state_dict = torch_load(str(path))
+  def load_from_pretrained(self):
+    state_dict = torch_load(str(fetch("https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth", "pt_inception-2015-12-05-6726825d.pth")))
    for k,v in state_dict.items():
      if k.endswith(".num_batches_tracked"):
        state_dict[k] = v.reshape(1)
@@ -249,5 +249,8 @@ def convert_from_gguf(weights:dict[str, Tensor], n_layers:int):
  return sd

 def fix_bf16(weights:dict[Any, Tensor]):
-  # TODO: without casting to float16, 70B llama OOM on tinybox.
-  return {k:v.cast(dtypes.float32).cast(dtypes.float16) if v.dtype == dtypes.bfloat16 else v for k,v in weights.items()}
+  if getenv("SUPPORT_BF16", 1):
+    # TODO: without casting to float16, 70B llama OOM on tinybox.
+    return {k:v.cast(dtypes.float32).cast(dtypes.float16) if v.dtype == dtypes.bfloat16 else v for k,v in weights.items()}
+  # TODO: check if device supports bf16
+  return {k:v.llvm_bf16_cast(dtypes.half).to(v.device) if v.dtype == dtypes.bfloat16 else v for k,v in weights.items()}
@@ -272,4 +272,4 @@ def compare_launch_state(states, good_states):

  return True, "PASS"

-# IOCTL=1 CUDA=1 CUDA_PTX=1 python3 test/test_ops.py TestOps.test_tiny_add
+# IOCTL=1 PTX=1 CUDA=1 python3 test/test_ops.py TestOps.test_tiny_add
@@ -7,7 +7,7 @@ rm $LOGOPS
 test/external/process_replay/reset.py

 CI=1 python3 -m pytest -n=auto test/test_ops.py test/test_nn.py test/test_winograd.py test/models/test_real_world.py --durations=20
-CL=1 python3 -m pytest test/test_tiny.py
+GPU=1 python3 -m pytest test/test_tiny.py

 # extract, sort and uniq
 extra/optimization/extract_dataset.py
@@ -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[str, int] = {k.expr:int(k.vmax+k.vmin)//2 for k in lin.ast.variables()}
+  var_vals: dict[Variable, int] = {k: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))
@@ -1,6 +1,6 @@
 import ctypes, array
 from hexdump import hexdump
-from tinygrad.runtime.ops_cl import CLDevice
+from tinygrad.runtime.ops_gpu import GPUDevice
 from tinygrad.helpers import getenv, to_mv, mv_address
 from tinygrad.dtype import dtypes
 from tinygrad import Tensor, TinyJit
@@ -8,7 +8,7 @@ from tinygrad.runtime.autogen import opencl as cl
 if getenv("IOCTL"): import extra.qcom_gpu_driver.opencl_ioctl  # noqa: F401  # pylint: disable=unused-import

 # create raw opencl buffer.
-gdev = CLDevice()
+gdev = GPUDevice()
 cl_buf = cl.clCreateBuffer(gdev.context, cl.CL_MEM_READ_WRITE, 0x100, None, status := ctypes.c_int32())
 assert status.value == 0

@@ -4,13 +4,13 @@ import struct
 import json
 import traceback
 import numpy as np
-from tinygrad.runtime.ops_cl import CLProgram, compile_gpu
+from tinygrad.runtime.ops_gpu import CLProgram, compile_gpu
 from tinygrad.device import Device
 from tinygrad.helpers import DEBUG, getenv
 from collections import defaultdict
 import pyopencl as cl
-from tinygrad.runtime.ops_cl import OSX_TIMING_RATIO
-CL = Device["CL"]
+from tinygrad.runtime.ops_gpu import OSX_TIMING_RATIO
+CL = Device["GPU"]

 DEBUGCL = getenv("DEBUGCL", 0)
 FLOAT16 = getenv("FLOAT16", 0)
@@ -110,7 +110,7 @@ class Thneed:
    prgs = {}
    for o in jdat['binaries']:
      nptr = ptr + o['length']
-      prgs[o['name']] = CLProgram(Device["CL"], o['name'], weights[ptr:nptr])
+      prgs[o['name']] = CLProgram(Device["GPU"], o['name'], weights[ptr:nptr])
      ptr = nptr

    # populate the cl_cache
@@ -267,7 +267,7 @@ class Thneed:
    for prg, args in self.cl_cache:
      events.append(prg.clprg(CL.queue, *args))
    mt = time.monotonic()
-    Device["CL"].synchronize()
+    Device["GPU"].synchronize()
    et = time.monotonic() - st
    print(f"submit in {(mt-st)*1000.0:.2f} ms, total runtime is {et*1000.0:.2f} ms")

@@ -35,7 +35,7 @@ def to_movement_ops(st: ShapeTracker) -> List[Tuple[MovementOps, Tuple]]:
  to_apply:List[Tuple[MovementOps, Tuple]] = []
  for i, v in enumerate(st.views):
    real_shape = tuple(y-x for x,y in v.mask) if v.mask else v.shape
-    offset = (v.offset or 0) + sum(st*(s-1) for s,st in zip(real_shape, v.strides) if st<0)
+    offset = v.offset + sum(st*(s-1) for s,st in zip(real_shape, v.strides) if st<0)
    real_offset = offset + (sum(x*st for (x,_),st in zip(v.mask, v.strides)) if v.mask else 0)
    real_real_shape = [s for s,st in zip(real_shape, v.strides) if st]
    strides: List[int] = [abs(st) if isinstance(st,int) else st for st in v.strides if st]
@@ -120,7 +120,7 @@ def st_equivalent(st1: ShapeTracker, st2: ShapeTracker):
    if i > 1000:
      print("WARNING: did not search all possible combinations")
      break
-    var_vals = {k.expr:v for k,v in zip(vs, ranges)}
+    var_vals = {k: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
@@ -177,28 +177,22 @@ def cached_to_movement_ops(shape, st) -> list:

 from tinygrad.shape.shapetracker import ShapeTracker, View
 from extra.to_movement_ops import to_movement_ops, apply_mop, MovementOps
-
-@wrap_view_op
-def _as_strided(tensor:Tensor, size, stride, storage_offset=None):
-  # multiple as_strided do not compound
-  base = canonical_base(tensor)
-  # TODO: this is heavyweight
-  st = ShapeTracker(base.uop.st.views + (View.create(tuple(size), tuple(stride), storage_offset),))
-  ret = base
-  if TORCH_DEBUG >= 1: print("**** as_strided", tensor.shape, size, stride, st)
-  if prod(size) == 1: return ret.flatten()[storage_offset].reshape(size)
-  for mo in cached_to_movement_ops(tuple(base.shape), st): ret = apply_mop(ret, mo)
-  return ret
-
@torch.library.impl("aten::as_strided", "privateuseone")
 def as_strided(tensor:torch.Tensor, size, stride, storage_offset=None):
  storage_offset = storage_offset or tensor.storage_offset()
+  @wrap_view_op
+  def _as_strided(tensor:Tensor, size, stride, storage_offset=None):
+    # multiple as_strided do not compound
+    base = canonical_base(tensor)
+    # TODO: this is heavyweight
+    st = ShapeTracker(base.uop.st.views + (View.create(tuple(size), tuple(stride), storage_offset),))
+    ret = base
+    if TORCH_DEBUG >= 1: print("**** as_strided", tensor.shape, size, stride, st)
+    if prod(size) == 1: return ret.flatten()[storage_offset].reshape(size)
+    for mo in cached_to_movement_ops(tuple(base.shape), st): ret = apply_mop(ret, mo)
+    return ret
  return _as_strided(tensor, size, stride, storage_offset)

-@torch.library.impl("aten::_reshape_alias", "privateuseone")
-def _reshape_alias(tensor:torch.Tensor, size, stride):
-  return _as_strided(tensor, size, stride)
-
@torch.library.impl("aten::empty_strided", "privateuseone")
 def empty_strided(size, stride, dtype, layout=None, device=None, pin_memory=False):
  if TORCH_DEBUG: print(f"empty_strided {size=} {stride=} {dtype=} {layout=} {device=} {pin_memory=}")
@@ -1,6 +1,2 @@
 [pytest]
 norecursedirs = extra
-timeout = 180
-timeout_method = thread
-timeout_func_only = true
-testpaths = test
@@ -9,12 +9,12 @@ with open(directory / 'README.md', encoding='utf-8') as f:

 testing_minimal = [
  "numpy",
-  "torch==2.8.0",
+  "torch==2.7.1",
  "pytest",
  "pytest-xdist",
-  "pytest-timeout",
  "hypothesis",
  "z3-solver",
+  "ml_dtypes"
 ]

 setup(name='tinygrad',
@@ -59,7 +59,7 @@ setup(name='tinygrad',
        'triton': ["triton-nightly>=2.1.0.dev20231014192330"],
        'linting': [
            "pylint",
-            "mypy==1.18.1",
+            "mypy==1.13.0",
            "typing-extensions",
            "pre-commit",
            "ruff",
@@ -87,7 +87,6 @@ setup(name='tinygrad',
            "tiktoken",
            "blobfile",
            "librosa",
-            "numba>=0.55",  # librosa needs numba but uv ignores python upper bounds and some numba versions require <python3.10
            "networkx",
            "nibabel",
            "bottle",
@@ -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.expr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val.expr: TestHCQ.d0.timeline_value}
+        var_vals = {virt_signal.base_buf.va_addr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val: 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.expr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val.expr: TestHCQ.d0.timeline_value}
+        var_vals = {virt_signal.base_buf.va_addr: TestHCQ.d0.timeline_signal.base_buf.va_addr, virt_val: TestHCQ.d0.timeline_value}
        q.submit(TestHCQ.d0, var_vals)
        TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
        TestHCQ.d0.timeline_value += 1
@@ -75,7 +75,7 @@ class TestHCQ(unittest.TestCase):
        TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
        TestHCQ.d0.timeline_value += 1

-  @unittest.skipIf(MOCKGPU or Device.DEFAULT in {"CPU"}, "Can't handle async update on MOCKGPU for now")
+  @unittest.skipIf(MOCKGPU or Device.DEFAULT in {"CPU", "LLVM"}, "Can't handle async update on MOCKGPU for now")
  def test_wait_late_set(self):
    for queue_type in [TestHCQ.d0.hw_compute_queue_t, TestHCQ.d0.hw_copy_queue_t]:
      if queue_type is None: continue
@@ -106,7 +106,7 @@ class TestHCQ(unittest.TestCase):

        fake_signal.value = 0x30

-        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr.expr: fake_signal.base_buf.va_addr, virt_val.expr: fake_signal.value})
+        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr: fake_signal.base_buf.va_addr, virt_val: fake_signal.value})
        TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
        TestHCQ.d0.timeline_value += 1

@@ -131,13 +131,13 @@ class TestHCQ(unittest.TestCase):
     .signal(TestHCQ.d0.timeline_signal, virt_val)

    for _ in range(100):
-      q.submit(TestHCQ.d0, {virt_val.expr: TestHCQ.d0.timeline_value})
+      q.submit(TestHCQ.d0, {virt_val: TestHCQ.d0.timeline_value})
      TestHCQ.d0.timeline_value += 1

    val = TestHCQ.a.uop.buffer.as_buffer().cast("f")[0]
    assert val == 200.0, f"got val {val}"

-  @unittest.skipIf(Device.DEFAULT in {"CPU"}, "No globals/locals on LLVM/CPU")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "No globals/locals on LLVM/CPU")
  def test_exec_update(self):
    sint_global = (Variable("sint_global", 0, 0xffffffff, dtypes.uint32),) + tuple(TestHCQ.runner.p.global_size[1:])
    sint_local = (Variable("sint_local", 0, 0xffffffff, dtypes.uint32),) + tuple(TestHCQ.runner.p.local_size[1:])
@@ -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].expr: 1, sint_local[0].expr: 1})
+    q.submit(TestHCQ.d0, {sint_global[0]: 1, sint_local[0]: 1})
    TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
    TestHCQ.d0.timeline_value += 1

@@ -155,7 +155,7 @@ class TestHCQ(unittest.TestCase):
    val = TestHCQ.b.uop.buffer.as_buffer().cast("f")[1]
    assert val == 0.0, f"got val {val}, should not be updated"

-  @unittest.skipIf(Device.DEFAULT in {"CPU"}, "No globals/locals on LLVM/CPU")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "No globals/locals on LLVM/CPU")
  def test_exec_update_fuzz(self):
    virt_val = Variable("sig_val", 0, 0xffffffff, dtypes.uint32)
    virt_local = [Variable(f"local_{i}", 0, 0xffffffff, dtypes.uint32) for i in range(3)]
@@ -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.expr: TestHCQ.d0.timeline_value, virt_local[0].expr: x, virt_local[1].expr: y, virt_local[2].expr: z})
+          q.submit(TestHCQ.d0, {virt_val: TestHCQ.d0.timeline_value, virt_local[0]: x, virt_local[1]: y, virt_local[2]: 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.expr: TestHCQ.a.uop.buffer._buf.va_addr, virt_dest_addr.expr: TestHCQ.b.uop.buffer._buf.va_addr})
+    q.submit(TestHCQ.d0, {virt_src_addr: TestHCQ.a.uop.buffer._buf.va_addr, virt_dest_addr: 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.expr: buf2._buf.va_addr, virt_dest_addr.expr: buf1._buf.va_addr})
+    q.submit(TestHCQ.d0, {virt_src_addr: buf2._buf.va_addr, virt_dest_addr: 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.expr: fake_signal.base_buf.va_addr, virt_val.expr: fake_signal.value})
+        q.submit(TestHCQ.d0, {virt_signal.base_buf.va_addr: fake_signal.base_buf.va_addr, virt_val: fake_signal.value})
        TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
        TestHCQ.d0.timeline_value += 1

@@ -336,7 +336,7 @@ class TestHCQ(unittest.TestCase):
    et = float(sig_en.timestamp - sig_st.timestamp)

    print(f"exec kernel time: {et:.2f} us")
-    assert 0.1 <= et <= (100000 if MOCKGPU or Device.DEFAULT in {"CPU"} else 100)
+    assert 0.1 <= et <= (100000 if MOCKGPU or Device.DEFAULT in {"CPU", "LLVM"} else 100)

  def test_speed_copy_bandwidth(self):
    if TestHCQ.d0.hw_copy_queue_t is None: self.skipTest("device does not support copy queue")
@@ -3,9 +3,9 @@ from tinygrad import Device
 from tinygrad.device import Buffer
 from tinygrad.dtype import dtypes
 from tinygrad.helpers import CI
-from tinygrad.runtime.ops_cl import CLDevice, CLAllocator, CLCompiler, CLProgram
+from tinygrad.runtime.ops_gpu import CLDevice, CLAllocator, CLCompiler, CLProgram

-@unittest.skipUnless(Device.DEFAULT == "CL", "Runs only on OpenCL")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Runs only on OpenCL (GPU)")
 class TestCLError(unittest.TestCase):
  @unittest.skipIf(CI, "dangerous for CI, it allocates tons of memory")
  def test_oom(self):
@@ -24,7 +24,7 @@ class TestCLError(unittest.TestCase):
  def test_unaligned_copy(self):
    data = list(range(65))
    unaligned = memoryview(bytearray(data))[1:]
-    buffer = Buffer("CL", 64, dtypes.uint8).allocate()
+    buffer = Buffer("GPU", 64, dtypes.uint8).allocate()
    buffer.copyin(unaligned)
    result = memoryview(bytearray(len(data) - 1))
    buffer.copyout(result)
@@ -1,7 +1,7 @@
 import random, os
 from tinygrad.helpers import Timing
 from tinygrad.runtime.ops_hip import compile_hip, HIPDevice
-from tinygrad.runtime.ops_cl import compile_cl, CLDevice
+from tinygrad.runtime.ops_gpu import compile_cl, CLDevice

 # OMP_NUM_THREADS=1 strace -tt -f -e trace=file python3 test/external/external_benchmark_hip_compile.py
 # AMD_COMGR_REDIRECT_LOGS=stdout AMD_COMGR_EMIT_VERBOSE_LOGS=1 python3 test/external/external_benchmark_hip_compile.py
@@ -27,7 +27,6 @@ if __name__ == "__main__":

