Q5_K

ggml_type_13
2026-01-29 17:04:03 +08:00 · 2026-01-28 21:19:52 +08:00
179 changed files with 4368 additions and 15597 deletions
@@ -16,36 +16,6 @@ on:
  workflow_dispatch:

 jobs:
-  # the goal of this test is to replicate a normal person on a laptop running the test
-  # no process replay, no benchmarks, no CI, just a normal laptop person
-  # the 3 minute timeout should not be raised
-  testmacpytest:
-    name: Mac pytest
-    runs-on: [self-hosted, macOS]
-    timeout-minutes: 3
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    # brew install uv
-    - name: setup python environment
-      run: |
-        rm -rf /tmp/tinygrad_pytest_ci
-        uv venv /tmp/tinygrad_pytest_ci
-        source /tmp/tinygrad_pytest_ci/bin/activate
-        uv pip install .[testing]
-    - name: setup staging db
-      run: |
-        echo "CACHEDB=/tmp/pytest-db-ci.db" >> $GITHUB_ENV
-        rm -f /tmp/pytest-db-ci*
-    - name: Run pytest -nauto
-      run: |
-        source /tmp/tinygrad_pytest_ci/bin/activate
-        pytest -nauto --durations=20
-
  testmacbenchmark:
    name: Mac Benchmark
    env:
@@ -175,10 +145,6 @@ jobs:
      run: |
        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: Kill stale pids
-      run: |
-        PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-        PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
    - name: UsbGPU boot time
      run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
    - name: UsbGPU tiny tests
@@ -366,9 +332,9 @@ jobs:
    - name: Setcap to python
      run: ./extra/amdpci/setup_python_cap.sh
    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+      run: ./extra/hcq/hcq_smi.py amd rmmod
    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+      run: ./extra/hcq/hcq_smi.py amd kill_pids
    #- name: Insert amdgpu
    #  run: sudo modprobe amdgpu
    - name: Symlink models and datasets
@@ -478,9 +444,9 @@ jobs:
    - name: Setcap to python
      run: ./extra/amdpci/setup_python_cap.sh
    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+      run: ./extra/hcq/hcq_smi.py amd rmmod
    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+      run: ./extra/hcq/hcq_smi.py amd kill_pids
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -530,9 +496,9 @@ jobs:
    - name: Setcap to python
      run: ./extra/amdpci/setup_python_cap.sh
    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+      run: ./extra/hcq/hcq_smi.py amd rmmod
    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+      run: ./extra/hcq/hcq_smi.py amd kill_pids
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -621,9 +587,9 @@ jobs:
    - name: Setcap to python
      run: ./extra/amdpci/setup_python_cap.sh
    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+      run: ./extra/hcq/hcq_smi.py amd rmmod
    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+      run: ./extra/hcq/hcq_smi.py amd kill_pids
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -685,9 +651,9 @@ jobs:
    - name: Setcap to python
      run: ./extra/amdpci/setup_python_cap.sh
    - name: Remove nv modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv rmmod
+      run: ./extra/hcq/hcq_smi.py nv rmmod
    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
+      run: ./extra/hcq/hcq_smi.py nv kill_pids
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
@@ -26,7 +26,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: llvm-speed
-        deps: testing_unit
+        deps: testing_minimal
        llvm: 'true'
    - name: Speed Test
      run: CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
@@ -98,7 +98,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_unit
+        deps: testing_minimal
        pydeps: "pillow torchvision expecttest"
        llvm: 'true'
    - name: Install ninja
@@ -134,7 +134,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_unit
+        deps: testing_minimal
        llvm: 'true'
    - name: Install ninja
      run: |
@@ -156,7 +156,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: be-minimal
-        deps: testing_unit
+        deps: testing_minimal
    - name: Test dtype with Python emulator
      run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
    - name: Test ops with Python emulator
@@ -239,7 +239,6 @@ jobs:
    - name: Run mypy with lineprecision report
      run: |
        python -m mypy --lineprecision-report .
-        grep -v autogen lineprecision.txt | awk 'NR>2 {lines+=$2; precise+=$3; imprecise+=$4; any+=$5; empty+=$6} END {t=lines-empty; printf "TOTAL: %d lines, %d precise (%.1f%%), %d imprecise (%.1f%%), %d any (%.1f%%)\n", t, precise, 100*precise/t, imprecise, 100*imprecise/t, any, 100*any/t}'
        cat lineprecision.txt
    - name: Run TYPED=1
      run: CHECK_OOB=0 DEV=CPU TYPED=1 python test/test_tiny.py
@@ -256,7 +255,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: unittest-13
-        pydeps: "pillow ftfy regex pre-commit"
+        pydeps: "pillow numpy ftfy regex pre-commit"
        deps: testing_unit
        llvm: 'true'
        amd: 'true'
@@ -266,19 +265,17 @@ jobs:
      run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
    - name: Run unit tests
      run: |
-        CPU=1 python test/null/test_device.py TestRunAsModule.test_module_runs
-        CPU=1 python -m pytest -n=auto test/unit/ --durations=20
-    - name: Run NULL backend tests
-      run: NULL=1 python -m pytest -n=auto test/null/ --durations=20
+        CPU=1 python test/unit/test_device.py TestRunAsModule.test_module_runs
+        CPU=1 python -m pytest -n=auto test/unit/ --durations=20 --deselect=test/unit/test_device.py::TestRunAsModule::test_module_runs
    - name: Run targetted tests on NULL backend
-      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step
+      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
    # TODO: too slow
    # - name: Run SDXL on NULL backend
    #   run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
    - name: Run Clip tests for SD MLPerf on NULL backend
      run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
    - name: Run AMD emulated BERT training on NULL backend
-      run: EMULATE=AMD_RDNA4 NULL=1 NULL_ALLOW_COPYOUT=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: EMULATE=AMD_RDNA4 NULL=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
    # TODO: support fake weights
    #- name: Run LLaMA 7B on 4 fake devices
    #  run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
@@ -316,7 +313,7 @@ jobs:
        deps: testing_unit
        python-version: '3.14'
    - name: Test SPEC=2
-      run: SPEC=2 pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore=test/null --ignore test/test_custom_kernel.py --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
+      run: SPEC=2 pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/test_custom_kernel.py --ignore test/unit/test_hashing.py --ignore test/unit/test_autogen.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}

  fuzzing:
    name: Fuzzing
@@ -350,7 +347,7 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: gpu-image
-          deps: testing_unit
+          deps: testing_minimal
          opencl: 'true'
      - name: Test CL IMAGE=2 ops
        run: |
@@ -426,7 +423,7 @@ jobs:
        with:
          key: onnxoptc
          deps: testing
-          python-version: '3.12'
+          python-version: '3.11'
          llvm: 'true'
      - name: Test ONNX (CPU)
        run: CPU=1 CPU_LLVM=0 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
@@ -454,7 +451,7 @@ jobs:
          key: onnxoptl
          deps: testing
          pydeps: "tensorflow==2.19"
-          python-version: '3.12'
+          python-version: '3.11'
          opencl: 'true'
      - name: Test ONNX (CL)
        run: CL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
@@ -467,11 +464,11 @@ jobs:
      - name: Test MLPerf stuff
        run: CL=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
      - name: NULL=1 beautiful_mnist_multigpu
-        run: NULL=1 NULL_ALLOW_COPYOUT=1 python examples/beautiful_mnist_multigpu.py
+        run: NULL=1 python examples/beautiful_mnist_multigpu.py
      - name: Test Bert training
-        run: NULL=1 NULL_ALLOW_COPYOUT=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+        run: NULL=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
      - name: Test llama 3 training
-        run: NULL=1 NULL_ALLOW_COPYOUT=1 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=1 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
+        run: NULL=1 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=1 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -528,7 +525,7 @@ jobs:
      with:
        key: metal
        deps: testing
-        python-version: '3.12'
+        python-version: '3.11'
    - name: Test models (Metal)
      run: METAL=1 python -m pytest -n=auto test/models --durations=20
    - name: Test LLaMA compile speed
@@ -547,7 +544,7 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: devectorize-minimal
-        deps: testing_unit
+        deps: testing_minimal
        pydeps: "pillow"
        llvm: "true"
    - name: Test LLVM=1 DEVECTORIZE=0
@@ -568,8 +565,8 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: dsp-minimal
-        deps: testing_unit
-        pydeps: "onnx==1.18.0 onnxruntime"
+        deps: testing_minimal
+        pydeps: "onnx==1.18.0 onnxruntime pillow"
        llvm: "true"
    - name: Set up Docker Buildx
      uses: docker/setup-buildx-action@v3
@@ -602,8 +599,8 @@ jobs:
      uses: ./.github/actions/setup-tinygrad
      with:
        key: webgpu-minimal
-        deps: testing_unit
-        python-version: '3.12'
+        deps: testing_minimal
+        python-version: '3.11'
        webgpu: 'true'
    - name: Check Device.DEFAULT (WEBGPU) and print some source
      run: |
@@ -611,7 +608,7 @@ jobs:
        WEBGPU=1 DEBUG=4 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
    - name: Run selected webgpu tests
      run: |
-          WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
+          WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
    - name: Run process replay tests
      uses: ./.github/actions/process-replay

@@ -636,7 +633,7 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: ${{ matrix.backend }}-minimal
-          deps: testing_unit
+          deps: testing_minimal
          amd: 'true'
          llvm: ${{ matrix.backend == 'amdllvm' && 'true' }}
      - name: Check Device.DEFAULT and print some source
@@ -678,9 +675,9 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: rdna3-emu
-          deps: testing_unit
+          deps: testing_minimal
          amd: 'true'
-          python-version: '3.14'
+          python-version: '3.13'
      - name: Verify AMD autogen is up to date
        run: |
          python -m extra.assembly.amd.generate
@@ -706,8 +703,6 @@ jobs:
      # TODO: run all once emulator is faster
      - name: Run RDNA3 ops tests
        run: SKIP_SLOW_TEST=1 AMD_LLVM=0 pytest -n=auto test/test_ops.py -k "test_sparse_categorical_crossentropy or test_tril or test_nonzero or test_softmax_argmax" --durations 20
-      - name: Run RDNA4 emulator tests
-        run: MOCKGPU_ARCH=rdna4 python -m pytest test/test_tiny.py -v --durations 20

  testnvidia:
    strategy:
@@ -728,7 +723,7 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: ${{ matrix.backend }}-minimal
-          deps: testing_unit
+          deps: testing_minimal
          cuda: 'true'
          ocelot: 'true'
      - name: Set env
@@ -739,7 +734,7 @@ jobs:
          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
      - name: Run pytest (cuda)
        # skip multitensor because it's slow
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
+        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
      - name: Run TestOps.test_add with PMA
        run: VIZ=-1 PMA=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
      - name: Run process replay tests
@@ -761,7 +756,7 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: ${{ matrix.backend }}-minimal
-          deps: testing_unit
+          deps: testing_minimal
          opencl: ${{ matrix.backend == 'opencl' && 'true' }}
          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
@@ -772,9 +767,12 @@ jobs:
          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
      - name: Run pytest (${{ matrix.backend }})
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
+        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
      - name: Run TRANSCENDENTAL math
        run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
+      - name: Test dtype with emulated long
+        if: matrix.backend != 'lvp' && matrix.backend != 'llvm'
+        run: EMULATED_DTYPES=long python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -792,15 +790,13 @@ jobs:
      with:
        key: metal
        deps: testing
-        python-version: '3.12'
+        python-version: '3.11'
        amd: 'true'
        cuda: 'true'
        ocelot: 'true'
        llvm: 'true'
    - name: Run unit tests
      run: METAL=1 python -m pytest -n=auto test/unit/ --durations=20
-    - name: Run NULL backend tests
-      run: NULL=1 python -m pytest -n=auto test/null/ --durations=20
    - name: Run ONNX
      run: METAL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
    - name: Test tensor core ops (fake)
@@ -892,7 +888,8 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: macos-${{ matrix.backend }}-minimal
-          deps: testing_unit
+          deps: testing_minimal
+          pydeps: "capstone"
          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
      - name: Set env
@@ -902,7 +899,7 @@ jobs:
          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU','LVP':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
      - name: Run pytest (${{ matrix.backend }})
-        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
+        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
      - name: Run process replay tests
        uses: ./.github/actions/process-replay
      - name: Run macOS-specific unit test
@@ -935,11 +932,7 @@ jobs:
      - name: Run unit tests
        if: matrix.backend=='llvm'
        # test_newton_schulz hits RecursionError
-        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
-      - name: Run NULL backend tests
-        if: matrix.backend=='llvm'
-        shell: bash
-        run: CPU=0 CPU_LLVM=0 NULL=1 python -m pytest -n=auto test/null/ --ignore=test/null/test_elf.py --durations=20
+        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
      - name: Run pytest (${{ matrix.backend }})
        shell: bash
        run: |
@@ -963,12 +956,12 @@ jobs:
        uses: ./.github/actions/setup-tinygrad
        with:
          key: compile-${{ matrix.backend }}
-          deps: testing_unit
+          deps: testing_minimal
          mesa: ${{ (matrix.backend == 'ir3' || matrix.backend == 'nak') && 'true' }}
-          python-version: '3.12'
+          python-version: '3.14'
      - name: Set env
        shell: bash
-        run: printf "NULL=1\nNULL_ALLOW_COPYOUT=1\n${{ matrix.backend == 'ir3' && 'NULL_IR3=1' || matrix.backend == 'nak' && 'NULL_NAK=1' }}" >> $GITHUB_ENV
+        run: printf "NULL=1\n${{ matrix.backend == 'ir3' && 'NULL_IR3=1' || matrix.backend == 'nak' && 'NULL_NAK=1' }}" >> $GITHUB_ENV
      - name: Run test_ops
        shell: bash
        run: |
@@ -28,7 +28,7 @@ repos:
        pass_filenames: false
      - id: tests
        name: comprehensive test suite
-        entry: env OMP_NUM_THREADS=1 SKIP_SLOW_TEST=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_schedule.py test/unit/test_assign.py test/test_tensor.py test/test_jit.py test/unit/test_schedule_cache.py test/null/test_pattern_matcher.py test/null/test_uop_symbolic.py test/unit/test_helpers.py
+        entry: env OMP_NUM_THREADS=1 SKIP_SLOW_TEST=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_schedule.py test/unit/test_assign.py test/test_tensor.py test/test_jit.py test/unit/test_schedule_cache.py test/unit/test_pattern_matcher.py test/unit/test_uop_symbolic.py test/unit/test_helpers.py
        language: system
        always_run: true
        pass_filenames: false
@@ -72,7 +72,7 @@ vliw_prepare = PatternMatcher([
  # cast is fake
  (UPat(Ops.CAST, name="c"), lambda c: c.src[0]),
  # rewrites to hardcode the addresses in memory
-  (UPat(Ops.PARAM, name="dg"), lambda dg: UOp.const(dtypes.uint, global_addrs[dg.arg])),
+  (UPat(Ops.DEFINE_GLOBAL, name="dg"), lambda dg: UOp.const(dtypes.uint, global_addrs[dg.arg])),
  # INDEX is just plus
  (UPat(Ops.INDEX, name="i"), lambda i: i.src[0]+i.src[1]),
 ])+symbolic
@@ -3,7 +3,7 @@ from pathlib import Path
 import multiprocessing

 from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
-from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling, profile_marker
+from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling
 from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
 from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW

@@ -1321,8 +1321,6 @@ def train_llama3():
  opt_base_learning_rate = LR
  opt_end_learning_rate = END_LR

-  Tensor.manual_seed(SEED)  # seed for weight initialization
-
  # ** init wandb **
  WANDB = getenv("WANDB")
  if WANDB:
@@ -1413,17 +1411,17 @@ def train_llama3():
    # https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
    if not getenv("DISABLE_GRAD_CLIP_NORM"):
      total_norm = Tensor(0.0, dtype=dtypes.float32, device=optim.params[0].device)
-      for g in grads:
-        total_norm += g.float().square().sum()
-      total_norm = total_norm.sqrt().contiguous().realize()
-      for g in grads:
-        g.assign((g * (opt_gradient_clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(g.dtype)).realize()
+      for p in optim.params:
+        total_norm += p.grad.float().square().sum()
+      total_norm = total_norm.sqrt().contiguous()
+      for p in optim.params:
+        p.grad.assign((p.grad * (opt_gradient_clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(p.dtype))

    optim.step()
    scheduler.step()

-    for g in grads:
-      g.assign(g.zeros_like().contiguous()).realize()
+    for p in optim.params:
+      p.grad.assign(p.grad.zeros_like().contiguous())

    lr = optim.lr
    Tensor.realize(lr, *grads)
@@ -1432,7 +1430,7 @@ def train_llama3():

  @TinyJit
  @Tensor.train(False)
-  def eval_step(tokens:Tensor):
+  def eval_step(model, tokens:Tensor):
    if (DP := getenv("DP", 1)) > 1:
      device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
      tokens = tokens.shard(device, 0)
@@ -1474,11 +1472,11 @@ def train_llama3():
  while i < MAX_STEPS:
    GlobalCounters.reset()
    if getenv("TRAIN", 1):
-      profile_marker(f"train @ {i}")
      st = time.perf_counter()

      stopped = False
-      for _ in range(grad_acc):
+      minibatches = grad_acc if i >= 3 else 1
+      for _ in range(minibatches):
        ist = time.perf_counter()
        try: tokens = next(train_iter)
        except StopIteration:
@@ -1500,7 +1498,7 @@ def train_llama3():
      gbs_time = gt - st
      optim_time = ot - gt
      data_time = dt - ist
-      dev_time = step_time - data_time * grad_acc
+      dev_time = step_time - data_time * minibatches
      if BENCHMARK: step_times.append(step_time)

      i += 1
@@ -1545,9 +1543,7 @@ def train_llama3():
              f"epoch global_mem: {GlobalCounters.global_mem:_}")

    if (sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1)) or (BENCHMARK and i == BENCHMARK):
-      if EVAL_BS == 0: return
      tqdm.write(f"evaluating after {sequences_seen} sequences")
-      profile_marker(f"eval @ {i}")

      # run eval
      eval_losses = []
@@ -1555,8 +1551,7 @@ def train_llama3():
      tqdm.write(f"evaluating {5760//EVAL_BS} batches of {EVAL_BS} sequences")

      for j,tokens in tqdm(enumerate(eval_iter), total=EVAL_SAMPLES//EVAL_BS):
-        eval_losses += eval_step(tokens).tolist()
-
+        eval_losses += eval_step(model, tokens).tolist()
        if BENCHMARK and (j+1) == min(BENCHMARK, EVAL_SAMPLES//EVAL_BS):
          return

@@ -1645,7 +1640,7 @@ def train_stable_diffusion():
    loss, out_lr = loss.detach().to("CPU"), optimizer.lr.to("CPU")
    Tensor.realize(loss, out_lr)
    return loss, out_lr
-
+    
  # checkpointing takes ~9 minutes without this, and ~1 minute with this
  @TinyJit
  def ckpt_to_cpu():
@@ -1684,7 +1679,7 @@ def train_stable_diffusion():
    if i == 3:
      for _ in range(3): ckpt_to_cpu() # do this at the beginning of run to prevent OOM surprises when checkpointing
      print("BEAM COMPLETE", flush=True) # allows wrapper script to detect BEAM search completion and retry if it failed
-
+      
    total_train_time = time.perf_counter() - train_start_time
    if WANDB:
      wandb.log({"train/loss": loss_item, "train/lr": lr_item, "train/loop_time_prev": loop_time, "train/dl_time": dl_time, "train/step": i,
@@ -8,11 +8,9 @@ export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
 export DEBUG=${DEBUG:-2}
 export FLASH_ATTENTION=${FLASH_ATTENTION:-1}
 export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-1}
-export ASM_GEMM=${ASM_GEMM:-1}

 export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=8 BS=16 EVAL_BS=8 GRADIENT_ACC_STEPS=1
+export DP=8 BS=8 EVAL_BS=8 GRADIENT_ACC_STEPS=2
 export GBS=$((BS * GRADIENT_ACC_STEPS))

 export MODEL="llama3"
@@ -2,18 +2,15 @@

 export PYTHONPATH="."
 export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
 export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000

 export DEBUG=${DEBUG:-0}
 export FLASH_ATTENTION=${FLASH_ATTENTION:-1}
 export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-1}
-export ASM_GEMM=${ASM_GEMM:-1}

 export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-8} BS=${BS:-16} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-1}
+export DP=8 BS=8 EVAL_BS=8 GRADIENT_ACC_STEPS=2
 export GBS=$((BS * GRADIENT_ACC_STEPS))

 export MODEL="llama3"
@@ -25,7 +22,7 @@ export LR="2.5e-4" END_LR="2.5e-5" WARMUP_SAMPLES=256 MAX_STEPS=1200000
 export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
 export SAMPLES=$((MAX_STEPS * GBS))

-export SEED=${SEED:-5760}
+export SEED=5760

 export JITBEAM=${JITBEAM:-3}
 export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
@@ -1,10 +0,0 @@
-#!/bin/bash
-export BENCHMARK=5
-export EVAL_BS=0
-export FAKEDATA=1
-export NULL_ALLOW_COPYOUT=1
-export HIP_VISIBLE_DEVICES=""
-export DEV=NULL
-export JITBEAM=0
-export LLAMA_LAYERS=${LLAMA_LAYERS:-"2"}
-time examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run.sh
@@ -93,7 +93,7 @@ if __name__ == "__main__":
    forward: Any = None

  sub_steps = [
-    Step(name = "textModel", input = [Tensor.randint(1, 77, low=0, high=49408, dtype=dtypes.int32)], forward = model.cond_stage_model.transformer.text_model),
+    Step(name = "textModel", input = [Tensor.randn(1, 77)], forward = model.cond_stage_model.transformer.text_model),
    Step(name = "diffusor", input = [Tensor.randn(1, 77, 768), Tensor.randn(1, 77, 768), Tensor.randn(1,4,64,64), Tensor.rand(1), Tensor.randn(1), Tensor.randn(1), Tensor.randn(1)], forward = model),
    Step(name = "decoder", input = [Tensor.randn(1,4,64,64)], forward = model.decode),
    Step(name = "f16tof32", input = [Tensor.randn(2097120, dtype=dtypes.uint32)], forward = u32_to_f16)
@@ -49,11 +49,10 @@ from tinygrad.helpers import Context, DEBUG, colored
 from tinygrad.engine.realize import get_runner

 from extra.assembly.amd import decode_inst
-from extra.assembly.amd.autogen.rdna3.str_pcode import PCODE as PCODE_RDNA3
-from extra.assembly.amd.autogen.rdna4.str_pcode import PCODE as PCODE_RDNA4
-from extra.assembly.amd.autogen.rdna3 import ins as ir3
-from extra.assembly.amd.autogen.rdna4 import ins as ir4
-from extra.assembly.amd.dsl import VCC_LO, EXEC_LO, SCC, ttmp
+from extra.assembly.amd.autogen.rdna3.str_pcode import PCODE
+from extra.assembly.amd.autogen.rdna3.ins import (SOP1, SOP2, SOPC, SOPK, SOPP, SMEM, VOP1, VOP1_SDST, VOP2, VOP3, VOP3_SDST, VOP3SD, VOP3P, VOPC,
+  DS, FLAT, GLOBAL, SCRATCH, VOPD, SOPPOp, SMEMOp, VOP1Op, VOP2Op, VOP3Op, VOPDOp)
+from extra.assembly.amd.dsl import VCC_LO, EXEC_LO, SCC
 from extra.assembly.amd.autogen.common import Fmt, OpType
 from extra.assembly.amd.pcode import parse_block, _FUNCS

@@ -80,23 +79,15 @@ def _apply_src_mods(val: UOp, mod_bit: int, abs_bits: int, neg_bits: int, bits:
  if neg_bits & (1 << mod_bit): fv = fv.neg()
  return fv.bitcast(ut).cast(dtypes.uint32) if bits == 16 else fv.bitcast(ut)

-# Map VOPD ops to VOP2 ops for pcode lookup (both RDNA3 and RDNA4)
+# Map VOPD ops to VOP2 ops for pcode lookup
 VOPD_TO_VOP2 = {
-  ir3.VOPDOp.V_DUAL_FMAC_F32: ir3.VOP2Op.V_FMAC_F32_E32, ir3.VOPDOp.V_DUAL_MUL_F32: ir3.VOP2Op.V_MUL_F32_E32,
-  ir3.VOPDOp.V_DUAL_ADD_F32: ir3.VOP2Op.V_ADD_F32_E32, ir3.VOPDOp.V_DUAL_SUB_F32: ir3.VOP2Op.V_SUB_F32_E32,
-  ir3.VOPDOp.V_DUAL_SUBREV_F32: ir3.VOP2Op.V_SUBREV_F32_E32, ir3.VOPDOp.V_DUAL_MAX_F32: ir3.VOP2Op.V_MAX_F32_E32,
-  ir3.VOPDOp.V_DUAL_MIN_F32: ir3.VOP2Op.V_MIN_F32_E32, ir3.VOPDOp.V_DUAL_ADD_NC_U32: ir3.VOP2Op.V_ADD_NC_U32_E32,
-  ir3.VOPDOp.V_DUAL_LSHLREV_B32: ir3.VOP2Op.V_LSHLREV_B32_E32, ir3.VOPDOp.V_DUAL_AND_B32: ir3.VOP2Op.V_AND_B32_E32,
-  ir3.VOPDOp.V_DUAL_MOV_B32: ir3.VOP1Op.V_MOV_B32_E32, ir3.VOPDOp.V_DUAL_CNDMASK_B32: ir3.VOP2Op.V_CNDMASK_B32_E32,
-  ir3.VOPDOp.V_DUAL_FMAAK_F32: ir3.VOP2Op.V_FMAAK_F32_E32, ir3.VOPDOp.V_DUAL_FMAMK_F32: ir3.VOP2Op.V_FMAMK_F32_E32,
-  # RDNA4 mappings (same VOP1/VOP2 targets, RDNA4 uses _NUM_ suffix for min/max)
-  ir4.VOPDOp.V_DUAL_FMAC_F32: ir3.VOP2Op.V_FMAC_F32_E32, ir4.VOPDOp.V_DUAL_MUL_F32: ir3.VOP2Op.V_MUL_F32_E32,
-  ir4.VOPDOp.V_DUAL_ADD_F32: ir3.VOP2Op.V_ADD_F32_E32, ir4.VOPDOp.V_DUAL_SUB_F32: ir3.VOP2Op.V_SUB_F32_E32,
-  ir4.VOPDOp.V_DUAL_SUBREV_F32: ir3.VOP2Op.V_SUBREV_F32_E32, ir4.VOPDOp.V_DUAL_MAX_NUM_F32: ir3.VOP2Op.V_MAX_F32_E32,
-  ir4.VOPDOp.V_DUAL_MIN_NUM_F32: ir3.VOP2Op.V_MIN_F32_E32, ir4.VOPDOp.V_DUAL_ADD_NC_U32: ir3.VOP2Op.V_ADD_NC_U32_E32,
-  ir4.VOPDOp.V_DUAL_LSHLREV_B32: ir3.VOP2Op.V_LSHLREV_B32_E32, ir4.VOPDOp.V_DUAL_AND_B32: ir3.VOP2Op.V_AND_B32_E32,
-  ir4.VOPDOp.V_DUAL_MOV_B32: ir3.VOP1Op.V_MOV_B32_E32, ir4.VOPDOp.V_DUAL_CNDMASK_B32: ir3.VOP2Op.V_CNDMASK_B32_E32,
-  ir4.VOPDOp.V_DUAL_FMAAK_F32: ir3.VOP2Op.V_FMAAK_F32_E32, ir4.VOPDOp.V_DUAL_FMAMK_F32: ir3.VOP2Op.V_FMAMK_F32_E32,
+  VOPDOp.V_DUAL_FMAC_F32: VOP2Op.V_FMAC_F32_E32, VOPDOp.V_DUAL_MUL_F32: VOP2Op.V_MUL_F32_E32,
+  VOPDOp.V_DUAL_ADD_F32: VOP2Op.V_ADD_F32_E32, VOPDOp.V_DUAL_SUB_F32: VOP2Op.V_SUB_F32_E32,
+  VOPDOp.V_DUAL_SUBREV_F32: VOP2Op.V_SUBREV_F32_E32, VOPDOp.V_DUAL_MAX_F32: VOP2Op.V_MAX_F32_E32,
+  VOPDOp.V_DUAL_MIN_F32: VOP2Op.V_MIN_F32_E32, VOPDOp.V_DUAL_ADD_NC_U32: VOP2Op.V_ADD_NC_U32_E32,
+  VOPDOp.V_DUAL_LSHLREV_B32: VOP2Op.V_LSHLREV_B32_E32, VOPDOp.V_DUAL_AND_B32: VOP2Op.V_AND_B32_E32,
+  VOPDOp.V_DUAL_MOV_B32: VOP1Op.V_MOV_B32_E32, VOPDOp.V_DUAL_CNDMASK_B32: VOP2Op.V_CNDMASK_B32_E32,
+  VOPDOp.V_DUAL_FMAAK_F32: VOP2Op.V_FMAAK_F32_E32, VOPDOp.V_DUAL_FMAMK_F32: VOP2Op.V_FMAMK_F32_E32,
 }
 WAVE_SIZE = 32
 # Special registers stored after inline constants (256-259)
@@ -155,15 +146,11 @@ _pcode_fixes = {
  'V_TRIG_PREOP_F64': ("result = 64'F((1201'B(2.0 / PI)[1200 : 0] << shift.u32) & 1201'0x1fffffffffffff)", "result = trig_preop_result(shift)"),
 }

-def _get_pcode_dict(op) -> dict:
-  """Return the PCODE dictionary for the given opcode based on its architecture."""
-  return PCODE_RDNA4 if 'rdna4' in type(op).__module__ else PCODE_RDNA3
-
 # Pcode parser
@functools.cache
 def get_pcode(op) -> str:
  op_name = op.name
-  pcode = _get_pcode_dict(op)[op]
+  pcode = PCODE[op]
  if op_name in _pcode_fixes: pcode = pcode.replace(*_pcode_fixes[op_name])
  if 'V_DIV_SCALE' in op_name:
    dt, exp_lim, ldexp_val = ('f32', '23', '64') if 'F32' in op_name else ('f64', '52', '128')
@@ -187,12 +174,7 @@ def get_pcode(op) -> str:
 def parse_pcode(pcode: str, srcs: dict[str, UOp] | None = None) -> tuple[dict, list[tuple[str, UOp]]]:
  vars: dict = srcs.copy() if srcs else {}
  assigns: list[tuple[str, UOp]] = []
-  raw_lines = [l.strip().rstrip(';') for l in pcode.split('\n') if l.strip() and not l.strip().startswith('//')]
-  # TODO: pcode.py should tokenize full pcode string instead of line-by-line, then this hack can be removed
-  lines: list[str] = []
-  for l in raw_lines:
-    if lines and lines[-1].endswith('&&'): lines[-1] = lines[-1] + ' ' + l
-    else: lines.append(l)
+  lines = [l.strip().rstrip(';') for l in pcode.split('\n') if l.strip() and not l.strip().startswith('//')]
  _, final, _ = parse_block(lines, 0, vars, assigns=assigns)
  sliced = set(d.split('[')[0] for d, _ in assigns if '[' in d)
  for var, val in final.items():
@@ -265,11 +247,11 @@ def _collect_data_slices(assigns: list[tuple[str, UOp]], data_prefix: str, pcode
 class _Ctx:
  """Context for instruction compilation - holds buffers and helpers."""
  __slots__ = ('inst_size', 'dyn_fields', '_axis_id')
-  sgpr = UOp(Ops.PARAM, dtypes.uint32.ptr(SGPR_COUNT), arg=0)
-  vgpr = UOp(Ops.PARAM, dtypes.uint32.ptr(VGPR_SIZE), arg=1)
-  vmem = UOp(Ops.PARAM, dtypes.uint32.ptr(1 << 46), arg=2)
-  lds = UOp(Ops.PARAM, dtypes.uint32.ptr(16384), arg=3)
-  scratch = UOp(Ops.PARAM, dtypes.uint8.ptr(1 << 30), arg=4)
+  sgpr = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(SGPR_COUNT), arg=0)
+  vgpr = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(VGPR_SIZE), arg=1)
+  vmem = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(1 << 46), arg=2)
+  lds = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(16384), arg=3)
+  scratch = UOp(Ops.DEFINE_GLOBAL, dtypes.uint8.ptr(1 << 30), arg=4)

  def __init__(self, inst_size: int):
    self.inst_size, self._axis_id = inst_size, 0
@@ -335,9 +317,9 @@ class _Ctx:
    return base, mask, size

  # Dynamic register access (takes UOp index instead of int)
-  def rsgpr_dyn(self, reg: UOp, valid: UOp | None = None) -> UOp:
+  def rsgpr_dyn(self, reg: UOp) -> UOp:
    """Read SGPR with dynamic register index."""
-    return self.sgpr.index(reg.cast(dtypes.int), valid, ptr=True).load() if valid is not None else self.sgpr.index(reg.cast(dtypes.int), ptr=True).load()
+    return self.sgpr.index(reg.cast(dtypes.int), ptr=True).load()

  def wsgpr_dyn(self, reg: UOp, val: UOp) -> UOp:
    """Write SGPR with dynamic register index. Writes to NULL (124) are discarded."""
@@ -359,18 +341,15 @@ class _Ctx:
    If lane is None, only scalar access is supported (off must be < 256).
    is_f64: True for F64 operations where 64-bit literals go in high 32 bits."""
    is_float_const = (off >= _c(240)) & (off <= _c(248))
-    is_vgpr = off >= _c(256)
-    is_sgpr = is_vgpr.ne(True)
-    sgpr_lo = self.rsgpr_dyn(off, is_sgpr)
+    sgpr_lo = self.rsgpr_dyn(off)

    if lane is not None:
-      vgpr_reg = off - _c(256)
+      is_vgpr, vgpr_reg = off >= _c(256), off - _c(256)
      vgpr_lo = self.rvgpr_dyn(vgpr_reg, lane, is_vgpr)
      vgpr_val = _u64(vgpr_lo, self.rvgpr_dyn(vgpr_reg + _c(1), lane, is_vgpr)) if bits == 64 else vgpr_lo

    if bits == 64:
-      sgpr_hi = self.rsgpr_dyn(off + _c(1), is_sgpr)
-      sgpr_val = _u64(sgpr_lo, sgpr_hi)
+      sgpr_val = _u64(sgpr_lo, self.rsgpr_dyn(off + _c(1)))
      # Integer inline constants: sign-extend 32-bit value from buffer to 64-bit
      # Float constants: cast F32 to F64
      int_inline = sgpr_lo.cast(dtypes.int32).cast(dtypes.int64)
@@ -423,19 +402,17 @@ class _Ctx:
    return UOp.sink(*self.scalar_stores(assigns, sdst_reg, sdst_size), *self.inc_pc())

  def compile_lane_pcode(self, op, inst) -> UOp:
-    """Compile cross-lane ops (READLANE/WRITELANE/PERMLANE) using pcode parser."""
+    """Compile READLANE/READFIRSTLANE/WRITELANE using pcode parser."""
    pcode = get_pcode(op)
    op_name = op.name if hasattr(op, 'name') else str(op)
    src0_off, vdst_off = self.inst_field(type(inst).src0), self.inst_field(type(inst).vdst)
    src0_reg = (src0_off >= _c(256)).where(src0_off - _c(256), _c(0))  # VGPR index or 0
    src1_off = self.inst_field(type(inst).src1) if hasattr(type(inst), 'src1') else None
-    src2_off = self.inst_field(type(inst).src2) if hasattr(type(inst), 'src2') else None
    exec_lo = self.rsgpr_dyn(_c(EXEC_LO.offset))
    srcs = {
      'SRC0': src0_reg, 'VDST': vdst_off, 'EXEC_LO': exec_lo, 'EXEC': exec_lo.cast(dtypes.uint64), '_vgpr': self.vgpr,
      'S0': self.rsrc_dyn(src0_off, _c(0, dtypes.int)) if 'WRITELANE' in op_name else src0_reg,
      'S1': self.rsrc_dyn(src1_off, _c(0, dtypes.int)) if src1_off is not None else _c(0),
-      'S2': self.rsrc_dyn(src2_off, _c(0, dtypes.int)) if src2_off is not None else _c(0),
    }
    _, assigns = parse_pcode(pcode, srcs)
    stores = []
@@ -450,8 +427,7 @@ class _Ctx:
    pcode = get_pcode(op)
    vcc_reg = sdst_reg if sdst_reg is not None else VCC_LO.offset
    if 'VCC' not in srcs: srcs['VCC'] = self.rsgpr_dyn(_c(vcc_reg))
-    srcs.update({'EXEC': exec_mask, 'SCC': self.rsgpr_dyn(_c(SCC.offset)), 'laneId': lane,
-                 'ROUND_MODE': _c(0), 'ROUND_TOWARD_ZERO': _c(0)})  # rounding mode: 0=RNE, RTZ constant
+    srcs.update({'EXEC': exec_mask, 'SCC': self.rsgpr_dyn(_c(SCC.offset)), 'laneId': lane})
    _, assigns = parse_pcode(pcode, srcs)

    raw_stores: list = []
@@ -503,14 +479,13 @@ class _Ctx:
 # INSTRUCTION HANDLERS
 # ═══════════════════════════════════════════════════════════════════════════════

-def _compile_sopp(inst: ir3.SOPP | ir4.SOPP, ctx: _Ctx) -> UOp:
-  simm16 = ctx.inst_field_signed(type(inst).simm16).cast(dtypes.int16)
-  if inst.op in (ir3.SOPPOp.S_ENDPGM, ir4.SOPPOp.S_ENDPGM):
+def _compile_sopp(inst: SOPP, ctx: _Ctx) -> UOp:
+  simm16 = ctx.inst_field_signed(SOPP.simm16).cast(dtypes.int16)
+  if inst.op == SOPPOp.S_ENDPGM:
    return UOp.sink(ctx.wsgpr_dyn(_c(PC_LO_IDX), UOp.const(dtypes.uint32, 0xFFFFFFFF)),
                          ctx.wsgpr_dyn(_c(PC_HI_IDX), UOp.const(dtypes.uint32, 0xFFFFFFFF)))
-  if inst.op in (ir3.SOPPOp.S_NOP, ir4.SOPPOp.S_NOP): return UOp.sink(*ctx.inc_pc())  # S_NOP is a no-op
  # NOTE: we ignore SOPPs without PCODE
-  if inst.op in _get_pcode_dict(inst.op):
+  if inst.op in PCODE:
    pcode = get_pcode(inst.op)
    pc_bytes = ctx.rpc()  # PC is already 64-bit byte address
    vcc, exec_lo = ctx.rsgpr_dyn(_c(VCC_LO.offset)), ctx.rsgpr_dyn(_c(EXEC_LO.offset))
@@ -522,57 +497,50 @@ def _compile_sopp(inst: ir3.SOPP | ir4.SOPP, ctx: _Ctx) -> UOp:
        return UOp.sink(ctx.wsgpr_dyn(_c(PC_LO_IDX), lo), ctx.wsgpr_dyn(_c(PC_HI_IDX), hi))
  return UOp.sink(*ctx.inc_pc())

-def _compile_smem(inst: ir3.SMEM | ir4.SMEM, ctx: _Ctx) -> UOp:
+def _compile_smem(inst: SMEM, ctx: _Ctx) -> UOp:
  # Cache invalidation instructions are no-ops in the emulator (we don't model caches)
-  cache_inv_ops = [ir3.SMEMOp.S_GL1_INV, ir3.SMEMOp.S_DCACHE_INV, ir4.SMEMOp.S_DCACHE_INV]
-  if hasattr(ir4.SMEMOp, 'S_GL1_INV'): cache_inv_ops.append(ir4.SMEMOp.S_GL1_INV)
-  if inst.op in cache_inv_ops:
+  if inst.op in (SMEMOp.S_GL1_INV, SMEMOp.S_DCACHE_INV):
    return UOp.sink(*ctx.inc_pc())
  # Dynamic sbase field (bits 5:0) - SGPR pair, field value * 2 = register offset
-  sbase = ctx.inst_field(type(inst).sbase) * _c(2)
+  sbase = ctx.inst_field(SMEM.sbase) * _c(2)
  # Dynamic sdata field (bits 12:6) - destination SGPR
-  sdata_reg = ctx.inst_field(type(inst).sdata)
-  # RDNA4 uses 'ioffset', RDNA3 uses 'offset' - use type(inst) to get correct field
-  offset_field = type(inst).ioffset if hasattr(type(inst), 'ioffset') else type(inst).offset
-  offset = ctx.inst_field_signed(offset_field)  # signed immediate
-  # Dynamic soffset field - SGPR for additional offset (NULL=124 reads as 0)
-  soffset = ctx.inst_field(type(inst).soffset)
+  sdata_reg = ctx.inst_field(SMEM.sdata)
+  offset = ctx.inst_field_signed(SMEM.offset)  # 21-bit signed immediate
+  # Dynamic soffset field (bits 63:57) - SGPR for additional offset (NULL=124 reads as 0)
+  soffset = ctx.inst_field(SMEM.soffset)
  addr = _u64(ctx.rsgpr_dyn(sbase), ctx.rsgpr_dyn(sbase + _c(1))) + offset.cast(dtypes.uint64) + ctx.rsgpr_dyn(soffset).cast(dtypes.uint64)
-  _SMEM_NDWORDS = {ir3.SMEMOp.S_LOAD_B32: 1, ir3.SMEMOp.S_LOAD_B64: 2, ir3.SMEMOp.S_LOAD_B128: 4,
-    ir3.SMEMOp.S_LOAD_B256: 8, ir3.SMEMOp.S_LOAD_B512: 16, ir4.SMEMOp.S_LOAD_B32: 1, ir4.SMEMOp.S_LOAD_B64: 2,
-    ir4.SMEMOp.S_LOAD_B96: 3, ir4.SMEMOp.S_LOAD_B128: 4, ir4.SMEMOp.S_LOAD_B256: 8, ir4.SMEMOp.S_LOAD_B512: 16}
-  ndwords = _SMEM_NDWORDS[inst.op]
+  ndwords = {SMEMOp.S_LOAD_B32: 1, SMEMOp.S_LOAD_B64: 2, SMEMOp.S_LOAD_B128: 4, SMEMOp.S_LOAD_B256: 8, SMEMOp.S_LOAD_B512: 16}.get(inst.op, 1)
  stores = [ctx.wsgpr_dyn(sdata_reg + _c(i), ctx.vmem.index((addr + UOp.const(dtypes.uint64, i * 4) >> UOp.const(dtypes.uint64, 2)).cast(dtypes.int)))
            for i in range(ndwords)]
  return UOp.sink(*stores, *ctx.inc_pc())

-def _compile_sop(inst: ir3.SOP1 | ir3.SOP2 | ir3.SOPC | ir3.SOPK | ir4.SOP1 | ir4.SOP2 | ir4.SOPC | ir4.SOPK, ctx: _Ctx) -> UOp:
+def _compile_sop(inst: SOP1 | SOP2 | SOPC | SOPK, ctx: _Ctx) -> UOp:
  bits = inst.canonical_op_bits
  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None

-  if isinstance(inst, (ir3.SOPK, ir4.SOPK)):
-    sdst_off = ctx.inst_field(type(inst).sdst)
-    simm16 = ctx.inst_field(type(inst).simm16)
+  if isinstance(inst, SOPK):
+    sdst_off = ctx.inst_field(SOPK.sdst)
+    simm16 = ctx.inst_field(SOPK.simm16)
    # Sign-extend simm16
    simm16_sext = simm16.cast(dtypes.int16).cast(dtypes.int32)
    srcs = {'S0': ctx.rsgpr_dyn(sdst_off), 'SIMM16': simm16_sext, 'D0': ctx.rsgpr_dyn(sdst_off)}
    dst_off, dst_size = sdst_off, 1
-  elif isinstance(inst, (ir3.SOP1, ir4.SOP1)):
-    sdst_off = ctx.inst_field(type(inst).sdst)
-    ssrc0_off = ctx.inst_field(type(inst).ssrc0)
+  elif isinstance(inst, SOP1):
+    sdst_off = ctx.inst_field(SOP1.sdst)
+    ssrc0_off = ctx.inst_field(SOP1.ssrc0)
    srcs = {'S0': ctx.rsrc_dyn(ssrc0_off, None, bits['s0'], literal)}
    dst_off, dst_size = sdst_off, bits['d'] // 32
-  elif isinstance(inst, (ir3.SOP2, ir4.SOP2)):
-    sdst_off = ctx.inst_field(type(inst).sdst)
-    ssrc0_off = ctx.inst_field(type(inst).ssrc0)
-    ssrc1_off = ctx.inst_field(type(inst).ssrc1)
+  elif isinstance(inst, SOP2):
+    sdst_off = ctx.inst_field(SOP2.sdst)
+    ssrc0_off = ctx.inst_field(SOP2.ssrc0)
+    ssrc1_off = ctx.inst_field(SOP2.ssrc1)
    srcs = {'S0': ctx.rsrc_dyn(ssrc0_off, None, bits['s0'], literal),
            'S1': ctx.rsrc_dyn(ssrc1_off, None, bits['s1'], literal)}
    if literal is not None: srcs['SIMM32'] = literal
    dst_off, dst_size = sdst_off, bits['d'] // 32
-  elif isinstance(inst, (ir3.SOPC, ir4.SOPC)):
-    ssrc0_off = ctx.inst_field(type(inst).ssrc0)
-    ssrc1_off = ctx.inst_field(type(inst).ssrc1)
+  elif isinstance(inst, SOPC):
+    ssrc0_off = ctx.inst_field(SOPC.ssrc0)
+    ssrc1_off = ctx.inst_field(SOPC.ssrc1)
    srcs = {'S0': ctx.rsrc_dyn(ssrc0_off, None, bits['s0'], literal),
            'S1': ctx.rsrc_dyn(ssrc1_off, None, bits['s1'], literal)}
    dst_off, dst_size = _c(0), 0  # SOPC writes to SCC, not sdst
@@ -581,18 +549,18 @@ def _compile_sop(inst: ir3.SOP1 | ir3.SOP2 | ir3.SOPC | ir3.SOPK | ir4.SOP1 | ir

  return ctx.compile_sop_pcode(inst.op, srcs, dst_off, dst_size)

