tests pass

assembly/amd: use Reg in emu
command line interface for sqtt viz (#13891 )
2025-12-30 13:49:05 +00:00 · 2025-12-30 12:52:03 +00:00 · 2025-12-30 12:33:21 +09:00 · 2025-12-29 21:35:46 -05:00 · 2025-12-29 18:41:29 -05:00 · 2025-12-29 17:42:28 -05:00
413 changed files with 95741 additions and 29936 deletions
@@ -61,7 +61,7 @@ runs:
      uses: actions/cache@v4
      with:
        path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ hashFiles('**/pyproject.toml') }}-${{ env.CACHE_VERSION }}
+        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}

    # **** Caching downloads ****

@@ -70,13 +70,13 @@ runs:
      uses: actions/cache@v4
      with:
        path: ~/.cache/tinygrad/downloads/
-        key: downloads-cache-${{ inputs.key }}-${{ env.CACHE_VERSION }}
+        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}
    - name: Cache downloads (macOS)
      if: inputs.key != '' && runner.os == 'macOS'
      uses: actions/cache@v4
      with:
        path: ~/Library/Caches/tinygrad/downloads/
-        key: osx-downloads-cache-${{ inputs.key }}-${{ env.CACHE_VERSION }}
+        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}

    # **** Python deps ****

@@ -221,7 +221,7 @@ runs:
        sudo mkdir -p /usr/local/lib
        curl -s -H "Authorization: token $GH_TOKEN" curl -s https://api.github.com/repos/nimlgen/amdcomgr_dylib/releases/latest | \
          jq -r '.assets[] | select(.name == "libamd_comgr.dylib").browser_download_url' | \
-          sudo xargs curl -L -o /usr/local/lib/libamd_comgr.dylib
+          sudo xargs curl -fL -o /usr/local/lib/libamd_comgr.dylib
        cargo build --release --manifest-path ./extra/remu/Cargo.toml

    # **** gpuocelot ****
@@ -278,7 +278,7 @@ runs:
      if: inputs.webgpu == 'true' && runner.os == 'Linux'
      shell: bash
      run: |
-        sudo curl -L https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
+        sudo curl -fL https://github.com/wpmed92/pydawn/releases/download/v0.1.6/libwebgpu_dawn.so -o /usr/local/lib/libwebgpu_dawn.so
        sudo ldconfig
    - name: Install WebGPU dawn (macOS)
      if: inputs.webgpu == 'true' && runner.os == 'macOS'
@@ -298,7 +298,7 @@ runs:
    - name: Install mesa (linux)
      if: inputs.mesa == 'true' && runner.os == 'Linux'
      shell: bash
-      run: sudo curl -L https://github.com/sirhcm/tinymesa/releases/download/tinymesa-32dc66c/libtinymesa_cpu-mesa-25.2.4-linux-amd64.so -o /usr/lib/libtinymesa_cpu.so
+      run: sudo curl -fL https://github.com/sirhcm/tinymesa/releases/download/v1/libtinymesa_cpu-mesa-25.2.7-linux-amd64.so -o /usr/lib/libtinymesa_cpu.so
    - name: Install mesa (macOS)
      if: inputs.mesa == 'true' && runner.os == 'macOS'
      shell: bash
@@ -13,9 +13,11 @@ on:
  pull_request:
    paths:
    - 'tinygrad/runtime/autogen/**/*'
+    - 'tinygrad/runtime/support/autogen.py'
  workflow_dispatch:
    paths:
    - 'tinygrad/runtime/autogen/**/*'
+    - 'tinygrad/runtime/support/autogen.py'

 jobs:
  autogen:
@@ -114,11 +116,9 @@ jobs:
    - name: Verify Qualcomm autogen
      run: |
        mv tinygrad/runtime/autogen/kgsl.py /tmp/kgsl.py.bak
-        mv tinygrad/runtime/autogen/adreno.py /tmp/adreno.py.bak
        mv tinygrad/runtime/autogen/qcom_dsp.py /tmp/qcom_dsp.py.bak
-        python3 -c "from tinygrad.runtime.autogen import kgsl, adreno, qcom_dsp"
+        python3 -c "from tinygrad.runtime.autogen import kgsl, qcom_dsp"
        diff /tmp/kgsl.py.bak tinygrad/runtime/autogen/kgsl.py
-        diff /tmp/adreno.py.bak tinygrad/runtime/autogen/adreno.py
        diff /tmp/qcom_dsp.py.bak tinygrad/runtime/autogen/qcom_dsp.py
    - name: Verify libusb autogen
      run: |
@@ -14,12 +14,6 @@ on:
      - update_benchmark
      - update_benchmark_staging
  workflow_dispatch:
-    inputs:
-      run_process_replay:
-        description: "Run process replay tests"
-        required: false
-        default: false
-        type: boolean

 jobs:
  testmacbenchmark:
@@ -39,6 +33,7 @@ jobs:
    - name: Symlink models and datasets
      run: |
        mkdir -p weights
+        mkdir -p extra/disassemblers
        ln -s ~/tinygrad/extra/disassemblers/applegpu extra/disassemblers/applegpu
        ln -s ~/tinygrad/weights/sd-v1-4.ckpt weights/sd-v1-4.ckpt
        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
@@ -54,9 +49,9 @@ jobs:
    - name: Print macOS version
      run: sw_vers
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
+      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
    - name: Run Stable Diffusion v2
      # TODO: very slow step time
      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=4500 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
@@ -64,7 +59,7 @@ jobs:
    - name: Run SDXL
      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
-      run: METAL=1 python3.11 test/external/external_model_benchmark.py
+      run: METAL=1 NOCLANG=1 python3.11 test/external/external_model_benchmark.py
    - name: Test speed vs torch
      run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
    - name: Test tensor cores
@@ -124,14 +119,6 @@ jobs:
    # TODO: too slow
    # - name: Run 10 CIFAR training steps w winograd
    #   run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    - 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
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/test_tiny.py
-    - name: UsbGPU copy speeds
-      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
-    #- name: UsbGPU openpilot test
-    #  run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (Mac)
@@ -165,6 +152,37 @@ jobs:
    - name: Run process replay tests
      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3.11 process_replay.py

+  testusbgpu:
+    name: UsbGPU Benchmark
+    env:
+      PYTHONPYCACHEPREFIX: /tmp/tiny_python_pycache
+    runs-on: [self-hosted, macOS]
+    timeout-minutes: 10
+    defaults:
+      run:
+        shell: bash -e -o pipefail {0}
+    if: github.repository_owner == 'tinygrad'
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: setup staging db
+      if: github.ref == 'refs/heads/update_benchmark_staging'
+      run: |
+        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
+        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
+    - 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
+      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/test_tiny.py
+    - name: UsbGPU copy speeds
+      run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
+    #- name: UsbGPU openpilot test
+    #  run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+    - name: UsbGPU (USB4/TB) boot time
+      run: PYTHONPATH=. DEBUG=3 NV=1 NV_IFACE=PCI NV_NAK=1 time python3.11 test/test_tiny.py TestTiny.test_plus
+    - name: UsbGPU (USB4/TB) tiny tests
+      run: PYTHONPATH=. NV=1 NV_IFACE=PCI NV_NAK=1 python3.11 test/test_tiny.py
+
  testnvidiabenchmark:
    name: tinybox green Benchmark
    runs-on: [self-hosted, Linux, tinyboxgreen]
@@ -318,31 +336,31 @@ jobs:
    # TODO: too slow
    # - name: Fuzz Padded Tensor Core GEMM (PTX)
    #   run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
+    - name: HEVC Decode Benchmark
+      run: VALIDATE=1 MAX_FRAMES=100 NV=1 PYTHONPATH=. python3 extra/hevc/decode.py
    - name: Train MNIST
      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-    # TODO: too slow
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=1300 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    # - name: Run 10 CIFAR training steps w HALF
-    #   run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=240 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
-    # - name: Run 10 CIFAR training steps w BF16
-    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    # TODO: too slow
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=120 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+    - name: Run 10 CIFAR training steps w HALF
+      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=110 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+    - name: Run 10 CIFAR training steps w BF16
+      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=120 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    # - name: Run 10 CIFAR training steps w winograd
    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    # - name: Run full CIFAR training w 1 GPU
-    #   run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
-    # - name: Run full CIFAR training steps w 6 GPUS
-    #   run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+    - name: Run full CIFAR training w 1 GPU
+      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+    - name: Run full CIFAR training steps w 6 GPUS
+      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
    - name: Run MLPerf resnet eval on training data
      run: time BENCHMARK_LOG=resnet_eval NV=1 MODEL=resnet python3 examples/mlperf/model_eval.py
-    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-    #  run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
-    #- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-    #  run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      run: BENCHMARK_LOG=resnet_10steps NV=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
+    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
+      run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - name: Run 10 MLPerf Bert training steps (6 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
+      run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (NVIDIA Training)
@@ -411,13 +429,15 @@ jobs:
    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
    - name: Test speed vs theoretical
      run: AMD=1 IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
-    - name: Test tensor cores
-      run: |
-        AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
-        AMD=1 AMD_LLVM=1 python3 test/opt/test_tensor_cores.py
-        AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+    - name: Test tensor cores AMD_LLVM=0
+      run: AMD=1 AMD_LLVM=0 python3 test/opt/test_tensor_cores.py
+    # TODO: this is flaky
+    # - name: Test tensor cores AMD_LLVM=1
+    #   run: AMD=1 AMD_LLVM=1 python3 test/opt/test_tensor_cores.py
    - name: Run Tensor Core GEMM (AMD)
-      run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
+      run: |
+        AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+        AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_amd.txt
    - name: Test AMD=1
      run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
    #- name: Test HIP=1
@@ -433,9 +453,8 @@ jobs:
      run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
    - name: Run Stable Diffusion
      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=550 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    # TODO: too slow
-    # - name: Run SDXL
-    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+    - name: Run SDXL
+      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA 7B
      run: |
        BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
@@ -525,22 +544,19 @@ jobs:
      run: test/external/process_replay/reset.py
    - name: Train MNIST
      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-    # TODO: too slow
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=2000 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
-    # - name: Run 10 CIFAR training steps w HALF
-    #   run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=390 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=200 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+    - name: Run 10 CIFAR training steps w HALF
+      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=200 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    # - name: Run 10 CIFAR training steps w BF16
    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
    # TODO: too slow
    # - name: Run 10 CIFAR training steps w winograd
    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
-    # - name: Run full CIFAR training w 1 GPU
-    #   run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
-    #- name: Run full CIFAR training steps w 6 GPUS
-    #  run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
-    #- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
-    #  run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
+    - name: Run full CIFAR training w 1 GPU
+      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+    - name: Run full CIFAR training steps w 6 GPUS
+      run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD Training)
@@ -552,7 +568,6 @@ jobs:
          train_cifar_wino.txt
          train_cifar_one_gpu.txt
          train_cifar_six_gpu.txt
-          train_cifar_six_gpu_remote.txt
    - name: Run process replay tests
      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py

@@ -590,13 +605,13 @@ jobs:
      run: test/external/process_replay/reset.py
    - name: Run MLPerf resnet eval
      run: time BENCHMARK_LOG=resnet_eval AMD=1 MODEL=resnet python3 examples/mlperf/model_eval.py
-    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-    #  run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
-    #- name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-    #  run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      run: BENCHMARK_LOG=resnet_10steps AMD=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet_one_gpu.txt
+    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
+      run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - name: Run 10 MLPerf Bert training steps (6 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
+      run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD MLPerf)
@@ -625,32 +640,28 @@ jobs:
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
      run: test/external/process_replay/reset.py
-    # - name: openpilot compile3 0.9.9 driving_vision
-    #   run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
-    # - name: openpilot compile3 0.9.9 driving_policy
-    #   run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
-    # - name: openpilot compile3 0.9.9 dmonitoring
-    #   run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 0.10.0 driving_policy
      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/driving_policy.onnx
    - name: openpilot compile3 0.10.0 dmonitoring
      run: BENCHMARK_LOG=openpilot_0_10_0_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=11 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.10.0/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: DEBUG=2 openpilot compile3 0.10.1 driving_vision
      run: PYTHONPATH="." DEBUG=2 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
+    - name: DEBUG=2 IMAGE=1 openpilot compile3 0.10.1 driving_vision
+      run: PYTHONPATH="." DEBUG=2 DEV=QCOM FLOAT16=1 IMAGE=1 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
    - name: openpilot compile3 0.10.1 driving_vision
      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=17 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
    - name: openpilot compile3 0.10.1 driving_policy
      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_policy.onnx
    - name: openpilot compile3 0.10.1 dmonitoring
      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=10 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
-    # - name: benchmark MobileNetV2 on DSP
-    #   run: |
-    #     # generate quantized weights
-    #     ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
-    #     ln -s /data/home/tiny/tinygrad/testsig-*.so .
-    #     PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
-    #     # benchmark on DSP with NOOPT=1, the devectorizer has issues
-    #     PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+    - name: benchmark MobileNetV2 on DSP
+      run: |
+        # generate quantized weights
+        ln -s /data/home/tiny/tinygrad/extra/datasets/imagenet extra/datasets/imagenet
+        ln -s /data/home/tiny/tinygrad/testsig-*.so .
+        PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
+        # benchmark on DSP with NOOPT=1, the devectorizer has issues
+        PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
    - name: Run process replay tests
      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py

@@ -706,10 +717,8 @@ jobs:
      run: |
        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
-    # TODO: too slow
-    # - name: Run full CIFAR training w 1 GPU
-    #   run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
-    # TODO: enable
+    - name: Run full CIFAR training w 1 GPU
+      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
    # - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
    #   run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
    - name: Run 10 MLPerf Bert training steps (1 gpu)
@@ -770,11 +779,10 @@ jobs:
        NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
    - name: Test LLAMA-3
      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
-    # TODO: too slow
-    # - name: Run full CIFAR training w 1 GPU
-    #   run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
-    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-    #  run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
+    - name: Run full CIFAR training w 1 GPU
+      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
    - name: Run 10 MLPerf Bert training steps (1 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
      run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt
@@ -56,15 +56,15 @@ jobs:
        uses: actions/checkout@v4
        with:
          path: base
-      - name: Set up Python 3.10
+      - name: Set up Python 3.12
        uses: actions/setup-python@v5
        with:
-          python-version: '3.10'
+          python-version: '3.12'
      - name: Count Line Diff
        run: |
-          pip install tabulate
          BASE="$GITHUB_WORKSPACE/base"
          PR="$GITHUB_WORKSPACE/pr"
+          pip install tabulate $BASE
          cp "$BASE/sz.py" .
          echo "loc_content<<EOF" >> "$GITHUB_ENV"
          python sz.py "$BASE" "$PR" >> "$GITHUB_ENV"
@@ -1,7 +1,7 @@
 name: Unit Tests
 env:
  # increment this when downloads substantially change to avoid the internet
-  CACHE_VERSION: '13'
+  CACHE_VERSION: '15'
  CAPTURE_PROCESS_REPLAY: 1
  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
  PYTHONPATH: ${{ github.workspace }}
@@ -71,9 +71,7 @@ jobs:
    - name: Test Docs Build
      run: python -m mkdocs build --strict
    - name: Test Docs
-      run: |
-        python docs/abstractions2.py
-        python docs/abstractions3.py
+      run: python docs/abstractions3.py
    - name: Test README
      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py &&  python README.py
    - name: Test Quickstart
@@ -86,65 +84,67 @@ jobs:
        clang -O2 recognize.c -lm -o recognize
        cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock

-  # TODO: fix the torch backend and reenable
-  # torchbackend:
-  #   name: Torch Backend Tests
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       pydeps: "pillow torchvision expecttest"
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Lint with ruff
-  #     run: |
-  #       pip3 install --upgrade --force-reinstall ruff==0.11.0
-  #       python3 -m ruff check extra/torch_backend/backend.py
-  #   - name: Test one op
-  #     run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
-  #   - name: Test ResNet-18
-  #     run: DEBUG=2 python3 extra/torch_backend/example.py
-  #   - name: My (custom) tests
-  #     run: python3 extra/torch_backend/test.py
-  #   - name: Test one op in torch tests
-  #     run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
-  #   - name: Test Ops with TINY_BACKEND
-  #     run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
-  #   - name: Test in-place operations on views
-  #     run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
-  #   - name: Test multi-gpu
-  #     run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+  torchbackend:
+    name: Torch Backend Tests
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: torch-backend-pillow-torchvision-et-pt
+        deps: testing_minimal
+        pydeps: "pillow torchvision expecttest"
+        llvm: 'true'
+    - name: Install ninja
+      run: |
+        sudo apt update || true
+        sudo apt install -y --no-install-recommends ninja-build
+    - name: Lint with ruff
+      run: |
+        pip3 install --upgrade --force-reinstall ruff==0.11.0
+        python3 -m ruff check extra/torch_backend/backend.py
+    - name: Test one op
+      run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
+    - name: Test ResNet-18
+      run: DEBUG=2 python3 extra/torch_backend/example.py
+    - name: My (custom) tests
+      run: python3 extra/torch_backend/test.py
+    - name: Test one op in torch tests
+      run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
+    - name: Test Ops with TINY_BACKEND
+      run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
+    - name: Test in-place operations on views
+      run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
+    - name: Test multi-gpu
+      run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+    - name: Test kernel fusion
+      run: python3 extra/torch_backend/test_kernel_fusion.py

-  # torchbackendmore:
-  #   name: Torch Backend Tests More
-  #   runs-on: ubuntu-latest
-  #   timeout-minutes: 15
-  #   steps:
-  #   - name: Checkout Code
-  #     uses: actions/checkout@v4
-  #   - name: Setup Environment
-  #     uses: ./.github/actions/setup-tinygrad
-  #     with:
-  #       key: torch-backend-pillow-torchvision-et-pt
-  #       deps: testing_minimal
-  #       llvm: 'true'
-  #   - name: Install ninja
-  #     run: |
-  #       sudo apt update || true
-  #       sudo apt install -y --no-install-recommends ninja-build
-  #   - name: Test beautiful_mnist in torch with TINY_BACKEND
-  #     run: STEPS=20 CPU=1 TARGET_EVAL_ACC_PCT=90.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
-  #   - name: Test some torch tests (expect failure)
-  #     run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
+
+  torchbackendmore:
+    name: Torch Backend Tests More
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: torch-backend-pillow-torchvision-et-pt
+        deps: testing_minimal
+        llvm: 'true'
+    - name: Install ninja
+      run: |
+        sudo apt update || true
+        sudo apt install -y --no-install-recommends ninja-build
+    - name: Test beautiful_mnist in torch with TINY_BACKEND
+      run: STEPS=20 CPU=1 TARGET_EVAL_ACC_PCT=90.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+    - name: Test some torch tests (expect failure)
+      run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true

  bepython:
    name: Python Backend
@@ -236,13 +236,13 @@ jobs:
        pip3 install --upgrade --force-reinstall ruff==0.11.0
        python3 -m ruff check .
        python3 -m ruff check examples/mlperf/ --ignore E501
+        python3 -m ruff check extra/thunder/tiny/ --ignore E501 --ignore F841 --ignore E722
    - name: Run mypy
      run: |
        python -m mypy --strict-equality --lineprecision-report .
        cat lineprecision.txt
-    # broken because of UPatAny
-    #- name: Run TYPED=1
-    #  run: TYPED=1 python -c "import tinygrad"
+    - name: Run TYPED=1
+      run: TYPED=1 python -c "import tinygrad"

  unittest:
    name: Unit Tests
@@ -261,7 +261,9 @@ jobs:
    - name: Check Device.DEFAULT
      run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
    - name: Run unit tests
-      run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
+      run: |
+        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 test/device/test_null.py
    # TODO: too slow
@@ -287,8 +289,8 @@ jobs:
        python extra/optimization/extract_dataset.py
        gzip -c /tmp/sops > extra/datasets/sops.gz
        #DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
-    - name: Repo line count < 19000 lines
-      run: MAX_LINE_COUNT=19000 python sz.py
+    - name: Repo line count < 20000 lines
+      run: MAX_LINE_COUNT=20000 python sz.py

  spec:
    strategy:
@@ -306,8 +308,9 @@ jobs:
      with:
        key: spec-unit
        deps: testing_unit
+        python-version: '3.14'
    - name: Test SPEC=2
-      run: IGNORE_OOB=0 SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
+      run: IGNORE_OOB=0 SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/test_custom_kernel.py --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}

  fuzzing:
    name: Fuzzing
@@ -323,6 +326,8 @@ jobs:
        deps: testing_unit
    - name: Fuzz Test symbolic
      run: python test/external/fuzz_symbolic.py
+    - name: Fuzz Test symbolic (symbolic divisors)
+      run: python test/external/fuzz_symbolic_symbolic_div.py
    - name: Fuzz Test fast idiv
      run: python test/external/fuzz_fast_idiv.py
    - name: Fuzz Test shape ops
@@ -442,7 +447,7 @@ jobs:
        with:
          key: onnxoptl
          deps: testing
-          pydeps: "tensorflow==2.15.1 tensorflow_addons"
+          pydeps: "tensorflow==2.19"
          python-version: '3.11'
          opencl: 'true'
      - name: Test ONNX (CL)
@@ -460,7 +465,7 @@ jobs:
      - name: Test Bert training
        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 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
+        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

@@ -636,7 +641,7 @@ jobs:
        if: matrix.backend=='amdllvm'
        run: python test/device/test_amd_llvm.py
      - name: Run pytest (amd)
-        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py --durations=20
+        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py test/testextra/test_cfg_viz.py --durations=20
      - name: Run pytest (amd)
        run: python -m pytest test/external/external_test_am.py --durations=20
      - name: Run TRANSCENDENTAL math
@@ -649,6 +654,37 @@ jobs:
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

+  testrdna3:
+    name: AMD ASM IDE
+    runs-on: ubuntu-24.04
+    timeout-minutes: 10
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: rdna3-emu
+          deps: testing_minimal
+          amd: 'true'
+      - name: Install LLVM 21
+        run: |
+          wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
+          echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-21 main" | sudo tee /etc/apt/sources.list.d/llvm.list
+          sudo apt-get update
+          sudo apt-get install llvm-21 llvm-21-tools cloc
+      - name: RDNA3 Line Count
+        run: cloc --by-file extra/assembly/amd/*.py
+      - name: Run RDNA3 emulator tests
+        run: python -m pytest -n=auto extra/assembly/amd/ --durations 20
+      - name: Install pdfplumber
+        run: pip install pdfplumber
+      - name: Verify AMD autogen is up to date
+        run: |
+          python -m extra.assembly.amd.dsl --arch all
+          python -m extra.assembly.amd.pcode --arch all
+          git diff --exit-code extra/assembly/amd/autogen/
+
  testnvidia:
    strategy:
      fail-fast: false
@@ -716,71 +752,6 @@ jobs:
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

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

  testmetal:
@@ -878,30 +849,6 @@ jobs:
    - name: Test ONNX Runner (WEBGPU)
      run: WEBGPU=1 python3 test/external/external_test_onnx_runner.py

-  osxremote:
-   name: MacOS (remote metal)
-   runs-on: macos-15
-   timeout-minutes: 10
-   env:
-     REMOTE: 1
-     REMOTEDEV: METAL
-   steps:
-     - name: Checkout Code
-       uses: actions/checkout@v4
-     - name: Setup Environment
-       uses: ./.github/actions/setup-tinygrad
-       with:
-         key: macos-remote
-         deps: testing_minimal
-     - name: Check Device.DEFAULT and print some source
-       run: |
-         python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-         python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
-         DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-     - name: Run REMOTE=1 Test
-       run: |
-         python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
-
  osxtests:
    strategy:
      fail-fast: false
@@ -967,3 +914,33 @@ jobs:
        run: |
          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
          python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20
+
+# ****** Compile-only Tests ******
+
+  compiletests:
+    strategy:
+      fail-fast: false
+      matrix:
+        backend: [ir3, nak]
+    name: Compile-only (${{ matrix.backend }})
+    runs-on: ubuntu-24.04
+    timeout-minutes: 15
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: compile-${{ matrix.backend }}
+          deps: testing_minimal
+          mesa: ${{ (matrix.backend == 'ir3' || matrix.backend == 'nak') && 'true' }}
+          python-version: '3.14'
+      - name: Set env
+        shell: bash
+        run: printf "NULL=1\n${{ matrix.backend == 'ir3' && 'NULL_IR3=1' || matrix.backend == 'nak' && 'NULL_NAK=1' }}" >> $GITHUB_ENV
+      - name: Run test_ops
+        shell: bash
+        run: |
+          python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
+          DEBUG=4 python3 test/test_ops.py TestOps.test_add
+          python -m pytest -n=auto test/test_ops.py --durations=20
@@ -27,8 +27,8 @@ repos:
        always_run: true
        pass_filenames: false
      - id: tests
-        name: subset of tests
-        entry: env OMP_NUM_THREADS=1 PYTHONPATH="." python3 -m pytest -n=6 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
+        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/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
@@ -0,0 +1,210 @@
+# Claude Code Guide for tinygrad
+
+## Architecture Overview
+
+tinygrad compiles tensor operations into optimized kernels. The pipeline:
+
+1. **Tensor** (`tensor.py`) - User-facing API, creates UOp graph
+2. **UOp** (`uop/ops.py`) - Unified IR for all operations (both tensor and kernel level)
+3. **Schedule** (`engine/schedule.py`, `schedule/`) - Converts tensor UOps to kernel UOps
+4. **Codegen** (`codegen/`) - Converts kernel UOps to device code
+5. **Runtime** (`runtime/`) - Device-specific execution
+
+## Key Concepts
+
+### UOp (Universal Operation)
+Everything is a UOp - tensors, operations, buffers, kernels. Key properties:
+- `op`: The operation type (Ops enum)
+- `dtype`: Data type
+- `src`: Tuple of source UOps
+- `arg`: Operation-specific argument
+- `tag`: Optional tag for graph transformations
+
+UOps are **immutable and cached** - creating the same UOp twice returns the same object (ucache).
+
+### PatternMatcher
+Used extensively for graph transformations:
+```python
+pm = PatternMatcher([
+  (UPat(Ops.ADD, src=(UPat.cvar("x"), UPat.cvar("x"))), lambda x: x * 2),
+])
+result = graph_rewrite(uop, pm)
+```
+
+### Schedule Cache
+Schedules are cached by graph structure. BIND nodes (variables with bound values) are unbound before cache key computation so different values hit the same cache.
+
+## Testing
+
+```bash
+# Run specific test
+python -m pytest test/unit/test_schedule_cache.py -xvs
+
+# Run with timeout
+python -m pytest test/test_symbolic_ops.py -x --timeout=60
+
+# Debug with print
+DEBUG=2 python -m pytest test/test_schedule.py::test_name -xvs
+
+# Visualize UOp graphs
+VIZ=1 python -c "from tinygrad import Tensor; Tensor.ones(10).sum().realize()"
+```
+
+## Common Environment Variables
+
+- `DEBUG=1-7` - Increasing verbosity (7 shows assembly output)
+- `VIZ=1` - Enable graph visualization
+- `SPEC=1` - Enable UOp spec verification
+- `NOOPT=1` - Disable optimizations
+- `DEVICE=CPU/CUDA/AMD/METAL` - Set default device
+
+## Debugging Tips
+
+1. **Print UOp graphs**: `print(tensor.uop)` or `print(tensor.uop.sink())`
+2. **Check schedule**: `tensor.schedule()` returns list of ExecItems
+3. **Trace graph rewrites**: Use `VIZ=1` or add print in PatternMatcher callbacks
+4. **Find UOps by type**: `[u for u in uop.toposort() if u.op is Ops.SOMETHING]`
+
+## Workflow Rules
+
+- **NEVER commit without explicit user approval** - always show the diff and wait for approval
+- **NEVER amend commits** - always create a new commit instead
+- Run `pre-commit run --all-files` before committing to catch linting/type errors
+- Run tests before proposing commits
+- Test with `SPEC=2` when modifying UOp-related code
+
+## Auto-generated Files (DO NOT EDIT)
+
+The following files are auto-generated and should never be edited manually:
+- `extra/assembly/amd/autogen/{arch}/__init__.py` - Generated by `python -m extra.assembly.amd.dsl --arch {arch}`
+- `extra/assembly/amd/autogen/{arch}/gen_pcode.py` - Generated by `python -m extra.assembly.amd.pcode --arch {arch}`
+
+Where `{arch}` is one of: `rdna3`, `rdna4`, `cdna`
+
+To add missing instruction implementations, add them to `extra/assembly/amd/emu.py` instead.
+
+## Style Notes
+
+- 2-space indentation, 150 char line limit
+- PatternMatchers should be defined at module level (slow to construct)
+- Prefer `graph_rewrite` over manual graph traversal
+- UOp methods like `.replace()` preserve tags unless explicitly changed
+- Use `.rtag(value)` to add tags to UOps
+
+## Lessons Learned
+
+### UOp ucache Behavior
+UOps are cached by their contents - creating a UOp with identical (op, dtype, src, arg) returns the **same object**. This means:
+- `uop.replace(tag=None)` on a tagged UOp returns the original untagged UOp if it exists in cache
+- Two UOps with same structure are identical (`is` comparison works)
+
+### Spec Validation
+When adding new UOp patterns, update `tinygrad/uop/spec.py`. Test with:
+```bash
+SPEC=2 python3 test/unit/test_something.py
+```
+Spec issues appear as `RuntimeError: SPEC ISSUE None: UOp(...)`.
+
+### Schedule Cache Key Normalization
+The schedule cache strips values from BIND nodes so different bound values (e.g., KV cache positions) hit the same cache entry:
+- `pm_pre_sched_cache`: BIND(DEFINE_VAR, CONST) → BIND(DEFINE_VAR) for cache key
+- `pm_post_sched_cache`: restores original BIND from context
+- When accessing `bind.src[1]`, check `len(bind.src) > 1` first (might be stripped)
+- Extract var_vals from `input_buffers` dict after graph_rewrite (avoids extra toposort)
+
+### Avoiding Extra Work
+- Use ctx dict from graph_rewrite to collect info during traversal instead of separate toposort
+- Only extract var_vals when schedule is non-empty (no kernels = no vars needed)
+- PatternMatchers are slow to construct - define at module level, not in functions
+
+### Readability Over Speed
+Don't add complexity for marginal performance gains. Simpler code that's slightly slower is often better:
+```python
+# BAD: "optimized" with extra complexity
+if has_afters:  # skip toposort if no AFTERs
+  after_map = [(u, u.buf_uop) for u in big_sink.toposort() if u.op is Ops.AFTER]
+
+# GOOD: simple, always works
+after_map = [(u, u.buf_uop) for u in big_sink.toposort() if u.op is Ops.AFTER]
+```
+The conditional check adds complexity, potential bugs, and often negligible speedup. Only optimize when profiling shows a real bottleneck.
+
+### Testing LLM Changes
+```bash
+# Quick smoke test
+echo "Hello" | DEBUG=1 python tinygrad/apps/llm.py --model "llama3.2:1b"
+
+# Check cache hits (should see "cache hit" after warmup)
+echo "Hello world" | DEBUG=1 python tinygrad/apps/llm.py --model "llama3.2:1b" 2>&1 | grep cache
+
+# Test with beam search
+echo "Hello" | BEAM=2 python tinygrad/apps/llm.py --model "llama3.2:1b"
+```
+
+## Common Patterns
+
+### Graph Transformation
+```python
+def my_transform(ctx, x):
+  # Return new UOp or None to skip
+  return x.replace(arg=new_arg)
+
+pm = PatternMatcher([
+  (UPat(Ops.SOMETHING, name="x"), my_transform),
+])
+result = graph_rewrite(input_uop, pm, ctx={})
+```
+
+### Finding Variables
+```python
+# Get all variables in a UOp graph
+variables = uop.variables()
+
+# Get bound variable values
+var, val = bind_uop.unbind()
+```
+
+### Shape Handling
+```python
+# Shapes can be symbolic (contain UOps)
+shape = tensor.shape  # tuple[sint, ...] where sint = int | UOp
+```
+
+## Performance Optimization
+
+When optimizing tinygrad internals:
+
+1. **Measure wall time, not just call counts** - Reducing `graph_rewrite` calls doesn't always improve wall time. The overhead of conditional checks can exceed the cost of the operation being skipped.
+
+2. **Profile each optimization individually** - Run benchmarks with and without each change to measure actual impact. Use `test/external/external_benchmark_schedule.py` for schedule/rewrite timing.
+
+3. **Early exits in hot paths are effective** - Simple checks like `if self.op is Ops.CONST: return self` in `simplify()` can eliminate many unnecessary `graph_rewrite` calls.
+
+4. **`graph_rewrite` is expensive** - Each call has overhead even for small graphs. Avoid calling it when the result is trivially known (e.g., simplifying a CONST returns itself).
+
+5. **Beware iterator overhead** - Checks like `all(x.op is Ops.CONST for x in self.src)` can be slower than just running the operation, especially for small sequences.
+
+6. **Verify cache hit rates before adding/keeping caches** - Measure actual hit rates with real workloads. A cache with 0% hit rate is pure overhead (e.g., `pm_cache` was removed because the algorithm guarantees each UOp is only passed to `pm_rewrite` once).
+
+7. **Use `TRACK_MATCH_STATS=2` to profile pattern matching** - This shows match rates and time per pattern. Look for patterns with 0% match rate that still cost significant time - these are pure overhead for that workload.
+
+8. **Cached properties beat manual traversal** - `backward_slice` uses `@functools.cached_property`. A DFS with early-exit sounds faster but is actually slower because it doesn't benefit from caching. The cache hit benefit often outweighs algorithmic improvements.
+
+9. **Avoid creating intermediate objects in hot paths** - For example, `any(x.op in ops for x in self.backward_slice)` is faster than `any(x.op in ops for x in {self:None, **self.backward_slice})` because it avoids dict creation.
+
+## Pattern Matching Profiling
+
+Use `TRACK_MATCH_STATS=2` to identify expensive patterns:
+
+```bash
+TRACK_MATCH_STATS=2 PYTHONPATH="." python3 test/external/external_benchmark_schedule.py
+```
+
+Output format: `matches / attempts -- match_time / total_time ms -- location`
+
+Key patterns to watch (from ResNet50 benchmark):
+- `split_load_store`: ~146ms, 31% match rate - does real work
+- `simplify_valid`: ~75ms, 0% match rate in this workload - checks AND ops for INDEX in backward slice
+- `vmin==vmax folding`: ~55ms, 0.33% match rate - checks 52K ops but rarely matches
+
+Patterns with 0% match rate are workload-specific overhead. They may be useful in other workloads, so don't remove them without understanding their purpose.
@@ -1,135 +0,0 @@
-# tinygrad is a tensor library, and as a tensor library it has multiple parts
-# 1. a "runtime". this allows buffer management, compilation, and running programs
-# 2. a "Device" that uses the runtime but specifies compute in an abstract way for all
-# 3. a "UOp" that fuses the compute into kernels, using memory only when needed
-# 4. a "Tensor" that provides an easy to use frontend with autograd ".backward()"
-
-
-print("******** first, the runtime ***********")
-
-from tinygrad.runtime.ops_cpu import ClangJITCompiler, CPUDevice, CPUProgram
-
-cpu = CPUDevice()
-
-# allocate some buffers
-out = cpu.allocator.alloc(4)
-a = cpu.allocator.alloc(4)
-b = cpu.allocator.alloc(4)
-
-# load in some values (little endian)
-cpu.allocator._copyin(a, memoryview(bytearray([2,0,0,0])))
-cpu.allocator._copyin(b, memoryview(bytearray([3,0,0,0])))
-
-# compile a program to a binary
-lib = ClangJITCompiler().compile("void add(int *out, int *a, int *b) { out[0] = a[0] + b[0]; }")
-
-# create a runtime for the program
-fxn = cpu.runtime("add", lib)
-
-# run the program
-fxn(out, a, b)
-
-# check the data out
-print(val := cpu.allocator._as_buffer(out).cast("I").tolist()[0])
-assert val == 5
-
-
-print("******** second, the Device ***********")
-
-DEVICE = "CPU"   # NOTE: you can change this!
-
-import struct
-from tinygrad.dtype import dtypes
-from tinygrad.device import Buffer, Device
-from tinygrad.uop.ops import UOp, Ops
-
-# allocate some buffers + load in values
-out = Buffer(DEVICE, 1, dtypes.int32).allocate()
-a = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
-b = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
-# NOTE: a._buf is the same as the return from cpu.allocator.alloc
-
-# describe the computation
-idx = UOp.const(dtypes.index, 0)
-buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
-buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
-alu = buf_1.index(idx) + buf_2.index(idx)
-output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
-st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.index(idx), alu))
-s = UOp(Ops.SINK, dtypes.void, (st_0,))
-
-# convert the computation to a "linearized" format (print the format)
-from tinygrad.engine.realize import get_program, CompiledRunner
-program = get_program(s, Device[DEVICE].renderer)
-
-# compile a program (and print the source)
-fxn = CompiledRunner(program)
-print(fxn.p.src)
-# NOTE: fxn.clprg is the CPUProgram
-
-# run the program
-fxn.exec([out, a, b])
-
-# check the data out
-assert out.as_buffer().cast('I')[0] == 5
-
-
-print("******** third, the UOp ***********")
-
-from tinygrad.engine.realize import run_schedule
-from tinygrad.engine.schedule import create_schedule_with_vars
-from tinygrad.schedule.rangeify import get_rangeify_map
-
-# allocate some values + load in values
-a = UOp.new_buffer(DEVICE, 1, dtypes.int32)
-b = UOp.new_buffer(DEVICE, 1, dtypes.int32)
-a.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
-b.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
-
-# describe the computation
-out = a + b
-s = UOp(Ops.SINK, dtypes.void, (out,))
-
-# group the computation into kernels
-becomes_map = get_rangeify_map(s)
-
-# the compute maps to an assign
-assign = becomes_map[a+b].base
-
-# the first source is the output buffer (data)
-assert assign.src[0].op is Ops.BUFFER
-# the second source is the kernel (compute)
-assert assign.src[1].op is Ops.KERNEL
-
-# schedule the kernel graph in a linear list
-s = UOp(Ops.SINK, dtypes.void, (assign,))
-sched, _ = create_schedule_with_vars(s)
-assert len(sched) == 1
-
-# DEBUGGING: print the compute ast
-print(sched[-1].ast)
-# NOTE: sched[-1].ast is the same as st_0 above
-
-# the output will be stored in a new buffer
-out = assign.buf_uop
-assert out.op is Ops.BUFFER and not out.buffer.is_allocated()
-print(out)
-
-# run that schedule
-run_schedule(sched)
-
-# check the data out
-assert out.is_realized and out.buffer.as_buffer().cast('I')[0] == 5
-
-
-print("******** fourth, the Tensor ***********")
-
-from tinygrad import Tensor
-
-a = Tensor([2], dtype=dtypes.int32, device=DEVICE)
-b = Tensor([3], dtype=dtypes.int32, device=DEVICE)
-out = a + b
-
-# check the data out
-print(val:=out.item())
-assert val == 5
@@ -38,25 +38,19 @@ optim.schedule_step()   # this will step the optimizer without running realize
 # The weight Tensors have been assigned to, but not yet realized. Everything is still lazy at this point
 # l1.uop and l2.uop define a computation graph

-from tinygrad.engine.schedule import ScheduleItem
-schedule: List[ScheduleItem] = Tensor.schedule(l1, l2)
+from tinygrad.engine.schedule import ExecItem
+schedule: List[ExecItem] = Tensor.schedule(l1, l2)

 print(f"The schedule contains {len(schedule)} items.")
 for si in schedule: print(str(si)[:80])

 # *****
-# 4. Lower a schedule.
+# 4. Lower and run the schedule.

-from tinygrad.engine.realize import lower_schedule_item, ExecItem
-lowered: List[ExecItem] = [lower_schedule_item(si) for si in tqdm(schedule)]
+for si in tqdm(schedule): si.run()

 # *****
-# 5. Run the schedule
-
-for ei in tqdm(lowered): ei.run()
-
-# *****
-# 6. Print the weight change
+# 5. Print the weight change

 print("first weight change\n", l1.numpy()-l1n)
 print("second weight change\n", l2.numpy()-l2n)
@@ -13,19 +13,19 @@ There's also a [doc describing speed](../developer/speed.md)

 Everything in [Tensor](../tensor/index.md) is syntactic sugar around constructing a graph of [UOps](../developer/uop.md).

-The `UOp` graph specifies the compute in terms of low level tinygrad ops. Not all UOps will actually become realized. There's two types of UOps, base and view. base contains compute into a contiguous buffer, and view is a view (specified by a ShapeTracker). Inputs to a base can be either base or view, inputs to a view can only be a single base.
+The `UOp` graph specifies the compute in terms of low level tinygrad ops. Not all UOps will actually become realized. There's two types of UOps, base and view. base contains compute into a contiguous buffer, and view is a view. Inputs to a base can be either base or view, inputs to a view can only be a single base.

 ## Scheduling

-The [scheduler](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/schedule.py) converts the graph of UOps into a list of `ScheduleItem`. One `ScheduleItem` is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. `ast` specifies what compute to run, and `bufs` specifies what buffers to run it on.
+The [scheduler](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/schedule.py) converts the graph of UOps into a list of `ExecItem`. One `ExecItem` is one kernel on the GPU, and the scheduler is responsible for breaking the large compute graph into subgraphs that can fit in a kernel. `ast` specifies what compute to run, and `bufs` specifies what buffers to run it on.

-::: tinygrad.engine.schedule.ScheduleItem
+::: tinygrad.engine.schedule.ExecItem

 ## Lowering

-The code in [realize](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py) lowers `ScheduleItem` to `ExecItem` with
+The code in [realize](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/engine/realize.py) lowers `ExecItem` by populating its `prg` field with

-::: tinygrad.engine.realize.lower_schedule
+::: tinygrad.engine.realize.run_schedule

 There's a ton of complexity hidden behind this, see the `codegen/` directory.

@@ -26,9 +26,9 @@ Transforms the ast into an optimized ast. This is where BEAM search and heuristi

 ## tinygrad/codegen

-Transform the optimized ast into a linearized list of UOps.
+Transform the optimized ast into a linearized and rendered program.

-::: tinygrad.codegen.full_rewrite
+::: tinygrad.codegen.get_program
    options:
        members: false
        show_labels: false
@@ -131,7 +131,7 @@ timeit.repeat(jit_step, repeat=5, number=1)

 1.0 ms is 75x faster! Note that we aren't syncing the GPU, so GPU time may be slower.

-The slowness the first two times is the JIT capturing the kernels. And this JIT will not run any Python in the function, it will just replay the tinygrad kernels that were run, so be aware that non tinygrad Python operations won't work. Randomness functions work as expected.
+The first two runs of the function execute normally, with the JIT capturing the kernels. Starting from the third run, only the tinygrad operations are replayed, removing the overhead by skipping Python code execution. So be aware that any non-tinygrad Python values affecting the kernels will be "frozen" from the second run. Note that `Tensor` randomness functions work as expected.

 Unlike other JITs, we JIT everything, including the optimizer. Think of it as a dumb replay on different data.

@@ -1,293 +0,0 @@
-#!/usr/bin/env python3
-
-# this file is a "ramp" for people new to tinygrad to think about how to approach it
-# it is runnable and editable.
-# whenever you see stuff like DEBUG=2 or CPU=1 discussed, these are environment variables
-# in a unix shell like bash `DEBUG=2 CPU=1 python docs/ramp.py`
-
-# this pip installs tinygrad master for the system
-# the -e allows you to edit the tinygrad folder and update system tinygrad
-# tinygrad is pure Python, so you are encouraged to do this
-# git pull in the tinygrad directory will also get you the latest
-"""
-git clone https://github.com/tinygrad/tinygrad.git
-cd tinygrad
-python3 -m pip install -e .
-"""
-
-# %% ********
-print("******* PART 1 *******")
-
-# we start with a Device.
-# a Device is where Tensors are stored and compute is run
-# tinygrad autodetects the best device on your system and makes it the DEFAULT
-from tinygrad import Device
-print(Device.DEFAULT)  # on Mac, you can see this prints METAL
-
-# now, lets create a Tensor
-from tinygrad import Tensor, dtypes
-t = Tensor([1,2,3,4])
-
-# you can see this Tensor is on the DEFAULT device with int dtype and shape (4,)
-assert t.device == Device.DEFAULT
-assert t.dtype == dtypes.int
-assert t.shape == (4,)
-
-# unlike in torch, if we print it, it doesn't print the contents
-# this is because tinygrad is lazy
-# this Tensor has not been computed yet
-print(t)
-# <Tensor <UOp METAL (4,) int (<Ops.COPY: 7>, None)> on METAL with grad None>
-
-# the ".uop" property on Tensor contains the specification of how to compute it
-print(t.uop)
-"""
-UOp(Ops.COPY, dtypes.int, arg=None, src=(
-  UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=0, src=()),
-    UOp(Ops.DEVICE, dtypes.void, arg='PYTHON', src=()),)),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# as you can see, it's specifying a copy from PYTHON device
-# which is where the [1,2,3,4] array lives
-
-# UOps are the specification language in tinygrad
-# they are immutable and form a DAG
-# they have a "Ops", a "dtype", a tuple of srcs (parents), and an arg
-
-t.realize()
-# if we want to "realize" a tensor, we can with the "realize" method
-# now when we look at the uop, it's changed
-print(t.uop)
-"""
-UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-  UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# the copy was actually run, and now the "uop" of the Tensor is just a BUFFER
-# if you run this script with DEBUG=2 in the environment, you can see the copy happen
-# *** METAL      1 copy       16,   METAL <- PYTHON ...
-
-# now let's do some compute
-# we look at the uop to see the specification of the compute
-t_times_2 = t * 2
-print(t_times_2.uop)
-"""
-UOp(Ops.MUL, dtypes.int, arg=None, src=(
-  UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-    x2:=UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),)),
-  UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-    UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-      UOp(Ops.CONST, dtypes.int, arg=2, src=(
-        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
-           x2,)),)),)),)),))
-"""
-# the BUFFER from above is being multiplied by a CONST 2
-# it's RESHAPEd and EXPANDed to broadcast the CONST to the BUFFER
-
-# we can check the result with
-assert t_times_2.tolist() == [2, 4, 6, 8]
-
-# UOps are both immutable and globally unique
-# if i multiply the Tensor by 4 twice, these result Tensors will have the same uop specification
-t_times_4_try_1 = t * 4
-t_times_4_try_2 = t * 4
-assert t_times_4_try_1.uop is t_times_4_try_2.uop
-# the specification isn't just the same, it's the exact same Python object
-assert t_times_4_try_1 is not t_times_4_try_2
-# the Tensor is a different Python object
-
-# if we realize `t_times_4_try_1` ...
-t_times_4_try_1.realize()
-print(t_times_4_try_2.uop)
-"""
-UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-  UOp(Ops.UNIQUE, dtypes.void, arg=4, src=()),
-  UOp(Ops.DEVICE, dtypes.void, arg='METAL', src=()),))
-"""
-# ... `t_times_4_try_2` also becomes the same BUFFER
-assert t_times_4_try_1.uop is t_times_4_try_2.uop
-# so this print doesn't require any computation, just a copy back to the CPU so we can print it
-print("** only the copy start")
-print(t_times_4_try_2.tolist())  # [4, 8, 12, 16]
-print("** only the copy end")
-# you can confirm this with DEBUG=2, seeing what's printed in between the "**" prints
-
-# tinygrad has an auto differentiation engine that operates according to these same principles
-# the derivative of "log(x)" is "1/x", and you can see this on line 20 of gradient.py
-t_float = Tensor([3.0])
-t_log = t_float.log()
-t_log_grad, = t_log.sum().gradient(t_float)
-# due to how log is implemented, this gradient contains a lot of UOps
-print(t_log_grad.uop)
-# ...not shown here...
-# but if you run with DEBUG=4 (CPU=1 used here for simpler code), you can see the generated code
-"""
-void E_(float* restrict data0, float* restrict data1) {
-  float val0 = *(data1+0);
-  *(data0+0) = (1/val0);
-}
-"""
-# the derivative is close to 1/3
-assert (t_log_grad.item() - 1/3) < 1e-6
-
-# %% ********
-print("******* PART 2 *******")
-
-# we redefine the same t here so this cell can run on it's own
-from tinygrad import Tensor
-t = Tensor([1,2,3,4])
-
-# what's above gives you enough of an understanding to go use tinygrad as a library
-# however, a lot of the beauty of tinygrad is in how easy it is to interact with the internals
-# NOTE: the APIs here are subject to change
-
-t_plus_3_plus_4 = t + 3 + 4
-print(t_plus_3_plus_4.uop)
-"""
-UOp(Ops.ADD, dtypes.int, arg=None, src=(
-  UOp(Ops.ADD, dtypes.int, arg=None, src=(
-    UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-      UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-      x3:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
-    UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-      UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-        UOp(Ops.CONST, dtypes.int, arg=3, src=(
-          x7:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(), strides=(), offset=0, mask=None, contiguous=True),)), src=(
-             x3,)),)),)),)),)),
-  UOp(Ops.EXPAND, dtypes.int, arg=(4,), src=(
-    UOp(Ops.RESHAPE, dtypes.int, arg=(1,), src=(
-      UOp(Ops.CONST, dtypes.int, arg=4, src=(
-         x7,)),)),)),))
-"""
-# you can see it's adding both 3 and 4
-
-# but by the time we are actually running the code, it's adding 7
-# `kernelize` will simplify and group the operations in the graph into kernels
-t_plus_3_plus_4.kernelize()
-print(t_plus_3_plus_4.uop)
-"""
-UOp(Ops.ASSIGN, dtypes.int, arg=None, src=(
-  x0:=UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-    UOp(Ops.UNIQUE, dtypes.void, arg=7, src=()),
-    x2:=UOp(Ops.DEVICE, dtypes.void, arg='CPU', src=()),)),
-  UOp(Ops.KERNEL, dtypes.void, arg=<Kernel 12 SINK(<Ops.STORE: 48>,) (__add__,)>, src=(
-     x0,
-    UOp(Ops.BUFFER, dtypes.int, arg=4, src=(
-      UOp(Ops.UNIQUE, dtypes.void, arg=1, src=()),
-       x2,)),)),))
-"""
-# ASSIGN has two srcs, src[0] is the BUFFER that's assigned to, and src[1] is the thing to assign
-# src[1] is the GPU Kernel that's going to be run
-# we can get the ast of the Kernel as follows
-kernel_ast = t_plus_3_plus_4.uop.src[1].arg.ast
-
-# almost everything in tinygrad functions as a rewrite of the UOps
-# the codegen rewrites the ast to a simplified form ready for "rendering"
-from tinygrad.codegen import full_rewrite_to_sink
-rewritten_ast = full_rewrite_to_sink(kernel_ast)
-print(rewritten_ast)
-"""
-UOp(Ops.SINK, dtypes.void, arg=None, src=(
-  UOp(Ops.STORE, dtypes.void, arg=None, src=(
-    UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
-      UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=0, src=()),
-      x3:=UOp(Ops.SPECIAL, dtypes.int, arg=('gidx0', 4), src=()),)),
-    UOp(Ops.ADD, dtypes.int, arg=None, src=(
-      UOp(Ops.LOAD, dtypes.int, arg=None, src=(
-        UOp(Ops.INDEX, dtypes.int.ptr(4), arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(4), arg=1, src=()),
-           x3,)),)),
-      UOp(Ops.CONST, dtypes.int, arg=7, src=()),)),)),))
-"""
-# you can see at this point we are adding 7, not 3 and 4
-
-# with DEBUG=4, we can see the code.
-# since optimizations are on, it UPCASTed the operation, explicitly writing out all 4 +7s
-t_plus_3_plus_4.realize()
-"""
-void E_4n2(int* restrict data0, int* restrict data1) {
-  int val0 = *(data1+0);
-  int val1 = *(data1+1);
-  int val2 = *(data1+2);
-  int val3 = *(data1+3);
-  *(data0+0) = (val0+7);
-  *(data0+1) = (val1+7);
-  *(data0+2) = (val2+7);
-  *(data0+3) = (val3+7);
-}
-"""
-# the function name E_4n2 is "E" for elementwise op (as opposed to "r" for reduce op)
-# "4" for the size, and "n2" for name deduping (it's the 3rd function with the same E and 4 in this session)
-# when you print the name with DEBUG=2, you'll see the 4 is yellow, meaning that it's upcasted
-# if you run with NOOPT=1 ...
-"""
-void E_4n2(int* restrict data0, int* restrict data1) {
-  for (int ridx0 = 0; ridx0 < 4; ridx0++) {
-    int val0 = *(data1+ridx0);
-    *(data0+ridx0) = (val0+7);
-  }
-}
-"""
-# ... you get this unoptimized code with a loop and the 4 is blue (for global). the color code is in kernel.py
-
-# %% ********
-print("******* PART 3 *******")
-
-# now, we go even lower and understand UOps better and how the graph rewrite engine works.
-# it's much simpler than what's in LLVM or MLIR
-
-from tinygrad import dtypes
-from tinygrad.uop.ops import UOp, Ops
-
-# first, we'll construct some const UOps
-a = UOp(Ops.CONST, dtypes.int, arg=2)
-b = UOp(Ops.CONST, dtypes.int, arg=2)
-
-# if you have been paying attention, you should know these are the same Python object
-assert a is b
-
-# UOps support normal Python math operations, so a_plus_b expresses the spec for 2 + 2
-a_plus_b = a + b
-print(a_plus_b)
-"""
-UOp(Ops.ADD, dtypes.int, arg=None, src=(
-  x0:=UOp(Ops.CONST, dtypes.int, arg=2, src=()),
-   x0,))
-"""
-
-# we could actually render this 2+2 into a language like c and run it
-# or, we can use tinygrad's graph rewrite engine to "constant fold"
-
-from tinygrad.uop.ops import graph_rewrite, UPat, PatternMatcher
-
-# a `PatternMatcher` is a list of tuples. for each element in the list:
-# [0] is the pattern to match, and [1] is the function to run.
-# this function can return either a UOp to replace the pattern with, or None to not replace
-simple_pm = PatternMatcher([
-  (UPat(Ops.ADD, src=(UPat(Ops.CONST, name="c1"), UPat(Ops.CONST, name="c2"))),
-   lambda c1,c2: UOp(Ops.CONST, dtype=c1.dtype, arg=c1.arg+c2.arg)),
-])
-# this pattern matches the addition of two CONST and rewrites it into a single CONST UOp
-
-# to actually apply the pattern to a_plus_b, we use graph_rewrite
-a_plus_b_simplified = graph_rewrite(a_plus_b, simple_pm)
-print(a_plus_b_simplified)
-"""
-UOp(Ops.CONST, dtypes.int, arg=4, src=())
-"""
-# 2+2 is in fact, 4
-
-# we can also use syntactic sugar to write the pattern nicer
-simpler_pm = PatternMatcher([
-  (UPat.cvar("c1")+UPat.cvar("c2"), lambda c1,c2: c1.const_like(c1.arg+c2.arg))
-])
-assert graph_rewrite(a_plus_b, simple_pm) is graph_rewrite(a_plus_b, simpler_pm)
-# note again the use of is, UOps are immutable and globally unique
-
-# %% ********
-
-# that brings you to an understanding of the most core concepts in tinygrad
-# you can run this with VIZ=1 to use the web based graph rewrite explorer
-# hopefully now you understand it. the nodes in the graph are just UOps
@@ -41,7 +41,7 @@ The BMC also has a web interface you can use if you find that easier.
 It is recommended that you change the BMC password after setting up the box, as the password on the screen is only the initial password.

 If you do decide to change the BMC password and no longer want the initial password to be displayed, remove the `/root/.bmc_password` file.
-Reboot after making these changes or restart the `displayservice.service` service.
+Reboot after making these changes or restart the `tinybox-display.service` service.

 ## What do I use it for?

@@ -1,9 +0,0 @@
-import globals from "globals";
-import pluginJs from "@eslint/js";
-import pluginHtml from "eslint-plugin-html";
-
-export default [
-  {files: ["**/*.html"], plugins: {html: pluginHtml}, rules:{"max-len": ["error", {"code": 150}]}},
-  {languageOptions: {globals: globals.browser}},
-  pluginJs.configs.recommended,
-];
@@ -21,7 +21,7 @@ if __name__ == "__main__":
  X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))

  model = Model()
-  opt = (nn.optim.Adam if not getenv("MUON") else nn.optim.Muon)(nn.state.get_parameters(model))
+  opt = (nn.optim.Muon if getenv("MUON") else nn.optim.SGD if getenv("SGD") else nn.optim.Adam)(nn.state.get_parameters(model))

  @TinyJit
  @Tensor.train()
@@ -1,93 +0,0 @@
-#!/usr/bin/env python3
-import os, sys, traceback
-sys.path.append(os.getcwd())
-
-from io import StringIO
-from contextlib import redirect_stdout
-from tinygrad import Tensor, nn
-from tinygrad.helpers import Timing, colored, getenv, fetch
-from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
-from sentencepiece import SentencePieceProcessor
-
-def create_fixed_tokenizer(output_file):
-  print("creating fixed tokenizer")
-  import extra.junk.sentencepiece_model_pb2 as spb2
-  mp = spb2.ModelProto()
-  mp.ParseFromString(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/tokenizer.model?download=true").read_bytes())
-  mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_end|>", score=0))
-  mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_start|>", score=0))
-  with open(output_file, "wb") as f:
-    f.write(mp.SerializeToString())
-
-# example:
-# echo -en "write 2+2\nwrite hello world\ny\n" | TEMP=0 python3 examples/coder.py
-
-if __name__ == "__main__":
-  # https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/config.json
-  with Timing("create model: "):
-    model = Transformer(4096, 14336, n_heads=32, n_layers=32, norm_eps=1e-5, vocab_size=32002, n_kv_heads=8, max_context=4096, jit=getenv("JIT", 1))
-
-  with Timing("download weights: "):
-    part1 = nn.state.torch_load(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/pytorch_model-00001-of-00002.bin?download=true"))
-    part2 = nn.state.torch_load(fetch("https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/resolve/main/pytorch_model-00002-of-00002.bin?download=true"))
-
-  with Timing("weights -> model: "):
-    nn.state.load_state_dict(model, fix_bf16(convert_from_huggingface(part1, 32, 32, 8)), strict=False)
-    nn.state.load_state_dict(model, fix_bf16(convert_from_huggingface(part2, 32, 32, 8)), strict=False)
-
-  if not os.path.isfile("/tmp/tokenizer.model"): create_fixed_tokenizer("/tmp/tokenizer.model")
-  spp = SentencePieceProcessor(model_file="/tmp/tokenizer.model")
-
-  # https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
-  #   "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
-  IM_END = 32000
-  IM_START = 32001
-  def encode_prompt(k, v): return [IM_START]+spp.encode(f"{k}\n{v}")+[IM_END]+spp.encode("\n")
-  def start_prompt(k): return [IM_START]+spp.encode(f"{k}\n")
-  def output(outputted, toks, color):
-    cur = spp.decode(toks)[len(outputted):]
-    sys.stdout.write(colored(cur, color))
-    sys.stdout.flush()
-    outputted += cur
-    return outputted
-
-  # *** app below this line ***
-
-  toks = [spp.bos_id()] + encode_prompt("system", "You are Quentin. Quentin is a useful assistant who writes Python code to answer questions. He keeps the code as short as possible and doesn't read from user input")
-
-  PROMPT = getenv("PROMPT", 1)
-  temperature = getenv("TEMP", 0.7)
-
-  start_pos = 0
-  outputted = output("", toks, "green")
-  turn = True
-  while 1:
-    if PROMPT:
-      toks += encode_prompt("user", input("Q: ")) + start_prompt("assistant")
-    else:
-      toks += start_prompt("user" if turn else "assistant")
-      turn = not turn
-    old_output_len = len(outputted)
-    while 1:
-      tok = model(Tensor([toks[start_pos:]]), start_pos, temperature).item()
-      start_pos = len(toks)
-      toks.append(tok)
-      outputted = output(outputted, toks, "blue" if not turn else "cyan")
-      if tok == IM_END: break
-      if tok == spp.eos_id(): break
-      new_output = outputted[old_output_len:]
-
-      if new_output.endswith("```") and '```python\n' in new_output:
-        python_code = new_output.split('```python\n')[1].split("```")[0]
-        # AI safety. Warning to user. Do not press y if the AI is trying to do unsafe things.
-        if input(colored(f" <-- PYTHON DETECTED, RUN IT? ", "red")).lower() == 'y':
-          my_stdout = StringIO()
-          try:
-            with redirect_stdout(my_stdout): exec(python_code)
-            result = my_stdout.getvalue()
-          except Exception as e:
-            result = ''.join(traceback.format_exception_only(e))
-          toks += spp.encode(f"\nOutput:\n```\n{result}```")
-          outputted = output(outputted, toks, "yellow")
-          old_output_len = len(outputted)
-    print("")
@@ -1,341 +0,0 @@
-import argparse
-import multiprocessing as mp
-import os
-import re
-import sys
-import time
-from contextlib import contextmanager
-from pathlib import Path
-
-import numpy as np
-import pyaudio
-import yaml
-from llama import LLaMa
-from vits import MODELS as VITS_MODELS
-from vits import Y_LENGTH_ESTIMATE_SCALARS, HParams, Synthesizer, TextMapper, get_hparams_from_file, load_model
-from whisper import init_whisper, transcribe_waveform
-from sentencepiece import SentencePieceProcessor
-
-from tinygrad.helpers import Timing, fetch
-from tinygrad import Tensor, dtypes
-
-# Whisper constants
-RATE = 16000
-CHUNK = 1600
-
-# LLaMa constants
-IM_START = 32001
-IM_END = 32002
-
-
-# Functions for encoding prompts to chatml md
-def encode_prompt(spp, k, v): return [IM_START]+spp.encode(f"{k}\n{v}")+[IM_END]+spp.encode("\n")
-def start_prompt(spp, k): return [IM_START]+spp.encode(f"{k}\n")
-
-def chunks(lst, n):
-  for i in range(0, len(lst), n): yield lst[i:i + n]
-
-def create_fixed_tokenizer():
-  """Function needed for extending tokenizer with additional chat tokens"""
-  import extra.junk.sentencepiece_model_pb2 as spb2
-  tokenizer_path = fetch("https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/resolve/main/tokenizer.model")
-  if SentencePieceProcessor(model_file=str(tokenizer_path)).vocab_size() != 32003:
-    print("creating fixed tokenizer")
-    mp = spb2.ModelProto()
-    mp.ParseFromString(tokenizer_path.read_bytes())
-    # https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/blob/main/added_tokens.json
-    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="[PAD]", score=0))
-    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_start|>", score=0))
-    mp.pieces.append(spb2.ModelProto.SentencePiece(piece="<|im_end|>", score=0))
-    tokenizer_path.write_bytes(mp.SerializeToString())
-  return tokenizer_path
-
-def llama_prepare(llama: LLaMa, temperature: float, pre_prompt_path: Path) -> tuple[list[int], str, str, str]:
-  """Prepares a llama model from a specified pre-prompt file"""
-  with open(str(pre_prompt_path)) as f:
-    config = yaml.safe_load(f.read())
-  toks = [llama.tokenizer.bos_id()] + encode_prompt(llama.tokenizer, "system", config["pre_prompt"].replace("\n", " "))
-  for i in config["examples"]:
-    toks += encode_prompt(llama.tokenizer, config["user_delim"], i["user_prompt"])
-    toks += encode_prompt(llama.tokenizer, config["resp_delim"], i["resp_prompt"])
-  llama.model(Tensor([toks]), 0, temperature).realize()  # NOTE: outputs are not used
-  return toks, config["user_delim"], config["resp_delim"], len(toks), llama.tokenizer.decode(toks)
-
-def llama_generate(
-  llama: LLaMa,
-  toks: list[int],
-  outputted: str,
-  prompt: str,
-  start_pos: int,
-  user_delim: str,
-  resp_delim: str,
-  temperature=0.7,
-  max_tokens=1000
-):
-  """Generates an output for the specified prompt"""
-  toks += encode_prompt(llama.tokenizer, user_delim, prompt)
-  toks += start_prompt(llama.tokenizer, resp_delim)
-
-  outputted = llama.tokenizer.decode(toks)
-  init_length = len(outputted)
-  for _ in range(max_tokens):
-    token = llama.model(Tensor([toks[start_pos:]]), start_pos, temperature).item()
-    start_pos = len(toks)
-    toks.append(token)
-
-    cur = llama.tokenizer.decode(toks)
-
-    # Print is just for debugging
-    sys.stdout.write(cur[len(outputted):])
-    sys.stdout.flush()
-    outputted = cur
-    if toks[-1] == IM_END: break
-  else:
-    toks.append(IM_END)
-  print() # because the output is flushed
-  return outputted, start_pos, outputted[init_length:].replace("<|im_end|>", "")
-
-def tts(
-  text_to_synthesize: str,
-  synth: Synthesizer,
-  hps: HParams,
-  emotion_embedding: Path,
-  speaker_id: int,
-  model_to_use: str,
-  noise_scale: float,
-  noise_scale_w: float,
-  length_scale: float,
-  estimate_max_y_length: bool,
-  text_mapper: TextMapper,
-  model_has_multiple_speakers: bool,
-  pad_length=600,
-  vits_pad_length=1000
-):
-  if model_to_use == "mmts-tts": text_to_synthesize = text_mapper.filter_oov(text_to_synthesize.lower())
-
-  # Convert the input text to a tensor.
-  stn_tst = text_mapper.get_text(text_to_synthesize, hps.data.add_blank, hps.data.text_cleaners)
-  init_shape = stn_tst.shape
-  assert init_shape[0] < pad_length, "text is too long"
-  x_tst, x_tst_lengths = stn_tst.pad(((0, pad_length - init_shape[0]),), value=1).unsqueeze(0), Tensor([init_shape[0]], dtype=dtypes.int64)
-  sid = Tensor([speaker_id], dtype=dtypes.int64) if model_has_multiple_speakers else None
-
-  # Perform inference.
-  audio_tensor = synth.infer(x_tst, x_tst_lengths, sid, noise_scale, length_scale, noise_scale_w, emotion_embedding=emotion_embedding,
-                             max_y_length_estimate_scale=Y_LENGTH_ESTIMATE_SCALARS[model_to_use] if estimate_max_y_length else None, pad_length=vits_pad_length)[0, 0]
-  # Save the audio output.
-  audio_data = (np.clip(audio_tensor.numpy(), -1.0, 1.0) * 32767).astype(np.int16)
-  return audio_data
-
-def init_vits(
-  model_to_use: str,
-  emotion_path: Path,
-  speaker_id: int,
-  seed: int,
-):
-  model_config = VITS_MODELS[model_to_use]
-
-  # Load the hyperparameters from the config file.
-  hps = get_hparams_from_file(fetch(model_config[0]))
-
-  # If model has multiple speakers, validate speaker id and retrieve name if available.
-  model_has_multiple_speakers = hps.data.n_speakers > 0
-  if model_has_multiple_speakers:
-    if speaker_id >= hps.data.n_speakers: raise ValueError(f"Speaker ID {speaker_id} is invalid for this model.")
-    if hps.__contains__("speakers"): # maps speaker ids to names
-      speakers = hps.speakers
-      if isinstance(speakers, list): speakers = {speaker: i for i, speaker in enumerate(speakers)}
-
-  # Load emotions if any. TODO: find an english model with emotions, this is untested atm.
-  emotion_embedding = None
-  if emotion_path is not None:
-    if emotion_path.endswith(".npy"): emotion_embedding = Tensor(np.load(emotion_path), dtype=dtypes.int64).unsqueeze(0)
-    else: raise ValueError("Emotion path must be a .npy file.")
-
-  # Load symbols, instantiate TextMapper and clean the text.
-  if hps.__contains__("symbols"): symbols = hps.symbols
-  elif model_to_use == "mmts-tts": symbols = [x.replace("\n", "") for x in fetch("https://huggingface.co/facebook/mms-tts/raw/main/full_models/eng/vocab.txt").open(encoding="utf-8").readlines()]
-  else: symbols = ['_'] + list(';:,.!?¡¿—…"«»“” ') + list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz') + list("ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ")
-  text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)
-
-  # Load the model.
-  if seed is not None:
-    Tensor.manual_seed(seed)
-    np.random.seed(seed)
-  net_g = load_model(text_mapper.symbols, hps, model_config)
-
-  return net_g, emotion_embedding, text_mapper, hps, model_has_multiple_speakers
-
-@contextmanager
-def output_stream(num_channels: int, sample_rate: int):
-  try:
-    p = pyaudio.PyAudio()
-    stream = p.open(format=pyaudio.paInt16, channels=num_channels, rate=sample_rate, output=True)
-    yield stream
-  except KeyboardInterrupt: pass
-  finally:
-    stream.stop_stream()
-    stream.close()
-    p.terminate()
-
-@contextmanager
-def log_writer():
-  try:
-    logs = []
-    yield logs
-  finally:
-    sep = "="*os.get_terminal_size()[1]
-    print(f"{sep[:-1]}\nCHAT LOG")
-    print(*logs, sep="\n")
-    print(sep)
-
-def listener(q: mp.Queue, event: mp.Event):
-  try:
-    p = pyaudio.PyAudio()
-    stream = p.open(format=pyaudio.paInt16, channels=1, rate=RATE, input=True, frames_per_buffer=CHUNK)
-    did_print = False
-    while True:
-      data = stream.read(CHUNK) # read data to avoid overflow
-      if event.is_set():
-        if not did_print:
-          print("listening")
-          did_print = True
-        q.put(((np.frombuffer(data, np.int16)/32768).astype(np.float32)*3))
-      else:
-        did_print = False
-  finally:
-    stream.stop_stream()
-    stream.close()
-    p.terminate()
-
-def mp_output_stream(q: mp.Queue, counter: mp.Value, num_channels: int, sample_rate: int):
-  with output_stream(num_channels, sample_rate) as stream:
-    while True:
-      try:
-        stream.write(q.get())
-        counter.value += 1
-      except KeyboardInterrupt:
-        break
-
-if __name__ == "__main__":
-  import nltk
-  nltk.download("punkt")
-  # Parse CLI arguments
-  parser = argparse.ArgumentParser("Have a tiny conversation with tinygrad")
-
-  # Whisper args
-  parser.add_argument("--whisper_model_name", type=str, default="tiny.en")
-
-  # LLAMA args
-  parser.add_argument("--llama_pre_prompt_path", type=Path, default=Path(__file__).parent / "conversation_data" / "pre_prompt_stacy.yaml", help="Path to yaml file which contains all pre-prompt data needed. ")
-  parser.add_argument("--llama_count", type=int, default=1000, help="Max number of tokens to generate")
-  parser.add_argument("--llama_temperature", type=float, default=0.7, help="Temperature in the softmax")
-  parser.add_argument("--llama_quantize", type=str, default=None, help="Quantize the weights to int8 or nf4 in memory")
-  parser.add_argument("--llama_model", type=Path, default=None, help="Folder with the original weights to load, or single .index.json, .safetensors or .bin file")
-  parser.add_argument("--llama_gen", type=str, default="tiny", required=False, help="Generation of the model to use")
-  parser.add_argument("--llama_size", type=str, default="1B-Chat", required=False, help="Size of model to use")
-  parser.add_argument("--llama_tokenizer", type=Path, default=None, required=False, help="Path to llama tokenizer.model")
-
-  # vits args
-  parser.add_argument("--vits_model_to_use", default="vctk", help="Specify the model to use. Default is 'vctk'.")
-  parser.add_argument("--vits_speaker_id", type=int, default=12, help="Specify the speaker ID. Default is 6.")
-  parser.add_argument("--vits_noise_scale", type=float, default=0.667, help="Specify the noise scale. Default is 0.667.")
-  parser.add_argument("--vits_noise_scale_w", type=float, default=0.8, help="Specify the noise scale w. Default is 0.8.")
-  parser.add_argument("--vits_length_scale", type=float, default=1, help="Specify the length scale. Default is 1.")
-  parser.add_argument("--vits_seed", type=int, default=None, help="Specify the seed (set to None if no seed). Default is 1337.")
-  parser.add_argument("--vits_num_channels", type=int, default=1, help="Specify the number of audio output channels. Default is 1.")
-  parser.add_argument("--vits_sample_width", type=int, default=2, help="Specify the number of bytes per sample, adjust if necessary. Default is 2.")
-  parser.add_argument("--vits_emotion_path", type=Path, default=None, help="Specify the path to emotion reference.")
-  parser.add_argument("--vits_estimate_max_y_length", type=str, default=False, help="If true, overestimate the output length and then trim it to the correct length, to prevent premature realization, much more performant for larger inputs, for smaller inputs not so much. Default is False.")
-  parser.add_argument("--vits_vocab_path", type=Path, default=None, help="Path to the TTS vocabulary.")
-
-  # conversation args
-  parser.add_argument("--max_sentence_length", type=int, default=20, help="Max words in one sentence to pass to vits")
-
-  args = parser.parse_args()
-
-  # Init models
-  model, enc = init_whisper(args.whisper_model_name)
-  synth, emotion_embedding, text_mapper, hps, model_has_multiple_speakers = init_vits(args.vits_model_to_use, args.vits_emotion_path, args.vits_speaker_id, args.vits_seed)
-
-  # Download tinyllama chat as a default model
-  if args.llama_model is None:
-    args.llama_model = fetch("https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4/resolve/main/model.safetensors", "tinyllamachat.safetensors")
-    args.llama_gen = "tiny"
-    args.llama_size = "1B-Chat"
-  # Add 3 more tokens to the tokenizer
-  if args.llama_gen == "tiny" and args.llama_size.endswith("Chat"): args.llama_tokenizer = create_fixed_tokenizer()
-  tokenizer_path = args.llama_tokenizer or args.llama_model.parent / "tokenizer.model"
-  llama = LLaMa.build(args.llama_model, tokenizer_path, args.llama_gen, args.llama_size, args.llama_quantize)
-  toks, user_delim, resp_delim, start_pos, outputted = llama_prepare(llama, args.llama_temperature, args.llama_pre_prompt_path)
-
-  # Start child process for mic input
-  q = mp.Queue()
-  is_listening_event = mp.Event()
-  p = mp.Process(target=listener, args=(q, is_listening_event,))
-  p.daemon = True
-  p.start()
-
-  # Start child process for speaker output
-  out_q = mp.Queue()
-  out_counter = mp.Value("i", 0)
-  out_p = mp.Process(target=mp_output_stream, args=(out_q, out_counter, args.vits_num_channels, hps.data.sampling_rate,))
-  out_p.daemon = True
-  out_p.start()
-
-  # JIT tts
-  for i in ["Hello, I'm a chat bot", "I am capable of doing a lot of things"]:
-    tts(
-      i, synth, hps, emotion_embedding,
-      args.vits_speaker_id, args.vits_model_to_use, args.vits_noise_scale,
-      args.vits_noise_scale_w, args.vits_length_scale,
-      args.vits_estimate_max_y_length, text_mapper, model_has_multiple_speakers
-    )
-
-  # Start the pipeline
-  with log_writer() as log:
-    while True:
-      tokens = [enc._special_tokens["<|startoftranscript|>"], enc._special_tokens["<|notimestamps|>"]]
-      total = np.array([])
-      out_counter.value = 0
-
-      s = time.perf_counter()
-      is_listening_event.set()
-      prev_text = None
-      while True:
-        for _ in range(RATE // CHUNK): total = np.concatenate([total, q.get()])
-        txt = transcribe_waveform(model, enc, [total], truncate=True)
-        print(txt, end="\r")
-        if txt == "[BLANK_AUDIO]" or re.match(r"^\([\w+ ]+\)$", txt.strip()): continue
-        if prev_text is not None and prev_text == txt:
-          is_listening_event.clear()
-          break
-        prev_text = txt
-      print() # to avoid llama printing on the same line
-      log.append(f"{user_delim.capitalize()}: {txt}")
-
-      # Generate with llama
-      with Timing("llama generation: "):
-        outputted, start_pos, response = llama_generate(
-          llama, toks, outputted, txt, start_pos,
-          user_delim=user_delim, resp_delim=resp_delim, temperature=args.llama_temperature,
-          max_tokens=args.llama_count
-        )
-        log.append(f"{resp_delim.capitalize()}: {response}")
-
-      # Convert to voice
-      with Timing("tts: "):
-        sentences = nltk.sent_tokenize(response.replace('"', ""))
-        for i in sentences:
-          total = np.array([], dtype=np.int16)
-          for j in chunks(i.split(), args.max_sentence_length):
-            audio_data = tts(
-              " ".join(j), synth, hps, emotion_embedding,
-              args.vits_speaker_id, args.vits_model_to_use, args.vits_noise_scale,
-              args.vits_noise_scale_w, args.vits_length_scale,
-              args.vits_estimate_max_y_length, text_mapper, model_has_multiple_speakers
-            )
-            total = np.concatenate([total, audio_data])
-          out_q.put(total.tobytes())
-      while out_counter.value < len(sentences): continue
-      log.append(f"Total: {time.perf_counter() - s}")
@@ -1,89 +0,0 @@
-# load weights from
-# https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth
-# a rough copy of
-# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
-import sys
-import ast
-import time
-import numpy as np
-from PIL import Image
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import getenv, fetch, Timing
-from tinygrad.engine.jit import TinyJit
-from extra.models.efficientnet import EfficientNet
-np.set_printoptions(suppress=True)
-
-# TODO: you should be able to put these in the jitted function
-bias = Tensor([0.485, 0.456, 0.406])
-scale = Tensor([0.229, 0.224, 0.225])
-
-@TinyJit
-def _infer(model, img):
-  img = img.permute((2,0,1))
-  img = img / 255.0
-  img = img - bias.reshape((1,-1,1,1))
-  img = img / scale.reshape((1,-1,1,1))
-  return model.forward(img).realize()
-
-def infer(model, img):
-  # preprocess image
-  aspect_ratio = img.size[0] / img.size[1]
-  img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0))))
-
-  img = np.array(img)
-  y0,x0=(np.asarray(img.shape)[:2]-224)//2
-  retimg = img = img[y0:y0+224, x0:x0+224]
-
-  # if you want to look at the image
-  """
-  import matplotlib.pyplot as plt
-  plt.imshow(img)
-  plt.show()
-  """
-
-  # run the net
-  out = _infer(model, Tensor(img.astype("float32"))).numpy()
-
-  # if you want to look at the outputs
-  """
-  import matplotlib.pyplot as plt
-  plt.plot(out[0])
-  plt.show()
-  """
-  return out, retimg
-
-if __name__ == "__main__":
-  # instantiate my net
-  model = EfficientNet(getenv("NUM", 0))
-  model.load_from_pretrained()
-
-  # category labels
-  lbls = ast.literal_eval(fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt").read_text())
-
-  # load image and preprocess
-  url = sys.argv[1] if len(sys.argv) >= 2 else "https://raw.githubusercontent.com/tinygrad/tinygrad/master/docs/showcase/stable_diffusion_by_tinygrad.jpg"
-  if url == 'webcam':
-    import cv2
-    cap = cv2.VideoCapture(0)
-    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
-    while 1:
-      _ = cap.grab() # discard one frame to circumvent capture buffering
-      ret, frame = cap.read()
-      img = Image.fromarray(frame[:, :, [2,1,0]])
-      lt = time.monotonic_ns()
-      out, retimg = infer(model, img)
-      print(f"{(time.monotonic_ns()-lt)*1e-6:7.2f} ms", np.argmax(out), np.max(out), lbls[np.argmax(out)])
-      SCALE = 3
-      simg = cv2.resize(retimg, (224*SCALE, 224*SCALE))
-      retimg = cv2.cvtColor(simg, cv2.COLOR_RGB2BGR)
-      cv2.imshow('capture', retimg)
-      if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-    cap.release()
-    cv2.destroyAllWindows()
-  else:
-    img = Image.open(fetch(url))
-    for i in range(getenv("CNT", 1)):
-      with Timing("did inference in "):
-        out, _ = infer(model, img)
-        print(np.argmax(out), np.max(out), lbls[np.argmax(out)])
@@ -1,498 +0,0 @@
-# pip3 install sentencepiece
-
-# This file incorporates code from the following:
-# Github Name                    | License | Link
-# black-forest-labs/flux         | Apache  | https://github.com/black-forest-labs/flux/tree/main/model_licenses
-
-from tinygrad import Tensor, nn, dtypes, TinyJit
-from tinygrad.nn.state import safe_load, load_state_dict
-from tinygrad.helpers import fetch, tqdm, colored
-from sdxl import FirstStage
-from extra.models.clip import FrozenClosedClipEmbedder
-from extra.models.t5 import T5Embedder
-import numpy as np
-
-import math, time, argparse, tempfile
-from typing import List, Dict, Optional, Union, Tuple, Callable
-from dataclasses import dataclass
-from pathlib import Path
-from PIL import Image
-
-urls:dict = {
-  "flux-schnell": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors",
-  "flux-dev": "https://huggingface.co/camenduru/FLUX.1-dev/resolve/main/flux1-dev.sft",
-  "ae": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors",
-  "T5_1_of_2": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder_2/model-00001-of-00002.safetensors",
-  "T5_2_of_2": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder_2/model-00002-of-00002.safetensors",
-  "T5_tokenizer": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/tokenizer_2/spiece.model",
-  "clip": "https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/text_encoder/model.safetensors"
-}
-
-def tensor_identity(x:Tensor) -> Tensor: return x
-
-class AutoEncoder:
-  def __init__(self, scale_factor:float, shift_factor:float):
-    self.decoder = FirstStage.Decoder(128, 3, 3, 16, [1, 2, 4, 4], 2, 256)
-    self.scale_factor = scale_factor
-    self.shift_factor = shift_factor
-
-  def decode(self, z:Tensor) -> Tensor:
-    z = z / self.scale_factor + self.shift_factor
-    return self.decoder(z)
-
-# Conditioner
-class ClipEmbedder(FrozenClosedClipEmbedder):
-  def __call__(self, texts:Union[str, List[str], Tensor]) -> Tensor:
-    if isinstance(texts, str): texts = [texts]
-    assert isinstance(texts, (list,tuple)), f"expected list of strings, got {type(texts).__name__}"
-    tokens = Tensor.cat(*[Tensor(self.tokenizer.encode(text)) for text in texts], dim=0)
-    return self.transformer.text_model(tokens.reshape(len(texts),-1))[:, tokens.argmax(-1)]
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/math.py
-def attention(q:Tensor, k:Tensor, v:Tensor, pe:Tensor) -> Tensor:
-  q, k = apply_rope(q, k, pe)
-  x = Tensor.scaled_dot_product_attention(q, k, v)
-  return x.rearrange("B H L D -> B L (H D)")
-
-def rope(pos:Tensor, dim:int, theta:int) -> Tensor:
-  assert dim % 2 == 0
-  scale = Tensor.arange(0, dim, 2, dtype=dtypes.float32, device=pos.device) / dim # NOTE: this is torch.float64 in reference implementation
-  omega = 1.0 / (theta**scale)
-  out = Tensor.einsum("...n,d->...nd", pos, omega)
-  out = Tensor.stack(Tensor.cos(out), -Tensor.sin(out), Tensor.sin(out), Tensor.cos(out), dim=-1)
-  out = out.rearrange("b n d (i j) -> b n d i j", i=2, j=2)
-  return out.float()
-
-def apply_rope(xq:Tensor, xk:Tensor, freqs_cis:Tensor) -> Tuple[Tensor, Tensor]:
-  xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-  xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
-  xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-  xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-  return xq_out.reshape(*xq.shape).cast(xq.dtype), xk_out.reshape(*xk.shape).cast(xk.dtype)
-
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
-class EmbedND:
-  def __init__(self, dim:int, theta:int, axes_dim:List[int]):
-    self.dim = dim
-    self.theta = theta
-    self.axes_dim = axes_dim
-
-  def __call__(self, ids:Tensor) -> Tensor:
-    n_axes = ids.shape[-1]
-    emb = Tensor.cat(*[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
-    return emb.unsqueeze(1)
-
-class MLPEmbedder:
-  def __init__(self, in_dim:int, hidden_dim:int):
-    self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
-    self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    return self.out_layer(self.in_layer(x).silu())
-
-class QKNorm:
-  def __init__(self, dim:int):
-    self.query_norm = nn.RMSNorm(dim)
-    self.key_norm = nn.RMSNorm(dim)
-
-  def __call__(self, q:Tensor, k:Tensor) -> Tuple[Tensor, Tensor]:
-    return self.query_norm(q), self.key_norm(k)
-
-class SelfAttention:
-  def __init__(self, dim:int, num_heads:int = 8, qkv_bias:bool = False):
-    self.num_heads = num_heads
-    head_dim = dim // num_heads
-
-    self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-    self.norm = QKNorm(head_dim)
-    self.proj = nn.Linear(dim, dim)
-
-  def __call__(self, x:Tensor, pe:Tensor) -> Tensor:
-    qkv = self.qkv(x)
-    q, k, v = qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-    q, k = self.norm(q, k)
-    x = attention(q, k, v, pe=pe)
-    return self.proj(x)
-
-@dataclass
-class ModulationOut:
-  shift:Tensor
-  scale:Tensor
-  gate:Tensor
-
-class Modulation:
-  def __init__(self, dim:int, double:bool):
-    self.is_double = double
-    self.multiplier = 6 if double else 3
-    self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
-
-  def __call__(self, vec:Tensor) -> Tuple[ModulationOut, Optional[ModulationOut]]:
-    out = self.lin(vec.silu())[:, None, :].chunk(self.multiplier, dim=-1)
-    return ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None
-
-class DoubleStreamBlock:
-  def __init__(self, hidden_size:int, num_heads:int, mlp_ratio:float, qkv_bias:bool = False):
-    mlp_hidden_dim = int(hidden_size * mlp_ratio)
-    self.num_heads = num_heads
-    self.hidden_size = hidden_size
-    self.img_mod = Modulation(hidden_size, double=True)
-    self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-    self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-    self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-    self.img_mlp = [nn.Linear(hidden_size, mlp_hidden_dim, bias=True), Tensor.gelu, nn.Linear(mlp_hidden_dim, hidden_size, bias=True)]
-
-    self.txt_mod = Modulation(hidden_size, double=True)
-    self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-    self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-    self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-    self.txt_mlp = [nn.Linear(hidden_size, mlp_hidden_dim, bias=True), Tensor.gelu, nn.Linear(mlp_hidden_dim, hidden_size, bias=True)]
-
-  def __call__(self, img:Tensor, txt:Tensor, vec:Tensor, pe:Tensor) -> tuple[Tensor, Tensor]:
-    img_mod1, img_mod2 = self.img_mod(vec)
-    txt_mod1, txt_mod2 = self.txt_mod(vec)
-    assert img_mod2 is not None and txt_mod2 is not None
-    # prepare image for attention
-    img_modulated = self.img_norm1(img)
-    img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-    img_qkv = self.img_attn.qkv(img_modulated)
-    img_q, img_k, img_v = img_qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-    img_q, img_k = self.img_attn.norm(img_q, img_k)
-
-    # prepare txt for attention
-    txt_modulated = self.txt_norm1(txt)
-    txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-    txt_qkv = self.txt_attn.qkv(txt_modulated)
-    txt_q, txt_k, txt_v = txt_qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-    txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k)
-
-    # run actual attention
-    q = Tensor.cat(txt_q, img_q, dim=2)
-    k = Tensor.cat(txt_k, img_k, dim=2)
-    v = Tensor.cat(txt_v, img_v, dim=2)
-
-    attn = attention(q, k, v, pe=pe)
-    txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-    # calculate the img bloks
-    img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-    img = img + img_mod2.gate * ((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift).sequential(self.img_mlp)
-
-    # calculate the txt bloks
-    txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
-    txt = txt + txt_mod2.gate * ((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift).sequential(self.txt_mlp)
-    return img, txt
-
-
-class SingleStreamBlock:
-  """
-  A DiT block with parallel linear layers as described in
-  https://arxiv.org/abs/2302.05442 and adapted modulation interface.
-  """
-
-  def __init__(self,hidden_size:int, num_heads:int, mlp_ratio:float=4.0, qk_scale:Optional[float]=None):
-    self.hidden_dim = hidden_size
-    self.num_heads = num_heads
-    head_dim = hidden_size // num_heads
-    self.scale = qk_scale or head_dim**-0.5
-
-    self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-    # qkv and mlp_in
-    self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
-    # proj and mlp_out
-    self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
-
-    self.norm = QKNorm(head_dim)
-
-    self.hidden_size = hidden_size
-    self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-
-    self.mlp_act = Tensor.gelu
-    self.modulation = Modulation(hidden_size, double=False)
-
-  def __call__(self, x:Tensor, vec:Tensor, pe:Tensor) -> Tensor:
-    mod, _ = self.modulation(vec)
-    x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
-    qkv, mlp = Tensor.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
-    q, k, v = qkv.rearrange("B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-    q, k = self.norm(q, k)
-
-    # compute attention
-    attn = attention(q, k, v, pe=pe)
-    # compute activation in mlp stream, cat again and run second linear layer
-    output = self.linear2(Tensor.cat(attn, self.mlp_act(mlp), dim=2))
-    return x + mod.gate * output
-
-
-class LastLayer:
-  def __init__(self, hidden_size:int, patch_size:int, out_channels:int):
-    self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-    self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-    self.adaLN_modulation:List[Callable[[Tensor], Tensor]] = [Tensor.silu, nn.Linear(hidden_size, 2 * hidden_size, bias=True)]
-
-  def __call__(self, x:Tensor, vec:Tensor) -> Tensor:
-    shift, scale = vec.sequential(self.adaLN_modulation).chunk(2, dim=1)
-    x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
-    return self.linear(x)
-
-def timestep_embedding(t:Tensor, dim:int, max_period:int=10000, time_factor:float=1000.0) -> Tensor:
-  """
-  Create sinusoidal timestep embeddings.
-  :param t: a 1-D Tensor of N indices, one per batch element.
-                    These may be fractional.
-  :param dim: the dimension of the output.
-  :param max_period: controls the minimum frequency of the embeddings.
-  :return: an (N, D) Tensor of positional embeddings.
-  """
-  t = time_factor * t
-  half = dim // 2
-  freqs = Tensor.exp(-math.log(max_period) * Tensor.arange(0, stop=half, dtype=dtypes.float32) / half).to(t.device)
-
-  args = t[:, None].float() * freqs[None]
-  embedding = Tensor.cat(Tensor.cos(args), Tensor.sin(args), dim=-1)
-  if dim % 2:  embedding = Tensor.cat(*[embedding, Tensor.zeros_like(embedding[:, :1])], dim=-1)
-  if Tensor.is_floating_point(t):  embedding = embedding.cast(t.dtype)
-  return embedding
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/model.py
-class Flux:
-  """
-  Transformer model for flow matching on sequences.
-  """
-
-  def __init__(
-      self,
-      guidance_embed:bool,
-      in_channels:int = 64,
-      vec_in_dim:int = 768,
-      context_in_dim:int = 4096,
-      hidden_size:int = 3072,
-      mlp_ratio:float = 4.0,
-      num_heads:int = 24,
-      depth:int = 19,
-      depth_single_blocks:int = 38,
-      axes_dim:Optional[List[int]] = None,
-      theta:int = 10_000,
-      qkv_bias:bool = True,
-      ):
-
-    axes_dim = axes_dim or [16, 56, 56]
-    self.guidance_embed = guidance_embed
-    self.in_channels = in_channels
-    self.out_channels = self.in_channels
-    if hidden_size % num_heads != 0:
-      raise ValueError(f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}")
-    pe_dim = hidden_size // num_heads
-    if sum(axes_dim) != pe_dim:
-      raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
-    self.hidden_size = hidden_size
-    self.num_heads = num_heads
-    self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim=axes_dim)
-    self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
-    self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-    self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
-    self.guidance_in:Callable[[Tensor], Tensor] = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else tensor_identity
-    self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
-
-    self.double_blocks = [DoubleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias) for _ in range(depth)]
-    self.single_blocks = [SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio) for _ in range(depth_single_blocks)]
-    self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
-
-  def __call__(self, img:Tensor, img_ids:Tensor, txt:Tensor, txt_ids:Tensor, timesteps:Tensor, y:Tensor, guidance:Optional[Tensor] = None) -> Tensor:
-    if img.ndim != 3 or txt.ndim != 3:
-      raise ValueError("Input img and txt tensors must have 3 dimensions.")
-    # running on sequences img
-    img = self.img_in(img)
-    vec = self.time_in(timestep_embedding(timesteps, 256))
-    if self.guidance_embed:
-      if guidance is None:
-        raise ValueError("Didn't get guidance strength for guidance distilled model.")
-      vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-    vec = vec + self.vector_in(y)
-    txt = self.txt_in(txt)
-    ids = Tensor.cat(txt_ids, img_ids, dim=1)
-    pe = self.pe_embedder(ids)
-    for double_block in self.double_blocks:
-      img, txt = double_block(img=img, txt=txt, vec=vec, pe=pe)
-
-    img = Tensor.cat(txt, img, dim=1)
-    for single_block in self.single_blocks:
-      img = single_block(img, vec=vec, pe=pe)
-
-    img = img[:, txt.shape[1] :, ...]
-
-    return self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/util.py
-def load_flow_model(name:str, model_path:str):
-  # Loading Flux
-  print("Init model")
-  model = Flux(guidance_embed=(name != "flux-schnell"))
-  if not model_path: model_path = fetch(urls[name])
-  state_dict = {k.replace("scale", "weight"): v for k, v in safe_load(model_path).items()}
-  load_state_dict(model, state_dict)
-  return model
-
-def load_T5(max_length:int=512):
-  # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
-  print("Init T5")
-  T5 = T5Embedder(max_length, fetch(urls["T5_tokenizer"]))
-  pt_1 = fetch(urls["T5_1_of_2"])
-  pt_2 = fetch(urls["T5_2_of_2"])
-  load_state_dict(T5.encoder, safe_load(pt_1) | safe_load(pt_2), strict=False)
-  return T5
-
-def load_clip():
-  print("Init Clip")
-  clip = ClipEmbedder()
-  load_state_dict(clip.transformer, safe_load(fetch(urls["clip"])))
-  return clip
-
-def load_ae() -> AutoEncoder:
-  # Loading the autoencoder
-  print("Init AE")
-  ae = AutoEncoder(0.3611, 0.1159)
-  load_state_dict(ae, safe_load(fetch(urls["ae"])))
-  return ae
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/sampling.py
-def prepare(T5:T5Embedder, clip:ClipEmbedder, img:Tensor, prompt:Union[str, List[str]]) -> Dict[str, Tensor]:
-  bs, _, h, w = img.shape
-  if bs == 1 and not isinstance(prompt, str):
-    bs = len(prompt)
-
-  img = img.rearrange("b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-  if img.shape[0] == 1 and bs > 1:
-    img = img.expand((bs, *img.shape[1:]))
-
-  img_ids = Tensor.zeros(h // 2, w // 2, 3).contiguous()
-  img_ids[..., 1] = img_ids[..., 1] + Tensor.arange(h // 2)[:, None]
-  img_ids[..., 2] = img_ids[..., 2] + Tensor.arange(w // 2)[None, :]
-  img_ids = img_ids.rearrange("h w c -> 1 (h w) c")
-  img_ids = img_ids.expand((bs, *img_ids.shape[1:]))
-
-  if isinstance(prompt, str):
-    prompt = [prompt]
-  txt = T5(prompt).realize()
-  if txt.shape[0] == 1 and bs > 1:
-    txt = txt.expand((bs, *txt.shape[1:]))
-  txt_ids = Tensor.zeros(bs, txt.shape[1], 3)
-
-  vec = clip(prompt).realize()
-  if vec.shape[0] == 1 and bs > 1:
-    vec = vec.expand((bs, *vec.shape[1:]))
-
-  return {"img": img, "img_ids": img_ids.to(img.device), "txt": txt.to(img.device), "txt_ids": txt_ids.to(img.device), "vec": vec.to(img.device)}
-
-
-def get_schedule(num_steps:int, image_seq_len:int, base_shift:float=0.5, max_shift:float=1.15, shift:bool=True) -> List[float]:
-  # extra step for zero
-  step_size = -1.0 / num_steps
-  timesteps = Tensor.arange(1, 0 + step_size, step_size)
-
-  # shifting the schedule to favor high timesteps for higher signal images
-  if shift:
-    # estimate mu based on linear estimation between two points
-    mu = 0.5 + (max_shift - base_shift) * (image_seq_len - 256) / (4096 - 256)
-    timesteps = math.exp(mu) / (math.exp(mu) + (1 / timesteps - 1))
-  return timesteps.tolist()
-
-@TinyJit
-def run(model, *args): return model(*args).realize()
-
-def denoise(model, img:Tensor, img_ids:Tensor, txt:Tensor, txt_ids:Tensor, vec:Tensor, timesteps:List[float], guidance:float=4.0) -> Tensor:
-  # this is ignored for schnell
-  guidance_vec = Tensor((guidance,), device=img.device, dtype=img.dtype).expand((img.shape[0],))
-  for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:])), "Denoising"):
-    t_vec = Tensor((t_curr,), device=img.device, dtype=img.dtype).expand((img.shape[0],))
-    pred = run(model, img, img_ids, txt, txt_ids, t_vec, vec, guidance_vec)
-    img = img + (t_prev - t_curr) * pred
-
-  return img
-
-def unpack(x:Tensor, height:int, width:int) -> Tensor:
-  return x.rearrange("b (h w) (c ph pw) -> b c (h ph) (w pw)", h=math.ceil(height / 16), w=math.ceil(width / 16), ph=2, pw=2)
-
-# https://github.com/black-forest-labs/flux/blob/main/src/flux/cli.py
-if __name__ == "__main__":
-  default_prompt = "bananas and a can of coke"
-  parser = argparse.ArgumentParser(description="Run Flux.1", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-  parser.add_argument("--name",       type=str,   default="flux-schnell", help="Name of the model to load")
-  parser.add_argument("--model_path", type=str,   default="",             help="path of the model file")
-  parser.add_argument("--width",      type=int,   default=512,            help="width of the sample in pixels (should be a multiple of 16)")
-  parser.add_argument("--height",     type=int,   default=512,            help="height of the sample in pixels (should be a multiple of 16)")
-  parser.add_argument("--seed",       type=int,   default=None,           help="Set a seed for sampling")
-  parser.add_argument("--prompt",     type=str,   default=default_prompt, help="Prompt used for sampling")
-  parser.add_argument('--out',        type=str,   default=Path(tempfile.gettempdir()) / "rendered.png", help="Output filename")
-  parser.add_argument("--num_steps",  type=int,   default=None,           help="number of sampling steps (default 4 for schnell, 50 for guidance distilled)") #noqa:E501
-  parser.add_argument("--guidance",   type=float, default=3.5,            help="guidance value used for guidance distillation")
-  parser.add_argument("--output_dir", type=str,   default="output",       help="output directory")
-  args = parser.parse_args()
-
-  if args.name not in ["flux-schnell", "flux-dev"]:
-    raise ValueError(f"Got unknown model name: {args.name}, chose from flux-schnell and flux-dev")
-
-  if args.num_steps is None:
-    args.num_steps = 4 if args.name == "flux-schnell" else 50
-
-  # allow for packing and conversion to latent space
-  height = 16 * (args.height // 16)
-  width = 16 * (args.width // 16)
-
-  if args.seed is None: args.seed = Tensor._seed
-  else: Tensor.manual_seed(args.seed)
-
-  print(f"Generating with seed {args.seed}:\n{args.prompt}")
-  t0 = time.perf_counter()
-
-  # prepare input noise
-  x = Tensor.randn(1, 16, 2 * math.ceil(height / 16), 2 * math.ceil(width / 16), dtype="bfloat16")
-
-  # load text embedders
-  T5 = load_T5(max_length=256 if args.name == "flux-schnell" else 512)
-  clip = load_clip()
-
-  # embed text to get inputs for model
-  inp = prepare(T5, clip, x, prompt=args.prompt)
-  timesteps = get_schedule(args.num_steps, inp["img"].shape[1], shift=(args.name != "flux-schnell"))
-
-  # done with text embedders
-  del T5, clip
-
-  # load model
-  model = load_flow_model(args.name, args.model_path)
-
-  # denoise initial noise
-  x = denoise(model, **inp, timesteps=timesteps, guidance=args.guidance)
-
-  # done with model
-  del model, run
-
-  # load autoencoder
-  ae = load_ae()
-
-  # decode latents to pixel space
-  x = unpack(x.float(), height, width)
-  x = ae.decode(x).realize()
-
-  t1 = time.perf_counter()
-  print(f"Done in {t1 - t0:.1f}s. Saving {args.out}")
-
-  # bring into PIL format and save
-  x = x.clamp(-1, 1)
-  x = x[0].rearrange("c h w -> h w c")
-  x = (127.5 * (x + 1.0)).cast("uint8")
-
-  img = Image.fromarray(x.numpy())
-
-  img.save(args.out)
-
-  # validation!
-  if args.prompt == default_prompt and args.name=="flux-schnell" and args.seed == 0 and args.width == args.height == 512:
-    ref_image = Tensor(np.array(Image.open("examples/flux1_seed0.png")))
-    distance = (((x.cast(dtypes.float) - ref_image.cast(dtypes.float)) / ref_image.max())**2).mean().item()
-    assert distance < 4e-3, colored(f"validation failed with {distance=}", "red")
-    print(colored(f"output validated with {distance=}", "green"))
@@ -0,0 +1,108 @@
+import itertools
+from typing import Callable
+from tinygrad import nn, Tensor, dtypes, Device, TinyJit
+from tinygrad.helpers import getenv, trange, partition
+
+class Model:
+  def __init__(self):
+    self.layers: list[Callable[[Tensor], Tensor]] = [
+      nn.Conv2d(1, 32, 5), Tensor.relu,
+      nn.Conv2d(32, 32, 5), Tensor.relu,
+      nn.BatchNorm(32), Tensor.max_pool2d,
+      nn.Conv2d(32, 64, 3), Tensor.relu,
+      nn.Conv2d(64, 64, 3), Tensor.relu,
+      nn.BatchNorm(64), Tensor.max_pool2d,
+      lambda x: x.flatten(1), nn.Linear(576, 10)]
+
+  def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
+
+# TODO: refactor this into optim/onnx
+def functional_adam(g:Tensor, m:Tensor, v:Tensor, b1_t:Tensor, b2_t:Tensor, lr=0.001, b1=0.9, b2=0.999, eps=1e-6) -> Tensor:
+  b1_t *= b1
+  b2_t *= b2
+  m.assign(b1 * m + (1.0 - b1) * g)
+  v.assign(b2 * v + (1.0 - b2) * (g * g))
+  m_hat = m / (1.0 - b1_t)
+  v_hat = v / (1.0 - b2_t)
+  return lr * (m_hat / (v_hat.sqrt() + eps))
+
+if __name__ == "__main__":
+  BS = getenv("BS", 512)
+  ACC_STEPS = getenv("ACC_STEPS", 8)
+
+  X_train, Y_train, X_test, Y_test = nn.datasets.mnist()
+  model = Model()
+
+  params = nn.state.get_parameters(model)
+
+  # init params, set requires grad on the ones we need gradients of
+  for x in params:
+    if x.requires_grad is None: x.requires_grad_()
+    x.replace(x.contiguous())
+  Tensor.realize(*params)
+
+  # split params (with grads) and buffers (without)
+  params, buffers = partition(params, lambda x: x.requires_grad)
+  print(f"params: {len(params)} buffers: {len(buffers)}")
+
+  # optim params
+  pos_params = list(itertools.accumulate(params, lambda x,y: x+y.numel(), initial=0))
+  adam_m = Tensor.zeros(pos_params[-1], device="CPU").contiguous()
+  adam_v = Tensor.zeros(pos_params[-1], device="CPU").contiguous()
+  adam_b1_t = Tensor.ones((1,), dtype=dtypes.float32, device="CPU", requires_grad=False).contiguous()
+  adam_b2_t = Tensor.ones((1,), dtype=dtypes.float32, device="CPU", requires_grad=False).contiguous()
+  adam_params = [adam_m, adam_v, adam_b1_t, adam_b2_t]
+
+  # create loss and grads. init all state so the JIT works on microbatch
+  for x in params: x.assign(x.detach())
+  loss = Tensor.zeros(tuple()).contiguous()
+  grads = Tensor.zeros(pos_params[-1]).contiguous()
+  Tensor.realize(*params, *buffers, *adam_params, loss, grads)
+
+  @TinyJit
+  @Tensor.train()
+  def microbatch():
+    samples = Tensor.randint(BS // ACC_STEPS, high=X_train.shape[0])
+    for t in params: t.grad = None
+    # divide by ACC_STEPS at the loss
+    uloss = (model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]) / ACC_STEPS).backward()
+    ugrads = Tensor.cat(*[t.grad.contiguous().flatten() for t in params], dim=0)
+    for t in params: t.grad = None
+    # concat the grads and assign them
+    loss.assign(loss + uloss)
+    grads.assign(grads + ugrads)
+    Tensor.realize(*params, *buffers, loss, grads)
+
+  @TinyJit
+  def optimizer():
+    # run optimizer (on CPU, where adam params live)
+    delta = functional_adam(grads.to("CPU"), adam_m, adam_v, adam_b1_t, adam_b2_t)
+
+    # update the params, copying back the delta one at a time to avoid OOM
+    # NOTE: the scheduler is ordering things poorly, all the copies are happening before the adds
+    for j,tt in enumerate(params):
+      tt.assign(tt.detach() - delta[pos_params[j]:pos_params[j+1]].reshape(tt.shape).to(Device.DEFAULT))
+
+    # realize everything, zero out loss and grads
+    loss.assign(Tensor.zeros_like(loss))
+    grads.assign(Tensor.zeros_like(grads))
+    Tensor.realize(*params, *adam_params, loss, grads)
+
+  @TinyJit
+  def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
+
+  test_acc = float('nan')
+  for i in (t:=trange(getenv("STEPS", 70))):
+    # microbatch sets the gradients
+    for _ in range(ACC_STEPS): microbatch()
+
+    # get the loss before the optimizer clears it
+    # this is already realized so this isn't a schedule
+    loss_item = loss.item()
+
+    # run the optimizer
+    optimizer()
+
+    # eval
+    if i%10 == 9: test_acc = get_test_acc().item()
+    t.set_description(f"loss: {loss_item:6.2f} test_accuracy: {test_acc:5.2f}%")
@@ -1,299 +0,0 @@
-from extra.models.mask_rcnn import MaskRCNN
-from extra.models.resnet import ResNet
-from extra.models.mask_rcnn import BoxList
-from torch.nn import functional as F
-from torchvision import transforms as T
-from torchvision.transforms import functional as Ft
-import random
-from tinygrad.tensor import Tensor
-from PIL import Image
-import numpy as np
-import torch
-import argparse
-import cv2
-
-
-class Resize:
-  def __init__(self, min_size, max_size):
-    if not isinstance(min_size, (list, tuple)):
-      min_size = (min_size,)
-    self.min_size = min_size
-    self.max_size = max_size
-
-  # modified from torchvision to add support for max size
-  def get_size(self, image_size):
-    w, h = image_size
-    size = random.choice(self.min_size)
-    max_size = self.max_size
-    if max_size is not None:
-      min_original_size = float(min((w, h)))
-      max_original_size = float(max((w, h)))
-      if max_original_size / min_original_size * size > max_size:
-        size = int(round(max_size * min_original_size / max_original_size))
-
-      if (w <= h and w == size) or (h <= w and h == size):
-        return (h, w)
-
-      if w < h:
-        ow = size
-        oh = int(size * h / w)
-      else:
-        oh = size
-        ow = int(size * w / h)
-
-      return (oh, ow)
-
-  def __call__(self, image):
-    size = self.get_size(image.size)
-    image = Ft.resize(image, size)
-    return image
-
-
-class Normalize:
-  def __init__(self, mean, std, to_bgr255=True):
-    self.mean = mean
-    self.std = std
-    self.to_bgr255 = to_bgr255
-
-  def __call__(self, image):
-    if self.to_bgr255:
-      image = image[[2, 1, 0]] * 255
-    else:
-      image = image[[0, 1, 2]] * 255
-    image = Ft.normalize(image, mean=self.mean, std=self.std)
-    return image
-
-transforms = lambda size_scale: T.Compose(
-  [
-    Resize(int(800*size_scale), int(1333*size_scale)),
-    T.ToTensor(),
-    Normalize(
-      mean=[102.9801, 115.9465, 122.7717], std=[1., 1., 1.], to_bgr255=True
-    ),
-  ]
-)
-
-def expand_boxes(boxes, scale):
-  w_half = (boxes[:, 2] - boxes[:, 0]) * .5
-  h_half = (boxes[:, 3] - boxes[:, 1]) * .5
-  x_c = (boxes[:, 2] + boxes[:, 0]) * .5
-  y_c = (boxes[:, 3] + boxes[:, 1]) * .5
-
-  w_half *= scale
-  h_half *= scale
-
-  boxes_exp = torch.zeros_like(boxes)
-  boxes_exp[:, 0] = x_c - w_half
-  boxes_exp[:, 2] = x_c + w_half
-  boxes_exp[:, 1] = y_c - h_half
-  boxes_exp[:, 3] = y_c + h_half
-  return boxes_exp
-
-
-def expand_masks(mask, padding):
-  N = mask.shape[0]
-  M = mask.shape[-1]
-  pad2 = 2 * padding
-  scale = float(M + pad2) / M
-  padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2))
-  padded_mask[:, :, padding:-padding, padding:-padding] = mask
-  return padded_mask, scale
-
-
-def paste_mask_in_image(mask, box, im_h, im_w, thresh=0.5, padding=1):
-  # TODO: remove torch
-  mask = torch.tensor(mask.numpy())
-  box = torch.tensor(box.numpy())
-  padded_mask, scale = expand_masks(mask[None], padding=padding)
-  mask = padded_mask[0, 0]
-  box = expand_boxes(box[None], scale)[0]
-  box = box.to(dtype=torch.int32)
-
-  TO_REMOVE = 1
-  w = int(box[2] - box[0] + TO_REMOVE)
-  h = int(box[3] - box[1] + TO_REMOVE)
-  w = max(w, 1)
-  h = max(h, 1)
-
-  mask = mask.expand((1, 1, -1, -1))
-
-  mask = mask.to(torch.float32)
-  mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
-  mask = mask[0][0]
-
-  if thresh >= 0:
-    mask = mask > thresh
-  else:
-    mask = (mask * 255).to(torch.uint8)
-
-  im_mask = torch.zeros((im_h, im_w), dtype=torch.uint8)
-  x_0 = max(box[0], 0)
-  x_1 = min(box[2] + 1, im_w)
-  y_0 = max(box[1], 0)
-  y_1 = min(box[3] + 1, im_h)
-
-  im_mask[y_0:y_1, x_0:x_1] = mask[
-                              (y_0 - box[1]): (y_1 - box[1]), (x_0 - box[0]): (x_1 - box[0])
-                              ]
-  return im_mask
-
-
-class Masker:
-  def __init__(self, threshold=0.5, padding=1):
-    self.threshold = threshold
-    self.padding = padding
-
-  def forward_single_image(self, masks, boxes):
-    boxes = boxes.convert("xyxy")
-    im_w, im_h = boxes.size
-    res = [
-      paste_mask_in_image(mask[0], box, im_h, im_w, self.threshold, self.padding)
-      for mask, box in zip(masks, boxes.bbox)
-    ]
-    if len(res) > 0:
-      res = torch.stack(*res, dim=0)[:, None]
-    else:
-      res = masks.new_empty((0, 1, masks.shape[-2], masks.shape[-1]))
-    return Tensor(res.numpy())
-
-  def __call__(self, masks, boxes):
-    if isinstance(boxes, BoxList):
-      boxes = [boxes]
-
-    results = []
-    for mask, box in zip(masks, boxes):
-      result = self.forward_single_image(mask, box)
-      results.append(result)
-    return results
-
-
-masker = Masker(threshold=0.5, padding=1)
-
-def select_top_predictions(predictions, confidence_threshold=0.9):
-  scores = predictions.get_field("scores").numpy()
-  keep = [idx for idx, score in enumerate(scores) if score > confidence_threshold]
-  return predictions[keep]
-
-def compute_prediction(original_image, model, confidence_threshold, size_scale=1.0):
-  image = transforms(size_scale)(original_image).numpy()
-  image = Tensor(image, requires_grad=False)
-  predictions = model(image)
-  prediction = predictions[0]
-  prediction = select_top_predictions(prediction, confidence_threshold)
-  width, height = original_image.size
-  prediction = prediction.resize((width, height))
-
-  if prediction.has_field("mask"):
-    masks = prediction.get_field("mask")
-    masks = masker([masks], [prediction])[0]
-    prediction.add_field("mask", masks)
-  return prediction
-
-def compute_prediction_batched(batch, model, size_scale=1.0):
-  imgs = []
-  for img in batch:
-    imgs.append(transforms(size_scale)(img).numpy())
-  image = [Tensor(image, requires_grad=False) for image in imgs]
-  predictions = model(image)
-  del image
-  return predictions
-
-palette = np.array([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
-
-def findContours(*args, **kwargs):
-  if cv2.__version__.startswith('4'):
-    contours, hierarchy = cv2.findContours(*args, **kwargs)
-  elif cv2.__version__.startswith('3'):
-    _, contours, hierarchy = cv2.findContours(*args, **kwargs)
-  return contours, hierarchy
-
-def compute_colors_for_labels(labels):
-  l = labels[:, None]
-  colors = l * palette
-  colors = (colors % 255).astype("uint8")
-  return colors
-
-def overlay_mask(image, predictions):
-  image = np.asarray(image)
-  masks = predictions.get_field("mask").numpy()
-  labels = predictions.get_field("labels").numpy()
-
-  colors = compute_colors_for_labels(labels).tolist()
-
-  for mask, color in zip(masks, colors):
-    thresh = mask[0, :, :, None]
-    contours, hierarchy = findContours(
-        thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
-    )
-    image = cv2.drawContours(image, contours, -1, color, 3)
-
-  composite = image
-
-  return composite
-
-CATEGORIES = [
-    "__background", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
-    "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
-    "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
-    "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle",
-    "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
-    "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table",
-    "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster",
-    "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
-]
-
-def overlay_boxes(image, predictions):
-  labels = predictions.get_field("labels").numpy()
-  boxes = predictions.bbox
-  image = np.asarray(image)
-  colors = compute_colors_for_labels(labels).tolist()
-
-  for box, color in zip(boxes, colors):
-    box = torch.tensor(box.numpy())
-    box = box.to(torch.int64)
-    top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
-    image = cv2.rectangle(
-        image, tuple(top_left), tuple(bottom_right), tuple(color), 1
-    )
-
-  return image
-
-def overlay_class_names(image, predictions):
-  scores = predictions.get_field("scores").numpy().tolist()
-  labels = predictions.get_field("labels").numpy().tolist()
-  labels = [CATEGORIES[int(i)] for i in labels]
-  boxes = predictions.bbox.numpy()
-  image = np.asarray(image)
-  template = "{}: {:.2f}"
-  for box, score, label in zip(boxes, scores, labels):
-    x, y = box[:2]
-    s = template.format(label, score)
-    x, y = int(x), int(y)
-    cv2.putText(
-        image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
-    )
-
-  return image
-
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser(description='Run MaskRCNN', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--image', type=str, help="Path of the image to run")
-  parser.add_argument('--threshold', type=float, default=0.7, help="Detector threshold")
-  parser.add_argument('--size_scale', type=float, default=1.0, help="Image resize multiplier")
-  parser.add_argument('--out', type=str, default="/tmp/rendered.png", help="Output filename")
-  args = parser.parse_args()
-
-  resnet = ResNet(50, num_classes=None, stride_in_1x1=True)
-  model_tiny = MaskRCNN(resnet)
-  model_tiny.load_from_pretrained()
-  img = Image.open(args.image)
-  top_result_tiny = compute_prediction(img, model_tiny, confidence_threshold=args.threshold, size_scale=args.size_scale)
-  bbox_image = overlay_boxes(img, top_result_tiny)
-  mask_image = overlay_mask(bbox_image, top_result_tiny)
-  final_image = overlay_class_names(mask_image, top_result_tiny)
-
-  im = Image.fromarray(final_image)
-  print(f"saving {args.out}")
-  im.save(args.out)
-  im.show()
@@ -763,48 +763,26 @@ class BlendedGPTDataset:

    return dataset_idx, dataset_sample_idx

-def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
+def get_llama3_dataset(samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True, small:bool=False) -> BlendedGPTDataset:
+  if small:
+    if val:
+      return BlendedGPTDataset(
+        [base_dir / "c4-validation-91205-samples.en_text_document"], [1.0], samples, seqlen, seed, shuffle=False)
+    return BlendedGPTDataset(
+      [base_dir / "c4-train.en_6_text_document"], [1.0], samples, seqlen, seed, shuffle=True)
  if val:
-    dataset = BlendedGPTDataset([
-      base_dir / "validation" / "c4-validationn-91205-samples.en_text_document",
-    ], [
-      1.0
-    ], samples, seqlen, seed, False)
-  else:
-    dataset = BlendedGPTDataset([
-      base_dir / "c4-train.en_6_text_document",
-      base_dir / "c4-train.en_7_text_document",
-    ], [
-      1.0, 1.0
-    ], samples, seqlen, seed, True)
+    return BlendedGPTDataset(
+      [base_dir / "validation" / "c4-validationn-91205-samples.en_text_document"], [1.0], samples, seqlen, seed, shuffle=False)
+  return BlendedGPTDataset(
+    [base_dir / "c4-train.en_6_text_document", base_dir / "c4-train.en_7_text_document"], [1.0, 1.0], samples, seqlen, seed, shuffle=True)

-  for b in range(math.ceil(samples / bs)):
-    batch = []
-    for i in range(bs):
-      tokens = dataset.get(b * bs + i)
-      batch.append(tokens)
+def iterate_llama3_dataset(dataset:BlendedGPTDataset, bs:int):
+  for b in range(math.ceil(dataset.samples / bs)):
+    batch = [dataset.get(b * bs + i) for i in range(bs)]
    yield Tensor.stack(batch, dim=0)

-def batch_load_llama3_small(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
-  if val:
-    dataset = BlendedGPTDataset([
-      base_dir / "c4-validation-91205-samples.en_text_document",
-    ], [
-      1.0
-    ], samples, seqlen, seed, False)
-  else:
-    dataset = BlendedGPTDataset([
-      base_dir / "c4-train.en_6_text_document",
-    ], [
-      1.0
-    ], samples, seqlen, seed, True)
-
-  for b in range(math.ceil(samples / bs)):
-    batch = []
-    for i in range(bs):
-      tokens = dataset.get(b * bs + i)
-      batch.append(tokens)
-    yield Tensor.stack(batch, dim=0)
+def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True, small:bool=False):
+  return iterate_llama3_dataset(get_llama3_dataset(samples, seqlen, base_dir, seed, val, small), bs)

 if __name__ == "__main__":
  def load_unet3d(val):
@@ -223,13 +223,13 @@ def get_mlperf_bert_model():

 def get_fake_data_bert(BS:int):
  return {
-    "input_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
-    "input_mask": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
-    "segment_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_positions": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_ids": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
-    "masked_lm_weights": Tensor.empty((BS, 76), dtype=dtypes.float32, device="CPU"),
-    "next_sentence_labels": Tensor.empty((BS, 1), dtype=dtypes.int32, device="CPU"),
+    "input_ids": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
+    "input_mask": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
+    "segment_ids": Tensor.zeros((BS, 512), dtype=dtypes.int32, device="CPU").contiguous(),
+    "masked_lm_positions": Tensor.zeros((BS, 76), dtype=dtypes.int32, device="CPU").contiguous(),
+    "masked_lm_ids": Tensor.zeros((BS, 76), dtype=dtypes.int32, device="CPU").contiguous(),
+    "masked_lm_weights": Tensor.zeros((BS, 76), dtype=dtypes.float32, device="CPU").contiguous(),
+    "next_sentence_labels": Tensor.zeros((BS, 1), dtype=dtypes.int32, device="CPU").contiguous(),
  }

 def find_matches(match_quality_matrix:np.ndarray, high_threshold:float=0.5, low_threshold:float=0.4, allow_low_quality_matches:bool=False) -> np.ndarray:
@@ -59,9 +59,7 @@ class EmbeddingBert(nn.Embedding):
    arange_shp, weight_shp, big_shp = (1, 1, self.vocab_sz, 1), (1, 1, self.vocab_sz, self.embed_sz), idx.shape+(self.vocab_sz, self.embed_sz,)
    if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz, requires_grad=False, device=self.weight.device).reshape(arange_shp)
    arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1,)).expand(big_shp), self.weight.cast(dtypes.default_float).reshape(weight_shp).expand(big_shp)
-    # TODO: contiguous() here because the embedding dropout creates different asts on each device, and search becomes very slow.
-    # Should fix with fixing random ast on multi device, and fuse arange to make embedding fast.
-    return (arange == idx).mul(vals).sum(2, dtype=vals.dtype).contiguous()
+    return (arange == idx).where(vals, 0).sum(2, dtype=vals.dtype)

 class LayerNormBert:
  def __init__(self, normalized_shape:Union[int, tuple[int, ...]], eps:float=1e-12, elementwise_affine:bool=True):
@@ -204,43 +204,6 @@ def eval_bert():

    st = time.perf_counter()

-def eval_mrcnn():
-  from tqdm import tqdm
-  from extra.models.mask_rcnn import MaskRCNN
-  from extra.models.resnet import ResNet
-  from extra.datasets.coco import BASEDIR, images, convert_prediction_to_coco_bbox, convert_prediction_to_coco_mask, accumulate_predictions_for_coco, evaluate_predictions_on_coco, iterate
-  from examples.mask_rcnn import compute_prediction_batched, Image
-  mdl = MaskRCNN(ResNet(50, num_classes=None, stride_in_1x1=True))
-  mdl.load_from_pretrained()
-
-  bbox_output = '/tmp/results_bbox.json'
-  mask_output = '/tmp/results_mask.json'
-
-  accumulate_predictions_for_coco([], bbox_output, rm=True)
-  accumulate_predictions_for_coco([], mask_output, rm=True)
-
-  #TODO: bs > 1 not as accurate
-  bs = 1
-
-  for batch in tqdm(iterate(images, bs=bs), total=len(images)//bs):
-    batch_imgs = []
-    for image_row in batch:
-      image_name = image_row['file_name']
-      img = Image.open(BASEDIR/f'val2017/{image_name}').convert("RGB")
-      batch_imgs.append(img)
-    batch_result = compute_prediction_batched(batch_imgs, mdl)
-    for image_row, result in zip(batch, batch_result):
-      image_name = image_row['file_name']
-      box_pred = convert_prediction_to_coco_bbox(image_name, result)
-      mask_pred = convert_prediction_to_coco_mask(image_name, result)
-      accumulate_predictions_for_coco(box_pred, bbox_output)
-      accumulate_predictions_for_coco(mask_pred, mask_output)
-    del batch_imgs
-    del batch_result
-
-  evaluate_predictions_on_coco(bbox_output, iou_type='bbox')
-  evaluate_predictions_on_coco(mask_output, iou_type='segm')
-
 def eval_llama3():
  from extra.models.llama import Transformer
  from examples.llama3 import MODEL_PARAMS, load, convert_from_huggingface
@@ -271,12 +234,9 @@ def eval_llama3():
    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
    return loss.flatten().float()

-  if SMALL:
-    from examples.mlperf.dataloader import batch_load_llama3_small
-    iter = batch_load_llama3_small(BS, 5760, SEQLEN, BASEDIR, val=True)
-  else:
-    from examples.mlperf.dataloader import batch_load_llama3
-    iter = batch_load_llama3(BS, 5760, SEQLEN, BASEDIR, val=True)
+  from examples.mlperf.dataloader import get_llama3_dataset, iterate_llama3_dataset
+  eval_dataset = get_llama3_dataset(5760, SEQLEN, BASEDIR, val=True, small=bool(SMALL))
+  iter = iterate_llama3_dataset(eval_dataset, BS)

  losses = []
  for tokens in tqdm(iter, total=5760//BS):
@@ -541,7 +501,7 @@ if __name__ == "__main__":
  # inference only
  Tensor.training = False

-  models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,mrcnn").split(",")
+  models = getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert").split(",")
  for m in models:
    nm = f"eval_{m}"
    if nm in globals():
@@ -918,40 +918,6 @@ def train_rnnt():
  # TODO: RNN-T
  pass

-@TinyJit
-def train_step_bert(model, optimizer, scheduler, loss_scaler:float, GPUS, grad_acc:int, **kwargs):
-  optimizer.zero_grad()
-
-  for i in range(grad_acc):
-    input_ids, segment_ids = kwargs[f"input_ids{i}"], kwargs[f"segment_ids{i}"]
-    # NOTE: these two have different names
-    attention_mask, masked_positions = kwargs[f"input_mask{i}"], kwargs[f"masked_lm_positions{i}"]
-    masked_lm_ids, masked_lm_weights, next_sentence_labels = kwargs[f"masked_lm_ids{i}"], kwargs[f"masked_lm_weights{i}"], kwargs[f"next_sentence_labels{i}"]
-
-    for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
-      if len(GPUS) > 1: t.shard_(GPUS, axis=0)
-      else: t.to_(GPUS[0])
-
-    lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
-    loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
-    (loss * loss_scaler).backward()
-    # TODO: OOM without this realize with large grad_acc
-    Tensor.realize(*[p.grad for p in optimizer.params])
-
-  global_norm = Tensor(0.0, dtype=dtypes.float32, device=optimizer[0].device)
-  for p in optimizer.params:
-    p.grad = p.grad / loss_scaler
-    global_norm += p.grad.float().square().sum()
-  global_norm = global_norm.sqrt().contiguous()
-  for p in optimizer.params:
-    p.grad = (global_norm > 1.0).where((p.grad/global_norm).cast(p.grad.dtype), p.grad)
-
-  optimizer.step()
-  scheduler.step()
-  # TODO: no to("CPU") here because it blocks and messes the python time
-  Tensor.realize(loss, global_norm, optimizer.optimizers[0].lr)
-  return loss, global_norm, optimizer.optimizers[0].lr
-
@TinyJit
 def eval_step_bert(model, input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor, masked_positions:Tensor, masked_lm_ids:Tensor,
                   masked_lm_weights:Tensor, next_sentence_labels:Tensor, GPUS):
@@ -1014,7 +980,8 @@ def train_bert():
  # ** hyperparameters **
  BS                 = config["BS"]                     = getenv("BS", 11 * len(GPUS) if dtypes.default_float in (dtypes.float16, dtypes.bfloat16) else 8 * len(GPUS))
  grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
-  # TODO: mlperf logging
+  # TODO: implement grad accumulation + mlperf logging
+  assert grad_acc == 1
  GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
  EVAL_BS            = config["EVAL_BS"]                = getenv("EVAL_BS", 1 * len(GPUS))
  max_lr             = config["OPT_BASE_LEARNING_RATE"] = getenv("OPT_BASE_LEARNING_RATE", 0.000175 * math.sqrt(GBS/96))
@@ -1073,8 +1040,8 @@ def train_bert():

  # ** Optimizer **
  parameters_no_wd = [v for k, v in get_state_dict(model).items() if "bias" in k or "LayerNorm" in k]
-  parameters = [x for x in parameters if x not in set(parameters_no_wd)]
-  optimizer_wd = LAMB(parameters, lr=max_lr, b1=opt_lamb_beta_1, b2=opt_lamb_beta_2, eps=epsilon, weight_decay=decay, adam=False)
+  parameters_wd = [x for x in parameters if x not in set(parameters_no_wd)]
+  optimizer_wd = LAMB(parameters_wd, lr=max_lr, b1=opt_lamb_beta_1, b2=opt_lamb_beta_2, eps=epsilon, weight_decay=decay, adam=False)
  optimizer_no_wd = LAMB(parameters_no_wd, lr=max_lr, b1=opt_lamb_beta_1, b2=opt_lamb_beta_2, eps=epsilon, weight_decay=0.0, adam=False)
  optimizer_group = OptimizerGroup(optimizer_wd, optimizer_no_wd)

@@ -1131,12 +1098,38 @@ def train_bert():
  # ** train loop **
  wc_start = time.perf_counter()

-  i, train_data = start_step, [next(train_it) for _ in range(grad_acc)]
+  i, train_data = start_step, next(train_it)

  if RUNMLPERF:
    if MLLOGGER:
      MLLOGGER.start(key=mllog_constants.EPOCH_START, value=i*GBS, metadata={"epoch_num": i*GBS})

+  @TinyJit
+  def train_step_bert(input_ids:Tensor, segment_ids:Tensor, attention_mask:Tensor,
+                      masked_positions:Tensor, masked_lm_ids:Tensor, masked_lm_weights:Tensor, next_sentence_labels:Tensor):
+    for t in [input_ids, segment_ids, attention_mask, masked_positions, masked_lm_ids, masked_lm_weights, next_sentence_labels]:
+      if len(GPUS) > 1: t.shard_(GPUS, axis=0)
+      else: t.to_(GPUS[0])
+    optimizer_group.zero_grad()
+
+    lm_logits, seq_relationship_logits = model(input_ids, attention_mask, masked_positions, segment_ids)
+    loss = model.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
+    (loss * loss_scaler).backward()
+
+    global_norm = Tensor(0.0, dtype=dtypes.float32, device=optimizer_group[0].device)
+    for p in optimizer_group.params:
+      p.grad = p.grad / loss_scaler
+      global_norm += p.grad.float().square().sum()
+    global_norm = global_norm.sqrt().contiguous()
+    for p in optimizer_group.params:
+      p.grad = (global_norm > 1.0).where((p.grad/global_norm).cast(p.grad.dtype), p.grad)
+
+    optimizer_group.step()
+    scheduler_group.step()
+    # TODO: no to("CPU") here because it blocks and messes the python time
+    Tensor.realize(loss, global_norm, optimizer_group.optimizers[0].lr)
+    return loss, global_norm, optimizer_group.optimizers[0].lr
+
  while train_data is not None and i < train_steps and not achieved:
    if getenv("TRAIN", 1):
      Tensor.training = True
@@ -1144,16 +1137,12 @@ def train_bert():
      st = time.perf_counter()
      GlobalCounters.reset()
      with WallTimeEvent(BenchEvent.STEP):
-        data = {f"{k}{i}":v for i,d in enumerate(train_data) for k,v in d.items()}
-        loss, global_norm, lr = train_step_bert(model, optimizer_group, scheduler_group, loss_scaler, GPUS, grad_acc, **data)
+        loss, global_norm, lr = train_step_bert(
+          train_data["input_ids"], train_data["segment_ids"], train_data["input_mask"], train_data["masked_lm_positions"], \
+          train_data["masked_lm_ids"], train_data["masked_lm_weights"], train_data["next_sentence_labels"])

        pt = time.perf_counter()
-
-        try:
-          next_data = [next(train_it) for _ in range(grad_acc)]
-        except StopIteration:
-          next_data = None
-
+        next_data = next(train_it)
        dt = time.perf_counter()

        device_str = parameters[0].device if isinstance(parameters[0].device, str) else f"{parameters[0].device[0]} * {len(parameters[0].device)}"
@@ -1188,8 +1177,8 @@ def train_bert():
      if MLLOGGER and RUNMLPERF:
        MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*GBS, "step_num": i})
      if getenv("RESET_STEP"): train_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 0) and train_step_bert.captured is not None:
-        # TODO: FREE_INTERMEDIATE nan'ed after jit step 2
+      elif getenv("FREE_INTERMEDIATE") and train_step_bert.captured is not None:
+        # TODO: this hangs on tiny green after 90 minutes of training
        train_step_bert.captured.free_intermediates()
      eval_lm_losses = []
      eval_clsf_losses = []
@@ -1224,7 +1213,7 @@ def train_bert():
          return

      if getenv("RESET_STEP"): eval_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 0) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()
+      elif getenv("FREE_INTERMEDIATE") and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()

      del eval_data
      avg_lm_loss = sum(eval_lm_losses) / len(eval_lm_losses)
@@ -1300,6 +1289,7 @@ def train_llama3():
  BASEDIR            = config["BASEDIR"]                = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
  BS                 = config["BS"]                     = getenv("BS", 16)
  grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
+  assert grad_acc == 1, f"{grad_acc=} is not supported"
  GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
  SEED               = config["SEED"]                   = getenv("SEED", 5760)
  SEQLEN             = config["SEQLEN"]                 = getenv("SEQLEN", 8192)
@@ -1324,12 +1314,21 @@ def train_llama3():
  opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152)  # NOTE: cannot change for benchmark
  opt_end_learning_rate = getenv("END_LR", 8e-7)

-  # TODO: confirm weights are in bf16
+  # ** init wandb **
+  WANDB = getenv("WANDB")
+  if WANDB:
+    import wandb
+    wandb_args = {"id": wandb_id, "resume": "must"} if (wandb_id := getenv("WANDB_RESUME", "")) else {}
+    wandb.init(config=config, **wandb_args, project="MLPerf-LLaMA3")
+
+  model_params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
  # vocab_size from the mixtral tokenizer
-  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
-  params = params | {"vocab_size": 32000} if not SMALL else params
-  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
-  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
+  if not SMALL: model_params |= {"vocab_size": 32000}
+  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: model_params['n_layers'] = llama_layers
+  model = Transformer(**model_params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
+  params = get_parameters(model)
+  # weights are all bfloat16 for now
+  assert params and all(p.dtype == dtypes.bfloat16 for p in params)

  if getenv("FAKEDATA"):
    for v in get_parameters(model):
@@ -1374,20 +1373,17 @@ def train_llama3():

  @TinyJit
  @Tensor.train()
-  def train_step(model, tokens:Tensor, grad_acc:int):
+  def train_step(model, tokens:Tensor):
    optim.zero_grad()
-    # grad acc
-    for batch in tokens.split(tokens.shape[0]//grad_acc):
-      if (DP := getenv("DP", 1)) > 1:
-        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
-        batch = batch.shard(device, 0)
-      if (MP := getenv("MP", 1)) > 1:
-        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
-        batch = batch.shard(device)
-      logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
-      loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
-      loss.backward()
-      Tensor.realize(*[p.grad for p in optim.params])
+    if (DP := getenv("DP", 1)) > 1:
+      device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
+      tokens = tokens.shard(device, 0)
+    if (MP := getenv("MP", 1)) > 1:
+      device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
+      tokens = tokens.shard(device)
+    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
+    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
+    loss.backward()
    # L2 norm grad clip
    # https://github.com/NVIDIA/NeMo/blob/3368c3fc0b4a186ab33a1d68a504315100c0b2a6/nemo/collections/nlp/modules/common/megatron/clip_grads.py#L57
    # https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
@@ -1422,55 +1418,62 @@ def train_llama3():
  # ** data iters **
  def fake_data(bs, samples):
    for _ in range(samples // bs):
-      yield Tensor.randint(bs, SEQLEN + 1, low=0, high=params["vocab_size"], dtype=dtypes.int32, device=Device.DEFAULT)
+      yield Tensor.randint(bs, SEQLEN + 1, low=0, high=model_params["vocab_size"], dtype=dtypes.int32, device=Device.DEFAULT)

  def get_train_iter():
    if getenv("FAKEDATA", 0):
-      return fake_data(GBS, SAMPLES)
+      return fake_data(BS, SAMPLES)
    else:
-      if SMALL:
-        from examples.mlperf.dataloader import batch_load_llama3_small
-        return batch_load_llama3_small(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
-      else:
-        from examples.mlperf.dataloader import batch_load_llama3
-        return batch_load_llama3(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
+      from examples.mlperf.dataloader import batch_load_llama3
+      return batch_load_llama3(BS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL), small=bool(SMALL))
+
+  if getenv("FAKEDATA", 0):
+    eval_dataset = None
+  else:
+    from examples.mlperf.dataloader import get_llama3_dataset
+    eval_dataset = get_llama3_dataset(5760, SEQLEN, BASEDIR, val=True, small=bool(SMALL))

  def get_eval_iter():
-    if getenv("FAKEDATA", 0):
+    if eval_dataset is None:
      return fake_data(EVAL_BS, 5760)
-    else:
-      if SMALL:
-        from examples.mlperf.dataloader import batch_load_llama3_small
-        return batch_load_llama3_small(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
-      else:
-        from examples.mlperf.dataloader import batch_load_llama3
-        return batch_load_llama3(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
+    from examples.mlperf.dataloader import iterate_llama3_dataset
+    return iterate_llama3_dataset(eval_dataset, EVAL_BS)

  iter = get_train_iter()
  i, sequences_seen = resume_ckpt, 0
  for tokens in tqdm(iter, total=SAMPLES//GBS):
-    t = time.perf_counter()
    GlobalCounters.reset()
-    loss, lr = train_step(model, tokens, grad_acc)
-    loss = loss.float().item()
+    if getenv("TRAIN", 1):
+      t = time.perf_counter()
+      loss, lr = train_step(model, tokens)
+      loss = loss.float().item()
+      lr = lr.item()

-    i += 1
-    sequences_seen += tokens.shape[0]
+      i += 1
+      sequences_seen += tokens.shape[0]

-    tqdm.write(f"{loss:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
-    if (fname:=getenv("LOSS_FILE", "")):
-      with open(fname, "a") as f:
-        f.write(f"{i} {loss:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
+      sec = time.perf_counter()-t
+      mem_gb = GlobalCounters.mem_used / 1e9
+      gflops = GlobalCounters.global_ops / 1e9 / sec
+      tqdm.write(
+        f"{i:5} {sec:.2f} s run, {loss:.4f} loss, {lr:.12f} LR, {mem_gb:.2f} GB used, {gflops:9.2f} GFLOPS")

-    if (ckpt_freq := getenv("CKPT")) and (i % ckpt_freq == 0 and (i != 1 or ckpt_freq == 1)):
-      tqdm.write("saving checkpoint")
-      if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
-      fn = f"{ckpt_dir}/llama3_{i}.safe"
-      safe_save(get_state_dict(model), fn)
+      if (fname:=getenv("LOSS_FILE", "")):
+        with open(fname, "a") as f:
+          f.write(f"{i} {loss:.4f} {lr:.12f} {mem_gb:.2f}\n")

-      tqdm.write("saving optim checkpoint")
-      fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
-      safe_save(get_state_dict(scheduler), fn)
+      if WANDB:
+        wandb.log({"lr": lr, "train/loss": loss, "train/step_time": sec, "train/GFLOPS": gflops, "train/sequences_seen": sequences_seen})
+
+      if (ckpt_freq := getenv("CKPT")) and (i % ckpt_freq == 0 and (i != 1 or ckpt_freq == 1)):
+        tqdm.write("saving checkpoint")
+        if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
+        fn = f"{ckpt_dir}/llama3_{i}.safe"
+        safe_save(get_state_dict(model), fn)
+
+        tqdm.write("saving optim checkpoint")
+        fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
+        safe_save(get_state_dict(scheduler), fn)

    if sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1):
      tqdm.write(f"evaluating after {sequences_seen} sequences")
@@ -1486,6 +1489,9 @@ def train_llama3():

      tqdm.write(f"eval log perplexity: {log_perplexity:.4f}")

+      if WANDB:
+        wandb.log({"eval/log_perplexity": log_perplexity, "eval/sequences_seen": sequences_seen})
+
      if log_perplexity < EVAL_TARGET:
        tqdm.write(f"target achieved after {sequences_seen} sequences")
        if getenv("CKPT"):
@@ -0,0 +1,31 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export SUBMISSION_PLATFORM="tinybox_8xMI350X"
+export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
+
+# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
+export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
+export TRAIN_STEPS=3900
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=5000000
+
+export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
+export BASEDIR="/raid/datasets/wiki"
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=1
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="bert_8xMI350x_${DATETIME}_${SEED}.log"
+
+BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -2,7 +2,7 @@

 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72

 export IGNORE_OOB=1
 export REWRITE_STACK_LIMIT=500000
@@ -2,7 +2,7 @@

 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72

 export IGNORE_OOB=1
 export REWRITE_STACK_LIMIT=500000
@@ -5,7 +5,7 @@ set -o pipefail  # Make pipeline fail if any command fails
 export PYTHONPATH="." NV=1
 export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=72 EVAL_BS=72

 export IGNORE_OOB=1
 export REWRITE_STACK_LIMIT=500000
@@ -1,118 +0,0 @@
-import json, pprint
-from tinygrad import fetch, nn, Tensor
-from tinygrad.helpers import DEBUG
-
-class FeedForward:
-  def __init__(self, model_dim, intermediate_dim):
-    self.proj_1 = nn.Linear(model_dim, 2*intermediate_dim, bias=False)
-    self.proj_2 = nn.Linear(intermediate_dim, model_dim, bias=False)
-
-  def __call__(self, x):
-    y_12 = self.proj_1(x)
-    y_1, y_2 = y_12.chunk(2, dim=-1)
-    return self.proj_2(y_1.silu() * y_2)
-
-# NOTE: this RoPE doesn't match LLaMA's?
-def _rotate_half(x: Tensor) -> Tensor:
-  x1, x2 = x.chunk(2, dim=-1)
-  return Tensor.cat(-x2, x1, dim=-1)
-
-def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor:
-  return (x * pos_cos) + (_rotate_half(x) * pos_sin)
-
-class Attention:
-  def __init__(self, model_dim, num_query_heads, num_kv_heads, head_dim):
-    self.qkv_proj = nn.Linear(model_dim, (num_query_heads + num_kv_heads*2) * head_dim, bias=False)
-    self.num_query_heads, self.num_kv_heads = num_query_heads, num_kv_heads
-    self.head_dim = head_dim
-    self.q_norm = nn.RMSNorm(head_dim)
-    self.k_norm = nn.RMSNorm(head_dim)
-    self.out_proj = nn.Linear(num_query_heads * head_dim, model_dim, bias=False)
-
-  def __call__(self, x:Tensor) -> Tensor:
-    batch_size, seq_len, embed_dim = x.shape
-    qkv = self.qkv_proj(x)
-    qkv = qkv.reshape(batch_size, seq_len, self.num_query_heads+self.num_kv_heads*2, self.head_dim).transpose(1, 2)
-    xq,xk,xv = qkv.split([self.num_query_heads, self.num_kv_heads, self.num_kv_heads], dim=1)
-    xq = self.q_norm(xq)
-    xk = self.k_norm(xk)
-
-    # add positional embedding (how many kernels is this?)
-    freq_constant = 10000
-    inv_freq = 1.0 / (freq_constant ** (Tensor.arange(0, self.head_dim, 2) / self.head_dim))
-    pos_index_theta = Tensor.einsum("i,j->ij", Tensor.arange(seq_len), inv_freq)
-    emb = Tensor.cat(pos_index_theta, pos_index_theta, dim=-1)
-    cos_emb, sin_emb = emb.cos()[None, None, :, :], emb.sin()[None, None, :, :]
-    xq = _apply_rotary_pos_emb(xq, sin_emb, cos_emb)
-    xk = _apply_rotary_pos_emb(xk, sin_emb, cos_emb)
-
-    # grouped-query attention
-    num_groups = self.num_query_heads // self.num_kv_heads
-    xk = xk.repeat_interleave(num_groups, dim=1)
-    xv = xv.repeat_interleave(num_groups, dim=1)
-
-    # masked attention
-    #start_pos = 0
-    #mask = Tensor.full((1, 1, seq_len, start_pos+seq_len), float("-inf"), dtype=xq.dtype, device=xq.device).triu(start_pos+1)
-    #attn_output = xq.scaled_dot_product_attention(xk, xv, mask).transpose(1, 2)
-
-    # causal is fine, no mask needed
-    attn_output = xq.scaled_dot_product_attention(xk, xv, is_causal=True).transpose(1, 2)
-    return self.out_proj(attn_output.reshape(batch_size, seq_len, self.num_query_heads * self.head_dim))
-
-class Layer:
-  def __init__(self, model_dim, intermediate_dim, num_query_heads, num_kv_heads, head_dim):
-    self.ffn = FeedForward(model_dim, intermediate_dim)
-    self.attn = Attention(model_dim, num_query_heads, num_kv_heads, head_dim)
-    self.ffn_norm = nn.RMSNorm(model_dim)
-    self.attn_norm = nn.RMSNorm(model_dim)
-
-  def __call__(self, x:Tensor) -> Tensor: # (batch, seq_len, embed_dim)
-    x = x + self.attn(self.attn_norm(x))
-    x = x + self.ffn(self.ffn_norm(x))
-    return x
-
-# stupidly complex
-def make_divisible(v, divisor):
-  new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
-  if new_v < 0.9 * v: new_v += divisor
-  return new_v
-
-class Transformer:
-  def __init__(self, cfg):
-    if DEBUG >= 3: pprint.pp(cfg)
-    self.layers = [Layer(cfg['model_dim'], make_divisible(int(cfg["model_dim"] * cfg['ffn_multipliers'][i]), cfg['ffn_dim_divisor']),
-                         cfg['num_query_heads'][i], cfg['num_kv_heads'][i], cfg['head_dim']) for i in range(cfg['num_transformer_layers'])]
-    self.norm = nn.RMSNorm(cfg['model_dim'])
-    self.token_embeddings = nn.Embedding(cfg['vocab_size'], cfg['model_dim'])
-
-  def __call__(self, tokens:Tensor):
-    # _bsz, seqlen = tokens.shape
-    x = self.token_embeddings(tokens)
-    for l in self.layers: x = l(x)
-    return self.norm(x) @ self.token_embeddings.weight.T
-
-if __name__ == "__main__":
-  #model_name = "OpenELM-270M-Instruct"
-  model_name = "OpenELM-270M"  # this is fp32
-  model = Transformer(json.loads(fetch(f"https://huggingface.co/apple/{model_name}/resolve/main/config.json?download=true").read_bytes()))
-  weights = nn.state.safe_load(fetch(f"https://huggingface.co/apple/{model_name}/resolve/main/model.safetensors?download=true"))
-  if DEBUG >= 3:
-    for k, v in weights.items(): print(k, v.shape)
-  nn.state.load_state_dict(model, {k.removeprefix("transformer."):v for k,v in weights.items()})
-
-  from sentencepiece import SentencePieceProcessor
-  tokenizer = SentencePieceProcessor(fetch("https://github.com/karpathy/llama2.c/raw/master/tokenizer.model").as_posix())
-  toks = [tokenizer.bos_id()] + tokenizer.encode("Some car brands include")
-  for i in range(100):
-    ttoks = Tensor([toks])
-    out = model(ttoks).realize()
-    t0 = out[0].argmax(axis=-1).tolist()
-    toks.append(t0[-1])
-    # hmmm...passthrough still doesn't match (it shouldn't, it outputs the most likely)
-    print(tokenizer.decode(toks))
-    #print(toks)
-    #print(tokenizer.decode(t0))
-    #print(t0)
-
-
@@ -1,55 +0,0 @@
-from tinygrad.helpers import trange
-from tinygrad.nn.datasets import mnist
-import mlx.core as mx
-import mlx.nn as nn
-import mlx.optimizers as optim
-from functools import partial
-
-class Model(nn.Module):
-  def __init__(self):
-    super().__init__()
-    self.c1 = nn.Conv2d(1, 32, 5)
-    self.c2 = nn.Conv2d(32, 32, 5)
-    self.bn1 = nn.BatchNorm(32)
-    self.m1 = nn.MaxPool2d(2)
-    self.c3 = nn.Conv2d(32, 64, 3)
-    self.c4 = nn.Conv2d(64, 64, 3)
-    self.bn2 = nn.BatchNorm(64)
-    self.m2 = nn.MaxPool2d(2)
-    self.lin = nn.Linear(576, 10)
-  def __call__(self, x):
-    x = mx.maximum(self.c1(x), 0)
-    x = mx.maximum(self.c2(x), 0)
-    x = self.m1(self.bn1(x))
-    x = mx.maximum(self.c3(x), 0)
-    x = mx.maximum(self.c4(x), 0)
-    x = self.m2(self.bn2(x))
-    return self.lin(mx.flatten(x, 1))
-
-if __name__ == "__main__":
-  X_train, Y_train, X_test, Y_test = mnist()
-  X_train = mx.array(X_train.float().permute((0,2,3,1)).numpy())
-  Y_train = mx.array(Y_train.numpy())
-  X_test = mx.array(X_test.float().permute((0,2,3,1)).numpy())
-  Y_test = mx.array(Y_test.numpy())
-
-  model = Model()
-  optimizer = optim.Adam(1e-3)
-  def loss_fn(model, x, y): return nn.losses.cross_entropy(model(x), y).mean()
-
-  state = [model.state, optimizer.state]
-  @partial(mx.compile, inputs=state, outputs=state)
-  def step(samples):
-    # Compiled functions will also treat any inputs not in the parameter list as constants.
-    X,Y = X_train[samples], Y_train[samples]
-    loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
-    loss, grads = loss_and_grad_fn(model, X, Y)
-    optimizer.update(model, grads)
-    return loss
-
-  test_acc = float('nan')
-  for i in (t:=trange(70)):
-    samples = mx.random.randint(0, X_train.shape[0], (512,))  # putting this in JIT didn't work well
-    loss = step(samples)
-    if i%10 == 9: test_acc = ((model(X_test).argmax(axis=-1) == Y_test).sum() * 100 / X_test.shape[0]).item()
-    t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
@@ -1,45 +0,0 @@
-import gymnasium as gym
-import numpy as np
-from gymnasium.envs.registration import register
-
-# a very simple game
-# one of <size> lights will light up
-# take the action of the lit up light
-# in <hard_mode>, you act differently based on the step number and need to track this
-
-class PressTheLightUpButton(gym.Env):
-  metadata = {"render_modes": []}
-  def __init__(self, render_mode=None, size=2, game_length=10, hard_mode=False):
-    self.size, self.game_length = size, game_length
-    self.observation_space = gym.spaces.Box(0, 1, shape=(self.size,), dtype=np.float32)
-    self.action_space = gym.spaces.Discrete(self.size)
-    self.step_num = 0
-    self.done = True
-    self.hard_mode = hard_mode
-
-  def _get_obs(self):
-    obs = [0]*self.size
-    if self.step_num < len(self.state):
-      obs[self.state[self.step_num]] = 1
-    return np.array(obs, dtype=np.float32)
-
-  def reset(self, seed=None, options=None):
-    super().reset(seed=seed)
-    self.state = np.random.randint(0, self.size, size=self.game_length)
-    self.step_num = 0
-    self.done = False
-    return self._get_obs(), {}
-
-  def step(self, action):
-    target = ((action + self.step_num) % self.size) if self.hard_mode else action
-    reward = int(target == self.state[self.step_num])
-    self.step_num += 1
-    if not reward:
-      self.done = True
-    return self._get_obs(), reward, self.done, self.step_num >= self.game_length, {}
-
-register(
-  id="PressTheLightUpButton-v0",
-  entry_point="examples.rl.lightupbutton:PressTheLightUpButton",
-  max_episode_steps=None,
-)
@@ -115,7 +115,7 @@ if __name__ == "__main__":