  # NOTE: the inputs to a JIT must be first level arguments
  run_onnx_jit = TinyJit(lambda **kwargs: run_onnx(kwargs), prune=True)
-  step_times = []
  for _ in range(20):
    GlobalCounters.reset()
    st = time.perf_counter_ns()
@@ -36,12 +35,7 @@ if __name__ == "__main__":
      inputs = {**{k:v for k,v in new_inputs_junk.items() if 'img' in k},
                **{k:Tensor(v) for k,v in new_inputs_junk_numpy.items() if 'img' not in k}}
      ret = next(iter(run_onnx_jit(**inputs).values())).cast(dtypes.float32).numpy()
-      step_times.append(t:=(time.perf_counter_ns() - st)*1e-6)
-    print(f"jitted:  {t:7.4f} ms")
-
-  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
-    min_time = min(step_times)
-    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
+    print(f"jitted:  {(time.perf_counter_ns() - st)*1e-6:7.4f} ms")

  suffix = ""
  if IMAGE.value < 2: suffix += f"_image{IMAGE.value}" # image=2 has no suffix for compatibility
@@ -1,4 +1,4 @@
-from tinygrad.runtime.ops_cl import CLDevice, CLProgram, compile_cl
+from tinygrad.runtime.ops_gpu import CLDevice, CLProgram, compile_cl

 if __name__ == "__main__":
  dev = CLDevice()
@@ -1,5 +1,5 @@
 # ugh, OS X OpenCL doesn't support half
-from tinygrad.runtime.ops_cl import CLDevice, CLProgram, CLCompiler
+from tinygrad.runtime.ops_gpu import CLDevice, CLProgram, CLCompiler

 src = """#pragma OPENCL EXTENSION cl_khr_fp16 : enable
 __kernel void max_half(__global half* data0, const __global half* data1) {
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # cd extra/disassemblers/ && git clone --recursive github.com:geohot/cuda_ioctl_sniffer.git
-# LD_PRELOAD=$PWD/extra/disassemblers/cuda_ioctl_sniffer/out/sniff.so CL=1 python3 test/external/external_multi_gpu.py
+# LD_PRELOAD=$PWD/extra/disassemblers/cuda_ioctl_sniffer/out/sniff.so GPU=1 python3 test/external/external_multi_gpu.py
 import numpy as np
 from tinygrad.tensor import Tensor
 from tinygrad.helpers import colored, Timing, getenv
@@ -1,4 +1,4 @@
-from tinygrad.runtime.ops_cl import CLProgram, CL, CLBuffer
+from tinygrad.runtime.ops_gpu import CLProgram, CL, CLBuffer
 from tinygrad import dtypes
 import time

@@ -4,7 +4,7 @@ import unittest
 import numpy as np
 if 'IMAGE' not in os.environ:
  os.environ['IMAGE'] = '2'
-os.environ['CL'] = '1'
+os.environ['GPU'] = '1'
 os.environ['OPT'] = '2'
 from tinygrad.tensor import Tensor
 from tinygrad.nn import Conv2d
@@ -193,12 +193,12 @@ backend_test.exclude('test_adam_cpu')
 backend_test.exclude('test_gradient_of_add_and_mul_cpu')
 backend_test.exclude('test_gradient_of_add_cpu')

-if Device.DEFAULT in ['CL', 'METAL']:
+if Device.DEFAULT in ['GPU', 'METAL']:
  backend_test.exclude('test_resize_upsample_sizes_nearest_axes_2_3_cpu')
  backend_test.exclude('test_resize_upsample_sizes_nearest_axes_3_2_cpu')
  backend_test.exclude('test_resize_upsample_sizes_nearest_cpu')

-if Device.DEFAULT == "METAL" or (OSX and Device.DEFAULT == "CL"):
+if Device.DEFAULT == "METAL" or (OSX and Device.DEFAULT == "GPU"):
  # numerical inaccuracy
  backend_test.exclude('test_mish_cpu')
  backend_test.exclude('test_mish_expanded_cpu')
@@ -34,7 +34,7 @@ from extra.models.efficientnet import EfficientNet
 from extra.models.resnet import ResNet18
 from extra.models.vit import ViT

-@unittest.skipUnless(Device.DEFAULT == "CL", "Not Implemented")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Not Implemented")
 class TestInferenceMinKernels(unittest.TestCase):
  def setUp(self):
    self.training_old = Tensor.training
@@ -90,7 +90,7 @@ class TestInferenceMinKernels(unittest.TestCase):
    with CLCache(100):
      model(inp, 0).realize()

-@unittest.skipUnless(Device.DEFAULT == "CL", "Not Implemented")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Not Implemented")
 class TestOptBinOp(unittest.TestCase):
  def _test_no_binop_rerun(self, f1, f2=None, allowed=1):
    a = Tensor.randn(16, 16)
@@ -117,7 +117,7 @@ class TestOptBinOp(unittest.TestCase):
  #def test_no_binop_rerun_reduce(self): return self._test_no_binop_rerun(lambda a,b: (a*b).sum(), lambda a,b: (a*b).reshape(16, 16, 1).sum())
  #def test_no_binop_rerun_reduce_alt(self): return self._test_no_binop_rerun(lambda a,b: a.sum(1)+b[0], lambda a,b: a.sum(1).reshape(1,16)+b[0])

-@unittest.skipUnless(Device.DEFAULT == "CL", "Not Implemented")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Not Implemented")
 class TestOptReduceLoop(unittest.TestCase):
  def test_loop_left(self):
    a = Tensor.randn(16, 16)
@@ -139,7 +139,7 @@ class TestOptReduceLoop(unittest.TestCase):
      c.realize()
      assert cache.count == 2, "loop right fusion broken"

-@unittest.skipUnless(Device.DEFAULT == "CL", "Not Implemented")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Not Implemented")
 class TestOptWChild(unittest.TestCase):
  @unittest.skip("this no longer happens, use realize")
  def test_unrealized_child(self):
@@ -152,7 +152,7 @@ class TestOptWChild(unittest.TestCase):
      d.realize()
      assert cache.count == 2, "don't fuse if you have children"

-@unittest.skipUnless(Device.DEFAULT == "CL", "Not Implemented")
+@unittest.skipUnless(Device.DEFAULT == "GPU", "Not Implemented")
 class TestOpt(unittest.TestCase):
  def test_muladd(self):
    a,b,c = [Tensor.randn(2,2).realize() for _ in range(3)]
@@ -20,7 +20,7 @@ class TestLLaMASpeed(unittest.TestCase):
  def test_llama_compile(self):
    backup_program = Device[Device.DEFAULT].runtime
    backup_allocator = Device[Device.DEFAULT].allocator
-    backup_compiler = Device[Device.DEFAULT].compiler.compile_cached
+    backup_compiler = Device[Device.DEFAULT].compiler
    Device[Device.DEFAULT].runtime = FakeProgram
    Device[Device.DEFAULT].allocator = FakeAllocator(Device.default)

@@ -44,14 +44,14 @@ class TestLLaMASpeed(unittest.TestCase):
    run_llama("codegen(1)")

    # test no compiler use for this
-    Device[Device.DEFAULT].compiler.compile_cached = None
+    Device[Device.DEFAULT].compiler = None
    run_llama("methodcache", False)
    with Profiling(sort='time', frac=0.1, fn="/tmp/llama.prof", ts=5):
      run_llama("profile", False)

    Device[Device.DEFAULT].runtime = backup_program
    Device[Device.DEFAULT].allocator = backup_allocator
-    Device[Device.DEFAULT].compiler.compile_cached = backup_compiler
+    Device[Device.DEFAULT].compiler = backup_compiler

 if __name__ == '__main__':
  TestLLaMASpeed().test_llama_compile()
@@ -1,6 +1,6 @@
 import gc
 from tinygrad import Tensor, UOp, Device
-from tinygrad.shape.shapetracker import views_to_valid_uop
+from tinygrad.shape.shapetracker import views_to_indexed_uops
 from tinygrad.engine.realize import method_cache, get_program

 def uops_allocated(): return sum([isinstance(x, UOp) for x in gc.get_objects()])
@@ -60,7 +60,7 @@ if __name__ == "__main__":

    # these caches will keep uops alive
    method_cache.clear()
-    views_to_valid_uop.cache_clear()
+    views_to_indexed_uops.cache_clear()

    new_uops = uops_allocated()
    gc.collect()
@@ -11,7 +11,7 @@ if __name__ == "__main__":
  for i in range(10_000):
    if i % 1000 == 0:
      print(f"Progress: {i}")
-    dt = random.choice(dtypes.ints + tuple(dt.vec(4) for dt in dtypes.ints))
+    dt = random.choice(dtypes.ints)
    u = UOp.variable('x', random.randint(dt.min, 0), random.randint(1, dt.max), dtype=dt)
    d = random.randint(1, max(1, u.arg[2]))
    if d in powers_of_two: continue
@@ -16,7 +16,7 @@ if os.getenv("VALIDATE_HCQ", 0) != 0:
  try:
    import extra.qcom_gpu_driver.opencl_ioctl
    from tinygrad import Device
-    _, _ = Device["QCOM"], Device["CL"]
+    _, _ = Device["QCOM"], Device["GPU"]
  except Exception: pass

 from tinygrad import Tensor, Device, dtypes
@@ -42,9 +42,9 @@ if getenv("VALIDATE_HCQ"):
    on_linearizer_did_run = extra.nv_gpu_driver.nv_ioctl.collect_last_launch_state
    compare_states = extra.nv_gpu_driver.nv_ioctl.compare_launch_state
  elif Device.DEFAULT == "QCOM":
-    print("VALIDATE_HCQ: Comparing QCOM to CL")
+    print("VALIDATE_HCQ: Comparing QCOM to GPU")
    import extra.qcom_gpu_driver.opencl_ioctl
-    validate_device = Device["CL"]
+    validate_device = Device["GPU"]
    on_linearizer_will_run = extra.qcom_gpu_driver.opencl_ioctl.before_launch
    on_linearizer_did_run = extra.qcom_gpu_driver.opencl_ioctl.collect_last_launch_state
    compare_states = extra.qcom_gpu_driver.opencl_ioctl.compare_launch_state
@@ -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.expr: v.min for v in lin.vars}
+  if var_vals is None: var_vals = {v: 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.expr: random.randint(v.vmin, v.vmax) for v in lin.ast.variables()}
+    var_vals = {v: 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)
@@ -302,7 +302,7 @@ if __name__ == "__main__":
    for i, ast in enumerate(ast_strs[:getenv("FUZZ_N", len(ast_strs))]):
      if (nth := getenv("FUZZ_NTH", -1)) != -1 and i != nth: continue
      if getenv("FUZZ_IMAGEONLY") and "dtypes.image" not in ast: continue
-      if "dtypes.image" in ast and Device.DEFAULT not in {"CL", "QCOM"}: continue  # IMAGE is only for CL
+      if "dtypes.image" in ast and Device.DEFAULT not in {"GPU", "QCOM"}: continue  # IMAGE is only for GPU
      if ast in seen_ast_strs: continue
      seen_ast_strs.add(ast)

@@ -57,8 +57,8 @@ def eval_uop(uop:UOp, inputs:list[tuple[DType, list[Any]]]|None=None):
  return out_buf.cast(uop.dtype.fmt).tolist()[0]

 def not_support_multi_device():
-  # CL and CUDA don't support multi device if in CI
-  return CI and REAL_DEV in ("CL", "CUDA")
+  # GPU and CUDA don't support multi device if in CI
+  return CI and REAL_DEV in ("GPU", "CUDA")

 # NOTE: This will open REMOTE if it's the default device
 REAL_DEV = (Device.DEFAULT if Device.DEFAULT != "REMOTE" else Device['REMOTE'].properties.real_device)
@@ -1,14 +1,14 @@
 #!/usr/bin/env python
 import unittest
-from tinygrad import Tensor
 import numpy as np
+from tinygrad.tensor import Tensor
 import torch

 def get_question_samp(bsz, seq_len, vocab_size, seed):
  np.random.seed(seed)
  in_ids= np.random.randint(vocab_size, size=(bsz, seq_len))
  mask = np.random.choice([True, False], size=(bsz, seq_len))
-  seg_ids = np.random.randint(2, size=(bsz, seq_len))  # type_vocab_size
+  seg_ids = np.random.randint(1, size=(bsz, seq_len))
  return in_ids, mask, seg_ids

 def set_equal_weights(mdl, torch_mdl):
@@ -45,7 +45,7 @@ class TestBert(unittest.TestCase):

    seeds = (1337, 3141)
    bsz, seq_len = 1, 16
-    for seed in seeds:
+    for _, seed in enumerate(seeds):
      in_ids, mask, seg_ids = get_question_samp(bsz, seq_len, config['vocab_size'], seed)
      out = mdl(Tensor(in_ids), Tensor(mask), Tensor(seg_ids))
      torch_out = torch_mdl.forward(torch.from_numpy(in_ids).long(), torch.from_numpy(mask), torch.from_numpy(seg_ids).long())[:2]
@@ -1,10 +1,12 @@
-import ast, pathlib, unittest
+import ast
+import pathlib
+import unittest

 import numpy as np
 from PIL import Image

-from tinygrad import Tensor
-from tinygrad.helpers import getenv, CI
+from tinygrad.helpers import getenv
+from tinygrad.tensor import Tensor
 from extra.models.efficientnet import EfficientNet
 from extra.models.vit import ViT
 from extra.models.resnet import ResNet50
@@ -38,13 +40,19 @@ def preprocess(img, new=False):
    img /= np.array([0.229, 0.224, 0.225]).reshape((1, -1, 1, 1))
  return img

-def _infer(model: EfficientNet, img):
-  with Tensor.train(False):
-    out = model.forward(Tensor(img)).argmax(axis=-1)
-  return out.tolist()

-chicken_img = preprocess(Image.open(pathlib.Path(__file__).parent / 'efficientnet/Chicken.jpg'))
-car_img = preprocess(Image.open(pathlib.Path(__file__).parent / 'efficientnet/car.jpg'))
+def _infer(model: EfficientNet, img, bs=1):
+  old_training = Tensor.training
+  Tensor.training = False
+  img = preprocess(img)
+  # run the net
+  if bs > 1: img = img.repeat(bs, axis=0)
+  out = model.forward(Tensor(img))
+  Tensor.training = old_training
+  return _LABELS[np.argmax(out.numpy()[0])]
+
+chicken_img = Image.open(pathlib.Path(__file__).parent / 'efficientnet/Chicken.jpg')
+car_img = Image.open(pathlib.Path(__file__).parent / 'efficientnet/car.jpg')

 class TestEfficientNet(unittest.TestCase):
  @classmethod
@@ -56,20 +64,17 @@ class TestEfficientNet(unittest.TestCase):
  def tearDownClass(cls):
    del cls.model

-  @unittest.skipIf(CI, "covered by test_chicken_car")
  def test_chicken(self):
-    labels = _infer(self.model, chicken_img)
-    self.assertEqual(_LABELS[labels[0]], "hen")
+    label = _infer(self.model, chicken_img)
+    self.assertEqual(label, "hen")
+
+  def test_chicken_bigbatch(self):
+    label = _infer(self.model, chicken_img, 2)
+    self.assertEqual(label, "hen")

-  @unittest.skipIf(CI, "covered by test_chicken_car")
  def test_car(self):
-    labels = _infer(self.model, car_img)
-    self.assertEqual(_LABELS[labels[0]], "sports car, sport car")
-
-  def test_chicken_car(self):
-    labels = _infer(self.model, np.concat([chicken_img, car_img], axis=0))
-    self.assertEqual(_LABELS[labels[0]], "hen")
-    self.assertEqual(_LABELS[labels[1]], "sports car, sport car")
+    label = _infer(self.model, car_img)
+    self.assertEqual(label, "sports car, sport car")

 class TestViT(unittest.TestCase):
  @classmethod
@@ -82,12 +87,12 @@ class TestViT(unittest.TestCase):
    del cls.model

  def test_chicken(self):
-    labels = _infer(self.model, chicken_img)
-    self.assertEqual(_LABELS[labels[0]], "cock")
+    label = _infer(self.model, chicken_img)
+    self.assertEqual(label, "cock")

  def test_car(self):
-    labels = _infer(self.model, car_img)
-    self.assertEqual(_LABELS[labels[0]], "racer, race car, racing car")
+    label = _infer(self.model, car_img)
+    self.assertEqual(label, "racer, race car, racing car")

 class TestResNet(unittest.TestCase):
  @classmethod
@@ -100,12 +105,12 @@ class TestResNet(unittest.TestCase):
    del cls.model

  def test_chicken(self):
-    labels = _infer(self.model, chicken_img)
-    self.assertEqual(_LABELS[labels[0]], "hen")
+    label = _infer(self.model, chicken_img)
+    self.assertEqual(label, "hen")

  def test_car(self):
-    labels = _infer(self.model, car_img)
-    self.assertEqual(_LABELS[labels[0]], "sports car, sport car")
+    label = _infer(self.model, car_img)
+    self.assertEqual(label, "sports car, sport car")

 if __name__ == '__main__':
  unittest.main()
@@ -5,8 +5,12 @@ from tinygrad.frontend.onnx import OnnxRunner
 from tinygrad.device import Device
 from tinygrad.helpers import fetch, Context