-def _compile_vop12(inst: ir3.VOP1 | ir3.VOP1_SDST | ir3.VOP2 | ir4.VOP1 | ir4.VOP1_SDST | ir4.VOP2, ctx: _Ctx) -> UOp:
+def _compile_vop12(inst: VOP1 | VOP1_SDST | VOP2, ctx: _Ctx) -> UOp:
  op_name = _op_name(inst)
-  if op_name in ('V_READFIRSTLANE_B32_E32', 'V_PERMLANE64_B32_E32'): return ctx.compile_lane_pcode(inst.op, inst)
+  if op_name == 'V_READFIRSTLANE_B32_E32': return ctx.compile_lane_pcode(inst.op, inst)
  lane, exec_mask, bits = ctx.range(), ctx.rsgpr_dyn(_c(EXEC_LO.offset)), inst.canonical_op_bits
  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None
-  vdst_reg = ctx.inst_field(type(inst).vdst)
+  vdst_reg = ctx.inst_field(VOP1.vdst)
  write_hi_half = bits['d'] == 16 and (vdst_reg >= _c(128))
  if isinstance(write_hi_half, UOp): vdst_reg = write_hi_half.where(vdst_reg - _c(128), vdst_reg)
  elif write_hi_half: vdst_reg -= 128
-  if isinstance(inst, (ir3.VOP1, ir4.VOP1)):
+  if isinstance(inst, VOP1):
    # Handle VOP1 hi-half source operand (src0 >= v[128] for 16-bit ops)
-    src0_off = ctx.inst_field(type(inst).src0)
+    src0_off = ctx.inst_field(VOP1.src0)
    s0 = ctx.rsrc_dyn(src0_off, lane, bits['s0'], literal)
    if bits['s0'] == 16:
      src0_hi = src0_off >= _c(384)
@@ -601,13 +569,13 @@ def _compile_vop12(inst: ir3.VOP1 | ir3.VOP1_SDST | ir3.VOP2 | ir4.VOP1 | ir4.VO
      s0 = src0_hi.where(_hi16(ctx.rvgpr_dyn(src0_reg, lane)), s0)
    srcs = {'S0': s0}
  else:
-    vsrc1_reg = ctx.inst_field(type(inst).vsrc1)
+    vsrc1_reg = ctx.inst_field(VOP2.vsrc1)
    vsrc1_hi = bits['s0'] == 16 and (vsrc1_reg >= _c(128))
    vsrc1_actual = _cond(vsrc1_hi, vsrc1_reg - _c(128), vsrc1_reg)
    s1 = _cond_hi16(vsrc1_hi, ctx.rvgpr_dyn(vsrc1_actual, lane))
    d0 = _cond_hi16(write_hi_half, ctx.rvgpr_dyn(vdst_reg, lane))  # FMAC/FMAMK hi-half dest needs hi-half accumulator
    # Handle VOP2 hi-half src0 operand (src0 >= v[128] for 16-bit ops)
-    src0_off = ctx.inst_field(type(inst).src0)
+    src0_off = ctx.inst_field(VOP2.src0)
    s0 = ctx.rsrc_dyn(src0_off, lane, bits['s0'], literal)
    if bits['s0'] == 16:
      src0_hi = src0_off >= _c(384)
@@ -615,20 +583,19 @@ def _compile_vop12(inst: ir3.VOP1 | ir3.VOP1_SDST | ir3.VOP2 | ir4.VOP1 | ir4.VO
      src0_reg = src0_hi.where(src0_off - _c(384), _c(0))
      s0 = src0_hi.where(_hi16(ctx.rvgpr_dyn(src0_reg, lane)), s0)
    srcs = {'S0': s0, 'S1': s1, 'D0': d0}
-    if inst.op in (ir3.VOP2Op.V_FMAAK_F32_E32, ir3.VOP2Op.V_FMAMK_F32_E32, ir3.VOP2Op.V_FMAAK_F16_E32,
-                   ir3.VOP2Op.V_FMAMK_F16_E32):
+    if inst.op in (VOP2Op.V_FMAAK_F32_E32, VOP2Op.V_FMAMK_F32_E32, VOP2Op.V_FMAAK_F16_E32, VOP2Op.V_FMAMK_F16_E32):
      assert literal is not None
      srcs['SIMM32'] = literal
  return ctx.compile_vop_pcode(inst.op, srcs, lane, vdst_reg, exec_mask, opsel_dst_hi=write_hi_half)

-def _compile_vopc(inst: ir3.VOPC | ir3.VOP3 | ir4.VOPC | ir4.VOP3, ctx: _Ctx, opsel: int = 0, abs_bits: int = 0, neg_bits: int = 0) -> UOp:
+def _compile_vopc(inst: VOPC | VOP3, ctx: _Ctx, opsel: int = 0, abs_bits: int = 0, neg_bits: int = 0) -> UOp:
  exec_mask, op_name, bits = ctx.rsgpr_dyn(_c(EXEC_LO.offset)), _op_name(inst), inst.canonical_op_bits
  is_cmpx, is_vopc = 'CMPX' in op_name, hasattr(inst, 'vsrc1')  # is_vopc: e32 vs e64

  # Handle both VOPC (vsrc1) and VOP3 (src1) instruction formats - read operands dynamically
  if is_vopc:
-    src0_off = ctx.inst_field(type(inst).src0)
-    vsrc1_off = ctx.inst_field(type(inst).vsrc1)
+    src0_off = ctx.inst_field(VOPC.src0)
+    vsrc1_off = ctx.inst_field(VOPC.vsrc1)
    # For 16-bit ops, vsrc1 >= 128 means hi-half of v[vsrc1-128]
    if bits['s0'] == 16:
      vsrc1_hi = vsrc1_off >= _c(128)
@@ -637,9 +604,9 @@ def _compile_vopc(inst: ir3.VOPC | ir3.VOP3 | ir4.VOPC | ir4.VOP3, ctx: _Ctx, op
      vsrc1_hi = False
      src1_off = _c(256) + vsrc1_off
  else:
-    src0_off = ctx.inst_field(type(inst).src0)
-    src1_off = ctx.inst_field(type(inst).src1)
-    dst_off = ctx.inst_field(type(inst).vdst)
+    src0_off = ctx.inst_field(VOP3.src0)
+    src1_off = ctx.inst_field(VOP3.src1)
+    dst_off = ctx.inst_field(VOP3.vdst)
    vsrc1_hi = False
  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None

@@ -668,7 +635,7 @@ def _compile_vopc(inst: ir3.VOPC | ir3.VOP3 | ir4.VOPC | ir4.VOP3, ctx: _Ctx, op
    stores = [ctx.wsgpr_dyn(dst_off, new_result)] if not is_vopc else [ctx.wsgpr_dyn(_c(VCC_LO.offset), new_result)]
  return UOp.sink(*stores, *ctx.inc_pc())

-def _compile_vop3(inst: ir3.VOP3 | ir4.VOP3, ctx: _Ctx) -> UOp:
+def _compile_vop3(inst: VOP3, ctx: _Ctx) -> UOp:
  exec_mask = ctx.rsgpr_dyn(_c(EXEC_LO.offset))
  bits = inst.canonical_op_bits
  opsel, op_name = getattr(inst, 'opsel', 0) or 0, _op_name(inst)
@@ -677,22 +644,18 @@ def _compile_vop3(inst: ir3.VOP3 | ir4.VOP3, ctx: _Ctx) -> UOp:
  if op_name in ('V_READLANE_B32', 'V_READFIRSTLANE_B32', 'V_READFIRSTLANE_B32_E64', 'V_WRITELANE_B32'):
    return ctx.compile_lane_pcode(inst.op, inst)

-  # V_PERMLANE16_B32 / V_PERMLANEX16_B32: cross-lane swizzle via pcode
-  if 'PERMLANE16' in op_name or 'PERMLANEX16' in op_name:
-    return ctx.compile_lane_pcode(inst.op, inst)
-
  # VOP3 VOPC (v_cmp_*_e64) - delegate to unified VOPC handler
  if 'V_CMP' in op_name or 'V_CMPX' in op_name:
    return _compile_vopc(inst, ctx, opsel=opsel, abs_bits=getattr(inst, 'abs', 0) or 0, neg_bits=getattr(inst, 'neg', 0) or 0)

  # Regular VOP3 - read operands dynamically
  lane = ctx.range()
-  vdst_reg = ctx.inst_field(type(inst).vdst)
+  vdst_reg = ctx.inst_field(VOP3.vdst)
  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None
  ops = inst.canonical_operands
-  src0 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src0), lane, bits['s0'], literal, 's0' in ops and ops['s0'][0] == Fmt.FMT_NUM_F64)
-  src1 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src1), lane, bits['s1'], literal, 's1' in ops and ops['s1'][0] == Fmt.FMT_NUM_F64)
-  src2 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src2), lane, bits['s2'], literal, 's2' in ops and ops['s2'][0] == Fmt.FMT_NUM_F64)
+  src0 = ctx.rsrc_dyn(ctx.inst_field(VOP3.src0), lane, bits['s0'], literal, 's0' in ops and ops['s0'][0] == Fmt.FMT_NUM_F64)
+  src1 = ctx.rsrc_dyn(ctx.inst_field(VOP3.src1), lane, bits['s1'], literal, 's1' in ops and ops['s1'][0] == Fmt.FMT_NUM_F64)
+  src2 = ctx.rsrc_dyn(ctx.inst_field(VOP3.src2), lane, bits['s2'], literal, 's2' in ops and ops['s2'][0] == Fmt.FMT_NUM_F64)
  if bits['s0'] == 16:
    src0 = _apply_opsel(src0, 0, opsel)
    src1 = _apply_opsel(src1, 1, opsel)
@@ -702,19 +665,19 @@ def _compile_vop3(inst: ir3.VOP3 | ir4.VOP3, ctx: _Ctx) -> UOp:
  src1 = _apply_src_mods(src1, 1, abs_bits, neg_bits, bits['s1'])
  src2 = _apply_src_mods(src2, 2, abs_bits, neg_bits, bits['s2'])
  srcs = {'S0': src0, 'S1': src1, 'S2': src2}
-  if inst.op in (ir3.VOP3Op.V_CNDMASK_B32_E64, ir3.VOP3Op.V_CNDMASK_B16) and src2 is not None: srcs['VCC'] = src2
+  if inst.op in (VOP3Op.V_CNDMASK_B32_E64, VOP3Op.V_CNDMASK_B16) and src2 is not None: srcs['VCC'] = src2
  # FMAC instructions need D0 (accumulator) from destination register
  if 'FMAC' in op_name: srcs['D0'] = ctx.rvgpr_dyn(vdst_reg, lane)
  opsel_dst_hi = bool(opsel & 0b1000) and bits['d'] == 16
  return ctx.compile_vop_pcode(inst.op, srcs, lane, vdst_reg, exec_mask, opsel_dst_hi=opsel_dst_hi, clmp=getattr(inst, 'clmp', 0))

-def _compile_vop3sd(inst: ir3.VOP3SD | ir4.VOP3SD, ctx: _Ctx) -> UOp:
+def _compile_vop3sd(inst: VOP3SD, ctx: _Ctx) -> UOp:
  exec_mask = ctx.rsgpr_dyn(_c(EXEC_LO.offset))
  bits, pcode, ops = inst.canonical_op_bits, get_pcode(inst.op), inst.canonical_operands

  # Read operands dynamically from instruction encoding
-  vdst_reg, sdst_off = ctx.inst_field(type(inst).vdst), ctx.inst_field(type(inst).sdst)
-  src0_off, src1_off, src2_off = ctx.inst_field(type(inst).src0), ctx.inst_field(type(inst).src1), ctx.inst_field(type(inst).src2)
+  vdst_reg, sdst_off = ctx.inst_field(VOP3SD.vdst), ctx.inst_field(VOP3SD.sdst)
+  src0_off, src1_off, src2_off = ctx.inst_field(VOP3SD.src0), ctx.inst_field(VOP3SD.src1), ctx.inst_field(VOP3SD.src2)
  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None

  has_carry_in = 's2' in ops and ops['s2'][2] == OpType.OPR_SREG
@@ -760,13 +723,13 @@ def _compile_vop3sd(inst: ir3.VOP3SD | ir4.VOP3SD, ctx: _Ctx) -> UOp:
  else:
    return ctx.compile_vop_pcode(inst.op, srcs, lane, vdst_reg, exec_mask, sdst_reg=inst.sdst.offset)

-def _compile_wmma(inst: ir3.VOP3P | ir4.VOP3P, ctx: _Ctx) -> UOp:
+def _compile_wmma(inst: VOP3P, ctx: _Ctx) -> UOp:
  op_name = _op_name(inst)
  exec_mask = ctx.rsgpr_dyn(_c(EXEC_LO.offset))
-  vdst_reg = ctx.inst_field(type(inst).vdst)
-  src0_r = ctx.inst_field(type(inst).src0) - _c(256)
-  src1_r = ctx.inst_field(type(inst).src1) - _c(256)
-  src2_r = ctx.inst_field(type(inst).src2) - _c(256)
+  vdst_reg = ctx.inst_field(VOP3P.vdst)
+  src0_r = ctx.inst_field(VOP3P.src0) - _c(256)
+  src1_r = ctx.inst_field(VOP3P.src1) - _c(256)
+  src2_r = ctx.inst_field(VOP3P.src2) - _c(256)
  is_f16_output = 'F16_16X16X16_F16' in op_name or 'BF16_16X16X16_BF16' in op_name  # F16/BF16 output vs F32 output
  is_bf16 = 'BF16' in op_name
  cvt = _FUNCS['bf16_to_f32'] if is_bf16 else _FUNCS['f16_to_f32']
@@ -793,16 +756,16 @@ def _compile_wmma(inst: ir3.VOP3P | ir4.VOP3P, ctx: _Ctx) -> UOp:
    stores = [ctx.wvgpr_dyn(vdst_reg + _c(i // 32), UOp.const(dtypes.int, i % 32), mat_d[i].bitcast(dtypes.uint32), exec_mask) for i in range(256)]
  return UOp.sink(*stores, *ctx.inc_pc())

-def _compile_vop3p(inst: ir3.VOP3P | ir4.VOP3P, ctx: _Ctx) -> UOp:
+def _compile_vop3p(inst: VOP3P, ctx: _Ctx) -> UOp:
  op_name = _op_name(inst)
  if 'WMMA' in op_name and ('16X16X16_F16' in op_name or '16X16X16_BF16' in op_name): return _compile_wmma(inst, ctx)

  lane = ctx.range()
  exec_mask = ctx.rsgpr_dyn(_c(EXEC_LO.offset))
-  vdst_reg = ctx.inst_field(type(inst).vdst)
-  src0 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src0), lane, 16)
-  src1 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src1), lane, 16)
-  src2 = ctx.rsrc_dyn(ctx.inst_field(type(inst).src2), lane, 16)
+  vdst_reg = ctx.inst_field(VOP3P.vdst)
+  src0 = ctx.rsrc_dyn(ctx.inst_field(VOP3P.src0), lane, 16)
+  src1 = ctx.rsrc_dyn(ctx.inst_field(VOP3P.src1), lane, 16)
+  src2 = ctx.rsrc_dyn(ctx.inst_field(VOP3P.src2), lane, 16)
  opsel, opsel_hi = getattr(inst, 'opsel', 0) or 0, getattr(inst, 'opsel_hi', 3) if getattr(inst, 'opsel_hi', 3) is not None else 3
  opsel_hi2 = getattr(inst, 'opsel_hi2', 1) if getattr(inst, 'opsel_hi2', 1) is not None else 1
  neg, neg_hi = getattr(inst, 'neg', 0) or 0, getattr(inst, 'neg_hi', 0) or 0
@@ -824,7 +787,7 @@ def _compile_vop3p(inst: ir3.VOP3P | ir4.VOP3P, ctx: _Ctx) -> UOp:
    s0_mod = apply_neg_mix(apply_abs(src0, 1, 1, 1), 1, 1, 1)
    s1_mod = apply_neg_mix(apply_abs(src1, 2, 2, 2), 2, 2, 2)
    s2_mod = apply_neg_mix(apply_abs(src2, 4, 4, 4), 4, 4, 4)
-    srcs = {'S@0': s0_mod, 'S@1': s1_mod, 'S@2': s2_mod,
+    srcs = {'S0': s0_mod, 'S1': s1_mod, 'S2': s2_mod,
            'OPSEL_HI': UOp.const(dtypes.uint32, combined_opsel_hi), 'OPSEL': UOp.const(dtypes.uint32, opsel)}
  else:
    def get_half_bits(val: UOp, use_hi: bool, apply_neg: bool = False) -> UOp:
@@ -833,28 +796,24 @@ def _compile_vop3p(inst: ir3.VOP3P | ir4.VOP3P, ctx: _Ctx) -> UOp:
      return bits
    def build_remapped_src(src: UOp, opsel_lo_bit: int, opsel_hi_bit: int, neg_lo_bit: int, neg_hi_bit: int) -> UOp:
      return get_half_bits(src, bool(opsel_lo_bit), bool(neg_lo_bit)) | (get_half_bits(src, bool(opsel_hi_bit), bool(neg_hi_bit)) << UOp.const(dtypes.uint32, 16))
-    # DOT IU instructions use NEG bits for signed/unsigned selection, not fp16 negation
-    is_dot_iu = 'DOT' in op_name and 'IU' in op_name
-    n0, n1, n2, nh0, nh1, nh2 = (0, 0, 0, 0, 0, 0) if is_dot_iu else (neg & 1, neg & 2, neg & 4, neg_hi & 1, neg_hi & 2, neg_hi & 4)
-    srcs = {'S0': build_remapped_src(src0, opsel & 1, opsel_hi & 1, n0, nh0),
-            'S1': build_remapped_src(src1, opsel & 2, opsel_hi & 2, n1, nh1),
-            'S2': build_remapped_src(src2, opsel & 4, 1 if opsel_hi2 else 0, n2, nh2)}
-    if is_dot_iu: srcs['NEG'] = UOp.const(dtypes.uint32, neg)
+    s0_new = build_remapped_src(src0, opsel & 1, opsel_hi & 1, neg & 1, neg_hi & 1)
+    s1_new = build_remapped_src(src1, opsel & 2, opsel_hi & 2, neg & 2, neg_hi & 2)
+    s2_new = build_remapped_src(src2, opsel & 4, 1 if opsel_hi2 else 0, neg & 4, neg_hi & 4)
+    srcs = {'S0': s0_new, 'S1': s1_new, 'S2': s2_new}
  return ctx.compile_vop_pcode(inst.op, srcs, lane, vdst_reg, exec_mask)

-def _compile_vopd(inst: ir3.VOPD | ir4.VOPD, ctx: _Ctx) -> UOp:
+def _compile_vopd(inst: VOPD, ctx: _Ctx) -> UOp:
  exec_mask = ctx.rsgpr_dyn(_c(EXEC_LO.offset))
-  # Read operands dynamically - use type(inst) to get correct field descriptors
-  inst_type = type(inst)
-  vdstx_reg = ctx.inst_field(inst_type.vdstx)
+  # Read operands dynamically
+  vdstx_reg = ctx.inst_field(VOPD.vdstx)
  # vdsty has complex encoding: actual = (raw << 1) | ((vdstx & 1) ^ 1)
-  vdsty_raw = ctx.inst_field(inst_type.vdsty)
+  vdsty_raw = ctx.inst_field(VOPD.vdsty)
  vdsty_reg = (vdsty_raw << _c(1)) | ((vdstx_reg & _c(1)) ^ _c(1))
-  srcx0_off = ctx.inst_field(inst_type.srcx0)
-  srcy0_off = ctx.inst_field(inst_type.srcy0)
-  vsrcx1_reg = ctx.inst_field(inst_type.vsrcx1)
-  vsrcy1_reg = ctx.inst_field(inst_type.vsrcy1)
-  literal = ctx.inst_field(inst_type.literal) if hasattr(inst_type, 'literal') else None
+  srcx0_off = ctx.inst_field(VOPD.srcx0)
+  srcy0_off = ctx.inst_field(VOPD.srcy0)
+  vsrcx1_reg = ctx.inst_field(VOPD.vsrcx1)
+  vsrcy1_reg = ctx.inst_field(VOPD.vsrcy1)
+  literal = ctx.inst_field(type(inst).literal) if hasattr(type(inst), 'literal') else None

  lane = ctx.range()
  srcy0, srcy1 = ctx.rsrc_dyn(srcy0_off, lane, literal=literal), ctx.rvgpr_dyn(vsrcy1_reg, lane)
@@ -865,64 +824,49 @@ def _compile_vopd(inst: ir3.VOPD | ir4.VOPD, ctx: _Ctx) -> UOp:
    assert vop is not None, f"no VOP mapping for VOPD {label}: {op}"
    if label == 'Y': srcs = {'S0': srcy0, 'S1': srcy1, 'D0': ctx.rvgpr_dyn(vdst_reg, lane)}
    else: srcs = {'S0': ctx.rsrc_dyn(src0_off, lane, literal=literal), 'S1': ctx.rvgpr_dyn(vsrc1_reg, lane), 'D0': ctx.rvgpr_dyn(vdst_reg, lane)}
-    if op in (ir3.VOPDOp.V_DUAL_FMAAK_F32, ir3.VOPDOp.V_DUAL_FMAMK_F32, ir4.VOPDOp.V_DUAL_FMAAK_F32, ir4.VOPDOp.V_DUAL_FMAMK_F32):
+    if op in (VOPDOp.V_DUAL_FMAAK_F32, VOPDOp.V_DUAL_FMAMK_F32):
      assert literal is not None
      srcs['SIMM32'] = literal
-    if op in (ir3.VOPDOp.V_DUAL_CNDMASK_B32, ir4.VOPDOp.V_DUAL_CNDMASK_B32): srcs['VCC'] = ctx.rsgpr_dyn(_c(VCC_LO.offset))
+    if op == VOPDOp.V_DUAL_CNDMASK_B32: srcs['VCC'] = ctx.rsgpr_dyn(_c(VCC_LO.offset))
    pcode = get_pcode(vop)
    srcs.update({'VCC': ctx.rsgpr_dyn(_c(VCC_LO.offset)), 'EXEC': exec_mask, 'SCC': ctx.rsgpr_dyn(_c(SCC.offset)), 'laneId': lane})
    for dest, val in parse_pcode(pcode, srcs)[1]:
      if dest.startswith('D0'): all_stores.append(ctx.wvgpr_dyn(vdst_reg, lane, _val_to_u32(val), exec_mask, after=srcy1))
  return UOp.sink(UOp.group(*all_stores).end(lane), *ctx.inc_pc())

-def _compile_mem_op(inst: ir3.DS | ir3.FLAT | ir3.GLOBAL | ir3.SCRATCH | ir4.DS | ir4.VFLAT | ir4.VGLOBAL | ir4.VSCRATCH, ctx: _Ctx) -> UOp:
+def _compile_mem_op(inst: DS | FLAT | GLOBAL | SCRATCH, ctx: _Ctx) -> UOp:
  """Unified memory operation compiler for DS, FLAT, GLOBAL, SCRATCH."""
  exec_mask, op_name = ctx.rsgpr_dyn(_c(EXEC_LO.offset)), _op_name(inst)
  pcode = get_pcode(inst.op)

-  is_lds = isinstance(inst, (ir3.DS, ir4.DS))
-  is_scratch = isinstance(inst, (ir3.SCRATCH, ir4.VSCRATCH))
+  is_lds = isinstance(inst, DS)
+  is_scratch = isinstance(inst, SCRATCH)
  mem = ctx.lds if is_lds else ctx.scratch if is_scratch else ctx.vmem
  addr_shift = UOp.const(dtypes.uint32 if is_lds else dtypes.uint64, 2)

  # Extract register info - all dynamic for deduplication
  if is_lds:
-    addr_reg = ctx.inst_field(type(inst).addr)
-    vdata_reg = ctx.inst_field(type(inst).data0)
-    vdst_reg = ctx.inst_field(type(inst).vdst)
-    offset0 = ctx.inst_field(type(inst).offset0)
-    offset1 = ctx.inst_field(type(inst).offset1)
+    addr_reg = ctx.inst_field(DS.addr)
+    vdata_reg = ctx.inst_field(DS.data0)
+    vdst_reg = ctx.inst_field(DS.vdst)
+    offset0 = ctx.inst_field(DS.offset0)
+    offset1 = ctx.inst_field(DS.offset1)
    offset = offset0  # DS uses offset0 as primary offset
    saddr_reg = None
-  elif isinstance(inst, (ir4.VGLOBAL, ir4.VSCRATCH, ir4.VFLAT)):  # RDNA4: vaddr, vsrc, ioffset
-    addr_reg = ctx.inst_field(type(inst).vaddr)
-    vdata_reg = ctx.inst_field(type(inst).vsrc)
-    vdst_reg = ctx.inst_field(type(inst).vdst)
-    offset = ctx.inst_field_signed(type(inst).ioffset)
-    offset0, offset1 = _c(0), _c(0)
-    saddr_reg = ctx.inst_field(type(inst).saddr) if hasattr(type(inst), 'saddr') else None
-  else:  # RDNA3: addr, data, offset
+  else:
    addr_reg = ctx.inst_field(type(inst).addr)
    vdata_reg = ctx.inst_field(type(inst).data)
    vdst_reg = ctx.inst_field(type(inst).vdst)
    offset = ctx.inst_field_signed(type(inst).offset)
    offset0, offset1 = _c(0), _c(0)
-    saddr_reg = ctx.inst_field(type(inst).saddr) if hasattr(type(inst), 'saddr') else None
+    # Dynamic saddr - read field, NULL (124) or >= 128 means no saddr
+    saddr_reg = ctx.inst_field(type(inst).saddr) if hasattr(inst, 'saddr') else None

  # Data width from canonical_op_bits (32/64/96/128), default to 32 for untyped ops
  data_bits_mem = inst.canonical_op_bits.get('data', 32)
  is_atomic, glc = 'ATOMIC' in op_name, getattr(inst, 'glc', 0)
  has_data1 = is_lds and hasattr(inst, 'data1') and inst.data1 is not None
-  data1_reg = ctx.inst_field(type(inst).data1) if is_lds else _c(0)
-
-  # DS_PERMUTE/DS_BPERMUTE: cross-lane VGPR access via pcode
-  if is_lds and 'PERMUTE' in op_name:
-    pcode = get_pcode(inst.op)
-    srcs = {'ADDR': addr_reg, 'DATA0': vdata_reg, 'VDST': vdst_reg, 'OFFSET': offset,
-            'EXEC': exec_mask.cast(dtypes.uint64), '_vgpr': ctx.vgpr}
-    _, assigns = parse_pcode(pcode, srcs)
-    stores = [ctx.vgpr.index(val[0].cast(dtypes.int)).store(val[1].cast(dtypes.uint32)) for dest, val in assigns if dest.startswith('VGPR[')]
-    return UOp.sink(*stores, *ctx.inc_pc())
+  data1_reg = ctx.inst_field(DS.data1) if is_lds else _c(0)

  def make_addr(lane: UOp) -> UOp:
    if is_lds: return ctx.rvgpr_dyn(addr_reg, lane)
@@ -964,26 +908,14 @@ def _compile_mem_op(inst: ir3.DS | ir3.FLAT | ir3.GLOBAL | ir3.SCRATCH | ir4.DS
      else:
        data = {'DATA': _u64(ctx.rvgpr_dyn(vdata_reg, lane), ctx.rvgpr_dyn(vdata_reg + _c(1), lane)),
                'DATA2': _u64(ctx.rvgpr_dyn(data1_reg, lane), ctx.rvgpr_dyn(data1_reg + _c(1), lane)) if has_data1 else UOp.const(dtypes.uint64, 0)}
-      # RDNA3 uses ADDR/OFFSET, RDNA4 uses vgpr_a/offset (lowercase) + CalcDsAddr function
-      return {'ADDR': addr, 'ADDR_BASE': addr, 'OFFSET': offset, 'OFFSET0': offset0, 'OFFSET1': offset1, '_lds': mem, 'laneId': lane,
-              'vgpr_a': ctx.rvgpr_dyn(addr_reg, lane), 'offset': offset, **data}
+      return {'ADDR': addr, 'ADDR_BASE': addr, 'OFFSET': offset, 'OFFSET0': offset0, 'OFFSET1': offset1, '_lds': mem, 'laneId': lane, **data}
    active = _lane_active(exec_mask, lane)
-    # saddr < 124 means valid SGPR pair, otherwise use 0 (NULL means no saddr contribution)
-    use_saddr = (saddr_reg < _c(124)) if saddr_reg is not None else UOp.const(dtypes.bool, False)
-    saddr_raw = _u64(ctx.rsgpr_dyn(saddr_reg), ctx.rsgpr_dyn(saddr_reg + _c(1))) if saddr_reg is not None else UOp.const(dtypes.uint64, 0)
-    saddr_base = use_saddr.where(saddr_raw, UOp.const(dtypes.uint64, 0))
-    # Sign-extend offset to 64-bit for the final address calculation
-    ioffset64 = offset.cast(dtypes.int64).cast(dtypes.uint64)
-    # v_addr for CalcGlobalAddr: when saddr valid, use low 32 bits as offset; otherwise full 64-bit address. Include ioffset.
-    vaddr_full = _u64(ctx.rvgpr_dyn(addr_reg, lane), ctx.rvgpr_dyn(addr_reg + _c(1), lane))
-    vaddr_lo = ctx.rvgpr_dyn(addr_reg, lane).cast(dtypes.uint64)
-    vaddr_base = use_saddr.where(vaddr_lo + ioffset64, vaddr_full + ioffset64)
    if is_atomic:
      return {'ADDR': addr, 'DATA': _u64(ctx.rvgpr_dyn(vdata_reg, lane), ctx.rvgpr_dyn(vdata_reg + _c(1), lane)) if data_bits_mem == 64 else ctx.rvgpr_dyn(vdata_reg, lane),
-              '_vmem': mem, '_active': active, 'laneId': lane, 'v_addr': vaddr_base, 's_saddr': saddr_base}
+              '_vmem': mem, '_active': active, 'laneId': lane}
    vdata = ctx.rvgpr_dyn(vdata_reg, lane).cast(dtypes.uint64) if 'STORE' in op_name else ctx.rvgpr_dyn(vdst_reg, lane) if 'D16' in op_name else UOp.const(dtypes.uint32, 0)
    if 'STORE' in op_name and data_bits_mem >= 64: vdata = vdata | (ctx.rvgpr_dyn(vdata_reg + _c(1), lane).cast(dtypes.uint64) << UOp.const(dtypes.uint64, 32))
-    srcs = {'ADDR': addr, 'VDATA': vdata, '_vmem': mem, '_active': active, 'laneId': lane, 'v_addr': vaddr_base, 's_saddr': saddr_base}
+    srcs = {'ADDR': addr, 'VDATA': vdata, '_vmem': mem, '_active': active, 'laneId': lane}
    for i in range(data_bits_mem // 32): srcs[f'VDATA{i}'] = ctx.rvgpr_dyn(vdata_reg + _c(i), lane) if 'STORE' in op_name else UOp.const(dtypes.uint32, 0)
    return srcs

@@ -1034,15 +966,10 @@ def _compile_mem_op(inst: ir3.DS | ir3.FLAT | ir3.GLOBAL | ir3.SCRATCH | ir4.DS

 # Dispatch table: instruction type -> handler function
 _INST_HANDLERS: dict[type, Callable[..., UOp]] = {
-  ir3.SOPP: _compile_sopp, ir3.SMEM: _compile_smem, ir3.SOP1: _compile_sop, ir3.SOP2: _compile_sop, ir3.SOPC: _compile_sop, ir3.SOPK: _compile_sop,
-  ir3.VOP1: _compile_vop12, ir3.VOP1_SDST: _compile_vop12, ir3.VOP2: _compile_vop12, ir3.VOPC: _compile_vopc, ir3.VOP3: _compile_vop3,
-  ir3.VOP3_SDST: _compile_vop3, ir3.VOP3SD: _compile_vop3sd, ir3.VOP3P: _compile_vop3p, ir3.VOPD: _compile_vopd,
-  ir3.DS: _compile_mem_op, ir3.FLAT: _compile_mem_op, ir3.GLOBAL: _compile_mem_op, ir3.SCRATCH: _compile_mem_op,
-  # RDNA4 instruction classes
-  ir4.SOPP: _compile_sopp, ir4.SMEM: _compile_smem, ir4.SOP1: _compile_sop, ir4.SOP2: _compile_sop, ir4.SOPC: _compile_sop, ir4.SOPK: _compile_sop,
-  ir4.VOP1: _compile_vop12, ir4.VOP1_SDST: _compile_vop12, ir4.VOP2: _compile_vop12, ir4.VOPC: _compile_vopc, ir4.VOP3: _compile_vop3,
-  ir4.VOP3_SDST: _compile_vop3, ir4.VOP3SD: _compile_vop3sd, ir4.VOP3P: _compile_vop3p, ir4.VOPD: _compile_vopd,
-  ir4.DS: _compile_mem_op, ir4.VFLAT: _compile_mem_op, ir4.VGLOBAL: _compile_mem_op, ir4.VSCRATCH: _compile_mem_op,
+  SOPP: _compile_sopp, SMEM: _compile_smem, SOP1: _compile_sop, SOP2: _compile_sop, SOPC: _compile_sop, SOPK: _compile_sop,
+  VOP1: _compile_vop12, VOP1_SDST: _compile_vop12, VOP2: _compile_vop12, VOPC: _compile_vopc, VOP3: _compile_vop3, VOP3_SDST: _compile_vop3,
+  VOP3SD: _compile_vop3sd, VOP3P: _compile_vop3p, VOPD: _compile_vopd,
+  DS: _compile_mem_op, FLAT: _compile_mem_op, GLOBAL: _compile_mem_op, SCRATCH: _compile_mem_op,
 }

 # ═══════════════════════════════════════════════════════════════════════════════
@@ -1052,9 +979,9 @@ _INST_HANDLERS: dict[type, Callable[..., UOp]] = {
 _canonical_runner_cache: list[tuple[int, int, int, object]] = []  # [(base, mask, size, runner), ...]

@functools.cache
-def _get_runner(inst_bytes: bytes, arch: str = "rdna3"):
+def _get_runner(inst_bytes: bytes):
  """Build and compile instruction to CompiledRunner. Cached by instruction bytes, with canonical dedup."""
-  inst = decode_inst(inst_bytes, arch)
+  inst = decode_inst(inst_bytes)
  inst_size = inst.size()
  inst_int = int.from_bytes(inst_bytes[:inst_size], 'little')

@@ -1077,21 +1004,21 @@ def _get_runner(inst_bytes: bytes, arch: str = "rdna3"):
  canonical_name = f"{_op_name(inst).lower()}_{base.to_bytes(size, 'little').hex()}"
  sink = sink.replace(arg=KernelInfo(name=canonical_name)).rtag(1)

-  with Context(NOOPT=1, CHECK_OOB=0, TUPLE_ORDER=0, EMULATED_DTYPES=""):
+  with Context(NOOPT=1, CHECK_OOB=0, TUPLE_ORDER=0):
    runner = get_runner('CPU', sink)
  _canonical_runner_cache.append((base, mask, size, runner))
  return runner, True

@functools.cache
-def decode_program(data: bytes, arch: str = "rdna3") -> dict[int, tuple[str, Callable, list[int], Any]]:
+def decode_program(data: bytes) -> dict[int, tuple[str, Callable, list[int], Any]]:
  """Decode program to {pc: (name, fxn, globals, runner)}."""
  result: dict[int, tuple[str, Callable, list[int], Any]] = {}
  i = 0
  while i < len(data):
-    inst = decode_inst(data[i:], arch)
-    if hasattr(inst, 'op') and inst.op in (ir3.SOPPOp.S_CODE_END, ir4.SOPPOp.S_CODE_END): break
+    inst = decode_inst(data[i:])
+    if isinstance(inst, SOPP) and inst.op == SOPPOp.S_CODE_END: break
    try:
-      runner, is_new = _get_runner(bytes(data[i:i + inst.size() + 4]), arch)
+      runner, is_new = _get_runner(bytes(data[i:i + inst.size() + 4]))
      if DEBUG >= 3:
        try: inst_str = repr(inst)
        except Exception: inst_str = f"<{type(inst).__name__} at PC={i}>"
@@ -1150,9 +1077,9 @@ class WaveState:
 # ═══════════════════════════════════════════════════════════════════════════════

 def run_asm(lib: int, lib_sz: int, gx: int, gy: int, gz: int, lx: int, ly: int, lz: int, args_ptr: int, rsrc2: int = 0x19c,
-            scratch_size: int = 0, arch: str = "rdna3") -> int:
+            scratch_size: int = 0) -> int:
  """Execute AMD assembly program. scratch_size is private_segment_fixed_size from kernel descriptor (per-lane)."""
-  program_raw = decode_program(bytes((ctypes.c_char * lib_sz).from_address(lib).raw), arch)
+  program_raw = decode_program(bytes((ctypes.c_char * lib_sz).from_address(lib).raw))
  program = {lib + offset: val for offset, val in program_raw.items()}  # Remap to actual addresses
  lds_size = ((rsrc2 & hsa.AMD_COMPUTE_PGM_RSRC_TWO_GRANULATED_LDS_SIZE) >> hsa.AMD_COMPUTE_PGM_RSRC_TWO_GRANULATED_LDS_SIZE_SHIFT) * 512
  total_threads = lx * ly * lz
@@ -1180,12 +1107,6 @@ def run_asm(lib: int, lib_sz: int, gx: int, gy: int, gz: int, lx: int, ly: int,
                                 (hsa.AMD_COMPUTE_PGM_RSRC_TWO_ENABLE_SGPR_WORKGROUP_ID_Z, gidz)]:
              if rsrc2 & enabled: st._write_sgpr(sgpr_idx, gid); sgpr_idx += 1

-            # RDNA4 uses TTMP registers for workgroup IDs: ttmp[9]=gidx, ttmp[10]=gidy, ttmp[11]=gidz
-            if arch == "rdna4":
-              st._write_sgpr(ttmp[9].offset, gidx)
-              st._write_sgpr(ttmp[10].offset, gidy)
-              st._write_sgpr(ttmp[11].offset, gidz)
-
            # v0 = packed workitem IDs, scratch stride in secret SGPR
            for lane in range(n_lanes):
              tid = wave_start + lane
@@ -1202,7 +1123,7 @@ def run_asm(lib: int, lib_sz: int, gx: int, gy: int, gz: int, lx: int, ly: int,
              assert fxn is not None, f"[emu] No fxn for {name} at PC={pc}"
              assert 4 not in globals_list or scratch_buf, f"SCRATCH instruction {name} but scratch_size=0"
              if DEBUG >= 6:
-                inst = decode_inst(bytes((ctypes.c_char * 12).from_address(pc).raw), arch)
+                inst = decode_inst(bytes((ctypes.c_char * 12).from_address(pc).raw))
                print(f"[emu] exec PC={pc:X}: {inst!r}")
              fxn(*[c_bufs[g] for g in globals_list])
            else: raise RuntimeError("exceeded 1M instructions, likely infinite loop")
@@ -94,19 +94,13 @@ def _trig_reduce(x, phase=0.0):
  return UOp(Ops.SIN, x.dtype, (x - n * _const(x.dtype, 6.283185307179586),))

 def _signext(val: UOp) -> UOp:
-  for bits, mask, ext in [(4, 0xF, 0xFFFFFFF0), (8, 0xFF, 0xFFFFFF00), (16, 0xFFFF, 0xFFFF0000)]:
+  for bits, mask, ext in [(8, 0xFF, 0xFFFFFF00), (16, 0xFFFF, 0xFFFF0000)]:
    if (val.op == Ops.AND and len(val.src) == 2 and val.src[1].op == Ops.CONST and val.src[1].arg == mask) or val.dtype.itemsize == bits // 8:
      v32 = val.cast(dtypes.uint32) if val.dtype != dtypes.uint32 else val
      sb = (v32 >> _u32(bits - 1)) & _u32(1)
      return sb.ne(_u32(0)).where(v32 | _u32(ext), v32).cast(dtypes.int)
  return val.cast(dtypes.int64) if val.dtype in (dtypes.int, dtypes.int32) else val

-def _signext_4bit(val: UOp) -> UOp:
-  """Sign extend a 4-bit value to 32-bit signed integer."""
-  v32 = val.cast(dtypes.uint32) if val.dtype != dtypes.uint32 else val
-  sb = (v32 >> _u32(3)) & _u32(1)  # sign bit at position 3
-  return sb.ne(_u32(0)).where(v32 | _u32(0xFFFFFFF0), v32).bitcast(dtypes.int)
-
 def _abs(val: UOp) -> UOp:
  if val.dtype not in (dtypes.float32, dtypes.float64, dtypes.half): return val
  _, _, _, _, shift = _float_info(val)
@@ -200,17 +194,6 @@ def _ff1(val: UOp, bits: int) -> UOp:
    result = cond.where(_const(dtypes.int, i), result)
  return result

-def _sad_u8(a: UOp, b: UOp, acc: UOp, masked: bool = False) -> UOp:
-  """Sum of absolute differences of 4 unsigned bytes + accumulator. If masked, skips bytes where a == 0."""
-  a, b, acc = a.cast(dtypes.uint32), b.cast(dtypes.uint32), acc.cast(dtypes.uint32)
-  result = acc
-  for i in range(4):
-    a_byte = (a >> _u32(i * 8)) & _u32(0xFF)
-    b_byte = (b >> _u32(i * 8)) & _u32(0xFF)
-    diff = (a_byte > b_byte).where(a_byte - b_byte, b_byte - a_byte)
-    result = result + (a_byte.ne(_u32(0)).where(diff, _u32(0)) if masked else diff)
-  return result
-
 _FUNCS: dict[str, Callable[..., UOp]] = {
  'sqrt': lambda a: UOp(Ops.SQRT, a.dtype, (a,)), 'trunc': lambda a: UOp(Ops.TRUNC, a.dtype, (a,)),
  'log2': lambda a: UOp(Ops.LOG2, a.dtype, (a,)), 'sin': lambda a: _trig_reduce(a),
@@ -244,53 +227,11 @@ _FUNCS: dict[str, Callable[..., UOp]] = {
  'signext_from_bit': _signext_from_bit, 'ldexp': _ldexp, 'frexp_mant': _frexp_mant, 'mantissa': _frexp_mant,
  'frexp_exp': _frexp_exp, 'trig_preop_result': _trig_preop,
  's_ff1_i32_b32': lambda a: _ff1(a, 32), 's_ff1_i32_b64': lambda a: _ff1(a, 64),
-  # Normalization conversions: map [-1,1] or [0,1] to integer range
-  # Use floor(x + 0.5) for round-to-nearest
-  # SNORM: round(value * 32767), range is [-32767, 32767] (hardware behavior)
-  'f16_to_snorm': lambda a: _floor(_f16_extract(a).cast(dtypes.float32) * _const(dtypes.float32, 32767) + _const(dtypes.float32, 0.5)).cast(dtypes.int).cast(dtypes.int16),
-  'f16_to_unorm': lambda a: _floor(_f16_extract(a).cast(dtypes.float32) * _const(dtypes.float32, 65535) + _const(dtypes.float32, 0.5)).cast(dtypes.uint16),
-  'f32_to_snorm': lambda a: _floor(a.bitcast(dtypes.float32) * _const(dtypes.float32, 32767) + _const(dtypes.float32, 0.5)).cast(dtypes.int).cast(dtypes.int16),
-  'f32_to_unorm': lambda a: _floor(a.bitcast(dtypes.float32) * _const(dtypes.float32, 65535) + _const(dtypes.float32, 0.5)).cast(dtypes.uint16),
-  'f32_to_u8': lambda a: _f_to_u(a.bitcast(dtypes.float32), dtypes.uint8),
-  # Integer truncation conversions
-  'i32_to_i16': lambda a: a.cast(dtypes.int).cast(dtypes.int16),
-  'u32_to_u16': lambda a: a.cast(dtypes.uint32).cast(dtypes.uint16),
-  'u16_to_u32': lambda a: (a.cast(dtypes.uint32) & _u32(0xFFFF)),
-  'u8_to_u32': lambda a: (a.cast(dtypes.uint32) & _u32(0xFF)),
-  'u4_to_u32': lambda a: (a.cast(dtypes.uint32) & _u32(0xF)),
-  # Signed extraction with sign extension for dot products
-  'i16_to_i32': lambda a: _signext(a.cast(dtypes.uint32) & _u32(0xFFFF)),
-  'i8_to_i32': lambda a: _signext(a.cast(dtypes.uint32) & _u32(0xFF)),
-  'i4_to_i32': lambda a: _signext_4bit(a.cast(dtypes.uint32) & _u32(0xF)),
-  # Float to int16 conversions
-  'v_cvt_i16_f32': lambda a: UOp(Ops.TRUNC, dtypes.float32, (a.bitcast(dtypes.float32),)).cast(dtypes.int16),
-  'v_cvt_u16_f32': lambda a: _f_to_u(a.bitcast(dtypes.float32), dtypes.uint16),
-  # SAD (Sum of Absolute Differences) - sum |a_i - b_i| for 4 bytes + accumulator
-  'v_sad_u8': lambda a, b, c: _sad_u8(a, b, c),
-  'v_msad_u8': lambda a, b, c: _sad_u8(a, b, c, masked=True),
-  # System NOPs - these are scheduling hints, no effect on emulation
-  'MIN': lambda a, b: (a < b).where(a, b),
-  's_nop': lambda a: _u32(0),
-  # Address calculation for memory operations
-  'CalcDsAddr': lambda a, o, *r: a.cast(dtypes.uint32) + o.cast(dtypes.uint32),
-  'CalcGlobalAddr': lambda v, s, *r: v.cast(dtypes.uint64) + s.cast(dtypes.uint64),
 }
 for is_max, name in [(False, 'min'), (True, 'max')]:
  for dt, sfx in [(dtypes.float32, 'f32'), (dtypes.int, 'i32'), (dtypes.uint32, 'u32'), (dtypes.int16, 'i16'), (dtypes.uint16, 'u16')]:
    _FUNCS[f'v_{name}_{sfx}'] = lambda *a, im=is_max, d=dt: _minmax_reduce(im, d, *a)
    _FUNCS[f'v_{name}3_{sfx}'] = lambda *a, im=is_max, d=dt: _minmax_reduce(im, d, *a)
-# f16 min/max/min3/max3/med3
-for is_max, name in [(False, 'min'), (True, 'max')]:
-  _FUNCS[f'v_{name}_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}3_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}_num_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}_num_f32'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.float32, *a)
-  _FUNCS[f'v_{name}3_num_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}3_num_f32'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.float32, *a)
-  _FUNCS[f'v_{name}imum_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}imum_f32'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.float32, *a)
-  _FUNCS[f'v_{name}imum3_f16'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.half, *[_f16_extract(x) for x in a])
-  _FUNCS[f'v_{name}imum3_f32'] = lambda *a, im=is_max: _minmax_reduce(im, dtypes.float32, *a)

 # ═══════════════════════════════════════════════════════════════════════════════
 # TOKENIZER/PARSER
@@ -298,7 +239,7 @@ for is_max, name in [(False, 'min'), (True, 'max')]:

 DTYPES = {'u32': dtypes.uint32, 'i32': dtypes.int, 'f32': dtypes.float32, 'b32': dtypes.uint32, 'u64': dtypes.uint64, 'i64': dtypes.int64,
          'f64': dtypes.float64, 'b64': dtypes.uint64, 'u16': dtypes.uint16, 'i16': dtypes.short, 'f16': dtypes.half, 'b16': dtypes.uint16,
-          'u8': dtypes.uint8, 'i8': dtypes.int8, 'b8': dtypes.uint8, 'u4': dtypes.uint8, 'i4': dtypes.int8, 'u1': dtypes.uint32}
+          'u8': dtypes.uint8, 'i8': dtypes.int8, 'b8': dtypes.uint8, 'u1': dtypes.uint32}
 _BITS_DT = {8: dtypes.uint8, 16: dtypes.uint16, 32: dtypes.uint32, 64: dtypes.uint64}
 _NUM_SUFFIXES = ('ULL', 'LL', 'UL', 'U', 'L', 'F', 'f')
 def _strip_suffix(num: str) -> tuple[str, str]:
@@ -455,7 +396,7 @@ class Parser:
        self.eat('DOT')
        dt_name = self.eat('IDENT').val
        return self._handle_mem_load(addr, DTYPES.get(dt_name, dtypes.uint32))
-      if name == 'VGPR' and self.at('LBRACKET'):
+      if name == 'VGPR':
        self.eat('LBRACKET')
        lane = self.parse()
        self.eat('RBRACKET')
@@ -482,21 +423,7 @@ class Parser:
      if self.try_eat('LBRACE'):
        idx = self.eat('NUM').val
        self.eat('RBRACE')
-        # Handle VGPR{lane}[reg] - 2D array access after loop unrolling
-        if name == 'VGPR' and self.at('LBRACKET'):
-          self.eat('LBRACKET')
-          reg = self.parse()
-          self.eat('RBRACKET')
-          vgpr = self.vars.get('_vgpr')
-          if vgpr is None: return _u32(0)
-          return vgpr.index(_to_u32(reg) * _u32(32) + _u32(int(idx)), ptr=True).load()
-        elem = self.vars.get(f'{name}@{idx}', self.vars.get(f'{name}{idx}'))
-        if elem is None:
-          # Extract bit idx from base variable (like var[idx])
-          base = self.vars.get(name)
-          assert isinstance(base, UOp), f"unknown variable: {name}{idx}"
-          dt = dtypes.uint64 if base.dtype in (dtypes.uint64, dtypes.int64) else dtypes.uint32
-          elem = (base.cast(dt) >> _const(dt, int(idx))) & _const(dt, 1)
+        elem = self.vars.get(f'{name}{idx}', _u32(0))
        if self.try_eat('DOT'):
          dt_name = self.eat('IDENT').val
          return _cast_to(elem, DTYPES.get(dt_name, dtypes.uint32))
@@ -505,17 +432,27 @@ class Parser:
        return elem
      if self.at('LBRACKET') and name not in self.vars:
        self.eat('LBRACKET')
+        if self.at('NUM'):
+          idx_num = int(self.peek().val)
+          if f'{name}{idx_num}' in self.vars:
+            self.eat('NUM')
+            self.eat('RBRACKET')
+            elem = self.vars[f'{name}{idx_num}']
+            if self.try_eat('DOT'): return _cast_to(elem, DTYPES.get(self.eat('IDENT').val, dtypes.uint32))
+            return elem
        first = self.parse()
        return self._handle_bracket_rest(first, _u32(0), name)
      if name in self.vars:
        v = self.vars[name]
-        assert isinstance(v, UOp), f"expected UOp for {name}, got {type(v)}"
-        return v
+        return v if isinstance(v, UOp) else _u32(0) if isinstance(v, dict) else _u32(0)
      raise RuntimeError(f"unknown variable: {name}")
    raise RuntimeError(f"unexpected token in primary: {self.peek()}")

  def _handle_dot(self, base, field: str) -> UOp:
-    assert isinstance(base, UOp), f"expected UOp for dot access, got {type(base)}"
+    if isinstance(base, str): return _u32(0)
+    if not isinstance(base, UOp):
+      if isinstance(base, dict): return base.get(field, _u32(0))
+      return _u32(0)
    if field == 'u64' and self.at('LBRACKET') and self.peek(1).type == 'IDENT' and self.peek(1).val == 'laneId':
      self.eat('LBRACKET')
      self.eat_val('laneId', 'IDENT')
@@ -530,7 +467,6 @@ class Parser:
    if dt == base.dtype: return base
    if dt.itemsize == 2 and base.dtype.itemsize == 4:
      return (base & _const(base.dtype, 0xFFFF)).cast(dtypes.uint16) if dt == dtypes.uint16 else (base & _const(base.dtype, 0xFFFF)).cast(dtypes.uint16).bitcast(dt)
-    if field == 'i4': return _signext_4bit(base)
    return _cast_to(base, dt)

  def _handle_bracket(self, base, var_name: str | None = None) -> UOp:
@@ -573,18 +509,16 @@ class Parser:
      var_name = self._find_var_name(base)
    if first.op == Ops.CONST:
      idx = int(first.arg)
-      # Check for array element (var@idx)
-      if var_name and f'{var_name}@{idx}' in self.vars:
-        v = self.vars[f'{var_name}@{idx}']
+      if var_name and f'{var_name}{idx}' in self.vars:
+        v = self.vars[f'{var_name}{idx}']
        return _cast_to(v, dt_suffix) if dt_suffix else v
-      # Bit extraction
      dt = dtypes.uint64 if base.dtype in (dtypes.uint64, dtypes.int64) else dtypes.uint32
      base_cast = base.cast(dt) if base.dtype != dt else base
      result = ((base_cast >> _const(dt, idx)) & _const(dt, 1))
      return _cast_to(result, dt_suffix) if dt_suffix else result
    if var_name:
      idx_u32 = _to_u32(first)
-      elems = [(i, self.vars[f'{var_name}@{i}']) for i in range(256) if f'{var_name}@{i}' in self.vars]
+      elems = [(i, self.vars[f'{var_name}{i}']) for i in range(256) if f'{var_name}{i}' in self.vars]
      if elems:
        result = elems[-1][1]
        for ei, ev in reversed(elems[:-1]):
@@ -603,7 +537,7 @@ class Parser:
    self.eat('RBRACE')
    var_name = self._find_var_name(base)
    if var_name:
-      elem = self.vars.get(f'{var_name}@{idx}', _u32(0))  # use @ to avoid collision with temps like A4
+      elem = self.vars.get(f'{var_name}{idx}', _u32(0))
      if self.try_eat('DOT'):
        dt_name = self.eat('IDENT').val
        return _cast_to(elem, DTYPES.get(dt_name, dtypes.uint32))
@@ -665,14 +599,13 @@ class Parser:
    raise RuntimeError(f"unexpected token after {bits}': {self.peek()}")

  def _parse_number(self, num: str) -> UOp:
-    if num.startswith('0x') or num.startswith('0X'):
-      is_u64 = num.upper().endswith('ULL') or num.upper().endswith('LL') or num.upper().endswith('UL')
-      return _const(dtypes.uint64 if is_u64 else dtypes.uint32, int(num.rstrip('ULul'), 16))
-    suffix, num_str = _strip_suffix(num)
-    if '.' in num_str or suffix in ('F', 'f'):
-      return _const(dtypes.float32 if suffix in ('F', 'f') else dtypes.float64, float(num_str))
-    val = int(num_str)
-    if 'ULL' in suffix or 'LL' in suffix or 'L' in suffix: return _const(dtypes.uint64, val)
+    if num.startswith('0x') or num.startswith('0X'): return _const(dtypes.uint64, int(num.rstrip('ULul'), 16))
+    suffix, num = _strip_suffix(num)
+    if '.' in num or suffix in ('F', 'f'):
+      return _const(dtypes.float32 if suffix in ('F', 'f') else dtypes.float64, float(num))
+    val = int(num)
+    if 'ULL' in suffix: return _const(dtypes.uint64, val)
+    if 'LL' in suffix or 'L' in suffix: return _const(dtypes.uint64, val)
    if 'U' in suffix: return _const(dtypes.uint32, val)
    return _const(dtypes.int if val < 0 else dtypes.uint32, val)

@@ -690,8 +623,7 @@ class Parser:
      if ';' in body or '\n' in body or 'return' in body.lower():
        lines = [l.strip() for l in body.replace(';', '\n').split('\n') if l.strip() and not l.strip().startswith('//')]
        _, _, result = parse_block(lines, 0, lv, self.funcs)
-        assert result is not None, f"lambda {name} must return a value"
-        return result
+        return result if result is not None else _u32(0)
      return parse_expr(body, lv, self.funcs)
    if name in self.funcs:
      return self.funcs[name](*args)
@@ -699,7 +631,7 @@ class Parser:

  def _handle_mem_load(self, addr: UOp, dt) -> UOp:
    mem = self.vars.get('_vmem') if '_vmem' in self.vars else self.vars.get('_lds')
-    assert mem is not None, "memory load requires _vmem or _lds"
+    if mem is None: return _const(dt, 0)
    adt = dtypes.uint64 if addr.dtype == dtypes.uint64 else dtypes.uint32
    active = self.vars.get('_active')
    gate = (active,) if active is not None else ()
@@ -761,9 +693,29 @@ def parse_tokens(toks: list[Token], vars: dict[str, VarVal], funcs: dict | None

 # Unified block parser for pcode
 def _subst_loop_var(line: str, loop_var: str, val: int) -> str:
-  """Substitute loop variable with its value."""
+  """Substitute loop variable and evaluate bracket expressions.
+  Converts var[loop_var] to var{val} for array element access (like the old regex parser)."""
  toks = tokenize(line)
-  return ' '.join(str(val) if t.type == 'IDENT' and t.val == loop_var else t.val for t in toks if t.type != 'EOF')
+  # First pass: convert var[loop_var] to var{loop_var} to mark for array element assignment
+  result_toks: list[Token] = []
+  j = 0
+  while j < len(toks):
+    t = toks[j]
+    # Check for pattern: IDENT[loop_var] where it's not preceded by a dot (not .type[...])
+    if t.type == 'IDENT' and j+3 < len(toks) and toks[j+1].type == 'LBRACKET' and toks[j+2].type == 'IDENT' and toks[j+2].val == loop_var and toks[j+3].type == 'RBRACKET':
+      # Check that it's not .type[loop_var]
+      if not result_toks or result_toks[-1].type != 'DOT':
+        result_toks.append(t)
+        result_toks.append(Token('LBRACE', '{'))
+        result_toks.append(Token('NUM', str(val)))
+        result_toks.append(Token('RBRACE', '}'))
+        j += 4
+        continue
+    result_toks.append(t)
+    j += 1
+  # Second pass: substitute loop variable in remaining positions
+  subst_parts = [str(val) if t.type == 'IDENT' and t.val == loop_var else t.val for t in result_toks if t.type != 'EOF']
+  return ' '.join(subst_parts)

 def _set_bits(old: UOp, val: UOp, width: int, offset: int) -> UOp:
  """Set bits [offset:offset+width) in old to val, masking and shifting appropriately."""
@@ -813,9 +765,8 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
      def parse_bound():
        if p.at('NUM') and p.peek(1).type == 'QUOTE': p.eat('NUM'); p.eat('QUOTE')
        if p.at('NUM'): return int(p.eat('NUM').val.rstrip('UuLl'))
-        expr = p.parse().simplify()
-        assert expr.op == Ops.CONST, f"loop bound must be constant, got {expr}"
-        return int(expr.arg)
+        expr = p.parse()
+        return int(expr.arg) if expr.op == Ops.CONST else 0
      start_val = parse_bound()
      p.eat('COLON')
      end_val = parse_bound()
@@ -836,7 +787,7 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
      if found_var: vars[found_var] = block_assigns[found_var] = _const(dtypes.bool, False)
      for loop_i in range(start_val, end_val + 1):
        subst_lines = [_subst_loop_var(bl, loop_var, loop_i) for bl in body_lines if not (has_break and bl.strip().lower() == 'break')]
-        _, iter_assigns, _ = parse_block(subst_lines, 0, {**vars, **block_assigns}, funcs, assigns)
+        _, iter_assigns, _ = parse_block(subst_lines, 0, vars, funcs, assigns)
        if has_break:
          assert found_var is not None
          found = block_assigns.get(found_var, vars.get(found_var))
@@ -861,9 +812,7 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di

    # declare
    if first == 'declare':
-      # Initialize scalar declarations (skip arrays and vars already passed as srcs)
-      if '[' not in line and len(toks) >= 2 and toks[1].type == 'IDENT':
-        vars.setdefault(toks[1].val, _u32(0))
+      if '[' not in line and len(toks) >= 2 and toks[1].type == 'IDENT': vars[toks[1].val] = _u32(0)
      i += 1; continue

    # lambda definition
@@ -921,7 +870,6 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
      j, lane_toks = _match_bracket(toks, 1)
      if j < len(toks) and toks[j].type == 'LBRACKET':
        j, reg_toks = _match_bracket(toks, j)
-        if j < len(toks) and toks[j].type == 'DOT': j += 2  # skip .type suffix
        if j < len(toks) and toks[j].type == 'EQUALS': j += 1
        ln, rg, val = parse_tokens(lane_toks, vars, funcs), parse_tokens(reg_toks, vars, funcs), parse_tokens(toks[j:], vars, funcs)
        if assigns is not None: assigns.append((f'VGPR[{_tok_str(lane_toks)}][{_tok_str(reg_toks)}]', (_to_u32(rg) * _u32(32) + _to_u32(ln), val)))
@@ -985,32 +933,19 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
            block_assigns[var] = vars[var] = _set_bit(existing, _to_u32(parse_tokens(bit_toks, vars, funcs)), parse_tokens(toks[j+1:], vars, funcs))
            i += 1; continue

-    # Array element: var[idx] = value (static index) or var[expr] = value (dynamic)
-    if len(toks) >= 4 and toks[0].type == 'IDENT' and toks[1].type == 'LBRACKET':
-      var = toks[0].val
-      j, idx_toks = _match_bracket(toks, 1)
-      if j < len(toks) and toks[j].type == 'EQUALS':
-        # Static index: var[NUM] = value
-        if len(idx_toks) == 1 and idx_toks[0].type == 'NUM':
-          idx = int(idx_toks[0].val.rstrip('UuLl'))
-          val = parse_tokens(toks[j+1:], vars, funcs)
-          existing = block_assigns.get(var, vars.get(var))
-          if existing is not None and isinstance(existing, UOp):
-            block_assigns[var] = vars[var] = _set_bit(existing, _u32(idx), val)
-          else:
-            block_assigns[f'{var}@{idx}'] = vars[f'{var}@{idx}'] = val
-          i += 1; continue
-        # Dynamic index: var[expr] = value where var has @-elements
-        elems = [(k.split('@')[1], v) for k, v in {**vars, **block_assigns}.items() if k.startswith(f'{var}@') and isinstance(v, UOp)]
-        if elems:
-          idx_expr = parse_tokens(idx_toks, vars, funcs)
-          val = parse_tokens(toks[j+1:], vars, funcs)
-          for elem_idx_str, old_elem in elems:
-            elem_idx = int(elem_idx_str)
-            cond = _to_u32(idx_expr).eq(_u32(elem_idx))
-            new_val = cond.where(val.cast(old_elem.dtype) if val.dtype != old_elem.dtype else val, old_elem)
-            block_assigns[f'{var}@{elem_idx}'] = vars[f'{var}@{elem_idx}'] = new_val
-          i += 1; continue
+    # Array element: var{idx} = value
+    if len(toks) >= 5 and toks[0].type == 'IDENT' and toks[1].type == 'LBRACE' and toks[2].type == 'NUM':
+      var, idx = toks[0].val, int(toks[2].val)
+      j = 4
+      while j < len(toks) and toks[j].type != 'EQUALS': j += 1
+      if j < len(toks):
+        val = parse_tokens(toks[j+1:], vars, funcs)
+        existing = block_assigns.get(var, vars.get(var))
+        if existing is not None and isinstance(existing, UOp):
+          block_assigns[var] = vars[var] = _set_bit(existing, _u32(idx), val)
+        else:
+          block_assigns[f'{var}{idx}'] = vars[f'{var}{idx}'] = val
+        i += 1; continue

    # Compound assignment: var += or var -=
    assign_op = next((j for j, t in enumerate(toks) if t.type == 'ASSIGN_OP'), None)
@@ -1057,14 +992,13 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
      def parse_cond(s, kw):
        ll = s.lower()
        return _to_bool(parse_expr(s[ll.find(kw) + len(kw):ll.rfind('then')].strip(), vars, funcs))
-      def is_const(c, v): return c.op == Ops.CONST and c.arg is v
+      def not_static_false(c): return c.op != Ops.CONST or c.arg is not False
      cond = parse_cond(line, 'if')
-      conditions: list[tuple[UOp, UOp | dict[str, VarVal] | None]] = [(cond, None)] if not is_const(cond, False) else []
+      conditions: list[tuple[UOp, UOp | dict[str, VarVal] | None]] = [(cond, None)] if not_static_false(cond) else []
      else_branch: tuple[UOp | None, dict[str, VarVal]] = (None, {})
      vars_snap = dict(vars)
-      static_true = is_const(cond, True)  # track if any condition is statically true
      i += 1
-      i, branch, ret = parse_block(lines, i, vars, funcs, assigns if not is_const(cond, False) else None)
+      i, branch, ret = parse_block(lines, i, vars, funcs, assigns)
      if conditions: conditions[0] = (cond, ret if ret is not None else branch)
      vars.clear(); vars.update(vars_snap)
      while i < len(lines):
@@ -1073,16 +1007,12 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
        lf = ltoks[0].val.lower()
        if lf == 'elsif':
          c = parse_cond(lines[i], 'elsif')
-          take = not static_true and not is_const(c, False)
-          i += 1; i, branch, ret = parse_block(lines, i, vars, funcs, assigns if take else None)
-          if take:
-            conditions.append((c, ret if ret is not None else branch))
-            if is_const(c, True): static_true = True
+          i += 1; i, branch, ret = parse_block(lines, i, vars, funcs, assigns)
+          if not_static_false(c): conditions.append((c, ret if ret is not None else branch))
          vars.clear(); vars.update(vars_snap)
        elif lf == 'else':
-          i += 1
-          i, branch, ret = parse_block(lines, i, vars, funcs, assigns if not static_true else None)
-          if not static_true: else_branch = (ret, branch)
+          i += 1; i, branch, ret = parse_block(lines, i, vars, funcs, assigns)
+          else_branch = (ret, branch)
          vars.clear(); vars.update(vars_snap)
        elif lf == 'endif': i += 1; break
        else: break
@@ -1094,21 +1024,17 @@ def parse_block(lines: list[str], start: int, vars: dict[str, VarVal], funcs: di
            if rv.dtype != result.dtype and rv.dtype.itemsize == result.dtype.itemsize: result = result.cast(rv.dtype)
            result = c.where(rv, result)
        return i, block_assigns, result
-      # If statically true, use that branch directly; otherwise merge with WHERE
-      if static_true:
-        ba = next((b for c, b in conditions if is_const(c, True) and isinstance(b, dict)), {})
-        block_assigns.update(ba); vars.update(ba)
-      else:
-        else_assigns = else_branch[1]
-        all_vars = set().union(*[ba.keys() for _, ba in conditions if isinstance(ba, dict)], else_assigns.keys())
-        for var in all_vars:
-          res: Any = else_assigns.get(var, block_assigns.get(var, vars.get(var, _u32(0))))
-          for cond, ba in reversed(conditions):
-            if isinstance(ba, dict) and var in ba:
-              tv = ba[var]
-              if isinstance(tv, UOp) and isinstance(res, UOp):
-                res = cond.where(tv, res.cast(tv.dtype) if tv.dtype != res.dtype and tv.dtype.itemsize == res.dtype.itemsize else res)
-          block_assigns[var] = vars[var] = res
+      # Main style: merge variable assignments with WHERE
+      else_assigns = else_branch[1]
+      all_vars = set().union(*[ba.keys() for _, ba in conditions if isinstance(ba, dict)], else_assigns.keys())
+      for var in all_vars:
+        res: Any = else_assigns.get(var, block_assigns.get(var, vars.get(var, _u32(0))))
+        for cond, ba in reversed(conditions):
+          if isinstance(ba, dict) and var in ba:
+            tv = ba[var]
+            if isinstance(tv, UOp) and isinstance(res, UOp):
+              res = cond.where(tv, res.cast(tv.dtype) if tv.dtype != res.dtype and tv.dtype.itemsize == res.dtype.itemsize else res)
+        block_assigns[var] = vars[var] = res
      continue

    # Regular assignment: var = value
@@ -2,8 +2,11 @@
 from dataclasses import dataclass
 from typing import Iterator

+from tinygrad.runtime.support.elf import elf_loader
+
 from extra.assembly.amd.sqtt import decode, print_packets, INST, VALUINST, IMMEDIATE, WAVESTART, WAVEEND, InstOp, PacketType, IMMEDIATE_MASK
 from extra.assembly.amd.dsl import Inst
+from extra.assembly.amd import decode_inst
 from extra.assembly.amd.autogen.rdna3.ins import SOPP, s_endpgm
 from extra.assembly.amd.autogen.rdna3.enum import SOPPOp

@@ -13,11 +16,19 @@ class InstructionInfo:
  wave: int
  inst: Inst

-def map_insts(data:bytes, lib:bytes, target:int) -> Iterator[tuple[PacketType, InstructionInfo|None]]:
+def map_insts(data:bytes, lib:bytes) -> Iterator[tuple[PacketType, InstructionInfo|None]]:
  """maps SQTT packets to instructions, yields (packet, instruction_info or None)"""
  # map pcs to insts
-  from tinygrad.viz.serve import amd_decode
-  pc_map = amd_decode(lib, target)
+  pc_map:dict[int, Inst] = {}
+  image, sections, _ = elf_loader(lib)
+  text = next((sh for sh in sections if sh.name == ".text"), None)
+  assert text is not None, "no .text section found"
+  text_off, text_size = text.header.sh_addr, text.header.sh_size
+  offset = text_off
+  while offset < text_off + text_size:
+    inst = decode_inst(image[offset:])
+    pc_map[offset-text_off] = inst
+    offset += inst.size()

  wave_pc:dict[int, int] = {}
  # only processing packets on one [CU, SIMD] unit
@@ -26,7 +37,7 @@ def map_insts(data:bytes, lib:bytes, target:int) -> Iterator[tuple[PacketType, I
    if not simd_select(p): continue
    if isinstance(p, WAVESTART):
      assert p.wave not in wave_pc, "only one inflight wave per unit"
-      wave_pc[p.wave] = next(iter(pc_map))
+      wave_pc[p.wave] = 0
      continue
    if isinstance(p, WAVEEND):
      pc = wave_pc.pop(p.wave)
@@ -69,22 +80,22 @@ def map_insts(data:bytes, lib:bytes, target:int) -> Iterator[tuple[PacketType, I
 # test to compare every packet with the rocprof decoder

 def test_rocprof_inst_traces_match(sqtt, prg, target):
-  from tinygrad.viz.serve import amd_decode
+  from tinygrad.viz.serve import llvm_disasm
  from extra.sqtt.roc import decode as roc_decode, InstExec
-  addr_table = amd_decode(prg.lib, target)
-  disasm = {addr+prg.base:(inst.disasm(), inst.size()) for addr,inst in addr_table.items()}
-  rctx = roc_decode([sqtt], {prg.tag:disasm})
-  rwaves = rctx.inst_execs.get((sqtt.kern, sqtt.exec_tag), [])
+  disasm = {addr+prg.base:inst_disasm for addr, inst_disasm in llvm_disasm(target, prg.lib).items()}
+  rctx = roc_decode([sqtt], {prg.name:disasm})
+  rwaves = rctx.inst_execs[(sqtt.kern, sqtt.exec_tag)]
  rwaves_iter:dict[int, list[Iterator[InstExec]]] = {} # wave unit (0-15) -> list of inst trace iterators for all executions on that unit
  for w in rwaves: rwaves_iter.setdefault(w.wave_id, []).append(w.unpack_insts())
+  rwaves_base = next(iter(disasm)) # base program counter

  passed_insts = 0
-  for pkt, info in map_insts(sqtt.blob, prg.lib, target):
+  for pkt, info in map_insts(sqtt.blob, prg.lib):
    if DEBUG >= 2: print_packets([pkt])
    if info is None: continue
    if DEBUG >= 2: print(f"{' '*29}{info.inst.disasm()}")
    rocprof_inst = next(rwaves_iter[info.wave][0])
-    ref_pc = rocprof_inst.pc-prg.base
+    ref_pc = rocprof_inst.pc-rwaves_base
    # always check pc matches
    assert ref_pc == info.pc, f"pc mismatch {ref_pc}:{disasm[rocprof_inst.pc][0]} != {info.pc}:{info.inst.disasm()}"
    # special handling for s_endpgm, it marks the wave completion.
@@ -99,8 +110,7 @@ def test_rocprof_inst_traces_match(sqtt, prg, target):
  for k,v in rwaves_iter.items():
    assert len(v) == 0, f"incomplete wave {k}"

-  if len(rwaves):
-    print(f"passed for {passed_insts} instructions across {len(rwaves)} waves scheduled on {len(rwaves_iter)} wave units")
+  print(f"passed for {passed_insts} instructions across {len(rwaves)} waves scheduled on {len(rwaves_iter)} wave units")

 if __name__ == "__main__":
  import argparse, pickle, pathlib
@@ -113,7 +123,7 @@ if __name__ == "__main__":
  with open(args.profile, "rb") as f:
    data = pickle.load(f)
  sqtt_events = [e for e in data if type(e).__name__ == "ProfileSQTTEvent"]
-  kern_events = {e.tag:e for e in data if type(e).__name__ == "ProfileProgramEvent"}
+  kern_events = {e.name:e for e in data if type(e).__name__ == "ProfileProgramEvent"}
  target = next((e for e in data if type(e).__name__ == "ProfileDeviceEvent" and e.device.startswith("AMD"))).props["gfx_target_version"]
  for e in sqtt_events:
    if args.kernel is not None and args.kernel != e.kern: continue
@@ -13,7 +13,7 @@ def _i32(f: float) -> int: return struct.unpack('<I', struct.pack('<f', f))[0]
 def _f32(i: int) -> float: return struct.unpack('<f', struct.pack('<I', i & 0xFFFFFFFF))[0]

 # f16 conversion helpers
-def f16(i: int) -> float: return struct.unpack('<e', struct.pack('<H', i & 0xFFFF))[0]
+def _f16(i: int) -> float: return struct.unpack('<e', struct.pack('<H', i & 0xFFFF))[0]
 def f32_to_f16(f: float) -> int:
  f = float(f)
  if math.isnan(f): return 0x7e00
@@ -43,23 +43,6 @@ VCC = VCC_LO  # For VOP3SD sdst field (VCC_LO is exported from dsl)
 USE_HW = os.environ.get("USE_HW", "0") == "1"
 FLOAT_TOLERANCE = 1e-5

-def get_gpu_target() -> tuple[int, int, int]:
-  """Get the GPU target as (major, minor, stepping) tuple."""
-  if not USE_HW: return (0, 0, 0)
-  from tinygrad.device import Device
-  return Device["AMD"].target
-
-def skip_unless_gfx(min_major: int, min_minor: int = 0, reason: str = ""):
-  """Skip test if GPU target is below the minimum required version."""
-  import unittest
-  def decorator(test_func):
-    if not USE_HW: return test_func
-    target = get_gpu_target()
-    if target[0] < min_major or (target[0] == min_major and target[1] < min_minor):
-      return unittest.skip(reason or f"requires gfx{min_major}{min_minor}0+")(test_func)
-    return test_func
-  return decorator
-
 # Output buffer layout: vgpr[16][32], sgpr[16], vcc, scc, exec
 N_VGPRS, N_SGPRS, WAVE_SIZE = 16, 16, 32
 VGPR_BYTES = N_VGPRS * WAVE_SIZE * 4  # 16 regs * 32 lanes * 4 bytes = 2048
@@ -229,12 +212,8 @@ amdhsa.kernels:

  return parse_output(bytes(out_buf), n_lanes)

-def compare_wave_states(emu_st: WaveState, hw_st: WaveState, n_lanes: int, n_vgprs: int = N_VGPRS, ulp_tolerance: int = 0) -> list[str]:
-  """Compare two WaveStates and return list of differences.
-
-  Args:
-    ulp_tolerance: Allow up to this many ULPs difference for float comparisons (0 = exact match required)
-  """
+def compare_wave_states(emu_st: WaveState, hw_st: WaveState, n_lanes: int, n_vgprs: int = N_VGPRS) -> list[str]:
+  """Compare two WaveStates and return list of differences."""
  import math
  diffs = []
  for i in range(n_vgprs):
@@ -245,11 +224,6 @@ def compare_wave_states(emu_st: WaveState, hw_st: WaveState, n_lanes: int, n_vgp
        emu_f, hw_f = _f32(emu_val), _f32(hw_val)
        if math.isnan(emu_f) and math.isnan(hw_f):
          continue
-        # Check ULP difference for floats (only for same-sign values)
-        if ulp_tolerance > 0 and (emu_val < 0x80000000) == (hw_val < 0x80000000):
-          ulp_diff = abs(int(emu_val) - int(hw_val))
-          if ulp_diff <= ulp_tolerance:
-            continue
        diffs.append(f"v[{i}] lane {lane}: emu=0x{emu_val:08x} ({emu_f:.6g}) hw=0x{hw_val:08x} ({hw_f:.6g})")
  for i in range(N_SGPRS):
    emu_val = emu_st.sgpr[i]
@@ -262,19 +236,16 @@ def compare_wave_states(emu_st: WaveState, hw_st: WaveState, n_lanes: int, n_vgp
    diffs.append(f"scc: emu={emu_st.scc} hw={hw_st.scc}")
  return diffs

-def run_program(instructions: list, n_lanes: int = 1, ulp_tolerance: int = 0) -> WaveState:
+def run_program(instructions: list, n_lanes: int = 1) -> WaveState:
  """Run instructions and return WaveState.

  If USE_HW=1, runs on both emulator and hardware, compares results, and raises if they differ.
  Otherwise, runs only on emulator.
-
-  Args:
-    ulp_tolerance: Allow up to this many ULPs difference for float comparisons (0 = exact match required)
  """
  emu_st = run_program_emu(instructions, n_lanes)
  if USE_HW:
    hw_st = run_program_hw(instructions, n_lanes)
-    diffs = compare_wave_states(emu_st, hw_st, n_lanes, ulp_tolerance=ulp_tolerance)
+    diffs = compare_wave_states(emu_st, hw_st, n_lanes)
    if diffs:
      raise AssertionError(f"Emulator vs Hardware mismatch:\n" + "\n".join(diffs))
    return hw_st
@@ -719,47 +719,5 @@ class TestAtomicOrdering(unittest.TestCase):
    self.assertEqual(st.vgpr[0][4], 150, "Final value should be 150")


-class TestDsPermute(unittest.TestCase):
-  """Tests for DS_PERMUTE_B32 and DS_BPERMUTE_B32 instructions."""
-
-  def test_ds_permute_b32_identity(self):
-    """DS_PERMUTE_B32 with identity permutation (lane 0 sends to lane 0)."""
-    # For simplicity, test with single lane
-    instructions = [
-      v_mov_b32_e32(v[0], 0),  # addr = 0 (lane 0)
-      v_mov_b32_e32(v[1], 0xDEADBEEF),  # data
-      ds_permute_b32(v[2], v[0], v[1]),
-      s_waitcnt(lgkmcnt=0),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    # Lane 0 sends to lane 0, so lane 0 gets 0xDEADBEEF
-    self.assertEqual(st.vgpr[0][2], 0xDEADBEEF)
-
-  def test_ds_bpermute_b32_identity(self):
-    """DS_BPERMUTE_B32 with identity permutation (each lane reads from itself)."""
-    instructions = [
-      v_mov_b32_e32(v[0], 0),  # addr = 0 (read from lane 0)
-      v_mov_b32_e32(v[1], 0xCAFEBABE),  # data in lane 0
-      ds_bpermute_b32(v[2], v[0], v[1]),
-      s_waitcnt(lgkmcnt=0),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    # Lane 0 reads from lane 0's v[1]
-    self.assertEqual(st.vgpr[0][2], 0xCAFEBABE)
-
-  def test_ds_permute_b32_broadcast(self):
-    """DS_PERMUTE_B32 broadcast - all lanes send to lane 0."""
-    # With 4 lanes, all sending to lane 0, highest lane wins
-    instructions = [
-      v_mov_b32_e32(v[0], 0),  # All lanes send to addr 0 (lane 0)
-      v_mov_b32_e32(v[1], 0x11111111),  # All lanes send same data
-      ds_permute_b32(v[2], v[0], v[1]),
-      s_waitcnt(lgkmcnt=0),
-    ]
-    st = run_program(instructions, n_lanes=4)
-    # Lane 0 receives data (highest numbered active lane wins)
-    self.assertEqual(st.vgpr[0][2], 0x11111111)
-
-
 if __name__ == '__main__':
  unittest.main()
@@ -62,7 +62,6 @@ class TestBasicScalar(unittest.TestCase):
    st = run_program(instructions, n_lanes=1)
    self.assertEqual(st.sgpr[1], 0x80000000)

-  @skip_unless_gfx(11, 5, "SALU FP ops require gfx1150+")
  def test_s_fmamk_f32(self):
    """S_FMAMK_F32: D = S0 * literal + S1."""
    # 2.0 * 3.0 + 1.0 = 7.0
@@ -74,7 +73,6 @@ class TestBasicScalar(unittest.TestCase):
    st = run_program(instructions, n_lanes=1)
    self.assertEqual(st.sgpr[2], f2i(7.0))

-  @skip_unless_gfx(11, 5, "SALU FP ops require gfx1150+")
  def test_s_fmamk_f32_negative(self):
    """S_FMAMK_F32 with negative values."""
    # -2.0 * 4.0 + 10.0 = 2.0
@@ -255,6 +255,7 @@ class TestF16Conversions(unittest.TestCase):

  def test_v_cvt_f16_f32_small(self):
    """V_CVT_F16_F32 converts small f32 value."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    instructions = [
      v_mov_b32_e32(v[0], 0.5),
      v_cvt_f16_f32_e32(v[1], v[0]),
@@ -292,6 +293,7 @@ class TestF16Conversions(unittest.TestCase):

  def test_v_cvt_f16_f32_reads_full_32bit_source(self):
    """V_CVT_F16_F32 must read full 32-bit f32 source."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x3fc00000),  # f32 1.5
      v_mov_b32_e32(v[0], s[0]),
@@ -300,7 +302,7 @@ class TestF16Conversions(unittest.TestCase):
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][1]
    lo_bits = result & 0xffff
-    self.assertEqual(lo_bits, 0x3e00, f"Expected f16(1.5)=0x3e00, got 0x{lo_bits:04x} ({f16(lo_bits)})")
+    self.assertEqual(lo_bits, 0x3e00, f"Expected f16(1.5)=0x3e00, got 0x{lo_bits:04x} ({_f16(lo_bits)})")

  def test_v_cvt_i16_f16_zero(self):
    """V_CVT_I16_F16 converts f16 zero to i16 zero."""
@@ -694,6 +696,7 @@ class TestCvtF16Modifiers(unittest.TestCase):

  def test_v_cvt_f32_f16_abs_negative(self):
    """V_CVT_F32_F16 with |abs| on negative value."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_neg1 = f32_to_f16(-1.0)  # 0xbc00
    instructions = [
      s_mov_b32(s[0], f16_neg1),
@@ -706,6 +709,7 @@ class TestCvtF16Modifiers(unittest.TestCase):

  def test_v_cvt_f32_f16_abs_positive(self):
    """V_CVT_F32_F16 with |abs| on positive value (should stay positive)."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_2 = f32_to_f16(2.0)  # 0x4000
    instructions = [
      s_mov_b32(s[0], f16_2),
@@ -718,6 +722,7 @@ class TestCvtF16Modifiers(unittest.TestCase):

  def test_v_cvt_f32_f16_neg_positive(self):
    """V_CVT_F32_F16 with neg on positive value."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_2 = f32_to_f16(2.0)  # 0x4000
    instructions = [
      s_mov_b32(s[0], f16_2),
@@ -730,6 +735,7 @@ class TestCvtF16Modifiers(unittest.TestCase):

  def test_v_cvt_f32_f16_neg_negative(self):
    """V_CVT_F32_F16 with neg on negative value (double negative)."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_neg2 = f32_to_f16(-2.0)  # 0xc000
    instructions = [
      s_mov_b32(s[0], f16_neg2),
@@ -742,6 +748,7 @@ class TestCvtF16Modifiers(unittest.TestCase):

  def test_v_cvt_f16_f32_then_pack_for_wmma(self):
    """CVT F32->F16 followed by pack (common WMMA pattern)."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    f32_val = 3.5
    instructions = [
      s_mov_b32(s[0], f2i(f32_val)),
@@ -750,8 +757,8 @@ class TestCvtF16Modifiers(unittest.TestCase):
      v_pack_b32_f16(v[2], v[1], v[1]),  # Pack same value
    ]
    st = run_program(instructions, n_lanes=1)
-    lo = f16(st.vgpr[0][2] & 0xffff)
-    hi = f16((st.vgpr[0][2] >> 16) & 0xffff)
+    lo = _f16(st.vgpr[0][2] & 0xffff)
+    hi = _f16((st.vgpr[0][2] >> 16) & 0xffff)
    self.assertAlmostEqual(lo, f32_val, places=1)
    self.assertAlmostEqual(hi, f32_val, places=1)

@@ -797,6 +804,7 @@ class TestConversionRounding(unittest.TestCase):

  def test_f16_to_f32_precision(self):
    """F16 to F32 conversion precision."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_val = f32_to_f16(1.5)
    instructions = [
      s_mov_b32(s[0], f16_val),
@@ -808,6 +816,7 @@ class TestConversionRounding(unittest.TestCase):

  def test_f16_denormal_to_f32(self):
    """F16 denormal converts to small positive f32."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    f16_denorm = 0x0001  # Smallest positive f16 denormal
    instructions = [
      v_mov_b32_e32(v[0], f16_denorm),
@@ -1503,82 +1512,5 @@ class TestReciprocalF16(unittest.TestCase):
    self.assertAlmostEqual(result, 0.25, places=2, msg="1/4.0 should be 0.25")


-class TestCvtNormF16(unittest.TestCase):
-  """Tests for V_CVT_NORM_I16_F16 and V_CVT_NORM_U16_F16."""
-
-  def test_cvt_norm_i16_f16_positive(self):
-    """V_CVT_NORM_I16_F16: f16 1.0 -> i16 max (32767)."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(1.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_cvt_norm_i16_f16_e32(v[1], v[0]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][1] & 0xffff
-    self.assertEqual(result, 32767)
-
-  def test_cvt_norm_i16_f16_negative(self):
-    """V_CVT_NORM_I16_F16: f16 -1.0 -> i16 -32767 (0x8001)."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(-1.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_cvt_norm_i16_f16_e32(v[1], v[0]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][1] & 0xffff
-    self.assertEqual(result, 0x8001)  # -32767, hardware uses symmetric range
-
-  def test_cvt_norm_i16_f16_zero(self):
-    """V_CVT_NORM_I16_F16: f16 0.0 -> i16 0."""
-    instructions = [
-      v_mov_b32_e32(v[0], 0),
-      v_cvt_norm_i16_f16_e32(v[1], v[0]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][1] & 0xffff
-    self.assertEqual(result, 0)
-
-  def test_cvt_norm_u16_f16_one(self):
-    """V_CVT_NORM_U16_F16: f16 1.0 -> u16 max (65535)."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(1.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_cvt_norm_u16_f16_e32(v[1], v[0]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][1] & 0xffff
-    self.assertEqual(result, 65535)
-
-  def test_cvt_norm_u16_f16_half(self):
-    """V_CVT_NORM_U16_F16: f16 0.5 -> u16 ~32768."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(0.5)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_cvt_norm_u16_f16_e32(v[1], v[0]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][1] & 0xffff
-    self.assertAlmostEqual(result, 32768, delta=1)
-
-
-class TestPermlane64(unittest.TestCase):
-  """Tests for V_PERMLANE64_B32 instruction (wave64 cross-half swap)."""
-
-  def test_v_permlane64_b32_is_nop_in_wave32(self):
-    """V_PERMLANE64_B32 is a NOP in wave32 mode.
-
-    Per AMD pcode: "if WAVE32 then s_nop(...) else ... endif"
-    The emulator runs in wave32 mode, so this instruction should not modify registers.
-    """
-    instructions = [
-      v_mov_b32_e32(v[0], 0xCAFEBABE),  # source
-      v_mov_b32_e32(v[1], 0x12345678),  # dest (should be preserved)
-      v_permlane64_b32_e32(v[1], v[0]),  # NOP in wave32
-    ]
-    st = run_program(instructions, n_lanes=1)
-    # Dest register should be unchanged (NOP behavior in wave32)
-    self.assertEqual(st.vgpr[0][1], 0x12345678)
-
-
 if __name__ == '__main__':
  unittest.main()
@@ -857,6 +857,7 @@ class TestF16Modifiers(unittest.TestCase):

  def test_v_fma_f16_inline_const_1_0(self):
    """V_FMA_F16: a*b + 1.0 should use f16 inline constant."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16, _f16
    f16_a = f32_to_f16(0.325928)  # ~0x3537
    f16_b = f32_to_f16(-0.486572)  # ~0xb7c9
    instructions = [
@@ -867,12 +868,13 @@ class TestF16Modifiers(unittest.TestCase):
      v_fma_f16(v[4], v[4], v[6], 1.0),  # 1.0 is inline constant
    ]
    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][4] & 0xffff)
+    result = _f16(st.vgpr[0][4] & 0xffff)
    expected = 0.325928 * (-0.486572) + 1.0
    self.assertAlmostEqual(result, expected, delta=0.01)

  def test_v_fma_f16_inline_const_0_5(self):
    """V_FMA_F16: a*b + 0.5 should use f16 inline constant."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16, _f16
    f16_a = f32_to_f16(2.0)
    f16_b = f32_to_f16(3.0)
    instructions = [
@@ -883,12 +885,13 @@ class TestF16Modifiers(unittest.TestCase):
      v_fma_f16(v[2], v[0], v[1], 0.5),  # 0.5 is inline constant
    ]
    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][2] & 0xffff)
+    result = _f16(st.vgpr[0][2] & 0xffff)
    expected = 2.0 * 3.0 + 0.5
    self.assertAlmostEqual(result, expected, delta=0.01)

  def test_v_fma_f16_inline_const_neg_1_0(self):
    """V_FMA_F16: a*b + (-1.0) should use f16 inline constant."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16, _f16
    f16_a = f32_to_f16(2.0)
    f16_b = f32_to_f16(3.0)
    instructions = [
@@ -899,12 +902,13 @@ class TestF16Modifiers(unittest.TestCase):
      v_fma_f16(v[2], v[0], v[1], -1.0),  # -1.0 is inline constant
    ]
    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][2] & 0xffff)
+    result = _f16(st.vgpr[0][2] & 0xffff)
    expected = 2.0 * 3.0 + (-1.0)
    self.assertAlmostEqual(result, expected, delta=0.01)

  def test_v_add_f16_abs_both(self):
    """V_ADD_F16 with abs on both operands."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16, _f16
    f16_neg2 = f32_to_f16(-2.0)
    f16_neg3 = f32_to_f16(-3.0)
    instructions = [
@@ -915,11 +919,12 @@ class TestF16Modifiers(unittest.TestCase):
      v_add_f16_e64(v[2], abs(v[0]), abs(v[1])),  # |-2| + |-3| = 5
    ]
    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][2] & 0xffff)
+    result = _f16(st.vgpr[0][2] & 0xffff)
    self.assertAlmostEqual(result, 5.0, delta=0.01)

  def test_v_mul_f16_neg_abs(self):
    """V_MUL_F16 with neg on one operand and abs on another."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16, _f16
    f16_2 = f32_to_f16(2.0)
    f16_neg3 = f32_to_f16(-3.0)
    instructions = [
@@ -930,7 +935,7 @@ class TestF16Modifiers(unittest.TestCase):
      v_mul_f16_e64(v[2], -v[0], abs(v[1])),  # -(2) * |-3| = -6
    ]
    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][2] & 0xffff)
+    result = _f16(st.vgpr[0][2] & 0xffff)
    self.assertAlmostEqual(result, -6.0, delta=0.01)

  def test_v_fmac_f16_hi_dest(self):
@@ -938,6 +943,7 @@ class TestF16Modifiers(unittest.TestCase):

    This tests the case from AMD_LLVM sin(0) where V_FMAC_F16 writes to v0.h.
    """
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x38003c00),  # v0 = {hi=0.5, lo=1.0}
      v_mov_b32_e32(v[0], s[0]),
@@ -948,8 +954,8 @@ class TestF16Modifiers(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    v0 = st.vgpr[0][0]
-    result_hi = f16((v0 >> 16) & 0xffff)
-    result_lo = f16(v0 & 0xffff)
+    result_hi = _f16((v0 >> 16) & 0xffff)
+    result_lo = _f16(v0 & 0xffff)
    self.assertAlmostEqual(result_hi, 0.5, delta=0.01, msg=f"Expected hi=0.5, got {result_hi}")
    self.assertAlmostEqual(result_lo, 1.0, delta=0.01, msg=f"Expected lo=1.0, got {result_lo}")

@@ -2949,394 +2955,5 @@ class TestVOP3Clamp(unittest.TestCase):
    self.assertAlmostEqual(i2f(st.vgpr[3][1]), 1.0, places=5, msg="lane 3: 2.5 should clamp to 1.0")