  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
    if not args.fakeweights:
-      default_weights_url = 'https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors'
+      default_weights_url = 'https://huggingface.co/sd2-community/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors'
      weights_fn = args.weights_fn
      if not weights_fn:
        weights_url = args.weights_url if args.weights_url else default_weights_url
@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-#inspired by https://github.com/Matuzas77/MNIST-0.17/blob/master/MNIST_final_solution.ipynb
-import sys
-import numpy as np
-from tinygrad.nn.state import get_parameters
-from tinygrad.tensor import Tensor
-from tinygrad.nn import BatchNorm2d, optim
-from tinygrad.helpers import getenv
-from extra.datasets import fetch_mnist
-from extra.augment import augment_img
-from extra.training import train, evaluate
-GPU = getenv("GPU")
-QUICK = getenv("QUICK")
-DEBUG = getenv("DEBUG")
-
-class SqueezeExciteBlock2D:
-  def __init__(self, filters):
-    self.filters = filters
-    self.weight1 = Tensor.scaled_uniform(self.filters, self.filters//32)
-    self.bias1 = Tensor.scaled_uniform(1,self.filters//32)
-    self.weight2 = Tensor.scaled_uniform(self.filters//32, self.filters)
-    self.bias2 = Tensor.scaled_uniform(1, self.filters)
-
-  def __call__(self, input):
-    se = input.avg_pool2d(kernel_size=(input.shape[2], input.shape[3])) #GlobalAveragePool2D
-    se = se.reshape(shape=(-1, self.filters))
-    se = se.dot(self.weight1) + self.bias1
-    se = se.relu()
-    se = se.dot(self.weight2) + self.bias2
-    se = se.sigmoid().reshape(shape=(-1,self.filters,1,1)) #for broadcasting
-    se = input.mul(se)
-    return se
-
-class ConvBlock:
-  def __init__(self, h, w, inp, filters=128, conv=3):
-    self.h, self.w = h, w
-    self.inp = inp
-    #init weights
-    self.cweights = [Tensor.scaled_uniform(filters, inp if i==0 else filters, conv, conv) for i in range(3)]
-    self.cbiases = [Tensor.scaled_uniform(1, filters, 1, 1) for i in range(3)]
-    #init layers
-    self._bn = BatchNorm2d(128)
-    self._seb = SqueezeExciteBlock2D(filters)
-
-  def __call__(self, input):
-    x = input.reshape(shape=(-1, self.inp, self.w, self.h))
-    for cweight, cbias in zip(self.cweights, self.cbiases):
-      x = x.pad(padding=[1,1,1,1]).conv2d(cweight).add(cbias).relu()
-    x = self._bn(x)
-    x = self._seb(x)
-    return x
-
-class BigConvNet:
-  def __init__(self):
-    self.conv = [ConvBlock(28,28,1), ConvBlock(28,28,128), ConvBlock(14,14,128)]
-    self.weight1 = Tensor.scaled_uniform(128,10)
-    self.weight2 = Tensor.scaled_uniform(128,10)
-
-  def parameters(self):
-    if DEBUG: #keeping this for a moment
-      pars = [par for par in get_parameters(self) if par.requires_grad]
-      no_pars = 0
-      for par in pars:
-        print(par.shape)
-        no_pars += np.prod(par.shape)
-      print('no of parameters', no_pars)
-      return pars
-    else:
-      return get_parameters(self)
-
-  def save(self, filename):
-    with open(filename+'.npy', 'wb') as f:
-      for par in get_parameters(self):
-        #if par.requires_grad:
-        np.save(f, par.numpy())
-
-  def load(self, filename):
-    with open(filename+'.npy', 'rb') as f:
-      for par in get_parameters(self):
-        #if par.requires_grad:
-        try:
-          par.numpy()[:] = np.load(f)
-          if GPU:
-            par.gpu()
-        except:
-          print('Could not load parameter')
-
-  def forward(self, x):
-    x = self.conv[0](x)
-    x = self.conv[1](x)
-    x = x.avg_pool2d(kernel_size=(2,2))
-    x = self.conv[2](x)
-    x1 = x.avg_pool2d(kernel_size=(14,14)).reshape(shape=(-1,128)) #global
-    x2 = x.max_pool2d(kernel_size=(14,14)).reshape(shape=(-1,128)) #global
-    xo = x1.dot(self.weight1) + x2.dot(self.weight2)
-    return xo
-
-
-if __name__ == "__main__":
-  lrs = [1e-4, 1e-5] if QUICK else [1e-3, 1e-4, 1e-5, 1e-5]
-  epochss = [2, 1] if QUICK else [13, 3, 3, 1]
-  BS = 32
-
-  lmbd = 0.00025
-  lossfn = lambda out,y: out.sparse_categorical_crossentropy(y) + lmbd*(model.weight1.abs() + model.weight2.abs()).sum()
-  X_train, Y_train, X_test, Y_test = fetch_mnist()
-  X_train = X_train.reshape(-1, 28, 28).astype(np.uint8)
-  X_test = X_test.reshape(-1, 28, 28).astype(np.uint8)
-  steps = len(X_train)//BS
-  np.random.seed(1337)
-  if QUICK:
-    steps = 1
-    X_test, Y_test = X_test[:BS], Y_test[:BS]
-
-  model = BigConvNet()
-
-  if len(sys.argv) > 1:
-    try:
-      model.load(sys.argv[1])
-      print('Loaded weights "'+sys.argv[1]+'", evaluating...')
-      evaluate(model, X_test, Y_test, BS=BS)
-    except:
-      print('could not load weights "'+sys.argv[1]+'".')
-
-  if GPU:
-    params = get_parameters(model)
-    [x.gpu_() for x in params]
-
-  for lr, epochs in zip(lrs, epochss):
-    optimizer = optim.Adam(model.parameters(), lr=lr)
-    for epoch in range(1,epochs+1):
-      #first epoch without augmentation
-      X_aug = X_train if epoch == 1 else augment_img(X_train)
-      train(model, X_aug, Y_train, optimizer, steps=steps, lossfn=lossfn, BS=BS)
-      accuracy = evaluate(model, X_test, Y_test, BS=BS)
-      model.save(f'examples/checkpoint{accuracy * 1e6:.0f}')
@@ -1,17 +0,0 @@
-from tinygrad.tensor import Tensor
-from tinygrad.nn import Conv2d, BatchNorm2d
-from tinygrad.nn.state import get_parameters
-
-if __name__ == "__main__":
-  with Tensor.train():
-
-    BS, C1, H, W = 4, 16, 224, 224
-    C2, K, S, P = 64, 7, 2, 1
-
-    x = Tensor.uniform(BS, C1, H, W)
-    conv = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
-    bn = BatchNorm2d(C2, track_running_stats=False)
-    for t in get_parameters([x, conv, bn]): t.realize()
-
-    print("running network")
-    x.sequential([conv, bn]).numpy()
@@ -1,669 +0,0 @@
-# original implementation: https://github.com/svc-develop-team/so-vits-svc
-from __future__ import annotations
-import sys, logging, time, io, math, argparse, operator, numpy as np
-from functools import partial, reduce
-from pathlib import Path
-from typing import Tuple, Optional, Type
-from tinygrad import nn, dtypes, Tensor
-from tinygrad.helpers import getenv, fetch
-from tinygrad.nn.state import torch_load
-from examples.vits import ResidualCouplingBlock, PosteriorEncoder, Encoder, ResBlock1, ResBlock2, LRELU_SLOPE, sequence_mask, split, get_hparams_from_file, load_checkpoint, weight_norm, HParams
-from examples.sovits_helpers import preprocess
-import soundfile
-
-DEBUG = getenv("DEBUG")
-
-F0_BIN = 256
-F0_MAX = 1100.0
-F0_MIN = 50.0
-F0_MEL_MIN = 1127 * np.log(1 + F0_MIN / 700)
-F0_MEL_MAX = 1127 * np.log(1 + F0_MAX / 700)
-
-class SpeechEncoder:
-  def __init__(self, hidden_dim, model:ContentVec): self.hidden_dim, self.model = hidden_dim, model
-  def encode(self, ): raise NotImplementedError("implement me")
-  @classmethod
-  def load_from_pretrained(cls, checkpoint_path:str, checkpoint_url:str) -> ContentVec:
-    contentvec = ContentVec.load_from_pretrained(checkpoint_path, checkpoint_url)
-    return cls(contentvec)
-
-class ContentVec256L9(SpeechEncoder):
-  def __init__(self, model:ContentVec): super().__init__(hidden_dim=256, model=model)
-  def encode(self, wav: Tensor):
-    feats = wav
-    if len(feats.shape) == 2:  # double channels
-      feats = feats.mean(-1)
-    assert len(feats.shape) == 1, feats.dim()
-    feats = feats.reshape(1, -1)
-    padding_mask = Tensor.zeros_like(feats).cast(dtypes.bool)
-    logits = self.model.extract_features(feats.to(wav.device), padding_mask=padding_mask.to(wav.device), output_layer=9)
-    feats = self.model.final_proj(logits[0])
-    return feats.transpose(1,2)
-
-class ContentVec768L12(SpeechEncoder):
-  def __init__(self, model:ContentVec): super().__init__(hidden_dim=768, model=model)
-  def encode(self, wav: Tensor):
-    feats = wav
-    if len(feats.shape) == 2:  # double channels
-      feats = feats.mean(-1)
-    assert len(feats.shape) == 1, feats.dim()
-    feats = feats.reshape(1, -1)
-    padding_mask = Tensor.zeros_like(feats).cast(dtypes.bool)
-    logits = self.model.extract_features(feats.to(wav.device), padding_mask=padding_mask.to(wav.device), output_layer=12)
-    return logits[0].transpose(1,2)
-
-# original code for contentvec: https://github.com/auspicious3000/contentvec/
-class ContentVec:
-  # self.final_proj dims are hardcoded and depend on fairseq.data.dictionary Dictionary in the checkpoint. This param can't yet be loaded since there is no pickle for it. See with DEBUG=2.
-  # This means that the ContentVec only works with the hubert weights used in all SVC models
-  def __init__(self, cfg: HParams):
-    self.feature_grad_mult, self.untie_final_proj = cfg.feature_grad_mult, cfg.untie_final_proj
-    feature_enc_layers = eval(cfg.conv_feature_layers)
-    self.embed = feature_enc_layers[-1][0]
-    final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
-    self.feature_extractor = ConvFeatureExtractionModel(conv_layers=feature_enc_layers, dropout=0.0, mode=cfg.extractor_mode, conv_bias=cfg.conv_bias)
-    self.post_extract_proj = nn.Linear(self.embed, cfg.encoder_embed_dim) if self.embed != cfg.encoder_embed_dim else None
-    self.encoder = TransformerEncoder(cfg)
-    self.layer_norm = nn.LayerNorm(self.embed)
-    self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim * 1) if self.untie_final_proj else nn.Linear(cfg.encoder_embed_dim, final_dim)
-    self.mask_emb = Tensor.uniform(cfg.encoder_embed_dim, dtype=dtypes.float32)
-    self.label_embs_concat = Tensor.uniform(504, final_dim, dtype=dtypes.float32)
-  def forward_features(self, source, padding_mask):
-    if self.feature_grad_mult > 0:
-      features = self.feature_extractor(source, padding_mask)
-      if self.feature_grad_mult != 1.0: pass  # training: GradMultiply.forward(features, self.feature_grad_mult)
-    else:
-      features = self.feature_extractor(source, padding_mask)
-    return features
-  def forward_padding_mask(self, features, padding_mask):  # replaces original forward_padding_mask for batch inference
-    lengths_org = tilde(padding_mask.cast(dtypes.bool)).cast(dtypes.int64).sum(1)  # ensure its bool for tilde
-    lengths = (lengths_org - 400).float().div(320).floor().cast(dtypes.int64) + 1  # intermediate float to divide
-    padding_mask = lengths_to_padding_mask(lengths)
-    return padding_mask
-  def extract_features(self, source: Tensor, spk_emb:Tensor=None, padding_mask=None, ret_conv=False, output_layer=None, tap=False):
-    features = self.forward_features(source, padding_mask)
-    if padding_mask is not None:
-      padding_mask = self.forward_padding_mask(features, padding_mask)
-    features = features.transpose(1, 2)
-    features = self.layer_norm(features)
-    if self.post_extract_proj is not None:
-      features = self.post_extract_proj(features)
-    x, _ = self.encoder(features, spk_emb, padding_mask=padding_mask, layer=(None if output_layer is None else output_layer - 1), tap=tap)
-    res = features if ret_conv else x
-    return res, padding_mask
-  @classmethod
-  def load_from_pretrained(cls, checkpoint_path:str, checkpoint_url:str) -> ContentVec:
-    fetch(checkpoint_url, checkpoint_path)
-    cfg = load_fairseq_cfg(checkpoint_path)
-    enc = cls(cfg.model)
-    _ = load_checkpoint_enc(checkpoint_path, enc, None)
-    logging.debug(f"{cls.__name__}: Loaded model with cfg={cfg}")
-    return enc
-
-class TransformerEncoder:
-  def __init__(self, cfg: HParams):
-    def make_conv() -> nn.Conv1d:
-      layer = nn.Conv1d(self.embedding_dim, self.embedding_dim, kernel_size=cfg.conv_pos, padding=cfg.conv_pos // 2, groups=cfg.conv_pos_groups)
-      std = std = math.sqrt(4 / (cfg.conv_pos * self.embedding_dim))
-      layer.weight, layer.bias = (Tensor.normal(*layer.weight.shape, std=std)), (Tensor.zeros(*layer.bias.shape))
-      # for training: layer.weights need to be weight_normed
-      return layer
-    self.dropout, self.embedding_dim, self.layer_norm_first, self.layerdrop, self.num_layers, self.num_layers_1 = cfg.dropout, cfg.encoder_embed_dim, cfg.layer_norm_first, cfg.encoder_layerdrop, cfg.encoder_layers, cfg.encoder_layers_1
-    self.pos_conv, self.pos_conv_remove = [make_conv()], (1 if cfg.conv_pos % 2 == 0 else 0)
-    self.layers = [
-      TransformerEncoderLayer(self.embedding_dim, cfg.encoder_ffn_embed_dim, cfg.encoder_attention_heads, self.dropout, cfg.attention_dropout, cfg.activation_dropout, cfg.activation_fn, self.layer_norm_first, cond_layer_norm=(i >= cfg.encoder_layers))
-      for i in range(cfg.encoder_layers + cfg.encoder_layers_1)
-      ]
-    self.layer_norm = nn.LayerNorm(self.embedding_dim)
-    self.cond_layer_norm = CondLayerNorm(self.embedding_dim) if cfg.encoder_layers_1 > 0 else None
-    # training: apply init_bert_params
-  def __call__(self, x, spk_emb, padding_mask=None, layer=None, tap=False):
-    x, layer_results = self.extract_features(x, spk_emb, padding_mask, layer, tap)
-    if self.layer_norm_first and layer is None:
-      x = self.cond_layer_norm(x, spk_emb) if (self.num_layers_1 > 0) else self.layer_norm(x)
-    return x, layer_results
-  def extract_features(self, x: Tensor, spk_emb: Tensor, padding_mask=None, tgt_layer=None, tap=False):
-    if tgt_layer is not None:  # and not self.training
-      assert tgt_layer >= 0 and tgt_layer < len(self.layers)
-    if padding_mask is not None:
-      # x[padding_mask] = 0
-      assert padding_mask.shape == x.shape[:len(padding_mask.shape)]  # first few dims of x must match padding_mask
-      tmp_mask = padding_mask.unsqueeze(-1).repeat((1, 1, x.shape[-1]))
-      tmp_mask = tilde(tmp_mask.cast(dtypes.bool))
-      x = tmp_mask.where(x, 0)
-    x_conv = self.pos_conv[0](x.transpose(1,2))
-    if self.pos_conv_remove > 0: x_conv = x_conv[:, :, : -self.pos_conv_remove]
-    x_conv = x_conv.gelu().transpose(1, 2)
-    x = (x + x_conv).transpose(0, 1)  # B x T x C -> T x B x C
-    if not self.layer_norm_first: x = self.layer_norm(x)
-    x = x.dropout(p=self.dropout)
-    layer_results = []
-    r = None
-    for i, layer in enumerate(self.layers):
-      if i < self.num_layers:  # if (not self.training or (dropout_probability > self.layerdrop)) and (i < self.num_layers):
-        assert layer.cond_layer_norm == False
-        x = layer(x, self_attn_padding_mask=padding_mask, need_weights=False)
-        if tgt_layer is not None or tap:
-          layer_results.append(x.transpose(0, 1))
-      if i>= self.num_layers:
-        assert layer.cond_layer_norm == True
-        x = layer(x, emb=spk_emb, self_attn_padding_mask=padding_mask, need_weights=False)
-      if i == tgt_layer:
-        r = x
-        break
-    if r is not None:
-      x = r
-    x = x.transpose(0, 1)  # T x B x C -> B x T x C
-    return x, layer_results
-
-class TransformerEncoderLayer:
-  def __init__(self, embedding_dim=768.0, ffn_embedding_dim=3072.0, num_attention_heads=8.0, dropout=0.1, attention_dropout=0.1, activation_dropout=0.1, activation_fn="relu", layer_norm_first=False, cond_layer_norm=False):
-    def get_activation_fn(activation):
-      if activation == "relu": return Tensor.relu
-      if activation == "gelu": return Tensor.gelu
-      else: raise RuntimeError(f"activation function={activation} is not forseen")
-    self.embedding_dim, self.dropout, self.activation_dropout, self.layer_norm_first, self.num_attention_heads, self.cond_layer_norm, self.activation_fn = embedding_dim, dropout, activation_dropout, layer_norm_first, num_attention_heads, cond_layer_norm, get_activation_fn(activation_fn)
-    self.self_attn = MultiHeadAttention(self.embedding_dim, self.num_attention_heads)
-    self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim) if not cond_layer_norm else CondLayerNorm(self.embedding_dim)
-    self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
-    self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
-    self.final_layer_norm = nn.LayerNorm(self.embedding_dim) if not cond_layer_norm else CondLayerNorm(self.embedding_dim)
-  def __call__(self, x:Tensor, self_attn_mask:Tensor=None, self_attn_padding_mask:Tensor=None, emb:Tensor=None, need_weights=False):
-    #self_attn_padding_mask = self_attn_padding_mask.reshape(x.shape[0], 1, 1, self_attn_padding_mask.shape[1]).expand(-1, self.num_attention_heads, -1, -1).reshape(x.shape[0] * self.num_attention_heads, 1, self_attn_padding_mask.shape[1]) if self_attn_padding_mask is not None else None
-    assert self_attn_mask is None and self_attn_padding_mask is not None
-    residual = x
-    if self.layer_norm_first:
-      x = self.self_attn_layer_norm(x) if not self.cond_layer_norm else self.self_attn_layer_norm(x, emb)
-      x = self.self_attn(x=x, mask=self_attn_padding_mask)
-      x = x.dropout(self.dropout)
-      x = residual + x
-      x = self.final_layer_norm(x) if not self.cond_layer_norm else self.final_layer_norm(x, emb)
-      x = self.activation_fn(self.fc1(x))
-      x = x.dropout(self.activation_dropout)
-      x = self.fc2(x)
-      x = x.dropout(self.dropout)
-      x = residual + x
-    else:
-      x = self.self_attn(x=x, mask=self_attn_padding_mask)
-      x = x.dropout(self.dropout)
-      x = residual + x
-      x = self.self_attn_layer_norm(x) if not self.cond_layer_norm else self.self_attn_layer_norm(x, emb)
-      residual = x
-      x = self.activation_fn(self.fc1(x))
-      x = x.dropout(self.activation_dropout)
-      x = self.fc2(x)
-      x = x.dropout(self.dropout)
-      x = residual + x
-      x = self.final_layer_norm(x) if not self.cond_layer_norm else self.final_layer_norm(x, emb)
-    return x
-
-class MultiHeadAttention:
-  def __init__(self, n_state, n_head):
-    self.n_state, self.n_head = n_state, n_head
-    self.q_proj, self.k_proj, self.v_proj, self.out_proj = [nn.Linear(n_state, n_state) for _ in range(4)]
-  def __call__(self, x:Tensor, xa:Optional[Tensor]=None, mask:Optional[Tensor]=None):
-    x = x.transpose(0,1)  # TxBxC -> BxTxC
-    q, k, v = self.q_proj(x), self.k_proj(xa or x), self.v_proj(xa or x)
-    q, k, v = [x.reshape(*q.shape[:2], self.n_head, -1) for x in (q, k, v)]
-    wv = Tensor.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), None).transpose(1, 2).reshape(*x.shape[:2], -1)
-    ret =  self.out_proj(wv).transpose(0,1)  # BxTxC -> TxBxC
-    return ret
-
-class ConvFeatureExtractionModel:
-  def __init__(self, conv_layers, dropout=.0, mode="default", conv_bias=False):
-    assert mode in {"default", "group_norm_masked", "layer_norm"}
-    def block(n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False):
-      def make_conv():
-        conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
-        conv.weight = Tensor.kaiming_normal(*conv.weight.shape)
-        return conv
-      assert (is_layer_norm and is_group_norm) == False, "layer norm and group norm are exclusive"
-      if is_layer_norm:
-        return [make_conv(), partial(Tensor.dropout, p=dropout),[partial(Tensor.transpose, dim0=-2, dim1=-1), nn.LayerNorm(dim, elementwise_affine=True), partial(Tensor.transpose, dim0=-2, dim1=-1)], Tensor.gelu]
-      elif is_group_norm and mode == "default":
-        return [make_conv(), partial(Tensor.dropout, p=dropout), nn.GroupNorm(dim, dim, affine=True), Tensor.gelu]
-      elif is_group_norm and mode == "group_norm_masked":
-        return [make_conv(), partial(Tensor.dropout, p=dropout), GroupNormMasked(dim, dim, affine=True), Tensor.gelu]
-      else:
-        return [make_conv(), partial(Tensor.dropout, p=dropout), Tensor.gelu]
-    in_d, self.conv_layers, self.mode = 1, [], mode
-    for i, cl in enumerate(conv_layers):
-      assert len(cl) == 3, "invalid conv definition: " + str(cl)
-      (dim, k, stride) = cl
-      if i == 0: self.cl = cl
-      self.conv_layers.append(block(in_d, dim, k, stride, is_layer_norm=(mode == "layer_norm"), is_group_norm=((mode == "default" or mode == "group_norm_masked") and i == 0), conv_bias=conv_bias))
-      in_d = dim
-  def __call__(self, x:Tensor, padding_mask:Tensor):
-    x = x.unsqueeze(1)  # BxT -> BxCxT
-    if self.mode == "group_norm_masked":
-      if padding_mask is not None:
-        _, k, stride = self.cl
-        lengths_org = tilde(padding_mask.cast(dtypes.bool)).cast(dtypes.int64).sum(1)  # ensure padding_mask is bool for tilde
-        lengths = (((lengths_org - k) / stride) + 1).floor().cast(dtypes.int64)
-        padding_mask = tilde(lengths_to_padding_mask(lengths)).cast(dtypes.int64)  # lengths_to_padding_mask returns bool tensor
-      x = self.conv_layers[0][0](x)  # padding_mask is numeric
-      x = self.conv_layers[0][1](x)
-      x = self.conv_layers[0][2](x, padding_mask)
-      x = self.conv_layers[0][3](x)
-    else:
-      x = x.sequential(self.conv_layers[0])  # default
-    for _, conv in enumerate(self.conv_layers[1:], start=1):
-      conv = reduce(lambda a,b: operator.iconcat(a,b if isinstance(b, list) else [b]), conv, [])  # flatten
-      x = x.sequential(conv)
-    return x
-
-class CondLayerNorm:  # https://github.com/auspicious3000/contentvec/blob/main/contentvec/modules/cond_layer_norm.py#L10
-  def __init__(self, dim_last, eps=1e-5, dim_spk=256, elementwise_affine=True):
-    self.dim_last, self.eps, self.dim_spk, self.elementwise_affine = dim_last, eps, dim_spk, elementwise_affine
-    if self.elementwise_affine:
-      self.weight_ln = nn.Linear(self.dim_spk, self.dim_last, bias=False)
-      self.bias_ln = nn.Linear(self.dim_spk, self.dim_last, bias=False)
-      self.weight_ln.weight, self.bias_ln.weight = (Tensor.ones(*self.weight_ln.weight.shape)), (Tensor.zeros(*self.bias_ln.weight.shape))
-  def __call__(self, x: Tensor, spk_emb: Tensor):
-    axis = tuple(-1-i for i in range(len(x.shape[1:])))
-    x = x.layernorm(axis=axis, eps=self.eps)
-    if not self.elementwise_affine: return x
-    weights, bias = self.weight_ln(spk_emb), self.bias_ln(spk_emb)
-    return weights * x + bias
-
-class GroupNormMasked:  # https://github.com/auspicious3000/contentvec/blob/d746688a32940f4bee410ed7c87ec9cf8ff04f74/contentvec/modules/fp32_group_norm.py#L16
-  def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
-    self.num_groups, self.num_channels, self.eps, self.affine = num_groups, num_channels, eps, affine
-    self.weight, self.bias = (Tensor.ones(num_channels)), (Tensor.zeros(num_channels)) if self.affine else (None, None)
-  def __call__(self, x:Tensor, mask:Tensor):
-    bsz, n_c, length = x.shape
-    assert n_c % self.num_groups == 0
-    x = x.reshape(bsz, self.num_groups, n_c // self.num_groups, length)
-    if mask is None: mask = Tensor.ones_like(x)
-    else: mask = mask.reshape(bsz, 1, 1, length)
-    x = x * mask
-    lengths = mask.sum(axis=3, keepdim=True)
-    assert x.shape[2] == 1
-    mean_ = x.mean(dim=3, keepdim=True)
-    mean = mean_ * length / lengths
-    var = (((x.std(axis=3, keepdim=True) ** 2) + mean_**2) * length / lengths - mean**2) + self.eps
-    return x.add(-mean).div(var.sqrt()).reshape(bsz, n_c, length).mul(self.weight.reshape(1,-1,1)).add(self.bias.reshape(1,-1,1))
-
-class Synthesizer:
-  def __init__(self, spec_channels, segment_size, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels, ssl_dim, n_speakers, sampling_rate=44100, vol_embedding=False, n_flow_layer=4, **kwargs):
-    self.spec_channels, self.inter_channels, self.hidden_channels, self.filter_channels, self.n_heads, self.n_layers, self.kernel_size, self.p_dropout, self.resblock, self.resblock_kernel_sizes, self.resblock_dilation_sizes, self.upsample_rates, self.upsample_initial_channel, self.upsample_kernel_sizes, self.segment_size, self.n_speakers, self.gin_channels, self.vol_embedding = spec_channels, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, segment_size, n_speakers, gin_channels, vol_embedding
-    self.emb_g = nn.Embedding(n_speakers, gin_channels)
-    if vol_embedding: self.emb_vol = nn.Linear(1, hidden_channels)
-    self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
-    self.enc_p = TextEncoder(inter_channels, hidden_channels, kernel_size, n_layers, filter_channels=filter_channels, n_heads=n_heads, p_dropout=p_dropout)
-    self.dec = Generator(sampling_rate, inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels)
-    self.emb_uv = nn.Embedding(vocab_size=2, embed_size=hidden_channels)
-  def infer(self, c:Tensor, f0:Tensor, uv:Tensor, g:Tensor=None, noise_scale=0.35, seed=52468, vol=None) -> Tuple[Tensor, Tensor]:
-    Tensor.manual_seed(getenv('SEED', seed))
-    c_lengths = (Tensor.ones([c.shape[0]]) * c.shape[-1]).to(c.device)
-    if len(g.shape) == 1: g = g.unsqueeze(0)
-    g = self.emb_g(g).transpose(1, 2)
-    x_mask = sequence_mask(c_lengths, c.shape[2]).unsqueeze(1).cast(c.dtype)
-    vol = self.emb_vol(vol[:,:,None]).transpose(1,2) if vol is not None and self.vol_embedding else 0
-    x = self.pre(c) * x_mask + self.emb_uv(uv.cast(dtypes.int64)).transpose(1, 2) + vol
-    z_p, _, _, c_mask = self.enc_p.forward(x, x_mask, f0=self._f0_to_coarse(f0), noise_scale=noise_scale)
-    z = self.flow.forward(z_p, c_mask, g=g, reverse=True)
-    o = self.dec.forward(z * c_mask, g=g, f0=f0)
-    return o,f0
-  def _f0_to_coarse(self, f0 : Tensor):
-    f0_mel = 1127 * (1 + f0 / 700).log()
-    a = (F0_BIN - 2) / (F0_MEL_MAX - F0_MEL_MIN)
-    b = F0_MEL_MIN * a - 1.
-    f0_mel = (f0_mel > 0).where(f0_mel * a - b, f0_mel)
-    f0_coarse = f0_mel.ceil().cast(dtype=dtypes.int64)
-    f0_coarse = f0_coarse * (f0_coarse > 0)
-    f0_coarse = f0_coarse + ((f0_coarse < 1) * 1)
-    f0_coarse = f0_coarse * (f0_coarse < F0_BIN)
-    f0_coarse = f0_coarse + ((f0_coarse >= F0_BIN) * (F0_BIN - 1))
-    return f0_coarse
-  @classmethod
-  def load_from_pretrained(cls, config_path:str, config_url:str, weights_path:str, weights_url:str) -> Synthesizer:
-    fetch(config_url, config_path)
-    hps = get_hparams_from_file(config_path)
-    fetch(weights_url, weights_path)
-    net_g = cls(hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model)
-    _ = load_checkpoint(weights_path, net_g, None, skip_list=["f0_decoder"])
-    logging.debug(f"{cls.__name__}:Loaded model with hps: {hps}")
-    return net_g, hps
-
-class TextEncoder:
-  def __init__(self, out_channels, hidden_channels, kernel_size, n_layers, gin_channels=0, filter_channels=None, n_heads=None, p_dropout=None):
-    self.out_channels, self.hidden_channels, self.kernel_size, self.n_layers, self.gin_channels = out_channels, hidden_channels, kernel_size, n_layers, gin_channels
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.f0_emb = nn.Embedding(256, hidden_channels)  # n_vocab = 256
-    self.enc_ = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
-  def forward(self, x, x_mask, f0=None, noise_scale=1):
-    x = x + self.f0_emb(f0).transpose(1, 2)
-    x = self.enc_.forward(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-    m, logs = split(stats, self.out_channels, dim=1)
-    z = (m + randn_like(m) * logs.exp() * noise_scale) * x_mask
-    return z, m, logs, x_mask
-
-class Upsample:
-  def __init__(self, scale_factor):
-    assert scale_factor % 1 == 0, "Only integer scale factor allowed."
-    self.scale = int(scale_factor)
-  def forward(self, x:Tensor):
-    repeats = tuple([1] * len(x.shape) + [self.scale])
-    new_shape = (*x.shape[:-1], x.shape[-1] * self.scale)
-    return x.unsqueeze(-1).repeat(repeats).reshape(new_shape)
-
-class SineGen:
-  def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=0.003, voice_threshold=0, flag_for_pulse=False):
-    self.sine_amp, self.noise_std, self.harmonic_num, self.sampling_rate, self.voiced_threshold, self.flag_for_pulse = sine_amp, noise_std, harmonic_num, samp_rate, voice_threshold, flag_for_pulse
-    self.dim = self.harmonic_num + 1
-  def _f02uv(self, f0): return (f0 > self.voiced_threshold).float()  #generate uv signal
-  def _f02sine(self, f0_values):
-    def padDiff(x : Tensor): return (x.pad((0,0,-1,1)) - x).pad((0,0,0,-1))
-    def mod(x: Tensor, n: int) -> Tensor: return x - n * x.div(n).floor()  # this is what the % operator does in pytorch.
-    rad_values = mod((f0_values / self.sampling_rate) , 1)  # convert to F0 in rad
-    rand_ini = Tensor.rand(f0_values.shape[0], f0_values.shape[2], device=f0_values.device)  # initial phase noise
-
-    #rand_ini[:, 0] = 0
-    m = Tensor.ones(f0_values.shape[0]).unsqueeze(1).pad((0,f0_values.shape[2]-1,0,0)).cast(dtypes.bool)
-    m = tilde(m)
-    rand_ini = m.where(rand_ini, 0)
-
-    #rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-    tmp = rad_values[:, 0, :] + rand_ini
-    m = Tensor.ones(tmp.shape).pad((0,0,0,rad_values.shape[1]-1,0)).cast(dtypes.bool)
-    m = tilde(m)
-    tmp = tmp.unsqueeze(1).pad((0,0,0,rad_values.shape[1]-1,0))
-    rad_values = m.where(rad_values, tmp)
-
-    tmp_over_one = mod(rad_values.cumsum(1), 1)
-    tmp_over_one_idx = padDiff(tmp_over_one) < 0
-    cumsum_shift = Tensor.zeros_like(rad_values)
-
-    #cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-    tmp_over_one_idx = (tmp_over_one_idx * -1.0).pad((0,0,1,0))
-    cumsum_shift = tmp_over_one_idx
-
-    sines = ((rad_values + cumsum_shift).cumsum(1) * 2 * np.pi).sin()
-    return sines
-  def forward(self, f0, upp=None):
-    fn = f0.mul(Tensor([[range(1, self.harmonic_num + 2)]], dtype=dtypes.float32).to(f0.device))
-    sine_waves = self._f02sine(fn) * self.sine_amp  #generate sine waveforms
-    uv = self._f02uv(f0)  # generate uv signal
-    noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-    noise = noise_amp * randn_like(sine_waves)
-    sine_waves = sine_waves * uv + noise
-    return sine_waves, uv, noise
-
-class SourceHnNSF:
-  def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshold=0):
-    self.sine_amp, self.noise_std = sine_amp, add_noise_std
-    self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshold)
-    self.l_linear = nn.Linear(harmonic_num + 1, 1)
-  def forward(self, x, upp=None):
-    sine_waves, uv, _ = self.l_sin_gen.forward(x, upp)
-    sine_merge = self.l_linear(sine_waves.cast(self.l_linear.weight.dtype)).tanh()
-    noise = randn_like(uv) * self.sine_amp / 3
-    return sine_merge, noise, uv
-
-# most of the hifigan in standard vits is reused here, but need to upsample and construct harmonic source from f0
-class Generator:
-  def __init__(self, sampling_rate, inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels):
-    self.sampling_rate, self.inter_channels, self.resblock, self.resblock_kernel_sizes, self.resblock_dilation_sizes, self.upsample_rates, self.upsample_initial_channel, self.upsample_kernel_sizes, self.gin_channels = sampling_rate, inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels
-    self.num_kernels, self.num_upsamples = len(resblock_kernel_sizes), len(upsample_rates)
-    self.conv_pre = nn.Conv1d(inter_channels, upsample_initial_channel, 7, 1, padding=3)
-    self.f0_upsamp = Upsample(scale_factor=np.prod(upsample_rates))
-    self.m_source = SourceHnNSF(sampling_rate, harmonic_num=8)
-    resblock = ResBlock1 if resblock == '1' else ResBlock2
-    self.ups, self.noise_convs, self.resblocks = [], [], []
-    for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-      c_cur = upsample_initial_channel//(2**(i+1))
-      self.ups.append(nn.ConvTranspose1d(upsample_initial_channel//(2**i), c_cur, k, u, padding=(k-u)//2))
-      stride_f0 = int(np.prod(upsample_rates[i + 1:]))
-      self.noise_convs.append(nn.Conv1d(1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2) if (i + 1 < len(upsample_rates)) else nn.Conv1d(1, c_cur, kernel_size=1))
-    for i in range(len(self.ups)):
-      ch = upsample_initial_channel // (2 ** (i + 1))
-      for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-        self.resblocks.append(resblock(ch, k, d))
-    self.conv_post = nn.Conv1d(ch, 1, 7, 1, padding=3)
-    if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-    self.upp = np.prod(upsample_rates)
-  def forward(self, x, f0, g=None):
-    f0 = self.f0_upsamp.forward(f0[:, None]).transpose(1, 2)  # bs,n,t
-    har_source, _, _ = self.m_source.forward(f0, self.upp)
-    har_source = har_source.transpose(1, 2)
-    x = self.conv_pre(x)
-    if g is not None:  x = x + self.cond(g)
-    for i in range(self.num_upsamples):
-      x, xs = self.ups[i](x.leaky_relu(LRELU_SLOPE)), None
-      x_source = self.noise_convs[i](har_source)
-      x = x + x_source
-      for j in range(self.num_kernels):
-        if xs is None: xs = self.resblocks[i * self.num_kernels + j].forward(x)
-        else: xs += self.resblocks[i * self.num_kernels + j].forward(x)
-      x = xs / self.num_kernels
-    return self.conv_post(x.leaky_relu()).tanh()
-
-# **** helpers ****
-
-def randn_like(x:Tensor) -> Tensor: return Tensor.randn(*x.shape, dtype=x.dtype).to(device=x.device)
-
-def tilde(x: Tensor) -> Tensor:
-  if x.dtype == dtypes.bool: return (1 - x).cast(dtypes.bool)
-  return (x + 1) * -1  # this seems to be what the ~ operator does in pytorch for non bool
-
-def lengths_to_padding_mask(lens:Tensor) -> Tensor:
-  bsz, max_lens = lens.shape[0], lens.max().numpy().item()
-  mask = Tensor.arange(max_lens).to(lens.device).reshape(1, max_lens)
-  mask = mask.expand(bsz, -1) >= lens.reshape(bsz, 1).expand(-1, max_lens)
-  return mask.cast(dtypes.bool)
-
-def repeat_expand_2d_left(content, target_len): # content : [h, t]
-  src_len = content.shape[-1]
-  temp = np.arange(src_len+1) * target_len / src_len
-  current_pos, cols = 0, []
-  for i in range(target_len):
-    if i >= temp[current_pos+1]:
-      current_pos += 1
-    cols.append(content[:, current_pos])
-  return Tensor.stack(*cols).transpose(0, 1)
-
-def load_fairseq_cfg(checkpoint_path):
-  assert Path(checkpoint_path).is_file()
-  state = torch_load(checkpoint_path)
-  cfg = state["cfg"] if ("cfg" in state and state["cfg"] is not None) else None
-  if cfg is None: raise RuntimeError(f"No cfg exist in state keys = {state.keys()}")
-  return HParams(**cfg)
-
-def load_checkpoint_enc(checkpoint_path, model: ContentVec, optimizer=None, skip_list=[]):
-  assert Path(checkpoint_path).is_file()
-  start_time = time.time()
-  checkpoint_dict = torch_load(checkpoint_path)
-  saved_state_dict = checkpoint_dict['model']
-  weight_g, weight_v, parent = None, None, None
-  for key, v in saved_state_dict.items():
-    if any(layer in key for layer in skip_list): continue
-    try:
-      obj, skip = model, False
-      for k in key.split('.'):
-        if k.isnumeric(): obj = obj[int(k)]
-        elif isinstance(obj, dict): obj = obj[k]
-        else:
-          if k in ["weight_g", "weight_v"]:
-            parent, skip = obj, True
-            if k == "weight_g": weight_g = v
-            else: weight_v = v
-          if not skip:
-            parent = obj
-            obj = getattr(obj, k)
-      if weight_g and weight_v:
-        setattr(obj, "weight_g", weight_g.numpy())
-        setattr(obj, "weight_v", weight_v.numpy())
-        obj, v = getattr(parent, "weight"), weight_norm(weight_v, weight_g, 0)
-        weight_g, weight_v, parent, skip = None, None, None, False
-      if not skip and obj.shape == v.shape:
-        if "feature_extractor" in key and (isinstance(parent, (nn.GroupNorm, nn.LayerNorm))):  # cast
-          obj.assign(v.to(obj.device).float())
-        else:
-          obj.assign(v.to(obj.device))
-      elif not skip: logging.error(f"MISMATCH SHAPE IN {key}, {obj.shape} {v.shape}")
-    except Exception as e: raise e
-  logging.info(f"Loaded checkpoint '{checkpoint_path}' in {time.time() - start_time:.4f}s")
-  return model, optimizer
-
-def pad_array(arr, target_length):
-  current_length = arr.shape[0]
-  if current_length >= target_length: return arr
-  pad_width = target_length - current_length
-  pad_left = pad_width // 2
-  pad_right = pad_width - pad_left
-  padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
-  return padded_arr
-
-def split_list_by_n(list_collection, n, pre=0):
-  for i in range(0, len(list_collection), n):
-    yield list_collection[i-pre if i-pre>=0 else i: i + n]
-
-def get_sid(spk2id:HParams, speaker:str) -> Tensor:
-  speaker_id = spk2id[speaker]
-  if not speaker_id and type(speaker) is int:
-    if len(spk2id.__dict__) >= speaker: speaker_id = speaker
-  if speaker_id is None: raise RuntimeError(f"speaker={speaker} not in the speaker list")
-  return Tensor([int(speaker_id)], dtype=dtypes.int64).unsqueeze(0)
-
-def get_encoder(ssl_dim) -> Type[SpeechEncoder]:
-  if ssl_dim == 256: return ContentVec256L9
-  if ssl_dim == 768: return ContentVec768L12
-
-#########################################################################################
-# CODE: https://github.com/svc-develop-team/so-vits-svc
-#########################################################################################
-# CONTENTVEC:
-#   CODE: https://github.com/auspicious3000/contentvec
-#   PAPER: https://arxiv.org/abs/2204.09224
-#########################################################################################
-# INSTALLATION: dependencies are for preprocessing and loading/saving audio.
-# pip3 install soundfile librosa praat-parselmouth
-#########################################################################################
-# EXAMPLE USAGE:
-# python3 examples/so_vits_svc.py --model tf2spy --file ~/recording.wav
-#########################################################################################
-# DEMO USAGE (uses audio sample from LJ-Speech):
-# python3 examples/so_vits_svc.py --model saul_goodman
-#########################################################################################
-SO_VITS_SVC_PATH = Path(__file__).parents[1] / "weights/So-VITS-SVC"
-VITS_MODELS = { # config_path, weights_path, config_url, weights_url
-  "saul_goodman" : (SO_VITS_SVC_PATH / "config_saul_gman.json", SO_VITS_SVC_PATH / "pretrained_saul_gman.pth", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/Saul_Goodman_80000/config.json", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/Saul_Goodman_80000/G_80000.pth"),
-  "drake" : (SO_VITS_SVC_PATH / "config_drake.json", SO_VITS_SVC_PATH / "pretrained_drake.pth", "https://huggingface.co/jaspa/so-vits-svc/resolve/main/aubrey/config_aubrey.json", "https://huggingface.co/jaspa/so-vits-svc/resolve/main/aubrey/pretrained_aubrey.pth"),
-  "cartman" : (SO_VITS_SVC_PATH / "config_cartman.json", SO_VITS_SVC_PATH / "pretrained_cartman.pth", "https://huggingface.co/marcoc2/so-vits-svc-4.0-models/resolve/main/EricCartman/config.json", "https://huggingface.co/marcoc2/so-vits-svc-4.0-models/resolve/main/EricCartman/G_10200.pth"),
-  "tf2spy" : (SO_VITS_SVC_PATH / "config_tf2spy.json", SO_VITS_SVC_PATH / "pretrained_tf2spy.pth", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/TF2_spy_60k/config.json", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/TF2_spy_60k/G_60000.pth"),
-  "tf2heavy" : (SO_VITS_SVC_PATH / "config_tf2heavy.json", SO_VITS_SVC_PATH / "pretrained_tf2heavy.pth", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/TF2_heavy_100k/config.json", "https://huggingface.co/Amo/so-vits-svc-4.0_GA/resolve/main/ModelsFolder/TF2_heavy_100k/G_100000.pth"),
-  "lady_gaga" : (SO_VITS_SVC_PATH / "config_gaga.json", SO_VITS_SVC_PATH / "pretrained_gaga.pth", "https://huggingface.co/marcoc2/so-vits-svc-4.0-models/resolve/main/LadyGaga/config.json", "https://huggingface.co/marcoc2/so-vits-svc-4.0-models/resolve/main/LadyGaga/G_14400.pth")
-}
-ENCODER_MODELS = { # weights_path, weights_url
-  "contentvec": (SO_VITS_SVC_PATH / "contentvec_checkpoint.pt", "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt")
-}
-ENCODER_MODEL = "contentvec"
-DEMO_PATH, DEMO_URL = Path(__file__).parents[1] / "temp/LJ037-0171.wav", "https://keithito.com/LJ-Speech-Dataset/LJ037-0171.wav"
-if __name__=="__main__":
-  logging.basicConfig(stream=sys.stdout, level=(logging.INFO if DEBUG < 1 else logging.DEBUG))
-  parser = argparse.ArgumentParser()
-  parser.add_argument("-m", "--model", default=None, help=f"Specify the model to use. All supported models: {VITS_MODELS.keys()}", required=True)
-  parser.add_argument("-f", "--file", default=DEMO_PATH, help=f"Specify the path of the input file")
-  parser.add_argument("--out_dir", default=str(Path(__file__).parents[1] / "temp"), help="Specify the output path.")
-  parser.add_argument("--out_path", default=None, help="Specify the full output path. Overrides the --out_dir and --name parameter.")
-  parser.add_argument("--base_name", default="test", help="Specify the base of the output file name. Default is 'test'.")
-  parser.add_argument("--speaker", default=None, help="If not specified, the first available speaker is chosen. Usually there is only one speaker per model.")
-  parser.add_argument("--noise_scale", default=0.4)
-  parser.add_argument("--tran", default=0.0, help="Pitch shift, supports positive and negative (semitone) values. Default 0.0")
-  parser.add_argument("--pad_seconds", default=0.5)
-  parser.add_argument("--lg_num", default=0.0)
-  parser.add_argument("--clip_seconds", default=0.0)
-  parser.add_argument("--slice_db", default=-40)
-  args = parser.parse_args()
-
-  vits_model = args.model
-  encoder_location, vits_location = ENCODER_MODELS[ENCODER_MODEL], VITS_MODELS[vits_model]
-
-  Tensor.training = False
-  # Get Synthesizer and ContentVec
-  net_g, hps = Synthesizer.load_from_pretrained(vits_location[0], vits_location[2], vits_location[1], vits_location[3])
-  Encoder = get_encoder(hps.model.ssl_dim)
-  encoder = Encoder.load_from_pretrained(encoder_location[0], encoder_location[1])
-
-  # model config args
-  target_sample, spk2id, hop_length, target_sample = hps.data.sampling_rate, hps.spk, hps.data.hop_length, hps.data.sampling_rate
-  vol_embedding = hps.model.vol_embedding if hasattr(hps.data, "vol_embedding") and hps.model.vol_embedding is not None else False
-
-  # args
-  slice_db, clip_seconds, lg_num, pad_seconds, tran, noise_scale, audio_path = args.slice_db, args.clip_seconds, args.lg_num, args.pad_seconds, args.tran, args.noise_scale, args.file
-  speaker = args.speaker if args.speaker is not None else list(hps.spk.__dict__.keys())[0]
-
-  ### Loading audio and slicing ###
-  if audio_path == DEMO_PATH: fetch(DEMO_URL, DEMO_PATH)
-  assert Path(audio_path).is_file() and Path(audio_path).suffix == ".wav"
-  chunks = preprocess.cut(audio_path, db_thresh=slice_db)
-  audio_data, audio_sr = preprocess.chunks2audio(audio_path, chunks)
-
-  per_size = int(clip_seconds * audio_sr)
-  lg_size = int(lg_num * audio_sr)
-
-  ### Infer per slice ###
-  global_frame = 0
-  audio = []
-  for (slice_tag, data) in audio_data:
-    print(f"\n====segment start, {round(len(data) / audio_sr, 3)}s====")
-    length = int(np.ceil(len(data) / audio_sr * target_sample))
-
-    if slice_tag:
-      print("empty segment")
-      _audio = np.zeros(length)
-      audio.extend(list(pad_array(_audio, length)))
-      global_frame += length // hop_length
-      continue
-
-    datas = [data] if per_size == 0 else split_list_by_n(data, per_size, lg_size)
-
-    for k, dat in enumerate(datas):
-      per_length = int(np.ceil(len(dat) / audio_sr * target_sample)) if clip_seconds!=0 else length
-      pad_len = int(audio_sr * pad_seconds)
-      dat = np.concatenate([np.zeros([pad_len]), dat, np.zeros([pad_len])])
-      raw_path = io.BytesIO()
-      soundfile.write(raw_path, dat, audio_sr, format="wav")
-      raw_path.seek(0)
-
-      ### Infer START ###
-      wav, sr = preprocess.load_audiofile(raw_path)
-      wav = preprocess.sinc_interp_resample(wav, sr, target_sample)[0]
-      wav16k, f0, uv = preprocess.get_unit_f0(wav, tran, hop_length, target_sample)
-      sid = get_sid(spk2id, speaker)
-      n_frames = f0.shape[1]
-
-      # ContentVec infer
-      start = time.time()
-      c = encoder.encode(wav16k)
-      c = repeat_expand_2d_left(c.squeeze(0).realize(), f0.shape[1])  # interpolate speech encoding to match f0
-      c = c.unsqueeze(0).realize()
-      enc_time = time.time() - start
-
-      # VITS infer
-      vits_start = time.time()
-      out_audio, f0 = net_g.infer(c, f0=f0, uv=uv, g=sid, noise_scale=noise_scale, vol=None)
-      out_audio = out_audio[0,0].float().realize()
-      vits_time = time.time() - vits_start
-
-      infer_time = time.time() - start
-      logging.info("total infer time:{:.2f}s, speech_enc time:{:.2f}s, vits time:{:.2f}s".format(infer_time, enc_time, vits_time))
-      ### Infer END ###
-
-      out_sr, out_frame = out_audio.shape[-1], n_frames
-      global_frame += out_frame
-      _audio = out_audio.numpy()
-      pad_len = int(target_sample * pad_seconds)
-      _audio = _audio[pad_len:-pad_len]
-      _audio = pad_array(_audio, per_length)
-      audio.extend(list(_audio))
-
-  audio = np.array(audio)
-  out_path = Path(args.out_path or Path(args.out_dir)/f"{args.model}{f'_spk_{speaker}'}_{args.base_name}.wav")
-  out_path.parent.mkdir(parents=True, exist_ok=True)
-  soundfile.write(out_path, audio, target_sample, format="flac")
-  logging.info(f"Saved audio output to {out_path}")
@@ -1,204 +0,0 @@
-import math
-from typing import Optional, Tuple
-from tinygrad import Tensor, dtypes
-import librosa
-import soundfile
-import numpy as np
-import parselmouth
-
-class PMF0Predictor:  # from https://github.com/svc-develop-team/so-vits-svc/
-  def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
-    self.hop_length, self.f0_min, self.f0_max, self.sampling_rate, self.name = hop_length, f0_min, f0_max, sampling_rate, "pm"
-  def interpolate_f0(self,f0):
-    vuv_vector = np.zeros_like(f0, dtype=np.float32)
-    vuv_vector[f0 > 0.0] = 1.0
-    vuv_vector[f0 <= 0.0] = 0.0
-    nzindex = np.nonzero(f0)[0]
-    data = f0[nzindex]
-    nzindex = nzindex.astype(np.float32)
-    time_org = self.hop_length / self.sampling_rate * nzindex
-    time_frame = np.arange(f0.shape[0]) * self.hop_length / self.sampling_rate
-    if data.shape[0] <= 0: return np.zeros(f0.shape[0], dtype=np.float32),vuv_vector
-    if data.shape[0] == 1: return np.ones(f0.shape[0], dtype=np.float32) * f0[0],vuv_vector
-    f0 = np.interp(time_frame, time_org, data, left=data[0], right=data[-1])
-    return f0,vuv_vector
-  def compute_f0(self,wav,p_len=None):
-    x = wav
-    if p_len is None: p_len = x.shape[0]//self.hop_length
-    else: assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
-    time_step = self.hop_length / self.sampling_rate * 1000
-    f0 = parselmouth.Sound(x, self.sampling_rate) \
-                    .to_pitch_ac(time_step=time_step / 1000, voicing_threshold=0.6,pitch_floor=self.f0_min, pitch_ceiling=self.f0_max) \
-                    .selected_array['frequency']
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-      f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-    f0,uv = self.interpolate_f0(f0)
-    return f0
-  def compute_f0_uv(self,wav,p_len=None):
-    x = wav
-    if p_len is None: p_len = x.shape[0]//self.hop_length
-    else: assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
-    time_step = self.hop_length / self.sampling_rate * 1000
-    f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
-      time_step=time_step / 1000, voicing_threshold=0.6,
-      pitch_floor=self.f0_min, pitch_ceiling=self.f0_max).selected_array['frequency']
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-      f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-    f0,uv = self.interpolate_f0(f0)
-    return f0,uv
-
-class Slicer:  # from https://github.com/svc-develop-team/so-vits-svc/
-  def __init__(self, sr: int, threshold: float = -40., min_length: int = 5000, min_interval: int = 300, hop_size: int = 20, max_sil_kept: int = 5000):
-    if not min_length >= min_interval >= hop_size:
-      raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
-    if not max_sil_kept >= hop_size:
-      raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
-    min_interval = sr * min_interval / 1000
-    self.threshold = 10 ** (threshold / 20.)
-    self.hop_size = round(sr * hop_size / 1000)
-    self.win_size = min(round(min_interval), 4 * self.hop_size)
-    self.min_length = round(sr * min_length / 1000 / self.hop_size)
-    self.min_interval = round(min_interval / self.hop_size)
-    self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-  def _apply_slice(self, waveform, begin, end):
-    if len(waveform.shape) > 1: return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
-    else: return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
-  def slice(self, waveform):
-    samples = librosa.to_mono(waveform) if len(waveform.shape) > 1 else waveform
-    if samples.shape[0] <= self.min_length: return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-    rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
-    sil_tags, silence_start, clip_start = [], None, 0
-    for i, rms in enumerate(rms_list):
-      if rms < self.threshold:  # Keep looping while frame is silent.
-        if silence_start is None:  # Record start of silent frames.
-          silence_start = i
-        continue
-      if silence_start is None: continue  # Keep looping while frame is not silent and silence start has not been recorded.
-      # Clear recorded silence start if interval is not enough or clip is too short
-      is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-      need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
-      if not is_leading_silence and not need_slice_middle:
-        silence_start = None
-        continue
-      if i - silence_start <= self.max_sil_kept:  # Need slicing. Record the range of silent frames to be removed.
-        pos = rms_list[silence_start: i + 1].argmin() + silence_start
-        sil_tags.append((0, pos) if silence_start == 0 else (pos, pos))
-        clip_start = pos
-      elif i - silence_start <= self.max_sil_kept * 2:
-        pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
-        pos += i - self.max_sil_kept
-        pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-        pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-        if silence_start == 0:
-          sil_tags.append((0, pos_r))
-          clip_start = pos_r
-        else:
-          sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-          clip_start = max(pos_r, pos)
-      else:
-        pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-        pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-        sil_tags.append((0, pos_r) if silence_start == 0 else (pos_l, pos_r))
-        clip_start = pos_r
-      silence_start = None
-    total_frames = rms_list.shape[0]
-    if silence_start is not None and total_frames - silence_start >= self.min_interval:  # Deal with trailing silence.
-      silence_end = min(total_frames, silence_start + self.max_sil_kept)
-      pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
-      sil_tags.append((pos, total_frames + 1))
-    if len(sil_tags) == 0: return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}  # Apply and return slices.
-    chunks = []
-    if sil_tags[0][0]:
-      chunks.append({"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
-    for i in range(0, len(sil_tags)):
-      if i: chunks.append({"slice": False, "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
-      chunks.append({"slice": True, "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
-    if sil_tags[-1][1] * self.hop_size < len(waveform):
-      chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
-    chunk_dict = {}
-    for i in range(len(chunks)): chunk_dict[str(i)] = chunks[i]
-    return chunk_dict
-
-# sinc_interp_hann audio resampling
-class Resample:
-  def __init__(self, orig_freq:int=16000, new_freq:int=16000, lowpass_filter_width:int=6, rolloff:float=0.99, beta:Optional[float]=None, dtype:Optional[dtypes]=None):
-    self.orig_freq, self.new_freq, self.lowpass_filter_width, self.rolloff, self.beta = orig_freq, new_freq, lowpass_filter_width, rolloff, beta
-    self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq))
-    self.kernel, self.width = self._get_sinc_resample_kernel(dtype) if self.orig_freq != self.new_freq else (None, None)
-  def __call__(self, waveform:Tensor) -> Tensor:
-    if self.orig_freq == self.new_freq: return waveform
-    return self._apply_sinc_resample_kernel(waveform)
-  def _apply_sinc_resample_kernel(self, waveform:Tensor):
-    if not waveform.is_floating_point(): raise TypeError(f"Waveform tensor expected to be of type float, but received {waveform.dtype}.")
-    orig_freq, new_freq = (int(self.orig_freq) // self.gcd), (int(self.new_freq) // self.gcd)
-    shape = waveform.shape
-    waveform = waveform.reshape(-1, shape[-1])  # pack batch
-    num_wavs, length = waveform.shape
-    target_length = int(math.ceil(new_freq * length / orig_freq))
-    waveform = waveform.pad((self.width, self.width + orig_freq))
-    resampled = waveform[:, None].conv2d(self.kernel, stride=orig_freq)
-    resampled = resampled.transpose(1, 2).reshape(num_wavs, -1)
-    resampled = resampled[..., :target_length]
-    resampled = resampled.reshape(shape[:-1] + resampled.shape[-1:])  # unpack batch
-    return resampled
-  def _get_sinc_resample_kernel(self, dtype=None):
-    orig_freq, new_freq = (int(self.orig_freq) // self.gcd), (int(self.new_freq) // self.gcd)
-    if self.lowpass_filter_width <= 0: raise ValueError("Low pass filter width should be positive.")
-    base_freq = min(orig_freq, new_freq)
-    base_freq *= self.rolloff
-    width = math.ceil(self.lowpass_filter_width * orig_freq / base_freq)
-    idx = Tensor.arange(-width, width + orig_freq, dtype=(dtype if dtype is not None else dtypes.float32))[None, None] / orig_freq
-    t = Tensor.arange(0, -new_freq, -1, dtype=dtype)[:, None, None] / new_freq + idx
-    t *= base_freq
-    t = t.clip(-self.lowpass_filter_width, self.lowpass_filter_width)
-    window = (t * math.pi / self.lowpass_filter_width / 2).cos() ** 2
-    t *= math.pi
-    scale = base_freq / orig_freq
-    kernels = Tensor.where(t == 0, Tensor(1.0, dtype=t.dtype).to(t.device), t.sin() / t)
-    kernels *= window * scale
-    if dtype is None: kernels = kernels.cast(dtype=dtypes.float32)
-    return kernels, width
-
-def sinc_interp_resample(x:Tensor, orig_freq:int=16000, new_freq:int=1600, lowpass_filter_width:int=6, rolloff:float=0.99, beta:Optional[float]=None):
-  resamp = Resample(orig_freq, new_freq, lowpass_filter_width, rolloff, beta, x.dtype)
-  return resamp(x)
-
-def cut(audio_path, db_thresh=-30, min_len=5000):
-  audio, sr = librosa.load(audio_path, sr=None)
-  slicer = Slicer(sr=sr, threshold=db_thresh, min_length=min_len)
-  chunks = slicer.slice(audio)
-  return chunks
-
-def chunks2audio(audio_path, chunks):
-  chunks = dict(chunks)
-  audio, sr = load_audiofile(audio_path)
-  if len(audio.shape) == 2 and audio.shape[1] >= 2:
-    audio = audio.mean(0).unsqueeze(0)
-  audio = audio.numpy()[0]
-  result = []
-  for k, v in chunks.items():
-    tag = v["split_time"].split(",")
-    if tag[0] != tag[1]:
-      result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-  return result, sr
-
-def load_audiofile(filepath:str, frame_offset:int=0, num_frames:int=-1, channels_first:bool=True):
-  with soundfile.SoundFile(filepath, "r") as file_:
-    frames = file_._prepare_read(frame_offset, None, num_frames)
-    waveform = file_.read(frames, "float32", always_2d=True)
-    sample_rate = file_.samplerate
-  waveform = Tensor(waveform)
-  if channels_first: waveform = waveform.transpose(0, 1)
-  return waveform, sample_rate
-
-def get_unit_f0(wav:Tensor, tran, hop_length, target_sample, f0_filter=False) -> Tuple[Tensor,Tensor,Tensor]:
-  f0_predictor = PMF0Predictor(hop_length, sampling_rate=target_sample)
-  f0, uv = f0_predictor.compute_f0_uv(wav.numpy())
-  if f0_filter and sum(f0) == 0: raise RuntimeError("No voice detected")
-  f0 = Tensor(f0.astype(np.float32)).float()
-  f0 = (f0 * 2 ** (tran / 12)).unsqueeze(0)
-  uv = Tensor(uv.astype(np.float32)).float().unsqueeze(0)
-  wav16k = sinc_interp_resample(wav[None,:], target_sample, 16000)[0]
-  return wav16k.realize(), f0.realize(), uv.realize()
@@ -9,7 +9,7 @@ from typing import Dict, Any
 from PIL import Image
 import numpy as np
 from tinygrad import Device, GlobalCounters, dtypes, Tensor, TinyJit
-from tinygrad.helpers import Timing, Context, getenv, fetch, colored, tqdm, flatten
+from tinygrad.helpers import Timing, Context, getenv, fetch, colored, tqdm, flatten, profile_marker
 from tinygrad.nn import Conv2d, GroupNorm
 from tinygrad.nn.state import torch_load, load_state_dict, get_state_dict
 from extra.models.clip import Closed, Tokenizer, FrozenOpenClipEmbedder
@@ -266,13 +266,16 @@ if __name__ == "__main__":
  parser.add_argument('--fakeweights', action='store_true', help="Skip loading checkpoints and use fake weights")
  args = parser.parse_args()

+  profile_marker("create model")
  model = StableDiffusion()

-  # load in weights
+  profile_marker("load in weights")
  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
    if not args.fakeweights:
      model_bin = fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt')
-      load_state_dict(model, torch_load(model_bin)['state_dict'], verbose=False, strict=False, realize=False)
+      state_dict = torch_load(model_bin)['state_dict']
+      profile_marker("state dict loaded")
+      load_state_dict(model, state_dict, verbose=False, strict=False, realize=False)

    if args.fp16:
      for k,v in get_state_dict(model).items():
@@ -281,12 +284,13 @@ if __name__ == "__main__":

    Tensor.realize(*get_state_dict(model).values())

-  # run through CLIP to get context
+  profile_marker("run clip (conditional)")
  tokenizer = Tokenizer.ClipTokenizer()
  prompt = Tensor([tokenizer.encode(args.prompt)])
  context = model.cond_stage_model.transformer.text_model(prompt).realize()
  print("got CLIP context", context.shape)

+  profile_marker("run clip (unconditional)")
  prompt = Tensor([tokenizer.encode("")])
  unconditional_context = model.cond_stage_model.transformer.text_model(prompt).realize()
  print("got unconditional CLIP context", unconditional_context.shape)
@@ -310,6 +314,7 @@ if __name__ == "__main__":
  step_times = []
  with Context(BEAM=getenv("LATEBEAM")):
    for index, timestep in (t:=tqdm(list(enumerate(timesteps))[::-1])):
+      profile_marker(f"step {len(timesteps)-index-1}")
      GlobalCounters.reset()
      st = time.perf_counter_ns()
      t.set_description("%3d %3d" % (index, timestep))
@@ -319,24 +324,26 @@ if __name__ == "__main__":
          latent = run(model, unconditional_context, context, latent, Tensor([timestep]), alphas[tid], alphas_prev[tid], Tensor([args.guidance]))
          if args.timing: Device[Device.DEFAULT].synchronize()
      step_times.append((time.perf_counter_ns() - st)*1e-6)
+    # done with diffusion model
    del run
+    del model.model

  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
    min_time = min(step_times)
    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
-  # upsample latent space to image with autoencoder
-  x = model.decode(latent)
+  profile_marker("run decoder") # upsample latent space to image with autoencoder
+  x = model.decode(latent).realize()
  print(x.shape)

-  # save image
+  profile_marker("save image")
  im = Image.fromarray(x.numpy())
  print(f"saving {args.out}")
  im.save(args.out)
  # Open image.
  if not args.noshow: im.show()

-  # validation!
  if args.prompt == default_prompt and args.steps == 6 and args.seed == 0 and args.guidance == 7.5:
+    profile_marker("validate")
    ref_image = Tensor(np.array(Image.open(Path(__file__).parent / "stable_diffusion_seed0.png")))
    distance = (((x.cast(dtypes.float) - ref_image.cast(dtypes.float)) / ref_image.max())**2).mean().item()
    assert distance < 3e-3, colored(f"validation failed with {distance=}", "red")  # higher distance with WINO
@@ -1,104 +0,0 @@
-import traceback
-import time
-from multiprocessing import Process, Queue
-import numpy as np
-from tinygrad.nn.state import get_parameters
-from tinygrad.nn import optim
-from tinygrad.helpers import getenv, trange
-from tinygrad.tensor import Tensor
-from extra.datasets import fetch_cifar
-from extra.models.efficientnet import EfficientNet
-
-class TinyConvNet:
-  def __init__(self, classes=10):
-    conv = 3
-    inter_chan, out_chan = 8, 16   # for speed
-    self.c1 = Tensor.uniform(inter_chan,3,conv,conv)
-    self.c2 = Tensor.uniform(out_chan,inter_chan,conv,conv)
-    self.l1 = Tensor.uniform(out_chan*6*6, classes)
-
-  def forward(self, x):
-    x = x.conv2d(self.c1).relu().max_pool2d()
-    x = x.conv2d(self.c2).relu().max_pool2d()
-    x = x.reshape(shape=[x.shape[0], -1])
-    return x.dot(self.l1)
-
-if __name__ == "__main__":
-  IMAGENET = getenv("IMAGENET")
-  classes = 1000 if IMAGENET else 10
-
-  TINY = getenv("TINY")
-  TRANSFER = getenv("TRANSFER")
-  if TINY:
-    model = TinyConvNet(classes)
-  elif TRANSFER:
-    model = EfficientNet(getenv("NUM", 0), classes, has_se=True)
-    model.load_from_pretrained()
-  else:
-    model = EfficientNet(getenv("NUM", 0), classes, has_se=False)
-
-  parameters = get_parameters(model)
-  print("parameter count", len(parameters))
-  optimizer = optim.Adam(parameters, lr=0.001)
-
-  BS, steps = getenv("BS", 64 if TINY else 16), getenv("STEPS", 2048)
-  print(f"training with batch size {BS} for {steps} steps")
-
-  if IMAGENET:
-    from extra.datasets.imagenet import fetch_batch
-    def loader(q):
-      while 1:
-        try:
-          q.put(fetch_batch(BS))
-        except Exception:
-          traceback.print_exc()
-    q = Queue(16)
-    for i in range(2):
-      p = Process(target=loader, args=(q,))
-      p.daemon = True
-      p.start()
-  else:
-    X_train, Y_train, _, _ = fetch_cifar()
-    X_train = X_train.reshape((-1, 3, 32, 32))
-    Y_train = Y_train.reshape((-1,))
-
-  with Tensor.train():
-    for i in (t := trange(steps)):
-      if IMAGENET:
-        X, Y = q.get(True)
-      else:
-        samp = np.random.randint(0, X_train.shape[0], size=(BS))
-        X, Y = X_train.numpy()[samp], Y_train.numpy()[samp]
-
-      st = time.time()
-      out = model.forward(Tensor(X.astype(np.float32), requires_grad=False))
-      fp_time = (time.time()-st)*1000.0
-
-      y = np.zeros((BS,classes), np.float32)
-      y[range(y.shape[0]),Y] = -classes
-      y = Tensor(y, requires_grad=False)
-      loss = out.log_softmax().mul(y).mean()
-
-      optimizer.zero_grad()
-
-      st = time.time()
-      loss.backward()
-      bp_time = (time.time()-st)*1000.0
-
-      st = time.time()
-      optimizer.step()
-      opt_time = (time.time()-st)*1000.0
-
-      st = time.time()
-      loss = loss.numpy()
-      cat = out.argmax(axis=1).numpy()
-      accuracy = (cat == Y).mean()
-      finish_time = (time.time()-st)*1000.0
-
-      # printing
-      t.set_description("loss %.2f accuracy %.2f -- %.2f + %.2f + %.2f + %.2f = %.2f" %
-        (loss, accuracy,
-        fp_time, bp_time, opt_time, finish_time,
-        fp_time + bp_time + opt_time + finish_time))
-
-      del out, y, loss
@@ -1,46 +0,0 @@
-import ast
-import numpy as np
-from PIL import Image
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import getenv, fetch
-from extra.models.vit import ViT
-"""
-fn = "gs://vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz"
-import tensorflow as tf
-with tf.io.gfile.GFile(fn, "rb") as f:
-  dat = f.read()
-  with open("cache/"+ fn.rsplit("/", 1)[1], "wb") as g:
-    g.write(dat)
-"""
-
-Tensor.training = False
-if getenv("LARGE", 0) == 1:
-  m = ViT(embed_dim=768, num_heads=12)
-else:
-  # tiny
-  m = ViT(embed_dim=192, num_heads=3)
-m.load_from_pretrained()
-
-# category labels
-lbls = ast.literal_eval(fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt").read_text())
-
-#url = "https://upload.wikimedia.org/wikipedia/commons/4/41/Chicken.jpg"
-url = "https://repository-images.githubusercontent.com/296744635/39ba6700-082d-11eb-98b8-cb29fb7369c0"
-
-# junk
-img = Image.open(fetch(url))
-aspect_ratio = img.size[0] / img.size[1]
-img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0))))
-img = np.array(img)
-y0,x0=(np.asarray(img.shape)[:2]-224)//2
-img = img[y0:y0+224, x0:x0+224]
-img = np.moveaxis(img, [2,0,1], [0,1,2])
-img = img.astype(np.float32)[:3].reshape(1,3,224,224)
-img /= 255.0
-img -= 0.5
-img /= 0.5
-
-out = m.forward(Tensor(img))
-outnp = out.numpy().ravel()
-choice = outnp.argmax()
-print(out.shape, choice, outnp[choice], lbls[choice])
@@ -1,740 +0,0 @@
-import json, logging, math, re, sys, time, wave, argparse, numpy as np
-from phonemizer.phonemize import default_separator, _phonemize
-from phonemizer.backend import EspeakBackend
-from phonemizer.punctuation import Punctuation
-from functools import reduce
-from pathlib import Path
-from typing import List
-from tinygrad import nn, dtypes
-from tinygrad.helpers import fetch
-from tinygrad.nn.state import torch_load
-from tinygrad.tensor import Tensor
-from tinygrad.engine.jit import TinyJit
-from unidecode import unidecode
-
-LRELU_SLOPE = 0.1
-
-class Synthesizer:
-  def __init__(self, n_vocab, spec_channels, segment_size, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, n_speakers=0, gin_channels=0, use_sdp=True, emotion_embedding=False, **kwargs):
-    self.n_vocab, self.spec_channels, self.inter_channels, self.hidden_channels, self.filter_channels, self.n_heads, self.n_layers, self.kernel_size, self.p_dropout, self.resblock, self.resblock_kernel_sizes, self.resblock_dilation_sizes, self.upsample_rates, self.upsample_initial_channel, self.upsample_kernel_sizes, self.segment_size, self.n_speakers, self.gin_channels, self.use_sdp = n_vocab, spec_channels, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, segment_size, n_speakers, gin_channels, use_sdp
-    self.enc_p = TextEncoder(n_vocab, inter_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, emotion_embedding)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-    self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) if use_sdp else DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-    if n_speakers > 1: self.emb_g = nn.Embedding(n_speakers, gin_channels)
-  def infer(self, x, x_lengths, sid=None, noise_scale=1.0, length_scale=1, noise_scale_w=1., max_len=None, emotion_embedding=None, max_y_length_estimate_scale=None, pad_length=-1):
-    x, m_p, logs_p, x_mask = self.enc_p.forward(x.realize(), x_lengths.realize(), emotion_embedding.realize() if emotion_embedding is not None else emotion_embedding)
-    g = self.emb_g(sid.reshape(1, 1)).squeeze(1).unsqueeze(-1) if self.n_speakers > 0 else None
-    logw = self.dp.forward(x, x_mask.realize(), g=g.realize(), reverse=self.use_sdp, noise_scale=noise_scale_w if self.use_sdp else 1.0)
-    w_ceil = Tensor.ceil(logw.exp() * x_mask * length_scale)
-    y_lengths = Tensor.maximum(w_ceil.sum([1, 2]), 1).cast(dtypes.int64)
-    return self.generate(g, logs_p, m_p, max_len, max_y_length_estimate_scale, noise_scale, w_ceil, x, x_mask, y_lengths, pad_length)
-  def generate(self, g, logs_p, m_p, max_len, max_y_length_estimate_scale, noise_scale, w_ceil, x, x_mask, y_lengths, pad_length):
-    max_y_length = y_lengths.max().item() if max_y_length_estimate_scale is None else max(15, x.shape[-1]) * max_y_length_estimate_scale
-    y_mask = sequence_mask(y_lengths, max_y_length).unsqueeze(1).cast(x_mask.dtype)
-    attn_mask = x_mask.unsqueeze(2) * y_mask.unsqueeze(-1)
-    attn = generate_path(w_ceil, attn_mask)
-    m_p_2 = attn.squeeze(1).matmul(m_p.transpose(1, 2)).transpose(1, 2)        # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p_2 = attn.squeeze(1).matmul(logs_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-    z_p = m_p_2 + Tensor.randn(*m_p_2.shape, dtype=m_p_2.dtype) * logs_p_2.exp() * noise_scale
-    row_len = y_mask.shape[2]
-    if pad_length > -1:
-      # Pad flow forward inputs to enable JIT
-      assert pad_length > row_len, "pad length is too small"
-      y_mask = y_mask.pad(((0, 0), (0, 0), (0, pad_length - row_len))).cast(z_p.dtype)
-      # New y_mask tensor to remove sts mask
-      y_mask = Tensor(y_mask.numpy(), device=y_mask.device, dtype=y_mask.dtype, requires_grad=y_mask.requires_grad)
-      z_p = z_p.squeeze(0).pad(((0, 0), (0, pad_length - z_p.shape[2])), value=1).unsqueeze(0)
-    z = self.flow.forward(z_p.realize(), y_mask.realize(), g=g.realize(), reverse=True)
-    result_length = reduce(lambda x, y: x * y, self.dec.upsample_rates, row_len)
-    o = self.dec.forward((z * y_mask)[:, :, :max_len], g=g)[:, :, :result_length]
-    if max_y_length_estimate_scale is not None:
-      length_scaler = o.shape[-1] / max_y_length
-      o.realize()
-      real_max_y_length = y_lengths.max().numpy()
-      if real_max_y_length > max_y_length:
-        logging.warning(f"Underestimated max length by {(((real_max_y_length / max_y_length) * 100) - 100):.2f}%, recomputing inference without estimate...")
-        return self.generate(g, logs_p, m_p, max_len, None, noise_scale, w_ceil, x, x_mask, y_lengths)
-      if real_max_y_length < max_y_length:
-        overestimation = ((max_y_length / real_max_y_length) * 100) - 100
-        logging.info(f"Overestimated max length by {overestimation:.2f}%")
-        if overestimation > 10: logging.warning("Warning: max length overestimated by more than 10%")
-      o = o[:, :, :(real_max_y_length * length_scaler).astype(np.int32)]
-    return o
-
-class StochasticDurationPredictor:
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels, self.filter_channels, self.kernel_size, self.p_dropout, self.n_flows, self.gin_channels = in_channels, filter_channels, kernel_size, p_dropout, n_flows, gin_channels
-    self.log_flow, self.flows = Log(), [ElementwiseAffine(2)]
-    for _ in range(n_flows):
-      self.flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(Flip())
-    self.post_pre, self.post_proj = nn.Conv1d(1, filter_channels, 1), nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = [ElementwiseAffine(2)]
-    for _ in range(4):
-      self.post_flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(Flip())
-    self.pre, self.proj = nn.Conv1d(in_channels, filter_channels, 1), nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-  @TinyJit
-  def forward(self, x: Tensor, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = self.pre(x.detach())
-    if g is not None: x = x + self.cond(g.detach())
-    x = self.convs.forward(x, x_mask)
-    x = self.proj(x) * x_mask
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-      log_det_tot_q = 0
-      h_w = self.post_proj(self.post_convs.forward(self.post_pre(w), x_mask)) * x_mask
-      e_q = Tensor.randn(w.size(0), 2, w.size(2), dtype=x.dtype).to(device=x.device) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, log_det_q = flow.forward(z_q, x_mask, g=(x + h_w))
-        log_det_tot_q += log_det_q
-      z_u, z1 = z_q.split([1, 1], 1)
-      u = z_u.sigmoid() * x_mask
-      z0 = (w - u) * x_mask
-      log_det_tot_q += Tensor.sum((z_u.logsigmoid() + (-z_u).logsigmoid()) * x_mask, [1,2])
-      log_q = Tensor.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - log_det_tot_q
-      log_det_tot = 0
-      z0, log_det = self.log_flow.forward(z0, x_mask)
-      log_det_tot += log_det
-      z = z0.cat(z1, 1)
-      for flow in flows:
-        z, log_det = flow.forward(z, x_mask, g=x, reverse=reverse)
-        log_det_tot = log_det_tot + log_det
-      nll = Tensor.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - log_det_tot
-      return (nll + log_q).realize() # [b]
-    flows = list(reversed(self.flows))
-    flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-    z = Tensor.randn(x.shape[0], 2, x.shape[2], dtype=x.dtype).to(device=x.device) * noise_scale
-    for flow in flows: z = flow.forward(z, x_mask, g=x, reverse=reverse)
-    z0, z1 = z.split([1, 1], 1)
-    return z0.realize()
-
-class DurationPredictor:
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    self.in_channels, self.filter_channels, self.kernel_size, self.p_dropout, self.gin_channels = in_channels, filter_channels, kernel_size, p_dropout, gin_channels
-    self.conv_1, self.norm_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2), LayerNorm(filter_channels)
-    self.conv_2, self.norm_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2), LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-    if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-  def forward(self, x: Tensor, x_mask, g=None):
-    x = x.detach()
-    if g is not None: x = x + self.cond(g.detach())
-    x = self.conv_1(x * x_mask).relu()
-    x = self.norm_1(x).dropout(self.p_dropout)
-    x = self.conv_2(x * x_mask).relu(x)
-    x = self.norm_2(x).dropout(self.p_dropout)
-    return self.proj(x * x_mask) * x_mask
-
-class TextEncoder:
-  def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, emotion_embedding):
-    self.n_vocab, self.out_channels, self.hidden_channels, self.filter_channels, self.n_heads, self.n_layers, self.kernel_size, self.p_dropout = n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-    if n_vocab!=0:self.emb = nn.Embedding(n_vocab, hidden_channels)
-    if emotion_embedding: self.emo_proj = nn.Linear(1024, hidden_channels)
-    self.encoder = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-  @TinyJit
-  def forward(self, x: Tensor, x_lengths: Tensor, emotion_embedding=None):
-    if self.n_vocab!=0: x = (self.emb(x) * math.sqrt(self.hidden_channels))
-    if emotion_embedding: x = x + self.emo_proj(emotion_embedding).unsqueeze(1)
-    x = x.transpose(1, -1)  # [b, t, h] -transpose-> [b, h, t]
-    x_mask = sequence_mask(x_lengths, x.shape[2]).unsqueeze(1).cast(x.dtype)
-    x = self.encoder.forward(x * x_mask, x_mask)
-    m, logs = (self.proj(x) * x_mask).split(self.out_channels, dim=1)
-    return x.realize(), m.realize(), logs.realize(), x_mask.realize()
-
-class ResidualCouplingBlock:
-  def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
-    self.channels, self.hidden_channels, self.kernel_size, self.dilation_rate, self.n_layers, self.n_flows, self.gin_channels = channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows, gin_channels
-    self.flows = []
-    for _ in range(n_flows):
-      self.flows.append(ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(Flip())
-  @TinyJit
-  def forward(self, x, x_mask, g=None, reverse=False):
-    for flow in reversed(self.flows) if reverse else self.flows: x = flow.forward(x, x_mask, g=g, reverse=reverse)
-    return x.realize()
-
-class PosteriorEncoder:
-  def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0):
-    self.in_channels, self.out_channels, self.hidden_channels, self.kernel_size, self.dilation_rate, self.n_layers, self.gin_channels = in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels
-    self.pre, self.proj = nn.Conv1d(in_channels, hidden_channels, 1), nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-  def forward(self, x, x_lengths, g=None):
-    x_mask = sequence_mask(x_lengths, x.size(2)).unsqueeze(1).cast(x.dtype)
-    stats = self.proj(self.enc.forward(self.pre(x) * x_mask, x_mask, g=g)) * x_mask
-    m, logs = stats.split(self.out_channels, dim=1)
-    z = (m + Tensor.randn(m.shape, m.dtype) * logs.exp()) * x_mask
-    return z, m, logs, x_mask
-
-class Generator:
-  def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-    self.num_kernels, self.num_upsamples = len(resblock_kernel_sizes), len(upsample_rates)
-    self.conv_pre = nn.Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-    resblock = ResBlock1 if resblock == '1' else ResBlock2
-    self.ups = [nn.ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), k, u, padding=(k-u)//2) for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes))]
-    self.resblocks = []
-    self.upsample_rates = upsample_rates
-    for i in range(len(self.ups)):
-      ch = upsample_initial_channel // (2 ** (i + 1))
-      for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-        self.resblocks.append(resblock(ch, k, d))
-    self.conv_post = nn.Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-    if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-  @TinyJit
-  def forward(self, x: Tensor, g=None):
-    x = self.conv_pre(x)
-    if g is not None:  x = x + self.cond(g)
-    for i in range(self.num_upsamples):
-      x = self.ups[i](x.leaky_relu(LRELU_SLOPE))
-      xs = sum(self.resblocks[i * self.num_kernels + j].forward(x) for j in range(self.num_kernels))
-      x = (xs / self.num_kernels).realize()
-    res = self.conv_post(x.leaky_relu()).tanh().realize()
-    return res
-
-class LayerNorm(nn.LayerNorm):
-  def __init__(self, channels, eps=1e-5): super().__init__(channels, eps, elementwise_affine=True)
-  def forward(self, x: Tensor): return self.__call__(x.transpose(1, -1)).transpose(1, -1)
-
-class WN:
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    assert (kernel_size % 2 == 1)
-    self.hidden_channels, self.kernel_size, self.dilation_rate, self.n_layers, self.gin_channels, self.p_dropout = hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout
-    self.in_layers, self.res_skip_layers = [], []
-    if gin_channels != 0: self.cond_layer = nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      self.in_layers.append(nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=int((kernel_size * dilation - dilation) / 2)))
-      self.res_skip_layers.append(nn.Conv1d(hidden_channels, 2 * hidden_channels if i < n_layers - 1 else hidden_channels, 1))
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = Tensor.zeros_like(x)
-    if g is not None: g = self.cond_layer(g)
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:, cond_offset:cond_offset + 2 * self.hidden_channels, :]
-      else:
-        g_l = Tensor.zeros_like(x_in)
-      acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, self.hidden_channels)
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        x = (x + res_skip_acts[:, :self.hidden_channels, :]) * x_mask
-        output = output + res_skip_acts[:, self.hidden_channels:, :]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-class ResBlock1:
-  def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-    self.convs1 = [nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[i], padding=get_padding(kernel_size, dilation[i])) for i in range(3)]
-    self.convs2 = [nn.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)) for _ in range(3)]
-  def forward(self, x: Tensor, x_mask=None):
-    for c1, c2 in zip(self.convs1, self.convs2):
-      xt = x.leaky_relu(LRELU_SLOPE)
-      xt = c1(xt if x_mask is None else xt * x_mask).leaky_relu(LRELU_SLOPE)
-      x = c2(xt if x_mask is None else xt * x_mask) + x
-    return x if x_mask is None else x * x_mask
-
-class ResBlock2:
-  def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-    self.convs = [nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[i], padding=get_padding(kernel_size, dilation[i])) for i in range(2)]
-  def forward(self, x, x_mask=None):
-    for c in self.convs:
-      xt = x.leaky_relu(LRELU_SLOPE)
-      xt = c(xt if x_mask is None else xt * x_mask)
-      x = xt + x
-    return x if x_mask is None else x * x_mask
-
-class DDSConv: # Dilated and Depth-Separable Convolution
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    self.channels, self.kernel_size, self.n_layers, self.p_dropout = channels, kernel_size, n_layers, p_dropout
-    self.convs_sep, self.convs_1x1, self.norms_1, self.norms_2 = [], [], [], []
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-  def forward(self, x, x_mask, g=None):
-    if g is not None: x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i].forward(y).gelu()
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i].forward(y).gelu()
-      x = x + y.dropout(self.p_dropout)
-    return x * x_mask
-
-class ConvFlow:
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    self.in_channels, self.filter_channels, self.kernel_size, self.n_layers, self.num_bins, self.tail_bound = in_channels, filter_channels, kernel_size, n_layers, num_bins, tail_bound
-    self.half_channels = in_channels // 2
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = x.split([self.half_channels] * 2, 1)
-    h = self.proj(self.convs.forward(self.pre(x0), x_mask, g=g)) * x_mask
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-    un_normalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    un_normalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    un_normalized_derivatives = h[..., 2 * self.num_bins:]
-    x1, log_abs_det = piecewise_rational_quadratic_transform(x1, un_normalized_widths, un_normalized_heights, un_normalized_derivatives, inverse=reverse, tails='linear', tail_bound=self.tail_bound)
-    x = x0.cat(x1, dim=1) * x_mask
-    return x if reverse else (x, Tensor.sum(log_abs_det * x_mask, [1,2]))
-
-class ResidualCouplingLayer:
-  def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    self.channels, self.hidden_channels, self.kernel_size, self.dilation_rate, self.n_layers, self.mean_only = channels, hidden_channels, kernel_size, dilation_rate, n_layers, mean_only
-    self.half_channels = channels // 2
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = x.split([self.half_channels] * 2, 1)
-    stats = self.post(self.enc.forward(self.pre(x0) * x_mask, x_mask, g=g)) * x_mask
-    if not self.mean_only:
-      m, logs = stats.split([self.half_channels] * 2, 1)
-    else:
-      m = stats
-      logs = Tensor.zeros_like(m)
-    if not reverse: return x0.cat((m + x1 * logs.exp() * x_mask), dim=1)
-    return x0.cat(((x1 - m) * (-logs).exp() * x_mask), dim=1)
-
-class Log:
-  def forward(self, x : Tensor, x_mask, reverse=False):
-    if not reverse:
-      y = x.maximum(1e-5).log() * x_mask
-      return y, (-y).sum([1, 2])
-    return x.exp() * x_mask
-
-class Flip:
-  def forward(self, x: Tensor, *args, reverse=False, **kwargs):
-    return x.flip([1]) if reverse else (x.flip([1]), Tensor.zeros(x.shape[0], dtype=x.dtype).to(device=x.device))
-
-class ElementwiseAffine:
-  def __init__(self, channels): self.m, self.logs = Tensor.zeros(channels, 1), Tensor.zeros(channels, 1)
-  def forward(self, x, x_mask, reverse=False, **kwargs): # x if reverse else y, logdet
-    return (x - self.m) * Tensor.exp(-self.logs) * x_mask if reverse \
-      else ((self.m + Tensor.exp(self.logs) * x) * x_mask, Tensor.sum(self.logs * x_mask, [1, 2]))
-
-class MultiHeadAttention:
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    assert channels % n_heads == 0
-    self.channels, self.out_channels, self.n_heads, self.p_dropout, self.window_size, self.heads_share, self.block_length, self.proximal_bias, self.proximal_init = channels, out_channels, n_heads, p_dropout, window_size, heads_share, block_length, proximal_bias, proximal_init
-    self.attn, self.k_channels  = None, channels // n_heads
-    self.conv_q, self.conv_k, self.conv_v = [nn.Conv1d(channels, channels, 1) for _ in range(3)]
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    if window_size is not None: self.emb_rel_k, self.emb_rel_v = [Tensor.randn(1 if heads_share else n_heads, window_size * 2 + 1, self.k_channels) * (self.k_channels ** -0.5) for _ in range(2)]
-  def forward(self, x, c, attn_mask=None):
-    q, k, v = self.conv_q(x), self.conv_k(c), self.conv_v(c)
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-    return self.conv_o(x)
-  def attention(self, query: Tensor, key: Tensor, value: Tensor, mask=None):# reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = key.shape[0], key.shape[1], key.shape[2], query.shape[2]
-    query = query.reshape(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.reshape(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.reshape(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    scores = (query / math.sqrt(self.k_channels)) @ key.transpose(-2, -1)
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
-      scores = scores + self._relative_position_to_absolute_position(rel_logits)
-    if mask is not None:
-      scores = Tensor.where(mask, scores, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        scores = Tensor.where(Tensor.ones_like(scores).triu(-self.block_length).tril(self.block_length), scores, -1e4)
-    p_attn = scores.softmax(axis=-1)  # [b, n_h, t_t, t_s]
-    output = p_attn.matmul(value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().reshape(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-  def _matmul_with_relative_values(self, x, y): return x.matmul(y.unsqueeze(0))                 # x: [b, h, l, m], y: [h or 1, m, d], ret: [b, h, l, d]
-  def _matmul_with_relative_keys(self, x, y): return x.matmul(y.unsqueeze(0).transpose(-2, -1)) # x: [b, h, l, d], y: [h or 1, m, d], re, : [b, h, l, m]
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    pad_length, slice_start_position = max(length - (self.window_size + 1), 0), max((self.window_size + 1) - length, 0)
-    padded_relative_embeddings = relative_embeddings if pad_length <= 0\
-      else relative_embeddings.pad(convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    return padded_relative_embeddings[:, slice_start_position:(slice_start_position + 2 * length - 1)] #used_relative_embeddings
-  def _relative_position_to_absolute_position(self, x: Tensor): # x: [b, h, l, 2*l-1] -> [b, h, l, l]
-    batch, heads, length, _ = x.shape
-    x = x.pad(convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-    x_flat = x.reshape([batch, heads, length * 2 * length]).pad(convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-    return x_flat.reshape([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-  def _absolute_position_to_relative_position(self, x: Tensor): # x: [b, h, l, l] -> [b, h, l, 2*l-1]
-    batch, heads, length, _ = x.shape
-    x = x.pad(convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.reshape([batch, heads, length**2 + length*(length -1)]).pad(convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    return x_flat.reshape([batch, heads, length, 2*length])[:,:,:,1:]
-
-class FFN:
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    self.in_channels, self.out_channels, self.filter_channels, self.kernel_size, self.p_dropout, self.activation, self.causal = in_channels, out_channels, filter_channels, kernel_size, p_dropout, activation, causal
-    self.padding = self._causal_padding if causal else self._same_padding
-    self.conv_1, self.conv_2 = nn.Conv1d(in_channels, filter_channels, kernel_size), nn.Conv1d(filter_channels, out_channels, kernel_size)
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    x = x * (1.702 * x).sigmoid() if self.activation == "gelu" else x.relu()
-    return self.conv_2(self.padding(x.dropout(self.p_dropout) * x_mask)) * x_mask
-  def _causal_padding(self, x):return x if self.kernel_size == 1 else x.pad(convert_pad_shape([[0, 0], [0, 0], [self.kernel_size - 1, 0]]))
-  def _same_padding(self, x): return x if self.kernel_size == 1 else x.pad(convert_pad_shape([[0, 0], [0, 0], [(self.kernel_size - 1) // 2, self.kernel_size // 2]]))
-
-class Encoder:
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    self.hidden_channels, self.filter_channels, self.n_heads, self.n_layers, self.kernel_size, self.p_dropout, self.window_size = hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, window_size
-    self.attn_layers, self.norm_layers_1, self.ffn_layers, self.norm_layers_2 = [], [], [], []
-    for _ in range(n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-  def forward(self, x, x_mask):
-    attn_mask, x = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1), x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i].forward(x, x, attn_mask).dropout(self.p_dropout)
-      x = self.norm_layers_1[i].forward(x + y)
-      y = self.ffn_layers[i].forward(x, x_mask).dropout(self.p_dropout)
-      x = self.norm_layers_2[i].forward(x + y)
-    return x * x_mask
-
-DEFAULT_MIN_BIN_WIDTH, DEFAULT_MIN_BIN_HEIGHT, DEFAULT_MIN_DERIVATIVE = 1e-3, 1e-3, 1e-3
-def piecewise_rational_quadratic_transform(inputs, un_normalized_widths, un_normalized_heights, un_normalized_derivatives, inverse=False, tails=None, tail_bound=1., min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-  if tails is None: spline_fn, spline_kwargs = rational_quadratic_spline, {}
-  else: spline_fn, spline_kwargs = unconstrained_rational_quadratic_spline, {'tails': tails, 'tail_bound': tail_bound}
-  return spline_fn(inputs=inputs, un_normalized_widths=un_normalized_widths, un_normalized_heights=un_normalized_heights, un_normalized_derivatives=un_normalized_derivatives, inverse=inverse, min_bin_width=min_bin_width, min_bin_height=min_bin_height, min_derivative=min_derivative, **spline_kwargs)
-def unconstrained_rational_quadratic_spline(inputs, un_normalized_widths, un_normalized_heights, un_normalized_derivatives, inverse=False, tails='linear', tail_bound=1., min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-  if not tails == 'linear': raise RuntimeError('{} tails are not implemented.'.format(tails))
-  constant = np.log(np.exp(1 - min_derivative) - 1).item()
-  un_normalized_derivatives = cat_lr(un_normalized_derivatives, constant, constant)
-  output, log_abs_det = rational_quadratic_spline(inputs=inputs.squeeze(dim=0).squeeze(dim=0), unnormalized_widths=un_normalized_widths.squeeze(dim=0).squeeze(dim=0), unnormalized_heights=un_normalized_heights.squeeze(dim=0).squeeze(dim=0), unnormalized_derivatives=un_normalized_derivatives.squeeze(dim=0).squeeze(dim=0), inverse=inverse, left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound, min_bin_width=min_bin_width, min_bin_height=min_bin_height, min_derivative=min_derivative)
-  return output.unsqueeze(dim=0).unsqueeze(dim=0), log_abs_det.unsqueeze(dim=0).unsqueeze(dim=0)
-def rational_quadratic_spline(inputs: Tensor, unnormalized_widths: Tensor, unnormalized_heights: Tensor, unnormalized_derivatives: Tensor, inverse=False, left=0., right=1., bottom=0., top=1., min_bin_width=DEFAULT_MIN_BIN_WIDTH, min_bin_height=DEFAULT_MIN_BIN_HEIGHT, min_derivative=DEFAULT_MIN_DERIVATIVE):
-  num_bins = unnormalized_widths.shape[-1]
-  if min_bin_width * num_bins > 1.0: raise ValueError('Minimal bin width too large for the number of bins')
-  if min_bin_height * num_bins > 1.0: raise ValueError('Minimal bin height too large for the number of bins')
-  widths = min_bin_width + (1 - min_bin_width * num_bins) * unnormalized_widths.softmax(axis=-1)
-  cum_widths = cat_lr(((right - left) * widths[..., :-1].cumsum(axis=1) + left), left, right + 1e-6 if not inverse else right)
-  widths = cum_widths[..., 1:] - cum_widths[..., :-1]
-  derivatives = min_derivative + (unnormalized_derivatives.exp()+1).log()
-  heights = min_bin_height + (1 - min_bin_height * num_bins) * unnormalized_heights.softmax(axis=-1)
-  cum_heights = cat_lr(((top - bottom) * heights[..., :-1].cumsum(axis=1) + bottom), bottom, top + 1e-6 if inverse else top)
-  heights = cum_heights[..., 1:] - cum_heights[..., :-1]
-  bin_idx = ((inputs[..., None] >= (cum_heights if inverse else cum_widths)).sum(axis=-1) - 1)[..., None]
-  input_cum_widths = gather(cum_widths, bin_idx, axis=-1)[..., 0]
-  input_bin_widths = gather(widths, bin_idx, axis=-1)[..., 0]
-  input_cum_heights = gather(cum_heights, bin_idx, axis=-1)[..., 0]
-  input_delta = gather(heights / widths, bin_idx, axis=-1)[..., 0]
-  input_derivatives = gather(derivatives, bin_idx, axis=-1)[..., 0]
-  input_derivatives_plus_one = gather(derivatives[..., 1:], bin_idx, axis=-1)[..., 0]
-  input_heights = gather(heights, bin_idx, axis=-1)[..., 0]
-  if inverse:
-    a = ((inputs - input_cum_heights) * (input_derivatives + input_derivatives_plus_one - 2 * input_delta) + input_heights * (input_delta - input_derivatives))
-    b = (input_heights * input_derivatives - (inputs - input_cum_heights) * (input_derivatives + input_derivatives_plus_one - 2 * input_delta))
-    c = - input_delta * (inputs - input_cum_heights)
-    discriminant = b.square() - 4 * a * c
-    # assert (discriminant.numpy() >= 0).all()
-    root = (2 * c) / (-b - discriminant.sqrt())
-    theta_one_minus_theta = root * (1 - root)
-    denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) * theta_one_minus_theta)
-    derivative_numerator = input_delta.square() * (input_derivatives_plus_one * root.square() + 2 * input_delta * theta_one_minus_theta + input_derivatives * (1 - root).square())
-    return root * input_bin_widths + input_cum_widths, -(derivative_numerator.log() - 2 * denominator.log())
-  theta = (inputs - input_cum_widths) / input_bin_widths
-  theta_one_minus_theta = theta * (1 - theta)
-  numerator = input_heights * (input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta)
-  denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta) * theta_one_minus_theta)
-  derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2) + 2 * input_delta * theta_one_minus_theta + input_derivatives * (1 - theta).pow(2))
-  return input_cum_heights + numerator / denominator, derivative_numerator.log() - 2 * denominator.log()
-
-def sequence_mask(length: Tensor, max_length): return Tensor.arange(max_length, dtype=length.dtype, device=length.device).unsqueeze(0) < length.unsqueeze(1)
-def generate_path(duration: Tensor, mask: Tensor):  # duration: [b, 1, t_x], mask: [b, 1, t_y, t_x]
-  b, _, t_y, t_x = mask.shape
-  path = sequence_mask(duration.cumsum(axis=2).reshape(b * t_x), t_y).cast(mask.dtype).reshape(b, t_x, t_y)
-  path = path - path.pad(convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  return path.unsqueeze(1).transpose(2, 3) * mask
-def fused_add_tanh_sigmoid_multiply(input_a: Tensor, input_b: Tensor, n_channels: int):
-  n_channels_int, in_act = n_channels, input_a + input_b
-  t_act, s_act = in_act[:, :n_channels_int, :].tanh(), in_act[:, n_channels_int:, :].sigmoid()
-  return t_act * s_act
-
-def cat_lr(t, left, right): return Tensor.full(get_shape(t), left).cat(t, dim=-1).cat(Tensor.full(get_shape(t), right), dim=-1)
-def get_shape(tensor):
-  (shape := list(tensor.shape))[-1] = 1
-  return tuple(shape)
-def convert_pad_shape(pad_shape): return tuple(tuple(x) for x in pad_shape)
-def get_padding(kernel_size, dilation=1): return int((kernel_size*dilation - dilation)/2)
-
-def gather(x, indices, axis):
-  indices = (indices < 0).where(indices + x.shape[axis], indices).transpose(0, axis)
-  permute_args = list(range(x.ndim))
-  permute_args[0], permute_args[axis] = permute_args[axis], permute_args[0]
-  permute_args.append(permute_args.pop(0))
-  x = x.permute(*permute_args)
-  reshape_arg = [1] * x.ndim + [x.shape[-1]]
-  return ((indices.unsqueeze(indices.ndim).expand(*indices.shape, x.shape[-1]) ==
-           Tensor.arange(x.shape[-1]).reshape(*reshape_arg).expand(*indices.shape, x.shape[-1])) * x).sum(indices.ndim).transpose(0, axis)
-
-def norm_except_dim(v, dim):
-  if dim == -1: return np.linalg.norm(v)
-  if dim == 0:
-    (output_shape := [1] * v.ndim)[0] = v.shape[0]
-    return np.linalg.norm(v.reshape(v.shape[0], -1), axis=1).reshape(output_shape)
-  if dim == v.ndim - 1:
-    (output_shape := [1] * v.ndim)[-1] = v.shape[-1]
-    return np.linalg.norm(v.reshape(-1, v.shape[-1]), axis=0).reshape(output_shape)
-  transposed_v = np.transpose(v, (dim,) + tuple(i for i in range(v.ndim) if i != dim))
-  return np.transpose(norm_except_dim(transposed_v, 0), (dim,) + tuple(i for i in range(v.ndim) if i != dim))
-def weight_norm(v: Tensor, g: Tensor, dim):
-  v, g = v.numpy(), g.numpy()
-  return Tensor(v * (g / norm_except_dim(v, dim)))
-
-# HPARAMS LOADING
-def get_hparams_from_file(path):
-  with open(path, "r") as f:
-    data = f.read()
-  return HParams(**json.loads(data))
-class HParams:
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items(): self[k] = v if type(v) != dict else HParams(**v)
-  def keys(self): return self.__dict__.keys()
-  def items(self): return self.__dict__.items()
-  def values(self): return self.__dict__.values()
-  def __len__(self): return len(self.__dict__)
-  def __getitem__(self, key): return getattr(self, key)
-  def __setitem__(self, key, value): return setattr(self, key, value)
-  def __contains__(self, key): return key in self.__dict__
-  def __repr__(self): return self.__dict__.__repr__()
-
-# MODEL LOADING
-def load_model(symbols, hps, model) -> Synthesizer:
-  net_g = Synthesizer(len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers = hps.data.n_speakers, **hps.model)
-  _ = load_checkpoint(fetch(model[1]), net_g, None)
-  return net_g
-def load_checkpoint(checkpoint_path, model: Synthesizer, optimizer=None, skip_list=[]):
-  assert Path(checkpoint_path).is_file()
-  start_time = time.time()
-  checkpoint_dict = torch_load(checkpoint_path)
-  iteration, learning_rate = checkpoint_dict['iteration'], checkpoint_dict['learning_rate']
-  if optimizer: optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  weight_g, weight_v, parent = None, None, None
-  for key, v in saved_state_dict.items():
-    if any(layer in key for layer in skip_list): continue
-    try:
-      obj, skip = model, False
-      for k in key.split('.'):
-        if k.isnumeric(): obj = obj[int(k)]
-        elif isinstance(obj, dict): obj = obj[k]
-        else:
-          if isinstance(obj, (LayerNorm, nn.LayerNorm)) and k in ["gamma", "beta"]:
-            k = "weight" if k == "gamma" else "bias"
-          elif k in ["weight_g", "weight_v"]:
-            parent, skip = obj, True
-            if k == "weight_g": weight_g = v
-            else: weight_v = v
-          if not skip: obj = getattr(obj, k)
-      if weight_g is not None and weight_v is not None:
-        setattr(obj, "weight_g", weight_g.numpy())
-        setattr(obj, "weight_v", weight_v.numpy())
-        obj, v = getattr(parent, "weight"), weight_norm(weight_v, weight_g, 0)
-        weight_g, weight_v, parent, skip = None, None, None, False
-      if not skip and obj.shape == v.shape: obj.assign(v.to(obj.device))
-      elif not skip: logging.error(f"MISMATCH SHAPE IN {key}, {obj.shape} {v.shape}")
-    except Exception as e: raise e
-  logging.info(f"Loaded checkpoint '{checkpoint_path}' (iteration {iteration}) in {time.time() - start_time:.4f}s")
-  return model, optimizer, learning_rate, iteration
-
-# Used for cleaning input text and mapping to symbols
-class TextMapper: # Based on https://github.com/keithito/tacotron
-  def __init__(self, symbols, apply_cleaners=True):
-    self.apply_cleaners, self.symbols, self._inflect = apply_cleaners, symbols, None
-    self._symbol_to_id, _id_to_symbol = {s: i for i, s in enumerate(symbols)}, {i: s for i, s in enumerate(symbols)}
-    self._whitespace_re, self._abbreviations = re.compile(r'\s+'), [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [('mrs', 'misess'), ('mr', 'mister'), ('dr', 'doctor'), ('st', 'saint'), ('co', 'company'), ('jr', 'junior'), ('maj', 'major'), ('gen', 'general'), ('drs', 'doctors'), ('rev', 'reverend'), ('lt', 'lieutenant'), ('hon', 'honorable'), ('sgt', 'sergeant'), ('capt', 'captain'), ('esq', 'esquire'), ('ltd', 'limited'), ('col', 'colonel'), ('ft', 'fort'), ]]
-    self.phonemizer = EspeakBackend(
-        language="en-us", punctuation_marks=Punctuation.default_marks(), preserve_punctuation=True, with_stress=True,
-    )
-  def text_to_sequence(self, text, cleaner_names):
-    if self.apply_cleaners:
-      for name in cleaner_names:
-        cleaner = getattr(self, name)
-        if not cleaner: raise ModuleNotFoundError('Unknown cleaner: %s' % name)
-        text = cleaner(text)
-    else: text = text.strip()
-    return [self._symbol_to_id[symbol] for symbol in text]
-  def get_text(self, text, add_blank=False, cleaners=('english_cleaners2',)):
-    text_norm = self.text_to_sequence(text, cleaners)
-    return Tensor(self.intersperse(text_norm, 0) if add_blank else text_norm, dtype=dtypes.int64)
-  def intersperse(self, lst, item):
-    (result := [item] * (len(lst) * 2 + 1))[1::2] = lst
-    return result
-  def phonemize(self, text, strip=True): return _phonemize(self.phonemizer, text, default_separator, strip, 1, False, False)
-  def filter_oov(self, text): return "".join(list(filter(lambda x: x in self._symbol_to_id, text)))
-  def base_english_cleaners(self, text): return self.collapse_whitespace(self.phonemize(self.expand_abbreviations(unidecode(text.lower()))))
-  def english_cleaners2(self, text): return self.base_english_cleaners(text)
-  def transliteration_cleaners(self, text): return self.collapse_whitespace(unidecode(text.lower()))
-  def cjke_cleaners(self, text): return re.sub(r'([^\.,!\?\-…~])$', r'\1.', re.sub(r'\s+$', '', self.english_to_ipa2(text).replace('ɑ', 'a').replace('ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')))
-  def cjke_cleaners2(self, text): return re.sub(r'([^\.,!\?\-…~])$', r'\1.', re.sub(r'\s+$', '', self.english_to_ipa2(text)))
-  def cjks_cleaners(self, text): return re.sub(r'([^\.,!\?\-…~])$', r'\1.', re.sub(r'\s+$', '', self.english_to_lazy_ipa(text)))
-  def english_to_ipa2(self, text):
-    _ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ ('r', 'ɹ'), ('ʤ', 'dʒ'), ('ʧ', 'tʃ')]]
-    return reduce(lambda t, rx: re.sub(rx[0], rx[1], t), _ipa_to_ipa2, self.mark_dark_l(self.english_to_ipa(text))).replace('...', '…')
-  def mark_dark_l(self, text): return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ' + x.group(1), text)
-  def english_to_ipa(self, text):
-    import eng_to_ipa as ipa
-    return self.collapse_whitespace(ipa.convert(self.normalize_numbers(self.expand_abbreviations(unidecode(text).lower()))))
-  def english_to_lazy_ipa(self, text):
-    _lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [('r', 'ɹ'), ('æ', 'e'), ('ɑ', 'a'), ('ɔ', 'o'), ('ð', 'z'), ('θ', 's'), ('ɛ', 'e'), ('ɪ', 'i'), ('ʊ', 'u'), ('ʒ', 'ʥ'), ('ʤ', 'ʥ'), ('ˈ', '↓')]]
-    return reduce(lambda t, rx: re.sub(rx[0], rx[1], t), _lazy_ipa, self.english_to_ipa(text))
-  def expand_abbreviations(self, text): return reduce(lambda t, abbr: re.sub(abbr[0], abbr[1], t), self._abbreviations, text)
-  def collapse_whitespace(self, text): return re.sub(self._whitespace_re, ' ', text)
-  def normalize_numbers(self, text):
-    import inflect
-    self._inflect = inflect.engine()
-    text = re.sub(re.compile(r'([0-9][0-9\,]+[0-9])'), self._remove_commas, text)
-    text = re.sub(re.compile(r'£([0-9\,]*[0-9]+)'), r'\1 pounds', text)
-    text = re.sub(re.compile(r'\$([0-9\.\,]*[0-9]+)'), self._expand_dollars, text)
-    text = re.sub(re.compile(r'([0-9]+\.[0-9]+)'), self._expand_decimal_point, text)
-    text = re.sub(re.compile(r'[0-9]+(st|nd|rd|th)'), self._expand_ordinal, text)
-    text = re.sub(re.compile(r'[0-9]+'), self._expand_number, text)
-    return text
-  def _remove_commas(self, m): return m.group(1).replace(',', '') # george won't like this
-  def _expand_dollars(self, m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2: return match + ' dollars'  # Unexpected format
-    dollars, cents = int(parts[0]) if parts[0] else 0, int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents: return '%s %s, %s %s' % (dollars, 'dollar' if dollars == 1 else 'dollars', cents, 'cent' if cents == 1 else 'cents')
-    if dollars: return '%s %s' % (dollars, 'dollar' if dollars == 1 else 'dollars')
-    if cents: return '%s %s' % (cents, 'cent' if cents == 1 else 'cents')
-    return 'zero dollars'
-  def _expand_decimal_point(self, m): return m.group(1).replace('.', ' point ')
-  def _expand_ordinal(self, m): return self._inflect.number_to_words(m.group(0))
-  def _expand_number(self, _inflect, m):
-    num = int(m.group(0))
-    if 1000 < num < 3000:
-      if num == 2000: return 'two thousand'
-      if 2000 < num < 2010: return 'two thousand ' + self._inflect.number_to_words(num % 100)
-      if num % 100 == 0: return self._inflect.number_to_words(num // 100) + ' hundred'
-      return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    return self._inflect.number_to_words(num, andword='')
-
-#########################################################################################
-# PAPER: https://arxiv.org/abs/2106.06103
-# CODE: https://github.com/jaywalnut310/vits/tree/main
-#########################################################################################
-# INSTALLATION: this is based on default config, dependencies are for preprocessing.
-# vctk, ljs                      | pip3 install unidecode phonemizer          | phonemizer requires [eSpeak](https://espeak.sourceforge.net) backend to be installed on your system
-# mmts-tts                       | pip3 install unidecode                     |
-# uma_trilingual, cjks, voistock | pip3 install unidecode inflect eng_to_ipa  |
-#########################################################################################
-# Some good speakers to try out, there may be much better ones, I only tried out a few:
-# male vctk 1  | --model_to_use vctk --speaker_id 2
-# male vctk 2  | --model_to_use vctk --speaker_id 6
-# anime lady 1 | --model_to_use uma_trilingual --speaker_id 36
-# anime lady 2 | --model_to_use uma_trilingual --speaker_id 121
-#########################################################################################
-VITS_PATH = Path(__file__).parents[1] / "weights/VITS/"
-MODELS = { # config_url, weights_url
-  "ljs": ("https://raw.githubusercontent.com/jaywalnut310/vits/main/configs/ljs_base.json", "https://drive.google.com/uc?export=download&id=1q86w74Ygw2hNzYP9cWkeClGT5X25PvBT&confirm=t"),
-  "vctk": ("https://huggingface.co/csukuangfj/vits-vctk/resolve/main/vctk_base.json", "https://huggingface.co/csukuangfj/vits-vctk/resolve/main/pretrained_vctk.pth"),
-  "mmts-tts": ("https://huggingface.co/facebook/mms-tts/raw/main/full_models/eng/config.json", "https://huggingface.co/facebook/mms-tts/resolve/main/full_models/eng/G_100000.pth"),
-  "uma_trilingual": ("https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/raw/main/configs/uma_trilingual.json", "https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/resolve/main/pretrained_models/G_trilingual.pth"),
-  "cjks": ("https://huggingface.co/spaces/skytnt/moe-tts/resolve/main/saved_model/14/config.json", "https://huggingface.co/spaces/skytnt/moe-tts/resolve/main/saved_model/14/model.pth"),
-  "voistock": ("https://huggingface.co/spaces/skytnt/moe-tts/resolve/main/saved_model/15/config.json", "https://huggingface.co/spaces/skytnt/moe-tts/resolve/main/saved_model/15/model.pth"),
-}
-Y_LENGTH_ESTIMATE_SCALARS = {"ljs": 2.8, "vctk": 1.74, "mmts-tts": 1.9, "uma_trilingual": 2.3, "cjks": 3.3, "voistock": 3.1}
-if __name__ == '__main__':
-  logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--model_to_use", default="vctk", help="Specify the model to use. Default is 'vctk'.")
-  parser.add_argument("--speaker_id", type=int, default=6, help="Specify the speaker ID. Default is 6.")
-  parser.add_argument("--out_path", default=None, help="Specify the full output path. Overrides the --out_dir and --name parameter.")
-  parser.add_argument("--out_dir", default=str(Path(__file__).parents[1] / "temp"), help="Specify the output path.")
-  parser.add_argument("--base_name", default="test", help="Specify the base of the output file name. Default is 'test'.")
-  parser.add_argument("--text_to_synthesize", default="""Hello person. If the code you are contributing isn't some of the highest quality code you've written in your life, either put in the effort to make it great, or don't bother.""", help="Specify the text to synthesize. Default is a greeting message.")
-  parser.add_argument("--noise_scale", type=float, default=0.667, help="Specify the noise scale. Default is 0.667.")
-  parser.add_argument("--noise_scale_w", type=float, default=0.8, help="Specify the noise scale w. Default is 0.8.")
-  parser.add_argument("--length_scale", type=float, default=1, help="Specify the length scale. Default is 1.")
-  parser.add_argument("--seed", type=int, default=1337, help="Specify the seed (set to None if no seed). Default is 1337.")
-  parser.add_argument("--num_channels", type=int, default=1, help="Specify the number of audio output channels. Default is 1.")
-  parser.add_argument("--sample_width", type=int, default=2, help="Specify the number of bytes per sample, adjust if necessary. Default is 2.")
-  parser.add_argument("--emotion_path", type=str, default=None, help="Specify the path to emotion reference.")
-  parser.add_argument("--estimate_max_y_length", type=str, default=False, help="If true, overestimate the output length and then trim it to the correct length, to prevent premature realization, much more performant for larger inputs, for smaller inputs not so much. Default is False.")
-  args = parser.parse_args()
-
-  model_config = MODELS[args.model_to_use]
-
-  # Load the hyperparameters from the config file.
-  hps = get_hparams_from_file(fetch(model_config[0]))
-
-  # If model has multiple speakers, validate speaker id and retrieve name if available.
-  model_has_multiple_speakers = hps.data.n_speakers > 0
-  if model_has_multiple_speakers:
-    logging.info(f"Model has {hps.data.n_speakers} speakers")
-    if args.speaker_id >= hps.data.n_speakers: raise ValueError(f"Speaker ID {args.speaker_id} is invalid for this model.")
-    speaker_name = "?"
-    if hps.__contains__("speakers"): # maps speaker ids to names
-      speakers = hps.speakers
-      if isinstance(speakers, List): speakers = {speaker: i for i, speaker in enumerate(speakers)}
-      speaker_name = next((key for key, value in speakers.items() if value == args.speaker_id), None)
-    logging.info(f"You selected speaker {args.speaker_id} (name: {speaker_name})")
-
-  # Load emotions if any. TODO: find an english model with emotions, this is untested atm.
-  emotion_embedding = None
-  if args.emotion_path is not None:
-    if args.emotion_path.endswith(".npy"): emotion_embedding = Tensor(np.load(args.emotion_path), dtype=dtypes.int64).unsqueeze(0)
-    else: raise ValueError("Emotion path must be a .npy file.")
-
-  # Load symbols, instantiate TextMapper and clean the text.
-  if hps.__contains__("symbols"): symbols = hps.symbols
-  elif args.model_to_use == "mmts-tts": symbols = [x.replace("\n", "") for x in fetch("https://huggingface.co/facebook/mms-tts/raw/main/full_models/eng/vocab.txt").open(encoding="utf-8").readlines()]
-  else: symbols = ['_'] + list(';:,.!?¡¿—…"«»“” ') + list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz') + list("ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ")
-  text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)
-
-  # Load the model.
-  if args.seed is not None:
-    Tensor.manual_seed(args.seed)
-    np.random.seed(args.seed)
-  net_g = load_model(text_mapper.symbols, hps, model_config)
-  logging.debug(f"Loaded model with hps: {hps}")
-
-  # Convert the input text to a tensor.
-  text_to_synthesize = args.text_to_synthesize
-  if args.model_to_use == "mmts-tts": text_to_synthesize = text_mapper.filter_oov(text_to_synthesize.lower())
-  stn_tst = text_mapper.get_text(text_to_synthesize, hps.data.add_blank, hps.data.text_cleaners)
-  logging.debug(f"Converted input text to tensor \"{text_to_synthesize}\" -> Tensor({stn_tst.shape}): {stn_tst.numpy()}")
-  x_tst, x_tst_lengths = stn_tst.unsqueeze(0), Tensor([stn_tst.shape[0]], dtype=dtypes.int64)
-  sid = Tensor([args.speaker_id], dtype=dtypes.int64) if model_has_multiple_speakers else None
-
-  # Perform inference.
-  start_time = time.time()
-  audio_tensor = net_g.infer(x_tst, x_tst_lengths, sid, args.noise_scale, args.length_scale, args.noise_scale_w, emotion_embedding=emotion_embedding,
-                             max_y_length_estimate_scale=Y_LENGTH_ESTIMATE_SCALARS[args.model_to_use] if args.estimate_max_y_length else None)[0, 0].realize()
-  logging.info(f"Inference took {(time.time() - start_time):.2f}s")
-
-  # Save the audio output.
-  audio_data = (np.clip(audio_tensor.numpy(), -1.0, 1.0) * 32767).astype(np.int16)
-  out_path = Path(args.out_path or Path(args.out_dir)/f"{args.model_to_use}{f'_sid_{args.speaker_id}' if model_has_multiple_speakers else ''}_{args.base_name}.wav")
-  out_path.parent.mkdir(parents=True, exist_ok=True)
-  with wave.open(str(out_path), 'wb') as wav_file:
-    wav_file.setnchannels(args.num_channels)
-    wav_file.setsampwidth(args.sample_width)
-    wav_file.setframerate(hps.data.sampling_rate)
-    wav_file.setnframes(len(audio_data))
-    wav_file.writeframes(audio_data.tobytes())
-  logging.info(f"Saved audio output to {out_path}")
@@ -26,11 +26,13 @@ def color_temp(temp):
 def color_voltage(voltage): return colored(f"{voltage/1000:>5.3f}V", "cyan")

 def draw_bar(percentage, width=40, fill='|', empty=' ', opt_text='', color='cyan'):
+  percentage = 0.0 if percentage != percentage else percentage  # NaN guard
+  percentage = max(0.0, min(1.0, float(percentage)))
  filled_width = int(width * percentage)
  if not opt_text: opt_text = f'{percentage*100:.1f}%'

  bar = fill * filled_width + empty * (width - filled_width)
-  bar = (bar[:-len(opt_text)] + opt_text) if opt_text else bar
+  if opt_text and len(opt_text) <= len(bar): bar = (bar[:-len(opt_text)] + opt_text)
  bar = colored(bar[:filled_width], color) + bar[filled_width:]
  return f'[{bar}]'