-from extra.onnx_helpers import validate
-from extra.huggingface_onnx.huggingface_manager import DOWNLOADS_DIR, snapshot_download_with_retry
+try:
+  from extra.onnx_helpers import validate
+  from extra.huggingface_onnx.huggingface_manager import DOWNLOADS_DIR, snapshot_download_with_retry
+  HUGGINGFACE_AVAILABLE = True
+except ModuleNotFoundError:
+  HUGGINGFACE_AVAILABLE = False

 def run_onnx_torch(onnx_model, inputs):
  import torch
@@ -58,7 +62,7 @@ class TestOnnxModel(unittest.TestCase):
    print(cls, _LABELS[cls])
    assert "car" in _LABELS[cls] or _LABELS[cls] == "convertible"

-@unittest.skipUnless(Device.DEFAULT == "METAL", "only run on METAL")
+@unittest.skipUnless(HUGGINGFACE_AVAILABLE and Device.DEFAULT == "METAL", "only run on METAL")
 class TestHuggingFaceOnnxModels(unittest.TestCase):
  @classmethod
  def setUpClass(cls):
@@ -53,8 +53,8 @@ class TestRealWorld(unittest.TestCase):
  @unittest.skipUnless(is_dtype_supported(dtypes.float16), "need dtypes.float16")
  def test_stable_diffusion(self):
    params = unet_params
-    params["model_ch"] = 8
-    params["ctx_dim"] = 8
+    params["model_ch"] = 16
+    params["ctx_dim"] = 16
    params["num_res_blocks"] = 1
    params["n_heads"] = 2
    model = UNetModel(**params)
@@ -114,7 +114,7 @@ class TestRealWorld(unittest.TestCase):

      helper_test("train_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 93)

-  @unittest.skipIf(CI and Device.DEFAULT in {"CPU", "CL"}, "slow")
+  @unittest.skipIf(CI and Device.DEFAULT in {"CPU", "GPU", "LLVM"}, "slow")
  def test_train_cifar(self):
    with Tensor.train():
      model = SpeedyResNet(Tensor.ones((12,3,2,2)))
@@ -144,7 +144,6 @@ class TestRealWorld(unittest.TestCase):
                                final_div_factor=1./(initial_div_factor*final_lr_ratio), total_steps=4)
      assert not np.isnan(lr_scheduler.min_lr), "lr too small or initial_div_facotr too big for half"

-  @unittest.skipIf(CI and Device.DEFAULT == "CPU", "slow")
  def test_bert(self):
    with Tensor.train():
      args_tiny = {"attention_probs_dropout_prob": 0.0, "hidden_dropout_prob": 0.0, "vocab_size": 30522, "type_vocab_size": 2,
@@ -168,5 +167,9 @@ class TestRealWorld(unittest.TestCase):
      helper_test("train_bert", lambda: (data["input_ids"], data["segment_ids"], data["input_mask"], data["masked_lm_positions"], \
          data["masked_lm_ids"], data["masked_lm_weights"], data["next_sentence_labels"]), train, 0.25, 347)

+  def test_bert_fuse_arange(self):
+    with Context(FUSE_ARANGE=1):
+      self.test_bert()
+
 if __name__ == '__main__':
  unittest.main()
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 import unittest
-from tinygrad import Tensor
-from extra.models.rnnt import LSTM
 import numpy as np
+from tinygrad.tensor import Tensor
+from extra.models.rnnt import LSTM
 import torch

 class TestRNNT(unittest.TestCase):
@@ -1,8 +1,9 @@
-import unittest, time
+import unittest
+import time
 import numpy as np
-from tinygrad import Device
 from tinygrad.nn.state import get_parameters
 from tinygrad.nn import optim
+from tinygrad.tensor import Device
 from tinygrad.helpers import getenv, CI
 from extra.training import train
 from extra.models.convnext import ConvNeXt
@@ -26,7 +27,7 @@ def train_one_step(model,X,Y):
  print("done in %.2f ms" % (et*1000.))

 def check_gc():
-  if Device.DEFAULT == "CL":
+  if Device.DEFAULT == "GPU":
    from extra.introspection import print_objects
    assert print_objects() == 0

@@ -39,6 +40,7 @@ class TestTrain(unittest.TestCase):
    check_gc()

  @unittest.skipIf(CI, "slow")
+  @unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "too many buffers for webgpu and metal")
  def test_efficientnet(self):
    model = EfficientNet(0)
    X = np.zeros((BS,3,224,224), dtype=np.float32)
@@ -47,6 +49,7 @@ class TestTrain(unittest.TestCase):
    check_gc()

  @unittest.skipIf(CI, "slow")
+  @unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "too many buffers for webgpu and metal")
  def test_vit(self):
    model = ViT()
    X = np.zeros((BS,3,224,224), dtype=np.float32)
@@ -54,7 +57,7 @@ class TestTrain(unittest.TestCase):
    train_one_step(model,X,Y)
    check_gc()

-  @unittest.skipIf(CI, "slow")
+  @unittest.skipIf(Device.DEFAULT in ["METAL", "WEBGPU"], "too many buffers for webgpu and metal")
  def test_transformer(self):
    # this should be small GPT-2, but the param count is wrong
    # (real ff_dim is 768*4)
@@ -1,7 +1,7 @@
 import unittest
 import pathlib
 from examples.whisper import init_whisper, load_file_waveform, transcribe_file, transcribe_waveform
-from tinygrad.helpers import CI, fetch, CPU_LLVM
+from tinygrad.helpers import CI, fetch
 from tinygrad import Device, dtypes
 from tinygrad.device import is_dtype_supported

@@ -16,7 +16,7 @@ TRANSCRIPTION_2 = "a slightly longer audio file so that we can test batch transc
 TEST_FILE_3_URL = 'https://homepage.ntu.edu.tw/~karchung/miniconversations/mc45.mp3'
 TRANSCRIPTION_3 = "Just lie back and relax. Is the level of pressure about right? Yes, it's fine, and I'd like conditioner please. Sure. I'm going to start the second lathering now. Would you like some Q-tips? How'd you like it cut? I'd like my bangs and the back trimmed, and I'd like the rest thinned out a bit and layered. Where would you like the part? On the left, right about here. Here, have a look. What do you think? It's fine. Here's a thousand anti-dollars. It's 30-ant extra for the rants. Here's your change and receipt. Thank you, and please come again. So how do you like it? It could have been worse, but you'll notice that I didn't ask her for her card. Hmm, yeah. Maybe you can try that place over there next time."   # noqa: E501

-@unittest.skipIf(Device.DEFAULT in ["CPU"], "slow")
+@unittest.skipIf(Device.DEFAULT in ["CPU", "LLVM"], "slow")
@unittest.skipUnless(is_dtype_supported(dtypes.float16), "need float16 support")
 class TestWhisper(unittest.TestCase):
  @classmethod
@@ -33,11 +33,11 @@ class TestWhisper(unittest.TestCase):
  def test_transcribe_file1(self):
    self.assertEqual(transcribe_file(self.model, self.enc, TEST_FILE_1),  TRANSCRIPTION_1)

-  @unittest.skipIf(CI or (Device.DEFAULT == "CPU" and CPU_LLVM), "too many tests for CI")
+  @unittest.skipIf(CI or Device.DEFAULT == "LLVM", "too many tests for CI")
  def test_transcribe_file2(self):
    self.assertEqual(transcribe_file(self.model, self.enc, TEST_FILE_2),  TRANSCRIPTION_2)

-  @unittest.skipIf(CI or (Device.DEFAULT == "CPU" and CPU_LLVM), "too many tests for CI")
+  @unittest.skipIf(CI or Device.DEFAULT == "LLVM", "too many tests for CI")
  def test_transcribe_batch12(self):
    waveforms = [load_file_waveform(TEST_FILE_1), load_file_waveform(TEST_FILE_2)]
    transcriptions = transcribe_waveform(self.model, self.enc, waveforms)
@@ -52,13 +52,13 @@ class TestWhisper(unittest.TestCase):
    self.assertEqual(TRANSCRIPTION_2,  transcriptions[0])
    self.assertEqual(TRANSCRIPTION_1,  transcriptions[1])

-  @unittest.skipIf(CI or (Device.DEFAULT == "CPU" and CPU_LLVM), "too long for CI")
+  @unittest.skipIf(CI or Device.DEFAULT == "LLVM", "too long for CI")
  def test_transcribe_long(self):
    waveform = [load_file_waveform(fetch(TEST_FILE_3_URL))]
    transcription = transcribe_waveform(self.model, self.enc, waveform)
    self.assertEqual(TRANSCRIPTION_3, transcription)

-  @unittest.skipIf(CI or (Device.DEFAULT == "CPU" and CPU_LLVM), "too long for CI")
+  @unittest.skipIf(CI or Device.DEFAULT == "LLVM", "too long for CI")
  def test_transcribe_long_no_batch(self):
    waveforms = [load_file_waveform(fetch(TEST_FILE_3_URL)), load_file_waveform(TEST_FILE_1)]

@@ -29,7 +29,7 @@ class TestFloat4(unittest.TestCase):

    assert TestFloat4.count_float4(program.uops) == (2, 1)

-  @unittest.skipIf(Device.DEFAULT in {"CPU"} and AMX, "CPU with AMX upcasts float up to size 16")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
  def test_float4_multidim(self):
    a = Tensor.empty(2, 8).realize()
    b = Tensor.empty(2, 8).realize()
@@ -39,7 +39,7 @@ class TestFloat4(unittest.TestCase):
    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
    assert TestFloat4.count_float4(uops) == (4, 2)

-  @unittest.skipUnless(Device.DEFAULT in {"CPU"} and AMX, "Only CPU with AMX upcasts float up to size 16")
+  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
  def test_float4_multidim_amx(self):
    def kernel_for_shape(size, shift):
      a = Tensor.empty(2, size).realize()
@@ -69,7 +69,7 @@ class TestFloat4(unittest.TestCase):

    assert TestFloat4.count_float4(program.uops) == (0, 1)

-  @unittest.skipIf(Device.DEFAULT in {"CPU"} and AMX, "CPU with AMX upcasts float up to size 16")
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
  def test_float4_multidim_unaligned_load(self):
    a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
    b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
@@ -80,7 +80,7 @@ class TestFloat4(unittest.TestCase):

    assert TestFloat4.count_float4(uops) == (0, 2)

-  @unittest.skipUnless(Device.DEFAULT in {"CPU"} and AMX, "Only CPU with AMX upcasts float up to size 16")
+  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
  def test_float4_multidim_unaligned_load_amx(self):
    def kernel_for_shape(size, shift):
      a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
@@ -93,7 +93,7 @@ class TestKernelOpts(unittest.TestCase):
    a = Tensor.rand(8, N, 8, N)
    r = a.sum(axis=(1,3))
    helper_linearizer_opt(r, [
-      # openCL / CL=1 is 256 max threads
+      # openCL / GPU=1 is 256 max threads
      [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
      [Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
      [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
@@ -327,29 +327,14 @@ class TestKernelOpts(unittest.TestCase):
  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
  def test_arange_opts(self):
    a = Tensor.arange(128)
-    # NOTE: arange no longer has reduce ops available for opt
    helper_linearizer_opt(a, [
-      #[Opt(OptOps.GROUP, 0, 32)],
-      #[Opt(OptOps.GROUPTOP, 0, 32)],
+      [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
+      [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()
@@ -7,7 +7,7 @@ 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, CPU_LLVM
+from tinygrad.helpers import AMX, CI, AMD_LLVM
 from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.codegen.opt import Opt, OptOps, KernelOptError

@@ -69,7 +69,7 @@ class TestTensorCores(unittest.TestCase):
      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 == "CPU" and CPU_LLVM:
+      if Device.DEFAULT == "LLVM":
        assert "0x201000" in prg.src
      elif Device.DEFAULT == "AMD" and AMD_LLVM:
        assert "@llvm.amdgcn.wmma" in prg.src
@@ -160,7 +160,7 @@ class TestTensorCores(unittest.TestCase):

  @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"}, "CPU does not support using a different type for accumulation")
+  @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)
@@ -174,7 +174,7 @@ class TestTensorCores(unittest.TestCase):

  @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"}, "CPU does not support using a different type for accumulation")
+  @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]
@@ -77,9 +77,9 @@ class TestCopySpeed(unittest.TestCase):
      np.testing.assert_equal(t.numpy(), x.numpy())

  @unittest.skipIf(CI, "CI doesn't have 6 GPUs")
-  @unittest.skipIf(Device.DEFAULT != "CL", "only test this on CL")
+  @unittest.skipIf(Device.DEFAULT != "GPU", "only test this on GPU")
  def testCopyCPUto6GPUs(self):
-    from tinygrad.runtime.ops_cl import CLDevice
+    from tinygrad.runtime.ops_gpu import CLDevice
    if len(CLDevice.device_ids) != 6: raise unittest.SkipTest("computer doesn't have 6 GPUs")
    t = Tensor.ones(N, N, device="CPU").contiguous().realize()
    print(f"buffer: {t.nbytes()*1e-9:.2f} GB")
@@ -87,8 +87,8 @@ class TestCopySpeed(unittest.TestCase):
      with Timing("sync:  ", on_exit=lambda ns: f" @ {t.nbytes()/ns:.2f} GB/s ({t.nbytes()*6/ns:.2f} GB/s total)"):
        with Timing("queue: "):
          for g in range(6):
-            t.to(f"CL:{g}").realize()
-        Device["CL"].synchronize()
+            t.to(f"gpu:{g}").realize()
+        Device["gpu"].synchronize()

 if __name__ == '__main__':
  unittest.main()
@@ -3,9 +3,9 @@ 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 import Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.uop.ops import Ops
-from tinygrad.renderer.ptx import PTXRenderer

 class TestArange(unittest.TestCase):
  def _get_flops(self, N, opts=None):
@@ -26,13 +26,10 @@ class TestArange(unittest.TestCase):
    print(f"{f1=}, {f2=}")
    # add 1 to avoid divide by 0. arange is 0 flops now!
    assert (f1 < 6000 and f2 < 6000) or ((f2+1) / (f1+1) < 16), f"bad complexity, flops {(f2+1) / (f1+1):.1f}X while inputs 10X"
-    if limit is not None and not isinstance(Device[Device.DEFAULT].renderer, PTXRenderer):
+    if limit is not None and not getenv("PTX"):
      # PTX counts index ALU in flops
      assert f1 <= limit, f"{f1=}, {limit=}"

-  # reduce collapse now happens before optimizations
-  """
-  from tinygrad.codegen.opt import Opt, OptOps
  def test_complexity_w_upcast(self): return self.test_complexity([Opt(OptOps.UPCAST, 0, 4)], limit=0)
  def test_complexity_w_unroll2(self): return self.test_complexity([Opt(OptOps.UNROLL, 0, 2)], limit=0)
  def test_complexity_w_unroll4(self): return self.test_complexity([Opt(OptOps.UNROLL, 0, 4)], limit=0)
@@ -49,7 +46,6 @@ class TestArange(unittest.TestCase):
    def test_complexity_w_local_unroll4(self): return self.test_complexity([Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.UNROLL, 0, 4)], limit=0)
    @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)])
-  """

 class TestRand(unittest.TestCase):
  def test_fused_rand_less_ops(self, noopt=1):
@@ -1,7 +1,7 @@
 import unittest, io
 from contextlib import redirect_stdout
 from tinygrad import Tensor, dtypes, Device
-from tinygrad.helpers import OSX, CPU_LLVM
+from tinygrad.helpers import OSX
 from tinygrad.engine.realize import lower_schedule
 from tinygrad.device import is_dtype_supported
 from tinygrad.engine.realize import get_program
@@ -19,7 +19,7 @@ class TestCompileFailures(unittest.TestCase):

 class TestDisassembly(unittest.TestCase):
  # TODO: fails on llvm. llvm.LLVMGetHostCPUName() returns "generic"
-  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and not CPU_LLVM and OSX, "m series cpus support fp16 arithmetic")
+  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and OSX, "m series cpus support fp16 arithmetic")
  def test_float16_alu(self):
    c = Tensor([1], dtype=dtypes.float16) + Tensor([1], dtype=dtypes.float16)
    s = c.schedule()[-1]
@@ -5,11 +5,11 @@ from typing import Any, List
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv, DEBUG, CI
 from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8, _to_np_dtype, _to_torch_dtype
-from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad import Device, Tensor, dtypes
 from hypothesis import assume, given, settings, strategies as strat
 from test.helpers import rand_for_dtype
 from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX
+import ml_dtypes
 import pytest
 pytestmark = pytest.mark.filterwarnings("ignore")

@@ -49,7 +49,7 @@ def _test_cast(a:Tensor, target_dtype:DType):