-class TestCvtPkF16(unittest.TestCase):
-  """Tests for V_CVT_PK_RTZ_F16_F32 - pack two f32 to f16 with round toward zero."""
-
-  def test_cvt_pk_rtz_f16_f32_basic(self):
-    """V_CVT_PK_RTZ_F16_F32: basic pack of two f32 values."""
-    instructions = [
-      v_mov_b32_e32(v[0], 1.0),
-      v_mov_b32_e32(v[1], 2.0),
-      v_cvt_pk_rtz_f16_f32_e64(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo_f16 = f16(result & 0xffff)
-    hi_f16 = f16((result >> 16) & 0xffff)
-    self.assertAlmostEqual(lo_f16, 1.0, delta=0.01)
-    self.assertAlmostEqual(hi_f16, 2.0, delta=0.01)
-
-
-class TestCvtPkNorm(unittest.TestCase):
-  """Tests for V_CVT_PK_NORM_I16_F32 and V_CVT_PK_NORM_U16_F32."""
-
-  def test_cvt_pk_norm_i16_f32_basic(self):
-    """V_CVT_PK_NORM_I16_F32: pack two f32 to normalized i16."""
-    instructions = [
-      v_mov_b32_e32(v[0], 1.0),
-      v_mov_b32_e32(v[1], -1.0),
-      v_cvt_pk_norm_i16_f32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    self.assertEqual(lo, 32767)
-    self.assertEqual(hi, 0x8001)  # -32767, hardware uses symmetric range
-
-  def test_cvt_pk_norm_u16_f32_basic(self):
-    """V_CVT_PK_NORM_U16_F32: pack two f32 to normalized u16."""
-    instructions = [
-      v_mov_b32_e32(v[0], 1.0),
-      v_mov_b32_e32(v[1], 0.5),
-      v_cvt_pk_norm_u16_f32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    self.assertEqual(lo, 65535)
-    self.assertAlmostEqual(hi, 32768, delta=1)
-
-
-class TestCvtPkInt(unittest.TestCase):
-  """Tests for V_CVT_PK_I16_I32, V_CVT_PK_U16_U32, V_CVT_PK_I16_F32, V_CVT_PK_U16_F32."""
-
-  def test_cvt_pk_i16_i32_basic(self):
-    """V_CVT_PK_I16_I32: pack two i32 to i16."""
-    instructions = [
-      s_mov_b32(s[0], 100),
-      s_mov_b32(s[1], -100 & 0xffffffff),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_cvt_pk_i16_i32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    lo_signed = lo if lo < 32768 else lo - 65536
-    hi_signed = hi if hi < 32768 else hi - 65536
-    self.assertEqual(lo_signed, 100)
-    self.assertEqual(hi_signed, -100)
-
-  def test_cvt_pk_u16_u32_basic(self):
-    """V_CVT_PK_U16_U32: pack two u32 to u16."""
-    instructions = [
-      s_mov_b32(s[0], 1000),
-      s_mov_b32(s[1], 2000),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_cvt_pk_u16_u32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    self.assertEqual(lo, 1000)
-    self.assertEqual(hi, 2000)
-
-  def test_cvt_pk_i16_f32_basic(self):
-    """V_CVT_PK_I16_F32: convert two f32 to packed i16."""
-    instructions = [
-      v_mov_b32_e32(v[0], 100.5),
-      v_mov_b32_e32(v[1], -50.7),
-      v_cvt_pk_i16_f32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    lo_signed = lo if lo < 32768 else lo - 65536
-    hi_signed = hi if hi < 32768 else hi - 65536
-    self.assertEqual(lo_signed, 100)
-    self.assertEqual(hi_signed, -50)
-
-  def test_cvt_pk_u16_f32_basic(self):
-    """V_CVT_PK_U16_F32: convert two f32 to packed u16."""
-    instructions = [
-      v_mov_b32_e32(v[0], 100.9),
-      v_mov_b32_e32(v[1], 200.1),
-      v_cvt_pk_u16_f32(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = result & 0xffff
-    hi = (result >> 16) & 0xffff
-    self.assertEqual(lo, 100)
-    self.assertEqual(hi, 200)
-
-  def test_cvt_pk_u8_f32_basic(self):
-    """V_CVT_PK_U8_F32: convert f32 to u8 and pack at byte position."""
-    instructions = [
-      v_mov_b32_e32(v[0], 128.5),
-      v_mov_b32_e32(v[1], 0),
-      v_mov_b32_e32(v[2], 0),
-      v_cvt_pk_u8_f32(v[2], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    byte0 = result & 0xff
-    self.assertEqual(byte0, 128)
-
-
-class TestDotProduct(unittest.TestCase):
-  """Tests for dot product instructions V_DOT4_U32_U8, V_DOT8_U32_U4."""
-
-  def test_v_dot4_u32_u8_basic(self):
-    """V_DOT4_U32_U8: 4-element dot product of u8 vectors."""
-    src0 = 0x04030201  # {4, 3, 2, 1}
-    src1 = 0x01010101  # {1, 1, 1, 1}
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot4_u32_u8(v[2], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    self.assertEqual(result, 10)
-
-  def test_v_dot4_u32_u8_with_accumulator(self):
-    """V_DOT4_U32_U8 with non-zero accumulator."""
-    src0 = 0x02020202  # {2, 2, 2, 2}
-    src1 = 0x03030303  # {3, 3, 3, 3}
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 100),
-      v_dot4_u32_u8(v[2], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    self.assertEqual(result, 124)
-
-  def test_v_dot8_u32_u4_basic(self):
-    """V_DOT8_U32_U4: 8-element dot product of u4 vectors."""
-    # src0 = 8 nibbles: {1,2,3,4,5,6,7,8} packed as 0x87654321
-    # src1 = 8 nibbles: {1,1,1,1,1,1,1,1} packed as 0x11111111
-    # result = 1+2+3+4+5+6+7+8 = 36
-    src0 = 0x87654321
-    src1 = 0x11111111
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot8_u32_u4(v[2], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    self.assertEqual(result, 36)
-
-
-class TestMinMaxF16Vop3(unittest.TestCase):
-  """Tests for V_MIN3_F16, V_MAX3_F16, V_MED3_F16, V_MINMAX_F16, V_MAXMIN_F16."""
-
-  def test_v_min3_f16_basic(self):
-    """V_MIN3_F16: minimum of three f16 values."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(3.0)),
-      s_mov_b32(s[1], f32_to_f16(1.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_min3_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 1.0, delta=0.01)
-
-  def test_v_max3_f16_basic(self):
-    """V_MAX3_F16: maximum of three f16 values."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(1.0)),
-      s_mov_b32(s[1], f32_to_f16(3.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_max3_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 3.0, delta=0.01)
-
-  def test_v_med3_f16_basic(self):
-    """V_MED3_F16: median of three f16 values."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(3.0)),
-      s_mov_b32(s[1], f32_to_f16(1.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_med3_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 2.0, delta=0.01)
-
-  def test_v_minmax_f16_basic(self):
-    """V_MINMAX_F16: clamp(src0, min=src1, max=src2)."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(2.5)),
-      s_mov_b32(s[1], f32_to_f16(1.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_minmax_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 2.0, delta=0.01)
-
-  def test_v_maxmin_f16_basic(self):
-    """V_MAXMIN_F16: clamp(src0, min=src2, max=src1)."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(0.5)),
-      s_mov_b32(s[1], f32_to_f16(2.0)),
-      s_mov_b32(s[2], f32_to_f16(1.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_maxmin_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 1.0, delta=0.01)
-
-  def test_v_min3_f16_with_neg(self):
-    """V_MIN3_F16 with neg modifier: min(-3, 1, 2) = -3."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(3.0)),
-      s_mov_b32(s[1], f32_to_f16(1.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_min3_f16(v[3], -v[0], v[1], v[2]),  # neg on first operand
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, -3.0, delta=0.01)
-
-  def test_v_max3_f16_with_abs(self):
-    """V_MAX3_F16 with abs modifier: max(|-3|, 1, 2) = 3."""
-    instructions = [
-      s_mov_b32(s[0], f32_to_f16(-3.0)),
-      s_mov_b32(s[1], f32_to_f16(1.0)),
-      s_mov_b32(s[2], f32_to_f16(2.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      v_max3_f16(v[3], abs(v[0]), v[1], v[2]),  # abs on first operand
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 3.0, delta=0.01)
-
-  def test_v_med3_f16_opsel_hi(self):
-    """V_MED3_F16 with opsel reading from hi half."""
-    # Pack two f16 values: hi=5.0, lo=1.0
-    packed = (f32_to_f16(5.0) << 16) | f32_to_f16(1.0)
-    instructions = [
-      s_mov_b32(s[0], packed),
-      s_mov_b32(s[1], f32_to_f16(3.0)),
-      s_mov_b32(s[2], f32_to_f16(4.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], s[2]),
-      # Read hi half of v[0] (5.0), med3(5, 3, 4) = 4
-      v_med3_f16(v[3], v[0].h, v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 4.0, delta=0.01)
-
-
-class TestSadHi(unittest.TestCase):
-  """Tests for V_SAD_HI_U8 instruction."""
-
-  def test_v_sad_hi_u8_basic(self):
-    """V_SAD_HI_U8: (sad << 16) + acc."""
-    # |1-5| + |2-6| + |3-7| + |4-8| = 16, << 16 = 0x100000, + 100 = 0x100064
-    instructions = [
-      v_mov_b32_e32(v[0], 0x04030201),
-      v_mov_b32_e32(v[1], 0x08070605),
-      v_mov_b32_e32(v[2], 100),
-      v_sad_hi_u8(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    self.assertEqual(st.vgpr[0][3], (16 << 16) + 100)
-
-  def test_v_sad_hi_u8_zero_diff(self):
-    """V_SAD_HI_U8: identical inputs gives acc only."""
-    instructions = [
-      v_mov_b32_e32(v[0], 0x12345678),
-      v_mov_b32_e32(v[2], 50),
-      v_sad_hi_u8(v[3], v[0], v[0], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    self.assertEqual(st.vgpr[0][3], 50)
-
-
-class TestPermlane(unittest.TestCase):
-  """Tests for V_PERMLANE16_B32 and V_PERMLANEX16_B32 instructions."""
-
-  def test_v_permlane16_b32_identity(self):
-    """V_PERMLANE16_B32 with identity permutation (lane i reads from lane i within row)."""
-    # lanesel encodes 4 bits per position: position i gets lanesel[i*4+3:i*4]
-    # Identity: position 0->0, 1->1, ..., 15->15
-    # lanesel = 0xFEDCBA9876543210 (positions 15-0 in nibbles)
-    instructions = [
-      v_mov_b32_e32(v[0], 0xDEADBEEF),  # source data
-      s_mov_b32(s[0], 0x76543210),       # lanesel low (positions 0-7)
-      s_mov_b32(s[1], 0xFEDCBA98),       # lanesel high (positions 8-15)
-      v_permlane16_b32(v[1], v[0], s[0], s[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    # Lane 0 reads from lane 0 (position 0 -> lanesel[3:0] = 0)
-    self.assertEqual(st.vgpr[0][1], 0xDEADBEEF)
-
-  def test_v_permlane16_b32_broadcast(self):
-    """V_PERMLANE16_B32 broadcast lane 0 to all lanes in row."""
-    # lanesel = all zeros -> all positions read from lane 0 within row
-    instructions = [
-      v_mov_b32_e32(v[0], 0xCAFEBABE),  # source data
-      s_mov_b32(s[0], 0),                # lanesel low = 0 (all read lane 0)
-      s_mov_b32(s[1], 0),                # lanesel high = 0
-      v_permlane16_b32(v[1], v[0], s[0], s[1]),
-    ]
-    st = run_program(instructions, n_lanes=4)
-    # All lanes read from lane 0 of their row
-    for lane in range(4):
-      self.assertEqual(st.vgpr[lane][1], 0xCAFEBABE)
-
-  def test_v_permlanex16_b32_identity(self):
-    """V_PERMLANEX16_B32 cross-row read with identity selection."""
-    # In wave32: row 0 (lanes 0-15) reads from row 1 (lanes 16-31) and vice versa
-    # With single lane in row 0, it reads from lane 0 of row 1 (lane 16)
-    # But lane 16 doesn't exist in 1-lane test, so use 32 lanes
-    instructions = [
-      v_mov_b32_e32(v[0], 0x11111111),  # All lanes have this initially
-      s_mov_b32(s[0], 0x76543210),       # lanesel low
-      s_mov_b32(s[1], 0xFEDCBA98),       # lanesel high
-      v_permlanex16_b32(v[1], v[0], s[0], s[1]),
-    ]
-    st = run_program(instructions, n_lanes=32)
-    # Lane 0 in row 0 reads from lane 0 of row 1 (lane 16)
-    self.assertEqual(st.vgpr[0][1], 0x11111111)
-    # Lane 16 in row 1 reads from lane 0 of row 0 (lane 0)
-    self.assertEqual(st.vgpr[16][1], 0x11111111)
-
-
 if __name__ == '__main__':
  unittest.main()
@@ -149,6 +149,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mix_f32_src2_f16_lo(self):
    """V_FMA_MIX_F32 with src2 as f16 from lo bits."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_2 = f32_to_f16(2.0)
    instructions = [
      s_mov_b32(s[0], f2i(1.0)),
@@ -165,6 +166,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mix_f32_src2_f16_hi(self):
    """V_FMA_MIX_F32 with src2 as f16 from hi bits."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_2 = f32_to_f16(2.0)
    val = (f16_2 << 16) | 0
    instructions = [
@@ -197,6 +199,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mix_f32_with_abs_f16_src2_lo(self):
    """V_FMA_MIX_F32 with abs modifier on f16 src2 (lo half). Regression test for sin(1.0) bug."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_neg1 = f32_to_f16(-1.0)  # 0xbc00
    instructions = [
      s_mov_b32(s[0], f2i(0.0)),  # src0 = 0.0 (f32)
@@ -214,6 +217,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mix_f32_with_neg_f16_src2_lo(self):
    """V_FMA_MIX_F32 with neg modifier on f16 src2 (lo half)."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_1 = f32_to_f16(1.0)  # 0x3c00
    instructions = [
      s_mov_b32(s[0], f2i(0.0)),  # src0 = 0.0 (f32)
@@ -231,6 +235,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mix_f32_with_abs_f16_src2_hi(self):
    """V_FMA_MIX_F32 with abs modifier on f16 src2 (hi half)."""
+    from extra.assembly.amd.test.hw.helpers import f32_to_f16
    f16_neg1 = f32_to_f16(-1.0)  # 0xbc00
    val = (f16_neg1 << 16) | 0  # -1.0 in hi, 0 in lo
    instructions = [
@@ -249,6 +254,7 @@ class TestFmaMix(unittest.TestCase):

  def test_v_fma_mixlo_f16(self):
    """V_FMA_MIXLO_F16 writes to low 16 bits of destination."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], f2i(2.0)),
      v_mov_b32_e32(v[0], s[0]),
@@ -261,13 +267,14 @@ class TestFmaMix(unittest.TestCase):
      VOP3P(VOP3POp.V_FMA_MIXLO_F16, vdst=v[3], src0=v[0], src1=v[1], src2=v[2], opsel=0, opsel_hi=0, opsel_hi2=0),
    ]
    st = run_program(instructions, n_lanes=1)
-    lo = f16(st.vgpr[0][3] & 0xffff)
+    lo = _f16(st.vgpr[0][3] & 0xffff)
    hi = (st.vgpr[0][3] >> 16) & 0xffff
    self.assertAlmostEqual(lo, 7.0, places=1)
    self.assertEqual(hi, 0xdead, f"hi should be preserved, got 0x{hi:04x}")

  def test_v_fma_mixlo_f16_all_f32_sources(self):
    """V_FMA_MIXLO_F16 with all f32 sources."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], f2i(1.0)),
      v_mov_b32_e32(v[0], s[0]),
@@ -279,12 +286,13 @@ class TestFmaMix(unittest.TestCase):
      VOP3P(VOP3POp.V_FMA_MIXLO_F16, vdst=v[3], src0=v[0], src1=v[1], src2=v[2], opsel=0, opsel_hi=0, opsel_hi2=0),
    ]
    st = run_program(instructions, n_lanes=1)
-    lo = f16(st.vgpr[0][3] & 0xffff)
+    lo = _f16(st.vgpr[0][3] & 0xffff)
    # 1*2+3 = 5
    self.assertAlmostEqual(lo, 5.0, places=1)

  def test_v_fma_mixlo_f16_sin_case(self):
    """V_FMA_MIXLO_F16 case from sin kernel."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x3f800000),  # f32 1.0
      v_mov_b32_e32(v[3], s[0]),
@@ -297,7 +305,7 @@ class TestFmaMix(unittest.TestCase):
      VOP3P(VOP3POp.V_FMA_MIXLO_F16, vdst=v[3], src0=v[3], src1=s[6], src2=v[5], opsel=0, opsel_hi=0, opsel_hi2=0),
    ]
    st = run_program(instructions, n_lanes=1)
-    lo = f16(st.vgpr[0][3] & 0xffff)
+    lo = _f16(st.vgpr[0][3] & 0xffff)
    self.assertAlmostEqual(lo, -3.14159, delta=0.01)


@@ -306,6 +314,7 @@ class TestVOP3P(unittest.TestCase):

  def test_v_pk_add_f16_basic(self):
    """V_PK_ADD_F16 adds two packed f16 values."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x40003c00),  # hi=2.0, lo=1.0
      s_mov_b32(s[1], 0x44004200),  # hi=4.0, lo=3.0
@@ -315,13 +324,14 @@ class TestVOP3P(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][2]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    self.assertAlmostEqual(lo, 4.0, places=2)
    self.assertAlmostEqual(hi, 6.0, places=2)

  def test_v_pk_mul_f16_basic(self):
    """V_PK_MUL_F16 multiplies two packed f16 values."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x42004000),  # hi=3.0, lo=2.0
      s_mov_b32(s[1], 0x45004400),  # hi=5.0, lo=4.0
@@ -331,13 +341,14 @@ class TestVOP3P(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][2]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    self.assertAlmostEqual(lo, 8.0, places=1)
    self.assertAlmostEqual(hi, 15.0, places=1)

  def test_v_pk_fma_f16_basic(self):
    """V_PK_FMA_F16: D = A * B + C for packed f16."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x42004000),  # A: hi=3.0, lo=2.0
      s_mov_b32(s[1], 0x45004400),  # B: hi=5.0, lo=4.0
@@ -349,8 +360,8 @@ class TestVOP3P(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][3]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    self.assertAlmostEqual(lo, 9.0, places=1)   # 2*4+1
    self.assertAlmostEqual(hi, 16.0, places=0)  # 3*5+1

@@ -359,6 +370,7 @@ class TestVOP3P(unittest.TestCase):
    Inline constants for VOP3P are f16 values in the low 16 bits only.
    hi half of inline constant is 0, so hi result = v0.hi + 0 = 1.0.
    """
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x3c003c00),  # packed f16: hi=1.0, lo=1.0
      v_mov_b32_e32(v[0], s[0]),
@@ -366,8 +378,8 @@ class TestVOP3P(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][1]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    # lo = 1.0 + 1.0 = 2.0, hi = 1.0 + 0.0 = 1.0 (inline const hi half is 0)
    self.assertAlmostEqual(lo, 2.0, places=2)
    self.assertAlmostEqual(hi, 1.0, places=2)
@@ -376,6 +388,7 @@ class TestVOP3P(unittest.TestCase):
    """V_PK_MUL_F16 with inline constant POS_TWO (2.0).
    Inline constant has value only in low 16 bits, hi is 0.
    """
+    from extra.assembly.amd.test.hw.helpers import _f16
    # v0 = packed (3.0, 4.0), multiply by POS_TWO
    # lo = 3.0 * 2.0 = 6.0, hi = 4.0 * 0.0 = 0.0 (inline const hi is 0)
    instructions = [
@@ -385,8 +398,8 @@ class TestVOP3P(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][1]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    self.assertAlmostEqual(lo, 6.0, places=1)
    self.assertAlmostEqual(hi, 0.0, places=1)

@@ -400,6 +413,7 @@ class TestWMMAF16(unittest.TestCase):

  def test_v_wmma_f16_16x16x16_f16_all_ones(self):
    """V_WMMA_F16_16X16X16_F16 with all ones produces 16.0 in f16."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = []
    instructions.append(s_mov_b32(s[0], 0x3c003c00))  # packed f16 1.0
    # Initialize A matrix in v[16:23] (8 regs)
@@ -418,12 +432,13 @@ class TestWMMAF16(unittest.TestCase):
    for lane in range(32):
      for reg in range(8):
        result = st.vgpr[lane][reg]
-        lo = f16(result & 0xffff)
+        lo = _f16(result & 0xffff)
        self.assertAlmostEqual(lo, 16.0, places=1, msg=f"v[{reg}] lane {lane}: expected 16.0, got {lo}")
        self.assertEqual(result >> 16, 0, msg=f"v[{reg}] lane {lane}: hi bits should be 0")

  def test_v_wmma_f16_16x16x16_f16_with_accumulator(self):
    """V_WMMA_F16_16X16X16_F16 with non-zero accumulator."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = []
    instructions.append(s_mov_b32(s[0], 0x3c003c00))  # packed f16 1.0
    instructions.append(s_mov_b32(s[1], 0x4500))  # f16 5.0 in lo bits only
@@ -443,7 +458,7 @@ class TestWMMAF16(unittest.TestCase):
    for lane in range(32):
      for reg in range(8):
        result = st.vgpr[lane][reg]
-        lo = f16(result & 0xffff)
+        lo = _f16(result & 0xffff)
        self.assertAlmostEqual(lo, 21.0, places=0, msg=f"v[{reg}] lane {lane}: expected 21.0, got {lo}")
        self.assertEqual(result >> 16, 0, msg=f"v[{reg}] lane {lane}: hi bits should be 0")

@@ -453,6 +468,7 @@ class TestWMMAF16(unittest.TestCase):
    Regression test: WMMA was using static register indices instead of dynamic.
    This test uses v[64:71] for A, v[80:87] for B, v[96:103] for C/D.
    """
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = []
    instructions.append(s_mov_b32(s[0], 0x3c003c00))  # packed f16 1.0
    # Initialize A matrix in v[64:71] (8 regs)
@@ -474,7 +490,7 @@ class TestWMMAF16(unittest.TestCase):
    for lane in range(32):
      for reg in range(8):
        result = st.vgpr[lane][reg]
-        lo = f16(result & 0xffff)
+        lo = _f16(result & 0xffff)
        self.assertAlmostEqual(lo, 16.0, places=1, msg=f"v[{reg}] lane {lane}: expected 16.0, got {lo}")
        self.assertEqual(result >> 16, 0, msg=f"v[{reg}] lane {lane}: hi bits should be 0")

@@ -697,6 +713,7 @@ class TestPackedMixedSigns(unittest.TestCase):

  def test_pk_add_f16_mixed_signs(self):
    """V_PK_ADD_F16 with mixed positive/negative values."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0xc0003c00),  # packed: hi=-2.0, lo=1.0
      s_mov_b32(s[1], 0x3c003c00),  # packed: hi=1.0, lo=1.0
@@ -706,13 +723,14 @@ class TestPackedMixedSigns(unittest.TestCase):
    ]
    st = run_program(instructions, n_lanes=1)
    result = st.vgpr[0][2]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
+    lo = _f16(result & 0xffff)
+    hi = _f16((result >> 16) & 0xffff)
    self.assertAlmostEqual(lo, 2.0, places=2)   # 1.0 + 1.0
    self.assertAlmostEqual(hi, -1.0, places=2)  # -2.0 + 1.0

  def test_pk_mul_f16_zero(self):
    """V_PK_MUL_F16 with zero."""
+    from extra.assembly.amd.test.hw.helpers import _f16
    instructions = [
      s_mov_b32(s[0], 0x40004000),  # packed: 2.0, 2.0
      s_mov_b32(s[1], 0x00000000),  # packed: 0.0, 0.0
@@ -725,277 +743,5 @@ class TestPackedMixedSigns(unittest.TestCase):
    self.assertEqual(result, 0x00000000, "2.0 * 0.0 should be 0.0")


-class TestDot2F32F16(unittest.TestCase):
-  """Tests for V_DOT2_F32_F16 - dot product of f16 pairs producing f32."""
-
-  def test_v_dot2_f32_f16_basic(self):
-    """V_DOT2_F32_F16: dot product of two packed f16 pairs -> f32."""
-    # src0 = {hi=2.0, lo=1.0}, src1 = {hi=4.0, lo=3.0}
-    # result = 1.0*3.0 + 2.0*4.0 + 0 = 3 + 8 = 11.0
-    src0 = (f32_to_f16(2.0) << 16) | f32_to_f16(1.0)
-    src1 = (f32_to_f16(4.0) << 16) | f32_to_f16(3.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot2_f32_f16(v[3], v[0], v[1], v[2], opsel_hi=3, opsel_hi2=1),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = i2f(st.vgpr[0][3])
-    self.assertAlmostEqual(result, 11.0, places=2)
-
-  def test_v_dot2_f32_f16_with_accumulator(self):
-    """V_DOT2_F32_F16 with non-zero f32 accumulator."""
-    # src0 = {hi=1.0, lo=1.0}, src1 = {hi=1.0, lo=1.0}, acc = 5.0
-    # result = 1.0*1.0 + 1.0*1.0 + 5.0 = 7.0
-    src0 = (f32_to_f16(1.0) << 16) | f32_to_f16(1.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], f2i(5.0)),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[0]),  # same as src0
-      v_mov_b32_e32(v[2], s[1]),
-      v_dot2_f32_f16(v[3], v[0], v[1], v[2], opsel_hi=3, opsel_hi2=1),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = i2f(st.vgpr[0][3])
-    self.assertAlmostEqual(result, 7.0, places=2)
-
-  def test_v_dot2_f32_f16_negative_values(self):
-    """V_DOT2_F32_F16 with negative f16 values."""
-    # src0 = {hi=-2.0, lo=3.0}, src1 = {hi=1.0, lo=2.0}
-    # result = 3.0*2.0 + (-2.0)*1.0 + 0 = 6 - 2 = 4.0
-    # NOTE: Hardware DOT2 may have up to 1 ULP difference due to internal implementation
-    src0 = (f32_to_f16(-2.0) << 16) | f32_to_f16(3.0)
-    src1 = (f32_to_f16(1.0) << 16) | f32_to_f16(2.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot2_f32_f16(v[3], v[0], v[1], v[2], opsel_hi=3, opsel_hi2=1),
-    ]
-    st = run_program(instructions, n_lanes=1, ulp_tolerance=1)
-    result = i2f(st.vgpr[0][3])
-    self.assertAlmostEqual(result, 4.0, places=2)
-
-
-class TestDot2F16F16(unittest.TestCase):
-  """Tests for V_DOT2_F16_F16 - dot product of f16 pairs producing f16."""
-
-  def test_v_dot2_f16_f16_basic(self):
-    """V_DOT2_F16_F16: dot product of two packed f16 pairs -> f16."""
-    # src0 = {hi=2.0, lo=1.0}, src1 = {hi=3.0, lo=2.0}
-    # result = 1.0*2.0 + 2.0*3.0 + 0 = 2 + 6 = 8.0 (f16)
-    src0 = (f32_to_f16(2.0) << 16) | f32_to_f16(1.0)
-    src1 = (f32_to_f16(3.0) << 16) | f32_to_f16(2.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot2_f16_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 8.0, places=1)
-
-  def test_v_dot2_f16_f16_with_accumulator(self):
-    """V_DOT2_F16_F16 with non-zero f16 accumulator."""
-    # src0 = {hi=1.0, lo=1.0}, src1 = {hi=1.0, lo=1.0}, acc = 3.0 (f16)
-    # result = 1.0*1.0 + 1.0*1.0 + 3.0 = 5.0 (f16)
-    src0 = (f32_to_f16(1.0) << 16) | f32_to_f16(1.0)
-    acc = f32_to_f16(3.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[2], acc),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[0]),  # same as src0
-      v_mov_b32_e32(v[2], s[2]),
-      v_dot2_f16_f16(v[3], v[0], v[1], v[2]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = f16(st.vgpr[0][3] & 0xffff)
-    self.assertAlmostEqual(result, 5.0, places=1)
-
-
-class TestSignedDotProducts(unittest.TestCase):
-  """Tests for V_DOT4_I32_IU8 and V_DOT8_I32_IU4 with signed inputs."""
-
-  def test_v_dot4_i32_iu8_signed_both(self):
-    """V_DOT4_I32_IU8 with both inputs signed (neg=0b011)."""
-    # src0 = {-1, -2, 3, 4} as i8 = {0xff, 0xfe, 0x03, 0x04}
-    # src1 = {1, 1, 1, 1} as i8
-    # result = (-1)*1 + (-2)*1 + 3*1 + 4*1 = -1 - 2 + 3 + 4 = 4
-    src0 = (0xff << 24) | (0xfe << 16) | (0x03 << 8) | 0x04  # -1, -2, 3, 4
-    src1 = 0x01010101  # 1, 1, 1, 1
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot4_i32_iu8(v[3], v[0], v[1], v[2], neg=0b011),  # both signed
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][3]
-    # Result is i32, interpret as signed
-    if result >= 0x80000000:
-      result = result - 0x100000000
-    self.assertEqual(result, 4)
-
-  def test_v_dot4_i32_iu8_src0_signed(self):
-    """V_DOT4_I32_IU8 with only src0 signed (neg=0b001)."""
-    # src0 = {-1, -1, -1, -1} as i8 = {0xff, 0xff, 0xff, 0xff}
-    # src1 = {2, 2, 2, 2} as u8
-    # result = (-1)*2 + (-1)*2 + (-1)*2 + (-1)*2 = -8
-    src0 = 0xffffffff  # -1, -1, -1, -1 (as i8)
-    src1 = 0x02020202  # 2, 2, 2, 2 (as u8)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot4_i32_iu8(v[3], v[0], v[1], v[2], neg=0b001),  # src0 signed
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][3]
-    if result >= 0x80000000:
-      result = result - 0x100000000
-    self.assertEqual(result, -8)
-
-  def test_v_dot4_i32_iu8_src1_signed(self):
-    """V_DOT4_I32_IU8 with only src1 signed (neg=0b010)."""
-    # src0 = {2, 2, 2, 2} as u8
-    # src1 = {-1, -1, -1, -1} as i8 = {0xff, 0xff, 0xff, 0xff}
-    # result = 2*(-1) + 2*(-1) + 2*(-1) + 2*(-1) = -8
-    src0 = 0x02020202  # 2, 2, 2, 2 (as u8)
-    src1 = 0xffffffff  # -1, -1, -1, -1 (as i8)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot4_i32_iu8(v[3], v[0], v[1], v[2], neg=0b010),  # src1 signed
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][3]
-    if result >= 0x80000000:
-      result = result - 0x100000000
-    self.assertEqual(result, -8)
-
-  def test_v_dot4_i32_iu8_unsigned_as_reference(self):
-    """V_DOT4_I32_IU8 with both unsigned (neg=0) - same as V_DOT4_U32_U8."""
-    # src0 = {0xff, 0xff, 0xff, 0xff} = 255 each as u8
-    # src1 = {1, 1, 1, 1}
-    # result = 255*1 + 255*1 + 255*1 + 255*1 = 1020
-    src0 = 0xffffffff
-    src1 = 0x01010101
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot4_i32_iu8(v[3], v[0], v[1], v[2], neg=0),  # both unsigned
-    ]
-    st = run_program(instructions, n_lanes=1)
-    self.assertEqual(st.vgpr[0][3], 1020)
-
-  def test_v_dot8_i32_iu4_signed_both(self):
-    """V_DOT8_I32_IU4 with both inputs signed (neg=0b011)."""
-    # src0 = 8 nibbles: {-1, -2, 3, 4, -1, -2, 3, 4} as i4
-    # i4 -1 = 0xf, -2 = 0xe, 3 = 0x3, 4 = 0x4
-    # src0 = 0xfe34fe34
-    # src1 = {1, 1, 1, 1, 1, 1, 1, 1} as i4 = 0x11111111
-    # result = 2 * ((-1)*1 + (-2)*1 + 3*1 + 4*1) = 2 * 4 = 8
-    src0 = 0xfe34fe34
-    src1 = 0x11111111
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot8_i32_iu4(v[3], v[0], v[1], v[2], neg=0b011),  # both signed
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][3]
-    if result >= 0x80000000:
-      result = result - 0x100000000
-    self.assertEqual(result, 8)
-
-  def test_v_dot8_i32_iu4_all_negative(self):
-    """V_DOT8_I32_IU4 with all negative signed values."""
-    # src0 = 8 nibbles all -1 (0xf) = 0xffffffff
-    # src1 = 8 nibbles all 1 = 0x11111111
-    # result = 8 * ((-1)*1) = -8
-    src0 = 0xffffffff  # all -1 as i4
-    src1 = 0x11111111  # all 1
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_mov_b32_e32(v[2], 0),
-      v_dot8_i32_iu4(v[3], v[0], v[1], v[2], neg=0b011),  # both signed
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][3]
-    if result >= 0x80000000:
-      result = result - 0x100000000
-    self.assertEqual(result, -8)
-
-
-class TestPkMinMaxF16(unittest.TestCase):
-  """Tests for V_PK_MIN_F16 and V_PK_MAX_F16."""
-
-  def test_v_pk_min_f16_basic(self):
-    """V_PK_MIN_F16: packed min of two f16 pairs."""
-    # src0 = {hi=3.0, lo=1.0}, src1 = {hi=2.0, lo=4.0}
-    # result = {min(3,2)=2, min(1,4)=1}
-    src0 = (f32_to_f16(3.0) << 16) | f32_to_f16(1.0)
-    src1 = (f32_to_f16(2.0) << 16) | f32_to_f16(4.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_pk_min_f16(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
-    self.assertAlmostEqual(lo, 1.0, delta=0.01)
-    self.assertAlmostEqual(hi, 2.0, delta=0.01)
-
-  def test_v_pk_max_f16_basic(self):
-    """V_PK_MAX_F16: packed max of two f16 pairs."""
-    # src0 = {hi=3.0, lo=1.0}, src1 = {hi=2.0, lo=4.0}
-    # result = {max(3,2)=3, max(1,4)=4}
-    src0 = (f32_to_f16(3.0) << 16) | f32_to_f16(1.0)
-    src1 = (f32_to_f16(2.0) << 16) | f32_to_f16(4.0)
-    instructions = [
-      s_mov_b32(s[0], src0),
-      s_mov_b32(s[1], src1),
-      v_mov_b32_e32(v[0], s[0]),
-      v_mov_b32_e32(v[1], s[1]),
-      v_pk_max_f16(v[2], v[0], v[1]),
-    ]
-    st = run_program(instructions, n_lanes=1)
-    result = st.vgpr[0][2]
-    lo = f16(result & 0xffff)
-    hi = f16((result >> 16) & 0xffff)
-    self.assertAlmostEqual(lo, 4.0, delta=0.01)
-    self.assertAlmostEqual(hi, 3.0, delta=0.01)
-
-
 if __name__ == '__main__':
  unittest.main()
@@ -180,7 +180,7 @@ class TestDSPcodePatterns(unittest.TestCase):
  def test_mem_read_parsing(self):
    """Test MEM[addr].type read expression parsing."""
    # Create a mock LDS buffer
-    lds = UOp(Ops.PARAM, dtypes.uint32.ptr(16384), arg=3)
+    lds = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(16384), arg=3)
    addr = UOp.const(dtypes.uint32, 0)
    vars = {'_lds': lds, 'ADDR': addr, 'OFFSET': UOp.const(dtypes.uint32, 0)}

@@ -213,7 +213,7 @@ class TestDSPcodePatterns(unittest.TestCase):
    """Test DS_LOAD_2ADDR_B32 pcode parsing produces RETURN_DATA assignments."""
    pcode = PCODE.get(DSOp.DS_LOAD_2ADDR_B32)
    self.assertIsNotNone(pcode)
-    lds = UOp(Ops.PARAM, dtypes.uint32.ptr(16384), arg=3)
+    lds = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(16384), arg=3)
    srcs = {
      'ADDR': UOp.const(dtypes.uint32, 0),
      'OFFSET0': UOp.const(dtypes.uint32, 0),
@@ -286,7 +286,7 @@ class TestAllPcode(unittest.TestCase):
  def _make_srcs(self):
    """Create dummy source variables for pcode parsing."""
    u32, u64 = lambda v=0: UOp.const(dtypes.uint32, v), lambda v=0: UOp.const(dtypes.uint64, v)
-    lds = UOp(Ops.PARAM, dtypes.uint32.ptr(16384), arg=3)
+    lds = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(16384), arg=3)
    return {'laneId': u32(), 'laneID': u32(), 'S0': u32(), 'S1': u32(), 'S2': u32(), 'S3': u32(), 'SRC0': u32(),
            'D0': u32(), 'D1': u32(), 'DST': u32(), 'VDST': u32(), 'SDST': u32(),
            'VCC': u64(), 'VCCZ': u32(), 'EXEC': u64(), 'EXEC_LO': u32(), 'EXECZ': u32(), 'SCC': u32(),
@@ -294,7 +294,7 @@ class TestAllPcode(unittest.TestCase):
            'ADDR': u32(), 'ADDR_BASE': u32(), 'TADDR': u32(), 'DATA': u32(), 'DATA0': u32(), 'DATA1': u32(), 'DATA2': u32(),
            'VDATA': u32(), 'VDATA0': u32(), 'VDATA1': u32(), 'VDATA2': u32(), 'VDATA3': u32(),
            'OPSEL': u32(), 'OPSEL_HI': u32(), 'NEG': u32(), 'NEG_HI': u32(), 'CLAMP': u32(),
-            'M0': u32(), 'PC': u64(), 'DENORM': u32(1), 'ROUND_MODE': u32(), 'ROUND_TOWARD_ZERO': u32(), 'ROUND_NEAREST_EVEN': u32(), 'WAVE_STATUS': u32(),
+            'M0': u32(), 'PC': u64(), 'DENORM': u32(1), 'ROUND_MODE': u32(), 'WAVE_STATUS': u32(),
            'MAX_FLOAT_F32': u32(0x7f7fffff), 'Unsigned': u32(1), 'clampedLOD': u32(),
            '_lds': lds, '_vmem': lds, '_active': UOp.const(dtypes.bool, True)}

@@ -1,98 +0,0 @@
-import unittest, ctypes
-from extra.assembly.amd.autogen.rdna4 import ins as ir4
-from extra.assembly.amd.dsl import v, s
-from extra.assembly.amd.emu import WaveState, decode_program
-from tinygrad.device import Buffer, BufferSpec
-from tinygrad.dtype import dtypes
-
-class TestRDNA4Emu(unittest.TestCase):
-  def _run(self, insts: list, sgprs: dict[int, int] = None, vgprs: dict[tuple[int, int], int] = None) -> WaveState:
-    """Run instructions and return final WaveState."""
-    # Add S_ENDPGM if not present
-    if not any(isinstance(i, ir4.SOPP) and i.op == ir4.SOPPOp.S_ENDPGM for i in insts):
-      insts = list(insts) + [ir4.SOPP(ir4.SOPPOp.S_ENDPGM, simm=0)]
-
-    # Assemble and decode
-    code = b''.join(i.to_bytes() for i in insts)
-    code_buf = (ctypes.c_uint8 * len(code)).from_buffer_copy(code)
-    code_addr = ctypes.addressof(code_buf)
-    program_raw = decode_program(code, "rdna4")
-    program = {code_addr + offset: val for offset, val in program_raw.items()}
-
-    # Setup wave state
-    st = WaveState(n_lanes=1)
-    st.pc = code_addr
-    if sgprs:
-      for idx, val in sgprs.items(): st._write_sgpr(idx, val)
-    if vgprs:
-      for (reg, lane), val in vgprs.items(): st._write_vgpr(reg, lane, val)
-
-    # Setup vmem buffer with external_ptr=0 (maps to address 0, allows any pointer access)
-    vmem_buf = Buffer('CPU', 1 << 40, dtypes.uint32, options=BufferSpec(external_ptr=0)).ensure_allocated()
-
-    # Execute
-    c_bufs = [ctypes.c_uint64(st.sgpr_buf._buf.va_addr), ctypes.c_uint64(st.vgpr_buf._buf.va_addr),
-              ctypes.c_uint64(vmem_buf._buf.va_addr), ctypes.c_uint64(0), ctypes.c_uint64(0)]
-    for _ in range(100):
-      if (pc := st.pc) == 0xFFFFFFFFFFFFFFFF or pc not in program: break
-      _, fxn, globals_list, _ = program[pc]
-      fxn(*[c_bufs[g] for g in globals_list])
-    return st
-
-  def test_vopd_dual_mov(self):
-    """Test VOPD with two V_DUAL_MOV_B32 operations: v[1]=s[1], v[2]=s[2]."""
-    insts = [ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-                      vdstx=v[1], vdsty=v[2], srcx0=s[1], srcy0=s[2], vsrcx1=v[0], vsrcy1=v[0])]
-    st = self._run(insts, sgprs={1: 0x40e00000, 2: 0x41100000})  # 7.0f, 9.0f
-    self.assertEqual(st._read_vgpr(1, 0), 0x40e00000)  # v[1] = 7.0
-    self.assertEqual(st._read_vgpr(2, 0), 0x41100000)  # v[2] = 9.0
-
-  def test_vopd_dual_mov_after_other_vopd(self):
-    """Test VOPD reuse: first VOPD(v[3]=0, v[0]=?), then VOPD(v[1]=s[1], v[2]=s[2])."""
-    # This matches the BEAM kernel sequence that fails
-    insts = [
-      ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-               vdstx=v[3], vdsty=v[0], srcx0=0, srcy0=s[0], vsrcx1=v[0], vsrcy1=v[0]),  # v[3]=0, v[0]=s[0]
-      ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-               vdstx=v[1], vdsty=v[2], srcx0=s[1], srcy0=s[2], vsrcx1=v[0], vsrcy1=v[0]),  # v[1]=s[1], v[2]=s[2]
-    ]
-    st = self._run(insts, sgprs={0: 0x40a00000, 1: 0x40e00000, 2: 0x41100000})  # 5.0f, 7.0f, 9.0f
-    self.assertEqual(st._read_vgpr(1, 0), 0x40e00000)  # v[1] = 7.0
-    self.assertEqual(st._read_vgpr(2, 0), 0x41100000)  # v[2] = 9.0
-
-  def test_vopd_with_s_add_f32_sequence(self):
-    """Test full BEAM kernel sequence: s_add_f32 then VOPD."""
-    # This is the exact sequence from the failing BEAM kernel
-    insts = [
-      ir4.SOP2(ir4.SOP2Op.S_ADD_F32, sdst=s[0], ssrc0=s[0], ssrc1=s[8]),   # s[0] = s[0] + s[8]
-      ir4.SOP2(ir4.SOP2Op.S_ADD_F32, sdst=s[1], ssrc0=s[1], ssrc1=s[9]),   # s[1] = s[1] + s[9]
-      ir4.SOP2(ir4.SOP2Op.S_ADD_F32, sdst=s[2], ssrc0=s[2], ssrc1=s[10]),  # s[2] = s[2] + s[10]
-      ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-               vdstx=v[3], vdsty=v[0], srcx0=0, srcy0=s[0], vsrcx1=v[0], vsrcy1=v[0]),
-      ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-               vdstx=v[1], vdsty=v[2], srcx0=s[1], srcy0=s[2], vsrcx1=v[0], vsrcy1=v[0]),
-    ]
-    # Input: s[0:2] = [1,2,3], s[8:10] = [4,5,6]
-    # After s_add_f32: s[0:2] = [5,7,9]
-    st = self._run(insts, sgprs={0: 0x3f800000, 1: 0x40000000, 2: 0x40400000,  # 1.0, 2.0, 3.0
-                                  8: 0x40800000, 9: 0x40a00000, 10: 0x40c00000})  # 4.0, 5.0, 6.0
-    self.assertEqual(st._read_vgpr(1, 0), 0x40e00000)  # v[1] = 7.0
-    self.assertEqual(st._read_vgpr(2, 0), 0x41100000)  # v[2] = 9.0
-
-  def test_s_mov_b32_then_vopd(self):
-    """Test s_mov_b32 followed by VOPD - simulates BEAM kernel sequence."""
-    # Use s_mov_b32 with SGPR source (copy from pre-initialized SGPRs)
-    # s[10:12] will have values set by test harness, copy to s[0:2], then VOPD to VGPRs
-    insts = [
-      ir4.SOP1(ir4.SOP1Op.S_MOV_B32, sdst=s[0], ssrc0=s[10]),  # s[0] = s[10]
-      ir4.SOP1(ir4.SOP1Op.S_MOV_B32, sdst=s[1], ssrc0=s[11]),  # s[1] = s[11]
-      ir4.SOP1(ir4.SOP1Op.S_MOV_B32, sdst=s[2], ssrc0=s[12]),  # s[2] = s[12]
-      ir4.VOPD(ir4.VOPDOp.V_DUAL_MOV_B32, ir4.VOPDOp.V_DUAL_MOV_B32,
-               vdstx=v[1], vdsty=v[2], srcx0=s[1], srcy0=s[2], vsrcx1=v[0], vsrcy1=v[0]),
-    ]
-    st = self._run(insts, sgprs={10: 0x40a00000, 11: 0x40e00000, 12: 0x41100000})  # 5.0, 7.0, 9.0
-    self.assertEqual(st._read_vgpr(1, 0), 0x40e00000)  # v[1] = 7.0
-    self.assertEqual(st._read_vgpr(2, 0), 0x41100000)  # v[2] = 9.0
-
-if __name__ == '__main__':
-  unittest.main()
@@ -203,12 +203,12 @@ class SQTTExamplesTestBase(unittest.TestCase):
 class TestSQTTExamplesRDNA3(SQTTExamplesTestBase):
  target = "gfx1100"
  expected = {
-    "profile_empty_run_0": [1844, 1885, 1905, 1956, 1983, 1889],
-    "profile_empty_run_1": [1780, 1885, 1905, 1956, 1983, 1889],
-    "profile_gemm_run_0": [2656, 2025, 2045, 2096, 2123, 2029, 3183, 2019, 2039, 2090, 2117, 2023, 19119, 2013, 2033, 2084, 2111, 2017],
-    "profile_gemm_run_1": [2662, 2025, 2045, 2096, 2123, 2029, 3179, 2019, 2039, 2090, 2117, 2023, 19113, 2071, 2091, 2142, 2169, 2075],
-    "profile_plus_run_0": [1886, 2013, 2033, 2084, 2111, 2017],
-    "profile_plus_run_1": [1988, 2071, 2091, 2142, 2169, 2075],
+    "profile_empty_run_0": [1803, 1908, 1928, 1979, 2006, 1912],
+    "profile_empty_run_1": [1803, 1908, 1928, 1979, 2006, 1912],
+    "profile_gemm_run_0": [2531, 1844, 1864, 1915, 1942, 1848, 3074, 1919, 1939, 1990, 2017, 1923, 19026, 1919, 1939, 1990, 2017, 1929],
+    "profile_gemm_run_1": [2554, 1844, 1864, 1915, 1942, 1848, 3084, 1919, 1939, 1990, 2017, 1923, 19010, 1919, 1939, 1990, 2017, 1923],
+    "profile_plus_run_0": [1900, 1908, 1928, 1979, 2006, 1912],
+    "profile_plus_run_1": [1856, 1908, 1928, 1979, 2006, 1912],
  }

 class TestSQTTExamplesRDNA4(SQTTExamplesTestBase): target = "gfx1200"
@@ -471,7 +471,7 @@ THREADS = 128

 def test_matmul():
  dev = Device[Device.DEFAULT]
-  print(f"Device arch: {dev.renderer.arch}")
+  print(f"Device arch: {dev.arch}")

  if getenv("STOCK", 0):
    # Load the stock kernel from amd_seb/kernel8_batched_gmem.s
@@ -479,7 +479,7 @@ def test_matmul():
    asm = stock_path.read_text()
    print(f"Loaded stock kernel from {stock_path}")
  else:
-    asm = build_kernel(dev.renderer.arch)
+    asm = build_kernel(dev.arch)