@@ -88,6 +90,7 @@ class SMICtx:
    self.opened_pci_resources = {}
    self.prev_lines_cnt = 0
    self.prev_terminal_width = 0
+    self.prev_terminal_height = 0

    remove_parts = ["Advanced Micro Devices, Inc. [AMD/ATI]", "VGA compatible controller:"]
    lspci = subprocess.check_output(["lspci"]).decode("utf-8").splitlines()
@@ -95,6 +98,20 @@ class SMICtx:
    for k,v in self.lspci.items():
      for part in remove_parts: self.lspci[k] = self.lspci[k].replace(part, "").strip().rstrip()

+  def _smuq10_round(self, v:int) -> int:
+    v = int(v)
+    return (v + 512) >> 10  # SMUQ10_ROUND
+
+  def _fmt_kb(self, kb:int) -> str:
+    kb = int(kb)
+    if kb < 1024: return f"{kb}KB"
+    mb = kb / 1024.0
+    if mb < 1024: return f"{mb:.1f}MB"
+    gb = mb / 1024.0
+    if gb < 1024: return f"{gb:.2f}GB"
+    tb = gb / 1024.0
+    return f"{tb:.2f}TB"
+
  def _open_am_device(self, pcibus):
    if pcibus not in self.opened_pci_resources:
      bar_fds = {bar: os.open(f"/sys/bus/pci/devices/{pcibus}/resource{bar}", os.O_RDWR | os.O_SYNC) for bar in [0, 2, 5]}
@@ -116,6 +133,7 @@ class SMICtx:
  def rescan_devs(self):
    pattern = os.path.join('/tmp', 'am_*.lock')
    for d in [f[8:-5] for f in glob.glob(pattern)]:
+      if d.startswith("usb"): continue
      if d not in self.opened_pcidevs:
        self._open_am_device(d)

@@ -131,21 +149,53 @@ class SMICtx:
        os.system('clear')
        if DEBUG >= 2: print(f"Removed AM device {d.pcibus}")

-  def collect(self): return {d: d.smu.read_metrics() if d.pci_state == "D0" else None for d in self.devs}
+  def collect(self):
+    tables = {}
+    for dev in self.devs:
+      match dev.ip_ver[am.MP1_HWIP]:
+        case (13,0,6): table_t = dev.smu.smu_mod.MetricsTableX_t
+        case (13,0,12): table_t = dev.smu.smu_mod.MetricsTableV2_t
+        case _: table_t = dev.smu.smu_mod.SmuMetricsExternal_t
+      tables[dev] = dev.smu.read_table(table_t, dev.smu.smu_mod.SMU_TABLE_SMU_METRICS) if dev.pci_state == "D0" else None
+    return tables

-  def get_gfx_activity(self, dev, metrics): return metrics.SmuMetrics.AverageGfxActivity
-  def get_mem_activity(self, dev, metrics): return metrics.SmuMetrics.AverageUclkActivity
+  def _pick_nonzero_avg(self, vals) -> int:
+    xs = [x for x in vals if x > 0]
+    return int(sum(xs) / len(xs)) if xs else 0
+
+  def get_gfx_activity(self, dev, metrics):
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return max(0, min(100, self._smuq10_round(metrics.SocketGfxBusy)))
+      case _: return metrics.SmuMetrics.AverageGfxActivity
+
+  def get_mem_activity(self, dev, metrics):
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return max(0, min(100, self._smuq10_round(metrics.DramBandwidthUtilization)))
+      case _: return metrics.SmuMetrics.AverageUclkActivity

  def get_temps(self, dev, metrics, compact=False):
-    temps_keys = [(k, name) for k, name in dev.smu.smu_mod.c__EA_TEMP_e__enumvalues.items()
-                  if k < dev.smu.smu_mod.TEMP_COUNT and metrics.SmuMetrics.AvgTemperature[k] != 0]
-    if compact: temps_keys = [(k, name) for k, name in temps_keys if k in (dev.smu.smu_mod.TEMP_HOTSPOT, dev.smu.smu_mod.TEMP_MEM)]
-    return {name: metrics.SmuMetrics.AvgTemperature[k] for k, name in temps_keys}
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6):
+        temps = {
+          "Hotspot": self._smuq10_round(metrics.MaxSocketTemperature),
+          "HBM": self._smuq10_round(metrics.MaxHbmTemperature),
+          "VR": self._smuq10_round(metrics.MaxVrTemperature),
+        }
+        if compact: return {k: temps[k] for k in ("Hotspot", "HBM") if temps.get(k, 0) != 0}
+        return {k: v for k, v in temps.items() if v != 0}
+      case _:
+        temps_keys = [(k, name) for k, name in dev.smu.smu_mod.TEMP_e.items()
+                      if k < dev.smu.smu_mod.TEMP_COUNT and metrics.SmuMetrics.AvgTemperature[k] != 0]
+        if compact: temps_keys = [(k, name) for k, name in temps_keys if k in (dev.smu.smu_mod.TEMP_HOTSPOT, dev.smu.smu_mod.TEMP_MEM)]
+        return {name: metrics.SmuMetrics.AvgTemperature[k] for k, name in temps_keys}

  def get_voltage(self, dev, metrics, compact=False):
-    voltage_keys = [(k, name) for k, name in dev.smu.smu_mod.c__EA_SVI_PLANE_e__enumvalues.items()
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return {}
+      case _:
+        voltage_keys = [(k, name) for k, name in dev.smu.smu_mod.SVI_PLANE_e.items()
                        if k < dev.smu.smu_mod.SVI_PLANE_COUNT and metrics.SmuMetrics.AvgVoltage[k] != 0]
-    return {name: metrics.SmuMetrics.AvgVoltage[k] for k, name in voltage_keys}
+        return {name: metrics.SmuMetrics.AvgVoltage[k] for k, name in voltage_keys}

  def get_busy_threshold(self, dev):
    match dev.ip_ver[am.MP1_HWIP]:
@@ -153,22 +203,40 @@ class SMICtx:
      case _: return 15

  def get_gfx_freq(self, dev, metrics):
-    return metrics.SmuMetrics.AverageGfxclkFrequencyPostDs if self.get_gfx_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
-          metrics.SmuMetrics.AverageGfxclkFrequencyPreDs
+    if metrics is None: return 0
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return self._smuq10_round(metrics.GfxclkFrequency[0])
+      case _:
+        return metrics.SmuMetrics.AverageGfxclkFrequencyPostDs if self.get_gfx_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
+               metrics.SmuMetrics.AverageGfxclkFrequencyPreDs

  def get_mem_freq(self, dev, metrics):
-    return metrics.SmuMetrics.AverageMemclkFrequencyPostDs if self.get_mem_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
-           metrics.SmuMetrics.AverageMemclkFrequencyPreDs
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return self._smuq10_round(metrics.UclkFrequency)
+      case _:
+        return metrics.SmuMetrics.AverageMemclkFrequencyPostDs if self.get_mem_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
+               metrics.SmuMetrics.AverageMemclkFrequencyPreDs

  def get_fckl_freq(self, dev, metrics):
-    return metrics.SmuMetrics.AverageFclkFrequencyPostDs if self.get_mem_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
-           metrics.SmuMetrics.AverageFclkFrequencyPreDs
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return self._smuq10_round(metrics.FclkFrequency)
+      case _:
+        return metrics.SmuMetrics.AverageFclkFrequencyPostDs if self.get_mem_activity(dev, metrics) <= self.get_busy_threshold(dev) else \
+               metrics.SmuMetrics.AverageFclkFrequencyPreDs

-  def get_fan_rpm_pwm(self, dev, metrics): return metrics.SmuMetrics.AvgFanRpm, metrics.SmuMetrics.AvgFanPwm
+  def get_fan_rpm_pwm(self, dev, metrics):
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return None, None
+      case _: return metrics.SmuMetrics.AvgFanRpm, metrics.SmuMetrics.AvgFanPwm

-  def get_power(self, dev, metrics): return metrics.SmuMetrics.AverageSocketPower, metrics.SmuMetrics.dGPU_W_MAX
+  def get_power(self, dev, metrics):
+    match dev.ip_ver[am.MP1_HWIP]:
+      case (13,0,6): return self._smuq10_round(metrics.SocketPower), self._smuq10_round(metrics.MaxSocketPowerLimit)
+      case _: return metrics.SmuMetrics.AverageSocketPower, metrics.SmuMetrics.dGPU_W_MAX

  def get_mem_usage(self, dev):
+    return 0
+
    usage = 0
    pt_stack = [dev.mm.root_page_table]
    while len(pt_stack) > 0:
@@ -177,7 +245,7 @@ class SMICtx:
        entry = pt.entries[i]

        if (entry & am.AMDGPU_PTE_VALID) == 0: continue
-        if pt.lv!=am.AMDGPU_VM_PTB and not dev.gmc.is_pte_huge_page(entry):
+        if pt.lv!=am.AMDGPU_VM_PTB and not dev.gmc.is_pte_huge_page(pt.lv, entry):
          pt_stack.append(AMPageTableEntry(dev, entry & 0x0000FFFFFFFFF000, lv=pt.lv+1))
          continue
        if (entry & am.AMDGPU_PTE_SYSTEM) != 0: continue
@@ -219,23 +287,28 @@ class SMICtx:
      temps_table_compact = ["Temps (°C):" + '/'.join([f"{color_temp(val)} {name}" for name, val in temps_data_compact.items()])]

      fan_rpm, fan_pwm = self.get_fan_rpm_pwm(dev, metrics)
-      power_table = ["=== Power ==="] + [f"Fan Speed: {fan_rpm} RPM"] + [f"Fan Power: {fan_pwm}%"]
+      power_table = ["=== Power ==="]
+      power_table += ["Fan: N/A"] if fan_rpm is None or fan_pwm is None else [f"Fan Speed: {fan_rpm} RPM", f"Fan Power: {fan_pwm}%"]

      total_power, max_power = self.get_power(dev, metrics)
-      power_line = [f"Power: " + draw_bar(total_power / max_power, 16, opt_text=f"{total_power}/{max_power}W")]
-      power_line_compact = [f"Power:       " + draw_bar(total_power / max_power, activity_line_width, opt_text=f"{total_power}/{max_power}W")]
+      if max_power > 0:
+        power_line = [f"Power: " + draw_bar(total_power / max_power, 16, opt_text=f"{total_power}/{max_power}W")]
+        power_line_compact = [f"Power:       " + draw_bar(total_power / max_power, activity_line_width, opt_text=f"{total_power}/{max_power}W")]
+      else:
+        power_line = ["Power: N/A"]
+        power_line_compact = ["Power: N/A"]

      voltage_data = self.get_voltage(dev, metrics)
-      voltage_table = ["=== Voltages ==="] + [f"{name:<20}: {color_voltage(voltage)}" for name, voltage in voltage_data.items()]
+      voltage_table = None if not voltage_data else (["=== Voltages ==="] + [f"{name:<20}: {color_voltage(voltage)}" for name, voltage in voltage_data.items()])

      gfx_freq = self.get_gfx_freq(dev, metrics)
      mclk_freq = self.get_mem_freq(dev, metrics)
      fclk_freq = self.get_fckl_freq(dev, metrics)
-
      frequency_table = ["=== Frequencies ===", f"GFXCLK: {gfx_freq:>4} MHz", f"FCLK  : {fclk_freq:>4} MHz", f"MCLK  : {mclk_freq:>4} MHz"]

      if self.prev_terminal_width >= 231:
-        power_table += power_line + [""] + voltage_table
+        power_table += power_line
+        if voltage_table is not None: power_table += [""] + voltage_table
        activity_line += [""]
      elif self.prev_terminal_width >= 171:
        power_table += power_line + [""] + frequency_table
@@ -307,4 +380,5 @@ if __name__ == "__main__":
      smi_ctx.draw(args.list)
      if args.list: break
      time.sleep(1)
-  except KeyboardInterrupt: print("Exiting...")
+  except KeyboardInterrupt:
+    print("Exiting...")
@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+from tinygrad.helpers import Context
+from tinygrad.runtime.support.system import System, PCIDevice, PCIDevImplBase
+from tinygrad.runtime.support.am.amdev import AMDev
+
+if __name__ == "__main__":
+  gpus = System.pci_scan_bus(0x1002, [(0xffff, [0x74a1, 0x75a0])])
+  pcidevs = [PCIDevice(f"reset:{gpu}", gpu, bars=[0, 2, 5]) for gpu in gpus]
+  amdevs = []
+  with Context(DEBUG=2):
+    for pcidev in pcidevs:
+      amdevs.append(AMDev(pcidev, reset_mode=True))
+    for amdev in amdevs: amdev.smu.mode1_reset()
@@ -1,48 +1,65 @@
 import re, ctypes, sys, importlib
+from tinygrad.helpers import getenv

 from tinygrad.runtime.support.am.amdev import AMDev, AMRegister
+
+class GFXFake:
+  def __init__(self): self.xccs = 8
+
 class AMDFake(AMDev):
-  def __init__(self, devfmt, vram, doorbell, mmio, dma_regions=None):
-    self.devfmt, self.vram, self.doorbell64, self.mmio, self.dma_regions = devfmt, vram, doorbell, mmio, dma_regions
+  def __init__(self, pci_dev, dma_regions=None):
+    self.pci_dev, self.devfmt, self.dma_regions = pci_dev, pci_dev.pcibus, dma_regions
+    self.vram, self.doorbell64, self.mmio = self.pci_dev.map_bar(0), self.pci_dev.map_bar(2, fmt='Q'), self.pci_dev.map_bar(5, fmt='I')
    self._run_discovery()
    self._build_regs()

+    self.gfx = GFXFake()
+
 amdev = importlib.import_module("tinygrad.runtime.support.am.amdev")
 amdev.AMDev = AMDFake
-
 from tinygrad.runtime.ops_amd import PCIIface

-def parse_amdgpu_logs(log_content, register_names=None):
-  register_map = register_names
+def parse_amdgpu_logs(log_content, register_names=None, *, only_xcc0: bool = False):
+  register_map = register_names or {}

-  final = ""
  def replace_register(match):
-    register = match.group(1)
-    return f"Reading register {register_map.get(int(register, base=16), register)}"
+    reg = match.group(1)
+    return f"Reading register {register_map.get(int(reg, 16), reg)}"

-  pattern = r'Reading register (0x[0-9a-fA-F]+)'
-
-  processed_log = re.sub(pattern, replace_register, log_content)
+  processed_log = re.sub(r'Reading register (0x[0-9a-fA-F]+)', replace_register, log_content)

  def replace_register_2(match):
-    register = match.group(1)
-    return f"Writing register {register_map.get(int(register, base=16), register)}"
+    reg = match.group(1)
+    return f"Writing register {register_map.get(int(reg, 16), reg)}"
+
+  processed_log = re.sub(r'Writing register (0x[0-9a-fA-F]+)', replace_register_2, processed_log)
+
+  # remove timing prefix
+  processed_log = re.sub(r'^\[\s*\d+(?:\.\d+)?\]\s*', '', processed_log, flags=re.MULTILINE)
+
+  # keep only xcc=0 lines (but keep lines with no xcc at all)
+  if only_xcc0:
+    kept = []
+    for line in processed_log.splitlines(True):
+      if "xcc=" not in line or re.search(r'\bxcc=0\b', line): kept.append(line)
+    processed_log = "".join(kept)

-  pattern = r'Writing register (0x[0-9a-fA-F]+)'
-  processed_log = re.sub(pattern, replace_register_2, processed_log)
  return processed_log

 def main():
+  only_xcc0 = bool(getenv("ONLY_XCC0", 0))
+
  reg_names = {}
  dev = PCIIface(None, 0)
  for x, y in dev.dev_impl.__dict__.items():
    if isinstance(y, AMRegister):
-      for inst, addr in y.addr.items(): reg_names[addr] = f"{x}, xcc={inst}"
+      for xcc, addr in y.addr.items():
+        reg_names[addr] = f"{x}, xcc={xcc}"

  with open(sys.argv[1], 'r') as f:
-    log_content = log_content_them = f.read()
+    log_content = f.read()

-  processed_log = parse_amdgpu_logs(log_content, reg_names)
+  processed_log = parse_amdgpu_logs(log_content, reg_names, only_xcc0=only_xcc0)