  _test_op(lambda: a.cast(target_dtype), target_dtype, list(a.numpy().astype(_to_np_dtype(target_dtype))))
 def _test_bitcast(a:Tensor, target_dtype:DType, target=None):
-  if isinstance(Device[Device.DEFAULT].renderer, PTXRenderer) and a.dtype == dtypes.int8 and target_dtype.itemsize != a.dtype.itemsize:
+  if getenv("PTX") and a.dtype == dtypes.int8 and target_dtype.itemsize != a.dtype.itemsize:
    raise unittest.SkipTest("shape changing bitcast of int8 broken on PTX")
  expected = torch.tensor(a.tolist(), dtype=_to_torch_storage_type(a.dtype)).view(_to_torch_dtype(target_dtype))
  _test_op(lambda: a.bitcast(target_dtype), target_dtype, target or expected.tolist())
@@ -100,7 +100,7 @@ class TestDType(unittest.TestCase):
    ))

  @unittest.skipIf(Device.DEFAULT == "PYTHON", "skip for now")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "skip for now")
+  @unittest.skipIf(getenv("PTX"), "skip for now")
  def test_uint_overflow(self):
    if not dtypes.is_unsigned(self.DTYPE): raise unittest.SkipTest("only for unsigned")
    v = dtypes.max(self.DTYPE)
@@ -128,10 +128,11 @@ class TestDType(unittest.TestCase):
      np.testing.assert_allclose(tin, tor, atol=1e-6, rtol=1e-3)

  def test_finfo(self):
-    if self.DTYPE not in [dtypes.float16, dtypes.float32, dtypes.float64]: return
-    info = np.finfo(_to_np_dtype(self.DTYPE))
-    self.assertEqual(info.bits, self.DTYPE.itemsize*8)
-    self.assertEqual((info.nexp, info.nmant), dtypes.finfo(self.DTYPE))
+    if self.DTYPE not in [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]: return
+    info = ml_dtypes.finfo(ml_dtypes.bfloat16 if self.DTYPE is dtypes.bfloat16 else _to_np_dtype(self.DTYPE))
+    assert info.bits == self.DTYPE.itemsize*8
+    assert info.nexp == dtypes.finfo(self.DTYPE)[0]
+    assert info.nmant == dtypes.finfo(self.DTYPE)[1]

 def _test_ops(a_dtype:DType, b_dtype:DType, target_dtype=None):
  target_dtype = target_dtype or least_upper_dtype(a_dtype, b_dtype)
@@ -149,40 +150,36 @@ class TestFp8s(unittest.TestCase):

 class TestFp8sConversions(unittest.TestCase):
  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=False, allow_infinity=False, min_value=-FP8E4M3_MAX, max_value=FP8E4M3_MAX))
-  def test_float_to_fp8e4m3(self, x):
-    np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3), torch.tensor(x, dtype=torch.float8_e4m3fn).view(torch.uint8).item())
+  def test_float_to_fp8e4m3(self, x): np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3), ml_dtypes.float8_e4m3fn(x).tobytes()[0])

  def test_float_to_fp8e4m3_extreme_values(self):
    np.testing.assert_equal(float_to_fp8(FP8E4M3_MAX, dtypes.fp8e4m3), 126)
    np.testing.assert_equal(float_to_fp8(FP8E4M3_MAX*1.01, dtypes.fp8e4m3), 126)
-    np.testing.assert_equal(float_to_fp8(math.inf, dtypes.fp8e4m3), 127)
+    np.testing.assert_equal(float_to_fp8(math.inf, dtypes.fp8e4m3), 126)
    np.testing.assert_equal(float_to_fp8(-FP8E4M3_MAX, dtypes.fp8e4m3), 254)
    np.testing.assert_equal(float_to_fp8(-FP8E4M3_MAX*1.01, dtypes.fp8e4m3), 254)
-    np.testing.assert_equal(float_to_fp8(-math.inf, dtypes.fp8e4m3), 255)
+    np.testing.assert_equal(float_to_fp8(-math.inf, dtypes.fp8e4m3), 254)
    np.testing.assert_equal(float_to_fp8(math.nan, dtypes.fp8e4m3), 127)
    np.testing.assert_equal(float_to_fp8(-math.nan, dtypes.fp8e4m3), 255)

  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=False, allow_infinity=False, min_value=-FP8E5M2_MAX, max_value=FP8E5M2_MAX))
-  def test_float_to_fp8e5m2(self, x):
-    np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e5m2), torch.tensor(x, dtype=torch.float8_e5m2).view(torch.uint8).item())
+  def test_float_to_fp8e5m2(self, x): np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e5m2), ml_dtypes.float8_e5m2(x).tobytes()[0])

  def test_float_to_fp8e5m2_extreme_values(self):
    np.testing.assert_equal(float_to_fp8(FP8E5M2_MAX, dtypes.fp8e5m2), 123)
    np.testing.assert_equal(float_to_fp8(FP8E5M2_MAX*1.01, dtypes.fp8e5m2), 123)
-    np.testing.assert_equal(float_to_fp8(math.inf, dtypes.fp8e5m2), 124)
+    np.testing.assert_equal(float_to_fp8(math.inf, dtypes.fp8e5m2), 123)
    np.testing.assert_equal(float_to_fp8(-FP8E5M2_MAX, dtypes.fp8e5m2), 251)
    np.testing.assert_equal(float_to_fp8(-FP8E5M2_MAX*1.01, dtypes.fp8e5m2), 251)
-    np.testing.assert_equal(float_to_fp8(-math.inf, dtypes.fp8e5m2), 252)
+    np.testing.assert_equal(float_to_fp8(-math.inf, dtypes.fp8e5m2), 251)
    np.testing.assert_equal(float_to_fp8(math.nan, dtypes.fp8e5m2), 126)
    np.testing.assert_equal(float_to_fp8(-math.nan, dtypes.fp8e5m2), 254)

  @given(strat.integers(min_value=0, max_value=255))
-  def test_fp8e4m3_to_float(self, x):
-    np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e4m3), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e4m3fn).float().item())
+  def test_fp8e4m3_to_float(self, x): np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e4m3), np.uint8(x).view(ml_dtypes.float8_e4m3fn).item())

  @given(strat.integers(min_value=0, max_value=255))
-  def test_fp8e5m2_to_float(self, x):
-    np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e5m2), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e5m2).float().item())
+  def test_fp8e5m2_to_float(self, x): np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e5m2), np.uint8(x).view(ml_dtypes.float8_e5m2).item())

@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), "bfloat16 not supported")
 class TestBFloat16(unittest.TestCase):
@@ -258,8 +255,7 @@ class TestFloatDType(TestDType):

 class TestDoubleDType(TestDType):
  DTYPE = dtypes.double
-  @unittest.skipIf((CI and Device.DEFAULT in {"CUDA", "NV"}) or \
-                    isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "conversion not supported on CI CUDA and PTX")  # TODO: why not?
+  @unittest.skipIf((CI and Device.DEFAULT in {"CUDA", "NV"}) or getenv("PTX"), "conversion not supported on CI CUDA and PTX")  # TODO: why not?
  def test_float64_increased_precision(self):
    for func in [
      lambda t: t.exp(),
@@ -283,21 +279,21 @@ class TestDoubleDType(TestDType):

 class TestInt8DType(TestDType):
  DTYPE = dtypes.int8
-  @unittest.skipIf(getenv("CUDA",0)==1 or isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "cuda saturation works differently")
+  @unittest.skipIf(getenv("CUDA",0)==1 or getenv("PTX", 0)==1, "cuda saturation works differently")
  def test_int8_to_uint8_negative(self):
    _test_op(lambda: Tensor([-1, -2, -3, -4], dtype=dtypes.int8).cast(dtypes.uint8), dtypes.uint8, [255, 254, 253, 252])

  def test_int8_to_uint16_negative(self):
    _test_op(lambda: Tensor([-1, -2, -3, -4], dtype=dtypes.int8).cast(dtypes.uint16), dtypes.uint16, [2**16-1, 2**16-2, 2**16-3, 2**16-4])

-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken in ptx")
+  @unittest.skipIf(getenv("PTX"), "broken in ptx")
  def test_bitcast_alt(self):
    a = Tensor([72, -90, 27, 40, -53, 70, 96, 51], dtype=dtypes.int8).bitcast(dtypes.short)
    self.assertListEqual(a.tolist(), [-22968, 10267, 18123, 13152])

 class TestUint8DType(TestDType):
  DTYPE = dtypes.uint8
-  @unittest.skipIf(getenv("CUDA",0)==1 or isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "cuda saturation works differently")
+  @unittest.skipIf(getenv("CUDA",0)==1 or getenv("PTX", 0)==1, "cuda saturation works differently")
  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])

@@ -305,7 +301,7 @@ 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):
    # NOTE: this has to be assume to prevent hypothesis from skipping all samples
-    assume(not (isinstance(Device[Device.DEFAULT].renderer, PTXRenderer) and dt1 == dtypes.int8)) # TODO: bitcasting int8 fails in PTX
+    assume(not (getenv("PTX") and dt1 == dtypes.int8)) # TODO: bitcasting int8 fails in PTX
    data = rand_for_dtype(dt1, 32).reshape(2, 2, 8)
    expected = torch.tensor(data.tolist(), dtype=_to_torch_storage_type(dt1)).view(_to_torch_dtype(dt2))
    _test_op(lambda: Tensor(data, dtype=dt1).bitcast(dt2), dt2, expected.tolist())
@@ -348,6 +344,7 @@ class TestUint64DType(TestDType):

 class TestBoolDType(TestDType): DTYPE = dtypes.bool

+@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
 class TestBFloat16Type(TestDType): DTYPE = dtypes.bfloat16

 class TestPtrDType(unittest.TestCase):
@@ -426,7 +423,7 @@ class TestDtypeUsage(unittest.TestCase):
 class TestOpsBFloat16(unittest.TestCase):
  def test_cast(self):
    # TODO: helper_test_op breaks in unrelated part
-    # TODO: wrong output with CL=1 on mac
+    # TODO: wrong output with GPU=1 on mac
    data = [60000.0, 70000.0, 80000.0]
    np.testing.assert_allclose(Tensor(data).cast("bfloat16").numpy(), torch.tensor(data).type(torch.bfloat16).float().numpy())

@@ -5,7 +5,6 @@ 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
-from tinygrad.renderer.ptx import PTXRenderer
 import numpy as np
 import pytest
 from hypothesis import given, strategies as strat, settings, HealthCheck
@@ -92,7 +91,7 @@ def universal_test_midcast(a, b, c, op1, op2, d1:DType, d2:DType):
  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()
  numpy_value = op2[1](op1[1](an, bn).astype(_to_np_dtype(d2)), cn)
-  np.testing.assert_allclose(tensor_value, numpy_value, rtol=1e-6 if isinstance(Device[Device.DEFAULT].renderer, PTXRenderer) else 1e-7)
+  np.testing.assert_allclose(tensor_value, numpy_value, rtol=1e-6 if getenv("PTX") else 1e-7)

 class TestDTypeALU(unittest.TestCase):
  @unittest.skipUnless(is_dtype_supported(dtypes.float64), f"no float64 on {Device.DEFAULT}")
@@ -26,10 +26,6 @@ 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
@@ -171,6 +167,34 @@ 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.

@@ -206,8 +230,10 @@ class TestUOpValidationIssue(unittest.TestCase):
  # these fail with UOp verification error.
  # we want more of these with diverse errors!

-  @unittest.skipIf((not is_dtype_supported(dtypes.long)) or MOCKGPU, "hangs gpuocelot")
+  @unittest.expectedFailure
  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])
@@ -247,4 +273,4 @@ class TestEdgeCases(unittest.TestCase):


 if __name__ == "__main__":
-  unittest.main()
+  unittest.main()
@@ -7,7 +7,7 @@ from tinygrad.engine.realize import lower_schedule
 from tinygrad.helpers import prod, unwrap
 from test.helpers import REAL_DEV

-IMAGE_SUPPORTED_DEVICES = ("QCOM", "CL")
+IMAGE_SUPPORTED_DEVICES = ("QCOM", "GPU")

@unittest.skipUnless(REAL_DEV in IMAGE_SUPPORTED_DEVICES, "Images not supported")
 class TestImageCopy(unittest.TestCase):
@@ -23,7 +23,7 @@ def _simple_test(add, extract=lambda x: x, N=10):
 class TestJit(unittest.TestCase):

  @settings(deadline=2e4)
-  @unittest.skipUnless(REAL_DEV in ["CPU"], f"no support on {REAL_DEV}")
+  @unittest.skipUnless(REAL_DEV in ["LLVM", "CPU"], f"no support on {REAL_DEV}")
  @given(strat.sampled_from([Tensor.exp2, Tensor.log2, Tensor.sin]))
  def test_approx_jit_timeout(self, op):
    with Context(TRANSCENDENTAL=2):
@@ -791,7 +791,7 @@ class TestJitGraphSplit(unittest.TestCase):
      hcqgraph=[self.ji_graph(5)])

  def test_jit_multidev_xfer(self):
-    if Device.DEFAULT in {"CPU"}: raise unittest.SkipTest("CPU is not a valid default device for this test (zero-copies)")
+    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
    if Device.DEFAULT == "METAL" or REAL_DEV == "METAL": raise unittest.SkipTest("Metal is flaky, with multidevice (same as metal llama 4gpu?)")

    try: Device[f"{Device.DEFAULT}:1"]
@@ -816,7 +816,7 @@ class TestJitGraphSplit(unittest.TestCase):

  @unittest.skipIf(getenv("MOCKGPU"), "MockGPU does not support parallel copies")
  def test_jit_multidev_copy(self):
-    if Device.DEFAULT in {"CPU"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
+    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")

    @TinyJit
    def f(inp):
@@ -16,14 +16,14 @@ class TestKernelCache(unittest.TestCase):

    a1 = Tensor.rand(4,4).realize()
    b1 = Tensor.rand(4,4).realize()
-    orig_compile_func = Device['CPU'].compiler.compile_cached
-    Device['CPU'].compiler.compile_cached = None # making it not callable
+    orig_compile_func = Device['CPU'].compiler
+    Device['CPU'].compiler = None # making it not callable

    try:
      x1 = a1 + b1 + unique_const
      x1.realize() # Same kernel should be from cache.
    finally:
-      Device['CPU'].compiler.compile_cached = orig_compile_func
+      Device['CPU'].compiler = orig_compile_func

 if __name__ == "__main__":
  unittest.main()
@@ -10,10 +10,9 @@ 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.helpers import Context, flatten, dedup, 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
-from tinygrad.renderer.ptx import PTXRenderer

 class TestLinearizer(unittest.TestCase):
  def test_arg_dedup(self):
@@ -156,7 +155,7 @@ class TestLinearizer(unittest.TestCase):
  @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")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken on ptx for some reason")
+  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
  def test_upcast_with_locals(self):
    x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
    r = (x@y).relu()
@@ -367,7 +366,7 @@ class TestLinearizer(unittest.TestCase):
    helper(Tensor.arange(255), max_ops=2)

  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken on ptx for some reason")
+  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
  def test_grouped_store_phis(self):
    """
    float4 acc0 = float4(0.0,0.0,0.0,0.0);
@@ -421,7 +420,7 @@ class TestLinearizer(unittest.TestCase):
  @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")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken on ptx for some reason")
+  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
  def test_grouped_store_local_only(self):
    x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
    r = (x@y).relu()
@@ -482,7 +481,7 @@ def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
  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)]
+  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):
@@ -504,7 +503,7 @@ def reset_bufs(bufs:list[Buffer]):

 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=[]):
-  outbufs = real_bufs[:len(realized_ast.src)]
+  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]

@@ -6,10 +6,10 @@ import unittest
 from tinygrad import Device, dtypes
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import UOp, Ops, AxisType, KernelInfo
+from tinygrad.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
-from tinygrad.renderer.ptx import PTXRenderer

 class TestLinearizerFailure(unittest.TestCase):
  @unittest.expectedFailure
@@ -47,8 +47,7 @@ class TestLinearizerDumb(unittest.TestCase):
    c10 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
    c11 = c1.store((c4.alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c8)).cast(dtypes.int)*(c9.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, -1, src=c10), UOp.const(dtypes.int, 0, src=c10)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,)))+UOp.const(dtypes.int, 1000, src=c8))))
    ast = c11.sink()
-    #opts = [Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8)]
-    opts = [Opt(op=OptOps.LOCAL, axis=0, arg=8)]
+    opts = [Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8)]
    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
    print(prg.src)
    assert prg.uops is not None and not any(uop.op is Ops.MAX for uop in prg.uops), "leftover MAX"
@@ -94,7 +93,7 @@ class TestLinearizerDumb(unittest.TestCase):