  binary = dev.compiler.compile(asm)
  print(f"Compiled! Binary size: {len(binary)} bytes")
@@ -1,95 +0,0 @@
-import atexit, functools
-from tinygrad.runtime.support.compiler_amd import HIPCompiler
-from tinygrad import Tensor, Device, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from tinygrad.renderer import Estimates
-from tinygrad.helpers import getenv, all_same, dedup
-from extra.gemm.asm.cdna.asm import build_kernel, GEMM_ARGS
-
-# ** CDNA4 assembly gemm
-
-WORKGROUP_SIZE = 256
-
-def custom_asm_gemm(C:UOp, A:UOp, B:UOp, dname:str, arch:str, wg:int) -> UOp:
-  batch, M, K = A.shape
-  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
-  assert K == K2
-  lidx = UOp.special(WORKGROUP_SIZE, "lidx0")
-  gidx = UOp.special(wg, "gidx0")
-  k = build_kernel(batch, M, N, K, A.dtype.base)
-  sink = UOp.sink(C.base, A.base, B.base, lidx, gidx,
-                  arg=KernelInfo(name=k.name, estimates=Estimates(ops=2*batch*M*N*K, mem=(batch*M*K + K*N + batch*M*N)*2)))
-  binary = HIPCompiler(arch).compile(k.to_asm())
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=dname), UOp(Ops.LINEAR, src=(*sink.src, sink)),
-                               UOp(Ops.SOURCE, arg=k.to_text()), UOp(Ops.BINARY, arg=binary)))
-
-counters = {"used":0, "todos":[]}
-def todo(msg:str) -> bool: counters["todos"].append(msg); return False
-atexit.register(lambda: print(f'asm_gemm: {counters["used"]} used, {len(counters["todos"])} not used'))
-
-def can_use_asm_gemm(a:Tensor, b:Tensor) -> bool:
-  if a.dtype != b.dtype: return todo(f"dtypes must match {a.dtype} != {b.dtype}")
-  if a.dtype not in {dtypes.bfloat16, dtypes.float16}: return todo(f"only bfloat16/float16, got {a.dtype}")
-  # only sharding on the batch is tested, others might work too
-  if isinstance(a.device, tuple) and not (a.ndim == 3 and a.uop.axis == 0 and b.uop.axis is None):
-    return todo(f"sharding mismatch a.ndim={a.ndim} a.uop.axis={a.uop.axis} b.uop.axis={b.uop.axis}")
-  batch, M, K = (1, *a.shape) if a.ndim == 2 else a.shape
-  N = b.shape[1]
-  if isinstance(a.device, tuple): batch //= len(a.device)
-  if batch not in {1, 2}: return todo(f"GEMM batch size {batch}")
-  if (key:=(M, N, K)) not in GEMM_ARGS: return todo(f"GEMM shape not supported {key}")
-  return True
-
-# ** UOp gemm to test Tensor.custom_kernel multi and backward correctness on non cdna4
-# note: this can be removed after we have GEMM on mixins
-
-def custom_uop_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
-  M, K = A.shape[0]*A.shape[1], A.shape[2]
-  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
-  assert K == K2
-  m = UOp.range(M, 1, AxisType.LOOP)
-  n = UOp.range(N, 2, AxisType.LOOP)
-  k = UOp.range(K, 0, AxisType.REDUCE)
-  mul = (A.index((m*UOp.const(dtypes.index, K)+k))*B.index((k*UOp.const(dtypes.index, N)+n))).cast(dtypes.float32)
-  red = mul.reduce(k, arg=Ops.ADD, dtype=dtypes.float32).cast(C.dtype.base)
-  store = C.index((m*UOp.const(dtypes.index, N)+n), ptr=True).store(red).end(m, n)
-  return store.sink(arg=KernelInfo(name=f'uop_gemm_{M}_{N}_{K}'))
-
-# ** backward gemm, might use the asm gemm
-
-def custom_gemm_bw(gradient:UOp, kernel:UOp):
-  out, a, b = kernel.src
-  assert all_same([gradient.device, a.device, b.device, out.device])
-  a_t, b_t, g_t = Tensor(a, device=a.device), Tensor(b, device=a.device), Tensor(gradient, device=a.device)
-  grad_a = (g_t @ b_t.T).uop
-  a_T = a_t.transpose(-2, -1)
-  a_T = a_T.reshape(*a_T.shape[:-1], 1, a_T.shape[-1])
-  g_r = g_t.reshape(*g_t.shape[:-2], 1, *g_t.shape[-2:]).transpose(-1, -2)
-  grad_b = (a_T * g_r).sum((-1, 0)).uop
-  return (None, grad_a, grad_b)
-
-# ** main gemm function
-
-def asm_gemm(a:Tensor, b:Tensor) -> Tensor:
-  assert can_use_asm_gemm(a, b), f"{counters['todos'][-1]}"
-  counters["used"] += 1
-  squeeze = a.ndim == 2
-  if squeeze: a = a.unsqueeze(0)
-
-  batch, M, K = a.shape
-  N = b.shape[1]
-  is_multi = isinstance(a.device, tuple)
-
-  if is_multi:
-    out = Tensor(Tensor.empty(batch//len(a.device), M, N, dtype=a.dtype, device=a.device).uop.multi(0), device=a.device)
-  else:
-    out = Tensor.empty(batch, M, N, dtype=a.dtype, device=a.device)
-
-  dname = a.device[0] if is_multi else a.device
-  arch = getattr(Device[dname].renderer, "arch", None)
-  if arch.startswith("gfx950") and getenv("USE_ASM", 1):
-    numWG = GEMM_ARGS[(M, N, K)][0]
-    out = Tensor.custom_kernel(out, a, b, fxn=functools.partial(custom_asm_gemm, dname=dname, wg=numWG, arch=arch), grad_fxn=custom_gemm_bw)[0]
-  else:
-    out = Tensor.custom_kernel(out, a, b, fxn=custom_uop_gemm, grad_fxn=custom_gemm_bw)[0]
-  return out.squeeze(0) if squeeze else out
@@ -0,0 +1,78 @@
+.text
+.section	.text.
+.global	gemm
+.p2align	8
+.type	gemm,@function
+
+gemm:
+INSTRUCTIONS
+
+.section .rodata,"a",@progbits
+.p2align 6, 0x0
+.amdhsa_kernel gemm
+  # basic memory requirements
+  .amdhsa_group_segment_fixed_size 133120
+  .amdhsa_private_segment_fixed_size 0
+  .amdhsa_kernarg_size 28
+  # register usage (RSRC1)
+  .amdhsa_next_free_vgpr 504
+  .amdhsa_next_free_sgpr 96
+  # workgroup / workitem IDs (RSRC2)
+  .amdhsa_system_sgpr_workgroup_id_x 1
+  .amdhsa_system_sgpr_workgroup_id_y 1
+  .amdhsa_system_sgpr_workgroup_id_z 1
+  # user SGPRs, we only specify the kernel args ptr in s[0:1]
+  .amdhsa_user_sgpr_kernarg_segment_ptr 1
+  .amdhsa_user_sgpr_count 2
+  .amdhsa_user_sgpr_kernarg_preload_length 0
+  .amdhsa_user_sgpr_kernarg_preload_offset 0
+  # gfx90a / gfx940 specifics (RSRC3)
+  .amdhsa_accum_offset 248
+  .amdhsa_uses_dynamic_stack 0
+  .amdhsa_tg_split 0
+.end_amdhsa_kernel
+
+.amdgpu_metadata
+---
+amdhsa.kernels:
+  - .name: gemm
+    .symbol: gemm.kd
+    .args:
+      - .name: C
+        .address_space: global
+        .offset: 0
+        .size: 8
+        .value_kind: global_buffer
+        .value_type: bf16
+      - .name: B
+        .address_space: global
+        .offset: 8
+        .size: 8
+        .value_kind: global_buffer
+        .value_type: bf16
+      - .name: A
+        .address_space: global
+        .offset: 16
+        .size: 8
+        .value_kind: global_buffer
+        .value_type: bf16
+      - .name: sz
+        .offset: 24
+        .size: 4
+        .value_kind: by_value
+        .value_type: u32
+    .group_segment_fixed_size: 133120
+    .private_segment_fixed_size: 0
+    .kernarg_segment_align: 8
+    .kernarg_segment_size: 28
+    .max_flat_workgroup_size: 256
+    .sgpr_count: 88
+    .sgpr_spill_count: 0
+    .vgpr_count: 248
+    .vgpr_spill_count: 0
+    .wavefront_size: 64
+amdhsa.version:
+  - 1
+  - 0
+...
+.end_amdgpu_metadata
@@ -0,0 +1,73 @@
+# Run assembly on the AMD runtime and check correctness
+# VIZ=2 to profile
+import pathlib
+from tinygrad import Tensor, Device, dtypes, Context
+from tinygrad.uop.ops import UOp, Ops, KernelInfo
+from tinygrad.engine.realize import Estimates
+from tinygrad.helpers import getenv
+
+fp = pathlib.Path(__file__).parent/"gemm.s"
+
+N = getenv("N", 8192)
+THREADS_PER_WG = 256
+NUM_WG = N//THREADS_PER_WG * N//THREADS_PER_WG
+
+assert N % THREADS_PER_WG == 0, "N must be divisible by THREADS_PER_WG"
+
+# ** generate inputs on CPU
+
+scale = 10.0
+
+import torch
+torch.manual_seed(0)
+A = (torch.randn(N, N, dtype=torch.float32, device="cpu") / scale).to(torch.bfloat16).contiguous()
+B = (torch.randn(N, N, dtype=torch.float32, device="cpu") / scale).to(torch.bfloat16).contiguous()
+Bt = B.t().contiguous() # transpose B for the asm gemm
+C_torch = A@B
+
+# ** copy buffers to AMD
+
+# input creation and validation run on the copy engine for simpler tracing
+
+def from_torch(t:torch.Tensor) -> Tensor:
+  return Tensor.from_blob(t.data_ptr(), t.shape, dtype=dtypes.bfloat16, device="cpu").to(Device.DEFAULT).realize()
+
+C_tiny = from_torch(A) @ from_torch(B)
+C_asm = Tensor.empty_like(C_tiny)
+
+# ** assembly custom kernel
+
+def custom_asm_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
+  lidx = UOp.special(THREADS_PER_WG, "lidx0")
+  gidx = UOp.special(NUM_WG, "gidx0")
+
+  src = (pathlib.Path(__file__).parent/"template.s").read_text().replace("INSTRUCTIONS", fp.read_text())
+
+  sz = UOp.variable("SZ", 256, 8192)
+
+  sink = UOp.sink(C.base, A.base, B.base, sz, lidx, gidx, arg=KernelInfo(name="gemm", estimates=Estimates(ops=N*N*N*2, mem=N*N*4*3)))
+  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=Device.DEFAULT), UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src)))
+
+C_asm = Tensor.custom_kernel(C_asm, from_torch(A), from_torch(Bt), fxn=custom_asm_gemm)[0]
+
+# ** run gemms
+
+sched = Tensor.schedule(C_tiny, C_asm)
+eis = [si.lower() for si in sched]
+
+with Context(DEBUG=2):
+  for ei in eis:
+    et = ei.run({"SZ":N}, wait=True)
+    print(f"{(N*N*N*2 / et)*1e-12:.2f} REAL TFLOPS")
+
+# ** correctness
+
+import ctypes
+
+def torch_bf16(t:Tensor) -> torch.tensor:
+  asm_out = t.to("cpu").realize().uop.buffer._buf
+  buf = (ctypes.c_uint16*C_asm.uop.size).from_address(asm_out.va_addr)
+  return torch.frombuffer(buf, dtype=torch.bfloat16, count=C_asm.uop.size).reshape(C_asm.shape)
+
+assert torch.allclose(torch_bf16(C_asm), C_torch, rtol=1e-2, atol=1e-3)
+assert torch.allclose(torch_bf16(C_tiny), C_torch, rtol=1e-2, atol=1e-3)
@@ -1,46 +0,0 @@
-import unittest
-from tinygrad import Tensor, Device, dtypes, Context
-from tinygrad.helpers import getenv
-from extra.gemm.asm.cdna.gemm import asm_gemm
-
-def verify_asm_gemm(batch:int, M:int, N:int, K:int, dtype=dtypes.bfloat16, multi=False) -> None:
-  Tensor.manual_seed(0)
-  a_rand = Tensor.randn((batch, M, K), dtype=dtypes.float).sub(0.5).cast(dtype)
-  b_rand = Tensor.randn((K, N), dtype=dtypes.float).sub(0.5).cast(dtype)
-  with Context(DEBUG=0):
-    Tensor.realize(a_rand, b_rand)
-
-  devs = tuple(f"{Device.DEFAULT}:{i}" for i in range(8)) if multi else None
-
-  a, b = Tensor(a_rand.numpy(), requires_grad=True).cast(dtype), Tensor(b_rand.numpy(), requires_grad=True).cast(dtype)
-  if multi: a, b = a.shard(devs, axis=0), b.shard(devs, axis=None)
-  tst = asm_gemm(a, b)
-  tst.sum().backward()
-  Tensor.realize(tst, a.grad, b.grad)
-
-  a_ref, b_ref = Tensor(a_rand.numpy(), requires_grad=True).cast(dtype), Tensor(b_rand.numpy(), requires_grad=True).cast(dtype)
-  if multi: a_ref, b_ref = a_ref.shard(devs, axis=0), b_ref.shard(devs, axis=None)
-  with Context(ASM_GEMM=0): ref = a_ref @ b_ref
-  ref.sum().backward()
-  Tensor.realize(ref, a_ref.grad, b_ref.grad)
-
-  with Context(DEBUG=0):
-    assert (tst - ref).square().max().float().item() < 1e-6, "forward mismatch"
-    assert (a.grad - a_ref.grad).square().max().float().item() < 1e-3, "grad_a mismatch"
-    assert (b.grad - b_ref.grad).square().max().float().item() < 1e-3, "grad_b mismatch"
-
-class TestGemm(unittest.TestCase):
-  def test_simple(self): verify_asm_gemm(1, N:=getenv("N", 4096), N, N, dtype=dtypes.half)
-
-  def test_gemm1(self): verify_asm_gemm(8, 8192, 4096, 14336, multi=True)
-  def test_gemm2(self): verify_asm_gemm(8, 8192, 128256, 4096, multi=True)
-  def test_gemm3(self): verify_asm_gemm(8, 8192, 14336, 4096, multi=True)
-  def test_gemm4(self): verify_asm_gemm(8, 4096, 14336, 4096, multi=True)
-  def test_gemm5(self): verify_asm_gemm(8, 4096, 4096, 14336, multi=True)
-  def test_gemm6(self): verify_asm_gemm(16, 4096, 4096, 14336, multi=True)
-  def test_gemm_unsupported(self):
-    with self.assertRaisesRegex(AssertionError, "shape not supported"):
-      verify_asm_gemm(8, 8192, 1024, 4096, multi=True)
-
-if __name__ == "__main__":
-  unittest.main()
@@ -1,15 +1,14 @@
 #!/usr/bin/env python3

 import argparse, glob, os, time, subprocess, sys
-from tinygrad.helpers import temp

 def scan_devs_based_on_lock(prefix:str, args) -> list[str]:
  target_dev = args.pci_bus if 'pci_bus' in args.__dir__() else ""

  devs = []
-  for dev in glob.glob(temp(f'{prefix}_*.lock')):
-    dev_id = dev.split('/')[-1][len(prefix)+1:-5]
-    if dev_id.startswith(target_dev): devs.append(dev_id)
+  for dev in glob.glob(f'/tmp/{prefix}_*.lock'):
+    dev_id = dev[8:-5]
+    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'")
@@ -54,7 +53,16 @@ def cmd_show_pids(args):

  for dev in devs:
    try:
-      pid = subprocess.check_output(['sudo', 'lsof', temp(f'{prefix}_{dev}.lock')]).decode('utf-8').strip().split('\n')[1].split()[1]
+      pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
+      print(f"{dev}: {pid}")
+    except subprocess.CalledProcessError: print(f"{dev}: No processes found using this device")
+
+def cmd_kill_pids(args):
+  devs = scan_devs_based_on_lock(prefix:={"amd":"am", "nv":"nv"}[args.backend], args)
+
+  for dev in devs:
+    try:
+      pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
      print(f"{dev}: {pid}")
    except subprocess.CalledProcessError: print(f"{dev}: No processes found using this device")

@@ -66,7 +74,7 @@ def cmd_kill_pids(args):
      if i > 0: time.sleep(0.2)

      try:
-        try: pid = subprocess.check_output(['sudo', 'lsof', temp(f'{prefix}_{dev}.lock')]).decode('utf-8').strip().split('\n')[1].split()[1]
+        try: pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
        except subprocess.CalledProcessError: break

        print(f"Killing process {pid} (which uses {dev})")
@@ -202,7 +202,7 @@ def ioctl(fd, request, argp):
        if s.hClass == nv_gpu.NV1_MEMORY_SYSTEM: dump_struct(get_struct(s.pAllocParms, nv_gpu.NV_MEMORY_ALLOCATION_PARAMS))
        if s.hClass == nv_gpu.GT200_DEBUGGER: dump_struct(get_struct(s.pAllocParms, nv_gpu.NV83DE_ALLOC_PARAMETERS))
        if s.hClass == nv_gpu.MAXWELL_PROFILER_DEVICE: dump_struct(get_struct(s.pAllocParms, nv_gpu.NVB2CC_ALLOC_PARAMETERS))
-        if s.hClass in {nv_gpu.AMPERE_CHANNEL_GPFIFO_A, nv_gpu.BLACKWELL_CHANNEL_GPFIFO_A}:
+        if s.hClass == nv_gpu.AMPERE_CHANNEL_GPFIFO_A:
          sx = get_struct(s.pAllocParms, nv_gpu.NV_CHANNELGPFIFO_ALLOCATION_PARAMETERS)
          dump_struct(sx)
          gpus_fifo.append((sx.gpFifoOffset, sx.gpFifoEntries))
@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-from __future__ import annotations
-import enum, collections
-from typing import Iterator
-from tinygrad.helpers import colored
-from extra.assembly.amd.sqtt import PacketType, bits
-
-# ═══════════════════════════════════════════════════════════════════════════════
-# STALL REASONS
-# ═══════════════════════════════════════════════════════════════════════════════
-
-class StallReason(enum.IntEnum):
-  # Based on CUpti_ActivityPCSamplingStallReason
-  INVALID = 0
-  NONE = 1              # selected, selected_not_issued
-  INST_FETCH = 2        # branch_resolving, no_instructions
-  EXEC_DEPENDENCY = 3   # short_scoreboard, wait
-  MEMORY_DEPENDENCY = 4 # long_scoreboard
-  TEXTURE = 5           # tex_throttle
-  SYNC = 6              # barrier, membar
-  CONSTANT_MEMORY = 7   # imc_miss
-  PIPE_BUSY = 8         # mio_throttle, math_pipe_throttle
-  MEMORY_THROTTLE = 9   # drain, lg_throttle
-  NOT_SELECTED = 10     # not_selected
-  OTHER = 11            # misc, dispatch_stall
-  SLEEPING = 12         # sleeping
-
-STALL_KEY_MAP_AMPERE: dict[int, StallReason] = {
-  1: StallReason.MEMORY_THROTTLE, 15: StallReason.MEMORY_THROTTLE,
-  2: StallReason.CONSTANT_MEMORY,
-  3: StallReason.SYNC,
-  6: StallReason.INST_FETCH, 11: StallReason.INST_FETCH,
-  7: StallReason.EXEC_DEPENDENCY, 10: StallReason.EXEC_DEPENDENCY,
-  9: StallReason.MEMORY_DEPENDENCY,
-  12: StallReason.PIPE_BUSY,
-  17: StallReason.OTHER, 20: StallReason.OTHER,
-  18: StallReason.NONE,
-}
-
-STALL_KEY_MAP_BLACKWELL: dict[int, StallReason] = {
-  0x01: StallReason.MEMORY_THROTTLE, 0x0e: StallReason.MEMORY_THROTTLE,
-  0x02: StallReason.SYNC,
-  0x05: StallReason.INST_FETCH, 0x0a: StallReason.INST_FETCH,
-  0x06: StallReason.EXEC_DEPENDENCY, 0x09: StallReason.EXEC_DEPENDENCY,
-  0x08: StallReason.MEMORY_DEPENDENCY,
-  0x0b: StallReason.PIPE_BUSY, 0x0f: StallReason.PIPE_BUSY,
-  0x10: StallReason.OTHER, 0x13: StallReason.OTHER,
-  0x11: StallReason.NONE,
-}
-
-# Lookup table for extracting sample bytes from 32-byte packet (bytes 0-3, 8-31, skipping header at 4-7)
-LOOKUP_28B = [0, 1, 2, 3, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]
-
-# ═══════════════════════════════════════════════════════════════════════════════
-# PACKET HEADER
-# ═══════════════════════════════════════════════════════════════════════════════
-
-class PMAHeader(PacketType):
-  num_bytes  = bits[4:0]    # number of sample bytes in this packet
-  tpc_id_lo  = bits[15:8]   # TPC identifier low 8 bits
-  tpc_id_hi  = bits[27:25]  # TPC identifier high 3 bits
-  dropped    = bits[28:28]  # dropped flag (resets byte accumulator)
-  @property
-  def tpc_id(self) -> int: return self.tpc_id_lo | (self.tpc_id_hi << 8)
-
-# ═══════════════════════════════════════════════════════════════════════════════
-# 8-BYTE SAMPLE FORMAT (Ampere/Ada/Hopper)
-# ═══════════════════════════════════════════════════════════════════════════════
-
-class PMASampleAmpere8B(PacketType):
-  pc_raw     = bits[44:0]   # raw PC value (pc_offset = pc_raw << 4)
-  stall_key  = bits[49:45]  # stall reason key
-  wave_id    = bits[55:50]  # warp/wave identifier
-  active     = bits[62:62]  # 1 if warp was executing, 0 if scheduled but not issued
-  @property
-  def pc_offset(self) -> int: return self.pc_raw << 4
-  @property
-  def stall_reason(self) -> StallReason: return STALL_KEY_MAP_AMPERE.get(self.stall_key, StallReason.OTHER)
-
-# ═══════════════════════════════════════════════════════════════════════════════
-# 9-BYTE SAMPLE FORMAT (Blackwell+)
-# ═══════════════════════════════════════════════════════════════════════════════
-
-class PMASampleBlackwell9B(PacketType):
-  stall_key  = bits[5:0]    # stall reason key
-  pc_raw     = bits[60:8]   # raw PC value (pc_offset = pc_raw << 4)
-  wave_hi    = bits[7:6]    # wave_id high 2 bits
-  wave_lo    = bits[71:68]  # wave_id low 4 bits
-  active     = bits[67:67]  # 1 if warp was executing, 0 if scheduled but not issued
-  @property
-  def pc_offset(self) -> int: return self.pc_raw << 4
-  @property
-  def stall_reason(self) -> StallReason: return STALL_KEY_MAP_BLACKWELL.get(self.stall_key, StallReason.OTHER)
-  @property
-  def wave_id(self) -> int: return (self.wave_hi << 4) | self.wave_lo
-
-PMASample = PMASampleAmpere8B|PMASampleBlackwell9B
-
-def decode(data: bytes, sm_version: int = 0x800) -> Iterator[tuple[PMASample, int]]:
-  use_9byte = sm_version >= 0xa04
-  record_size = 9 if use_9byte else 8
-  sample_cls = PMASampleBlackwell9B if use_9byte else PMASampleAmpere8B
-
-  tpc_state: dict[int, list[int]] = collections.defaultdict(list)
-  for pkt_idx in range(len(data) // 32):
-    pkt = data[pkt_idx * 32:(pkt_idx + 1) * 32]
-    hdr = PMAHeader.from_raw(int.from_bytes(pkt[4:8], 'little'))
-
-    if hdr.dropped: tpc_state[hdr.tpc_id].clear()
-
-    for i in range(hdr.num_bytes):
-      tpc_state[hdr.tpc_id].append(pkt[LOOKUP_28B[i]])
-
-    while len(tpc_state[hdr.tpc_id]) >= record_size:
-      yield sample_cls.from_raw(int.from_bytes(bytes(tpc_state[hdr.tpc_id][:record_size]), 'little')), hdr.tpc_id
-      del tpc_state[hdr.tpc_id][:record_size]
-
-# ═══════════════════════════════════════════════════════════════════════════════
-# CLI
-# ═══════════════════════════════════════════════════════════════════════════════
-
-STALL_COLORS = {
-  StallReason.NONE: "green", StallReason.INST_FETCH: "yellow", StallReason.EXEC_DEPENDENCY: "cyan",
-  StallReason.MEMORY_DEPENDENCY: "red", StallReason.SYNC: "magenta", StallReason.CONSTANT_MEMORY: "blue",
-  StallReason.PIPE_BUSY: "yellow", StallReason.MEMORY_THROTTLE: "RED", StallReason.OTHER: "white",
-}
-
-def decode_tpc_id(tpc_id:int) -> tuple[int, int, int]:
-  # NOTE: valid only for ops_nv, cuda encoding is different
-  return (tpc_id >> 5, (tpc_id >> 1) & 0xf, tpc_id & 1)
-
-def print_samples(samples:list[tuple[PMASample, int]]) -> None:
-  if not samples: return
-  base_pc = min(s.pc_offset for s, _ in samples)
-  for s, tpc_id in samples:
-    gpc, tpc, sm = decode_tpc_id(tpc_id)
-    stall_str = colored(f"{s.stall_reason.name:17}", STALL_COLORS.get(s.stall_reason, "white"))
-    print(f"pc=0x{s.pc_offset - base_pc:06x} {stall_str} ev={s.stall_key:2d} active={s.active} wave={s.wave_id:2d} gpc={gpc} tpc={tpc} sm={sm}")
-
-def print_packets(data:bytes, sm_version:int=0x800) -> None:
-  record_size = 9 if sm_version >= 0x890 else 8
-  tpc_state: dict[int, list[int]] = collections.defaultdict(list)
-  for i in range(len(data) // 32):
-    pkt = data[i * 32:(i + 1) * 32]
-    hdr = PMAHeader.from_raw(int.from_bytes(pkt[4:8], 'little'))
-    if hdr.dropped: tpc_state[hdr.tpc_id].clear()
-    for j in range(hdr.num_bytes): tpc_state[hdr.tpc_id].append(pkt[LOOKUP_28B[j]])
-    # Show complete records extracted from this packet
-    records = []
-    while len(tpc_state[hdr.tpc_id]) >= record_size:
-      records.append(bytes(tpc_state[hdr.tpc_id][:record_size]).hex())
-      del tpc_state[hdr.tpc_id][:record_size]
-    leftover = len(tpc_state[hdr.tpc_id])
-    print(f"Pkt {i:3d}: tpc={hdr.tpc_id:4d} n={hdr.num_bytes:2d} drop={hdr.dropped} left={leftover} | {' '.join(records)}")
-
-def print_aggregated(samples:list[tuple[PMASample, int]]) -> None:
-  if not samples: return
-  base_pc = min(s.pc_offset for s, _ in samples)
-  counter: collections.Counter[tuple[int, StallReason]] = collections.Counter((s.pc_offset, s.stall_reason) for s, _ in samples)
-  print(f"\nAggregated samples (base_pc=0x{base_pc:x}):")
-  for (pc, reason), cnt in sorted(counter.items()):
-    stall_str = colored(f"{reason.name:17}", STALL_COLORS.get(reason, "white"))
-    print(f"  pc=0x{pc - base_pc:06x} {stall_str} samples={cnt:4d}")
-
-if __name__ == "__main__":
-  import sys, pickle
-
-  if len(sys.argv) < 2:
-    print("Usage: python decode.py <pkl_file> [--raw] [--sm=0xNNN]")
-    sys.exit(1)
-
-  with open(sys.argv[1], "rb") as f:
-    data = pickle.load(f)
-
-  if isinstance(data, dict):
-    sm_version = 0x800  # default to Ampere
-    for arg in sys.argv:
-      if arg.startswith("--sm="): sm_version = int(arg[5:], 0)
-    dumps = [(i, x, sm_version) for i, x in enumerate(data["pma_raw_dumps"])]
-  else:
-    devs = {e.device: e for e in data if type(e).__name__ == "ProfileDeviceEvent"}
-    dumps = []
-    for i, e in enumerate(e for e in data if type(e).__name__ == "ProfilePMAEvent"):
-      dumps.append((i, e.blob, devs[e.device].props.get('sm_version', 0x800)))
-
-  for dump_idx, raw, sm_ver in dumps:
-    print(f"\n{'='*60}\nDump {dump_idx} ({len(raw)} bytes, {len(raw)//32} packets)\n{'='*60}")
-    if "--raw" in sys.argv: print_packets(raw, sm_ver)
-    else:
-      samples = list(decode(raw, sm_ver))
-      print(f"\nDecoded {len(samples)} samples:")
-      print_samples(samples)
-      print_aggregated(samples)
@@ -1,76 +0,0 @@
-import pickle, unittest
-from collections import Counter
-from pathlib import Path
-
-from extra.nv_pma.decode import decode
-from tinygrad.helpers import DEBUG
-
-EXAMPLES_DIR = Path(__file__).parent.parent / "examples"
-EXAMPLES_5090_DIR = Path(__file__).parent.parent / "examples_5090"
-
-def decode_and_aggregate(raw_dumps: list[bytes], sm_version: int = 0x800) -> Counter[tuple[int, int]]:
-  """Decode all PMA buffers and aggregate by (relative_pc, stall_reason). Each dump is normalized separately."""
-  result: Counter[tuple[int, int]] = Counter()
-  for raw in raw_dumps:
-    samples = [s for s, _ in decode(raw, sm_version)]
-    if not samples: continue
-    base_pc = min(s.pc_offset for s in samples)
-    result += Counter((s.pc_offset - base_pc, int(s.stall_reason)) for s in samples)
-  return result
-
-def cupti_to_counter(cupti_records: list[dict]) -> Counter[tuple[int, int]]:
-  """Convert CUPTI records to Counter[(pcOffset, stallReason)]."""
-  counter: Counter[tuple[int, int]] = Counter()
-  for r in cupti_records:
-    counter[(r['pcOffset'], r['stallReason'])] += r['samples']
-  return counter
-
-class TestNVProf(unittest.TestCase):
-  def _test_example(self, name: str, sm_version: int = 0x800, examples_dir: Path = EXAMPLES_DIR):
-    pkl_file = examples_dir / f"{name}.pkl"
-    if not pkl_file.exists():
-      self.skipTest(f"Example data not found: {pkl_file}. Run collect.py first.")
-
-    with open(pkl_file, "rb") as f:
-      data = pickle.load(f)
-
-    self.assertEqual(data["test_name"], name)
-    pma_agg = decode_and_aggregate(data["pma_raw_dumps"], sm_version)
-    cupti_agg = cupti_to_counter(data["cupti_pc_samples"])
-
-    if DEBUG >= 2:
-      total = sum(cupti_agg.values())
-      mismatched = sum(abs(pma_agg.get(k, 0) - v) for k, v in cupti_agg.items())
-      mismatched += sum(v for k, v in pma_agg.items() if k not in cupti_agg)
-      mismatched //= 2
-
-      print(f"\n=== Test: {name} ===")
-      print(f"Total samples: {total}, Mismatched: {mismatched} ({mismatched/total*100 if total else 0:.1f}%)")
-
-    self.assertEqual(pma_agg, cupti_agg, f"PMA: {dict(pma_agg)}\nCUPTI: {dict(cupti_agg)}")
-
-  # Ampere tests (8-byte format)
-  def test_decode_test_plus(self): self._test_example("test_plus")
-  def test_decode_test_reduce_sum(self): self._test_example("test_reduce_sum")
-  def test_decode_test_broadcast(self): self._test_example("test_broadcast")
-  def test_decode_test_matmul(self): self._test_example("test_matmul")
-  def test_decode_test_plus_big(self): self._test_example("test_plus_big")
-  def test_decode_test_elementwise_chain(self): self._test_example("test_elementwise_chain")
-  def test_decode_test_conv2d(self): self._test_example("test_conv2d")
-  def test_decode_test_large_matmul(self): self._test_example("test_large_matmul")
-
-  # Blackwell/5090 tests (9-byte format)
-  def test_5090_test_plus(self): self._test_example("test_plus", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_plus_big(self): self._test_example("test_plus_big", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_broadcast(self): self._test_example("test_broadcast", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_matmul(self): self._test_example("test_matmul", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_large_matmul(self): self._test_example("test_large_matmul", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_reduce_sum(self): self._test_example("test_reduce_sum", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_reduce_max(self): self._test_example("test_reduce_max", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_elementwise_chain(self): self._test_example("test_elementwise_chain", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_conv2d(self): self._test_example("test_conv2d", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_exp(self): self._test_example("test_exp", 0xa04, EXAMPLES_5090_DIR)
-  def test_5090_test_softmax(self): self._test_example("test_softmax", 0xa04, EXAMPLES_5090_DIR)
-
-if __name__ == "__main__":
-  unittest.main()
@@ -0,0 +1,31 @@
+import random
+from extra.optimization.helpers import load_worlds, ast_str_to_lin
+from tinygrad.codegen.opt.search import actions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.helpers import tqdm
+
+tactions = set()
+def test_rebuild(lin):
+  linr = Kernel(lin.ast)
+  for o in lin.applied_opts:
+    assert o in actions, f"{o} is not in actions"
+    tactions.add(o)
+    linr.apply_opt(o)
+
+  assert len(lin.sts) == len(linr.sts)
+  for st1,st2 in zip(lin.sts, linr.sts):
+    assert st1 == st2, f"{st1} != {st2}"
+
+if __name__ == "__main__":
+  ast_strs = load_worlds(False, False, False)
+  random.shuffle(ast_strs)
+  ast_strs = ast_strs[:2000]
+  for ast_str in tqdm(ast_strs):
+    lin = ast_str_to_lin(ast_str)
+    #if not lin.apply_tensor_cores():
+    lin.apply_opts(hand_coded_optimizations(lin))
+    test_rebuild(lin)
+
+  print(len(tactions), len(actions))
+  print(sorted(list(tactions)))
@@ -0,0 +1,76 @@
+import os
+import numpy as np
+import math, random
+from tinygrad.tensor import Tensor
+from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
+from tinygrad.codegen.opt.search import actions, bufs_from_lin, get_kernel_actions
+from tinygrad.nn.optim import Adam
+from extra.optimization.extract_policynet import PolicyNet
+from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
+
+if __name__ == "__main__":
+  net = PolicyNet()
+  if os.path.isfile("/tmp/policynet.safetensors"): load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
+  optim = Adam(get_parameters(net))
+
+  ast_strs = load_worlds()
+
+  # select a world
+  all_feats, all_acts, all_rews = [], [], []
+  while 1:
+    Tensor.training = False
+    lin = ast_str_to_lin(random.choice(ast_strs))
+    rawbufs = bufs_from_lin(lin)
+    tm = last_tm = base_tm = time_linearizer(lin, rawbufs)
+
+    # take actions
+    feats, acts, rews = [], [], []
+    while 1:
+      feat = lin_to_feats(lin)
+      feats.append(feat)
+      probs = net(Tensor([feat])).exp()[0].numpy()
+
+      # mask valid actions
+      valid_action_mask = np.zeros((len(actions)+1), dtype=np.float32)
+      for x in get_kernel_actions(lin): valid_action_mask[x] = 1
+      probs *= valid_action_mask
+      probs /= sum(probs)
+
+      act = np.random.choice(len(probs), p=probs)
+      acts.append(act)
+      if act == 0:
+        rews.append(0)
+        break
+      try:
+        lin.apply_opt(actions[act-1])
+        tm = time_linearizer(lin, rawbufs)
+        if math.isinf(tm): raise Exception("failed")
+        rews.append(((last_tm-tm)/base_tm))
+        last_tm = tm
+      except Exception:
+        rews.append(-0.5)
+        break
+      #print(f"{tm*1e6:10.2f}", lin.colored_shape())
+
+    assert len(feats) == len(acts) and len(acts) == len(rews)
+    #print(rews)
+    print(f"***** EPISODE {len(rews)} steps, {sum(rews):5.2f} reward, {base_tm*1e6:12.2f} -> {tm*1e6:12.2f} : {lin.colored_shape()}")
+    all_feats += feats
+    all_acts += acts
+    # rewards to go
+    for i in range(len(rews)-2, -1, -1): rews[i] += rews[i+1]
+    all_rews += rews
+
+    BS = 32
+    if len(all_feats) >= BS:
+      Tensor.training = True
+      x = Tensor(all_feats[:BS])
+      mask = np.zeros((BS, len(actions)+1), dtype=np.float32)
+      mask[range(BS), all_acts[:BS]] = all_rews[:BS]
+      loss = -(net(x) * Tensor(mask)).mean()
+      optim.zero_grad()
+      loss.backward()
+      optim.step()
+      all_feats = all_feats[BS:]
+      all_acts = all_acts[BS:]
+      all_rews = all_rews[BS:]
@@ -0,0 +1,32 @@
+from typing import List, Tuple
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import get_kernel_actions, actions
+
+_net = None
+def beam_q_estimate(beam:List[Tuple[Kernel, float]]) -> List[Tuple[Kernel, float]]:
+  global _net
+  if _net is None:
+    from tinygrad.nn.state import load_state_dict, safe_load
+    from extra.optimization.pretrain_valuenet import ValueNet
+    _net = ValueNet(1021+len(actions), 2)
+    load_state_dict(_net, safe_load("/tmp/qnet.safetensors"), verbose=False)
+  from tinygrad.tensor import Tensor
+  from tinygrad.helpers import Context
+  from extra.optimization.helpers import lin_to_feats
+  import numpy as np
+  feats = []
+  lins = []
+  base_tms = []
+  for lin,tm in beam:
+    lin_feats = lin_to_feats(lin)
+    for a,v in get_kernel_actions(lin, include_0=False).items():
+      acts = np.zeros(len(actions))
+      acts[a-1] = 1.0
+      feats.append(np.concatenate([lin_feats, acts]))
+      lins.append(v)
+      base_tms.append(tm)
+  with Context(BEAM=0):
+    with Tensor.train(False):
+      preds = _net(Tensor(feats)).numpy()
+  pred_time = np.array(base_tms) / np.exp(preds[:, 0])
+  return sorted(zip(lins, pred_time), key=lambda x: x[1])
@@ -0,0 +1,34 @@
+import argparse
+from extra.optimization.helpers import ast_str_to_lin, time_linearizer
+
+from tinygrad import dtypes
+from tinygrad.helpers import BEAM, getenv
+from tinygrad.device import Device, Compiled
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(description="Run a search for the optimal opts for a kernel", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument("--ast", type=str, default=None, help="the ast for the kernel to be optimized")
+  parser.add_argument("--file", type=str, default=None, help="a file containing asts to be optimized, one per line")
+  args = parser.parse_args()
+
+  device: Compiled = Device[Device.DEFAULT]
+  print(f"optimizing for {Device.DEFAULT}")
+
+  if args.ast is not None:
+    ast_strs = [args.ast]
+  elif args.file is not None:
+    with open(args.file, 'r') as file:
+      ast_strs = file.readlines()
+
+  for i, ast_str in enumerate(ast_strs):
+    print(f"optimizing {i}/{len(ast_strs)}\nast={ast_str}")
+    lin = ast_str_to_lin(ast_str, opts=device.renderer)
+    rawbufs = bufs_from_lin(lin)
+    lin = beam_search(lin, rawbufs, getenv("BEAM", 8), bool(getenv("BEAM_ESTIMATE", 1)))
+
+    tm = time_linearizer(lin, rawbufs, allow_test_size=False, cnt=10)
+    print(f"final time {tm*1e6:9.0f} us: {lin.colored_shape()}")
+    print(lin.applied_opts)
@@ -0,0 +1,19 @@
+import unittest
+
+from extra.optimization.helpers import load_worlds
+
+class TestKernelDataset(unittest.TestCase):
+  def test_load_worlds_filters(self):
+    all_kernels = load_worlds(filter_reduce=False, filter_noimage=False, filter_novariable=False)
+
+    reduce_kernels = load_worlds(filter_reduce=True, filter_noimage=False, filter_novariable=False)
+    self.assertGreater(len(all_kernels), len(reduce_kernels))
+
+    image_kernels = load_worlds(filter_reduce=False, filter_noimage=True, filter_novariable=False)
+    self.assertGreater(len(all_kernels), len(image_kernels))
+
+    variable_kernels = load_worlds(filter_reduce=False, filter_noimage=False, filter_novariable=True)
+    self.assertGreater(len(all_kernels), len(variable_kernels))
+
+if __name__ == '__main__':
+  unittest.main()
@@ -0,0 +1,67 @@
+import numpy as np
+import math
+import random
+np.set_printoptions(suppress=True)
+from copy import deepcopy
+from tinygrad.helpers import getenv, colored
+from tinygrad.tensor import Tensor
+from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
+from tinygrad.codegen.opt.search import bufs_from_lin, actions, get_kernel_actions
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
+from extra.optimization.extract_policynet import PolicyNet
+from extra.optimization.pretrain_valuenet import ValueNet
+
+VALUE = getenv("VALUE")
+
+if __name__ == "__main__":
+  if VALUE:
+    net = ValueNet()
+    load_state_dict(net, safe_load("/tmp/valuenet.safetensors"))
+  else:
+    net = PolicyNet()
+    load_state_dict(net, safe_load("/tmp/policynet.safetensors"))
+
+  ast_strs = load_worlds()
+
+  # real randomness
+  random.seed()
+  random.shuffle(ast_strs)
+
+  wins = 0
+  for ep_num,ast_str in enumerate(ast_strs):
+    print("\nEPISODE", ep_num, f"win {wins*100/max(1,ep_num):.2f}%")
+    lin = ast_str_to_lin(ast_str)
+    rawbufs = bufs_from_lin(lin)
+
+    linhc = deepcopy(lin)
+    linhc.applied_opts(hand_coded_optimizations(linhc))
+    tmhc = time_linearizer(linhc, rawbufs)
+    print(f"{tmhc*1e6:10.2f}     HC    ", linhc.colored_shape())
+
+    pred_time = float('nan')
+    tm = float('inf')
+    while 1:
+      if VALUE:
+        acts,feats = [], []
+        for k,v in get_kernel_actions(lin).items():
+          acts.append(k)
+          feats.append(lin_to_feats(v))
+        preds = net(Tensor(feats))
+        pred_time = math.exp(preds.numpy().min())
+        act = acts[preds.numpy().argmin()]
+      else:
+        probs = net(Tensor([lin_to_feats(lin)]))
+        dist = probs.exp().numpy()
+        act = dist.argmax()
+      if act == 0: break
+      try:
+        lin.apply_opt(actions[act-1])
+      except Exception:
+        print("FAILED")
+        break
+      tm = time_linearizer(lin, rawbufs)
+      print(f"{tm*1e6:10.2f} {pred_time*1e6:10.2f}", lin.colored_shape())
+
+    print(f"{colored('BEAT', 'green') if tm < tmhc else colored('lost', 'red')} hand coded {tmhc/tm:5.2f}x")
+    wins += int(tm < tmhc)
@@ -0,0 +1,21 @@
+from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
+from tinygrad.codegen.opt.search import bufs_from_lin, get_kernel_actions
+
+if __name__ == "__main__":
+  ast_strs = load_worlds()
+  for i, ast_str in enumerate(ast_strs):
+    lin = ast_str_to_lin(ast_str)
+    rawbufs = bufs_from_lin(lin)
+    test_tm = time_linearizer(lin, rawbufs)
+    if test_tm < 1e-2: continue
+    print(f"EXAMPLE {i}")
+    acted_lins = get_kernel_actions(lin)
+    ok_avg, short_avg = 0, 0
+    for k,v in acted_lins.items():
+      tm1 = time_linearizer(v, rawbufs)
+      tm2 = time_linearizer(v, rawbufs)
+      tm3 = time_linearizer(v, rawbufs, False)
+      print(v.colored_shape(50), f"{tm1*1e3:10.2f} {tm2*1e3:10.2f} {tm3*1e3:10.2f}  :  {((tm1-tm2)/tm1)*100:5.2f}%  vs  {((tm1-tm3)/tm1)*100:5.2f}%")
+      ok_avg += (tm1-tm2)/tm1
+      short_avg += (tm1-tm3)/tm1
+    print(f"{ok_avg/len(acted_lins)*100:5.2f}% vs {short_avg/len(acted_lins)*100:5.2f}%")
@@ -2,15 +2,15 @@ import math

 from tinygrad import Tensor, dtypes
 from tinygrad.helpers import DEBUG
-from tinygrad.uop.ops import UOp, Ops
+from tinygrad.uop.ops import UOp

 from extra.thunder.tiny.tk import WARP_THREADS
 from extra.thunder.tiny.tk.kernel import Kernel
 from extra.thunder.tiny.tk.tiles import GL, TileLayout

 NUM_WORKERS = 1
-Q_BLOCK_SIZE = 32
-KV_BLOCK_SIZE = 32
+Q_BLOCK_SIZE = 16
+KV_BLOCK_SIZE = 16

 def _sharded_empty(shape:Tensor, ref:Tensor, axis:int|None) -> Tensor:
  if not isinstance(ref.device, tuple): return Tensor.empty(*shape, dtype=ref.dtype, device=ref.device)
@@ -43,12 +43,11 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
  B_local = B // num_devices
  if DEBUG >= 2: print(f"Flash Attention {B=} {B_local=} {N=} {H=} {D=} {H_KV=} {GROUP_SIZE=}")

-  def _custom_forward_impl(ou:UOp, l_vecu:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp|None) -> UOp:
+  def custom_forward(ou:UOp, l_vecu:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp) -> UOp:
    with Kernel("fa_custom_forward", (H, N // (Q_BLOCK_SIZE*NUM_WORKERS), B_local), NUM_WORKERS * WARP_THREADS) as ker:
      warp = ker.warp

-      o, q, k, v, l_vec = GL(ou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker), GL(l_vecu, ker)
-      mask = GL(masku, ker) if masku is not None else None
+      o, q, k, v, mask, l_vec = GL(ou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker), GL(masku, ker), GL(l_vecu, ker)

      head = ker.blockIdx_x
      head_kv = head // GROUP_SIZE
@@ -71,10 +70,10 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
      mask_reg = ker.rt((Q_BLOCK_SIZE, KV_BLOCK_SIZE), dtypes.float32)
      mask_reg_transposed = ker.rt((KV_BLOCK_SIZE, Q_BLOCK_SIZE), dtypes.float32, TileLayout.COL)

-      max_vec_last = ker.rv(Q_BLOCK_SIZE, dtypes.float32)
-      max_vec = ker.rv(Q_BLOCK_SIZE, dtypes.float32)
-      norm_vec = ker.rv(Q_BLOCK_SIZE, dtypes.float32)
-      scale_vec = ker.rv(Q_BLOCK_SIZE, dtypes.float32)
+      max_vec_last = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
+      max_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
+      norm_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
+      scale_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)

      max_vec = warp.neg_inf(max_vec)
      norm_vec = warp.zero(norm_vec)
@@ -87,8 +86,7 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
      q_reg = warp.copy(q_reg, q_reg_fl)
      q_reg_transposed = warp.transpose(q_reg_transposed, q_reg)

-      num_kv_blocks = (q_seq + 1) if is_causal else (N // KV_BLOCK_SIZE)
-      for kv_idx in ker.range(num_kv_blocks):
+      for kv_idx in ker.range(N // KV_BLOCK_SIZE):
        k_smem = warp.load(k_smem, k, (), (batch, kv_idx, head_kv, 0), axis=1)
        v_smem = warp.load(v_smem, v, (), (batch, kv_idx, head_kv, 0), axis=1)

@@ -101,20 +99,13 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
        att_block = warp.mma_AtB(att_block, k_reg_transposed, q_reg_transposed)

        # apply attention mask
-        if is_causal:
-          bs_rows, bs_cols, bs_stride = att_block.base_shape.rows, att_block.base_shape.cols, att_block.base_shape.stride
-          q_base = q_seq * Q_BLOCK_SIZE + (warp.laneid % bs_cols)
-          kv_base = kv_idx * KV_BLOCK_SIZE + (warp.laneid // bs_cols) * bs_stride
-          att_block = warp.map(att_block,
-            lambda x, idx: ((kv_base + idx[0]*bs_rows + idx[2]) > (q_base + idx[1]*bs_cols)).alu(Ops.WHERE, UOp.ufix(x._uop, -math.inf), x))
-        elif mask is not None:
-          mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_seq, kv_idx), axis=2)
-          mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
-          att_block += mask_reg_transposed
+        mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_seq, kv_idx), axis=2)
+        mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
+        att_block += mask_reg_transposed

        # softmax
        max_vec_last = warp.copy(max_vec_last.after(kv_idx), max_vec)
-        max_vec = warp.col_reduce(max_vec.after(max_vec_last), att_block, lambda a, b: a.maximum(b), init_value=-math.inf)
+        max_vec = warp.row_reduce(max_vec.after(max_vec_last), att_block, lambda a, b: a.maximum(b), init_value=-math.inf)

        scale_vec = warp.map(scale_vec.after(max_vec_last, max_vec), lambda _, idx: max_vec_last[*idx] - max_vec[*idx])
        scale_vec = scale_vec.exp2()
@@ -125,7 +116,7 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
        att_block -= max_vec
        att_block = att_block.exp2()

-        norm_vec = warp.col_reduce(norm_vec.after(scale_vec), att_block, lambda a, b: a + b)
+        norm_vec = warp.row_reduce(norm_vec.after(scale_vec), att_block, lambda a, b: a + b)

        # mma av
        att_block_mma = warp.copy(att_block_mma.after(kv_idx, norm_vec), att_block)
@@ -150,18 +141,11 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False

      return ker.finish()

-  def custom_forward_causal(ou:UOp, l_vecu:UOp, qu:UOp, ku:UOp, vu:UOp) -> UOp:
-    return _custom_forward_impl(ou, l_vecu, qu, ku, vu, None)
-
-  def custom_forward_masked(ou:UOp, l_vecu:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp) -> UOp:
-    return _custom_forward_impl(ou, l_vecu, qu, ku, vu, masku)
-
-  def _custom_backward_q_impl(dqu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp|None, l_vecu:UOp, delta_vecu:UOp) -> UOp:
+  def custom_backward_q(dqu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp, l_vecu:UOp, delta_vecu:UOp) -> UOp:
    with Kernel("fa_custom_backward_q", (H, N // (Q_BLOCK_SIZE*NUM_WORKERS), B_local), NUM_WORKERS * WARP_THREADS) as ker:
      warp = ker.warp

-      dq, do, q, k, v = GL(dqu, ker), GL(dou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker)
-      mask = GL(masku, ker) if masku is not None else None
+      dq, do, q, k, v, mask = GL(dqu, ker), GL(dou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker), GL(masku, ker)
      l_vec, delta_vec = GL(l_vecu, ker), GL(delta_vecu, ker)

      head = ker.blockIdx_x
@@ -210,8 +194,7 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
      l_vec_reg *= 1.0 / math.log(2)
      delta_vec_reg = warp.load(delta_vec_reg, delta_vec, (), (batch, head, 0, q_seq), axis=2)

-      num_kv_blocks = (q_seq + 1) if is_causal else (N // KV_BLOCK_SIZE)
-      for kv_idx in ker.range(num_kv_blocks):
+      for kv_idx in ker.range(N // KV_BLOCK_SIZE):
        k_smem = warp.load(k_smem, k, (), (batch, kv_idx, head_kv, 0), axis=1)
        v_smem = warp.load(v_smem, v, (), (batch, kv_idx, head_kv, 0), axis=1)

@@ -226,16 +209,9 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
        att_block = warp.mma_AtB(att_block, k_reg_t, q_reg_t)

