fix that

simpler
2025-10-23 10:24:25 +08:00 · 2025-10-23 10:21:27 +08:00
189 changed files with 2887 additions and 5919 deletions
@@ -1,3 +0,0 @@
-[run]
-source = tinygrad
-branch = True
@@ -2,7 +2,7 @@ name: Autogen
 env:
  # increment this when downloads substantially change to avoid the internet
  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
+  PYTHON_CACHE_VERSION: '3'
  APT_CACHE_VERSION: '1'
  BUILD_CACHE_VERSION: '1'
  CAPTURE_PROCESS_REPLAY: 1
@@ -199,7 +199,7 @@ jobs:
    - name: Test speed vs torch
      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
    - name: Test speed vs theoretical
-      run: NV=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+      run: NV=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
    - name: Test benchmark allreduce
      run: NV=1 python test/external/external_benchmark_multitensor_allreduce.py
    - name: Test tensor cores
@@ -211,7 +211,6 @@ jobs:
        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
-        CUDA=1 SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_fp8.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
    - name: Run Tensor Core GEMM (NV)
@@ -239,8 +238,6 @@ jobs:
      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
      run: BENCHMARK_LOG=llama3_beam_4gpu NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
-    - name: Run quantized LLaMA3
-      run: BENCHMARK_LOG=llama3_fp8 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --temperature 0 --benchmark --quantize fp8 | tee llama3_fp8.txt
    # - name: Run LLaMA-3 8B on 6 GPUs
    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
    # - name: Run LLaMA-2 70B
@@ -274,7 +271,6 @@ jobs:
          llama3_beam.txt
          llama3_four_gpu.txt
          llama3_six_gpu.txt
-          llama3_fp8.txt
          llama_2_70B.txt
          mixtral.txt
          gpt2_unjitted.txt
@@ -409,7 +405,7 @@ jobs:
    #    python3 -c "import torch; print(torch.__version__)"
    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
    - name: Test speed vs theoretical
-      run: AMD=1 IGNORE_BEAM_CACHE=1 DISABLE_COMPILER_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+      run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
    - name: Test tensor cores
      run: |
        AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
@@ -527,7 +523,7 @@ jobs:
    - name: Run 10 CIFAR training steps
      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=390 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    # - name: Run 10 CIFAR training steps w BF16
    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    # TODO: too slow
@@ -630,17 +626,17 @@ jobs:
    - name: openpilot compile3 0.9.9 dmonitoring
      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 0.10.0 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
    - name: openpilot compile3 0.10.0 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=12 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 0.10.1 driving_vision
      # TODO: ASSERT_MIN_STEP_TIME=17
-      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=21 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=25 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
    - name: openpilot compile3 0.10.1 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
    - name: openpilot compile3 0.10.1 dmonitoring
      # TODO: ASSERT_MIN_STEP_TIME=10
-      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=12 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=13 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: benchmark MobileNetV2 on DSP
      run: |
        # generate quantized weights
@@ -27,4 +27,4 @@ jobs:
      run: |
        rm "~/.cache/tinygrad/cache_mlperf.db" || true
        BENCHMARK_LOG=mlpert_train_resnet LOGMLPERF=0 CACHEDB="~/.cache/tinygrad/cache_mlperf.db" examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
-        rm "~/.cache/tinygrad/cache_mlperf.db"
+        rm "~/.cache/tinygrad/cache_mlperf.db"
@@ -2,7 +2,7 @@ name: Unit Tests
 env:
  # increment this when downloads substantially change to avoid the internet
  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '4'
+  PYTHON_CACHE_VERSION: '3'
  APT_CACHE_VERSION: '1'
  BUILD_CACHE_VERSION: '1'
  CAPTURE_PROCESS_REPLAY: 1
@@ -230,7 +230,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: linting-only
-        python-version: '3.11'
+        python-version: '3.10'
        deps: linting
    - name: Lint bad-indentation and trailing-whitespace with pylint
      run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string='  ' --recursive=y .
@@ -243,9 +243,8 @@ jobs:
      run: |
        python -m mypy --strict-equality --lineprecision-report .
        cat lineprecision.txt
-    # broken because of UPatAny
-    #- name: Run TYPED=1
-    #  run: TYPED=1 python -c "import tinygrad"
+    - name: Run TYPED=1
+      run: TYPED=1 python -c "import tinygrad"

  unittest:
    name: Unit Tests
@@ -265,6 +264,8 @@ jobs:
      run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
    - name: Run unit tests
      run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Check SPEC=1
+      run: SPEC=1 python3 test/test_tiny.py
    - name: Run targetted tests on NULL backend
      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
    # TODO: too slow
@@ -290,27 +291,8 @@ jobs:
        python extra/optimization/extract_dataset.py
        gzip -c /tmp/sops > extra/datasets/sops.gz
        #DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
-    - name: Repo line count < 18500 lines
-      run: MAX_LINE_COUNT=18500 python sz.py
-
-  spec:
-    strategy:
-      fail-fast: false
-      matrix:
-        group: [1, 2]
-    name: SPEC=2 (${{ matrix.group }})
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: spec-unit
-        deps: testing_unit
-    - name: Test SPEC=2
-      run: IGNORE_OOB=0 SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
+    - name: Repo line count < 18000 lines
+      run: MAX_LINE_COUNT=18000 python sz.py

  fuzzing:
    name: Fuzzing
@@ -369,7 +351,7 @@ jobs:
      - 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 test/test_optim.py -k "not muon"
+        run: CL=1 FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py
      - name: Upload artifact
        uses: actions/upload-artifact@v4
        with:
@@ -540,11 +522,11 @@ 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
+      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"
    - name: Test LLVM=1 DEVECTORIZE=0 for model
      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
    - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py
+      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"

  testdsp:
    name: Linux (DSP)
@@ -645,7 +627,6 @@ jobs:
        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 TestOps.test_add with SQTT
        run: |
-          VIZ=1 PMC=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
          VIZ=1 SQTT=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
          extra/sqtt/rgptool.py create "/tmp/profile.pkl.$USER" -o /tmp/gpu0.rgp
      - name: Run process replay tests
@@ -63,5 +63,3 @@ profile_stats
 *.log
 target
 .mypy_cache
-mutants
-.mutmut-cache
@@ -28,7 +28,7 @@ repos:
        pass_filenames: false
      - id: tests
        name: subset of tests
-        entry: env OMP_NUM_THREADS=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
+        entry: env PYTHONPATH="." python3 -m pytest -n=8 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
        language: system
        always_run: true
-        pass_filenames: false
+        pass_filenames: false
@@ -31,9 +31,7 @@ $(for p in "$@"; do echo "  $p,"; done)
 ]
 def _try_dlopen_$name():
  library = ctypes.util.find_library("$name")
-  if library:
-    try: return ctypes.CDLL(library)
-    except OSError: pass
+  if library: return ctypes.CDLL(library)
  for candidate in PATHS_TO_TRY:
    try: return ctypes.CDLL(candidate)
    except OSError: pass
@@ -188,7 +186,6 @@ nv_status_codes = {}
    extra/nv_gpu_driver/g_rpc-message-header.h \
    extra/nv_gpu_driver/gsp_static_config.h \
    extra/nv_gpu_driver/vbios.h \
-    extra/nv_gpu_driver/pci_exp_table.h \
    --clang-args="-DRPC_MESSAGE_STRUCTURES -DRPC_STRUCTURES -include $NVKERN_SRC/src/common/sdk/nvidia/inc/nvtypes.h -I$NVKERN_SRC/src/nvidia/generated -I$NVKERN_SRC/src/common/inc -I$NVKERN_SRC/src/nvidia/inc -I$NVKERN_SRC/src/nvidia/interface/ -I$NVKERN_SRC/src/nvidia/inc/kernel -I$NVKERN_SRC/src/nvidia/inc/libraries -I$NVKERN_SRC/src/nvidia/arch/nvalloc/common/inc -I$NVKERN_SRC/kernel-open/nvidia-uvm -I$NVKERN_SRC/kernel-open/common/inc -I$NVKERN_SRC/src/common/sdk/nvidia/inc -I$NVKERN_SRC/src/nvidia/arch/nvalloc/unix/include -I$NVKERN_SRC/src/common/sdk/nvidia/inc/ctrl" \
    -o $BASE/nv/nv.py

@@ -435,13 +432,11 @@ generate_sqtt() {
    $ROCPROF_SRC/include/rocprof_trace_decoder.h \
    $ROCPROF_SRC/include/trace_decoder_instrument.h \
    $ROCPROF_SRC/include/trace_decoder_types.h \
-    -o $BASE/rocprof.py
-  fixup $BASE/rocprof.py
-  sed -i '1s/^/# pylint: skip-file\n/' $BASE/rocprof.py
-  sed -i "s/import ctypes/import ctypes, ctypes.util/g" $BASE/rocprof.py
-  patch_dlopen $BASE/rocprof.py rocprof-trace-decoder "'/usr/local/lib/librocprof-trace-decoder.so'" "'/usr/local/lib/librocprof-trace-decoder.dylib'"
-  sed -i "s/def _try_dlopen_rocprof-trace-decoder():/def _try_dlopen_rocprof_trace_decoder():/g" $BASE/rocprof.py
-  sed -i "s|FunctionFactoryStub()|_try_dlopen_rocprof_trace_decoder()|g" $BASE/rocprof.py
+    -o extra/sqtt/rocprof/rocprof.py
+  fixup extra/sqtt/rocprof/rocprof.py
+  sed -i '1s/^/# pylint: skip-file\n/' extra/sqtt/rocprof/rocprof.py
+  sed -i "s/import ctypes/import ctypes, ctypes.util/g" extra/sqtt/rocprof/rocprof.py
+  sed -i "s|FunctionFactoryStub()|ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder'))|g" extra/sqtt/rocprof/rocprof.py
 }

 generate_webgpu() {
@@ -536,7 +531,7 @@ generate_mesa() {
  sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py
  sed -i "s/('\(\w\+\)', pipe_shader_type, 8)/('\1', ctypes.c_ubyte)/" $BASE/mesa.py
  sed -i "s/\([0-9]\+\)()/\1/" $BASE/mesa.py
-  sed -i '/struct_nir_builder._pack_ = 1 # source:False/d' "$BASE/mesa.py"
+  sed -i "s/\(struct_nir_builder._pack_\) = 1/\1 = 0/" $BASE/mesa.py
  python3 -c "import tinygrad.runtime.autogen.mesa"
 }

@@ -550,6 +545,7 @@ elif [ "$1" == "kfd" ]; then generate_kfd
 elif [ "$1" == "nv" ]; then generate_nv
 elif [ "$1" == "amd" ]; then generate_amd
 elif [ "$1" == "am" ]; then generate_am
+elif [ "$1" == "nvdrv" ]; then generate_nvdrv
 elif [ "$1" == "sqtt" ]; then generate_sqtt
 elif [ "$1" == "qcom" ]; then generate_qcom
 elif [ "$1" == "io_uring" ]; then generate_io_uring
@@ -53,7 +53,9 @@ b = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struc
 idx = UOp.const(dtypes.index, 0)
 buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
 buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
-alu = buf_1.index(idx) + buf_2.index(idx)
+ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1.index(idx),))
+ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2.index(idx),))
+alu = ld_1 + ld_2
 output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
 st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.index(idx), alu))
 s = UOp(Ops.SINK, dtypes.void, (st_0,))
@@ -0,0 +1,109 @@
+# Kernel Creation
+
+Tinygrad lazily builds up a graph of Tensor operations. The Tensor graph includes a mix of:
+
+- Buffer and Assignment Ops: `BUFFER`, `BUFFER_VIEW`, `COPY`, `ASSIGN`
+- Movement Ops: `RESHAPE`, `EXPAND`, `PERMUTE`, `PAD`, `SHRINK`, `FLIP`
+- Compute Ops: `ADD`, `MUL`, `REDUCE_AXIS`, ...
+
+`Tensor.kernelize` creates the kernels and buffers needed to realize the output Tensor(s).
+
+## Kernelize flow
+
+Let's see how a multiply add Tensor graph becomes a fused elementwise kernel.
+
+```py
+# initialize 3 input buffers on the device
+a = Tensor([1]).realize()
+b = Tensor([2]).realize()
+c = Tensor([3]).realize()
+
+# create the Tensor graph
+mul = a*b
+out = mul+c
+
+print(mul) # <Tensor <UOp METAL (1,) int (<Ops.MUL: 48>, None)> on METAL with grad None>
+print(out) # <Tensor <UOp METAL (1,) int (<Ops.ADD: 52>, None)> on METAL with grad None>
+
+out.kernelize()
+
+print(mul) # <Tensor <UOp METAL (1,) int (<Ops.MUL: 48>, None)> on METAL with grad None>
+print(out) # <Tensor <UOp METAL (1,) int (<Ops.ASSIGN: 66>, None)> on METAL with grad None>
+```
+
+The multiply Tensor stays the same because it is fused. The output Tensor's UOp becomes a new ASSIGN UOp:
+
+```py
+print(out.uop)
+```
+
+The first source is the output BUFFER:
+
+```
+UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
+  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),
+  UOp(Ops.UNIQUE, dtypes.void, arg=6, src=()),))
+```
+
+And the second source is the KERNEL and its 4 buffer edges (output_buffer, a, b, c):
+
+```
+UOp(Ops.KERNEL, dtypes.void, arg=<Kernel 12 SINK(<Ops.STORE: 45>,) (__add__, __mul__)>, src=(
+  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
+    x1:=UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),
+    UOp(Ops.UNIQUE, dtypes.void, arg=6, src=()),)),
+  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
+     x1,
+    UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),)),
+  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
+     x1,
+    UOp(Ops.UNIQUE, dtypes.void, arg=3, src=()),)),
+  UOp(Ops.BUFFER, dtypes.int, arg=1, src=(
+     x1,
+    UOp(Ops.UNIQUE, dtypes.void, arg=5, src=()),)),))
+```
+
+KERNEL describes the compute AST, metadata and memory dependencies.
+
+BUFFER holds a reference to the device memory where the output will be stored.
+
+Once a Tensor is kernelized, all children will LOAD its BUFFER, instead of fusing it:
+
+```py
+child = out+2
+child.kernelize()
+print(child.uop.src[1].arg.ast)
+```
+
+```
+UOp(Ops.SINK, dtypes.void, arg=None, src=(
+  UOp(Ops.STORE, dtypes.void, arg=None, src=(
+    UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=0, src=()),
+    x2:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1,), strides=(0,), offset=0, mask=None, contiguous=True),)), src=()),
+    UOp(Ops.ADD, dtypes.int, arg=None, src=(
+      UOp(Ops.LOAD, dtypes.int, arg=None, src=(
+        UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=()),
+         x2,)),
+      UOp(Ops.CONST, dtypes.int, arg=2, src=(
+         x2,)),)),)),))
+```
+
+`Tensor.realize` will execute the kernels and write outputs to memory:
+
+```py
+Tensor.realize(out)
+print(out)        # <Tensor <UOp METAL (1,) int (<Ops.BUFFER: 23>, <buf real:True device:METAL size:1 dtype:dtypes.int offset:0>)> on METAL with grad None>
+print(out.item()) # 5
+```
+
+<hr />
+
+**Summary**
+
+- The large Tensor graph is built from a mix of data, compute and movement Ops.
+
+- `Tensor.kernelize` splits the Tensor graph into data (BUFFER), compute (KERNEL) and links dependencies with ASSIGN.
+
+- `Tensor.realize` executes KERNELs on device and replaces the Tensor graph with just a BUFFER.
+
+- Kernelize can be called multiple times on a Tensor. This allows for incrementally building the kernel fusion layout of a large Tensor graph, without having to call `realize` or `schedule`.
@@ -41,7 +41,7 @@ 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
 FLOAT16             | [1]        | use float16 for images instead of float32
-HCQ_VISIBLE_DEVICES | [list[int]]| restricts the HCQ devices that are available. The format is a comma-separated list of identifiers (indexing starts with 0).
+VISIBLE_DEVICES     | [list[int]]| restricts the NV/AMD devices that are available. The format is a comma-separated list of identifiers (indexing starts with 0).
 JIT                 | [0-2]      | 0=disabled, 1=[jit enabled](quickstart.md#jit) (default), 2=jit enabled, but graphs are disabled
 VIZ                 | [1]        | 0=disabled, 1=[viz enabled](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/viz)
 ALLOW_TF32          | [1]        | enable TensorFloat-32 tensor cores on Ampere or newer GPUs.
@@ -145,41 +145,6 @@ def NF4Linear(block_size):
      return new_state_dict
  return _NF4Linear

-def quantize_to_fp8(x: Tensor, dtype=dtypes.fp8e4m3):
-  fp8_min = -448.0 if dtype == dtypes.fp8e4m3 else -57344.0
-  fp8_max = 448.0 if dtype == dtypes.fp8e4m3 else 57344.0
-  scale = fp8_max / x.abs().max()
-  x_scl_sat = (x * scale).clamp(fp8_min, fp8_max)
-  return x_scl_sat.cast(dtype), scale.float().reciprocal()
-
-class FP8Linear:
-  def __init__(self, in_features, out_features, bias=True):
-    self.weight = Tensor.empty(out_features, in_features, dtype=dtypes.fp8e4m3)
-    self.bias = Tensor.empty(out_features, dtype=dtypes.float16) if bias else None
-    self.weight_scale = Tensor.empty((), dtype=dtypes.float16)
-
-  def __call__(self, x:Tensor):
-    y = x.dot(self.weight.T.cast(dtypes.float32)) * self.weight_scale
-    if self.bias is not None: y = y + self.bias.cast(y.dtype)
-    return y.cast(x.dtype)
-
-  @staticmethod
-  def quantize(tensors, device, scale_dtype=dtypes.float16, quantize_embeds=False):
-    assert not quantize_embeds
-    new_tensors = {}
-    for name,v in tensors.items():
-      if "feed_forward" in name or "attention.w" in name:
-        assert "weight" in name, name
-        fp8_weight, scale = quantize_to_fp8(v)
-        new_tensors[name] = fp8_weight
-        new_tensors[name.replace('weight', 'weight_scale')] = scale.cast(scale_dtype)
-        if isinstance(device, tuple):
-          new_tensors[name].shard_(device, axis=-1)
-          new_tensors[name.replace('weight', 'weight_scale')].shard_(device, axis=None)
-      else:
-        new_tensors[name] = v
-    return new_tensors
-
 MODEL_PARAMS = {
  "1B": {
    "args": {"dim": 2048, "n_heads": 32, "n_kv_heads": 8, "n_layers": 16, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192},
@@ -202,7 +167,6 @@ def build_transformer(model_path: Path, model_size="8B", quantize=None, scale_dt
  # build model
  if quantize == "int8": linear, embedding, quantize_embeds = Int8Linear, Int8Embedding, True
  elif quantize == "nf4": linear, embedding, quantize_embeds = NF4Linear(64), nn.Embedding, False
-  elif quantize == "fp8": linear, embedding, quantize_embeds = FP8Linear, nn.Embedding, False
  else: linear, embedding, quantize_embeds = nn.Linear, nn.Embedding, False
  model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, embedding=embedding, max_context=max_context, jit=True)

@@ -278,7 +242,7 @@ if __name__ == "__main__":
  parser.add_argument("--model", type=Path, help="Model path")
  parser.add_argument("--size", choices=["1B", "8B", "70B", "405B"], default="1B", help="Model size")
  parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
-  parser.add_argument("--quantize", choices=["int8", "nf4", "float16", "fp8"], help="Quantization method")
+  parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
  parser.add_argument("--no_api", action="store_true", help="Disable the api and run a cli test interface")
  parser.add_argument("--host", type=str, default="0.0.0.0", help="Web server bind address")
  parser.add_argument("--port", type=int, default=7776, help="Web server port")
@@ -15,7 +15,7 @@ export IGNORE_JIT_FIRST_BEAM=1
 export BASEDIR="/raid/datasets/wiki"

 # pip install -e ".[mlperf]"
-export LOGMLPERF=${LOGMLPERF:-1}
+export LOGMLPERF=1

 export SEED=$RANDOM
 DATETIME=$(date "+%m%d%H%M")
@@ -4,6 +4,8 @@ import numpy as np
 from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters, Device, dtypes
 from tinygrad.helpers import DEBUG, getenv
 from tinygrad.engine.realize import CompiledRunner
+
+import onnx
 from tinygrad.nn.onnx import OnnxRunner

 OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
@@ -38,7 +40,7 @@ def compile(onnx_file):
  np.testing.assert_equal(test_val, ret, "JIT run failed")
  print("jit run validated")

-  # check gated read_image usage
+  # checks from compile2
  kernel_count = 0
  read_image_count = 0
  gated_read_image_count = 0
@@ -94,7 +96,6 @@ def test_vs_compile(run, inputs, test_val=None):
  return val

 def test_vs_onnx(new_inputs, test_val, onnx_file, tol):
-  import onnx
  import onnxruntime as ort
  
  onnx_inputs = {k:v.numpy() for k,v in new_inputs.items()}
@@ -3,7 +3,7 @@
 import sys, base64, multiprocessing, itertools, collections
 from typing import Optional, Union, Literal, List

-from tinygrad import Tensor, TinyJit, Variable, nn, dtypes
+from tinygrad import Tensor, TinyJit, Variable, nn
 from tinygrad.nn.state import torch_load, load_state_dict
 from tinygrad.helpers import getenv, fetch

@@ -244,16 +244,15 @@ def transcribe_waveform(model: Whisper, enc, waveforms, truncate=False):

  log_spec = prep_audio(waveforms, model.batch_size, truncate)
  nsample = model.decoder.max_tokens_to_sample
-  nctx = model.decoder.max_self_attn_cache_len

  def inferloop(ctx: Union[np.ndarray, List[np.ndarray]], encoded_audio):
    pos, next_tokens = 0, ctx
-    for i in range(nsample):
-      next_tokens = model.decoder(Tensor(next_tokens, dtype=dtypes.int32), pos, encoded_audio)[:, -1].argmax(axis=-1).numpy().astype(np.int32).reshape(-1, 1)
+    for i in range((nsample-len(start_tokens))*2):
+      next_tokens = model.decoder(Tensor(next_tokens), pos, encoded_audio)[:, -1].argmax(axis=-1).numpy().astype(np.int32).reshape(-1, 1)
      next_tokens[ctx[:, -1] == eot] = eot
      ctx = np.concatenate((ctx, next_tokens), axis=1)
      pos = ctx.shape[-1] - 1
-      if (next_tokens == eot).all() or pos == nctx: break
+      if (next_tokens == eot).all(): break
    return ctx

  def gettexttoks(line): return [tok for tok in line if tok < eot or tok > enc._special_tokens["<|notimestamps|>"]][-nsample+len(start_tokens):]
@@ -1,4 +0,0 @@
-# source extra/cl_android.sh
-export LD_LIBRARY_PATH=/data/data/com.termux/files/usr/lib:/system/vendor/lib64
-export LD_PRELOAD=/system/vendor/lib64/libOpenCL.so
-
@@ -1,168 +1,353 @@
 from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
-from tinygrad.uop.ops import UOp, KernelInfo, sint, AxisType
-from tinygrad.engine.realize import ExecItem, get_runner
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite, AxisType, PatternMatcher, UPat
+from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.dtype import AddrSpace
-from tinygrad.helpers import getenv
+from tinygrad.helpers import getenv, colored, prod, unwrap
+from tinygrad.shape.shapetracker import ShapeTracker, View
+from tinygrad.shape.view import strides_for_shape
+from tinygrad.codegen.opt.kernel import axis_colors, Opt, OptOps
+from tinygrad.codegen.opt.swizzler import merge_views, view_left
+
+def to_colored(full_shape, axis_types): return '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])

 N = 4096
-M = K = N
 run_count = 5

-# ---------------------------
-# launch/config constants
-# ---------------------------
+BN = 128
+BM = 128
+BK = 8

-WARP_SIZE = 32
+TN = 4
+TM = 4

-# Threadblock tile sizes (block-level tile of C that a block computes)
-BLOCK_N = 128   # columns of C (N-dim) per block
-BLOCK_M = 128   # rows of C (M-dim) per block
-BLOCK_K = 8     # K-slice per block iteration
+# NOTE: this is from testgrad
+# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
+# src->r->view  -->   src->view->r
+def swizzle_reduceop(src:UOp, r:UOp, view:UOp):
+  if r.tag is not None: return None
+  # confirm the input is in order
+  # TODO: replace this with a UOp that allows for nothing else then remove this
+  permute = tuple(i for i in range(len(src.shape)) if i not in r.axis_arg)+r.axis_arg
+  assert permute == tuple(range(len(permute))), f"reduce axis must already be in order, {permute} isn't"

-# Register tile sizes (per-thread accumulator tile of C)
-TN = 4     # columns per thread
-TM = 4     # rows per thread
+  # append the reduce shape to each of the views
+  prshape = prod(rshape:=src.shape[-len(r.axis_arg):])
+  rstrides = strides_for_shape(rshape)
+  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+rstrides, v.offset*prshape,
+                    v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]

-is_kernel5 = getenv("K5", 0)
-THREADS_PER_BLOCK = 128 if is_kernel5 else 256
-assert THREADS_PER_BLOCK % BLOCK_N == 0, "THREADS_PER_BLOCK must be divisible by BLOCK_N"
-assert THREADS_PER_BLOCK % BLOCK_K == 0, "THREADS_PER_BLOCK must be divisible by BLOCK_K"
-assert (BLOCK_N * BLOCK_K) % THREADS_PER_BLOCK == 0
-assert (BLOCK_M * BLOCK_K) % THREADS_PER_BLOCK == 0
+  # no reshape required with shrinking REDUCE_AXIS
+  return UOp(Ops.REDUCE_AXIS, r.dtype, (src.view(ShapeTracker(tuple(nv))),),
+             (r.arg[0], tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))))

-WARPS_PER_BLOCK = THREADS_PER_BLOCK // WARP_SIZE
-WAVE_TILE_N = 128 if is_kernel5 else 64
-WAVE_TILE_M = BLOCK_N * BLOCK_M // WARPS_PER_BLOCK // WAVE_TILE_N
-assert BLOCK_N % WAVE_TILE_N == 0, "BN must be a multiple of WN"
-assert BLOCK_M % WAVE_TILE_M == 0, "BM must be a multiple of WM"
-WAVES_IN_BLOCK_X = BLOCK_N // WAVE_TILE_N
-WAVES_IN_BLOCK_Y = BLOCK_M // WAVE_TILE_M
-assert WAVES_IN_BLOCK_X * WAVES_IN_BLOCK_Y == WARPS_PER_BLOCK, "wave grid must match warps/block"
+pm = PatternMatcher([
+  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
+])

-LANES_PER_WAVE_X = 8
-LANES_PER_WAVE_Y = 4
-ITERS_PER_WAVE_N = WAVE_TILE_N // (LANES_PER_WAVE_X * TN)
-ITERS_PER_WAVE_M = WAVE_TILE_M // (LANES_PER_WAVE_Y * TM)
-assert WAVE_TILE_N % (LANES_PER_WAVE_X * TN) == 0, "WAVE_TILE_N must be divisible by LANES_PER_WAVE_X*TN"
-assert WAVE_TILE_M % (LANES_PER_WAVE_Y * TM) == 0, "WAVE_TILE_M must be divisible by LANES_PER_WAVE_Y*TM"
+def rangeify_kernel3():
+  a = Tensor.empty(N,N)
+  b = Tensor.empty(N,N)
+  c = a@b
+  #c = c.reshape((32,2,16,4,32,2,16,4)).contiguous()
+  sink = c.schedule()[-1].ast
+  #print(sink)

-def rngs_for_shape(shape:tuple[sint, ...], rng:int, axis_type=AxisType.LOOP): return [UOp.range(s, rng+i, axis_type) for i,s in enumerate(shape)]
-def copy(dest:UOp, src:UOp, rng:int, set=False, upcast=False):
-  assert dest.shape == src.shape
-  rngs = rngs_for_shape(src.shape, rng, AxisType.UPCAST if upcast else AxisType.LOOP)
-  copy = dest[*rngs].store(src[*rngs]).end(*rngs)
-  return dest.after(copy) if set else copy
+  opts  = [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.UPCAST, 0, 2)]
+  opts += [Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.LOCAL, 1, 16), Opt(OptOps.UPCAST, 1, 2)]
+  opts += [Opt(OptOps.UNROLL, 0, 8)]

-def hand_spec_kernel3():
-  # ---------------------------
-  # block indices & placeholders
-  # ---------------------------
-  blockIdx_x = UOp.special(N // BLOCK_N, "gidx0")
-  blockIdx_y = UOp.special(N // BLOCK_M, "gidx1")
+  return sink.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))

-  a = UOp.placeholder((N, N), dtypes.float, slot=1)
-  b = UOp.placeholder((N, N), dtypes.float, slot=2)
-  c = UOp.placeholder((N, N), dtypes.float, slot=0)
+def top_spec_kernel3():
+  a = Tensor.empty(N,N)
+  b = Tensor.empty(N,N)
+  c = a@b
+  sink = c.schedule()[-1].ast
+  L = 16
+  sink = sink.reshape((N//L, L, N//L, L)) #.lift({0:UOp.range(N//BM, 0), 2:UOp.range(N//BN, 1)})
+  sink = graph_rewrite(sink, view_left+pm)
+  axis_types = (AxisType.GLOBAL, AxisType.LOCAL, AxisType.GLOBAL, AxisType.LOCAL, AxisType.REDUCE)
+  return sink.replace(arg=KernelInfo(name="top_"+to_colored(sink.full_shape, axis_types), axis_types=axis_types))

-  # index the output with the globals
-  c = c.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_N, BLOCK_N)[blockIdx_y, :, blockIdx_x, :]
+def hl_spec_kernel3():
+  nbIterWaveM = 2
+  nbIterWaveN = 2

-  # open the main reduction range
-  k_tile_range = UOp.range(N // BLOCK_K, 0, AxisType.REDUCE)
-  a = a.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_K, BLOCK_K)[blockIdx_y, :, k_tile_range, :]
-  b = b.reshape(N // BLOCK_K, BLOCK_K, N // BLOCK_N, BLOCK_N)[k_tile_range, :, blockIdx_x, :]
+  # define buffers
+  # TODO: remove these views once the defines have a shape
+  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N)))
+  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2).view(ShapeTracker.from_shape((N,N))).permute((1,0))
+  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
+  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0).view(ShapeTracker.from_shape((BK, BM))).permute((1,0))
+  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1).view(ShapeTracker.from_shape((BK, BN))).permute((1,0))
+  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((nbIterWaveM * TM,)))
+  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1).view(ShapeTracker.from_shape((nbIterWaveN * TN,)))

-  # globals are no longer used, they are already in the indexes
-  del blockIdx_y, blockIdx_x
+  # shape buffers. TODO: permutes
+  full_shape = (N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, N//BK, BK)
+  a = a.reshape((N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, 1, 1, 1, 1, N//BK, BK)).expand(full_shape)
+  b = b.reshape((1, 1, 1, 1, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, N//BK, BK)).expand(full_shape)
+  c = c.reshape((N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, 1, 1))
+  As = As.reshape((1, nbIterWaveM, BM//(nbIterWaveM * TM), TM, 1, 1, 1, 1, 1, BK)).expand(full_shape)
+  Bs = Bs.reshape((1, 1, 1, 1, 1, nbIterWaveN, BN//(nbIterWaveN * TN), TN, 1, BK)).expand(full_shape)
+  A_col = A_col.reshape((1, nbIterWaveM, 1, TM, 1, 1, 1, 1, 1, 1)).expand(full_shape)
+  B_row = B_row.reshape((1, 1, 1, 1, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape)

-  # ---------------------------
-  # GLOBAL -> LOCAL (As, Bs)
-  # ---------------------------
-  tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
+  #                     U1   L2 L3 L4 L5   U6 U7      U9   L10 L11 L12 L13   U14 U15      U17  U18  U19
+  expanded_shape = (32, 2,   2, 2, 2, 2,   2, 2,  32, 2,   2,  2,  2,  2,    2,  2,  512, 2,   2,   2)
+  assert len(expanded_shape) == 20
+  permute_a = list(range(len(expanded_shape)))
+  permute_b = permute_a[:]

-  # A: read BM x BK tiles (permute on store into locals)
-  BM_As_stride = (BLOCK_M + 4) if is_kernel5 else BLOCK_M
-  As = UOp.placeholder((BLOCK_K, BM_As_stride), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL).shrink_to((BLOCK_K, BLOCK_M))
-  As_store = copy(As.permute((1,0)).reshape(-1, THREADS_PER_BLOCK)[:, tid], a.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=100)
+  # this makes all the global loads match
+  # this can also be more simply done by rebinding the RANGEs
+  # but sadly, rebinding the RANGEs doesn't work to change the order of the local axes
+  permute_a[17:20] = [11,12,13]
+  permute_a[11:14] = [17,18,19]
+  permute_a[7], permute_a[10] = permute_a[10], permute_a[7]
+  permute_a[2:7] = [3,4,5,6,2]

-  # B: read BK x BN tiles
-  Bs = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
-  Bs_store = copy(Bs.reshape(-1, THREADS_PER_BLOCK)[:, tid], b.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=200)
+  permute_b[2:16] = [19,9,10,11,17,18,8,2,12,13,14,15,3,4]
+  permute_b[17:20] = [5,6,7]

-  # TODO: can we automate barrier?
-  barrier = UOp.barrier(As_store, Bs_store)
-  As, Bs = As.after(barrier), Bs.after(barrier)
+  a_permute   = a.reshape(expanded_shape).permute(tuple(permute_a)).reshape(full_shape)
+  As_permute = As.reshape(expanded_shape).permute(tuple(permute_a)).reshape(full_shape)

-  # open inner k range
-  k = UOp.range(BLOCK_K, 3, AxisType.REDUCE)
+  b_permute   = b.reshape(expanded_shape).permute(tuple(permute_b)).reshape(full_shape)
+  Bs_permute = Bs.reshape(expanded_shape).permute(tuple(permute_b)).reshape(full_shape)

-  # ---------------------------
-  # LOCAL -> REG (per-wave tiles)
-  # ---------------------------
-  waveIdx = (tid // WARP_SIZE) % WAVES_IN_BLOCK_X
-  waveIdy = (tid // WARP_SIZE) // WAVES_IN_BLOCK_X
-  assert waveIdy.vmax+1 == WAVES_IN_BLOCK_Y
+  #out = (a.load() * b.load()).r(Ops.ADD, (8, 9))
+  out = (As.load(As_permute.store(a_permute.load())) * Bs.load(Bs_permute.store(b_permute.load()))).r(Ops.ADD, (8, 9))
+  #out = (A_col.load(A_col.store(As.load(As.store(a.load())))) * B_row.load(B_row.store(Bs.load(Bs.store(b.load()))))).r(Ops.ADD, (8, 9))

-  laneIdx = (tid % WARP_SIZE) % LANES_PER_WAVE_X
-  laneIdy = (tid % WARP_SIZE) // LANES_PER_WAVE_X
-  assert laneIdy.vmax+1 == LANES_PER_WAVE_Y
+  axis_types = (
+    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
+    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
+    AxisType.REDUCE, AxisType.REDUCE)

-  A_col = UOp.placeholder((ITERS_PER_WAVE_M, TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
-  A_col = copy(A_col, As[k, :].reshape(WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM)[waveIdy, :, laneIdy, :], 300, set=True, upcast=True)
+  sink = c.store(out).sink(arg=KernelInfo(name="tg_"+to_colored(full_shape, axis_types), axis_types=axis_types))
+  sink = graph_rewrite(sink, merge_views)
+  return sink

-  B_row = UOp.placeholder((ITERS_PER_WAVE_N, TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
-  B_row = copy(B_row, Bs[k, :].reshape(WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)[waveIdx, :, laneIdx, :], 400, set=True, upcast=True)
+def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
+  BLOCK_SIZE = 128 if kernel5 else 256

-  # ---------------------------
-  # FMA: c_regs += A_col * B_row
-  # ---------------------------
-  c_regs = UOp.placeholder((ITERS_PER_WAVE_M, TM, ITERS_PER_WAVE_N, TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
-  i = UOp.range(c_regs.size, 16)
-  c_regs = c_regs.after(c_regs.flatten()[i].store(0.0).end(i))
+  nbWaves = BLOCK_SIZE // 32
+  WN = 128 if kernel5 else 64
+  WM = BN * BM // nbWaves // WN

-  # TODO: why don't these work as upcast?
-  # why if the ranges merge is it slow?!? (if you change the order on end, they will merge. big slowdown on METAL)
-  iterWaveM, yt, iterWaveN, xt = rngs = rngs_for_shape(c_regs.shape, 500)
-  sink = c_regs[*rngs].store(c_regs.after(k)[*rngs] + A_col[iterWaveM, yt] * B_row[iterWaveN, xt]).end(iterWaveM, iterWaveN, yt, xt)
+  nbWaveX = BN // WN
+  nbWaveY = BM // WM

-  # Close k, sync, and close K tiles
-  sink = sink.end(k).barrier().end(k_tile_range)
+  threadIdx_x = UOp(Ops.SPECIAL, dtypes.int, arg=("lidx0", BLOCK_SIZE))
+  waveIndex = threadIdx_x // 32
+  waveIdx = waveIndex % nbWaveX
+  waveIdy = waveIndex // nbWaveX
+  indexInWave = threadIdx_x % 32

-  # ---------------------------
-  # REG -> GLOBAL (epilogue)
-  # ---------------------------
-  c = c.reshape(WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM,
-                WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)
-  c = c[waveIdy, :, laneIdy, :,
-        waveIdx, :, laneIdx, :]
-  sink = copy(c, c_regs.after(sink), rng=600)
+  nbThreadXPerWave = 8
+  nbThreadYPerWave = 4

-  return sink.sink(arg=KernelInfo(opts_to_apply=())).simplify()
+  idxInWave = indexInWave % nbThreadXPerWave
+  idyInWave = indexInWave // nbThreadXPerWave

-def test_matmul(sink:UOp, N=N):
-  with Context(DEBUG=0):
-    a = Tensor.randn(N, N)
-    b = Tensor.randn(N, N)
-    hc = Tensor.empty(N, N)
-    Tensor.realize(a, b, hc)
+  nbIterWaveN = WN // (nbThreadXPerWave * TN)
+  nbIterWaveM = WM // (nbThreadYPerWave * TM)

-  ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in [hc, a, b]])
+  SUBWN = WN // nbIterWaveN
+  SUBWM = WM // nbIterWaveM

-  GlobalCounters.reset()
-  ets = []
-  with Context(DEBUG=2):
-    for _ in range(run_count):
-      ets.append(ei.run(wait=True))
-  print(f"REAL TFLOPS {N * N * N * 2 / min(ets) * 1e-12:.2f}")
+  # Thread mapping to read BKxBN block from A
+  rAIdx = threadIdx_x % BK
+  rAIdy = threadIdx_x // BK
+  # Thread mapping to read BNxBK block from B
+  rBIdx = threadIdx_x % BN
+  rBIdy = threadIdx_x // BN