  with open(sys.argv[2], 'w') as f:
    f.write(processed_log)
@@ -51,5 +68,4 @@ if __name__ == '__main__':
  if len(sys.argv) != 3:
    print("Usage: <input_file_path> <output_file_path>")
    sys.exit(1)
-
-  main()
+  main()
@@ -0,0 +1,760 @@
+# RDNA3 assembler and disassembler
+from __future__ import annotations
+import re
+from extra.assembly.amd.dsl import Inst, RawImm, Reg, SrcMod, SGPR, VGPR, TTMP, s, v, ttmp, _RegFactory, FLOAT_ENC, SRC_FIELDS, unwrap
+from extra.assembly.amd.dsl import VCC_LO, VCC_HI, VCC, EXEC_LO, EXEC_HI, EXEC, SCC, M0, NULL, OFF
+
+# Decoding helpers
+SPECIAL_GPRS = {106: "vcc_lo", 107: "vcc_hi", 124: "null", 125: "m0", 126: "exec_lo", 127: "exec_hi", 253: "scc"}
+SPECIAL_DEC = {**SPECIAL_GPRS, **{v: str(k) for k, v in FLOAT_ENC.items()}}
+SPECIAL_PAIRS = {106: "vcc", 126: "exec"}  # Special register pairs (for 64-bit ops)
+# GFX11 hwreg names (IDs 16-17 are TBA - not supported, IDs 18-19 are PERF_SNAPSHOT)
+HWREG_NAMES = {1: 'HW_REG_MODE', 2: 'HW_REG_STATUS', 3: 'HW_REG_TRAPSTS', 4: 'HW_REG_HW_ID', 5: 'HW_REG_GPR_ALLOC',
+               6: 'HW_REG_LDS_ALLOC', 7: 'HW_REG_IB_STS', 15: 'HW_REG_SH_MEM_BASES', 18: 'HW_REG_PERF_SNAPSHOT_PC_LO',
+               19: 'HW_REG_PERF_SNAPSHOT_PC_HI', 20: 'HW_REG_FLAT_SCR_LO', 21: 'HW_REG_FLAT_SCR_HI',
+               22: 'HW_REG_XNACK_MASK', 23: 'HW_REG_HW_ID1', 24: 'HW_REG_HW_ID2', 25: 'HW_REG_POPS_PACKER', 28: 'HW_REG_IB_STS2'}
+HWREG_IDS = {v.lower(): k for k, v in HWREG_NAMES.items()}  # Reverse map for assembler
+MSG_NAMES = {128: 'MSG_RTN_GET_DOORBELL', 129: 'MSG_RTN_GET_DDID', 130: 'MSG_RTN_GET_TMA',
+             131: 'MSG_RTN_GET_REALTIME', 132: 'MSG_RTN_SAVE_WAVE', 133: 'MSG_RTN_GET_TBA'}
+_16BIT_TYPES = ('f16', 'i16', 'u16', 'b16')
+def _is_16bit(s: str) -> bool: return any(s.endswith(x) for x in _16BIT_TYPES)
+
+def decode_src(val: int) -> str:
+  if val <= 105: return f"s{val}"
+  if val in SPECIAL_DEC: return SPECIAL_DEC[val]
+  if 108 <= val <= 123: return f"ttmp{val - 108}"
+  if 128 <= val <= 192: return str(val - 128)
+  if 193 <= val <= 208: return str(-(val - 192))
+  if 256 <= val <= 511: return f"v{val - 256}"
+  return "lit" if val == 255 else f"?{val}"
+
+def _reg(prefix: str, base: int, cnt: int = 1) -> str: return f"{prefix}{base}" if cnt == 1 else f"{prefix}[{base}:{base+cnt-1}]"
+def _sreg(base: int, cnt: int = 1) -> str: return _reg("s", base, cnt)
+def _vreg(base: int, cnt: int = 1) -> str: return _reg("v", base, cnt)
+
+def _fmt_sdst(v: int, cnt: int = 1) -> str:
+  """Format SGPR destination with special register names."""
+  if v == 124: return "null"
+  if 108 <= v <= 123: return _reg("ttmp", v - 108, cnt)
+  if cnt > 1 and v in SPECIAL_PAIRS: return SPECIAL_PAIRS[v]
+  if cnt > 1: return _sreg(v, cnt)
+  return {126: "exec_lo", 127: "exec_hi", 106: "vcc_lo", 107: "vcc_hi", 125: "m0"}.get(v, f"s{v}")
+
+def _fmt_ssrc(v: int, cnt: int = 1) -> str:
+  """Format SGPR source with special register names and pairs."""
+  if cnt == 2:
+    if v in SPECIAL_PAIRS: return SPECIAL_PAIRS[v]
+    if v <= 105: return _sreg(v, 2)
+    if 108 <= v <= 123: return _reg("ttmp", v - 108, 2)
+  return decode_src(v)
+
+def _fmt_src_n(v: int, cnt: int) -> str:
+  """Format source with given register count (1, 2, or 4)."""
+  if cnt == 1: return decode_src(v)
+  if v >= 256: return _vreg(v - 256, cnt)
+  if v <= 105: return _sreg(v, cnt)
+  if cnt == 2 and v in SPECIAL_PAIRS: return SPECIAL_PAIRS[v]
+  if 108 <= v <= 123: return _reg("ttmp", v - 108, cnt)
+  return decode_src(v)
+
+def _fmt_src64(v: int) -> str:
+  """Format 64-bit source (VGPR pair, SGPR pair, or special pair)."""
+  return _fmt_src_n(v, 2)
+
+def _parse_sop_sizes(op_name: str) -> tuple[int, ...]:
+  """Parse dst and src sizes from SOP instruction name. Returns (dst_cnt, src0_cnt) or (dst_cnt, src0_cnt, src1_cnt)."""
+  if op_name in ('s_bitset0_b64', 's_bitset1_b64'): return (2, 1)
+  if op_name in ('s_lshl_b64', 's_lshr_b64', 's_ashr_i64', 's_bfe_u64', 's_bfe_i64'): return (2, 2, 1)
+  if op_name in ('s_bfm_b64',): return (2, 1, 1)
+  # SOPC: s_bitcmp0_b64, s_bitcmp1_b64 - 64-bit src0, 32-bit src1 (bit index)
+  if op_name in ('s_bitcmp0_b64', 's_bitcmp1_b64'): return (1, 2, 1)
+  if m := re.search(r'_(b|i|u)(32|64)_(b|i|u)(32|64)$', op_name):
+    return (2 if m.group(2) == '64' else 1, 2 if m.group(4) == '64' else 1)
+  if m := re.search(r'_(b|i|u)(32|64)$', op_name):
+    sz = 2 if m.group(2) == '64' else 1
+    return (sz, sz)
+  return (1, 1)
+
+# Waitcnt helpers (RDNA3 format: bits 15:10=vmcnt, bits 9:4=lgkmcnt, bits 3:0=expcnt)
+def waitcnt(vmcnt: int = 0x3f, expcnt: int = 0x7, lgkmcnt: int = 0x3f) -> int:
+  return (expcnt & 0x7) | ((lgkmcnt & 0x3f) << 4) | ((vmcnt & 0x3f) << 10)
+def decode_waitcnt(val: int) -> tuple[int, int, int]:
+  return (val >> 10) & 0x3f, val & 0xf, (val >> 4) & 0x3f  # vmcnt, expcnt, lgkmcnt
+
+# VOP3SD opcodes (shared encoding with VOP3 but different field layout)
+# Note: opcodes 0-255 are VOPC promoted to VOP3 - never treat as VOP3SD
+VOP3SD_OPCODES = {288, 289, 290, 764, 765, 766, 767, 768, 769, 770}
+
+# Disassembler
+def disasm(inst: Inst) -> str:
+  op_val = unwrap(inst._values.get('op', 0))
+  cls_name = inst.__class__.__name__
+  # VOP3 and VOP3SD share encoding - check opcode to determine which
+  is_vop3sd = cls_name == 'VOP3' and op_val in VOP3SD_OPCODES
+  try:
+    from extra.assembly.amd.autogen import rdna3 as autogen
+    if is_vop3sd:
+      op_name = autogen.VOP3SDOp(op_val).name.lower()
+    else:
+      op_name = getattr(autogen, f"{cls_name}Op")(op_val).name.lower() if hasattr(autogen, f"{cls_name}Op") else f"op_{op_val}"
+  except (ValueError, KeyError): op_name = f"op_{op_val}"
+  def fmt_src(v): return f"0x{inst._literal:x}" if v == 255 and inst._literal is not None else decode_src(v)
+
+  # VOP1
+  if cls_name == 'VOP1':
+    vdst, src0 = unwrap(inst._values['vdst']), unwrap(inst._values['src0'])
+    if op_name == 'v_nop': return 'v_nop'
+    if op_name == 'v_pipeflush': return 'v_pipeflush'
+    parts = op_name.split('_')
+    is_16bit_dst = any(p in _16BIT_TYPES for p in parts[-2:-1]) or (len(parts) >= 2 and parts[-1] in _16BIT_TYPES and 'cvt' not in op_name)
+    is_16bit_src = parts[-1] in _16BIT_TYPES and 'sat_pk' not in op_name
+    _F64_OPS = ('v_ceil_f64', 'v_floor_f64', 'v_fract_f64', 'v_frexp_mant_f64', 'v_rcp_f64', 'v_rndne_f64', 'v_rsq_f64', 'v_sqrt_f64', 'v_trunc_f64')
+    is_f64_dst = op_name in _F64_OPS or op_name in ('v_cvt_f64_f32', 'v_cvt_f64_i32', 'v_cvt_f64_u32')
+    is_f64_src = op_name in _F64_OPS or op_name in ('v_cvt_f32_f64', 'v_cvt_i32_f64', 'v_cvt_u32_f64', 'v_frexp_exp_i32_f64')
+    if op_name == 'v_readfirstlane_b32':
+      return f"v_readfirstlane_b32 {decode_src(vdst)}, v{src0 - 256 if src0 >= 256 else src0}"
+    dst_str = _vreg(vdst, 2) if is_f64_dst else f"v{vdst & 0x7f}.{'h' if vdst >= 128 else 'l'}" if is_16bit_dst else f"v{vdst}"
+    src_str = _fmt_src64(src0) if is_f64_src else f"v{(src0 - 256) & 0x7f}.{'h' if src0 >= 384 else 'l'}" if is_16bit_src and src0 >= 256 else fmt_src(src0)
+    return f"{op_name}_e32 {dst_str}, {src_str}"
+
+  # VOP2
+  if cls_name == 'VOP2':
+    vdst, src0_raw, vsrc1 = unwrap(inst._values['vdst']), unwrap(inst._values['src0']), unwrap(inst._values['vsrc1'])
+    suffix = "" if op_name == "v_dot2acc_f32_f16" else "_e32"
+    is_16bit_op = ('_f16' in op_name or '_i16' in op_name or '_u16' in op_name) and '_f32' not in op_name and '_i32' not in op_name and 'pk_' not in op_name
+    if is_16bit_op:
+      dst_str = f"v{vdst & 0x7f}.{'h' if vdst >= 128 else 'l'}"
+      src0_str = f"v{(src0_raw - 256) & 0x7f}.{'h' if src0_raw >= 384 else 'l'}" if src0_raw >= 256 else fmt_src(src0_raw)
+      vsrc1_str = f"v{vsrc1 & 0x7f}.{'h' if vsrc1 >= 128 else 'l'}"
+    else:
+      dst_str, src0_str, vsrc1_str = f"v{vdst}", fmt_src(src0_raw), f"v{vsrc1}"
+    return f"{op_name}{suffix} {dst_str}, {src0_str}, {vsrc1_str}" + (", vcc_lo" if op_name == "v_cndmask_b32" else "")
+
+  # VOPC
+  if cls_name == 'VOPC':
+    src0, vsrc1 = unwrap(inst._values['src0']), unwrap(inst._values['vsrc1'])
+    is_64bit = any(x in op_name for x in ('f64', 'i64', 'u64'))
+    is_64bit_vsrc1 = is_64bit and 'class' not in op_name
+    is_16bit = any(x in op_name for x in ('_f16', '_i16', '_u16')) and 'f32' not in op_name
+    is_cmpx = op_name.startswith('v_cmpx')  # VOPCX writes to exec, no vcc destination
+    src0_str = _fmt_src64(src0) if is_64bit else f"v{(src0 - 256) & 0x7f}.{'h' if src0 >= 384 else 'l'}" if is_16bit and src0 >= 256 else fmt_src(src0)
+    vsrc1_str = _vreg(vsrc1, 2) if is_64bit_vsrc1 else f"v{vsrc1 & 0x7f}.{'h' if vsrc1 >= 128 else 'l'}" if is_16bit else f"v{vsrc1}"
+    return f"{op_name}_e32 {src0_str}, {vsrc1_str}" if is_cmpx else f"{op_name}_e32 vcc_lo, {src0_str}, {vsrc1_str}"
+
+  # SOPP
+  if cls_name == 'SOPP':
+    simm16 = unwrap(inst._values.get('simm16', 0))
+    # No-operand instructions (simm16 is ignored)
+    no_imm_ops = ('s_endpgm', 's_barrier', 's_wakeup', 's_icache_inv', 's_ttracedata', 's_ttracedata_imm',
+                  's_wait_idle', 's_endpgm_saved', 's_code_end', 's_endpgm_ordered_ps_done')
+    if op_name in no_imm_ops: return op_name
+    if op_name == 's_waitcnt':
+      vmcnt, expcnt, lgkmcnt = decode_waitcnt(simm16)
+      parts = []
+      if vmcnt != 0x3f: parts.append(f"vmcnt({vmcnt})")
+      if expcnt != 0x7: parts.append(f"expcnt({expcnt})")
+      if lgkmcnt != 0x3f: parts.append(f"lgkmcnt({lgkmcnt})")
+      return f"s_waitcnt {' '.join(parts)}" if parts else "s_waitcnt 0"
+    if op_name == 's_delay_alu':
+      dep_names = ['VALU_DEP_1','VALU_DEP_2','VALU_DEP_3','VALU_DEP_4','TRANS32_DEP_1','TRANS32_DEP_2','TRANS32_DEP_3','FMA_ACCUM_CYCLE_1','SALU_CYCLE_1','SALU_CYCLE_2','SALU_CYCLE_3']
+      skip_names = ['SAME','NEXT','SKIP_1','SKIP_2','SKIP_3','SKIP_4']
+      id0, skip, id1 = simm16 & 0xf, (simm16 >> 4) & 0x7, (simm16 >> 7) & 0xf
+      def dep_name(v): return dep_names[v-1] if 0 < v <= len(dep_names) else str(v)
+      parts = [f"instid0({dep_name(id0)})"] if id0 else []
+      if skip: parts.append(f"instskip({skip_names[skip]})")
+      if id1: parts.append(f"instid1({dep_name(id1)})")
+      return f"s_delay_alu {' | '.join(p for p in parts if p)}" if parts else "s_delay_alu 0"
+    if op_name.startswith('s_cbranch') or op_name.startswith('s_branch'):
+      return f"{op_name} {simm16}"
+    # Most SOPP ops require immediate (s_nop, s_setkill, s_sethalt, s_sleep, s_setprio, s_sendmsg*, etc.)
+    return f"{op_name} 0x{simm16:x}"
+
+  # SMEM
+  if cls_name == 'SMEM':
+    if op_name in ('s_gl1_inv', 's_dcache_inv'): return op_name
+    sdata, sbase, soffset, offset = unwrap(inst._values['sdata']), unwrap(inst._values['sbase']), unwrap(inst._values['soffset']), unwrap(inst._values.get('offset', 0))
+    glc, dlc = unwrap(inst._values.get('glc', 0)), unwrap(inst._values.get('dlc', 0))
+    # Format offset: "soffset offset:X" if both, "0x{offset:x}" if only imm, or decode_src(soffset)
+    off_str = f"{decode_src(soffset)} offset:0x{offset:x}" if offset and soffset != 124 else f"0x{offset:x}" if offset else decode_src(soffset)
+    sbase_idx, sbase_cnt = sbase * 2, 4 if (8 <= op_val <= 12 or op_name == 's_atc_probe_buffer') else 2
+    sbase_str = _fmt_ssrc(sbase_idx, sbase_cnt) if sbase_cnt == 2 else _sreg(sbase_idx, sbase_cnt) if sbase_idx <= 105 else _reg("ttmp", sbase_idx - 108, sbase_cnt)
+    if op_name in ('s_atc_probe', 's_atc_probe_buffer'): return f"{op_name} {sdata}, {sbase_str}, {off_str}"
+    width = {0:1, 1:2, 2:4, 3:8, 4:16, 8:1, 9:2, 10:4, 11:8, 12:16}.get(op_val, 1)
+    mods = [m for m in ["glc" if glc else "", "dlc" if dlc else ""] if m]
+    return f"{op_name} {_fmt_sdst(sdata, width)}, {sbase_str}, {off_str}" + (" " + " ".join(mods) if mods else "")
+
+  # FLAT
+  if cls_name == 'FLAT':
+    vdst, addr, data, saddr, offset, seg = [unwrap(inst._values.get(f, 0)) for f in ['vdst', 'addr', 'data', 'saddr', 'offset', 'seg']]
+    instr = f"{['flat', 'scratch', 'global'][seg] if seg < 3 else 'flat'}_{op_name.split('_', 1)[1] if '_' in op_name else op_name}"
+    width = {'b32':1, 'b64':2, 'b96':3, 'b128':4, 'u8':1, 'i8':1, 'u16':1, 'i16':1}.get(op_name.split('_')[-1], 1)
+    addr_str = _vreg(addr, 2) if saddr == 0x7F else _vreg(addr)
+    saddr_str = "" if saddr == 0x7F else f", {_sreg(saddr, 2)}" if saddr < 106 else ", off" if saddr == 124 else f", {decode_src(saddr)}"
+    off_str = f" offset:{offset}" if offset else ""
+    vdata_str = _vreg(data if 'store' in op_name else vdst, width)
+    return f"{instr} {addr_str}, {vdata_str}{saddr_str}{off_str}" if 'store' in op_name else f"{instr} {vdata_str}, {addr_str}{saddr_str}{off_str}"
+
+  # VOP3: vector ops with modifiers (can be 1, 2, or 3 sources depending on opcode range)
+  if cls_name == 'VOP3':
+    # Handle VOP3SD opcodes (same encoding, different field layout)
+    if is_vop3sd:
+      vdst = unwrap(inst._values.get('vdst', 0))
+      # VOP3SD: sdst is at bits [14:8], but VOP3 decodes opsel at [14:11], abs at [10:8], clmp at [15]
+      # We need to reconstruct sdst from these fields
+      opsel_raw = unwrap(inst._values.get('opsel', 0))
+      abs_raw = unwrap(inst._values.get('abs', 0))
+      clmp_raw = unwrap(inst._values.get('clmp', 0))
+      sdst = (clmp_raw << 7) | (opsel_raw << 3) | abs_raw
+      src0, src1, src2 = [unwrap(inst._values.get(f, 0)) for f in ('src0', 'src1', 'src2')]
+      neg = unwrap(inst._values.get('neg', 0))
+      omod = unwrap(inst._values.get('omod', 0))
+      omod_str = {1: " mul:2", 2: " mul:4", 3: " div:2"}.get(omod, "")
+      is_f64 = 'f64' in op_name
+      # v_mad_i64_i32/v_mad_u64_u32: 64-bit dst and src2, 32-bit src0/src1
+      is_mad64 = 'mad_i64_i32' in op_name or 'mad_u64_u32' in op_name
+      def fmt_sd_src(v, neg_bit, is_64bit=False):
+        s = _fmt_src64(v) if (is_64bit or is_f64) else fmt_src(v)
+        return f"-{s}" if neg_bit else s
+      src0_str, src1_str = fmt_sd_src(src0, neg & 1), fmt_sd_src(src1, neg & 2)
+      src2_str = fmt_sd_src(src2, neg & 4, is_mad64)
+      dst_str = _vreg(vdst, 2) if (is_f64 or is_mad64) else f"v{vdst}"
+      sdst_str = _fmt_sdst(sdst, 1)
+      # v_add_co_u32, v_sub_co_u32, v_subrev_co_u32, v_add_co_ci_u32, etc. only use 2 sources
+      if op_name in ('v_add_co_u32', 'v_sub_co_u32', 'v_subrev_co_u32', 'v_add_co_ci_u32', 'v_sub_co_ci_u32', 'v_subrev_co_ci_u32'):
+        return f"{op_name} {dst_str}, {sdst_str}, {src0_str}, {src1_str}"
+      # v_div_scale uses 3 sources
+      return f"{op_name} {dst_str}, {sdst_str}, {src0_str}, {src1_str}, {src2_str}" + omod_str
+
+    vdst = unwrap(inst._values.get('vdst', 0))
+    src0, src1, src2 = [unwrap(inst._values.get(f, 0)) for f in ('src0', 'src1', 'src2')]
+    neg, abs_, clmp = unwrap(inst._values.get('neg', 0)), unwrap(inst._values.get('abs', 0)), unwrap(inst._values.get('clmp', 0))
+    opsel = unwrap(inst._values.get('opsel', 0))
+    # Check if 64-bit op (needs register pairs)
+    is_f64 = 'f64' in op_name or 'i64' in op_name or 'u64' in op_name or 'b64' in op_name
+    # v_cmp_class_* has 64-bit src0 but 32-bit src1 (class mask)
+    is_class = 'class' in op_name
+    # Shift ops: v_*rev_*64 have 32-bit shift amount (src0), 64-bit value (src1)
+    is_shift64 = 'rev' in op_name and '64' in op_name and op_name.startswith('v_')
+    # v_ldexp_f64: 64-bit src0 (mantissa), 32-bit src1 (exponent)
+    is_ldexp64 = op_name == 'v_ldexp_f64'
+    # v_trig_preop_f64: 64-bit dst/src0, 32-bit src1 (exponent/scale)
+    is_trig_preop = op_name == 'v_trig_preop_f64'
+    # v_readlane_b32: destination is SGPR (despite vdst field)
+    is_readlane = op_name == 'v_readlane_b32'
+    # SAD/QSAD/MQSAD instructions have mixed sizes
+    # v_qsad_pk_u16_u8, v_mqsad_pk_u16_u8: 64-bit dst/src0/src2, 32-bit src1
+    # v_mqsad_u32_u8: 128-bit (4 reg) dst/src2, 64-bit src0, 32-bit src1
+    is_sad64 = any(x in op_name for x in ('qsad_pk', 'mqsad_pk'))
+    is_mqsad_u32 = 'mqsad_u32' in op_name
+    # Detect 16-bit and 64-bit operand sizes for various instruction patterns
+    if 'cvt_pk' in op_name:
+      is_f16_dst, is_f16_src, is_f16_src2 = False, op_name.endswith('16'), False
+    elif m := re.match(r'v_(?:cvt|frexp_exp)_([a-z0-9_]+)_([a-z0-9]+)', op_name):
+      dst_type, src_type = m.group(1), m.group(2)
+      is_f16_dst, is_f16_src, is_f16_src2 = _is_16bit(dst_type), _is_16bit(src_type), _is_16bit(src_type)
+      is_f64_dst, is_f64_src, is_f64 = '64' in dst_type, '64' in src_type, False
+    elif re.match(r'v_mad_[iu]32_[iu]16', op_name):
+      is_f16_dst, is_f16_src, is_f16_src2 = False, True, False  # 32-bit dst, 16-bit src0/src1, 32-bit src2
+    elif 'pack_b32' in op_name:
+      is_f16_dst, is_f16_src, is_f16_src2 = False, True, True  # 32-bit dst, 16-bit sources
+    else:
+      is_16bit_op = any(x in op_name for x in _16BIT_TYPES) and not any(x in op_name for x in ('dot2', 'pk_', 'sad', 'msad', 'qsad', 'mqsad'))
+      is_f16_dst = is_f16_src = is_f16_src2 = is_16bit_op
+    # Check if any opsel bit is set (any operand uses .h) - if so, we need explicit .l for low-half
+    any_hi = opsel != 0
+    def fmt_vop3_src(v, neg_bit, abs_bit, hi_bit=False, reg_cnt=1, is_16=False):
+      s = _fmt_src_n(v, reg_cnt) if reg_cnt > 1 else f"v{v - 256}.h" if is_16 and v >= 256 and hi_bit else f"v{v - 256}.l" if is_16 and v >= 256 and any_hi else fmt_src(v)
+      if abs_bit: s = f"|{s}|"
+      return f"-{s}" if neg_bit else s
+    # Determine register count for each source (check for cvt-specific 64-bit flags first)
+    is_src0_64 = locals().get('is_f64_src', is_f64 and not is_shift64) or is_sad64 or is_mqsad_u32
+    is_src1_64 = is_f64 and not is_class and not is_ldexp64 and not is_trig_preop
+    src0_cnt = 2 if is_src0_64 else 1
+    src1_cnt = 2 if is_src1_64 else 1
+    src2_cnt = 4 if is_mqsad_u32 else 2 if (is_f64 or is_sad64) else 1
+    src0_str = fmt_vop3_src(src0, neg & 1, abs_ & 1, opsel & 1, src0_cnt, is_f16_src)
+    src1_str = fmt_vop3_src(src1, neg & 2, abs_ & 2, opsel & 2, src1_cnt, is_f16_src)
+    src2_str = fmt_vop3_src(src2, neg & 4, abs_ & 4, opsel & 4, src2_cnt, is_f16_src2)
+    # Format destination - for 16-bit ops, use .h/.l suffix; readlane uses SGPR dest
+    is_dst_64 = locals().get('is_f64_dst', is_f64) or is_sad64
+    dst_cnt = 4 if is_mqsad_u32 else 2 if is_dst_64 else 1
+    if is_readlane:
+      dst_str = _fmt_sdst(vdst, 1)
+    elif dst_cnt > 1:
+      dst_str = _vreg(vdst, dst_cnt)
+    elif is_f16_dst:
+      dst_str = f"v{vdst}.h" if (opsel & 8) else f"v{vdst}.l" if any_hi else f"v{vdst}"
+    else:
+      dst_str = f"v{vdst}"
+    clamp_str = " clamp" if clmp else ""
+    omod = unwrap(inst._values.get('omod', 0))
+    omod_str = {1: " mul:2", 2: " mul:4", 3: " div:2"}.get(omod, "")
+    # op_sel for non-VGPR sources (when opsel bits are set but source is not a VGPR)
+    # For 16-bit ops with VGPR sources, opsel is encoded in .h/.l suffix
+    # For non-VGPR sources or non-16-bit ops, we need explicit op_sel
+    has_nonvgpr_opsel = (src0 < 256 and (opsel & 1)) or (src1 < 256 and (opsel & 2)) or (src2 < 256 and (opsel & 4))
+    need_opsel = has_nonvgpr_opsel or (opsel and not is_f16_src)
+    # Helper to format opsel string based on source count
+    def fmt_opsel(num_src):
+      if not need_opsel: return ""
+      # When dst is .h (for 16-bit ops) and non-VGPR sources have opsel, use all 1s
+      if is_f16_dst and (opsel & 8):  # dst is .h
+        return f" op_sel:[1,1,1{',1' if num_src == 3 else ''}]"
+      # Otherwise output actual opsel values
+      if num_src == 3:
+        return f" op_sel:[{opsel & 1},{(opsel >> 1) & 1},{(opsel >> 2) & 1},{(opsel >> 3) & 1}]"
+      return f" op_sel:[{opsel & 1},{(opsel >> 1) & 1},{(opsel >> 2) & 1}]"
+    # Determine number of sources based on opcode range:
+    # 0-255: VOPC promoted (comparison, 2 src, sdst)
+    # 256-383: VOP2 promoted (2 src)
+    # 384-511: VOP1 promoted (1 src)
+    # 512+: Native VOP3 (2 or 3 src depending on instruction)
+    if op_val < 256:  # VOPC promoted
+      # VOPCX (v_cmpx_*) writes to exec, no explicit destination
+      if op_name.startswith('v_cmpx'):
+        return f"{op_name}_e64 {src0_str}, {src1_str}"
+      return f"{op_name}_e64 {_fmt_sdst(vdst, 1)}, {src0_str}, {src1_str}"
+    elif op_val < 384:  # VOP2 promoted
+      # v_cndmask_b32 in VOP3 format has 3 sources (src2 is mask selector)
+      if 'cndmask' in op_name:
+        return f"{op_name}_e64 {dst_str}, {src0_str}, {src1_str}, {src2_str}" + fmt_opsel(3) + clamp_str + omod_str
+      return f"{op_name}_e64 {dst_str}, {src0_str}, {src1_str}" + fmt_opsel(2) + clamp_str + omod_str
+    elif op_val < 512:  # VOP1 promoted
+      if op_name in ('v_nop', 'v_pipeflush'): return f"{op_name}_e64"
+      return f"{op_name}_e64 {dst_str}, {src0_str}" + fmt_opsel(1) + clamp_str + omod_str
+    else:  # Native VOP3 - determine 2 vs 3 sources based on instruction name
+      # 3-source ops: fma, mad, min3, max3, med3, div_fixup, div_fmas, sad, msad, qsad, mqsad, lerp, alignbit/byte, cubeid/sc/tc/ma, bfe, bfi, perm_b32, permlane, cndmask
+      # Note: v_writelane_b32 is 2-src (src0, src1 with vdst as 3rd operand - read-modify-write)
+      is_3src = any(x in op_name for x in ('fma', 'mad', 'min3', 'max3', 'med3', 'div_fix', 'div_fmas', 'sad', 'lerp', 'align', 'cube',
+                                            'bfe', 'bfi', 'perm_b32', 'permlane', 'cndmask', 'xor3', 'or3', 'add3', 'lshl_or', 'and_or', 'lshl_add',
+                                            'add_lshl', 'xad', 'maxmin', 'minmax', 'dot2', 'cvt_pk_u8', 'mullit'))
+      if is_3src:
+        return f"{op_name} {dst_str}, {src0_str}, {src1_str}, {src2_str}" + fmt_opsel(3) + clamp_str + omod_str
+      return f"{op_name} {dst_str}, {src0_str}, {src1_str}" + fmt_opsel(2) + clamp_str + omod_str
+
+  # VOP3SD: 3-source with scalar destination (v_div_scale_*, v_add_co_u32, v_mad_*64_*32, etc.)
+  if cls_name == 'VOP3SD':
+    vdst, sdst = unwrap(inst._values.get('vdst', 0)), unwrap(inst._values.get('sdst', 0))
+    src0, src1, src2 = [unwrap(inst._values.get(f, 0)) for f in ('src0', 'src1', 'src2')]
+    neg, omod, clmp = unwrap(inst._values.get('neg', 0)), unwrap(inst._values.get('omod', 0)), unwrap(inst._values.get('clmp', 0))
+    is_f64, is_mad64 = 'f64' in op_name, 'mad_i64_i32' in op_name or 'mad_u64_u32' in op_name
+    def fmt_neg(v, neg_bit, is_64=False): return f"-{_fmt_src64(v) if (is_64 or is_f64) else fmt_src(v)}" if neg_bit else _fmt_src64(v) if (is_64 or is_f64) else fmt_src(v)
+    srcs = [fmt_neg(src0, neg & 1), fmt_neg(src1, neg & 2), fmt_neg(src2, neg & 4, is_mad64)]
+    dst_str, sdst_str = _vreg(vdst, 2) if (is_f64 or is_mad64) else f"v{vdst}", _fmt_sdst(sdst, 1)
+    clamp_str, omod_str = " clamp" if clmp else "", {1: " mul:2", 2: " mul:4", 3: " div:2"}.get(omod, "")
+    is_2src = op_name in ('v_add_co_u32', 'v_sub_co_u32', 'v_subrev_co_u32')
+    suffix = "_e64" if op_name.startswith('v_') and 'co_' in op_name else ""
+    return f"{op_name}{suffix} {dst_str}, {sdst_str}, {', '.join(srcs[:2] if is_2src else srcs)}" + clamp_str + omod_str
+
+  # VOPD: dual-issue instructions
+  if cls_name == 'VOPD':
+    from extra.assembly.amd.autogen import rdna3 as autogen
+    opx, opy, vdstx, vdsty_enc = [unwrap(inst._values.get(f, 0)) for f in ('opx', 'opy', 'vdstx', 'vdsty')]
+    srcx0, vsrcx1, srcy0, vsrcy1 = [unwrap(inst._values.get(f, 0)) for f in ('srcx0', 'vsrcx1', 'srcy0', 'vsrcy1')]
+    vdsty = (vdsty_enc << 1) | ((vdstx & 1) ^ 1)  # Decode vdsty
+    def fmt_vopd(op, vdst, src0, vsrc1):
+      try: name = autogen.VOPDOp(op).name.lower()
+      except (ValueError, KeyError): name = f"op_{op}"
+      return f"{name} v{vdst}, {fmt_src(src0)}" if 'mov' in name else f"{name} v{vdst}, {fmt_src(src0)}, v{vsrc1}"
+    return f"{fmt_vopd(opx, vdstx, srcx0, vsrcx1)} :: {fmt_vopd(opy, vdsty, srcy0, vsrcy1)}"
+
+  # VOP3P: packed vector ops
+  if cls_name == 'VOP3P':
+    vdst, clmp = unwrap(inst._values.get('vdst', 0)), unwrap(inst._values.get('clmp', 0))
+    src0, src1, src2 = [unwrap(inst._values.get(f, 0)) for f in ('src0', 'src1', 'src2')]
+    neg, neg_hi = unwrap(inst._values.get('neg', 0)), unwrap(inst._values.get('neg_hi', 0))
+    opsel, opsel_hi, opsel_hi2 = unwrap(inst._values.get('opsel', 0)), unwrap(inst._values.get('opsel_hi', 0)), unwrap(inst._values.get('opsel_hi2', 0))
+    is_wmma, is_3src = 'wmma' in op_name, any(x in op_name for x in ('fma', 'mad', 'dot', 'wmma'))
+    def fmt_bits(name, val, n): return f"{name}:[{','.join(str((val >> i) & 1) for i in range(n))}]"
+    # WMMA: f16/bf16 use 8-reg sources, iu8 uses 4-reg, iu4 uses 2-reg; all have 8-reg dst
+    if is_wmma:
+      src_cnt = 2 if 'iu4' in op_name else 4 if 'iu8' in op_name else 8
+      src0_str, src1_str, src2_str = _fmt_src_n(src0, src_cnt), _fmt_src_n(src1, src_cnt), _fmt_src_n(src2, 8)
+      dst_str = _vreg(vdst, 8)
+    else:
+      src0_str, src1_str, src2_str = _fmt_src_n(src0, 1), _fmt_src_n(src1, 1), _fmt_src_n(src2, 1)
+      dst_str = f"v{vdst}"
+    n = 3 if is_3src else 2
+    full_opsel_hi = opsel_hi | (opsel_hi2 << 2)
+    mods = [fmt_bits("op_sel", opsel, n)] if opsel else []
+    if full_opsel_hi != (0b111 if is_3src else 0b11): mods.append(fmt_bits("op_sel_hi", full_opsel_hi, n))
+    if neg: mods.append(fmt_bits("neg_lo", neg, n))
+    if neg_hi: mods.append(fmt_bits("neg_hi", neg_hi, n))
+    if clmp: mods.append("clamp")
+    mod_str = " " + " ".join(mods) if mods else ""
+    return f"{op_name} {dst_str}, {src0_str}, {src1_str}, {src2_str}{mod_str}" if is_3src else f"{op_name} {dst_str}, {src0_str}, {src1_str}{mod_str}"
+
+  # VINTERP: interpolation instructions
+  if cls_name == 'VINTERP':
+    vdst = unwrap(inst._values.get('vdst', 0))
+    src0, src1, src2 = [unwrap(inst._values.get(f, 0)) for f in ('src0', 'src1', 'src2')]
+    neg, waitexp, clmp = unwrap(inst._values.get('neg', 0)), unwrap(inst._values.get('waitexp', 0)), unwrap(inst._values.get('clmp', 0))
+    def fmt_neg_vi(v, neg_bit): return f"-{v}" if neg_bit else v
+    srcs = [fmt_neg_vi(f"v{s - 256}" if s >= 256 else fmt_src(s), neg & (1 << i)) for i, s in enumerate([src0, src1, src2])]
+    mods = [m for m in [f"wait_exp:{waitexp}" if waitexp else "", "clamp" if clmp else ""] if m]
+    return f"{op_name} v{vdst}, {', '.join(srcs)}" + (" " + " ".join(mods) if mods else "")
+
+  # MUBUF/MTBUF helpers
+  def _buf_vaddr(vaddr, offen, idxen): return _vreg(vaddr, 2) if offen and idxen else f"v{vaddr}" if offen or idxen else "off"
+  def _buf_srsrc(srsrc): srsrc_base = srsrc * 4; return _reg("ttmp", srsrc_base - 108, 4) if 108 <= srsrc_base <= 123 else _sreg(srsrc_base, 4)
+
+  # MUBUF: buffer load/store
+  if cls_name == 'MUBUF':
+    vdata, vaddr, srsrc, soffset = [unwrap(inst._values.get(f, 0)) for f in ('vdata', 'vaddr', 'srsrc', 'soffset')]
+    offset, offen, idxen = unwrap(inst._values.get('offset', 0)), unwrap(inst._values.get('offen', 0)), unwrap(inst._values.get('idxen', 0))
+    glc, dlc, slc, tfe = [unwrap(inst._values.get(f, 0)) for f in ('glc', 'dlc', 'slc', 'tfe')]
+    if op_name in ('buffer_gl0_inv', 'buffer_gl1_inv'): return op_name
+    # Determine data width from op name
+    if 'd16' in op_name: width = 2 if any(x in op_name for x in ('xyz', 'xyzw')) else 1
+    elif 'atomic' in op_name:
+      base_width = 2 if any(x in op_name for x in ('b64', 'u64', 'i64')) else 1
+      width = base_width * 2 if 'cmpswap' in op_name else base_width
+    else: width = {'b32':1, 'b64':2, 'b96':3, 'b128':4, 'b16':1, 'x':1, 'xy':2, 'xyz':3, 'xyzw':4}.get(op_name.split('_')[-1], 1)
+    if tfe: width += 1
+    mods = [m for m in ["offen" if offen else "", "idxen" if idxen else "", f"offset:{offset}" if offset else "",
+                        "glc" if glc else "", "dlc" if dlc else "", "slc" if slc else "", "tfe" if tfe else ""] if m]
+    return f"{op_name} {_vreg(vdata, width)}, {_buf_vaddr(vaddr, offen, idxen)}, {_buf_srsrc(srsrc)}, {decode_src(soffset)}" + (" " + " ".join(mods) if mods else "")
+
+  # MTBUF: typed buffer load/store
+  if cls_name == 'MTBUF':
+    vdata, vaddr, srsrc, soffset = [unwrap(inst._values.get(f, 0)) for f in ('vdata', 'vaddr', 'srsrc', 'soffset')]
+    offset, tbuf_fmt, offen, idxen = [unwrap(inst._values.get(f, 0)) for f in ('offset', 'format', 'offen', 'idxen')]
+    glc, dlc, slc = [unwrap(inst._values.get(f, 0)) for f in ('glc', 'dlc', 'slc')]
+    mods = [f"format:{tbuf_fmt}"] + [m for m in ["idxen" if idxen else "", "offen" if offen else "", f"offset:{offset}" if offset else "",
+                                                  "glc" if glc else "", "dlc" if dlc else "", "slc" if slc else ""] if m]
+    width = 2 if 'd16' in op_name and any(x in op_name for x in ('xyz', 'xyzw')) else 1 if 'd16' in op_name else {'x':1, 'xy':2, 'xyz':3, 'xyzw':4}.get(op_name.split('_')[-1], 1)
+    return f"{op_name} {_vreg(vdata, width)}, {_buf_vaddr(vaddr, offen, idxen)}, {_buf_srsrc(srsrc)}, {decode_src(soffset)} {' '.join(mods)}"
+
+  # SOP1/SOP2/SOPC/SOPK
+  if cls_name in ('SOP1', 'SOP2', 'SOPC', 'SOPK'):
+    sizes = _parse_sop_sizes(op_name)
+    dst_cnt, src0_cnt = sizes[0], sizes[1]
+    src1_cnt = sizes[2] if len(sizes) > 2 else src0_cnt
+    if cls_name == 'SOP1':
+      sdst, ssrc0 = unwrap(inst._values.get('sdst', 0)), unwrap(inst._values.get('ssrc0', 0))
+      if op_name == 's_getpc_b64': return f"{op_name} {_fmt_sdst(sdst, 2)}"
+      if op_name in ('s_setpc_b64', 's_rfe_b64'): return f"{op_name} {_fmt_ssrc(ssrc0, 2)}"
+      if op_name == 's_swappc_b64': return f"{op_name} {_fmt_sdst(sdst, 2)}, {_fmt_ssrc(ssrc0, 2)}"
+      if op_name in ('s_sendmsg_rtn_b32', 's_sendmsg_rtn_b64'):
+        return f"{op_name} {_fmt_sdst(sdst, 2 if 'b64' in op_name else 1)}, sendmsg({MSG_NAMES.get(ssrc0, str(ssrc0))})"
+      ssrc0_str = fmt_src(ssrc0) if src0_cnt == 1 else _fmt_ssrc(ssrc0, src0_cnt)
+      return f"{op_name} {_fmt_sdst(sdst, dst_cnt)}, {ssrc0_str}"
+    if cls_name == 'SOP2':
+      sdst, ssrc0, ssrc1 = [unwrap(inst._values.get(f, 0)) for f in ('sdst', 'ssrc0', 'ssrc1')]
+      ssrc0_str = fmt_src(ssrc0) if ssrc0 == 255 else _fmt_ssrc(ssrc0, src0_cnt)
+      ssrc1_str = fmt_src(ssrc1) if ssrc1 == 255 else _fmt_ssrc(ssrc1, src1_cnt)
+      return f"{op_name} {_fmt_sdst(sdst, dst_cnt)}, {ssrc0_str}, {ssrc1_str}"
+    if cls_name == 'SOPC':
+      return f"{op_name} {_fmt_ssrc(unwrap(inst._values.get('ssrc0', 0)), src0_cnt)}, {_fmt_ssrc(unwrap(inst._values.get('ssrc1', 0)), src1_cnt)}"
+    if cls_name == 'SOPK':
+      sdst, simm16 = unwrap(inst._values.get('sdst', 0)), unwrap(inst._values.get('simm16', 0))
+      if op_name == 's_version': return f"{op_name} 0x{simm16:x}"
+      if op_name in ('s_setreg_b32', 's_getreg_b32'):
+        hwreg_id, hwreg_offset, hwreg_size = simm16 & 0x3f, (simm16 >> 6) & 0x1f, ((simm16 >> 11) & 0x1f) + 1
+        hwreg_str = f"0x{simm16:x}" if hwreg_id in (16, 17) else f"hwreg({HWREG_NAMES.get(hwreg_id, str(hwreg_id))}, {hwreg_offset}, {hwreg_size})"
+        return f"{op_name} {hwreg_str}, {_fmt_sdst(sdst, 1)}" if op_name == 's_setreg_b32' else f"{op_name} {_fmt_sdst(sdst, 1)}, {hwreg_str}"
+      return f"{op_name} {_fmt_sdst(sdst, dst_cnt)}, 0x{simm16:x}"
+
+  # Generic fallback
+  def fmt_field(n, v):
+    v = unwrap(v)
+    if n in SRC_FIELDS: return fmt_src(v) if v != 255 else "0xff"
+    if n in ('sdst', 'vdst'): return f"{'s' if n == 'sdst' else 'v'}{v}"
+    return f"v{v}" if n == 'vsrc1' else f"0x{v:x}" if n == 'simm16' else str(v)
+  ops = [fmt_field(n, inst._values.get(n, 0)) for n in inst._fields if n not in ('encoding', 'op')]
+  return f"{op_name} {', '.join(ops)}" if ops else op_name
+
+# Assembler
+SPECIAL_REGS = {'vcc_lo': RawImm(106), 'vcc_hi': RawImm(107), 'vcc': RawImm(106), 'null': RawImm(124), 'off': RawImm(124), 'm0': RawImm(125),
+                'exec_lo': RawImm(126), 'exec_hi': RawImm(127), 'exec': RawImm(126), 'scc': RawImm(253), 'src_scc': RawImm(253)}
+FLOAT_CONSTS = {'0.5': 0.5, '-0.5': -0.5, '1.0': 1.0, '-1.0': -1.0, '2.0': 2.0, '-2.0': -2.0, '4.0': 4.0, '-4.0': -4.0}
+REG_MAP: dict[str, _RegFactory] = {'s': s, 'v': v, 't': ttmp, 'ttmp': ttmp}
+
+def parse_operand(op: str) -> tuple:
+  op = op.strip().lower()
+  neg = op.startswith('-') and not op[1:2].isdigit(); op = op[1:] if neg else op
+  abs_ = op.startswith('|') and op.endswith('|') or op.startswith('abs(') and op.endswith(')')
+  op = op[1:-1] if op.startswith('|') else op[4:-1] if op.startswith('abs(') else op
+  hi_half = op.endswith('.h')
+  op = re.sub(r'\.[lh]$', '', op)
+  if op in FLOAT_CONSTS: return (FLOAT_CONSTS[op], neg, abs_, hi_half)
+  if re.match(r'^-?\d+$', op): return (int(op), neg, abs_, hi_half)
+  if m := re.match(r'^-?0x([0-9a-f]+)$', op):
+    v = -int(m.group(1), 16) if op.startswith('-') else int(m.group(1), 16)
+    return (v, neg, abs_, hi_half)
+  if op in SPECIAL_REGS: return (SPECIAL_REGS[op], neg, abs_, hi_half)
+  if op == 'lit': return (RawImm(255), neg, abs_, hi_half)  # literal marker (actual value comes from literal word)
+  if m := re.match(r'^([svt](?:tmp)?)\[(\d+):(\d+)\]$', op): return (REG_MAP[m.group(1)][int(m.group(2)):int(m.group(3))], neg, abs_, hi_half)
+  if m := re.match(r'^([svt](?:tmp)?)(\d+)$', op):
+    reg = REG_MAP[m.group(1)][int(m.group(2))]
+    reg.hi = hi_half
+    return (reg, neg, abs_, hi_half)
+  # hwreg(name, offset, size) or hwreg(name) -> simm16 encoding
+  if m := re.match(r'^hwreg\((\w+)(?:,\s*(\d+),\s*(\d+))?\)$', op):
+    name_str = m.group(1).lower()
+    hwreg_id = HWREG_IDS.get(name_str, int(name_str) if name_str.isdigit() else None)
+    if hwreg_id is None: raise ValueError(f"unknown hwreg name: {name_str}")
+    offset, size = int(m.group(2)) if m.group(2) else 0, int(m.group(3)) if m.group(3) else 32
+    return (((size - 1) << 11) | (offset << 6) | hwreg_id, neg, abs_, hi_half)
+  raise ValueError(f"cannot parse operand: {op}")
+
+SMEM_OPS = {'s_load_b32', 's_load_b64', 's_load_b128', 's_load_b256', 's_load_b512',
+            's_buffer_load_b32', 's_buffer_load_b64', 's_buffer_load_b128', 's_buffer_load_b256', 's_buffer_load_b512'}
+SOP1_SRC_ONLY = {'s_setpc_b64', 's_rfe_b64'}
+SOP1_MSG_IMM = {'s_sendmsg_rtn_b32', 's_sendmsg_rtn_b64'}
+SOPK_IMM_ONLY = {'s_version'}
+SOPK_IMM_FIRST = {'s_setreg_b32'}
+SOPK_UNSUPPORTED = {'s_setreg_imm32_b32'}
+
+def _operand_to_dsl(op: str) -> str:
+  """Transform a single operand from LLVM assembly syntax to DSL expression string."""
+  op = op.strip()
+  # Handle negation prefix
+  neg = False
+  if op.startswith('-') and not (op[1:2].isdigit() or (len(op) > 2 and op[1] == '0' and op[2] in 'xX')):
+    neg, op = True, op[1:]
+  # Handle abs modifier: |x| or abs(x)
+  abs_ = False
+  if op.startswith('|') and op.endswith('|'):
+    abs_, op = True, op[1:-1]
+  elif op.startswith('abs(') and op.endswith(')'):
+    abs_, op = True, op[4:-1]
+  # Handle .h/.l suffix for 16-bit ops
+  hi_suffix = ""
+  if op.endswith('.h'): hi_suffix, op = ".h", op[:-2]
+  elif op.endswith('.l'): hi_suffix, op = ".l", op[:-2]
+  op_lower = op.lower()
+
+  # Helper to apply modifiers
+  def apply_mods(base: str) -> str:
+    if not neg and not abs_: return f"{base}{hi_suffix}"
+    if abs_: return f"{'-' if neg else ''}abs({base}){hi_suffix}"
+    return f"-{base}{hi_suffix}"
+
+  # Special registers - vcc maps to VCC_LO (64-bit alias)
+  special_map = {'vcc_lo': 'VCC_LO', 'vcc_hi': 'VCC_HI', 'vcc': 'VCC_LO', 'null': 'NULL', 'off': 'OFF',
+                 'm0': 'M0', 'exec_lo': 'EXEC_LO', 'exec_hi': 'EXEC_HI', 'exec': 'EXEC_LO', 'scc': 'SCC',
+                 'src_scc': 'SCC'}
+  if op_lower in special_map: return apply_mods(special_map[op_lower])
+  # Float constants
+  float_map = {'0.5': '0.5', '-0.5': '-0.5', '1.0': '1.0', '-1.0': '-1.0', '2.0': '2.0', '-2.0': '-2.0', '4.0': '4.0', '-4.0': '-4.0'}
+  if op in float_map: return apply_mods(float_map[op])
+  # Register range: v[0:3], s[4:7]
+  if m := re.match(r'^([svt](?:tmp)?)\[(\d+):(\d+)\]$', op_lower):
+    prefix = {'s': 's', 'v': 'v', 't': 'ttmp', 'ttmp': 'ttmp'}[m.group(1)]
+    return apply_mods(f"{prefix}[{m.group(2)}:{m.group(3)}]")
+  # Single register: v0, s1, ttmp5
+  if m := re.match(r'^([svt](?:tmp)?)(\d+)$', op_lower):
+    prefix = {'s': 's', 'v': 'v', 't': 'ttmp', 'ttmp': 'ttmp'}[m.group(1)]
+    return apply_mods(f"{prefix}[{m.group(2)}]")
+  # Integer literals (decimal or hex) - use SrcMod wrapper when modifiers present
+  if re.match(r'^-?\d+$', op) or re.match(r'^-?0x([0-9a-fA-F]+)$', op):
+    if neg or abs_:
+      return f"SrcMod({op}, neg={neg}, abs_={abs_})"
+    return op
+  # hwreg(name, offset, size) -> pass through
+  if op_lower.startswith('hwreg('): return apply_mods(op)
+  # sendmsg(...) -> pass through
+  if op_lower.startswith('sendmsg('): return apply_mods(op)
+  # Fallback: return as-is
+  return apply_mods(op)
+
+def _parse_operands(op_str: str) -> list[str]:
+  """Parse comma-separated operands, respecting brackets and pipes."""
+  operands, current, depth, in_pipe = [], "", 0, False
+  for ch in op_str:
+    if ch in '[(': depth += 1
+    elif ch in '])': depth -= 1
+    elif ch == '|': in_pipe = not in_pipe
+    if ch == ',' and depth == 0 and not in_pipe:
+      operands.append(current.strip())
+      current = ""
+    else:
+      current += ch
+  if current.strip(): operands.append(current.strip())
+  return operands
+
+def _unwrap_dsl(s: str) -> str:
+  """Unwrap a DSL expression to get the raw value for literals."""
+  if re.match(r'^-?\d+$', s): return s
+  if re.match(r'^-?0x[0-9a-fA-F]+$', s): return s
+  return s
+
+def get_dsl(text: str) -> str:
+  """Transform LLVM-style assembly instruction to Python DSL expression string."""
+  text = text.strip()
+  # Extract and remove trailing modifiers (must happen before operand parsing)
+  kwargs = []
+  # Extract mul:N and div:N modifiers (omod)
+  omod_val = 0
+  if m := re.search(r'\s+mul:2(?:\s|$)', text, re.I):
+    omod_val = 1; text = text[:m.start()] + text[m.end():]
+  elif m := re.search(r'\s+mul:4(?:\s|$)', text, re.I):
+    omod_val = 2; text = text[:m.start()] + text[m.end():]
+  elif m := re.search(r'\s+div:2(?:\s|$)', text, re.I):
+    omod_val = 3; text = text[:m.start()] + text[m.end():]
+  if omod_val: kwargs.append(f'omod={omod_val}')
+  # Extract clamp modifier
+  if m := re.search(r'\s+clamp(?:\s|$)', text, re.I):
+    kwargs.append('clmp=1')
+    text = text[:m.start()] + text[m.end():]
+  # Extract op_sel:[...] modifier - interpretation depends on format:
+  # VOP3: [src0, src1, dst] or [src0, src1, src2, dst] -> bits 0, 1, (2), 3
+  # VOP3P/WMMA: [src0, src1, src2] -> bits 0, 1, 2 (no dst bit, 3-source ops)
+  opsel_explicit = None
+  if m := re.search(r'\s+op_sel:\[([^\]]+)\]', text, re.I):
+    bits = [int(x.strip()) for x in m.group(1).split(',')]
+    # Check if this is a VOP3P instruction (v_pk_*, v_wmma_*, v_dot*)
+    mnemonic = text.split()[0].lower()
+    is_vop3p = mnemonic.startswith(('v_pk_', 'v_wmma_', 'v_dot'))
+    if len(bits) == 3:
+      if is_vop3p:
+        # VOP3P: [src0, src1, src2] -> bits 0, 1, 2
+        opsel_explicit = bits[0] | (bits[1] << 1) | (bits[2] << 2)
+      else:
+        # VOP3: [src0, src1, dst] -> bits 0, 1, 3
+        opsel_explicit = bits[0] | (bits[1] << 1) | (bits[2] << 3)
+    else:
+      opsel_explicit = sum(b << i for i, b in enumerate(bits))
+    text = text[:m.start()] + text[m.end():]
+  if m := re.search(r'\s+wait_exp:(\d+)', text, re.I):
+    kwargs.append(f'waitexp={m.group(1)}')
+    text = text[:m.start()] + text[m.end():]
+  # Extract offset:N for FLAT/GLOBAL/SCRATCH/SMEM (can be hex or decimal)
+  offset_val = None
+  if m := re.search(r'\s+offset:(0x[0-9a-fA-F]+|-?\d+)', text, re.I):
+    offset_val = m.group(1)
+    text = text[:m.start()] + text[m.end():]
+  # Extract dlc modifier (before glc to avoid partial match issues)
+  dlc_val = None
+  if m := re.search(r'\s+dlc(?:\s|$)', text, re.I):
+    dlc_val = 1
+    text = text[:m.start()] + text[m.end():]
+  # Extract glc modifier
+  glc_val = None
+  if m := re.search(r'\s+glc(?:\s|$)', text, re.I):
+    glc_val = 1
+    text = text[:m.start()] + text[m.end():]
+  # Extract neg_lo:[...] and neg_hi:[...] for VOP3P
+  neg_lo_val = None
+  if m := re.search(r'\s+neg_lo:\[([^\]]+)\]', text, re.I):
+    bits = [int(x.strip()) for x in m.group(1).split(',')]
+    neg_lo_val = sum(b << i for i, b in enumerate(bits))
+    text = text[:m.start()] + text[m.end():]
+  neg_hi_val = None
+  if m := re.search(r'\s+neg_hi:\[([^\]]+)\]', text, re.I):
+    bits = [int(x.strip()) for x in m.group(1).split(',')]
+    neg_hi_val = sum(b << i for i, b in enumerate(bits))
+    text = text[:m.start()] + text[m.end():]
+  parts = text.replace(',', ' ').split()
+  if not parts: raise ValueError("empty instruction")
+  mnemonic, op_str = parts[0].lower(), text[len(parts[0]):].strip()
+  # Handle s_waitcnt specially
+  if mnemonic == 's_waitcnt':
+    vmcnt, expcnt, lgkmcnt = 0x3f, 0x7, 0x3f
+    for part in op_str.replace(',', ' ').split():
+      if m := re.match(r'vmcnt\((\d+)\)', part): vmcnt = int(m.group(1))
+      elif m := re.match(r'expcnt\((\d+)\)', part): expcnt = int(m.group(1))
+      elif m := re.match(r'lgkmcnt\((\d+)\)', part): lgkmcnt = int(m.group(1))
+      elif re.match(r'^0x[0-9a-f]+$|^\d+$', part): return f"s_waitcnt(simm16={int(part, 0)})"
+    wc = waitcnt(vmcnt, expcnt, lgkmcnt)
+    return f"s_waitcnt(simm16={wc})"
+  # Handle VOPD dual-issue: opx dst, src :: opy dst, src
+  if '::' in text:
+    x_part, y_part = text.split('::')
+    x_parts, y_parts = x_part.strip().replace(',', ' ').split(), y_part.strip().replace(',', ' ').split()
+    opx_name, opy_name = x_parts[0].upper(), y_parts[0].upper()
+    x_ops = [_operand_to_dsl(p) for p in x_parts[1:]]
+    y_ops = [_operand_to_dsl(p) for p in y_parts[1:]]
+    vdstx, srcx0 = x_ops[0], x_ops[1] if len(x_ops) > 1 else '0'
+    vsrcx1 = x_ops[2] if len(x_ops) > 2 else 'v[0]'
+    vdsty, srcy0 = y_ops[0], y_ops[1] if len(y_ops) > 1 else '0'
+    vsrcy1 = y_ops[2] if len(y_ops) > 2 else 'v[0]'
+    lit = None
+    if 'fmaak' in opx_name.lower() and len(x_ops) > 3: lit = x_ops[3]
+    elif 'fmamk' in opx_name.lower() and len(x_ops) > 3: lit, vsrcx1 = x_ops[2], x_ops[3]
+    elif 'fmaak' in opy_name.lower() and len(y_ops) > 3: lit = y_ops[3]
+    elif 'fmamk' in opy_name.lower() and len(y_ops) > 3: lit, vsrcy1 = y_ops[2], y_ops[3]
+    lit_str = f", literal={lit}" if lit else ""
+    return f"VOPD(VOPDOp.{opx_name}, VOPDOp.{opy_name}, vdstx={vdstx}, vdsty={vdsty}, srcx0={srcx0}, vsrcx1={vsrcx1}, srcy0={srcy0}, vsrcy1={vsrcy1}{lit_str})"
+  operands = _parse_operands(op_str)
+  dsl_args = [_operand_to_dsl(op) for op in operands]
+  # Handle special instructions
+  if mnemonic in SOPK_UNSUPPORTED: raise ValueError(f"unsupported instruction: {mnemonic}")
+  if mnemonic in SOP1_SRC_ONLY: return f"{mnemonic}(ssrc0={dsl_args[0]})"
+  if mnemonic in SOP1_MSG_IMM: return f"{mnemonic}(sdst={dsl_args[0]}, ssrc0=RawImm({_unwrap_dsl(dsl_args[1])}))"
+  if mnemonic in SOPK_IMM_ONLY: return f"{mnemonic}(simm16={dsl_args[0]})"
+  if mnemonic in SOPK_IMM_FIRST: return f"{mnemonic}(simm16={dsl_args[0]}, sdst={dsl_args[1]})"
+  # SMEM with immediate offset (offset in operand[2] or offset: modifier)
+  if mnemonic in SMEM_OPS:
+    glc_str = ", glc=1" if glc_val else ""
+    dlc_str = ", dlc=1" if dlc_val else ""
+    # Pure immediate offset in operand[2]
+    if len(operands) >= 3 and re.match(r'^-?[0-9]|^-?0x', operands[2].strip().lower()):
+      return f"{mnemonic}(sdata={dsl_args[0]}, sbase={dsl_args[1]}, offset={dsl_args[2]}, soffset=RawImm(124){glc_str}{dlc_str})"
+    # Register soffset with offset: modifier
+    if offset_val and len(operands) >= 3:
+      return f"{mnemonic}(sdata={dsl_args[0]}, sbase={dsl_args[1]}, offset={offset_val}, soffset={dsl_args[2]}{glc_str}{dlc_str})"
+    # Register soffset only (no offset modifier)
+    if len(operands) >= 3:
+      return f"{mnemonic}(sdata={dsl_args[0]}, sbase={dsl_args[1]}, soffset={dsl_args[2]}{glc_str}{dlc_str})"
+  # Buffer ops with 'off'
+  if mnemonic.startswith('buffer_') and len(operands) >= 2 and operands[1].strip().lower() == 'off':
+    soff = f"RawImm({_unwrap_dsl(dsl_args[3])})" if len(dsl_args) > 3 else "RawImm(0)"
+    return f"{mnemonic}(vdata={dsl_args[0]}, vaddr=0, srsrc={dsl_args[2]}, soffset={soff})"
+  # FLAT/GLOBAL/SCRATCH load
+  if (mnemonic.startswith('flat_load') or mnemonic.startswith('global_load') or mnemonic.startswith('scratch_load')) and len(dsl_args) >= 3:
+    off = f", offset={offset_val}" if offset_val else ""
+    return f"{mnemonic}(vdst={dsl_args[0]}, addr={dsl_args[1]}, saddr={dsl_args[2]}{off})"
+  # FLAT/GLOBAL/SCRATCH store
+  if (mnemonic.startswith('flat_store') or mnemonic.startswith('global_store') or mnemonic.startswith('scratch_store')) and len(dsl_args) >= 3:
+    off = f", offset={offset_val}" if offset_val else ""
+    return f"{mnemonic}(addr={dsl_args[0]}, data={dsl_args[1]}, saddr={dsl_args[2]}{off})"
+  # Handle v_fmaak/v_fmamk literals
+  lit_str = ""
+  if mnemonic in ('v_fmaak_f32', 'v_fmaak_f16') and len(dsl_args) == 4:
+    lit_str, dsl_args = f", literal={_unwrap_dsl(dsl_args[3])}", dsl_args[:3]
+  elif mnemonic in ('v_fmamk_f32', 'v_fmamk_f16') and len(dsl_args) == 4:
+    lit_str, dsl_args = f", literal={_unwrap_dsl(dsl_args[2])}", [dsl_args[0], dsl_args[1], dsl_args[3]]
+  # Handle v_add_co_ci_u32_e32 etc with vcc operands - strip implicit vcc sdst and carry_in, add _e32 suffix
+  vcc_ops = {'v_add_co_ci_u32', 'v_sub_co_ci_u32', 'v_subrev_co_ci_u32'}
+  if mnemonic.replace('_e32', '') in vcc_ops and len(dsl_args) >= 5:
+    mnemonic = mnemonic.replace('_e32', '') + '_e32'  # Ensure _e32 suffix for VOP2 encoding
+    dsl_args = [dsl_args[0], dsl_args[2], dsl_args[3]]
+  # v_cmp_*_e32: strip implicit vcc_lo dest
+  if mnemonic.startswith('v_cmp') and not mnemonic.endswith('_e64') and len(dsl_args) >= 3 and operands[0].strip().lower() in ('vcc_lo', 'vcc_hi', 'vcc'):
+    dsl_args = dsl_args[1:]
+  # CMPX with _e64: prepend implicit EXEC_LO (vdst=126)
+  if 'cmpx' in mnemonic and mnemonic.endswith('_e64') and len(dsl_args) == 2:
+    dsl_args = ['RawImm(126)'] + dsl_args
+  # Build the function name - use mnemonic as-is, replacing . with _
+  func_name = mnemonic.replace('.', '_')
+  # When explicit opsel is given, strip .h/.l from register args (opsel overrides)
+  if opsel_explicit is not None:
+    dsl_args = [re.sub(r'\.[hl]$', '', a) for a in dsl_args]
+  args_str = ', '.join(dsl_args)
+  all_kwargs = list(kwargs)
+  if lit_str: all_kwargs.append(lit_str.lstrip(', '))
+  if opsel_explicit is not None: all_kwargs.append(f'opsel={opsel_explicit}')
+  if neg_lo_val is not None: all_kwargs.append(f'neg={neg_lo_val}')
+  if neg_hi_val is not None: all_kwargs.append(f'neg_hi={neg_hi_val}')
+  kwargs_str = ', '.join(all_kwargs)
+  if kwargs_str:
+    return f"{func_name}({args_str}, {kwargs_str})" if args_str else f"{func_name}({kwargs_str})"
+  return f"{func_name}({args_str})"
+
+def asm(text: str) -> Inst:
+  """Assemble LLVM-style instruction text to Inst by transforming to DSL and eval."""
+  from extra.assembly.amd.autogen import rdna3 as autogen
+  dsl_expr = get_dsl(text)
+  namespace = {name: getattr(autogen, name) for name in dir(autogen) if not name.startswith('_')}
+  namespace.update({'s': s, 'v': v, 'ttmp': ttmp, 'abs': abs, 'RawImm': RawImm, 'SrcMod': SrcMod, 'VGPR': VGPR, 'SGPR': SGPR, 'TTMP': TTMP,
+                    'VCC_LO': VCC_LO, 'VCC_HI': VCC_HI, 'VCC': VCC, 'EXEC_LO': EXEC_LO, 'EXEC_HI': EXEC_HI, 'EXEC': EXEC,
+                    'SCC': SCC, 'M0': M0, 'NULL': NULL, 'OFF': OFF})
+  try:
+    return eval(dsl_expr, namespace)
+  except NameError:
+    # Try with _e32 suffix for VOP1/VOP2/VOPC (only for v_* instructions)
+    if m := re.match(r'^(v_\w+)(\(.*\))$', dsl_expr):
+      return eval(f"{m.group(1)}_e32{m.group(2)}", namespace)
+    raise
@@ -0,0 +1,640 @@
+# library for RDNA3 assembly DSL
+# mypy: ignore-errors
+from __future__ import annotations
+from enum import IntEnum
+from typing import overload, Annotated, TypeVar, Generic
+
+# Bit field DSL
+class BitField:
+  def __init__(self, hi: int, lo: int, name: str | None = None): self.hi, self.lo, self.name = hi, lo, name
+  def __set_name__(self, owner, name): self.name, self._owner = name, owner
+  def __eq__(self, val: int) -> tuple[BitField, int]: return (self, val)  # type: ignore
+  def mask(self) -> int: return (1 << (self.hi - self.lo + 1)) - 1
+  @property
+  def marker(self) -> type | None:
+    # Get marker from Annotated type hint if present
+    import typing
+    if hasattr(self, '_owner') and self.name:
+      hints = typing.get_type_hints(self._owner, include_extras=True)
+      if self.name in hints:
+        hint = hints[self.name]
+        if typing.get_origin(hint) is Annotated:
+          args = typing.get_args(hint)
+          return args[1] if len(args) > 1 else None
+    return None
+  @overload
+  def __get__(self, obj: None, objtype: type) -> BitField: ...
+  @overload
+  def __get__(self, obj: object, objtype: type | None = None) -> int: ...
+  def __get__(self, obj, objtype=None):
+    if obj is None: return self
+    val = unwrap(obj._values.get(self.name, 0))
+    # Convert to IntEnum if marker is an IntEnum subclass
+    if self.marker and isinstance(self.marker, type) and issubclass(self.marker, IntEnum):
+      try: return self.marker(val)
+      except ValueError: pass
+    return val
+
+class _Bits:
+  def __getitem__(self, key) -> BitField: return BitField(key.start, key.stop) if isinstance(key, slice) else BitField(key, key)
+bits = _Bits()
+
+# Source operand with modifiers - base class for anything that can be a src with neg/abs
+class SrcMod:
+  __slots__ = ('val', 'neg', 'abs_')
+  def __init__(self, val: int, neg: bool = False, abs_: bool = False): self.val, self.neg, self.abs_ = val, neg, abs_
+  def __repr__(self): return f"{'-' if self.neg else ''}{'|' if self.abs_ else ''}{self.val}{'|' if self.abs_ else ''}"
+  def __neg__(self): return SrcMod(self.val, not self.neg, self.abs_)
+  def __abs__(self): return SrcMod(self.val, self.neg, True)
+
+# Register types
+class Reg(SrcMod):
+  __slots__ = ('idx', 'count', 'hi')
+  def __init__(self, idx: int, count: int = 1, hi: bool = False, neg: bool = False, abs_: bool = False):
+    self.idx, self.count, self.hi = idx, count, hi
+    super().__init__(idx, neg, abs_)
+  def __repr__(self): return f"{self.__class__.__name__.lower()[0]}[{self.idx}]" if self.count == 1 else f"{self.__class__.__name__.lower()[0]}[{self.idx}:{self.idx + self.count}]"
+  def __neg__(self): return self.__class__(self.idx, self.count, self.hi, not self.neg, self.abs_)
+  def __abs__(self): return self.__class__(self.idx, self.count, self.hi, self.neg, True)
+  @property
+  def l(self): return self.__class__(self.idx, self.count, False, self.neg, self.abs_)
+  @property
+  def h(self): return self.__class__(self.idx, self.count, True, self.neg, self.abs_)
+
+T = TypeVar('T', bound=Reg)
+class _RegFactory(Generic[T]):
+  def __init__(self, cls: type[T], name: str): self._cls, self._name = cls, name
+  @overload
+  def __getitem__(self, key: int) -> Reg: ...
+  @overload
+  def __getitem__(self, key: slice) -> Reg: ...
+  def __getitem__(self, key: int | slice) -> Reg:
+    return self._cls(key.start, key.stop - key.start + 1) if isinstance(key, slice) else self._cls(key)
+  def __repr__(self): return f"<{self._name} factory>"
+
+class SGPR(Reg): pass
+class VGPR(Reg): pass
+class TTMP(Reg): pass
+s: _RegFactory[SGPR] = _RegFactory(SGPR, "SGPR")
+v: _RegFactory[VGPR] = _RegFactory(VGPR, "VGPR")
+ttmp: _RegFactory[TTMP] = _RegFactory(TTMP, "TTMP")
+
+# Special registers as SrcMod objects (support -VCC_LO, abs(EXEC_LO), etc.)
+VCC_LO, VCC_HI, VCC = SrcMod(106), SrcMod(107), SrcMod(106)
+EXEC_LO, EXEC_HI, EXEC = SrcMod(126), SrcMod(127), SrcMod(126)
+SCC, M0, NULL, OFF = SrcMod(253), SrcMod(125), SrcMod(124), SrcMod(124)
+
+# Field type markers (runtime classes for validation)
+class _SSrc: pass
+class _Src: pass
+class _Imm: pass
+class _SImm: pass
+class _VDSTYEnc: pass  # VOPD vdsty: encoded = actual >> 1, actual = (encoded << 1) | ((vdstx & 1) ^ 1)
+class _SGPRField: pass
+class _VGPRField: pass
+
+# Type aliases for annotations - tells mypy it's a BitField while preserving marker info
+SSrc = Annotated[BitField, _SSrc]
+Src = Annotated[BitField, _Src]
+Imm = Annotated[BitField, _Imm]
+SImm = Annotated[BitField, _SImm]
+VDSTYEnc = Annotated[BitField, _VDSTYEnc]
+SGPRField = Annotated[BitField, _SGPRField]
+VGPRField = Annotated[BitField, _VGPRField]
+class RawImm:
+  def __init__(self, val: int): self.val = val
+  def __repr__(self): return f"RawImm({self.val})"
+  def __eq__(self, other): return isinstance(other, RawImm) and self.val == other.val
+
+def unwrap(val) -> int:
+  if isinstance(val, RawImm): return val.val
+  if isinstance(val, SrcMod) and not isinstance(val, Reg): return val.val  # Special registers like VCC_LO, NULL
+  if hasattr(val, 'value'): return val.value  # IntEnum
+  if hasattr(val, 'idx'): return val.idx  # Reg
+  return val
+
+# Encoding helpers
+FLOAT_ENC = {0.5: 240, -0.5: 241, 1.0: 242, -1.0: 243, 2.0: 244, -2.0: 245, 4.0: 246, -4.0: 247}
+SRC_FIELDS = {'src0', 'src1', 'src2', 'ssrc0', 'ssrc1', 'soffset', 'srcx0', 'srcy0'}
+RAW_FIELDS = {'vdata', 'vdst', 'vaddr', 'addr', 'data', 'data0', 'data1', 'sdst', 'sdata', 'vsrc1'}
+
+def _encode_reg(val: Reg) -> int:
+  if isinstance(val, TTMP): return 108 + val.idx
+  return val.idx  # hi bit is handled via opsel, not in register encoding
+
+def encode_src(val) -> int:
+  if isinstance(val, VGPR): return 256 + _encode_reg(val)
+  if isinstance(val, Reg): return _encode_reg(val)
+  if isinstance(val, SrcMod) and not isinstance(val, Reg):
+    # SrcMod wraps either special registers (VCC_LO=106, EXEC_LO=126, etc.) or literals
+    # Special register values are in valid encoding ranges - return as-is
+    # Literals (large integers) need 255 marker
+    v = val.val
+    # Valid source encoding ranges: 0-127 (SGPRs/special), 128-192 (inline const), 193-208 (neg inline), 240-247 (float), 251-253 (special)
+    if 0 <= v <= 127 or 240 <= v <= 255: return v  # SGPRs, special regs, float constants
+    if 128 <= v <= 192: return v  # Inline positive constants (0-64)
+    if 193 <= v <= 208: return v  # Inline negative constants (-1 to -16)
+    return 255  # Literal marker - value stored separately
+  if hasattr(val, 'value'): return val.value  # IntEnum
+  if isinstance(val, float): return 128 if val == 0.0 else FLOAT_ENC.get(val, 255)
+  return 128 + val if isinstance(val, int) and 0 <= val <= 64 else 192 + (-val) if isinstance(val, int) and -16 <= val <= -1 else 255
+
+# Instruction base class
+class Inst:
+  _fields: dict[str, BitField]
+  _encoding: tuple[BitField, int] | None = None
+  _defaults: dict[str, int] = {}
+  _values: dict[str, int | RawImm]
+  _words: int  # size in 32-bit words, set by decode_program
+  _literal: int | None
+
+  def __init_subclass__(cls, **kwargs):
+    super().__init_subclass__(**kwargs)
+    cls._fields = {n: v[0] if isinstance(v, tuple) else v for n, v in cls.__dict__.items() if isinstance(v, BitField) or (isinstance(v, tuple) and len(v) == 2 and isinstance(v[0], BitField))}
+    if 'encoding' in cls._fields and isinstance(cls.__dict__.get('encoding'), tuple): cls._encoding = cls.__dict__['encoding']
+
+  def __init__(self, *args, literal: int | None = None, **kwargs):
+    self._values, self._literal = dict(self._defaults), literal
+    # Map positional args to field names
+    field_names = [n for n in self._fields if n != 'encoding']
+    orig_args = dict(zip(field_names, args))
+    orig_args.update(kwargs)
+    self._values.update(orig_args)
+    # Validate register counts for SMEM instructions (before encoding)
+    if self.__class__.__name__ == 'SMEM':
+      op_val = orig_args.get(field_names[0]) if args else orig_args.get('op')
+      if op_val is not None:
+        if hasattr(op_val, 'value'): op_val = op_val.value
+        expected_cnt = {0:1, 1:2, 2:4, 3:8, 4:16, 8:1, 9:2, 10:4, 11:8, 12:16}.get(op_val)
+        sdata_val = orig_args.get('sdata')
+        if expected_cnt is not None and isinstance(sdata_val, Reg) and sdata_val.count != expected_cnt:
+          raise ValueError(f"SMEM op {op_val} expects {expected_cnt} registers, got {sdata_val.count}")
+    # Validate register counts for SOP1 instructions (b32 = 1 reg, b64 = 2 regs)
+    if self.__class__.__name__ == 'SOP1':
+      op_val = orig_args.get(field_names[0]) if args else orig_args.get('op')
+      if op_val is not None and hasattr(op_val, 'name'):
+        expected = 2 if op_val.name.endswith('_B64') else 1
+        sdst_val, ssrc0_val = orig_args.get('sdst'), orig_args.get('ssrc0')
+        if isinstance(sdst_val, Reg) and sdst_val.count != expected:
+          raise ValueError(f"SOP1 {op_val.name} expects {expected} destination register(s), got {sdst_val.count}")
+        if isinstance(ssrc0_val, Reg) and ssrc0_val.count != expected:
+          raise ValueError(f"SOP1 {op_val.name} expects {expected} source register(s), got {ssrc0_val.count}")
+    # Type check and encode values
+    for name, val in list(self._values.items()):
+      if name == 'encoding': continue
+      # For RawImm, only process RAW_FIELDS to unwrap to int
+      if isinstance(val, RawImm):
+        if name in RAW_FIELDS: self._values[name] = val.val
+        continue
+      field = self._fields.get(name)
+      marker = field.marker if field else None
+      # Type validation
+      if marker is _SGPRField:
+        if isinstance(val, VGPR): raise TypeError(f"field '{name}' requires SGPR, got VGPR")
+        if not isinstance(val, (SGPR, TTMP, SrcMod, int, RawImm)): raise TypeError(f"field '{name}' requires SGPR, got {type(val).__name__}")
+      if marker is _VGPRField:
+        if not isinstance(val, VGPR): raise TypeError(f"field '{name}' requires VGPR, got {type(val).__name__}")
+      if marker is _SSrc and isinstance(val, VGPR): raise TypeError(f"field '{name}' requires scalar source, got VGPR")
+      # Encode source fields as RawImm for consistent disassembly
+      if name in SRC_FIELDS:
+        encoded = encode_src(val)
+        # For VOP1/VOP2/VOPC (no opsel field), encode hi bit in src value
+        if isinstance(val, Reg) and val.hi and 'opsel' not in self._fields:
+          encoded |= 0x80
+        self._values[name] = RawImm(encoded)
+        # Handle neg/abs/opsel modifiers for VOP3 instructions
+        if isinstance(val, SrcMod):
+          if val.neg and 'neg' in self._fields:
+            neg_bit = {'src0': 1, 'src1': 2, 'src2': 4}.get(name, 0)
+            cur_neg = self._values.get('neg', 0)
+            self._values['neg'] = (cur_neg.val if isinstance(cur_neg, RawImm) else cur_neg) | neg_bit
+          if val.abs_ and 'abs' in self._fields:
+            abs_bit = {'src0': 1, 'src1': 2, 'src2': 4}.get(name, 0)
+            cur_abs = self._values.get('abs', 0)
+            self._values['abs'] = (cur_abs.val if isinstance(cur_abs, RawImm) else cur_abs) | abs_bit
+        # Handle hi (opsel) for 16-bit ops - only for formats with opsel field
+        if isinstance(val, Reg) and val.hi and 'opsel' in self._fields:
+          opsel_bit = {'src0': 1, 'src1': 2, 'src2': 4}.get(name, 0)
+          cur_opsel = self._values.get('opsel', 0)
+          self._values['opsel'] = (cur_opsel.val if isinstance(cur_opsel, RawImm) else cur_opsel) | opsel_bit
+        # Track literal value if needed (encoded as 255)
+        # For 64-bit ops, store literal in high 32 bits (to match from_bytes decoding and to_bytes encoding)
+        if encoded == 255 and self._literal is None:
+          if isinstance(val, SrcMod) and not isinstance(val, Reg):
+            # SrcMod wrapping a literal value
+            self._literal = (val.val << 32) if self._is_64bit_op() else val.val
+          elif isinstance(val, int) and not isinstance(val, IntEnum):
+            self._literal = (val << 32) if self._is_64bit_op() else val
+          elif isinstance(val, float):
+            import struct
+            lit32 = struct.unpack('<I', struct.pack('<f', val))[0]
+            self._literal = (lit32 << 32) if self._is_64bit_op() else lit32
+      # Encode raw register fields for consistent repr
+      elif name in RAW_FIELDS:
+        if isinstance(val, Reg):
+          encoded = _encode_reg(val)
+          # For VOP1/VOP2/VOPC (no opsel field), encode hi bit in register value
+          if val.hi and 'opsel' not in self._fields:
+            encoded |= 0x80
+          self._values[name] = encoded
+          # Handle vdst hi (opsel bit 3) for 16-bit ops - only for formats with opsel field
+          if name == 'vdst' and val.hi and 'opsel' in self._fields:
+            cur_opsel = self._values.get('opsel', 0)
+            self._values['opsel'] = (cur_opsel.val if isinstance(cur_opsel, RawImm) else cur_opsel) | 8
+        elif hasattr(val, 'value'): self._values[name] = val.value  # IntEnum like SrcEnum.NULL
+      # Encode sbase (divided by 2) and srsrc/ssamp (divided by 4)
+      elif name == 'sbase':
+        if isinstance(val, Reg): self._values[name] = val.idx // 2
+        elif isinstance(val, SrcMod): self._values[name] = val.val // 2  # Special regs like VCC_LO
+      elif name in {'srsrc', 'ssamp'} and isinstance(val, Reg):
+        self._values[name] = val.idx // 4
+      # VOPD vdsty: encode as actual >> 1 (constraint: vdsty parity must be opposite of vdstx)
+      elif marker is _VDSTYEnc and isinstance(val, VGPR):
+        self._values[name] = val.idx >> 1
+
+  def _encode_field(self, name: str, val) -> int:
+    if isinstance(val, RawImm): return val.val
+    if isinstance(val, SrcMod) and not isinstance(val, Reg): return val.val  # Special regs like VCC_LO
+    if name in {'srsrc', 'ssamp'}: return val.idx // 4 if isinstance(val, Reg) else val
+    if name == 'sbase': return val.idx // 2 if isinstance(val, Reg) else val.val // 2 if isinstance(val, SrcMod) else val
+    if name in RAW_FIELDS: return _encode_reg(val) if isinstance(val, Reg) else val
+    if isinstance(val, Reg) or name in SRC_FIELDS: return encode_src(val)
+    return val.value if hasattr(val, 'value') else val
+
+  def to_int(self) -> int:
+    word = (self._encoding[1] & self._encoding[0].mask()) << self._encoding[0].lo if self._encoding else 0
+    for n, bf in self._fields.items():
+      if n != 'encoding' and n in self._values: word |= (self._encode_field(n, self._values[n]) & bf.mask()) << bf.lo
+    return word
+
+  def _get_literal(self) -> int | None:
+    for n in SRC_FIELDS:
+      if n in self._values and not isinstance(v := self._values[n], RawImm) and isinstance(v, int) and not isinstance(v, IntEnum) and not (0 <= v <= 64 or -16 <= v <= -1): return v
+    return None
+
+  def _is_64bit_op(self) -> bool:
+    """Check if this instruction uses 64-bit operands (and thus 64-bit literals).
+    Exception: V_LDEXP_F64 has 32-bit integer src1, so its literal is 32-bit."""
+    op = self._values.get('op')
+    if op is None: return False
+    # op may be an enum (from __init__) or an int (from from_int)
+    op_name = op.name if hasattr(op, 'name') else None
+    if op_name is None and self.__class__.__name__ == 'VOP3':
+      from extra.assembly.amd.autogen.rdna3 import VOP3Op
+      try: op_name = VOP3Op(op).name
+      except ValueError: pass
+    if op_name is None: return False
+    # V_LDEXP_F64 has 32-bit integer exponent in src1, so literal is 32-bit
+    if op_name == 'V_LDEXP_F64': return False
+    return op_name.endswith(('_F64', '_B64', '_I64', '_U64'))
+
+  def to_bytes(self) -> bytes:
+    result = self.to_int().to_bytes(self._size(), 'little')
+    lit = self._get_literal() or getattr(self, '_literal', None)
+    if lit is None: return result
+    # For 64-bit ops, literal is stored in high 32 bits internally, but encoded as 4 bytes
+    lit32 = (lit >> 32) if self._is_64bit_op() else lit
+    return result + (lit32 & 0xffffffff).to_bytes(4, 'little')
+
+  @classmethod
+  def _size(cls) -> int: return 4 if issubclass(cls, Inst32) else 8
+  def size(self) -> int:
+    # Literal is always 4 bytes in the binary (for 64-bit ops, it's in high 32 bits)
+    return self._size() + (4 if self._literal is not None else 0)
+
+  @classmethod
+  def from_int(cls, word: int):
+    inst = object.__new__(cls)
+    inst._values = {n: RawImm(v) if n in SRC_FIELDS else v for n, bf in cls._fields.items() if n != 'encoding' for v in [(word >> bf.lo) & bf.mask()]}
+    inst._literal = None
+    return inst
+
+  @classmethod
+  def from_bytes(cls, data: bytes):
+    inst = cls.from_int(int.from_bytes(data[:cls._size()], 'little'))
+    op_val = inst._values.get('op', 0)
+    has_literal = cls.__name__ == 'VOP2' and op_val in (44, 45, 55, 56)
+    has_literal = has_literal or (cls.__name__ == 'SOP2' and op_val in (69, 70))
+    for n in SRC_FIELDS:
+      if n in inst._values and isinstance(inst._values[n], RawImm) and inst._values[n].val == 255: has_literal = True
+    if has_literal:
+      # For 64-bit ops, the literal is 32 bits placed in the HIGH 32 bits of the 64-bit value
+      # (low 32 bits are zero). This is how AMD hardware interprets 32-bit literals for 64-bit ops.
+      if len(data) >= cls._size() + 4:
+        lit32 = int.from_bytes(data[cls._size():cls._size()+4], 'little')
+        inst._literal = (lit32 << 32) if inst._is_64bit_op() else lit32
+    return inst
+
+  def __repr__(self):
+    # Use _fields order and exclude fields that are 0/default (for consistent repr after roundtrip)
+    def is_zero(v): return (isinstance(v, int) and v == 0) or (isinstance(v, VGPR) and v.idx == 0 and v.count == 1)
+    items = [(k, self._values[k]) for k in self._fields if k in self._values and k != 'encoding'
+             and not (is_zero(self._values[k]) and k not in {'op'})]
+    lit = f", literal={hex(self._literal)}" if self._literal is not None else ""
+    return f"{self.__class__.__name__}({', '.join(f'{k}={v}' for k, v in items)}{lit})"
+
+  def __eq__(self, other):
+    if not isinstance(other, Inst): return NotImplemented
+    return self.__class__ == other.__class__ and self._values == other._values and self._literal == other._literal
+
+  def __hash__(self): return hash((self.__class__.__name__, tuple(sorted((k, repr(v)) for k, v in self._values.items())), self._literal))
+
+  def disasm(self) -> str:
+    from extra.assembly.amd.asm import disasm
+    return disasm(self)
+
+class Inst32(Inst): pass
+class Inst64(Inst): pass
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# CODE GENERATION: generates autogen/__init__.py by parsing AMD ISA PDFs
+# Supports both RDNA3.5 and CDNA4 instruction set PDFs - auto-detects format
+# ═══════════════════════════════════════════════════════════════════════════════
+
+PDF_URLS = {
+  "rdna3": "https://docs.amd.com/api/khub/documents/UVVZM22UN7tMUeiW_4ShTQ/content",  # RDNA3.5
+  "rdna4": "https://docs.amd.com/api/khub/documents/uQpkEvk3pv~kfAb2x~j4uw/content",
+  "cdna": ["https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-mi300-cdna3-instruction-set-architecture.pdf",
+           "https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-cdna4-instruction-set-architecture.pdf"],
+}
+FIELD_TYPES = {'SSRC0': 'SSrc', 'SSRC1': 'SSrc', 'SOFFSET': 'SSrc', 'SADDR': 'SSrc', 'SRC0': 'Src', 'SRC1': 'Src', 'SRC2': 'Src',
+  'SDST': 'SGPRField', 'SBASE': 'SGPRField', 'SDATA': 'SGPRField', 'SRSRC': 'SGPRField', 'VDST': 'VGPRField', 'VSRC1': 'VGPRField', 'VDATA': 'VGPRField',
+  'VADDR': 'VGPRField', 'ADDR': 'VGPRField', 'DATA': 'VGPRField', 'DATA0': 'VGPRField', 'DATA1': 'VGPRField', 'SIMM16': 'SImm', 'OFFSET': 'Imm',
+  'OPX': 'VOPDOp', 'OPY': 'VOPDOp', 'SRCX0': 'Src', 'SRCY0': 'Src', 'VSRCX1': 'VGPRField', 'VSRCY1': 'VGPRField', 'VDSTX': 'VGPRField', 'VDSTY': 'VDSTYEnc'}
+FIELD_ORDER = {
+  'SOP2': ['op', 'sdst', 'ssrc0', 'ssrc1'], 'SOP1': ['op', 'sdst', 'ssrc0'], 'SOPC': ['op', 'ssrc0', 'ssrc1'],
+  'SOPK': ['op', 'sdst', 'simm16'], 'SOPP': ['op', 'simm16'], 'VOP1': ['op', 'vdst', 'src0'], 'VOPC': ['op', 'src0', 'vsrc1'],
+  'VOP2': ['op', 'vdst', 'src0', 'vsrc1'], 'VOP3SD': ['op', 'vdst', 'sdst', 'src0', 'src1', 'src2', 'clmp'],
+  'SMEM': ['op', 'sdata', 'sbase', 'soffset', 'offset', 'glc', 'dlc'], 'DS': ['op', 'vdst', 'addr', 'data0', 'data1'],
+  'VOP3': ['op', 'vdst', 'src0', 'src1', 'src2', 'omod', 'neg', 'abs', 'clmp', 'opsel'],
+  'VOP3P': ['op', 'vdst', 'src0', 'src1', 'src2', 'neg', 'neg_hi', 'opsel', 'opsel_hi', 'clmp'],
+  'FLAT': ['op', 'vdst', 'addr', 'data', 'saddr', 'offset', 'seg', 'dlc', 'glc', 'slc'],
+  'MUBUF': ['op', 'vdata', 'vaddr', 'srsrc', 'soffset', 'offset', 'offen', 'idxen', 'glc', 'dlc', 'slc', 'tfe'],
+  'MTBUF': ['op', 'vdata', 'vaddr', 'srsrc', 'soffset', 'offset', 'format', 'offen', 'idxen', 'glc', 'dlc', 'slc', 'tfe'],
+  'MIMG': ['op', 'vdata', 'vaddr', 'srsrc', 'ssamp', 'dmask', 'dim', 'unrm', 'dlc', 'glc', 'slc'],
+  'EXP': ['en', 'target', 'vsrc0', 'vsrc1', 'vsrc2', 'vsrc3', 'done', 'row'],
+  'VINTERP': ['op', 'vdst', 'src0', 'src1', 'src2', 'waitexp', 'clmp', 'opsel', 'neg'],
+  'VOPD': ['opx', 'opy', 'vdstx', 'vdsty', 'srcx0', 'vsrcx1', 'srcy0', 'vsrcy1'],
+  'LDSDIR': ['op', 'vdst', 'attr', 'attr_chan', 'wait_va']}
+SRC_EXTRAS = {233: 'DPP8', 234: 'DPP8FI', 250: 'DPP16', 251: 'VCCZ', 252: 'EXECZ', 254: 'LDS_DIRECT'}
+FLOAT_MAP = {'0.5': 'POS_HALF', '-0.5': 'NEG_HALF', '1.0': 'POS_ONE', '-1.0': 'NEG_ONE', '2.0': 'POS_TWO', '-2.0': 'NEG_TWO',
+  '4.0': 'POS_FOUR', '-4.0': 'NEG_FOUR', '1/(2*PI)': 'INV_2PI', '0': 'ZERO'}
+
+def _parse_bits(s: str) -> tuple[int, int] | None:
+  import re
+  return (int(m.group(1)), int(m.group(2) or m.group(1))) if (m := re.match(r'\[(\d+)(?::(\d+))?\]', s)) else None
+
+def _parse_fields_table(table: list, fmt: str, enums: set[str]) -> list[tuple]:
+  import re
+  fields = []
+  for row in table[1:]:
+    if not row or not row[0]: continue
+    name, bits_str = row[0].split('\n')[0].strip(), (row[1] or '').split('\n')[0].strip()
+    if not (bits := _parse_bits(bits_str)): continue
+    enc_val, hi, lo = None, bits[0], bits[1]
+    if name == 'ENCODING' and row[2]:
+      # Handle both RDNA3 ('bXX) and CDNA4 (Must be: XX) encoding formats
+      if m := re.search(r"(?:'b|Must be:\s*)([01_]+)", row[2]):
+        enc_bits = m.group(1).replace('_', '')
+        enc_val = int(enc_bits, 2)
+        declared_width, actual_width = hi - lo + 1, len(enc_bits)
+        if actual_width > declared_width: lo = hi - actual_width + 1
+    ftype = f"{fmt}Op" if name == 'OP' and f"{fmt}Op" in enums else FIELD_TYPES.get(name.upper())
+    fields.append((name, hi, lo, enc_val, ftype))
+  return fields
+
+def _parse_single_pdf(url: str) -> dict:
+  """Parse a single PDF and return raw data (formats, enums, src_enum, doc_name, is_cdna)."""
+  import re, pdfplumber
+  from tinygrad.helpers import fetch
+
+  pdf = pdfplumber.open(fetch(url))
+
+  # Auto-detect document type from first page
+  first_page_text = pdf.pages[0].extract_text() or ''
+  is_cdna4 = 'CDNA4' in first_page_text or 'CDNA 4' in first_page_text
+  is_cdna3 = 'CDNA3' in first_page_text or 'CDNA 3' in first_page_text or 'MI300' in first_page_text
+  is_cdna = is_cdna3 or is_cdna4
+  is_rdna4 = 'RDNA4' in first_page_text or 'RDNA 4' in first_page_text
+  is_rdna35 = 'RDNA3.5' in first_page_text or 'RDNA 3.5' in first_page_text  # Check 3.5 before 3
+  is_rdna3 = not is_rdna35 and ('RDNA3' in first_page_text or 'RDNA 3' in first_page_text)
+  doc_name = "CDNA4" if is_cdna4 else "CDNA3" if is_cdna3 else "RDNA4" if is_rdna4 else "RDNA3.5" if is_rdna35 else "RDNA3" if is_rdna3 else "Unknown"
+
+  # Find the "Microcode Formats" section - search for SOP2 format definition
+  microcode_start = None
+  total_pages = len(pdf.pages)
+  # Search from likely locations (formats are typically 20-95% through the document - RDNA3 has them at ~25%)
+  for i in range(int(total_pages * 0.2), total_pages):
+    text = pdf.pages[i].extract_text() or ''
+    # Look for "X.Y.Z. SOP2" section header or "Chapter X. Microcode Formats"
+    if re.search(r'\d+\.\d+\.\d+\.\s+SOP2\b', text) or re.search(r'Chapter \d+\.\s+Microcode Formats', text):
+      microcode_start = i
+      break
+  if microcode_start is None: microcode_start = int(total_pages * 0.9)
+
+  pages = pdf.pages[microcode_start:microcode_start + 50]
+  page_texts = [p.extract_text() or '' for p in pages]
+  page_tables = [[t.extract() for t in p.find_tables()] for p in pages]
+  full_text = '\n'.join(page_texts)
+
+  # parse SSRC encoding from first page with VCC_LO
+  src_enum = dict(SRC_EXTRAS)
+  for text in page_texts[:10]:
+    if 'SSRC0' in text and 'VCC_LO' in text:
+      for m in re.finditer(r'^(\d+)\s+(\S+)', text, re.M):
+        val, name = int(m.group(1)), m.group(2).rstrip('.:')
+        if name in FLOAT_MAP: src_enum[val] = FLOAT_MAP[name]
+        elif re.match(r'^[A-Z][A-Z0-9_]*$', name): src_enum[val] = name
+      break
+
+  # parse opcode tables
+  enums: dict[str, dict[int, str]] = {}
+  for m in re.finditer(r'Table \d+\. (\w+) Opcodes(.*?)(?=Table \d+\.|\n\d+\.\d+\.\d+\.\s+\w+\s*\nDescription|$)', full_text, re.S):
+    if ops := {int(x.group(1)): x.group(2) for x in re.finditer(r'(\d+)\s+([A-Z][A-Z0-9_]+)', m.group(2))}:
+      enums[m.group(1) + "Op"] = ops
+  if vopd_m := re.search(r'Table \d+\. VOPD Y-Opcodes\n(.*?)(?=Table \d+\.|15\.\d)', full_text, re.S):
+    if ops := {int(x.group(1)): x.group(2) for x in re.finditer(r'(\d+)\s+(V_DUAL_\w+)', vopd_m.group(1))}:
+      enums["VOPDOp"] = ops
+  enum_names = set(enums.keys())
+
+  def is_fields_table(t) -> bool: return t and len(t) > 1 and t[0] and 'Field' in str(t[0][0] or '')
+  def has_encoding(fields) -> bool: return any(f[0] == 'ENCODING' for f in fields)
+  def has_header_before_fields(text) -> bool:
+    return (pos := text.find('Field Name')) != -1 and bool(re.search(r'\d+\.\d+\.\d+\.\s+\w+\s*\n', text[:pos]))
+
+  # find format headers with their page indices
+  format_headers = []
+  for i, text in enumerate(page_texts):
+    for m in re.finditer(r'\d+\.\d+\.\d+\.\s+(\w+)\s*\n?Description', text): format_headers.append((m.group(1), i, m.start()))
+    for m in re.finditer(r'\d+\.\d+\.\d+\.\s+(\w+)\s*\n', text):
+      fmt_name = m.group(1)
+      if is_cdna and fmt_name.isupper() and len(fmt_name) >= 2:
+        format_headers.append((fmt_name, i, m.start()))
+      elif m.start() > len(text) - 200 and 'Description' not in text[m.end():] and i + 1 < len(page_texts):
+        next_text = page_texts[i + 1].lstrip()
+        if next_text.startswith('Description') or (next_text.startswith('"RDNA') and 'Description' in next_text[:200]):
+          format_headers.append((fmt_name, i, m.start()))
+
+  # parse instruction formats
+  formats: dict[str, list] = {}
+  for fmt_name, page_idx, header_pos in format_headers:
+    if fmt_name in formats: continue
+    text, tables = page_texts[page_idx], page_tables[page_idx]
+    field_pos = text.find('Field Name', header_pos)
+
+    fields = None
+    for offset in range(3):
+      if page_idx + offset >= len(pages): break
+      if offset > 0 and has_header_before_fields(page_texts[page_idx + offset]): break
+      for t in page_tables[page_idx + offset] if offset > 0 or field_pos > header_pos else []:
+        if is_fields_table(t) and (f := _parse_fields_table(t, fmt_name, enum_names)) and has_encoding(f):
+          fields = f
+          break
+      if fields: break
+
+    if not fields and field_pos > header_pos:
+      for t in tables:
+        if is_fields_table(t) and (f := _parse_fields_table(t, fmt_name, enum_names)):
+          fields = f
+          break
+
+    if not fields: continue
+    field_names = {f[0] for f in fields}
+
+    for pg_offset in range(1, 3):
+      if page_idx + pg_offset >= len(pages) or has_header_before_fields(page_texts[page_idx + pg_offset]): break
+      for t in page_tables[page_idx + pg_offset]:
+        if is_fields_table(t) and (extra := _parse_fields_table(t, fmt_name, enum_names)) and not has_encoding(extra):
+          for ef in extra:
+            if ef[0] not in field_names:
+              fields.append(ef)
+              field_names.add(ef[0])
+          break
+    formats[fmt_name] = fields
+
+  # fix known PDF errors
+  if 'SMEM' in formats:
+    formats['SMEM'] = [(n, 13 if n == 'DLC' else 14 if n == 'GLC' else h, 13 if n == 'DLC' else 14 if n == 'GLC' else l, e, t)
+                       for n, h, l, e, t in formats['SMEM']]
+
+  return {"formats": formats, "enums": enums, "src_enum": src_enum, "doc_name": doc_name, "is_cdna": is_cdna}
+
+def _merge_results(results: list[dict]) -> dict:
+  """Merge multiple PDF parse results into a superset. Asserts if any conflicts."""
+  merged = {"formats": {}, "enums": {}, "src_enum": dict(SRC_EXTRAS), "doc_names": [], "is_cdna": False}
+  for r in results:
+    merged["doc_names"].append(r["doc_name"])
+    merged["is_cdna"] = merged["is_cdna"] or r["is_cdna"]
+    # Merge src_enum (union, assert no conflicts)
+    for val, name in r["src_enum"].items():
+      if val in merged["src_enum"]:
+        assert merged["src_enum"][val] == name, f"SrcEnum conflict: {val} = {merged['src_enum'][val]} vs {name}"
+      else:
+        merged["src_enum"][val] = name
+    # Merge enums (union of ops per enum, assert no conflicts)
+    for enum_name, ops in r["enums"].items():
+      if enum_name not in merged["enums"]: merged["enums"][enum_name] = {}
+      for val, name in ops.items():
+        if val in merged["enums"][enum_name]:
+          assert merged["enums"][enum_name][val] == name, f"{enum_name} conflict: {val} = {merged['enums'][enum_name][val]} vs {name}"
+        else:
+          merged["enums"][enum_name][val] = name
+    # Merge formats (union of fields, assert no bit position conflicts for same field name)
+    for fmt_name, fields in r["formats"].items():
+      if fmt_name not in merged["formats"]:
+        merged["formats"][fmt_name] = list(fields)
+      else:
+        existing = {f[0]: (f[1], f[2]) for f in merged["formats"][fmt_name]}  # name -> (hi, lo)
+        for f in fields:
+          name, hi, lo = f[0], f[1], f[2]
+          if name in existing:
+            assert existing[name] == (hi, lo), f"Format {fmt_name} field {name} conflict: bits {existing[name]} vs ({hi}, {lo})"
+          else:
+            merged["formats"][fmt_name].append(f)
+  return merged
+
+def generate(output_path: str | None = None, arch: str = "rdna3") -> dict:
+  """Generate instruction definitions from AMD ISA PDF(s). Returns dict with formats for testing."""
+  urls = PDF_URLS[arch]
+  if isinstance(urls, str): urls = [urls]
+
+  # Parse all PDFs and merge
+  results = [_parse_single_pdf(url) for url in urls]
+  if len(results) == 1:
+    merged = results[0]
+    doc_name = merged["doc_name"]
+  else:
+    merged = _merge_results(results)
+    doc_name = "+".join(merged["doc_names"])
+
+  formats, enums, src_enum = merged["formats"], merged["enums"], merged["src_enum"]
+
+  # generate output
+  def enum_lines(name, items):
+    return [f"class {name}(IntEnum):"] + [f"  {n} = {v}" for v, n in sorted(items.items())] + [""]
+  def field_key(f): return order.index(f[0].lower()) if f[0].lower() in order else 1000
+  lines = [f"# autogenerated from AMD {doc_name} ISA PDF by dsl.py - do not edit", "from enum import IntEnum",
+           "from typing import Annotated",
+           "from extra.assembly.amd.dsl import bits, BitField, Inst32, Inst64, SGPR, VGPR, TTMP as TTMP, s as s, v as v, ttmp as ttmp, SSrc, Src, SImm, Imm, VDSTYEnc, SGPRField, VGPRField",
+           "import functools", ""]
+  lines += enum_lines("SrcEnum", src_enum) + sum([enum_lines(n, ops) for n, ops in sorted(enums.items())], [])
+  # Format-specific field defaults (verified against LLVM test vectors)
+  format_defaults = {'VOP3P': {'opsel_hi': 3, 'opsel_hi2': 1}}
+  lines.append("# instruction formats")
+  for fmt_name, fields in sorted(formats.items()):
+    base = "Inst64" if max(f[1] for f in fields) > 31 or fmt_name == 'VOP3SD' else "Inst32"
+    order = FIELD_ORDER.get(fmt_name, [])
+    lines.append(f"class {fmt_name}({base}):")
+    if enc := next((f for f in fields if f[0] == 'ENCODING'), None):
+      enc_str = f"bits[{enc[1]}:{enc[2]}] == 0b{enc[3]:b}" if enc[1] != enc[2] else f"bits[{enc[1]}] == {enc[3]}"
+      lines.append(f"  encoding = {enc_str}")
+    if defaults := format_defaults.get(fmt_name):
+      lines.append(f"  _defaults = {defaults}")
+    for name, hi, lo, _, ftype in sorted([f for f in fields if f[0] != 'ENCODING'], key=field_key):
+      if ftype and ftype.endswith('Op'):
+        ann = f":Annotated[BitField, {ftype}]"
+      else:
+        ann = f":{ftype}" if ftype else ""
+      lines.append(f"  {name.lower()}{ann} = bits[{hi}]" if hi == lo else f"  {name.lower()}{ann} = bits[{hi}:{lo}]")
+    lines.append("")
+  lines.append("# instruction helpers")
+  for cls_name, ops in sorted(enums.items()):
+    fmt = cls_name[:-2]
+    for op_val, name in sorted(ops.items()):
+      seg = {"GLOBAL": ", seg=2", "SCRATCH": ", seg=2"}.get(fmt, "")
+      tgt = {"GLOBAL": "FLAT, GLOBALOp", "SCRATCH": "FLAT, SCRATCHOp"}.get(fmt, f"{fmt}, {cls_name}")
+      if fmt in formats or fmt in ("GLOBAL", "SCRATCH"):
+        if fmt in ("VOP1", "VOP2", "VOPC"):
+          suffix = "_e32"
+        elif fmt == "VOP3" and op_val < 512:
+          suffix = "_e64"
+        else:
+          suffix = ""
+        if name in ('V_FMAMK_F32', 'V_FMAMK_F16'):
+          lines.append(f"def {name.lower()}{suffix}(vdst, src0, K, vsrc1): return {fmt}({cls_name}.{name}, vdst, src0, vsrc1, literal=K)")
+        elif name in ('V_FMAAK_F32', 'V_FMAAK_F16'):
+          lines.append(f"def {name.lower()}{suffix}(vdst, src0, vsrc1, K): return {fmt}({cls_name}.{name}, vdst, src0, vsrc1, literal=K)")
+        else:
+          lines.append(f"{name.lower()}{suffix} = functools.partial({tgt}.{name}{seg})")
+  skip_exports = {'DPP8', 'DPP16'}
+  src_names = {name for _, name in src_enum.items()}
+  lines += [""] + [f"{name} = SrcEnum.{name}" for _, name in sorted(src_enum.items()) if name not in skip_exports]
+  if "NULL" in src_names: lines.append("OFF = NULL\n")
+
+  if output_path is not None:
+    import pathlib
+    pathlib.Path(output_path).write_text('\n'.join(lines))
+  return {"formats": formats, "enums": enums, "src_enum": src_enum}
+
+if __name__ == "__main__":
+  import argparse
+  parser = argparse.ArgumentParser(description="Generate instruction definitions from AMD ISA PDF")
+  parser.add_argument("--arch", choices=list(PDF_URLS.keys()) + ["all"], default="rdna3", help="Target architecture (default: rdna3)")
+  args = parser.parse_args()
+  if args.arch == "all":
+    for arch in PDF_URLS.keys():
+      result = generate(f"extra/assembly/amd/autogen/{arch}/__init__.py", arch=arch)
+      print(f"{arch}: generated SrcEnum ({len(result['src_enum'])}) + {len(result['enums'])} opcode enums + {len(result['formats'])} format classes")
+  else:
+    result = generate(f"extra/assembly/amd/autogen/{args.arch}/__init__.py", arch=args.arch)
+    print(f"generated SrcEnum ({len(result['src_enum'])}) + {len(result['enums'])} opcode enums + {len(result['formats'])} format classes")
@@ -0,0 +1,759 @@
+# RDNA3 emulator - executes compiled pseudocode from AMD ISA PDF
+# mypy: ignore-errors
+from __future__ import annotations
+import ctypes, os
+from extra.assembly.amd.dsl import Inst, RawImm
+from extra.assembly.amd.pcode import _f32, _i32, _sext, _f16, _i16, _f64, _i64, Reg
+from extra.assembly.amd.autogen.rdna3.gen_pcode import get_compiled_functions
+from extra.assembly.amd.autogen.rdna3 import (
+  SOP1, SOP2, SOPC, SOPK, SOPP, SMEM, VOP1, VOP2, VOP3, VOP3SD, VOP3P, VOPC, DS, FLAT, VOPD, SrcEnum,
+  SOP1Op, SOP2Op, SOPCOp, SOPKOp, SOPPOp, SMEMOp, VOP1Op, VOP2Op, VOP3Op, VOP3SDOp, VOP3POp, VOPCOp, DSOp, FLATOp, GLOBALOp, VOPDOp