  @unittest.expectedFailure
  @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "this is somehow correct in PTX")
+  @unittest.skipIf(getenv("PTX"), "this is somehow correct in PTX")
  def test_upcasted_stores_out_of_order(self):
    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=())
    c1 = c0.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)))
@@ -5,9 +5,9 @@ from tinygrad.nn.state import get_state_dict

 class TestMethodCache(unittest.TestCase):
  def setUp(self):
-    self.backup_compiler = Device[Device.DEFAULT].compiler.compile_cached
+    self.backup_compiler = Device[Device.DEFAULT].compiler
  def tearDown(self):
-    Device[Device.DEFAULT].compiler.compile_cached = self.backup_compiler
+    Device[Device.DEFAULT].compiler = self.backup_compiler

  def test_simple_methodcache(self):
    a = Tensor([1])
@@ -15,19 +15,19 @@ class TestMethodCache(unittest.TestCase):
    c = Tensor([3])
    d = Tensor([4])
    (a+b).realize()
-    Device[Device.DEFAULT].compiler.compile_cached = None
+    Device[Device.DEFAULT].compiler = None
    (c+d).realize()

  def test_nested_methodcache(self):
    a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
    ((a+b)+(a+b)).realize()
-    Device[Device.DEFAULT].compiler.compile_cached = None
+    Device[Device.DEFAULT].compiler = None
    ((c+d)+(c+d)).realize()

  def test_nested_methodcache_swap(self):
    a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
    ((a+b)+(c+d)).realize()
-    Device[Device.DEFAULT].compiler.compile_cached = None
+    Device[Device.DEFAULT].compiler = None
    ((c+d)+(a+b)).realize()

  @unittest.skip("incorrect use of transformer")
@@ -38,7 +38,7 @@ class TestMethodCache(unittest.TestCase):
    # NOTE: you have to do this twice due to the k-v cache
    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
-    Device[Device.DEFAULT].compiler.compile_cached = None
+    Device[Device.DEFAULT].compiler = None
    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()

 if __name__ == '__main__':
@@ -178,14 +178,16 @@ class TestMultiTensor(unittest.TestCase):
      run_schedule(sched)
      np.testing.assert_equal(xt.numpy(), X_np[i*2:i*2+2])

-  @given(strat.sampled_from((devices_2, devices_3)),
+  @given(strat.sampled_from((4, 5)), strat.sampled_from((devices_2, devices_3)),
         strat.sampled_from((Ops.ADD, Ops.MUL, Ops.MAX)),
-         strat.sampled_from((None, 0, 1)), strat.sampled_from((None, 0, 1)))
-  def test_simple_reduce(self, devices, rop, shard_axis, reduce_axis):
-    N = 4 * len(devices)
-    X = (Tensor.rand(N*N)-1).reshape(N, N).shard_(devices, shard_axis)
+         strat.sampled_from((None, 0, 1)), strat.sampled_from((None, 0, 1)), strat.sampled_from((1, 0, -1)))
+  def test_simple_reduce(self, N, devices, rop, shard_axis, reduce_axis, sign):
+    N = N * len(devices)
+    X = Tensor.rand(N*N).reshape(N, N).mul(sign)
    n = X.numpy()
-    f = {Ops.ADD: lambda x: x.sum(reduce_axis), Ops.MUL: lambda x: x.prod(reduce_axis), Ops.MAX: lambda x: x.max(reduce_axis)}[rop]
+    X.shard_(devices, shard_axis)
+    f = {Ops.ADD: lambda x: x.sum(reduce_axis), Ops.MUL: lambda x: x.prod(reduce_axis),
+         Ops.MAX: lambda x: x.max(reduce_axis)}[rop]
    fX = f(X)
    fn = f(n)
    np.testing.assert_allclose(fX.numpy(), fn, rtol=1e-6, atol=1e-6)
@@ -371,7 +373,7 @@ class TestMultiTensor(unittest.TestCase):
    np.testing.assert_allclose(y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6)

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

@@ -407,7 +409,7 @@ class TestMultiTensor(unittest.TestCase):
    # sometimes there is zeros in these grads... why?
    np.testing.assert_allclose(grad, shard_grad, atol=1e-5, rtol=1e-5)

-  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "CPU", "AMD"), "slow, and flaky on CPU")
+  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU", "AMD"), "slow, and flaky on LLVM/CPU")
  def test_data_parallel_resnet_train_step(self):
    from extra.models.resnet import ResNet18
    fake_image = Tensor.rand((2, 3, 224//16, 224//16))
@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
 import numpy as np
 from typing import List, Callable
 import torch
-from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, CPU_LLVM, AMD_LLVM, RANGEIFY
+from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, AMD_LLVM
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
@@ -234,8 +234,7 @@ class TestOps(unittest.TestCase):
  def test_unfold(self):
    helper_test_op([(8,)], lambda x: x.unfold(0, 2, 1))
    helper_test_op([(8,)], lambda x: x.unfold(0, 2, 2))
-    # TODO: something is wrong with unfold
-    if not getenv("TINY_BACKEND"): helper_test_op([(8,)], lambda x: x.unfold(0, 7, 3))
+    helper_test_op([(8,)], lambda x: x.unfold(0, 7, 3))
    helper_test_op([(3,3,3)], lambda x: x.unfold(2, 2, 8))
    helper_test_op([(3,3,3)], lambda x: x.unfold(1, 0, 8))
    helper_test_op([(3,3,3,3,3)], lambda x: x.unfold(-1, 2, 2))
@@ -1305,7 +1304,7 @@ class TestOps(unittest.TestCase):
                                                                         np.arange(64,128,dtype=np.float32).reshape(8,8)])
  def test_small_gemm_eye(self):
    helper_test_op(None, lambda x,y: x.matmul(y), lambda x,y: x@y, vals=[np.eye(8).astype(np.float32), np.eye(8).astype(np.float32)])
-  @unittest.skipIf(CI and Device.DEFAULT in ["NV", "CL", "CUDA"] or (Device.DEFAULT == "CPU" and CPU_LLVM) or IMAGE
+  @unittest.skipIf(CI and Device.DEFAULT in ["NV", "LLVM", "GPU", "CUDA"] or IMAGE
  or (Device.DEFAULT == "WEBGPU" and platform.system() == "Windows"), "not supported on these in CI/IMAGE")
  def test_gemm_fp16(self):
    helper_test_op([(64,64), (64,64)], lambda x,y: x.half().matmul(y.half()), atol=5e-3, rtol=5e-3)
@@ -1459,7 +1458,6 @@ class TestOps(unittest.TestCase):
  def test_mean_zero_axis(self):
    helper_test_op([(1,0,3,0,5)], lambda x: x.mean(axis=(1,3)))

-  @slow_test
  def test_var(self):
    helper_test_op([(15, 25, 35)], lambda x: x.var())
    helper_test_op([(15, 25, 35)], lambda x: x.var(correction=0))
@@ -1495,7 +1493,6 @@ class TestOps(unittest.TestCase):
    helper_test_op([(15, 25, 35)], lambda x: x.var(keepdim=True))
    helper_test_op([(15, 25, 35)], lambda x: x.var(0, keepdim=True, correction=0))

-  @slow_test
  def test_std(self):
    helper_test_op([(15, 25, 35)], lambda x: x.std())
    helper_test_op([(15, 25, 35)], lambda x: x.std(correction=0))
@@ -1528,7 +1525,6 @@ class TestOps(unittest.TestCase):
  def test_std_keepdim(self):
    helper_test_op([(15, 25, 35)], lambda x: x.std(keepdim=True))
    helper_test_op([(15, 25, 35)], lambda x: x.std(0, keepdim=True, correction=0))
-  @slow_test
  def test_std_mean(self):
    helper_test_op([(15,25,35)], lambda x: torch.stack(torch.std_mean(x)),
                                 lambda x: Tensor.stack(*x.std_mean()))
@@ -2044,14 +2040,12 @@ class TestOps(unittest.TestCase):
      lambda x,w,b: torch.nn.functional.conv2d(x,w,b),
      lambda x,w,b: Tensor.conv2d(x,w,b), grad_rtol=1e-5)

-  @slow_test
  @unittest.skipIf(IMAGE>0, "no conv3d on images")
  def test_simple_conv3d(self):
    helper_test_op([(1,4,9,9,9), (4,4,3,3,3)],
      lambda x,w: torch.nn.functional.conv3d(x,w),
      lambda x,w: Tensor.conv2d(x,w), grad_rtol=1e-5)

-  @slow_test
  @unittest.skipIf(IMAGE>0, "no conv3d on images")
  def test_padded_conv3d(self):
    helper_test_op([(1,4,5,5,5), (4,4,3,3,3)],
@@ -2108,7 +2102,6 @@ class TestOps(unittest.TestCase):
      lambda x,w: torch.nn.functional.conv_transpose2d(x,w,groups=2),
      lambda x,w: Tensor.conv_transpose2d(x,w,groups=2), grad_rtol=1e-5)

-  @slow_test
  def test_padded_conv_transpose2d(self):
    for padding in [(1,2), (2,1), 2, 1, 0]:
      helper_test_op([(2,4,9,9), (4,4,3,3)],
@@ -2117,7 +2110,6 @@ class TestOps(unittest.TestCase):
    self.helper_test_exception([(2,16,2,2), (32,16,3,3)], lambda x,w: torch.nn.functional.conv_transpose2d(x,w,padding=(1,1,1)),
                   lambda x,w: Tensor.conv_transpose2d(x,w,padding=(1,1,1)), expected=(RuntimeError, ValueError))

-  @slow_test
  def test_dilated_conv_transpose2d(self):
    for dilation in [(1,2), (2,1), 2, 1]:
      helper_test_op([(2,4,9,9), (4,4,3,3)],
@@ -2130,7 +2122,6 @@ class TestOps(unittest.TestCase):
        lambda x,w: torch.nn.functional.conv_transpose2d(x,w, stride=stride),
        lambda x,w: Tensor.conv_transpose2d(x,w,stride=stride), atol=1e-5, grad_rtol=1e-5)

-  @slow_test
  def test_output_padded_conv_transpose2d(self):
    for output_padding, stride in [((1,1), (2,3)), ((2,1), (3,2))]:
      helper_test_op([(2,4,6,5), (4,4,3,3),(4,)],
@@ -2192,10 +2183,8 @@ class TestOps(unittest.TestCase):
              lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups),
              lambda x,w: Tensor.conv2d(x,w,groups=groups), grad_rtol=1e-5)
  def test_conv2d(self): self._test_conv2d(bs=1, cin=3)
-  @slow_test
  def test_conv2d_bs_4_cin_3(self): self._test_conv2d(bs=4, cin=3, cout=2)
  def test_conv2d_bs_1_cin_1(self): self._test_conv2d(bs=1, cin=1)
-  @slow_test
  def test_conv2d_bs_4_cin_1(self): self._test_conv2d(bs=4, cin=1)

  def test_conv2d_errors(self):
@@ -2267,18 +2256,16 @@ class TestOps(unittest.TestCase):
      lambda x,w: torch.nn.functional.conv2d(x,w,groups=groups),
      lambda x,w: Tensor.conv2d(x,w,groups=groups), grad_rtol=1e-5)

-  @slow_test
  def test_strided_conv2d_simple(self):
    bs,H,W = 2,3,1
    helper_test_op([(bs,1,5,1), (1,1,H,W)],
      lambda x,w: torch.nn.functional.conv2d(x,w,stride=2),
      lambda x,w: Tensor.conv2d(x,w,stride=2))

-  @unittest.skipUnless(Device.DEFAULT == "CPU" and CPU_LLVM, "DEVECTORIZE=0 only for LLVM")
+  @unittest.skipIf(Device.DEFAULT != "LLVM", "DEVECTORIZE=0 only for LLVM")
  def test_strided_conv2d_simple_vec(self):
    with Context(DEVECTORIZE=0): self.test_strided_conv2d_simple()

-  @slow_test
  def test_strided_conv2d(self):
    bs = 4
    cin = 3
@@ -2514,7 +2501,6 @@ class TestOps(unittest.TestCase):
      ),
      forward_only=True)

-  @slow_test
  def test_avg_pool2d(self):
    shape = (32,2,111,28)
    for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
@@ -2942,13 +2928,13 @@ class TestOps(unittest.TestCase):
  @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), requires_grad=False)
+    a = Tensor(b.detach().cpu().numpy().astype(np.int32), dtype=dtypes.int32, requires_grad=False)
    for reduce in ("sum", "prod", "mean", "amin", "amax"):
      for dim in (-1,1,-3):
-        helper_test_op([(3,4,5), (3,4,5)],
+        helper_test_op([(4,5,6), (4,5,6)],
          lambda x,src: x.scatter_reduce(dim=dim, index=b, src=src, reduce=reduce),
          lambda x,src: x.scatter_reduce(dim=dim, index=a, src=src, reduce=reduce), forward_only=True)
-        helper_test_op([(3,4,5), (3,4,5)],
+        helper_test_op([(4,5,6), (4,5,6)],
          lambda x,src: x.scatter_reduce(dim=dim, index=b, src=src, reduce=reduce, include_self=False),
          lambda x,src: x.scatter_reduce(dim=dim, index=a, src=src, reduce=reduce, include_self=False), forward_only=True)

@@ -3029,8 +3015,6 @@ class TestOps(unittest.TestCase):
    helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.binary_cross_entropy_with_logits(x,y.clip(0,1),
                                                                                                        pos_weight=torch.tensor(pos_weight)),
                                       lambda x,y: x.binary_crossentropy_logits(y.clip(0,1),pos_weight=Tensor(pos_weight)))
-
-  @unittest.skipIf(RANGEIFY > 1, "broken on RANGEIFY > 1, TODO: fix")
  def test_cross_entropy_class_probabilities(self):
    helper_test_op([(32,), (32,)], lambda x,y: torch.nn.functional.cross_entropy(x, y), lambda x,y: x.cross_entropy(y))
    helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.cross_entropy(x, y), lambda x,y: x.cross_entropy(y))
@@ -1,6 +1,6 @@
 import unittest
 from tinygrad import Tensor, Device
-from tinygrad.helpers import RANGEIFY, CPU_LLVM
+from tinygrad.helpers import RANGEIFY
 from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.engine.realize import get_program

@@ -13,7 +13,7 @@ class TestOpts(unittest.TestCase):
    out = (a+b).contiguous(arg=opts)
    s = out.schedule()
    self.assertEqual(s[-1].ast.arg.opts_to_apply, opts)
-    if Device.DEFAULT in {"CPU", "CL", "METAL"} and not CPU_LLVM:
+    if Device.DEFAULT in {"CPU", "GPU", "METAL"}:
      prg = get_program(s[-1].ast)
      self.assertIn('float4', prg.src)

@@ -31,7 +31,7 @@ def helper_profile_filter_device(profile, device:str):
  return [x for x in profile if getattr(x, "device", None) == device], dev_events[0]

 # TODO: support in HCQCompiled
-is_cpu_hcq = Device.DEFAULT in {"CPU"}
+is_cpu_hcq = Device.DEFAULT in {"CPU", "LLVM"}

@unittest.skipUnless((issubclass(type(Device[Device.DEFAULT]), HCQCompiled) and not is_cpu_hcq) or Device.DEFAULT in {"METAL"}, "Dev not supported")
 class TestProfiler(unittest.TestCase):
@@ -9,7 +9,6 @@ from tinygrad.device import is_dtype_supported
 from tinygrad.engine.realize import lower_schedule, CompiledRunner
 from hypothesis import given, settings, strategies as strat
 from test.helpers import not_support_multi_device
-from tinygrad.renderer.ptx import PTXRenderer

 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
 settings.load_profile("my_profile")
@@ -99,7 +98,7 @@ class TestRandomness(unittest.TestCase):

    np.testing.assert_allclose(jr, r)

-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "fails with PTX")
+  @unittest.skipIf(getenv("PTX"), "fails with PTX")
  def test_threefry_doesnt_use_long(self):
    for (_,ei) in lower_schedule(Tensor.rand(20).schedule()):
      if isinstance(ei.prg, CompiledRunner):
@@ -1,21 +1,8 @@
 import unittest
-from tinygrad import Tensor, nn
+from tinygrad import Tensor
 from tinygrad.helpers import RANGEIFY, Context, GlobalCounters
 from tinygrad.uop.ops import UOp

-@unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
-class TestRangeifyAssign(unittest.TestCase):
-  def test_assign_permuted(self):
-    A = Tensor.empty(4, 4, dtype='int')
-    B = Tensor.arange(16).reshape(4,4)
-    ret = A.permute(1,0).assign(B)
-    lst = ret.tolist()
-    lst2 = A.tolist()
-    lst3 = B.tolist()
-    print(lst)
-    print(lst2)
-    print(lst3)
-
 N = 256

@unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
@@ -106,16 +93,6 @@ class TestRangeify(unittest.TestCase):
    w2 = Tensor.empty(12, 8, 3, 3)
    x.conv2d(w1).conv2d(w2).realize()