-  GlobalCounters.reset()
-  with Context(DEBUG=2):
-    tc = (a @ b).realize()
-  with Context(DEBUG=0):
-    err = (hc - tc).square().mean().item()
-  print(f"mean squared error {err}")
-  if err > 1e-06:
-    raise RuntimeError("matmul is wrong!")
+  strideReadB = BLOCK_SIZE // BN
+  strideReadA = BLOCK_SIZE // BK
+  nbReadsB = BN * BK // BLOCK_SIZE
+  nbReadsA = BM * BK // BLOCK_SIZE
+
+  blockIdx_x = UOp(Ops.SPECIAL, dtypes.int, arg=("gidx0", N//BN))
+  blockIdx_y = UOp(Ops.SPECIAL, dtypes.int, arg=("gidx1", N//BM))
+
+  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1)
+  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2)
+  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0)
+
+  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0)
+  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1)
+
+  BM_As_stride = (BM+4) if kernel5 else BM
+  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM_As_stride, AddrSpace.LOCAL), arg=0)
+  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1)
+
+  c_regs = UOp(Ops.DEFINE_REG, dtypes.float.ptr(TM * nbIterWaveM * TN * nbIterWaveN), arg=2)
+
+  i = UOp.range(c_regs.dtype.size, 16)
+  init_store = c_regs[i].store(UOp.const(dtypes.float, 0.0), i)
+
+  if kernel4:
+    regA = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbReadsA, AddrSpace.REG), arg=3)
+    regB = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbReadsB, AddrSpace.REG), arg=4)
+
+    # initial load from globals into locals (0)
+    kId = 0
+
+    # load from globals into locals
+    i = UOp.range(nbReadsB, 0)
+    index_x = BN * blockIdx_x + rBIdx
+    index_y = rBIdy + i * strideReadB + kId
+    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
+
+    i = UOp.range(nbReadsA, 1)
+    index_x = rAIdx + kId
+    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
+
+    # iterate over the middle chunk
+    kId_range = UOp.range(N//BK-1, 2)
+    kId = kId_range*BK
+
+    barrier = UOp.barrier(As_store, Bs_store)
+
+    # load from globals into registers (next round)
+    i = UOp.range(nbReadsB, 3)
+    index_x = BN * blockIdx_x + rBIdx
+    index_y = rBIdy + i * strideReadB + kId + BK
+    regB_store = regB[i].store(b[N * index_y + index_x].load(), i)
+
+    i = UOp.range(nbReadsA, 4)
+    index_x = rAIdx + kId + BK
+    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+    regA_store = regA[i].store(a[N * index_y + index_x].load(), i)
+
+    def inner_loop(first_range, inp_dep=()):
+      # inner unroll
+      k = UOp.range(BK, first_range+0)
+
+      # load from locals into registers
+      iterWave = UOp.range(nbIterWaveN, first_range+1)
+      i = UOp.range(TN, first_range+2)
+      index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
+      B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(*inp_dep), iterWave, i)
+
+      iterWave = UOp.range(nbIterWaveM, first_range+3)
+      i = UOp.range(TM, first_range+4)
+      index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
+      A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(*inp_dep), iterWave, i)
+
+      # do the GEMM math
+      iterWaveM = UOp.range(nbIterWaveM, first_range+5)
+      yt = UOp.range(TM, first_range+6)
+      iterWaveN = UOp.range(nbIterWaveN, first_range+7)
+      xt = UOp.range(TN, first_range+8)
+      x = iterWaveN * TN + xt
+      y = iterWaveM * TM + yt
+      c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
+      # sketchy, this should end the kId_range but it doesn't
+      sink = c_regs_idx.store(c_regs_idx.load(init_store) + A_col[y].load(A_col_store) * B_row[x].load(B_row_store),
+                              iterWaveM, iterWaveN, yt, xt, k)
+      return sink
+
+    # TODO: kId_range should endrange after a barrier
+    sink = inner_loop(5, (barrier, regB_store, regA_store)).barrier()
+
+    # load from registers into locals
+    i = UOp.range(nbReadsB, 14)
+    index_x = BN * blockIdx_x + rBIdx
+    index_y = rBIdy + i * strideReadB + kId + BK
+    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(regB[i].load(sink), i, kId_range)
+
+    i = UOp.range(nbReadsA, 15)
+    index_x = rAIdx + kId + BK
+    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(regA[i].load(sink), i, kId_range)
+
+    # final iteration without the copy
+    sink = inner_loop(16, (UOp.barrier(Bs_store, As_store),))
+  else:
+    kId_range = UOp.range(N//BK, 0)
+    kId = kId_range*BK
+
+    # load from globals into locals
+    i = UOp.range(nbReadsB, 1)
+    index_x = BN * blockIdx_x + rBIdx
+    index_y = rBIdy + i * strideReadB + kId
+    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
+
+    i = UOp.range(nbReadsA, 2)
+    index_x = rAIdx + kId
+    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
+
+    barrier = UOp.barrier(As_store, Bs_store)
+
+    k = UOp.range(BK, 3)
+
+    # load from locals into registers
+    iterWave = UOp.range(nbIterWaveN, 4)
+    i = UOp.range(TN, 5)
+    index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
+    B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(barrier), iterWave, i)
+
+    iterWave = UOp.range(nbIterWaveM, 6)
+    i = UOp.range(TM, 7)
+    index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
+    A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(barrier), iterWave, i)
+
+    # do the GEMM math
+    iterWaveM = UOp.range(nbIterWaveM, 8)
+    yt = UOp.range(TM, 9)
+    iterWaveN = UOp.range(nbIterWaveN, 10)
+    xt = UOp.range(TN, 12)
+    x = iterWaveN * TN + xt
+    y = iterWaveM * TM + yt
+    c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
+    sink = c_regs_idx.store(c_regs_idx.load(init_store) + A_col[y].load(A_col_store) * B_row[x].load(B_row_store),
+                            iterWaveM, iterWaveN, yt, xt, k, kId_range)
+
+  # store c_regs into c
+  iterWaveM = UOp.range(nbIterWaveM, 1000)
+  yt = UOp.range(TM, 1001)
+  iterWaveN = UOp.range(nbIterWaveN, 1002)
+  xt = UOp.range(TN, 1003)
+  xOut = blockIdx_x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave
+  yOut = blockIdx_y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave
+  indexC = N * (yOut + yt) + xOut + xt
+  sink = c[indexC].store(c_regs[TN * nbIterWaveN * (iterWaveM * TM + yt) + (iterWaveN * TN + xt)].load(sink),
+                         iterWaveM, iterWaveN, yt, xt)
+
+  return sink.sink(arg=KernelInfo(name="tinygemm"))

 if __name__ == "__main__":
-  test_matmul(hand_spec_kernel3(), N=N)
+  HL = getenv("HL")
+  if HL == 3: hprg = rangeify_kernel3()
+  elif HL == 2: hprg = top_spec_kernel3()
+  elif HL == 1: hprg = hl_spec_kernel3()
+  else: hprg = hand_spec_kernel3()
+  if HL == 3:
+    prg = get_program(hprg, Device.default.renderer)
+  else:
+    prg = get_program(hprg, Device.default.renderer)
+  print(prg.src)
+  if getenv("SRC"): exit(0)
+  hrunner = CompiledRunner(prg)
+
+  a = Tensor.randn(N, N).realize()
+  b = Tensor.randn(N, N).realize()
+  hc = Tensor.zeros(N, N).contiguous().realize()
+
+  GlobalCounters.reset()
+  with Context(DEBUG=2):
+    for _ in range(run_count): tc = (a@b).realize()
+
+  GlobalCounters.reset()
+  buffers = [hc.uop.buffer, a.uop.buffer, b.uop.buffer]
+  ei = ExecItem(hrunner, buffers)
+  with Context(DEBUG=2):
+    for _ in range(run_count): ei.run(wait=True)
+  err = (hc-tc).square().mean().item()
+  print(f"hrunner {err}")
+  if err > 1e-06: raise RuntimeError("matmul is wrong!")
@@ -1,11 +1,17 @@
 import numpy as np, os
 from tinygrad.helpers import getenv, flat_mv
 from tinygrad import dtypes
+from typing import Optional, List, Tuple, cast, Dict, Final, DefaultDict, Self
 from tinygrad.engine.realize import get_program

 # for copied uops
-from tinygrad import dtypes
-from tinygrad.dtype import DTYPES_DICT
+from tinygrad.codegen.opt.kernel import Kernel, KernelOptError
+from tinygrad.uop.ops import UOp, Ops, BinaryOps, UnaryOps, TernaryOps, KernelInfo
+from tinygrad.codegen.opt.search import Opt, OptOps
+from tinygrad import Device, dtypes, Tensor
+from tinygrad.dtype import PtrDType, DType, DTYPES_DICT
+from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.shape.view import View

 script_dir = os.path.dirname(os.path.abspath(__file__))

@@ -47,6 +53,12 @@ def randoms():
    nc = nc.astype(np.bfloat16 if DTYPE_IN == dtypes.bfloat16 else np.float16)
  return na, nb, nc

+def ast_to_cuda_prog(compiler, ast, opts):
+  k = Kernel(ast)
+  k.apply_opts(opts)
+  p = get_program(k.ast, k.opts, k.applied_opts)
+  return CUDAProgram(device, p.function_name, compiler.compile(p.src))
+
 if __name__ == "__main__":
  print(f"gemm variation: {GEMM_VARIATION=} {M=} {N=} {K=} {DTYPE_IN=} {DTYPE_OUT=} {DTYPE_ACC=}")
  prog, global_size, local_size = None, None, None
@@ -177,11 +189,11 @@ if __name__ == "__main__":

    tms = []
    na, nb, nc = randoms()
-    cudaalloc._copyin(a, memoryview(bytearray(na)))
-    cudaalloc._copyin(b, memoryview(bytearray(nb)))
+    cudaalloc.copyin(a, bytearray(na))
+    cudaalloc.copyin(b, bytearray(nb))
    for i in range(CNT):
      tms.append(prog(*args, **kwargs))
-    cudaalloc._copyout(flat_mv(nc.data), c)
+    cudaalloc.copyout(flat_mv(nc.data), c)
    comp = na.astype(np.float32) @ nb.astype(np.float32)
    result = nc.reshape(M, N).astype(np.float32)

@@ -1,42 +0,0 @@
-from tinygrad import UOp, dtypes
-from tinygrad.uop.ops import AxisType, Ops, KernelInfo, AddrSpace
-from extra.gemm.amd_uop_matmul import test_matmul
-
-N = 2048
-
-# metal has an 8x8 tensor core. this is the indexing
-def mat_idx(buf, g0, g1, warp, u):
-  l = [(warp//2**i)%2 for i in range(5)]
-  return buf[g0, l[4]*4 + l[2]*2 + l[1], g1, l[3]*4 + l[0]*2 + u]
-
-def hand_spec_tc_cores():
-  gx = UOp.special(N // 8, "gidx0")
-  gy = UOp.special(N // 8, "gidx1")
-  warp = UOp.special(32, "lidx0")
-
-  c = UOp.placeholder((N, N), dtypes.float, slot=0).reshape((N//8, 8, N//8, 8))
-  a = UOp.placeholder((N, N), dtypes.float, slot=1).reshape((N//8, 8, N//8, 8))
-  b = UOp.placeholder((N, N), dtypes.float, slot=2).reshape((N//8, 8, N//8, 8))
-
-  gk = UOp.range(N // 8, 0, AxisType.REDUCE)
-
-  a_tc = UOp.vectorize(*[mat_idx(a, gx, gk, warp, i) for i in range(2)])
-  b_tc = UOp.vectorize(*[mat_idx(b, gk, gy, warp, i) for i in range(2)])
-
-  acc = UOp.placeholder((2,), dtypes.float, slot=0, addrspace=AddrSpace.REG)
-  acc = acc[0].set(0.0)
-  acc = acc[1].set(0.0)
-
-  # TODO: make this simple
-  wmma_arg = ('WMMA_8_8_8_float_float', (8, 8, 8), dtypes.float, dtypes.float, 'METAL', 32, (((3, 2),), ((3, 2),), ((3, 2),)), ())
-
-  acc_load = UOp.vectorize(acc.after(gk)[0], acc.after(gk)[1])
-  out = UOp(Ops.WMMA, dtypes.float.vec(2), (a_tc, b_tc, acc_load), arg=wmma_arg)
-
-  end_loop = UOp.group(*[acc[i].store(out.gep(i)) for i in range(2)]).end(gk)
-
-  sink = UOp.group(*[mat_idx(c.after(end_loop), gx, gy, warp, i).store(acc[i]) for i in range(2)])
-  return sink.sink(arg=KernelInfo(name="custom_metal_matmul", opts_to_apply=())).simplify()
-
-if __name__ == "__main__":
-  test_matmul(hand_spec_tc_cores(), N=N)
@@ -1,229 +0,0 @@
-import os
-import numpy as np
-np.set_printoptions(linewidth=1000000)
-os.environ["AMD_LLVM"] = "0"
-
-from tinygrad import Tensor, Context, dtypes, UOp, GlobalCounters
-from tinygrad.helpers import DEBUG, getenv
-from tinygrad.dtype import AddrSpace
-from tinygrad.uop.ops import AxisType, KernelInfo, Ops
-
-WARP_SIZE = 64
-
-# Reg tile sizes (tensor cores)
-TC_M = 16
-TC_N = 16
-TC_K = 32
-
-# 1024 matrix cores
-# 16 cycle mfma
-# 2.2 GHz
-# 16x16x32x2 FLOPS/mma = 16384
-# 2.2*1e9*16384*1024/16*1e-12 TFLOPS = 2306 TFLOPS
-
-#N,M,K = 256,256,64
-N,M,K = 4096,4096,4096
-
-# Threadblock tile sizes (block-level tile of C that a block computes)
-#BLOCK_M = 128   # rows of C (M-dim) per block
-#BLOCK_N = 128   # columns of C (N-dim) per block
-#BLOCK_K = 128   # K-slice per block iteration
-
-BLOCK_M = 64
-BLOCK_N = 64
-BLOCK_K = 128
-
-WARPGROUP_SIZE = 1
-BLOCK_M = BLOCK_M * WARPGROUP_SIZE
-
-# TODO: improve the syntax of this. better syntax, faster iteration
-#  -- DONE: add working slice a[gx, :, i] -> shape of the : (aka (16,16,32) becomes (16,))
-#  -- DONE(ish): add argfix to movement (traits shared with Tensor)
-#  -- fix WMMA to not require all the junk
-#  -- improve syntax for vectorized loads/stores (both with DEVECTORIZE and without)
-#  -- DONE: be able to use CONTRACT on a range
-#  -- fix upcasted RANGE on an already vectorized buffer
-#  -- improve "all ranges not ended error" / fix the bug with after on ended ranges (if you are after end of range, range is closed)
-
-CUS_PER_GPU = 256
-assert ((M//BLOCK_M) * (N//BLOCK_N)) >= CUS_PER_GPU, "not enough globals"
-
-def custom_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
-  # A = (M x K)
-  # B = (K x N)
-  # C = (M x N)
-
-  # check it's proper matmul
-  assert C.shape[0] == A.shape[0]
-  assert C.shape[1] == B.shape[1]
-  assert A.shape[1] == B.shape[0]
-
-  gx, gy = UOp.special(M//BLOCK_M, "gidx0"), UOp.special(N//BLOCK_N, "gidx1")
-  warp = UOp.special(WARP_SIZE, "lidx0")
-  warpgroup = UOp.special(WARPGROUP_SIZE, "lidx1")
-
-  # generic copy logic (not good)
-  def generic_copy(glbl, gargs, lcl, rng):
-    # Fully coalesced 128-bit loads/stores.
-    INNER_SIZE = 8
-    cp_i = UOp.range(lcl.size//(WARPGROUP_SIZE*WARP_SIZE*INNER_SIZE), rng)
-    cp_inner = UOp.range(INNER_SIZE, rng+1, AxisType.UPCAST)
-    idx_i = cp_i*WARPGROUP_SIZE*WARP_SIZE*INNER_SIZE + warpgroup*WARP_SIZE*INNER_SIZE + warp*INNER_SIZE + cp_inner
-    return lcl[idx_i].store(glbl[*gargs, idx_i]).end(cp_i, cp_inner)
-
-  # split out the globals into blocks
-  C = C.reshape((M//BLOCK_M, BLOCK_M, N//BLOCK_N, BLOCK_N))
-  A = A.reshape((M//BLOCK_M, BLOCK_M, K//BLOCK_K, BLOCK_K))
-  B = B.reshape((K//BLOCK_K, BLOCK_K, N//BLOCK_N, BLOCK_N))
-
-  # this is the big accumulator
-  acc = UOp.placeholder((BLOCK_N//TC_N, BLOCK_M//TC_M//WARPGROUP_SIZE), dtypes.float.vec(4), 0, AddrSpace.REG)
-  assert acc.size*WARP_SIZE*WARPGROUP_SIZE*4 == BLOCK_M*BLOCK_N
-  acc = acc[init_l:=UOp.range(acc.size, 500)].set(UOp.const(dtypes.float.vec(4), 0.0), end=init_l)
-
-  # create locals (note A is permuted, and the stride is changed to avoid bank conflicts)
-  def make_locals(slot) -> tuple[UOp, UOp]:
-    BM_As_stride = (BLOCK_M + 1)
-    BN_Bs_stride = (BLOCK_N + 0)
-    INNER_SLICE = 8
-    As = UOp.placeholder((BLOCK_K//INNER_SLICE, BM_As_stride, INNER_SLICE), dtypes.half, slot=slot, addrspace=AddrSpace.LOCAL)
-    INNER_SLICE = 1
-    Bs = UOp.placeholder((BLOCK_K//INNER_SLICE, BN_Bs_stride, INNER_SLICE), dtypes.half, slot=slot+1, addrspace=AddrSpace.LOCAL)
-    As = As.permute((0,2,1)).reshape((BLOCK_K, BM_As_stride)).shrink_to((BLOCK_K, BLOCK_M))
-    Bs = Bs.permute((0,2,1)).reshape((BLOCK_K, BN_Bs_stride)).shrink_to((BLOCK_K, BLOCK_N))
-    return As, Bs
-
-  # load from globals into locals (TODO: use the warpgroup)
-
-  def load_to_locals(l_K_outer_loop:UOp, Asl:UOp, Bsl:UOp, rng:int, barrier=True) -> tuple[UOp, UOp]:
-    if getenv("FAKE"):
-      return Asl[0].set(0), Bsl[0].set(0)
-    else:
-      pA = A.permute((0,2,1,3)).reshape((M//BLOCK_M, K//BLOCK_K, BLOCK_M*BLOCK_K))
-      pas = Asl.permute((1,0)).reshape((BLOCK_M*BLOCK_K,))
-      As_store = generic_copy(pA, (gx, l_K_outer_loop), pas, rng)
-
-      pB = B.permute((0,2,1,3)).reshape((K//BLOCK_K, N//BLOCK_N, BLOCK_K*BLOCK_N))
-      pbs = Bsl.reshape((BLOCK_K*BLOCK_N,))
-      Bs_store = generic_copy(pB, (l_K_outer_loop, gy), pbs, rng+2)
-
-      barrier = UOp.barrier(As_store, Bs_store) if barrier else UOp.group(As_store, Bs_store)
-      return Asl.after(barrier), Bsl.after(barrier)
-
-  def compute_on_locals(acc:UOp, Asl:UOp, Bsl:UOp, rng:int, afters:tuple[UOp, ...]=()) -> UOp:
-    K_inner_loop = UOp.range(BLOCK_K//TC_K, rng, AxisType.REDUCE)
-
-    # load from locals into registers
-    Ar = UOp.placeholder((BLOCK_M//TC_M//WARPGROUP_SIZE,), dtypes.half.vec(8), slot=1, addrspace=AddrSpace.REG)
-    Br = UOp.placeholder((BLOCK_N//TC_N,), dtypes.half.vec(8), slot=2, addrspace=AddrSpace.REG)
-
-    M_load_loop = UOp.range(BLOCK_M//TC_M//WARPGROUP_SIZE, rng+10)
-    Asl = Asl.reshape((BLOCK_K//TC_K, TC_K, BLOCK_M//TC_M//WARPGROUP_SIZE, WARPGROUP_SIZE, TC_M))
-    load_rng = UOp.range(8, rng+11, axis_type=AxisType.UPCAST)
-    A_in = Asl[K_inner_loop, (warp//16)*8+load_rng, M_load_loop, warpgroup, warp%16].contract(load_rng)
-    Ar = Ar[M_load_loop].set(A_in, end=M_load_loop)
-
-    N_load_loop = UOp.range(BLOCK_N//TC_N, rng+20)
-    Bsl = Bsl.reshape((BLOCK_K//TC_K, TC_K, BLOCK_N//TC_N, TC_N))
-    load_rng = UOp.range(8, rng+21, axis_type=AxisType.UPCAST)
-    B_in = Bsl[K_inner_loop, (warp//16)*8+load_rng, N_load_loop, warp%16].contract(load_rng)
-    Br = Br[N_load_loop].set(B_in, end=N_load_loop)
-
-    M_inner_loop = UOp.range(BLOCK_M//TC_M//WARPGROUP_SIZE, rng+30)
-    N_inner_loop = UOp.range(BLOCK_N//TC_N, rng+31)
-
-    # load values
-    acc_after = acc.after(*afters, M_inner_loop, N_inner_loop, K_inner_loop)
-    acc_load = acc_after[N_inner_loop, M_inner_loop]
-
-    # do WMMA
-    wmma_arg = ('WMMA_16_16_32_half_float', (16, 16, 32), dtypes.half, dtypes.float, 'AMD', 64, ((), (), ((3, 2), (2, 2))), ())
-    out = UOp(Ops.WMMA, dtypes.float.vec(4), (Ar[M_inner_loop], Br[N_inner_loop], acc_load), arg=wmma_arg)
-
-    # store back the acc
-    acc_store = acc[N_inner_loop, M_inner_loop].store(out)
-    return acc_store.end(M_inner_loop, N_inner_loop, K_inner_loop)
-
-  # **** START INNER LOOP *****
-  # inner loop -- locals -> regs
-
-  # no pipeline
-  if not getenv("PIPELINE"):
-    As, Bs = make_locals(slot=0)
-
-    K_outer_loop = UOp.range(K//BLOCK_K, 0, AxisType.REDUCE)
-    As, Bs = load_to_locals(K_outer_loop, As, Bs, 1000, barrier=True)
-    acc_store = compute_on_locals(acc, As, Bs, 1500, afters=(K_outer_loop,))
-    acc = acc.after(acc_store.barrier().end(K_outer_loop))
-  else:
-    # this doesn't work
-    As0, Bs0 = make_locals(slot=0)
-    As1, Bs1 = make_locals(slot=2)
-    As0, Bs0 = load_to_locals(0, As0, Bs0, 1000)
-
-    K_outer_loop = UOp.range((K//BLOCK_K-2)//2, 0, AxisType.REDUCE)
-    As1, Bs1 = load_to_locals(K_outer_loop+1, As1, Bs1, 2000, barrier=False)
-    acc_store = compute_on_locals(acc, As0, Bs0, 1500, afters=(K_outer_loop,))
-    As0, Bs0 = load_to_locals(K_outer_loop+2, As0, Bs0, 3000, barrier=False)
-    acc_store = compute_on_locals(acc, As1, Bs1, 2500, afters=(acc_store, As0, Bs0))
-    acc = acc.after(acc_store.barrier().end(K_outer_loop))
-
-    #acc_store = compute_on_locals(acc, As0, Bs0, 3500, afters=(acc_store.barrier().end(K_outer_loop)))
-    """
-    As1, Bs1 = load_to_locals(K//BLOCK_K-1, As1, Bs1, 4000)
-    acc_store = compute_on_locals(acc, As1, Bs1, 4500, afters=(acc_store))
-    """
-    #acc = acc.after(acc_store)
-
-  # **** END LOOPS *****
-
-  # store the acc into gmem
-  cp_i, cp_j = UOp.range(BLOCK_M//TC_M//WARPGROUP_SIZE, 10004), UOp.range(BLOCK_N//TC_N, 10005)
-  c_load = lambda i: C[gx, cp_i*TC_M*WARPGROUP_SIZE + warpgroup*TC_M + (warp//16)*4+i, gy, cp_j*TC_N + warp%16]
-  store = UOp.group(*[c_load(i).store(acc[cp_j, cp_i].gep(i)) for i in range(4)])
-  store = store.end(cp_i, cp_j)
-
-  return store.sink(arg=KernelInfo(name="custom_gemm", opts_to_apply=())).simplify()
-
-# simplest WMMA
-"""
-# init the acc
-acc = UOp.placeholder((4,), dtypes.float, 0, AddrSpace.REG)
-acc = acc[init_l:=UOp.range(4, 1)].set(0.0, end=init_l)
-
-# do the wmma
-acc_load = UOp.vectorize(*[acc.after(K_loop)[i] for i in range(4)])
-wmma_arg = ('WMMA_16_16_32_half_float', (16, 16, 32), dtypes.half, dtypes.float, 'AMD', 64, ((), (), ((3, 2), (2, 2))), ())
-out = UOp(Ops.WMMA, dtypes.float.vec(4), (A_in, B_in, acc_load), arg=wmma_arg)
-
-# store back the acc
-acc = acc.after(UOp.group(*[acc[i].store(out.gep(i)) for i in range(4)]).end(K_loop))
-
-# store the acc into gmem
-store = UOp.group(*[C[gx, (warp//16)*4+i, gy, warp%16].store(acc[i]) for i in range(4)])
-"""
-
-if __name__ == "__main__":
-  a = Tensor.randn(M, K, dtype=dtypes.half)
-  b = Tensor.randn(K, N, dtype=dtypes.half)
-
-  #a = Tensor.zeros(M, K, dtype=dtypes.half).contiguous()
-  #a[0,16] = 1
-  #b = Tensor.ones(K, N, dtype=dtypes.half).contiguous()
-
-  c = Tensor.empty(M, N, dtype=dtypes.float)
-  with Context(DEBUG=0): Tensor.realize(a,b)
-
-  ref = a.dot(b, dtype=dtypes.float)
-  ref.realize()
-
-  GlobalCounters.reset()
-  with Context(DEBUG=max(2, DEBUG.value), DEVECTORIZE=2):
-    tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm)[0]
-    tst.realize()
-  print(f"{(N*M*K*2 / GlobalCounters.time_sum_s)*1e-12:.2f} REAL TFLOPS")
-
-  with Context(DEBUG=0):
-    #print(ref.numpy())
-    #print(tst.numpy())
-    assert Tensor.isclose(ref, tst, atol=1e-2).all().item(), "matrix not close"
@@ -17,7 +17,7 @@ M = getenv("M", N)
 K = getenv("K", N)
 CNT = getenv("CNT", 10)

-atol, rtol = {dtypes.half:{1e-3, 1e-2}, dtypes.bfloat16:(1e-3, 1e-2), dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2:(1.0, 5e-1)}.get(dtype_in, (1e-4, 3e-2))
+atol, rtol = {dtypes.bfloat16:(1e-3, 1e-2), dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2:(1.0, 5e-1)}.get(dtype_in, (1e-4, 3e-2))
 ATOL, RTOL = getenv("ATOL", atol), getenv("RTOL", rtol)

 INT_LOW = getenv("INT_LOW", 0)
@@ -8,23 +8,19 @@ import torch
 torch.set_num_threads(1)
 from tinygrad.helpers import getenv
 CUDA = getenv("CUDA", 1)
-MPS = getenv("MPS", 0)
-if getenv("FP16_ACC"): torch.backends.cuda.matmul.allow_fp16_accumulation = True

-for dtype in [torch.float32, torch.float16, torch.bfloat16]:
+for dtype in [torch.float32, torch.float16]:
  for N in [256, 512, 1024, 2048, 4096]:
    FLOPS = N*N*N*2

    b = torch.rand((N,N), dtype=dtype)
    c = torch.rand((N,N), dtype=dtype)
    if CUDA: b,c = b.cuda(),c.cuda()
-    if MPS: b,c = b.to('mps'),c.to('mps')

    def torch_prog(b, c):
      st = time.perf_counter()
      a = b@c
      if CUDA: torch.cuda.synchronize()
-      if MPS: torch.mps.synchronize()
      return time.perf_counter() - st
    tm = min([torch_prog(b, c) for _ in range(20)])
    print(f"{N*N:10d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS {N:4d}x{N:4d}x{N:4d} matmul in {dtype}")
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3

-import argparse, glob, os, time, subprocess, sys
+from tinygrad.runtime.support.system import System
+import argparse, glob, os, re, time, subprocess, sys

 def scan_devs_based_on_lock(prefix:str, args) -> list[str]:
  target_dev = args.pci_bus if 'pci_bus' in args.__dir__() else ""
@@ -11,7 +12,7 @@ def scan_devs_based_on_lock(prefix:str, args) -> list[str]:
    if os.path.exists(f"/sys/bus/pci/devices/{dev_id}") and dev_id.startswith(target_dev): devs.append(dev_id)
  return devs

-def _do_reset_device(pci_bus): os.system(f"sudo sh -c 'echo 1 > /sys/bus/pci/devices/{pci_bus}/reset'")
+def _do_reset_device(pci_bus): System.pci_reset(pci_bus)
 def _is_module_loaded(name: str) -> bool: return os.path.isdir(f"/sys/module/{name}")

 def cmd_remove_module(args):
@@ -84,14 +84,12 @@ if __name__=="__main__":
    NUM_WORKGROUPS = 256
    WAVE_SIZE = 64
    NUM_WAVES = 4
-    launchBenchmark("v_mfma_f32_16x16x16_f16", (3,0,1), accum=True)
    launchBenchmark("v_mfma_f32_16x16x16_bf16", (3,0,1), accum=True)
    FLOPS_PER_MATMUL = 16*16*32*2
-    launchBenchmark("v_mfma_f32_16x16x32_f16", (3,0,3), accum=True)
    launchBenchmark("v_mfma_f32_16x16x32_bf16", (3,0,3), accum=True)
    FLOPS_PER_MATMUL = 16*16*128*2
    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,7), accum=True) # fp8
    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,5), accum=True, extra=", cbsz:2 blgp:2") # fp6
    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,3), accum=True, extra=", cbsz:4 blgp:4") # fp4
  else:
-    raise RuntimeError(f"arch {DEV.arch} not supported.")
+    raise RuntimeError(f"arch {DEV.arch} not supported.")
@@ -89,20 +89,6 @@ class Attention:
    keys, values = repeat_kv(keys, self.n_rep), repeat_kv(values, self.n_rep)
    xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
    attn = xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2)
-    if getenv("STUB_ATTENTION"):
-      # TODO: do we need mask?
-      from tinygrad.uop.ops import UOp, KernelInfo
-      def fa_custom_forward(attn:UOp, q:UOp, k:UOp, v:UOp) -> UOp:
-        return UOp.sink(arg=KernelInfo(name="fa_custom_forward"))
-      def fa_custom_backward(out_q:UOp, out_k:UOp, out_v:UOp, grad:UOp, q:UOp, k:UOp, v:UOp) -> UOp:
-        return UOp.sink(arg=KernelInfo(name="fa_custom_backward"))
-      def fa_backward(grad:UOp, kernel:UOp) -> tuple[None, UOp, UOp, UOp]:
-        grad_q = Tensor.empty_like(q:=Tensor(kernel.src[1]))
-        grad_k = Tensor.empty_like(k:=Tensor(kernel.src[2]))
-        grad_v = Tensor.empty_like(v:=Tensor(kernel.src[3]))
-        ck = Tensor.custom_kernel(grad_q, grad_k, grad_v, Tensor(grad), q, k, v, fxn=fa_custom_backward)[:3]
-        return (None, ck[0].uop, ck[1].uop, ck[2].uop)
-      attn = Tensor.empty_like(attn).custom_kernel(xq, keys, values, fxn=fa_custom_forward, grad_fxn=fa_backward)[0]
    attn = attn.reshape(bsz, seqlen, -1)
    return self.wo(attn)

@@ -1,134 +0,0 @@
-/*
- * SPDX-FileCopyrightText: Copyright (c) 1993-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a
- * copy of this software and associated documentation files (the "Software"),
- * to deal in the Software without restriction, including without limitation
- * the rights to use, copy, modify, merge, publish, distribute, sublicense,
- * and/or sell copies of the Software, and to permit persons to whom the
- * Software is furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
- * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- * DEALINGS IN THE SOFTWARE.
- */
-
-#ifndef PCIEXPTBL_H
-#define PCIEXPTBL_H
-
-#define NV_BCRT_HASH_INFO_BASE_CODE_TYPE_VBIOS_BASE   0x00
-#define NV_BCRT_HASH_INFO_BASE_CODE_TYPE_VBIOS_EXT    0xE0
-
-//
-// The VBIOS object comes from walking the PCI expansion code block
-// The following structure holds the expansion code format.
-//
-#define PCI_EXP_ROM_SIGNATURE     0xaa55
-#define PCI_EXP_ROM_SIGNATURE_NV  0x4e56 // "VN" in word format
-#define PCI_EXP_ROM_SIGNATURE_NV2 0xbb77
-#define IS_VALID_PCI_ROM_SIG(sig) ((sig == PCI_EXP_ROM_SIGNATURE) ||    \
-                                   (sig == PCI_EXP_ROM_SIGNATURE_NV) || \
-                                   (sig == PCI_EXP_ROM_SIGNATURE_NV2))
-
-#define OFFSETOF_PCI_EXP_ROM_SIG                    0x0
-#define OFFSETOF_PCI_EXP_ROM_NBSI_DATA_OFFSET       0x16
-#define OFFSETOF_PCI_EXP_ROM_PCI_DATA_STRUCT_PTR    0x18
-
-#pragma pack(1)
-typedef struct _PCI_EXP_ROM_STANDARD
-{
-    NvU16       sig;                //  00h: ROM Signature 0xaa55
-    NvU8        reserved [0x16];    //  02h: Reserved (processor architecture unique data)
-    NvU16       pciDataStrucPtr;    //  18h: Pointer to PCI Data Structure
-    NvU32       sizeOfBlock;        //  1Ah: <NBSI-specific appendage>
-} PCI_EXP_ROM_STANDARD, *PPCI_EXP_ROM_STANDARD;
-#pragma pack()
-
-#pragma pack(1)
-typedef struct _PCI_EXP_ROM_NBSI
-{
-    NvU16       sig;                //  00h: ROM Signature 0xaa55
-    NvU8        reserved [0x14];    //  02h: Reserved (processor architecture unique data)
-    NvU16       nbsiDataOffset;     //  16h: Offset from header to NBSI image
-    NvU16       pciDataStrucPtr;    //  18h: Pointer to PCI Data Structure
-    NvU32       sizeOfBlock;        //  1Ah: <NBSI-specific appendage>
-} PCI_EXP_ROM_NBSI, *PPCI_EXP_ROM_NBSI;
-#pragma pack()
-
-typedef union _PCI_EXP_ROM {
-    PCI_EXP_ROM_STANDARD standard;
-    PCI_EXP_ROM_NBSI nbsi;
-} PCI_EXP_ROM, *PPCI_EXP_ROM;
-
-#define PCI_DATA_STRUCT_SIGNATURE     0x52494350 // "PCIR" in dword format
-#define PCI_DATA_STRUCT_SIGNATURE_NV  0x5344504E // "NPDS" in dword format
-#define PCI_DATA_STRUCT_SIGNATURE_NV2 0x53494752 // "RGIS" in dword format
-#define IS_VALID_PCI_DATA_SIG(sig) ((sig == PCI_DATA_STRUCT_SIGNATURE) ||    \
-                                    (sig == PCI_DATA_STRUCT_SIGNATURE_NV) || \
-                                    (sig == PCI_DATA_STRUCT_SIGNATURE_NV2))
-
-#define PCI_LAST_IMAGE NVBIT(7)
-#define PCI_ROM_IMAGE_BLOCK_SIZE 512U
-
-#define OFFSETOF_PCI_DATA_STRUCT_SIG        0x0
-#define OFFSETOF_PCI_DATA_STRUCT_VENDOR_ID  0x4
-#define OFFSETOF_PCI_DATA_STRUCT_LEN        0xa
-#define OFFSETOF_PCI_DATA_STRUCT_CLASS_CODE 0xd
-#define OFFSETOF_PCI_DATA_STRUCT_CODE_TYPE  0x14
-#define OFFSETOF_PCI_DATA_STRUCT_IMAGE_LEN  0x10
-#define OFFSETOF_PCI_DATA_STRUCT_LAST_IMAGE 0x15
-
-#pragma pack(1)
-typedef struct _PCI_DATA_STRUCT
-{
-    NvU32       sig;                //  00h: Signature, the string "PCIR" or NVIDIA's alternate "NPDS"
-    NvU16       vendorID;           //  04h: Vendor Identification
-    NvU16       deviceID;           //  06h: Device Identification
-    NvU16       deviceListPtr;      //  08h: Device List Pointer
-    NvU16       pciDataStructLen;   //  0Ah: PCI Data Structure Length
-    NvU8        pciDataStructRev;   //  0Ch: PCI Data Structure Revision
-    NvU8        classCode[3];       //  0Dh: Class Code
-    NvU16       imageLen;           //  10h: Image Length (units of 512 bytes)
-    NvU16       vendorRomRev;       //  12h: Revision Level of the Vendor's ROM
-    NvU8        codeType;           //  14h: holds NBSI_OBJ_CODE_TYPE (0x70) and others
-    NvU8        lastImage;          //  15h: Last Image Indicator: bit7=1 is lastImage
-    NvU16       maxRunTimeImageLen; //  16h: Maximum Run-time Image Length (units of 512 bytes)
-} PCI_DATA_STRUCT, *PPCI_DATA_STRUCT;
-#pragma pack()
-
-#define NV_PCI_DATA_EXT_SIG 0x4544504E // "NPDE" in dword format
-#define NV_PCI_DATA_EXT_REV_10 0x100      // 1.0
-#define NV_PCI_DATA_EXT_REV_11 0x101      // 1.1
-
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_SIG            0x0
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_LEN            0x6
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_REV            0x4
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_SUBIMAGE_LEN   0x8
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_LAST_IMAGE     0xa
-#define OFFSETOF_PCI_DATA_EXT_STRUCT_FLAGS          0xb
-
-#define PCI_DATA_EXT_STRUCT_FLAGS_CHECKSUM_DISABLED 0x04
-
-#pragma pack(1)
-typedef struct _NV_PCI_DATA_EXT_STRUCT
-{
-    NvU32   signature;          //  00h: Signature, the string "NPDE"
-    NvU16   nvPciDataExtRev;    //  04h: NVIDIA PCI Data Extension Revision
-    NvU16   nvPciDataExtLen;    //  06h: NVIDIA PCI Data Extension Length
-    NvU16   subimageLen;        //  08h: Sub-image Length
-    NvU8    privLastImage;      //  0Ah: Private Last Image Indicator
-    NvU8    flags;              //  0Bh: Private images enabled if bit0=1
-} NV_PCI_DATA_EXT_STRUCT, *PNV_PCI_DATA_EXT_STRUCT;
-#pragma pack()
-
-#endif // PCIEXPTBL_H
-
-
@@ -10,8 +10,6 @@ import ctypes


 class AsDictMixin:
-    import sys
-    if sys.version_info >= (3, 14): _layout_ = 'ms'
    @classmethod
    def as_dict(cls, self):
        result = {}
@@ -882,11 +882,6 @@ impl<'a> Thread<'a> {
                            let s1 = sign_ext((s1 & 0xffffff) as u64, 24) as i32;
                            (s0 * s1) as u32
                        }
-                        10 => {
-                            let s0 = sign_ext((s0 & 0xffffff) as u64, 24) as i64;
-                            let s1 = sign_ext((s1 & 0xffffff) as u64, 24) as i64;
-                            ((s0 * s1) >> 32) as u32
-                        }
                        17 | 18 | 26 => {
                            let (s0, s1) = (s0 as i32, s1 as i32);
                            (match op {
@@ -935,7 +930,7 @@ impl<'a> Thread<'a> {

            let op = ((instr >> 16) & 0x3ff) as u32;
            match op {
-                764 | 765 | 288 | 289 | 290 | 766 | 767 | 768 | 769 | 770 => {
+                764 | 765 | 288 | 289 | 290 | 766 | 767 | 768 | 769 => {
                    let vdst = (instr & 0xff) as usize;
                    let sdst = ((instr >> 8) & 0x7f) as usize;
                    let f = |i: u32| -> usize { ((instr >> i) & 0x1ff) as usize };
@@ -1001,10 +996,6 @@ impl<'a> Thread<'a> {
                                    let ret = s0.wrapping_sub(s1);
                                    (ret as u32, s1 > s0)
                                }
-                                770 => {
-                                    let ret = s1.wrapping_sub(s0);
-                                    (ret as u32, s0 > s1)
-                                }
                                _ => todo_instr!(instruction)?,
                            };
                            if self.exec.read() {
@@ -1,32 +1,98 @@
 import numpy as np
 import unittest
 import subprocess, struct, math
-from tinygrad import Tensor, dtypes, Device, UOp
-from tinygrad.helpers import getenv
-from tinygrad.runtime.support.compiler_amd import amdgpu_disassemble
-from tinygrad.renderer import ProgramSpec
-from tinygrad.engine.realize import CompiledRunner
+from typing import cast
+from tinygrad.runtime.ops_amd import AMDProgram, AMDDevice
+from tinygrad import Tensor, dtypes, Device
+from tinygrad.helpers import diskcache, OSX, getenv