+)
+
+Program = dict[int, Inst]
+WAVE_SIZE, SGPR_COUNT, VGPR_COUNT = 32, 128, 256
+VCC_LO, VCC_HI, NULL, EXEC_LO, EXEC_HI, SCC = SrcEnum.VCC_LO, SrcEnum.VCC_HI, SrcEnum.NULL, SrcEnum.EXEC_LO, SrcEnum.EXEC_HI, SrcEnum.SCC
+
+# VOP3 ops that use 64-bit operands (and thus 64-bit literals when src is 255)
+# Exception: V_LDEXP_F64 has 32-bit integer src1, so literal should NOT be 64-bit when src1=255
+_VOP3_64BIT_OPS = {op.value for op in VOP3Op if op.name.endswith(('_F64', '_B64', '_I64', '_U64'))}
+# Ops where src1 is 32-bit (exponent/shift amount) even though the op name suggests 64-bit
+_VOP3_64BIT_OPS_32BIT_SRC1 = {VOP3Op.V_LDEXP_F64.value}
+# Ops with 16-bit types in name (for source/dest handling)
+# Exception: SAD/MSAD ops take 32-bit packed sources and extract 16-bit/8-bit chunks internally
+_VOP3_16BIT_OPS = {op for op in VOP3Op if any(s in op.name for s in ('_F16', '_B16', '_I16', '_U16')) and 'SAD' not in op.name}
+_VOP1_16BIT_OPS = {op for op in VOP1Op if any(s in op.name for s in ('_F16', '_B16', '_I16', '_U16'))}
+_VOP2_16BIT_OPS = {op for op in VOP2Op if any(s in op.name for s in ('_F16', '_B16', '_I16', '_U16'))}
+# CVT ops with 32/64-bit source (despite 16-bit in name)
+_CVT_32_64_SRC_OPS = {op for op in VOP3Op if op.name.startswith('V_CVT_') and op.name.endswith(('_F32', '_I32', '_U32', '_F64', '_I64', '_U64'))} | \
+                     {op for op in VOP1Op if op.name.startswith('V_CVT_') and op.name.endswith(('_F32', '_I32', '_U32', '_F64', '_I64', '_U64'))}
+# 16-bit dst ops (PACK has 32-bit dst despite F16 in name)
+_VOP3_16BIT_DST_OPS = {op for op in _VOP3_16BIT_OPS if 'PACK' not in op.name}
+_VOP1_16BIT_DST_OPS = {op for op in _VOP1_16BIT_OPS if 'PACK' not in op.name}
+
+# Inline constants for src operands 128-254. Build tables for f32, f16, and f64 formats.
+import struct as _struct
+_FLOAT_CONSTS = {SrcEnum.POS_HALF: 0.5, SrcEnum.NEG_HALF: -0.5, SrcEnum.POS_ONE: 1.0, SrcEnum.NEG_ONE: -1.0,
+                 SrcEnum.POS_TWO: 2.0, SrcEnum.NEG_TWO: -2.0, SrcEnum.POS_FOUR: 4.0, SrcEnum.NEG_FOUR: -4.0, SrcEnum.INV_2PI: 0.15915494309189535}
+def _build_inline_consts(neg_mask, float_to_bits):
+  tbl = list(range(65)) + [((-i) & neg_mask) for i in range(1, 17)] + [0] * (127 - 81)
+  for k, v in _FLOAT_CONSTS.items(): tbl[k - 128] = float_to_bits(v)
+  return tbl
+_INLINE_CONSTS = _build_inline_consts(0xffffffff, lambda f: _struct.unpack('<I', _struct.pack('<f', f))[0])
+_INLINE_CONSTS_F16 = _build_inline_consts(0xffff, lambda f: _struct.unpack('<H', _struct.pack('<e', f))[0])
+_INLINE_CONSTS_F64 = _build_inline_consts(0xffffffffffffffff, lambda f: _struct.unpack('<Q', _struct.pack('<d', f))[0])
+
+# Memory access
+_valid_mem_ranges: list[tuple[int, int]] = []
+def set_valid_mem_ranges(ranges: set[tuple[int, int]]) -> None: _valid_mem_ranges.clear(); _valid_mem_ranges.extend(ranges)
+def _mem_valid(addr: int, size: int) -> bool:
+  for s, z in _valid_mem_ranges:
+    if s <= addr and addr + size <= s + z: return True
+  return not _valid_mem_ranges
+def _ctypes_at(addr: int, size: int): return (ctypes.c_uint8 if size == 1 else ctypes.c_uint16 if size == 2 else ctypes.c_uint32).from_address(addr)
+def mem_read(addr: int, size: int) -> int: return _ctypes_at(addr, size).value if _mem_valid(addr, size) else 0
+def mem_write(addr: int, size: int, val: int) -> None:
+  if _mem_valid(addr, size): _ctypes_at(addr, size).value = val
+
+# Memory op tables (not pseudocode - these are format descriptions)
+def _mem_ops(ops, suffix_map):
+  return {getattr(e, f"{p}_{s}"): v for e in ops for s, v in suffix_map.items() for p in [e.__name__.replace("Op", "")]}
+_LOAD_MAP = {'LOAD_B32': (1,4,0), 'LOAD_B64': (2,4,0), 'LOAD_B96': (3,4,0), 'LOAD_B128': (4,4,0), 'LOAD_U8': (1,1,0), 'LOAD_I8': (1,1,1), 'LOAD_U16': (1,2,0), 'LOAD_I16': (1,2,1)}
+_STORE_MAP = {'STORE_B32': (1,4), 'STORE_B64': (2,4), 'STORE_B96': (3,4), 'STORE_B128': (4,4), 'STORE_B8': (1,1), 'STORE_B16': (1,2)}
+FLAT_LOAD, FLAT_STORE = _mem_ops([GLOBALOp, FLATOp], _LOAD_MAP), _mem_ops([GLOBALOp, FLATOp], _STORE_MAP)
+# D16 ops: load/store 16-bit to lower or upper half of VGPR. Format: (size, sign, hi) where hi=1 means upper 16 bits
+_D16_LOAD_MAP = {'LOAD_D16_U8': (1,0,0), 'LOAD_D16_I8': (1,1,0), 'LOAD_D16_B16': (2,0,0),
+                 'LOAD_D16_HI_U8': (1,0,1), 'LOAD_D16_HI_I8': (1,1,1), 'LOAD_D16_HI_B16': (2,0,1)}
+_D16_STORE_MAP = {'STORE_D16_HI_B8': (1,1), 'STORE_D16_HI_B16': (2,1)}  # (size, hi)
+FLAT_D16_LOAD = _mem_ops([GLOBALOp, FLATOp], _D16_LOAD_MAP)
+FLAT_D16_STORE = _mem_ops([GLOBALOp, FLATOp], _D16_STORE_MAP)
+DS_LOAD = {DSOp.DS_LOAD_B32: (1,4,0), DSOp.DS_LOAD_B64: (2,4,0), DSOp.DS_LOAD_B128: (4,4,0), DSOp.DS_LOAD_U8: (1,1,0), DSOp.DS_LOAD_I8: (1,1,1), DSOp.DS_LOAD_U16: (1,2,0), DSOp.DS_LOAD_I16: (1,2,1)}
+DS_STORE = {DSOp.DS_STORE_B32: (1,4), DSOp.DS_STORE_B64: (2,4), DSOp.DS_STORE_B128: (4,4), DSOp.DS_STORE_B8: (1,1), DSOp.DS_STORE_B16: (1,2)}
+SMEM_LOAD = {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}
+
+# VOPD op -> VOP3 op mapping (VOPD is dual-issue of VOP1/VOP2 ops, use VOP3 enums for pseudocode lookup)
+_VOPD_TO_VOP = {
+  VOPDOp.V_DUAL_FMAC_F32: VOP3Op.V_FMAC_F32, VOPDOp.V_DUAL_FMAAK_F32: VOP2Op.V_FMAAK_F32, VOPDOp.V_DUAL_FMAMK_F32: VOP2Op.V_FMAMK_F32,
+  VOPDOp.V_DUAL_MUL_F32: VOP3Op.V_MUL_F32, VOPDOp.V_DUAL_ADD_F32: VOP3Op.V_ADD_F32, VOPDOp.V_DUAL_SUB_F32: VOP3Op.V_SUB_F32,
+  VOPDOp.V_DUAL_SUBREV_F32: VOP3Op.V_SUBREV_F32, VOPDOp.V_DUAL_MUL_DX9_ZERO_F32: VOP3Op.V_MUL_DX9_ZERO_F32,
+  VOPDOp.V_DUAL_MOV_B32: VOP3Op.V_MOV_B32, VOPDOp.V_DUAL_CNDMASK_B32: VOP3Op.V_CNDMASK_B32,
+  VOPDOp.V_DUAL_MAX_F32: VOP3Op.V_MAX_F32, VOPDOp.V_DUAL_MIN_F32: VOP3Op.V_MIN_F32,
+  VOPDOp.V_DUAL_ADD_NC_U32: VOP3Op.V_ADD_NC_U32, VOPDOp.V_DUAL_LSHLREV_B32: VOP3Op.V_LSHLREV_B32, VOPDOp.V_DUAL_AND_B32: VOP3Op.V_AND_B32,
+}
+
+# Compiled pseudocode functions (lazy loaded)
+_COMPILED: dict | None = None
+
+def _get_compiled() -> dict:
+  global _COMPILED
+  if _COMPILED is None: _COMPILED = get_compiled_functions()
+  return _COMPILED
+
+class WaveState:
+  __slots__ = ('sgpr', 'vgpr', 'scc', 'pc', 'literal', '_pend_sgpr', '_scc_reg', '_vcc_reg', '_exec_reg')
+  def __init__(self):
+    self.sgpr = [Reg(0) for _ in range(SGPR_COUNT)]
+    self.vgpr = [[Reg(0) for _ in range(VGPR_COUNT)] for _ in range(WAVE_SIZE)]
+    self.sgpr[EXEC_LO]._val = 0xffffffff
+    self.scc, self.pc, self.literal, self._pend_sgpr = 0, 0, 0, {}
+    # Reg wrappers for pseudocode access
+    self._scc_reg = Reg(0)
+    self._vcc_reg = self.sgpr[VCC_LO]
+    self._exec_reg = self.sgpr[EXEC_LO]
+
+  @property
+  def vcc(self) -> int: return self.sgpr[VCC_LO]._val | (self.sgpr[VCC_HI]._val << 32)
+  @vcc.setter
+  def vcc(self, v: int): self.sgpr[VCC_LO]._val, self.sgpr[VCC_HI]._val = v & 0xffffffff, (v >> 32) & 0xffffffff
+  @property
+  def exec_mask(self) -> int: return self.sgpr[EXEC_LO]._val | (self.sgpr[EXEC_HI]._val << 32)
+  @exec_mask.setter
+  def exec_mask(self, v: int): self.sgpr[EXEC_LO]._val, self.sgpr[EXEC_HI]._val = v & 0xffffffff, (v >> 32) & 0xffffffff
+
+  def rsgpr(self, i: int) -> int: return 0 if i == NULL else self.scc if i == SCC else self.sgpr[i]._val if i < SGPR_COUNT else 0
+  def wsgpr(self, i: int, v: int):
+    if i < SGPR_COUNT and i != NULL: self.sgpr[i]._val = v & 0xffffffff
+  def rsgpr64(self, i: int) -> int: return self.rsgpr(i) | (self.rsgpr(i+1) << 32)
+  def wsgpr64(self, i: int, v: int): self.wsgpr(i, v & 0xffffffff); self.wsgpr(i+1, (v >> 32) & 0xffffffff)
+
+  def rsrc(self, v: int, lane: int) -> int:
+    if v < SGPR_COUNT: return self.sgpr[v]._val
+    if v == SCC: return self.scc
+    if v < 255: return _INLINE_CONSTS[v - 128]
+    if v == 255: return self.literal
+    return self.vgpr[lane][v - 256]._val if v <= 511 else 0
+
+  def rsrc_reg(self, v: int, lane: int) -> Reg:
+    """Return the Reg object for a source operand."""
+    if v < SGPR_COUNT: return self.sgpr[v]
+    if v == SCC: self._scc_reg._val = self.scc; return self._scc_reg
+    if v < 255: return Reg(_INLINE_CONSTS[v - 128])
+    if v == 255: return Reg(self.literal)
+    return self.vgpr[lane][v - 256] if v <= 511 else Reg(0)
+
+  def rsrc_f16(self, v: int, lane: int) -> int:
+    """Read source operand for VOP3P packed f16 operations. Uses f16 inline constants."""
+    if v < SGPR_COUNT: return self.sgpr[v]._val
+    if v == SCC: return self.scc
+    if v < 255: return _INLINE_CONSTS_F16[v - 128]
+    if v == 255: return self.literal
+    return self.vgpr[lane][v - 256]._val if v <= 511 else 0
+
+  def rsrc_reg_f16(self, v: int, lane: int) -> Reg:
+    """Return Reg for VOP3P source. Inline constants are f16 in low 16 bits only."""
+    if v < SGPR_COUNT: return self.sgpr[v]
+    if v == SCC: self._scc_reg._val = self.scc; return self._scc_reg
+    if v < 255: return Reg(_INLINE_CONSTS_F16[v - 128])  # f16 inline constant
+    if v == 255: return Reg(self.literal)
+    return self.vgpr[lane][v - 256] if v <= 511 else Reg(0)
+
+  def rsrc64(self, v: int, lane: int) -> int:
+    """Read 64-bit source operand. For inline constants, returns 64-bit representation."""
+    if 128 <= v < 255: return _INLINE_CONSTS_F64[v - 128]
+    if v == 255: return self.literal
+    return self.rsrc(v, lane) | ((self.rsrc(v+1, lane) if v < VCC_LO or 256 <= v <= 511 else 0) << 32)
+
+  def rsrc_reg64(self, v: int, lane: int) -> Reg:
+    """Return Reg for 64-bit source operand. For inline constants, returns 64-bit f64 value."""
+    if 128 <= v < 255: return Reg(_INLINE_CONSTS_F64[v - 128])
+    if v == 255: return Reg(self.literal)
+    if v < SGPR_COUNT: return Reg(self.sgpr[v]._val | (self.sgpr[v+1]._val << 32))
+    if 256 <= v <= 511:
+      vgpr_idx = v - 256
+      return Reg(self.vgpr[lane][vgpr_idx]._val | (self.vgpr[lane][vgpr_idx + 1]._val << 32))
+    return Reg(0)
+
+  def pend_sgpr_lane(self, reg: int, lane: int, val: int):
+    if reg not in self._pend_sgpr: self._pend_sgpr[reg] = 0
+    if val: self._pend_sgpr[reg] |= (1 << lane)
+  def commit_pends(self):
+    for reg, val in self._pend_sgpr.items(): self.sgpr[reg]._val = val
+    self._pend_sgpr.clear()
+
+# Instruction decode
+def decode_format(word: int) -> tuple[type[Inst] | None, bool]:
+  hi2 = (word >> 30) & 0x3
+  if hi2 == 0b11:
+    enc = (word >> 26) & 0xf
+    if enc == 0b1101: return SMEM, True
+    if enc == 0b0101:
+      op = (word >> 16) & 0x3ff
+      return (VOP3SD, True) if op in (288, 289, 290, 764, 765, 766, 767, 768, 769, 770) else (VOP3, True)
+    return {0b0011: (VOP3P, True), 0b0110: (DS, True), 0b0111: (FLAT, True), 0b0010: (VOPD, True)}.get(enc, (None, True))
+  if hi2 == 0b10:
+    enc = (word >> 23) & 0x7f
+    return {0b1111101: (SOP1, False), 0b1111110: (SOPC, False), 0b1111111: (SOPP, False)}.get(enc, (SOPK, False) if ((word >> 28) & 0xf) == 0b1011 else (SOP2, False))
+  enc = (word >> 25) & 0x7f
+  return (VOPC, False) if enc == 0b0111110 else (VOP1, False) if enc == 0b0111111 else (VOP2, False)
+
+def _unwrap(v) -> int: return v.val if isinstance(v, RawImm) else v.value if hasattr(v, 'value') else v
+
+def decode_program(data: bytes) -> Program:
+  result: Program = {}
+  i = 0
+  while i < len(data):
+    word = int.from_bytes(data[i:i+4], 'little')
+    inst_class, is_64 = decode_format(word)
+    if inst_class is None: i += 4; continue
+    base_size = 8 if is_64 else 4
+    # Pass enough data for potential 64-bit literal (base + 8 bytes max)
+    inst = inst_class.from_bytes(data[i:i+base_size+8])
+    for name, val in inst._values.items(): setattr(inst, name, _unwrap(val))
+    # from_bytes already handles literal reading - only need fallback for cases it doesn't handle
+    if inst._literal is None:
+      has_literal = any(getattr(inst, fld, None) == 255 for fld in ('src0', 'src1', 'src2', 'ssrc0', 'ssrc1', 'srcx0', 'srcy0'))
+      if inst_class == VOP2 and inst.op in (44, 45, 55, 56): has_literal = True
+      if inst_class == VOPD and (inst.opx in (1, 2) or inst.opy in (1, 2)): has_literal = True
+      if inst_class == SOP2 and inst.op in (69, 70): has_literal = True
+      if has_literal:
+        # For 64-bit ops, the 32-bit literal is placed in HIGH 32 bits (low 32 bits = 0)
+        # Exception: some ops have mixed src sizes (e.g., V_LDEXP_F64 has 32-bit src1)
+        op_val = inst._values.get('op')
+        if hasattr(op_val, 'value'): op_val = op_val.value
+        is_64bit = inst_class is VOP3 and op_val in _VOP3_64BIT_OPS
+        # Don't treat literal as 64-bit if the op has 32-bit src1 and src1 is the literal
+        if is_64bit and op_val in _VOP3_64BIT_OPS_32BIT_SRC1 and getattr(inst, 'src1', None) == 255:
+          is_64bit = False
+        lit32 = int.from_bytes(data[i+base_size:i+base_size+4], 'little')
+        inst._literal = (lit32 << 32) if is_64bit else lit32
+    inst._words = inst.size() // 4
+    result[i // 4] = inst
+    i += inst._words * 4
+  return result
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# EXECUTION - All ALU ops use pseudocode from PDF
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def exec_scalar(st: WaveState, inst: Inst) -> int:
+  """Execute scalar instruction. Returns PC delta or negative for special cases."""
+  compiled = _get_compiled()
+  inst_type = type(inst)
+
+  # SOPP: control flow (not ALU)
+  if inst_type is SOPP:
+    op = inst.op
+    if op == SOPPOp.S_ENDPGM: return -1
+    if op == SOPPOp.S_BARRIER: return -2
+    if op == SOPPOp.S_BRANCH: return _sext(inst.simm16, 16)
+    if op == SOPPOp.S_CBRANCH_SCC0: return _sext(inst.simm16, 16) if st.scc == 0 else 0
+    if op == SOPPOp.S_CBRANCH_SCC1: return _sext(inst.simm16, 16) if st.scc == 1 else 0
+    if op == SOPPOp.S_CBRANCH_VCCZ: return _sext(inst.simm16, 16) if (st.vcc & 0xffffffff) == 0 else 0
+    if op == SOPPOp.S_CBRANCH_VCCNZ: return _sext(inst.simm16, 16) if (st.vcc & 0xffffffff) != 0 else 0
+    if op == SOPPOp.S_CBRANCH_EXECZ: return _sext(inst.simm16, 16) if st.exec_mask == 0 else 0
+    if op == SOPPOp.S_CBRANCH_EXECNZ: return _sext(inst.simm16, 16) if st.exec_mask != 0 else 0
+    # Valid SOPP range is 0-61 (max defined opcode); anything above is invalid
+    if op > 61: raise NotImplementedError(f"Invalid SOPP opcode {op}")
+    return 0  # waits, hints, nops
+
+  # SMEM: memory loads (not ALU)
+  if inst_type is SMEM:
+    addr = st.rsgpr64(inst.sbase * 2) + _sext(inst.offset, 21)
+    if inst.soffset not in (NULL, 0x7f): addr += st.rsrc(inst.soffset, 0)
+    if (cnt := SMEM_LOAD.get(inst.op)) is None: raise NotImplementedError(f"SMEM op {inst.op}")
+    for i in range(cnt): st.wsgpr(inst.sdata + i, mem_read((addr + i * 4) & 0xffffffffffffffff, 4))
+    return 0
+
+  # SOP1: special handling for ops not in pseudocode
+  if inst_type is SOP1:
+    op = SOP1Op(inst.op)
+    # S_GETPC_B64: Get program counter (PC is stored as byte offset, convert from words)
+    if op == SOP1Op.S_GETPC_B64:
+      pc_bytes = st.pc * 4  # PC is in words, convert to bytes
+      st.wsgpr64(inst.sdst, pc_bytes)
+      return 0
+    # S_SETPC_B64: Set program counter to source value (indirect jump)
+    # Returns delta such that st.pc + inst_words + delta = target_words
+    if op == SOP1Op.S_SETPC_B64:
+      target_bytes = st.rsrc64(inst.ssrc0, 0)
+      target_words = target_bytes // 4
+      inst_words = 1  # SOP1 is always 1 word
+      return target_words - st.pc - inst_words
+
+  # Get op enum and lookup compiled function
+  if inst_type is SOP1: op_cls, ssrc0, sdst = SOP1Op, inst.ssrc0, inst.sdst
+  elif inst_type is SOP2: op_cls, ssrc0, sdst = SOP2Op, inst.ssrc0, inst.sdst
+  elif inst_type is SOPC: op_cls, ssrc0, sdst = SOPCOp, inst.ssrc0, None
+  elif inst_type is SOPK: op_cls, ssrc0, sdst = SOPKOp, inst.sdst, inst.sdst  # sdst is both src and dst
+  else: raise NotImplementedError(f"Unknown scalar type {inst_type}")
+
+  op = op_cls(inst.op)
+  fn = compiled.get(op_cls, {}).get(op)
+  if fn is None: raise NotImplementedError(f"{op.name} not in pseudocode")
+
+  # Build context - handle 64-bit ops that need 64-bit source reads
+  # 64-bit source ops: name ends with _B64, _I64, _U64 or contains _U64, _I64 before last underscore
+  is_64bit_s0 = op.name.endswith(('_B64', '_I64', '_U64')) or '_U64_' in op.name or '_I64_' in op.name
+  is_64bit_s0s1 = op_cls is SOPCOp and op in (SOPCOp.S_CMP_EQ_U64, SOPCOp.S_CMP_LG_U64)
+  s0 = st.rsrc64(ssrc0, 0) if is_64bit_s0 or is_64bit_s0s1 else (st.rsrc(ssrc0, 0) if inst_type != SOPK else st.rsgpr(inst.sdst))
+  is_64bit_sop2 = is_64bit_s0 and inst_type is SOP2
+  s1 = st.rsrc64(inst.ssrc1, 0) if (is_64bit_sop2 or is_64bit_s0s1) else (st.rsrc(inst.ssrc1, 0) if inst_type in (SOP2, SOPC) else inst.simm16 if inst_type is SOPK else 0)
+  d0 = st.rsgpr64(sdst) if (is_64bit_s0 or is_64bit_s0s1) and sdst is not None else (st.rsgpr(sdst) if sdst is not None else 0)
+  literal = inst.simm16 if inst_type is SOPK else st.literal
+
+  # Create Reg objects for new calling convention
+  S0, S1, S2, D0 = Reg(s0), Reg(s1), Reg(0), Reg(d0)
+  SCC, VCC, EXEC = Reg(st.scc), Reg(st.vcc), Reg(st.exec_mask)
+
+  # Execute compiled function - fn(S0, S1, S2, D0, SCC, VCC, laneId, EXEC, SIMM16, VGPR, SRC0, VDST)
+  fn(S0, S1, S2, D0, SCC, VCC, 0, EXEC, Reg(literal), None, 0, 0)
+
+  # Apply results from Reg objects
+  is_64bit_d0 = is_64bit_s0 or is_64bit_s0s1
+  if sdst is not None:
+    if is_64bit_d0:
+      st.wsgpr64(sdst, D0._val)
+    else:
+      st.wsgpr(sdst, D0._val)
+  st.scc = SCC._val
+  st.exec_mask = EXEC._val
+  return 0
+
+def exec_vector(st: WaveState, inst: Inst, lane: int, lds: bytearray | None = None,
+                d0_override: 'Reg | None' = None, vcc_override: 'Reg | None' = None) -> None:
+  """Execute vector instruction for one lane.
+  d0_override: For VOPC/VOP3-VOPC, use this Reg instead of st.sgpr[vdst] for D0 output.
+  vcc_override: For VOP3SD, use this Reg instead of st.sgpr[sdst] for VCC output.
+  """
+  compiled = _get_compiled()
+  inst_type, V = type(inst), st.vgpr[lane]
+
+  # Memory ops (not ALU pseudocode)
+  if inst_type is FLAT:
+    op, addr_reg, data_reg, vdst, offset, saddr = inst.op, inst.addr, inst.data, inst.vdst, _sext(inst.offset, 13), inst.saddr
+    addr = V[addr_reg]._val | (V[addr_reg+1]._val << 32)
+    addr = (st.rsgpr64(saddr) + V[addr_reg]._val + offset) & 0xffffffffffffffff if saddr not in (NULL, 0x7f) else (addr + offset) & 0xffffffffffffffff
+    if op in FLAT_LOAD:
+      cnt, sz, sign = FLAT_LOAD[op]
+      for i in range(cnt): val = mem_read(addr + i * sz, sz); V[vdst + i]._val = _sext(val, sz * 8) & 0xffffffff if sign else val
+    elif op in FLAT_STORE:
+      cnt, sz = FLAT_STORE[op]
+      for i in range(cnt): mem_write(addr + i * sz, sz, V[data_reg + i]._val & ((1 << (sz * 8)) - 1))
+    elif op in FLAT_D16_LOAD:
+      sz, sign, hi = FLAT_D16_LOAD[op]
+      val = mem_read(addr, sz)
+      if sign: val = _sext(val, sz * 8) & 0xffff
+      if hi: V[vdst]._val = (V[vdst]._val & 0xffff) | (val << 16)
+      else: V[vdst]._val = (V[vdst]._val & 0xffff0000) | (val & 0xffff)
+    elif op in FLAT_D16_STORE:
+      sz, hi = FLAT_D16_STORE[op]
+      val = (V[data_reg]._val >> 16) & 0xffff if hi else V[data_reg]._val & 0xffff
+      mem_write(addr, sz, val & ((1 << (sz * 8)) - 1))
+    else: raise NotImplementedError(f"FLAT op {op}")
+    return
+
+  if inst_type is DS:
+    op, addr, vdst = inst.op, (V[inst.addr]._val + inst.offset0) & 0xffff, inst.vdst
+    if op in DS_LOAD:
+      cnt, sz, sign = DS_LOAD[op]
+      for i in range(cnt): val = int.from_bytes(lds[addr+i*sz:addr+i*sz+sz], 'little'); V[vdst + i]._val = _sext(val, sz * 8) & 0xffffffff if sign else val
+    elif op in DS_STORE:
+      cnt, sz = DS_STORE[op]
+      for i in range(cnt): lds[addr+i*sz:addr+i*sz+sz] = (V[inst.data0 + i]._val & ((1 << (sz * 8)) - 1)).to_bytes(sz, 'little')
+    else: raise NotImplementedError(f"DS op {op}")
+    return
+
+  # VOPD: dual-issue, execute two ops using VOP2/VOP3 compiled functions
+  if inst_type is VOPD:
+    vdsty = (inst.vdsty << 1) | ((inst.vdstx & 1) ^ 1)
+    # Read all source operands BEFORE any writes (dual-issue semantics)
+    sx0, sx1 = Reg(st.rsrc(inst.srcx0, lane)), Reg(V[inst.vsrcx1]._val)
+    sy0, sy1 = Reg(st.rsrc(inst.srcy0, lane)), Reg(V[inst.vsrcy1]._val)
+    dx0, dy0 = Reg(V[inst.vdstx]._val), Reg(V[vdsty]._val)
+    st._scc_reg._val = st.scc
+    if (op_x := _VOPD_TO_VOP.get(inst.opx)):
+      if (fn_x := compiled.get(type(op_x), {}).get(op_x)):
+        fn_x(sx0, sx1, Reg(0), dx0, st._scc_reg, st.sgpr[VCC_LO], lane, st.sgpr[EXEC_LO], Reg(st.literal), None, Reg(0), Reg(inst.vdstx))
+    if (op_y := _VOPD_TO_VOP.get(inst.opy)):
+      if (fn_y := compiled.get(type(op_y), {}).get(op_y)):
+        fn_y(sy0, sy1, Reg(0), dy0, st._scc_reg, st.sgpr[VCC_LO], lane, st.sgpr[EXEC_LO], Reg(st.literal), None, Reg(0), Reg(vdsty))
+    V[inst.vdstx]._val, V[vdsty]._val = dx0._val, dy0._val
+    st.scc = st._scc_reg._val
+    return
+
+  # Determine instruction format and get function
+  is_vop3_vopc = False
+  is_readlane = False
+  if inst_type is VOP1:
+    if inst.op == VOP1Op.V_NOP: return
+    op_cls, op, src0, src1, src2, vdst = VOP1Op, VOP1Op(inst.op), inst.src0, None, None, inst.vdst
+    # V_READFIRSTLANE_B32 writes to SGPR, not VGPR
+    is_readlane = inst.op == VOP1Op.V_READFIRSTLANE_B32
+  elif inst_type is VOP2:
+    op_cls, op, src0, src1, src2, vdst = VOP2Op, VOP2Op(inst.op), inst.src0, inst.vsrc1 + 256, None, inst.vdst
+  elif inst_type is VOP3:
+    if inst.op < 256:
+      # VOP3-encoded VOPC - destination is an SGPR (vdst field)
+      op_cls, op, src0, src1, src2, vdst = VOPCOp, VOPCOp(inst.op), inst.src0, inst.src1, None, inst.vdst
+      is_vop3_vopc = True
+    else:
+      op_cls, op, src0, src1, src2, vdst = VOP3Op, VOP3Op(inst.op), inst.src0, inst.src1, inst.src2, inst.vdst
+      # V_READFIRSTLANE_B32 and V_READLANE_B32 write to SGPR
+      is_readlane = inst.op in (VOP3Op.V_READFIRSTLANE_B32, VOP3Op.V_READLANE_B32)
+  elif inst_type is VOP3SD:
+    op_cls, op, src0, src1, src2, vdst = VOP3SDOp, VOP3SDOp(inst.op), inst.src0, inst.src1, inst.src2, inst.vdst
+  elif inst_type is VOPC:
+    op_cls, op, src0, src1, src2, vdst = VOPCOp, VOPCOp(inst.op), inst.src0, inst.vsrc1 + 256, None, VCC_LO
+  elif inst_type is VOP3P:
+    op_cls, op, src0, src1, src2, vdst = VOP3POp, VOP3POp(inst.op), inst.src0, inst.src1, inst.src2, inst.vdst
+    # WMMA instructions are handled specially (only execute for lane 0)
+    if op in (VOP3POp.V_WMMA_F32_16X16X16_F16, VOP3POp.V_WMMA_F16_16X16X16_F16):
+      if lane == 0: exec_wmma(st, inst, op)
+      return
+  else: raise NotImplementedError(f"Unknown vector type {inst_type}")
+
+  fn = compiled.get(op_cls, {}).get(op)
+  if fn is None: raise NotImplementedError(f"{op.name} not in pseudocode")
+
+  # Build source Regs - get the actual register or create temp for inline constants
+  # VOP3P uses f16 inline constants (16-bit value in low half only)
+  if inst_type is VOP3P:
+    S0 = st.rsrc_reg_f16(src0, lane)
+    S1 = st.rsrc_reg_f16(src1, lane) if src1 is not None else Reg(0)
+    S2 = st.rsrc_reg_f16(src2, lane) if src2 is not None else Reg(0)
+    # Apply op_sel_hi modifiers: control which half is used for hi-half computation
+    # opsel_hi[0]=0 means src0 hi comes from lo half, =1 means from hi half (default)
+    # opsel_hi[1]=0 means src1 hi comes from lo half, =1 means from hi half (default)
+    # opsel_hi2=0 means src2 hi comes from lo half, =1 means from hi half (default)
+    opsel_hi = getattr(inst, 'opsel_hi', 3)  # default 0b11
+    opsel_hi2 = getattr(inst, 'opsel_hi2', 1)  # default 1
+    # If opsel_hi bit is 0, replicate lo half to hi half
+    if not (opsel_hi & 1):  # src0 hi from lo
+      lo = S0._val & 0xffff
+      S0 = Reg((lo << 16) | lo)
+    if not (opsel_hi & 2):  # src1 hi from lo
+      lo = S1._val & 0xffff
+      S1 = Reg((lo << 16) | lo)
+    if not opsel_hi2:  # src2 hi from lo
+      lo = S2._val & 0xffff
+      S2 = Reg((lo << 16) | lo)
+  else:
+    # Check if this is a 64-bit F64 op - needs 64-bit source reads for f64 operands
+    # V_LDEXP_F64: S0 is f64, S1 is i32 (exponent)
+    # V_ADD_F64, V_MUL_F64, etc: S0 and S1 are f64
+    # VOP1 F64 ops (V_TRUNC_F64, V_FLOOR_F64, etc): S0 is f64
+    is_f64_op = hasattr(op, 'name') and '_F64' in op.name
+    is_ldexp_f64 = hasattr(op, 'name') and op.name == 'V_LDEXP_F64'
+    if is_f64_op:
+      S0 = st.rsrc_reg64(src0, lane)
+      # V_LDEXP_F64: S1 is i32 exponent, not f64
+      if is_ldexp_f64:
+        S1 = st.rsrc_reg(src1, lane) if src1 is not None else Reg(0)
+      else:
+        S1 = st.rsrc_reg64(src1, lane) if src1 is not None else Reg(0)
+      S2 = st.rsrc_reg64(src2, lane) if src2 is not None else Reg(0)
+    else:
+      S0 = st.rsrc_reg(src0, lane)
+      S1 = st.rsrc_reg(src1, lane) if src1 is not None else Reg(0)
+      S2 = st.rsrc_reg(src2, lane) if src2 is not None else Reg(0)
+    # VOP3SD V_MAD_U64_U32 and V_MAD_I64_I32 need S2 as 64-bit from VGPR pair
+    if inst_type is VOP3SD and op in (VOP3SDOp.V_MAD_U64_U32, VOP3SDOp.V_MAD_I64_I32) and src2 is not None:
+      if 256 <= src2 <= 511:  # VGPR
+        vgpr_idx = src2 - 256
+        S2 = Reg(V[vgpr_idx]._val | (V[vgpr_idx + 1]._val << 32))
+
+  # Apply source modifiers (neg, abs) for VOP3/VOP3SD
+  if inst_type in (VOP3, VOP3SD):
+    neg, abs_mod = getattr(inst, 'neg', 0), getattr(inst, 'abs', 0)
+    if neg or abs_mod:
+      # Apply to f32 values - need to handle as float
+      import struct
+      def apply_mods(reg, neg_bit, abs_bit):
+        val = reg._val
+        f = struct.unpack('<f', struct.pack('<I', val & 0xffffffff))[0]
+        if abs_bit: f = abs(f)
+        if neg_bit: f = -f
+        return Reg(struct.unpack('<I', struct.pack('<f', f))[0])
+      if neg & 1 or abs_mod & 1: S0 = apply_mods(S0, neg & 1, abs_mod & 1)
+      if neg & 2 or abs_mod & 2: S1 = apply_mods(S1, neg & 2, abs_mod & 2)
+      if neg & 4 or abs_mod & 4: S2 = apply_mods(S2, neg & 4, abs_mod & 4)
+
+  # Apply opsel for VOP3 f16 operations - select which half to use
+  # opsel[0]: src0, opsel[1]: src1, opsel[2]: src2 (0=lo, 1=hi)
+  if inst_type is VOP3:
+    opsel = getattr(inst, 'opsel', 0)
+    if opsel:
+      # If opsel bit is set, swap lo and hi so that .f16 reads the hi half
+      if opsel & 1:  # src0 from hi
+        S0 = Reg(((S0._val >> 16) & 0xffff) | (S0._val << 16))
+      if opsel & 2:  # src1 from hi
+        S1 = Reg(((S1._val >> 16) & 0xffff) | (S1._val << 16))
+      if opsel & 4:  # src2 from hi
+        S2 = Reg(((S2._val >> 16) & 0xffff) | (S2._val << 16))
+
+  # For VOPC and VOP3-encoded VOPC, D0 is an SGPR (VCC_LO for VOPC, vdst for VOP3 VOPC)
+  # V_READFIRSTLANE_B32 and V_READLANE_B32 also write to SGPR
+  # Use d0_override if provided (for batch execution with shared output register)
+  is_vopc = inst_type is VOPC or (inst_type is VOP3 and is_vop3_vopc)
+  if is_vopc:
+    D0 = d0_override if d0_override is not None else st.sgpr[VCC_LO if inst_type is VOPC else vdst]
+  elif is_readlane:
+    D0 = st.sgpr[vdst]
+  else:
+    D0 = V[vdst]
+
+  # Execute compiled function - D0 is modified in place
+  st._scc_reg._val = st.scc
+  # For VOP3SD, pass sdst register as VCC parameter (carry-out destination)
+  # Use vcc_override if provided (for batch execution with shared output register)
+  # For VOP3 V_CNDMASK_B32, src2 specifies the condition selector (not VCC)
+  if inst_type is VOP3SD:
+    vcc_reg = vcc_override if vcc_override is not None else st.sgpr[inst.sdst]
+  elif inst_type is VOP3 and op == VOP3Op.V_CNDMASK_B32 and src2 is not None:
+    vcc_reg = st.rsrc_reg(src2, lane)  # Use src2 as condition
+  else:
+    vcc_reg = st.sgpr[VCC_LO]
+  # SRC0/VDST are VGPR indices (0-255), not hardware encoding (256-511)
+  src0_idx = (src0 - 256) if src0 and src0 >= 256 else (src0 if src0 else 0)
+  result = fn(S0, S1, S2, D0, st._scc_reg, vcc_reg, lane, st.sgpr[EXEC_LO], Reg(st.literal), st.vgpr, Reg(src0_idx), Reg(vdst))
+  st.scc = st._scc_reg._val
+
+  # Handle special results
+  if result:
+    if 'vgpr_write' in result:
+      wr_lane, wr_idx, wr_val = result['vgpr_write']
+      st.vgpr[wr_lane][wr_idx]._val = wr_val
+
+  # 64-bit destination: write high 32 bits to next VGPR (determined from op name)
+  is_64bit_dst = not is_vopc and not is_readlane and hasattr(op, 'name') and \
+                 any(s in op.name for s in ('_B64', '_I64', '_U64', '_F64'))
+  if is_64bit_dst:
+    V[vdst + 1]._val = (D0._val >> 32) & 0xffffffff
+    D0._val = D0._val & 0xffffffff  # Keep only low 32 bits in D0
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# WMMA (Wave Matrix Multiply-Accumulate)
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def exec_wmma(st: WaveState, inst, op: VOP3POp) -> None:
+  """Execute WMMA instruction - 16x16x16 matrix multiply across the wave."""
+  src0, src1, src2, vdst = inst.src0, inst.src1, inst.src2, inst.vdst
+  # Read matrix A (16x16 f16/bf16) from lanes 0-15, VGPRs src0 to src0+7 (2 f16 per VGPR = 16 values per lane)
+  # Layout: A[row][k] where row = lane (0-15), k comes from 8 VGPRs × 2 halves
+  mat_a = []
+  for lane in range(16):
+    for reg in range(8):
+      val = st.vgpr[lane][src0 - 256 + reg] if src0 >= 256 else st.rsgpr(src0 + reg)
+      mat_a.append(_f16(val & 0xffff))
+      mat_a.append(_f16((val >> 16) & 0xffff))
+  # Read matrix B (16x16 f16/bf16) - same layout, B[col][k] where col comes from lane
+  mat_b = []
+  for lane in range(16):
+    for reg in range(8):
+      val = st.vgpr[lane][src1 - 256 + reg] if src1 >= 256 else st.rsgpr(src1 + reg)
+      mat_b.append(_f16(val & 0xffff))
+      mat_b.append(_f16((val >> 16) & 0xffff))
+
+  # Read matrix C (16x16 f32) from lanes 0-31, VGPRs src2 to src2+7
+  # Layout: element i is at lane (i % 32), VGPR (i // 32) + src2
+  mat_c = []
+  for i in range(256):
+    lane, reg = i % 32, i // 32
+    val = st.vgpr[lane][src2 - 256 + reg] if src2 >= 256 else st.rsgpr(src2 + reg)
+    mat_c.append(_f32(val))
+
+  # Compute D = A × B + C (16x16 matrix multiply)
+  mat_d = [0.0] * 256
+  for row in range(16):
+    for col in range(16):
+      acc = 0.0
+      for k in range(16):
+        a_val = mat_a[row * 16 + k]
+        b_val = mat_b[col * 16 + k]
+        acc += a_val * b_val
+      mat_d[row * 16 + col] = acc + mat_c[row * 16 + col]
+
+  # Write result matrix D back - same layout as C
+  if op == VOP3POp.V_WMMA_F16_16X16X16_F16:
+    # Output is f16, pack 2 values per VGPR
+    for i in range(0, 256, 2):
+      lane, reg = (i // 2) % 32, (i // 2) // 32
+      lo = _i16(mat_d[i]) & 0xffff
+      hi = _i16(mat_d[i + 1]) & 0xffff
+      st.vgpr[lane][vdst + reg]._val = (hi << 16) | lo
+  else:
+    # Output is f32
+    for i in range(256):
+      lane, reg = i % 32, i // 32
+      st.vgpr[lane][vdst + reg]._val = _i32(mat_d[i])
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# MAIN EXECUTION LOOP
+# ═══════════════════════════════════════════════════════════════════════════════
+
+SCALAR_TYPES = {SOP1, SOP2, SOPC, SOPK, SOPP, SMEM}
+VECTOR_TYPES = {VOP1, VOP2, VOP3, VOP3SD, VOPC, FLAT, DS, VOPD, VOP3P}
+
+# Pre-cache compiled functions for fast lookup
+_COMPILED_CACHE: dict | None = None
+def _get_fn(op_cls, op):
+  global _COMPILED_CACHE
+  if _COMPILED_CACHE is None: _COMPILED_CACHE = _get_compiled()
+  return _COMPILED_CACHE.get(op_cls, {}).get(op)
+
+def exec_vector_batch(st: WaveState, inst: Inst, exec_mask: int, n_lanes: int, lds: bytearray | None = None) -> None:
+  """Execute vector instruction for all active lanes at once."""
+  compiled = _get_compiled()
+  inst_type = type(inst)
+  vgpr = st.vgpr
+
+  # Memory ops - still per-lane but inlined
+  if inst_type is FLAT:
+    op, addr_reg, data_reg, vdst, offset, saddr = inst.op, inst.addr, inst.data, inst.vdst, _sext(inst.offset, 13), inst.saddr
+    if op in FLAT_LOAD:
+      cnt, sz, sign = FLAT_LOAD[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = V[addr_reg]._val | (V[addr_reg+1]._val << 32)
+        addr = (st.rsgpr64(saddr) + V[addr_reg]._val + offset) & 0xffffffffffffffff if saddr not in (NULL, 0x7f) else (addr + offset) & 0xffffffffffffffff
+        for i in range(cnt): val = mem_read(addr + i * sz, sz); V[vdst + i]._val = _sext(val, sz * 8) & 0xffffffff if sign else val
+    elif op in FLAT_STORE:
+      cnt, sz = FLAT_STORE[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = V[addr_reg]._val | (V[addr_reg+1]._val << 32)
+        addr = (st.rsgpr64(saddr) + V[addr_reg]._val + offset) & 0xffffffffffffffff if saddr not in (NULL, 0x7f) else (addr + offset) & 0xffffffffffffffff
+        for i in range(cnt): mem_write(addr + i * sz, sz, V[data_reg + i]._val & ((1 << (sz * 8)) - 1))
+    elif op in FLAT_D16_LOAD:
+      sz, sign, hi = FLAT_D16_LOAD[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = V[addr_reg]._val | (V[addr_reg+1]._val << 32)
+        addr = (st.rsgpr64(saddr) + V[addr_reg]._val + offset) & 0xffffffffffffffff if saddr not in (NULL, 0x7f) else (addr + offset) & 0xffffffffffffffff
+        val = mem_read(addr, sz)
+        if sign: val = _sext(val, sz * 8) & 0xffff
+        if hi: V[vdst]._val = (V[vdst]._val & 0xffff) | (val << 16)
+        else: V[vdst]._val = (V[vdst]._val & 0xffff0000) | (val & 0xffff)
+    elif op in FLAT_D16_STORE:
+      sz, hi = FLAT_D16_STORE[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = V[addr_reg]._val | (V[addr_reg+1]._val << 32)
+        addr = (st.rsgpr64(saddr) + V[addr_reg]._val + offset) & 0xffffffffffffffff if saddr not in (NULL, 0x7f) else (addr + offset) & 0xffffffffffffffff
+        val = (V[data_reg]._val >> 16) & 0xffff if hi else V[data_reg]._val & 0xffff
+        mem_write(addr, sz, val & ((1 << (sz * 8)) - 1))
+    else: raise NotImplementedError(f"FLAT op {op}")
+    return
+
+  if inst_type is DS:
+    op, vdst = inst.op, inst.vdst
+    if op in DS_LOAD:
+      cnt, sz, sign = DS_LOAD[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = (V[inst.addr]._val + inst.offset0) & 0xffff
+        for i in range(cnt): val = int.from_bytes(lds[addr+i*sz:addr+i*sz+sz], 'little'); V[vdst + i]._val = _sext(val, sz * 8) & 0xffffffff if sign else val
+    elif op in DS_STORE:
+      cnt, sz = DS_STORE[op]
+      for lane in range(n_lanes):
+        if not (exec_mask & (1 << lane)): continue
+        V = vgpr[lane]
+        addr = (V[inst.addr]._val + inst.offset0) & 0xffff
+        for i in range(cnt): lds[addr+i*sz:addr+i*sz+sz] = (V[inst.data0 + i]._val & ((1 << (sz * 8)) - 1)).to_bytes(sz, 'little')
+    else: raise NotImplementedError(f"DS op {op}")
+    return
+
+  # For VOPC, VOP3-encoded VOPC, and VOP3SD, we write per-lane bits to an SGPR.
+  # The pseudocode does D0.u64[laneId] = bit or VCC.u64[laneId] = bit.
+  # To avoid corrupting reads from the same SGPR, use a shared output Reg(0).
+  # Exception: CMPX instructions write to EXEC (not D0/VCC).
+  d0_override, vcc_override = None, None
+  vopc_dst, vop3sd_dst = None, None
+  is_cmpx = False
+  if inst_type is VOPC:
+    op = VOPCOp(inst.op)
+    is_cmpx = 'CMPX' in op.name
+    if not is_cmpx:  # Regular CMP writes to VCC
+      d0_override, vopc_dst = Reg(0), VCC_LO
+    else:  # CMPX writes to EXEC - clear it first, accumulate per-lane
+      st.sgpr[EXEC_LO]._val = 0
+  elif inst_type is VOP3 and inst.op < 256:  # VOP3-encoded VOPC
+    op = VOPCOp(inst.op)
+    is_cmpx = 'CMPX' in op.name
+    if not is_cmpx:  # Regular CMP writes to destination SGPR
+      d0_override, vopc_dst = Reg(0), inst.vdst
+    else:  # CMPX writes to EXEC - clear it first, accumulate per-lane
+      st.sgpr[EXEC_LO]._val = 0
+  if inst_type is VOP3SD:
+    vcc_override, vop3sd_dst = Reg(0), inst.sdst
+
+  # For other vector ops, dispatch to exec_vector per lane (can optimize later)
+  for lane in range(n_lanes):
+    if exec_mask & (1 << lane): exec_vector(st, inst, lane, lds, d0_override, vcc_override)
+
+  # Write accumulated per-lane bit results to destination SGPRs
+  # (CMPX writes directly to EXEC in the pseudocode, so no separate write needed)
+  if vopc_dst is not None: st.sgpr[vopc_dst]._val = d0_override._val
+  if vop3sd_dst is not None: st.sgpr[vop3sd_dst]._val = vcc_override._val
+
+def step_wave(program: Program, st: WaveState, lds: bytearray, n_lanes: int) -> int:
+  inst = program.get(st.pc)
+  if inst is None: return 1
+  inst_words, st.literal, inst_type = inst._words, getattr(inst, '_literal', None) or 0, type(inst)
+
+  if inst_type in SCALAR_TYPES:
+    delta = exec_scalar(st, inst)
+    if delta == -1: return -1  # endpgm
+    if delta == -2: st.pc += inst_words; return -2  # barrier
+    st.pc += inst_words + delta
+  else:
+    # V_READFIRSTLANE_B32 and V_READLANE_B32 write to SGPR, so they should only execute once per wave (lane 0)
+    is_readlane = (inst_type is VOP1 and inst.op == VOP1Op.V_READFIRSTLANE_B32) or \
+                  (inst_type is VOP3 and inst.op in (VOP3Op.V_READFIRSTLANE_B32, VOP3Op.V_READLANE_B32))
+    if is_readlane:
+      exec_vector(st, inst, 0, lds)  # Execute once with lane 0
+    else:
+      exec_vector_batch(st, inst, st.exec_mask, n_lanes, lds)
+    st.commit_pends()
+    st.pc += inst_words
+  return 0
+
+def exec_wave(program: Program, st: WaveState, lds: bytearray, n_lanes: int) -> int:
+  while st.pc in program:
+    result = step_wave(program, st, lds, n_lanes)
+    if result == -1: return 0
+    if result == -2: return -2
+  return 0
+
+def exec_workgroup(program: Program, workgroup_id: tuple[int, int, int], local_size: tuple[int, int, int], args_ptr: int,
+                   wg_id_sgpr_base: int, wg_id_enables: tuple[bool, bool, bool]) -> None:
+  lx, ly, lz = local_size
+  total_threads, lds = lx * ly * lz, bytearray(65536)
+  waves: list[tuple[WaveState, int, int]] = []
+  for wave_start in range(0, total_threads, WAVE_SIZE):
+    n_lanes, st = min(WAVE_SIZE, total_threads - wave_start), WaveState()
+    st.exec_mask = (1 << n_lanes) - 1
+    st.wsgpr64(0, args_ptr)
+    gx, gy, gz = workgroup_id
+    # Set workgroup IDs in SGPRs based on USER_SGPR_COUNT and enable flags from COMPUTE_PGM_RSRC2
+    sgpr_idx = wg_id_sgpr_base
+    if wg_id_enables[0]: st.sgpr[sgpr_idx]._val = gx; sgpr_idx += 1
+    if wg_id_enables[1]: st.sgpr[sgpr_idx]._val = gy; sgpr_idx += 1
+    if wg_id_enables[2]: st.sgpr[sgpr_idx]._val = gz
+    for i in range(n_lanes):
+      tid = wave_start + i
+      st.vgpr[i][0]._val = tid if local_size == (lx, 1, 1) else ((tid // (lx * ly)) << 20) | (((tid // lx) % ly) << 10) | (tid % lx)
+    waves.append((st, n_lanes, wave_start))
+  has_barrier = any(isinstance(inst, SOPP) and inst.op == SOPPOp.S_BARRIER for inst in program.values())
+  for _ in range(2 if has_barrier else 1):
+    for st, n_lanes, _ in waves: exec_wave(program, st, lds, n_lanes)
+
+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) -> int:
+  data = (ctypes.c_char * lib_sz).from_address(lib).raw
+  program = decode_program(data)
+  if not program: return -1
+  # Parse COMPUTE_PGM_RSRC2 for SGPR layout
+  user_sgpr_count = (rsrc2 >> 1) & 0x1f
+  enable_wg_id_x = bool((rsrc2 >> 7) & 1)
+  enable_wg_id_y = bool((rsrc2 >> 8) & 1)
+  enable_wg_id_z = bool((rsrc2 >> 9) & 1)
+  wg_id_enables = (enable_wg_id_x, enable_wg_id_y, enable_wg_id_z)
+  for gidz in range(gz):
+    for gidy in range(gy):
+      for gidx in range(gx): exec_workgroup(program, (gidx, gidy, gidz), (lx, ly, lz), args_ptr, user_sgpr_count, wg_id_enables)
+  return 0
@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+"""Benchmark comparing Python vs Rust RDNA3 emulators on synthetic and real tinygrad kernels."""
+import ctypes, time, os, struct, cProfile, pstats, io
+from pathlib import Path
+from typing import Callable
+
+# Set AMD=1 before importing tinygrad
+os.environ["AMD"] = "1"
+
+from extra.assembly.amd.emu import run_asm as python_run_asm, set_valid_mem_ranges, decode_program, step_wave, WaveState, WAVE_SIZE
+
+REMU_PATH = Path(__file__).parents[3] / "remu/target/release/libremu.so"
+if not REMU_PATH.exists():
+  REMU_PATH = Path(__file__).parents[3] / "remu/target/release/libremu.dylib"
+
+def get_rust_remu():
+  """Load the Rust libremu shared library."""
+  if not REMU_PATH.exists(): return None
+  remu = ctypes.CDLL(str(REMU_PATH))
+  remu.run_asm.restype = ctypes.c_int32
+  remu.run_asm.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32,
+                           ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_void_p]
+  return remu
+
+def count_instructions(kernel: bytes) -> int:
+  """Count instructions in a kernel."""
+  return len(decode_program(kernel))
+
+def setup_buffers(buf_sizes: list[int], init_data: dict[int, bytes] | None = None):
+  """Allocate buffers and return args pointer + valid ranges."""
+  if init_data is None: init_data = {}
+  buffers = []
+  for i, size in enumerate(buf_sizes):
+    padded = ((size + 15) // 16) * 16 + 16
+    data = init_data.get(i, b'\x00' * padded)
+    data_list = list(data) + [0] * (padded - len(data))
+    buf = (ctypes.c_uint8 * padded)(*data_list[:padded])
+    buffers.append(buf)
+  args = (ctypes.c_uint64 * len(buffers))(*[ctypes.addressof(b) for b in buffers])
+  args_ptr = ctypes.addressof(args)
+  ranges = {(ctypes.addressof(b), len(b)) for b in buffers}
+  ranges.add((args_ptr, ctypes.sizeof(args)))
+  return buffers, args, args_ptr, ranges
+
+def benchmark_emulator(name: str, run_fn, kernel: bytes, global_size, local_size, args_ptr, iterations: int = 5):
+  """Benchmark an emulator and return average time."""
+  gx, gy, gz = global_size
+  lx, ly, lz = local_size
+  kernel_buf = (ctypes.c_char * len(kernel)).from_buffer_copy(kernel)
+  lib_ptr = ctypes.addressof(kernel_buf)
+
+  # Warmup
+  run_fn(lib_ptr, len(kernel), gx, gy, gz, lx, ly, lz, args_ptr)
+
+  # Timed runs
+  times = []
+  for _ in range(iterations):
+    start = time.perf_counter()
+    result = run_fn(lib_ptr, len(kernel), gx, gy, gz, lx, ly, lz, args_ptr)
+    end = time.perf_counter()
+    if result != 0:
+      print(f"  {name} returned error: {result}")
+      return None
+    times.append(end - start)
+
+  return sum(times) / len(times)
+
+def create_synthetic_kernel(n_ops: int) -> bytes:
+  """Create a synthetic kernel with n_ops vector operations."""
+  instructions = []
+  # VOP2 instructions: v_add_f32, v_mul_f32, v_max_f32, v_min_f32
+  ops = [
+    (0b0000011 << 25) | (1 << 17) | (0 << 9) | 256,  # v_add_f32 v0, v0, v1
+    (0b0001000 << 25) | (1 << 17) | (0 << 9) | 256,  # v_mul_f32 v0, v0, v1
+    (0b0010000 << 25) | (1 << 17) | (0 << 9) | 256,  # v_max_f32 v0, v0, v1
+    (0b0001111 << 25) | (1 << 17) | (0 << 9) | 256,  # v_min_f32 v0, v0, v1
+  ]
+  for i in range(n_ops):
+    instructions.append(ops[i % len(ops)])
+  # S_ENDPGM
+  instructions.append((0b101111111 << 23) | (48 << 16) | 0)
+  return b''.join(struct.pack('<I', inst) for inst in instructions)
+
+def get_tinygrad_kernel(op_name: str) -> tuple[bytes, tuple, tuple, list[int], dict[int, bytes]] | None:
+  """Get a real tinygrad kernel by operation name. Returns (code, global_size, local_size, buf_sizes, buf_data)."""
+  try:
+    from tinygrad import Tensor
+    from tinygrad.runtime.support.elf import elf_loader
+    import numpy as np
+    np.random.seed(42)
+
+    ops = {
+      "add": lambda: Tensor.empty(1024) + Tensor.empty(1024),
+      "mul": lambda: Tensor.empty(1024) * Tensor.empty(1024),
+      "matmul_small": lambda: Tensor.empty(16, 16) @ Tensor.empty(16, 16),
+      "matmul_medium": lambda: Tensor.empty(64, 64) @ Tensor.empty(64, 64),
+      "reduce_sum": lambda: Tensor.empty(4096).sum(),
+      "reduce_max": lambda: Tensor.empty(4096).max(),
+      "softmax": lambda: Tensor.empty(256).softmax(),
+      "layernorm": lambda: Tensor.empty(32, 64).layernorm(),
+      "conv2d": lambda: Tensor.empty(1, 4, 16, 16).conv2d(Tensor.empty(4, 4, 3, 3)),
+      "gelu": lambda: Tensor.empty(1024).gelu(),
+      "exp": lambda: Tensor.empty(1024).exp(),
+      "sin": lambda: Tensor.empty(1024).sin(),
+    }
+
+    if op_name not in ops: return None
+    out = ops[op_name]()
+    sched = out.schedule()
+
+    for ei in sched:
+      lowered = ei.lower()
+      if ei.ast.op.name == 'SINK' and lowered.prg and lowered.prg.p.lib:
+        lib = bytes(lowered.prg.p.lib)
+        _, sections, _ = elf_loader(lib)
+        for sec in sections:
+          if sec.name == '.text':
+            buf_sizes = [b.nbytes for b in lowered.bufs]
+            # Get initial data from numpy arrays if available
+            buf_data = {}
+            for i, buf in enumerate(lowered.bufs):
+              if hasattr(buf, 'base') and buf.base is not None and hasattr(buf.base, '_buf'):
+                try: buf_data[i] = bytes(buf.base._buf)
+                except: pass
+            return (bytes(sec.content), tuple(lowered.prg.p.global_size), tuple(lowered.prg.p.local_size), buf_sizes, buf_data)
+    return None
+  except Exception as e:
+    print(f"  Error getting kernel: {e}")
+    return None
+
+def profile_python_emu(kernel: bytes, global_size, local_size, args_ptr, n_runs: int = 1):
+  """Profile the Python emulator to find bottlenecks."""
+  gx, gy, gz = global_size
+  lx, ly, lz = local_size
+  kernel_buf = (ctypes.c_char * len(kernel)).from_buffer_copy(kernel)
+  lib_ptr = ctypes.addressof(kernel_buf)
+
+  pr = cProfile.Profile()
+  pr.enable()
+  for _ in range(n_runs):
+    python_run_asm(lib_ptr, len(kernel), gx, gy, gz, lx, ly, lz, args_ptr)
+  pr.disable()
+
+  s = io.StringIO()
+  ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+  ps.print_stats(20)
+  return s.getvalue()
+
+def measure_step_rate(kernel: bytes, n_steps: int = 10000) -> float:
+  """Measure raw step_wave() performance (steps per second)."""
+  program = decode_program(kernel)
+  if not program: return 0.0
+
+  st = WaveState()
+  st.exec_mask = 0xffffffff
+  lds = bytearray(65536)
+  n_lanes = 32
+
+  # Reset PC for each measurement
+  start = time.perf_counter()
+  for _ in range(n_steps):
+    st.pc = 0
+    while st.pc in program:
+      result = step_wave(program, st, lds, n_lanes)
+      if result == -1: break
+  elapsed = time.perf_counter() - start
+  return n_steps / elapsed if elapsed > 0 else 0
+
+# Test configurations
+SYNTHETIC_TESTS = [
+  ("synthetic_10ops", 10, (1, 1, 1), (32, 1, 1)),
+  ("synthetic_100ops", 100, (1, 1, 1), (32, 1, 1)),
+  ("synthetic_500ops", 500, (1, 1, 1), (32, 1, 1)),
+  ("synthetic_100ops_4wg", 100, (4, 1, 1), (32, 1, 1)),
+  ("synthetic_100ops_16wg", 100, (16, 1, 1), (32, 1, 1)),
+]
+
+TINYGRAD_TESTS = ["add", "mul", "reduce_sum", "softmax", "exp", "gelu", "matmul_small"]
+
+def main():
+  import argparse
+  parser = argparse.ArgumentParser(description="Benchmark RDNA3 emulators")
+  parser.add_argument("--profile", action="store_true", help="Profile Python emulator")
+  parser.add_argument("--synthetic-only", action="store_true", help="Only run synthetic tests")
+  parser.add_argument("--tinygrad-only", action="store_true", help="Only run tinygrad tests")
+  parser.add_argument("--iterations", type=int, default=3, help="Number of iterations per benchmark")
+  args = parser.parse_args()
+
+  rust_remu = get_rust_remu()
+  if rust_remu is None:
+    print("Rust libremu not found. Build with: cargo build --release --manifest-path extra/remu/Cargo.toml")
+    print("Running Python-only benchmarks...\n")
+
+  print("=" * 90)
+  print("RDNA3 Emulator Benchmark: Python vs Rust")
+  print("=" * 90)
+
+  results = []
+
+  # Synthetic workloads
+  if not args.tinygrad_only:
+    print("\n[SYNTHETIC WORKLOADS]")
+    print("-" * 90)
+
+    for name, n_ops, global_size, local_size in SYNTHETIC_TESTS:
+      kernel = create_synthetic_kernel(n_ops)
+      n_insts = count_instructions(kernel)
+      n_workgroups = global_size[0] * global_size[1] * global_size[2]
+      n_threads = local_size[0] * local_size[1] * local_size[2]
+      total_work = n_insts * n_workgroups * n_threads
+
+      print(f"\n{name}: {n_insts} insts × {n_workgroups} WGs × {n_threads} threads = {total_work:,} ops")
+
+      buf_sizes = [4096]
+      buffers, args_arr, args_ptr, ranges = setup_buffers(buf_sizes)
+      set_valid_mem_ranges(ranges)
+
+      # Benchmark
+      py_time = benchmark_emulator("Python", python_run_asm, kernel, global_size, local_size, args_ptr, args.iterations)
+      rust_time = benchmark_emulator("Rust", rust_remu.run_asm, kernel, global_size, local_size, args_ptr, args.iterations) if rust_remu else None
+
+      if py_time:
+        py_rate = total_work / py_time / 1e6
+        print(f"  Python: {py_time*1000:8.3f} ms  ({py_rate:7.2f} M ops/s)")
+      if rust_time:
+        rust_rate = total_work / rust_time / 1e6
+        speedup = py_time / rust_time if py_time else 0
+        print(f"  Rust:   {rust_time*1000:8.3f} ms  ({rust_rate:7.2f} M ops/s)  [{speedup:.1f}x faster]")
+
+      results.append(("synthetic", name, n_insts, n_workgroups, py_time, rust_time))
+
+  # Tinygrad kernels
+  if not args.synthetic_only:
+    print("\n[TINYGRAD KERNELS]")
+    print("-" * 90)
+
+    for op_name in TINYGRAD_TESTS:
+      print(f"\n{op_name}:", end=" ", flush=True)
+      kernel_info = get_tinygrad_kernel(op_name)
+      if kernel_info is None:
+        print("failed to compile")
+        continue
+
+      kernel, global_size, local_size, buf_sizes, buf_data = kernel_info
+      n_insts = count_instructions(kernel)
+      n_workgroups = global_size[0] * global_size[1] * global_size[2]
+      n_threads = local_size[0] * local_size[1] * local_size[2]
+      total_work = n_insts * n_workgroups * n_threads
+
+      print(f"{n_insts} insts × {n_workgroups} WGs × {n_threads} threads = {total_work:,} ops")
+
+      buffers, args_arr, args_ptr, ranges = setup_buffers(buf_sizes, buf_data)
+      set_valid_mem_ranges(ranges)
+
+      py_time = benchmark_emulator("Python", python_run_asm, kernel, global_size, local_size, args_ptr, args.iterations)
+      rust_time = benchmark_emulator("Rust", rust_remu.run_asm, kernel, global_size, local_size, args_ptr, args.iterations) if rust_remu else None
+
+      if py_time:
+        py_rate = total_work / py_time / 1e6
+        print(f"  Python: {py_time*1000:8.3f} ms  ({py_rate:7.2f} M ops/s)")
+      if rust_time:
+        rust_rate = total_work / rust_time / 1e6
+        speedup = py_time / rust_time if py_time else 0
+        print(f"  Rust:   {rust_time*1000:8.3f} ms  ({rust_rate:7.2f} M ops/s)  [{speedup:.1f}x faster]")
+
+      results.append(("tinygrad", op_name, n_insts, n_workgroups, py_time, rust_time))
+
+      # Optional profiling
+      if args.profile and py_time:
+        print("\n  [PROFILE - Top 10 functions]")
+        profile_output = profile_python_emu(kernel, global_size, local_size, args_ptr)
+        for line in profile_output.split('\n')[5:15]:
+          if line.strip(): print(f"    {line}")
+
+  # Summary table
+  print("\n" + "=" * 90)
+  print("SUMMARY")
+  print("=" * 90)
+  print(f"{'Type':<10} {'Name':<25} {'Insts':<8} {'WGs':<6} {'Python (ms)':<14} {'Rust (ms)':<14} {'Speedup':<10}")
+  print("-" * 90)
+
+  for test_type, name, n_insts, n_wgs, py_time, rust_time in results:
+    py_ms = f"{py_time*1000:.3f}" if py_time else "error"
+    if rust_time:
+      rust_ms = f"{rust_time*1000:.3f}"
+      speedup = f"{py_time/rust_time:.1f}x" if py_time else "N/A"
+    else:
+      rust_ms, speedup = "N/A", "N/A"
+    print(f"{test_type:<10} {name:<25} {n_insts:<8} {n_wgs:<6} {py_ms:<14} {rust_ms:<14} {speedup:<10}")
+
+
+
+if __name__ == "__main__":
+  main()
@@ -0,0 +1,196 @@
+# Usability tests for the RDNA3 ASM DSL
+# These tests demonstrate how the DSL *should* work for a good user experience
+# Currently many of these tests fail - they document desired behavior
+
+import unittest
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.dsl import Inst, RawImm, SGPR, VGPR
+
+class TestRegisterSliceSyntax(unittest.TestCase):
+  """
+  Issue: Register slice syntax should use AMD assembly convention (inclusive end).
+
+  In AMD assembly, s[4:7] means registers s4, s5, s6, s7 (4 registers, inclusive).
+  The DSL should match this convention so that:
+  - s[4:7] gives 4 registers
+  - Disassembler output can be copied directly back into DSL code
+
+  Fix: Change _RegFactory.__getitem__ to use inclusive end:
+    key.stop - key.start + 1  (instead of key.stop - key.start)
+  """
+  def test_register_slice_count(self):
+    # s[4:7] should give 4 registers: s4, s5, s6, s7 (AMD convention, inclusive)
+    reg = s[4:7]
+    self.assertEqual(reg.count, 4, "s[4:7] should give 4 registers (s4, s5, s6, s7)")
+
+  def test_register_slice_roundtrip(self):
+    # Round-trip: DSL -> disasm -> DSL should preserve register count
+    reg = s[4:7]  # 4 registers in AMD convention
+    inst = s_load_b128(reg, s[0:1], NULL, 0)
+    disasm = inst.disasm()
+    # Disasm shows s[4:7] - user should be able to copy this back
+    self.assertIn("s[4:7]", disasm)
+    # And s[4:7] in DSL should give the same 4 registers
+    reg_from_disasm = s[4:7]
+    self.assertEqual(reg_from_disasm.count, 4, "s[4:7] from disasm should give 4 registers")
+
+
+class TestReprReadability(unittest.TestCase):
+  """
+  Issue: repr() leaks internal RawImm type and omits zero-valued fields.
+
+  When you create v_mov_b32_e32(v[0], v[1]), the repr shows:
+    VOP1(op=1, src0=RawImm(257))
+
+  Problems:
+  1. vdst=v[0] is omitted because 0 is treated as "default"
+  2. src0 shows RawImm(257) instead of v[1]
+  3. User sees encoded values (257 = 256 + 1) instead of register names
+
+  Expected repr: VOP1(op=1, vdst=v[0], src0=v[1])
+  """
+  def test_repr_shows_registers_not_raw_imm(self):
+    inst = v_mov_b32_e32(v[0], v[1])
+    # Should show v[1], not RawImm(257)
+    self.assertNotIn("RawImm", repr(inst), "repr should not expose RawImm internal type")
+    self.assertIn("v[1]", repr(inst), "repr should show register name")
+
+  def test_repr_includes_zero_dst(self):
+    inst = v_mov_b32_e32(v[0], v[1])
+    # v[0] is a valid destination register, should be shown
+    self.assertIn("vdst", repr(inst), "repr should include vdst even when 0")
+
+  def test_repr_roundtrip(self):
+    # repr should produce something that can be eval'd back
+    inst = v_mov_b32_e32(v[0], v[1])
+    # This would require repr to output valid Python, e.g.:
+    # "VOP1(op=VOP1Op.V_MOV_B32, vdst=v[0], src0=v[1])"
+    r = repr(inst)
+    # At minimum, it should be human-readable
+    self.assertIn("v[", r, "repr should show register syntax")
+
+
+class TestInstructionEquality(unittest.TestCase):
+  """
+  Issue: No __eq__ method - instruction comparison requires repr() workaround.
+
+  Two identical instructions should compare equal with ==, but currently:
+    inst1 == inst2 returns False
+
+  The test_handwritten.py works around this with:
+    self.assertEqual(repr(self.inst), repr(reasm))
+  """
+  def test_identical_instructions_equal(self):
+    inst1 = v_mov_b32_e32(v[0], v[1])
+    inst2 = v_mov_b32_e32(v[0], v[1])
+    self.assertEqual(inst1, inst2, "identical instructions should be equal")
+
+  def test_different_instructions_not_equal(self):
+    inst1 = v_mov_b32_e32(v[0], v[1])
+    inst2 = v_mov_b32_e32(v[0], v[2])
+    self.assertNotEqual(inst1, inst2, "different instructions should not be equal")
+
+
+class TestVOPDHelperSignature(unittest.TestCase):
+  """
+  Issue: VOPD helper functions have confusing semantics.
+
+  v_dual_mul_f32 is defined as:
+    v_dual_mul_f32 = functools.partial(VOPD, VOPDOp.V_DUAL_MUL_F32)
+
+  This binds VOPDOp.V_DUAL_MUL_F32 to the FIRST positional arg of VOPD.__init__,
+  which is 'opx'. So v_dual_mul_f32 sets the X operation.
+
+  But then test_dual_mul in test_handwritten.py does:
+    v_dual_mul_f32(VOPDOp.V_DUAL_MUL_F32, vdstx=v[0], ...)
+
+  This passes V_DUAL_MUL_F32 as the SECOND positional arg (opy), making both
+  X and Y operations the same. This is confusing because:
+  1. The function name suggests it handles the X operation
+  2. But you still pass an opcode as the first arg (which becomes opy)
+
+  Expected: Either make the helper fully specify both ops, or make the
+  signature clearer about what the positional arg means.
+  """
+  def test_vopd_helper_opy_should_be_required(self):
+    # Using only keyword args "works" but opy silently defaults to 0
+    inst = v_dual_mul_f32(vdstx=v[0], vdsty=v[1], srcx0=v[2], vsrcx1=v[3], srcy0=v[4], vsrcy1=v[5])
+    self.assertEqual(inst.opx, VOPDOp.V_DUAL_MUL_F32)
+    # Bug: opy defaults to 0 (V_DUAL_FMAC_F32) silently - should require explicit opy
+    # This test documents the bug - it should fail once fixed
+    self.assertNotEqual(inst.opy, VOPDOp.V_DUAL_FMAC_F32, "opy should not silently default to FMAC")
+
+  def test_vopd_helper_positional_arg_is_opy(self):
+    # The first positional arg after the partial becomes opy, not a second opx
+    inst = v_dual_mul_f32(VOPDOp.V_DUAL_MOV_B32, vdstx=v[0], vdsty=v[1], srcx0=v[2], vsrcx1=v[3], srcy0=v[4], vsrcy1=v[5])
+    self.assertEqual(inst.opx, VOPDOp.V_DUAL_MUL_F32)  # From partial
+    self.assertEqual(inst.opy, VOPDOp.V_DUAL_MOV_B32)  # From first positional arg
+
+
+class TestFieldAccessPreservesType(unittest.TestCase):
+  """
+  Issue: Field access loses type information.
+
+  After creating an instruction, accessing fields returns encoded int values:
+    inst = v_mov_b32_e32(v[0], v[1])
+    inst.vdst  # returns 0, not VGPR(0)
+
+  This makes it impossible to round-trip register types through field access.
+  """
+  def test_vdst_returns_register(self):
+    inst = v_mov_b32_e32(v[5], v[1])
+    vdst = inst.vdst
+    # Should return a VGPR, not an int
+    self.assertIsInstance(vdst, (VGPR, int), "vdst should return VGPR or at least be usable")
+    # Ideally: self.assertIsInstance(vdst, VGPR)
+
+  def test_src_returns_register_for_vgpr_source(self):
+    inst = v_mov_b32_e32(v[0], v[1])
+    # src0 is encoded as 257 (256 + 1 for v1)
+    # Ideally it should decode back to v[1]
+    src0_raw = inst._values.get('src0')
+    # Currently returns RawImm(257), should return VGPR(1) or similar
+    self.assertNotIsInstance(src0_raw, RawImm, "source should not be RawImm internally")
+
+
+class TestArgumentDiscoverability(unittest.TestCase):
+  """
+  Issue: No clear signature for positional arguments.
+
+  inspect.signature(s_load_b128) shows: (*args, literal=None, **kwargs)
+
+  Users have no way to know the argument order without reading source code.
+  The order is implicitly defined by the class field definition order.
+
+  Possible fixes:
+  1. Add explicit parameter names to functools.partial
+  2. Generate type stubs with proper signatures
+  3. Add docstrings listing the expected arguments
+  """
+  def test_signature_has_named_params(self):
+    import inspect
+    sig = inspect.signature(s_load_b128)
+    params = list(sig.parameters.keys())
+    # Currently: ['args', 'literal', 'kwargs'] (from *args, literal=None, **kwargs)
+    # Expected: something like ['sdata', 'sbase', 'soffset', 'offset', 'literal']
+    self.assertIn('sdata', params, "signature should show field names")
+
+
+class TestSpecialConstants(unittest.TestCase):
+  """
+  Issue: NULL and other constants are IntEnum values that might be confusing.
+
+  NULL = SrcEnum.NULL = 124, but users might expect NULL to be a special object
+  that clearly represents "no register" rather than a magic number.
+  """
+  def test_null_has_clear_repr(self):
+    # NULL should have a clear string representation
+    self.assertIn("NULL", str(NULL) or repr(NULL), "NULL should be clearly identifiable")
+
+  def test_null_is_distinguishable_from_int(self):
+    # NULL should be distinguishable from the raw integer 124
+    self.assertNotEqual(type(NULL), int, "NULL should not be plain int")
+
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,24 @@
+"""Shared test helpers for RDNA3 tests."""
+import shutil
+from dataclasses import dataclass
+
+@dataclass
+class KernelInfo:
+  code: bytes
+  global_size: tuple[int, int, int]
+  local_size: tuple[int, int, int]
+  buf_idxs: list[int]  # indices into shared buffer pool
+  buf_sizes: list[int]  # sizes for each buffer index
+
+# LLVM tool detection (shared across test files)
+def get_llvm_mc():
+  """Find llvm-mc executable, preferring newer versions."""
+  for p in ['llvm-mc', 'llvm-mc-21', 'llvm-mc-20']:
+    if shutil.which(p): return p
+  raise FileNotFoundError("llvm-mc not found")
+
+def get_llvm_objdump():
+  """Find llvm-objdump executable, preferring newer versions."""
+  for p in ['llvm-objdump', 'llvm-objdump-21', 'llvm-objdump-20']:
+    if shutil.which(p): return p
+  raise FileNotFoundError("llvm-objdump not found")
@@ -0,0 +1,398 @@
+# Test to compare Python and Rust RDNA3 emulators by running real tinygrad kernels
+import unittest, ctypes, os
+from dataclasses import dataclass
+from pathlib import Path
+
+# Set environment before any tinygrad imports to use MOCKGPU
+# This allows generating AMD GPU kernels without requiring real hardware
+os.environ["AMD"] = "1"
+os.environ["MOCKGPU"] = "1"
+os.environ["PYTHON_REMU"] = "1"
+
+from extra.assembly.amd.emu import WaveState, decode_program, step_wave, WAVE_SIZE, set_valid_mem_ranges
+from extra.assembly.amd.test.helpers import KernelInfo
+
+REMU_PATH = Path(__file__).parents[3] / "remu/target/release/libremu.so"
+
+def _is_f32_nan(bits: int) -> bool:
+  """Check if 32-bit value is a NaN (exponent all 1s, mantissa non-zero)."""
+  return (bits & 0x7f800000) == 0x7f800000 and (bits & 0x007fffff) != 0
+
+def _vals_equal(a: int, b: int) -> bool:
+  """Compare two 32-bit values, treating all NaN bit patterns as equal."""
+  if a == b: return True
+  return _is_f32_nan(a) and _is_f32_nan(b)
+
+@dataclass
+class StateSnapshot:
+  pc: int
+  scc: int
+  vcc: int
+  exec_mask: int
+  sgpr: list[int]
+  vgpr: list[list[int]]
+
+  def diff(self, other: 'StateSnapshot', n_lanes: int, arrow: str = " vs ") -> list[str]:
+    """Return list of differences between two states."""
+    diffs = []
+    if self.pc != other.pc: diffs.append(f"pc: {self.pc}{arrow}{other.pc}")
+    if self.scc != other.scc: diffs.append(f"scc: {self.scc}{arrow}{other.scc}")
+    if self.vcc != other.vcc: diffs.append(f"vcc: 0x{self.vcc:08x}{arrow}0x{other.vcc:08x}")
+    if self.exec_mask != other.exec_mask: diffs.append(f"exec: 0x{self.exec_mask:08x}{arrow}0x{other.exec_mask:08x}")
+    for i, (a, b) in enumerate(zip(self.sgpr, other.sgpr)):
+      # Skip VCC_LO/HI (106/107) and EXEC_LO/HI (126/127) as they alias vcc/exec_mask which are compared separately
+      if i in (106, 107, 126, 127): continue
+      if not _vals_equal(a, b): diffs.append(f"sgpr[{i}]: 0x{a:08x}{arrow}0x{b:08x}")
+    for lane in range(n_lanes):
+      for i, (a, b) in enumerate(zip(self.vgpr[lane], other.vgpr[lane])):
+        if not _vals_equal(a, b): diffs.append(f"vgpr[{lane}][{i}]: 0x{a:08x}{arrow}0x{b:08x}")
+    return diffs
+
+class CStateSnapshot(ctypes.Structure):
+  _fields_ = [("pc", ctypes.c_uint32), ("scc", ctypes.c_uint32), ("vcc", ctypes.c_uint32), ("exec_mask", ctypes.c_uint32),
+              ("sgpr", ctypes.c_uint32 * 128), ("vgpr", (ctypes.c_uint32 * 256) * 32)]
+
+  def to_snapshot(self) -> StateSnapshot:
+    return StateSnapshot(pc=self.pc, scc=self.scc, vcc=self.vcc, exec_mask=self.exec_mask,
+                         sgpr=list(self.sgpr), vgpr=[list(self.vgpr[i]) for i in range(32)])
+
+class RustEmulator:
+  def __init__(self):
+    self.lib = ctypes.CDLL(str(REMU_PATH))
+    self.lib.wave_create.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32]
+    self.lib.wave_create.restype = ctypes.c_void_p
+    self.lib.wave_step.argtypes = [ctypes.c_void_p]
+    self.lib.wave_step.restype = ctypes.c_int32
+    self.lib.wave_get_snapshot.argtypes = [ctypes.c_void_p, ctypes.POINTER(CStateSnapshot)]
+    self.lib.wave_set_sgpr.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32]
+    self.lib.wave_set_vgpr.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32]
+    self.lib.wave_init_lds.argtypes = [ctypes.c_void_p, ctypes.c_uint32]
+    self.lib.wave_free.argtypes = [ctypes.c_void_p]
+    self.ctx = None
+
+  def create(self, kernel: bytes, n_lanes: int):
+    kernel_buf = (ctypes.c_char * len(kernel)).from_buffer_copy(kernel)
+    self.ctx = self.lib.wave_create(ctypes.addressof(kernel_buf), len(kernel), n_lanes)
+    self._kernel_buf = kernel_buf
+
+  def step(self) -> int: return self.lib.wave_step(self.ctx)
+  def set_sgpr(self, idx: int, val: int): self.lib.wave_set_sgpr(self.ctx, idx, val)
+  def set_vgpr(self, lane: int, idx: int, val: int): self.lib.wave_set_vgpr(self.ctx, lane, idx, val)
+  def init_lds(self, size: int): self.lib.wave_init_lds(self.ctx, size)
+
+  def get_snapshot(self) -> StateSnapshot:
+    snap = CStateSnapshot()
+    self.lib.wave_get_snapshot(self.ctx, ctypes.byref(snap))
+    return snap.to_snapshot()
+
+  def free(self):
+    if self.ctx: self.lib.wave_free(self.ctx); self.ctx = None
+
+class PythonEmulator:
+  def __init__(self):
+    self.state: WaveState | None = None
+    self.program: dict | None = None
+    self.lds: bytearray | None = None
+    self.n_lanes = 0
+
+  def create(self, kernel: bytes, n_lanes: int):
+    self.program = decode_program(kernel)
+    self.state = WaveState()
+    self.state.exec_mask = (1 << n_lanes) - 1
+    self.lds = bytearray(65536)
+    self.n_lanes = n_lanes
+
+  def step(self) -> int:
+    assert self.program is not None and self.state is not None and self.lds is not None
+    return step_wave(self.program, self.state, self.lds, self.n_lanes)
+  def set_sgpr(self, idx: int, val: int):
+    assert self.state is not None
+    self.state.sgpr[idx]._val = val & 0xffffffff
+  def set_vgpr(self, lane: int, idx: int, val: int):
+    assert self.state is not None
+    self.state.vgpr[lane][idx]._val = val & 0xffffffff
+
+  def get_snapshot(self) -> StateSnapshot:
+    assert self.state is not None
+    return StateSnapshot(pc=self.state.pc, scc=self.state.scc, vcc=self.state.vcc & 0xffffffff,
+                         exec_mask=self.state.exec_mask & 0xffffffff, sgpr=[r._val for r in self.state.sgpr],
+                         vgpr=[[r._val for r in self.state.vgpr[i]] for i in range(WAVE_SIZE)])
+
+def run_single_kernel(kernel: bytes, n_lanes: int, args_ptr: int, global_size: tuple[int, int, int],
+                      program, max_steps: int, debug: bool, trace_len: int, kernel_idx: int = 0,
+                      max_workgroups: int = 8) -> tuple[bool, str, int]:
+  """Run a single kernel through both emulators. Returns (success, message, total_steps)."""
+  gx, gy, gz = global_size
+  total_steps = 0
+  wg_count = 0
+
+  for gidz in range(gz):
+    for gidy in range(gy):
+      for gidx in range(gx):
+        if wg_count >= max_workgroups: return True, f"Completed {wg_count} workgroups (limit reached)", total_steps
+        wg_count += 1
+        rust = RustEmulator()
+        python = PythonEmulator()
+        rust.create(kernel, n_lanes)
+        python.create(kernel, n_lanes)
+
+        # Initialize LDS (64KB, standard size for AMD GPUs)
+        rust.init_lds(65536)
+
+        for emu in (rust, python):
+          emu.set_sgpr(0, args_ptr & 0xffffffff)
+          emu.set_sgpr(1, (args_ptr >> 32) & 0xffffffff)
+          emu.set_sgpr(13, gidx)
+          emu.set_sgpr(14, gidy)
+          emu.set_sgpr(15, gidz)
+
+        step = 0
+        trace: list[tuple[int, int, str, StateSnapshot, StateSnapshot]] = []
+        try:
+          while step < max_steps:
+            rust_before = rust.get_snapshot()
+            python_before = python.get_snapshot()
+
+            inst = program.get(python_before.pc)
+            inst_str = inst.disasm() if inst else f"unknown at PC={python_before.pc}"
+            trace.append((step, python_before.pc, inst_str, rust_before, python_before))
+            if len(trace) > trace_len: trace.pop(0)
+
+            if debug: print(f"K{kernel_idx} WG({gidx},{gidy},{gidz}) Step {step}: PC={python_before.pc}, inst={inst_str}")
+
+            # Instructions with known Rust emulator bugs - sync Python to Rust after execution
+            # v_div_scale/v_div_fixup: Rust has different VCC handling
+            # v_cvt_f16_f32: Rust clears high 16 bits, but hardware (and Python) preserves them
+            sync_after = any(x in inst_str for x in ('v_div_scale_f32', 'v_div_scale_f64', 'v_div_fixup_f32', 'v_div_fixup_f64',
+                                                      'v_cvt_f16_f32'))
+            diffs = rust_before.diff(python_before, n_lanes)
+            if diffs:
+              trace_lines = []
+              for idx, (s, pc, d, rb, pb) in enumerate(trace):
+                trace_lines.append(f"    step {s}: PC={pc:3d} {d}")
+                if idx < len(trace) - 1:
+                  next_rb, next_pb = trace[idx + 1][3:5]
+                  rust_diffs = rb.diff(next_rb, n_lanes, "->")
+                  python_diffs = pb.diff(next_pb, n_lanes, "->")
+                  if rust_diffs: trace_lines.append(f"             rust:   {', '.join(rust_diffs[:5])}")
+                  if python_diffs: trace_lines.append(f"             python: {', '.join(python_diffs[:5])}")
+                  elif rust_diffs: trace_lines.append(f"             python: (no changes)")
+                else:
+                  # Last traced instruction - compare with current state
+                  rust_diffs = rb.diff(rust_before, n_lanes, "->")
+                  python_diffs = pb.diff(python_before, n_lanes, "->")
+                  if rust_diffs: trace_lines.append(f"             rust:   {', '.join(rust_diffs[:5])}")
+                  if python_diffs: trace_lines.append(f"             python: {', '.join(python_diffs[:5])}")
+                  elif rust_diffs: trace_lines.append(f"             python: (no changes)")
+              trace_str = "\n".join(trace_lines)
+              return False, f"K{kernel_idx} WG({gidx},{gidy},{gidz}) Step {step} before inst '{inst_str}': states differ (rust vs python):\n  " + "\n  ".join(diffs[:10]) + f"\n  Recent instructions:\n{trace_str}", total_steps
+
+            rust_result = rust.step()
+            python_result = python.step()
+
+            if rust_result != python_result:
+              trace_str = "\n".join(f"    step {s}: PC={pc:3d} {d}" for s, pc, d, _, _ in trace)
+              return False, f"K{kernel_idx} WG({gidx},{gidy},{gidz}) Step {step}: different return codes: rust={rust_result}, python={python_result}, inst={inst_str}\n  Recent instructions:\n{trace_str}", total_steps
+
+            # Sync Python state to Rust after instructions with known Rust emulator differences
+            if sync_after:
+              rust_after = rust.get_snapshot()
+              for i in range(128): python.set_sgpr(i, rust_after.sgpr[i])
+              for lane in range(n_lanes):
+                for i in range(256): python.set_vgpr(lane, i, rust_after.vgpr[lane][i])
+              assert python.state is not None
+              python.state.pc, python.state.scc, python.state.vcc, python.state.exec_mask = rust_after.pc, rust_after.scc, rust_after.vcc, rust_after.exec_mask
+
+            if rust_result == -1:
+              total_steps += step + 1
+              break
+            if rust_result == 1:
+              total_steps += step + 1
+              break
+            if rust_result < 0 and rust_result != -2:
+              return False, f"K{kernel_idx} WG({gidx},{gidy},{gidz}) Step {step}: error code {rust_result}", total_steps
+
+            step += 1
+          else:
+            return False, f"K{kernel_idx} WG({gidx},{gidy},{gidz}) Max steps ({max_steps}) reached", total_steps
+        finally:
+          rust.free()
+
+  return True, f"Completed {gx*gy*gz} workgroups", total_steps
+
+def compare_emulators_multi_kernel(kernels: list[KernelInfo], buf_pool: dict[int, int], max_steps: int = 1000,
+                                    debug: bool = False, trace_len: int = 10, buf_data: dict[int, bytes] | None = None) -> tuple[bool, str]:
+  """Run all kernels through both emulators with shared buffer pool."""
+  if buf_data is None: buf_data = {}
+
+  # Allocate shared buffer pool with padding for over-reads (GPU loads up to 16 bytes at once)
+  buf_id_to_ptr: dict[int, int] = {}
+  buffers = []
+  for buf_id, size in buf_pool.items():
+    padded_size = ((size + 15) // 16) * 16 + 16  # round up to 16 bytes + extra padding
+    # Initialize with data from COPY if available
+    init_data = buf_data.get(buf_id, b'\x00' * padded_size)
+    init_list = list(init_data) + [0] * (padded_size - len(init_data))
+    buf = (ctypes.c_uint8 * padded_size)(*init_list[:padded_size])
+    buffers.append((buf, padded_size))
+    buf_id_to_ptr[buf_id] = ctypes.addressof(buf)
+
+  # Set up valid memory ranges
+  ranges = {(ctypes.addressof(b), size) for b, size in buffers}
+
+  total_steps = 0
+  for ki, kernel in enumerate(kernels):
+    # Create args array for this kernel's buffers
+    args = (ctypes.c_uint64 * len(kernel.buf_idxs))(*[buf_id_to_ptr[bid] for bid in kernel.buf_idxs])
+    args_ptr = ctypes.addressof(args)
+
+    # Update valid ranges to include this args array
+    kernel_ranges = ranges | {(args_ptr, ctypes.sizeof(args))}
+    set_valid_mem_ranges(kernel_ranges)
+
+    program = decode_program(kernel.code)
+    n_lanes = kernel.local_size[0] * kernel.local_size[1] * kernel.local_size[2]
+
+    ok, msg, steps = run_single_kernel(
+      kernel.code, min(n_lanes, 32), args_ptr, kernel.global_size,
+      program, max_steps, debug, trace_len, ki
+    )
+    total_steps += steps
+    if not ok:
+      return False, msg
+
+  return True, f"Completed {len(kernels)} kernels, {total_steps} total steps"
+
+def compare_emulators_with_memory(kernel: bytes, n_lanes: int, buf_sizes: list, max_steps: int = 1000, debug: bool = False,
+                                   global_size: tuple[int, int, int] = (1, 1, 1), trace_len: int = 10) -> tuple[bool, str]:
+  """Run both emulators with memory set up for tinygrad kernels, executing all workgroups. Legacy wrapper."""
+  # Allocate buffers
+  buffers = []
+  for size in buf_sizes:
+    buf = (ctypes.c_uint8 * size)(*[0] * size)
+    buffers.append(buf)
+
+  # Create args array with buffer pointers
+  args = (ctypes.c_uint64 * len(buffers))(*[ctypes.addressof(b) for b in buffers])
+  args_ptr = ctypes.addressof(args)
+
+  # Set up valid memory ranges for Python emulator
+  ranges = {(ctypes.addressof(b), len(b)) for b in buffers}
+  ranges.add((args_ptr, ctypes.sizeof(args)))
+  set_valid_mem_ranges(ranges)
+
+  program = decode_program(kernel)
+  ok, msg, _ = run_single_kernel(kernel, n_lanes, args_ptr, global_size, program, max_steps, debug, trace_len)
+  return ok, msg
+
+def get_kernels_from_tinygrad(op_fn) -> tuple[list[KernelInfo], dict[int, int], dict[int, bytes]]:
+  """Compile a tinygrad operation and extract all kernels with their buffer mappings."""
+  from tinygrad import Tensor
+  from tinygrad.runtime.support.elf import elf_loader
+
+  out = op_fn(Tensor)
+  sched = out.schedule()
+  kernels = []
+  buf_pool: dict[int, int] = {}  # buffer id -> size
+  buf_data: dict[int, bytes] = {}  # buffer id -> initial data from COPY
+
+  for ei in sched:
+    lowered = ei.lower()
+    if ei.ast.op.name == 'COPY':
+      # Handle COPY: extract source data to initialize destination buffer
+      if len(lowered.bufs) >= 2:
+        dst_buf, src_buf = lowered.bufs[0], lowered.bufs[1]
+        dst_id = id(dst_buf)
+        if dst_id not in buf_pool:
+          buf_pool[dst_id] = dst_buf.nbytes
+        # Get source data if it's from numpy/CPU
+        if hasattr(src_buf, 'base') and src_buf.base is not None and hasattr(src_buf.base, '_buf'):
+          src_data = bytes(src_buf.base._buf)
+          buf_data[dst_id] = src_data
+    elif ei.ast.op.name == 'SINK':
+      if lowered.prg and lowered.prg.p.lib:
+        lib = bytes(lowered.prg.p.lib)
+        _, sections, _ = elf_loader(lib)
+        for sec in sections:
+          if sec.name == '.text':
+            buf_idxs = []
+            buf_sizes = []
+            for b in lowered.bufs:
+              buf_id = id(b)
+              if buf_id not in buf_pool:
+                buf_pool[buf_id] = b.nbytes
+              buf_idxs.append(buf_id)
+              buf_sizes.append(b.nbytes)
+            kernels.append(KernelInfo(
+              code=bytes(sec.content),
+              global_size=tuple(lowered.prg.p.global_size),
+              local_size=tuple(lowered.prg.p.local_size),
+              buf_idxs=buf_idxs,
+              buf_sizes=buf_sizes
+            ))
+  if not kernels: raise RuntimeError("No kernel found")
+  return kernels, buf_pool, buf_data
+
+def get_kernel_from_tinygrad(op_fn) -> tuple[bytes, tuple[int, int, int], tuple[int, int, int], list]:
+  """Compile a tinygrad operation and extract the last (main) kernel binary. Legacy wrapper."""
+  kernels, _, _ = get_kernels_from_tinygrad(op_fn)
+  k = kernels[-1]
+  return k.code, k.global_size, k.local_size, k.buf_sizes
+
+class TestTinygradKernels(unittest.TestCase):
+  """Compare emulators on real tinygrad-compiled kernels."""
+
+  def _test_kernel(self, op_fn, max_steps=10000):
+    kernels, buf_pool, buf_data = get_kernels_from_tinygrad(op_fn)
+    ok, msg = compare_emulators_multi_kernel(kernels, buf_pool, max_steps=max_steps, buf_data=buf_data)
+    self.assertTrue(ok, msg)
+
+  # Basic ops - consolidated tests covering key instruction patterns
+  def test_unary_ops(self): self._test_kernel(lambda T: T([-1.0, 0.0, 1.0, 2.0]).relu().exp().log().sqrt().reciprocal())
+  def test_binary_ops(self): self._test_kernel(lambda T: (T([1.0, 2.0]) + T([3.0, 4.0])) * T([0.5, 0.5]) - T([1.0, 1.0]))
+  def test_trig(self): self._test_kernel(lambda T: T([0.1, 1.0, 3.14, -1.0]*8).sin() + T([0.1, 1.0, 3.14, -1.0]*8).cos())
+  def test_compare(self): self._test_kernel(lambda T: (T.empty(64) < T.empty(64)).where(T.empty(64), T.empty(64)))
+  def test_bitwise(self): self._test_kernel(lambda T: (T([0xF0, 0x0F, 0xFF]*11).int() & T([0x0F, 0x0F, 0x00]*11).int()) | T([1]*33).int())
+  def test_int_ops(self): self._test_kernel(lambda T: ((T.empty(64).int() + T.empty(64).int()) * T.empty(64).int()).float())
+
+  # Reductions
+  def test_reduce(self): self._test_kernel(lambda T: T.empty(64).sum() + T.empty(64).max())
+  def test_argmax(self): self._test_kernel(lambda T: T.empty(64).argmax())
+
+  # Matmul
+  def test_gemm(self): self._test_kernel(lambda T: T.empty(8, 8) @ T.empty(8, 8), max_steps=100000)
+  def test_gemm_fp16(self): self._test_kernel(lambda T: T.empty(16, 16).half() @ T.empty(16, 16).half(), max_steps=100000)
+
+  # Complex ops
+  def test_softmax(self): self._test_kernel(lambda T: T.empty(16).softmax())
+  def test_layernorm(self): self._test_kernel(lambda T: T.empty(8, 8).layernorm())
+
+  # Memory patterns
+  def test_memory(self): self._test_kernel(lambda T: T.empty(4, 4).permute(1, 0).contiguous() + T.empty(4, 1).expand(4, 4))
+
+  # Cast ops
+  def test_cast(self): self._test_kernel(lambda T: T.empty(32).half().float() + T.empty(32).int().float())
+
+  # Pooling - regression for VCC wave32 mode
+  def test_pool2d(self): self._test_kernel(lambda T: T.empty(1, 1, 8, 8).avg_pool2d(kernel_size=(4,4)) + T.empty(1, 1, 8, 8).max_pool2d(kernel_size=(4,4)))
+
+  # Convolution
+  def test_conv2d(self): self._test_kernel(lambda T: T.empty(1, 2, 8, 8).conv2d(T.empty(2, 2, 3, 3)), max_steps=50000)
+
+  # Regression tests
+  def test_topk(self): self._test_kernel(lambda T: T.empty(64).topk(3)[0])
+  def test_interpolate(self): self._test_kernel(lambda T: T.empty(1,2,16,16).relu().cast('uint8').interpolate((8,8), mode="linear"))
+  def test_index_int64(self):
+    from tinygrad import dtypes
+    self._test_kernel(lambda T: T.empty(4, 4)[T.arange(4).cast(dtypes.int64), :])
+  def test_gelu(self): self._test_kernel(lambda T: T.empty(32, 32).gelu())
+  def test_cross_entropy(self):
+    import numpy as np
+    np.random.seed(0)
+    classes = np.random.randint(0, 10, (16,), dtype=np.int32).tolist()
+    x_np = np.random.randn(16, 10).astype(np.float32)
+    self._test_kernel(lambda T: (T(x_np.tolist()).reshape(16,10) + 0).cross_entropy((T(classes).int().reshape(16) + 0)))
+  def test_isinf(self): self._test_kernel(lambda T: T([float('-inf'), 0., float('inf'), 1.1]*8).isinf())
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+"""Test MUBUF, MTBUF, MIMG, EXP, DS formats against LLVM."""
+import unittest
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.dsl import encode_src
+
+class TestMUBUF(unittest.TestCase):
+  """Test MUBUF (buffer) instructions."""
+
+  def test_buffer_load_b32_basic(self):
+    # buffer_load_b32 v5, off, s[8:11], s3 offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_load_b32_idxen(self):
+    # buffer_load_b32 v5, v0, s[8:11], s3 idxen offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x50,0xe0,0x00,0x05,0x82,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, idxen=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x50,0xe0,0x00,0x05,0x82,0x03]))
+
+  def test_buffer_load_b32_offen(self):
+    # buffer_load_b32 v5, v0, s[8:11], s3 offen offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x50,0xe0,0x00,0x05,0x42,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, offen=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x50,0xe0,0x00,0x05,0x42,0x03]))
+
+  def test_buffer_load_b32_glc(self):
+    # buffer_load_b32 v5, off, s[8:11], s3 offset:4095 glc
+    # GFX11: encoding: [0xff,0x4f,0x50,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, glc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x4f,0x50,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_load_b32_slc(self):
+    # buffer_load_b32 v5, off, s[8:11], s3 offset:4095 slc
+    # GFX11: encoding: [0xff,0x1f,0x50,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, slc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x1f,0x50,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_load_b32_dlc(self):
+    # buffer_load_b32 v5, off, s[8:11], s3 offset:4095 dlc
+    # GFX11: encoding: [0xff,0x2f,0x50,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, dlc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x2f,0x50,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_load_b32_all_flags(self):
+    # buffer_load_b32 v5, off, s[8:11], s3 offset:4095 glc slc dlc
+    # GFX11: encoding: [0xff,0x7f,0x50,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, glc=1, slc=1, dlc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x7f,0x50,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_store_b32(self):
+    # buffer_store_b32 v1, off, s[12:15], s4 offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x68,0xe0,0x00,0x01,0x03,0x04]
+    inst = buffer_store_b32(vdata=v[1], vaddr=v[0], srsrc=s[12:16], soffset=s[4], offset=4095)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x68,0xe0,0x00,0x01,0x03,0x04]))
+
+  def test_buffer_load_b64(self):
+    # buffer_load_b64 v[5:6], off, s[8:11], s3 offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x54,0xe0,0x00,0x05,0x02,0x03]
+    inst = buffer_load_b64(vdata=v[5:7], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x54,0xe0,0x00,0x05,0x02,0x03]))
+
+  def test_buffer_load_soffset_m0(self):
+    # buffer_load_b32 v5, off, s[8:11], m0 offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x7d]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=M0, offset=4095)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x7d]))
+
+  def test_buffer_load_soffset_inline_const(self):
+    # buffer_load_b32 v5, off, s[8:11], 0 offset:4095
+    # GFX11: encoding: [0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x80]
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=0, offset=4095)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0x50,0xe0,0x00,0x05,0x02,0x80]))
+
+  def test_buffer_disasm_roundtrip(self):
+    inst = buffer_load_b32(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, glc=1)
+    decoded = MUBUF.from_bytes(inst.to_bytes())
+    self.assertEqual(decoded.to_bytes(), inst.to_bytes())