-  def test_conv_maxpool_contig(self): self.test_conv_maxpool(True)
-  def test_conv_maxpool(self, contig=False):
-    GlobalCounters.reset()
-    x = Tensor.empty(32, 16, 64, 64)
-    l1 = nn.Conv2d(16, 16, 3)
-    for p in nn.state.get_parameters(l1): p.replace(Tensor.empty(p.shape))
-    x = l1(x)
-    if contig: x = x.contiguous()
-    x.max_pool2d().realize()
-
  def test_double_conv2d_half_contig(self):
    x = Tensor.empty(1, 4, 32, 32)
    w1 = Tensor.empty(8, 4, 3, 3)
@@ -167,7 +144,6 @@ class TestRangeify(unittest.TestCase):

 # contiguous + reduce can support ranges?

-@unittest.skip("okay to disable this for now")
@unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
 class TestOuterworld(unittest.TestCase):
  def test_passthrough_range(self):
@@ -12,9 +12,9 @@ from tinygrad import nn, dtypes, Device, Tensor
 from tinygrad.device import is_dtype_supported
 from tinygrad.dtype import DType, ImageDType
 from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.uop.ops import UOp, Ops, GroupOp, UPat, graph_rewrite, track_rewrites
+from tinygrad.uop.ops import PatternMatcher, UOp, Ops, GroupOp, UPat, graph_rewrite, track_rewrites
 from tinygrad.uop.symbolic import symbolic_simple
-from tinygrad.helpers import CI, DEBUG, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp, RANGEIFY
+from tinygrad.helpers import CI, DEBUG, FUSE_ARANGE, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp
 from tinygrad.schedule.kernelize import merge_views, get_kernelize_map, Kernel
 from tinygrad.engine.schedule import create_schedule_with_vars
 from tinygrad.engine.realize import CompiledRunner, run_schedule, lower_schedule
@@ -1024,6 +1024,20 @@ class TestSchedule(unittest.TestCase):
    run_schedule(check_schedule(out, 2))
    np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)

+  def test_argmin_multireduce_fusion(self):
+    Tensor.manual_seed(0)
+    x = Tensor.randn(4, 32).realize()
+    out = x.argmin(-1)
+    run_schedule(check_schedule(out, 2))
+    np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))
+
+  def test_argmax_multireduce_fusion(self):
+    Tensor.manual_seed(0)
+    x = Tensor.randn(4, 32).realize()
+    out = x.argmax(-1)
+    run_schedule(check_schedule(out, 2))
+    np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))
+
  def test_scaled_dot_product_attention_multireduce_fusion(self):
    Tensor.manual_seed(0)
    q = Tensor.randn(32,8,16,8).realize()
@@ -1640,7 +1654,7 @@ class TestSchedule(unittest.TestCase):
    constv = Tensor.empty(2, 2).uop.const_like(10).contiguous()
    check_schedule(constv, 1)

-  @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
+  @unittest.skipIf(Device.DEFAULT != "GPU", "image only supported on GPU")
  def test_image_matmul(self):
    with Context(IMAGE=2):
      x = Tensor.randn((9, 9)).realize()
@@ -1714,41 +1728,53 @@ class TestSchedule(unittest.TestCase):
    run_schedule(check_schedule(realized_const_view, 1))
    np.testing.assert_equal(realized_const_view.numpy(), [[0], [1], [0]])

+class TestIndexing(unittest.TestCase):
+  def check_schedule(self, xt:Tensor|list[Tensor], cnt:int):
+    with Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1)):
+      lst = [xt] if isinstance(xt, Tensor) else xt
+      s = Tensor.schedule(*lst)
+      lowered = [x[1] for x in lower_schedule(s.copy())]
+      kernels = [ei for ei in list(lowered) if isinstance(ei.prg, CompiledRunner)]
+      if FUSE_ARANGE and len(kernels) != cnt:
+        raise KernelCountException(f"{len(kernels)} != {cnt}")
+      for ei in lowered: ei.run(do_update_stats=True)
+    return s
+
  def test_simple_indexing(self):
    X = Tensor.randn(10, 10).realize()
    idxs = Tensor([0, 2]).realize()
    xt = X[idxs]
-    run_schedule(check_schedule(xt, 2))
+    self.check_schedule(xt, 2)
    np.testing.assert_equal(xt.numpy(), X.numpy()[idxs.numpy()])

  def test_simple_indexing_alt(self):
    X = Tensor.arange(16).reshape(4, 4)
    xt = X[[1, 2], [-1, 2]]
-    run_schedule(check_schedule(xt, 1))
+    self.check_schedule(xt, 1)
    np.testing.assert_equal(xt.numpy(), (np.arange(16).reshape(4, 4))[[1, 2], [-1, 2]])

  def test_advanced_indexing(self):
    X = Tensor.arange(10)+1
    xt = X[[0, -1]]
-    run_schedule(check_schedule(xt, 1))
+    self.check_schedule(xt, 1)
    np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0, -1]])

  def test_advanced_indexing_alt(self):
    X = Tensor.arange(6).reshape(3, 2)+1
    xt = X[[Tensor([2]), Tensor([1])]]
-    run_schedule(check_schedule(xt, 3))
+    self.check_schedule(xt, 3)
    np.testing.assert_equal(xt.numpy(), 6)

  def test_advanced_simple_indexing_combined(self):
    X = Tensor.arange(16).reshape(4, 4)
    xt = X[1:2, [-1, 2]]
-    run_schedule(check_schedule(xt, 1))
+    self.check_schedule(xt, 1)

  def test_push_through_reshape(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(10, 20).realize()
    out = x.argmax(1)
-    run_schedule(check_schedule(out, 2))
+    self.check_schedule(out, 2)
    np.testing.assert_allclose(out.numpy(), np.argmax(x.numpy(), 1))

  def test_arange_push_through_expand(self):
@@ -1756,35 +1782,35 @@ class TestSchedule(unittest.TestCase):
    a = Tensor.arange(4,)
    b = Tensor.randn(4, 4).realize()
    out = (a+b).sum()
-    run_schedule(check_schedule(out, 1))
+    self.check_schedule(out, 1)
    np.testing.assert_allclose(out.numpy(), (np.arange(4)+b.numpy()).sum(), atol=1e-5)

  def test_argmin(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.argmin(-1)
-    run_schedule(check_schedule(out, 2))
+    self.check_schedule(out, 2)
    np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))

  def test_argmax(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 32).realize()
    out = x.argmax(-1)
-    run_schedule(check_schedule(out, 2))
+    self.check_schedule(out, 2)
    np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))

  def test_arange_transposed(self):
    Tensor.manual_seed(0)
    x = Tensor.randint(4, 1).realize()
    a = ((Tensor.arange(4,)*x).T).sum()
-    run_schedule(check_schedule(a, 1))
+    self.check_schedule(a, 1)
    np.testing.assert_equal(a.numpy(), (np.arange(4)*x.numpy()).T.sum())

  def test_div_padded_arange(self):
    x = Tensor.full((2,2), 16)
    y = x.idiv(Tensor.linspace(2, 8, steps=4, dtype=dtypes.int).reshape(2,2)).pad(((1,1), (1,1)))
    out = y.sum(axis=1)
-    run_schedule(check_schedule(out, 2))
+    with Context(FUSE_ARANGE=1): run_schedule(check_schedule(out, 2))
    self.assertListEqual(out.tolist(), [0, 12, 4, 0])

  def test_arange_transposed_descendants(self):
@@ -1793,7 +1819,7 @@ class TestSchedule(unittest.TestCase):
    a = (Tensor.arange(4,)*x).T
    b = Tensor.randint(4, 4).realize()
    out = (a+b).sum()
-    run_schedule(check_schedule(out, 1))
+    self.check_schedule(out, 1)
    np.testing.assert_equal(out.numpy(), ((np.arange(4)*x.numpy()).T+b.numpy()).sum())

  def test_arange_index(self):
@@ -1801,7 +1827,7 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(5, 2).realize()
    a = Tensor.arange(10)
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 1))
+    self.check_schedule(out, 1)
    np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)

  def test_arange_index_shrink(self):
@@ -1810,14 +1836,14 @@ class TestSchedule(unittest.TestCase):
      x = Tensor.randn(11).realize()
    a = Tensor.arange(22)
    out = (x + a[:11]).sum()
-    check_schedule(out, 1)
+    self.check_schedule(out, 1)

  def test_arange_index_contiguous(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(5, 2).realize()
    a = Tensor.arange(10).contiguous()
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 3))
+    self.check_schedule(out, 3)
    np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)

  def test_arange_index_child(self):
@@ -1825,24 +1851,62 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.randn(5, 2).realize()
    a = Tensor.arange(10)+1
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 1))
+    self.check_schedule(out, 1)
    np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)

-  def test_user_contiguous(self):
+  def test_arange_index_contiguous_child(self):
    Tensor.manual_seed(0)
    x = Tensor.randn(5, 2).realize()
    a = (Tensor.arange(10)+1).contiguous()
    out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 3))
+    self.check_schedule(out, 3)
    np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)

+  def test_arange_childless_base(self):
+    a = Tensor.arange(4)
+    self.check_schedule(a, 1)
+    np.testing.assert_equal(a.numpy(), np.arange(4))
+
+  def test_arange_childless_view(self):
+    a = Tensor.arange(4).reshape(2, 2)
+    a[0] = 4
+    np.testing.assert_equal(a.numpy(), [[4, 4], [2, 3]])
+
+  def test_arange_group_childless_base(self):
+    Tensor.manual_seed(0)
+    x = Tensor.randint(4).realize()
+    a = Tensor.arange(4)+x
+    self.check_schedule(a, 1)
+    np.testing.assert_equal(a.numpy(), np.arange(4)+x.numpy())
+
+  def test_arange_group_childless_view(self):
+    Tensor.manual_seed(0)
+    x = Tensor.ones(4).contiguous().realize()
+    a = Tensor.arange(4)+x
+    a[0] = 6
+    np.testing.assert_equal(a.numpy(), [6., 2., 3., 4.])
+
  @unittest.skip("BUFFER_VIEW no longer supported on non-disk devices")
  def test_arange_view_op(self):
    a = Tensor.arange(12).reshape(4, 3).shrink(((1, 2), (1, 3))).contiguous()
-    sched = run_schedule(check_schedule(a, 1))
+    sched = self.check_schedule(a, 1)
    self.assertIs(sched[1].ast.op, Ops.BUFFER_VIEW)
    np.testing.assert_equal(a.numpy(), [[4, 5]])

+  @unittest.skipIf(Device.DEFAULT == "CPU", "tests copy from ext device")
+  def test_arange_shrink_copy(self):
+    a = Tensor.arange(12).reshape(4, 3).shrink(((1, 2), (1, 3))).to("CPU")
+    sched = self.check_schedule(a, 2) # NOTE: there is a contiguous between REDUCE_AXIS and COPY
+    self.assertIs(sched[-1].ast.op, Ops.COPY)
+    np.testing.assert_equal(a.numpy(), [[4, 5]])
+
+  @unittest.skipIf(Device.DEFAULT == "CPU", "tests copy from ext device")
+  def test_arange_expand_copy(self):
+    a = Tensor.arange(4).reshape(2, 2, 1).expand(2, 2, 2).contiguous().to("CPU")
+    sched = self.check_schedule(a, 2) # NOTE: there is a contiguous between REDUCE_AXIS and COPY
+    self.assertIs(sched[2].ast.op, Ops.COPY)
+    np.testing.assert_equal(a.numpy(), [[[0, 0], [1, 1]], [[2, 2], [3, 3]]])
+
  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
  def test_precompute_freqs_cis(self):
    from extra.models.llama import precompute_freqs_cis
@@ -1858,33 +1922,23 @@ class TestSchedule(unittest.TestCase):
  def test_fuse_assign_contiguous(self):
    x = Tensor.zeros(4, 4, dtype=dtypes.int).contiguous().realize()
    a = Tensor.arange(8).reshape(4, 2)
-    run_schedule(check_schedule(x.shrink((None, (0, 2))).assign(a.contiguous()), 2))
+    self.check_schedule(x.shrink((None, (0, 2))).assign(a.contiguous()), 2)
    np.testing.assert_equal(x.numpy(), [[0, 1, 0, 0], [2, 3, 0, 0], [4, 5, 0, 0], [6, 7, 0, 0]])

-  def test_assign_non_contiguous_alt(self): self.test_assign_non_contiguous(alt=True)
-  def test_assign_non_contiguous(self, alt=False):
-    x = (Tensor.arange(16)-100).reshape(4,4).contiguous().realize()
-    xref = x.numpy()
-    if alt:
-      y = Tensor.randint(2, 4).contiguous().realize()
-      a = Tensor.arange(8).reshape(2, 4)+y
-      tst = x.shrink(((0, 2), None)).assign(a).realize()
-      xref[:2, :] = np.arange(8).reshape(2, 4)+y.numpy()
-    else:
-      y = Tensor.randint(4, 2).contiguous().realize()
-      a = Tensor.arange(8).reshape(4, 2)+y
-      tst = x.shrink((None, (0, 2))).assign(a).realize()
-      xref[:, :2] = np.arange(8).reshape(4, 2)+y.numpy()
+  def test_assign_non_contiguous(self):
+    x = Tensor.zeros(4, 4, dtype=dtypes.int).contiguous().realize()
+    y = Tensor.randint(4, 2)
+    a = Tensor.arange(8).reshape(4, 2)+y
+    x.shrink((None, (0, 2))).assign(a).realize()
+    xref = np.zeros((4, 4), dtype=int)
+    xref[:, :2] = np.arange(8).reshape(4, 2)+y.numpy()
    np.testing.assert_equal(x.numpy(), xref)
-    if RANGEIFY > 0:
-      # NOTE: this is a bug on non rangeify
-      np.testing.assert_equal(tst.numpy(), a.numpy())

  def test_sparse_categorical_crossentropy_simple(self):
    X = Tensor([[0, 2, 3], [1, 2, 3]]).realize()
    Y = Tensor([1, 2]).realize()
    loss = X.sparse_categorical_crossentropy(Y)
-    run_schedule(check_schedule(loss, 4))
+    self.check_schedule(loss, 4)
    np.testing.assert_allclose(loss.item(), 0.878309, atol=1e-5, rtol=1e-6)

  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "Validation error on WebGPU")
@@ -1896,7 +1950,7 @@ class TestSchedule(unittest.TestCase):
    yt = Tensor.randn(BS, 10).realize()
    with Context(SPLIT_REDUCEOP=0):
      loss = yt.sparse_categorical_crossentropy(Y_train[samples])
-      run_schedule(check_schedule(loss, 6))
+      self.check_schedule(loss, 6)
      loss_fused = loss.numpy()
    loss_ref = torch.nn.CrossEntropyLoss()(torch.tensor(yt.numpy()), torch.tensor(Y_train.numpy())[torch.tensor(samples.numpy())])
    np.testing.assert_allclose(loss_fused, loss_ref.numpy(), atol=1e-6, rtol=1e-6)
@@ -1907,11 +1961,17 @@ class TestSchedule(unittest.TestCase):
    r = (X+Tensor.arange(16).reshape(4, 4)).sum()
    out0 = r+2
    out1 = r+3
-    run_schedule(check_schedule([out0, out1], 1))
+    self.check_schedule([out0, out1], 1)
    r_ref = (X.numpy()+np.arange(16).reshape(4, 4)).sum()
    np.testing.assert_allclose(out0.numpy(), r_ref+2, rtol=2e-7)
    np.testing.assert_allclose(out1.numpy(), r_ref+3, rtol=2e-7)

+  def test_dont_fold_arange_contiguous_view(self):
+    X = Tensor.randn(4, 4).realize()
+    r = (X+Tensor.arange(16).reshape(4, 4).contiguous()).sum(1, keepdim=True)
+    self.check_schedule([r], 2)
+    np.testing.assert_allclose(r.numpy(), (X.numpy()+np.arange(16).reshape(4, 4)).sum(1, keepdims=True), atol=1e-5, rtol=1e-6)
+
  @unittest.skip("multi output isn't supported")
  def test_multiview_arange_children(self):
    X = Tensor.randn(2,3,4,4).numpy()
@@ -2148,6 +2208,84 @@ class TestSimplifier(unittest.TestCase):
    assert UPat(Ops.CONST, arg=False).match(sink, {}), f"expected {sink} to collapse to a const False"
    assert sink.shape == a.shape