-def get_output(asm:str, n_threads:int=1):
-  input_asm = "\n".join([ln if ln.strip().startswith('asm volatile') else f'asm volatile("{ln.strip().lstrip()}" : "+v"(a), "+v"(b));'
-                         for ln in asm.strip().splitlines() if ln.strip()])
-  src = f"""
-  typedef long unsigned int size_t;
-  extern "C" __attribute__((device, const)) size_t __ockl_get_local_id(unsigned int);
-  extern "C" __attribute__((global)) void __attribute__((amdgpu_flat_work_group_size(1, {n_threads}))) test(unsigned int* data0_1) {{
-    int l = __ockl_get_local_id(0);
-    unsigned a = 0, b = 0, c = 0;
-    {input_asm}
-    unsigned res;
-    asm volatile("v_mov_b32 %0, %1" : "=v"(res) : "v"(a));
-    *(data0_1+l) = res;
-  }}"""
-  t = Tensor.zeros(n_threads, dtype=dtypes.uint32).contiguous().realize()
-  prg = ProgramSpec("test", src, Device.DEFAULT, UOp.sink(t), global_size=[1, 1, 1], local_size=[n_threads, 1, 1])
-  car = CompiledRunner(prg)
-  if getenv("PRINT_ASM"): amdgpu_disassemble(car.lib)
-  car([t.uop.buffer], {}, wait=True)
-  return t.numpy()
+@diskcache
+def assemble(code:str) -> bytes:
+  try:
+    LLVM_MC = "llvm-mc" if OSX else "/opt/rocm/llvm/bin/llvm-mc"
+    return subprocess.run([LLVM_MC, "--arch=amdgcn", "--mcpu=gfx1100", "--triple=amdgcn-amd-amdhsa", "-filetype=obj", "-o", "-"],
+                           input=code.encode("utf-8"), stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True).stdout
+  except subprocess.CalledProcessError as e:
+    print("stderr:")
+    print(e.stderr.decode())
+    raise
+
+# copied from extra/rdna
+def get_prg(code:str, v_cnt:int, s_cnt:int):
+  function_name = "test"
+  metadata = f"""
+amdhsa.kernels:
+- .args:
+  - .address_space: global
+    .name: buf_0
+    .offset: 0
+    .size: 8
+    .type_name: unsigned int*
+    .value_kind: global_buffer
+  .group_segment_fixed_size: 0
+  .kernarg_segment_align: 8
+  .kernarg_segment_size: 8
+  .language: OpenCL C
+  .language_version:
+  - 1
+  - 2
+  .max_flat_workgroup_size: 256
+  .name: test
+  .private_segment_fixed_size: 0
+  .sgpr_count: {s_cnt}
+  .sgpr_spill_count: 0
+  .symbol: test.kd
+  .uses_dynamic_stack: false
+  .vgpr_count: {v_cnt}
+  .vgpr_spill_count: 0
+  .wavefront_size: 32
+amdhsa.target: amdgcn-amd-amdhsa--gfx1100
+amdhsa.version:
+- 1
+- 2
+  """
+  boilerplate_start = f"""
+  .rodata
+  .global {function_name}.kd
+  .type {function_name}.kd,STT_OBJECT
+  .align 0x10
+  .amdhsa_kernel {function_name}"""
+  kernel_desc = {
+    '.amdhsa_group_segment_fixed_size': 0, '.amdhsa_private_segment_fixed_size': 0, '.amdhsa_kernarg_size': 0,
+    '.amdhsa_next_free_vgpr': v_cnt,   # this matters!
+    '.amdhsa_reserve_vcc': 0, '.amdhsa_reserve_xnack_mask': 0,
+    '.amdhsa_next_free_sgpr': s_cnt,
+    '.amdhsa_float_round_mode_32': 0, '.amdhsa_float_round_mode_16_64': 0, '.amdhsa_float_denorm_mode_32': 3, '.amdhsa_float_denorm_mode_16_64': 3,
+    '.amdhsa_dx10_clamp': 1, '.amdhsa_ieee_mode': 1, '.amdhsa_fp16_overflow': 0,
+    '.amdhsa_workgroup_processor_mode': 1, '.amdhsa_memory_ordered': 1, '.amdhsa_forward_progress': 0, '.amdhsa_enable_private_segment': 0,
+    '.amdhsa_system_sgpr_workgroup_id_x': 1, '.amdhsa_system_sgpr_workgroup_id_y': 1, '.amdhsa_system_sgpr_workgroup_id_z': 1,
+    '.amdhsa_system_sgpr_workgroup_info': 0, '.amdhsa_system_vgpr_workitem_id': 2, # is amdhsa_system_vgpr_workitem_id real?
+    '.amdhsa_exception_fp_ieee_invalid_op': 0, '.amdhsa_exception_fp_denorm_src': 0,
+    '.amdhsa_exception_fp_ieee_div_zero': 0, '.amdhsa_exception_fp_ieee_overflow': 0, '.amdhsa_exception_fp_ieee_underflow': 0,
+    '.amdhsa_exception_fp_ieee_inexact': 0, '.amdhsa_exception_int_div_zero': 0,
+    '.amdhsa_user_sgpr_dispatch_ptr': 0, '.amdhsa_user_sgpr_queue_ptr': 0, '.amdhsa_user_sgpr_kernarg_segment_ptr': 1,
+    '.amdhsa_user_sgpr_dispatch_id': 0, '.amdhsa_user_sgpr_private_segment_size': 0, '.amdhsa_wavefront_size32': 1, '.amdhsa_uses_dynamic_stack': 0}
+  code_start = f""".end_amdhsa_kernel
+  .text
+  .global {function_name}
+  .type {function_name},@function
+  .p2align 8
+  {function_name}:
+  """
+  ret = ".amdgpu_metadata\n" + metadata + ".end_amdgpu_metadata" + boilerplate_start + "\n" + '\n'.join("%s %d" % x for x in kernel_desc.items()) \
+      + "\n" + code_start + code + f"\n.size {function_name}, .-{function_name}"
+  return AMDProgram(cast(AMDDevice, Device["AMD"]), function_name, assemble(ret))
+
+def get_output(s:str, n_threads:int=1):
+  assert n_threads <= 32
+  code = "\n".join(["s_load_b64 s[0:1], s[0:1], null", "v_lshlrev_b32_e32 v0, 2, v0", s,
+    "s_waitcnt 0",
+    "global_store_b32 v0, v1, s[0:1]",
+    "s_nop 0", "s_sendmsg sendmsg(MSG_DEALLOC_VGPRS)", "s_endpgm"])
+  test = Tensor.zeros((n_threads,), dtype=dtypes.uint32).contiguous().realize().uop.buffer
+  prg = get_prg(code, 32, 32)
+  prg(test._buf, global_size=(1, 1, 1), local_size=(n_threads, 1, 1), wait=True)
+  return test.numpy()

 def f16_to_bits(x:float) -> int: return struct.unpack('<H', struct.pack('<e', x))[0]
 def f32_from_bits(x:int) -> float: return struct.unpack('<f', struct.pack('<I', x))[0]
@@ -39,57 +105,54 @@ class TestHW(unittest.TestCase):

  def test_simple(self):
    out = get_output("""
-    v_mov_b32_e32 %1 42
-    v_mov_b32_e32 %2 %1
-    """)[0]
+    v_mov_b32_e32 v10 42
+    v_mov_b32_e32 v1 v10
+    """, n_threads=2)
    np.testing.assert_equal(out, 42)

  def test_exec_mov(self):
    out = get_output("""
-    v_mov_b32_e32 %1 42
+    v_mov_b32_e32 v10 42
    s_mov_b32_e32 exec_lo 0b10
-    v_mov_b32_e32 %1 10
+    v_mov_b32_e32 v10 10
    s_mov_b32_e32 exec_lo 0b11
-    v_mov_b32_e32 %2 %1
+    v_mov_b32_e32 v1 v10
    """, n_threads=2)
    np.testing.assert_equal(out, [42, 10])

  def test_exec_cmp_vopc(self):
    out = get_output("""
    s_mov_b32 vcc_lo 0 // reset vcc
-    v_mov_b32_e32 %1 42
-    v_mov_b32_e32 %2 10
+    v_mov_b32_e32 v10 42
+    v_mov_b32_e32 v11 10
    s_mov_b32_e32 exec_lo 0b01
-    v_cmp_ne_u32 %1 %2
+    v_cmp_ne_u32 v10 v11
    s_mov_b32_e32 exec_lo 0b11
-    v_mov_b32_e32 %2 vcc_lo
+    v_mov_b32_e32 v1 vcc_lo
    """, n_threads=2)
    np.testing.assert_equal(out, 0b01)

  def test_exec_cmpx_vop3(self):
    out = get_output("""
-    s_mov_b32_e32 exec_lo 0b11
-    v_mov_b32_e32 %1 42
-    v_mov_b32_e32 %2 10
+    v_mov_b32_e32 v10 42
+    v_mov_b32_e32 v11 10
    s_mov_b32_e32 exec_lo 0b01
-    v_cmpx_ne_u32 %1 %2
+    v_cmpx_ne_u32 v10 v11
    s_mov_b32_e32 s10 exec_lo
    s_mov_b32_e32 exec_lo 0b11
-    v_mov_b32_e32 %2 s10
-    """, n_threads=2)[0]
-    np.testing.assert_equal(out & 0b11, 0b01)
+    v_mov_b32_e32 v1 s10
+    """, n_threads=2)
+    np.testing.assert_equal(out, 0b01)

  def test_fmac_vop3_modifier(self):
    init_state = f"""
-    asm volatile("v_mov_b32_e32 %1, {f16_to_bits(4.0)}" : "+v"(a));
-    asm volatile("v_mov_b32_e32 %1, {f16_to_bits(3.0)}" : "+v"(b));
-    asm volatile("v_mov_b32_e32 %1, {f16_to_bits(2.0)}" : "+v"(c));
+    v_mov_b32_e32 v10 {f16_to_bits(4.0)}
+    v_mov_b32_e32 v11 {f16_to_bits(3.0)}
+    v_mov_b32_e32 v1 {f16_to_bits(2.0)}
    """
-    mov = """asm volatile("v_mov_b32_e32 %1, %2" : "+v"(c), "+v"(a));"""
-    def fmac(a, b, c): return f"""asm volatile("v_fmac_f16_e64 {c}, {a}, {b}" : "+v"(c) : "v"(a), "v"(b));"""+"\n"+mov
-    self.assertEqual(get_output(init_state+"\n"+fmac("%1", "%2", "%3")), f16_to_bits(14.))
-    self.assertEqual(get_output(init_state+"\n"+fmac("%1", "-%2", "%3")), f16_to_bits(-10.))
-    self.assertEqual(get_output(init_state+"\n"+fmac("-%1", "-%2", "%3")), f16_to_bits(14.))
+    self.assertEqual(get_output(init_state+"\n"+"v_fmac_f16_e64 v1 v11 v10"), f16_to_bits(14.))
+    self.assertEqual(get_output(init_state+"\n"+"v_fmac_f16_e64 v1 -v11 v10"), f16_to_bits(-10.))
+    self.assertEqual(get_output(init_state+"\n"+"v_fmac_f16_e64 v1 -v11 -v10"), f16_to_bits(14.))

  def test_s_abs_i32(self):
    def s_abs_i32(x, y, dst="s10", scc=0):
@@ -97,7 +160,7 @@ class TestHW(unittest.TestCase):
        self.assertEqual(get_output(f"""
        s_mov_b32_e32 {dst} {x}
        s_abs_i32 {dst} {dst}
-        v_mov_b32_e32 %2 {reg}
+        v_mov_b32_e32 v1 {reg}
        """)[0], val)
    s_abs_i32(0x00000001, 0x00000001, scc=1)
    s_abs_i32(0x7fffffff, 0x7fffffff, scc=1)
@@ -110,8 +173,8 @@ class TestHW(unittest.TestCase):
  def test_v_rcp_f32_neg_vop3(self):
    def v_neg_rcp_f32(x:float, y:float):
      out = get_output(f"""
-      v_mov_b32_e32 %2 {f32_to_bits(x)}
-      v_rcp_f32_e64 %2, -%2
+      v_mov_b32_e32 v1 {f32_to_bits(x)}
+      v_rcp_f32_e64 v1, -v1
      """)[0]
      assert out == f32_to_bits(y), f"{f32_from_bits(out)} != {y} / {out} != {f32_to_bits(y)}"
    v_neg_rcp_f32(math.inf, -0.0)
@@ -123,11 +186,10 @@ class TestHW(unittest.TestCase):

  def test_v_cndmask_b32_neg(self):
    def v_neg(x:int|float, y:float):
-      # always pick -v1
      out = get_output(f"""
-      v_mov_b32_e32 %2 {f32_to_bits(x)}
-      s_mov_b32_e32 s10 1
-      v_cndmask_b32 %2, %2, -%2 s10
+      v_mov_b32_e32 v1 {f32_to_bits(x)}
+      s_mov_b32_e32 s10 1 // always pick -v1
+      v_cndmask_b32 v1, v1, -v1 s10
      """)[0]
      assert out == f32_to_bits(y), f"{f32_from_bits(out)} != {y} / {out} != {f32_to_bits(y)}"
    v_neg(-0.0, 0.0)
@@ -136,12 +198,5 @@ class TestHW(unittest.TestCase):
    v_neg(math.inf, -math.inf)
    v_neg(-math.inf, math.inf)

-  def test_v_subrev_wrap(self):
-    out = get_output("""
-    v_dual_mov_b32 %1, 0xffffffff :: v_dual_mov_b32 %2, 0x0
-    v_subrev_co_u32 %2, vcc_lo, %2, %1
-    """)[0]
-    self.assertEqual(out, 0xffff_ffff)
-
 if __name__ == "__main__":
  unittest.main()
@@ -2,6 +2,8 @@

 ## Getting SQ Thread Trace

+Only supported on 7900XTX, requires either AM (`rmmod amdgpu`) or disabling power gating on AMD (`ppfeaturemask=0xffff3fff`, don't forget to rebuild initramfs)
+
 SQTT is implemented on top of normal tinygrad profiling, `VIZ=1 SQTT=1` to get profile pickle with sqtt data embedded in it.

 `SQTT_BUFFER_SIZE=X` to change size of SQTT buffer (per shader engine, 6 SEs on 7900xtx) in megabytes, default 256.
@@ -0,0 +1,68 @@
+import ctypes
+from dataclasses import dataclass
+import tinygrad.runtime.autogen.comgr as comgr
+from tinygrad.runtime.support.compiler_amd import check
+
+@dataclass
+class InstrCtx:
+  pc:int=0
+  inst:str=""
+
+@comgr.amd_comgr_create_disassembly_info.argtypes[2]
+def instr_cb(text, user_data):
+  c = ctypes.cast(user_data, ctypes.POINTER(ctypes.py_object)).contents.value
+  c.inst = ctypes.string_at(text).decode("utf-8","replace").strip()
+  return comgr.AMD_COMGR_STATUS_SUCCESS
+
+# nop callback
+@comgr.amd_comgr_create_disassembly_info.argtypes[3]
+def addr_cb(*args): return comgr.AMD_COMGR_STATUS_SUCCESS
+
+def comgr_get_address_table(lib:bytes) -> dict[int, tuple[str, int]]:
+  check(comgr.amd_comgr_create_data(comgr.AMD_COMGR_DATA_KIND_EXECUTABLE, ctypes.byref(data_src:=comgr.amd_comgr_data_t())))
+  lib_buf = ctypes.create_string_buffer(lib, len(lib))
+  check(comgr.amd_comgr_set_data(data_src, len(lib), lib_buf))
+  check(comgr.amd_comgr_get_data_isa_name(data_src, isa_sz:=ctypes.c_size_t(128), isa:=(ctypes.c_char*isa_sz.value)()))
+
+  @comgr.amd_comgr_create_disassembly_info.argtypes[1]
+  def memory_cb(from_addr, to, size, _):
+    base, buf_len = ctypes.addressof(lib_buf), len(lib_buf)
+    start = int(from_addr) - base
+    if start < 0 or start >= buf_len: return 0
+    ctypes.memmove(to, base + start, n:=min(int(size), buf_len - start))
+    return n
+
+  info_src = comgr.amd_comgr_disassembly_info_t()
+  check(comgr.amd_comgr_create_disassembly_info(ctypes.cast(isa, ctypes.POINTER(ctypes.c_char)), memory_cb, instr_cb, addr_cb, info_src))
+
+  @comgr.amd_comgr_iterate_symbols.argtypes[1]
+  def sym_callback(sym, udata):
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_TYPE, ctypes.byref(sym_type:=ctypes.c_int())))
+    if sym_type.value != comgr.AMD_COMGR_SYMBOL_TYPE_FUNC: return comgr.AMD_COMGR_STATUS_SUCCESS
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_VALUE, ctypes.byref(vaddr:=ctypes.c_uint64())))
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_SIZE, ctypes.byref(size:=ctypes.c_uint64())))
+    check(comgr.amd_comgr_map_elf_virtual_address_to_code_object_offset(data_src, vaddr.value, ctypes.byref(offset:=ctypes.c_uint64()),
+                                                                        ctypes.byref(ctypes.c_uint64()), ctypes.byref(nobits:=ctypes.c_bool())))
+    check(nobits.value)
+    base = ctypes.addressof(lib_buf)
+    pc = base + offset.value
+    end = pc + size.value
+    addr_table = ctypes.cast(udata, ctypes.POINTER(ctypes.py_object)).contents.value
+    instr_ref = ctypes.py_object(ctx:=InstrCtx())
+    instr_ptr = ctypes.cast(ctypes.pointer(instr_ref), ctypes.c_void_p)
+    while pc < end:
+      size_read = ctypes.c_uint64(0)
+      ctx.pc = pc
+      st = comgr.amd_comgr_disassemble_instruction(info_src, ctypes.c_uint64(pc), instr_ptr, ctypes.byref(size_read))
+      if st == comgr.AMD_COMGR_STATUS_SUCCESS and size_read.value:
+        rel = (pc - base) - offset.value
+        addr_table[vaddr.value + rel] = (ctx.inst, int(size_read.value))
+        pc += size_read.value
+      else: # don't inf loop if comgr fails
+        b = ctypes.c_ubyte.from_buffer(lib_buf, pc - base).value
+        addr_table[vaddr.value + (pc - base - offset.value)] = (f"DISASSEMBLER ISSUE 0x{b:02x}", 1)
+        pc += 1
+    return comgr.AMD_COMGR_STATUS_SUCCESS
+  addr_table:dict[int, tuple[str, int]] = {}
+  check(comgr.amd_comgr_iterate_symbols(data_src, sym_callback, ctypes.cast(ctypes.pointer(ctypes.py_object(addr_table)), ctypes.c_void_p)))
+  return addr_table
@@ -4,7 +4,7 @@ import argparse, ctypes, struct, hashlib, pickle, code, typing, functools
 import tinygrad.runtime.autogen.sqtt as sqtt
 from tinygrad.device import ProfileEvent, ProfileDeviceEvent, ProfileProgramEvent
 from tinygrad.runtime.ops_amd import ProfileSQTTEvent
-from tinygrad.helpers import round_up, flatten, all_same, temp
+from tinygrad.helpers import round_up, flatten, all_same
 from dataclasses import dataclass

 CHUNK_CLASSES = {
@@ -154,22 +154,8 @@ class RGP:
      if device not in device_events: raise RuntimeError(f"Device {device} not found in profile, devices in profile: {', '.join(device_events.keys())} ")
      device_event = device_events[device]
    sqtt_events = [x for x in profile if isinstance(x, ProfileSQTTEvent) and x.device == device_event.device]
-    device_props = device_event.props
-    # merge events per SE
-    merged_sqtt_events:dict[int, ProfileSQTTEvent] = {}
-    for ev in sqtt_events:
-      if ev.se not in merged_sqtt_events: merged_sqtt_events[ev.se] = ev
-      else:
-        merged_sqtt_events[ev.se] = ProfileSQTTEvent(
-          device=ev.device,
-          kern=ev.kern,
-          se=ev.se,
-          itrace=merged_sqtt_events[ev.se].itrace or ev.itrace,
-          blob=merged_sqtt_events[ev.se].blob + ev.blob,
-        )
-    sqtt_events = list(merged_sqtt_events.values())
-
    if len(sqtt_events) == 0: raise RuntimeError(f"Device {device_event.device} doesn't contain SQTT data")
+    device_props = sqtt_events[0].props
    gfx_ver = device_props['gfx_target_version'] // 10000
    gfx_iplvl = getattr(sqtt, f"SQTT_GFXIP_LEVEL_GFXIP_{device_props['gfx_target_version']//10000}_{(device_props['gfx_target_version']//100)%100}",
                        getattr(sqtt, f"SQTT_GFXIP_LEVEL_GFXIP_{device_props['gfx_target_version']//10000}", None))
@@ -210,7 +196,7 @@ class RGP:
        flags=0,
        trace_shader_core_clock=0x93f05080,
        trace_memory_clock=0x4a723a40,
-        device_id={110000: 0x744c, 110003: 0x7480, 120001: 0x7550, 120000: 0x7550}[device_props['gfx_target_version']],
+        device_id={110000: 0x744c, 110003: 0x7480, 120001: 0x7550}[device_props['gfx_target_version']],
        device_revision_id=0xc8,
        vgprs_per_simd=1536,
        sgprs_per_simd=128*16,
@@ -324,7 +310,7 @@ class RGP:
 if __name__ == '__main__':
  parser = argparse.ArgumentParser(prog='rgptool', description='A tool to create (from pickled tinygrad profile), inspect and modify Radeon GPU Profiler files')
  parser.add_argument('command')
-  parser.add_argument('input', nargs='?', default=temp("profile.pkl", append_user=True))
+  parser.add_argument('input')
  parser.add_argument('-d', '--device')
  parser.add_argument('-o', '--output')
  args = parser.parse_args()
@@ -346,4 +332,3 @@ if __name__ == '__main__':

  if args.output is not None:
    with open(args.output, 'wb+') as fd: fd.write(rgp.to_bytes())
-    print(f"Saved to {args.output}")
@@ -1,32 +1,9 @@
-import ctypes, pathlib, argparse, pickle, re, functools, dataclasses, itertools
-from tinygrad.helpers import temp, unwrap, DEBUG
-from tinygrad.device import ProfileEvent, ProfileDeviceEvent, ProfileProgramEvent
-from tinygrad.runtime.ops_amd import ProfileSQTTEvent, ProfilePMCEvent
-from tinygrad.runtime.autogen import llvm, rocprof
-from tinygrad.runtime.support.elf import elf_loader
-
-# to pass NULL to callbacks
-llvm.LLVMCreateDisasmCPUFeatures.argtypes = tuple(llvm.LLVMCreateDisasmCPUFeatures.argtypes[:5]) + (ctypes.c_void_p, ctypes.c_void_p)
-def llvm_disasm(arch:str, lib:bytes) -> dict[int, tuple[str, int]]:
-  llvm.LLVMInitializeAMDGPUTargetInfo()
-  llvm.LLVMInitializeAMDGPUTargetMC()
-  llvm.LLVMInitializeAMDGPUAsmParser()
-  llvm.LLVMInitializeAMDGPUDisassembler()
-  ctx = llvm.LLVMCreateDisasmCPUFeatures("amdgcn-amd-amdhsa".encode(), arch.encode(), "".encode(), None, 0, None, None)
-
-  image, sections, relocs = elf_loader(lib)
-  text = next((sh.header for sh in sections if sh.name == ".text"), None)
-  off, sz = unwrap(text).sh_addr, unwrap(text).sh_size
-
-  addr_table:dict[int, tuple[str, int]] = {}
-  out = ctypes.create_string_buffer(128)
-  cur_off = off
-  while cur_off < sz + off:
-    view = (ctypes.c_ubyte * ((sz + off) - cur_off)).from_buffer_copy(memoryview(image)[cur_off:])
-    instr_sz = llvm.LLVMDisasmInstruction(ctx, view, ctypes.c_uint64(len(view)), ctypes.c_uint64(0), out, ctypes.c_size_t(128))
-    addr_table[cur_off] = (out.value.decode("utf-8", "replace").strip(), instr_sz)
-    cur_off += instr_sz
-  return addr_table
+import ctypes, pathlib, argparse, pickle, re, functools, dataclasses
+from extra.sqtt.rocprof import rocprof
+from extra.sqtt.disasm import comgr_get_address_table
+from tinygrad.helpers import temp, DEBUG
+from tinygrad.device import ProfileEvent, ProfileProgramEvent
+from tinygrad.runtime.ops_amd import ProfileSQTTEvent

@dataclasses.dataclass
 class InstInfo:
@@ -40,77 +17,51 @@ class InstInfo:
  def on_ev(self, ev):
    self.hit, self.lat, self.stall = self.hit + 1, self.lat + ev.duration, self.stall + ev.stall

-@dataclasses.dataclass(frozen=True)
-class InstExec:
-  typ:str
-  inst:str
-  stall:int
-  dur:int
-  time:int
-
-@dataclasses.dataclass(frozen=True)
-class PrgExec:
-  name:str
-  wave:int
-  cu:int
-  simd:int
-  def __str__(self): return f"{self.name},{self.wave},{self.cu},{self.simd}"
-
-@dataclasses.dataclass(frozen=True)
-class WaveExec:
-  wave_id:int
-  cu:int
-  simd:int
-  insts:list[InstExec]
-
 class _ROCParseCtx:
-  def __init__(self, dev_evs:dict[str, ProfileDeviceEvent], sqtt_evs:list[ProfileSQTTEvent], prog_evs:list[ProfileProgramEvent]):
-    self.dev_evs, self.sqtt_evs, self.prog_evs = dev_evs, iter(sqtt_evs), prog_evs
-    self.wave_events:dict[PrgExec, dict[int, InstInfo]] = {}
-    self.disasms:dict[tuple[str, int], tuple[str, int]] = {}
-    self.inst_execs:dict[str, list[WaveExec]] = {}
+  def __init__(self, sqtt_evs:list[ProfileSQTTEvent], prog_evs:list[ProfileProgramEvent]):
+    self.sqtt_evs, self.prog_evs = iter(sqtt_evs), prog_evs
+    self.wave_events, self.disasms, self.addr2prg = {}, {}, {}

    for prog in prog_evs:
-      arch = "gfx%d%x%x" % ((trgt:=unwrap(dev_evs[prog.device].props)['gfx_target_version']) // 10000, (trgt // 100) % 100, trgt % 100)
-      for addr, info in llvm_disasm(arch, unwrap(prog.lib)).items():
-        self.disasms[(prog.name, unwrap(prog.base) + addr)] = info
+      for addr, info in comgr_get_address_table(prog.lib).items():
+        self.disasms[prog.base + addr] = info
+        self.addr2prg[prog.base + addr] = prog

  def next_sqtt(self):
    x = next(self.sqtt_evs, None)
-    self.active_kern = x.kern if x is not None else None
    self.active_se = x.se if x is not None else None
    return x

+  def find_program(self, addr): return self.addr2prg[addr]
+
  def on_occupancy_ev(self, ev):
-    if DEBUG >= 5: print("OCC", ev.time, self.active_se, ev.cu, ev.simd, ev.wave_id, ev.start)
+    if DEBUG >= 4: print("OCC", ev.time, self.active_se, ev.cu, ev.simd, ev.wave_id, ev.start)

  def on_wave_ev(self, ev):
-    if DEBUG >= 5: print("WAVE", ev.wave_id, self.active_se, ev.cu, ev.simd, ev.contexts, ev.begin_time, ev.end_time)
+    if DEBUG >= 4: print("WAVE", ev.wave_id, self.active_se, ev.cu, ev.simd, ev.contexts, ev.begin_time, ev.end_time)

-    asm:dict[int, InstInfo] = {}
-    inst_execs:list[InstExec] = []
+    asm = {}
    for j in range(ev.instructions_size):
      inst_ev = ev.instructions_array[j]
      inst_typ = rocprof.rocprofiler_thread_trace_decoder_inst_category_t__enumvalues[inst_ev.category]
-      inst_disasm = self.disasms[(unwrap(self.active_kern), unwrap(inst_ev.pc.address))][0]
-      asm.setdefault(inst_ev.pc.address, InstInfo(typ=inst_typ, inst=inst_disasm))
+      asm.setdefault(inst_ev.pc.address, InstInfo(typ=inst_typ, inst=self.disasms[inst_ev.pc.address][0]))
      asm[inst_ev.pc.address].on_ev(inst_ev)
-      inst_execs.append(InstExec(inst_typ, inst_disasm, inst_ev.stall, inst_ev.duration, inst_ev.time))

-    if ev.instructions_size > 0:
-      self.wave_events[key:=PrgExec(unwrap(self.active_kern), ev.wave_id, ev.cu, ev.simd)] = asm
-      self.inst_execs.setdefault(key.name, []).append(WaveExec(ev.wave_id, ev.cu, ev.simd, inst_execs))
+    self.wave_events[(self.find_program(ev.instructions_array[0].pc.address).name, ev.wave_id, ev.cu, ev.simd)] = asm

-def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:
-  dev_events:dict[str, ProfileDeviceEvent] = {}
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--profile', type=pathlib.Path, help='Path to profile', default=pathlib.Path(temp("profile.pkl", append_user=True)))
+  args = parser.parse_args()
+
+  with args.profile.open("rb") as f: profile = pickle.load(f)
  sqtt_events:list[ProfileSQTTEvent] = []
  prog_events:list[ProfileProgramEvent] = []
  for e in profile:
-    if isinstance(e, ProfileDeviceEvent): dev_events[e.device] = e
    if isinstance(e, ProfileSQTTEvent): sqtt_events.append(e)
    if isinstance(e, ProfileProgramEvent) and e.device.startswith("AMD"): prog_events.append(e)

-  ROCParseCtx = _ROCParseCtx(dev_events, sqtt_events, prog_events)
+  ROCParseCtx = _ROCParseCtx(sqtt_events, prog_events)

  @rocprof.rocprof_trace_decoder_se_data_callback_t
  def copy_cb(buf, buf_size, data_ptr):
@@ -127,12 +78,12 @@ def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:
      case rocprof.ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE:
        for ev in (rocprof.rocprofiler_thread_trace_decoder_wave_t * n).from_address(events_ptr): ROCParseCtx.on_wave_ev(ev)
      case _:
-        if DEBUG >= 5: print(rocprof.rocprofiler_thread_trace_decoder_record_type_t__enumvalues[record_type], events_ptr, n)
+        if DEBUG >= 2: print(rocprof.rocprofiler_thread_trace_decoder_record_type_t__enumvalues[record_type], events_ptr, n)
    return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS

  @rocprof.rocprof_trace_decoder_isa_callback_t
  def isa_cb(instr_ptr, mem_size_ptr, size_ptr, pc, data_ptr):
-    instr, mem_size_ptr[0] = ROCParseCtx.disasms[(unwrap(ROCParseCtx.active_kern), pc.address)]
+    instr, mem_size_ptr[0] = ROCParseCtx.disasms[pc.address]

    # this is the number of bytes to next instruction, set to 0 for end_pgm
    if instr == "s_endpgm": mem_size_ptr[0] = 0
@@ -145,27 +96,5 @@ def decode(profile:list[ProfileEvent]) -> _ROCParseCtx:

    return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS

-  try:
-    rocprof.rocprof_trace_decoder_parse_data(copy_cb, trace_cb, isa_cb, None)
-  except AttributeError as e: raise RuntimeError("Failed to find rocprof-trace-decoder. Run ./extra/sqtt/install_sqtt_decoder.py to install") from e
-  return ROCParseCtx
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--profile', type=pathlib.Path, help='Path to profile', default=pathlib.Path(temp("profile.pkl", append_user=True)))
-  args = parser.parse_args()
-
-  with args.profile.open("rb") as f: profile = pickle.load(f)
-  rctx = decode(profile)
-  print('SQTT:', rctx.wave_events.keys())
-
-  for ev in profile:
-    if not isinstance(ev, ProfilePMCEvent): continue
-    print(f"PMC Event: dev={ev.device} kern={ev.kern}")
-    ptr = 0
-    for s in ev.sched:
-      view = memoryview(ev.blob).cast('Q')
-      print(f"\t{s.name}")
-      for xcc, inst, se_idx, sa_idx, wgp_idx in itertools.product(range(s.xcc), range(s.inst), range(s.se), range(s.sa), range(s.wgp)):
-        print(f"\t\tXCC {xcc} Inst {inst} SE {se_idx} SA {sa_idx} WGP {wgp_idx}: {view[ptr]:#x}")
-        ptr += 1
+  rocprof.rocprof_trace_decoder_parse_data(copy_cb, trace_cb, isa_cb, None)
+  print(ROCParseCtx.wave_events.keys())
@@ -13,6 +13,6 @@ if __name__ == "__main__":
    os.chmod(fp, 0o755)
    os.system(f"sudo {fp} --prefix={fp.parent} --include-subdir")
  else:
-    lib = fetch("https://github.com/ROCm/rocprof-trace-decoder/raw/43bf0fef74a83c3c25badfc5a09c0bd39ed8c6f9/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so", name="librocprof-trace-decoder.so")
+    lib = fetch("https://github.com/ROCm/rocprof-trace-decoder/raw/5420409ad0963b2d76450add067b9058493ccbd0/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so", name="librocprof-trace-decoder.so")
  shutil.copy2(lib, DEST)
  print(f"Installed {lib.name} to", DEST)
@@ -8,19 +8,6 @@
 # LONGDOUBLE_SIZE is: 16
 #
 import ctypes, ctypes.util
-PATHS_TO_TRY = [
-  '/usr/local/lib/librocprof-trace-decoder.so',
-  '/usr/local/lib/librocprof-trace-decoder.dylib',
-]
-def _try_dlopen_rocprof_trace_decoder():
-  library = ctypes.util.find_library("rocprof-trace-decoder")
-  if library:
-    try: return ctypes.CDLL(library)
-    except OSError: pass
-  for candidate in PATHS_TO_TRY:
-    try: return ctypes.CDLL(candidate)
-    except OSError: pass
-  return None


 class AsDictMixin:
@@ -168,7 +155,7 @@ class FunctionFactoryStub:
 # You can either re-run clan2py with -l /path/to/library.so
 # Or manually fix this by comment the ctypes.CDLL loading
 _libraries = {}
-_libraries['FIXME_STUB'] = _try_dlopen_rocprof_trace_decoder() #  ctypes.CDLL('FIXME_STUB')
+_libraries['FIXME_STUB'] = ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder')) #  ctypes.CDLL('FIXME_STUB')