        # apply attention mask
-        if is_causal:
-          bs_rows, bs_cols, bs_stride = att_block.base_shape.rows, att_block.base_shape.cols, att_block.base_shape.stride
-          q_base = q_seq * Q_BLOCK_SIZE + (warp.laneid % bs_cols)
-          kv_base = kv_idx * KV_BLOCK_SIZE + (warp.laneid // bs_cols) * bs_stride
-          att_block = warp.map(att_block,
-            lambda x, idx: ((kv_base + idx[0]*bs_rows + idx[2]) > (q_base + idx[1]*bs_cols)).alu(Ops.WHERE, UOp.ufix(x._uop, -math.inf), x))
-        elif mask is not None:
-          mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_seq, kv_idx), axis=2)
-          mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
-          att_block += mask_reg_transposed
+        mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_seq, kv_idx), axis=2)
+        mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
+        att_block += mask_reg_transposed

        att_block -= l_vec_reg
        att_block = att_block.exp2()
@@ -255,18 +231,11 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False

      return ker.finish()

-  def custom_backward_q_causal(dqu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, l_vecu:UOp, delta_vecu:UOp) -> UOp:
-    return _custom_backward_q_impl(dqu, dou, qu, ku, vu, None, l_vecu, delta_vecu)
-
-  def custom_backward_q_masked(dqu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp, l_vecu:UOp, delta_vecu:UOp) -> UOp:
-    return _custom_backward_q_impl(dqu, dou, qu, ku, vu, masku, l_vecu, delta_vecu)
-
-  def _custom_backward_kv_impl(dku:UOp, dvu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp|None, l_vecu:UOp, delta_vecu:UOp):
+  def custom_backward_kv(dku:UOp, dvu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp, l_vecu:UOp, delta_vecu:UOp):
    with Kernel("fa_custom_backward_kv", (H_KV, N // (KV_BLOCK_SIZE*NUM_WORKERS), B_local), NUM_WORKERS * WARP_THREADS) as ker:
      warp = ker.warp

-      dk, dv, do, q, k, v = GL(dku, ker), GL(dvu, ker), GL(dou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker)
-      mask = GL(masku, ker) if masku is not None else None
+      dk, dv, do, q, k, v, mask = GL(dku, ker), GL(dvu, ker), GL(dou, ker), GL(qu, ker), GL(ku, ker), GL(vu, ker), GL(masku, ker)
      l_vec, delta_vec = GL(l_vecu, ker), GL(delta_vecu, ker)

      head_kv = ker.blockIdx_x
@@ -333,16 +302,9 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
          att_block *= (1.0 / math.sqrt(D)) * (1.0 / math.log(2))

          # apply attention mask
-          if is_causal:
-            bs_rows, bs_cols, bs_stride = att_block.base_shape.rows, att_block.base_shape.cols, att_block.base_shape.stride
-            q_base = q_idx * Q_BLOCK_SIZE + (warp.laneid % bs_cols)
-            kv_base = kv_seq * KV_BLOCK_SIZE + (warp.laneid // bs_cols) * bs_stride
-            att_block = warp.map(att_block,
-              lambda x, idx: ((kv_base + idx[0]*bs_rows + idx[2]) > (q_base + idx[1]*bs_cols)).alu(Ops.WHERE, UOp.ufix(x._uop, -math.inf), x))
-          elif mask is not None:
-            mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_idx, kv_seq), axis=2)
-            mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
-            att_block += mask_reg_transposed
+          mask_reg = warp.load(mask_reg, mask, (), (batch, 0, q_idx, kv_seq), axis=2)
+          mask_reg_transposed = warp.transpose(mask_reg_transposed, mask_reg)
+          att_block += mask_reg_transposed

          att_block -= l_vec_reg
          att_block = att_block.exp2()
@@ -351,7 +313,7 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
          att_block_transposed = warp.transpose(att_block_transposed, att_block_mma)
          att_smem = warp.store(att_smem, att_block_transposed)
          att_block_row = warp.load(att_block_row, att_smem)
-          dv_reg_ = warp.mma_AtB(dv_reg, att_block_row, do_reg_col)
+          dv_reg_ = warp.mma_AB(dv_reg, att_block_row, do_reg_col)

          dp_block = warp.zero(dp_block.after(g, q_idx, dv_reg_))
          dp_block = warp.mma_ABt(dp_block, v_reg, do_reg)
@@ -363,7 +325,7 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
          att_block_transposed = warp.transpose(att_block_transposed, att_block_mma)
          att_smem = warp.store(att_smem, att_block_transposed)
          att_block_row = warp.load(att_block_row, att_smem)
-          dk_reg = warp.mma_AtB(dk_reg, att_block_row, q_reg_col)
+          dk_reg = warp.mma_AB(dk_reg, att_block_row, q_reg_col)
      dk_reg = ker.endrange(2)
      dv_reg = dv_reg.after(dk_reg)

@@ -374,31 +336,24 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False

      return ker.finish(2)

-  def custom_backward_kv_causal(dku:UOp, dvu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, l_vecu:UOp, delta_vecu:UOp):
-    return _custom_backward_kv_impl(dku, dvu, dou, qu, ku, vu, None, l_vecu, delta_vecu)
-
-  def custom_backward_kv_masked(dku:UOp, dvu:UOp, dou:UOp, qu:UOp, ku:UOp, vu:UOp, masku:UOp, l_vecu:UOp, delta_vecu:UOp):
-    return _custom_backward_kv_impl(dku, dvu, dou, qu, ku, vu, masku, l_vecu, delta_vecu)
-
  single_device = xq.device[0] if isinstance(xq.device, tuple) else xq.device

  if is_causal:
    if attn_mask is not None: raise RuntimeError("cannot set attn_mask when is_causal=True")
-  elif attn_mask is not None:
+    attn_mask = Tensor.ones((B, 1, N, N), requires_grad=False, device=single_device, dtype=dtypes.bool).tril()
+  if attn_mask is not None:
    if attn_mask.dtype == dtypes.bool: attn_mask = attn_mask.where(0, -float("inf"))
-    if attn_mask.shape != (B, 1, N, N):
-      attn_mask = attn_mask.expand(B, 1, N, N)
-    if isinstance(xq.device, tuple) and not isinstance(attn_mask.device, tuple):
-      attn_mask = attn_mask.shard(xq.device, axis=0)
  else:
    attn_mask = Tensor.zeros((B, 1, N, N), requires_grad=False, device=single_device, dtype=dtypes.float32)
-    if isinstance(xq.device, tuple):
-      attn_mask = attn_mask.shard(xq.device, axis=0)
+  if attn_mask.shape != (B, 1, N, N):
+    attn_mask = attn_mask.expand(B, 1, N, N)
+  if isinstance(xq.device, tuple) and not isinstance(attn_mask.device, tuple):
+    attn_mask = attn_mask.shard(xq.device, axis=0)

  attn = _sharded_empty_like(xq, axis=0)
  l_vec = _sharded_empty((B, H, 1, N), xq, axis=0)

-  def grad_causal(gradu:UOp, _) -> tuple[None, None, UOp, UOp, UOp]:
+  def grad(gradu:UOp, _) -> tuple[None, None, UOp, UOp, UOp, None]:
    grad = Tensor(gradu, device=gradu.device)
    grad_q = _sharded_empty_like(xq, axis=0)
    grad_k = _sharded_empty_like(xk, axis=0)
@@ -406,26 +361,11 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False

    delta_vec = (grad * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()

-    grad_q = Tensor.custom_kernel(grad_q, grad, xq, xk, xv, l_vec, delta_vec, fxn=custom_backward_q_causal)[0]
-    grad_k, grad_v = Tensor.custom_kernel(grad_k, grad_v, grad, xq, xk, xv, l_vec, delta_vec, fxn=custom_backward_kv_causal)[:2]
-    return (None, None, grad_q.uop, grad_k.uop, grad_v.uop)
-
-  def grad_masked(gradu:UOp, _) -> tuple[None, None, UOp, UOp, UOp, None]:
-    grad = Tensor(gradu, device=gradu.device)
-    grad_q = _sharded_empty_like(xq, axis=0)
-    grad_k = _sharded_empty_like(xk, axis=0)
-    grad_v = _sharded_empty_like(xv, axis=0)
-
-    delta_vec = (grad * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()
-
-    grad_q = Tensor.custom_kernel(grad_q, grad, xq, xk, xv, attn_mask, l_vec, delta_vec, fxn=custom_backward_q_masked)[0]
-    grad_k, grad_v = Tensor.custom_kernel(grad_k, grad_v, grad, xq, xk, xv, attn_mask, l_vec, delta_vec, fxn=custom_backward_kv_masked)[:2]
+    grad_q = Tensor.custom_kernel(grad_q, grad, xq, xk, xv, attn_mask, l_vec, delta_vec, fxn=custom_backward_q)[0]
+    grad_k, grad_v = Tensor.custom_kernel(grad_k, grad_v, grad, xq, xk, xv, attn_mask, l_vec, delta_vec, fxn=custom_backward_kv)[:2]
    return (None, None, grad_q.uop, grad_k.uop, grad_v.uop, None)

-  if is_causal:
-    attn, l_vec = Tensor.custom_kernel(attn, l_vec, xq, xk, xv, fxn=custom_forward_causal, grad_fxn=grad_causal)[:2]
-  else:
-    attn, l_vec = Tensor.custom_kernel(attn, l_vec, xq, xk, xv, attn_mask, fxn=custom_forward_masked, grad_fxn=grad_masked)[:2]
+  attn, l_vec = Tensor.custom_kernel(attn, l_vec, xq, xk, xv, attn_mask, fxn=custom_forward, grad_fxn=grad)[:2]
  attn_ = attn[:, :N_, :, :D_]

  return attn_.transpose(1, 2).cast(odtype)
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
-import os, zlib, struct, hashlib
-from tinygrad.helpers import getenv
+import sys, os, zlib, struct, hashlib
+from tinygrad.helpers import DEBUG, getenv, fetch
 from tinygrad.runtime.support.usb import USB3

 SUPPORTED_CONTROLLERS = [
@@ -50,7 +50,7 @@ patched_fw = patch(file_path, file_hash, patches)
 dev = None
 for vendor, device in SUPPORTED_CONTROLLERS:
  try:
-    dev = USB3(vendor, device, 0x81, 0x83, 0x02, 0x04, use_bot=True)
+    dev = USB3(vendor, device, 0x81, 0x83, 0x02, 0x04)
    break
  except RuntimeError: pass
 if dev is None:
@@ -0,0 +1,43 @@
+import random
+from tinygrad.helpers import getenv
+from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
+
+def optimize_kernel(k):
+  # TODO: update this
+  return hand_coded_optimizations(k)
+
+if __name__ == '__main__':
+  hcopt_wins = beam_wins = tie = 0
+  hcopt_total = beam_total = 0.0
+
+  worlds = load_worlds(filter_reduce=False, filter_noimage=True, filter_novariable=False)
+  random.seed(0)
+  random.shuffle(worlds)
+
+  for world in worlds[:500]:
+    k = ast_str_to_lin(world)
+    rawbufs = bufs_from_lin(k)
+
+    k_hcopt = k.copy()
+    k_hcopt.apply_opts(optimize_kernel(k_hcopt))
+    k_beam = beam_search(k.copy(), rawbufs, getenv("BEAM", 2))
+
+    disable_cache = bool(getenv("NOCACHE", 0))
+    t_hcopt = time_linearizer(k_hcopt, rawbufs, allow_test_size=False, cnt=10, disable_cache=disable_cache, clear_l2=True) * 1e6
+    t_beam = time_linearizer(k_beam, rawbufs, allow_test_size=False, cnt=10, disable_cache=disable_cache, clear_l2=True) * 1e6
+
+    if t_hcopt == t_beam: tie += 1
+    elif t_hcopt < t_beam: hcopt_wins += 1
+    else: beam_wins += 1
+    hcopt_total += t_hcopt
+    beam_total += t_beam
+
+    print(f"{t_hcopt=:5.2f} {k_hcopt.applied_opts=}")
+    print("")
+    print(f"{t_beam=:5.2f} {k_beam.applied_opts=}")
+    print("*"*20)
+
+  print(f"{hcopt_wins=}, {beam_wins=}, {tie=}")
+  print(f"{hcopt_total=:.2f}, {beam_total=:.2f}")
@@ -11,7 +11,7 @@ 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.PARAM, dtypes.imageh((16, 1024, 4)), (), 0)
+  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)
@@ -21,13 +21,13 @@ def vision_conv_143():
  c26 = UOp.range(7, 1, AxisType.REDUCE)
  c27 = c2*2+c26
  c32 = ((c27<3)!=True)&(c27<67)
-  c34 = UOp(Ops.PARAM, dtypes.imageh((32, 1024, 4)), (), 1)
+  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.PARAM, dtypes.imageh((64, 49, 4)), (), 2)
+  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.PARAM, dtypes.float.ptr(128), (), 3)
+  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)

@@ -37,7 +37,7 @@ def vision_conv_143():
  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))

 def vision_conv_153():
-  c0 = UOp(Ops.PARAM, dtypes.imageh((8, 1024, 4)), (), 0)
+  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)
@@ -47,13 +47,13 @@ def vision_conv_153():
  c26 = UOp.range(7, 1, AxisType.REDUCE)
  c27 = c2*2+c26
  c32 = ((c27<3)!=True)&(c27<35)
-  c34 = UOp(Ops.PARAM, dtypes.imageh((16, 1024, 4)), (), 1)
+  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.PARAM, dtypes.imageh((128, 49, 4)), (), 2)
+  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.PARAM, dtypes.float.ptr(256), (), 3)
+  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)

@@ -63,16 +63,16 @@ def vision_conv_153():
  return c67.sink(arg=KernelInfo(name="conv", opts_to_apply=opts))

 def dm_conv_172():
-  c0 = UOp(Ops.PARAM, dtypes.imageh((1, 240, 4)), (), 0)
+  c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((1, 240, 4)), (), 0)
  c2 = UOp.range(960, 4, AxisType.LOOP)
-  c5 = UOp(Ops.PARAM, dtypes.imageh((8, 384, 4)), (), 1)
+  c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((8, 384, 4)), (), 1)
  c7 = UOp.range(32, 0, AxisType.REDUCE)
  c10 = UOp.range(4, 1, AxisType.REDUCE)
  c13 = UOp.range(12, 3, AxisType.REDUCE)
  c18 = UOp.range(8, 2, AxisType.REDUCE)
-  c23 = UOp(Ops.PARAM, dtypes.imageh((240, 128, 4)), (), 2)
+  c23 = UOp(Ops.DEFINE_GLOBAL, dtypes.imageh((240, 128, 4)), (), 2)
  c35 = c5.index((c7*4+c10+c13*128+c18*1536))*c23.index((c10*4+c2%4+c7*16+c2//4*512))
-  c37 = UOp(Ops.PARAM, dtypes.float.ptr(960), (), 3)
+  c37 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(960), (), 3)
  c39 = c35.reduce(c7, c10, arg=Ops.ADD)+c37.index(c2)
  c50 = (1.0+((c39+0.044708251953125*(c39*(c39*c39)))*-2.3021129851685216).exp2()).reciprocal()*c39
  c53 = c50.reduce(c18, c13, arg=Ops.ADD)*0.010416666666666666
@@ -91,7 +91,7 @@ allocator = Device.default.allocator
 ps = get_program(ast, renderer)
 cr = CompiledRunner(replace(ps, device=Device.DEFAULT))

-gs = sorted(dedup([u for u in ast.toposort() if u.op is Ops.PARAM]), key=lambda u: u.arg)
+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]
@@ -1,137 +0,0 @@
-# ruff: noqa: F405
-import unittest, subprocess, os
-from extra.assembly.amd.autogen.rdna3.ins import *  # noqa: F403
-from extra.assembly.amd.dsl import s, v, Inst, NULL
-
-def assemble_kernel(insts:list[Inst], name:str="test") -> str:
-  kd = {"next_free_vgpr": 8, "next_free_sgpr": 8, "wavefront_size32": 1, "user_sgpr_kernarg_segment_ptr": 1, "kernarg_size": 8}
-  disasm = "\n".join(inst.disasm() for inst in insts)
-  hsasrc = f".text\n.globl {name}\n.p2align 8\n.type {name},@function\n{name}:\n{disasm}\n"
-  return hsasrc + f".rodata\n.p2align 6\n.amdhsa_kernel {name}\n" + "\n".join(f".amdhsa_{k} {v}" for k, v in kd.items()) + "\n.end_amdhsa_kernel"
-
-def _run(code:str, timeout:float=15.0) -> subprocess.CompletedProcess:
-  # TODO: AM_RESET is required for now, so subprocesses
-  return subprocess.run(["python", "-c", code], env={**os.environ, "AMD": "1"}, capture_output=True, text=True, timeout=timeout)
-
-def _run_asm(asm_src:str) -> subprocess.CompletedProcess:
-  return _run('from tinygrad.device import Device; from tinygrad.runtime.ops_amd import AMDProgram; '
-              'from tinygrad.runtime.support.compiler_amd import HIPCompiler; dev = Device["AMD"]; '
-              f'AMDProgram(dev, "test", HIPCompiler(dev.arch).compile("""{asm_src}"""))('
-              'dev.allocator.alloc(64), global_size=(1,1,1), local_size=(1,1,1), wait=True)')
-
-def _verify_recovery() -> subprocess.CompletedProcess:
-  return _run('from tinygrad import Tensor; t = Tensor([1.0, 2.0], device="AMD").realize(); assert (t + 1).numpy().tolist() == [2.0, 3.0]')
-
-_ILLEGAL_INST_ASM = ".text\n.globl test\n.p2align 8\n.type test,@function\ntest:\n.byte 0xff,0xff,0xff,0xff\ns_endpgm\n" \
-  ".rodata\n.p2align 6\n.amdhsa_kernel test\n.amdhsa_next_free_vgpr 8\n.amdhsa_next_free_sgpr 8\n" \
-  ".amdhsa_wavefront_size32 1\n.amdhsa_user_sgpr_kernarg_segment_ptr 1\n.amdhsa_kernarg_size 8\n.end_amdhsa_kernel"
-
-@unittest.skipIf(os.environ.get("AMD") != "1" or os.environ.get("MOCKGPU") == "1", "AMD with AM driver required")
-class TestAMFaultRecovery(unittest.TestCase):
-  def _run_kernel(self, insts: list[Inst]) -> subprocess.CompletedProcess: return _run_asm(assemble_kernel(insts))
-
-  def _assert_fault_and_recovery(self, result:subprocess.CompletedProcess):
-    if result.stdout.strip(): print(f"\nstdout: {result.stdout.strip()}")
-    if result.stderr.strip(): print(f"\nstderr: {result.stderr.strip()}")
-    self.assertNotEqual(result.returncode, 0, f"Expected fault but succeeded: {result.stdout}")
-    self.assertEqual(_verify_recovery().returncode, 0)
-
-
-class TestGlobalMemoryFaults(TestAMFaultRecovery):
-  def test_global_load_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_global_store_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 0x12345678),
-             global_store_b32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_global_null_ptr(self):
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_global_misaligned_b64(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0001), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b64(v[2:3], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_global_misaligned_b128(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0004), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b128(v[2:5], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-
-class TestSMEMFaults(TestAMFaultRecovery):
-  def test_smem_null_base(self):
-    insts = [s_mov_b32(s[2], 0), s_mov_b32(s[3], 0),
-             s_load_b32(s[4], s[2:3], 0, soffset=NULL), s_waitcnt(lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_smem_unmapped_address(self):
-    insts = [s_mov_b32(s[2], 0xBEEF0000), s_mov_b32(s[3], 0xDEAD),
-             s_load_b32(s[4], s[2:3], 0, soffset=NULL), s_waitcnt(lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_smem_misaligned_b64(self):
-    insts = [s_mov_b32(s[2], 0xBEEF0004), s_mov_b32(s[3], 0xDEAD),
-             s_load_b64(s[4:5], s[2:3], 0, soffset=NULL), s_waitcnt(lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_smem_misaligned_b128(self):
-    insts = [s_mov_b32(s[2], 0xBEEF0004), s_mov_b32(s[3], 0xDEAD),
-             s_load_b128(s[4:7], s[2:3], 0, soffset=NULL), s_waitcnt(lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-
-class TestIllegalInstruction(TestAMFaultRecovery):
-  def test_malformed_encoding(self):
-    self._assert_fault_and_recovery(_run_asm(_ILLEGAL_INST_ASM))
-
-
-class TestFlatFaults(TestAMFaultRecovery):
-  def test_flat_load_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD),
-             flat_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0, lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_flat_store_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 0x12345678),
-             flat_store_b32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(vmcnt=0, lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-
-class TestAtomicFaults(TestAMFaultRecovery):
-  def test_global_atomic_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 1),
-             global_atomic_add_u32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-  def test_flat_atomic_unmapped(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 1),
-             flat_atomic_add_u32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(vmcnt=0, lgkmcnt=0), s_endpgm()]
-    self._assert_fault_and_recovery(self._run_kernel(insts))
-
-
-class TestRecovery(TestAMFaultRecovery):
-  def test_recovery_after_memviol(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    self.assertNotEqual(self._run_kernel(insts).returncode, 0)
-    self.assertEqual(_verify_recovery().returncode, 0)
-
-  def test_recovery_after_illegal_inst(self):
-    self.assertNotEqual(_run_asm(_ILLEGAL_INST_ASM).returncode, 0)
-    self.assertEqual(_verify_recovery().returncode, 0)
-
-  def test_multiple_faults_recovery(self):
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(vmcnt=0), s_endpgm()]
-    for _ in range(3):
-      self.assertNotEqual(self._run_kernel(insts).returncode, 0)
-      self.assertEqual(_verify_recovery().returncode, 0)
-
-if __name__ == "__main__":
-  unittest.main()
@@ -1,126 +0,0 @@
-# ruff: noqa: F405
-"""Tests for GPU crash scenarios using AMD assembly to trigger invalid operations.
-
-These tests intentionally cause GPU faults to verify error handling.
-Run with: AMD=1 python -m pytest test/external/external_test_gpu_crash.py -v
-"""
-import unittest, re
-from tinygrad.device import Device
-from extra.assembly.amd.autogen.rdna3.ins import *  # noqa: F403
-from extra.assembly.amd.dsl import s, v, Inst, NULL
-
-def assemble(code:str, name:str="test") -> str:
-  kd = {"next_free_vgpr": 8, "next_free_sgpr": 8, "wavefront_size32": 1, "user_sgpr_kernarg_segment_ptr": 1, "kernarg_size": 8}
-  return f".text\n.globl {name}\n.p2align 8\n.type {name},@function\n{name}:\n{code}\n.rodata\n.p2align 6\n.amdhsa_kernel {name}\n" + \
-         "\n".join(f".amdhsa_{k} {v}" for k,v in kd.items()) + "\n.end_amdhsa_kernel"
-
-@unittest.skipIf(Device.DEFAULT != "AMD", "AMD required")
-class TestGPUCrash(unittest.TestCase):
-  @classmethod
-  def setUpClass(cls):
-    from tinygrad.runtime.support.compiler_amd import HIPCompiler
-    cls.dev = Device["AMD"]
-    cls.compiler = HIPCompiler(cls.dev.arch)
-
-  def setUp(self):
-    # Verify device works before each test
-    from tinygrad import Tensor
-    try:
-      t = Tensor([1.0, 2.0], device="AMD").realize()
-      assert (t + 1).numpy().tolist() == [2.0, 3.0]
-    except Exception:
-      self.fail("Device not working before test")
-
-  def _run(self, code: str):
-    from tinygrad.runtime.ops_amd import AMDProgram
-    prg = AMDProgram(self.dev, "test", self.compiler.compile(assemble(code)))
-    prg(self.dev.allocator.alloc(64), global_size=(1,1,1), local_size=(1,1,1), wait=True)
-
-  def _run_insts(self, insts: list[Inst]): self._run("\n".join(i.disasm() for i in insts))
-
-  def _assert_gpu_fault(self, func):
-    """Assert that func raises a RuntimeError indicating a GPU fault (not a setup error)."""
-    with self.assertRaises(RuntimeError) as cm:
-      func()
-    err_msg = str(cm.exception).lower()
-    # Verify it's a GPU fault, not a setup/device initialization error
-    self.assertTrue(
-      re.search(r'fault|hang|timeout|illegal|memviol', err_msg),
-      f"Expected GPU fault error, got: {cm.exception}"
-    )
-
-
-class TestOutOfBoundsMemoryAccess(TestGPUCrash):
-  """Tests for out-of-bounds memory accesses."""
-
-  def test_global_load_null_ptr(self):
-    """Global load from NULL pointer."""
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_global_store_null_ptr(self):
-    """Global store to NULL pointer."""
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0), v_mov_b32_e32(v[2], 0xDEADBEEF),
-             global_store_b32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_global_load_unmapped_high_address(self):
-    """Global load from high unmapped address (0xDEAD00000000)."""
-    insts = [v_mov_b32_e32(v[0], 0x00000000), v_mov_b32_e32(v[1], 0xDEAD),
-             global_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_global_store_unmapped_high_address(self):
-    """Global store to high unmapped address."""
-    insts = [v_mov_b32_e32(v[0], 0x00000000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 0x12345678),
-             global_store_b32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_global_atomic_unmapped(self):
-    """Atomic operation on unmapped memory."""
-    insts = [v_mov_b32_e32(v[0], 0xBEEF0000), v_mov_b32_e32(v[1], 0xDEAD), v_mov_b32_e32(v[2], 1),
-             global_atomic_add_u32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-
-class TestSMEMFaults(TestGPUCrash):
-  """Tests for scalar memory (SMEM) faults."""
-
-  def test_smem_load_null(self):
-    """SMEM load from NULL base."""
-    insts = [s_mov_b32(s[2], 0), s_mov_b32(s[3], 0),
-             s_load_b32(s[4], s[2:3], 0, soffset=NULL), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_smem_load_unmapped(self):
-    """SMEM load from unmapped address."""
-    insts = [s_mov_b32(s[2], 0xBEEF0000), s_mov_b32(s[3], 0xDEAD),
-             s_load_b32(s[4], s[2:3], 0, soffset=NULL), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-
-class TestFlatMemoryFaults(TestGPUCrash):
-  """Tests for FLAT memory instruction faults."""
-
-  def test_flat_load_null(self):
-    """FLAT load from NULL address."""
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0),
-             flat_load_b32(v[2], addr=v[0:1], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_flat_store_null(self):
-    """FLAT store to NULL address."""
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0), v_mov_b32_e32(v[2], 0xDEADBEEF),
-             flat_store_b32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-  def test_flat_atomic_null(self):
-    """FLAT atomic on NULL address."""
-    insts = [v_mov_b32_e32(v[0], 0), v_mov_b32_e32(v[1], 0), v_mov_b32_e32(v[2], 1),
-             flat_atomic_add_u32(addr=v[0:1], data=v[2], saddr=NULL, offset=0), s_waitcnt(0), s_endpgm()]
-    self._assert_gpu_fault(lambda: self._run_insts(insts))
-
-
-if __name__ == "__main__":
-  unittest.main()
@@ -0,0 +1,339 @@
+import random, traceback, ctypes, argparse, os
+from typing import Any
+import numpy as np
+from collections import defaultdict
+from extra.optimization.helpers import load_worlds, ast_str_to_lin, kern_str_to_lin
+from tinygrad.engine.realize import get_program
+
+# We need to insert ioctl before opening devices.
+if os.getenv("VALIDATE_HCQ", 0) != 0:
+  try:
+    import extra.nv_gpu_driver.nv_ioctl
+    from tinygrad import Device
+    _, _ = Device["NV"], Device["CUDA"]
+  except Exception: pass
+
+  try:
+    import extra.qcom_gpu_driver.opencl_ioctl
+    from tinygrad import Device
+    _, _ = Device["QCOM"], Device["CL"]
+  except Exception: pass
+
+from tinygrad import Tensor, Device, dtypes
+from tinygrad.tensor import _to_np_dtype
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt import Opt, OptOps
+from tinygrad.codegen.opt.search import get_kernel_actions, bufs_from_lin
+from tinygrad.engine.realize import CompiledRunner
+from tinygrad.helpers import getenv, from_mv, prod, colored, Context, DEBUG, Timing
+from tinygrad.uop.ops import UOp, Ops
+from tinygrad.device import is_dtype_supported
+
+def on_linearizer_will_run(): pass
+def on_linearizer_did_run(): pass
+def compare_states(x, y): return (True, "")
+
+if getenv("VALIDATE_HCQ"):
+  if Device.DEFAULT == "NV":
+    print("VALIDATE_HCQ: Comparing NV to CUDA")
+    import extra.nv_gpu_driver.nv_ioctl
+    validate_device = Device["CUDA"]
+    on_linearizer_will_run = extra.nv_gpu_driver.nv_ioctl.before_launch
+    on_linearizer_did_run = extra.nv_gpu_driver.nv_ioctl.collect_last_launch_state
+    compare_states = extra.nv_gpu_driver.nv_ioctl.compare_launch_state
+  elif Device.DEFAULT == "QCOM":
+    print("VALIDATE_HCQ: Comparing QCOM to CL")
+    import extra.qcom_gpu_driver.opencl_ioctl
+    validate_device = Device["CL"]
+    on_linearizer_will_run = extra.qcom_gpu_driver.opencl_ioctl.before_launch
+    on_linearizer_did_run = extra.qcom_gpu_driver.opencl_ioctl.collect_last_launch_state
+    compare_states = extra.qcom_gpu_driver.opencl_ioctl.compare_launch_state
+  else:
+    print(colored("VALIDATE_HCQ options is ignored", 'red'))
+
+def tuplize_uops(uops:list[UOp]) -> tuple:
+  return tuple([(x.op, x.dtype, tuple(uops.index(x) for x in x.src), x.arg) for x in uops])
+
+def get_fuzz_rawbufs(lin):
+  rawbufs = bufs_from_lin(lin)
+
+  # Reallocate output buffer with additional area to detect out-of-bounds writes.
+  RED_AREA_SIZE = 1024
+  # setting output  # TODO: multi-output kernel
+  rawbufs[0] = get_fuzz_rawbuf_like(rawbufs[0], zero=True, size=rawbufs[0].size+RED_AREA_SIZE)
+  # setting inputs
+  with Context(DEBUG=0):
+    for rawbuf in rawbufs[1:]:
+      if dtypes.is_unsigned(rawbuf.dtype):
+        data = np.random.randint(0, 100, size=rawbuf.size, dtype=_to_np_dtype(rawbuf.dtype))
+      elif dtypes.is_int(rawbuf.dtype):
+        data = np.random.randint(-100, 100, size=rawbuf.size, dtype=_to_np_dtype(rawbuf.dtype))
+      elif rawbuf.dtype == dtypes.bool:
+        data = np.random.choice([True, False], size=rawbuf.size)
+      elif rawbuf.dtype == dtypes.half:
+        data = np.random.uniform(-1, 1, size=rawbuf.size).astype(dtype=_to_np_dtype(rawbuf.dtype))
+      else:
+        data = np.random.uniform(-10, 10, size=rawbuf.size).astype(dtype=_to_np_dtype(rawbuf.dtype))
+      rawbuf.copyin(Tensor(data, device=lin.opts.device).realize().uop.base.realized.as_buffer())
+  return rawbufs
+
+def get_fuzz_rawbuf_like(old_rawbuf, zero=False, copy=False, size=None, force_device=None):
+  rawbuf = type(old_rawbuf)(force_device or old_rawbuf.device, old_rawbuf.size if size is None else size, old_rawbuf.dtype).allocate()
+  if copy:
+    with Context(DEBUG=0): rawbuf.copyin(old_rawbuf.as_buffer())
+  elif zero:
+    with Context(DEBUG=0):
+      mv = memoryview(bytearray(rawbuf.size * rawbuf.dtype.itemsize))
+      ctypes.memset(from_mv(mv), 0, len(mv))
+      rawbuf.copyin(mv)
+  return rawbuf
+
+def run_linearizer(lin: Kernel, rawbufs=None, var_vals=None) -> tuple[str, Any]: # (error msg, run state)
+  if rawbufs is None: rawbufs = bufs_from_lin(lin)
+  if var_vals is None: var_vals = {v.expr: v.min for v in lin.vars}
+
+  # TODO: images needs required_optimization
+  try:
+    prg = CompiledRunner(get_program(lin.get_optimized_ast(), lin.opts))
+  except KeyboardInterrupt: raise
+  except Exception:
+    traceback.print_exc()
+    return "COMPILE_ERROR", None
+
+  if getenv("VALIDATE_HCQ"): on_linearizer_will_run()
+  try:
+    prg(rawbufs, var_vals, wait=True)
+  except KeyboardInterrupt: raise
+  except Exception:
+    traceback.print_exc()
+    return "EXEC_ERROR", None
+
+  if getenv("VALIDATE_HCQ"): run_state = on_linearizer_did_run()
+  else: run_state = None
+
+  return "PASS", run_state
+
+def compare_linearizer(lin: Kernel, rawbufs=None, var_vals=None, ground_truth=None, rtol=1e-2, atol=1e-2):
+  # TODO: for bfloat16 it compiles linearizer, but it does not run because numpy cannot generate bf16 buffer.
+  has_bf16 = any(b.dtype.base == dtypes.bfloat16 for b in lin.bufs)
+
+  # TODO: raise specific fuzzing errors instead of str, and propagate the error message
+  try:
+    if rawbufs is None:
+      rawbufs = get_fuzz_rawbufs(lin)
+    else:
+      rawbufs[0] = get_fuzz_rawbuf_like(rawbufs[0], zero=True) # get a new output buffer
+  except KeyboardInterrupt: raise
+  except BaseException:
+    return ("RAWBUFS_ERROR", rawbufs, var_vals, ground_truth, None)
+
+  if var_vals is None:
+    # TODO: handle symbolic max case
+    var_vals = {v.expr: random.randint(v.vmin, v.vmax) for v in lin.ast.variables()}
+
+  if ground_truth is None and not has_bf16:
+    unoptimized = Kernel(lin.ast)
+    if run_linearizer(unoptimized, rawbufs, var_vals)[0] != "PASS":
+      return ("BASELINE_ERROR", rawbufs, var_vals, ground_truth, None)
+    ground_truth = np.frombuffer(rawbufs[0].as_buffer(), _to_np_dtype(rawbufs[0].dtype)).copy()
+
+  rawbufs[0] = get_fuzz_rawbuf_like(rawbufs[0], zero=True) # get a new output buffer
+  run_msg, run_state = run_linearizer(lin, rawbufs, var_vals)
+  if run_msg != "PASS": return (run_msg, rawbufs, var_vals, ground_truth, run_state)
+
+  try:
+    if not has_bf16:
+      result = np.frombuffer(rawbufs[0].as_buffer(), _to_np_dtype(rawbufs[0].dtype))
+      np.testing.assert_allclose(result, ground_truth, rtol=rtol, atol=atol)
+  except KeyboardInterrupt: raise
+  except AssertionError as e:
+    if DEBUG >= 2:
+      print(f"COMPARE_ERROR details: {e}")
+      if getenv("DEBUG_VALUES") > 0:
+        mismatch_indices = np.where(~np.isclose(result, ground_truth, rtol=rtol, atol=atol))
+        mismatched_result = result[mismatch_indices]
+        mismatched_ground_truth = ground_truth[mismatch_indices]
+        for i, idx in enumerate(mismatch_indices[0]):
+          print(f"mismatch at {idx=}: result={mismatched_result[i]} <> ground_truth={mismatched_ground_truth[i]}")
+    return ("COMPARE_ERROR", rawbufs, var_vals, ground_truth, run_state)
+
+  return ("PASS", rawbufs, var_vals, ground_truth, run_state)
+
+def fuzz_linearizer(lin: Kernel, rtol=1e-2, atol=1e-2, opts_list=None):
+  SEED = getenv("SEED", 42)
+  random.seed(SEED)
+  np.random.seed(SEED)
+  print(lin.ast)
+  print(lin.colored_shape())
+  seen_uops = {}
+  last_lins = [lin]
+  failures:defaultdict[str, list[tuple[tuple[UOp, ...], list[Opt]]]] = defaultdict(list)
+  rawbufs, var_vals, ground_truth, validate_rawbufs = None, None, None, None
+
+  FUZZ_ALL_ACTIONS = getenv("FUZZ_ALL_ACTIONS", 0)
+  FUZZ_MAX_SIZE = getenv("FUZZ_MAX_SIZE", 0)
+  FUZZ_IGNORE_SIMPLE_OPS = getenv("FUZZ_IGNORE_SIMPLE_OPS", 1)
+
+  if FUZZ_MAX_SIZE > 0 and prod(lin.full_shape) > FUZZ_MAX_SIZE:
+    print("skipping large kernel")
+    return failures
+  if FUZZ_IGNORE_SIMPLE_OPS and _is_simple(lin):
+    print("skipping simple kernel")
+    return failures
+
+  test_depth = 1 if opts_list is not None else getenv("DEPTH", 1 if FUZZ_ALL_ACTIONS else 10)
+  for depth in range(test_depth):
+    next_lins = []
+    for lin in last_lins:
+      if opts_list is None: actions = get_kernel_actions(lin, include_0=False)
+      else:
+        actions = {}
+        for oi,opts in enumerate(opts_list):
+          lin2 = lin.copy()
+          for o in opts: lin2.apply_opt(o)
+          actions[oi] = lin2
+
+      if not actions: continue
+      if depth == 0 and getenv("FUZZ_REQUIRE_TC", 0):
+        tc_acts = {i: k for k in actions.values() if k.applied_opts[0].op == OptOps.TC}
+        if len(tc_acts) == 0: return failures
+        else: actions = tc_acts
+
+      test_lins = list(actions.values())
+      if FUZZ_ALL_ACTIONS: print(f"testing {lin.applied_opts=} with {len(actions)} actions")
+      elif opts_list is None: test_lins = [random.choice(test_lins)]
+
+      for test_lin in test_lins:
+        if not FUZZ_ALL_ACTIONS and test_lin.applied_opts: print(f"applied opts: {test_lin.applied_opts}")
+
+        # stop if kernel uops repeat
+        try: tuops = tuplize_uops(get_program(test_lin.get_optimized_ast(), test_lin.ren).uops)
+        except KeyboardInterrupt: raise
+        except BaseException as e:
+          print(test_lin.ast)
+          print(test_lin.applied_opts)
+          print(e)
+          failures["LINEARIZE_ERROR"].append((test_lin.ast, test_lin.applied_opts))
+          continue
+
+        if tuops in seen_uops: continue
+        seen_uops[tuops] = tuple(test_lin.applied_opts)
+
+        if not FUZZ_ALL_ACTIONS: print(test_lin.colored_shape())
+
+        (msg, rawbufs, var_vals, ground_truth, state1) = compare_linearizer(test_lin, rawbufs, var_vals, ground_truth, rtol=rtol, atol=atol)
+        if state1 is not None and validate_device is not None:
+          validate_lin = test_lin.copy()
+          validate_lin.ren = validate_device.renderer
+          if validate_rawbufs is None:
+            validate_rawbufs = [get_fuzz_rawbuf_like(x, copy=True, force_device=validate_device.device) for x in rawbufs]
+          (_msg, _, _, _, state2) = compare_linearizer(validate_lin, validate_rawbufs, var_vals, ground_truth, rtol=rtol, atol=atol)
+
+          if _msg != "PASS": failures[f"VALIDATE_DEV_{_msg}"].append((validate_lin.ast, validate_lin.applied_opts))
+
+          ok, err_msg = compare_states(state1, state2)
+          if not ok: failures["HCQ_COMPARE_FAILURE"].append((err_msg, test_lin.ast, test_lin.applied_opts, state1, state2))
+
+        if msg != "PASS":
+          print(test_lin.ast)
+          print(test_lin.applied_opts)
+          print(msg)
+          failures[msg].append((test_lin.ast, test_lin.applied_opts))
+          continue
+
+        next_lins.append(test_lin)
+
+    last_lins = next_lins
+    if FUZZ_ALL_ACTIONS: print(f"depth={depth} total_lins={len(last_lins)} {failures=}")
+  return failures
+
+def _is_simple(lin: Kernel) -> bool:
+  if len(lin.ast.src) > 1: return False
+  ast:UOp = lin.ast.src[0]
+  if ast.src[0].op is Ops.CAST and ast.src[0].src[0].op is Ops.LOAD: return True
+  return False
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(description="Run a fuzz testing on one or more kernels", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument("--ast", type=str, default=None, help="the ast for the kernel to be optimized")
+  parser.add_argument("--file", type=str, default=None, help="a file containing asts to be optimized, one per line")
+  parser.add_argument("--beamreplay", type=str, default=None, help="replay asts and opts got from beam with CAPTURE_BEAM")
+  parser.add_argument("--logfile", type=str, default=None, help="a file containing a tuple of ast and applied_opts, one per line")
+  parser.add_argument("--expected-failures", type=int, default=0, help="the number of expected failed kernels")
+  parser.add_argument("--rtol", type=float, default=1e-2, help="relative tolerance for numerical comparison")
+  parser.add_argument("--atol", type=float, default=1e-2, help="absolute tolerance for numerical comparison")
+  args = parser.parse_args()
+
+  opts_list = None
+  if args.ast is not None:
+    print("loaded AST from CLI")
+    ast_strs = [args.ast]
+  elif args.file is not None:
+    print(f"loading ASTs from file '{args.file}'")
+    with open(args.file, 'r') as file:
+      ast_strs = file.readlines()
+  elif args.beamreplay is not None:
+    print(f"loading BEAM replay from file '{args.beamreplay}'")
+    with open(args.beamreplay, 'r') as file: fdata = file.readlines()
+    ast_strs, opts_list = [x.split(' :: ')[0] for x in fdata if not x.startswith("#")], [x.split(' :: ')[1] for x in fdata if not x.startswith("#")]
+
+    # dedup ast_strs and opts_list
+    dct = defaultdict(list)
+    for i in range(len(ast_strs)): dct[ast_strs[i]].append(eval(opts_list[i]))
+    ast_strs_items = list(dct.keys())
+    opts_list = [dct[c] for c in ast_strs_items]
+  elif args.logfile is not None:
+    print(f"loading ASTs from LOGKERNS file '{args.file}'")
+    with open(args.logfile, 'r') as file:
+      kern_strs = file.readlines()
+      test_lins = [kern_str_to_lin(kern_str) for kern_str in kern_strs]
+      ast_strs = [f"{lin.ast}" for lin in test_lins]
+  else:
+    print("loading ASTs from world")
+    ast_strs = load_worlds(filter_reduce=False, filter_novariable=False)
+
+  print(f"{len(ast_strs)=}")
+  tested = 0
+  failed_ids = []
+  failures = defaultdict(list)
+  seen_ast_strs = set()
+
+  try:
+    for i, ast in enumerate(ast_strs[:getenv("FUZZ_N", len(ast_strs))]):
+      if (nth := getenv("FUZZ_NTH", -1)) != -1 and i != nth: continue
+      if getenv("FUZZ_IMAGEONLY") and "dtypes.image" not in ast: continue
+      if "dtypes.image" in ast and Device.DEFAULT not in {"CL", "QCOM"}: continue  # IMAGE is only for CL
+      if ast in seen_ast_strs: continue
+      seen_ast_strs.add(ast)
+
+      lin = ast_str_to_lin(ast)
+      if not all(is_dtype_supported(buf.dtype) for buf in lin.bufs):
+        print("skipping kernel due to not supported dtype")
+        continue
+
+      with Timing(f"tested ast {i}: "):
+        tested += 1
+        fuzz_failures = fuzz_linearizer(lin, rtol=args.rtol, atol=args.atol, opts_list=(opts_list[i] if opts_list else None))
+        if fuzz_failures: failed_ids.append(i)
+        for k, v in fuzz_failures.items():
+          for f in v:
+            failures[k].append(f)
+  except KeyboardInterrupt: print(colored("STOPPING...", 'red'))
+
+  for msg, errors in failures.items():
+    for i, payload in enumerate(errors):
+      print(f"{msg} {i} kernel: {payload}") # easier to use with output with verify_kernel.py
+
+  print(f"{tested=}")
+  if failures:
+    print(f"{failed_ids=}")
+    for msg, errors in failures.items():
+      print(f"{msg}: {len(errors)}")
+    if len(failed_ids) == args.expected_failures:
+      print(colored(f"{len(failed_ids)} failed as expected", "yellow"))
+  if len(failed_ids) != args.expected_failures:
+    print(colored(f"failed on {len(failed_ids)} kernels, expected {args.expected_failures}", "red"))
+    # TODO: fix this
+    # raise RuntimeError(f"failed on {len(failed_ids)} kernels, expected {args.expected_failures}")
+  else:
+    print(colored("all passed", "green"))
@@ -0,0 +1,157 @@
+from tinygrad import Device, dtypes
+from tinygrad.helpers import getenv, colorize_float, DEBUG
+from extra.optimization.helpers import load_worlds, ast_str_to_lin
+from test.external.fuzz_linearizer import get_fuzz_rawbufs
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.tensor import _to_np_dtype
+from tinygrad.runtime.ops_amd import AMDDevice
+from contextlib import contextmanager
+import numpy as np
+import os, random, statistics
+
+am_signal_pages, am_signal_pool, am_devices = [], [], []
+amd_signal_pages, amd_signal_pool, amd_devices = [], [], []
+
+def rebind_vfio(pcibus="0000:44:00.0"):
+  print("rebind ", pcibus)
+  os.system("sudo rmmod amdgpu")
+  os.system("sudo modprobe vfio-pci")
+
+  base = f"/sys/bus/pci/devices/{pcibus}"
+  if os.path.exists(f"{base}/driver"):
+    with open(f"{base}/driver/unbind", "w") as f: f.write(pcibus)
+  with open(f"{base}/driver_override", "w") as f: f.write("vfio-pci")
+  with open("/sys/bus/pci/drivers_probe", "w") as f: f.write(pcibus)
+
+  os.system("sudo modprobe amdgpu")
+  os.system("rocm-smi --setprofile compute")
+  os.system("rocm-smi --setperflevel high")
+
+@contextmanager
+def run_amd():
+  global amd_signal_pages, amd_signal_pool, amd_devices
+  AMDDevice.driverless = False
+  AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices = amd_signal_pages, amd_signal_pool, amd_devices
+  yield
+  amd_signal_pages, amd_signal_pool, amd_devices = AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices
+  AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices = [], [], []
+
+@contextmanager
+def run_am():
+  global am_signal_pages, am_signal_pool, am_devices
+  AMDDevice.driverless = True
+  AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices = am_signal_pages, am_signal_pool, am_devices
+  yield
+  am_signal_pages, am_signal_pool, am_devices = AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices
+  AMDDevice.signal_pages, AMDDevice.signal_pool, AMDDevice.devices = [], [], []
+
+if __name__ == "__main__":
+  CHECK_CPU = getenv("CHECK_CPU", 0)
+  SEED = getenv("SEED", 42)
+  CNT = getenv("CNT", 7)
+  random.seed(SEED)
+  np.random.seed(SEED)
+
+  # TODO: NUM=780 is super slow
+  # NUM=1907 is broken on AMD and AM have some mismatches (0 vs 1)
+  # kfd feels so bad when taking gpu out while it's running... Need hacks to rebind it before running.
+  rebind_vfio(pcibus="0000:44:00.0")
+
+  ast_strs = load_worlds(filter_reduce=False, filter_novariable=True)
+
+  with run_am():
+    amdev = Device["AMD:1"]
+
+  with run_amd():
+    amddev = Device["AMD"]
+
+  if CHECK_CPU: cpudev = Device["CPU"]
+
+  single = getenv("NUM", -1)
+  if single != -1: ast_strs = ast_strs[single:single+1]
+
+  average_tm_amd, average_tm_am = 0, 0
+  for num,ast in enumerate(ast_strs):
+    with run_amd():
+      amdlin = ast_str_to_lin(ast, opts=amddev.renderer)
+      amdlin.apply_opts(hand_coded_optimizations(amdlin))
+      has_bf16 = any(b.dtype == dtypes.bfloat16 for b in amdlin.bufs)
+
+      amd_prg = CompiledRunner(get_program(amdlin.get_optimized_ast(), amdlin.opts))
+      amdbufs = bufs_from_lin(amdlin)
+      test_amdbufs = get_fuzz_rawbufs(amdlin) if not has_bf16 else amdbufs
+      if not has_bf16: contents = [buf.as_buffer() for buf in test_amdbufs]
+
+    with run_am():
+      rdr = amdev.renderer
+      rdr.device = "AMD:1"
+      amlin = ast_str_to_lin(ast, opts=amdev.renderer)
+      amlin.apply_opts(hand_coded_optimizations(amlin))
+      am_prg = CompiledRunner(get_program(amlin.get_optimized_ast(), amlin.opts))
+      ambufs = bufs_from_lin(amlin)
+      test_ambufs = get_fuzz_rawbufs(amlin) if not has_bf16 else ambufs
+      if not has_bf16:
+        for i,rawbuf in enumerate(test_ambufs): rawbuf.copyin(contents[i])
+
+    if CHECK_CPU:
+      cpu_rdr = cpudev.renderer
+      cpu_rdr.device = "CPU"
+      cpulin = ast_str_to_lin(ast, opts=cpu_rdr)
+      cpulin.apply_opts(hand_coded_optimizations(cpulin))
+      cpu_prg = CompiledRunner(get_program(cpulin.get_optimized_ast(), cpulin.opts))
+      cpubufs = bufs_from_lin(cpulin)
+      test_cpubufs = get_fuzz_rawbufs(cpulin) if not has_bf16 else ambufs
+      if not has_bf16:
+        for i,rawbuf in enumerate(test_cpubufs): rawbuf.copyin(contents[i])
+
+    # warmup
+    tm_amd, tm_am, failed = [], [], False
+    with run_amd():
+      try:
+        amd_prg(test_amdbufs, {}, wait=True)
+        for i in range(CNT): tm_amd.append(amd_prg(amdbufs, {}, wait=True))
+      except RuntimeError:
+        print("AMD FAILED")
+        tm_amd = [1e9]
+        failed = True
+
+    with run_am():
+      try:
+        am_prg(test_ambufs, {}, wait=True)
+        for i in range(CNT): tm_am.append(am_prg(ambufs, {}, wait=True))
+      except RuntimeError:
+        print("AM FAILED")
+        tm_am = [1e9]
+        failed = True
+
+    if CHECK_CPU:
+      cpu_prg(test_cpubufs, {}, wait=True)
+      for i in range(1): cpu_prg(cpubufs, {}, wait=True)
+
+    if not failed and not has_bf16:
+      with run_amd():
+        curesult = np.frombuffer(test_amdbufs[0].as_buffer(), _to_np_dtype(test_amdbufs[0].dtype))
+
+      with run_am():
+        amresult = np.frombuffer(test_ambufs[0].as_buffer(), _to_np_dtype(test_ambufs[0].dtype))
+
+      if CHECK_CPU:
+        cpuresult = np.frombuffer(test_cpubufs[0].as_buffer(), _to_np_dtype(test_cpubufs[0].dtype))
+        np.testing.assert_allclose(amresult, cpuresult, rtol=1e-2, atol=1e-2)
+        np.testing.assert_allclose(curesult, cpuresult, rtol=1e-2, atol=1e-2)
+
+      try:
+        np.testing.assert_allclose(curesult, amresult, rtol=1e-2, atol=1e-2)
+      except AssertionError as e:
+        print("AM and AMD results do not match")
+        print(e)
+
+    bam = statistics.median(tm_am)
+    bamd = statistics.median(tm_amd)
+    average_tm_amd += bamd
+    average_tm_am += bam
+    ratio = bam/bamd
+    print(f"{average_tm_am/average_tm_amd:5.2f}x -- {num:4d} {colorize_float(ratio)} {bam*1e6:7.2f} vs {bamd*1e6:7.2f} us", amlin.name)
+    if DEBUG > 3 and ratio > 1.04: print(f"AM slower {ratio}", amlin.ast, amlin.applied_opts)
@@ -0,0 +1,69 @@
+from tinygrad import Device, dtypes
+from tinygrad.helpers import getenv, colorize_float
+from extra.optimization.helpers import load_worlds, ast_str_to_lin
+from test.external.fuzz_linearizer import get_fuzz_rawbufs
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.tensor import _to_np_dtype
+import numpy as np
+
+if __name__ == "__main__":
+  ast_strs = load_worlds(filter_reduce=False, filter_novariable=True)
+  cudev = Device["CUDA"]
+  nvdev = Device["NV"]
+
+  # NUM=112 python3 test/external/speed_compare_cuda_nv.py
+
+  single = getenv("NUM", -1)
+  if single != -1: ast_strs = ast_strs[single:single+1]
+
+  average_tm_cuda, average_tm_nv = 0, 0
+  for num,ast in enumerate(ast_strs):
+    # cuda compile
+    culin = ast_str_to_lin(ast, opts=cudev.renderer)
+    culin.apply_opts(hand_coded_optimizations(culin))
+    has_bf16 = any(b.dtype == dtypes.bfloat16 for b in culin.bufs)
+
+    cuda_prg = CompiledRunner(get_program(culin.get_optimized_ast(), culin.opts))
+    cubufs = bufs_from_lin(culin)
+    test_cubufs = get_fuzz_rawbufs(culin) if not has_bf16 else cubufs
+
+    rdr = nvdev.renderer
+    rdr.device = "NV"
+    nvlin = ast_str_to_lin(ast, opts=rdr)
+    nvlin.apply_opts(hand_coded_optimizations(nvlin))
+    nv_prg = CompiledRunner(get_program(nvlin.get_optimized_ast(), nvlin.opts))
+    nvbufs = bufs_from_lin(nvlin)
+    test_nvbufs = get_fuzz_rawbufs(nvlin) if not has_bf16 else nvbufs
+    if not has_bf16:
+      for i,rawbuf in enumerate(test_nvbufs): rawbuf.copyin(test_cubufs[i].as_buffer())
+
+    # warmup
+    tm_cuda, tm_nv, failed = [], [], False
+    try:
+      cuda_prg(test_cubufs, {}, wait=True)
+      for i in range(5): tm_cuda.append(cuda_prg(cubufs, {}, wait=True))
+    except RuntimeError:
+      print("CUDA FAILED")
+      tm_cuda = [1e9]
+      failed = True
+
+    try:
+      nv_prg(test_nvbufs, {}, wait=True)
+      for i in range(5): tm_nv.append(nv_prg(nvbufs, {}, wait=True))
+    except RuntimeError:
+      print("NV FAILED")
+      tm_nv = [1e9]
+      failed = True
+
+    if not failed and not has_bf16:
+      curesult = np.frombuffer(test_cubufs[0].as_buffer(), _to_np_dtype(test_cubufs[0].dtype))
+      nvresult = np.frombuffer(test_nvbufs[0].as_buffer(), _to_np_dtype(test_nvbufs[0].dtype))
+      np.testing.assert_allclose(curesult, nvresult, rtol=1e-2, atol=1e-2)
+
+    average_tm_cuda += min(tm_cuda)
+    average_tm_nv += min(tm_nv)
+    ratio = min(tm_nv)/min(tm_cuda)
+    print(f"{average_tm_nv/average_tm_cuda:5.2f}x -- {num:4d} {colorize_float(ratio)}  {min(tm_nv)*1e6:7.2f} us", nvlin.name)
+    if ratio > 1.04: print(f"NV slower {ratio}", nvlin.ast, nvlin.applied_opts)
@@ -0,0 +1,60 @@
+import itertools
+from tinygrad import Device
+from tinygrad.engine.realize import CompiledRunner, get_program
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.helpers import getenv, colorize_float
+from extra.optimization.helpers import load_worlds, ast_str_to_lin
+from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.runtime.ops_cuda import PTXCompiler, PTXRenderer, CUDACompiler
+
+if __name__ == "__main__":
+  ast_strs = load_worlds(filter_reduce=False, filter_novariable=True)
+  # no bfloat16 for ptx at the moment
+  ast_strs = [x for x in ast_strs if "dtypes.bfloat16" not in x]
+  dev = Device["CUDA"]
+  ptx = PTXRenderer(dev.arch)
+
+  # NUM=112 python3 test/external/speed_compare_cuda_ptx.py
+
+  single = getenv("NUM", -1)
+  if single != -1: ast_strs = ast_strs[single:single+1]
+
+  average_tm_cuda, average_tm_ptx = 0, 0
+  for num,ast in enumerate(ast_strs):
+    # cuda compile
+    dev.compiler = CUDACompiler(dev.arch)
+    lin = ast_str_to_lin(ast, opts=dev.renderer)
+    lin.apply_opts(hand_coded_optimizations(lin))
+    cuda_prg = CompiledRunner(get_program(lin.get_optimized_ast(), lin.opts))
+
+    bufs = bufs_from_lin(lin)
+
+    # ptx compile
+    dev.compiler = PTXCompiler(dev.arch)
+    lin = ast_str_to_lin(ast, opts=ptx)
+    lin.apply_opts(hand_coded_optimizations(lin))
+    ptx_prg = CompiledRunner(get_program(lin.get_optimized_ast(), lin.opts))
+
+    # warmup
+    try:
+      cuda_prg(bufs, {}, wait=True)
+    except RuntimeError:
+      print("cuda failed ast:", num)
+      continue
+    ptx_prg(bufs, {}, wait=True)
+
+    tm_cuda, tm_ptx = [], []
+    for i in range(5):
+      tm_cuda.append(cuda_prg(bufs, {}, wait=True))
+      tm_ptx.append(ptx_prg(bufs, {}, wait=True))
+    average_tm_cuda += min(tm_cuda)
+    average_tm_ptx += min(tm_ptx)
+    ratio = min(tm_ptx)/min(tm_cuda)
+    print(f"{average_tm_ptx/average_tm_cuda:5.2f}x -- {num:4d} {colorize_float(ratio)}  {min(tm_ptx)*1e6:7.2f} us", lin.name)
+    if ratio > 1.5:
+      def fix(x): return x.replace('\t', ' ').strip()
+      ll1, ll2 = cuda_prg.lib.decode().split('\n'), ptx_prg.lib.decode().split('\n')
+      if single != -1:
+        for ln, (l1, l2) in enumerate(itertools.zip_longest(ll1, ll2, fillvalue='')):
+          print(f"{ln:5d} | {fix(l1):80s} | {fix(l2):80s}")
+      print(len(ll1), len(ll2), "RATIO", ratio, "us", min(tm_ptx)*1e6)
@@ -0,0 +1,78 @@
+import argparse
+from collections import defaultdict
+from extra.optimization.helpers import kern_str_to_lin, time_linearizer
+from test.external.fuzz_linearizer import compare_linearizer
+from tinygrad.helpers import colored
+from tinygrad.codegen.opt.kernel import Kernel
+
+# Use this with the LOGKERNS options to verify that all executed kernels are valid and evaluate to the same ground truth results
+
+# Example for GPT2:
+# 1) Run the model to log all kernels: `PYTHONPATH=. LOGKERNS=/tmp/gpt2_kerns.txt JIT=1 HALF=1 BEAM=2 CACHELEVEL=0 python3 examples/gpt2.py --count 10 --temperature 0 --timing`   # noqa: E501
+# 2) Validate the kernel correctness: `PYTHONPATH=. python3 ./test/external/verify_kernel.py --file /tmp/gpt2_kerns.txt`
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(description="Verify the correctness of one or more kernel", formatter_class=argparse.ArgumentDefaultsHelpFormatter)    # noqa: E501
+  parser.add_argument("--kernel", type=str, default=None, help="a string of a tuple of (ast, applied_opts,)")
+  parser.add_argument("--file", type=str, default=None, help="a file containing a tuple of ast and applied_opts, one per line")
+  parser.add_argument("--pkl", type=str, default=None, help="a pickle file containing a single tuple of ast and applied_opts")
+  parser.add_argument("--rtol", type=float, default=1e-2, help="relative tolerance for numerical comparison")
+  parser.add_argument("--atol", type=float, default=1e-2, help="absolute tolerance for numerical comparison")
+  parser.add_argument("--timing", action='store_true', help="show final timing for the kernel")
+  parser.add_argument("--expected-failures", type=int, default=0, help="the number of expected failed kernels")
+  args = parser.parse_args()
+
+  if args.kernel is not None:
+    print("loading kernel from args")
+    test_lins = [kern_str_to_lin(args.kernel)]
+  elif args.file is not None:
+    print(f"loading kernel from file '{args.file}'")
+    with open(args.file, 'r') as file:
+      kern_strs = file.readlines()
+      test_lins = [kern_str_to_lin(kern_str) for kern_str in kern_strs]
+  elif args.pkl is not None:
+    print(f"loading kernel from pickle file '{args.file}'")
+    import pickle
+    with open(args.pkl, 'rb') as file:
+      (ast, applied_opts,) = pickle.load(file)
+      lin = Kernel(ast)
+      lin.apply_opts(applied_opts)
+      test_lins = [lin]
+
+  else:
+    raise RuntimeError("no kernel specified; use --kernel, --file, or --pkl options")
+
+  print(f"verifying {len(test_lins)} kernels")
+
+  failed_ids = []
+  failures = defaultdict(list)
+  for i, test_lin in enumerate(test_lins):
+    print(f"testing kernel {i}")
+    print(test_lin.ast)
+    print(test_lin.applied_opts)
+    unoptimized_lin = Kernel(test_lin.ast)
+    print(f"{unoptimized_lin.colored_shape()} -> {test_lin.colored_shape()}")
+    (msg,rb,vv,gt) = compare_linearizer(test_lin, None, None, None, rtol=args.rtol, atol=args.atol)
+    if msg != "PASS":
+      failed_ids.append(i)
+      failures[msg].append((test_lin.ast, test_lin.applied_opts))
+    if args.timing:
+      tm = time_linearizer(test_lin, rb, allow_test_size=False, cnt=10)
+      print(f"final time {tm*1e6:9.0f} us")
+
+  for msg, errors in failures.items():
+    for i, (ast, opts) in enumerate(errors):
+      print(f"{msg} {i} AST: {ast}")
+      print(f"{msg} {i} OPTS: {opts}\n")
+
+  print(f"tested {len(test_lins)} kernels")
+  if failures:
+    print(f"{failed_ids=}")
+    for msg, errors in failures.items():
+      print(f"{msg}: {len(errors)}")
+    if len(failed_ids) == args.expected_failures:
+      print(colored(f"{len(failed_ids)} failed as expected", "yellow"))
+  if len(failed_ids) != args.expected_failures:
+    raise RuntimeError(f"failed on {len(failed_ids)} kernels, expected {args.expected_failures}")
+  else:
+    print(colored("all passed", "green"))
@@ -61,7 +61,7 @@ def eval_uop(uop:UOp, inputs:list[tuple[DType, list[Any]]]|None=None):
  for buf_dt, data in inputs or []:
    bufs.append(buf:=allocator.alloc(len(data) * buf_dt.itemsize))
    allocator._copyin(buf, memoryview(struct.pack(str(len(data)) + (buf_dt.fmt or ""), *data)))
-  g = UOp(Ops.PARAM, uop.dtype.ptr(), arg=0, src=())
+  g = UOp(Ops.DEFINE_GLOBAL, uop.dtype.ptr(), arg=0, src=())
  prg = get_program(UOp.store(g.index(UOp.const(dtypes.int, 0)), uop).sink(), PythonRenderer())
  prog = PythonProgram("run", PythonCompiler().compile(prg.src))
  prog(out_buf:=allocator.alloc(uop.dtype.itemsize), *bufs)
@@ -4,7 +4,7 @@ import tinygrad.runtime.autogen.am.am as am
 import tinygrad.runtime.autogen.amdgpu_drm as amdgpu_drm
 from tinygrad.helpers import from_mv
 from test.mockgpu.driver import VirtDriver, VirtFileDesc, TextFileDesc, DirFileDesc, VirtFile
-from test.mockgpu.amd.amdgpu import AMDGPU, gpu_props, GFX_TARGET_VERSION, MOCKGPU_ARCH
+from test.mockgpu.amd.amdgpu import AMDGPU, gpu_props