+tensor_const_pm = PatternMatcher([
+  (UPat(Ops.CONST, src=(UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),)),)), lambda: True),
+  (UPat(Ops.BIND, src=(UPat(Ops.DEFINE_VAR, src=(UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),)))), UPat(Ops.CONST))), lambda: True),
+])
+class TestConst(unittest.TestCase):
+  # ** part 1: basic functionality of a tensor directly created from CONST
+
+  def test_tensor_const(self):
+    a = Tensor(1)
+    print(a.uop)
+    self.assertTrue(tensor_const_pm.rewrite(a.uop))
+
+  def test_tensor_variable(self):
+    vv = UOp.variable("a", 0, 10).bind(1)
+    a = Tensor(vv)
+    print(a.uop)
+    self.assertTrue(tensor_const_pm.rewrite(a.uop))
+
+  def test_const_schedule(self):
+    a = Tensor.ones((4, 4))
+    sched = a.schedule()
+    self.assertEqual(len(sched), 0)
+
+  def test_const_contiguous_schedule(self):
+    # this ends up in the big graph
+    a = Tensor.ones((4,)).contiguous()
+    sched = a.schedule()
+    self.assertEqual(len(sched), 1)
+
+  # ** part 2: scheduler behavior when const folding happens later
+
+  def test_const_folding_no_realize(self):
+    a = Tensor([1, 2, 3, 4])*0
+    sched = a.schedule()
+    self.assertEqual(len(sched), 0)
+
+  def test_src_const_folding(self):
+    with Context(TRACK_MATCH_STATS=0):
+      a = Tensor.full((4,), 1).contiguous().realize()
+      b = Tensor.full((4,), 2).contiguous().realize()
+    mul0 = a*0
+    add = b+mul0
+    sched = add.schedule()
+    self.assertEqual(len(sched), 0)
+    # b+0 and b share the same underlying device memory
+    self.assertIs(add.uop.buffer, b.uop.buffer)
+    self.assertListEqual(add.tolist(), [2, 2, 2, 2])
+
+  def test_src_masked_const_folding(self):
+    with Context(TRACK_MATCH_STATS=0):
+      a = Tensor.full((4,), 1).contiguous().realize()
+      b = Tensor.full((6,), 2).contiguous().realize()
+    mul0 = a*0
+    add = b+mul0.pad((1, 1), value=2)
+    sched = add.schedule()
+    self.assertEqual(len(sched), 1)
+    run_schedule(sched)
+    # add gets assigned to a new buffer
+    self.assertIsNot(add.uop.base.realized, b.uop.base.realized)
+    self.assertListEqual(add.tolist(), [4, 2, 2, 2, 2, 4])
+
+  # ** part 3: Tensor variable bindings
+
+  #@unittest.expectedFailure # TODO: should schedule assert if you try to realize a Variable?
+  def test_var_schedule(self):
+    vv = UOp.variable("a", 0, 10).bind(1)
+    a = Tensor(vv)
+    sched = a.schedule()
+    self.assertEqual(len(sched), 0)
+
+  def test_add_tvar(self):
+    vv = UOp.variable("a", 0, 10).bind(1)
+    a = Tensor(vv)+2
+    sched, var_vals = a.schedule_with_vars()
+    self.assertEqual(len(sched), 1)
+    run_schedule(sched, var_vals)
+    self.assertEqual(a.tolist(), 3)
+
@unittest.skipIf(Device.DEFAULT == "CPU", "tests copy from another device to cpu")
 class TestCopyFolding(unittest.TestCase):
  def test_const_copy_is_free(self):
@@ -165,17 +165,6 @@ class TestSetitem(unittest.TestCase):
    t[idx] = val
    self.assertEqual(t.tolist(), [val]*idx_size+[idx_size])

-  def test_setitem_advanced_indexing(self):
-    # Example from https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
-    t = Tensor.zeros(10,20,30,40,50).contiguous()
-    ind_1 = Tensor([5,3,7,8])
-    ind_2 = Tensor([[[0],[1],[2]],[[3],[4],[5]]])
-    v = Tensor.arange(2*3*4*10*30*50).reshape(2,3,4,10,30,50)
-    t[:, ind_1, :, ind_2, :] = v
-    n = np.zeros((10,20,30,40,50))
-    n[:, ind_1.numpy(), :, ind_2.numpy(), :] = v.numpy()
-    np.testing.assert_allclose(t.numpy(), n)
-
 class TestWithGrad(unittest.TestCase):
  def test_no_requires_grad_works(self):
    z = Tensor.rand(8, 8)
@@ -16,18 +16,6 @@ class TestSymbolicJit(unittest.TestCase):
      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
    assert_jit_cache_len(jf, 1)

-  @unittest.expectedFailure # TODO: fix, this works without jit
-  def test_plus1_pad(self):
-    def f(a): return (a+1).pad((None, (0, 10-a.shape[1]))).realize()
-    jf = TinyJit(f)
-    a = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      vi = Variable("i", 1, 10).bind(i)
-      symbolic = jf(a[:, :vi]).numpy()
-      expected = f(a[:, :i]).numpy()
-      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-    assert_jit_cache_len(jf, 1)
-
  def test_add(self):
    def f(a, b): return (a+b).realize()
    jf = TinyJit(f)
@@ -17,15 +17,6 @@ class TestSymbolicOps(unittest.TestCase):
      expected = f(a[:, :i]).numpy()
      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)

-  def test_plus1_pad(self):
-    def f(a): return (a+1).pad((None, (0, 10-a.shape[1]))).realize()
-    a = Tensor.rand(3, 10)
-    for i in range(1, 5):
-      vi = Variable("i", 1, 10).bind(i)
-      symbolic = f(a[:, :vi]).numpy()
-      expected = f(a[:, :i]).numpy()
-      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-
  def test_add(self):
    def f(a, b): return (a+b).realize()
    a = Tensor.rand(3, 10)
@@ -9,7 +9,7 @@ from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
 from hypothesis import given, settings, strategies as strat
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import Ops, UOp
-from tinygrad.renderer.ptx import PTXRenderer
+from tinygrad.runtime.support.compiler_cuda import PTX
 from tinygrad.codegen import full_rewrite
 from tinygrad.dtype import DType

@@ -516,6 +516,10 @@ class TestTinygrad(unittest.TestCase):
    print(c)

  def test_env_overwrite_default_device(self):
+    subprocess.run(['DISK=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"DISK\\""'],
+                    shell=True, check=True)
+    subprocess.run(['NPY=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"NPY\\""'],
+                    shell=True, check=True)
    subprocess.run([f'{Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
                    shell=True, check=True)
    subprocess.run([f'DISK=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
@@ -911,24 +915,19 @@ class TestIdxUpcast(unittest.TestCase):
  def test_regular_sym(self):
    self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 1, 64).bind(32))

-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX always convert Ops.INDEX to int64")
+  @unittest.skipIf(PTX, "PTX always convert Ops.INDEX to int64")
  def test_symfold(self):
    # This would cause an overflow, but after sym fold it's within int32
    a = Tensor.arange(65535)
    uops = self._schedule_render(a)
    assert all(uop.dtype is not dtypes.long for uop in uops)

-  def test_arange_raise_overflow(self):
-    with self.assertRaises(ValueError):
-      self._schedule_render(Tensor.arange(2**33, dtype=dtypes.int))
-
  @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
  def test_int64_unsupported_overflow_sym(self):
    with self.assertRaises(KeyError):
      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)
@@ -7,14 +7,6 @@ class TestTiny(unittest.TestCase):

  # *** basic functionality ***

-  def test_const(self):
-    const = Tensor(2.0)
-    self.assertEqual(const.item(), 2.0)
-
-  def test_copy(self):
-    out = Tensor([1.,2,3])
-    self.assertListEqual(out.tolist(), [1.0, 2.0, 3.0])
-
  def test_plus(self):
    out = Tensor([1.,2,3]) + Tensor([4.,5,6])
    self.assertListEqual(out.tolist(), [5.0, 7.0, 9.0])
@@ -107,7 +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(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
+  @unittest.skipIf(IMAGE>0 or (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,
@@ -126,7 +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(CI and Device.DEFAULT == "DSP", "failing because of make things that can't be images not images")
+  @unittest.skipIf(IMAGE>0 or (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 = [
@@ -145,7 +137,7 @@ class TestTiny(unittest.TestCase):

  # *** image ***

-  @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
+  @unittest.skipIf(Device.DEFAULT != "GPU", "image only supported on GPU")
  def test_image(self):
    with Context(IMAGE=2): self.test_gemm(N=4, out_dtype=dtypes.imagef((4, 1, 4)))

@@ -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.index, arg=0)
+    c0 = UOp(Ops.CONST, dtypes.int, 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, dtypes.int)
+    v = UOp.variable("tmp", 0, 1)
    c2 = UOp(Ops.CONST, dtypes.int, arg=2)
    c4 = UOp(Ops.CONST, dtypes.int, arg=4)
    vc = UOp(Ops.ADD, dtypes.int, (v, c2))
@@ -417,49 +417,6 @@ 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_where_on_gated_load_fold(self):
-    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
-    ld = d0.index(ridx0, ridx0<50).load()
-    w = (ridx0<50).where(ld, 5)
-    uops = to_uops_list([w])
-    for u in uops:
-      assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD: assert u.src[1].arg==5
-
-  def test_where_on_gated_load_folds_swapped_branches(self):
-    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
-    ld = d0.index(ridx0, (ridx0<50).logical_not()).load()
-    w = (ridx0<50).where(5, ld)
-    uops = to_uops_list([w])
-    for u in uops:
-      assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD: assert u.src[1].arg==5
-
-  def test_where_in_store_becomes_gate(self):
-    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
-    idx = d0.index(ridx0)
-    ld = idx.load()
-    val = (ridx0<50).where(5, ld)
-    st = idx.store(val, ridx0)
-    uops = to_uops_list([st])
-    for u in uops:
-      assert u.op is not Ops.WHERE
-      if u.op is Ops.STORE: assert u.src[1].arg==5
-
-  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)
@@ -555,7 +512,7 @@ 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.index, (UOp.const(dtypes.index, 42),), "gidx0")
+      gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 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])
@@ -579,9 +536,9 @@ 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.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)
+      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)),))
      to_uops_list([ld1])

      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<64)),))
@@ -592,7 +549,7 @@ class TestUOpGraph(unittest.TestCase):
    glbl1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 1)
    glbl2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 2)
    idx = UOp.const(dtypes.int, 0)
-    ld0 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(UOp.invalid()),))
+    ld0 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(idx, UOp.const(dtypes.bool, False)),))
    ld1 = UOp(Ops.LOAD, dtypes.int, (glbl2.index(idx, UOp.const(dtypes.bool, True)),))
    uops = to_uops_list([UOp(Ops.STORE, dtypes.void, (glbl0.index(idx), ld1+ld0))])
    ld0 = uops[-1].src[-1]
@@ -605,7 +562,7 @@ class TestUOpGraph(unittest.TestCase):
    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
    st = UOp(Ops.STORE, dtypes.void, (smem.index(lidx), UOp.load(glbl0.index(lidx), dtype=dtypes.int)))
    barrier = UOp(Ops.BARRIER, dtypes.void, (st, ))
-    ld0 = UOp(Ops.LOAD, dtypes.int, (smem.index(UOp.invalid()), barrier))
+    ld0 = UOp(Ops.LOAD, dtypes.int, (smem.index(lidx+1, UOp.const(dtypes.bool, False)), barrier))
    ld1 = UOp(Ops.LOAD, dtypes.int, (smem.index(lidx+2, UOp.const(dtypes.bool, True)), barrier))
    uops = to_uops_list([UOp(Ops.STORE, dtypes.void, (glbl0.index(lidx), ld1+ld0))])

@@ -618,7 +575,7 @@ class TestUOpGraph(unittest.TestCase):
    idx0 = UOp.const(dtypes.int, 0)
    idx1 = UOp.const(dtypes.int, 0)
    val = UOp.const(dtypes.int, 42)
-    st0 = glbl.index(UOp.invalid()).store(val)
+    st0 = glbl.index(idx0, UOp.const(dtypes.bool, False)).store(val)
    st1 = glbl.index(idx0, UOp.const(dtypes.bool, True)).store(val)
    uops = to_uops_list([st0, st1])
    # only the second store happens
@@ -15,7 +15,6 @@ from tinygrad.codegen import full_rewrite
 from tinygrad.uop.symbolic import sym
 from tinygrad.device import is_dtype_supported
 from tinygrad.codegen.opt import Opt, OptOps
-from tinygrad.renderer.ptx import PTXRenderer

 def to_uops_list(u:list[UOp], opts=None, skip_check=False) -> list[UOp]: return full_rewrite(UOp.sink(*u), opts)

@@ -131,9 +130,9 @@ class TestFloatUOps(TestUOps):
 class TestNonFloatUOps(TestUOps):
  def test_add_int32(self): self._test_bop_fxn(Ops.ADD, lambda a,b: int(a)+int(b), (dtypes.int32, dtypes.int32))
  def test_mul_int32(self): self._test_bop_fxn(Ops.MUL, lambda a,b: int(a)*int(b), (dtypes.int32, dtypes.int32))
-  @unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "only ptx uses bitshifts")
+  @unittest.skipUnless(getenv("PTX"), "only ptx uses bitshifts")
  def test_shr_int32(self): self._test_bop_fxn(Ops.SHR, lambda a,b: int(a)>>int(b), (dtypes.int32, dtypes.int32), no_b_neg=True)
-  @unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "only ptx uses bitshifts")
+  @unittest.skipUnless(getenv("PTX"), "only ptx uses bitshifts")
  def test_shl_int32(self): self._test_bop_fxn(Ops.SHL, lambda a,b: int(a)<<int(b), (dtypes.int32, dtypes.int32), no_b_neg=True)
  def test_div_int32(self):
    self._test_bop_fxn(Ops.IDIV, lambda a,b: int(a/b), (dtypes.int32, dtypes.int32), no_b_zero=True)
@@ -371,7 +370,7 @@ class TestLocalAccess(unittest.TestCase):
    sres = uop(uops, Ops.LOAD, dtypes.int32, (smem.index(ofs),))
    self.assertEqual(_test_uops_result(dtypes.int32, uops, sres), 42)

-@unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "This only tests assembly backends")
+@unittest.skipUnless(getenv("PTX"), "This only tests assembly backends")
 class TestAssembly(unittest.TestCase):
  def test_bitshift_left(self):
    g1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
@@ -477,7 +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.expr)
+    self.assertEqual(list(var_vals)[0], a)

  def test_const_factor(self):
    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 8),), 'gidx0')
@@ -513,7 +512,7 @@ class TestUOpStr(unittest.TestCase):
    assert str(eval(str(vec))) == str(vec)

  def test_device_arg(self):
-    device = UOp(Ops.DEVICE, arg="CL")
+    device = UOp(Ops.DEVICE, arg="GPU")
    assert str(eval(str(device))) == str(device)

  def test_reduceop_arg(self):
@@ -8,7 +8,6 @@ from tinygrad.uop.ops import Ops, UOp
 from tinygrad.dtype import dtypes
 from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.device import Device
-from tinygrad.renderer.ptx import PTXRenderer

 def flops_mem(uops, ignore_indexing=False):
  est = Estimates.from_uops(uops, ignore_indexing)
@@ -159,7 +158,7 @@ class TestUOpsStats(unittest.TestCase):
    self.assertEqual(flops_mem(uops), flops_mem(uops_fma))

 N = 64
-@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "wrong in PTX") # maybe?
+@unittest.skipIf(getenv("PTX"), "wrong in PTX") # maybe?
 class TestStatsOptimized(unittest.TestCase):
  @classmethod
  def setUpClass(cls):
@@ -13,7 +13,7 @@ def time_tensor_numpy(out:Tensor):

 N = 4096
 class TestZeroCopy(unittest.TestCase):
-  @unittest.skipIf(Device.DEFAULT not in {"CPU", "METAL"}, "device isn't zero copy")
+  @unittest.skipIf(Device.DEFAULT not in {"CPU", "LLVM", "METAL"}, "device isn't zero copy")
  def test_zero_copy_from_default_to_cpu(self):
    demo = Tensor.rand(1).realize()
    t1 = time_tensor_numpy(demo)
@@ -2,19 +2,19 @@
 import unittest, os, subprocess, sys
 from tinygrad import Tensor
 from tinygrad.device import Device, Compiler
-from tinygrad.helpers import diskcache_get, diskcache_put, getenv, Context, WIN, CI
+from tinygrad.helpers import diskcache_get, diskcache_put, getenv, Context

 class TestDevice(unittest.TestCase):
  def test_canonicalize(self):
    self.assertEqual(Device.canonicalize(None), Device.DEFAULT)
    self.assertEqual(Device.canonicalize("CPU"), "CPU")
    self.assertEqual(Device.canonicalize("cpu"), "CPU")
-    self.assertEqual(Device.canonicalize("CL"), "CL")
-    self.assertEqual(Device.canonicalize("CL:0"), "CL")
-    self.assertEqual(Device.canonicalize("cl:0"), "CL")
-    self.assertEqual(Device.canonicalize("CL:1"), "CL:1")
-    self.assertEqual(Device.canonicalize("cl:1"), "CL:1")
-    self.assertEqual(Device.canonicalize("CL:2"), "CL:2")
+    self.assertEqual(Device.canonicalize("GPU"), "GPU")
+    self.assertEqual(Device.canonicalize("GPU:0"), "GPU")
+    self.assertEqual(Device.canonicalize("gpu:0"), "GPU")
+    self.assertEqual(Device.canonicalize("GPU:1"), "GPU:1")
+    self.assertEqual(Device.canonicalize("gpu:1"), "GPU:1")
+    self.assertEqual(Device.canonicalize("GPU:2"), "GPU:2")
    self.assertEqual(Device.canonicalize("disk:/dev/shm/test"), "DISK:/dev/shm/test")
    self.assertEqual(Device.canonicalize("disk:000.txt"), "DISK:000.txt")