@@ -1,105 +0,0 @@
-import os
-os.environ["PYTHONPATH"] = "."
-os.environ["SQTT"] = "1"
-os.environ["AMD"] = "1"
-os.environ["VIZ"] = "1"
-os.environ["AMD_LLVM"] = "0"
-
-import unittest
-import sys, contextlib
-from tinygrad import Tensor
-from tinygrad.dtype import dtypes
-from tinygrad.renderer import ProgramSpec
-from tinygrad.uop.ops import UOp, Ops, KernelInfo
-from tinygrad.engine.realize import CompiledRunner
-from tinygrad.device import Device, ProfileDeviceEvent
-
-from extra.sqtt.roc import decode, InstExec, PrgExec
-
-dev = Device["AMD"]
-
-def custom(arg:str, s:UOp|None=None) -> UOp: return UOp(Ops.CUSTOM, src=(s,) if s is not None else (), arg=arg)
-
-def asm_kernel(instrs:list[str], l:int=1, g:int=1) -> Tensor:
-  name = sys._getframe(1).f_code.co_name
-  def fxn(_):
-    L = UOp.special(l, "lidx0")
-    G = UOp.special(g, "gidx0")
-    op = custom("asm volatile (")
-    for inst in instrs: op = custom(f'  "{inst}\\n\\t"', op)
-    op = custom(");", op)
-    return UOp.sink(op, L, G, arg=KernelInfo(name=name))
-  k = Tensor.custom_kernel(Tensor.empty(1), fxn=fxn)[0]
-  return k
-
-@contextlib.contextmanager
-def save_sqtt():
-  # clear the old traces
-  dev.profile_events.clear()
-  sqtt:dict[PrgExec, list[InstExec]] = {}
-  yield sqtt
-  # decode sqtt
-  rctx = decode(dev.profile_events+[ProfileDeviceEvent("AMD", props=dev.device_props())])
-  assert len(rctx.inst_execs) > 0, "empty sqtt output"
-  sqtt.update(rctx.inst_execs)
-
-class TestTiming(unittest.TestCase):
-  def test_v_add(self):
-    with save_sqtt() as sqtt:
-      asm_kernel([f"v_add_f32 v{10+i} v{10+i+1} {10+i}" for i in range(3)]).realize()
-    wave = list(sqtt.values())[0][:-1]
-    assert all(s.dur == 1 for s in wave)
-    assert all(s.stall == 0 for s in wave)
-
-  def test_chain_v_add_1l(self):
-    with save_sqtt() as sqtt:
-      asm_kernel([
-        "v_add_f32_e32 v1 v0 v0",
-        "v_add_f32_e32 v2 v1 v1",
-      ]).realize()
-    wave = list(sqtt.values())[0][:-1]
-    assert all(s.dur == 1 for s in wave)
-    assert all(s.stall == 0 for s in wave)
-
-  def test_multi_cycle_inst(self):
-    with save_sqtt() as sqtt:
-      asm_kernel([
-        "v_mov_b32_e32 v4 0x3f800000",
-        "v_rcp_f32_e32 v5 v4",
-        "v_mul_f32_e32 v6 v5 v4",
-      ]).realize()
-    w = list(sqtt.values())[0]
-    rcp, mul = w[1], w[2]
-    self.assertGreater(rcp.dur, 1) # 4 cycles on gfx11
-    self.assertEqual(mul.dur, 1)
-    # mul depends on v5, how can it run before rcp is done?
-    self.assertGreaterEqual(mul.time, rcp.time+rcp.dur)
-
-  def test_wmma(self):
-    with save_sqtt() as sqtt:
-      asm_kernel([
-        "v_wmma_f32_16x16x16_f16 v[16:23], v[0:7], v[8:15], v[16:23]",
-        "v_add_f32_e32 v0 v16 v0",
-      ], l=32*4).realize()
-    assert len(sqtt) == 2, f"expected two waves, got {len(sqtt)} {list(sqtt.keys())}"
-    wmma = list(sqtt.values())[0][0]
-    self.assertGreater(wmma.dur, 1) # rgp says 32 clocks
-
-  def test_sleep(self):
-    n = 1
-    def sleep_kernel(data0):
-      assert data0.dtype.base == dtypes.ulong
-      op = custom("unsigned long long t0 = __builtin_readcyclecounter();")
-      op = custom(f"__builtin_amdgcn_s_sleep({n});", op)
-      op = custom(f"unsigned long long t1 = __builtin_readcyclecounter();", op)
-      op = custom(f"data0_{data0.size}[0] = t1 - t0;", op)
-      return UOp.sink(data0, op, arg=KernelInfo(name=f"sleep_{n}"))
-    diff_hw_reg = Tensor.empty(1, dtype=dtypes.ulong)
-    diff_hw_reg = Tensor.custom_kernel(diff_hw_reg, fxn=sleep_kernel)[0]
-    with save_sqtt() as sqtt:
-      diff_hw_reg.realize()
-    diff_sqtt = list(sqtt.values())[0][2]
-    self.assertEqual(diff_sqtt.dur, diff_hw_reg.item()-1) # 1 cycle for reading the counter register
-
-if __name__ == "__main__":
-  unittest.main()
@@ -1,106 +0,0 @@
-#include "kittens.cuh"
-
-using namespace kittens;
-
-constexpr int NUM_WORKERS = 4;
-constexpr int PIPE_STAGES = 3;
-
-constexpr int ATTN_B = 16;
-constexpr int ATTN_N = 1024;
-constexpr int ATTN_H = 16;
-constexpr int ATTN_D = 64;
-
-template<int D> constexpr size_t ROWS = 16*(64/D); // height of each worker tile (rows)
-template<int D, typename T=bf16, typename L=row_l> using qkvo_tile = rt<T, ROWS<D>, D, L>;
-template<int D, typename T=float> using attn_tile = rt<T, ROWS<D>, ROWS<D>>;
-template<int D> using shared_tile = st_bf<ROWS<D>, D>;
-template<int D> using global_layout = gl<bf16, -1, -1, -1, D>; // B, N, H, specified at runtime, D known at compile time for this kernel
-template<int D> struct globals { global_layout<D> Qg, Kg, Vg, Og; };
-
-__launch_bounds__(NUM_WORKERS*WARP_THREADS, 1)
-__global__ void attend_ker(bf16 *O_ptr, bf16 *Q_ptr, bf16 *K_ptr, bf16 *V_ptr) {
-    constexpr int D = ATTN_D;
-    global_layout<D> Qg{Q_ptr, ATTN_B, ATTN_N, ATTN_H, nullptr};
-    global_layout<D> Kg{K_ptr, ATTN_B, ATTN_N, ATTN_H, nullptr};
-    global_layout<D> Vg{V_ptr, ATTN_B, ATTN_N, ATTN_H, nullptr};
-    global_layout<D> Og{O_ptr, ATTN_B, ATTN_N, ATTN_H, nullptr};
-    globals<D> g(Qg, Kg, Vg, Og);
-
-    using load_group = kittens::group<2>; // pairs of workers collaboratively load k, v tiles
-    int loadid = load_group::groupid(), workerid = kittens::warpid(); // which worker am I?
-    constexpr int LOAD_BLOCKS = NUM_WORKERS / load_group::GROUP_WARPS;
-    const int batch = blockIdx.z, head = blockIdx.y, q_seq = blockIdx.x * NUM_WORKERS + workerid;
-
-    extern __shared__ alignment_dummy __shm[];
-    shared_allocator al((int*)&__shm[0]);
-
-    shared_tile<D> (&k_smem)[LOAD_BLOCKS][PIPE_STAGES] = al.allocate<shared_tile<D>, LOAD_BLOCKS, PIPE_STAGES>();
-    shared_tile<D> (&v_smem)[LOAD_BLOCKS][PIPE_STAGES] = al.allocate<shared_tile<D>, LOAD_BLOCKS, PIPE_STAGES>();
-
-    shared_tile<D> (&qo_smem)[NUM_WORKERS] = reinterpret_cast<shared_tile<D>(&)[NUM_WORKERS]>(k_smem);
-    // Initialize all of the register tiles.
-    qkvo_tile<D, bf16> q_reg, k_reg; // Q and K are both row layout, as we use mma_ABt.
-    qkvo_tile<D, bf16, col_l> v_reg; // V is column layout, as we use mma_AB.
-    qkvo_tile<D, float> o_reg; // Output tile.
-    attn_tile<D, float> att_block; // attention tile, in float. (We want to use float wherever possible.)
-    attn_tile<D, bf16> att_block_mma; // bf16 attention tile for the second mma_AB. We cast right before that op.
-    typename attn_tile<D, float>::col_vec max_vec_last, max_vec, norm_vec; // these are column vectors for the in-place softmax.
-    // each warp loads its own Q tile of 16x64
-    if (q_seq*ROWS<D> < g.Qg.depth()) {
-      warp::load<1, false>(qo_smem[workerid], g.Qg, {batch, q_seq, head, 0});  // going through shared memory improves coalescing of dram reads.
-      __syncwarp();
-      warp::load(q_reg, qo_smem[workerid]);
-    }
-    __syncthreads();
-
-    if constexpr(D == 64) q_reg *= __float2bfloat16(0.125f * 1.44269504089f);
-    else if constexpr(D == 128) q_reg *= __float2bfloat16(0.08838834764f * 1.44269504089f);
-
-    max_vec = base_types::constants<float>::neg_infty();
-    norm_vec = 0.f;
-    o_reg = 0.f;
-    // launch the load of the first k, v tiles
-    int kv_blocks = (g.Kg.depth() + LOAD_BLOCKS*ROWS<D>-1) / (LOAD_BLOCKS*ROWS<D>), tic = 0;
-    load_group::load_async<1, false>(k_smem[loadid][0], g.Kg, {batch, loadid, head, 0});
-    load_group::load_async<1, false>(v_smem[loadid][0], g.Vg, {batch, loadid, head, 0});
-    // iterate over k, v for these q's that have been loaded
-    for(auto kv_idx = 0; kv_idx < kv_blocks; kv_idx++, tic=(tic+1)%3) {
-        int next_load_idx = (kv_idx+1)*LOAD_BLOCKS + loadid;
-        if(next_load_idx*ROWS<D> < g.Kg.depth()) {
-            int next_tic = (tic+1)%3;
-            load_group::load_async<1, false>(k_smem[loadid][next_tic], g.Kg, {batch, next_load_idx, head, 0});
-            load_group::load_async<1, false>(v_smem[loadid][next_tic], g.Vg, {batch, next_load_idx, head, 0});
-            load_async_wait<1>(); // next k, v can stay in flight.
-        }
-        else load_async_wait();
-        __syncthreads();
-
-        #pragma unroll LOAD_BLOCKS
-        for(int subtile = 0; subtile < LOAD_BLOCKS && (kv_idx*LOAD_BLOCKS + subtile)*ROWS<D> < g.Kg.depth(); subtile++) {
-          warp::load(k_reg, k_smem[subtile][tic]); // load k from shared into registers
-          att_block = 0.f; // zero 16x16 attention tile
-          warp::mma<transpose::N, transpose::T>(att_block, q_reg, k_reg, att_block); // [email protected]
-          // int first_index = (kv_idx*LOAD_BLOCKS + subtile)*ROWS<D>; // one past the last KV index of this tile
-          // int start_fill = g.Kg.depth()-first_index < ROWS<D> ? g.Kg.depth()-first_index : ROWS<D>;
-          // right_fill(att_block, att_block, start_fill, base_types::constants<float>::neg_infty());
-          max_vec_last = max_vec;
-          max_vec = warp::max<axis::COL>(att_block, max_vec); 
-          att_block = warp::exp2(att_block - max_vec); 
-          max_vec_last = warp::exp2(max_vec_last - max_vec); 
-          norm_vec *= max_vec_last; 
-          norm_vec = warp::sum<axis::COL>(att_block, norm_vec); 
-          att_block_mma = att_block; // copy to bf16 tile
-          warp::load(v_reg, v_smem[subtile][tic]);
-          o_reg *= max_vec_last;
-          warp::mma<transpose::N, transpose::N>(o_reg, att_block_mma, v_reg, o_reg);
-        }
-    }
-
-    o_reg /= norm_vec;
-    __syncthreads();
-    if (q_seq*ROWS<D> < g.Og.depth()) { // write out o.
-      warp::store(qo_smem[workerid], o_reg); // going through shared memory improves coalescing of dram writes.
-      __syncwarp();
-      warp::store<1, false>(g.Og, qo_smem[workerid], {batch, q_seq, head, 0});
-    }
-}
@@ -1,43 +0,0 @@
-import pathlib
-from tinygrad import Device, Tensor
-from tinygrad.helpers import Context
-from tinygrad.runtime.support.compiler_cuda import pretty_ptx, NVCCCompiler
-
-if __name__ == "__main__":
-  code = (pathlib.Path(__file__).parent / "fa.cu").read_text()
-  device = Device["CUDA"]
-  kitten_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "--expt-relaxed-constexpr", "-DKITTENS_4090"]
-  lib = NVCCCompiler(device.compiler.arch, kitten_args).compile(code)
-  kernel_name = lib.decode().split(".globl\t")[1].split("\n")[0]
-  print("kernel name", kernel_name)
-  print(pretty_ptx(lib.decode()))
-
-  prg = device.runtime(kernel_name, lib)
-  prg.smem = 16384 * 3
-
-  B, N, H, D = 16, 1024, 16, 64
-  q = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
-  k = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
-  v = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
-  out = Tensor.empty(B, N, H, D, device='CUDA', dtype="bfloat16")
-  Tensor.realize(q, k, v, out)
-
-  NUM_WORKERS = 4
-  ROWS = 16 * (64 // D)
-
-  gsz = (N // (ROWS*NUM_WORKERS), H, B)
-  for _ in range(5):
-    et = prg(out.uop.buffer.ensure_allocated()._buf, q.uop.buffer._buf, k.uop.buffer._buf, v.uop.buffer._buf,
-             global_size=gsz, local_size=(ROWS*NUM_WORKERS,1,1), wait=True)
-
-    attn_flops = 2 * B * H * N * N * D + \
-                 4 * B * H * N * N + \
-                 2 * B * H * N * N * D
-    print(f"{attn_flops/(et*1e9):2f} GFLOPS")
-
-  for _ in range(5):
-    with Context(DEBUG=2):
-      ref = q.scaled_dot_product_attention(k, v)
-
-  ref, out = ref.float(), out.float()
-  print((ref-out).mean().item(), (ref-out).max().item())
@@ -46,9 +46,9 @@ __device__ static inline void arrive(int id) {
 #include "memory/memory.cuh"
 #include "shared/shared.cuh"
 #include "register/register.cuh"
-#include "mma/mma.cuh"

 #ifdef KITTENS_HOPPER
+#include "mma/mma.cuh"

 template<int n_reg> __device__ static inline void increase_registers() {
    static_assert(n_reg % 8 == 0, "n_reg must be a multiple of 8");
@@ -93,4 +93,4 @@ __device__ static inline void sync() {
 using warp = group<1>;      // scope used by most pre-Hopper GPUs, and also for most register operations.
 using warpgroup = group<4>; // special scope commonly used by Hopper and later.

-}
+}
@@ -65,8 +65,8 @@ template<typename _T, int _axis=-9999, bool _swizzle_flag=true> struct descripto
 namespace detail {
 template<typename... Args>
 struct descriptor_dict {
-    __host__ __device__ descriptor_dict() {}
-    template<typename T> __host__ __device__ descriptor_dict(T _, int b, int d, int r, int c) {}
+    __host__ descriptor_dict() {}
+    template<typename T> __host__ descriptor_dict(T _, int b, int d, int r, int c) {}
    __host__ __device__ descriptor_dict(const descriptor_dict &other) {}
 #ifdef KITTENS_HOPPER
    template<typename T, int U> __device__ const CUtensorMap* get() const {
@@ -85,8 +85,8 @@ struct descriptor_dict<_T, Args...> {
    using DESC = kittens::tma::descriptor<_T>; // copy or initialize with a default value
    CUtensorMap tma_desc;
    descriptor_dict<Args...> other_descs;
-    __host__ __device__ descriptor_dict() {}
-    __host__ __device__ descriptor_dict(typename DESC::T::dtype *data, int b, int d, int r, int c): other_descs(data, b, d, r, c) {
+    __host__ descriptor_dict() {}
+    __host__ descriptor_dict(typename DESC::T::dtype *data, int b, int d, int r, int c): other_descs(data, b, d, r, c) {
        kittens::detail::tma::create_tensor_map<typename DESC::T, DESC::axis, DESC::swizzle_flag>(&tma_desc, data, b, d, r, c);
    }
    __host__ __device__ inline descriptor_dict(const descriptor_dict &other) :
@@ -135,7 +135,7 @@ struct gl {

    detail::descriptor_dict<TMA_Types...> tma_descs;

-    __host__ __device__ inline gl(T *_data,
+    __host__ inline gl(T *_data,
                        ducks::gl::make_arg_t<b> _batch,
                        ducks::gl::make_arg_t<d> _depth,
                        ducks::gl::make_arg_t<r> _rows,
@@ -425,4 +425,4 @@ __host__ static inline CUtensorMap* allocate_and_create_tensor_map(const typenam

 } // namespace tma
 } // namespace detail
-} // namespace kittens
+} // namespace kittens
@@ -1,45 +0,0 @@
-// https://github.com/HazyResearch/ThunderKittens/blob/main/kernels/matmul/educational/level_04.cu
-#include "kittens.cuh"
-using namespace kittens;
-
-constexpr int g_N = 8192;
-constexpr int BLOCK_SIZE = 32;
-#define NUM_WORKERS (1)
-
-using sub_tile = st_bf<BLOCK_SIZE,BLOCK_SIZE>;
-using tile_gl =  gl<bf16, 1, 1, g_N, g_N>;
-
-__launch_bounds__(NUM_WORKERS*WARP_THREADS, 1)
-__global__ void kernel(bf16 *c_ptr, bf16 *a_ptr, bf16 *b_ptr) {
-  tile_gl g_C{c_ptr, nullptr, nullptr, nullptr, nullptr};
-  tile_gl g_A{a_ptr, nullptr, nullptr, nullptr, nullptr};
-  tile_gl g_B{b_ptr, nullptr, nullptr, nullptr, nullptr};
-
-  extern __shared__ alignment_dummy __shm[];
-  shared_allocator al((int*)&__shm[0]);
-  st_bf<BLOCK_SIZE,BLOCK_SIZE> &As = al.allocate<st_bf<BLOCK_SIZE,BLOCK_SIZE>>();
-  st_bf<BLOCK_SIZE,BLOCK_SIZE> &Bs = al.allocate<st_bf<BLOCK_SIZE,BLOCK_SIZE>>();
-
-  rt_bf<BLOCK_SIZE,BLOCK_SIZE> A_reg;
-  rt_bf<BLOCK_SIZE,BLOCK_SIZE> B_reg;
-  rt_bf<BLOCK_SIZE,BLOCK_SIZE, ducks::rt_layout::col> B_reg_col;
-  rt_fl<BLOCK_SIZE,BLOCK_SIZE> C_accum;
-
-  int col = blockIdx.x;
-  int row = blockIdx.y;
-
-  warp::zero(C_accum);
-  int num_tiles = (g_N + BLOCK_SIZE - 1) / BLOCK_SIZE;
-  for (int tile = 0; tile < num_tiles; ++tile) {
-    warp::load(As, g_A, {0, 0, row, tile});
-    warp::load(Bs, g_B, {0, 0, tile, col});
-    __syncthreads();
-    warp::load(A_reg, As);
-    warp::load(B_reg, Bs);
-    warp::swap_layout(B_reg_col, B_reg);
-    __syncthreads();
-    warp::mma_AB(C_accum, A_reg, B_reg_col, C_accum);
-    __syncthreads();
-  }
-  warp::store(g_C, C_accum, {0, 0, row, col});
-}
@@ -1,55 +0,0 @@
-import pathlib
-from tinygrad import Device, Tensor
-from tinygrad.helpers import Context, getenv
-from tinygrad.runtime.support.compiler_cuda import pretty_ptx, NVCCCompiler
-
-if __name__ == "__main__":
-  if getenv("MATMUL2"):
-    code = (pathlib.Path(__file__).parent / "matmul2.cu").read_text()
-  else:
-    code = (pathlib.Path(__file__).parent / "matmul.cu").read_text()
-
-  device = Device["CUDA"]
-  kitten_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "--expt-relaxed-constexpr"]
-  lib = NVCCCompiler(device.compiler.arch, kitten_args).compile(code)
-  kernel_name = lib.decode().split(".globl\t")[1].split("\n")[0]
-  print("kernel name", kernel_name)
-  print(pretty_ptx(lib.decode()))
-
-  prg = device.runtime(kernel_name, lib)
-  if getenv("MATMUL2"):
-    prg.smem = 16384 * 2
-  else:
-    prg.smem = 10000
-
-  N = 8192
-  a = Tensor.randn(N, N, device='CUDA', dtype="bfloat16")
-  b = Tensor.randn(N, N, device='CUDA', dtype="bfloat16")
-  c = Tensor.empty(N, N, device='CUDA', dtype="bfloat16")
-  Tensor.realize(a, b, c)
-
-  WARP_THREADS = 32
-  if getenv("MATMUL2"):
-    SUPER_N = 2
-    SUPER_M = 2
-    NUM_WORKERS = SUPER_N * SUPER_M
-    BLOCK_SIZE = 32
-    gsz = (N // (BLOCK_SIZE * SUPER_N), N // (BLOCK_SIZE * SUPER_M), 1)
-  else:
-    NUM_WORKERS = 1
-    BLOCK_SIZE = 32
-    gsz = (N // (BLOCK_SIZE), N // (BLOCK_SIZE), 1)
-
-  for _ in range(5):
-    et = prg(c.uop.buffer.ensure_allocated()._buf, a.uop.buffer._buf, b.uop.buffer._buf,
-             global_size=gsz, local_size=(NUM_WORKERS*WARP_THREADS,1,1), wait=True)
-    print(f"{N*N*N*2/(et*1e9):2f} GFLOPS")
-
-  # print(c.tolist())
-
-  for _ in range(5):
-    with Context(DEBUG=2):
-      ref = (a@b).realize()
-
-  ref, c = ref.float(), c.float()
-  print((ref-c).mean().item(), (ref-c).max().item())
@@ -1,105 +0,0 @@
-#include "kittens.cuh"
-using namespace kittens;
-
-constexpr int g_N = 8192;
-
-constexpr int SUPER_N = 2;
-constexpr int SUPER_M = 2;
-constexpr int NUM_WORKERS = SUPER_N * SUPER_M;
-constexpr int LOAD_TASKS = SUPER_N + SUPER_M;
-
-constexpr int WORKER_M = 32;
-constexpr int WORKER_N = 32;
-
-constexpr int BLOCK_K = 32;
-constexpr int BLOCK_M = WORKER_M * SUPER_M;
-constexpr int BLOCK_N = WORKER_N * SUPER_N;
-
-constexpr int PIPE_STAGES = 2;
-
-using reg_tile_A = rt_bf<WORKER_M, BLOCK_K>;
-using reg_tile_B_col = rt_bf<BLOCK_K, WORKER_N, ducks::rt_layout::col>;
-using reg_tile_C = rt_fl<WORKER_M, WORKER_N>;
-
-using shared_tile_A = st_bf<WORKER_M, BLOCK_K>;
-using shared_tile_B = st_bf<BLOCK_K, WORKER_N>;
-using shared_tile_C = st_bf<WORKER_M, WORKER_N>;
-
-using gl_tile_A = gl<bf16, 1, 1, g_N, g_N, shared_tile_A>;
-using gl_tile_B = gl<bf16, 1, 1, g_N, g_N, shared_tile_B>;
-using gl_tile_C = gl<bf16, 1, 1, g_N, g_N, shared_tile_C>;
-
-__launch_bounds__(NUM_WORKERS *WARP_THREADS, 1) __global__
-    void kernel(bf16 *c_ptr, bf16 *a_ptr, bf16 *b_ptr) {
-  gl_tile_C g_C{c_ptr, nullptr, nullptr, nullptr, nullptr};
-  gl_tile_A g_A{a_ptr, nullptr, nullptr, nullptr, nullptr};
-  gl_tile_B g_B{b_ptr, nullptr, nullptr, nullptr, nullptr};
-
-  extern __shared__ alignment_dummy __shm[];
-  shared_allocator al((int *)&__shm[0]);
-
-  shared_tile_A(&As)[SUPER_M][PIPE_STAGES] =
-      al.allocate<shared_tile_A, SUPER_M, PIPE_STAGES>();
-  shared_tile_B(&Bs)[SUPER_N][PIPE_STAGES] =
-      al.allocate<shared_tile_B, SUPER_N, PIPE_STAGES>();
-
-  reg_tile_A A_reg;
-  reg_tile_B_col B_reg_col;
-  reg_tile_C C_accum;
-
-  int warpid = kittens::warpid();
-  int warp_m = warpid % SUPER_M;
-  int warp_n = warpid / SUPER_M;
-
-  int load_group_id = warpgroup::groupid();
-
-  int block_row = blockIdx.y * SUPER_M;
-  int block_col = blockIdx.x * SUPER_N;
-
-  warp::zero(C_accum);
-  int num_tiles = (g_N + BLOCK_K - 1) / BLOCK_K;
-
-  for (int load_tile = 0; load_tile < (PIPE_STAGES - 1); load_tile++) {
-    if (load_tile < num_tiles) {
-      int load_smem_idx = load_tile % PIPE_STAGES;
-      for (int task_id = warpid; task_id < LOAD_TASKS; task_id += NUM_WORKERS) {
-        if (task_id < SUPER_M) {
-          warp::load_async(As[task_id][load_smem_idx], g_A, {0, 0, block_row + task_id, load_tile});
-        } else {
-          int n_index = task_id - SUPER_M;
-          warp::load_async(Bs[n_index][load_smem_idx], g_B, {0, 0, load_tile, block_col + n_index});
-        }
-      }
-    }
-  }
-
-  for (int tile = 0; tile < num_tiles; tile++) {
-    int compute_smem_idx = tile % PIPE_STAGES;
-
-    int load_tile = tile + PIPE_STAGES - 1;
-    int load_smem_idx = load_tile % PIPE_STAGES;
-
-    if (load_tile < num_tiles) {
-      for (int task_id = warpid; task_id < LOAD_TASKS; task_id += NUM_WORKERS) {
-        if (task_id < SUPER_M) {
-          warp::load_async(As[task_id][load_smem_idx], g_A,
-                           {0, 0, block_row + task_id, load_tile});
-        } else {
-          int n_index = task_id - SUPER_M;
-          warp::load_async(Bs[n_index][load_smem_idx], g_B,
-                           {0, 0, load_tile, block_col + n_index});
-        }
-      }
-      load_async_wait<1>();
-    } else
-      load_async_wait();
-    __syncthreads();
-
-    warp::load(A_reg, As[warp_m][compute_smem_idx]);
-    warp::load(B_reg_col, Bs[warp_n][compute_smem_idx]);
-
-    warp::mma_AB(C_accum, A_reg, B_reg_col, C_accum);
-    __syncthreads();
-  }
-  warp::store(g_C, C_accum, {0, 0, block_row + warp_m, block_col + warp_n});
-}
@@ -1 +0,0 @@
-WARP_THREADS = 32
@@ -1,272 +0,0 @@
-import math, functools
-from typing import cast, Callable
-from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
-from tinygrad.uop.ops import AxisType, UOp, KernelInfo, Ops
-from tinygrad.engine.realize import ExecItem, get_runner
-from tinygrad.dtype import AddrSpace, PtrDType
-from tinygrad.helpers import getenv, prod
-
-from extra.thunder.tiny.tk import WARP_THREADS
-from extra.thunder.tiny.tk.tiles import TILE_ROW_DIM, TILE_COL_DIM, RT_BASE_TILE_NEPT, slots
-
-class Group:
-  def __init__(self, warps:int, ker):
-    self.warps = warps
-    self.group_threads = warps * WARP_THREADS
-    self.threadIdx_x = ker.threadIdx_x
-    self.ker = ker
-
-  # helpers
-  @property
-  def laneid(self): return self.threadIdx_x % self.group_threads
-  @property
-  def warpid(self): return self.laneid // WARP_THREADS
-  @property
-  def groupid(self): return self.threadIdx_x // self.group_threads
-
-  # ops that only work on a single warp
-
-  clear_rid = 1000
-  def clear(self, reg:UOp, value:float=0):
-    assert self.warps == 1
-
-    i = UOp.range(reg.size, Group.clear_rid)
-    Group.clear_rid += 1
-    return reg.reshape((reg.size,))[i].set(value, end=i).after(reg).reshape(reg.shape)
-
-  def zero(self, reg:UOp): return self.clear(reg, 0)
-  def neg_inf(self, reg:UOp): return self.clear(reg, -math.inf)
-
-  copy_rid = 300
-  def copy(self, dst:UOp, src:UOp):
-    assert self.warps == 1
-
-    assert dst.shape == src.shape
-    assert cast(PtrDType, dst.dtype).addrspace == AddrSpace.REG
-    assert cast(PtrDType, src.dtype).addrspace == AddrSpace.REG
-
-    rngs_for_shape = tuple(UOp.range(dim, Group.copy_rid + i) for i, dim in enumerate(dst.shape))
-    Group.copy_rid += len(dst.shape)
-
-    dst_store = dst[*rngs_for_shape].store(src[*rngs_for_shape].cast(dst.dtype.base)).end(*rngs_for_shape)
-
-    self.ker.push_store(dst_store, dst)
-    return dst.after(dst_store).reshape(dst.shape)
-
-  mma_rid = 600
-  def mma_AB(self, c:UOp, a:UOp, b:UOp, after=True):
-    assert self.warps == 1
-
-    mma_i_height = UOp.range(c.shape[-3], Group.mma_rid)
-    mma_i_width = UOp.range(c.shape[-2], Group.mma_rid+1)
-    mma_i_inner = UOp.range(a.shape[-2], Group.mma_rid+2, AxisType.REDUCE)
-    Group.mma_rid += 3
-
-    wmma_arg = ("WMMA_8_16_16_bfloat16_float", (8, 16, 16), dtypes.bfloat16, dtypes.float, "CUDA", 32, (((4, 2), (3, 2), (8, 2)), ((4, 2), (3, 2)), ((4, 2), (3, 2))), ())
-
-    a_in = UOp.vectorize(*[a[mma_i_height, mma_i_inner, i] for i in range(8)])
-    b_in1 = UOp.vectorize(*([b[mma_i_inner, mma_i_width, i] for i in range(2)] + [b[mma_i_inner, mma_i_width, 4+i] for i in range(2)]))
-    c_out1 = UOp.vectorize(*[c[mma_i_height, mma_i_width, i] for i in range(4)])
-    b_in2 = UOp.vectorize(*([b[mma_i_inner, mma_i_width, 2+i] for i in range(2)] + [b[mma_i_inner, mma_i_width, 6+i] for i in range(2)]))
-    c_out2 = UOp.vectorize(*[c[mma_i_height, mma_i_width, 4+i] for i in range(4)])
-
-    out1 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in1, c_out1), arg=wmma_arg)
-    out2 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in2, c_out2), arg=wmma_arg)
-    c_i = [c[mma_i_height, mma_i_width, i].store(out1.gep(i)) for i in range(4)] + [c[mma_i_height, mma_i_width, 4+i].store(out2.gep(i)) for i in range(4)]
-    c_store = UOp.group(*c_i).end(mma_i_height, mma_i_width, mma_i_inner)
-
-    self.ker.push_store(c_store, c)
-    return c.after(c_store).reshape(c.shape) if after else c_store
-
-  def mma_ABt(self, c:UOp, a:UOp, b:UOp, after=True):
-    assert self.warps == 1
-
-    mma_i_height = UOp.range(c.shape[-3], Group.mma_rid)
-    mma_i_width = UOp.range(c.shape[-2], Group.mma_rid+1)
-    mma_i_inner = UOp.range(a.shape[-2], Group.mma_rid+2, AxisType.REDUCE)
-    Group.mma_rid += 3
-
-    wmma_arg = ("WMMA_8_16_16_bfloat16_float", (8, 16, 16), dtypes.bfloat16, dtypes.float, "CUDA", 32, (((4, 2), (3, 2), (8, 2)), ((4, 2), (3, 2)), ((4, 2), (3, 2))), ())
-
-    a_in = UOp.vectorize(*[a[mma_i_height, mma_i_inner, i] for i in range(8)])
-    b_in1 = UOp.vectorize(*([b[mma_i_width, mma_i_inner, i] for i in range(2)] + [b[mma_i_width, mma_i_inner, 4+i] for i in range(2)]))
-    c_out1 = UOp.vectorize(*[c[mma_i_height, mma_i_width, i] for i in range(4)])
-    b_in2 = UOp.vectorize(*([b[mma_i_width, mma_i_inner, 2+i] for i in range(2)] + [b[mma_i_width, mma_i_inner, 6+i] for i in range(2)]))
-    c_out2 = UOp.vectorize(*[c[mma_i_height, mma_i_width, 4+i] for i in range(4)])
-
-    out1 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in1, c_out1), arg=wmma_arg)
-    out2 = UOp(Ops.WMMA, dtypes.float32.vec(4), (a_in, b_in2, c_out2), arg=wmma_arg)
-    c_i = [c[mma_i_height, mma_i_width, i].store(out1.gep(i)) for i in range(4)] + [c[mma_i_height, mma_i_width, 4+i].store(out2.gep(i)) for i in range(4)]
-    c_store = UOp.group(*c_i).end(mma_i_height, mma_i_width, mma_i_inner)
-
-    self.ker.push_store(c_store, c)
-    return c.after(c_store).reshape(c.shape) if after else c_store
-
-  map_rid = 400
-  def map(self, a:UOp, op:Callable[[UOp], UOp]|Callable[[UOp, tuple], UOp]):
-    assert self.warps == 1
-
-    rngs_for_shape = tuple(UOp.range(dim, Group.map_rid + i) for i, dim in enumerate(a.shape))
-    Group.map_rid += len(a.shape)
-
-    if op.__code__.co_argcount == 1:
-      to_store = op(a[*rngs_for_shape])
-    else:
-      to_store = op(a[*rngs_for_shape], rngs_for_shape)
-
-    a_store = a[*rngs_for_shape].store(to_store).end(*rngs_for_shape)
-
-    self.ker.push_store(a_store, a)
-    return a.after(a_store).reshape(a.shape)
-
-  def row_reduce(self, vec:UOp, src:UOp, op:Callable[[UOp, UOp], UOp]):
-    assert self.warps == 1
-
-    red_local = UOp.placeholder((self.group_threads, 2), src.dtype.base, addrspace=AddrSpace.LOCAL, slot=slots.shared_slot)
-    slots.shared_slot += 1
-
-    for height in self.ker.range(src.shape[-3], track=False):
-      for i_outer in self.ker.range(2, track=False):
-        for width in self.ker.range(src.shape[-2], AxisType.REDUCE, track=False):
-          for i_inner in self.ker.range(4, AxisType.REDUCE, track=False):
-            elem_index = i_inner + 2 * (i_inner // 2) + i_outer * 2
-            vec_store = vec[height, 0, i_outer].store(op(vec[height, 0, i_outer], src[height, width, elem_index])).end(width, i_inner, i_outer)
-            vec = vec.after(vec_store).reshape(vec.shape)
-
-      # store to shared memory
-      for i_outer in self.ker.range(2, track=False):
-        red_local_store = red_local[self.laneid, i_outer].store(vec[height, 0, i_outer]).end(i_outer)
-        red_local = red_local.after(red_local_store).reshape(red_local.shape)
-
-      # reduce from shared memory
-      for i_outer in self.ker.range(2, track=False):
-        for i_inner in self.ker.range(3, AxisType.REDUCE, track=False):
-          offset = (self.laneid // 4) * 4 + ((self.laneid + 1 + i_inner) % 4)
-          vec_store = vec[height, 0, i_outer].store(op(vec[height, 0, i_outer], red_local[offset, i_outer])).end(i_inner, i_outer)
-
-    self.ker.push_store(vec_store, vec)
-    return vec.after(vec_store).reshape(vec.shape)
-
-  # ops that can work across multiple warps
-
-  LOAD_INNER = 8
-  load_rid = 100
-  def load(self, dst:UOp, src:UOp, dst_idxs:tuple[UOp|int,...]=(), idxs:tuple[UOp|int,...]=(), axis:int=0, transpose:bool=False):
-    assert isinstance(dst.dtype, PtrDType) and isinstance(src.dtype, PtrDType)
-    dst_dtype, src_dtype = cast(PtrDType, dst.dtype), cast(PtrDType, src.dtype)
-    if dst_dtype.addrspace == AddrSpace.REG and src_dtype.addrspace == AddrSpace.LOCAL:
-      srcf = src.flatten(-2)
-
-      load_i_height = UOp.range(dst.shape[-3], Group.load_rid)
-      load_i_width = UOp.range(dst.shape[-2], Group.load_rid+1)
-      load_i_inner = UOp.range(RT_BASE_TILE_NEPT, Group.load_rid+2)
-      Group.load_rid += 3
-
-      if self.warps % 4 == 0: local_warpid = (self.warpid // 4) + (self.warpid % 4) * (self.warps // 4)
-      else: local_warpid = self.warpid
-      warp_laneid = self.threadIdx_x % WARP_THREADS
-
-      if not transpose:
-        row = (local_warpid * dst.shape[-3] + load_i_height) * TILE_ROW_DIM + (warp_laneid // 4)
-        col = load_i_width * TILE_COL_DIM + 2 * (warp_laneid % 4)
-
-        row_offset = ((load_i_inner % 4) // 2) * 8
-        col_offset = (load_i_inner % 2) + (load_i_inner // 4) * 8
-      else:
-        row = (local_warpid * dst.shape[-3] + load_i_height) * TILE_ROW_DIM + 2 * (warp_laneid % 4)
-        col = load_i_width * TILE_COL_DIM + (warp_laneid // 4)
-
-        row_offset = (load_i_inner % 2) + (load_i_inner // 4) * 8
-        col_offset = ((load_i_inner % 4) // 2) * 8
-
-      src_i_last = (row + row_offset) * src.shape[-1] + col + col_offset
-
-      dst_store = dst[*dst_idxs, load_i_height, load_i_width, load_i_inner].store(srcf[*idxs[:-2], src_i_last])
-      dst_store = dst_store.end(load_i_height, load_i_width, load_i_inner)
-    elif dst_dtype.addrspace == AddrSpace.LOCAL and src_dtype.addrspace == AddrSpace.GLOBAL:
-      dstf = dst.flatten(-2)
-
-      srcf = src.flatten()
-      row_stride = prod(src.shape[axis+1:])
-
-      idxs = tuple(idx * dst.shape[-2] if i == axis else idx for i, idx in enumerate(idxs))
-      idxs = tuple(idx * dst.shape[-1] if i == 3 else idx for i, idx in enumerate(idxs))
-      src_i = ((idxs[0] * src.shape[-3] + idxs[1]) * src.shape[-2] + idxs[2]) * src.shape[-1] + idxs[3]
-
-      memcpy_per_row = dst.shape[-1] // Group.LOAD_INNER
-      total_calls = prod(dst.shape[-2:]) // (self.group_threads * Group.LOAD_INNER)
-
-      load_i_outer = UOp.range(total_calls, Group.load_rid)
-      load_i_inner = UOp.range(Group.LOAD_INNER, Group.load_rid+1)
-      Group.load_rid += 2
-
-      load_idx = load_i_outer * self.group_threads + self.laneid
-      row = load_idx // memcpy_per_row
-      col = (load_idx * Group.LOAD_INNER) % dst.shape[-1]
-
-      dst_i = row * dst.shape[-1] + col + load_i_inner
-      src_i += row * row_stride + col + load_i_inner
-
-      dst_store = dstf[*dst_idxs, dst_i].store(srcf[src_i]).end(load_i_outer, load_i_inner)
-    else:
-      raise NotImplementedError(f"load from {src_dtype.addrspace} to {dst_dtype.addrspace} not implemented")
-
-    return dst.after(dst_store.barrier()).reshape(dst.shape)
-
-  STORE_INNER = 8
-  store_rid = 200
-  def store(self, dst:UOp, src:UOp, idxs:tuple[UOp|int,...]=(), src_idxs:tuple[UOp|int,...]=(), axis=0, after=True):
-    assert isinstance(dst.dtype, PtrDType) and isinstance(src.dtype, PtrDType)
-    dst_dtype, src_dtype = cast(PtrDType, dst.dtype), cast(PtrDType, src.dtype)
-    if src_dtype.addrspace == AddrSpace.REG and dst_dtype.addrspace == AddrSpace.LOCAL:
-      dstf = dst.flatten(-2)
-
-      store_i_height = UOp.range(src.shape[-3], Group.store_rid)
-      store_i_width = UOp.range(src.shape[-2], Group.store_rid+1)
-      store_i_inner = UOp.range(RT_BASE_TILE_NEPT, Group.store_rid+2)
-      Group.store_rid += 3
-
-      if self.warps % 4 == 0: local_warpid = (self.warpid // 4) + (self.warpid % 4) * (self.warps // 4)
-      else: local_warpid = self.warpid
-      warp_laneid = self.threadIdx_x % WARP_THREADS
-
-      row = (local_warpid * src.shape[-3] + store_i_height) * TILE_ROW_DIM + (warp_laneid // 4)
-      col = store_i_width * TILE_COL_DIM + 2 * (warp_laneid % 4)
-
-      row_offset = ((store_i_inner % 4) // 2) * 8
-      col_offset = (store_i_inner % 2) + (store_i_inner // 4) * 8
-
-      dst_i_last = (row + row_offset) * dst.shape[-1] + col + col_offset
-
-      dst_store = dstf[*idxs[:-2], dst_i_last].store(src[*src_idxs, store_i_height, store_i_width, store_i_inner])
-      dst_store = dst_store.end(store_i_height, store_i_width, store_i_inner)
-    elif src_dtype.addrspace == AddrSpace.LOCAL and dst_dtype.addrspace == AddrSpace.GLOBAL:
-      dstf = dst.flatten()
-      row_stride = prod(dst.shape[axis+1:])
-
-      idxs = tuple(idx * src.shape[-2] if i == axis else idx for i, idx in enumerate(idxs))
-      idxs = tuple(idx * src.shape[-1] if i == 3 else idx for i, idx in enumerate(idxs))
-      dst_i = ((idxs[0] * dst.shape[-3] + idxs[1]) * dst.shape[-2] + idxs[2]) * dst.shape[-1] + idxs[3]
-
-      srcf = src.flatten(-2)
-
-      memcpy_per_row = src.shape[-1] // Group.STORE_INNER
-      total_calls = prod(src.shape[-2:]) // (self.group_threads * Group.STORE_INNER)
-
-      store_i_outer = UOp.range(total_calls, Group.store_rid)
-      store_i_inner = UOp.range(Group.STORE_INNER, Group.store_rid+1)
-      Group.store_rid += 2
-
-      load_idx = store_i_outer * self.group_threads + self.laneid
-      row = load_idx // memcpy_per_row
-      col = (load_idx * Group.STORE_INNER) % src.shape[-1]
-
-      src_i = row * src.shape[-1] + col + store_i_inner
-      dst_i += row * row_stride + col + store_i_inner
-
-      dst_store = dstf[dst_i].store(srcf[*src_idxs, src_i]).end(store_i_outer, store_i_inner)
-    else:
-      raise NotImplementedError(f"store from {src_dtype.addrspace} to {dst_dtype.addrspace} not implemented")
-
-    self.ker.push_store(dst_store, dst)
-    return dst.after(dst_store.barrier()).reshape(dst.shape) if after else dst_store
@@ -1,57 +0,0 @@
-from contextlib import AbstractContextManager
-from tinygrad.uop.ops import UOp, KernelInfo, AxisType
-from extra.thunder.tiny.tk import WARP_THREADS
-from extra.thunder.tiny.tk.group import Group
-
-class _tk_range:
-  user_rid = 0
-  def __init__(self, end:int, axis_type:AxisType): self.end, self.axis_type, self.done = end, axis_type, False
-  def __iter__(self): return self
-  def __next__(self):
-    if not self.done:
-      self.done = True
-      _tk_range.user_rid += 1
-      self._rng = UOp.range(self.end, _tk_range.user_rid-1, axis_type=self.axis_type)
-      return self._rng
-    raise StopIteration
-
-class Kernel(AbstractContextManager):
-  def __init__(self, grid_size:tuple[int, int, int], block_size:int):
-    self.blockIdx_x = UOp.special(grid_size[0], "gidx0")
-    self.blockIdx_y = UOp.special(grid_size[1], "gidx1")
-    self.blockIdx_z = UOp.special(grid_size[2], "gidx2")
-    self.threadIdx_x = UOp.special(block_size, "lidx0")
-
-    self.range_stack = []
-    self.store_stack = []
-
-  @property
-  def warpid(self): return self.threadIdx_x // WARP_THREADS
-
-  def __enter__(self): return self
-  def __exit__(self, exc_type, exc_value, traceback): pass
-
-  def group(self, size:int): return Group(size, self)
-  @property
-  def warp(self): return self.group(1)
-  @property
-  def warpgroup(self): return self.group(4)
-
-  def range(self, end:int, axis_type:AxisType=AxisType.LOOP, track:bool=True):
-    rng = _tk_range(end, axis_type)
-    if track: self.range_stack.append(rng)
-    return rng
-
-  def push_store(self, store:UOp, uop:UOp): self.store_stack.append((store, uop))
-
-  def finish(self):
-    # end all ranges
-    rngs = []
-    while self.range_stack: rngs.append(self.range_stack.pop(0)._rng)
-
-    return self.store_stack.pop()[0].end(*rngs).sink(arg=KernelInfo(opts_to_apply=())).simplify()
-
-  def endrange(self):
-    last_store = self.store_stack.pop()
-    last_range = self.range_stack.pop()
-    return last_store[1].after(last_store[0].barrier().end(last_range._rng)).reshape(last_store[1].shape)
@@ -1,52 +0,0 @@
-import math
-from typing import cast, Callable
-from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
-from tinygrad.uop.ops import AxisType, UOp, KernelInfo, Ops
-from tinygrad.engine.realize import ExecItem, get_runner
-from tinygrad.dtype import AddrSpace, PtrDType
-from tinygrad.helpers import getenv, prod
-
-from extra.thunder.tiny.tk import WARP_THREADS
-
-class _Slots:
-  def __init__(self):
-    self.global_slot = 0
-    self.shared_slot = 0
-    self.register_slot = 0
-slots = _Slots()
-
-def gl(shape, dtype):
-  slots.global_slot += 1
-  return UOp.placeholder(shape, dtype, slot=slots.global_slot-1)
-
-shared_slot = 0
-def st(shape, dtype):
-  slots.shared_slot += 1
-  return UOp.placeholder(shape, dtype, addrspace=AddrSpace.LOCAL, slot=slots.shared_slot-1)
-
-TILE_ROW_DIM, TILE_COL_DIM = 16, 16
-RT_BASE_TILE_NE = TILE_ROW_DIM * TILE_COL_DIM
-RT_BASE_TILE_NEPT = RT_BASE_TILE_NE // WARP_THREADS
-register_slot = 0
-def rt(shape, dtype):
-  assert len(shape) == 2
-
-  height = shape[0] // TILE_ROW_DIM
-  width = shape[1] // TILE_COL_DIM
-
-  slots.register_slot += 1
-  return UOp.placeholder((height, width, RT_BASE_TILE_NEPT), dtype, addrspace=AddrSpace.REG, slot=slots.register_slot-1)
-
-def rv(length, dtype, layout="naive"):
-  tiles = length // TILE_ROW_DIM
-  match layout:
-    case "naive":
-      inner_dim = 1
-      outer_dim = (tiles + 1) // 2
-    case "ortho":
-      inner_dim = 1
-      outer_dim = tiles
-    case _: raise NotImplementedError(f"rv layout {layout} not implemented")
-
-  slots.register_slot += 1
-  return UOp.placeholder((outer_dim, inner_dim, 2), dtype, addrspace=AddrSpace.REG, slot=slots.register_slot-1)
@@ -3,9 +3,9 @@ import argparse

 if __name__ == "__main__":
  parser = argparse.ArgumentParser()
-  parser.add_argument("--hash", type=str, required=True, help="file hash to fetch")
-  parser.add_argument("--len", type=int, required=True, help="file length to fetch")
-  parser.add_argument("--dest", type=str, required=True, help="destination path to save the file")
+  parser.add_argument("hash", type=str, required=True, help="file hash to fetch")
+  parser.add_argument("len", type=int, required=True, help="file length to fetch")
+  parser.add_argument("dest", type=str, required=True, help="destination path to save the file")
  args = parser.parse_args()