 libc = ctypes.CDLL(ctypes.util.find_library("c"))
 libc.mmap.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_long]
@@ -90,30 +90,35 @@ class AMDDriver(VirtDriver):
  def _prepare_gpu(self, gpu_id):
    self.doorbells[gpu_id] = memoryview(bytearray(0x2000))
    self.gpus[gpu_id] = AMDGPU(gpu_id)
-    # IP versions: rdna3 = GC 11.0.0, NBIF 4.3.0; rdna4 = GC 12.0.0, NBIF 6.3.1
-    ip_versions = {"rdna3": {"gc": (11, 0, 0), "sdma": (6, 0, 0), "nbif": (4, 3, 0)},
-                   "rdna4": {"gc": (12, 0, 0), "sdma": (6, 0, 0), "nbif": (6, 3, 1)}}[MOCKGPU_ARCH]
-    def ip_discovery_files(hwid, ver, base_addr):
-      p = f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{hwid}/0'
-      return [VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{hwid}', functools.partial(DirFileDesc, child_names=['0'])),
-              VirtFile(f'{p}/major', functools.partial(TextFileDesc, text=str(ver[0]))),
-              VirtFile(f'{p}/minor', functools.partial(TextFileDesc, text=str(ver[1]))),
-              VirtFile(f'{p}/revision', functools.partial(TextFileDesc, text=str(ver[2]))),
-              VirtFile(f'{p}/base_addr', functools.partial(TextFileDesc, text=base_addr))]
    self.tracked_files += [
      VirtFile('/sys/module/amdgpu', functools.partial(TextFileDesc, text="1")),
      VirtFile('/sys/module/amdgpu/parameters/ppfeaturemask', functools.partial(TextFileDesc, text="0xffff3fff")),
      VirtFile(f'/sys/devices/virtual/kfd/kfd/topology/nodes/{gpu_id}', functools.partial(DirFileDesc, child_names=['gpu_id', 'properties'])),
      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, gfx_target_version=GFX_TARGET_VERSION))),
+        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)])),
-      *ip_discovery_files(am.GC_HWID, ip_versions["gc"], '0x00001260\n0x0000A000\n0x0001C000\n0x02402C00'),
-      *ip_discovery_files(am.SDMA0_HWID, ip_versions["sdma"], '0x00001260\n0x0000A000\n0x0001C000\n0x02402C00'),
-      *ip_discovery_files(am.NBIF_HWID, ip_versions["nbif"], '0x00000000\n0x00000014\n0x00000D20\n0x00010400\n0x0241B000\n0x04040000'),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/major', functools.partial(TextFileDesc, text='11')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/minor', functools.partial(TextFileDesc, text='0')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/base_addr',
+               functools.partial(TextFileDesc, text='0x00001260\n0x0000A000\n0x0001C000\n0x02402C00')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/major', functools.partial(TextFileDesc, text='6')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/minor', functools.partial(TextFileDesc, text='0')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/base_addr',
+               functools.partial(TextFileDesc, text='0x00001260\n0x0000A000\n0x0001C000\n0x02402C00')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/major', functools.partial(TextFileDesc, text='4')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/minor', functools.partial(TextFileDesc, text='3')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/base_addr',
+               functools.partial(TextFileDesc, text='0x00000000\n0x00000014\n0x00000D20\n0x00010400\n0x0241B000\n0x04040000')),
      VirtFile(f'/dev/dri/renderD{gpu_id}', functools.partial(DRMFileDesc, driver=self, gpu=f"{self.gpus[gpu_id]}")),
    ]

@@ -1,11 +1,8 @@
 import ctypes, time
 from test.mockgpu.gpu import VirtGPU
 from test.mockgpu.helpers import _try_dlopen_remu
-from tinygrad.helpers import getbits, to_mv, getenv
+from tinygrad.helpers import getbits, to_mv
 from tinygrad.runtime.support import c
-
-MOCKGPU_ARCH = getenv("MOCKGPU_ARCH", "rdna3")
-GFX_TARGET_VERSION = {"rdna3": 110000, "rdna4": 120000}[MOCKGPU_ARCH]
 import tinygrad.runtime.autogen.amd_gpu as amd_gpu, tinygrad.runtime.autogen.am.pm4_nv as pm4

 SDMA_MAX_COPY_SIZE = 0x400000
@@ -197,11 +194,10 @@ class PM4Executor(AMDQueue):
    scratch_size = wavesize * 4  # This gives the scratch size per thread (lane)

    assert prg_sz > 0, "Invalid prg ptr (not found in mapped ranges)"
-    # Pass valid memory ranges, rsrc2, scratch_size and arch to Python emulator
+    # Pass valid memory ranges, rsrc2, and scratch_size to Python emulator
    if hasattr(remu, 'valid_mem_ranges'): remu.valid_mem_ranges = self.gpu.mapped_ranges
    if hasattr(remu, 'rsrc2'): remu.rsrc2 = rsrc2
    if hasattr(remu, 'scratch_size'): remu.scratch_size = scratch_size
-    if hasattr(remu, 'arch'): remu.arch = self.gpu.arch
    err = remu.run_asm(prg_addr, prg_sz, *gl, *lc, args_addr)
    if err != 0: raise RuntimeError("remu does not support the new instruction introduced in this kernel")

@@ -318,7 +314,6 @@ class AMDGPU(VirtGPU):
    self.regs = AMDGPURegisters()
    self.mapped_ranges = set()
    self.queues = []
-    self.arch = MOCKGPU_ARCH

  def map_range(self, vaddr, size): self.mapped_ranges.add((vaddr, size))
  def unmap_range(self, vaddr, size): self.mapped_ranges.remove((vaddr, size))
@@ -347,7 +342,7 @@ simd_arrays_per_engine 2
 cu_per_simd_array 8
 simd_per_cu 2
 max_slots_scratch_cu 32
-gfx_target_version {gfx_target_version}
+gfx_target_version 110000
 vendor_id 4098
 device_id 29772
 location_id 34304
@@ -16,15 +16,14 @@ def _try_dlopen_gpuocelot():
  return None

 class PythonRemu:
-  """Python RDNA3/RDNA4 emulator wrapper that matches the libremu.so interface."""
+  """Python RDNA3 emulator wrapper that matches the libremu.so interface."""
  valid_mem_ranges: set[tuple[int, int]] = set()
  rsrc2: int = 0x19c  # Default: USER_SGPR_COUNT=14, enable X and Y workgroup IDs
  scratch_size: int = 0  # private_segment_fixed_size from kernel descriptor
-  arch: str = "rdna3"  # Architecture: rdna3 or rdna4

  def run_asm(self, lib: int, lib_sz: int, gx: int, gy: int, gz: int, lx: int, ly: int, lz: int, args_ptr: int) -> int:
    from extra.assembly.amd.emu import run_asm
-    return run_asm(lib, lib_sz, gx, gy, gz, lx, ly, lz, args_ptr, self.rsrc2, self.scratch_size, self.arch)
+    return run_asm(lib, lib_sz, gx, gy, gz, lx, ly, lz, args_ptr, self.rsrc2, self.scratch_size)

 def _try_dlopen_remu():
  # Use Python emulator only if PYTHON_REMU=1
@@ -42,9 +42,7 @@ def _memoryview(cls, mem):
      for st,en,rcb,wcb in d.tracked_addresses:
        if st <= addr <= en: return TrackedMemoryView(mem, rcb, wcb)
  return original_memoryview(mem)
-class _MockMemoryviewMeta(type):
-  def __instancecheck__(cls, instance): return isinstance(instance, (original_memoryview, TrackedMemoryView))
-builtins.memoryview = _MockMemoryviewMeta("memoryview", (), {'__new__': _memoryview}) # type: ignore
+builtins.memoryview = type("memoryview", (), {'__new__': _memoryview}) # type: ignore

 def _open(path, flags):
  for d in drivers:
@@ -1,94 +0,0 @@
-import unittest
-from tinygrad import Tensor, dtypes
-from tinygrad.tensor import _METADATA
-from tinygrad.helpers import Context
-
-class TestTensorMetadata(unittest.TestCase):
-  def setUp(self) -> None:
-    _METADATA.set(None)
-    self._ctx = Context(SCACHE=0)
-    self._ctx.__enter__()
-  def tearDown(self) -> None:
-    self._ctx.__exit__(None, None, None)
-
-  @unittest.skip("why would this be true?")
-  def test_exclude_noop_metadata(self):
-    a = Tensor.rand(4, 4)*1
-    self.assertEqual(a.uop.metadata[0].name, "__mul__")
-    k = a.schedule()[-1]
-    self.assertEqual([m.name for m in k.metadata], ["rand"])
-
-  @unittest.skip("metadata not reaching kernel schedule")
-  def test_exclude_const_metadata(self):
-    a = Tensor.arange(4)
-    b = Tensor.full((4,), -1, dtype=dtypes.int).contiguous()
-    sched = Tensor.schedule(a, b)
-    self.assertEqual([m.name for m in sched[0].metadata], ["arange"])
-    self.assertEqual([m.name for m in sched[1].metadata], ["contiguous"])
-
-  def test_matmul(self):
-    x = Tensor.rand(3, requires_grad=True)
-    W = Tensor.rand(3, 3, requires_grad=True)
-    out = x.matmul(W)
-    self.assertEqual(out.uop.metadata[0].name, "matmul")
-    si = out.schedule()[-1]
-    self.assertEqual(len(si.metadata), 1)
-    self.assertEqual(si.metadata[0].name, "matmul")
-
-  def test_relu(self):
-    x = Tensor.rand(3, requires_grad=True)
-    out = x.relu()
-    self.assertEqual(out.uop.metadata[0].name, "relu")
-    si = out.schedule()[-1]
-    self.assertEqual(len(si.metadata), 1)
-    self.assertEqual(si.metadata[0].name, "relu")
-
-  @unittest.skip("assign metadata no longer captured")
-  def test_assign(self):
-    x = Tensor.empty(10, 10).realize()
-    x.assign(Tensor.ones(10, 10).contiguous())
-    si = x.schedule()[-1]
-    self.assertEqual(len(si.metadata), 1)
-    self.assertEqual(si.metadata[0].name, "assign")
-
-  def test_complex(self):
-    x = Tensor.rand(3, requires_grad=True)
-    y = Tensor.rand(3, requires_grad=True)
-    out = x.relu() * y.sigmoid()
-    self.assertEqual(out.uop.metadata[0].name, "__mul__")
-    self.assertEqual(out.uop.src[0].metadata[0].name, "relu")
-    self.assertEqual(out.uop.src[1].metadata[0].name, "sigmoid")
-    si = out.schedule()[-1]
-    self.assertEqual(len(si.metadata), 3)
-    self.assertEqual(set(m.name for m in si.metadata), {"relu", "sigmoid", "__mul__"})
-
-  def test_complex_backward(self):
-    x = Tensor.rand(3, requires_grad=True).realize()
-    y = Tensor.rand(3, requires_grad=True).realize()
-    out = (x.relu() * y.sigmoid()).sum()
-    self.assertEqual(out.uop.metadata[0].name, "sum")
-    out.backward()
-    self.assertEqual(x.grad.uop.metadata[0].name, "relu")
-    #self.assertTrue(x.grad.uop.metadata[0].backward)  # TODO: backward flag is False
-    self.assertEqual(y.grad.uop.metadata[0].name, "sigmoid")
-    #self.assertTrue(y.grad.uop.metadata[0].backward)  # TODO: backward flag is False
-    si = Tensor.schedule(out, x.grad, y.grad)[-1]
-    #self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
-    # skip numpy, this is schedule cache
-    self.assertSetEqual(set(m.name for m in si.metadata if m.name != "numpy"), {"sigmoid", "relu"})
-    #bw = [m for m in si.metadata if m.backward]
-    #self.assertEqual(len(bw), 1)
-    #self.assertEqual(bw[0].name, "sigmoid")
-
-  def test_tracemeta_0(self):
-    with Context(TRACEMETA=0):
-      x = Tensor.rand(3, requires_grad=True)
-      y = Tensor.rand(3, requires_grad=True)
-      out = (x.relu() * y.sigmoid()).sum()
-      self.assertIsNone(out.uop.metadata)
-      self.assertIsNone(out.uop.src[0].metadata)
-      si = out.schedule()[-1]
-      self.assertEqual(si.metadata, ())
-
-if __name__ == '__main__':
-  unittest.main()
@@ -163,30 +163,5 @@ class TestIndexing(unittest.TestCase):
  # at least the arange is being fused
  def test_llama_embedding_opt(self): self.test_llama_embedding(0, 1_736_704_000)

-  # NOTE: call doesn't work with SPEC=2
-  @unittest.skipIf(Device.DEFAULT not in ("CPU", "AMD"), "atomics only on AMD/CPU")
-  @Context(USE_ATOMICS=1, SPEC=1)
-  def test_llama_8b_embedding_backward(self):
-    from tinygrad.renderer.cstyle import CStyleLanguage
-    if Device.DEFAULT == "CPU" and not isinstance(Device["CPU"].renderer, CStyleLanguage): self.skipTest("CPU needs Clang renderer")
-    vocab_size, embed_size = 1000, 128
-    bs, seqlen = 4, 256
-    idx = Tensor.randint(bs, seqlen, high=vocab_size)
-    emb = nn.Embedding(vocab_size, embed_size)
-    emb.weight = Tensor.ones(vocab_size, embed_size, requires_grad=True)
-    gt = Tensor.zeros(bs, seqlen, embed_size)
-    Tensor.realize(idx, emb.weight, gt)
-    GlobalCounters.reset()
-    loss = (emb(idx)-gt).square().sum()
-    loss.backward()
-    emb.weight.grad.realize()
-    bwd_ops = GlobalCounters.global_ops
-    print(f"embedding bwd: {GlobalCounters.kernel_count} kernels, {bwd_ops:,} ops")
-    self.assertLess(bwd_ops, bs*seqlen*embed_size*20, f"backward ops {bwd_ops:,} should be less than 20 per with atomic scatter-add")
-    # correctness check
-    expected_grad = np.zeros((vocab_size, embed_size), dtype=np.float32)
-    for i in idx.flatten().numpy(): expected_grad[i] += 2
-    np.testing.assert_allclose(emb.weight.grad.numpy(), expected_grad, rtol=1e-5, atol=1e-5)
-
 if __name__ == "__main__":
  unittest.main()
@@ -247,39 +247,5 @@ class TestCustomKernel(unittest.TestCase):
    err = (O_custom - O_ref).square().max()
    self.assertLess(err.item(), 1e-6)

-  def test_multi_after_schedule_order(self):
-    """Test correct scheduling order when custom_kernel has multiple outputs.
-
-    custom_kernel with 4 arguments creates 4 AFTERs from the same kernel.
-    The custom_kernel depends on both A2 and B2, so it must be scheduled after both.
-    E only depends on A2, so E can run before custom_kernel finishes waiting for B2.
-
-    Expected schedule order: [A2, B2, E, custom_addmul, final_sum]
-    The custom_addmul kernel should be at index 3.
-    """
-    from tinygrad.engine.schedule import create_schedule
-    from tinygrad.schedule.rangeify import get_rangeify_map
-
-    A, B = Tensor.empty(4, 4), Tensor.empty(4, 4)
-    A2 = (A + 1).contiguous()                      # kernel 0: depends on A
-    B2 = (B * 2).contiguous()                      # kernel 1: depends on B
-    C, D = Tensor.empty(4, 4), Tensor.empty(4, 4)
-    C, D, _, _ = Tensor.custom_kernel(C, D, A2, B2, fxn=custom_elementwise_addmul_kernel)  # depends on A2 AND B2
-    E = (A2 * 3).contiguous()                      # kernel 2: depends only on A2
-    result = (C + D + E).sum()                     # kernel 3: custom_addmul, then kernel 4: sum
-
-    big_sink = result.uop.sink()
-    tensor_map = get_rangeify_map(big_sink)
-    sched_sink = big_sink.substitute(tensor_map)
-    schedule, _ = create_schedule(sched_sink)
-
-    # Find the custom_addmul kernel position
-    custom_idx = next((i for i, item in enumerate(schedule)
-                       if hasattr(item.ast, "arg") and hasattr(item.ast.arg, "name")
-                       and "custom_addmul" in item.ast.arg.name), None)
-
-    self.assertIsNotNone(custom_idx, "custom_addmul kernel not found in schedule")
-    self.assertEqual(custom_idx, 3, f"custom_addmul should be at index 3, got {custom_idx}")
-
 if __name__ == '__main__':
  unittest.main()
@@ -1,4 +1,4 @@
-import contextlib, unittest, math
+import unittest, math
 import numpy as np
 import torch
 from typing import Any, List
@@ -7,8 +7,7 @@ from tinygrad.helpers import getenv, DEBUG, CI, EMULATED_DTYPES
 from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8, _to_np_dtype, _to_torch_dtype, truncate
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.nir import NIRRenderer
-from tinygrad import Context, Device, Tensor, dtypes
-from tinygrad.uop import Ops
+from tinygrad import Device, Tensor, dtypes
 from hypothesis import given, settings, strategies as strat
 from test.helpers import rand_for_dtype
 from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX
@@ -337,37 +336,18 @@ class TestUint16DType(TestDType):
 class TestInt32DType(TestDType): DTYPE = dtypes.int32
 class TestUint32DType(TestDType): DTYPE = dtypes.uint32

-class TestInt64DType(TestDType): DTYPE = dtypes.int64
-
-@unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-class TestEmulatedInt64DType(TestInt64DType):
+class TestInt64DType(TestDType):
+  DTYPE = dtypes.int64
  @classmethod
-  def setUpClass(cls):
-    cls.stack = contextlib.ExitStack()
-    cls.stack.enter_context(Context(EMULATED_DTYPES="long"))
-    cls.DATA = rand_for_dtype(cls.DTYPE, 10)
-
-  @classmethod
-  def tearDownClass(cls): cls.stack.close()
+  def setUpClass(cls): cls.DATA = rand_for_dtype(cls.DTYPE, 10)

 class TestUint64DType(TestDType):
+  @classmethod
+  def setUpClass(cls): cls.DATA = rand_for_dtype(cls.DTYPE, 10)
  DTYPE = dtypes.uint64
  def test_uint64_load(self):
    assert Tensor(2**64 - 1, dtype=dtypes.uint64).numpy() == 2**64 - 1

-@unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-class TestEmulatedUInt64DType(TestUint64DType):
-  @classmethod
-  def setUpClass(cls):
-    cls.stack = contextlib.ExitStack()
-    cls.stack.enter_context(Context(EMULATED_DTYPES="long"))
-    cls.DATA = rand_for_dtype(cls.DTYPE, 10)
-
-  @classmethod
-  def tearDownClass(cls): cls.stack.close()
-
 class TestBoolDType(TestDType): DTYPE = dtypes.bool

 class TestBFloat16Type(TestDType): DTYPE = dtypes.bfloat16
@@ -1,5 +1,5 @@
 import unittest, operator, math
-from tinygrad import Context, Tensor, dtypes, Device
+from tinygrad import Tensor, dtypes, Device
 from tinygrad.dtype import DType, truncate
 from tinygrad.helpers import CI, getenv
 from tinygrad.tensor import _to_np_dtype
@@ -7,7 +7,6 @@ from tinygrad.device import is_dtype_supported
 from tinygrad.runtime.ops_python import from_storage_scalar
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.nir import NIRRenderer
-from tinygrad.uop import Ops
 import numpy as np
 import pytest
 from hypothesis import assume, given, strategies as strat, settings
@@ -166,16 +165,9 @@ class TestDTypeALU(unittest.TestCase):
  @given(ht.uint32, ht.uint32, strat.sampled_from(integer_binary_operations))
  def test_uint32(self, a, b, op): universal_test(a, b, dtypes.uint32, op)

-  @unittest.skipUnless(is_dtype_supported(dtypes.uint64), f"no uint64 on {Device.DEFAULT}")
  @given(ht.uint64, ht.uint64, strat.sampled_from(integer_binary_operations))
  def test_uint64(self, a, b, op): universal_test(a, b, dtypes.uint64, op)

-  @unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-  @given(ht.uint64, ht.uint64, strat.sampled_from(integer_binary_operations))
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_uint64(self, a, b, op): universal_test(a, b, dtypes.uint64, op)
-
  @given(ht.int8, ht.int8, strat.sampled_from(integer_binary_operations))
  def test_int8(self, a, b, op): universal_test(a, b, dtypes.int8, op)

@@ -185,16 +177,9 @@ class TestDTypeALU(unittest.TestCase):
  @given(ht.int32, ht.int32, strat.sampled_from(integer_binary_operations))
  def test_int32(self, a, b, op): universal_test(a, b, dtypes.int32, op)

-  @unittest.skipUnless(is_dtype_supported(dtypes.int64), f"no int64 on {Device.DEFAULT}")
  @given(ht.int64, ht.int64, strat.sampled_from(integer_binary_operations))
  def test_int64(self, a, b, op): universal_test(a, b, dtypes.int64, op)

-  @unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-  @given(ht.int64, ht.int64, strat.sampled_from(integer_binary_operations))
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_int64(self, a, b, op): universal_test(a, b, dtypes.int64, op)
-
  @given(ht.uint8, strat.sampled_from(integer_unary_operations))
  def test_uint8_unary(self, a, op): universal_test_unary(a, dtypes.uint8, op)

@@ -206,16 +191,9 @@ class TestDTypeALU(unittest.TestCase):
  @given(ht.uint32, strat.sampled_from(integer_unary_operations))
  def test_uint32_unary(self, a, op): universal_test_unary(a, dtypes.uint32, op)

-  @unittest.skipUnless(is_dtype_supported(dtypes.uint64), f"no uint64 on {Device.DEFAULT}")
  @given(ht.uint64, strat.sampled_from(integer_unary_operations))
  def test_uint64_unary(self, a, op): universal_test_unary(a, dtypes.uint64, op)

-  @unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-  @given(ht.uint64, strat.sampled_from(integer_unary_operations))
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_uint64_unary(self, a, op): universal_test_unary(a, dtypes.uint64, op)
-
  @given(ht.int8, strat.sampled_from(integer_unary_operations))
  def test_int8_unary(self, a, op): universal_test_unary(a, dtypes.int8, op)

@@ -225,16 +203,9 @@ class TestDTypeALU(unittest.TestCase):
  @given(ht.int32, strat.sampled_from(integer_unary_operations))
  def test_int32_unary(self, a, op): universal_test_unary(a, dtypes.int32, op)

-  @unittest.skipUnless(is_dtype_supported(dtypes.int64), f"no int64 on {Device.DEFAULT}")
  @given(ht.int64, strat.sampled_from(integer_unary_operations))
  def test_int64_unary(self, a, op): universal_test_unary(a, dtypes.int64, op)

-  @unittest.skipUnless(Ops.SHL in Device[Device.DEFAULT].renderer.code_for_op, "long decomp requires bitshift")
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
-  @given(ht.int64, strat.sampled_from(integer_unary_operations))
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_int64_unary(self, a, op): universal_test_unary(a, dtypes.int64, op)
-
  @given(ht.bool, ht.bool, strat.sampled_from(((operator.add, operator.add), (operator.mul, operator.mul))))
  def test_bool(self, a, b, op): universal_test(a, b, dtypes.bool, op)

@@ -195,10 +195,8 @@ class TestAssignIssues(unittest.TestCase):
    t.shrink(((1, 3), (1, 3))).assign(Tensor.ones(2, 2))
    np.testing.assert_allclose(t.numpy(), torch_tensor.numpy())

-  @unittest.expectedFailure
  def test_assign_broadcast(self):
    # broadcasting during assign should behave like PyTorch
-    # NOTE: we don't want implicit dtype casting (int64 -> float32 loses precision), so this fails
    torch_tensor = torch.zeros(3, 5)
    torch_tensor[:] = torch.arange(5)
    t = Tensor.zeros(3, 5)
@@ -188,8 +188,9 @@ class TestLinearizer(unittest.TestCase):
    assert any(x.op is Ops.DEFINE_LOCAL for x in stores[0].toposort())
    # the second store is to gds with no upcasts
    assert stores[1].src[1].dtype == dtypes.float
-    assert any(x.op is Ops.PARAM for x in stores[1].toposort())
+    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)
@@ -449,7 +450,7 @@ class TestLinearizer(unittest.TestCase):
    def get_recursive(uop): return set.union(set(uop.src), [uop], *[get_recursive(v) for v in uop.src])
    uops = get_program(ast, renderer=Device[Device.DEFAULT].renderer, opts=opt).uops
    local_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_LOCAL for x in get_recursive(u.src[0]))]
-    global_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.PARAM for x in get_recursive(u.src[0]))]
+    global_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_GLOBAL for x in get_recursive(u.src[0]))]
    barrier = [u for u in uops if u.op is Ops.BARRIER]
    assert len(barrier) == 1
    # check that the float4 cast collapses for all stores
@@ -11,16 +11,16 @@ from tinygrad.engine.realize import get_program
 class TestLinearizerFailure(unittest.TestCase):
  @unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
  def test_failure_beam_mnist(self):
-    c0 = UOp(Ops.PARAM, dtypes.uchar.ptr(4014080), arg=0, src=())
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.uchar.ptr(4014080), arg=0, src=())
    c1 = UOp.range(UOp.const(dtypes.index, 512), 0, AxisType.GLOBAL)
    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.PARAM, dtypes.int.ptr(512), arg=1, src=())
+    c4 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(512), arg=1, src=())
    c5 = c4.index(c1.valid(UOp.const(dtypes.bool, True)))
    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.PARAM, dtypes.uchar.ptr(47040000), arg=2, src=())
+    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)))
    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)
@@ -7,14 +7,14 @@ from tinygrad.device import Device

 class TestLinearizerFailures(unittest.TestCase):
  def test_fail_1(self):
-    c0 = UOp(Ops.PARAM, dtypes.float.ptr(64), arg=0, src=())
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=0, src=())
    c1 = UOp.range(UOp.const(dtypes.index, 2), 1, AxisType.LOOP)
    c2 = UOp.range(UOp.const(dtypes.index, 32), 2, AxisType.LOOP)
    c3 = ((c1*UOp.const(dtypes.index, 32))+c2)
-    c4 = UOp(Ops.PARAM, dtypes.float.ptr(163840), arg=1, src=())
+    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))))
-    c7 = UOp(Ops.PARAM, dtypes.float.ptr(64), arg=2, src=())
+    c7 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=2, src=())
    c8 = c7.index(c3)
    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)
@@ -1,5 +1,5 @@
 import unittest
-from tinygrad import dtypes
+from tinygrad import dtypes, Device
 from tinygrad.device import Buffer
 from tinygrad.engine.memory import _internal_memory_planner

@@ -7,7 +7,7 @@ global_map = {}
 def b(i, base=None, offset=0, pin=False, size=16):
  global global_map
  if i in global_map: return global_map[i]
-  global_map[i] = Buffer("NULL", size, dtypes.int8, base=global_map[base] if base is not None else None, offset=offset)
+  global_map[i] = Buffer(Device.DEFAULT, size, dtypes.int8, base=global_map[base] if base is not None else None, offset=offset)
  if pin: global_map[i].ref(1)
  return global_map[i]

@@ -256,18 +256,6 @@ class TestMultiTensor(unittest.TestCase):
      a,b = _test_allreduce(Tensor.rand(256, 256))
      np.testing.assert_almost_equal(a.numpy(), b.numpy(), decimal=5)

-  def test_multiple_to_single_device_naive(self):
-    with Context(RING=0):
-      t = Tensor.arange(32).shard(devices_4, 0).to(Device.DEFAULT).realize()
-    self.assertEqual(t.device, Device.DEFAULT)
-    np.testing.assert_equal(t.numpy(), np.arange(32))
-
-  def test_multiple_to_single_device_ring(self):
-    with Context(RING=2):
-      t = Tensor.arange(32).shard(devices_4, 0).to(Device.DEFAULT).realize()
-    self.assertEqual(t.device, Device.DEFAULT)
-    np.testing.assert_equal(t.numpy(), np.arange(32))
-
  def test_allreduce_all2all(self):
    with Context(ALL2ALL=2):
      a,b = _test_allreduce(Tensor.rand(256, 256))
@@ -421,28 +409,6 @@ class TestMultiTensor(unittest.TestCase):

    np.testing.assert_allclose(z.numpy(), z_shard.numpy(), atol=1e-6, rtol=1e-6)

-  def test_embedding_backward(self, shard_weight_axis=None):
-    B, T, embed_size, vocab_size = 4, 10, 20, 28
-
-    layer = nn.Embedding(vocab_size, embed_size)
-    layer.weight.requires_grad = True
-    x = Tensor(np.random.randint(0, vocab_size, (B, T), dtype=np.int32))
-    z = layer(x)
-    z.sum().backward()
-    grad = layer.weight.grad.numpy()
-
-    layer_sharded = nn.Embedding(vocab_size, embed_size)
-    layer_sharded.weight.replace(layer.weight.shard(devices_2, axis=shard_weight_axis)).realize()
-    layer_sharded.weight.requires_grad = True
-    x_sharded = x.shard(devices_2, axis=None)
-    z_shard = layer_sharded(x_sharded)
-    z_shard.sum().backward()
-    grad_shard = layer_sharded.weight.grad.numpy()
-
-    np.testing.assert_allclose(grad, grad_shard, atol=1e-6, rtol=1e-6)
-
-  def test_embedding_backward_shard_weight(self): self.test_embedding_backward(shard_weight_axis=1)
-
  def test_rmsnorm(self):
    B, T, embed_size = 4, 10, 20

@@ -1285,20 +1251,19 @@ class TestMultiRamUsage(unittest.TestCase):
    _ = Tensor.zeros(self.N, self.N).contiguous().shard(devices_2, axis=0).contiguous().realize()
    self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage

-  def _test_matmul_half(self, dev_count:int):
+  def _test_matmul_half(self, devs):
    N = 32
    total_mem = {}
-    devs = tuple(f"NULL:{i}" for i in range(dev_count))
    for dtype in {dtypes.float, dtypes.half}:
      GlobalCounters.reset()
-      a = Tensor.empty((N, N), dtype=dtype, device=devs[0]).shard(devs, axis=0)
-      b = Tensor.empty((N, N), dtype=dtype, device=devs[0]).shard(devs, axis=None)
+      a = Tensor.empty((N, N), dtype=dtype).shard(devs, axis=0)
+      b = Tensor.empty((N, N), dtype=dtype).shard(devs, axis=None)
      (a @ b).realize()
      total_mem[dtype] = GlobalCounters.global_mem
    self.assertEqual(total_mem[dtypes.half], total_mem[dtypes.float] // 2)

-  def test_matmul_half(self): self._test_matmul_half(dev_count=2)
-  def test_matmul_half_alt(self): self._test_matmul_half(dev_count=4)
+  def test_matmul_half(self): self._test_matmul_half(devices_2)
+  def test_matmul_half_alt(self): self._test_matmul_half(devices_4)