@@ -28,51 +28,6 @@ class TestDevice(unittest.TestCase):
    self.assertEqual(Device.canonicalize(None), device)
    Device.DEFAULT = device

-  @unittest.skipIf(WIN and CI, "skipping windows test") # TODO: subproccess causes memory violation?
-  def test_env_overwrite_default_compiler(self):
-    expect_failure = "\ntry: assert Device[Device.DEFAULT].compiler is None;\nexcept RuntimeError: pass"
-
-    if Device.DEFAULT == "CPU":
-      from tinygrad.runtime.support.compiler_cpu import CPULLVMCompiler, ClangJITCompiler
-      try: _, _ = CPULLVMCompiler(), ClangJITCompiler()
-      except Exception as e: self.skipTest(f"skipping compiler test: not all compilers: {e}")
-
-      imports = "from tinygrad import Device; from tinygrad.runtime.support.compiler_cpu import CPULLVMCompiler, ClangJITCompiler"
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, CPULLVMCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "CPU", "CPU_LLVM": "1"})
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, ClangJITCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "CPU", "CPU_LLVM": "0"})
-      subprocess.run([f'python3 -c "{imports}; {expect_failure}"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "CPU", "CPU_CLANGJIT": "0", "CPU_LLVM": "0"})
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, CPULLVMCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "CPU", "CPU_CLANGJIT": "0"})
-      subprocess.run([f'python3 -c "{imports}; {expect_failure}"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "CPU", "CPU_CLANGJIT": "1", "CPU_LLVM": "1"})
-    elif Device.DEFAULT == "AMD":
-      from tinygrad.runtime.support.compiler_amd import HIPCompiler, AMDLLVMCompiler
-      try: _, _ = HIPCompiler(Device[Device.DEFAULT].arch), AMDLLVMCompiler(Device[Device.DEFAULT].arch)
-      except Exception as e: self.skipTest(f"skipping compiler test: not all compilers: {e}")
-
-      imports = "from tinygrad import Device; from tinygrad.runtime.support.compiler_amd import HIPCompiler, AMDLLVMCompiler"
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, AMDLLVMCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "AMD", "AMD_LLVM": "1"})
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, HIPCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "AMD", "AMD_LLVM": "0"})
-      subprocess.run([f'python3 -c "{imports}; assert isinstance(Device[Device.DEFAULT].compiler, HIPCompiler)"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "AMD", "AMD_HIP": "1"})
-      subprocess.run([f'python3 -c "{imports}; {expect_failure}"'],
-                        shell=True, check=True, env={**os.environ, "DEV": "AMD", "AMD_HIP": "1", "AMD_LLVM": "1"})
-    else: self.skipTest("only run on CPU/AMD")
-
-  def test_compiler_envvar(self):
-    d = Device[Device.DEFAULT]
-    dname = Device.DEFAULT.split(':')[0].upper()
-    assert d._get_compiler_envvar(type("Compiler", (), {})) == f"{dname}_COMPILER"
-    assert d._get_compiler_envvar(type("LLVMCompiler", (), {})) == f"{dname}_LLVM"
-    assert d._get_compiler_envvar(type("RandomCompiler", (), {})) == f"{dname}_RANDOM"
-    assert d._get_compiler_envvar(type(f"{dname}Compiler", (), {})) == f"{dname}_{dname}COMPILER" # do not repeat device name alone
-    assert d._get_compiler_envvar(type(f"{dname}LLVMCompiler", (), {})) == f"{dname}_LLVM" # do not repeat device name
-
 class MockCompiler(Compiler):
  def __init__(self, key): super().__init__(key)
  def compile(self, src) -> bytes: return src.encode()
@@ -101,7 +56,7 @@ class TestCompiler(unittest.TestCase):
 class TestRunAsModule(unittest.TestCase):
  def test_module_runs(self):
    p = subprocess.run([sys.executable, "-m", "tinygrad.device"],stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-      env={**os.environ, "DEBUG": "1"}, timeout=30,)
+      env={**os.environ, "DEBUG": "1"}, timeout=10,)
    out = (p.stdout + p.stderr).decode()
    self.assertEqual(p.returncode, 0, msg=out)
    self.assertIn("CPU", out) # for sanity check
@@ -318,7 +318,7 @@ class TestDiskTensor(unittest.TestCase):
    with open(temp('dt_bf16_disk_write_read_bf16'), "wb") as f: f.write(adat)

    t = Tensor.empty(5, dtype=dtypes.bfloat16, device=f"disk:{temp('dt_bf16_disk_write_read_bf16')}")
-    ct = t.to(Device.DEFAULT).cast(dtypes.float)
+    ct = t.llvm_bf16_cast(dtypes.float)
    assert ct.numpy().tolist() == [9984., -1, -1000, -9984, 20]

  def test_copy_from_disk(self):
@@ -6,6 +6,7 @@ from tinygrad.helpers import getenv, CI, DEBUG
 from hypothesis import given, settings, strategies as strat
 import numpy as np
 import torch
+import ml_dtypes

 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
 settings.load_profile("my_profile")
@@ -185,19 +186,15 @@ class TestHelpers(unittest.TestCase):

  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=True, allow_infinity=True))
  def test_truncate_fp8e4m3(self, x):
-    if math.isnan(x): np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), x)
-    elif math.isinf(x): np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), math.copysign(math.nan, x))
-    elif x > FP8E4M3_MAX: np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), FP8E4M3_MAX)
+    if x > FP8E4M3_MAX: np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), FP8E4M3_MAX)
    elif x < -FP8E4M3_MAX: np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), -FP8E4M3_MAX)
-    else: np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), torch.tensor(x, dtype=torch.float8_e4m3fn).float().item())
+    else: np.testing.assert_equal(truncate[dtypes.fp8e4m3](x), ml_dtypes.float8_e4m3fn(x))

  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=True, allow_infinity=True))
  def test_truncate_fp8e5m2(self, x):
-    if math.isnan(x): np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), x)
-    elif math.isinf(x): np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), x)
-    elif x > FP8E5M2_MAX: np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), FP8E5M2_MAX)
+    if x > FP8E5M2_MAX: np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), FP8E5M2_MAX)
    elif x < -FP8E5M2_MAX: np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), -FP8E5M2_MAX)
-    else: np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), torch.tensor(x, dtype=torch.float8_e5m2).float().item())
+    else: np.testing.assert_equal(truncate[dtypes.fp8e5m2](x), ml_dtypes.float8_e5m2(x))

 class TestTypeSpec(unittest.TestCase):
  def setUp(self):
@@ -377,7 +374,7 @@ class TestTypePromotion(unittest.TestCase):
    assert least_upper_dtype(dtypes.int32, dtypes.uint32) == dtypes.int64
    assert least_upper_dtype(dtypes.uint32, dtypes.int64) == dtypes.int64
    # similar to jax but we don't use weak type
-    assert least_upper_dtype(dtypes.int64, dtypes.uint64) == dtypes.fp8e4m3
+    assert least_upper_dtype(dtypes.int64, dtypes.uint64) == dtypes.float16
    assert least_upper_dtype(dtypes.float16, dtypes.float32) == dtypes.float32
    assert least_upper_dtype(dtypes.float32, dtypes.float64) == dtypes.float64

@@ -386,14 +383,6 @@ class TestTypePromotion(unittest.TestCase):
    assert least_upper_dtype(dtypes.float16, dtypes.int64) == dtypes.float16
    assert least_upper_dtype(dtypes.float16, dtypes.uint64) == dtypes.float16
    assert least_upper_dtype(dtypes.fp8e4m3, dtypes.fp8e5m2) == dtypes.half
-    assert least_upper_dtype(dtypes.fp8e4m3, dtypes.bfloat16) == dtypes.bfloat16
-    assert least_upper_dtype(dtypes.fp8e5m2, dtypes.bfloat16) == dtypes.bfloat16
-    assert least_upper_dtype(dtypes.fp8e4m3, dtypes.float16) == dtypes.float16
-    assert least_upper_dtype(dtypes.fp8e5m2, dtypes.float16) == dtypes.float16
-    assert least_upper_dtype(dtypes.fp8e4m3, dtypes.int64) == dtypes.fp8e4m3
-    assert least_upper_dtype(dtypes.fp8e4m3, dtypes.uint64) == dtypes.fp8e4m3
-    assert least_upper_dtype(dtypes.fp8e5m2, dtypes.int64) == dtypes.fp8e5m2
-    assert least_upper_dtype(dtypes.fp8e5m2, dtypes.uint64) == dtypes.fp8e5m2

 class TestAutoCastType(unittest.TestCase):
  def setUp(self):
@@ -450,10 +439,8 @@ class TestAutoCastType(unittest.TestCase):
    assert (Tensor([0, 1], dtype=dtypes.uint16)).sum().dtype == dtypes.uint32
    assert (Tensor([0, 1], dtype=dtypes.uint32)).sum().dtype == dtypes.uint32
    assert (Tensor([0, 1], dtype=dtypes.uint64)).sum().dtype == dtypes.uint64
-    assert (Tensor([0, 1], dtype=dtypes.fp8e4m3)).sum().dtype == dtypes.fp8e4m3
-    assert (Tensor([0, 1], dtype=dtypes.fp8e5m2)).sum().dtype == dtypes.fp8e5m2
    assert (Tensor([0, 1], dtype=dtypes.float16)).sum().dtype == dtypes.float16
-    assert (Tensor([0, 1], dtype=dtypes.bfloat16)).sum().dtype == dtypes.bfloat16
+    #assert (Tensor([0, 1], dtype=dtypes.bfloat16)).sum().dtype == dtypes.bfloat16
    assert (Tensor([0, 1], dtype=dtypes.float32)).sum().dtype == dtypes.float32
    assert (Tensor([0, 1], dtype=dtypes.float64)).sum().dtype == dtypes.float64

@@ -484,10 +471,8 @@ class TestAutoCastType(unittest.TestCase):
    assert (Tensor([0, 1], dtype=dtypes.uint16)).mean().dtype == dtypes.float32
    assert (Tensor([0, 1], dtype=dtypes.uint32)).mean().dtype == dtypes.float32
    assert (Tensor([0, 1], dtype=dtypes.uint64)).mean().dtype == dtypes.float32
-    assert (Tensor([0, 1], dtype=dtypes.fp8e4m3)).mean().dtype == dtypes.fp8e4m3
-    assert (Tensor([0, 1], dtype=dtypes.fp8e5m2)).mean().dtype == dtypes.fp8e5m2
    assert (Tensor([0, 1], dtype=dtypes.float16)).mean().dtype == dtypes.float16
-    assert (Tensor([0, 1], dtype=dtypes.bfloat16)).mean().dtype == dtypes.bfloat16
+    #assert (Tensor([0, 1], dtype=dtypes.bfloat16)).mean().dtype == dtypes.bfloat16
    assert (Tensor([0, 1], dtype=dtypes.float32)).mean().dtype == dtypes.float32
    assert (Tensor([0, 1], dtype=dtypes.float64)).mean().dtype == dtypes.float64

@@ -501,10 +486,8 @@ class TestAutoCastType(unittest.TestCase):
    assert (Tensor([0, 1], dtype=dtypes.uint16)).cumsum(0).dtype == dtypes.uint32
    assert (Tensor([0, 1], dtype=dtypes.uint32)).cumsum(0).dtype == dtypes.uint32
    assert (Tensor([0, 1], dtype=dtypes.uint64)).cumsum(0).dtype == dtypes.uint64
-    assert (Tensor([0, 1], dtype=dtypes.fp8e4m3)).cumsum(0).dtype == dtypes.fp8e4m3
-    assert (Tensor([0, 1], dtype=dtypes.fp8e5m2)).cumsum(0).dtype == dtypes.fp8e5m2
    assert (Tensor([0, 1], dtype=dtypes.float16)).cumsum(0).dtype == dtypes.float16
-    assert (Tensor([0, 1], dtype=dtypes.bfloat16)).cumsum(0).dtype == dtypes.bfloat16
+    #assert (Tensor([0, 1], dtype=dtypes.bfloat16)).cumsum(0).dtype == dtypes.bfloat16
    assert (Tensor([0, 1], dtype=dtypes.float32)).cumsum(0).dtype == dtypes.float32
    assert (Tensor([0, 1], dtype=dtypes.float64)).cumsum(0).dtype == dtypes.float64

@@ -67,7 +67,7 @@ class TestGGUF(unittest.TestCase):
      return np.array([E] + packed, dtype=np.uint8)

    def decode(code, E):
-      sign = -1.0 if (code & 0b1000) else 1.0
+      sign = -1.0 if code * 0b1000 else 1.0
      exp = (code >> 1) & 0b11
      mant = code & 0b1
      val = (1.0 + 0.5 * mant) * np.exp2(exp - 1) if exp else 0.5 * mant
@@ -83,8 +83,7 @@ class TestGGUF(unittest.TestCase):
      expected.extend(decode(c, E) for c in codes)
    tensor = Tensor(np.concatenate(blocks))
    out = ggml_data_to_tensor(tensor, len(expected), MXFP4)
-    # TODO: should this be exact equal? somehow failed on CI
-    np.testing.assert_allclose(out.numpy(), expected, atol=0.0, rtol=1e-6)
+    self.assertListEqual(out.numpy().tolist(), np.array(expected, dtype=np.float32).tolist())

  def test_expected_failure_unknown_type(self):
    with self.assertRaises(ValueError):
@@ -97,22 +97,19 @@ class TestFoldingAndReduction(unittest.TestCase):

 class TestModuloAndDivisionFolding(unittest.TestCase):
  def test_full_graph_rewrite_modulo_folding_with_define_var(self):
-    # index dtype because div-mod rules only work on index
-    x_var_uop = UOp.variable('x', 0, 100).cast(dtypes.index)
+    x_var_uop = UOp.variable('x', 0, 100)
    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):
-    # index dtype because div-mod rules only work on index
-    n_var_uop = UOp.variable('n', 1, 1000).cast(dtypes.index)
+    n_var_uop = UOp.variable('n', 1, 1000)
    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):
-    # index dtype because div-mod rules only work on index
-    k_var_uop = UOp.variable('k', 0, 50).cast(dtypes.index)
+    k_var_uop = UOp.variable('k', 0, 50)
    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)
@@ -129,9 +126,8 @@ 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.cast(dtypes.index) % 10).render(simplify=False), x_var_uop.render(simplify=False))
+    self.assertIs(apply_rewrite(x_var_uop % 10), x_var_uop)

  def test_full_graph_rewrite_division_with_remainder(self):
    x_var_uop = UOp.variable('x', 7, 9)
@@ -181,7 +181,7 @@ class TestIndexing(unittest.TestCase):
    # self.assertRaises(TypeError, delitem)

  # TODO: LLVM is quite fast, why are other compiled backends slow?
-  @unittest.skipIf(CI and Device.DEFAULT in ["CPU", "CL", "METAL", "NV", "AMD"], "slow")
+  @unittest.skipIf(CI and Device.DEFAULT in ["CPU", "GPU", "METAL", "NV", "AMD"], "slow")
  def test_advancedindex(self):
    # integer array indexing

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
geohot	652e395efd	not a part of regen	2025-09-05 15:41:34 -07:00
geohot	13e4cbd508	fix torch frontend	2025-09-05 15:39:52 -07:00
geohot	f6b661eb3c	imports	2025-09-05 15:34:49 -07:00
George HotzandGitHub	3e8dabbffc	Merge branch 'master' into delete_kernel_py	2025-09-05 15:31:39 -07:00
geohot	5bac311df4	don't test search	2025-09-05 15:15:05 -07:00
George HotzandGitHub	9a6d7f9a1e	Merge branch 'master' into delete_kernel_py	2025-09-05 15:13:36 -07:00
geohot	8f254d6c48	rip and tear	2025-09-05 14:56:58 -07:00
geohot	9d07e6ae03	delete that file	2025-09-05 14:53:48 -07:00
geohot	645b3c5ae3	delete kernel.py	2025-09-05 14:51:29 -07:00