  Tensor(bytes.fromhex(args.hash), device="CPU").load(args.len).to(f"disk:{args.dest}").realize()
@@ -119,7 +119,14 @@ extension TinyGPUViewModel: OSSystemExtensionRequestDelegate {

 		os_log("sysex actionForReplacingExtension: %@ %@", existing, ext)

+		// Add appropriate logic here to determine whether to replace the extension
+		// with the new extension. Common things to check for include
+		// testing whether the new extension's version number is newer than
+		// the current version number, or whether the bundleIdentifier is different.
+		// For simplicity, this sample always replaces the current extension
+		// with the new one.
 		replacementAction = .replace
+
 		self.state = .activating
 		return replacementAction
 	}
@@ -7,48 +7,30 @@
 struct TinyGPUDriverUserClient_IVars
 {
 	OSSharedPtr<TinyGPUDriver> provider = nullptr;
-
-	TinyGPUCreateDMAResp *dmas = nullptr;
-	size_t dmaCount = 0;
-	size_t dmaCap = 0;
-
-	int ensureDMACap(size_t need)
-	{
-		// not thread-safe
-		if (need <= dmaCap) return 0;
-
-		size_t newCap = dmaCap ? dmaCap * 2 : 16;
-		while (newCap < need) newCap *= 2;
-
-		auto *newArr = IONewZero(TinyGPUCreateDMAResp, newCap);
-		if (!newArr) return -kIOReturnNoMemory;
-
-		if (dmas && dmaCount) {
-			memcpy(newArr, dmas, dmaCount * sizeof(TinyGPUCreateDMAResp));
-		}
-
-		IOSafeDeleteNULL(dmas, TinyGPUCreateDMAResp, dmaCap);
-		dmas = newArr;
-		dmaCap = newCap;
-		return 0;
-	}
 };

 bool TinyGPUDriverUserClient::init()
 {
-	auto ok = super::init();
-	if (!ok) return false;
+	auto theAnswer = super::init();
+	if (!theAnswer) {
+		return false;
+	}

 	ivars = IONewZero(TinyGPUDriverUserClient_IVars, 1);
-	if (!ivars) return false;
+	if (ivars == nullptr) {
+		return false;
+	}
+
 	return true;
 }

 void TinyGPUDriverUserClient::free()
 {
-	if (ivars) {
-		IOSafeDeleteNULL(ivars, TinyGPUDriverUserClient_IVars, 1);
+	if (ivars != nullptr) {
+		ivars->provider.reset();
 	}
+
+	IOSafeDeleteNULL(ivars, TinyGPUDriverUserClient_IVars, 1);
 	super::free();
 }

@@ -77,22 +59,6 @@ error:

 kern_return_t TinyGPUDriverUserClient::Stop_Impl(IOService* in_provider)
 {
-	// release all DMA allocations for this client
-	if (ivars) {
-		for (size_t i = 0; i < ivars->dmaCount; i++) {
-			auto &d = ivars->dmas[i];
-			if (d.dmaCmd) {
-				d.dmaCmd->CompleteDMA(kIODMACommandCompleteDMANoOptions);
-				d.dmaCmd->release();
-				d.dmaCmd = nullptr;
-			}
-		}
-		ivars->dmaCount = 0;
-		IOSafeDeleteNULL(ivars->dmas, TinyGPUCreateDMAResp, ivars->dmaCap);
-		ivars->dmas = nullptr;
-		ivars->provider.reset();
-	}
-
 	return Stop(in_provider, SUPERDISPATCH);
 }

@@ -136,26 +102,26 @@ kern_return_t TinyGPUDriverUserClient::ExternalMethod(uint64_t selector, IOUserC

 kern_return_t IMPL(TinyGPUDriverUserClient, CopyClientMemoryForType)
 {
-	if (!memory) return kIOReturnBadArgument;
-	if (!ivars->provider.get()) return kIOReturnNotAttached;
+	if (!memory) {
+		return kIOReturnBadArgument;
+	}
+
+	if (ivars->provider.get() == nullptr) {
+		return kIOReturnNotAttached;
+	}

-	// bar handling, type is bar num
 	if (type < 6) {
 		uint32_t bar = (uint32_t)type;
 		return ivars->provider->MapBar(bar, memory);
 	}

-	// dma handling, type is size
-	if (ivars->ensureDMACap(ivars->dmaCount + 1)) {
-		os_log(OS_LOG_DEFAULT, "tinygpu: cannot grow dma array");
-		return kIOReturnNoMemory;
+	// dma page buffer
+	TinyGPUCreateDMAResp buf;
+	kern_return_t err = ivars->provider->CreateDMA(type, &buf);
+	if (err) {
+		return err;
 	}

-	TinyGPUCreateDMAResp buf{};
-	kern_return_t err = ivars->provider->CreateDMA(type, &buf);
-	if (err) return err;
-
-	ivars->dmas[ivars->dmaCount++] = buf;
 	*memory = buf.sharedBuf;
 	return 0;
 }
@@ -1,7 +1,7 @@
 # extra/weekly_commits_table.py
 import os, subprocess, datetime as dt

-NAMES = ["chenyu","George Hotz","nimlgen","qazal","wozeparrot"]
+NAMES = ["chenyu","George Hotz","nimlgen","qazal","Sieds Lykles","wozeparrot"]
 REPO  = os.environ.get("REPO_PATH",".")
 today = dt.date.today()
 days  = [(today - dt.timedelta(i)).strftime("%Y-%m-%d") for i in range(6,-1,-1)]
@@ -40,4 +40,4 @@ for d in days:
 print("** Commits by day (last 7) **")
 print("```")
 print("\n".join([header, rule] + rows))
-print("```")
+print("```")
@@ -25,6 +25,8 @@ nav:
    - Layout: developer/layout.md
    - Speed: developer/speed.md
    - UOp: developer/uop.md
+    - Grouper:
+      - developer/kernelize.md
    - Runtime:
      - developer/runtime.md
      - HCQ: developer/hcq.md
@@ -1,8 +1,5 @@
 [pytest]
-norecursedirs =
-    extra
-    .hypothesis
-    .git
+norecursedirs = extra
 timeout = 300
 timeout_method = thread
 timeout_func_only = true
@@ -1,6 +1,6 @@
 indent-width = 2
 preview = true
-target-version = "py311"
+target-version = "py310"

 lint.select = [
  "F",  # Pyflakes
@@ -1,21 +0,0 @@
-[mutmut]
-paths_to_mutate=tinygrad
-do_not_mutate=
-  tinygrad/apps/*
-  tinygrad/codegen/*
-  tinygrad/engine/*
-  tinygrad/nn/*
-  tinygrad/renderer/*
-  tinygrad/runtime/*
-  tinygrad/schedule/*
-  tinygrad/uop/*
-  tinygrad/viz/*
-  tinygrad/device.py
-  tinygrad/dtype.py
-  tinygrad/gradient.py
-  tinygrad/helpers.py
-  tinygrad/tensor.py
-tests_dir=
-  test/test_tiny.py
-  test/test_ops.py
-debug=true
@@ -13,7 +13,6 @@ testing_minimal = [
  "pytest",
  "pytest-xdist",
  "pytest-timeout",
-  "pytest-split",
  "hypothesis",
  "z3-solver",
 ]
@@ -32,7 +31,6 @@ setup(name='tinygrad',
        'tinygrad.codegen.opt',
        'tinygrad.codegen.late',
        'tinygrad.engine',
-        'tinygrad.mixin',
        'tinygrad.nn',
        'tinygrad.renderer',
        'tinygrad.runtime',
@@ -53,7 +51,7 @@ setup(name='tinygrad',
        "License :: OSI Approved :: MIT License"
      ],
      install_requires=[],
-      python_requires='>=3.11',
+      python_requires='>=3.10',
      extras_require={
        'arm': ["unicorn"],
        'triton': ["triton-nightly>=2.1.0.dev20231014192330"],
@@ -1,163 +0,0 @@
-# ruff: noqa: E501 E712
-from tinygrad import dtypes, Device
-from tinygrad.uop.ops import UOp, AxisType, Ops, KernelInfo
-from tinygrad.codegen import full_rewrite
-from tinygrad.renderer import ProgramSpec
-from tinygrad.engine.realize import CompiledRunner
-from tinygrad.helpers import dedup
-from tinygrad.device import Buffer
-from tinygrad.dtype import ImageDType, Invalid
-
-c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1576), (), 0)
-c2 = UOp.range(1576, 20, AxisType.LOOP)
-c5 = c2<55
-c6 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 16, 4)), (), 1)
-c8 = UOp.range(16, 0, AxisType.REDUCE)
-c11 = UOp.range(4, 1, AxisType.REDUCE)
-c14 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((14, 64, 4)), (), 2)
-c25 = c5.where((c2%4*4+c11+c8*16+c2//4*256), UOp.const(dtypes.index, Invalid))
-c27 = c6.index((c8*4+c11))*c14.index(c25)
-c29 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(55), (), 3)
-c30 = c5.where(c2, UOp.const(dtypes.index, Invalid))
-c34 = c5.where((c27.reduce(c8, c11, arg=Ops.ADD)+c29.index(c30)), UOp.const(dtypes.float, 0.0))
-c38 = c2<87
-c39 = (c5!=True)&c38
-c40 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 8, 4)), (), 4)
-c42 = UOp.range(8, 2, AxisType.REDUCE)
-c44 = UOp.range(4, 3, AxisType.REDUCE)
-c47 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((8, 32, 4)), (), 5)
-c49 = c2+1
-c51 = c49%4*4
-c57 = c49//4*128
-c61 = c39.where((c51+c44+c42*16+c57+-1792), UOp.const(dtypes.index, Invalid))
-c63 = c40.index((c42*4+c44))*c47.index(c61)
-c65 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(32), (), 6)
-c68 = c39.where((c2+-55), UOp.const(dtypes.index, Invalid))
-c71 = c39.where((c63.reduce(c42, c44, arg=Ops.ADD)+c65.index(c68)), UOp.const(dtypes.float, 0.0))
-c75 = c2<99
-c76 = (c38!=True)&c75
-c77 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 8, 4)), (), 7)
-c78 = UOp.range(8, 4, AxisType.REDUCE)
-c80 = UOp.range(4, 5, AxisType.REDUCE)
-c83 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((3, 32, 4)), (), 8)
-c90 = c76.where((c51+c80+c78*16+c57+-2816), UOp.const(dtypes.index, Invalid))
-c92 = c77.index((c78*4+c80))*c83.index(c90)
-c94 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(12), (), 9)
-c97 = c76.where((c2+-87), UOp.const(dtypes.index, Invalid))
-c100 = c76.where((c92.reduce(c78, c80, arg=Ops.ADD)+c94.index(c97)), UOp.const(dtypes.float, 0.0))
-c104 = c2<105
-c105 = (c75!=True)&c104
-c106 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 8, 4)), (), 10)
-c107 = UOp.range(8, 6, AxisType.REDUCE)
-c109 = UOp.range(4, 7, AxisType.REDUCE)
-c112 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((2, 32, 4)), (), 11)
-c119 = c105.where((c51+c109+c107*16+c57+-3200), UOp.const(dtypes.index, Invalid))
-c121 = c106.index((c107*4+c109))*c112.index(c119)
-c123 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(6), (), 12)
-c126 = c105.where((c2+-99), UOp.const(dtypes.index, Invalid))
-c129 = c105.where((c121.reduce(c107, c109, arg=Ops.ADD)+c123.index(c126)), UOp.const(dtypes.float, 0.0))
-c133 = c2<117
-c134 = (c104!=True)&c133
-c135 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 8, 4)), (), 13)
-c136 = UOp.range(8, 8, AxisType.REDUCE)
-c138 = UOp.range(4, 9, AxisType.REDUCE)
-c141 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((3, 32, 4)), (), 14)
-c143 = c2+3
-c145 = c143%4*4
-c149 = c143//4
-c150 = c149*128
-c154 = c134.where((c145+c138+c136*16+c150+-3456), UOp.const(dtypes.index, Invalid))
-c156 = c135.index((c136*4+c138))*c141.index(c154)
-c158 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(12), (), 15)
-c161 = c134.where((c2+-105), UOp.const(dtypes.index, Invalid))
-c164 = c134.where((c156.reduce(c136, c138, arg=Ops.ADD)+c158.index(c161)), UOp.const(dtypes.float, 0.0))
-c168 = c2<645
-c169 = (c133!=True)&c168
-c170 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 16, 4)), (), 16)
-c171 = UOp.range(16, 10, AxisType.REDUCE)
-c173 = UOp.range(4, 11, AxisType.REDUCE)
-c176 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((132, 64, 4)), (), 17)
-c180 = c149*256
-c184 = c169.where((c145+c173+c171*16+c180+-7680), UOp.const(dtypes.index, Invalid))
-c186 = c170.index((c171*4+c173))*c176.index(c184)
-c188 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(528), (), 18)
-c191 = c169.where((c2+-117), UOp.const(dtypes.index, Invalid))
-c194 = c169.where((c186.reduce(c171, c173, arg=Ops.ADD)+c188.index(c191)), UOp.const(dtypes.float, 0.0))
-c198 = c2<653
-c199 = (c168!=True)&c198
-c200 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 4, 4)), (), 19)
-c201 = UOp.range(4, 12, AxisType.REDUCE)
-c203 = UOp.range(4, 13, AxisType.REDUCE)
-c206 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((2, 16, 4)), (), 20)
-c215 = c199.where((c145+c203+c201*16+c149*64+-10368), UOp.const(dtypes.index, Invalid))
-c217 = c200.index((c201*4+c203))*c206.index(c215)
-c219 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(8), (), 21)
-c222 = c199.where((c2+-645), UOp.const(dtypes.index, Invalid))
-c225 = c199.where((c217.reduce(c201, c203, arg=Ops.ADD)+c219.index(c222)), UOp.const(dtypes.float, 0.0))
-c229 = c2<917
-c230 = (c198!=True)&c229
-c231 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 8, 4)), (), 22)
-c232 = UOp.range(8, 14, AxisType.REDUCE)
-c234 = UOp.range(4, 15, AxisType.REDUCE)
-c237 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((66, 32, 4)), (), 23)
-c244 = c230.where((c145+c234+c232*16+c150+-20992), UOp.const(dtypes.index, Invalid))
-c246 = c231.index((c232*4+c234))*c237.index(c244)
-c248 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(264), (), 24)
-c251 = c230.where((c2+-653), UOp.const(dtypes.index, Invalid))
-c254 = c230.where((c246.reduce(c232, c234, arg=Ops.ADD)+c248.index(c251)), UOp.const(dtypes.float, 0.0))
-c258 = c2<1061
-c259 = (c229!=True)&c258
-c260 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 16, 4)), (), 25)
-c261 = UOp.range(16, 16, AxisType.REDUCE)
-c263 = UOp.range(4, 17, AxisType.REDUCE)
-c266 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((36, 64, 4)), (), 26)
-c273 = c259.where((c145+c263+c261*16+c180+-58880), UOp.const(dtypes.index, Invalid))
-c275 = c260.index((c261*4+c263))*c266.index(c273)
-c277 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), (), 27)
-c280 = c259.where((c2+-917), UOp.const(dtypes.index, Invalid))
-c283 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), (), 28)
-c286 = c259.where(((c275.reduce(c261, c263, arg=Ops.ADD)+c277.index(c280))*c283.index(c280)), UOp.const(dtypes.float, 0.0))
-c290 = c2<1064
-c291 = (c258!=True)&c290
-c292 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 4, 4)), (), 29)
-c293 = UOp.range(4, 18, AxisType.REDUCE)
-c295 = UOp.range(4, 19, AxisType.REDUCE)
-c298 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 16, 4)), (), 30)
-c305 = c291.where((c2*4+c295+c293*16+-4244), UOp.const(dtypes.index, Invalid))
-c307 = c292.index((c293*4+c295))*c298.index(c305)
-c309 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(3), (), 31)
-c312 = c291.where((c2+-1061), UOp.const(dtypes.index, Invalid))
-c315 = c291.where((c307.reduce(c293, c295, arg=Ops.ADD)+c309.index(c312)), UOp.const(dtypes.float, 0.0))
-c317 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 128, 4)), (), 32)
-c321 = (c290!=True).where((c2+-1064), UOp.const(dtypes.index, Invalid))
-c323 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), (), 33)
-c328 = c290.where(UOp.const(dtypes.float, 0.0), (c317.index(c321)*c323.index(UOp.const(dtypes.index, 0)).reciprocal()))
-c329 = c34+c71+c100+c129+c164+c194+c225+c254+c286+c315+c328
-c331 = c0.index(c2, ptr=True).store(c329).end(c2)
-ast = c331.sink(arg=KernelInfo(name="cat", opts_to_apply=None))
-
-compiler = Device.default.compiler
-renderer = Device.default.renderer
-allocator = Device.default.allocator
-
-uops = full_rewrite(ast, renderer)
-src = renderer.render(uops)
-
-# NOLOCALS=1 IMAGE=2 DEV=CL
-lib = compiler.compile(src)
-
-ps = ProgramSpec("cat", src, Device.DEFAULT, ast, uops)
-# print(ps.src)
-# print(ps.applied_opts)
-# NOTE: this is faster with no GROUP and with NOLOCALS
-# (Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=19, arg=4), Opt(op=OptOps.UNROLL, axis=17, arg=4), Opt(op=OptOps.UNROLL, axis=15, arg=4), Opt(op=OptOps.UNROLL, axis=13, arg=4), Opt(op=OptOps.UNROLL, axis=11, arg=4), Opt(op=OptOps.UNROLL, axis=9, arg=4), Opt(op=OptOps.UNROLL, axis=7, arg=4), Opt(op=OptOps.UNROLL, axis=5, arg=4), Opt(op=OptOps.UNROLL, axis=3, arg=4), Opt(op=OptOps.UNROLL, axis=1, arg=4), Opt(op=OptOps.NOLOCALS, axis=None, arg=None))
-cr = CompiledRunner(ps, precompiled=lib)
-
-gs = sorted(dedup([u for u in ast.toposort() if u.op is Ops.DEFINE_GLOBAL]), key=lambda u: u.arg)
-print(len(gs))
-print([g.dtype for g in gs])
-
-bufs = [Buffer(ps.device, g.size, g.dtype if isinstance(g.dtype, ImageDType) else g.dtype._base).ensure_allocated() for g in gs]
-
-t = cr(bufs, wait=True)
-print(f"{t*1e6:.2f} us")
@@ -1,81 +0,0 @@
-# ruff: noqa: E501 E712
-from tinygrad import dtypes, Device
-from tinygrad.uop.ops import UOp, AxisType, Ops, KernelInfo
-from tinygrad.codegen import full_rewrite
-# from tinygrad.codegen.opt import Opt, OptOps
-from tinygrad.renderer import ProgramSpec
-from tinygrad.engine.realize import CompiledRunner
-from tinygrad.helpers import dedup, getenv
-from tinygrad.device import Buffer
-from tinygrad.dtype import ImageDType, Invalid
-
-# PYTHONPATH="." DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
-
-def vision_conv_143():
-  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 1024, 4)), (), 0)
-  c2 = UOp.range(32, 3, AxisType.LOOP)
-  c5 = UOp.range(128, 4, AxisType.LOOP)
-  c8 = UOp.range(16, 2, AxisType.LOOP)
-  c16 = UOp.range(7, 0, AxisType.REDUCE)
-  c17 = c8*2+c16
-  c24 = ((c17<3)!=True)&(c17<35)
-  c26 = UOp.range(7, 1, AxisType.REDUCE)
-  c27 = c2*2+c26
-  c32 = ((c27<3)!=True)&(c27<67)
-  c34 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((32, 1024, 4)), (), 1)
-  c38 = c5//2
-  c45 = (c32&c24).where((c27*64+c38+c17*4096+-12480), UOp.const(dtypes.index, Invalid))
-  c48 = (c24&c32).where(c34.index(c45), UOp.const(dtypes.float, 0.0))
-  c49 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((64, 49, 4)), (), 2)
-  c61 = c48*c49.index((c26*4+c5%2+c16*28+c38*196))
-  c63 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), (), 3)
-  c65 = c61.reduce(c16, c26, arg=Ops.ADD)+c63.index(c5)
-  c67 = c0.index((c2*128+c5+c8*4096), ptr=True).store(c65).end(c8, c2, c5)
-
-  opts = None
-  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))
-
-def vision_conv_153():
-  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((8, 1024, 4)), (), 0)
-  c2 = UOp.range(16, 3, AxisType.LOOP)
-  c5 = UOp.range(256, 4, AxisType.LOOP)
-  c8 = UOp.range(8, 2, AxisType.LOOP)
-  c16 = UOp.range(7, 0, AxisType.REDUCE)
-  c17 = c8*2+c16
-  c24 = ((c17<3)!=True)&(c17<19)
-  c26 = UOp.range(7, 1, AxisType.REDUCE)
-  c27 = c2*2+c26
-  c32 = ((c27<3)!=True)&(c27<35)
-  c34 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((16, 1024, 4)), (), 1)
-  c38 = c5//2
-  c45 = (c32&c24).where((c27*128+c38+c17*4096+-12672), UOp.const(dtypes.index, Invalid))
-  c48 = (c24&c32).where(c34.index(c45), UOp.const(dtypes.float, 0.0))
-  c49 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((128, 49, 4)), (), 2)
-  c61 = c48*c49.index((c26*4+c5%2+c16*28+c38*196))
-  c63 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), (), 3)
-  c65 = c61.reduce(c16, c26, arg=Ops.ADD)+c63.index(c5)
-  c67 = c0.index((c2*256+c5+c8*4096), ptr=True).store(c65).end(c8, c2, c5)
-
-  opts = None
-  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))
-
-ast = vision_conv_143() if getenv("NUM", 143) == 143 else vision_conv_153()
-
-compiler = Device.default.compiler
-renderer = Device.default.renderer
-allocator = Device.default.allocator
-
-uops = full_rewrite(ast, renderer)
-src = renderer.render(uops)
-
-lib = compiler.compile(src)
-ps = ProgramSpec("conv", src, Device.DEFAULT, ast, uops)
-cr = CompiledRunner(ps, precompiled=lib)
-
-gs = sorted(dedup([u for u in ast.toposort() if u.op is Ops.DEFINE_GLOBAL]), key=lambda u: u.arg)
-# print(len(gs))
-# print([g.dtype for g in gs])
-bufs = [Buffer(ps.device, g.size, g.dtype if isinstance(g.dtype, ImageDType) else g.dtype._base).ensure_allocated() for g in gs]
-
-t = cr(bufs, wait=True)
-print(f"{t*1e6:.2f} us")
@@ -3,7 +3,7 @@ from tinygrad import Tensor, nn, Device
 from tinygrad.helpers import Profiling, Timing, getenv
 from tinygrad.uop.ops import Ops
 from tinygrad.codegen import full_rewrite_to_sink
-from tinygrad.codegen.late.linearizer import linearize
+from tinygrad.codegen.late.control_flow import linearize
 from tinygrad.uop.spec import type_verify, program_spec

 if __name__ == "__main__":
@@ -0,0 +1,39 @@
+import subprocess
+import random
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+def run_test(i, full_run=False):
+  print(f"\rRunning iteration {i}...", end=" ", flush=True)
+
+  p = subprocess.Popen(['python3', 'test/test_tiny.py', 'TestTiny.test_plus'],  stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+  if not full_run:
+    time.sleep(random.uniform(0, 1200) / 1000)
+    p.kill()
+    _, stderr = p.communicate()
+  else:
+    _, stderr = p.communicate()
+
+  if full_run:
+    stderr_text = stderr.decode()
+    print(stderr_text)
+    assert "Ran 1 test in" in stderr_text and "OK" in stderr_text
+
+max_workers = 4
+with ThreadPoolExecutor(max_workers=max_workers) as executor:
+  futures = []
+  for i in range(1000000):
+    if i % 100 == 0:
+      for future in as_completed(futures):
+        try: future.result()
+        except Exception as e:
+          print(f"\nError in iteration: {e}")
+      futures = []
+
+      run_test(i, True)
+    else:
+      future = executor.submit(run_test, i, False)
+      futures.append(future)
+
+    if len(futures) > max_workers * 2: futures = [f for f in futures if not f.done()]
@@ -1,44 +0,0 @@
-import subprocess
-import random
-import time
-from concurrent.futures import ProcessPoolExecutor, as_completed
-from tinygrad.helpers import getenv
-
-# checks that HCQ drivers can be killed during operation without causing issues
-
-def run_test(i, full_run=False, force_ok=False):
-  print(f"\rRunning iteration {i}...", end=" ", flush=True)
-
-  p = subprocess.Popen(["python3", "test/test_tiny.py", "TestTiny.test_plus"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-
-  if not full_run:
-    time.sleep(random.uniform(0, 1200) / 1000.0)
-    p.kill()
-    _, stderr = p.communicate()
-  else:
-    _, stderr = p.communicate()
-    stderr_text = stderr.decode()
-    assert ("Ran 1 test in" in stderr_text and "OK" in stderr_text) or (not force_ok and "Failed to take lock file" in stderr_text), stderr_text
-
-if __name__ == "__main__":
-  max_workers = getenv("MAX_WORKERS", 4)
-  with ProcessPoolExecutor(max_workers=max_workers) as executor:
-    futures = []
-    for i in range(1000000):
-      if i % 100 == 0:
-        # wait for everything we launched so far
-        for f in as_completed(futures):
-          try:
-            f.result()
-          except Exception as e:
-            print(f"\nError in iteration: {e}")
-        futures = []
-
-        # do a full run in the main proc
-        run_test(i, True, force_ok=True)
-      else:
-        futures.append(executor.submit(run_test, i, bool(getenv("FULL_RUN", 0))))
-
-      # keep list small
-      if len(futures) > max_workers * 2:
-        futures = [f for f in futures if not f.done()]
@@ -1,38 +0,0 @@
-from tinygrad import Tensor, nn, Context, GlobalCounters
-
-if __name__ == "__main__":
-  conv = nn.Conv2d(64, 128, 3)
-  img = Tensor.randn((1,64,128,128))
-  with Context(DEBUG=0, BEAM=0):
-    Tensor.realize(img, conv.weight, conv.bias)
-
-  tst = conv(img).permute(0,2,3,1).realize()
-  print(tst.shape)
-
-  print("NEW")
-  img_perm = img.permute(0,2,3,1).contiguous()
-  print(img_perm.shape)
-  pp = img_perm.permute(0,3,1,2)._pool((3,3)).permute(0,2,3,4,5,1)
-
-  def hwio(pp, conv):
-    pp = pp.unsqueeze(-1)
-    weight = conv.weight.permute(2,3,1,0).contiguous()
-    print(pp.shape, weight.shape, (pp*weight).shape)
-    return (pp * weight).sum([-4,-3, -2])
-
-  def ohwi(pp, conv):
-    pp = pp.unsqueeze(-4)
-    weight = conv.weight.permute(0,2,3,1).contiguous()
-    print(pp.shape, weight.shape, (pp*weight).shape)
-    return (pp * weight).sum([-3,-2,-1])
-
-  for f in [hwio, ohwi]:
-    GlobalCounters.reset()
-    print("\n**************", f.__name__, "**************")
-    out = f(pp, conv)
-    out.realize()
-    print(out.shape)
-
-    with Context(DEBUG=0, BEAM=0):
-      err = (tst-out).square()
-      print(err.mean().item(), err.max().item())
@@ -1,20 +0,0 @@
-import os
-if "DEV" not in os.environ: os.environ["DEV"] = "AMD"
-
-import unittest, time
-from tinygrad import Device
-
-class TestOpen(unittest.TestCase):
-  def generate_test_open(n):
-    def test(self):
-      dev = Device[Device.DEFAULT]
-      for i in range(10):
-        dev.allocator.alloc(10 << 20)
-        time.sleep(0.5)
-    test.__name__ = f'test_open_{n}'
-    return test
-
-  for i in range(64): locals()[f'test_open_{i}'] = generate_test_open(i)
-
-if __name__ == '__main__':
-  unittest.main()
@@ -272,10 +272,6 @@ class TestMainOnnxOps(TestOnnxOps):
  def test_qlinearmatmul_2D_int8_float32(self): self._run_qlinearmatmul_test(np.int8, np.float32, 2)
  def test_qlinearmatmul_3D_int8_float32(self): self._run_qlinearmatmul_test(np.int8, np.float32, 3)

-  def test_reduce_l2_half(self):
-    inputs = {"data": np.random.randn(1, 1, 32, 32, 32).astype(np.half)*100}
-    self.helper_test_single_op("ReduceL2", inputs, {}, ["reduced"])
-
 class TestTrainingOnnxOps(TestOnnxOps):
  # NOTE: ORT doesn't actually support training ops on cpu so we test using functions provided by onnx
  DOMAIN = AI_ONNX_PREVIEW_TRAINING_DOMAIN
@@ -491,4 +487,4 @@ class TestContribOnnxOps(TestOnnxOps):
        self.helper_test_single_op("QLinearGlobalAveragePool", inputs, attributes, outputs)

 if __name__ == "__main__":
-  unittest.main()
+  unittest.main()
@@ -1,345 +0,0 @@
-import unittest
-
-from tinygrad import Tensor, Device, dtypes, Context
-from tinygrad.engine.realize import ExecItem, get_runner
-
-from extra.thunder.tiny.tk import WARP_THREADS
-from extra.thunder.tiny.tk.kernel import Kernel
-from extra.thunder.tiny.tk.tiles import gl, st, rt, rv
-
-class TestTK(unittest.TestCase):
-  @unittest.skip("store from float rt is wrong")
-  def test_simple_matmul(self):
-    N = 32
-    BLOCK_SIZE = 16
-    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      c = gl((1, 1, N, N), dtypes.float32)
-      a = gl((1, 1, N, N), dtypes.bfloat16)
-      b = gl((1, 1, N, N), dtypes.bfloat16)
-
-      a_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      b_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      c_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      a_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      b_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      c_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      col, row = ker.blockIdx_x, ker.blockIdx_y
-
-      c_reg = warp.zero(c_reg)
-      for tile in ker.range(N // BLOCK_SIZE):
-        a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
-        b_smem = warp.load(b_smem, b, (), (0, 0, tile, col), axis=2)
-
-        a_reg = warp.load(a_reg, a_smem)
-        b_reg = warp.load(b_reg, b_smem, transpose=True)
-
-        c_reg = warp.mma_AB(c_reg, a_reg, b_reg)
-      c_reg = ker.endrange()
-
-      c_smem = warp.store(c_smem, c_reg)
-      c = warp.store(c, c_smem, (0, 0, row, col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
-      b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
-      c = Tensor.empty(1, 1, N, N, dtype="float32")
-      Tensor.realize(a, b, c)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (c, a, b)])
-    for _ in range(5): ei.run(wait=True)
-    c = c.float()
-
-    ref = a.matmul(b, dtype=dtypes.float32).float()
-
-    assert ref.allclose(c)
-
-  @unittest.skip("store from float rt is wrong")
-  def test_simple_matmul_transposed(self):
-    N = 32
-    BLOCK_SIZE = 16
-    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      c = gl((1, 1, N, N), dtypes.float32)
-      a = gl((1, 1, N, N), dtypes.bfloat16)
-      b = gl((1, 1, N, N), dtypes.bfloat16)
-
-      a_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      b_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      c_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      a_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      b_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
-      c_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      col, row = ker.blockIdx_x, ker.blockIdx_y
-
-      c_reg = warp.zero(c_reg)
-      for tile in ker.range(N // BLOCK_SIZE):
-        a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
-        b_smem = warp.load(b_smem, b, (), (0, 0, col, tile), axis=2)
-
-        a_reg = warp.load(a_reg, a_smem)
-        b_reg = warp.load(b_reg, b_smem)
-
-        c_reg = warp.mma_ABt(c_reg, a_reg, b_reg)
-      c_reg = ker.endrange()
-
-      c_smem = warp.store(c_smem, c_reg)
-      c = warp.store(c, c_smem, (0, 0, row, col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
-      b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
-      c = Tensor.empty(1, 1, N, N, dtype="float32")
-      Tensor.realize(a, b, c)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (c, a, b)])
-    for _ in range(5): ei.run(wait=True)
-    c = c.float()
-
-    ref = a.matmul(b.transpose(2, 3), dtype=dtypes.float32).float()
-
-    assert ref.allclose(c)
-
-  def test_load_store(self):
-    N = 32
-    BLOCK_SIZE = 16
-    with Kernel((N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      b = gl((1, 1, N, N), dtypes.float32)
-      a = gl((1, 1, N, N), dtypes.float32)
-
-      a_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      a_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      col, row = ker.blockIdx_x, ker.blockIdx_y
-
-      a_smem = warp.load(a_smem, a, (), (0, 0, row, col), axis=2)
-      a_reg = warp.load(a_reg, a_smem)
-      b_reg = warp.copy(b_reg, a_reg)
-      b_smem = warp.store(b_smem, b_reg)
-      b = warp.store(b, b_smem, (0, 0, row, col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
-      b = Tensor.empty(1, 1, N, N, dtype="float32")
-      Tensor.realize(a, b)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
-    for _ in range(5): ei.run(wait=True)
-    b = b.float()
-
-    ref = a.float()
-
-    assert ref.allclose(b)
-
-  def test_max(self):
-    N = 16
-    BLOCK_SIZE = 16
-    with Kernel((1, 1, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      b = gl((1, 1, N, N), dtypes.float32)
-      a = gl((1, 1, N, N), dtypes.float32)
-
-      a_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      a_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      max_reg = rv(BLOCK_SIZE, dtypes.float32, "ortho")
-
-      max_reg = warp.neg_inf(max_reg)
-
-      for tile_row in ker.range(N // BLOCK_SIZE):
-        for tile_col in ker.range(N // BLOCK_SIZE):
-          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
-          a_reg = warp.load(a_reg, a_smem)
-          max_reg = warp.row_reduce(max_reg, a_reg, lambda a, b: a.maximum(b))
-        sum_reg = ker.endrange()
-
-        b_reg = warp.zero(b_reg).after(tile_row)
-        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
-        b_smem = warp.store(b_smem, b_reg)
-
-        for tile_col in ker.range(N // BLOCK_SIZE):
-          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
-      b = Tensor.empty(1, 1, N, N, dtype="float32")
-      Tensor.realize(a, b)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
-    for _ in range(5): ei.run(wait=True)
-    b = b.float()
-
-    ref = a.float().max(axis=3, keepdim=True).expand(a.shape)
-
-    assert ref.allclose(b)
-
-  def test_max_nonsquare(self):
-    N, M = 16, 64
-    BLOCK_N, BLOCK_M = 16, 64
-    with Kernel((1, 1, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      b = gl((1, 1, N, M), dtypes.float32)
-      a = gl((1, 1, N, M), dtypes.float32)
-
-      a_smem = st((BLOCK_N, BLOCK_M), dtypes.float32)
-      b_smem = st((BLOCK_N, BLOCK_M), dtypes.float32)
-
-      a_reg = rt((BLOCK_N, BLOCK_M), dtypes.float32)
-      b_reg = rt((BLOCK_N, BLOCK_M), dtypes.float32)
-
-      max_reg = rv(BLOCK_N, dtypes.float32, "ortho")
-
-      max_reg = warp.zero(max_reg)
-
-      for tile_row in ker.range(N // BLOCK_N):
-        for tile_col in ker.range(M // BLOCK_M):
-          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
-          a_reg = warp.load(a_reg, a_smem)
-          sum_reg = warp.row_reduce(max_reg, a_reg, lambda a, b: a.maximum(b))
-        sum_reg = ker.endrange()
-
-        b_reg = warp.zero(b_reg).after(tile_row)
-        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
-        b_smem = warp.store(b_smem, b_reg)
-
-        for tile_col in ker.range(M // BLOCK_M):
-          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
-      b = Tensor.empty(1, 1, N, M, dtype="float32")
-      Tensor.realize(a, b)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
-    for _ in range(5): ei.run(wait=True)
-    b = b.float()
-
-    ref = a.float().max(axis=3, keepdim=True).expand(a.shape)
-
-    assert ref.allclose(b)
-
-  def test_sum(self):
-    N = 16
-    BLOCK_SIZE = 16
-    with Kernel((1, 1, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      b = gl((1, 1, N, N), dtypes.float32)
-      a = gl((1, 1, N, N), dtypes.float32)
-
-      a_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_smem = st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      a_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-      b_reg = rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
-
-      sum_reg = rv(BLOCK_SIZE, dtypes.float32, "ortho")
-
-      for tile_row in ker.range(N // BLOCK_SIZE):
-        sum_reg = warp.zero(sum_reg).after(tile_row)
-
-        for tile_col in ker.range(N // BLOCK_SIZE):
-          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
-          a_reg = warp.load(a_reg, a_smem)
-          sum_reg = warp.row_reduce(sum_reg, a_reg, lambda a, b: a + b)
-        sum_reg = ker.endrange()
-
-        b_reg = warp.zero(b_reg).after(tile_row)
-        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
-        b_smem = warp.store(b_smem, b_reg)
-
-        for tile_col in ker.range(N // BLOCK_SIZE):
-          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
-      a = Tensor.arange(1 * 1 * N * N).reshape(1, 1, N, N).cast(dtypes.float32).contiguous()
-      b = Tensor.empty(1, 1, N, N, dtype="float32")
-      Tensor.realize(a, b)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
-    for _ in range(5): ei.run(wait=True)
-    b = b.float()
-
-    ref = a.float().sum(axis=3, keepdim=True).expand(a.shape)
-
-    assert ref.allclose(b)
-
-  def test_sum_nonsquare(self):
-    N, M = 16, 64
-    BLOCK_N, BLOCK_M = 16, 64
-    with Kernel((1, 1, 1), WARP_THREADS) as ker:
-      warp = ker.warp
-
-      b = gl((1, 1, N, M), dtypes.float32)
-      a = gl((1, 1, N, M), dtypes.float32)
-
-      a_smem = st((BLOCK_N, BLOCK_M), dtypes.float32)
-      b_smem = st((BLOCK_N, BLOCK_M), dtypes.float32)
-
-      a_reg = rt((BLOCK_N, BLOCK_M), dtypes.float32)
-      b_reg = rt((BLOCK_N, BLOCK_M), dtypes.float32)
-
-      sum_reg = rv(BLOCK_N, dtypes.float32, "ortho")
-
-      sum_reg = warp.zero(sum_reg)
-
-      for tile_row in ker.range(N // BLOCK_N):
-        for tile_col in ker.range(M // BLOCK_M):
-          a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
-          a_reg = warp.load(a_reg, a_smem)
-          sum_reg = warp.row_reduce(sum_reg, a_reg, lambda a, b: a + b)
-        sum_reg = ker.endrange()
-
-        b_reg = warp.zero(b_reg).after(tile_row)
-        b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[0], 0, (idx[2]%4)//2])
-        b_smem = warp.store(b_smem, b_reg)
-
-        for tile_col in ker.range(M // BLOCK_M):
-          b = warp.store(b, b_smem, (0, 0, tile_row, tile_col), (), axis=2)
-
-      sink = ker.finish()
-
-    with Context(DEBUG=0):
-      a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
-      b = Tensor.empty(1, 1, N, M, dtype="float32")
-      Tensor.realize(a, b)
-
-    ei = ExecItem(get_runner(Device.DEFAULT, sink), [t.uop.buffer for t in (b, a)])
-    for _ in range(5): ei.run(wait=True)
-    b = b.float()
-
-    ref = a.float().sum(axis=3, keepdim=True).expand(a.shape)
-
-    assert ref.allclose(b)
-
-if __name__ == "__main__":
-  unittest.main()
@@ -13,7 +13,7 @@ try:
  from tinygrad.engine.realize import get_program
  from tinygrad.uop.ops import UOp, Ops, KernelInfo
  from tinygrad.codegen.opt import Opt
-  from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm, BEAM
+  from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm
  from tinygrad.device import Device
 except ImportError as e:
  print(repr(e))
@@ -51,8 +51,8 @@ def replay_get_rangeify_map(ret:dict[UOp, UOp], big_sink:UOp) -> tuple[str, str,
  return to_str(new_sink), to_str(big_sink.substitute(ret)), (big_sink,)

 def replay_get_program(p:ProgramSpec, ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None) -> tuple[str, str, tuple[Any, ...]]:
-  # the ast.arg is non None if we are inside of search.py
-  sink_arg = ast.arg or KernelInfo(opts_to_apply=tuple(opts) if opts is not None else p.applied_opts if BEAM>=1 else None)
+  # NOTE: this always uses the opts_to_apply path
+  sink_arg = ast.arg or KernelInfo(opts_to_apply=p.applied_opts)
  input_ast = ast.replace(arg=replace(sink_arg, name=p.name))
  # if no renderer was provided, open the device to get it
  if renderer is None: renderer = Device[p.device].renderer
@@ -85,11 +85,12 @@ class TestKernelSpeed(unittest.TestCase):
    gbs = mems / tm / 1e9
    self._compare(tm, tflops, gbs, nv_tflops, nv_gbs, amd_tflops, amd_gbs)