@unittest.skipIf(not_support_multi_device(), "need multi")
 class TestMultiFromUnrenderable(unittest.TestCase):
@@ -698,7 +698,6 @@ class TestOps(unittest.TestCase):
        tiny_out = get_tiny_gradient(x, c)
        torch_out = get_torch_gradient(x, c)
        if math.isnan(tiny_out):
-          if Device.DEFAULT == "WEBGPU": continue # TODO: WEBGPU issue with nan
          assert math.isnan(torch_out)
        else:
          self.assertAlmostEqual(tiny_out, torch_out, msg=f"{x}, {c}")
@@ -950,8 +949,6 @@ class TestOps(unittest.TestCase):
    helper_test_op([(45,65), (45,1)], torch.copysign, Tensor.copysign)
    helper_test_op([(45,1), (1,65)], torch.copysign, Tensor.copysign)
    helper_test_op([(), ()], torch.copysign, Tensor.copysign)
-
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "fails locally")
  def test_copysign_exact(self):
    # NOTE: -nan (negative nan) is not tested because we can't detect its sign bit without bitcast
    v = [-1., -0., 0., 1., math.inf, -math.inf, math.nan]
@@ -26,8 +26,8 @@ def _test_uop_result(inputs:list[Tensor], prg, local_size=None):

 def _setup_and_test_alu(alu_op:Ops, input_val:ConstType, *alu_src_uops:UOp):
  dtype = alu_src_uops[0].dtype
-  a = UOp(Ops.PARAM, dtype.ptr(), (), 0)
-  b = UOp(Ops.PARAM, dtype.ptr(), (), 1)
+  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)
  alu = ld.alu(alu_op, *alu_src_uops)
@@ -39,7 +39,7 @@ def _setup_and_test_alu(alu_op:Ops, input_val:ConstType, *alu_src_uops:UOp):
 class TestRendererFailures(unittest.TestCase):
  @unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
  def test_gated_store_with_alu(self):
-    a = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    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)))
    sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
@@ -49,7 +49,7 @@ class TestRendererFailures(unittest.TestCase):

  @unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
  def test_gated_store_with_alu_2d(self):
-    a = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    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)))
@@ -94,7 +94,7 @@ class TestWGSLFailures(unittest.TestCase):
 class TestPTXFailures(unittest.TestCase):
  @unittest.skip("INDEX can only have a gate ALU parent, not an IF")
  def test_gated_store_with_if(self):
-    a = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    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)
    val = UOp.const(dtypes.int, 1)
    if_uop = UOp(Ops.IF, dtypes.void, (gate_alu,))
@@ -1092,18 +1092,6 @@ class TestSchedule(unittest.TestCase):
    np.testing.assert_allclose(out[0].numpy(), np.sqrt(np.square(x.numpy() - np_mu).sum(-1)/x.shape[-1]), atol=1e-4, rtol=1e-4)
    np.testing.assert_allclose(out[1].numpy(), np.sqrt(np.square(y.numpy() - np_mu).sum(-1)/y.shape[-1]), atol=1e-4, rtol=1e-4)

-  def test_cumsum_parallel_reduce_fused(self):
-    # two-stage cumsum + ops triggers parallel REDUCEs in one kernel that must share an END
-    step, num_steps = 513, 10
-    t = Tensor.arange(step).float().realize()
-    phase = t.cumsum()
-    tiled = phase.repeat((num_steps,)).reshape(num_steps, step)
-    pattern = Tensor([1,0,0,1,0,0,0,0,1,0]).reshape(num_steps, 1)
-    out = (tiled * pattern).flatten()
-    expected = np.tile(np.arange(step).astype(np.float32).cumsum(), num_steps).reshape(num_steps, step)
-    expected = (expected * np.array([1,0,0,1,0,0,0,0,1,0]).reshape(num_steps, 1)).flatten()
-    np.testing.assert_allclose(out.numpy(), expected, atol=1e-4, rtol=1e-4)
-
  def test_multimatmul_fusion(self):
    Tensor.manual_seed(0)
    a,b = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
@@ -2196,14 +2184,6 @@ class TestBufferUOp(unittest.TestCase):
    run_schedule(check_schedule(a, 0))
    self.assertIsNone(a.uop.base.realized)

-  def test_unused_var_not_in_var_vals(self):
-    # unused variable should not appear in var_vals even when there's other work
-    a = Tensor(UOp.variable("unused", 0, 10).bind(1))
-    b = Tensor.empty(3) + 1
-    _, var_vals = Tensor.schedule_with_vars(a, b)
-    self.assertEqual(var_vals, {})
-    self.assertIsNone(a.uop.base.realized)
-
  def test_view_does_not_realize(self):
    a = Tensor.randn(1, 4).expand(4, 4)
    a.realize()
@@ -169,6 +169,7 @@ class TestSymbolicOps(unittest.TestCase):
      vi = Variable("i", 1, 10).bind(i)
      a = Tensor.rand(7, 11)
      symbolic = a[3:5, vi:vi+2]
+      print(symbolic.shape)
      symbolic = symbolic.numpy()
      expected = a[3:5, i:i+2].numpy()
      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
@@ -2,7 +2,8 @@ import numpy as np
 import torch
 import unittest, copy, mmap, random, math, array
 from tinygrad import Tensor, Device, dtypes, nn
-from tinygrad.helpers import getenv, temp, mv_address
+from tinygrad.tensor import _METADATA
+from tinygrad.helpers import Context, getenv, temp, mv_address
 from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
 from hypothesis import given, settings, strategies as strat
 from tinygrad.device import is_dtype_supported
@@ -795,6 +796,92 @@ class TestInferenceMode(unittest.TestCase):
      assert W.grad is None
    f(x, m, W)

+class TestTensorMetadata(unittest.TestCase):
+  def setUp(self) -> None: _METADATA.set(None)
+
+  # NOOPs are not included in kernel metadata
+  @unittest.skip("why would this be true?")
+  def test_exclude_noop_metadata(self):
+    a = Tensor.rand(4, 4)*1
+    self.assertEqual(a.uop.metadata[0].name, "__mul__")
+    k = a.schedule()[-1]
+    self.assertEqual([m.name for m in k.metadata], ["rand"])
+
+  # we exclude const from kernel metadata because tensor methods can share the same CONST UOp
+  @unittest.skip("TODO: flaky")
+  def test_exclude_const_metadata(self):
+    a = Tensor.arange(4)
+    b = Tensor.full((4,), -1, dtype=dtypes.int).contiguous()
+    sched = Tensor.schedule(a, b)
+    self.assertEqual([m.name for m in sched[0].metadata], ["arange"])
+    self.assertEqual([m.name for m in sched[1].metadata], ["contiguous"])
+
+  def test_matmul(self):
+    x = Tensor.rand(3, requires_grad=True)
+    W = Tensor.rand(3, 3, requires_grad=True)
+    out = x.matmul(W)
+    self.assertEqual(out.uop.metadata[0].name, "matmul")
+    si = out.schedule()[-1]
+    self.assertEqual(len(si.metadata), 1)
+    self.assertEqual(si.metadata[0].name, "matmul")
+
+  def test_relu(self):
+    x = Tensor.rand(3, requires_grad=True)
+    out = x.relu()
+    self.assertEqual(out.uop.metadata[0].name, "relu")
+    si = out.schedule()[-1]
+    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())
+    si = x.schedule()[-1]
+    self.assertEqual(len(si.metadata), 1)
+    self.assertEqual(si.metadata[0].name, "assign")
+
+  def test_complex(self):
+    x = Tensor.rand(3, requires_grad=True)
+    y = Tensor.rand(3, requires_grad=True)
+    out = x.relu() * y.sigmoid()
+    self.assertEqual(out.uop.metadata[0].name, "__mul__")
+    self.assertEqual(out.uop.src[0].metadata[0].name, "relu")
+    self.assertEqual(out.uop.src[1].metadata[0].name, "sigmoid")
+    si = out.schedule()[-1]
+    self.assertEqual(len(si.metadata), 3)
+    self.assertEqual(set(m.name for m in si.metadata), {"relu", "sigmoid", "__mul__"})
+
+  @unittest.skip("metadata is no longer promised to be exact with schedulecache")
+  def test_complex_backward(self):
+    x = Tensor.rand(3, requires_grad=True).realize()
+    y = Tensor.rand(3, requires_grad=True).realize()
+    out = (x.relu() * y.sigmoid()).sum()
+    self.assertEqual(out.uop.metadata[0].name, "sum")
+    out.backward()
+    self.assertEqual(x.grad.uop.metadata[0].name, "relu")
+    self.assertTrue(x.grad.uop.metadata[0].backward)
+    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}")
+    # skip numpy, this is schedule cache
+    self.assertSetEqual(set(m.name for m in si.metadata if m.name != "numpy"), {"sigmoid", "relu"})
+    #bw = [m for m in si.metadata if m.backward]
+    #self.assertEqual(len(bw), 1)
+    #self.assertEqual(bw[0].name, "sigmoid")
+
+  @unittest.skip("metadata is no longer promised to be exact with schedulecache")
+  def test_tracemeta_0(self):
+    with Context(TRACEMETA=0):
+      x = Tensor.rand(3, requires_grad=True)
+      y = Tensor.rand(3, requires_grad=True)
+      out = (x.relu() * y.sigmoid()).sum()
+      self.assertIsNone(out.uop.metadata)
+      self.assertIsNone(out.uop.src[0].metadata)
+      si = out.schedule()[-1]
+      self.assertEqual(si.metadata, ())
+
 class TestIdxUpcast(unittest.TestCase):
  def _find_op(self, ast: UOp, op: Ops):
    if ast.op is op: return ast
@@ -253,7 +253,7 @@ class TestUOpGraph(unittest.TestCase):

  @unittest.skip("this test isn't valid uops")
  def test_noop_vectorize_fold(self):
-    d0 = UOp(Ops.PARAM, dtypes.float.ptr(), arg=0)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0)
    idx = UOp.const(dtypes.int, 0)
    ld = UOp(Ops.LOAD, dtypes.float.vec(2), (d0, idx))
    vec = UOp(Ops.VECTORIZE, dtypes.float.vec(2), (ld,))
@@ -265,9 +265,9 @@ class TestUOpGraph(unittest.TestCase):

  @unittest.skip("this test isn't valid uops")
  def test_gep_vec_fold(self):
-    d0 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 0)
-    d1 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 1)
-    d2 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 2)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
+    d2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 2)
    idx = UOp.const(dtypes.int, 0)
    def _test_vec(geps, count=4):
      vec = UOp(Ops.VECTORIZE, dtypes.float.vec(count), geps)
@@ -373,8 +373,8 @@ class TestUOpGraph(unittest.TestCase):
      self.assertEqual(uops[-2], wmma)  # -2 to skip SINK

  def test_cast_alu_fold(self):
-    d0 = UOp(Ops.PARAM, dtypes.bool.ptr(), arg=0)
-    d1 = UOp(Ops.PARAM, dtypes.int.ptr(), arg=1)
+    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)
    alu = (ld<1).cast(dtypes.bool)
@@ -383,8 +383,8 @@ class TestUOpGraph(unittest.TestCase):
    self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 0)

  def test_double_cast_fold(self):
-    d0 = UOp(Ops.PARAM, dtypes.float.ptr(), arg=0)
-    d1 = UOp(Ops.PARAM, dtypes.int.ptr(), arg=1)
+    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)
    alu = ld.cast(dtypes.float).cast(dtypes.float)
@@ -407,7 +407,7 @@ class TestUOpGraph(unittest.TestCase):

  def test_bitcast_to_same_dtype_fold(self):
    for dt in dtypes.ints + dtypes.floats + (dtypes.bool,):
-      d0 = UOp(Ops.PARAM, dt.ptr(), arg=0)
+      d0 = UOp(Ops.DEFINE_GLOBAL, dt.ptr(), arg=0)
      v = 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 +420,7 @@ class TestUOpGraph(unittest.TestCase):

  def test_where_on_gated_load_fold(self):
    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.PARAM, dtypes.long.ptr(), (), 0)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
    ld = d0.index(ridx0.valid(ridx0<50))
    w = (ridx0<50).where(ld, 5)
    uops = to_uops_list([w])
@@ -430,7 +430,7 @@ class TestUOpGraph(unittest.TestCase):

  def test_where_on_gated_load_folds_swapped_branches(self):
    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.PARAM, dtypes.long.ptr(), (), 0)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
    ld = d0.index(ridx0.valid((ridx0<50).logical_not()))
    w = (ridx0<50).where(5, ld)
    uops = to_uops_list([w])
@@ -440,7 +440,7 @@ class TestUOpGraph(unittest.TestCase):

  def test_where_on_gated_load_with_cast(self):
    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    gate_idx = ridx0.valid((ridx0<50))
    ld = d0.index(gate_idx).cast(dtypes.float)
    w = (ridx0<50).where(ld, 5.0)
@@ -451,7 +451,7 @@ class TestUOpGraph(unittest.TestCase):

  def test_where_in_store_becomes_gate(self):
    ridx0 = UOp.range(100, 0)
-    d0 = UOp(Ops.PARAM, dtypes.long.ptr(), (), 0)
+    d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(), (), 0)
    idx = d0.index(ridx0)
    ld = idx.load()
    val = (ridx0<50).where(5, ld)
@@ -464,14 +464,14 @@ class TestUOpGraph(unittest.TestCase):
  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.PARAM, dtypes.uchar.ptr(128000), arg=0, src=())
+    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.PARAM, dtypes.int.ptr(512), arg=1, src=())
+    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.PARAM, dtypes.uchar.ptr(60000), arg=2, src=())
+    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)
@@ -479,30 +479,10 @@ class TestUOpGraph(unittest.TestCase):
    for u in uops:
      self.assertNotEqual(u.dtype, dtypes.long)

-  def test_load_idx_no_math_on_loaded(self):
-    # test the (x+y)<c pattern where x has loads - we shouldn't do math on loaded indices
-    c0 = UOp(Ops.PARAM, 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.PARAM, dtypes.int.ptr(512), arg=1, src=())
-    c4 = c3.index(c1)  # c4 is a load
-    c5 = UOp.range(UOp.const(dtypes.index, 240), 0, AxisType.REDUCE)
-    c6 = ((c2*UOp.const(dtypes.index, 240))+c5)
-    c7 = UOp(Ops.PARAM, dtypes.uchar.ptr(60000), arg=2, src=())
-    c8 = c7.index(c6)
-    # (loaded + range) < const pattern - loaded value shouldn't be promoted to long
-    loaded_idx = c4.cast(dtypes.index)
-    comparison = (loaded_idx + c5) < UOp.const(dtypes.index, 60000)
-    c9 = comparison.where(c8.cast(dtypes.uint).cast(dtypes.uchar), 0).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])
-    for u in uops:
-      self.assertNotEqual(u.dtype, dtypes.long)
-
  def test_fold_gated_load(self):
-    glbl0 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
-    glbl1 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 1)
-    glbl2 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 2)
+    glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
+    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)))
@@ -512,7 +492,7 @@ class TestUOpGraph(unittest.TestCase):
    self.assertEqual(ld0, UOp.load(glbl2.index(idx, ptr=True), dtype=dtypes.int))

  def test_fold_gated_load_local(self):
-    glbl0 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    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()))
@@ -526,7 +506,7 @@ class TestUOpGraph(unittest.TestCase):
    self.assertEqual(ld0.src[0], smem.after(barrier).index(lidx+2, ptr=True))

  def test_fold_gated_store(self):
-    glbl = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    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)
@@ -539,7 +519,7 @@ class TestUOpGraph(unittest.TestCase):

  @unittest.skip("this is a uop type error")
  def test_asserts_bad_gate(self):
-    glbl0 = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    idx = UOp.const(dtypes.int, 0)
    bad_gate = UOp.const(dtypes.int, 1)
    with self.assertRaises(AssertionError): to_uops_list([UOp(Ops.STORE, dtypes.void, (glbl0, idx, UOp.const(dtypes.int, 42), bad_gate))])
@@ -706,97 +686,6 @@ class TestExpander(unittest.TestCase):
    sink = expander_rewrite(sink)
    print(sink)

-class TestReduceCollapse(unittest.TestCase):
-  def test_multi_range_reduce_add(self):
-    """Test that (x + y).reduce(r1, r2) distributes over multiple ranges"""
-    from tinygrad.codegen.simplify import pm_reduce_collapse
-    # Create two ranges
-    r1 = UOp.range(3, 0)
-    r2 = UOp.range(4, 1)
-    # Create x + y where x and y depend on different ranges
-    x = r1.cast(dtypes.float)
-    y = r2.cast(dtypes.float)
-    # (x + y).reduce(r1, r2) should be rewritten
-    red = (x + y).reduce(r1, r2, arg=Ops.ADD)
-    self.assertEqual(len(red.src), 3)  # value + 2 ranges
-    result = graph_rewrite(red, pm_reduce_collapse, name='test')
-    # Should become add of two separate reduces
-    self.assertEqual(result.op, Ops.ADD)
-
-class TestLoadStoreFolding(unittest.TestCase):
-  def test_gated_load_gep_preserves_alt(self):
-    """Test that LOAD(GEP, alt) preserves alt value after rewrite"""
-    from tinygrad.codegen.late.devectorizer import load_store_folding
-    buf = UOp(Ops.PARAM, dtypes.float.vec(4).ptr(), (), 0)
-    idx = UOp.const(dtypes.int, 0)
-    gate = UOp.const(dtypes.bool, True)
-    gated_index = buf.index(idx, gate)
-    gep = gated_index.gep(0)
-    alt = UOp.const(dtypes.float, 42.0)
-    gated_load = gep.load(alt)
-    self.assertEqual(len(gated_load.src), 2)  # GEP + alt
-    result = graph_rewrite(gated_load, load_store_folding, name='test')
-    # After rewrite, should still have alt value preserved
-    self.assertEqual(result.op, Ops.GEP)
-    inner_load = result.src[0]
-    self.assertEqual(inner_load.op, Ops.LOAD)
-    self.assertEqual(len(inner_load.src), 2)  # INDEX + alt
-
-  def test_gated_load_ptrcat_preserves_alt(self):
-    """Test that LOAD(PTRCAT, alt) preserves alt value after rewrite"""
-    from tinygrad.codegen.late.devectorizer import load_store_folding
-    buf1 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 0)
-    buf2 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 1)
-    idx = UOp.const(dtypes.int, 0)
-    idx1 = buf1.index(idx)
-    idx2 = buf2.index(idx)
-    ptrcat = UOp(Ops.PTRCAT, dtypes.float.ptr().vec(2), (idx1, idx2))
-    alt = UOp.const(dtypes.float.vec(2), 42.0)
-    gated_load = ptrcat.load(alt)
-    self.assertEqual(len(gated_load.src), 2)  # PTRCAT + alt
-    result = graph_rewrite(gated_load, load_store_folding, name='test')
-    # After rewrite, should be CAT of LOADs, each preserving alt
-    self.assertEqual(result.op, Ops.CAT)
-    for inner_load in result.src:
-      self.assertEqual(inner_load.op, Ops.LOAD)
-      self.assertEqual(len(inner_load.src), 2)  # INDEX + alt
-    self.assertEqual(inner_load.src[1].arg, 42.0)  # alt value preserved
-
-class TestConstBufferize(unittest.TestCase):
-  def test_const_bufferize_with_ranges(self):
-    """Test that CONST.BUFFERIZE with ranges is folded correctly.
-
-    BUFFERIZE can have ranges as additional sources beyond the value.
-    The pattern at rangeify.py uses allow_any_len=True because
-    CONST doesn't depend on ranges (constant is same value everywhere).
-    """
-    from tinygrad.schedule.rangeify import pm_const_buffer_folding, BufferizeOpts
-    c = UOp.const(dtypes.float, 42.0)
-    r1 = UOp.range(3, 0)
-    bufferize_with_range = UOp(Ops.BUFFERIZE, dtypes.float, (c, r1), arg=BufferizeOpts(device="CPU"))
-    self.assertEqual(len(bufferize_with_range.src), 2)  # const + 1 range
-
-    result = graph_rewrite(bufferize_with_range, pm_const_buffer_folding, name='test')
-    # BUFFERIZE should be removed, result is const broadcast to shape
-    self.assertNotEqual(result.op, Ops.BUFFERIZE)
-    const_vals = [u.arg for u in result.toposort() if u.op is Ops.CONST and u.dtype == dtypes.float]
-    self.assertIn(42.0, const_vals)
-
-  def test_const_bufferize_with_multiple_ranges(self):
-    """Test CONST.BUFFERIZE with multiple ranges is also folded."""
-    from tinygrad.schedule.rangeify import pm_const_buffer_folding, BufferizeOpts
-    c = UOp.const(dtypes.float, 3.14)
-    r1 = UOp.range(3, 0)
-    r2 = UOp.range(4, 1)
-    bufferize_with_ranges = UOp(Ops.BUFFERIZE, dtypes.float, (c, r1, r2), arg=BufferizeOpts(device="CPU"))
-    self.assertEqual(len(bufferize_with_ranges.src), 3)  # const + 2 ranges
-
-    result = graph_rewrite(bufferize_with_ranges, pm_const_buffer_folding, name='test')
-    # BUFFERIZE should be removed
-    self.assertNotEqual(result.op, Ops.BUFFERIZE)
-    const_vals = [u.arg for u in result.toposort() if u.op is Ops.CONST and u.dtype == dtypes.float]
-    self.assertIn(3.14, const_vals)
-
 class TestUOpTags(unittest.TestCase):
  def test_inc_by_one(self):
    g = UOp.const(dtypes.int, 1) + UOp.const(dtypes.int, 1)
@@ -36,8 +36,8 @@ def uop(uops:list[UOp], op:Ops, dtype:Optional[DType], src:tuple[UOp, ...], arg:
 def _test_single_value(vals, op, dts):
  uops = []
  output_dtype = dtypes.bool if op in (Ops.CMPLT, Ops.CMPNE) else dts[-1]
-  buf_store = uop(uops, Ops.PARAM, output_dtype.ptr(), (), 0)
-  buf_loads = [uop(uops, Ops.PARAM, dtype.ptr(), (), i+1) for i,dtype in enumerate(dts)]
+  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))
  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))
@@ -52,7 +52,7 @@ def _test_single_value(vals, op, dts):
 def _test_single_value_const(vals, op, dts):
  uops = []
  output_dtype = dtypes.bool if op in (Ops.CMPLT, Ops.CMPNE) else dts[-1]
-  buf_store = uop(uops, Ops.PARAM, output_dtype.ptr(), (), 0)
+  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)
@@ -65,7 +65,7 @@ def _test_single_value_const(vals, op, dts):

 def _test_uops_result(output_dtype, uops, res):
  # uops = []
-  buf_store = uop(uops, Ops.PARAM, output_dtype.ptr(), (), 0)
+  buf_store = uop(uops, Ops.DEFINE_GLOBAL, output_dtype.ptr(), (), 0)
  # res = output_fn(uops)
  out = uop(uops, Ops.STORE, dtypes.void, (buf_store.index(uop(uops, Ops.CONST, dtypes.int32, (), 0)), res))
  buf = Buffer(Device.DEFAULT, 1, output_dtype).allocate()
@@ -273,7 +273,7 @@ class TestConstantFolding(unittest.TestCase):

 class TestGatedStoreRewrite(unittest.TestCase):
  def test_tiny_gate_store(self):
-    gmem = UOp(Ops.PARAM, dtypes.float.ptr(), (), 0)
+    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)))
@@ -289,8 +289,8 @@ class TestGatedStoreRewrite(unittest.TestCase):
    self.assertIs(gated_uops[-1].op, Ops.STORE)

  def test_gate_some_stores(self):
-    gmem0 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 0)
-    gmem1 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 1)
+    gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    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))))
@@ -309,8 +309,8 @@ class TestGatedStoreRewrite(unittest.TestCase):
  # scaled down version of TestLinearizerDumb.test_unmerged_ifs
  @unittest.skip("we don't merge ifs anymore")
  def test_merge_ifs_alt(self):
-    gmem0 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 0)
-    gmem1 = UOp(Ops.PARAM, dtypes.float.ptr(), (), 1)
+    gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
+    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)
    gate = gidx0<UOp.const(dtypes.int, 1)
@@ -380,7 +380,7 @@ class TestLocalAccess(unittest.TestCase):
 class TestFastIdiv(unittest.TestCase):
  def test_division_power_of_two(self):
    for dt in (dtypes.int32, dtypes.uint32):
-      g = UOp(Ops.PARAM, dt.ptr(), (), 0)
+      g = UOp(Ops.DEFINE_GLOBAL, dt.ptr(), (), 0)
      c = UOp.const(dt, 2)
      l = g.index(c)
      a = UOp(Ops.IDIV, dt, (l, c))
@@ -392,7 +392,7 @@ class TestFastIdiv(unittest.TestCase):

  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU doesn't support long")
  def test_fast_idiv_and_mod(self):
-    g = UOp(Ops.PARAM, dtypes.uint32.ptr(), (), 0)
+    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp.const(dtypes.uint, 3)
    l = g.index(c)
    a = UOp(Ops.IDIV, dtypes.uint, (l, c))
@@ -420,7 +420,7 @@ class TestFastIdiv(unittest.TestCase):
  @unittest.expectedFailure
  def test_fast_idiv_overflow(self):
    # This will be possible with a slightly different method for fast_idiv
-    g = UOp(Ops.PARAM, dtypes.uint32.ptr(), (), 0)
+    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp.const(dtypes.uint, 7)
    l = UOp(Ops.LOAD, dtypes.uint, (g.index(c),))
    a = UOp(Ops.IDIV, dtypes.uint, (l, c))
@@ -431,7 +431,7 @@ class TestFastIdiv(unittest.TestCase):
    self.assertNotIn(Ops.IDIV, ops)

  def test_disable_fast_idiv(self):
-    g = UOp(Ops.PARAM, dtypes.uint32.ptr(), (), 0)
+    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp.const(dtypes.uint, 3)
    l = g.index(c)
    a = UOp(Ops.IDIV, dtypes.uint, (l, c))
@@ -445,7 +445,7 @@ class TestFastIdiv(unittest.TestCase):
@unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "This only tests assembly backends")
 class TestAssembly(unittest.TestCase):
  def test_bitshift_left(self):
-    g1 = UOp(Ops.PARAM, dtypes.int32.ptr(), (), 0)
+    g1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
    c1 = UOp.const(dtypes.int, 2)
    c2 = UOp.const(dtypes.int, 3)
    l1 = g1.index(c1)
@@ -471,7 +471,7 @@ class TestAssembly(unittest.TestCase):
    self.assertEqual(len([x.op for x in uops if x.op is Ops.MULACC]), 4)

  def test_use_cmpeq(self):
-    g = UOp(Ops.PARAM, dtypes.uint32.ptr(), (), 0)
+    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
    c = UOp.const(dtypes.uint, 7)
    comp = g.index(c).ne(c).ne(True)
    uops = to_uops_list([comp], ren=Device[Device.DEFAULT].renderer)
@@ -507,7 +507,7 @@ class TestUOpMethod(unittest.TestCase):
    self.assertEqual((gidx0*3+1).const_factor(), 1)

  def test_replace(self):
-    x = UOp(Ops.PARAM, dtypes.int.ptr(), (), 0)
+    x = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
    self.assertIs(x.replace(arg=None).arg, None)
    with self.assertRaises(AssertionError): x.replace(field="a")

@@ -137,7 +137,7 @@ class TestUOpsStats(unittest.TestCase):

  #MULACC should have the same stats as MUL + ADD
  def test_mulacc(self):
-    globl = UOp(Ops.PARAM, dtypes.int.ptr(), tuple())
+    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)
@@ -147,7 +147,7 @@ class TestUOpsStats(unittest.TestCase):
    u5 = UOp(Ops.ADD, dtypes.int, (u4,u3))
    uops = list(u5.toposort())

-    globl = UOp(Ops.PARAM, dtypes.int.ptr(), tuple())
+    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)
@@ -2,40 +2,82 @@
 # allow define from star imports

 import unittest
-import functools
+import textwrap, functools

 from tinygrad import Device, Tensor
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
+from tinygrad.helpers import getenv
 from tinygrad.device import Compiler
-from tinygrad.runtime.support.compiler_amd import HIPCompiler
 from tinygrad.viz.serve import amdgpu_cfg

 from extra.assembly.amd.autogen.rdna3.ins import *
-from extra.assembly.amd.dsl import s
+from extra.assembly.amd.dsl import Inst

-# TODO: this belongs to the dsl infrastructure
-from extra.gemm.amd_asm_matmul import Kernel
+template = """.text
+.globl fn_name
+.p2align 8
+.type fn_name,@function
+fn_name:
+  INSTRUCTION
+
+.rodata
+.p2align 6
+.amdhsa_kernel fn_name
+  .amdhsa_kernarg_size 8
+  .amdhsa_user_sgpr_kernarg_segment_ptr 1
+  .amdhsa_next_free_vgpr .amdgcn.next_free_vgpr
+  .amdhsa_next_free_sgpr .amdgcn.next_free_sgpr
+  .amdhsa_wavefront_size32 1
+.end_amdhsa_kernel
+
+.amdgpu_metadata
+---
+amdhsa.version:
+  - 1
+  - 0
+amdhsa.kernels:
+  - .name: fn_name
+    .symbol: fn_name.kd
+    .group_segment_fixed_size: 0
+    .private_segment_fixed_size: 0
+    .wavefront_size: 32
+    .sgpr_count: 8
+    .vgpr_count: 8
+    .max_flat_workgroup_size: 1024
+    .kernarg_segment_align: 8
+    .kernarg_segment_size: 8
+    .args:
+      - .address_space:  global
+        .name:           a
+        .offset:         0
+        .size:           8
+        .type_name:      'float*'
+        .value_kind:     global_buffer
+...
+.end_amdgpu_metadata
+"""

 # TODO: shouldn't need compiler once we can output ELF
 # outputs a text disassembly for humans and a machine readable binary
-def assemble(name:str, k:Kernel, compiler:Compiler) -> tuple[str, bytes]:
-  src = k.to_asm()
+def assemble(name:str, insts:list[str|Inst], compiler:Compiler) -> tuple[str, bytes]:
+  asm = "\n".join([inst if isinstance(inst, str) else inst.disasm() for inst in insts])
+  src = template.replace("fn_name", name).replace("INSTRUCTION", textwrap.dedent(asm))
  return (src, compiler.compile(src))

-def asm_kernel(out:UOp, k:Kernel, name:str, device:str, compiler:Compiler, n_threads:int=1, n_workgroups:int=1) -> UOp:
+def asm_kernel(out:UOp, insts:list[str|Inst], name:str, device:str, compiler:Compiler, n_threads:int=1, n_workgroups:int=1) -> UOp:
  lidx = UOp.special(n_threads, "lidx0")
  gidx = UOp.special(n_workgroups, "gidx0")
  sink = UOp.sink(out, lidx, gidx, arg=KernelInfo(name=name))
-  src, lib = assemble(name, k, compiler)
+  src, lib = assemble(name, insts, compiler)
  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=device), UOp(Ops.LINEAR, src=(*sink.src, sink)),
                               UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=lib)))

-def run_asm(name:str, k:Kernel) -> None:
-  fxn = functools.partial(asm_kernel, k=k, name=name, device=Device.DEFAULT, compiler=HIPCompiler(Device[Device.DEFAULT].renderer.arch))
+def run_asm(name:str, insts:list) -> None:
+  fxn = functools.partial(asm_kernel, insts=insts, name=name, device=Device.DEFAULT, compiler=Device[Device.DEFAULT].compiler)
  out = Tensor.custom_kernel(Tensor.empty(1), fxn=fxn)[0]
  out.realize()

-@unittest.skipUnless(Device.DEFAULT == "AMD", "only on AMD")
+@unittest.skipUnless(Device.DEFAULT == "AMD" and not getenv("AMD_LLVM"), "only on AMD with comgr")
 class TestCfg(unittest.TestCase):
  def setUp(self):
    arch = Device["AMD"].arch
@@ -43,32 +85,32 @@ class TestCfg(unittest.TestCase):
      self.skipTest(f"tests written for RDNA, got arch {arch}")

  def test_simple(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_branch(), target="bb1")
-    k.label("bb1")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("simple", k)
+    run_asm("simple", [
+      "entry:",
+        "s_branch bb1",
+      "bb1:",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_diamond(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[0], 0))
-    k.emit(s_mov_b32(s[1], 0))
-    k.emit(s_cmp_eq_u64(s[0:1], 0))
-    k.emit(s_cbranch_scc1(), target="if")
-    k.emit(s_branch(), target="else")
-    k.label("if")
-    k.emit(s_nop(1))
-    k.emit(s_branch(), target="end")
-    k.label("else")
-    k.emit(s_nop(0))
-    k.label("end")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("diamond", k)
-    _, lib = assemble("diamond", k, HIPCompiler(Device[Device.DEFAULT].arch))
+    run_asm("diamond", insts:=[
+      "entry:",
+        s_mov_b32(s[0], 0),
+        s_mov_b32(s[1], 0),
+        s_cmp_eq_u64(s[0:1], 0),
+        "s_cbranch_scc1 if",
+        "s_branch else",
+      "if:",
+        s_nop(1),
+        "s_branch end",
+      "else:",
+        s_nop(0),
+      "end:",
+        s_endpgm(),
+        s_code_end(),
+    ])
+    _, lib = assemble("diamond", insts, Device[Device.DEFAULT].compiler)
    cfg = amdgpu_cfg(lib, Device[Device.DEFAULT].device_props()["gfx_target_version"])["data"]
    self.assertEqual(len(cfg["blocks"]), 5)
    edge_count = sum(len(v) for v in cfg["paths"].values())
@@ -82,138 +124,119 @@ class TestCfg(unittest.TestCase):
    self.assertEqual(insts, ['s_mov_b32', 's_cmp_eq_u64'])

  def test_loop(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[1], 4))
-    k.label("loop")
-    k.emit(s_add_u32(s[1], s[1], -1))
-    k.emit(s_cmp_eq_i32(s[1], 0))
-    k.emit(s_cbranch_scc0(), target="loop")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("simple_loop", k)
+    run_asm("simple_loop", [
+      "entry:",
+        s_mov_b32(s[1], 4),
+      "loop:",
+        s_add_u32(s[1], s[1], -1),
+        s_cmp_eq_i32(s[1], 0),
+        "s_cbranch_scc0 loop",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_loop_branch(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[1], 4))
-    k.label("loop")
-    k.emit(s_add_u32(s[1], s[1], -1))
-    k.emit(s_cmp_eq_i32(s[1], 2))
-    k.emit(s_cbranch_scc1(), target="cond")
-    k.emit(s_branch(), target="cont")
-    k.label("cond")
-    k.emit(s_add_u32(s[1], s[1], -2))
-    k.label("cont")
-    k.emit(s_cmp_eq_i32(s[1], 0))
-    k.emit(s_cbranch_scc0(), target="loop")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("loop_if", k)
+    run_asm("loop_if", [
+      "entry:",
+        s_mov_b32(s[1], 4),
+      "loop:",
+        s_add_u32(s[1], s[1], -1),
+        s_cmp_eq_i32(s[1], 2),
+        "s_cbranch_scc1 cond",
+        "s_branch cont",
+      "cond:",
+        s_add_u32(s[1], s[1], -2),
+      "cont:",
+        s_cmp_eq_i32(s[1], 0),
+        "s_cbranch_scc0 loop",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_loop_break(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[1], 8))
-    k.label("loop")
-    k.emit(s_add_u32(s[1], s[1], -1))
-    k.emit(s_cmp_eq_i32(s[1], 5))
-    k.emit(s_cbranch_scc1(), target="break")
-    k.emit(s_cmp_eq_i32(s[1], 0))
-    k.emit(s_cbranch_scc0(), target="loop")
-    k.label("break")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("loop_break", k)
+    run_asm("loop_break", [
+      "entry:",
+        s_mov_b32(s[1], 8),
+      "loop:",
+        s_add_u32(s[1], s[1], -1),
+        s_cmp_eq_i32(s[1], 5),
+        "s_cbranch_scc1 break",
+        s_cmp_eq_i32(s[1], 0),
+        "s_cbranch_scc0 loop",
+      "break:",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_switch(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_cmp_eq_i32(s[0], 0))
-    k.emit(s_cbranch_scc1(), target="case0")
-    k.emit(s_cmp_eq_i32(s[0], 1))
-    k.emit(s_cbranch_scc1(), target="case1")
-    k.emit(s_branch(), target="case2")
-    k.label("case0")
-    k.emit(s_nop(0))
-    k.emit(s_branch(), target="join")
-    k.label("case1")
-    k.emit(s_nop(1))
-    k.emit(s_branch(), target="join")
-    k.label("case2")
-    k.emit(s_nop(2))
-    k.emit(s_branch(), target="join")
-    k.label("join")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("switch_case", k)
+    run_asm("switch_case", [
+      "entry:",
+        s_cmp_eq_i32(s[0], 0),
+        "s_cbranch_scc1 case0",
+        s_cmp_eq_i32(s[0], 1),
+        "s_cbranch_scc1 case1",
+        "s_branch case2",
+      "case0:",
+        s_nop(0),
+        "s_branch join",
+      "case1:",
+        s_nop(1),
+        "s_branch join",
+      "case2:",
+        s_nop(2),
+        "s_branch join",
+      "join:",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_ping_pong(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_cmp_eq_i32(s[0], 0))
-    k.emit(s_cbranch_scc1(), target="ping")
-    k.emit(s_branch(), target="pong")
-    k.label("ping")
-    k.emit(s_cmp_eq_i32(s[1], 0))
-    k.emit(s_cbranch_scc1(), target="pong")
-    k.emit(s_branch(), target="end")
-    k.label("pong")
-    k.emit(s_cmp_eq_i32(s[2], 0))
-    k.emit(s_cbranch_scc1(), target="ping")
-    k.label("end")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("ping_pong", k)
+    run_asm("ping_pong", [
+      "entry:",
+        s_cmp_eq_i32(s[0], 0),
+        "s_cbranch_scc1 ping",
+        "s_branch pong",
+      "ping:",
+        s_cmp_eq_i32(s[1], 0),
+        "s_cbranch_scc1 pong",
+        "s_branch end",
+      "pong:",
+        s_cmp_eq_i32(s[2], 0),
+        "s_cbranch_scc1 ping",
+      "end:",
+        s_endpgm(),
+        s_code_end(),
+    ])

  def test_colored_blocks(self):
    N = 10
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_branch(), target="init0")
+    asm = ["entry:", "s_branch init0"]
    for i in range(N):
-      loop = f"loop{i}"
-      k.label(f"init{i}")
-      k.emit(s_mov_b32(s[1], i + 1))
-      k.emit(s_branch(), target=loop)
-      k.label(loop)
-      k.emit(s_nop(i & 7))
-      k.emit(s_add_u32(s[1], s[1], -1))
-      k.emit(s_cmp_eq_i32(s[1], 0))
-      k.emit(s_cbranch_scc0(), target=loop)
-      k.emit(s_branch(), target=f"init{i+1}" if i + 1 < N else "end")
-    k.label("end")
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("test_colored_blocks", k)
+      asm += [f"init{i}:", s_mov_b32(s[1], i + 1), f"s_branch {(loop:=f'loop{i}')}"]
+      asm += [
+        f"{loop}:",
+          s_nop(i & 7),
+          s_add_u32(s[1], s[1], -1),
+          s_cmp_eq_i32(s[1], 0),
+          f"s_cbranch_scc0 {loop}",
+          f"s_branch {'init' + str(i+1) if i + 1 < N else 'end'}",
+      ]
+    asm += ["end:", s_endpgm(), s_code_end()]
+    run_asm("test_colored_blocks", asm)

  def test_jump_back_to_end(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[1], 2))
-    k.emit(s_cbranch_execz(), target="loop")
-    k.label("end")
-    k.emit(s_endpgm())
-    k.label("loop")
-    k.emit(s_add_u32(s[1], s[1], -1))
-    k.emit(s_cmp_eq_i32(s[1], 0))
-    k.emit(s_branch(), target="end")
-    k.emit(s_code_end())
-    run_asm("jump_back_to_end", k)
-
-  def test_hit_count(self):
-    k = Kernel(arch=Device["AMD"].arch)
-    k.label("entry")
-    k.emit(s_mov_b32(s[1], 1))
-    k.emit(s_branch(), target="alt")
-    k.label("continue")
-    k.emit(s_mov_b32(s[2], 2))
-    k.emit(s_add_u32(s[1], s[1], s[2]))
-    k.label("alt")
-    k.emit(s_add_u32(s[1], s[1], -1))
-    k.emit(s_endpgm())
-    k.emit(s_code_end())
-    run_asm("test_hit_count", k)
+    run_asm("jump_back_to_end", [
+      "entry:",
+        s_mov_b32(s[1], 2),
+        "s_cbranch_execz loop",
+      "end:",
+        s_endpgm(),
+      "loop:",
+        s_add_u32(s[1], s[1], -1),
+        s_cmp_eq_i32(s[1], 0),
+        "s_branch end",
+        s_code_end(),
+    ])

 if __name__ == "__main__":
  unittest.main()
@@ -750,35 +750,6 @@ class TestTK(unittest.TestCase):

    fa_jitted = TinyJit(flash_attention)

-    for _ in range(10):
-      st = time.perf_counter()
-      out = fa_jitted(q, k, v, is_causal=False)
-      et = time.perf_counter() - st
-      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")
-    out = out.float().transpose(1, 2)
-
-    ref = q.scaled_dot_product_attention(k, v, is_causal=False, enable_gqa=True).float().transpose(1, 2)
-
-    np.testing.assert_allclose(out.numpy(), ref.numpy(), atol=2e-2, rtol=2e-2)
-
-  def test_fast_fa_causal(self):
-    from extra.thunder.tiny.fa import flash_attention
-
-    B, N, H, H_KV, D = 2, 8192, 32, 8, 128
-
-    with Context(DEBUG=0):
-      q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16).contiguous()
-      k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
-      v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
-      Tensor.realize(q, k, v)
-
-    q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
-
-    fa_jitted = TinyJit(flash_attention)
-
    for _ in range(10):
      st = time.perf_counter()
      out = fa_jitted(q, k, v, is_causal=True)
@@ -867,7 +838,7 @@ class TestTK(unittest.TestCase):

    np.testing.assert_allclose(q.grad.numpy(), q_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
    np.testing.assert_allclose(v.grad.numpy(), v_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
-    np.testing.assert_allclose(k.grad.numpy(), k_ref.grad.numpy(), atol=6e-2, rtol=2e-2)
+    np.testing.assert_allclose(k.grad.numpy(), k_ref.grad.numpy(), atol=5e-2, rtol=2e-2)

  def test_fast_fa_bwd_causal_jitted(self):
    from extra.thunder.tiny.fa import flash_attention
@@ -456,32 +456,6 @@ class TestAssign(unittest.TestCase):
    assign.realize()
    np.testing.assert_allclose(a.numpy(), [2., 2., 2., 2., 1., 1., 1., 1.])

-  def test_setitem_list(self):
-    a = Tensor.zeros(8).contiguous().realize()
-    a[2:5] = [1, 2, 3]
-    np.testing.assert_allclose(a.numpy(), [0., 0., 1., 2., 3., 0., 0., 0.])
-
-  def test_assign_bitcast(self):
-    # assign to a bitcast view should modify the underlying buffer
-    a = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
-    # IEEE 754: 1.0f = 0x3f800000, 2.0f = 0x40000000, 3.0f = 0x40400000, 4.0f = 0x40800000
-    a.bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000, 0x40000000, 0x3f800000], dtype=dtypes.uint32)).realize()
-    np.testing.assert_allclose(a.numpy(), [4.0, 3.0, 2.0, 1.0])
-    # double bitcast
-    b = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
-    b.bitcast(dtypes.uint32).bitcast(dtypes.int32).assign(Tensor([0x40800000, 0x40400000, 0x40000000, 0x3f800000], dtype=dtypes.int32)).realize()
-    np.testing.assert_allclose(b.numpy(), [4.0, 3.0, 2.0, 1.0])
-    # shrink then bitcast
-    c = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
-    c[0:2].bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000], dtype=dtypes.uint32)).realize()
-    np.testing.assert_allclose(c.numpy(), [4.0, 3.0, 3.0, 4.0])
-
-  def test_assign_bitcast_different_size(self):
-    # different-size bitcast creates a new tensor, not a view, so assign doesn't modify the original
-    a = Tensor([0]*8, dtype=dtypes.uint8).realize()
-    a.bitcast(dtypes.int64).assign(Tensor([12345], dtype=dtypes.int64)).realize()
-    np.testing.assert_equal(a.numpy(), [0]*8)
-
  @unittest.skip("don't use output buffer, and mismatch dtype no longer supported")
  def test_cast_assignment(self):
    a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
@@ -493,38 +467,6 @@ class TestAssign(unittest.TestCase):
    assert oba1 is None and oba2 is None
    np.testing.assert_allclose(a.numpy(), np.arange(N*N,dtype=np.int32).reshape((N,N)))

-  def test_assign_dtype_mismatch(self):
-    # assign should not implicitly cast dtypes - this can lose precision
-    a = Tensor.zeros(4, dtype=dtypes.float32).contiguous().realize()
-    b = Tensor([1, 2, 3, 4], dtype=dtypes.int32)
-    with self.assertRaisesRegex(RuntimeError, "assign dtype mismatch"):
-      a.assign(b)
-
-  def test_assign_dtype_mismatch_int64_to_float32(self):
-    # int64 -> float32 loses precision for large values, should not be implicit
-    a = Tensor.zeros(1, dtype=dtypes.float32).contiguous().realize()
-    b = Tensor([16777217], dtype=dtypes.int64)  # 2^24 + 1, not exactly representable in float32
-    with self.assertRaisesRegex(RuntimeError, "assign dtype mismatch"):
-      a.assign(b)
-
-  def test_assign_shape_broadcast(self):
-    # shape broadcasting should work when dtypes match
-    a = Tensor.zeros(3, 5, dtype=dtypes.float32).contiguous().realize()
-    b = Tensor([1., 2., 3., 4., 5.], dtype=dtypes.float32)
-    a.assign(b)
-    a.realize()
-    expected = np.array([[1., 2., 3., 4., 5.]] * 3)
-    np.testing.assert_allclose(a.numpy(), expected)
-
-  def test_assign_shape_broadcast_2d(self):
-    # broadcast (1, 5) to (3, 5)
-    a = Tensor.zeros(3, 5, dtype=dtypes.float32).contiguous().realize()
-    b = Tensor([[1., 2., 3., 4., 5.]], dtype=dtypes.float32)
-    a.assign(b)
-    a.realize()
-    expected = np.array([[1., 2., 3., 4., 5.]] * 3)
-    np.testing.assert_allclose(a.numpy(), expected)
-
  def test_disk_assignment(self):
    a = Tensor.empty(5, device=f"disk:{temp('disk_assignment')}").assign(Tensor.ones(5)).numpy()
    np.testing.assert_equal(a, np.ones(5))
@@ -619,12 +561,12 @@ class TestAssignOrdering(unittest.TestCase):
  def test_slice_write_then_full_read(self):
    """Write to slice, then read full buffer."""
    # without .realize(): orphan slice assign not triggered by .numpy()
-    buf = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
+    buf = Tensor.zeros(4).contiguous().realize()
    buf[1:3].assign(Tensor([5, 6]))
    np.testing.assert_equal(buf.numpy(), [0, 0, 0, 0])  # TODO: wrong! should be [0, 5, 6, 0]

    # with .realize(): assign executes
-    buf = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
+    buf = Tensor.zeros(4).contiguous().realize()
    buf[1:3].assign(Tensor([5, 6])).realize()
    np.testing.assert_equal(buf.numpy(), [0, 5, 6, 0])

@@ -706,7 +648,7 @@ class TestAssignOrdering(unittest.TestCase):

  def test_three_buffer_chain(self):
    """Chain: A depends on B, B depends on C - ordering matters."""
-    a = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
+    a = Tensor.zeros(4).contiguous().realize()
    b = Tensor([1, 2, 3, 4]).contiguous().realize()
    c = Tensor([10, 10, 10, 10]).contiguous().realize()
    # b reads from c, a reads from b
@@ -718,8 +660,8 @@ class TestAssignOrdering(unittest.TestCase):

  def test_interleaved_assign_read_patterns(self):
    """Complex interleaved pattern: write A, read A into B, write B, read B."""
-    a = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
-    b = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
+    a = Tensor.zeros(4).contiguous().realize()
+    b = Tensor.zeros(4).contiguous().realize()

    a.assign(Tensor([1, 2, 3, 4]))
    b.assign(a.contiguous())       # b should get [1,2,3,4]
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
geohot	8c79751937	Q5_K	2026-01-29 17:04:03 +08:00
geohot	aeacd3b2fb	ggml_type_13	2026-01-28 21:19:52 +08:00