+  # NOTE: tiny7 was slower than tiny12
  # TODO: why are convs so slow?!?
  def test_conv_3x3_256_32_32_256_256(self): self._test_conv_3x3(256, 32, 32, 256, 256, nv_tflops=27, amd_tflops=14)

  # theoretical is nv_tflops=165, amd_tflops=123
-  def test_gemm_4096(self): self._test_matmul(4096, nv_tflops=110, amd_tflops=65)
+  def test_gemm_4096(self): self._test_matmul(4096, nv_tflops=115, amd_tflops=65)
  def test_gemm_8192(self): self._test_matmul(8192, nv_tflops=115, amd_tflops=60)

  # theoretical is nv_gbs=1008, amd_gbs=960
@@ -85,8 +85,6 @@ class AMDDriver(VirtDriver):
      VirtFile(f'/sys/devices/virtual/kfd/kfd/topology/nodes/{gpu_id}/gpu_id', functools.partial(TextFileDesc, text=f"{gpu_id}")),
      VirtFile(f'/sys/devices/virtual/kfd/kfd/topology/nodes/{gpu_id}/properties',
        functools.partial(TextFileDesc, text=gpu_props.format(drm_render_minor=gpu_id))),
-      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/power_dpm_force_performance_level',
-               functools.partial(TextFileDesc, text='profile_standard\n')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0',
               functools.partial(DirFileDesc, child_names=[str(am.GC_HWID), str(am.SDMA0_HWID), str(am.NBIF_HWID)])),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
@@ -14,9 +14,6 @@ regSQ_THREAD_TRACE_BUF0_BASE = 0x39e8 + amd_gpu.GC_BASE__INST0_SEG1
 regSQ_THREAD_TRACE_BUF0_SIZE = 0x39e9 + amd_gpu.GC_BASE__INST0_SEG1
 regSQ_THREAD_TRACE_WPTR = 0x39ef + amd_gpu.GC_BASE__INST0_SEG1
 regSQ_THREAD_TRACE_STATUS = 0x39f4 + amd_gpu.GC_BASE__INST0_SEG1
-regCP_PERFMON_CNTL = 0x3808 + amd_gpu.GC_BASE__INST0_SEG1
-regCPG_PERFCOUNTER1_LO = 0x3000 + amd_gpu.GC_BASE__INST0_SEG1
-regGUS_PERFCOUNTER_HI = 0x3643 + amd_gpu.GC_BASE__INST0_SEG1

 class SQTT_EVENTS:
  THREAD_TRACE_FINISH = 0x00000037
@@ -133,7 +130,7 @@ class PM4Executor(AMDQueue):
    _src_addr_hi = self._next_dword()
    dst_addr_lo = self._next_dword()
    dst_addr_hi = self._next_dword()
-    assert copy_data_flags in {0x100204, 0x000204}, hex(copy_data_flags) # better fail than silently do the wrong thing
+    assert copy_data_flags == 0x100204, hex(copy_data_flags) # better fail than silently do the wrong thing
    to_mv(dst_addr_hi<<32|dst_addr_lo, 4).cast('I')[0] = self.gpu.regs[src_addr_lo]

  def _exec_wait_reg_mem(self, n):
@@ -283,9 +280,6 @@ class AMDGPURegisters:
    self.regs: dict[tuple[int, int], int] = {}
  def __getitem__(self, addr:int) -> int:
    if addr == regGRBM_GFX_INDEX: return self.grbm_index
-    if regCPG_PERFCOUNTER1_LO < addr < regGUS_PERFCOUNTER_HI:
-      assert self.regs[(regCP_PERFMON_CNTL, 0)] == 0x401, "read mode should be enabled"
-      return addr << 16 | self.grbm_index
    return self.regs[(addr, getbits(self.grbm_index, 16, 23))]
  def __setitem__(self, addr:int, val:int):
    if addr == regGRBM_GFX_INDEX: self.grbm_index = val
@@ -112,7 +112,7 @@ class TestRealWorld(unittest.TestCase):
        loss.backward()
        optimizer.step()

-      helper_test("train_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 103)
+      helper_test("train_mnist", lambda: (Tensor.randn(BS, 1, 28, 28),), train, 0.07, 102)

  @unittest.skipIf(CI and Device.DEFAULT in {"CPU", "CL"}, "slow")
  def test_forward_cifar(self):
@@ -176,7 +176,7 @@ class TestRealWorld(unittest.TestCase):
      for v in data.values(): v.to_(Device.DEFAULT)

      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, 0.31, 427)
+          data["masked_lm_ids"], data["masked_lm_weights"], data["next_sentence_labels"]), train, 0.31, 358)

 if __name__ == '__main__':
  unittest.main()
@@ -14,8 +14,6 @@ from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 # TODO: write a clean version of this
 from test.test_linearizer import helper_realized_ast, helper_linearizer_opt

-# NOTE: get_program always passes in Device[Device.DEFAULT].renderer explicitly for process_replay!!!
-
 def helper_tc_ensure_uops_and_opts_count(N: int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0,
                                         ensure_triggered:bool=True):
  a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
@@ -43,7 +41,7 @@ def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axi
  if dtype_in == dtypes.bfloat16: r = r.float()
  realized_ast, bufs = helper_realized_ast(r)
  opts = [Opt(op=OptOps.TC, axis=axis, arg=(tc_select, tc_opt, use_tensor_cores))]
-  prg = CompiledRunner(replace(get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts), device=Device.DEFAULT))
+  prg = CompiledRunner(replace(get_program(realized_ast, opts=opts), device=Device.DEFAULT))
  if use_tensor_cores == 1: assert len([uop for uop in prg.p.uops if uop.op is Ops.WMMA]) > 0, "wmma not triggered"
  assert len([x for x in prg.p.uops[-1].arg.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
  prg.exec(bufs)
@@ -70,7 +68,7 @@ class TestTensorCores(unittest.TestCase):
      n, m, k = tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2]
      a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in)
      r = a.matmul(b, dtype=tc.dtype_out)
-      prg = get_program(r.schedule()[-1].ast, Device[Device.DEFAULT].renderer, opts=[Opt(op=OptOps.TC, axis=0, arg=(-1, 2, 1))])
+      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:
        assert "0x201000" in prg.src
      elif Device.DEFAULT == "AMD" and AMD_LLVM:
@@ -156,7 +154,7 @@ class TestTensorCores(unittest.TestCase):
    r = x.matmul(y, dtype=tc.dtype_out)
    opts = [Opt(OptOps.UNROLL, 0, 4)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
-    for u in get_program(ast, Device[Device.DEFAULT].renderer, opts=opts).uops:
+    for u in get_program(ast, opts=opts).uops:
      if u.op is Ops.WMMA:
        assert u.src[-1].src[0].op != Ops.STORE

@@ -169,7 +167,7 @@ class TestTensorCores(unittest.TestCase):
    r = x.matmul(y, dtype=tc.dtype_out)
    opts = [Opt(OptOps.UNROLL, 0, 4)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
-    for u in get_program(ast, Device[Device.DEFAULT].renderer, opts=opts).uops:
+    for u in get_program(ast, opts=opts).uops:
      if u.op is Ops.WMMA:
        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
        assert u.src[-1].src[0].op != Ops.STORE
@@ -184,7 +182,7 @@ class TestTensorCores(unittest.TestCase):
    r = x.matmul(y, dtype=tc.dtype_out).relu()
    opts = [Opt(OptOps.UNROLL, 0, 4)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
-    for u in get_program(ast, Device[Device.DEFAULT].renderer, opts=opts).uops:
+    for u in get_program(ast, opts=opts).uops:
      if u.op is Ops.WMMA:
        #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
        assert u.src[-1].src[0].op != Ops.STORE
@@ -1,5 +1,5 @@
 import unittest, itertools, math
-from tinygrad import Tensor, Device, dtypes, Context
+from tinygrad import Tensor, Device, dtypes
 from tinygrad.dtype import DType, ConstType
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.codegen import full_rewrite_to_sink
@@ -126,8 +126,7 @@ class TestBitcastConstFolding(unittest.TestCase):
    t({dtypes.int64: 4598983288165178391, dtypes.uint64: 4598983288165178391, dtypes.float64: 0.29485681936461233})

  def test_vec_bitcast(self):
-    with Context(SPEC=0):
-      r = full_rewrite_to_sink(UOp.const(dtypes.int32.vec(3), (-1, -2**31, 75)).bitcast(dtypes.uint32.vec(3)).sink()).src[0]
+    r = full_rewrite_to_sink(UOp.const(dtypes.int32.vec(3), (-1, -2**31, 75)).bitcast(dtypes.uint32.vec(3)).sink()).src[0]
    self.assertEqual(r.op, Ops.VECTORIZE)
    self.assertEqual(r.dtype, dtypes.uint32.vec(3))
    self.assertEqual(tuple(x.arg for x in r.src), (2**32-1, 2**31, 75))
@@ -183,7 +182,7 @@ class TestReduceOpsConstFolding(unittest.TestCase):
    np.testing.assert_equal(Tensor(4).sum().numpy(), 4)

  def test_padded_const_sum(self):
-    _check_ast_count(0, Tensor.ones(4).pad(((1, 1),)).sum())
+    _check_ast_count(1, Tensor.ones(4).pad(((1, 1),)).sum())
    np.testing.assert_equal(Tensor.ones(4).pad(((1, 1),)).sum().numpy(), 4)

    # NOTE: cannot just count the non-padded area because some Ops f do not have f(0) = 0.
@@ -1,221 +0,0 @@
-import unittest
-from tinygrad import Tensor, UOp, Context
-from tinygrad.dtype import AddrSpace
-from tinygrad.uop.ops import KernelInfo, AxisType
-
-# **** kernels ****
-
-def custom_arange_kernel(C:UOp) -> UOp:
-  i = UOp.range(C.size, 0)
-  return C[i].store(i.cast(C.dtype.base)).end(i).sink(arg=KernelInfo(name=f"custom_arange_{C.size}"))
-
-def custom_eye_kernel(C:UOp) -> UOp:
-  i = UOp.range(C.shape[0], 0)
-  j = UOp.range(C.shape[1], 1)
-  return C[i, j].store((i.eq(j)).cast(C.dtype.base)).end(i, j).sink(arg=KernelInfo(name=f"custom_eye_{C.size}"))
-
-def custom_add_one_kernel(B:UOp, A:UOp) -> UOp:
-  A,B = A.flatten(), B.flatten()
-  assert B.size == A.size
-  i = UOp.range(A.size, 0)
-  return B[i].store(A[i] + 1).end(i).sink(arg=KernelInfo(name=f"add_one_{A.size}"))
-
-def custom_elementwise_add_kernel(C:UOp, A:UOp, B:UOp) -> UOp:
-  C,A,B = C.flatten(), A.flatten(), B.flatten()
-  i = UOp.range(C.size, 0)
-  return C[i].store(A[i]+B[i]).end(i).sink(arg=KernelInfo(name=f"custom_add_kernel_{C.size}")).simplify()
-
-def custom_elementwise_addmul_kernel(C:UOp, D:UOp, A:UOp, B:UOp) -> UOp:
-  C,D,A,B = C.flatten(), D.flatten(), A.flatten(), B.flatten()
-  assert C.size == D.size
-  i = UOp.range(C.size, 0)
-  store_c = C[i].store(A[i]+B[i])
-  store_d = D[i].store(A[i]*B[i])
-  return UOp.group(store_c, store_d).end(i).sink(arg=KernelInfo(name=f"custom_addmul_kernel_{C.size}")).simplify()
-
-def custom_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
-  assert A.shape[1] == B.shape[0]
-  i, j, k = UOp.range(C.shape[0], 0), UOp.range(C.shape[1], 1), UOp.range(A.shape[1], 2, axis_type=AxisType.REDUCE)
-  C = C[i, j].set(0.0)
-  C = C[i, j].set(C.after(k)[i, j] + A[i, k] * B[k, j], end=k)
-  prog = C.end(i, j)
-  return prog.sink(arg=KernelInfo(name=f"custom_gemm_{C.shape[0]}_{C.shape[1]}_{A.shape[1]}", opts_to_apply=()))
-
-def custom_sum(B:UOp, A:UOp) -> UOp:
-  i = UOp.range(A.shape[0], 0, axis_type=AxisType.REDUCE)
-  B = B[0].set(0.0)
-  B = B[0].set(B.after(i)[0] + A[i], end=i)
-  return B.sink(arg=KernelInfo(name=f"custom_sum_{A.shape[0]}", opts_to_apply=()))
-
-def flip_contract_kernel(dest:UOp, src:UOp):
-  i = UOp.range(dest.shape[0], 0)
-  j = UOp.range(dest.shape[1], 1, AxisType.UPCAST)
-  vec = src[i, j].contract(j)
-  store = UOp.group(*[dest[i, k].store(vec.gep(3-k)) for k in range(4)])
-  return store.end(i).sink(arg=KernelInfo(name=f"flip_contract_{dest.size}", opts_to_apply=()))
-
-def slice_sum_kernel(dest:UOp, src:UOp):
-  G = UOp.range(src.shape[0], 0)
-  slice_src = src[G, :]
-  reg = UOp.placeholder((1,), dest.dtype.base, 0, addrspace=AddrSpace.REG)
-  reg = reg.after(G)[0].set(0)
-  R = UOp.range(src.shape[1], 1, AxisType.REDUCE)
-  reg = reg[0].set(reg.after(R)[0] + slice_src[R], end=R)
-  ast = dest[G].set(reg[0], end=G)
-  return ast.sink(arg=KernelInfo(name=f"slice_sum_{src.shape[0]}_{src.shape[1]}", opts_to_apply=()))
-
-def simple_qkv_kernel(O:UOp, Q:UOp, K:UOp, V:UOp) -> UOp:
-  # attention without softmax
-  N, d = Q.shape[0], Q.shape[1]
-
-  i = UOp.range(N, 0)  # output row
-  d_out = UOp.range(d, 1)  # output column
-  j = UOp.range(N, 2, axis_type=AxisType.REDUCE)
-
-  k_inner = UOp.range(d, 3, axis_type=AxisType.REDUCE)
-  qk_acc = UOp.placeholder((1,), Q.dtype.base, 0, addrspace=AddrSpace.REG)
-  qk_acc = qk_acc.after(i, j)[0].set(0.0)
-  qk_acc = qk_acc[0].set(qk_acc.after(k_inner)[0] + Q[i, k_inner] * K[j, k_inner], end=k_inner)
-  qk_score = qk_acc[0] / (d ** 0.5)
-
-  out_acc = UOp.placeholder((1,), Q.dtype.base, 1, addrspace=AddrSpace.REG)
-  out_acc = out_acc.after(i, d_out)[0].set(0.0)
-  out_acc = out_acc[0].set(out_acc.after(j)[0] + qk_score * V[j, d_out], end=j)
-
-  store = O[i, d_out].store(out_acc[0])
-  return store.end(d_out).end(i).sink(arg=KernelInfo(name=f"simple_qkv_{N}_{d}", opts_to_apply=()))
-
-# **** backward callbacks ****
-
-def backward_gemm(gradient:UOp, kernel:UOp) -> tuple[UOp, UOp]:
-  out, a, b = kernel.src
-  grad_a = (Tensor(gradient) @ Tensor(b).T).uop
-  grad_b = (Tensor(a).T @ Tensor(gradient)).uop
-  return (None, grad_a, grad_b)
-
-def backward_gemm_custom(gradient:UOp, kernel:UOp) -> tuple[UOp, UOp]:
-  out, a, b = kernel.src
-  grad_a = Tensor.empty_like(Tensor(a)).custom_kernel(Tensor(gradient), Tensor(b).T, fxn=custom_gemm)[0].uop
-  grad_b = Tensor.empty_like(Tensor(b)).custom_kernel(Tensor(a).T, Tensor(gradient), fxn=custom_gemm)[0].uop
-  return (None, grad_a, grad_b)
-
-# **** tests ****
-
-class TestCustomKernel(unittest.TestCase):
-  def test_simple(self):
-    a = Tensor.ones(16, 16).contiguous()
-    b = Tensor.ones(16, 16).contiguous()
-    c = Tensor.empty(16, 16)
-
-    c = Tensor.custom_kernel(c,a,b, fxn=custom_elementwise_add_kernel)[0]
-
-    out = c.flatten().tolist()
-    assert all(x == 2 for x in out), "all 2"
-
-  def test_multioutput(self):
-    a = Tensor.full((16, 16), 3.).contiguous()
-    b = Tensor.full((16, 16), 3.).contiguous()
-    c = Tensor.empty(16, 16)
-    d = Tensor.empty(16, 16)
-
-    c,d = Tensor.custom_kernel(c,d,a,b, fxn=custom_elementwise_addmul_kernel)[:2]
-    Tensor.realize(c,d)
-
-    assert all(x == 6 for x in c.flatten().tolist()), "all 6"
-    assert all(x == 9 for x in d.flatten().tolist()), "all 9"
-
-  def test_arange(self):
-    ref = Tensor.arange(100)
-    tst = Tensor.empty_like(ref)
-    tst = tst.custom_kernel(fxn=custom_arange_kernel)[0]
-    self.assertTrue((ref == tst).all().item())
-
-  def test_eye(self):
-    ref = Tensor.eye(1024).contiguous().realize()
-    tst = Tensor.empty_like(ref)
-    tst = tst.custom_kernel(fxn=custom_eye_kernel)[0]
-    self.assertTrue((ref == tst).all().item())
-
-  def test_flip_contract(self):
-    a = Tensor.randn(10,4)
-    b = Tensor.empty_like(a)
-    b = b.custom_kernel(a, fxn=flip_contract_kernel)[0]
-    self.assertTrue((a.flip(1) == b).all().item())
-
-  def test_noncontig(self):
-    a = Tensor.ones(16, 16).contiguous()
-    tst = Tensor.empty_like(a)
-    b = a+1
-    b_p1 = Tensor.custom_kernel(tst, b, fxn=custom_add_one_kernel)[0]
-    self.assertTrue((b_p1 == 3).all().item())
-
-  def test_sum(self):
-    # TODO: this only works for float, and silently fails with int
-    a = Tensor([1.0, 2, 3, 4, 5])
-    tst = Tensor.empty(1)
-    b = Tensor.custom_kernel(tst, a, fxn=custom_sum)[0]
-    self.assertEqual(b.item(), 15)
-
-  def test_slice_sum(self):
-    A = Tensor.randn(16, 16).contiguous()
-    B = Tensor.empty(16)
-    B = Tensor.custom_kernel(B, A, fxn=slice_sum_kernel)[0]
-    self.assertTrue(B.allclose(A.sum(1)))
-
-  def test_gemm(self):
-    N = 16
-    a = Tensor.randn(N, N)
-    b = Tensor.randn(N, N)
-    c = Tensor.empty(N, N)
-
-    tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm)[0]
-    err = (tst - (a@b)).square().max()
-    self.assertLess(err.item(), 1e-6)
-
-  def test_gemm_backward_custom(self): self.test_gemm_backward(True)
-  # NOTE: grad_fxn doesn't work with pyrender
-  @Context(SPEC=1)
-  def test_gemm_backward(self, custom_backward_gemm=False):
-    N = 4
-    a_rand = Tensor.randn(N, 8)
-    b_rand = Tensor.randn(8, N)
-    Tensor.realize(a_rand, b_rand)
-
-    a, b = Tensor(a_rand.numpy(), requires_grad=True), Tensor(b_rand.numpy(), requires_grad=True)
-    c = Tensor.empty(N, N)
-    tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm, grad_fxn=backward_gemm_custom if custom_backward_gemm else backward_gemm)[0]
-    tst.sum().backward()
-    grad_a, grad_b = a.grad, b.grad
-    Tensor.realize(tst, grad_a, grad_b)
-
-    a, b = Tensor(a_rand.numpy(), requires_grad=True), Tensor(b_rand.numpy(), requires_grad=True)
-    ref = (a@b)
-    ref.sum().backward()
-    real_grad_a, real_grad_b = a.grad, b.grad
-    Tensor.realize(ref, real_grad_a, real_grad_b)
-
-    err = (tst - ref).square().max()
-    self.assertLess(err.item(), 1e-6)
-
-    err = (grad_a - real_grad_a).square().max()
-    self.assertLess(err.item(), 1e-6)
-
-    err = (grad_b - real_grad_b).square().max()
-    self.assertLess(err.item(), 1e-6)
-
-  def test_simple_qkv(self):
-    N, d = 8, 4
-    Q = Tensor.randn(N, d)
-    K = Tensor.randn(N, d)
-    V = Tensor.randn(N, d)
-    O = Tensor.empty(N, d)
-
-    O_custom = Tensor.custom_kernel(O, Q, K, V, fxn=lambda o,q,k,v: simple_qkv_kernel(o,q,k,v))[0]
-    O_ref = ((Q @ K.T) / (d ** 0.5)) @ V
-
-    Tensor.realize(O_custom, O_ref)
-    err = (O_custom - O_ref).square().max()
-    self.assertLess(err.item(), 1e-6)
-
-if __name__ == '__main__':
-  unittest.main()
@@ -194,7 +194,6 @@ class TestDTypeALU(unittest.TestCase):
         strat.floats(width=32, min_value=0, max_value=10.0) if skip_overflow else ht.float32,
         ht.int32, strat.sampled_from(binary_operations), strat.sampled_from(integer_binary_operations))
  @unittest.skipIf(Device.DEFAULT == "PYTHON", "TODO: fix cast inf to int32 in PYTHON")
-  @unittest.skip("broken on Mac")
  def test_float_midcast_int32(self, a, b, c, op1, op2): universal_test_midcast(a, b, c, op1, op2, dtypes.float32, dtypes.int32)

  @unittest.skip("broken. TODO: fix it")
@@ -4,7 +4,7 @@ from dataclasses import replace

 from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.codegen.gpudims import get_grouped_dims
-from tinygrad.uop.ops import UOp, Ops, GroupOp, AxisType, PatternMatcher, graph_rewrite, UPat
+from tinygrad.uop.ops import UOp, Ops, GroupOp
 from tinygrad.device import Device, Buffer, is_dtype_supported
 from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
@@ -38,22 +38,6 @@ class TestLinearizer(unittest.TestCase):
    np.testing.assert_equal(a.numpy(), ta)
    np.testing.assert_equal(b.numpy(), tb)

-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "broken on ptx")
-  def test_late_bias_load(self):
-    img = Tensor.empty(1, 3, 16, 16)
-    w = Tensor.empty(16, 3, 3, 3)
-    b = Tensor.empty(16)
-    out = img.conv2d(w, b)
-    ast = helper_linearizer_opt(out)
-    uops = get_program(ast, opts=[]).uops
-    # slice at the last loop end
-    uslice = [i for i,u in enumerate(uops) if u.op == Ops.END][-1]
-    # only valid test if outermost range is the reduce
-    if uops[uslice].src[-1].arg[-1] == AxisType.REDUCE:
-      load_types = [u.src[0].dtype for u in uops[uslice+1:] if u.op == Ops.LOAD]
-      # assert that there is a global load after the reduce ends
-      assert any(dt.addrspace == AddrSpace.GLOBAL for dt in load_types)
-
  def _test_no_nested_ranges(self, lins, skip=None):
    for l in lins:
      range_in_acc = flatten([[x for x in u.src if x.op is Ops.RANGE] for u in l.uops if u.op is Ops.DEFINE_REG])
@@ -171,7 +155,6 @@ class TestLinearizer(unittest.TestCase):
    assert stores[1].src[1].dtype == dtypes.float
    assert any(x.op is Ops.DEFINE_GLOBAL for x in stores[1].toposort())

-  @unittest.skipIf(Device.DEFAULT=="CPU", "CPU splits the cat so cant upcast")
  def test_zero_fold(self):
    a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
    r = Tensor.stack(a, b)
@@ -278,8 +261,6 @@ class TestLinearizer(unittest.TestCase):
    _assert_grouped_dims("gidx", (65536,), (16,16,256), False, [16,16,256], False)
    #                              2             ->             3
    _assert_grouped_dims("gidx", (128,128), (16,16,256), False, [16,16,64], False)
-    #                              2             ->             2
-    _assert_grouped_dims("gidx", (65536,2), (65535,65535,65535), False, [32768,4], False)
    # test when the only divisor is the square root of dim
    _assert_grouped_dims("gidx", (121,), (12,12,12), False, [11,11], False)

@@ -304,27 +285,6 @@ class TestLinearizer(unittest.TestCase):
    with self.assertRaises(RuntimeError):
      get_grouped_dims("gidx", (2,3,4,5,6), (16,16,16))

-    # TODO: In the above cases we only test if the shape after reshape is correct, never the indices.
-    # We should check if the returned indices are correct, for all cases.
-    # (65536, 2) -> (32768, 4)
-    dims, expected_limited_dims = (65536,2), (32768, 4)
-    idxs = get_grouped_dims("gidx", dims, (65535,65535,65535))
-    def match_div(): raise RuntimeError("match_div")
-    def match_mod(): raise RuntimeError("match_mod")
-    flat_idx_pattern = UPat(Ops.SPECIAL, arg='gidx0')*expected_limited_dims[1]+UPat(Ops.SPECIAL, arg='gidx1')
-    pm = PatternMatcher([
-      (flat_idx_pattern//dims[1], match_div),
-      (flat_idx_pattern%dims[1], match_mod)
-    ])
-
-    with self.assertRaises(RuntimeError) as error:
-      graph_rewrite(idxs[0], pm)
-    self.assertIn("match_div", str(error.exception))
-
-    with self.assertRaises(RuntimeError) as error:
-      graph_rewrite(idxs[1], pm)
-    self.assertIn("match_mod", str(error.exception))
-
    # # variable too large
    # with self.assertRaises(AssertionError):
    #   get_grouped_dims("gidx", (Variable("start_pos",0,16),3,4), (16,16,16), False,)
@@ -471,8 +431,6 @@ def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
  # now all input buffers in s[-1] should be realized
  # create fresh buffers for the outputs
  bufs = [Buffer(x.device, x.size, x.dtype).allocate() if i < len(s[-1].ast.src) else x for i,x in enumerate(s[-1].bufs)]
-  # ensure buffers are allocated
-  for b in bufs: b.ensure_allocated()
  return s[-1].ast, bufs

 def helper_linearizer_ast(ast:UOp, inputs:list[Tensor], *args, **kwargs):
@@ -16,14 +16,14 @@ class TestLinearizerFailure(unittest.TestCase):
    c2 = UOp.range(UOp.const(dtypes.index, 784), 1, AxisType.GLOBAL)
    c3 = UOp.range(UOp.const(dtypes.index, 10), 3, AxisType.GLOBAL)
    c4 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(512), arg=1, src=())
-    c5 = c4.index(c1.valid(UOp.const(dtypes.bool, True)))
+    c5 = c4.index(c1.valid(UOp.const(dtypes.bool, True))).load()
    c6 = UOp.range(UOp.const(dtypes.index, 6000), 1004, AxisType.REDUCE)
    c7 = UOp.range(UOp.const(dtypes.index, 3750), 2006, AxisType.REDUCE)
    c8 = UOp.range(UOp.const(dtypes.index, 16), 2007, AxisType.GROUP_REDUCE)
    c9 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(47040000), arg=2, src=())
-    c10 = c9.index((((c3*UOp.const(dtypes.index, 4704000))+c2)+(c6*UOp.const(dtypes.index, 784))).valid(UOp.const(dtypes.bool, True)))
+    c10 = c9.index((((c3*UOp.const(dtypes.index, 4704000))+c2)+(c6*UOp.const(dtypes.index, 784))).valid(UOp.const(dtypes.bool, True))).load()
    c11 = c5.alu(Ops.CMPNE, ((((c3*UOp.const(dtypes.index, 6000))+c6)+((c7*UOp.const(dtypes.index, 16))+c8)).alu(Ops.CMPLT, UOp.const(dtypes.index, 59999)).where(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1)).reduce(c7, c8, arg=Ops.ADD)+UOp.const(dtypes.int, -1))).where(UOp.const(dtypes.uchar, 0), c10).reduce(c6, arg=Ops.ADD)
-    c12 = c0.index((((c1*UOp.const(dtypes.index, 7840))+(c2*UOp.const(dtypes.index, 10)))+c3).valid(UOp.const(dtypes.bool, True))).store(c11).end(c1, c2, c3)
+    c12 = c0.index((((c1*UOp.const(dtypes.index, 7840))+(c2*UOp.const(dtypes.index, 10)))+c3).valid(UOp.const(dtypes.bool, True))).store(c11, c1, c2, c3)
    ast = c12.sink(arg=KernelInfo(name='test', axis_types=(), dont_use_locals=False, applied_opts=(Opt(op=OptOps.GROUP, axis=1, arg=16),), opts_to_apply=None))
    _ = get_program(ast, Device["METAL"].renderer)

@@ -12,11 +12,11 @@ class TestLinearizerFailures(unittest.TestCase):
    c3 = ((c1*UOp.const(dtypes.index, 32))+c2)
    c4 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(163840), arg=1, src=())
    c5 = UOp.range(UOp.const(dtypes.index, 2560), 0, AxisType.REDUCE)
-    c6 = c4.index(((((((c5//UOp.const(dtypes.index, 8))%UOp.const(dtypes.index, 8))*UOp.const(dtypes.index, 8))+(c5%UOp.const(dtypes.index, 8)))+(((c2*UOp.const(dtypes.index, 40))+(c5//UOp.const(dtypes.index, 64)))*UOp.const(dtypes.index, 64)))+(c1*UOp.const(dtypes.index, 81920))))
+    c6 = c4.index(((((((c5//UOp.const(dtypes.index, 8))%UOp.const(dtypes.index, 8))*UOp.const(dtypes.index, 8))+(c5%UOp.const(dtypes.index, 8)))+(((c2*UOp.const(dtypes.index, 40))+(c5//UOp.const(dtypes.index, 64)))*UOp.const(dtypes.index, 64)))+(c1*UOp.const(dtypes.index, 81920)))).load()
    c7 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=2, src=())
-    c8 = c7.index(c3)
+    c8 = c7.index(c3).load()
    c9 = ((((c6+(c8*UOp.const(dtypes.float, -1.0)))*(c6+(c8*UOp.const(dtypes.float, -1.0)))).reduce(c5, arg=Ops.ADD)*UOp.const(dtypes.float, 0.000390625))+UOp.const(dtypes.float, 1e-05)).sqrt().reciprocal()
-    c10 = c0.index(c3).store(c9).end(c1, c2)
+    c10 = c0.index(c3).store(c9, c1, c2)
    ast = c10.sink()
    get_program(ast)

@@ -596,12 +596,6 @@ class TestMultiTensor(unittest.TestCase):
    # ast are the same on devices
    self.assertEqual(len(set(asts)), 1)

-  def test_flip(self):
-    rng = Tensor.rand((10, 10, 10))
-    t0 = rng.shard(devices_2, axis=1)
-    out = t0.flip(0) + 1
-    self.assertTrue((rng.flip(0)+1).allclose(out.to(rng.device)))
-
  def test_reshape_on_axis(self):
    t0 = Tensor.rand((26, 15, 7)).shard(devices_3, axis=1)

@@ -1551,10 +1551,8 @@ class TestOps(unittest.TestCase):
                                lambda x: Tensor.stack(*x.std_mean(axis=(1,2))))

  def test_std_mean_loaded_nan(self):
-    with warnings.catch_warnings():
-      warnings.filterwarnings("ignore", message="std_mean\\(\\): degrees of freedom is <= 0")
-      helper_test_op([(1,0,3,0,5)], lambda x: torch.stack(torch.std_mean(x, axis=(1,3))),
-                                    lambda x: Tensor.stack(*x.std_mean(axis=(1,3))))
+    helper_test_op([(1,0,3,0,5)], lambda x: torch.stack(torch.std_mean(x, axis=(1,3))),
+                                  lambda x: Tensor.stack(*x.std_mean(axis=(1,3))))
  def test_softmax(self):
    helper_test_op([(45,65)], torch.nn.Softmax(dim=1), Tensor.softmax, atol=1e-7, grad_atol=1e-7)
    helper_test_op([(45)], torch.nn.Softmax(dim=0), Tensor.softmax, atol=1e-7, grad_atol=1e-7)
@@ -2604,13 +2602,18 @@ class TestOps(unittest.TestCase):
      lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(111,28)),
      lambda x: Tensor.avg_pool2d(x, kernel_size=(111,28)), rtol=1e-5)

-  def test_avg_pool3d(self):
-    # TODO: AMD_LLVM has larger atol
-    # TODO: PYTHON=1 backward hangs?
-    atol = 1e-2 if AMD_LLVM else 1e-6
-    helper_test_op([(1,1,16,16,16)],
-      lambda x: torch.nn.functional.avg_pool3d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False),
-      lambda x: Tensor.avg_pool2d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False), atol=atol, rtol=1e-5, forward_only=True)
+  @unittest.skipIf(Device.DEFAULT == "AMD" and CI, "remu failure?")
+  def test_avg_pool3d_failure(self):
+    with Context(NOOPT=0):
+      helper_test_op([(1,1,16,16,16)],
+        lambda x: torch.nn.functional.avg_pool3d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False),
+        lambda x: Tensor.avg_pool2d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False), rtol=1e-5, forward_only=True)
+
+  def test_avg_pool3d_noopt(self):
+    with Context(NOOPT=1):
+      helper_test_op([(1,1,16,16,16)],
+        lambda x: torch.nn.functional.avg_pool3d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False),
+        lambda x: Tensor.avg_pool2d(x, kernel_size=(8,8,8), stride=5, padding=1, count_include_pad=False), rtol=1e-5, forward_only=True)

  def test_interpolate_linear(self):
    for in_sz, out_sz in [((52,),(29,)), ((29,),(52,))]:
@@ -2822,13 +2825,13 @@ class TestOps(unittest.TestCase):
  @slow_test
  def test_slice_fancy_indexing_list_indices(self):
    a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[((0,),)])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(0,),b,c,d,:], lambda x: x[(0,),j,k,o,:])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[0],b,c,d,:], lambda x: x[[0],j,k,o,:])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[[0]]],b,c,d,[[1]]], lambda x: x[[[[0]]],j,k,o,[[1]]])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(1,0,-1),b,c,d,:], lambda x: x[(1,0,-1),j,k,o,:])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,(1,2,3),...], lambda x: x[i,j,k,(1,2,3),...])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[1,0,-1],b,c,d,:], lambda x: x[[1,0,-1],j,k,o,:])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[1,2,3],...], lambda x: x[i,j,k,[1,2,3],...])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,(2,1,0),c,(-2,1,0),e], lambda x: x[i,(2,1,0),k,(-2,1,0),p])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[-2,1,0],e], lambda x: x[i,[2,1,0],k,[-2,1,0],p])

  @slow_test
  def test_slice_fancy_indexing_tuple_indices(self):
@@ -2843,10 +2846,11 @@ class TestOps(unittest.TestCase):
  @slow_test
  def test_slice_fancy_indexing_list_with_tensors(self):
    a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,)], lambda x: x[(i,)])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,1)], lambda x: x[(i,1)])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,(1,1))], lambda x: x[(i,(1,1))])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[(a,b,c,d,e)], lambda x: x[(i,j,k,o,p)])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,1]], lambda x: x[[i,1]])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,[1,1]]], lambda x: x[[i,[1,1]]])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,(1,1)]], lambda x: x[[i,(1,1)]])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a,b,c,d,e]], lambda x: x[[i,j,k,o,p]])

  def test_slice_fancy_indexing_errors(self):
    a = Tensor.ones(10,11,12)
@@ -1,18 +0,0 @@
-from tinygrad import Tensor, UOp
-from tinygrad.uop.ops import Ops, AxisType
-import unittest
-# this test is only focused on transformers and using range for the layers
-
-class TestOuterworldTransformer(unittest.TestCase):
-  def test_three_mats(self):
-    w = Tensor.empty(3, 1024, 1024)
-    inp = Tensor.empty(1, 1024)
-    i = UOp.range(3, -1, AxisType.OUTER)
-    inp_after = Tensor(inp.uop.after(i))
-    inp_gemm = inp_after@w[i]
-    inp = inp.uop.after(inp.uop.store(inp_gemm.uop).end(i)).contiguous()
-    inp = Tensor(inp)
-    inp.realize()
-
-if __name__ == "__main__":
-  unittest.main()
@@ -199,7 +199,6 @@ class TestProfiler(unittest.TestCase):
    #self.assertLess(e1.st, e2.st)
    #self.assertGreater(e1.en-e1.st, e2.en-e2.st)

-  @unittest.skipIf(not CI, "this test is flaky locally")
  @unittest.skipUnless(Device[Device.DEFAULT].graph is not None, "graph support required")
  def test_graph(self):
    from test.test_graph import helper_alloc_rawbuffer, helper_exec_op, helper_test_graphs
@@ -1,56 +1,10 @@
 import unittest
 from tinygrad import Tensor, nn, Device
-from tinygrad.helpers import Context, GlobalCounters, CI, getenv, PCONTIG, DEBUG
+from tinygrad.helpers import Context, GlobalCounters, CI, getenv, PCONTIG
 from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops
-from tinygrad.codegen.opt import OptOps, Opt
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.nir import NIRRenderer

-@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (NIRRenderer, PTXRenderer)), "broken in LVP and PTX")
-class TestDoubleMatmul(unittest.TestCase):
-  def setUp(self):
-    with Context(DEBUG=0):
-      self.a, self.b, self.c = [Tensor.randn(16, 16).contiguous().realize() for _ in range(3)]
-      self.ref = (self.a @ self.b @ self.c).realize()
-
-  def _test(self, opts):
-    with Context(PCONTIG=2, DEBUG=max(2, DEBUG.value)):
-      out = (self.a @ self.b @ self.c).contiguous(arg=opts).realize()
-
-    with Context(DEBUG=0):
-      err = (out-self.ref).square()
-      self.assertLess(err.max().item(), 1e-4)
-      self.assertLess(err.mean().item(), 1e-6)
-
-  def test_baseline(self): self._test(())
-  def test_upcast_0(self): self._test((Opt(OptOps.UPCAST, 0, 4),))
-  def test_upcast_1(self): self._test((Opt(OptOps.UPCAST, 1, 4),))
-  def test_upcast_2(self): self._test((Opt(OptOps.UPCAST, 2, 4),))
-  def test_upcast_01(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)))
-  def test_upcast_01_mismatch(self): self._test((Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 4)))
-  def test_upcast_02(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 2, 4)))
-  def test_upcast_12(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 2, 4)))
-
-  def test_unroll_0(self): self._test((Opt(OptOps.UNROLL, 0, 4),))
-  def test_unroll_1(self): self._test((Opt(OptOps.UNROLL, 1, 4),))
-  def test_unroll_01(self): self._test((Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
-
-  def test_upcast_0_unroll_0(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)))
-  def test_upcast_1_unroll_0(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)))
-  def test_upcast_2_unroll_0(self): self._test((Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 0, 4)))
-
-  def test_upcast_0_unroll_1(self): self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
-  def test_upcast_1_unroll_1(self): self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 1, 4)))
-  def test_upcast_2_unroll_1(self): self._test((Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 1, 4)))
-
-  def test_upcast_1_unroll_1_small(self): self._test((Opt(OptOps.UPCAST, 1, 2), Opt(OptOps.UNROLL, 1, 2)))
-  def test_upcast_1_unroll_1_rev(self): self._test((Opt(OptOps.UNROLL, 1, 2), Opt(OptOps.UPCAST, 1, 2)))
-
-  def test_upcast_01_unroll_01(self):
-    self._test((Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
-  def test_upcast_12_unroll_01(self):
-    self._test((Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 2, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)))
-
 class TestRangeifyAssign(unittest.TestCase):
  def test_assign_permuted(self):
    A = Tensor.empty(4, 4, dtype='int')
@@ -84,39 +38,32 @@ elif getenv("BIG") > 1:
  BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
 elif getenv("BIG") > 0:
  # bigger
-  BS, HEADS, SEQLEN, EMB = 4, 32, 128, 128
+  BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
 else:
  BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8

-def fa():
-  Tensor.manual_seed(1337)
-  with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
-  GlobalCounters.reset()
-  return q.scaled_dot_product_attention(k, v)
-
-def fa_bw():
-  Tensor.manual_seed(1337)
-  with Context(DEBUG=0):
-    q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize().requires_grad_() for _ in range(3)]
-    attn_output = nn.Linear(HEADS*EMB, HEADS*EMB, bias=False)
-    attn_output.weight.requires_grad_().realize()
-    target = Tensor.rand(BS, SEQLEN, HEADS*EMB).contiguous().realize()
-
-  GlobalCounters.reset()
-  attn = q.scaled_dot_product_attention(k, v).contiguous().contiguous_backward()
-  attn = attn.transpose(1, 2).reshape(BS, SEQLEN, -1)
-  out = attn_output(attn)
-  loss = (out - target).square().mean()
-  loss.backward()
-  #ret = [out, Tensor.stack(q.grad, k.grad, v.grad, dim=-1)]
-  #ret = [out, Tensor.stack(q.grad, k.grad, dim=-1), v.grad]
-  ret = [out, q.grad, k.grad, v.grad]
-  Tensor.realize(*ret)
-  return ret
-
@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (NIRRenderer, PTXRenderer)), "broken in LVP and PTX")
 class TestPcontig(unittest.TestCase):
  def test_flash_attention_bw(self):
+    def fa_bw():
+      Tensor.manual_seed(1337)
+      with Context(DEBUG=0):
+        q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize().requires_grad_() for _ in range(3)]
+        attn_output = nn.Linear(HEADS*EMB, HEADS*EMB, bias=False)
+        attn_output.weight.requires_grad_().realize()
+        target = Tensor.rand(BS, SEQLEN, HEADS*EMB).contiguous().realize()
+
+      GlobalCounters.reset()
+      attn = q.scaled_dot_product_attention(k, v).contiguous().contiguous_backward()
+      attn = attn.transpose(1, 2).reshape(BS, SEQLEN, -1)
+      out = attn_output(attn)
+      loss = (out - target).square().mean()
+      loss.backward()
+      #ret = [out, Tensor.stack(q.grad, k.grad, v.grad)]
+      ret = [out, q.grad, k.grad, v.grad]
+      Tensor.realize(*ret)
+      return ret
+
    with Context(PCONTIG=max(2, PCONTIG.value), DEBUG=2):
      grads = fa_bw()
      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
@@ -131,27 +78,24 @@ class TestPcontig(unittest.TestCase):
    print(f"mse: {mse}")
    self.assertLessEqual(mse, 1e-6)

-  def test_flash_attention(self, opts=None):
-    with Context(PCONTIG=2, DEBUG=max(2, DEBUG.value)):
-      ret = fa().realize() if opts is None else fa().contiguous(arg=opts).realize()
+  def test_flash_attention(self):
+    def fa():
+      Tensor.manual_seed(1337)
+      with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
+      GlobalCounters.reset()
+      return q.scaled_dot_product_attention(k, v).realize()
+
+    with Context(PCONTIG=2, DEBUG=2):
+      ret = fa()
      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
    with Context(DEBUG=2):
-      cmp = fa().realize()
+      cmp = fa()
      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
    with Context(DEBUG=0):
      mse = ((cmp-ret)**2).sum().item()
    print(f"mse: {mse}")
    self.assertLessEqual(mse, 1e-6)

-  def test_flash_attention_opt(self):
-    opts = ()
-    # columns in top matrix
-    opts += (Opt(OptOps.UPCAST, 0, 4),)
-    # columns in bottom matrix
-    opts += (Opt(OptOps.UPCAST, 3, 4),)
-    # rows in all the matrix
-    opts += (Opt(OptOps.UPCAST, 4, 4),)
-    self.test_flash_attention(opts)

 # *** non CI rangeify tests below this line ***

@@ -300,12 +244,6 @@ class TestRangeify(unittest.TestCase):
    w2 = Tensor.empty(12, 8, 3, 3)
    x.conv2d(w1).conv2d(w2).realize()

-  def test_resnet_conv2d(self):
-    x = Tensor.empty(1, 8, 32, 32)
-    w1 = Tensor.empty(8, 8, 3, 3)
-    w2 = Tensor.empty(8, 8, 1, 1)
-    x.conv2d(w1).conv2d(w2).realize()
-
  def test_xception_conv2d(self):
    # NOTE: this fusion is bad, it's recomputing the inner many times
    x = Tensor.empty(1, 4, 32, 32)
@@ -1,4 +1,5 @@
 import unittest
+from typing import List, cast
 import numpy as np
 from tinygrad.device import Buffer, Device, is_dtype_supported
 from tinygrad.dtype import dtypes, ConstType
@@ -14,15 +15,15 @@ from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.codegen import full_rewrite
 from tinygrad.engine.realize import lower_schedule_item

-def _test_uop_result(inputs:list[Tensor], stores:list[UOp], local_size=None):
+def _test_uop_result(inputs:List[Tensor], stores:List[UOp], local_size=None):
  for x in inputs: x.realize()
  # NOTE: we only toposort the stores
-  uops: list[UOp] = []
-  def _recursive_add(uop:UOp) -> list[UOp]: return flatten([_recursive_add(x) for x in uop.src])+[uop]
+  uops: List[UOp] = []
+  def _recursive_add(uop:UOp) -> List[UOp]: return flatten([_recursive_add(x) for x in uop.src])+[uop]
  uops = dedup(flatten(_recursive_add(st) for st in stores))
  outbufs = [Buffer(Device.DEFAULT, sz:=(1 if local_size is None else prod(local_size)), (dtype:=u.src[1].dtype), \
      initial_value=np.zeros(sz, dtype=_to_np_dtype(dtype)).data) for u in uops if u.op is Ops.STORE]
-  inbufs = [x.uop.base.buffer for x in inputs]
+  inbufs = [cast(UOp,x.uop).base.buffer for x in inputs]
  src = Device[Device.DEFAULT].renderer.render(uops)
  ei = CompiledRunner(ProgramSpec(uops[-1].arg.name if uops[-1].arg is not None else "test",
                                  src, Device.DEFAULT, uops[-1], uops=uops, local_size=local_size))
@@ -34,7 +35,7 @@ def _setup_and_test_alu(alu_op:Ops, input_val:ConstType, *alu_src_uops:UOp):
  a = UOp(Ops.DEFINE_GLOBAL, dtype.ptr(), (), 0)
  b = UOp(Ops.DEFINE_GLOBAL, dtype.ptr(), (), 1)
  idx = UOp.const(dtypes.int, 0)
-  ld = b.index(idx)
+  ld = UOp(Ops.LOAD, dtype, (b.index(idx),))
  alu = ld.alu(alu_op, *alu_src_uops)
  store = UOp.store(a.index(idx), alu)
  sink = UOp(Ops.SINK, dtypes.void, (store,))
@@ -46,7 +47,7 @@ class TestRendererFailures(unittest.TestCase):
  def test_gated_store_with_alu(self):
    a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
-    gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0.valid(gate_alu)), UOp.const(dtypes.int, 1)))
+    gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0, gate_alu), UOp.const(dtypes.int, 1)))
    sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
    uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
    ret = _test_uop_result([], uops, local_size=[4, 1, 1])[0]
@@ -57,7 +58,7 @@ class TestRendererFailures(unittest.TestCase):
    a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    gate_alu_0 = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
    gate_alu_1 = (lidx1:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 2),), 'lidx1')).ne(0)
-    gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index((lidx0+lidx1*4).valid(gate_alu_0&gate_alu_1)), UOp.const(dtypes.int, 1)))
+    gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0+lidx1*4, gate_alu_0&gate_alu_1), UOp.const(dtypes.int, 1)))
    sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
    uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
    ret = _test_uop_result([], uops, local_size=[4, 2, 1])[0]
@@ -370,7 +370,6 @@ class TestSchedule(unittest.TestCase):

  # NOTE: this is causing "LAZYCACHE=1 incorrectly reuses contiguous const" #4562
  # should contiguous dedup?
-  @unittest.skip("we do the exact opposite now")
  def test_dedup_contiguous(self):
    a = Tensor.ones(4).contiguous()
    b = Tensor.ones(4).contiguous()
@@ -447,7 +446,7 @@ class TestSchedule(unittest.TestCase):
  @unittest.skipUnless(is_dtype_supported(dtypes.ulong), "Needs ulong")
  def test_fold_conv_batchnorm_optim(self):
    # this is too high
-    for optim, cnt in [(nn.optim.Adam, 27), (nn.optim.SGD, 7)]:
+    for optim, cnt in [(nn.optim.Adam, 21), (nn.optim.SGD, 8)]:
      with self.subTest(optim=optim.__name__):
        with Tensor.train():
          img = Tensor.ones(1,3,4,4)
@@ -711,7 +710,7 @@ class TestSchedule(unittest.TestCase):
    self.assertEqual(b.buffer.numpy(), [12])

  # unlike schedule, kernelize can be called multiple times on a Tensor
-  def test_double_kernelize(self):
+  def test_double_kerenlize(self):
    a = Tensor.empty(10)
    b = Tensor.empty(10)
    c = (a+b)
@@ -760,7 +759,7 @@ class TestSchedule(unittest.TestCase):

  def test_pow_neg_05_is_rsqrt(self):
    t = Tensor([1.0, 2.0, 3.0]) ** -0.5
-    self.assertEqual(self._alu_from_tensor(t), [Ops.RECIPROCAL, Ops.SQRT])
+    self.assertEqual(self._alu_from_tensor(t), [Ops.RECIP, Ops.SQRT])

  def test_pow_2_has_1_mul(self):
    t = Tensor([1.0, 2.0, 3.0]) ** Tensor(2.0)
@@ -1042,12 +1041,13 @@ class TestSchedule(unittest.TestCase):
      compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
      np.testing.assert_allclose(out.numpy(), compare.numpy(), atol=1e-6, rtol=1e-3)

-    out = Tensor.scaled_dot_product_attention(q,k,v)
-    run_schedule(check_schedule(out, 4)) # TODO: should be 1?
-    if getenv("CHECK", 1):
-      import torch
-      compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
-      np.testing.assert_allclose(out.numpy(), compare.numpy(), atol=1e-6, rtol=1e-3)
+    with Context(FUSE_ATTENTION=1):
+      out = Tensor.scaled_dot_product_attention(q,k,v)
+      run_schedule(check_schedule(out, 4)) # TODO: should be 1?
+      if getenv("CHECK", 1):
+        import torch
+        compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
+        np.testing.assert_allclose(out.numpy(), compare.numpy(), atol=1e-6, rtol=1e-3)

  def test_ugly_reduceop_pairing(self):
    Tensor.manual_seed(0)
@@ -1220,7 +1220,7 @@ class TestSchedule(unittest.TestCase):
      _realize_weights(layer)
      opt = nn.optim.Adam(nn.state.get_parameters(layer), lr=1e-4)
      layer(x).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 19)
+      check_schedule(opt.schedule_step(), 16)

  def test_adam_conv_fuse(self):
    with Tensor.train():
@@ -1230,7 +1230,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.Adam(nn.state.get_parameters(c1), lr=1e-4)
      opt.zero_grad()
      c1(img).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 19)
+      check_schedule(opt.schedule_step(), 16)

  def test_adam_2convs_fuse(self):
    with Tensor.train():
@@ -1241,7 +1241,7 @@ class TestSchedule(unittest.TestCase):
      opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
      opt.zero_grad()
      c2(c1(img).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 21)
+      check_schedule(opt.schedule_step(), 18)

  def test_sgd_conv_fuse(self):
    with Tensor.train():
@@ -1502,18 +1502,6 @@ class TestSchedule(unittest.TestCase):
    run_schedule(sched)
    np.testing.assert_allclose(dx.numpy(), [[[[0.,3.,9.],[0,1.,3.],[0.,0.,0.]]]*3]*3)

-  def test_fuse_arange_avg_pool2d_ceil_mode(self):
-    x = Tensor.avg_pool2d(Tensor.empty(1,1,6,6), kernel_size=(3,3), padding=1, stride=3, ceil_mode=True)
-    sched = check_schedule(x, 1)
-    self.assertEqual(len([x for x in sched[0].ast.backward_slice_with_self if x.op is Ops.REDUCE]), 1)
-
-  def test_fuse_arange_pad_circular_mode_bw(self):
-    x = Tensor.empty(1,1,5,5,5)
-    out = x.pad((1,2,3,5,1,2), mode="circular")
-    g = out.sum().gradient(x)[0]
-    sched = check_schedule(g, 1)
-    self.assertEqual(len([x for x in sched[0].ast.backward_slice_with_self if x.op is Ops.REDUCE]), 0)
-
  # TODO like openpilot with imagef
  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
  def test_base_change_expand_expand(self):
@@ -1573,13 +1561,6 @@ class TestSchedule(unittest.TestCase):
  def test_conv2d(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
  def test_conv2d_fused(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)

-  def test_resnet_conv2d(self):
-    x = Tensor.empty(1, 8, 32, 32)
-    w1 = Tensor.empty(8, 8, 3, 3)
-    w2 = Tensor.empty(8, 8, 1, 1)
-    out = x.conv2d(w1).conv2d(w2)
-    check_schedule(out, 2)
-
  @unittest.skipUnless(is_dtype_supported(dtypes.half) and is_dtype_supported(dtypes.ulong), "need half and ulong")
  def test_conv2d_half(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4, dtype=dtypes.half)
  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
@@ -2181,8 +2162,8 @@ class TestCopyFolding(unittest.TestCase):
    self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])

  def test_permute_on_disk_contiguous(self):
-    with open(temp('dt_arange_4_permute_contig'), "wb") as f: f.write(Tensor.arange(4).realize().uop.base.buffer.as_buffer())
-    a = Tensor.empty(4, dtype=dtypes.int32, device=f"disk:{temp('dt_arange_4_permute_contig')}")
+    with open(temp('dt_arange_4_permute'), "wb") as f: f.write(Tensor.arange(4).realize().uop.base.buffer.as_buffer())
+    a = Tensor.empty(4, dtype=dtypes.int32, device=f"disk:{temp('dt_arange_4_permute')}")
    b = a.reshape(2, 2).permute(1, 0).contiguous().to("CPU")
    b.realize()
    self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
@@ -2285,7 +2266,7 @@ class TestContiguous(unittest.TestCase):
  def test_double_contiguous_realizes_once(self):
    a = Tensor.empty(4, 1)
    b = a.expand((4, 4)).contiguous().contiguous()
-    check_schedule(b, 1)
+    check_schedule(b, 2) # TODO: should be 1?

  def test_view_does_not_realize(self):
    a = Tensor.empty(4)
@@ -32,7 +32,7 @@ def run_one_schedule_item(out): lower_schedule_item(get_single_element(out.sched
 class TestFuse(unittest.TestCase):
  def _test_fuse(self, fxn, *args, atol=1e-6, allow_multiple=False, **kwargs):
    GlobalCounters.reset()
-    out_single = fxn(*args, **kwargs)
+    out_single = fxn(*args, **kwargs).fuse()
    if not allow_multiple: run_one_schedule_item(out_single)
    np_single = out_single.numpy()
    GlobalCounters.reset()
@@ -100,7 +100,7 @@ class TestFuse(unittest.TestCase):
    q = (x @ wq).contiguous()
    k = (x @ wk).contiguous()
    v = (x @ wv).contiguous()
-    attn = q.scaled_dot_product_attention(k, v)
+    attn = q.scaled_dot_product_attention(k, v).fuse()
    s = attn.schedule()
    self.assertEqual(len(s), 4) # 3 matmul and 1 attention

@@ -121,7 +121,7 @@ class TestFuse(unittest.TestCase):
  def test_mismatch_reduce(self):
    a = Tensor.ones(16, 10).contiguous().realize()
    b = Tensor.ones(16, 20).contiguous().realize()
-    c = (a.sum(axis=1) + b.sum(axis=1))
+    c = (a.sum(axis=1) + b.sum(axis=1)).fuse()
    self.assertListEqual(c.tolist(), [30]*16)

  @unittest.skipUnless(Device.DEFAULT == "METAL", "METAL TC")
@@ -129,7 +129,7 @@ class TestFuse(unittest.TestCase):
    A = Tensor.randn(8, 8).realize()
    B = Tensor.randn(8, 8).realize()
    C = Tensor.ones(1, 8, 8).pad(((1,1), None, None),).sum(0)
-    out = (C + (A @ B))
+    out = (C + (A @ B)).fuse()
    out.realize()

 class TestSoftmaxFusion(unittest.TestCase):
@@ -180,7 +180,7 @@ class TestSoftmaxFusion(unittest.TestCase):

    print("*** auto single kernel softmax ***")
    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2)):
-      out = self.test.contiguous().softmax(-1)
+      out = self.test.contiguous().softmax(-1).fuse()
      run_one_schedule_item(out)

    np.testing.assert_allclose(sout.numpy(), out.numpy(), atol=3e-7)
@@ -810,7 +810,6 @@ class TestTensorMetadata(unittest.TestCase):
    self.assertEqual(len(si.metadata), 1)
    self.assertEqual(si.metadata[0].name, "relu")

-  @unittest.skip("this no longer works")
  def test_assign(self):
    x = Tensor.empty(10, 10).realize()
    x.assign(Tensor.ones(10, 10).contiguous())
@@ -840,11 +839,11 @@ class TestTensorMetadata(unittest.TestCase):
    self.assertEqual(y.grad.uop.metadata[0].name, "sigmoid")
    self.assertTrue(y.grad.uop.metadata[0].backward)
    si = Tensor.schedule(out, x.grad, y.grad)[-1]
-    #self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
+    self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
    self.assertSetEqual(set(m.name for m in si.metadata), {"sigmoid", "relu"})
-    #bw = [m for m in si.metadata if m.backward]
-    #self.assertEqual(len(bw), 1)
-    #self.assertEqual(bw[0].name, "sigmoid")
+    bw = [m for m in si.metadata if m.backward]
+    self.assertEqual(len(bw), 1)
+    self.assertEqual(bw[0].name, "sigmoid")

 class TestIdxUpcast(unittest.TestCase):
  def _find_op(self, ast: UOp, op: Ops):
@@ -920,38 +919,5 @@ class TestIdxUpcast(unittest.TestCase):
    a = Tensor.empty(2**11, 2**11, 1, dtype=dtypes.int8).permute((2, 0, 1)).expand((2**9+10, -1, -1)).contiguous()
    a.realize()

-class TestTensorUnique(unittest.TestCase):
-  def test_empty_bufs_unique(self):
-    a = Tensor.empty(10, 10).contiguous()
-    b = Tensor.empty(10, 10).contiguous()
-    Tensor.realize(a,b)
-    self.assertIsNot(a.uop.buffer, b.uop.buffer)
-
-  def test_zeros_bufs_unique_sep(self):
-    a = Tensor.zeros(10, 10).contiguous()
-    Tensor.realize(a)
-    b = Tensor.zeros(10, 10).contiguous()
-    Tensor.realize(b)
-    self.assertIsNot(a.uop.buffer, b.uop.buffer)
-
-  def test_zeros_bufs_unique(self):
-    a = Tensor.zeros(10, 10).contiguous()
-    b = Tensor.zeros(10, 10).contiguous()
-    Tensor.realize(a,b)
-    self.assertIsNot(a.uop.buffer, b.uop.buffer)
-
-  def test_eye_bufs_unique(self):
-    a = Tensor.eye(10).contiguous()
-    b = Tensor.eye(10).contiguous()
-    Tensor.realize(a,b)
-    self.assertIsNot(a.uop.buffer, b.uop.buffer)
-
-  def test_times_2_not_unique(self):
-    a = Tensor.zeros(10, 10).contiguous()
-    b = a * 2
-    c = a * 2
-    Tensor.realize(b,c)
-    self.assertIs(b.uop.buffer, c.uop.buffer)
-
 if __name__ == '__main__':
  unittest.main()
@@ -1,11 +1,12 @@
+from typing import List
 import unittest, pytest
 from tinygrad import dtypes, Variable
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import DEBUG, Context
-from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, track_rewrites, graph_rewrite, GroupOp, AxisType
+from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, track_rewrites, graph_rewrite, GroupOp, KernelInfo
 from tinygrad.uop.symbolic import sym
+from tinygrad.codegen import full_rewrite, full_rewrite_to_sink
 from tinygrad.codegen.late.expander import expander
-from test.test_uops import to_uops_list

 simple_pm = PatternMatcher([
  (UPat.cvar('x', dtypes.int), lambda x: UOp.const(dtypes.float, 1.0) + UOp.const(dtypes.float, 2.0)),
@@ -14,6 +15,12 @@ simple_pm = PatternMatcher([
  ((UPat.var('x') + UPat.cvar('c1')) + UPat.cvar('c2'), lambda x,c1,c2: x + (c1.arg+c2.arg)),
 ])

+def to_uops_list(u:List[UOp]) -> List[UOp]:
+  # we strip the SINK here for legacy reasons
+  ret = full_rewrite(UOp.sink(*u, arg=KernelInfo(opts_to_apply=())))
+  assert ret[-1].op is Ops.SINK
+  return ret[:-1]
+
 class TestGraphRewriteConst(unittest.TestCase):
  def test_gep_const(self):
    v1 = UOp.const(dtypes.int.vec(3), (0,1,2))
@@ -263,7 +270,6 @@ class TestUOpGraph(unittest.TestCase):
    uops = to_uops_list([out])
    self.assertEqual(len([x for x in uops if x.op is Ops.VECTORIZE]), 0)

-  @unittest.skip("this test isn't valid uops")
  def test_gep_vec_fold(self):
    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
@@ -307,10 +313,9 @@ class TestUOpGraph(unittest.TestCase):
    for vec_size in [2, 4, 8]:
      consts = [UOp.const(dtypes.float, float(i)) for i in range(vec_size)]
      vec = UOp(Ops.VECTORIZE, dtypes.float.vec(vec_size), tuple(consts))
-      with Context(SPEC=0):
-        uops = to_uops_list([UOp(Ops.GEP, dtypes.float, (vec,), (i,)) for i in range(vec_size)])
-        for uop, const in zip(uops, consts):
-          self.assertEqual(uop, const)
+      uops = to_uops_list([UOp(Ops.GEP, dtypes.float, (vec,), (i,)) for i in range(vec_size)])
+      for uop, const in zip(uops, consts):
+        self.assertEqual(uop, const)

  @unittest.skip("no longer testable standalone")
  def test_wmma_vectorize_fold(self):
@@ -376,7 +381,7 @@ class TestUOpGraph(unittest.TestCase):
    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.bool.ptr(), arg=0)
    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), arg=1)
    idx = UOp.const(dtypes.int, 0)
-    ld = d1.index(idx)
+    ld = UOp(Ops.LOAD, dtypes.int, (d1.index(idx),))
    alu = (ld<1).cast(dtypes.bool)
    out = UOp(Ops.STORE, dtypes.void, (d0.index(idx), alu))
    uops = to_uops_list([out])
@@ -386,7 +391,7 @@ class TestUOpGraph(unittest.TestCase):
    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0)
    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), arg=1)
    idx = UOp.const(dtypes.int, 0)
-    ld = d1.index(idx)
+    ld = UOp(Ops.LOAD, dtypes.int, (d1.index(idx),))
    alu = ld.cast(dtypes.float).cast(dtypes.float)
    out = UOp(Ops.STORE, dtypes.void, (d0.index(idx), alu))
    uops = to_uops_list([out])
@@ -408,7 +413,7 @@ class TestUOpGraph(unittest.TestCase):
  def test_bitcast_to_same_dtype_fold(self):
    for dt in dtypes.ints + dtypes.floats + (dtypes.bool,):
      d0 = UOp(Ops.DEFINE_GLOBAL, dt.ptr(), arg=0)
-      v = d0.index(UOp.const(dtypes.int, 0))
+      v = UOp(Ops.LOAD, dt, (d0.index(UOp.const(dtypes.int, 0)),))
      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}")

@@ -420,7 +425,7 @@ class TestUOpGraph(unittest.TestCase):
  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.valid(ridx0<50))
+    ld = d0.index(ridx0.valid(ridx0<50)).load()
    w = (ridx0<50).where(ld, 5)
    uops = to_uops_list([w])
    for u in uops:
@@ -430,7 +435,7 @@ class TestUOpGraph(unittest.TestCase):
  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.valid((ridx0<50).logical_not()))
+    ld = d0.index(ridx0.valid((ridx0<50).logical_not())).load()
    w = (ridx0<50).where(5, ld)
    uops = to_uops_list([w])
    for u in uops:
@@ -441,7 +446,7 @@ class TestUOpGraph(unittest.TestCase):
    ridx0 = UOp.range(100, 0)
    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    gate_idx = ridx0.valid((ridx0<50))
-    ld = d0.index(gate_idx).cast(dtypes.float)
+    ld = d0.index(gate_idx).load().cast(dtypes.float)
    w = (ridx0<50).where(ld, 5.0)
    uops = to_uops_list([w])
    for u in uops:
@@ -454,62 +459,63 @@ class TestUOpGraph(unittest.TestCase):
    idx = d0.index(ridx0)
    ld = idx.load()
    val = (ridx0<50).where(5, ld)
-    st = idx.store(val).end(ridx0)
+    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):
-    # mnist indexing with split reduceop
-    # Make sure we are not doign math on the loaded index, which would promote it to long
-    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(128000), arg=0, src=())
-    c1 = UOp.range(UOp.const(dtypes.index, 512), 1, AxisType.LOOP)
-    c2 = UOp.range(UOp.const(dtypes.index, 250), 2, AxisType.LOOP)
-    c3 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(512), arg=1, src=())
-    c4 = c3.index(c1)
-    c5 = UOp.range(UOp.const(dtypes.index, 240), 0, AxisType.REDUCE)
-    c6 = ((c2*UOp.const(dtypes.index, 240))+c5)
-    c7 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(60000), arg=2, src=())
-    c8 = c7.index(c6)
-    c9 = ((c4<0).where((c4+60000), c4)!=c6.cast(dtypes.int)).where(0, c8.cast(dtypes.uint).cast(dtypes.uchar)).reduce(c5, arg=Ops.ADD)
-    c10 = c0.index(((c1*UOp.const(dtypes.index, 250))+c2)).store(c9).end(c1, c2)
-    uops = to_uops_list([c10])
+    # These loads wont overflow int since we know from the gate that the value is bounded
+    r0 = UOp.range(10, 0)
+    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)),)).cast(dtypes.index)
+    idx = l0 * 600
+    valid = (l0<-1).ne(True)&(l0<3000)
+    l1 = valid.where(UOp(Ops.LOAD, dtypes.long, (d1.index(idx),)),0)
+    uops = to_uops_list([l1])
    for u in uops:
-      self.assertNotEqual(u.dtype, dtypes.long)
+      if u.op is Ops.INDEX: self.assertEqual(u.src[1].dtype, dtypes.int)
+
+    valid = (10*r0<5-l0).ne(True)&(l0<3000)
+    l2 = UOp(Ops.LOAD, dtypes.long, (d1.index(idx.valid(valid)),))
+    uops = to_uops_list([l2])
+    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)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 0), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 0)),))
      to_uops_list([ld0])
-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 15), ptr=True),))
+      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 15)),))
      to_uops_list([ld1])
-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 7), ptr=True),))
+      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 7)),))
      to_uops_list([ld1])

-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 42), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 42)),))
      with self.assertRaises(RuntimeError): to_uops_list([ld0])

  def test_in_out_of_bounds_access_symbolic(self):
    with Context(IGNORE_OOB=0):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 1, 10), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 1, 10)),))
      to_uops_list([ld0])
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 0, 15), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 0, 15)),))
      to_uops_list([ld0])

-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 0, 20), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 0, 20)),))
      with self.assertRaises(RuntimeError): to_uops_list([ld0])

  def test_in_out_of_bounds_access_gated_store(self):
    with Context(IGNORE_OOB=0):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), src=(), arg=0)
      v = Variable("v", 0, 20)
-      st0 = UOp(Ops.STORE, dtypes.void, src=(glbl0.index(v.valid(v<16)), UOp.const(dtypes.int, 0)))
+      st0 = UOp(Ops.STORE, dtypes.void, src=(glbl0.index(v, v<16), UOp.const(dtypes.int, 0)))
      to_uops_list([st0])

-      st1 = UOp(Ops.STORE, dtypes.void, (glbl0.index(v.valid(v<20)), v))
+      st1 = UOp(Ops.STORE, dtypes.void, (glbl0.index(v), v, v<20))
      with self.assertRaises(RuntimeError): to_uops_list([st1])

  @unittest.skip("if not allowed in graph")
@@ -531,7 +537,7 @@ class TestUOpGraph(unittest.TestCase):
      if_barrier = UOp(Ops.IF, dtypes.void, (gate, barrier))

      # Load from local memory (after the IF/barrier)
-      local_load = UOp(Ops.LOAD, dtypes.uint, (sbuf.index(lidx, ptr=True), if_barrier))
+      local_load = UOp(Ops.LOAD, dtypes.uint, (sbuf.index(lidx), if_barrier))

      # Store to global memory
      global_store = UOp(Ops.STORE, dtypes.void, (gbuf.index(gidx), local_load))
@@ -542,18 +548,18 @@ class TestUOpGraph(unittest.TestCase):
      ridx = UOp.range(20, 0)
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
      i = (ridx.cast(dtypes.float)*0.68).trunc().cast(dtypes.int)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i.valid((0<=i)&(i<16)), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16))),))
      to_uops_list([ld0])
      glblfloat = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(20), (), 0)
      ldfloat = UOp(Ops.LOAD, dtypes.float, (glblfloat.index(ridx),))
      i = (ldfloat+3.14).cast(dtypes.int)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16)), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16))),))

  def test_load_cast_to_bool(self):
    with Context(IGNORE_OOB=0):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), (), 0)
      ridx = UOp.range(20, 0)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(ridx.valid(ridx.cast(dtypes.bool).logical_not()), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(ridx, ridx.cast(dtypes.bool).logical_not()),))
      to_uops_list([ld0])

  @unittest.skip("Bool load is not supported yet")
@@ -562,48 +568,48 @@ class TestUOpGraph(unittest.TestCase):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
      mask = UOp(Ops.DEFINE_GLOBAL, dtypes.bool.ptr(16), (), 0)
      ridx = UOp.range(20, 0)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(ridx, ridx<16&mask), ptr=True)))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(ridx, ridx<16&mask),)))
      to_uops_list([ld0])

  def test_out_of_bounds_off_by_one_access(self):
    with Context(IGNORE_OOB=0):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 16), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(dtypes.int, 16)),))
      with self.assertRaises(RuntimeError): to_uops_list([ld0])

  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.range(42, 0, AxisType.GLOBAL)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0.valid((5<gidx0)&(gidx0<16)), ptr=True),))
-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0.valid(gidx0<16), ptr=True),))
+      gidx0 = UOp(Ops.SPECIAL, dtypes.index, (UOp.const(dtypes.index, 42),), "gidx0")
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, (5<gidx0)&(gidx0<16)),))
+      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<16),))
      to_uops_list([ld0, ld1])

-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0.valid(gidx0<17), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<17),))
      with self.assertRaises(RuntimeError): to_uops_list([ld0])

  def test_in_out_of_bounds_access_symbolic_mask(self):
    with Context(IGNORE_OOB=0):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
      i = Variable("i", 1, 80)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i.valid(i<10), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, i<10),))
      to_uops_list([ld0])
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i.valid(i<15), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, i<15),))
      to_uops_list([ld0])

-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i.valid(i<20), ptr=True),))
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, i<20),))
      with self.assertRaises(RuntimeError): to_uops_list([ld0])

  def test_in_out_of_bounds_access_index_load(self):
    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.range(42, 0, AxisType.GLOBAL)
-      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0.valid(gidx0<8), ptr=True),)).cast(dtypes.index)
-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index((ld0*2).valid((ld0>=0)&(ld0<32)), ptr=True),))
+      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)),))
      to_uops_list([ld1])

-      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index((ld0*2).valid((ld0>=0)&(ld0<64)), ptr=True),))
+      ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<64)),))
      with self.assertRaises(RuntimeError): to_uops_list([ld1])

  def test_bounds_with_loaded_bool(self):
@@ -611,8 +617,8 @@ class TestUOpGraph(unittest.TestCase):
      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.bool.ptr(16), (), 0)
      glbl1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(8), (), 0)
      gidx0 = UOp(Ops.SPECIAL, dtypes.index, (UOp.const(dtypes.index, 16),), "gidx0")
-      ld0 = glbl0.index(gidx0, ptr=True).load()
-      ld1 = glbl1.index(gidx0.valid(ld0), ptr=True).load()
+      ld0 = glbl0.index(gidx0).load()
+      ld1 = glbl1.index(gidx0.valid(ld0)).load()
      with self.assertRaises(RuntimeError): to_uops_list([ld1])

  def test_fold_gated_load(self):
@@ -620,38 +626,38 @@ 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 = glbl1.index(UOp.invalid())
-    ld1 = glbl2.index(idx.valid(UOp.const(dtypes.bool, True)))
+    ld0 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(UOp.invalid()),))
+    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]
    # the gate and invalid value are deleted from ld1
-    self.assertEqual(ld0, UOp.load(glbl2.index(idx, ptr=True), dtype=dtypes.int))
+    self.assertEqual(ld0, UOp.load(glbl2.index(idx), dtype=dtypes.int))

  def test_fold_gated_load_local(self):
    glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    smem = UOp(Ops.DEFINE_LOCAL, dtypes.int.ptr(size=18, addrspace=AddrSpace.LOCAL), (), "temp")
    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
-    st = UOp(Ops.STORE, dtypes.void, (smem.index(lidx, ptr=True), glbl0.index(lidx, ptr=True).load()))
+    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 = smem.after(barrier).index(UOp.invalid())
-    ld1 = smem.after(barrier).index((lidx+2).valid(UOp.const(dtypes.bool, True)))
+    ld0 = UOp(Ops.LOAD, dtypes.int, (smem.after(barrier).index(UOp.invalid()),))
+    ld1 = UOp(Ops.LOAD, dtypes.int, (smem.after(barrier).index(lidx+2, UOp.const(dtypes.bool, True)),))
    uops = to_uops_list([UOp(Ops.STORE, dtypes.void, (glbl0.index(lidx), ld1+ld0))])

    ld0 = uops[-1].src[-1]
    # the gate and invalid value are deleted from ld1
-    self.assertEqual(ld0.src[0], smem.after(barrier).index(lidx+2, ptr=True))
+    self.assertEqual(ld0.src[0], smem.after(barrier).index(lidx+2))

  def test_fold_gated_store(self):
    glbl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    idx0 = UOp.const(dtypes.int, 0)
    idx1 = UOp.const(dtypes.int, 0)
    val = UOp.const(dtypes.int, 42)
-    st0 = glbl.index(UOp.invalid(), ptr=True).store(val)
-    st1 = glbl.index(idx0.valid(UOp.const(dtypes.bool, True)), ptr=True).store(val)
+    st0 = glbl.index(UOp.invalid()).store(val)
+    st1 = glbl.index(idx0, UOp.const(dtypes.bool, True)).store(val)
    uops = to_uops_list([st0, st1])
    # only the second store happens
    self.assertEqual(len(uops), 5)
-    self.assertEqual(uops[-1], glbl.index(idx1, ptr=True).store(val))
+    self.assertEqual(uops[-1], glbl.index(idx1).store(val))

  @unittest.skip("this is a uop type error")
  def test_asserts_bad_gate(self):
@@ -721,7 +727,7 @@ class TestExpander(unittest.TestCase):
    self.assertTupleEqual(sink.src[0].arg, (0,2,1,3,4,6,5,7))

  def test_contract_no_expand(self):
-    e1 = UOp.variable("i", 0, 10, dtype=dtypes.int)
+    e1 = UOp(Ops.DEFINE_VAR, dtypes.int)
    con = UOp(Ops.CONTRACT, dtypes.int.vec(2), (e1,), ((2,2),))
    sink = expander_rewrite(con)
    assert sink.op is Ops.VECTORIZE and len(sink.src) == 2
@@ -810,6 +816,54 @@ class TestExpander(unittest.TestCase):
    sink = expander_rewrite(sink)
    print(sink)

+class TestIFUOps(unittest.TestCase):
+  def test_create_ifs(self):
+    gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=4, addrspace=AddrSpace.LOCAL), (), "smem")
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
+    gate = valid&(lidx.ne(2))
+    idx = UOp.const(dtypes.int, 0)
+    st = UOp(Ops.STORE, dtypes.void, (sbuf.index(idx), UOp.const(dtypes.float, 42)))
+    barrier = UOp(Ops.BARRIER, dtypes.void, (st,))
+    lbuf = UOp(Ops.LOAD, dtypes.float, (sbuf.index(UOp.const(dtypes.int, 0)), barrier))
+    store = UOp(Ops.STORE, dtypes.void, (gbuf.index(UOp.const(dtypes.int, 0), gate), lbuf))
+    sink = UOp(Ops.SINK, dtypes.void, (store,))
+    sink = full_rewrite_to_sink(sink)
+    if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
+    self.assertEqual(len(if_uops), 1)
+    self.assertEqual(if_uops[0].src[0], gate)
+
+  def test_expand_ifs_one_gate(self):
+    gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=16, addrspace=AddrSpace.LOCAL), (), "smem")
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "gidx0")<1
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
+    gate = valid&(lidx.ne(2))
+    st = UOp(Ops.STORE, dtypes.void, (sbuf, lidx, UOp.const(dtypes.float, 42)))
+    barrier = UOp(Ops.BARRIER, dtypes.void, (st,))
+    lbufs = [UOp(Ops.LOAD, dtypes.float, (sbuf.index(UOp.const(dtypes.int, i)), barrier)) for i in range(4)]
+    stores = [UOp(Ops.STORE, dtypes.void, (gbuf.index(UOp.const(dtypes.int, i), gate), lbufs[i])) for i in range(4)]
+    sink = UOp(Ops.SINK, dtypes.void, tuple(stores))
+    sink = full_rewrite_to_sink(sink)
+    if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
+    self.assertEqual(len(if_uops), 1)
+    self.assertEqual(if_uops[0].src[0], gate)
+
+  # this will be fixed with the merge gated stores bounty
+  @unittest.expectedFailure
+  def test_expand_ifs_dumb(self):
+    buf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
+    gate = valid&(lidx.ne(2))
+    stores = [UOp(Ops.STORE, dtypes.void, (buf, UOp.const(dtypes.int, i), UOp.const(dtypes.float, i), gate)) for i in range(4)]
+    sink = UOp(Ops.SINK, dtypes.void, tuple(stores))
+    sink = full_rewrite_to_sink(sink)
+    if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
+    self.assertEqual(len(if_uops), 1)
+    self.assertEqual(if_uops[0].src[0], gate)
+
 class TestUOpTags(unittest.TestCase):
  def test_inc_by_one(self):
    g = UOp.const(dtypes.int, 1) + UOp.const(dtypes.int, 1)
@@ -2,26 +2,20 @@ from typing import Optional, Any
 import unittest, math
 import numpy as np
 from tinygrad.tensor import Tensor, _to_np_dtype
-from tinygrad.helpers import CI, DEBUG, getenv, Timing, Context
+from tinygrad.helpers import CI, DEBUG, getenv, Timing
 from tinygrad.dtype import dtypes, DType, AddrSpace
 from tinygrad.device import Buffer, Device
-from tinygrad.uop.ops import Ops, UOp, UPat, KernelInfo, exec_alu, AxisType
+from tinygrad.uop.ops import Ops, UOp, UPat, KernelInfo, exec_alu # noqa F401
 from tinygrad.uop.spec import shared_spec
 from tinygrad.renderer import ProgramSpec
-from tinygrad.engine.realize import CompiledRunner, get_program, get_runner, ExecItem
+from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.symbolic import sym
 from tinygrad.device import is_dtype_supported
 from tinygrad.codegen.opt import Opt, OptOps
 from tinygrad.renderer.ptx import PTXRenderer

-def to_uops_list(u:list[UOp], ren=None) -> list[UOp]:
-  sink = UOp.group(*u)
-  for r in sink.ranges: sink = sink.end(r)
-  # we strip the SINK here for legacy reasons
-  ret = full_rewrite(sink.sink(arg=KernelInfo(opts_to_apply=())), ren)
-  assert ret[-1].op is Ops.SINK
-  return ret[:-1]
+def to_uops_list(u:list[UOp], opts=None, skip_check=False) -> list[UOp]: return full_rewrite(UOp.sink(*u), opts)

 def _uops_to_prg(uops_list):
  uops = full_rewrite(ast:=UOp.sink(*uops_list), ren=Device[Device.DEFAULT].renderer)
@@ -39,9 +33,9 @@ def _test_single_value(vals, op, dts):
  output_dtype = dtypes.bool if op in (Ops.CMPLT, Ops.CMPNE) else dts[-1]
  buf_store = uop(uops, Ops.DEFINE_GLOBAL, output_dtype.ptr(), (), 0)
  buf_loads = [uop(uops, Ops.DEFINE_GLOBAL, dtype.ptr(), (), i+1) for i,dtype in enumerate(dts)]
-  loads = (buf_loads[i].index(uop(uops, Ops.CONST, dtypes.int32, (), 0)) for i, dtype in enumerate(dts))
+  loads = (uop(uops, Ops.LOAD, dtype, [buf_loads[i].index(uop(uops, Ops.CONST, dtypes.int32, (), 0))]) for i, dtype in enumerate(dts))
  alu = uop(uops, op, output_dtype, loads)
-  out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0), ptr=True), alu))
+  out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), alu))
  buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
  buf2 = [Buffer(Device.DEFAULT, 1, dtype).allocate().copyin(np.array([a], dtype=_to_np_dtype(dtype)).data) for a,dtype in zip(vals, dts)]
  prg = _uops_to_prg([out])
@@ -56,7 +50,7 @@ def _test_single_value_const(vals, op, dts):
  buf_store = uop(uops, Ops.DEFINE_GLOBAL, output_dtype.ptr(), (), 0)
  loads = (uop(uops, Ops.CONST, dtype, [], a) for a,dtype in zip(vals, dts))
  alu = uop(uops, op, output_dtype, loads)
-  out = buf_store[UOp.const(dtypes.int32, 0)].store(alu)
+  out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), alu))
  buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
  prg = _uops_to_prg([out])
  prg.exec([buf])
@@ -115,7 +109,7 @@ class TestFloatUOps(TestUOps):
  def test_log2(self): self._test_uop_fxn(Ops.LOG2, lambda a: math.log2(a) if a > 0 else float('-inf' if a==0 else 'nan'))
  @unittest.skipIf(Device.DEFAULT == "CPU", 'not supported as uop')
  def test_sin(self): self._test_uop_fxn(Ops.SIN, lambda a: math.sin(a))
-  def test_recip(self): self._test_uop_fxn(Ops.RECIPROCAL, lambda a: 1/a if a != 0 else float('inf'))
+  def test_recip(self): self._test_uop_fxn(Ops.RECIP, lambda a: 1/a if a != 0 else float('inf'))
  def test_sqrt(self): self._test_uop_fxn(Ops.SQRT, lambda a: math.sqrt(a) if a >= 0 else float('nan'))

  def test_add(self): self._test_bop_fxn(Ops.ADD, lambda a,b: a+b)
@@ -218,18 +212,18 @@ class TestExecALU(TestUOps):
    self.assertEqual(exec_alu(Ops.IDIV, dtypes.int8, (7, -3)), -2)
    self.assertEqual(exec_alu(Ops.IDIV, dtypes.int8, (-50, 6)), -8)

-    np.testing.assert_allclose(exec_alu(Ops.MUL, dtypes.float32, (7.0, exec_alu(Ops.RECIPROCAL, dtypes.float32, (3.0,)))), 2+(1.0/3.0))
-    np.testing.assert_allclose(exec_alu(Ops.MUL, dtypes.float32, (7.0, exec_alu(Ops.RECIPROCAL, dtypes.float32, (-3.0,)))), -2-(1.0/3.0))
+    np.testing.assert_allclose(exec_alu(Ops.MUL, dtypes.float32, (7.0, exec_alu(Ops.RECIP, dtypes.float32, (3.0,)))), 2+(1.0/3.0))
+    np.testing.assert_allclose(exec_alu(Ops.MUL, dtypes.float32, (7.0, exec_alu(Ops.RECIP, dtypes.float32, (-3.0,)))), -2-(1.0/3.0))

  def test_recip(self):
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, (8,)), 1/8)
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, (7,)), 1/7)
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, (-3,)), 1/-3)
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, (-50,)), 1/-50)
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, (8,)), 1/8)
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, (7,)), 1/7)
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, (-3,)), 1/-3)
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, (-50,)), 1/-50)

-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, ((32+521+3),)), 1/(32+521+3))
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, ((34**2),)), 1/(34**2))
-    np.testing.assert_allclose(exec_alu(Ops.RECIPROCAL, dtypes.float32, (10,)), 1/10)
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, ((32+521+3),)), 1/(32+521+3))
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, ((34**2),)), 1/(34**2))
+    np.testing.assert_allclose(exec_alu(Ops.RECIP, dtypes.float32, (10,)), 1/10)

  def test_bool_cmplt(self):
    self.assertEqual(exec_alu(Ops.CMPLT, dtypes.bool, (False, False)), False)
@@ -277,7 +271,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
    gmem = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
    gate = gidx0<UOp.const(dtypes.int, 1)
-    idx = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem, (gidx0 * UOp.const(dtypes.int, 2)).valid(gate)))
+    idx = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem, gidx0 * UOp.const(dtypes.int, 2), gate))
    val = UOp.const(dtypes.float, 42.0)
    store = UOp(Ops.STORE, dtypes.void, (idx, val))
    uops = to_uops_list([store])
@@ -294,7 +288,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
    gmem1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
    idx = gidx0 * UOp.const(dtypes.int, 2)
-    idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx.valid(gidx0<UOp.const(dtypes.int, 1))))
+    idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx, gidx0<UOp.const(dtypes.int, 1)))
    idx1 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem1, idx))
    val = UOp.const(dtypes.float, 42.0)
    stores = [UOp.store(idx0, val), UOp.store(idx1, val)]
@@ -338,7 +332,7 @@ class TestLocalAccess(unittest.TestCase):
    smem = uop(uops, Ops.DEFINE_LOCAL, dtypes.float32.ptr(size=16, addrspace=AddrSpace.LOCAL), (), 'smem')
    st = uop(uops, Ops.STORE, dtypes.void, (smem.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), uop(uops, Ops.CONST, dtypes.float32, (), 42.0)))
    barr = uop(uops, Ops.BARRIER, dtypes.void, (st,))
-    sres = uop(uops, Ops.LOAD, dtypes.float32, (smem.after(barr).index(uop(uops, Ops.CONST, dtypes.int32, (), 0), ptr=True),))
+    sres = uop(uops, Ops.LOAD, dtypes.float32, (smem.after(barr).index(uop(uops, Ops.CONST, dtypes.int32, (), 0)),))
    self.assertEqual(_test_uops_result(dtypes.float32, uops, sres), 42)

  # NOTE: webgpu specific, since only webgpu performs bitpacking
@@ -348,7 +342,7 @@ class TestLocalAccess(unittest.TestCase):
    smem = uop(uops, Ops.DEFINE_LOCAL, dtypes.uint8.ptr(size=16, addrspace=AddrSpace.LOCAL), (), 'smem')
    st = uop(uops, Ops.STORE, dtypes.void, (smem.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), uop(uops, Ops.CONST, dtypes.uint8, (), 42)))
    barr = uop(uops, Ops.BARRIER, dtypes.void, (st,))
-    sres = smem.after(barr).index(uop(uops, Ops.CONST, dtypes.int32, (), 0))
+    sres = uop(uops, Ops.LOAD, dtypes.uint8, (smem.after(barr).index(uop(uops, Ops.CONST, dtypes.int32, (), 0)),))
    self.assertEqual(_test_uops_result(dtypes.uint8, uops, sres), 42)

  # NOTE: webgpu specific, since only webgpu performs bitpacking
@@ -358,7 +352,7 @@ class TestLocalAccess(unittest.TestCase):
    size = 16
    for dtype in _dtypes:
      temp = UOp(Ops.DEFINE_LOCAL, dtype.ptr(size=size, addrspace=AddrSpace.LOCAL), (), 'smem')
-      uops = to_uops_list([temp], ren=Device[Device.DEFAULT].renderer)
+      uops = to_uops_list([temp], opts=Device[Device.DEFAULT].renderer)
      out = Device[Device.DEFAULT].renderer.render(uops)
      # half is supported in wgsl, so it doesn't have to be packed
      corrected_size = size//(4//dtype.itemsize) if dtype != dtypes.half else size
@@ -382,10 +376,10 @@ class TestAssembly(unittest.TestCase):
    g1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
    c1 = UOp(Ops.CONST, dtypes.int, (), 2)
    c2 = UOp(Ops.CONST, dtypes.int, (), 3)
-    l1 = g1.index(c1)
+    l1 = UOp(Ops.LOAD, dtypes.int, (g1.index(c1),))
    a1 = UOp(Ops.MUL, dtypes.int, (l1, c1))
    a2 = UOp(Ops.MUL, dtypes.int, (l1, c2))
-    uops = to_uops_list([a1,a2], ren=Device[Device.DEFAULT].renderer)
+    uops = to_uops_list([a1,a2], opts=Device[Device.DEFAULT].renderer)
    Device[Device.DEFAULT].renderer.render(uops)
    ops = [x.op for x in uops]
    self.assertIn(Ops.SHL, ops)
@@ -395,9 +389,9 @@ class TestAssembly(unittest.TestCase):
    for dt in (dtypes.int32, dtypes.uint32):
      g = UOp(Ops.DEFINE_GLOBAL, dt.ptr(), (), 0)
      c = UOp(Ops.CONST, dt, (), 2)
-      l = g.index(c)
+      l = UOp(Ops.LOAD, dt, (g.index(c),))
      a = UOp(Ops.IDIV, dt, (l, c))
-      uops = to_uops_list([a], ren=Device[Device.DEFAULT].renderer)
+      uops = to_uops_list([a], opts=Device[Device.DEFAULT].renderer)
      Device[Device.DEFAULT].renderer.render(uops)
      ops = [x.op for x in uops]
      self.assertIn(Ops.SHR, ops, f"For dtype={dt} divison by power of two did not simplify to shift")
@@ -406,16 +400,16 @@ class TestAssembly(unittest.TestCase):
  def test_fast_idiv_and_mod(self):
    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp(Ops.CONST, dtypes.uint, (), 3)
-    l = g.index(c)
+    l = UOp(Ops.LOAD, dtypes.uint, (g.index(c),))
    a = UOp(Ops.IDIV, dtypes.uint, (l, c))
-    uops = to_uops_list([a], ren=Device[Device.DEFAULT].renderer)
+    uops = to_uops_list([a], opts=Device[Device.DEFAULT].renderer)
    Device[Device.DEFAULT].renderer.render(uops)
    ops = [x.op for x in uops]
    self.assertIn(Ops.SHR, ops)
    self.assertNotIn(Ops.IDIV, ops)

    b = UOp(Ops.MOD, dtypes.uint, (l, c))
-    uops = to_uops_list([b], ren=Device[Device.DEFAULT].renderer)
+    uops = to_uops_list([b], opts=Device[Device.DEFAULT].renderer)
    Device[Device.DEFAULT].renderer.render(uops)
    ops = [x.op for x in uops]
    self.assertIn(Ops.SHR, ops)
@@ -428,7 +422,7 @@ class TestAssembly(unittest.TestCase):
    c = UOp(Ops.CONST, dtypes.uint, (), 7)
    l = UOp(Ops.LOAD, dtypes.uint, (g.index(c),))
    a = UOp(Ops.IDIV, dtypes.uint, (l, c))
-    uops = to_uops_list([a], ren=Device[Device.DEFAULT].renderer)
+    uops = to_uops_list([a], opts=Device[Device.DEFAULT].renderer)
    Device[Device.DEFAULT].renderer.render(uops)
    ops = [x.op for x in uops]
    self.assertIn(Ops.SHR, ops)
@@ -436,7 +430,7 @@ class TestAssembly(unittest.TestCase):

  def test_fast_idiv_remove_powers_of_two(self):
    ridx = UOp.range(2**20, 0)
-    uops = to_uops_list([ridx//(7*64)], ren=Device[Device.DEFAULT].renderer)
+    uops = to_uops_list([ridx//(7*64)], opts=Device[Device.DEFAULT].renderer)
    ops = [x.op for x in uops]
    # this requires shifting out the powers of two before doing fast_idiv
    # (((ridx0>>6)*18725)>>17) instead of (int)((((long)(ridx0)*1198373)>>29))
@@ -458,8 +452,9 @@ class TestAssembly(unittest.TestCase):
  def test_use_cmpeq(self):
    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp(Ops.CONST, dtypes.uint, (), 7)
-    comp = g.index(c).ne(c).ne(True)
-    uops = to_uops_list([comp], ren=Device[Device.DEFAULT].renderer)
+    l = UOp(Ops.LOAD, dtypes.uint, (g.index(c),))
+    comp = l.ne(c).ne(True)
+    uops = to_uops_list([comp], opts=Device[Device.DEFAULT].renderer)
    Device[Device.DEFAULT].renderer.render(uops)
    ops = [x.op for x in uops]
    self.assertIn(Ops.CMPEQ, ops)
@@ -517,7 +512,7 @@ class TestUOpStr(unittest.TestCase):

 class TestUPatHelpers(unittest.TestCase):
  def test_location(self):
-    self.assertEqual(sym.patterns[-1][0].location[0].replace("\\", "/").split("/")[-1], "math.py")
+    self.assertEqual(sym.patterns[-1][0].location[0].replace("\\", "/").split("/")[-1], "symbolic.py")
    self.assertEqual(shared_spec.patterns[0][0].location[0].replace("\\", "/").split("/")[-1], "spec.py")
    test_upat = UPat(Ops.CONST, dtypes.bool)
    self.assertEqual(test_upat.location[0].split("/")[-1], __file__.replace("\\", "/").split("/")[-1])
@@ -546,92 +541,11 @@ class TestUopsObject(unittest.TestCase):

 class TestUOpRender(unittest.TestCase):
  def test_render_vectorize_same(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
-    self.assertEqual(u.render(simplify=False), "{0, ...}")
+    u = UOp(Ops.VECTORIZE, src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
+    self.assertEqual(u.render(), "{0, ...}")
  def test_render_vectorize_different(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
-    self.assertEqual(u.render(simplify=False), "{0,1,2}")
-  def test_render_vectorize_same_simplified(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
-    self.assertEqual(u.render(), "0")
-  def test_render_vectorize_different_simplified(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
-    self.assertEqual(u.render(), "(0, 1, 2)")
-
-class TestZeroRange(unittest.TestCase):
-  def test_reduce_variable(self):
-    for i in range(3,-1,-1):
-      v = UOp.variable("i", 0, 5).bind(i)
-      out = Tensor.ones(10, dtype=dtypes.int).contiguous().shrink(((0,v),)).sum()
-      self.assertEqual(out.item(), i)
-
-class TestUOpPrograms(unittest.TestCase):
-  def _run(self, prog:UOp, *tensors:Tensor):
-    ExecItem(get_runner(Device.DEFAULT, prog), [t.uop.buffer for t in tensors]).run(wait=True)
-
-  def test_simple(self):
-    out = Tensor.empty(10,10,dtype=dtypes.int)
-
-    ptr = UOp.placeholder(out.shape, out.dtype, slot=0)
-    i, j = UOp.range(10, axis_id=0), UOp.range(10, axis_id=1)
-    prog = ptr[i,j].set(42).end(i,j)
-    self._run(prog.sink(), out)
-
-    with Context(DEBUG=0): self.assertTrue((out == 42).all().item())
-
-  def test_matmul(self):
-    a = Tensor.randn(10,10)
-    b = Tensor.randn(10,10)
-    c = Tensor.empty(10,10)
-    ref = (a@b)
-    with Context(DEBUG=0): Tensor.realize(a, b, c, ref)
-
-    # C[i,j] = sum_k A[i,k] * B[k,j]
-    # Shapes: A[M,K], B[K,N], C[M,N]
-    M = N = K = 10
-    DT = dtypes.float32
-
-    # Placeholders (bind slots explicitly)
-    A = UOp.placeholder((M, K), DT, slot=0)
-    B = UOp.placeholder((K, N), DT, slot=1)
-    C = UOp.placeholder((M, N), DT, slot=2)
-
-    # Axes: i,j are spatial; k is a reduction axis over the shared dim K
-    i = UOp.range(M, axis_id=0)                             # rows of A/C
-    j = UOp.range(N, axis_id=1)                             # cols of B/C
-    k = UOp.range(K, axis_id=2, axis_type=AxisType.REDUCE)  # reduction over K
-
-    # Zero-init: write a scalar 0 to each (i,j).
-    C = C[i, j].set(0.0)
-
-    # Accumulate: C_after(k) enforces the dependency along the reduction axis
-    C = C[i, j].set(C.after(k)[i, j] + A[i, k] * B[k, j])
-
-    # Finalize the loop nest / schedule in (i, j, k) order
-    prog = C.end(i, j, k)
-
-    # run program
-    # TODO: make this work with opts_to_apply
-    self._run(prog.sink(arg=KernelInfo(opts_to_apply=())), a, b, c)
-
-    with Context(DEBUG=0): self.assertLessEqual((c-ref).square().mean().item(), 1e-6)
-
-  def test_matmul_relu(self):
-    a, b, c = Tensor.randn(10,10), Tensor.randn(10,10), Tensor.empty(10,10)
-    ref = (a@b).relu()
-    with Context(DEBUG=0): Tensor.realize(a, b, c, ref)
-
-    A, B, C = a.uop.placeholder_like(0), b.uop.placeholder_like(1), c.uop.placeholder_like(2)
-    i, j, k = UOp.range(10, 0), UOp.range(10, 1), UOp.range(10, 2, axis_type=AxisType.REDUCE)
-
-    C = C[i, j].set(0.0)
-    C = C[i, j].set(C.after(k)[i, j] + A[i, k] * B[k, j], end=k)
-    C = C[i, j].set(C[i, j].maximum(0.0))
-
-    prog = C.end(i, j)
-
-    self._run(prog.sink(arg=KernelInfo(opts_to_apply=())), a, b, c)
-    with Context(DEBUG=0): self.assertLessEqual((c-ref).square().mean().item(), 1e-6)
+    u = UOp(Ops.VECTORIZE, src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
+    self.assertEqual(u.render(), "{0,1,2}")

 if __name__ == '__main__':
  unittest.main(verbosity=2)
@@ -141,8 +141,8 @@ class TestUOpsStats(unittest.TestCase):
    globl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), tuple())
    o1 = UOp(Ops.CONST, dtypes.int, tuple(), 1)
    o2 = UOp(Ops.CONST, dtypes.int, tuple(), 2)
-    u1 = globl.index(o1)
-    u2 = globl.index(o2)
+    u1 = UOp(Ops.LOAD, dtypes.int, (globl.index(o1),))
+    u2 = UOp(Ops.LOAD, dtypes.int, (globl.index(o2),))
    u3 = UOp(Ops.CONST, dtypes.int, tuple(), 3)
    u4 = UOp(Ops.MUL, dtypes.int, (u1,u2))
    u5 = UOp(Ops.ADD, dtypes.int, (u4,u3))
@@ -151,8 +151,8 @@ class TestUOpsStats(unittest.TestCase):
    globl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), tuple())
    o1 = UOp(Ops.CONST, dtypes.int, tuple(), 1)
    o2 = UOp(Ops.CONST, dtypes.int, tuple(), 2)
-    u1 = globl.index(o1)
-    u2 = globl.index(o2)
+    u1 = UOp(Ops.LOAD, dtypes.int, (globl.index(o1),))
+    u2 = UOp(Ops.LOAD, dtypes.int, (globl.index(o2),))
    u3 = UOp(Ops.CONST, dtypes.int, tuple(), 3)
    u4 = UOp(Ops.MULACC, dtypes.int, (u1,u2,u3))
    uops_fma = full_rewrite(u4.sink())
@@ -30,7 +30,7 @@ class TestDevice(unittest.TestCase):

  @unittest.skipIf(WIN and CI, "skipping windows test") # TODO: subproccess causes memory violation?
  def test_env_overwrite_default_compiler(self):
-    expect_failure = "\ntry: assert Device[Device.DEFAULT].compiler is None;\nexcept Exception: pass"
+    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
@@ -1,12 +1,11 @@
 import unittest, math
 from tinygrad import dtypes
-from tinygrad.helpers import all_same, Context
+from tinygrad.helpers import all_same
 from tinygrad.uop.ops import GroupOp, UOp, Ops, exec_alu, PatternMatcher, TrackedPatternMatcher, UPat
 from tinygrad.codegen import full_rewrite_to_sink
 from hypothesis import given, strategies as strat

 # Helper function to apply the graph rewrite
-@Context(SPEC=0)
 def apply_rewrite(expr):
  return full_rewrite_to_sink(expr.sink()).src[0]

@@ -306,19 +305,19 @@ class TestRecurse(unittest.TestCase):
    graph_rewrite(a, pm, bottom_up=True)

  def test_inf_loop(self):
-    a = UOp.const(dtypes.int, 3)
+    a = UOp.variable('a', 0, 10)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.CONST)),
+      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm)

  def test_inf_loop_bottom_up(self):
-    a = UOp.const(dtypes.int, 3)
+    a = UOp.variable('a', 0, 10)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.CONST)),
+      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm, bottom_up=True)
@@ -3,8 +3,6 @@ import hashlib, random, unittest
 from tinygrad import Tensor, Device, getenv, dtypes
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import CI
-from tinygrad.uop.ops import UOp
-from tinygrad.engine.jit import TinyJit

@unittest.skipUnless(is_dtype_supported(dtypes.uint8) and is_dtype_supported(dtypes.uint64), "Device must support uint8 and uint64")
@unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashes in NV CI")
@@ -74,52 +72,5 @@ class TestKeccak(unittest.TestCase):
    data = b"\x00" * 1000
    self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))

-  def test_variable_bs(self):
-    data = Tensor([b"abc", b"abc", b"abc"], dtype=dtypes.uint8).repeat(2048, 1)
-
-    bs = UOp.variable("bs", 1, 4096).bind(1)
-    out = data.shrink_to(bs, data.shape[-1]).keccak().shrink_to(1, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-
-    bs = UOp.variable("bs", 1, 4096).bind(2)
-    out = data.shrink_to(bs, data.shape[-1]).keccak().shrink_to(2, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[1].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-
-    bs = UOp.variable("bs", 1, 4096).bind(3)
-    data = Tensor([b"abc", b"abc", b"def"], dtype=dtypes.uint8).repeat(2048, 1)
-    out = data.shrink_to(bs, data.shape[-1]).keccak().shrink_to(3, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[1].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[2].tolist()), bytearray.fromhex("8e0d8f672252acb0 ffc5093db8653b18 1513bf9a2097e737 b4f73533dcaf46df"))
-
-  def test_variable_bs_jit(self):
-    def f(data):
-      return data.keccak()
-    jit_f = TinyJit(f)
-
-    data = Tensor([b"abc", b"abc", b"abc"], dtype=dtypes.uint8).repeat(2048, 1)
-
-    # initialize jit
-    for _ in range(3):
-      bs = UOp.variable("bs", 1, 4096).bind(4096)
-      _ = jit_f(data.shrink_to(bs, data.shape[-1]))
-
-    bs = UOp.variable("bs", 1, 4096).bind(1)
-    out = jit_f(data.shrink_to(bs, data.shape[-1])).shrink_to(1, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-
-    bs = UOp.variable("bs", 1, 4096).bind(2)
-    out = jit_f(data.shrink_to(bs, data.shape[-1])).shrink_to(2, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[1].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-
-    bs = UOp.variable("bs", 1, 4096).bind(3)
-    data = Tensor([b"abc", b"abc", b"def"], dtype=dtypes.uint8).repeat(2048, 1)
-    out = jit_f(data.shrink_to(bs, data.shape[-1])).shrink_to(3, 32)
-    self.assertEqual(bytes(out[0].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[1].tolist()), bytearray.fromhex("3a985da74fe225b2 045c172d6bd390bd 855f086e3e9d525b 46bfe24511431532"))
-    self.assertEqual(bytes(out[2].tolist()), bytearray.fromhex("8e0d8f672252acb0 ffc5093db8653b18 1513bf9a2097e737 b4f73533dcaf46df"))
-
 if __name__ == "__main__":
  unittest.main()
@@ -99,11 +99,6 @@ class TestStripParens(unittest.TestCase):
  def test_simple(self): self.assertEqual("1+2", strip_parens("(1+2)"))
  def test_nested(self): self.assertEqual("1+(2+3)", strip_parens("(1+(2+3))"))
  def test_casted_no_strip(self): self.assertEqual("(int)(1+2)", strip_parens("(int)(1+2)"))
-  def test_unmatched_parens(self): self.assertEqual("((c35+c39>>23&255)+-127).cast(dtypes.float)",
-    strip_parens("((c35+c39>>23&255)+-127).cast(dtypes.float)"))
-  def test_single_paren_left(self): self.assertEqual("(abc", strip_parens("(abc"))
-  def test_single_paren_right(self): self.assertEqual("abc)", strip_parens("abc)"))
-  def test_parens_at_different_depths(self): self.assertEqual("(a+(b))*(c)", strip_parens("(a+(b))*(c)"))

 class TestProd(unittest.TestCase):
  def test_empty(self): self.assertEqual(1, prod(tuple()))
@@ -894,7 +894,7 @@ class TestNumpy(unittest.TestCase):
    a = Tensor([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])
-    self.assertIs(a[...], a)
+    self.assertIsNot(a[...], a)
    numpy_testing_assert_equal_helper(a[...], a)
    # `a[...]` was `a` in numpy <1.9.
    #numpy_testing_assert_equal_helper(data_ptr(a[...]), data_ptr(a))
@@ -1037,9 +1037,9 @@ class TestNumpy(unittest.TestCase):
    # Before `...` would return a itself.
    a = Tensor([5])

-    self.assertIs(a, a[()])
-    self.assertIs(a, a[...])
-    self.assertIs(a, a[:])
+    self.assertIsNot(a, a[()])
+    self.assertIsNot(a, a[...])
+    self.assertIsNot(a, a[:])

  def test_broaderrors_indexing(self):
    a = Tensor.zeros(5, 5)
@@ -1,5 +1,5 @@
 import unittest, functools
-from tinygrad import Tensor, Context
+from tinygrad import Tensor
 import numpy as np

 def orthogonality_helper(A:Tensor, tolerance=1e-5):
@@ -27,16 +27,15 @@ class TestLinAlg(unittest.TestCase):
      reconstruction_helper([U,s_diag,V],a)

  def _test_svd_nonfull(self, size):
-    with Context(IGNORE_OOB=1):  # sometimes this is slow in CI
-      a = Tensor.randn(size).realize()
-      U,S,V = a.svd(full_matrices=False)
-      b_shape,m,n = size[0:-2],size[-2],size[-1]
-      k = min(m,n)
-      s_diag = (S.unsqueeze(-2) * Tensor.eye(k).reshape((1,) * len(b_shape) + (k,k)).expand(b_shape + (k,k)))
-      #reduced U,V is only orthogonal along smaller dim
-      if (m < n): orthogonality_helper(U),orthogonality_helper(V)
-      else: orthogonality_helper(U.transpose(-2,-1)),orthogonality_helper(V.transpose(-2,-1))
-      reconstruction_helper([U,s_diag,V],a)
+    a = Tensor.randn(size).realize()
+    U,S,V = a.svd(full_matrices=False)
+    b_shape,m,n = size[0:-2],size[-2],size[-1]
+    k = min(m,n)
+    s_diag = (S.unsqueeze(-2) * Tensor.eye(k).reshape((1,) * len(b_shape) + (k,k)).expand(b_shape + (k,k)))
+    #reduced U,V is only orthogonal along smaller dim
+    if (m < n): orthogonality_helper(U),orthogonality_helper(V)
+    else: orthogonality_helper(U.transpose(-2,-1)),orthogonality_helper(V.transpose(-2,-1))
+    reconstruction_helper([U,s_diag,V],a)

  # faster for parallel pytest
  def test_svd_nonfull_2_2(self): self._test_svd_nonfull((2,2))
@@ -76,4 +75,4 @@ class TestLinAlg(unittest.TestCase):
        orthogonality_helper(b if size[-1] > size[-2] else b.transpose(-2, -1), tolerance=1e-3)

 if __name__ == "__main__":
-  unittest.main()
+  unittest.main()
@@ -50,7 +50,7 @@ class TestPatternMatcher(unittest.TestCase):
    def fxn(ctx, x):
      ctx.append(True)
      assert len(x.src) == 0
-      return x.replace(src=(UOp(Ops.DEVICE, arg="blah"),))
+      return UOp(Ops.CONST, src=(UOp(Ops.CONST),))
    matcher = PatternMatcher([(UPat(Ops.CONST, src=(), name="x"), fxn)])
    c1 = UOp(Ops.CONST, dtypes.float, arg=1.0)
    # second rewrite shouldn't match anything
@@ -9,13 +9,13 @@ from test.unit.test_uop_symbolic import check_uop_against_string

 def get_gated_load_uop(valid:UOp, idx:UOp):
  return UOp(Ops.LOAD, dtypes.float, (
-    UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0).index(idx.valid(valid), ptr=True),
+    UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0).index(idx.valid(valid)),
    UOp.const(dtypes.float, 0.0)
  ))

 def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UOp]):
  return UOp(Ops.LOAD, dtypes.float.vec(4), (
-    UOp(Ops.DEFINE_GLOBAL, dtypes.imagef(image_shape), arg=0).index(UOp(Ops.VECTORIZE, dtypes.index.vec(2), idx).valid(valid), ptr=True),
+    UOp(Ops.DEFINE_GLOBAL, dtypes.imagef(image_shape), arg=0).index(UOp(Ops.VECTORIZE, dtypes.index.vec(2), idx).valid(valid)),
    UOp(Ops.VECTORIZE, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
  ))

@@ -41,13 +41,13 @@ class TestHelpers(unittest.TestCase):
    self.assertTrue(f2.is_increasing())
    self.assertTrue(f3.is_increasing())

-    rng = UOp.range(5, 2)
+    rng = UOp(Ops.RANGE, dtypes.int, arg=(2, True), src=(UOp(Ops.CONST, dtypes.int, arg=5, src=()),))
    self.assertTrue(rng.is_increasing())
    self.assertTrue((rng+2).is_increasing())

 class TestValidIdxSimplification(unittest.TestCase):
  def check(self, load, sidx, svalid):
-    with Context(NOOPT=1, SPEC=0):
+    with Context(NOOPT=1):
      load = full_rewrite_to_sink(load.sink()).src[0]
    idx, valid = load.src[0].src[1], load.src[0].src[2]
    check_uop_against_string(self, idx, sidx)
@@ -213,7 +213,7 @@ class TestValidIdxSimplification(unittest.TestCase):

 class TestImageSimplification(unittest.TestCase):
  def check(self, load, svalid, sidx0, sidx1):
-    with Context(NOOPT=1, SPEC=0):
+    with Context(NOOPT=1):
      load = full_rewrite_to_sink(load.sink()).src[0]
    idx = load.src[0].src[1]
    self.assertEqual(idx.op, Ops.VECTORIZE)
@@ -283,8 +283,7 @@ class TestImageSimplification(unittest.TestCase):

    # empty -> invalid
    load = get_load_image_uop(shape, (gidx0<8) & (gidx0<8).ne(True), idx)
-    with Context(NOOPT=1, SPEC=0):
-      load = full_rewrite_to_sink(load.sink()).src[0]
+    load = full_rewrite_to_sink(load.sink()).src[0]
    self.assertEqual(load.op, Ops.VECTORIZE)
    self.assertEqual(load.dtype.count, 4)

@@ -11,7 +11,7 @@ class TestTranscendentalFunctions(unittest.TestCase):
    # TODO: Test constant input when constant folding is fixed (or maybe test both variants)
    # Load input value from a buffer to prevent constant folding
    input_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.double.ptr(), arg=1, src=())
-    loaded_value = input_buf.index(UOp.const(dtypes.int, 0))
+    loaded_value = UOp.load(input_buf.index(UOp.const(dtypes.int, 0)), dtype=dtypes.double)
    def eval_payne_hanek_reduction(v:float) -> tuple[float, int]:
      return tuple(eval_uop(u, [(dtypes.float64, [v])]) for u in payne_hanek_reduction(loaded_value))

@@ -643,10 +643,6 @@ class TestSymbolic(unittest.TestCase):
    self.helper_test_variable(lidx+(gidx//4)*8+2*(gidx%4), 0, 372, "(lidx+(gidx*2))")
    self.helper_test_variable(lidx+2*(gidx%4)+(gidx//4)*8, 0, 372, "(lidx+(gidx*2))")

-  def test_div_mod_recombine_partial(self):
-    gidx = Variable("gidx", 0, 15)
-    self.helper_test_variable((gidx//2)%4+(gidx//8)*4, 0, 7, "gidx//2")
-
  def test_div_mod_recombine_folded_mod(self):
    a = Variable("a", 0, 2)
    b = Variable("b", 0, 100)
@@ -773,10 +769,6 @@ class TestSymbolic(unittest.TestCase):
    self.helper_test_variable(numerator, 3, 390, "(a*((a*4)+-1))")
    self.helper_test_variable((numerator//denominator)<=0, 1, 1, "True")

-  def test_symbolic_range_doesnt_collapse(self):
-    r0 = UOp.range((Variable("a", 1, 10)<5).cast(dtypes.index), 0)
-    self.helper_test_variable(r0, 0, 0, "r0")
-
  def test_const_reciprocal(self):
    a = Variable("a", 1, 10, dtypes.float)
    # TODO: bounds for reciprocal
@@ -1023,7 +1015,10 @@ class TestSymbolicRealWorld(unittest.TestCase):
    #print(idx.render())
    # NOTE: this used to have 13,151,129,600 in the output which is out of int32 range.
    self.assertIn(idx.render(),
-      ("(lidx3+((lidx5+1)//16*802816+(lidx5+1)%16*49+gidx0*3211264+gidx1*784+gidx2*8+lidx4*100352)+2207744)",))
+      ("((((((((((lidx5+1)//16)*802816)+(((lidx5+1)%16)*49))+(gidx0*3211264))+(gidx1*784))+(gidx2*8))+(lidx4*100352))+lidx3)+2207744)",
+       '((lidx3+((((((((lidx5+1)//16)*802816)+(((lidx5+1)%16)*49))+(gidx0*3211264))+(gidx1*784))+(gidx2*8))+(lidx4*100352)))+2207744)',
+       '((lidx3+((lidx4*100352)+((gidx2*8)+((gidx1*784)+((gidx0*3211264)+((((lidx5+1)//16)*802816)+(((lidx5+1)%16)*49)))))))+2207744)',
+      ))

 class TestBounds(unittest.TestCase):
  def test_unrolled_arange(self):
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
geohot	076bfa50e3	fix that	2025-10-23 10:24:25 +08:00
geohot	f4cea6a403	simpler	2025-10-23 10:21:27 +08:00