+
+
+class TestMTBUF(unittest.TestCase):
+  """Test MTBUF (typed buffer) instructions."""
+
+  def test_tbuffer_load_format_x(self):
+    # tbuffer_load_format_x v5, off, s[8:11], s3 format:[BUF_FMT_32_FLOAT] offset:4095
+    # BUF_FMT_32_FLOAT = 22
+    # GFX11: encoding: [0xff,0x0f,0xb0,0xe8,0x00,0x05,0x02,0x03]
+    inst = tbuffer_load_format_x(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, format=22)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0xb0,0xe8,0x00,0x05,0x02,0x03]))
+
+  def test_tbuffer_store_format_x(self):
+    # tbuffer_store_format_x v5, off, s[8:11], s3 format:[BUF_FMT_32_FLOAT] offset:4095
+    # BUF_FMT_32_FLOAT = 22
+    # GFX11: encoding: [0xff,0x0f,0xb2,0xe8,0x00,0x05,0x02,0x03]
+    inst = tbuffer_store_format_x(vdata=v[5], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, format=22)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x0f,0xb2,0xe8,0x00,0x05,0x02,0x03]))
+
+  def test_tbuffer_load_format_xy(self):
+    # tbuffer_load_format_xy v[5:6], off, s[8:11], s3 format:[BUF_FMT_32_32_FLOAT] offset:4095
+    # BUF_FMT_32_32_FLOAT = 50
+    # GFX11: encoding: [0xff,0x8f,0x90,0xe9,0x00,0x05,0x02,0x03]
+    inst = tbuffer_load_format_xy(vdata=v[5:7], vaddr=v[0], srsrc=s[8:12], soffset=s[3], offset=4095, format=50)
+    self.assertEqual(inst.to_bytes(), bytes([0xff,0x8f,0x90,0xe9,0x00,0x05,0x02,0x03]))
+
+
+class TestMIMG(unittest.TestCase):
+  """Test MIMG (image) instructions."""
+
+  def test_image_load_2d(self):
+    # image_load v[0:3], v[4:5], s[0:7] dmask:0xf dim:SQ_RSRC_IMG_2D
+    # GFX11: encoding: [0x04,0x0f,0x00,0xf0,0x04,0x00,0x00,0x00]
+    inst = image_load(vdata=v[0:4], vaddr=v[4:6], srsrc=s[0:8], dmask=0xf, dim=1)  # dim=1 is SQ_RSRC_IMG_2D
+    self.assertEqual(inst.to_bytes(), bytes([0x04,0x0f,0x00,0xf0,0x04,0x00,0x00,0x00]))
+
+  def test_image_store_2d(self):
+    # image_store v[0:3], v[4:5], s[0:7] dmask:0xf dim:SQ_RSRC_IMG_2D
+    # GFX11: encoding: [0x04,0x0f,0x18,0xf0,0x04,0x00,0x00,0x00]
+    inst = image_store(vdata=v[0:4], vaddr=v[4:6], srsrc=s[0:8], dmask=0xf, dim=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x04,0x0f,0x18,0xf0,0x04,0x00,0x00,0x00]))
+
+  def test_image_load_1d(self):
+    # image_load v[0:3], v4, s[0:7] dmask:0xf dim:SQ_RSRC_IMG_1D
+    # GFX11: encoding: [0x00,0x0f,0x00,0xf0,0x04,0x00,0x00,0x00]
+    inst = image_load(vdata=v[0:4], vaddr=v[4], srsrc=s[0:8], dmask=0xf, dim=0)  # dim=0 is SQ_RSRC_IMG_1D
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x0f,0x00,0xf0,0x04,0x00,0x00,0x00]))
+
+  def test_image_sample(self):
+    # image_sample v[0:3], v[4:5], s[0:7], s[8:11] dmask:0xf dim:SQ_RSRC_IMG_2D
+    # GFX11: encoding: [0x04,0x0f,0x6c,0xf0,0x04,0x00,0x00,0x08]
+    inst = image_sample(vdata=v[0:4], vaddr=v[4:6], srsrc=s[0:8], ssamp=s[8:12], dmask=0xf, dim=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x04,0x0f,0x6c,0xf0,0x04,0x00,0x00,0x08]))
+
+  def test_image_load_d16(self):
+    # image_load v[0:1], v[4:5], s[0:7] dmask:0xf dim:SQ_RSRC_IMG_2D d16
+    # GFX11: encoding: [0x04,0x0f,0x02,0xf0,0x04,0x00,0x00,0x00]
+    inst = image_load(vdata=v[0:2], vaddr=v[4:6], srsrc=s[0:8], dmask=0xf, dim=1, d16=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x04,0x0f,0x02,0xf0,0x04,0x00,0x00,0x00]))
+
+
+class TestEXP(unittest.TestCase):
+  """Test EXP (export) instructions."""
+
+  def test_exp_mrt0(self):
+    # exp mrt0 v0, v1, v2, v3
+    # GFX11: encoding: [0x0f,0x00,0x00,0xf8,0x00,0x01,0x02,0x03]
+    inst = EXP(en=0xf, target=0, vsrc0=v[0], vsrc1=v[1], vsrc2=v[2], vsrc3=v[3])
+    self.assertEqual(inst.to_bytes(), bytes([0x0f,0x00,0x00,0xf8,0x00,0x01,0x02,0x03]))
+
+  def test_exp_mrtz(self):
+    # exp mrtz v4, v3, v2, v1
+    # GFX11: encoding: [0x8f,0x00,0x00,0xf8,0x04,0x03,0x02,0x01]
+    inst = EXP(en=0xf, target=8, vsrc0=v[4], vsrc1=v[3], vsrc2=v[2], vsrc3=v[1])
+    self.assertEqual(inst.to_bytes(), bytes([0x8f,0x00,0x00,0xf8,0x04,0x03,0x02,0x01]))
+
+  def test_exp_mrtz_done(self):
+    # exp mrtz v4, v3, v2, v1 done
+    # GFX11: encoding: [0x8f,0x08,0x00,0xf8,0x04,0x03,0x02,0x01]
+    inst = EXP(en=0xf, target=8, vsrc0=v[4], vsrc1=v[3], vsrc2=v[2], vsrc3=v[3], done=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x8f,0x08,0x00,0xf8,0x04,0x03,0x02,0x03]))
+
+  def test_exp_partial_mask(self):
+    # exp mrt0 v0, v1, off, off (en=0x3, only first two components)
+    # GFX11: encoding: [0x03,0x00,0x00,0xf8,0x00,0x01,0x00,0x00]
+    inst = EXP(en=0x3, target=0, vsrc0=v[0], vsrc1=v[1], vsrc2=v[0], vsrc3=v[0])
+    self.assertEqual(inst.to_bytes(), bytes([0x03,0x00,0x00,0xf8,0x00,0x01,0x00,0x00]))
+
+  def test_exp_row_en(self):
+    # exp mrtz v4, v3, v2, v1 row_en
+    # GFX11: encoding: [0x8f,0x20,0x00,0xf8,0x04,0x03,0x02,0x01]
+    inst = EXP(en=0xf, target=8, vsrc0=v[4], vsrc1=v[3], vsrc2=v[2], vsrc3=v[1], row=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x8f,0x20,0x00,0xf8,0x04,0x03,0x02,0x01]))
+
+
+class TestDS(unittest.TestCase):
+  """Test DS (data share / LDS) instructions."""
+
+  def test_ds_store_b32(self):
+    # ds_store_b32 v0, v1
+    # GFX11: encoding: [0x00,0x00,0x34,0xd8,0x00,0x01,0x00,0x00]
+    inst = ds_store_b32(addr=v[0], data0=v[1])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x34,0xd8,0x00,0x01,0x00,0x00]))
+
+  def test_ds_load_b32(self):
+    # ds_load_b32 v0, v1
+    # GFX11: encoding: [0x00,0x00,0xd8,0xd8,0x01,0x00,0x00,0x00]
+    inst = ds_load_b32(vdst=v[0], addr=v[1])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0xd8,0xd8,0x01,0x00,0x00,0x00]))
+
+  def test_ds_store_b32_offset(self):
+    # ds_store_b32 v0, v1 offset:64
+    # GFX11: encoding: [0x40,0x00,0x34,0xd8,0x00,0x01,0x00,0x00]
+    inst = ds_store_b32(addr=v[0], data0=v[1], offset0=64)
+    self.assertEqual(inst.to_bytes(), bytes([0x40,0x00,0x34,0xd8,0x00,0x01,0x00,0x00]))
+
+  def test_ds_load_b64(self):
+    # ds_load_b64 v[0:1], v2
+    # GFX11: encoding: [0x00,0x00,0xd8,0xd9,0x02,0x00,0x00,0x00]
+    inst = ds_load_b64(vdst=v[0:2], addr=v[2])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0xd8,0xd9,0x02,0x00,0x00,0x00]))
+
+  def test_ds_add_u32(self):
+    # ds_add_u32 v0, v1
+    # GFX11: encoding: [0x00,0x00,0x00,0xd8,0x00,0x01,0x00,0x00]
+    inst = ds_add_u32(addr=v[0], data0=v[1])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x00,0xd8,0x00,0x01,0x00,0x00]))
+
+  def test_ds_store_b32_gds(self):
+    # ds_store_b32 v0, v1 gds
+    # GFX11: encoding: [0x00,0x00,0x36,0xd8,0x00,0x01,0x00,0x00]
+    inst = ds_store_b32(addr=v[0], data0=v[1], gds=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x36,0xd8,0x00,0x01,0x00,0x00]))
+
+
+class TestVOP3(unittest.TestCase):
+  """Test VOP3 (3-operand vector) instructions."""
+
+  def test_v_fma_f32(self):
+    # v_fma_f32 v0, v1, v2, v3
+    # GFX11: encoding: [0x00,0x00,0x13,0xd6,0x01,0x05,0x0e,0x04]
+    inst = v_fma_f32(vdst=v[0], src0=v[1], src1=v[2], src2=v[3])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x13,0xd6,0x01,0x05,0x0e,0x04]))
+
+  def test_v_mad_f32(self):
+    # v_fmac_f32_e64 v0, v1, v2 (fmac is fma with implicit dst as src2)
+    # Use v_fma_f32 with vdst == src2
+    inst = v_fma_f32(vdst=v[0], src0=v[1], src1=v[2], src2=v[0])
+    self.assertEqual(inst.to_bytes()[:4], bytes([0x00,0x00,0x13,0xd6]))
+
+  def test_v_add3_u32(self):
+    # v_add3_u32 v0, v1, v2, v3
+    # GFX11: encoding: [0x00,0x00,0x55,0xd6,0x01,0x05,0x0e,0x04]
+    inst = v_add3_u32(vdst=v[0], src0=v[1], src1=v[2], src2=v[3])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x55,0xd6,0x01,0x05,0x0e,0x04]))
+
+
+class TestFLAT(unittest.TestCase):
+  """Test FLAT/GLOBAL/SCRATCH memory instructions."""
+
+  def test_global_load_b32(self):
+    # global_load_b32 v0, v[1:2], off (seg=2 for global)
+    # GFX11: encoding: [0x00,0x00,0x52,0xdc,0x01,0x00,0x7c,0x00]
+    inst = global_load_b32(vdst=v[0], addr=v[1:3], saddr=OFF)
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x52,0xdc,0x01,0x00,0x7c,0x00]))
+
+  def test_global_store_b32(self):
+    # global_store_b32 v[0:1], v2, off (seg=2 for global)
+    # GFX11: encoding: [0x00,0x00,0x6a,0xdc,0x00,0x02,0x7c,0x00]
+    inst = global_store_b32(addr=v[0:2], data=v[2], saddr=OFF)
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x6a,0xdc,0x00,0x02,0x7c,0x00]))
+
+  def test_global_load_b32_saddr(self):
+    # global_load_b32 v0, v1, s[0:1] (seg=2 for global)
+    # GFX11: encoding: [0x00,0x00,0x52,0xdc,0x01,0x00,0x00,0x00]
+    inst = global_load_b32(vdst=v[0], addr=v[1], saddr=s[0:2])
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x52,0xdc,0x01,0x00,0x00,0x00]))
+
+  def test_global_load_b32_offset(self):
+    # global_load_b32 v0, v[1:2], off offset:256 (seg=2 for global)
+    # GFX11: encoding: [0x00,0x01,0x52,0xdc,0x01,0x00,0x7c,0x00]
+    inst = global_load_b32(vdst=v[0], addr=v[1:3], saddr=OFF, offset=256)
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x01,0x52,0xdc,0x01,0x00,0x7c,0x00]))
+
+  def test_global_load_b64(self):
+    # global_load_b64 v[0:1], v[2:3], off (seg=2 for global)
+    # GFX11: encoding: [0x00,0x00,0x56,0xdc,0x02,0x00,0x7c,0x00]
+    inst = global_load_b64(vdst=v[0:2], addr=v[2:4], saddr=OFF)
+    self.assertEqual(inst.to_bytes(), bytes([0x00,0x00,0x56,0xdc,0x02,0x00,0x7c,0x00]))
+
+
+class TestSMEM(unittest.TestCase):
+  """Test SMEM (scalar memory) instructions - regression tests for glc/dlc bit positions."""
+
+  def test_smem_dlc_bit_position(self):
+    # s_load_b32 s5, s[2:3], s0 dlc - tests that DLC is at bit 13 (not bit 14)
+    # GFX11: encoding: [0x41,0x21,0x00,0xf4,0x00,0x00,0x00,0x00]
+    inst = s_load_b32(sdata=s[5], sbase=s[2], soffset=s[0], dlc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x41,0x21,0x00,0xf4,0x00,0x00,0x00,0x00]))
+
+  def test_smem_glc_bit_position(self):
+    # s_load_b32 s5, s[2:3], s0 glc - tests that GLC is at bit 14 (not bit 16)
+    # GFX11: encoding: [0x41,0x41,0x00,0xf4,0x00,0x00,0x00,0x00]
+    inst = s_load_b32(sdata=s[5], sbase=s[2], soffset=s[0], glc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x41,0x41,0x00,0xf4,0x00,0x00,0x00,0x00]))
+
+  def test_smem_glc_dlc_combined(self):
+    # s_load_b32 s5, s[2:3], s0 glc dlc - tests both flags together
+    # GFX11: encoding: [0x41,0x61,0x00,0xf4,0x00,0x00,0x00,0x00]
+    inst = s_load_b32(sdata=s[5], sbase=s[2], soffset=s[0], glc=1, dlc=1)
+    self.assertEqual(inst.to_bytes(), bytes([0x41,0x61,0x00,0xf4,0x00,0x00,0x00,0x00]))
+
+  def test_smem_disasm_roundtrip_dlc(self):
+    # Test that disassembly/reassembly preserves DLC bit correctly
+    data = bytes([0x41,0x21,0x00,0xf4,0x00,0x00,0x00,0x00])
+    decoded = SMEM.from_bytes(data)
+    self.assertEqual(decoded.to_bytes(), data)
+
+  def test_smem_disasm_roundtrip_glc_dlc(self):
+    # Test that disassembly/reassembly preserves GLC+DLC bits correctly
+    data = bytes([0x41,0x61,0x00,0xf4,0x00,0x00,0x00,0x00])
+    decoded = SMEM.from_bytes(data)
+    self.assertEqual(decoded.to_bytes(), data)
+
+
+class TestVOP3Literal(unittest.TestCase):
+  """Test VOP3 literal handling - regression tests for Inst64 literal encoding."""
+
+  def test_vop3_with_literal(self):
+    # v_add3_u32 v5, vcc_hi, 0xaf123456, v255
+    # GFX11: encoding: [0x05,0x00,0x55,0xd6,0x6b,0xfe,0xfd,0x07,0x56,0x34,0x12,0xaf]
+    from extra.assembly.amd.dsl import RawImm
+    inst = VOP3(VOP3Op.V_ADD3_U32, vdst=v[5], src0=RawImm(107), src1=0xaf123456, src2=v[255])
+    expected = bytes([0x05,0x00,0x55,0xd6,0x6b,0xfe,0xfd,0x07,0x56,0x34,0x12,0xaf])
+    self.assertEqual(inst.to_bytes(), expected)
+
+  def test_vop3_literal_null_operand(self):
+    # v_add3_u32 v5, null, exec_lo, 0xaf123456
+    # GFX11: encoding: [0x05,0x00,0x55,0xd6,0x7c,0xfc,0xfc,0x03,0x56,0x34,0x12,0xaf]
+    from extra.assembly.amd.dsl import RawImm
+    inst = VOP3(VOP3Op.V_ADD3_U32, vdst=v[5], src0=NULL, src1=RawImm(126), src2=0xaf123456)
+    expected = bytes([0x05,0x00,0x55,0xd6,0x7c,0xfc,0xfc,0x03,0x56,0x34,0x12,0xaf])
+    self.assertEqual(inst.to_bytes(), expected)
+
+  def test_vop3p_with_literal(self):
+    # Test VOP3P literal encoding (also uses Inst64)
+    from extra.assembly.amd.dsl import RawImm
+    inst = VOP3P(VOP3POp.V_PK_ADD_F16, vdst=v[5], src0=RawImm(240), src1=0x12345678, src2=v[0])
+    self.assertEqual(len(inst.to_bytes()), 12)  # 8 bytes + 4 byte literal
+
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,178 @@
+# do not change these tests. we need to fix bugs to make them pass
+# the Inst constructor should be looking at the types of the fields to correctly set the value
+
+import unittest, struct
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.dsl import Inst
+from extra.assembly.amd.asm import asm
+from extra.assembly.amd.test.test_roundtrip import compile_asm
+
+class TestIntegration(unittest.TestCase):
+  inst: Inst
+  def tearDown(self):
+    if not hasattr(self, 'inst'): return
+    b = self.inst.to_bytes()
+    st = self.inst.disasm()
+    reasm = asm(st)
+    desc = f"{st:25s} {self.inst} {b!r} {reasm}"
+    self.assertEqual(b, compile_asm(st), desc)
+    # TODO: this compare should work for valid things
+    #self.assertEqual(self.inst, reasm)
+    self.assertEqual(repr(self.inst), repr(reasm))
+    print(desc)
+
+  def test_load_b128(self):
+    self.inst = s_load_b128(s[4:7], s[0:1], NULL, 0)
+
+  def test_load_b128_wrong_size(self):
+    # this should have to be 4 regs on the loaded to
+    with self.assertRaises(Exception):
+      self.inst = s_load_b128(s[4:6], s[0:1], NULL, 0)
+
+  def test_mov_b32(self):
+    self.inst = s_mov_b32(s[80], s[0])
+
+  def test_mov_b64(self):
+    self.inst = s_mov_b64(s[80:81], s[0:1])
+
+  def test_mov_b32_wrong(self):
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b32(s[80:81], s[0:1])
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b32(s[80:81], s[0])
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b32(s[80], s[0:1])
+
+  def test_mov_b64_wrong(self):
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b64(s[80], s[0])
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b64(s[80], s[0:1])
+    with self.assertRaises(Exception):
+      self.inst = s_mov_b64(s[80:81], s[0])
+
+  def test_load_b128_no_0(self):
+    self.inst = s_load_b128(s[4:7], s[0:1], NULL)
+
+  def test_load_b128_s(self):
+    self.inst = s_load_b128(s[4:7], s[0:1], s[8], 0)
+
+  def test_load_b128_v(self):
+    with self.assertRaises(TypeError):
+      self.inst = s_load_b128(s[4:7], s[0:1], v[8], 0)
+
+  def test_load_b128_off(self):
+    self.inst = s_load_b128(s[4:7], s[0:1], NULL, 3)
+
+  def test_simple_stos(self):
+    self.inst = s_mov_b32(s[0], s[1])
+
+  def test_simple_wrong(self):
+    with self.assertRaises(TypeError):
+      self.inst = s_mov_b32(v[0], s[1])
+
+  def test_simple_vtov(self):
+    self.inst = v_mov_b32_e32(v[0], v[1])
+
+  def test_simple_stov(self):
+    self.inst = v_mov_b32_e32(v[0], s[2])
+
+  def test_simple_float_to_v(self):
+    self.inst = v_mov_b32_e32(v[0], 1.0)
+
+  def test_simple_v_to_float(self):
+    with self.assertRaises(TypeError):
+      self.inst = v_mov_b32_e32(1, v[0])
+
+  def test_simple_int_to_v(self):
+    self.inst = v_mov_b32_e32(v[0], 1)
+
+  def test_three_add(self):
+    self.inst = v_add_co_ci_u32_e32(v[3], s[7], v[3])
+
+  def test_three_add_v(self):
+    self.inst = v_add_co_ci_u32_e32(v[3], v[7], v[3])
+
+  def test_three_add_const(self):
+    self.inst = v_add_co_ci_u32_e32(v[3], 2.0, v[3])
+
+  def test_swaitcnt_lgkm(self): self.inst = s_waitcnt(0xfc07)
+  def test_swaitcnt_vm(self): self.inst = s_waitcnt(0x03f7)
+
+  def test_vmad(self):
+    self.inst = v_mad_u64_u32(v[1:2], NULL, s[2], 3, v[1:2])
+
+  def test_large_imm(self):
+    self.inst = v_mov_b32_e32(v[0], 0x1234)
+
+  def test_dual_mov(self):
+    self.inst = VOPD(VOPDOp.V_DUAL_MOV_B32, VOPDOp.V_DUAL_MOV_B32, vdstx=v[0], vdsty=v[1], srcx0=v[2], srcy0=v[4])
+
+  def test_dual_mul(self):
+    self.inst = v_dual_mul_f32(VOPDOp.V_DUAL_MUL_F32, vdstx=v[0], vdsty=v[1], srcx0=v[2], vsrcx1=v[3], srcy0=v[4], vsrcy1=v[5])
+
+  def test_simple_int_to_s(self):
+    self.inst = s_mov_b32(s[0], 3)
+
+  def test_complex_int_to_s(self):
+    self.inst = s_mov_b32(s[0], 0x235646)
+
+  def test_simple_float_to_s(self):
+    self.inst = s_mov_b32(s[0], 1.0)
+
+  def test_complex_float_to_s(self):
+    self.inst = s_mov_b32(s[0], 1337.0)
+    int_inst = s_mov_b32(s[0], struct.unpack("I", struct.pack("f", 1337.0))[0])
+    self.assertEqual(self.inst, int_inst)
+
+class TestRegisterSliceSyntax(unittest.TestCase):
+  """
+  Issue: Register slice syntax should use AMD assembly convention (inclusive end).
+
+  In AMD assembly, s[4:7] means registers s4, s5, s6, s7 (4 registers, inclusive).
+  The DSL should match this convention so that:
+  - s[4:7] gives 4 registers
+  - Disassembler output can be copied directly back into DSL code
+
+  Fix: Change _RegFactory.__getitem__ to use inclusive end:
+    key.stop - key.start + 1  (instead of key.stop - key.start)
+  """
+  def test_register_slice_count(self):
+    # s[4:7] should give 4 registers: s4, s5, s6, s7 (AMD convention, inclusive)
+    reg = s[4:7]
+    self.assertEqual(reg.count, 4, "s[4:7] should give 4 registers (s4, s5, s6, s7)")
+
+  def test_register_slice_roundtrip(self):
+    # Round-trip: DSL -> disasm -> DSL should preserve register count
+    reg = s[4:7]  # 4 registers in AMD convention
+    inst = s_load_b128(reg, s[0:1], NULL, 0)
+    disasm = inst.disasm()
+    # Disasm shows s[4:7] - user should be able to copy this back
+    self.assertIn("s[4:7]", disasm)
+    # And s[4:7] in DSL should give the same 4 registers
+    reg_from_disasm = s[4:7]
+    self.assertEqual(reg_from_disasm.count, 4, "s[4:7] from disasm should give 4 registers")
+
+class TestInstructionEquality(unittest.TestCase):
+  """
+  Issue: No __eq__ method - instruction comparison requires repr() workaround.
+
+  Two identical instructions should compare equal with ==, but currently:
+    inst1 == inst2 returns False
+
+  The test_handwritten.py works around this with:
+    self.assertEqual(repr(self.inst), repr(reasm))
+  """
+  def test_identical_instructions_equal(self):
+    inst1 = v_mov_b32_e32(v[0], v[1])
+    inst2 = v_mov_b32_e32(v[0], v[1])
+    self.assertEqual(inst1, inst2, "identical instructions should be equal")
+
+  def test_different_instructions_not_equal(self):
+    inst1 = v_mov_b32_e32(v[0], v[1])
+    inst2 = v_mov_b32_e32(v[0], v[2])
+    self.assertNotEqual(inst1, inst2, "different instructions should not be equal")
+
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+"""Integration test: round-trip RDNA3 assembly through AMD toolchain."""
+import unittest, re, io, sys, subprocess
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.asm import waitcnt, asm
+from extra.assembly.amd.test.helpers import get_llvm_mc
+
+def disassemble(lib: bytes, arch: str = "gfx1100") -> str:
+  """Disassemble ELF binary using tinygrad's compiler, return raw output."""
+  from tinygrad.runtime.support.compiler_amd import HIPCompiler
+  old_stdout = sys.stdout
+  sys.stdout = io.StringIO()
+  HIPCompiler(arch).disassemble(lib)
+  output = sys.stdout.getvalue()
+  sys.stdout = old_stdout
+  return output
+
+def parse_disassembly(raw: str) -> list[str]:
+  """Parse disassembly output to list of instruction mnemonics."""
+  lines = []
+  for line in raw.splitlines():
+    if line.startswith('\t'):
+      instr = line.split('//')[0].strip()
+      if instr: lines.append(instr)
+  return lines
+
+def assemble_and_disassemble(instructions: list, arch: str = "gfx1100") -> list[str]:
+  """Assemble instructions with our DSL, then disassemble with AMD toolchain."""
+  from tinygrad.runtime.support.compiler_amd import HIPCompiler
+
+  # Generate bytes from our DSL
+  code_bytes = b''.join(inst.to_bytes() for inst in instructions)
+
+  # Wrap in minimal ELF-compatible assembly with .byte directives
+  byte_str = ', '.join(f'0x{b:02x}' for b in code_bytes)
+  asm_src = f".text\n.globl test\n.p2align 8\n.type test,@function\ntest:\n.byte {byte_str}\n"
+
+  # Assemble with AMD COMGR and disassemble
+  lib = HIPCompiler(arch).compile(asm_src)
+  return parse_disassembly(disassemble(lib, arch))
+
+class TestIntegration(unittest.TestCase):
+  """Test our assembler output matches LLVM disassembly."""
+
+  def test_simple_sop1(self):
+    """Test SOP1 instructions round-trip."""
+    instructions = [
+      s_mov_b32(s[0], s[1]),
+      s_mov_b32(s[2], 0),
+      s_not_b32(s[3], s[4]),
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    self.assertIn('s_mov_b32', disasm[0])
+    self.assertIn('s_mov_b32', disasm[1])
+    self.assertIn('s_not_b32', disasm[2])
+
+  def test_simple_sop2(self):
+    """Test SOP2 instructions round-trip."""
+    instructions = [
+      s_add_u32(s[0], s[1], s[2]),
+      s_sub_u32(s[3], s[4], 10),
+      s_and_b32(s[5], s[6], s[7]),
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    self.assertIn('s_add_u32', disasm[0])
+    self.assertIn('s_sub_u32', disasm[1])
+    self.assertIn('s_and_b32', disasm[2])
+
+  def test_simple_vop2(self):
+    """Test VOP2 instructions round-trip."""
+    instructions = [
+      v_add_f32_e32(v[0], v[1], v[2]),
+      v_mul_f32_e32(v[3], 1.0, v[4]),  # 1.0 is inline constant
+      v_and_b32_e32(v[5], 10, v[6]),  # small inline constant
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    self.assertIn('v_add_f32', disasm[0])
+    self.assertIn('v_mul_f32', disasm[1])
+
+  def test_control_flow(self):
+    """Test control flow instructions."""
+    instructions = [
+      s_waitcnt(simm16=waitcnt(lgkmcnt=0)),
+      s_endpgm(),
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    self.assertIn('s_waitcnt', disasm[0])
+    self.assertIn('s_endpgm', disasm[1])
+
+  def test_memory_ops(self):
+    """Test memory instructions."""
+    instructions = [
+      s_load_b32(s[0], s[0:2], NULL),
+      s_waitcnt(simm16=waitcnt(lgkmcnt=0)),
+      global_store_b32(addr=v[0:2], data=v[2], saddr=OFF),
+      s_endpgm(),
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    self.assertIn('s_load_b32', disasm[0])
+    self.assertIn('s_waitcnt', disasm[1])
+    self.assertIn('global_store_b32', disasm[2])
+
+  def test_full_kernel(self):
+    """Test a complete kernel similar to tinygrad output."""
+    # Simple kernel: load value, add 1, store back
+    instructions = [
+      # Get thread ID
+      v_mov_b32_e32(v[0], s[0]),  # base addr low
+      v_mov_b32_e32(v[1], s[1]),  # base addr high
+      # Load value
+      global_load_b32(vdst=v[2], addr=v[0:2], saddr=OFF),
+      s_waitcnt(simm16=waitcnt(vmcnt=0)),
+      # Add 1.0
+      v_add_f32_e32(v[2], 1.0, v[2]),
+      # Store result
+      global_store_b32(addr=v[0:2], data=v[2], saddr=OFF),
+      s_endpgm(),
+    ]
+    disasm = assemble_and_disassemble(instructions)
+    # Verify key instructions are present
+    self.assertTrue(any('global_load' in d for d in disasm))
+    self.assertTrue(any('v_add_f32' in d for d in disasm))
+    self.assertTrue(any('global_store' in d for d in disasm))
+    self.assertTrue(any('s_endpgm' in d for d in disasm))
+
+  def test_bytes_roundtrip(self):
+    """Test that our bytes match what AMD assembler produces."""
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+
+    # Simple instruction
+    inst = s_mov_b32(s[0], s[1])
+    our_bytes = inst.to_bytes()
+
+    # Assemble same instruction with AMD toolchain
+    asm_src = ".text\n.globl test\n.p2align 8\n.type test,@function\ntest:\ns_mov_b32 s0, s1\n"
+    compiler = HIPCompiler("gfx1100")
+    lib = compiler.compile(asm_src)
+    raw = disassemble(lib)
+
+    for line in raw.splitlines():
+      if 's_mov_b32' in line and '//' in line:
+        # Extract hex bytes from comment: "// 000000001300: BE800001"
+        comment = line.split('//')[1].strip()
+        hex_str = comment.split(':')[1].strip()
+        # Convert big-endian hex string to little-endian bytes
+        amd_bytes = bytes.fromhex(hex_str)[::-1]  # reverse for little-endian
+        self.assertEqual(our_bytes, amd_bytes, f"Bytes mismatch: ours={our_bytes.hex()} AMD={amd_bytes.hex()}")
+        return
+    self.fail("Could not find s_mov_b32 in disassembly")
+
+class TestAsm(unittest.TestCase):
+  """Test asm() string parsing."""
+
+  def test_asm_basic(self):
+    """Test basic instruction parsing."""
+    inst = asm('s_mov_b32 s0, s1')
+    self.assertEqual(inst.to_bytes(), s_mov_b32(s[0], s[1]).to_bytes())
+
+  def test_asm_with_immediates(self):
+    """Test parsing with immediate values."""
+    inst = asm('s_add_u32 s0, s1, 10')
+    self.assertEqual(inst.to_bytes(), s_add_u32(s[0], s[1], 10).to_bytes())
+
+  def test_asm_float_const(self):
+    """Test parsing float constants."""
+    inst = asm('v_mul_f32_e32 v0, 1.0, v1')
+    self.assertEqual(inst.to_bytes(), v_mul_f32_e32(v[0], 1.0, v[1]).to_bytes())
+
+  def test_asm_hex_immediate(self):
+    """Test parsing hex immediates."""
+    inst = asm('s_waitcnt 0xfc07')
+    self.assertEqual(inst.to_bytes(), s_waitcnt(simm16=0xfc07).to_bytes())
+
+  def test_asm_special_regs(self):
+    """Test parsing special registers."""
+    inst = asm('s_mov_b32 s0, vcc_lo')
+    self.assertEqual(inst.to_bytes(), s_mov_b32(s[0], VCC_LO).to_bytes())
+
+  def test_asm_register_range(self):
+    """Test parsing register ranges."""
+    inst = asm('s_load_b128 s[4:7], s[0:1], null')
+    self.assertEqual(inst.to_bytes(), s_load_b128(s[4:7], s[0:1], NULL).to_bytes())
+
+  def test_asm_matches_llvm(self):
+    """Test asm() output matches LLVM assembler."""
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+    compiler = HIPCompiler('gfx1100')
+
+    def get_llvm_bytes(instr: str) -> bytes:
+      src = f'.text\n.globl test\n.p2align 8\n.type test,@function\ntest:\n{instr}\n'
+      lib = compiler.compile(src)
+      raw = disassemble(lib)
+      for line in raw.splitlines():
+        if instr.split()[0] in line and '//' in line:
+          hex_str = line.split('//')[1].strip().split(':')[1].strip()
+          return bytes.fromhex(hex_str)[::-1]
+      return b''
+
+    tests = ['s_mov_b32 s0, s1', 's_endpgm', 'v_add_f32_e32 v0, v1, v2']
+    for t in tests:
+      self.assertEqual(asm(t).to_bytes(), get_llvm_bytes(t), f"mismatch for: {t}")
+
+  def test_asm_vop3_modifiers(self):
+    """Test asm() with VOP3 modifiers (neg, abs, clamp)."""
+    def get_llvm_encoding(instr: str) -> str:
+      result = subprocess.run([get_llvm_mc(), '-triple=amdgcn', '-mcpu=gfx1100', '-show-encoding'],
+                              input=instr, capture_output=True, text=True)
+      if m := re.search(r'encoding:\s*\[(.*?)\]', result.stdout):
+        return m.group(1).replace('0x','').replace(',','').replace(' ','')
+      return ''
+
+    tests = [
+      'v_fma_f32 v0, -v1, v2, v3',       # neg on src0
+      'v_fma_f32 v0, v1, |v2|, v3',      # abs on src1
+      'v_fma_f32 v0, v1, v2, v3 clamp',  # clamp
+      'v_fma_f32 v0, -v1, |v2|, v3 clamp',  # all modifiers
+      'v_fma_f32 v0, -|v1|, v2, v3',     # neg+abs on same operand
+    ]
+    for t in tests:
+      our_hex = asm(t).to_bytes().hex()
+      llvm_hex = get_llvm_encoding(t)
+      self.assertEqual(our_hex, llvm_hex, f"mismatch for: {t}")
+
+class TestTinygradIntegration(unittest.TestCase):
+  """Test that we can parse disassembled tinygrad kernels."""
+
+  def test_simple_add_kernel(self):
+    """Generate a simple add kernel from tinygrad and verify disassembly."""
+    from tinygrad import Tensor
+    from tinygrad.codegen import get_program
+    from tinygrad.renderer.cstyle import AMDHIPRenderer
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+    from tinygrad.uop.ops import Ops
+
+    # Create a computation that generates a real kernel
+    a = Tensor([1.0, 2.0, 3.0, 4.0]).realize()
+    b = Tensor([5.0, 6.0, 7.0, 8.0]).realize()
+    c = a + b
+
+    # Get schedule and find SINK
+    schedule = c.schedule()
+    sink_items = [si for si in schedule if si.ast.op == Ops.SINK]
+    self.assertTrue(len(sink_items) > 0, "No SINK in schedule")
+
+    # Generate program
+    renderer = AMDHIPRenderer('gfx1100')
+    prg = get_program(sink_items[0].ast, renderer)
+    self.assertIsNotNone(prg.src)
+
+    # Compile and disassemble
+    compiler = HIPCompiler('gfx1100')
+    lib = compiler.compile(prg.src)
+    raw_disasm = disassemble(lib)
+    instrs = parse_disassembly(raw_disasm)
+
+    # Verify we got some instructions
+    self.assertTrue(len(instrs) > 0, "No instructions in disassembly")
+    # Should have an endpgm
+    self.assertTrue(any('s_endpgm' in i for i in instrs), "Missing s_endpgm")
+
+  def test_matmul_kernel(self):
+    """Generate a matmul kernel and verify disassembly has expected patterns."""
+    from tinygrad import Tensor
+    from tinygrad.codegen import get_program
+    from tinygrad.renderer.cstyle import AMDHIPRenderer
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+    from tinygrad.uop.ops import Ops
+
+    # Create a small matmul
+    a = Tensor.rand(4, 4).realize()
+    b = Tensor.rand(4, 4).realize()
+    c = a @ b
+
+    # Get schedule
+    schedule = c.schedule()
+    sink_items = [si for si in schedule if si.ast.op == Ops.SINK]
+    self.assertTrue(len(sink_items) > 0)
+
+    # Generate and compile
+    renderer = AMDHIPRenderer('gfx1100')
+    prg = get_program(sink_items[0].ast, renderer)
+    compiler = HIPCompiler('gfx1100')
+    lib = compiler.compile(prg.src)
+    raw_disasm = disassemble(lib)
+    instrs = parse_disassembly(raw_disasm)
+
+    # Matmul should have multiply and add instructions
+    has_mul = any('mul' in i.lower() for i in instrs)
+    has_add = any('add' in i.lower() for i in instrs)
+    self.assertTrue(has_mul or has_add, "Matmul should have mul/add ops")
+
+  def test_disasm_to_bytes_roundtrip(self):
+    """Parse disassembled instructions and verify we can re-encode some of them."""
+    from tinygrad import Tensor
+    from tinygrad.codegen import get_program
+    from tinygrad.renderer.cstyle import AMDHIPRenderer
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+    from tinygrad.uop.ops import Ops
+
+    # Simple kernel
+    a = Tensor([1.0, 2.0, 3.0, 4.0]).realize()
+    b = (a * 2.0)
+
+    schedule = b.schedule()
+    sink_items = [si for si in schedule if si.ast.op == Ops.SINK]
+    if not sink_items: return  # skip if no kernel
+
+    renderer = AMDHIPRenderer('gfx1100')
+    prg = get_program(sink_items[0].ast, renderer)
+    compiler = HIPCompiler('gfx1100')
+    lib = compiler.compile(prg.src)
+    raw_disasm = disassemble(lib)
+
+    # Find s_endpgm and verify we can encode it
+    for line in raw_disasm.splitlines():
+      if 's_endpgm' in line and '//' in line:
+        # Extract bytes from comment
+        comment = line.split('//')[1].strip()
+        hex_str = comment.split(':')[1].strip()
+        amd_bytes = bytes.fromhex(hex_str)[::-1]
+
+        # Our encoding
+        our_inst = s_endpgm()
+        our_bytes = our_inst.to_bytes()
+
+        self.assertEqual(our_bytes, amd_bytes, f"s_endpgm mismatch: ours={our_bytes.hex()} AMD={amd_bytes.hex()}")
+        return
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,195 @@
+#!/usr/bin/env python3
+"""Test RDNA3 assembler/disassembler against LLVM test vectors."""
+import unittest, re, subprocess
+from tinygrad.helpers import fetch
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.asm import asm
+from extra.assembly.amd.test.helpers import get_llvm_mc
+
+LLVM_BASE = "https://raw.githubusercontent.com/llvm/llvm-project/main/llvm/test/MC/AMDGPU"
+
+# Format info: (filename, format_class, op_enum)
+LLVM_TEST_FILES = {
+  # Scalar ALU
+  'sop1': ('gfx11_asm_sop1.s', SOP1, SOP1Op),
+  'sop2': ('gfx11_asm_sop2.s', SOP2, SOP2Op),
+  'sopp': ('gfx11_asm_sopp.s', SOPP, SOPPOp),
+  'sopk': ('gfx11_asm_sopk.s', SOPK, SOPKOp),
+  'sopc': ('gfx11_asm_sopc.s', SOPC, SOPCOp),
+  # Vector ALU
+  'vop1': ('gfx11_asm_vop1.s', VOP1, VOP1Op),
+  'vop2': ('gfx11_asm_vop2.s', VOP2, VOP2Op),
+  'vopc': ('gfx11_asm_vopc.s', VOPC, VOPCOp),
+  'vop3': ('gfx11_asm_vop3.s', VOP3, VOP3Op),
+  'vop3p': ('gfx11_asm_vop3p.s', VOP3P, VOP3POp),
+  'vop3sd': ('gfx11_asm_vop3.s', VOP3SD, VOP3SDOp),  # VOP3SD shares file with VOP3
+  'vinterp': ('gfx11_asm_vinterp.s', VINTERP, VINTERPOp),
+  'vopd': ('gfx11_asm_vopd.s', VOPD, VOPDOp),
+  'vopcx': ('gfx11_asm_vopcx.s', VOPC, VOPCOp),  # VOPCX uses VOPC format
+  # VOP3 promotions (VOP1/VOP2/VOPC promoted to VOP3 encoding)
+  'vop3_from_vop1': ('gfx11_asm_vop3_from_vop1.s', VOP3, VOP3Op),
+  'vop3_from_vop2': ('gfx11_asm_vop3_from_vop2.s', VOP3, VOP3Op),
+  'vop3_from_vopc': ('gfx11_asm_vop3_from_vopc.s', VOP3, VOP3Op),
+  'vop3_from_vopcx': ('gfx11_asm_vop3_from_vopcx.s', VOP3, VOP3Op),
+  # Memory
+  'ds': ('gfx11_asm_ds.s', DS, DSOp),
+  'smem': ('gfx11_asm_smem.s', SMEM, SMEMOp),
+  'flat': ('gfx11_asm_flat.s', FLAT, FLATOp),
+  'mubuf': ('gfx11_asm_mubuf.s', MUBUF, MUBUFOp),
+  'mtbuf': ('gfx11_asm_mtbuf.s', MTBUF, MTBUFOp),
+  'mimg': ('gfx11_asm_mimg.s', MIMG, MIMGOp),
+  # WMMA (matrix multiply)
+  'wmma': ('gfx11_asm_wmma.s', VOP3P, VOP3POp),
+  # Additional features
+  'vop3_features': ('gfx11_asm_vop3_features.s', VOP3, VOP3Op),
+  'vop3p_features': ('gfx11_asm_vop3p_features.s', VOP3P, VOP3POp),
+  'vopd_features': ('gfx11_asm_vopd_features.s', VOPD, VOPDOp),
+  # Alias files (alternative mnemonics)
+  'vop3_alias': ('gfx11_asm_vop3_alias.s', VOP3, VOP3Op),
+  'vop3p_alias': ('gfx11_asm_vop3p_alias.s', VOP3P, VOP3POp),
+  'vopc_alias': ('gfx11_asm_vopc_alias.s', VOPC, VOPCOp),
+  'vopcx_alias': ('gfx11_asm_vopcx_alias.s', VOPC, VOPCOp),
+  'vinterp_alias': ('gfx11_asm_vinterp_alias.s', VINTERP, VINTERPOp),
+  'smem_alias': ('gfx11_asm_smem_alias.s', SMEM, SMEMOp),
+  'mubuf_alias': ('gfx11_asm_mubuf_alias.s', MUBUF, MUBUFOp),
+  'mtbuf_alias': ('gfx11_asm_mtbuf_alias.s', MTBUF, MTBUFOp),
+}
+
+def parse_llvm_tests(text: str) -> list[tuple[str, bytes]]:
+  """Parse LLVM test format into (asm, expected_bytes) pairs."""
+  tests, lines = [], text.split('\n')
+  for i, line in enumerate(lines):
+    line = line.strip()
+    if not line or line.startswith(('//', '.', ';')): continue
+    asm_text = line.split('//')[0].strip()
+    if not asm_text: continue
+    for j in range(i, min(i + 3, len(lines))):
+      # Match GFX11, W32, or W64 encodings (all valid for gfx11)
+      if m := re.search(r'(?:GFX11|W32|W64)[^:]*:.*?encoding:\s*\[(.*?)\]', lines[j]):
+        hex_bytes = m.group(1).replace('0x', '').replace(',', '').replace(' ', '')
+        if hex_bytes:
+          try: tests.append((asm_text, bytes.fromhex(hex_bytes)))
+          except ValueError: pass
+        break
+  return tests
+
+def try_assemble(text: str):
+  """Try to assemble instruction text, return bytes or None on failure."""
+  try: return asm(text).to_bytes()
+  except: return None
+
+def compile_asm_batch(instrs: list[str]) -> list[bytes]:
+  """Compile multiple instructions with a single llvm-mc call."""
+  if not instrs: return []
+  asm_text = ".text\n" + "\n".join(instrs) + "\n"
+  result = subprocess.run(
+    [get_llvm_mc(), '-triple=amdgcn', '-mcpu=gfx1100', '-mattr=+real-true16,+wavefrontsize32', '-show-encoding'],
+    input=asm_text, capture_output=True, text=True, timeout=30)
+  if result.returncode != 0: raise RuntimeError(f"llvm-mc batch failed: {result.stderr.strip()}")
+  # Parse all encodings from output
+  results = []
+  for line in result.stdout.split('\n'):
+    if 'encoding:' not in line: continue
+    enc = line.split('encoding:')[1].strip()
+    if enc.startswith('[') and enc.endswith(']'):
+      results.append(bytes.fromhex(enc[1:-1].replace('0x', '').replace(',', '').replace(' ', '')))
+  if len(results) != len(instrs): raise RuntimeError(f"expected {len(instrs)} encodings, got {len(results)}")
+  return results
+
+class TestLLVM(unittest.TestCase):
+  """Test assembler and disassembler against all LLVM test vectors."""
+  tests: dict[str, list[tuple[str, bytes]]] = {}
+
+  @classmethod
+  def setUpClass(cls):
+    for name, (filename, _, _) in LLVM_TEST_FILES.items():
+      try:
+        data = fetch(f"{LLVM_BASE}/{filename}").read_bytes()
+        cls.tests[name] = parse_llvm_tests(data.decode('utf-8', errors='ignore'))
+      except Exception as e:
+        print(f"Warning: couldn't fetch {filename}: {e}")
+        cls.tests[name] = []
+
+# Generate test methods dynamically for each format
+def _make_asm_test(name):
+  def test(self):
+    passed, failed, skipped = 0, 0, 0
+    for asm_text, expected in self.tests.get(name, []):
+      result = try_assemble(asm_text)
+      if result is None: skipped += 1
+      elif result == expected: passed += 1
+      else: failed += 1
+    print(f"{name.upper()} asm: {passed} passed, {failed} failed, {skipped} skipped")
+    self.assertEqual(failed, 0)
+  return test
+
+def _make_disasm_test(name):
+  def test(self):
+    _, fmt_cls, op_enum = LLVM_TEST_FILES[name]
+    # VOP3SD opcodes that share encoding with VOP3 (only for vop3sd test, not vopc promotions)
+    vop3sd_opcodes = {288, 289, 290, 764, 765, 766, 767, 768, 769, 770}
+    is_vopc_promotion = name in ('vop3_from_vopc', 'vop3_from_vopcx')
+    undocumented = {'smem': {34, 35}, 'sopk': {22, 23}, 'sopp': {8, 58, 59}}
+
+    # First pass: decode all instructions and collect disasm strings
+    to_test: list[tuple[str, bytes, str | None, str | None]] = []  # (asm_text, data, disasm_str, error)
+    skipped = 0
+    for asm_text, data in self.tests.get(name, []):
+      if len(data) > fmt_cls._size(): continue
+      temp_inst = fmt_cls.from_bytes(data)
+      temp_op = temp_inst._values.get('op', 0)
+      temp_op = temp_op.val if hasattr(temp_op, 'val') else temp_op
+      if temp_op in undocumented.get(name, set()): skipped += 1; continue
+      if name == 'sopp':
+        simm16 = temp_inst._values.get('simm16', 0)
+        simm16 = simm16.val if hasattr(simm16, 'val') else simm16
+        sopp_no_imm = {48, 54, 53, 55, 60, 61, 62}
+        if temp_op in sopp_no_imm and simm16 != 0: skipped += 1; continue
+      try:
+        if fmt_cls.__name__ in ('VOP3', 'VOP3SD'):
+          temp = VOP3.from_bytes(data)
+          op_val = temp._values.get('op', 0)
+          op_val = op_val.val if hasattr(op_val, 'val') else op_val
+          is_vop3sd = (op_val in vop3sd_opcodes) and not is_vopc_promotion
+          decoded = VOP3SD.from_bytes(data) if is_vop3sd else VOP3.from_bytes(data)
+          if is_vop3sd: VOP3SDOp(op_val)
+          else: VOP3Op(op_val)
+        else:
+          decoded = fmt_cls.from_bytes(data)
+          op_val = decoded._values.get('op', 0)
+          op_val = op_val.val if hasattr(op_val, 'val') else op_val
+          op_enum(op_val)
+        if decoded.to_bytes()[:len(data)] != data:
+          to_test.append((asm_text, data, None, "decode roundtrip failed"))
+          continue
+        to_test.append((asm_text, data, decoded.disasm(), None))
+      except Exception as e:
+        to_test.append((asm_text, data, None, f"exception: {e}"))
+
+    # Batch compile all disasm strings with single llvm-mc call
+    disasm_strs = [(i, t[2]) for i, t in enumerate(to_test) if t[2] is not None]
+    llvm_results = compile_asm_batch([s for _, s in disasm_strs]) if disasm_strs else []
+    llvm_map = {i: llvm_results[j] for j, (i, _) in enumerate(disasm_strs)}
+
+    # Match results back
+    passed, failed = 0, 0
+    failures: list[str] = []
+    for idx, (asm_text, data, disasm_str, error) in enumerate(to_test):
+      if error:
+        failed += 1; failures.append(f"{error} for {data.hex()}")
+      elif disasm_str is not None and idx in llvm_map:
+        llvm_bytes = llvm_map[idx]
+        if llvm_bytes is not None and llvm_bytes == data: passed += 1
+        elif llvm_bytes is not None: failed += 1; failures.append(f"'{disasm_str}': expected={data.hex()} got={llvm_bytes.hex()}")
+
+    print(f"{name.upper()} disasm: {passed} passed, {failed} failed" + (f", {skipped} skipped" if skipped else ""))
+    if failures[:10]: print("  " + "\n  ".join(failures[:10]))
+    self.assertEqual(failed, 0)
+  return test
+
+for name in LLVM_TEST_FILES:
+  setattr(TestLLVM, f'test_{name}_asm', _make_asm_test(name))
+  setattr(TestLLVM, f'test_{name}_disasm', _make_disasm_test(name))
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""Test that invalid instructions raise exceptions through the mock GPU stack."""
+import unittest, subprocess, os, time
+
+class TestMockGPUInvalidInstruction(unittest.TestCase):
+  def test_unsupported_instruction_raises(self):
+    """Test that unsupported instructions raise immediately through the full MOCKGPU stack."""
+    test_code = '''
+import struct
+from tinygrad import Device, Tensor
+from tinygrad.engine.realize import get_runner
+from tinygrad.runtime.ops_amd import AMDProgram
+
+dev = Device["AMD"]
+a = Tensor([1.0]).realize()
+b = a + 1
+si = b.schedule()[-1]
+runner = get_runner(dev.device, si.ast)
+
+prg = runner._prg
+lib = bytearray(prg.lib)
+
+# Find s_endpgm (0xBFB00000) and replace with invalid SOPP op=127 (0xBFFF0000)
+found = False
+for i in range(0, len(lib) - 4, 4):
+  if struct.unpack("<I", lib[i:i+4])[0] == 0xBFB00000:
+    lib[i:i+4] = struct.pack("<I", 0xBFFF0000)
+    found = True
+    break
+assert found, "s_endpgm not found"
+
+patched_prg = AMDProgram(dev, "patched", bytes(lib))
+b.uop.buffer.allocate()
+patched_prg(b.uop.buffer._buf, a.uop.buffer._buf, global_size=(1,1,1), local_size=(1,1,1))
+dev.synchronize()
+'''
+
+    env = os.environ.copy()
+    env["AMD"] = "1"
+    env["MOCKGPU"] = "1"
+    env["PYTHON_REMU"] = "1"
+    env["HCQDEV_WAIT_TIMEOUT_MS"] = "10000"
+
+    st = time.perf_counter()
+    result = subprocess.run(["python", "-c", test_code], env=env, capture_output=True, text=True, timeout=60)
+    elapsed = time.perf_counter() - st
+
+    self.assertNotEqual(result.returncode, 0, "should have raised")
+    self.assertTrue("NotImplementedError" in result.stderr or "ValueError" in result.stderr,
+                    f"expected NotImplementedError or ValueError in stderr")
+    # Should exit immediately, not wait for the full timeout
+    self.assertLess(elapsed, 9.0, f"should exit immediately on emulator exception, took {elapsed:.1f}s")
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,399 @@
+#!/usr/bin/env python3
+"""Tests for the RDNA3 pseudocode DSL."""
+import unittest
+from extra.assembly.amd.pcode import (Reg, TypedView, SliceProxy, ExecContext, compile_pseudocode, _expr, MASK32, MASK64,
+                                       _f32, _i32, _f16, _i16, f32_to_f16, _isnan, _bf16, _ibf16, bf16_to_f32, f32_to_bf16,
+                                       BYTE_PERMUTE, v_sad_u8, v_msad_u8)
+from extra.assembly.amd.autogen.rdna3.gen_pcode import _VOP3SDOp_V_DIV_SCALE_F32, _VOPCOp_V_CMP_CLASS_F32
+
+class TestReg(unittest.TestCase):
+  def test_u32_read(self):
+    r = Reg(0xDEADBEEF)
+    self.assertEqual(int(r.u32), 0xDEADBEEF)
+
+  def test_u32_write(self):
+    r = Reg(0)
+    r.u32 = 0x12345678
+    self.assertEqual(r._val, 0x12345678)
+
+  def test_f32_read(self):
+    r = Reg(0x40400000)  # 3.0f
+    self.assertAlmostEqual(float(r.f32), 3.0)
+
+  def test_f32_write(self):
+    r = Reg(0)
+    r.f32 = 3.0
+    self.assertEqual(r._val, 0x40400000)
+
+  def test_i32_signed(self):
+    r = Reg(0xFFFFFFFF)  # -1 as signed
+    self.assertEqual(int(r.i32), -1)
+
+  def test_u64(self):
+    r = Reg(0xDEADBEEFCAFEBABE)
+    self.assertEqual(int(r.u64), 0xDEADBEEFCAFEBABE)
+
+  def test_f64(self):
+    r = Reg(0x4008000000000000)  # 3.0 as f64
+    self.assertAlmostEqual(float(r.f64), 3.0)
+
+class TestTypedView(unittest.TestCase):
+  def test_bit_slice(self):
+    r = Reg(0xDEADBEEF)
+    # Slices return SliceProxy which supports .u32, .u16 etc (matching pseudocode like S1.u32[1:0].u32)
+    self.assertEqual(r.u32[7:0].u32, 0xEF)
+    self.assertEqual(r.u32[15:8].u32, 0xBE)
+    self.assertEqual(r.u32[23:16].u32, 0xAD)
+    self.assertEqual(r.u32[31:24].u32, 0xDE)
+    # Also works with int() for arithmetic
+    self.assertEqual(int(r.u32[7:0]), 0xEF)
+
+  def test_single_bit_read(self):
+    r = Reg(0b11010101)
+    self.assertEqual(r.u32[0], 1)
+    self.assertEqual(r.u32[1], 0)
+    self.assertEqual(r.u32[2], 1)
+    self.assertEqual(r.u32[3], 0)
+
+  def test_single_bit_write(self):
+    r = Reg(0)
+    r.u32[5] = 1
+    r.u32[3] = 1
+    self.assertEqual(r._val, 0b00101000)
+
+  def test_nested_bit_access(self):
+    # S0.u32[S1.u32[4:0]] - access bit at position from another register
+    s0 = Reg(0b11010101)
+    s1 = Reg(3)
+    bit_pos = s1.u32[4:0]  # SliceProxy, int value = 3
+    bit_val = s0.u32[int(bit_pos)]  # bit 3 of s0 = 0
+    self.assertEqual(int(bit_pos), 3)
+    self.assertEqual(bit_val, 0)
+
+  def test_arithmetic(self):
+    r1 = Reg(0x40400000)  # 3.0f
+    r2 = Reg(0x40800000)  # 4.0f
+    result = r1.f32 + r2.f32
+    self.assertAlmostEqual(result, 7.0)
+
+  def test_comparison(self):
+    r1 = Reg(5)
+    r2 = Reg(3)
+    self.assertTrue(r1.u32 > r2.u32)
+    self.assertFalse(r1.u32 < r2.u32)
+    self.assertTrue(r1.u32 != r2.u32)
+
+class TestSliceProxy(unittest.TestCase):
+  def test_slice_read(self):
+    r = Reg(0x56781234)
+    self.assertEqual(r[15:0].u16, 0x1234)
+    self.assertEqual(r[31:16].u16, 0x5678)
+
+  def test_slice_write(self):
+    r = Reg(0)
+    r[15:0].u16 = 0x1234
+    r[31:16].u16 = 0x5678
+    self.assertEqual(r._val, 0x56781234)
+
+  def test_slice_f16(self):
+    r = Reg(0)
+    r[15:0].f16 = 3.0
+    self.assertAlmostEqual(_f16(r._val & 0xffff), 3.0, places=2)
+
+class TestCompiler(unittest.TestCase):
+  def test_ternary(self):
+    result = _expr("a > b ? 1 : 0")
+    self.assertIn("if", result)
+    self.assertIn("else", result)
+
+  def test_type_prefix_strip(self):
+    self.assertEqual(_expr("1'0U"), "0")
+    self.assertEqual(_expr("32'1"), "1")
+    self.assertEqual(_expr("16'0xFFFF"), "0xFFFF")
+
+  def test_suffix_strip(self):
+    self.assertEqual(_expr("0ULL"), "0")
+    self.assertEqual(_expr("1LL"), "1")
+    self.assertEqual(_expr("5U"), "5")
+    self.assertEqual(_expr("3.14F"), "3.14")
+
+  def test_boolean_ops(self):
+    self.assertIn("and", _expr("a && b"))
+    self.assertIn("or", _expr("a || b"))
+    self.assertIn("!=", _expr("a <> b"))
+
+  def test_pack16(self):
+    result = _expr("{ a, b }")
+    self.assertIn("_pack", result)
+
+  def test_type_cast_strip(self):
+    self.assertEqual(_expr("64'U(x)"), "(x)")
+    self.assertEqual(_expr("32'I(y)"), "(y)")
+
+class TestExecContext(unittest.TestCase):
+  def test_float_add(self):
+    ctx = ExecContext(s0=0x40400000, s1=0x40800000)  # 3.0f, 4.0f
+    ctx.D0.f32 = ctx.S0.f32 + ctx.S1.f32
+    self.assertAlmostEqual(_f32(ctx.D0._val), 7.0)
+
+  def test_float_mul(self):
+    ctx = ExecContext(s0=0x40400000, s1=0x40800000)  # 3.0f, 4.0f
+    ctx.run("D0.f32 = S0.f32 * S1.f32")
+    self.assertAlmostEqual(_f32(ctx.D0._val), 12.0)
+
+  def test_scc_comparison(self):
+    ctx = ExecContext(s0=42, s1=42)
+    ctx.run("SCC = S0.u32 == S1.u32")
+    self.assertEqual(ctx.SCC._val, 1)
+
+  def test_scc_comparison_false(self):
+    ctx = ExecContext(s0=42, s1=43)
+    ctx.run("SCC = S0.u32 == S1.u32")
+    self.assertEqual(ctx.SCC._val, 0)
+
+  def test_ternary(self):
+    code = compile_pseudocode("D0.u32 = S0.u32 > S1.u32 ? 1'1U : 1'0U")
+    ctx = ExecContext(s0=5, s1=3)
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val, 1)
+
+  def test_pack(self):
+    code = compile_pseudocode("D0 = { S1[15:0].u16, S0[15:0].u16 }")
+    ctx = ExecContext(s0=0x1234, s1=0x5678)
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val, 0x56781234)
+
+  def test_tmp_with_typed_access(self):
+    code = compile_pseudocode("""tmp = S0.u32 + S1.u32
+D0.u32 = tmp.u32""")
+    ctx = ExecContext(s0=100, s1=200)
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val, 300)
+
+  def test_s_add_u32_pattern(self):
+    # Real pseudocode pattern from S_ADD_U32
+    code = compile_pseudocode("""tmp = 64'U(S0.u32) + 64'U(S1.u32)
+SCC = tmp >= 0x100000000ULL ? 1'1U : 1'0U
+D0.u32 = tmp.u32""")
+    # Test overflow case
+    ctx = ExecContext(s0=0xFFFFFFFF, s1=0x00000001)
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val, 0)  # Wraps to 0
+    self.assertEqual(ctx.SCC._val, 1)  # Carry set
+
+  def test_s_add_u32_no_overflow(self):
+    code = compile_pseudocode("""tmp = 64'U(S0.u32) + 64'U(S1.u32)
+SCC = tmp >= 0x100000000ULL ? 1'1U : 1'0U
+D0.u32 = tmp.u32""")
+    ctx = ExecContext(s0=100, s1=200)
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val, 300)
+    self.assertEqual(ctx.SCC._val, 0)  # No carry
+
+  def test_vcc_lane_read(self):
+    ctx = ExecContext(vcc=0b1010, lane=1)
+    # Lane 1 is set
+    self.assertEqual(ctx.VCC.u64[1], 1)
+    self.assertEqual(ctx.VCC.u64[2], 0)
+
+  def test_vcc_lane_write(self):
+    ctx = ExecContext(vcc=0, lane=0)
+    ctx.VCC.u64[3] = 1
+    ctx.VCC.u64[1] = 1
+    self.assertEqual(ctx.VCC._val, 0b1010)
+
+  def test_for_loop(self):
+    # CTZ pattern - find first set bit
+    code = compile_pseudocode("""tmp = -1
+for i in 0 : 31 do
+  if S0.u32[i] == 1 then
+    tmp = i
+D0.i32 = tmp""")
+    ctx = ExecContext(s0=0b1000)  # Bit 3 is set
+    ctx.run(code)
+    self.assertEqual(ctx.D0._val & MASK32, 3)
+
+  def test_result_dict(self):
+    ctx = ExecContext(s0=5, s1=3)
+    ctx.D0.u32 = 42
+    ctx.SCC._val = 1
+    result = ctx.result()
+    self.assertEqual(result['d0'], 42)
+    self.assertEqual(result['scc'], 1)
+
+class TestPseudocodeRegressions(unittest.TestCase):
+  """Regression tests for pseudocode instruction emulation bugs."""
+
+  def test_v_div_scale_f32_vcc_always_returned(self):
+    """V_DIV_SCALE_F32 must set VCC bit for the lane when scaling is needed.
+    The new calling convention uses Reg objects and modifies VCC in place."""
+    # Normal case: 1.0 / 3.0, no scaling needed, VCC should be 0
+    S0 = Reg(0x3f800000)  # 1.0
+    S1 = Reg(0x40400000)  # 3.0
+    S2 = Reg(0x3f800000)  # 1.0 (numerator)
+    D0 = Reg(0)
+    VCC = Reg(0)
+    _VOP3SDOp_V_DIV_SCALE_F32(S0, S1, S2, D0, Reg(0), VCC, 0, Reg(0xffffffff), Reg(0), None, Reg(0), Reg(0))
+    # VCC bit 0 should be 0 when no scaling needed
+    self.assertEqual(VCC._val & 1, 0, "VCC bit should be 0 when no scaling needed")
+
+  def test_v_cmp_class_f32_detects_quiet_nan(self):
+    """V_CMP_CLASS_F32 must correctly identify quiet NaN vs signaling NaN.
+    Bug: isQuietNAN and isSignalNAN both used math.isnan which can't distinguish them."""
+    quiet_nan = 0x7fc00000   # quiet NaN: exponent=255, bit22=1
+    signal_nan = 0x7f800001  # signaling NaN: exponent=255, bit22=0
+    # Test quiet NaN detection (bit 1 in mask)
+    s1_quiet = 0b0000000010  # bit 1 = quiet NaN
+    D0 = Reg(0)
+    _VOPCOp_V_CMP_CLASS_F32(Reg(quiet_nan), Reg(s1_quiet), Reg(0), D0, Reg(0), Reg(0), 0, Reg(0xffffffff), Reg(0), None, Reg(0), Reg(0))
+    self.assertEqual(D0._val & 1, 1, "Should detect quiet NaN with quiet NaN mask")
+    # Test signaling NaN detection (bit 0 in mask)
+    s1_signal = 0b0000000001  # bit 0 = signaling NaN
+    D0 = Reg(0)
+    _VOPCOp_V_CMP_CLASS_F32(Reg(signal_nan), Reg(s1_signal), Reg(0), D0, Reg(0), Reg(0), 0, Reg(0xffffffff), Reg(0), None, Reg(0), Reg(0))
+    self.assertEqual(D0._val & 1, 1, "Should detect signaling NaN with signaling NaN mask")
+    # Test that quiet NaN doesn't match signaling NaN mask
+    D0 = Reg(0)
+    _VOPCOp_V_CMP_CLASS_F32(Reg(quiet_nan), Reg(s1_signal), Reg(0), D0, Reg(0), Reg(0), 0, Reg(0xffffffff), Reg(0), None, Reg(0), Reg(0))
+    self.assertEqual(D0._val & 1, 0, "Quiet NaN should not match signaling NaN mask")
+    # Test that signaling NaN doesn't match quiet NaN mask
+    D0 = Reg(0)
+    _VOPCOp_V_CMP_CLASS_F32(Reg(signal_nan), Reg(s1_quiet), Reg(0), D0, Reg(0), Reg(0), 0, Reg(0xffffffff), Reg(0), None, Reg(0), Reg(0))
+    self.assertEqual(D0._val & 1, 0, "Signaling NaN should not match quiet NaN mask")
+
+  def test_isnan_with_typed_view(self):
+    """_isnan must work with TypedView objects, not just Python floats.
+    Bug: _isnan checked isinstance(x, float) which returned False for TypedView."""
+    nan_reg = Reg(0x7fc00000)  # quiet NaN
+    normal_reg = Reg(0x3f800000)  # 1.0
+    inf_reg = Reg(0x7f800000)  # +inf
+    self.assertTrue(_isnan(nan_reg.f32), "_isnan should return True for NaN TypedView")
+    self.assertFalse(_isnan(normal_reg.f32), "_isnan should return False for normal TypedView")
+    self.assertFalse(_isnan(inf_reg.f32), "_isnan should return False for inf TypedView")
+
+class TestBF16(unittest.TestCase):
+  """Tests for BF16 (bfloat16) support."""
+
+  def test_bf16_conversion(self):
+    """Test bf16 <-> f32 conversion."""
+    # bf16 is just the top 16 bits of f32
+    # 1.0f = 0x3f800000, bf16 = 0x3f80
+    self.assertAlmostEqual(_bf16(0x3f80), 1.0, places=2)
+    self.assertEqual(_ibf16(1.0), 0x3f80)
+    # 2.0f = 0x40000000, bf16 = 0x4000
+    self.assertAlmostEqual(_bf16(0x4000), 2.0, places=2)
+    self.assertEqual(_ibf16(2.0), 0x4000)
+    # -1.0f = 0xbf800000, bf16 = 0xbf80
+    self.assertAlmostEqual(_bf16(0xbf80), -1.0, places=2)
+    self.assertEqual(_ibf16(-1.0), 0xbf80)
+
+  def test_bf16_special_values(self):
+    """Test bf16 special values (inf, nan)."""
+    import math
+    # +inf: f32 = 0x7f800000, bf16 = 0x7f80
+    self.assertTrue(math.isinf(_bf16(0x7f80)))
+    self.assertEqual(_ibf16(float('inf')), 0x7f80)
+    # -inf: f32 = 0xff800000, bf16 = 0xff80
+    self.assertTrue(math.isinf(_bf16(0xff80)))
+    self.assertEqual(_ibf16(float('-inf')), 0xff80)
+    # NaN: quiet NaN bf16 = 0x7fc0
+    self.assertTrue(math.isnan(_bf16(0x7fc0)))
+    self.assertEqual(_ibf16(float('nan')), 0x7fc0)
+
+  def test_bf16_register_property(self):
+    """Test Reg.bf16 property."""
+    r = Reg(0)
+    r.bf16 = 3.0  # 3.0f = 0x40400000, bf16 = 0x4040
+    self.assertEqual(r._val & 0xffff, 0x4040)
+    self.assertAlmostEqual(float(r.bf16), 3.0, places=1)
+
+  def test_bf16_slice_property(self):
+    """Test SliceProxy.bf16 property."""
+    r = Reg(0x40404040)  # Two bf16 3.0 values
+    self.assertAlmostEqual(r[15:0].bf16, 3.0, places=1)
+    self.assertAlmostEqual(r[31:16].bf16, 3.0, places=1)
+
+class TestBytePermute(unittest.TestCase):
+  """Tests for BYTE_PERMUTE helper function (V_PERM_B32)."""
+
+  def test_byte_select_0_to_7(self):
+    """Test selecting bytes 0-7 from 64-bit data."""
+    # data = {s0, s1} where s0 is bytes 0-3, s1 is bytes 4-7
+    # Combined: 0x0706050403020100 (byte 0 = 0x00, byte 7 = 0x07)
+    data = 0x0706050403020100
+    for i in range(8):
+      self.assertEqual(BYTE_PERMUTE(data, i), i, f"byte {i} should be {i}")
+
+  def test_sign_extend_bytes(self):
+    """Test sign extension selectors 8-11."""
+    # sel 8: sign of byte 1 (bits 15:8)
+    # sel 9: sign of byte 3 (bits 31:24)
+    # sel 10: sign of byte 5 (bits 47:40)
+    # sel 11: sign of byte 7 (bits 63:56)
+    data = 0x8000800080008000  # All relevant bytes have sign bit set
+    self.assertEqual(BYTE_PERMUTE(data, 8), 0xff)
+    self.assertEqual(BYTE_PERMUTE(data, 9), 0xff)
+    self.assertEqual(BYTE_PERMUTE(data, 10), 0xff)
+    self.assertEqual(BYTE_PERMUTE(data, 11), 0xff)
+    data = 0x7f007f007f007f00  # No sign bits set
+    self.assertEqual(BYTE_PERMUTE(data, 8), 0x00)
+    self.assertEqual(BYTE_PERMUTE(data, 9), 0x00)
+    self.assertEqual(BYTE_PERMUTE(data, 10), 0x00)
+    self.assertEqual(BYTE_PERMUTE(data, 11), 0x00)
+
+  def test_constant_zero(self):
+    """Test selector 12 returns 0x00."""
+    self.assertEqual(BYTE_PERMUTE(0xffffffffffffffff, 12), 0x00)
+
+  def test_constant_ff(self):
+    """Test selectors >= 13 return 0xFF."""
+    for sel in [13, 14, 15, 255]:
+      self.assertEqual(BYTE_PERMUTE(0, sel), 0xff, f"sel {sel} should be 0xff")
+
+class TestSADHelpers(unittest.TestCase):
+  """Tests for V_SAD_U8 and V_MSAD_U8 helper functions."""
+
+  def test_v_sad_u8_basic(self):
+    """Test v_sad_u8 with simple values."""
+    # s0 = 0x04030201, s1 = 0x04030201 -> diff = 0 for all bytes
+    result = v_sad_u8(0x04030201, 0x04030201, 0)
+    self.assertEqual(result, 0)
+    # s0 = 0x05040302, s1 = 0x04030201 -> diff = 1+1+1+1 = 4
+    result = v_sad_u8(0x05040302, 0x04030201, 0)
+    self.assertEqual(result, 4)
+
+  def test_v_sad_u8_with_accumulator(self):
+    """Test v_sad_u8 with non-zero accumulator."""
+    # s0 = 0x05040302, s1 = 0x04030201, s2 = 100 -> 4 + 100 = 104
+    result = v_sad_u8(0x05040302, 0x04030201, 100)
+    self.assertEqual(result, 104)
+
+  def test_v_sad_u8_large_diff(self):
+    """Test v_sad_u8 with maximum byte differences."""
+    # s0 = 0xffffffff, s1 = 0x00000000 -> diff = 255*4 = 1020
+    result = v_sad_u8(0xffffffff, 0x00000000, 0)
+    self.assertEqual(result, 1020)
+
+  def test_v_msad_u8_basic(self):
+    """Test v_msad_u8 masks when reference byte is 0."""
+    # s0 = 0x10101010, s1 = 0x00000000 -> all masked, result = 0
+    result = v_msad_u8(0x10101010, 0x00000000, 0)
+    self.assertEqual(result, 0)
+    # s0 = 0x10101010, s1 = 0x01010101 -> diff = |0x10-0x01|*4 = 15*4 = 60
+    result = v_msad_u8(0x10101010, 0x01010101, 0)
+    self.assertEqual(result, 60)
+
+  def test_v_msad_u8_partial_mask(self):
+    """Test v_msad_u8 with partial masking."""
+    # s0 = 0x10101010, s1 = 0x00010001 -> bytes 1 and 3 masked
+    # diff = |0x10-0x01| + |0x10-0x01| = 15 + 15 = 30
+    result = v_msad_u8(0x10101010, 0x00010001, 0)
+    self.assertEqual(result, 30)
+
+  def test_v_msad_u8_with_accumulator(self):
+    """Test v_msad_u8 with non-zero accumulator."""
+    result = v_msad_u8(0x10101010, 0x01010101, 50)
+    self.assertEqual(result, 110)  # 60 + 50
+
+if __name__ == '__main__':
+  unittest.main()
@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""Test that PDF parser correctly extracts format fields."""
+import unittest, os
+from extra.assembly.amd.autogen.rdna3 import (
+  SOP1, SOP2, SOPK, SOPP, VOP1, VOP2, VOP3SD, VOPC, FLAT, VOPD,
+  SOP1Op, SOP2Op, VOP1Op, VOP3Op
+)
+
+# expected formats with key fields and whether they have ENCODING
+EXPECTED_FORMATS = {
+  'DPP16': (['SRC0', 'DPP_CTRL', 'BANK_MASK', 'ROW_MASK'], False),
+  'DPP8': (['SRC0', 'LANE_SEL0', 'LANE_SEL7'], False),
+  'DS': (['OP', 'ADDR', 'DATA0', 'DATA1', 'VDST'], True),
+  'EXP': (['EN', 'TARGET', 'VSRC0', 'VSRC1', 'VSRC2', 'VSRC3'], True),
+  'FLAT': (['OP', 'ADDR', 'DATA', 'SADDR', 'VDST', 'OFFSET'], True),
+  'LDSDIR': (['VDST', 'OP'], True),
+  'MIMG': (['OP', 'VADDR', 'VDATA', 'SRSRC', 'DMASK'], True),
+  'MTBUF': (['OP', 'VADDR', 'VDATA', 'SRSRC', 'FORMAT', 'SOFFSET'], True),
+  'MUBUF': (['OP', 'VADDR', 'VDATA', 'SRSRC', 'SOFFSET'], True),
+  'SMEM': (['OP', 'SBASE', 'SDATA', 'OFFSET', 'SOFFSET'], True),
+  'SOP1': (['OP', 'SDST', 'SSRC0'], True),
+  'SOP2': (['OP', 'SDST', 'SSRC0', 'SSRC1'], True),
+  'SOPC': (['OP', 'SSRC0', 'SSRC1'], True),
+  'SOPK': (['OP', 'SDST', 'SIMM16'], True),
+  'SOPP': (['OP', 'SIMM16'], True),
+  'VINTERP': (['OP', 'VDST', 'SRC0', 'SRC1', 'SRC2'], True),
+  'VOP1': (['OP', 'VDST', 'SRC0'], True),
+  'VOP2': (['OP', 'VDST', 'SRC0', 'VSRC1'], True),
+  'VOP3': (['OP', 'VDST', 'SRC0', 'SRC1', 'SRC2'], True),
+  'VOP3P': (['OP', 'VDST', 'SRC0', 'SRC1', 'SRC2'], True),
+  'VOP3SD': (['OP', 'VDST', 'SDST', 'SRC0', 'SRC1', 'SRC2'], True),
+  'VOPC': (['OP', 'SRC0', 'VSRC1'], True),
+  'VOPD': (['OPX', 'OPY', 'SRCX0', 'SRCY0', 'VDSTX', 'VDSTY'], True),
+}
+
+# Skip PDF parsing tests by default - only run with TEST_PDF_PARSER=1
+# These are slow (~5s) and only needed when regenerating autogen/
+@unittest.skipUnless(os.environ.get("TEST_PDF_PARSER"), "set TEST_PDF_PARSER=1 to run PDF parser tests")
+class TestPDFParserGenerate(unittest.TestCase):
+  """Test the PDF parser by running generate() and checking results."""
+
+  def test_pdf_parser(self):
+    """Single test that validates all PDF parser outputs."""
+    from extra.assembly.amd.dsl import generate
+    result = generate()
+
+    # test_all_formats_present
+    for fmt_name in EXPECTED_FORMATS:
+      self.assertIn(fmt_name, result["formats"], f"missing format {fmt_name}")
+
+    # test_format_count
+    self.assertEqual(len(result["formats"]), 23)
+
+    # test_no_duplicate_fields
+    for fmt_name, fields in result["formats"].items():
+      field_names = [f[0] for f in fields]
+      self.assertEqual(len(field_names), len(set(field_names)), f"{fmt_name} has duplicate fields: {field_names}")
+
+    # test_expected_fields
+    for fmt_name, (expected_fields, has_encoding) in EXPECTED_FORMATS.items():
+      fields = {f[0] for f in result["formats"].get(fmt_name, [])}
+      for field in expected_fields:
+        self.assertIn(field, fields, f"{fmt_name} missing {field}")
+      if has_encoding:
+        self.assertIn("ENCODING", fields, f"{fmt_name} should have ENCODING")
+      else:
+        self.assertNotIn("ENCODING", fields, f"{fmt_name} should not have ENCODING")
+
+    # test_vopd_no_dpp16_fields
+    vopd_fields = {f[0] for f in result["formats"].get("VOPD", [])}
+    for field in ['DPP_CTRL', 'BANK_MASK', 'ROW_MASK']:
+      self.assertNotIn(field, vopd_fields, f"VOPD should not have {field}")
+
+    # test_dpp16_no_vinterp_fields
+    dpp16_fields = {f[0] for f in result["formats"].get("DPP16", [])}
+    for field in ['VDST', 'WAITEXP']:
+      self.assertNotIn(field, dpp16_fields, f"DPP16 should not have {field}")
+
+    # test_sopp_no_smem_fields
+    sopp_fields = {f[0] for f in result["formats"].get("SOPP", [])}
+    for field in ['SBASE', 'SDATA']:
+      self.assertNotIn(field, sopp_fields, f"SOPP should not have {field}")
+
+class TestPDFParser(unittest.TestCase):
+  """Verify format classes have correct fields from PDF parsing."""
+
+  def test_sop2_fields(self):
+    """SOP2 should have op, sdst, ssrc0, ssrc1."""
+    for field in ['op', 'sdst', 'ssrc0', 'ssrc1']:
+      self.assertIn(field, SOP2._fields)
+    self.assertEqual(SOP2._fields['op'].hi, 29)
+    self.assertEqual(SOP2._fields['op'].lo, 23)
+
+  def test_sop1_fields(self):
+    """SOP1 should have op, sdst, ssrc0 with correct bit positions."""
+    for field in ['op', 'sdst', 'ssrc0']:
+      self.assertIn(field, SOP1._fields)
+    self.assertNotIn('simm16', SOP1._fields)
+    self.assertEqual(SOP1._fields['ssrc0'].hi, 7)
+    self.assertEqual(SOP1._fields['ssrc0'].lo, 0)
+    assert SOP1._encoding is not None
+    self.assertEqual(SOP1._encoding[0].hi, 31)
+    self.assertEqual(SOP1._encoding[1], 0b101111101)
+
+  def test_vop3sd_fields(self):
+    """VOP3SD should have all fields including src0/src1/src2 from page continuation."""
+    for field in ['op', 'vdst', 'sdst', 'src0', 'src1', 'src2']:
+      self.assertIn(field, VOP3SD._fields)
+    self.assertEqual(VOP3SD._fields['src0'].hi, 40)
+    self.assertEqual(VOP3SD._fields['src0'].lo, 32)
+    self.assertEqual(VOP3SD._size(), 8)
+
+  def test_flat_has_vdst(self):
+    """FLAT should have vdst field."""
+    self.assertIn('vdst', FLAT._fields)
+    self.assertEqual(FLAT._fields['vdst'].hi, 63)
+    self.assertEqual(FLAT._fields['vdst'].lo, 56)
+
+  def test_encoding_bits(self):
+    """Verify encoding bits are correct for major formats."""
+    tests = [
+      (SOP2, 31, 30, 0b10),
+      (SOPK, 31, 28, 0b1011),
+      (SOPP, 31, 23, 0b101111111),
+      (VOP1, 31, 25, 0b0111111),
+      (VOP2, 31, 31, 0b0),
+      (VOPC, 31, 25, 0b0111110),
+      (FLAT, 31, 26, 0b110111),
+    ]
+    for cls, hi, lo, val in tests:
+      assert cls._encoding is not None
+      self.assertEqual(cls._encoding[0].hi, hi, f"{cls.__name__} encoding hi")
+      self.assertEqual(cls._encoding[0].lo, lo, f"{cls.__name__} encoding lo")
+      self.assertEqual(cls._encoding[1], val, f"{cls.__name__} encoding val")
+
+  def test_opcode_enums_exist(self):
+    """Verify opcode enums are generated with expected counts."""
+    self.assertGreater(len(SOP1Op), 50)
+    self.assertGreater(len(SOP2Op), 50)
+    self.assertGreater(len(VOP1Op), 50)
+    self.assertGreater(len(VOP3Op), 200)
+
+  def test_vopd_no_duplicate_fields(self):
+    """VOPD should not have duplicate fields and should not include DPP16 fields."""
+    field_names = list(VOPD._fields.keys())
+    self.assertEqual(len(field_names), len(set(field_names)))
+    for field in ['srcx0', 'srcy0', 'opx', 'opy']:
+      self.assertIn(field, VOPD._fields)
+    for field in ['dpp_ctrl', 'bank_mask', 'row_mask']:
+      self.assertNotIn(field, VOPD._fields)
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+import unittest, subprocess
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.test.helpers import get_llvm_mc
+
+def llvm_assemble(asm: str) -> bytes:
+  """Assemble using llvm-mc and return bytes."""
+  result = subprocess.run(
+    [get_llvm_mc(), "-triple=amdgcn", "-mcpu=gfx1100", "-show-encoding"],
+    input=asm, capture_output=True, text=True
+  )
+  out = b''
+  for line in result.stdout.split('\n'):
+    if 'encoding:' in line:
+      enc = line.split('encoding:')[1].strip()
+      enc = enc.strip('[]').replace('0x', '').replace(',', '')
+      out += bytes.fromhex(enc)
+  if not out: raise ValueError(f"no encoding found: {result.stdout} {result.stderr}")
+  return out
+
+class TestRDNA3Asm(unittest.TestCase):
+  def test_full_program(self):
+    """Test the full program from rdna3fun.py matches llvm-mc output."""
+    program = [
+      v_bfe_u32(v[1], v[0], 10, 10),
+      s_load_b128(s[4:7], s[0:1], NULL),
+      v_and_b32_e32(v[0], 0x3FF, v[0]),
+      s_mulk_i32(s[3], 0x87),
+      v_mad_u64_u32(v[1:2], NULL, s[2], 3, v[1:2]),
+      v_mul_u32_u24_e32(v[0], 45, v[0]),
+      v_ashrrev_i32_e32(v[2], 31, v[1]),
+      v_add3_u32(v[0], v[0], s[3], v[1]),
+      v_lshlrev_b64(v[2:3], 2, v[1:2]),
+      v_ashrrev_i32_e32(v[1], 31, v[0]),
+      v_lshlrev_b64(v[0:1], 2, v[0:1]),
+      s_waitcnt(0xfc07),  # lgkmcnt(0)
+      v_add_co_u32(v[2], VCC_LO, s[6], v[2]),
+      v_add_co_ci_u32_e32(v[3], s[7], v[3]),
+      v_add_co_u32(v[0], VCC_LO, s[4], v[0]),
+      global_load_b32(vdst=v[2], addr=v[2], saddr=OFF),
+      v_add_co_ci_u32_e32(v[1], s[5], v[1]),
+      s_waitcnt(0x03f7),  # vmcnt(0)
+      global_store_b32(addr=v[0], data=v[2], saddr=OFF),
+      s_endpgm(),
+    ]
+
+    asm = """
+v_bfe_u32 v1, v0, 10, 10
+s_load_b128 s[4:7], s[0:1], null
+v_and_b32_e32 v0, 0x3FF, v0
+s_mulk_i32 s3, 0x87
+v_mad_u64_u32 v[1:2], null, s2, 3, v[1:2]
+v_mul_u32_u24_e32 v0, 45, v0
+v_ashrrev_i32_e32 v2, 31, v1
+v_add3_u32 v0, v0, s3, v1
+v_lshlrev_b64 v[2:3], 2, v[1:2]
+v_ashrrev_i32_e32 v1, 31, v0
+v_lshlrev_b64 v[0:1], 2, v[0:1]
+s_waitcnt lgkmcnt(0)
+v_add_co_u32 v2, vcc_lo, s6, v2
+v_add_co_ci_u32_e32 v3, vcc_lo, s7, v3, vcc_lo
+v_add_co_u32 v0, vcc_lo, s4, v0
+global_load_b32 v2, v[2:3], off
+v_add_co_ci_u32_e32 v1, vcc_lo, s5, v1, vcc_lo
+s_waitcnt vmcnt(0)
+global_store_b32 v[0:1], v2, off
+s_endpgm
+"""
+    expected = llvm_assemble(asm)
+    for inst,rt in zip(program, asm.strip().split("\n")): print(f"{inst.disasm():50s} {rt}")
+    actual = b''.join(inst.to_bytes() for inst in program)
+    self.assertEqual(actual, expected)
+
+  def test_sop2_s_add_u32(self):
+    inst = SOP2(SOP2Op.S_ADD_U32, s[3], s[0], s[1])
+    expected = llvm_assemble("s_add_u32 s3, s0, s1")
+    self.assertEqual(inst.to_bytes(), expected)
+
+  def test_vop2_v_and_b32_inline_const(self):
+    inst = v_and_b32_e32(v[0], 10, v[0])
+    expected = llvm_assemble("v_and_b32_e32 v0, 10, v0")
+    self.assertEqual(inst.to_bytes(), expected)
+
+  def test_sopp_s_endpgm(self):
+    inst = s_endpgm()
+    expected = llvm_assemble("s_endpgm")
+    self.assertEqual(inst.to_bytes(), expected)
+
+  def test_sop1_s_mov_b32(self):
+    inst = s_mov_b32(s[0], s[1])
+    expected = llvm_assemble("s_mov_b32 s0, s1")
+    self.assertEqual(inst.to_bytes(), expected)
+
+if __name__ == "__main__":
+  unittest.main()
@@ -0,0 +1,300 @@
+#!/usr/bin/env python3
+"""Roundtrip tests: generate tinygrad kernels, decode instructions, re-encode, verify match."""
+import unittest, io, sys, re, subprocess, os
+from extra.assembly.amd.autogen.rdna3 import *
+from extra.assembly.amd.dsl import Inst
+from extra.assembly.amd.asm import asm
+from extra.assembly.amd.test.helpers import get_llvm_mc, get_llvm_objdump
+
+# Instruction format detection based on encoding bits
+def detect_format(data: bytes) -> type[Inst] | None:
+  """Detect instruction format from machine code bytes."""
+  if len(data) < 4: return None
+  word = int.from_bytes(data[:4], 'little')
+  enc_9bit = (word >> 23) & 0x1FF  # 9-bit encoding for SOP1/SOPC/SOPP
+  enc_8bit = (word >> 24) & 0xFF
+
+  # Check 9-bit encodings first (most specific)
+  if enc_9bit == 0x17D: return SOP1  # bits 31:23 = 101111101
+  if enc_9bit == 0x17E: return SOPC  # bits 31:23 = 101111110
+  if enc_9bit == 0x17F: return SOPP  # bits 31:23 = 101111111
+  # SOPK: bits 31:28 = 1011, bits 27:23 = opcode (check after SOP1/SOPC/SOPP)
+  if enc_8bit in range(0xB0, 0xC0): return SOPK
+  # SOP2: bits 31:23 in range 0x100-0x17C (0x80-0xBE in bits 31:24, but not SOPK)
+  if 0x80 <= enc_8bit <= 0x9F: return SOP2
+  # VOP1: bits 31:25 = 0111111 (0x3F)
+  if (word >> 25) == 0x3F: return VOP1
+  # VOPC: bits 31:25 = 0111110 (0x3E)
+  if (word >> 25) == 0x3E: return VOPC
+  # VOP2: bits 31:30 = 00
+  if (word >> 30) == 0: return VOP2
+
+  # Check 64-bit formats
+  if len(data) >= 8:
+    if enc_8bit in (0xD4, 0xD5, 0xD7): return VOP3
+    if enc_8bit == 0xD6: return VOP3SD
+    if enc_8bit == 0xCC: return VOP3P
+    if enc_8bit == 0xCD: return VINTERP
+    if enc_8bit in (0xC8, 0xC9): return VOPD
+    if enc_8bit == 0xF4: return SMEM
+    if enc_8bit == 0xD8: return DS
+    if enc_8bit in (0xDC, 0xDD, 0xDE, 0xDF): return FLAT
+    if enc_8bit in (0xE0, 0xE1, 0xE2, 0xE3): return MUBUF
+    if enc_8bit in (0xE8, 0xE9, 0xEA, 0xEB): return MTBUF
+
+  return None
+
+def disassemble_lib(lib: bytes, compiler) -> list[tuple[str, bytes]]:
+  """Disassemble ELF binary and return list of (instruction_text, machine_code_bytes)."""
+  old_stdout = sys.stdout
+  sys.stdout = io.StringIO()
+  compiler.disassemble(lib)
+  output = sys.stdout.getvalue()
+  sys.stdout = old_stdout
+
+  results = []
+  for line in output.splitlines():
+    if '//' not in line: continue
+    instr = line.split('//')[0].strip()
+    if not instr: continue
+    comment = line.split('//')[1].strip()
+    if ':' not in comment: continue
+    hex_str = comment.split(':')[1].strip().split()[0]
+    try:
+      machine_bytes = bytes.fromhex(hex_str)[::-1]  # big-endian to little-endian
+      results.append((instr, machine_bytes))
+    except ValueError:
+      continue
+  return results
+
+def compile_asm(instr: str, compiler=None) -> bytes:
+  """Compile a single instruction with llvm-mc and return the machine code bytes."""
+  llvm_mc = get_llvm_mc()
+  result = subprocess.run(
+    [llvm_mc, '-triple=amdgcn', '-mcpu=gfx1100', '-mattr=+real-true16,+wavefrontsize32', '-show-encoding'],
+    input=f".text\n{instr}\n", capture_output=True, text=True)
+  if result.returncode != 0: raise RuntimeError(f"llvm-mc failed for '{instr}': {result.stderr.strip()}")
+  # Parse encoding: [0x01,0x39,0x0a,0x7e]
+  for line in result.stdout.split('\n'):
+    if 'encoding:' in line:
+      enc = line.split('encoding:')[1].strip()
+      if enc.startswith('[') and enc.endswith(']'):
+        hex_vals = enc[1:-1].replace('0x', '').replace(',', '').replace(' ', '')
+        return bytes.fromhex(hex_vals)
+  raise RuntimeError(f"no encoding found in llvm-mc output for: {instr}")
+
+def compile_asm_batch(instrs: list[str]) -> list[bytes]:
+  """Compile multiple instructions with a single llvm-mc call."""
+  if not instrs: return []
+  llvm_mc = get_llvm_mc()
+  src = ".text\n" + "\n".join(instrs) + "\n"
+  result = subprocess.run(
+    [llvm_mc, '-triple=amdgcn', '-mcpu=gfx1100', '-mattr=+real-true16,+wavefrontsize32', '-show-encoding'],
+    input=src, capture_output=True, text=True)
+  if result.returncode != 0: raise RuntimeError(f"llvm-mc batch failed: {result.stderr.strip()}")
+  # Parse all encodings in order
+  encodings = []
+  for line in result.stdout.split('\n'):
+    if 'encoding:' in line:
+      enc = line.split('encoding:')[1].strip()
+      if enc.startswith('[') and enc.endswith(']'):
+        hex_vals = enc[1:-1].replace('0x', '').replace(',', '').replace(' ', '')
+        encodings.append(bytes.fromhex(hex_vals))
+  if len(encodings) != len(instrs): raise RuntimeError(f"expected {len(instrs)} encodings, got {len(encodings)}")
+  return encodings
+
+def compile_and_disasm_batch(instrs: list[str], compiler) -> list[str]:
+  """Compile instructions with LLVM and get LLVM's disassembly."""
+  import tempfile, os
+  if not instrs: return []
+  # Build assembly source with all instructions
+  src = ".text\n.globl test\n.p2align 8\n.type test,@function\ntest:\n"
+  src += "\n".join(f"  {instr}" for instr in instrs) + "\n"
+  # Use llvm-mc to assemble to object file
+  with tempfile.NamedTemporaryFile(suffix='.o', delete=False) as f:
+    obj_path = f.name
+  try:
+    result = subprocess.run(
+      [get_llvm_mc(), '-triple=amdgcn', '-mcpu=gfx1100', '-mattr=+real-true16,+wavefrontsize32', '-filetype=obj', '-o', obj_path],
+      input=src, capture_output=True, text=True)
+    if result.returncode != 0: raise RuntimeError(f"llvm-mc failed: {result.stderr.strip()}")
+    # Disassemble with llvm-objdump
+    result = subprocess.run([get_llvm_objdump(), '-d', '--mcpu=gfx1100', obj_path], capture_output=True, text=True)
+    if result.returncode != 0: raise RuntimeError(f"llvm-objdump failed: {result.stderr.strip()}")
+    # Parse disassembly output
+    results: list[str] = []
+    for line in result.stdout.splitlines():
+      if '//' not in line: continue
+      instr = line.split('//')[0].strip()
+      if instr: results.append(instr)
+    return results[:len(instrs)]
+  finally:
+    os.unlink(obj_path)
+
+class TestTinygradKernelRoundtrip(unittest.TestCase):
+  """Test roundtrip on real tinygrad-generated kernels using get_kernels_from_tinygrad pattern."""
+
+  def _test_kernel_roundtrip(self, op_fn):
+    """Generate kernel from op_fn, test:
+    1. decode -> reencode matches original bytes
+    2. asm(disasm()) matches LLVM output
+    3. our disasm() matches LLVM's disassembly string exactly
+    """
+    from extra.assembly.amd.test.test_compare_emulators import get_kernels_from_tinygrad
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler
+
+    kernels, _, _ = get_kernels_from_tinygrad(op_fn)
+    compiler = HIPCompiler('gfx1100')
+
+    # First pass: decode all instructions and collect info
+    decoded_instrs: list[tuple] = []  # list of (ki, offset, orig_bytes, decoded, our_disasm, decode_ok, decode_err)
+    for ki, kernel in enumerate(kernels):
+      offset = 0
+      while offset < len(kernel.code):
+        remaining = kernel.code[offset:]
+        fmt = detect_format(remaining)
+        if fmt is None:
+          decoded_instrs.append((ki, offset, None, None, None, False, "no format"))
+          offset += 4
+          continue
+
+        base_size = fmt._size()
+        if len(remaining) < base_size:
+          break
+
+        try:
+          decoded = fmt.from_bytes(remaining)  # pass all remaining bytes so from_bytes can read literal
+          size = decoded.size()  # actual size including literal
+          orig_bytes = remaining[:size]
+          reencoded = decoded.to_bytes()
+          our_disasm = decoded.disasm()
+          decode_ok = reencoded == orig_bytes
+          decode_err: str | None = None if decode_ok else f"orig={orig_bytes.hex()} reenc={reencoded.hex()}"
+          decoded_instrs.append((ki, offset, orig_bytes, decoded, our_disasm, decode_ok, decode_err))
+        except Exception as e:
+          decoded_instrs.append((ki, offset, remaining[:base_size], None, None, False, str(e)))
+          size = base_size
+
+        offset += size
+
+    # Collect disasm strings for batched LLVM calls - skip unknown opcodes (op_X) that LLVM can't compile
+    asm_test_instrs: list[tuple[int, str]] = []  # (idx, our_disasm) for asm test
+    disasm_test_instrs: list[tuple[int, str]] = []  # (idx, our_disasm) for disasm comparison test
+
+    for idx, (ki, offset, orig_bytes, decoded, our_disasm, decode_ok, decode_err) in enumerate(decoded_instrs):
+      if our_disasm is None: continue
+      # Skip unknown opcodes and malformed instructions for both tests
+      if our_disasm.startswith('op_') or re.search(r', \d+, \d+, \d+,', our_disasm): continue
+      asm_test_instrs.append((idx, our_disasm))
+      disasm_test_instrs.append((idx, our_disasm))
+
+    # Batch compile for asm test
+    asm_llvm_results = compile_asm_batch([d for _, d in asm_test_instrs])
+    asm_llvm_map = {idx: result for (idx, _), result in zip(asm_test_instrs, asm_llvm_results)}
+
+    # Batch compile+disasm for disasm comparison test
+    disasm_llvm_results = compile_and_disasm_batch([d for _, d in disasm_test_instrs], compiler)
+    disasm_llvm_map = {idx: result for (idx, _), result in zip(disasm_test_instrs, disasm_llvm_results)}
+
+    # Now evaluate results
+    decode_passed, decode_failed, decode_skipped = 0, 0, 0
+    asm_passed, asm_failed, asm_skipped = 0, 0, 0
+    disasm_passed, disasm_failed, disasm_skipped = 0, 0, 0
+    decode_failures: list[str] = []
+    asm_failures: list[str] = []
+    disasm_failures: list[str] = []
+
+    for idx, (ki, offset, orig_bytes, decoded, our_disasm, decode_ok, decode_err) in enumerate(decoded_instrs):
+      # Decode test
+      if decode_ok:
+        decode_passed += 1
+      elif decode_err == "no format":
+        decode_skipped += 1
+      else:
+        decode_failed += 1
+        decode_failures.append(f"K{ki}@{offset}: {our_disasm}: {decode_err}")
+
+      # Asm test
+      if our_disasm is None:
+        asm_skipped += 1
+      elif idx in asm_llvm_map:
+        llvm_bytes = asm_llvm_map[idx]
+        try:
+          our_bytes = asm(our_disasm).to_bytes()
+          if our_bytes[:len(llvm_bytes)] == llvm_bytes:
+            asm_passed += 1
+          else:
+            asm_failed += 1
+            asm_failures.append(f"K{ki}@{offset}: '{our_disasm}': ours={our_bytes[:len(llvm_bytes)].hex()} llvm={llvm_bytes.hex()}")
+        except Exception:
+          asm_skipped += 1
+      else:
+        asm_skipped += 1
+
+      # Disasm comparison test
+      if our_disasm is None:
+        disasm_skipped += 1
+      elif idx in disasm_llvm_map:
+        llvm_disasm = disasm_llvm_map[idx]
+        if our_disasm == llvm_disasm:
+          disasm_passed += 1
+        else:
+          disasm_failed += 1
+          disasm_failures.append(f"K{ki}@{offset}: ours='{our_disasm}' llvm='{llvm_disasm}'")
+      else:
+        disasm_skipped += 1
+
+    print(f"decode roundtrip: {decode_passed} passed, {decode_failed} failed, {decode_skipped} skipped")
+    print(f"asm vs llvm: {asm_passed} passed, {asm_failed} failed, {asm_skipped} skipped")
+    print(f"disasm vs llvm: {disasm_passed} passed, {disasm_failed} failed, {disasm_skipped} skipped")
+    self.assertEqual(decode_failed, 0, f"Decode failures:\n" + "\n".join(decode_failures[:20]))
+    self.assertEqual(asm_failed, 0, f"Asm failures:\n" + "\n".join(asm_failures[:20]))
+    # Note: disasm string comparison is informational only - formatting differences between LLVM versions are expected
+
+  # Basic unary ops
+  def test_neg(self): self._test_kernel_roundtrip(lambda T: -T([1.0, -2.0, 3.0, -4.0]))
+  def test_relu(self): self._test_kernel_roundtrip(lambda T: T([-1.0, 0.0, 1.0, 2.0]).relu())
+  def test_exp(self): self._test_kernel_roundtrip(lambda T: T([0.0, 1.0, 2.0]).exp())
+  def test_log(self): self._test_kernel_roundtrip(lambda T: T([1.0, 2.0, 3.0]).log())
+  def test_sin(self): self._test_kernel_roundtrip(lambda T: T([0.0, 1.0, 2.0]).sin())
+  def test_sqrt(self): self._test_kernel_roundtrip(lambda T: T([1.0, 4.0, 9.0]).sqrt())
+  def test_recip(self): self._test_kernel_roundtrip(lambda T: T([1.0, 2.0, 4.0]).reciprocal())
+
+  # Binary ops
+  def test_add(self): self._test_kernel_roundtrip(lambda T: T([1.0, 2.0]) + T([3.0, 4.0]))
+  def test_sub(self): self._test_kernel_roundtrip(lambda T: T([5.0, 6.0]) - T([1.0, 2.0]))
+  def test_mul(self): self._test_kernel_roundtrip(lambda T: T([2.0, 3.0]) * T([4.0, 5.0]))
+  def test_div(self): self._test_kernel_roundtrip(lambda T: T([10.0, 20.0]) / T([2.0, 4.0]))
+  def test_max_binary(self): self._test_kernel_roundtrip(lambda T: T([1.0, 5.0]).maximum(T([3.0, 2.0])))
+
+  # Reductions
+  def test_sum_reduce(self): self._test_kernel_roundtrip(lambda T: T.empty(64).sum())
+  def test_max_reduce(self): self._test_kernel_roundtrip(lambda T: T.empty(64).max())
+  def test_mean_reduce(self): self._test_kernel_roundtrip(lambda T: T.empty(32).mean())
+
+  # Matmul
+  def test_gemm_4x4(self): self._test_kernel_roundtrip(lambda T: T.empty(4, 4) @ T.empty(4, 4))
+  def test_gemv(self): self._test_kernel_roundtrip(lambda T: T.empty(1, 16) @ T.empty(16, 16))
+
+  # Complex ops
+  def test_softmax(self): self._test_kernel_roundtrip(lambda T: T.empty(16).softmax())
+  def test_layernorm(self): self._test_kernel_roundtrip(lambda T: T.empty(8, 8).layernorm())
+
+  # Memory patterns
+  def test_contiguous(self): self._test_kernel_roundtrip(lambda T: T.empty(4, 4).permute(1, 0).contiguous())
+  def test_reshape(self): self._test_kernel_roundtrip(lambda T: (T.empty(16) + 1).reshape(4, 4).contiguous())
+  def test_expand(self): self._test_kernel_roundtrip(lambda T: T.empty(4, 1).expand(4, 4).contiguous())
+
+  # Cast ops
+  def test_cast_int(self): self._test_kernel_roundtrip(lambda T: T.empty(16).int().float())
+  def test_cast_half(self): self._test_kernel_roundtrip(lambda T: T.empty(16).half().float())
+
+  # Comparison ops
+  def test_cmp_lt(self): self._test_kernel_roundtrip(lambda T: (T.empty(64) < T.empty(64)).where(T.empty(64), T.empty(64)))
+  def test_where(self): self._test_kernel_roundtrip(lambda T: (T.empty(64) > 0).where(T.empty(64), T.empty(64)))
+
+  # Fused ops
+  def test_fma(self): self._test_kernel_roundtrip(lambda T: (T([1.0, 2.0]) * T([3.0, 4.0]) + T([5.0, 6.0])))
+
+if __name__ == "__main__":
+  unittest.main()
@@ -1,189 +0,0 @@
-from typing import Tuple, List, NamedTuple, Any, Dict, Optional, Union, DefaultDict, cast
-from tinygrad.codegen.opt.kernel import Ops, MemOp, UOp
-from tinygrad.uop.ops import BinaryOps, UnaryOps
-from tinygrad.dtype import DType, dtypes
-from tinygrad.helpers import DEBUG
-from tinygrad.uop.ops import Variable, NumNode, MulNode, DivNode, ModNode, LtNode, SumNode, AndNode
-import functools
-import math
-from collections import defaultdict
-
-_type_to_letter = {dtypes.float32: 'f', dtypes.bool: 'p', dtypes.int32: 'i', dtypes.int64: 'a', dtypes.uint32: 'u', dtypes.uint64: 'b', dtypes.float.vec(4): 'x', dtypes.uint8: 'uc', dtypes.float16: 'h',
-                   dtypes.int8: 'c', dtypes.uint16: 'us', dtypes.float64: 'd'}
-
-class Register(NamedTuple):
-  nm:str
-  dtype:DType
-  scalar:bool
-  off:Optional[int] = None
-  def __repr__(self): return self.nm if self.off is None else f"{self.nm}:{self.off}"
-  def subregs(self):
-    if self.dtype == dtypes.float.vec(4):
-      return [Register(self.nm, dtypes.float, False, off=off) for off in range(4)]
-    return []
-
-class AssemblyInstruction(NamedTuple):
-  op: Ops
-  out: Optional[Register]
-  vin: List[Union[Register, int, float]]
-  arg: Any = None
-
-# warp size of 32, s registers are shared across the warp, v are 32-wide vectors
-class AssemblyLanguage:
-  supports_load3: bool = False
-  sin_is_sin2pi: bool = False
-  no_div: bool = False
-  #TODO: these should be global vars
-  cnts:DefaultDict[Tuple[DType, bool], int] = defaultdict(int)
-  tor: Dict[Any, Register] = {}
-  ins: List[AssemblyInstruction] = []
-
-  def type_to_letter(self,x): return _type_to_letter[x[0]].upper() if x[1] else _type_to_letter[x[0]]
-  def newreg(self, tok, dtype=dtypes.float32, scalar=False) -> Register:
-    self.tor[tok] = ret = Register(f"%{self.type_to_letter((dtype, scalar))}{self.cnts[(dtype, scalar)]}", dtype, scalar)
-    if dtype == dtypes.float.vec(4):
-      for off in range(4):
-        self.tor[tok] = Register(ret.nm, dtypes.float, ret.scalar, off)
-    self.cnts[(dtype, scalar)] += 1
-    return ret
-
-  def render_numnode(self, b) -> Register:
-    key = ("num", b)
-    if key not in self.tor: self.ins.append(AssemblyInstruction(Ops.LOAD, self.newreg(key, scalar=True, dtype=dtypes.int32), [], b))
-    return self.tor[key]
-
-  def render_alu(self, op, a:Register, b:Union[Register, int, float], dtype=dtypes.int32) -> Register:
-    key = (op, a, b)
-    if key not in self.tor:
-      #if not isinstance(b, Register): b = render_numnode(b)
-      self.ins.append(AssemblyInstruction(Ops.ALU, self.newreg(key, dtype=dtype, scalar=a.scalar and (not isinstance(b, Register) or b.scalar)), [a, b], op))
-    return self.tor[key]
-
-  def render_cast(self, a:Register, new_dtype:DType) -> Register:
-    if a.dtype == new_dtype: return a
-    key = (a, new_dtype)
-    if key not in self.tor:
-      self.ins.append(AssemblyInstruction(Ops.CAST, self.newreg(key, dtype=new_dtype), [a]))
-    return self.tor[key]
-
-  render_ops: Any = { Variable: lambda self, ops, ctx: ctx.tor[self], NumNode: lambda self, ops, ctx: ctx.render_numnode(self.b),
-                 MulNode: lambda self, ops, ctx: ctx.render_alu(BinaryOps.MUL, self.a.render(ops, ctx), self.b),
-                 DivNode: lambda self, ops, ctx: ctx.render_alu(BinaryOps.DIV, self.a.render(ops, ctx), self.b),
-                 ModNode: lambda self, ops, ctx: ctx.render_alu(BinaryOps.MOD, self.a.render(ops, ctx), self.b),
-                 LtNode: lambda self, ops, ctx: ctx.render_alu(BinaryOps.CMPLT, self.a.render(ops, ctx), self.b, dtype=dtypes.bool),
-    SumNode: lambda self,ops,ctx: functools.reduce(lambda a,b: ctx.render_alu(BinaryOps.ADD, a, b.render(ops,ctx)), self.nodes[1:], self.nodes[0].render(ops,ctx)),
-    AndNode: lambda self,ops,ctx: functools.reduce(lambda a,b: ctx.render_alu(BinaryOps.MUL, a, b.render(ops,ctx), dtype=dtypes.bool), self.nodes[1:], self.nodes[0].render(ops,ctx)) }
-
-  def addr_w_offset(self, args):
-    assert isinstance(args, MemOp)
-    idx = args.idx*args.memory_dtype.itemsize
-    off = 0  # TODO: should this be None?
-    if isinstance(idx, SumNode):
-      nums = [n.b for n in idx.nodes if isinstance(n, NumNode)]
-      if nums and nums[0] < 4096 and (idx-nums[0]).min >= 0:  # TODO: different for each GPU?
-        idx -= nums[0]
-        off = cast(int, nums[0])
-    reg = idx.render(self.render_ops, self)
-    if self.supports_load3:
-      if reg.scalar:
-        new_reg = self.newreg((reg.nm, 'vec'), dtype=reg.dtype)
-        self.ins.append(AssemblyInstruction(Ops.ALU, new_reg, [reg], UnaryOps.NOOP))
-        reg = new_reg
-      return self.tor[args.name], reg, off
-    reg = self.render_alu(BinaryOps.ADD, self.render_cast(reg, dtypes.uint64), self.tor[args.name], dtype=dtypes.uint64)
-    return reg, None, off
-
-def uops_to_asmstyle(lang, function_name:str, uops:List[UOp]):
-  #TODO: Do not use clear()
-  lang.ins.clear()
-  lang.tor.clear()
-  lang.cnts.clear()
-  buf_to_dtype = {args:dtype for uop,dtype,_,args,_ in uops if uop == Ops.DEFINE_GLOBAL}
-  global_size, local_size = [], []
-  skipload_branch = 0
-  lang.ins += [AssemblyInstruction(Ops.SPECIAL, lang.newreg(buf, dtype=dtypes.uint64, scalar=True), [], buf) for buf in buf_to_dtype]
-  for u in uops:
-    uop,dtype,vin,args,_ = u
-    if uop == Ops.DEFINE_LOCAL:
-      lang.ins.append(AssemblyInstruction(Ops.DEFINE_LOCAL, None, [], args))
-      lang.ins.append(AssemblyInstruction(Ops.ALU, lang.newreg(args[0], dtype=dtypes.uint64), [args[0]], UnaryOps.NOOP))
-    elif uop == Ops.LOOP:
-      if args[1] == "global":
-        for i,var in enumerate(args[0]):
-          global_size.append(var.max+1)
-          lang.ins.append(AssemblyInstruction(Ops.SPECIAL, lang.newreg(var, dtype=dtypes.int32), [], f"gid{len(args[0])-1-i}"))
-      elif args[1] == "local":
-        for i,var in enumerate(args[0]):
-          local_size.append(var.max+1)
-          lang.ins.append(AssemblyInstruction(Ops.SPECIAL, lang.newreg(var, dtype=dtypes.int32), [], f"lid{len(args[0])-1-i}"))
-      else:
-        for var in args[0]:
-          if not isinstance(var, NumNode):  # TODO: why is this coming through?
-            lang.ins.append(AssemblyInstruction(Ops.LOAD, lang.newreg(var, dtype=dtypes.int32, scalar=True), [], 0))
-            lang.ins.append(AssemblyInstruction(Ops.LABEL, None, [], "$loop_"+var.expr))
-    elif uop == Ops.ENDLOOP:
-      if args[1] not in ["global", "local", "global+local"]:
-        for var in reversed(args[0]):
-          if not isinstance(var, NumNode):  # TODO: why is this coming through?
-            lang.ins.append(AssemblyInstruction(Ops.ALU, lang.tor[var], [lang.tor[var], 1], BinaryOps.ADD))
-            pred = lang.render_alu(BinaryOps.CMPLT, lang.tor[var], var.max+1, dtypes.bool)
-            lang.ins.append(AssemblyInstruction(Ops.COND_BRANCH, None, [pred], ("$loop_"+var.expr, True)))
-      elif args[1] == "global+local":
-        for i, var in enumerate(reversed(args[0])):
-          lang.ins.append(AssemblyInstruction(Ops.ENDLOOP, None, [lang.tor[var]], (var.max+1, f"gid{i}")))
-      elif args[1] == 'local':
-        for i, var in enumerate(reversed(args[0])):
-          lang.ins.append(AssemblyInstruction(Ops.ENDLOOP, None, [lang.tor[var]], (var.max+1, f"lid{i}")))
-    elif uop == Ops.CAST:
-      # TODO: we should reconsider outputting CAST in the linearizer. these are needless copies
-      out = lang.newreg(u, dtype)
-      for i,sr in enumerate(out.subregs()):
-        lang.ins.append(AssemblyInstruction(Ops.ALU, sr, [lang.tor[vin[i]]], UnaryOps.NOOP))
-    elif uop == Ops.ALU:
-      out = lang.newreg(u, dtype) if u not in lang.tor else lang.tor[u]
-      # this is the only thing that can violate SSA
-      if args in [BinaryOps.CMPLT]:
-        pred_reg = lang.newreg((u, 'pred'), dtype=dtypes.bool)
-        lang.ins.append(AssemblyInstruction(Ops.ALU, pred_reg, [lang.tor[x] for x in vin], args))
-        lang.ins.append(AssemblyInstruction(Ops.CAST, out, [pred_reg], args))
-      elif args == BinaryOps.DIV and lang.no_div:
-        tmp = lang.newreg((u, "rcp"))
-        lang.ins.append(AssemblyInstruction(Ops.ALU, tmp, [lang.tor[vin[1]]], UnaryOps.RECIP))
-        lang.ins.append(AssemblyInstruction(Ops.ALU, out, [lang.tor[vin[0]], tmp], BinaryOps.MUL))
-      elif args == UnaryOps.SIN and lang.sin_is_sin2pi:
-        tmp = lang.newreg((u, "2pi"))
-        lang.ins.append(AssemblyInstruction(Ops.ALU, tmp, [lang.tor[vin[0]], 1/(math.pi*2)], BinaryOps.MUL))
-        lang.ins.append(AssemblyInstruction(Ops.ALU, out, [tmp], args))
-      else:
-        lang.ins.append(AssemblyInstruction(Ops.ALU, out, [lang.tor[x] for x in vin], args))
-    elif uop == Ops.DEFINE_REG:
-      reg = lang.newreg(u, dtype=dtype)
-      lang.ins.append(AssemblyInstruction(Ops.LOAD, reg, [], args))
-    elif uop == Ops.SPECIAL:
-      lang.tor[u] = lang.tor[args]
-    elif uop == Ops.CONST:
-      lang.ins.append(AssemblyInstruction(Ops.LOAD, lang.newreg(u, dtype=dtype), [], args))
-    elif uop == Ops.LOAD:
-      idx, treg, off = lang.addr_w_offset(args)
-      reg = lang.newreg(u, dtype=dtype, scalar=(idx.scalar and (not isinstance(treg, Register) or treg.scalar)))
-      if args.valid.min == 0:
-        lang.ins.append(AssemblyInstruction(Ops.LOAD, reg, [], 0))
-        if args.valid.max == 1:
-          pred = args.valid.render(lang.render_ops, lang)
-          lang.ins.append(AssemblyInstruction(Ops.COND_BRANCH, None, [pred], (f"$skipload_{skipload_branch}", False)))
-      if args.valid.max == 1:
-          # NOTE: you can't compute the index in here, because it assumes it's all available later
-        lang.ins.append(AssemblyInstruction(Ops.LOAD, reg, [idx] + ([treg] if treg is not None else []), (off, 'global' if not args.local else 'shared', args.memory_dtype if args.memory_dtype != dtypes.float else None)))
-      if args.valid.min == 0 and args.valid.max == 1:
-        lang.ins.append(AssemblyInstruction(Ops.LABEL, None, [], f"$skipload_{skipload_branch}"))
-        skipload_branch += 1
-    elif uop == Ops.STORE:
-      if args is None:
-        lang.ins.append(AssemblyInstruction(Ops.ALU, lang.tor[vin[0]], [lang.tor[vin[1]]], UnaryOps.NOOP))
-      else:
-        idx, treg, off = lang.addr_w_offset(args)
-        lang.ins.append(AssemblyInstruction(Ops.STORE, None, [idx, lang.tor[vin[0]]] + ([treg] if treg is not None else []), (off, 'global' if not args.local else 'shared', args.memory_dtype if args.memory_dtype != dtypes.float else None)))
-
-  if DEBUG >= 4:
-    for tins in lang.ins: print(tins)
-  return global_size, local_size
@@ -1,177 +0,0 @@
-import struct
-from platform import system
-from typing import Tuple, Dict, List, Optional
-from tinygrad import dtypes
-from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
-from tinygrad.codegen.opt.kernel import Ops, UOp
-from tinygrad.helpers import CI
-from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage
-
-def float_to_hex(x): return "%02X%02X%02X%02X" % tuple(struct.pack("f",x)[::-1])
-def compute_offsets(total):
-  quotient, remainder = divmod(total, 4096)
-  return [4096]*quotient + [remainder] if remainder else [4096]*quotient
-
-#NOTE: Darwin needs names to start with a "_"
-def get_name(name): return ('_' if system() == 'Darwin' else '') + name
-
-class ARM64Language(AssemblyLanguage): pass
-
-def specialize_to_arm64(fn_nm, asm):
-  var_size = 16
-  prev_uop:Optional[Ops] = None
-  ins = []
-  x_regs = ['x' + str(i) for i in reversed(range(12))]
-  s_regs = ['s' + str(i) for i in reversed(range(3,32)) if i <= 7 or i >= 16]
-  type_to_reg = {dtypes.double: "d", dtypes.half: 'h', dtypes.float32: 's', dtypes.bool: 'w', dtypes.int8:'w', dtypes.int32: 'w', dtypes.int64: 'x', dtypes.uint8:'w', dtypes.uint32: 'w', dtypes.uint64: 'x'}
-  alu = {BinaryOps.ADD: "add", BinaryOps.SUB: "sub", BinaryOps.MUL: "mul", BinaryOps.DIV: "div", BinaryOps.MAX: "max",
-          BinaryOps.MOD: "", BinaryOps.CMPLT: "subs",
-          UnaryOps.NOOP: "mov", UnaryOps.NEG: "neg",
-          UnaryOps.SIN:'bl ' + get_name('sinf'), UnaryOps.LOG2: 'bl ' + get_name("log2f"), UnaryOps.EXP2: 'bl ' + get_name("exp2f"), UnaryOps.SQRT: 'bl ' + get_name("sqrtf"),
-          TernaryOps.MULACC: "madd", TernaryOps.WHERE: "fcsel"}
-
-  def mov_imm(value, reg):
-    # Manually move value into reg if value can't fit
-    if value.__class__ is not float and abs(value) > abs(65535):
-      ins.append(f"movz w15, #{value & 0xffff}")
-      ins.append(f"movk w15, #{(value >> 16) & 0xffff}, lsl #16")
-      ins.append(f"sxtw {reg}, w15")
-    elif reg[0] == 's':
-      ins.append(f"movz x15, 0x{float_to_hex(value)[4:]}")
-      ins.append(f"movk x15, 0x{float_to_hex(value)[:4]}, lsl #16")
-      ins.append("str x15, [sp, 16]")
-      ins.append(f"ldr {reg}, [sp, 16]")
-    else:
-      ins.append(f"mov {reg}, #{value}")
-
-  # Get variables intervals
-  live_range:Dict[str, List[int]] = {}
-  for i, (uop, out, vin, arg) in enumerate(asm):
-    for var in ([v for v in [out] + vin if v is not None and v.__class__ is not int]):
-      live_range[var.nm] = [i,i] if var.nm not in live_range else [live_range[var.nm][0], i]
-
-  mem_vars:Dict[str, int] = {}
-  rtor:Dict[str, str] = {}
-  def allocate_regs(mvars):
-    nonlocal var_size
-    for v in [v for v in mvars if v is not None and v.__class__ is not int and v.nm not in rtor]:
-      available_regs = s_regs if dtypes.is_float(v[1]) else x_regs
-      #NOTE: Very simple spill, everything that don't fit in regs goes to mem
-      if not available_regs:
-        # ARM needs the stack 16-byte aligned
-        var_size += 16
-        available_regs.append('s0' if dtypes.is_float(out[1]) else 'x12')
-        mem_vars[v.nm] = var_size
-      rtor[v.nm] = available_regs.pop()
-
-  temp_floats = ['s0', 's1', 's2']
-  temp_ints = ['x12', 'x13', 'x16']
-  for i, (uop, out, vin, arg) in enumerate(asm):
-    # Clear regs out of interval
-    for var, reg in list(rtor.items()):
-      available_regs = s_regs if reg[0] == 's' else x_regs
-      if var[1] not in 'B' and var not in mem_vars and i > live_range[var][1]:
-        available_regs.append(rtor.pop(var))
-    # Assign a registers to the variables using live ranges.
-    allocate_regs([out] + vin)
-    # Assign temp regs to vin and load them before direct use
-    for i, v in enumerate([v for v in vin if v.__class__ is not int and v.nm in mem_vars]):
-      rtor[v.nm] = temp_floats[i] if dtypes.is_float(v[1]) else temp_ints[i]
-      # ARM64 addressing constraints https://devblogs.microsoft.com/oldnewthing/20220728-00/?p=106912
-      ins.append(f"mov x15, {mem_vars[v.nm]}")
-      ins.append(f"ldr {rtor[v.nm]}, [sp, x15]")
-
-    if uop == Ops.SPECIAL:
-      if arg.startswith('data'):
-        # data 8 to n into the stack
-        if int(arg[4:]) >= 8:
-          ins.append(f"ldr x15, [x17, #{(int(arg[4:]) - 8) * 8}]")
-          ins.append(f"mov {rtor[out.nm]}, x15")
-      else:
-        ins.append(f"mov {rtor[out.nm]}, #0")
-        ins.append(f"loop_{arg}:")
-    elif uop == Ops.CAST:
-      if arg == BinaryOps.CMPLT:
-        if rtor[out.nm][0] == 's':
-          mov_imm(0.0, 's0')
-          mov_imm(1.0, 's1')
-          ins.append(f"fcsel {rtor[out.nm]}, s1, s0, lt")
-        if rtor[out.nm][0] == 'x':
-          mov_imm(0, 'x14')
-          mov_imm(1, 'x15')
-          ins.append(f"csel {rtor[out.nm]}, x15, x14, lt")
-      else:
-        ins.append(f"sxtw {rtor[out.nm]}, w{rtor[vin[0].nm][1:]}")
-    elif uop == Ops.ALU:
-      if len(vin)==2 and vin[1].__class__ is int: mov_imm(vin[1], 'x15')
-      if arg == BinaryOps.MUL and out.dtype == dtypes.bool:
-        ins.append(f"ands {','.join('x15' if v.__class__ is int else rtor[v.nm] for v in [out] + vin)}")
-      elif arg == TernaryOps.WHERE:
-        ins.append(f"fcmp {rtor[vin[0].nm]}, #0.0" if rtor[vin[0].nm][0] == 's' else f"cmp {rtor[vin[0].nm]}, #0")
-        ins.append(f"{alu[arg]} {rtor[out.nm]}, {rtor[vin[1].nm]}, {rtor[vin[2].nm]}, ne")
-      elif arg in [UnaryOps.LOG2, UnaryOps.SIN, UnaryOps.EXP2, UnaryOps.SQRT]:
-        #NOTE: Not a real instruction, use to emulate a ext call in unicorn
-        if CI: ins.append(f"{alu[arg]} {rtor[out.nm]} {rtor[vin[0].nm]}")
-        else:
-          save_regs = [k for k in rtor.keys() if k != out.nm and k not in mem_vars]
-          ins.append(f"sub sp, sp, #{(len(save_regs))*16}")
-          # Save the registers before they are cleared by func call
-          for i,k in enumerate(save_regs,1):
-            ins.append(f"str {rtor[k]}, [sp, #{16*i}]")
-          ins.append("stp x29, x30, [sp, #0]!")
-          ins.append("mov x29, sp")
-          ins.append(f"fmov s0, {rtor[vin[0].nm]}")
-          ins.append(alu[arg])
-          ins.append(f"fmov {rtor[out.nm]}, s0")
-          ins.append("mov sp, x29")
-          ins.append("ldp x29, x30, [sp], #0")
-          for i,k in enumerate(save_regs,1):
-            ins.append(f"ldr {rtor[k]}, [sp, #{16*i}]")
-          ins.append(f"add sp, sp, #{len(save_regs)*16}")
-      elif arg == BinaryOps.CMPLT:
-        ins.append(f"{alu[arg]} {','.join('x15' if v.__class__ is int else rtor[v.nm] for v in [out] + vin)}" if not dtypes.is_float(vin[0][1]) else f"fcmp {rtor[vin[0].nm]}, {rtor[vin[1].nm]}")
-      elif arg == BinaryOps.MOD:
-        rhs = 'x15' if vin[1].__class__ is int else rtor[vin[1].nm]
-        ins.append(f"udiv x14, {rtor[vin[0].nm]}, {rhs}")
-        ins.append(f"msub {rtor[out.nm]}, x14, {rhs}, {rtor[vin[0].nm]}")
-      else:
-        ins.append(f"{'f' if dtypes.is_float(vin[0][1]) else 's' if arg == BinaryOps.DIV else ''}{alu[arg]} {', '.join('x15' if v.__class__ is int else rtor[v.nm] for v in [out] + vin)}")
-    elif uop == Ops.LOAD:
-      if arg.__class__ in (int, float):
-        mov_imm(arg, rtor[out.nm])
-      else:
-        #NOTE: if need casting load var in s/h0 or x/w12 temp regs
-        reg_in = type_to_reg[arg[2]] + ('0' if dtypes.is_float(arg[2]) else '12') if arg[2] is not None else rtor[out.nm]
-        mov_imm(arg[0], "x15")
-        ins.append(f"add x15, {rtor[vin[0].nm]}, x15")
-        ins.append(f"ldr{'sb' if arg[2] is not None and arg[2] in (dtypes.int8, dtypes.uint8, dtypes.bool) else ''} {reg_in}, [x15]")
-        if arg[2] is not None: ins.append(f"{'fcvt' if arg[2] in [dtypes.half, dtypes.double] else 'scvtf'} {rtor[out.nm]}, {reg_in}")
-    elif uop == Ops.STORE:
-      #NOTE: if need casting load var in s/h0 or x/w12 temp regs
-      reg_out = (type_to_reg[arg[2]] + ('0' if dtypes.is_float(arg[2]) else '12') if arg[2] is not None else rtor[vin[1].nm])
-      if arg[2] is not None: ins.append(f"fcvt{'zs' if arg[2] not in [dtypes.half, dtypes.double] else '' } {reg_out}, {rtor[vin[1].nm]}")
-      ins.append(f"mov x15, #{arg[0]}")
-      ins.append(f"str {reg_out}, [{rtor[vin[0].nm]}, x15, lsl #0]")
-    elif uop == Ops.COND_BRANCH:
-      #TODO: this is a hack it shouldn't always be a cmp before a cond branch?
-      if prev_uop == Ops.LOAD:
-        ins.append(f"cmp {rtor[vin[0].nm]}, #0")
-      ins.append(f"b.{'lt' if arg[1] else 'ge'} {arg[0][1:]}")
-    elif uop == Ops.LABEL:
-      ins.append(f"{arg[1:]}:")
-    elif uop == Ops.ENDLOOP:
-      mov_imm(arg[0], "x15")
-      ins.append(f"add {rtor[vin[0].nm]}, {rtor[vin[0].nm]}, #1")
-      ins.append(f"cmp {rtor[vin[0].nm]}, x15")
-      ins.append(f"b.lt loop_{arg[1]}")
-    prev_uop = uop
-    # store regs into memory if needed
-    if out is not None and out.nm in mem_vars:
-      ins.append(f"mov x15, {mem_vars[out.nm]}")
-      ins.append(f"str {rtor[out.nm]}, [sp, x15]")
-  return "\n".join([f"//varsize {var_size}",".arch armv8-a",".text", f".global {get_name(fn_nm)}",".p2align 2", f"{get_name(fn_nm)}:", "mov x17, sp"] + [f"sub sp, sp, #{offset}" for offset in compute_offsets(var_size)]+ ins + [f"add sp, sp, #{offset}" for offset in compute_offsets(var_size)] +["ret", "\n"])
-
-def uops_to_arm64_asm(fn_nm:str, uops:List[UOp]) -> Tuple[str, List[int], List[int], bool]:
-  lang = ARM64Language()
-  global_size, local_size = uops_to_asmstyle(lang, fn_nm, uops)
-  return specialize_to_arm64(fn_nm, lang.ins), global_size[::-1], local_size[::-1], True
@@ -1,105 +0,0 @@
-from typing import List
-import struct
-from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage
-from tinygrad.codegen.opt.kernel import Ops, UOp
-from tinygrad import dtypes
-from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
-from tinygrad.runtime.ops_cuda import arch
-
-dtype_to_nvtype = {dtypes.float32: "f32", dtypes.float16: "f16", dtypes.int64: "s64", dtypes.int32: "s32", dtypes.int8: "s8", dtypes.bool: "pred", dtypes.uint64: "u64", dtypes.uint32: "u32", dtypes.uint16: "u16", dtypes.uint8: "u8", "bits16": "b16", dtypes.float64: "f64"}
-def float_to_hex(x): return "%02X%02X%02X%02X" % tuple(struct.pack("f",x)[::-1])
-
-def ptx_needs_cast(dest_dtype, src_dtype): return dtypes.is_float(dest_dtype) and dtypes.is_int(src_dtype) or dtypes.is_int(dest_dtype) and dtypes.is_float(src_dtype) or (dtypes.is_float(src_dtype) and dtypes.is_float(dest_dtype) and dest_dtype.itemsize != src_dtype.itemsize)
-
-def render_cast(ins, inp, out):
-  if inp.dtype == dtypes.bool and (dtypes.is_float(out.dtype) or dtypes.is_int(out.dtype)):
-    ins.append(f"selp.{dtype_to_nvtype[out.dtype]} {out}, {'0f3F800000, 0f00000000' if dtypes.is_float(out.dtype) else '1, 0'}, {inp};")
-  elif out.dtype == dtypes.bool:
-    if inp.dtype == dtypes.bool:
-      ins.append(f"mov.pred {out}, {inp};")
-    else:
-      ins.append(f"setp.ne.{dtype_to_nvtype[inp.dtype]} {out}, {'0f00000000' if dtypes.is_float(inp.dtype) else '0'}, {inp};")
-  else:
-    round_mod = ".rzi" if dtypes.is_int(out.dtype) and dtypes.is_float(inp.dtype) else '.rz' if dtypes.is_float(out.dtype) and (dtypes.is_int(inp.dtype) or dtypes.is_float(inp.dtype) and inp.dtype.itemsize > out.dtype.itemsize) else ''
-    ins.append(f"cvt{round_mod}.{dtype_to_nvtype[out.dtype]}.{dtype_to_nvtype[inp.dtype]} {out}, {inp};")
-
-# https://docs.nvidia.com/cuda/parallel-thread-execution/#
-
-class PTXLanguage(AssemblyLanguage):
-  supports_constant_folding: bool = True
-
-def specialize_to_ptx(lang, function_name):
-  param_cnt = 0
-  ins = []
-  alu = {BinaryOps.ADD: "add", BinaryOps.SUB: "sub", BinaryOps.MUL: "mul", BinaryOps.DIV: "div", BinaryOps.MAX: "max",
-         BinaryOps.MOD: "rem", BinaryOps.CMPLT: "setp.lt", UnaryOps.SQRT: "sqrt.approx",
-         UnaryOps.NOOP: "mov", UnaryOps.NEG: "neg",
-         UnaryOps.SIN: "sin.approx", UnaryOps.LOG2: "lg2.approx", UnaryOps.EXP2: "ex2.approx.ftz",
-         TernaryOps.MULACC: "fma.rn", TernaryOps.WHERE: "selp"}
-  for uop, out, vin, arg in lang.ins:
-    if uop == Ops.ENDLOOP:
-      ins.append("bar.sync 0;")
-    elif uop == Ops.DEFINE_LOCAL:
-      ins.append(f".shared .align 4 .b8 {arg[0]}[{arg[1]*4}];")
-    elif uop == Ops.SPECIAL:
-      if arg.startswith('data'):
-        param_cnt += 1
-        ins.append(f"ld.param.u64 {out}, [{arg}];")
-        # TODO: we sometimes want this to be local, nvcc converts to global most of the time, not sure when we would need to?
-        # ins.append(f"cvta.to.global.u64 {out}, {out};")
-      elif arg.startswith('gid'):
-        ins.append(f"mov.u32 {out}, %ctaid.{'xyz'[int(arg[3:])]};")
-      elif arg.startswith('lid'):
-        ins.append(f"mov.u32 {out}, %tid.{'xyz'[int(arg[3:])]};")
-    elif uop == Ops.ALU:
-      if arg == BinaryOps.MUL and out.dtype == dtypes.bool:
-        ins.append(f"and.pred {out}, {', '.join(str(x) for x in vin)};")
-      else:
-        otype = vin[0].dtype if arg in [BinaryOps.CMPLT] else out.dtype
-        if arg == TernaryOps.WHERE:
-          if vin[0].dtype == dtypes.bool:
-            reg = vin[0]
-          else:
-            reg = lang.newreg((vin[0], 'bool'), dtypes.bool)
-            ins.append(f"setp.ne.{dtype_to_nvtype[vin[0].dtype]} {reg}, {'0f00000000' if dtypes.is_float(vin[0].dtype) else '0'}, {vin[0]};")
-          vin = vin[1:] + [reg]
-        ins.append(f"{alu[arg]}{'.lo' if arg == BinaryOps.MUL and out.dtype != dtypes.float32 else ''}{'.rn' if arg == BinaryOps.DIV and out.dtype == dtypes.float32 else ''}.{dtype_to_nvtype[otype]} {out}, {', '.join(str(x) for x in vin)};")
-    elif uop == Ops.LOAD:
-      if arg.__class__ in (int, float):
-        ins.append(f"mov.{dtype_to_nvtype[out.dtype]} {out}, {'0f'+float_to_hex(arg) if dtypes.is_float(out.dtype) else int(arg)};")
-      elif arg[2] is not None and (arg[2] == dtypes.bool or arg[2] != out.dtype):
-        dt = ('u16', dtypes.uint16) if arg[2] == dtypes.bool == out.dtype else ('u8', dtypes.uint8) if arg[2] == dtypes.bool else ('b16', dtypes.float16) if arg[2] == dtypes.half else (dtype_to_nvtype[arg[2]], arg[2])
-        reg = lang.newreg((out, dt[0]), dtype=dt[1])
-        ins.append(f"ld.{arg[1]}.{dt[0]} {reg}, [{vin[0]}{f'+{arg[0]}' if arg[0] is not None else ''}];")
-        render_cast(ins, reg, out)
-      else:
-        ins.append(f"ld.{arg[1]}.{dtype_to_nvtype[dtypes.float if arg[2] is None else arg[2]]} {out}, [{vin[0]}{f'+{arg[0]}' if arg[0] is not None else ''}];")
-    elif uop == Ops.STORE:
-      if ptx_needs_cast(dtypes.float if arg[2] is None else arg[2], vin[1].dtype) or arg[2] == dtypes.bool:
-        if arg[2] == dtypes.bool != vin[1].dtype:
-          prereg = lang.newreg((vin[1],'bool'), dtype=dtypes.bool)
-          render_cast(ins, vin[1], prereg)
-        else: prereg = vin[1]
-        reg = lang.newreg((prereg, dtypes.uint16 if arg[2] == dtypes.bool else arg[2]), dtype=dtypes.uint16 if arg[2] == dtypes.bool else dtypes.float if arg[2] is None else arg[2])
-        render_cast(ins, prereg, reg)
-        ins.append(f"st.{arg[1]}.{dtype_to_nvtype['bits16' if arg[2] == dtypes.float16 else dtypes.uint8 if arg[2] == dtypes.bool else dtypes.float if arg[2] is None else arg[2]]} [{vin[0]}{f'+{arg[0]}' if arg[0] is not None else ''}], {reg};")
-      else:
-        ins.append(f"st.{arg[1]}.{dtype_to_nvtype[dtypes.float if arg[2] is None else arg[2]]} [{vin[0]}{f'+{arg[0]}' if arg[0] is not None else ''}], {vin[1]};")
-    elif uop == Ops.CAST:
-      render_cast(ins, vin[0], out)
-    elif uop == Ops.LABEL:
-      ins.append(f"{arg}:")
-    elif uop == Ops.COND_BRANCH:
-      ins.append(f"@{'!' if not arg[1] else ''}{vin[0]} bra {arg[0]};")
-
-  ins_prefix = [".version 7.8", ".target " + arch(), ".address_size 64",
-                f".visible .entry {function_name}({', '.join(f'.param .u64 data{i}' for i in range(param_cnt))}) {{"]
-  for arg in [(dtype, lang.type_to_letter(dtype), c) for dtype,c in lang.cnts.items()]: ins_prefix.append(f".reg .{dtype_to_nvtype[arg[0][0]]} %{arg[1]}<{arg[2]}>;",)
-  ins = ins_prefix + ins
-  ins += ["ret;", "}"]
-  return '\n'.join(ins)
-
-def uops_to_ptx_asm(function_name:str, uops:List[UOp]):
-  lang = PTXLanguage()
-  global_size, local_size = uops_to_asmstyle(lang, function_name, uops)
-  return specialize_to_ptx(lang, function_name), global_size[::-1], local_size[::-1], True
@@ -1,203 +0,0 @@
-import yaml
-from typing import Tuple, Set, Dict
-from tinygrad import dtypes
-from tinygrad.codegen.assembly import AssemblyCodegen, Register
-from tinygrad.codegen.opt.kernel import Ops
-from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
-from tinygrad.runtime.ops_cl import ROCM_LLVM_PATH
-
-# ugh, is this really needed?
-from extra.helpers import enable_early_exec
-early_exec = enable_early_exec()
-
-boilerplate_start = """
-.global _start
-_start:
-.rodata
-.align 0x10
-.global code.kd
-.type code.kd,STT_OBJECT
-.amdhsa_kernel code"""
-
-code_start = """.end_amdhsa_kernel
-.text
-code:
-"""
-
-# https://github.com/RadeonOpenCompute/ROCm_Documentation/blob/master/ROCm_Compiler_SDK/ROCm-Codeobj-format.rst
-# https://github.com/ROCm-Developer-Tools/ROCm-ComputeABI-Doc/blob/master/AMDGPU-ABI.md#initial-kernel-register-state
-# RDNA3 is actually a SIMD machine!
-class RDNACodegen(AssemblyCodegen):
-  supports_float4: bool = True
-  supports_float4_alu: bool = True
-  supports_load3: bool = True
-  sin_is_sin2pi: bool = True
-  no_div: bool = True
-
-  def specialize(self, asm) -> Tuple[str, str]:
-    args = []
-    for i,b in enumerate(self.bufs): args.append({'.address_space': 'global', '.name': f'buf_{i}', '.offset': i*8, '.size': 8, '.type_name': b.dtype.name+"*", '.value_kind': 'global_buffer'})
-    ins = []
-
-    v_cnt = 3  # v[0:2] is local_xyz
-    s_cnt = 5  # s[0:1] is the address, s[2:4] is global_xyz
-
-    dtype_to_rdnatype = {dtypes.float32: "f32", dtypes.int64: "i64", dtypes.int32: "i32", dtypes.uint64: "u64", dtypes.bool: "i32"}
-    alu = {BinaryOps.ADD: "add", BinaryOps.SUB: "sub", BinaryOps.MUL: "mul", TernaryOps.MULACC: "fma",
-           BinaryOps.MAX: "max", UnaryOps.RECIP: "rcp",
-           UnaryOps.NOOP: "mov", UnaryOps.SIN: "sin", UnaryOps.LOG2: "log", UnaryOps.EXP2: "exp",
-           BinaryOps.CMPLT: "cmp_lt"}
-
-    pend_regs:Set[Register] = set()
-    rtor:Dict[Register, str] = {}
-    def reg_in(x):
-      nonlocal pend_regs
-      #print("reg_in", x, rtor[x], pend_regs)
-      if x in pend_regs:
-        #print("clear")
-        ins.append('s_waitcnt lgkmcnt(0), vmcnt(0)')
-        pend_regs.clear()
-      return rtor[x]
-    def reg_out(x):
-      return rtor[x]
-    for uop, out, vin, arg in asm:
-      if uop == Ops.DEFINE_REGISTER:
-        if arg[0][0] in [dtypes.uint32, dtypes.uint64, dtypes.int64, dtypes.int32, dtypes.float32, dtypes.float.vec(4)]:
-          for i in range(arg[2]):
-            # TODO: Re-use gaps created by this to avoid wasting registers
-            align = int(arg[0][0].itemsize / 4)
-            if arg[0][1]:
-              s_cnt += s_cnt % align
-              reg_name = f"s[{s_cnt}:{s_cnt + align - 1}]" if align > 1 else f"s{s_cnt}"
-              s_cnt += align
-            else:
-              v_cnt += v_cnt % align
-              reg_name = f"v[{v_cnt}:{v_cnt + align - 1}]" if align > 1 else f"v{v_cnt}"
-              v_cnt += align
-            rtor[Register(f"%{arg[1]}{i}", *arg[0])] = reg_name
-
-            if arg[0][0] == dtypes.float.vec(4):
-              for off in range(4):
-                reg_name = f"s{s_cnt-align+off}" if arg[0][1] else f"v{v_cnt-align+off}"
-                rtor[Register(f"%{arg[1]}{i}", dtypes.float, False, off=off)] = reg_name
-        elif arg[0][0] == dtypes.bool:
-          for i in range(arg[2]):
-            reg_name = "scc" if arg[0][1] else "vcc_lo" # `_lo` suffix since we're running wavefront_size=32
-            rtor[Register(f"%{arg[1]}{i}", *arg[0])] = reg_name
-        else:
-          raise NotImplementedError("DEFINE_REGISTER not implemented for arg: ", arg)
-      elif uop == Ops.SPECIAL:
-        if arg.startswith('buf'):
-          i = int(arg[3:])
-          ins.append(f's_load_b64 {reg_out(out)}, s[0:1], {i*8}')
-          pend_regs.add(out)
-          for r in out.subregs(): pend_regs.add(r)
-        elif arg.startswith('gid'):
-          ins.append(f'v_mov_b32 {reg_out(out)}, s{2+int(arg[3])}')
-          # the docs lied, this is actually y
-          if int(arg[3]) == 2: ins.append("v_bfe_u32 v2, v0, 20, 10")  # untested
-          if int(arg[3]) == 1: ins.append("v_bfe_u32 v1, v0, 10, 10")
-          elif int(arg[3]) == 0: ins.append("v_and_b32_e32 v0, 0x3ff, v0")
-          # get local size
-          offset = len(args)*8
-          args.append({".offset": offset, ".value_kind": f"hidden_group_size_{'xyz'[int(arg[3])]}", ".size": 8})
-          ins.append(f's_load_b32 s{2+int(arg[3])}, s[0:1], {offset}')
-          ins.append('s_waitcnt vmcnt(0) lgkmcnt(0)')
-          pend_regs.clear()
-          ins.append(f'v_mul_i32_i24 {reg_out(out)}, {reg_out(out)}, s{2+int(arg[3])}')
-          ins.append(f'v_add_nc_u32 {reg_out(out)}, v{int(arg[3])}, {reg_out(out)}')
-      elif uop == Ops.CONST:
-        if arg == float('inf'): arg = "0x7f800000"
-        elif arg == float('-inf'): arg = "0xff800000"
-        if out.dtype == dtypes.float.vec(4):
-          for off in range(4):
-            ins.append(f"{'s_' if out.scalar else 'v_'}mov_b32 {reg_out(Register(out.nm, dtypes.float, False, off=off))}, {arg}")
-        else:
-          ins.append(f"{'s_' if out.scalar else 'v_'}mov_b32 {reg_out(out)}, {arg}")
-      elif uop == Ops.ALU:
-        if arg in [BinaryOps.CMPLT]:
-          ins.append(f"{'s' if out.scalar else 'v'}_{alu[arg]}_{dtype_to_rdnatype[out.dtype]} {', '.join(reg_in(x) if x.__class__ is Register else str(x) for x in vin)}")
-        else:
-          alu_arg = alu[arg]
-          if arg == TernaryOps.MULACC and out == vin[2]:
-            alu_arg = "fmac"
-            vin = vin[0:2]
-          if out.dtype == dtypes.float.vec(4):
-            for rr in zip(*[x.subregs() if x.dtype == dtypes.float.vec(4) else [x,x,x,x] for x in [out]+vin]):
-              ins.append(f"{'s_' if rr[0].scalar else 'v_'}{alu_arg}_{dtype_to_rdnatype[rr[0].dtype]} {reg_out(rr[0])}, {', '.join(reg_in(x) if x.__class__ is Register else str(x) for x in rr[1:])}")
-          else:
-            ins.append(f"{'s_' if out.scalar else 'v_'}{alu_arg}_{dtype_to_rdnatype[out.dtype] if arg != UnaryOps.NOOP else 'b32'}{'_i24' if arg == BinaryOps.MUL and out.dtype != dtypes.float32 and not out.scalar else ''} {reg_out(out)}, {', '.join(reg_in(x) if x.__class__ is Register else str(x) for x in vin)}")
-      elif uop == Ops.LOAD:
-        if out.scalar:
-          # swap arg order
-          ins.append(f's_load_b32 {reg_out(out)}, {reg_in(vin[0])}, {reg_in(vin[1])} offset:{arg[0]}')
-        else:
-          ins.append(f'global_load_{"b128" if out.dtype == dtypes.float.vec(4) else "b32"} {reg_out(out)}, {reg_in(vin[1])}, {reg_in(vin[0])} offset:{arg[0]}')
-        pend_regs.add(out)
-        for r in out.subregs(): pend_regs.add(r)
-      elif uop == Ops.STORE:
-        ins.append(f'global_store_{"b128" if vin[1].dtype == dtypes.float.vec(4) else "b32"} {reg_in(vin[2])}, {reg_in(vin[1])}, {reg_in(vin[0])} offset:{arg[0]}')
-      elif uop == Ops.LABEL:
-        ins.append(f"{arg}:")
-      elif uop == Ops.COND_BRANCH:
-        ins.append(f"s_cbranch_scc{'1' if arg[1] else '0'} {arg[0]}")
-      elif uop == Ops.CAST:
-        if vin[0].dtype == dtypes.bool:
-          if out.dtype == dtypes.float32:
-            ins.append(f"v_cndmask_b32 {reg_out(out)}, 0.0, 1.0, {reg_in(vin[0])}")
-        else:
-          raise NotImplementedError(f"cast {vin[0].dtype} -> {out.dtype}")
-      else:
-        raise NotImplementedError(uop)
-
-    ins += ['s_sendmsg sendmsg(MSG_DEALLOC_VGPRS)', 's_endpgm', 's_code_end']
-
-    # dual alu group
-    seen = set()
-    new_ins = []
-    for i,tins in enumerate(ins):
-      if tins in seen: continue
-      if tins.startswith("v_fmac_f32"):
-        for gins in reversed(ins[i+1:]):
-          if gins in seen: continue
-          if gins.startswith("v_fmac_f32"):
-            r0 = [int(x[1:].strip(',')) for x in tins.split(" ")[1:]]
-            r1 = [int(x[1:].strip(',')) for x in gins.split(" ")[1:]]
-            if r0[0]%2 == r1[0]%2: continue
-            if r0[1]%2 == r1[1]%2: continue
-            if r0[2]%2 == r1[2]%2: continue
-            new_ins.append(tins.replace("v_", "v_dual_")+" :: " + gins.replace("v_", "v_dual_"))
-            seen.add(tins)
-            seen.add(gins)
-            break
-      if tins not in seen:
-        new_ins.append(tins)
-    ins = new_ins
-
-    return 'code', self.assemble(args, ins, v_cnt, s_cnt)
-
-  def assemble(self, args, ins, v_cnt, s_cnt):
-    kernel_desc = {'.amdhsa_group_segment_fixed_size': 0, '.amdhsa_private_segment_fixed_size': 0, '.amdhsa_kernarg_size': 0,
-                   '.amdhsa_next_free_vgpr': v_cnt,   # this matters!
-                   '.amdhsa_reserve_vcc': 0, '.amdhsa_reserve_xnack_mask': 0,
-                   '.amdhsa_next_free_sgpr': s_cnt,
-                   '.amdhsa_float_round_mode_32': 0, '.amdhsa_float_round_mode_16_64': 0, '.amdhsa_float_denorm_mode_32': 3, '.amdhsa_float_denorm_mode_16_64': 3, '.amdhsa_dx10_clamp': 1, '.amdhsa_ieee_mode': 1,
-                   '.amdhsa_fp16_overflow': 0, '.amdhsa_workgroup_processor_mode': 1, '.amdhsa_memory_ordered': 1, '.amdhsa_forward_progress': 0, '.amdhsa_enable_private_segment': 0,
-                   '.amdhsa_system_sgpr_workgroup_id_x': 1, '.amdhsa_system_sgpr_workgroup_id_y': 1, '.amdhsa_system_sgpr_workgroup_id_z': 1,
-                   '.amdhsa_system_sgpr_workgroup_info': 0, '.amdhsa_system_vgpr_workitem_id': 2, # is amdhsa_system_vgpr_workitem_id real?
-                   '.amdhsa_exception_fp_ieee_invalid_op': 0, '.amdhsa_exception_fp_denorm_src': 0, '.amdhsa_exception_fp_ieee_div_zero': 0, '.amdhsa_exception_fp_ieee_overflow': 0, '.amdhsa_exception_fp_ieee_underflow': 0,
-                   '.amdhsa_exception_fp_ieee_inexact': 0, '.amdhsa_exception_int_div_zero': 0, '.amdhsa_user_sgpr_dispatch_ptr': 0, '.amdhsa_user_sgpr_queue_ptr': 0, '.amdhsa_user_sgpr_kernarg_segment_ptr': 1,
-                   '.amdhsa_user_sgpr_dispatch_id': 0, '.amdhsa_user_sgpr_private_segment_size': 0, '.amdhsa_wavefront_size32': 1, '.amdhsa_uses_dynamic_stack': 0}
-
-    metadata = {'amdhsa.kernels': [{'.args': args,
-                  '.group_segment_fixed_size': 0, '.kernarg_segment_align': 8, '.kernarg_segment_size': args[-1][".offset"] + args[-1][".size"],
-                  '.language': 'OpenCL C', '.language_version': [1, 2], '.max_flat_workgroup_size': 256,
-                  '.name': 'code', '.private_segment_fixed_size': 0, '.sgpr_count': s_cnt, '.sgpr_spill_count': 0,
-                  '.symbol': 'code.kd', '.uses_dynamic_stack': False, '.vgpr_count': v_cnt, '.vgpr_spill_count': 0,
-                  '.wavefront_size': 32}],
-                'amdhsa.target': 'amdgcn-amd-amdhsa--gfx1100', 'amdhsa.version': [1, 2]}
-
-    code = boilerplate_start + "\n" + '\n'.join("%s %d" % x for x in kernel_desc.items()) + "\n" +  code_start + '\n'.join(ins) + "\n.amdgpu_metadata\n" + yaml.dump(metadata) + ".end_amdgpu_metadata"
-    obj = early_exec(([ROCM_LLVM_PATH / "llvm-mc", '--arch=amdgcn', '--mcpu=gfx1100', '--triple=amdgcn-amd-amdhsa', '--filetype=obj', '-'], code.encode("utf-8")))
-    asm = early_exec(([ROCM_LLVM_PATH / "ld.lld", "/dev/stdin", "-o", "/dev/stdout", "--pie"], obj))
-    return asm
@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-import numpy as np
-from tinygrad.runtime.ops_cuda import CUDAProgram, RawCUDABuffer
-
-if __name__ == "__main__":
-  test = RawCUDABuffer.fromCPU(np.zeros(10, np.float32))
-  prg = CUDAProgram("test", """
-  .version 7.8
-  .target sm_86
-  .address_size 64
-  .visible .entry test(.param .u64 x) {
-    .reg .b32       %r<2>;
-    .reg .b64       %rd<3>;
-
-    ld.param.u64    %rd1, [x];
-    cvta.to.global.u64      %rd2, %rd1;
-    mov.u32         %r1, 0x40000000; // 2.0 in float
-    st.global.u32   [%rd2], %r1;
-    ret;
-  }""", binary=True)
-  prg([1], [1], test)
-  print(test.toCPU())
-
@@ -1,4 +0,0 @@
-*.deb
-build
-src
-sniffer/sniff.so
@@ -1,20 +0,0 @@
-Built ROCT-Thunk-Interface (hsakmt)
-  hsakmt-roct-dev_5.4.4.99999-local_amd64.deb
-  note: installs to /opt/rocm
-Built ROCm-Device-Libs
-  Works with ROCM_PATH=/home/tiny/build/ROCm-Device-Libs/build/dist
-  rocm-device-libs_1.0.0.99999-local_amd64.deb
-Built ROCm-CompilerSupport (amd_comgr)
-  no deb, sudo make install to /usr/local
-Built ROCR-Runtime
-  hsa-rocr_1.8.0-local_amd64.deb
-  hsa-rocr-dev_1.8.0-local_amd64.deb
-Built ROCm-OpenCL-Runtime
-  rocm-ocl-icd_2.0.0-local_amd64.deb
-  ISSUE: these depend on "comgr"
-  rocm-opencl_2.0.0-local_amd64.deb
-  rocm-opencl-dev_2.0.0-local_amd64.deb
-  Did sudo make install
-
-
-
@@ -1,41 +0,0 @@
-# run two "rocm-bandwidth-test" in a loop
-# amdgpu-6.0.5-1581431.20.04
-# fixed in kernel 6.2.14
-
-[   72.153646] RIP: 0010:pm_send_runlist+0x4a/0x630 [amdgpu]
-[   72.153815] Code: 30 65 48 8b 04 25 28 00 00 00 48 89 45 d0 31 c0 80 fb 01 0f 87 aa 9d 49 00 83 e3 01 0f 85 1c 05 00 00 49 8b 3f b8 01 00 00 00 <48> 8b 97 30 01 00 00 44 8b b7 6c 01 00 00 8b 9f 70 01 00 00 8b 8a
-[   72.153900] RSP: 0018:ffffb48445c03c30 EFLAGS: 00010246
-[   72.153928] RAX: 0000000000000001 RBX: 0000000000000000 RCX: 0000000000000000
-[   72.153962] RDX: 000000000000007b RSI: ffff9395e1562558 RDI: 0000000000000000
-[   72.153996] RBP: ffffb48445c03cb8 R08: 0000000000000000 R09: 0000000000000001
-[   72.154030] R10: ffff9395c900d840 R11: 0000000000000000 R12: 0000000000000000
-[   72.154065] R13: ffff9395c9e00400 R14: 0000000000000001 R15: ffff9395e15624e0
-[   72.154099] FS:  00007f345c6463c0(0000) GS:ffff93a4aee80000(0000) knlGS:0000000000000000
-[   72.154137] CS:  0010 DS: 0000 ES: 0000 CR0: 0000000080050033
-[   72.154165] CR2: 0000000000000130 CR3: 0000000112840000 CR4: 0000000000750ee0
-[   72.154201] PKRU: 55555554
-[   72.154215] Call Trace:
-[   72.154230]  <TASK>
-[   72.154244]  map_queues_cpsch+0x75/0xc0 [amdgpu]
-[   72.154365]  debug_map_and_unlock+0x51/0x90 [amdgpu]
-[   72.154480]  debug_refresh_runlist+0x1f/0x30 [amdgpu]
-[   72.154591]  kfd_dbg_runtime_disable+0x13c/0x240 [amdgpu]
-[   72.154705]  kfd_ioctl_dbg_set_debug_trap+0x69d/0x8b0 [amdgpu]
-[   72.154820]  kfd_ioctl+0x24a/0x5b0 [amdgpu]
-[   72.154925]  ? kfd_ioctl_create_queue+0x770/0x770 [amdgpu]
-[   72.155035]  ? syscall_exit_to_user_mode+0x27/0x50
-[   72.155061]  ? exit_to_user_mode_prepare+0x3d/0x1c0
-[   72.155088]  __x64_sys_ioctl+0x95/0xd0
-[   72.155109]  do_syscall_64+0x5c/0xc0
-[   72.155128]  ? syscall_exit_to_user_mode+0x27/0x50
-[   72.155151]  ? do_syscall_64+0x69/0xc0
-[   72.155172]  entry_SYSCALL_64_after_hwframe+0x61/0xcb
-[   72.155198] RIP: 0033:0x7f345c7f63ab
-[   72.155218] Code: 0f 1e fa 48 8b 05 e5 7a 0d 00 64 c7 00 26 00 00 00 48 c7 c0 ff ff ff ff c3 66 0f 1f 44 00 00 f3 0f 1e fa b8 10 00 00 00 0f 05 <48> 3d 01 f0 ff ff 73 01 c3 48 8b 0d b5 7a 0d 00 f7 d8 64 89 01 48
-[   72.155301] RSP: 002b:00007ffc97cc89f8 EFLAGS: 00000246 ORIG_RAX: 0000000000000010
-[   72.155339] RAX: ffffffffffffffda RBX: 00007ffc97cc8a30 RCX: 00007f345c7f63ab
-[   72.155375] RDX: 00007ffc97cc8a30 RSI: 00000000c0284b82 RDI: 0000000000000003
-[   72.155411] RBP: 00000000c0284b82 R08: 0000000000000000 R09: 0000000000000000
-[   72.155447] R10: 00007f345cd4ddb0 R11: 0000000000000246 R12: 00007ffc97cc8a30
-[   72.155481] R13: 0000000000000003 R14: 00007ffc97cc8d20 R15: 0000000000000000
-[   72.155517]  </TASK>
@@ -1,41 +0,0 @@
-# run two tinygrad matrix example in a loop
-# amdgpu-6.0.5-1581431.20.04
-# NOT fixed in kernel 6.2.14
-
-[  553.016624] gmc_v11_0_process_interrupt: 30 callbacks suppressed
-[  553.016631] amdgpu 0000:0b:00.0: amdgpu: [gfxhub] page fault (src_id:0 ring:24 vmid:9 pasid:32770, for process python3 pid 10001 thread python3 pid 10001)
-[  553.016790] amdgpu 0000:0b:00.0: amdgpu:   in page starting at address 0x00007f0000000000 from client 10
-[  553.016892] amdgpu 0000:0b:00.0: amdgpu: GCVM_L2_PROTECTION_FAULT_STATUS:0x00901A30
-[  553.016974] amdgpu 0000:0b:00.0: amdgpu:      Faulty UTCL2 client ID: SDMA0 (0xd)
-[  553.017051] amdgpu 0000:0b:00.0: amdgpu:      MORE_FAULTS: 0x0
-[  553.017111] amdgpu 0000:0b:00.0: amdgpu:      WALKER_ERROR: 0x0
-[  553.017173] amdgpu 0000:0b:00.0: amdgpu:      PERMISSION_FAULTS: 0x3
-[  553.017238] amdgpu 0000:0b:00.0: amdgpu:      MAPPING_ERROR: 0x0
-[  553.017300] amdgpu 0000:0b:00.0: amdgpu:      RW: 0x0
-[  553.123921] [drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=2
-[  553.124153] amdgpu: failed to add hardware queue to MES, doorbell=0x1a16
-[  553.124195] amdgpu: MES might be in unrecoverable state, issue a GPU reset
-[  553.124237] amdgpu: Failed to restore queue 2
-[  553.124266] amdgpu: Failed to restore process queues
-[  553.124270] amdgpu: Failed to evict queue 3
-[  553.124297] amdgpu: amdgpu_amdkfd_restore_userptr_worker: Failed to resume KFD
-
-# alternative crash in kernel 6.2.14
-
-[  151.097948] gmc_v11_0_process_interrupt: 30 callbacks suppressed
-[  151.097953] amdgpu 0000:0b:00.0: amdgpu: [gfxhub] page fault (src_id:0 ring:24 vmid:8 pasid:32771, for process python3 pid 7525 thread python3 pid 7525)
-[  151.097993] amdgpu 0000:0b:00.0: amdgpu:   in page starting at address 0x00007f0000000000 from client 10
-[  151.098008] amdgpu 0000:0b:00.0: amdgpu: GCVM_L2_PROTECTION_FAULT_STATUS:0x00801A30
-[  151.098020] amdgpu 0000:0b:00.0: amdgpu:      Faulty UTCL2 client ID: SDMA0 (0xd)
-[  151.098032] amdgpu 0000:0b:00.0: amdgpu:      MORE_FAULTS: 0x0
-[  151.098042] amdgpu 0000:0b:00.0: amdgpu:      WALKER_ERROR: 0x0
-[  151.098052] amdgpu 0000:0b:00.0: amdgpu:      PERMISSION_FAULTS: 0x3
-[  151.098062] amdgpu 0000:0b:00.0: amdgpu:      MAPPING_ERROR: 0x0
-[  151.098071] amdgpu 0000:0b:00.0: amdgpu:      RW: 0x0
-[  151.209517] [drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=2
-[  151.209724] amdgpu: failed to add hardware queue to MES, doorbell=0x1002
-[  151.209734] amdgpu: MES might be in unrecoverable state, issue a GPU reset
-[  151.209743] amdgpu: Failed to restore queue 1
-[  151.209751] amdgpu: Failed to restore process queues
-[  151.209759] amdgpu: amdgpu_amdkfd_restore_userptr_worker: Failed to resume KFD
-[  151.209858] amdgpu 0000:0b:00.0: amdgpu: GPU reset begin!
@@ -1,20 +0,0 @@
-# two tinygrad + two bandwidth test
-# RDNA2, driver 6.0.5
-# recovered from this!
-
-[  136.971209] gmc_v10_0_process_interrupt: 39 callbacks suppressed
-[  136.971218] amdgpu 0000:0b:00.0: amdgpu: [gfxhub] page fault (src_id:0 ring:24 vmid:11 pasid:32773, for process rocm-bandwidth- pid 20281 thread rocm-bandwidth- pid 20281)
-[  136.971228] amdgpu 0000:0b:00.0: amdgpu:   in page starting at address 0x00007f5c2b800000 from client 0x1b (UTCL2)
-[  136.971232] amdgpu 0000:0b:00.0: amdgpu: GCVM_L2_PROTECTION_FAULT_STATUS:0x00B01A31
-[  136.971233] amdgpu 0000:0b:00.0: amdgpu:      Faulty UTCL2 client ID: SDMA0 (0xd)
-[  136.971235] amdgpu 0000:0b:00.0: amdgpu:      MORE_FAULTS: 0x1
-[  136.971236] amdgpu 0000:0b:00.0: amdgpu:      WALKER_ERROR: 0x0
-[  136.971236] amdgpu 0000:0b:00.0: amdgpu:      PERMISSION_FAULTS: 0x3
-[  136.971237] amdgpu 0000:0b:00.0: amdgpu:      MAPPING_ERROR: 0x0
-[  136.971238] amdgpu 0000:0b:00.0: amdgpu:      RW: 0x0
-...
-[  136.993979] amdgpu 0000:0b:00.0: amdgpu: IH ring buffer overflow (0x000BE5A0, 0x0003C480, 0x0003E5C0)
-[  138.209072] amdgpu 0000:0b:00.0: AMD-Vi: Event logged [IO_PAGE_FAULT domain=0x001a address=0x7c00004000 flags=0x0000]
-[  138.209078] amdgpu 0000:0b:00.0: AMD-Vi: Event logged [IO_PAGE_FAULT domain=0x001a address=0x7c00004d80 flags=0x0000]
-[  138.209081] amdgpu 0000:0b:00.0: AMD-Vi: Event logged [IO_PAGE_FAULT domain=0x001a address=0x7c00005000 flags=0x0000]
-[  138.209084] amdgpu 0000:0b:00.0: AMD-Vi: Event logged [IO_PAGE_FAULT domain=0x001a address=0x7c00005d80 flags=0x0000]
@@ -1,33 +0,0 @@
-# ROCK-Kernel-Driver 0b579de9622f5c93021dcb7927d13926313740a2
-# non fatal "crash"
-
-[  127.418045] ------------[ cut here ]------------
-[  127.418046] User pages unexpectedly invalid
-[  127.418056] WARNING: CPU: 16 PID: 260 at drivers/gpu/drm/amd/amdgpu/amdgpu_amdkfd_gpuvm.c:3000 amdgpu_amdkfd_restore_userptr_worker+0x4d9/0x500 [amdgpu]
-[  127.418235] Modules linked in: rfcomm cmac algif_hash algif_skcipher af_alg bnep nls_iso8859_1 iwlmvm mac80211 intel_rapl_msr intel_rapl_common edac_mce_amd snd_hda_codec_realtek snd_hda_codec_generic snd_hda_codec_hdmi kvm_amd binfmt_misc snd_hda_intel snd_intel_dspcfg kvm libarc4 snd_intel_sdw_acpi snd_hda_codec btusb iwlwifi btrtl snd_hda_core btbcm btintel irqbypass btmtk snd_hwdep crct10dif_pclmul snd_pcm polyval_clmulni bluetooth snd_seq_midi snd_seq_midi_event snd_rawmidi snd_seq polyval_generic cfg80211 ghash_clmulni_intel eeepc_wmi snd_seq_device snd_timer aesni_intel asus_wmi ecdh_generic snd platform_profile crypto_simd ledtrig_audio cryptd ecc ccp soundcore sparse_keymap rapl k10temp wmi_bmof mac_hid sch_fq_codel msr parport_pc ppdev lp parport ramoops pstore_blk efi_pstore reed_solomon pstore_zone ip_tables x_tables autofs4 amdgpu hid_generic usbhid hid i2c_algo_bit drm_ttm_helper ttm video iommu_v2 drm_buddy gpu_sched drm_display_helper drm_kms_helper syscopyarea
-[  127.418276]  sysfillrect sysimgblt fb_sys_fops drm nvme nvme_core cec r8169 ahci crc32_pclmul rc_core i2c_piix4 xhci_pci libahci nvme_common xhci_pci_renesas realtek wmi
-[  127.418284] CPU: 16 PID: 260 Comm: kworker/16:1 Tainted: G        W          6.0.0 #4
-[  127.418286] Hardware name: System manufacturer System Product Name/TUF GAMING X570-PLUS (WI-FI), BIOS 3603 03/20/2021
-[  127.418287] Workqueue: events amdgpu_amdkfd_restore_userptr_worker [amdgpu]
-[  127.418455] RIP: 0010:amdgpu_amdkfd_restore_userptr_worker+0x4d9/0x500 [amdgpu]
-[  127.418601] Code: ff e8 2b 8a 96 d1 e9 66 fe ff ff 48 c7 c7 40 4f f5 c0 e8 56 7b 8a d1 0f 0b e9 2e ff ff ff 48 c7 c7 d8 d0 ed c0 e8 43 7b 8a d1 <0f> 0b e9 0a fe ff ff 4c 89 ef e8 f8 89 96 d1 e9 cb fd ff ff e8 ce
-[  127.418603] RSP: 0018:ffffb36740a83dc8 EFLAGS: 00010282
-[  127.418604] RAX: 0000000000000000 RBX: ffff9d159ee9df30 RCX: 0000000000000027
-[  127.418605] RDX: 0000000000000027 RSI: ffffb36740a83c88 RDI: ffff9d242a220568
-[  127.418606] RBP: ffffb36740a83e58 R08: ffff9d242a220560 R09: 0000000000000001
-[  127.418607] R10: 0000000000000001 R11: 0000000000000020 R12: ffff9d159ee9df98
-[  127.418607] R13: ffff9d159ee9df70 R14: ffff9d159ee9dee0 R15: ffff9d159ee9dee0
-[  127.418608] FS:  0000000000000000(0000) GS:ffff9d242a200000(0000) knlGS:0000000000000000
-[  127.418609] CS:  0010 DS: 0000 ES: 0000 CR0: 0000000080050033
-[  127.418610] CR2: 00007fd5d4715000 CR3: 0000000120ffe000 CR4: 0000000000750ee0
-[  127.418611] PKRU: 55555554
-[  127.418611] Call Trace:
-[  127.418612]  <TASK>
-[  127.418613]  process_one_work+0x21f/0x3f0
-[  127.418615]  worker_thread+0x4a/0x3c0
-[  127.418617]  ? process_one_work+0x3f0/0x3f0
-[  127.418618]  kthread+0xf0/0x120
-[  127.418619]  ? kthread_complete_and_exit+0x20/0x20
-[  127.418620]  ret_from_fork+0x22/0x30
-[  127.418622]  </TASK>
-[  127.418623] ---[ end trace 0000000000000000 ]---
@@ -1,80 +0,0 @@
-import numpy as np
-import pathlib
-from hexdump import hexdump
-from tinygrad.helpers import colored
-from extra.helpers import enable_early_exec
-early_exec = enable_early_exec()
-
-from tinygrad.runtime.ops_cl import CLProgram, CLBuffer, ROCM_LLVM_PATH
-
-ENABLE_NON_ASM = False
-
-WMMA = True
-DUAL_ALU = True
-F32 = True
-
-if ENABLE_NON_ASM:
-  buf = CLBuffer.fromCPU(np.zeros(10, np.float32))
-  prg_empty = CLProgram("code", "__kernel void code(__global float *a) { a[0] = 1; }")
-  asm_real = prg_empty.binary()
-  with open("/tmp/cc.elf", "wb") as f:
-    f.write(asm_real)
-  prg_empty([1], [1], buf, wait=True)
-  print(buf.toCPU())
-
-print(colored("creating CLBuffer", "green"))
-buf = CLBuffer.fromCPU(np.zeros(10, np.float32))
-code = open(pathlib.Path(__file__).parent / "prog.s", "r").read()
-
-gen = []
-FLOPS = 0
-MAX_REG = 251
-for j in range(1):
-  if WMMA:
-    KY, KX = 4, 4
-    for y in range(KY):
-      for x in range(KX):
-        c = (y*KX+x)*8
-        a = (KY*KX*8) + y*8
-        b = (KY*KX*8) + (KY*8) + x*8
-        gen.append(f"v_wmma_f32_16x16x16_f16 v[{c}:{c+7}], v[{a}:{a+7}], v[{b}:{b+7}], v[{c}:{c+7}]")
-        FLOPS += 16*8*2
-  else:
-    for i in range(0, MAX_REG, 6):
-      if DUAL_ALU:
-        if F32:
-          gen.append(f"v_dual_fmac_f32 v{i+0}, v{i+1}, v{i+2} :: v_dual_fmac_f32 v{i+3}, v{i+4}, v{i+5}")
-          FLOPS += 4
-        else:
-          gen.append(f"v_dual_dot2acc_f32_f16 v{i+0}, v{i+1}, v{i+2} :: v_dual_dot2acc_f32_f16 v{i+3}, v{i+4}, v{i+5}")
-          FLOPS += 8
-      else:
-        assert F32
-        gen.append(f"v_fmac_f32 v{i+0}, v{i+1}, v{i+2}")
-        gen.append(f"v_fmac_f32 v{i+3}, v{i+4}, v{i+5}")
-code = code.replace("// FLOPS", '\n'.join(gen))
-print(code)
-
-
-# fix: COMGR failed to get code object ISA name. set triple to 'amdgcn-amd-amdhsa'
-
-object = early_exec(([ROCM_LLVM_PATH / "llvm-mc", '--arch=amdgcn', '--mcpu=gfx1100', '--triple=amdgcn-amd-amdhsa', '--filetype=obj', '-'], code.encode("utf-8")))
-asm = early_exec(([ROCM_LLVM_PATH / "ld.lld", "/dev/stdin", "-o", "/dev/stdout", "--pie"], object))
-
-with open("/tmp/cc2.o", "wb") as f:
-  f.write(object)
-with open("/tmp/cc2.elf", "wb") as f:
-  f.write(asm)
-
-print(colored("creating CLProgram", "green"))
-prg = CLProgram("code", asm)
-
-print(colored("running program", "green"))
-G = 512
-FLOPS *= 100000*G*G  # loop * global_size
-for i in range(3):
-  tm = prg(buf, global_size=[G//256, G, 1], local_size=[256, 1, 1], wait=True)
-  print(f"ran in {tm*1e3:.2f} ms, {FLOPS/(tm*1e9):.2f} GFLOPS")
-
-print(colored("transferring buffer", "green"))
-print(buf.toCPU())
@@ -1,80 +0,0 @@
-.global _start
-_start:
-.rodata
-.align 0x10
-.global code.kd
-.type code.kd,STT_OBJECT
-# amd_kernel_code_t (must be at 0x440 for kernel_code_entry_byte_offset to be right)
-code.kd:
-# amd_kernel_..., amd_machine_...
-.long 0,0,0,0
-# kernel_code_entry_byte_offset, kernel_code_prefetch_byte_offset
-.long 0x00000bc0,0x00000000,0x00000000,0x00000000
-# kernel_code_prefetch_byte_size, max_scratch_backing_memory_byte_size
-.long 0,0,0,0
-# compute_pgm_rsrc1, compute_pgm_rsrc2, kernel_code_properties, workitem_private_segment_byte_size
-.long 0x60af0000,0x0000009e,0x00000408,0x00000000
-# compute_pgm_rsrc1 |= AMD_COMPUTE_PGM_RSRC_ONE_FLOAT_DENORM_MODE_32 | AMD_COMPUTE_PGM_RSRC_ONE_FLOAT_DENORM_MODE_16_64
-# compute_pgm_rsrc1 |= AMD_COMPUTE_PGM_RSRC_ONE_ENABLE_DX10_CLAMP | AMD_COMPUTE_PGM_RSRC_ONE_ENABLE_IEEE_MODE
-# compute_pgm_rsrc2 |= AMD_COMPUTE_PGM_RSRC_TWO_USER_SGPR_COUNT = 0xF
-# compute_pgm_rsrc2 |= AMD_COMPUTE_PGM_RSRC_TWO_ENABLE_SGPR_WORKGROUP_ID_X
-# kernel_code_properties |= AMD_KERNEL_CODE_PROPERTIES_ENABLE_SGPR_KERNARG_SEGMENT_PTR = 1
-# kernel_code_properties |= AMD_KERNEL_CODE_PROPERTIES_RESERVED1 = 1
-.text
-.global code
-.type code,STT_FUNC
-code:
-# https://llvm.org/docs/AMDGPUUsage.html#initial-kernel-execution-state
-# s[0:1] contains the kernarg_address
-# TODO: can we use s[2:3] if this was really a wave since we only alloced 2 SGPRs?
-s_load_b64 s[2:3], s[0:1], null
-
-s_mov_b32 s8, 0
-loop:
-s_addk_i32 s8, 1
-s_cmp_eq_u32 s8, 100000
-// FLOPS
-s_cbranch_scc0 loop
-
-# wait for the s_load_b64
-s_waitcnt lgkmcnt(0)
-
-v_dual_mov_b32 v0, 4 :: v_dual_mov_b32 v1, 2.0
-global_store_b32 v0, v1, s[2:3]
-
-# Deallocate all VGPRs for this wave. Use only when next instruction is S_ENDPGM.
-s_sendmsg sendmsg(MSG_DEALLOC_VGPRS)
-s_endpgm
-s_code_end
-
-.amdgpu_metadata
-amdhsa.kernels:
-  - .args:
-      - .address_space:  global
-        .name:           a
-        .offset:         0
-        .size:           8
-        .type_name:      'float*'
-        .value_kind:     global_buffer
-    .group_segment_fixed_size: 0
-    .kernarg_segment_align: 8
-    .kernarg_segment_size: 8
-    .language:       OpenCL C
-    .language_version:
-      - 1
-      - 2
-    .max_flat_workgroup_size: 256
-    .name:           code
-    .private_segment_fixed_size: 0
-    .sgpr_count:     2
-    .sgpr_spill_count: 0
-    .symbol:         code.kd
-    .uses_dynamic_stack: false
-    .vgpr_count:     256
-    .vgpr_spill_count: 0
-    .wavefront_size: 32
-amdhsa.target:   amdgcn-amd-amdhsa--gfx1100
-amdhsa.version:
-  - 1
-  - 2
-.end_amdgpu_metadata
@@ -1,11 +0,0 @@
-#!/bin/bash
-mkdir -p src
-cd src
-git clone https://github.com/RadeonOpenCompute/ROCT-Thunk-Interface.git -b rocm-5.5.0
-git clone https://github.com/RadeonOpenCompute/ROCm-Device-Libs.git -b rocm-5.5.0
-git clone https://github.com/RadeonOpenCompute/llvm-project.git -b rocm-5.5.0 --depth 1
-git clone https://github.com/RadeonOpenCompute/ROCR-Runtime.git -b rocm-5.5.0
-git clone https://github.com/ROCm-Developer-Tools/ROCclr.git -b rocm-5.5.0
-git clone https://github.com/RadeonOpenCompute/ROCm-CompilerSupport.git -b rocm-5.5.0
-git clone https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime.git -b rocm-5.5.0
-cd ../
@@ -1,69 +0,0 @@
-#!/bin/bash
-mkdir -p build/debs
-cd build
-
-# ROCT-Thunk-Interface (hsakmt)
-if [ ! -f debs/hsakmt-roct-dev_5.5.0.99999-local_amd64.deb ]
-then
-  mkdir -p ROCT-Thunk-Interface
-  cd ROCT-Thunk-Interface
-  cmake ../../src/ROCT-Thunk-Interface
-  make -j32 package
-  cp hsakmt-roct-dev_5.5.0.99999-local_amd64.deb ../debs
-  cd ../
-fi
-
-
-# build custom LLVM
-if [ ! -f llvm-project/bin/clang ]
-then
-  mkdir -p llvm-project
-  cd llvm-project
-  cmake -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_PROJECTS="llvm;clang;lld" -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" ../../src/llvm-project/llvm
-  make -j32
-  cd ..
-fi
-
-# use custom LLVM
-export PATH="$PWD/llvm-project/bin:$PATH"
-
-# ROCm-Device-Libs
-if [ ! -f debs/rocm-device-libs_1.0.0.99999-local_amd64.deb ]
-then
-  mkdir -p ROCm-Device-Libs
-  cd ROCm-Device-Libs
-  cmake ../../src/ROCm-Device-Libs
-  make -j32 package
-  cp rocm-device-libs_1.0.0.99999-local_amd64.deb ../debs
-  cd ../
-fi
-
-# ROCR-Runtime
-if [ ! -f debs/hsa-rocr_1.8.0-local_amd64.deb ]
-then
-  mkdir -p ROCR-Runtime
-  cd ROCR-Runtime
-  cmake ../../src/ROCR-Runtime/src
-  make -j32 package
-  cp hsa-rocr_1.8.0-local_amd64.deb ../debs
-  cp hsa-rocr-dev_1.8.0-local_amd64.deb ../debs
-  cd ../
-fi
-
-# ROCm-OpenCL-Runtime (needs ROCclr)
-if [ ! -f debs/rocm-opencl_2.0.0-local_amd64.deb ]
-then
-  mkdir -p ROCm-OpenCL-Runtime
-  cd ROCm-OpenCL-Runtime
-  cmake ../../src/ROCm-OpenCL-Runtime
-  make -j32 package
-  cp rocm-opencl_2.0.0-local_amd64.deb ../debs
-  cp rocm-opencl-dev_2.0.0-local_amd64.deb ../debs
-  cp rocm-ocl-icd_2.0.0-local_amd64.deb ../debs
-fi
-
-# ROCm-CompilerSupport (broken)
-#mkdir -p ROCm-CompilerSupport
-#cd ROCm-CompilerSupport
-#cmake ../../src/ROCm-CompilerSupport/lib/comgr
-#make -j32
@@ -1,14 +0,0 @@
-#!/bin/bash
-rm amdgpu-install_5.5.50500-1_all.deb
-wget https://repo.radeon.com/amdgpu-install/5.5/ubuntu/$(lsb_release -cs)/amdgpu-install_5.5.50500-1_all.deb
-sudo dpkg -i amdgpu-install_5.5.50500-1_all.deb
-sudo apt-get update
-
-# kernel driver
-sudo apt-get install amdgpu-dkms
-
-# for opencl
-sudo apt-get install rocm-opencl-runtime
-
-# for HIP
-sudo apt-get install hip-runtime-amd rocm-device-libs hip-dev
@@ -1,11 +0,0 @@
-#!/bin/bash -e
-clang sniff.cc -Werror -shared -fPIC -I../src/ -I../src/ROCT-Thunk-Interface/include -I../src/ROCm-Device-Libs/ockl/inc -o sniff.so -lstdc++
-#AMD_LOG_LEVEL=4 HSAKMT_DEBUG_LEVEL=7 LD_PRELOAD=$PWD/sniff.so /home/tiny/build/HIP-Examples/HIP-Examples-Applications/HelloWorld/HelloWorld
-#AMD_LOG_LEVEL=4 LD_PRELOAD=$PWD/sniff.so $HOME/build/HIP-Examples/HIP-Examples-Applications/HelloWorld/HelloWorld
-#AMD_LOG_LEVEL=5 LD_PRELOAD=$PWD/sniff.so python3 ../rdna3/asm.py
-DEBUG=5 LD_PRELOAD=$PWD/sniff.so python3 ../rdna3/asm.py
-#AMD_LOG_LEVEL=5 HSAKMT_DEBUG_LEVEL=7 DEBUG=5 LD_PRELOAD=$PWD/sniff.so strace -F python3 ../rdna3/asm.py
-#LD_PRELOAD=$PWD/sniff.so python3 ../rdna3/asm.py
-#AMD_LOG_LEVEL=4 LD_PRELOAD=$PWD/sniff.so FORWARD_ONLY=1 DEBUG=2 python3 ../../../test/test_ops.py TestOps.test_add
-#AMD_LOG_LEVEL=4 HSAKMT_DEBUG_LEVEL=7 LD_PRELOAD=$PWD/sniff.so rocm-bandwidth-test -s 0 -d 1 -m 1
-#AMD_LOG_LEVEL=4 HSAKMT_DEBUG_LEVEL=7 LD_PRELOAD=$PWD/sniff.so rocm-bandwidth-test -s 1 -d 2 -m 1
@@ -1,282 +0,0 @@
-// template copied from https://github.com/geohot/cuda_ioctl_sniffer/blob/master/sniff.cc
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <dlfcn.h>
-#include <signal.h>
-#include <ucontext.h>
-
-#include <sys/mman.h>
-
-// includes from the ROCm sources
-#include <linux/kfd_ioctl.h>
-#include <hsa.h>
-#include <amd_hsa_kernel_code.h>
-#include <ROCR-Runtime/src/core/inc/sdma_registers.h>
-using namespace rocr::AMD;
-
-#include <string>
-#include <map>
-std::map<int, std::string> files;
-std::map<uint64_t, uint64_t> ring_base_addresses;
-
-#define D(args...) fprintf(stderr, args)
-
-uint64_t doorbell_offset = -1;
-std::map<uint64_t, int> queue_types;
-
-void hexdump(void *d, int l) {
-  for (int i = 0; i < l; i++) {
-    if (i%0x10 == 0 && i != 0) printf("\n");
-    if (i%0x10 == 8) printf(" ");
-    if (i%0x10 == 0) printf("%8X: ", i);
-    printf("%2.2X ", ((uint8_t*)d)[i]);
-  }
-  printf("\n");
-}
-
-extern "C" {
-
-// https://defuse.ca/online-x86-assembler.htm#disassembly2
-static void handler(int sig, siginfo_t *si, void *unused) {
-  ucontext_t *u = (ucontext_t *)unused;
-  uint8_t *rip = (uint8_t*)u->uc_mcontext.gregs[REG_RIP];
-
-  int store_size = 0;
-  uint64_t value;
-  if (rip[0] == 0x48 && rip[1] == 0x89 && rip[2] == 0x30) {
-    // 0:  48 89 30                mov    QWORD PTR [rax],rsi
-    store_size = 8;
-    value = u->uc_mcontext.gregs[REG_RSI];
-    u->uc_mcontext.gregs[REG_RIP] += 3;
-  } else if (rip[0] == 0x4c && rip[1] == 0x89 && rip[2] == 0x28) {
-    // 0:  4c 89 28                mov    QWORD PTR [rax],r13
-    store_size = 8;
-    value = u->uc_mcontext.gregs[REG_R13];
-    u->uc_mcontext.gregs[REG_RIP] += 3;
-  } else {
-    D("segfault %02X %02X %02X %02X %02X %02X %02X %02X rip: %p addr: %p\n", rip[0], rip[1], rip[2], rip[3], rip[4], rip[5], rip[6], rip[7], rip, si->si_addr);
-    D("rax: %llx rcx: %llx rdx: %llx rsi: %llx rbx: %llx\n", u->uc_mcontext.gregs[REG_RAX], u->uc_mcontext.gregs[REG_RCX], u->uc_mcontext.gregs[REG_RDX], u->uc_mcontext.gregs[REG_RSI], u->uc_mcontext.gregs[REG_RBX]);
-    exit(-1);
-  }
-
-  uint64_t ring_base_address = ring_base_addresses[((uint64_t)si->si_addr)&0xFFF];
-  int queue_type = queue_types[((uint64_t)si->si_addr)&0xFFF];
-  D("%16p: \u001b[31mDING DONG\u001b[0m (queue_type %d) store(%d): 0x%8lx -> %p ring_base_address:0x%lx\n", rip, queue_type, store_size, value, si->si_addr, ring_base_address);
-
-  if (queue_type == KFD_IOC_QUEUE_TYPE_SDMA) {
-    uint8_t *sdma_ptr = (uint8_t*)(ring_base_address);
-    while (sdma_ptr < ((uint8_t*)(ring_base_address)+value)) {
-      D("0x%3lx: ", sdma_ptr-(uint8_t*)(ring_base_address));
-      if (sdma_ptr[0] == SDMA_OP_TIMESTAMP) {
-        D("SDMA_PKT_TIMESTAMP\n");
-        sdma_ptr += sizeof(SDMA_PKT_TIMESTAMP);
-      } else if (sdma_ptr[0] == SDMA_OP_GCR) {
-        D("SDMA_PKT_GCR\n");
-        sdma_ptr += sizeof(SDMA_PKT_GCR);
-      } else if (sdma_ptr[0] == SDMA_OP_ATOMIC) {
-        D("SDMA_PKT_ATOMIC\n");
-        sdma_ptr += sizeof(SDMA_PKT_ATOMIC);
-      } else if (sdma_ptr[0] == SDMA_OP_FENCE) {
-        D("SDMA_PKT_FENCE\n");
-        sdma_ptr += sizeof(SDMA_PKT_FENCE);
-      } else if (sdma_ptr[0] == SDMA_OP_TRAP) {
-        D("SDMA_PKT_TRAP\n");
-        sdma_ptr += sizeof(SDMA_PKT_TRAP);
-      } else if (sdma_ptr[0] == SDMA_OP_COPY && sdma_ptr[1] == SDMA_SUBOP_COPY_LINEAR) {
-        SDMA_PKT_COPY_LINEAR *pkt = (SDMA_PKT_COPY_LINEAR *)sdma_ptr;
-        D("SDMA_PKT_COPY_LINEAR: count:0x%x src:0x%lx dst:0x%lx\n", pkt->COUNT_UNION.count+1,
-          (uint64_t)pkt->SRC_ADDR_LO_UNION.src_addr_31_0 | ((uint64_t)pkt->SRC_ADDR_HI_UNION.src_addr_63_32 << 32),
-          (uint64_t)pkt->DST_ADDR_LO_UNION.dst_addr_31_0 | ((uint64_t)pkt->DST_ADDR_HI_UNION.dst_addr_63_32 << 32)
-        );
-        sdma_ptr += sizeof(SDMA_PKT_COPY_LINEAR);
-      } else {
-        D("unhandled packet type %d %d, exiting\n", sdma_ptr[0], sdma_ptr[1]);
-        break;
-      }
-    }
-
-    //hexdump((void*)(ring_base_address), 0x100);
-  } else if (queue_type == KFD_IOC_QUEUE_TYPE_COMPUTE_AQL) {
-    hsa_kernel_dispatch_packet_t *pkt = (hsa_kernel_dispatch_packet_t *)(ring_base_address+value*0x40);
-    if ((pkt->header&0xFF) == HSA_PACKET_TYPE_KERNEL_DISPATCH) {
-      D("HSA_PACKET_TYPE_KERNEL_DISPATCH -- setup:%d workgroup[%d, %d, %d] grid[%d, %d, %d] kernel_object:0x%lx kernarg_address:%p\n", pkt->setup, pkt->workgroup_size_x, pkt->workgroup_size_y, pkt->workgroup_size_z, pkt->grid_size_x, pkt->grid_size_y, pkt->grid_size_z, pkt->kernel_object, pkt->kernarg_address);
-      amd_kernel_code_t *code = (amd_kernel_code_t *)pkt->kernel_object;
-      D("kernel_code_entry_byte_offset:%lx\n", code->kernel_code_entry_byte_offset);
-      uint32_t *kernel_code = (uint32_t*)(pkt->kernel_object + code->kernel_code_entry_byte_offset);
-      int code_len = 0;
-      while (kernel_code[code_len] != 0xbf9f0000 && kernel_code[code_len] != 0) code_len++;
-      hexdump(kernel_code, code_len*4);
-      /*FILE *f = fopen("/tmp/kernel_code", "wb");
-      fwrite(kernel_code, 4, code_len, f);
-      fclose(f);
-      system("python -c 'print(\" \".join([(\"0x%02X\"%x) for x in open(\"/tmp/kernel_code\", \"rb\").read()]))' | ../build/llvm-project/bin/llvm-mc --disassemble --arch=amdgcn --mcpu=gfx1100 --show-encoding");*/
-      D("kernargs (kernarg_segment_byte_size:0x%lx)\n", code->kernarg_segment_byte_size);
-      // get length
-      int i;
-      for (i = 0; i < 0x400; i+=0x10) {
-        if (memcmp((void*)((uint64_t)pkt->kernarg_address+i), "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00", 0x10) == 0) break;
-      }
-      hexdump((void*)pkt->kernarg_address, i+0x10);
-    } else if ((pkt->header&0xFF) == HSA_PACKET_TYPE_BARRIER_AND) {
-      hsa_barrier_and_packet_t *pkt_and = (hsa_barrier_and_packet_t *)(ring_base_address+value*0x40);
-      D("HSA_PACKET_TYPE_BARRIER_AND completion_signal:0x%lx\n", pkt_and->completion_signal.handle);
-      //hexdump((void*)(ring_base_address+value*0x40), 0x40);
-    } else if ((pkt->header&0xFF) == HSA_PACKET_TYPE_VENDOR_SPECIFIC) {
-      D("HSA_PACKET_TYPE_VENDOR_SPECIFIC\n");
-      hexdump((void*)(ring_base_address+value*0x40), 0x40);
-    } else {
-      hexdump((void*)(ring_base_address+value*0x40), 0x40);
-    }
-  }
-
-  mprotect((void *)((uint64_t)si->si_addr & ~0xFFF), 0x2000, PROT_READ | PROT_WRITE);
-  if (store_size == 8) {
-    *(volatile uint64_t*)(si->si_addr) = value;
-  } else if (store_size == 4) {
-    *(volatile uint32_t*)(si->si_addr) = value;
-  } else if (store_size == 2) {
-    *(volatile uint16_t*)(si->si_addr) = value;
-  } else {
-    D("store size not supported\n");
-    exit(-1);
-  }
-  mprotect((void *)((uint64_t)si->si_addr & ~0xFFF), 0x2000, PROT_NONE);
-}
-
-void register_sigsegv_handler() {
-  struct sigaction sa = {0};
-  sa.sa_flags = SA_SIGINFO;
-  sigemptyset(&sa.sa_mask);
-  sa.sa_sigaction = handler;
-  if (sigaction(SIGSEGV, &sa, NULL) == -1) {
-    D("ERROR: failed to register sigsegv handler");
-    exit(-1);
-  }
-  // NOTE: python (or ocl runtime?) blocks the SIGSEGV signal
-  sigset_t x;
-  sigemptyset(&x);
-  sigaddset(&x, SIGSEGV);
-  sigprocmask(SIG_UNBLOCK, &x, NULL);
-}
-
-int (*my_open)(const char *pathname, int flags, mode_t mode);
-#undef open
-int open(const char *pathname, int flags, mode_t mode) {
-  if (my_open == NULL) my_open = reinterpret_cast<decltype(my_open)>(dlsym(RTLD_NEXT, "open"));
-  int ret = my_open(pathname, flags, mode);
-  //D("open %s (0o%o) = %d\n", pathname, flags, ret);
-  files[ret] = pathname;
-  return ret;
-}
-
-
-int (*my_open64)(const char *pathname, int flags, mode_t mode);
-#undef open
-int open64(const char *pathname, int flags, mode_t mode) {
-  if (my_open64 == NULL) my_open64 = reinterpret_cast<decltype(my_open64)>(dlsym(RTLD_NEXT, "open64"));
-  int ret = my_open64(pathname, flags, mode);
-  //D("open %s (0o%o) = %d\n", pathname, flags, ret);
-  files[ret] = pathname;
-  return ret;
-}
-
-void *(*my_mmap)(void *addr, size_t length, int prot, int flags, int fd, off_t offset);
-#undef mmap
-void *mmap(void *addr, size_t length, int prot, int flags, int fd, off_t offset) {
-  if (my_mmap == NULL) my_mmap = reinterpret_cast<decltype(my_mmap)>(dlsym(RTLD_NEXT, "mmap"));
-  void *ret = my_mmap(addr, length, prot, flags, fd, offset);
-
-  if (doorbell_offset != -1 && offset == doorbell_offset) {
-    D("HIDDEN DOORBELL %p, handled by %p\n", addr, handler);
-    register_sigsegv_handler();
-    mprotect(addr, length, PROT_NONE);
-  }
-
-  if (fd != -1) D("mmapped %p (target %p) with flags 0x%x length 0x%zx fd %d %s offset 0x%lx\n", ret, addr, flags, length, fd, files[fd].c_str(), offset);
-  return ret;
-}
-
-void *(*my_mmap64)(void *addr, size_t length, int prot, int flags, int fd, off_t offset);
-#undef mmap64
-void *mmap64(void *addr, size_t length, int prot, int flags, int fd, off_t offset) { return mmap(addr, length, prot, flags, fd, offset); }
-
-int ioctl_num = 1;
-int (*my_ioctl)(int filedes, unsigned long request, void *argp) = NULL;
-#undef ioctl
-int ioctl(int filedes, unsigned long request, void *argp) {
-  if (my_ioctl == NULL) my_ioctl = reinterpret_cast<decltype(my_ioctl)>(dlsym(RTLD_NEXT, "ioctl"));
-  int ret = 0;
-  ret = my_ioctl(filedes, request, argp);
-  if (!files.count(filedes)) return ret;
-
-  uint8_t type = (request >> 8) & 0xFF;
-  uint8_t nr = (request >> 0) & 0xFF;
-  uint16_t size = (request >> 16) & 0xFFF;
-
-  D("%3d: %d = %3d(%20s) 0x%3x ", ioctl_num, ret, filedes, files[filedes].c_str(), size);
-
-  if (request == AMDKFD_IOC_SET_EVENT) {
-    kfd_ioctl_set_event_args *args = (kfd_ioctl_set_event_args *)argp;
-    D("AMDKFD_IOC_SET_EVENT event_id:%d", args->event_id);
-  } else if (request == AMDKFD_IOC_ALLOC_MEMORY_OF_GPU) {
-    kfd_ioctl_alloc_memory_of_gpu_args *args = (kfd_ioctl_alloc_memory_of_gpu_args *)argp;
-    D("AMDKFD_IOC_ALLOC_MEMORY_OF_GPU va_addr:0x%llx size:0x%llx handle:%llX gpu_id:0x%x", args->va_addr, args->size, args->handle, args->gpu_id);
-  } else if (request == AMDKFD_IOC_MAP_MEMORY_TO_GPU) {
-    kfd_ioctl_map_memory_to_gpu_args *args = (kfd_ioctl_map_memory_to_gpu_args *)argp;
-    D("AMDKFD_IOC_MAP_MEMORY_TO_GPU handle:%llX", args->handle);
-  } else if (request == AMDKFD_IOC_CREATE_EVENT) {
-    kfd_ioctl_create_event_args *args = (kfd_ioctl_create_event_args *)argp;
-    D("AMDKFD_IOC_CREATE_EVENT event_page_offset:0x%llx event_type:%d event_id:%d", args->event_page_offset, args->event_type, args->event_id);
-  } else if (request == AMDKFD_IOC_WAIT_EVENTS) {
-    D("AMDKFD_IOC_WAIT_EVENTS");
-  } else if (request == AMDKFD_IOC_SET_XNACK_MODE) {
-    D("AMDKFD_IOC_SET_XNACK_MODE");
-  } else if (request == AMDKFD_IOC_SVM || (type == 0x4b && nr == 0x20)) {
-    // NOTE: this one is variable length
-    kfd_ioctl_svm_args *args = (kfd_ioctl_svm_args *)argp;
-    D("AMDKFD_IOC_SVM start_addr:0x%llx size:0x%llx op:%d", args->start_addr, args->size, args->op);
-  } else if (request == AMDKFD_IOC_UNMAP_MEMORY_FROM_GPU) {
-    kfd_ioctl_unmap_memory_from_gpu_args *args = (kfd_ioctl_unmap_memory_from_gpu_args *)argp;
-    D("AMDKFD_IOC_UNMAP_MEMORY_FROM_GPU handle:%llX", args->handle);
-  } else if (request == AMDKFD_IOC_FREE_MEMORY_OF_GPU) {
-    D("AMDKFD_IOC_FREE_MEMORY_OF_GPU");
-  } else if (request == AMDKFD_IOC_SET_SCRATCH_BACKING_VA) {
-    D("AMDKFD_IOC_SET_SCRATCH_BACKING_VA");
-  } else if (request == AMDKFD_IOC_GET_TILE_CONFIG) {
-    D("AMDKFD_IOC_GET_TILE_CONFIG");
-  } else if (request == AMDKFD_IOC_SET_TRAP_HANDLER) {
-    D("AMDKFD_IOC_SET_TRAP_HANDLER");
-  } else if (request == AMDKFD_IOC_GET_VERSION) {
-    kfd_ioctl_get_version_args *args = (kfd_ioctl_get_version_args *)argp;
-    D("AMDKFD_IOC_GET_VERSION major_version:%d minor_version:%d", args->major_version, args->minor_version);
-  } else if (request == AMDKFD_IOC_GET_PROCESS_APERTURES_NEW) {
-    D("AMDKFD_IOC_GET_PROCESS_APERTURES_NEW");
-  } else if (request == AMDKFD_IOC_ACQUIRE_VM) {
-    D("AMDKFD_IOC_ACQUIRE_VM");
-  } else if (request == AMDKFD_IOC_SET_MEMORY_POLICY) {
-    D("AMDKFD_IOC_SET_MEMORY_POLICY");
-  } else if (request == AMDKFD_IOC_GET_CLOCK_COUNTERS) {
-    D("AMDKFD_IOC_GET_CLOCK_COUNTERS");
-  } else if (request == AMDKFD_IOC_CREATE_QUEUE) {
-    kfd_ioctl_create_queue_args *args = (kfd_ioctl_create_queue_args *)argp;
-    D("AMDKFD_IOC_CREATE_QUEUE\n");
-    D("queue_type:%d ring_base_address:0x%llx\n", args->queue_type, args->ring_base_address);
-    D("eop_buffer_address:0x%llx ctx_save_restore_address:0x%llx\n", args->eop_buffer_address, args->ctx_save_restore_address);
-    D("ring_size:0x%x queue_priority:%d\n", args->ring_size, args->queue_priority);
-    D("RETURNS write_pointer_address:0x%llx read_pointer_address:0x%llx doorbell_offset:0x%llx queue_id:%d\n", args->write_pointer_address, args->read_pointer_address, args->doorbell_offset, args->queue_id);
-    //D("RETURNS *write_pointer_address:0x%llx *read_pointer_address:0x%llx\n", *(uint64_t*)args->write_pointer_address, *(uint64_t*)args->read_pointer_address);
-    ring_base_addresses[args->doorbell_offset&0xFFF] = args->ring_base_address;
-    queue_types[args->doorbell_offset&0xFFF] = args->queue_type;
-    doorbell_offset = args->doorbell_offset&~0xFFF;
-  } else {
-    D("type:0x%x nr:0x%x size:0x%x", type, nr, size);
-  }
-
-  D("\n");
-  ioctl_num++;
-  return ret;
-}
-
-}
@@ -1,42 +0,0 @@
-import numpy as np
-from PIL import Image
-from pathlib import Path
-import sys
-cwd = Path.cwd()
-sys.path.append(cwd.as_posix())
-sys.path.append((cwd / 'test').as_posix())
-from extra.datasets import fetch_mnist
-from tqdm import trange
-
-def augment_img(X, rotate=10, px=3):
-  Xaug = np.zeros_like(X)
-  for i in trange(len(X)):
-    im = Image.fromarray(X[i])
-    im = im.rotate(np.random.randint(-rotate,rotate), resample=Image.BICUBIC)
-    w, h = X.shape[1:]
-    #upper left, lower left, lower right, upper right
-    quad = np.random.randint(-px,px,size=(8)) + np.array([0,0,0,h,w,h,w,0])
-    im = im.transform((w, h), Image.QUAD, quad, resample=Image.BICUBIC)
-    Xaug[i] = im
-  return Xaug
-
-if __name__ == "__main__":
-  import matplotlib.pyplot as plt
-  X_train, Y_train, X_test, Y_test = fetch_mnist()
-  X_train = X_train.reshape(-1, 28, 28).astype(np.uint8)
-  X_test = X_test.reshape(-1, 28, 28).astype(np.uint8)
-  X = np.vstack([X_train[:1]]*10+[X_train[1:2]]*10)
-  fig, a = plt.subplots(2,len(X))
-  Xaug = augment_img(X)
-  for i in range(len(X)):
-    a[0][i].imshow(X[i], cmap='gray')
-    a[1][i].imshow(Xaug[i],cmap='gray')
-    a[0][i].axis('off')
-    a[1][i].axis('off')
-  plt.show()
-
-  #create some nice gifs for doc?!
-  for i in range(10):
-    im = Image.fromarray(X_train[7353+i])
-    im_aug = [Image.fromarray(x) for x in augment_img(np.array([X_train[7353+i]]*100))]
-    im.save(f"aug{i}.gif", save_all=True, append_images=im_aug, duration=100, loop=0)
@@ -1,39 +0,0 @@
-from typing import List, Dict, cast
-import ctypes
-from tinygrad.helpers import dedup, cpu_time_execution, DEBUG
-from tinygrad.engine.jit import GraphRunner, GraphException
-from tinygrad.device import Buffer, Device
-from tinygrad.engine.realize import ExecItem, CompiledRunner
-from tinygrad.uop.ops import Variable
-from tinygrad.runtime.ops_cpu import ClangProgram
-from tinygrad.renderer.cstyle import ClangRenderer
-render_dtype = ClangRenderer().render_dtype
-
-class ClangGraph(GraphRunner):
-  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[str, int]):
-    super().__init__(jit_cache, input_rawbuffers, var_vals)
-    if not all(isinstance(ji.prg, CompiledRunner) for ji in jit_cache): raise GraphException
-
-    prgs = '\n'.join(dedup([cast(CompiledRunner, ji.prg).p.src for ji in jit_cache]))
-    args = [f"{render_dtype(x.dtype)}* arg{i}" for i,x in enumerate(input_rawbuffers)]
-    args += sorted([f"int {v}" for v in var_vals])
-    code = ["void batched("+','.join(args)+") {"]
-    for ji in jit_cache:
-      args = []
-      for buf in ji.bufs:
-        assert buf is not None
-        if buf in input_rawbuffers:
-          args.append(f"arg{input_rawbuffers.index(buf)}")
-        else:
-          args.append(f"({render_dtype(buf.dtype)}*)0x{ctypes.addressof(buf._buf):X}")
-      args += [x.expr for x in cast(CompiledRunner, ji.prg).p.vars]
-      code.append(f"  {cast(CompiledRunner, ji.prg).p.function_name}({','.join(args)});")
-    code.append("}")
-    if DEBUG >= 4: print("\n".join(code))
-    compiler = Device["CPU"].compiler
-    assert compiler is not None
-    self._prg = ClangProgram("batched", compiler.compile(prgs+"\n"+"\n".join(code))) # no point in caching the pointers
-
-  def __call__(self, rawbufs: List[Buffer], var_vals: Dict[str, int], wait=False):
-    return cpu_time_execution(
-    lambda: self._prg(*[x._buf for x in rawbufs], *[x[1] for x in sorted(var_vals.items(), key=lambda x: x[0])]), enable=wait)
@@ -1,27 +0,0 @@
-import ctypes
-from typing import Tuple
-import tinygrad.runtime.autogen.hip as hip
-from tinygrad.helpers import init_c_var, time_execution_cuda_style
-from tinygrad.runtime.ops_hip import check, hip_set_device
-from tinygrad.runtime.graph.cuda import CUDAGraph
-
-# TODO: this is only used in graph
-def hip_time_execution(cb, enable=False): return time_execution_cuda_style(cb, hip.hipEvent_t, hip.hipEventCreate, hip.hipEventRecord, hip.hipEventSynchronize, hip.hipEventDestroy, hip.hipEventElapsedTime, enable=enable)  # noqa: E501
-
-class HIPGraph(CUDAGraph):
-  def __del__(self):
-    if hasattr(self, 'graph'): check(hip.hipGraphDestroy(self.graph))
-    if hasattr(self, 'instance'): check(hip.hipGraphExecDestroy(self.instance))
-  def set_device(self): hip_set_device(self.dev)
-  def encode_args_info(self): return (hip.hipDeviceptr_t, (1,2,3))
-  def graph_create(self): return init_c_var(hip.hipGraph_t(), lambda x: check(hip.hipGraphCreate(ctypes.byref(x), 0)))
-  def graph_instantiate(self, graph):
-    return init_c_var(hip.hipGraphExec_t(), lambda x: check(hip.hipGraphInstantiate(ctypes.byref(x), graph, None, None, 0)))
-  def graph_add_kernel_node(self, graph, c_deps, c_params):
-    return init_c_var(hip.hipGraphNode_t(), lambda x: check(hip.hipGraphAddKernelNode(ctypes.byref(x), graph, c_deps, ctypes.sizeof(c_deps)//8 if c_deps else 0, ctypes.byref(c_params))))  # noqa: E501
-  def graph_launch(self, *args, wait=False): return hip_time_execution(lambda: check(hip.hipGraphLaunch(*args)), enable=wait)
-  def graph_exec_kernel_node_set_params(self, *args): return check(hip.hipGraphExecKernelNodeSetParams(*args))
-  def build_kernel_node_params(self, prg, global_size, local_size, c_config):
-    return hip.hipKernelNodeParams(hip.dim3(*local_size), c_config, ctypes.cast(prg.clprg.prg, ctypes.c_void_p), hip.dim3(*global_size), None, 0)
-  def set_kernel_node_launch_dims(self, node, global_size: Tuple[int, int, int], local_size: Tuple[int, int, int]):
-    node.blockDim.x, node.blockDim.y, node.blockDim.z, node.gridDim.x, node.gridDim.y, node.gridDim.z = *local_size, *global_size
@@ -1,143 +0,0 @@
-import ctypes, collections
-import tinygrad.runtime.autogen.hsa as hsa
-from tinygrad.helpers import init_c_var
-
-def check(status):
-  if status != 0:
-    hsa.hsa_status_string(status, ctypes.byref(status_str := ctypes.POINTER(ctypes.c_char)()))
-    raise RuntimeError(f"HSA Error {status}: {ctypes.string_at(status_str).decode()}")
-
-# Precalulated AQL info
-AQL_PACKET_SIZE = ctypes.sizeof(hsa.hsa_kernel_dispatch_packet_t)
-EMPTY_SIGNAL = hsa.hsa_signal_t()
-
-DISPATCH_KERNEL_SETUP = 3 << hsa.HSA_KERNEL_DISPATCH_PACKET_SETUP_DIMENSIONS
-DISPATCH_KERNEL_HEADER  = 1 << hsa.HSA_PACKET_HEADER_BARRIER
-DISPATCH_KERNEL_HEADER |= hsa.HSA_FENCE_SCOPE_SYSTEM << hsa.HSA_PACKET_HEADER_SCACQUIRE_FENCE_SCOPE
-DISPATCH_KERNEL_HEADER |= hsa.HSA_FENCE_SCOPE_SYSTEM << hsa.HSA_PACKET_HEADER_SCRELEASE_FENCE_SCOPE
-DISPATCH_KERNEL_HEADER |= hsa.HSA_PACKET_TYPE_KERNEL_DISPATCH << hsa.HSA_PACKET_HEADER_TYPE
-
-BARRIER_HEADER  = 1 << hsa.HSA_PACKET_HEADER_BARRIER
-BARRIER_HEADER |= hsa.HSA_FENCE_SCOPE_SYSTEM << hsa.HSA_PACKET_HEADER_SCACQUIRE_FENCE_SCOPE
-BARRIER_HEADER |= hsa.HSA_FENCE_SCOPE_SYSTEM << hsa.HSA_PACKET_HEADER_SCRELEASE_FENCE_SCOPE
-BARRIER_HEADER |= hsa.HSA_PACKET_TYPE_BARRIER_AND << hsa.HSA_PACKET_HEADER_TYPE
-
-class AQLQueue:
-  def __init__(self, device, sz=-1):
-    self.device = device
-
-    check(hsa.hsa_agent_get_info(self.device.agent, hsa.HSA_AGENT_INFO_QUEUE_MAX_SIZE, ctypes.byref(max_queue_size := ctypes.c_uint32())))
-    queue_size = min(max_queue_size.value, sz) if sz != -1 else max_queue_size.value
-
-    null_func = ctypes.CFUNCTYPE(None, hsa.hsa_status_t, ctypes.POINTER(hsa.struct_hsa_queue_s), ctypes.c_void_p)()
-    self.hw_queue = init_c_var(ctypes.POINTER(hsa.hsa_queue_t)(), lambda x: check(
-      hsa.hsa_queue_create(self.device.agent, queue_size, hsa.HSA_QUEUE_TYPE_SINGLE, null_func, None, (1<<32)-1, (1<<32)-1, ctypes.byref(x))))
-
-    self.next_doorbell_index = 0
-    self.queue_base = self.hw_queue.contents.base_address
-    self.queue_size = self.hw_queue.contents.size * AQL_PACKET_SIZE # in bytes
-    self.write_addr = self.queue_base
-    self.write_addr_end = self.queue_base + self.queue_size - 1 # precalc saves some time
-    self.available_packet_slots = self.hw_queue.contents.size
-
-    check(hsa.hsa_amd_queue_set_priority(self.hw_queue, hsa.HSA_AMD_QUEUE_PRIORITY_HIGH))
-    check(hsa.hsa_amd_profiling_set_profiler_enabled(self.hw_queue, 1))
-
-  def __del__(self):
-    if hasattr(self, 'hw_queue'): check(hsa.hsa_queue_destroy(self.hw_queue))
-
-  def submit_kernel(self, prg, global_size, local_size, kernargs, completion_signal=None):
-    if self.available_packet_slots == 0: self._wait_queue()
-
-    packet = hsa.hsa_kernel_dispatch_packet_t.from_address(self.write_addr)
-    packet.workgroup_size_x = local_size[0]
-    packet.workgroup_size_y = local_size[1]
-    packet.workgroup_size_z = local_size[2]
-    packet.reserved0 = 0
-    packet.grid_size_x = global_size[0] * local_size[0]
-    packet.grid_size_y = global_size[1] * local_size[1]
-    packet.grid_size_z = global_size[2] * local_size[2]
-    packet.private_segment_size = prg.private_segment_size
-    packet.group_segment_size = prg.group_segment_size
-    packet.kernel_object = prg.handle
-    packet.kernarg_address = kernargs
-    packet.reserved2 = 0
-    packet.completion_signal = completion_signal if completion_signal else EMPTY_SIGNAL
-    packet.setup = DISPATCH_KERNEL_SETUP
-    packet.header = DISPATCH_KERNEL_HEADER
-    self._submit_packet()
-
-  def submit_barrier(self, wait_signals=None, completion_signal=None):
-    assert wait_signals is None or len(wait_signals) <= 5
-    if self.available_packet_slots == 0: self._wait_queue()
-
-    packet = hsa.hsa_barrier_and_packet_t.from_address(self.write_addr)
-    packet.reserved0 = 0
-    packet.reserved1 = 0
-    for i in range(5):
-      packet.dep_signal[i] = wait_signals[i] if wait_signals and len(wait_signals) > i else EMPTY_SIGNAL
-    packet.reserved2 = 0
-    packet.completion_signal = completion_signal if completion_signal else EMPTY_SIGNAL
-    packet.header = BARRIER_HEADER
-    self._submit_packet()
-
-  def blit_packets(self, packet_addr, packet_cnt):
-    if self.available_packet_slots < packet_cnt: self._wait_queue(packet_cnt)
-
-    tail_blit_packets = min((self.queue_base + self.queue_size - self.write_addr) // AQL_PACKET_SIZE, packet_cnt)
-    rem_packet_cnt = packet_cnt - tail_blit_packets
-    ctypes.memmove(self.write_addr, packet_addr, AQL_PACKET_SIZE * tail_blit_packets)
-    if rem_packet_cnt > 0: ctypes.memmove(self.queue_base, packet_addr + AQL_PACKET_SIZE * tail_blit_packets, AQL_PACKET_SIZE * rem_packet_cnt)
-
-    self._submit_packet(packet_cnt)
-
-  def wait(self):
-    self.submit_barrier([], finish_signal := self.device.alloc_signal(reusable=True))
-    hsa.hsa_signal_wait_scacquire(finish_signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-    self.available_packet_slots = self.queue_size // AQL_PACKET_SIZE
-
-  def _wait_queue(self, need_packets=1):
-    while self.available_packet_slots < need_packets:
-      rindex = hsa.hsa_queue_load_read_index_relaxed(self.hw_queue)
-      self.available_packet_slots = self.queue_size // AQL_PACKET_SIZE - (self.next_doorbell_index - rindex)
-
-  def _submit_packet(self, cnt=1):
-    self.available_packet_slots -= cnt
-    self.next_doorbell_index += cnt
-    hsa.hsa_queue_store_write_index_relaxed(self.hw_queue, self.next_doorbell_index)
-    hsa.hsa_signal_store_screlease(self.hw_queue.contents.doorbell_signal, self.next_doorbell_index-1)
-
-    self.write_addr += AQL_PACKET_SIZE * cnt
-    if self.write_addr > self.write_addr_end:
-      self.write_addr = self.queue_base + (self.write_addr - self.queue_base) % self.queue_size
-
-def scan_agents():
-  agents = collections.defaultdict(list)
-
-  @ctypes.CFUNCTYPE(hsa.hsa_status_t, hsa.hsa_agent_t, ctypes.c_void_p)
-  def __scan_agents(agent, data):
-    status = hsa.hsa_agent_get_info(agent, hsa.HSA_AGENT_INFO_DEVICE, ctypes.byref(device_type := hsa.hsa_device_type_t()))
-    if status == 0: agents[device_type.value].append(agent)
-    return hsa.HSA_STATUS_SUCCESS
-
-  hsa.hsa_iterate_agents(__scan_agents, None)
-  return agents
-
-def find_memory_pool(agent, segtyp=-1, location=-1):
-  @ctypes.CFUNCTYPE(hsa.hsa_status_t, hsa.hsa_amd_memory_pool_t, ctypes.c_void_p)
-  def __filter_amd_memory_pools(mem_pool, data):
-    check(hsa.hsa_amd_memory_pool_get_info(mem_pool, hsa.HSA_AMD_MEMORY_POOL_INFO_SEGMENT, ctypes.byref(segment := hsa.hsa_amd_segment_t())))
-    if segtyp >= 0 and segment.value != segtyp: return hsa.HSA_STATUS_SUCCESS
-
-    check(hsa.hsa_amd_memory_pool_get_info(mem_pool, hsa.HSA_AMD_MEMORY_POOL_INFO_LOCATION, ctypes.byref(loc:=hsa.hsa_amd_memory_pool_location_t())))
-    if location >= 0 and loc.value != location: return hsa.HSA_STATUS_SUCCESS
-
-    check(hsa.hsa_amd_memory_pool_get_info(mem_pool, hsa.HSA_AMD_MEMORY_POOL_INFO_SIZE, ctypes.byref(sz := ctypes.c_size_t())))
-    if sz.value == 0: return hsa.HSA_STATUS_SUCCESS
-
-    ret = ctypes.cast(data, ctypes.POINTER(hsa.hsa_amd_memory_pool_t))
-    ret[0] = mem_pool
-    return hsa.HSA_STATUS_INFO_BREAK
-
-  hsa.hsa_amd_agent_iterate_memory_pools(agent, __filter_amd_memory_pools, ctypes.byref(region := hsa.hsa_amd_memory_pool_t()))
-  return region
@@ -1,171 +0,0 @@
-import ctypes, collections, time, itertools
-from typing import List, Any, Dict, cast, Optional, Tuple
-from tinygrad.helpers import init_c_var, round_up
-from tinygrad.device import Buffer, BufferSpec
-from tinygrad.device import Compiled, Device
-from tinygrad.uop.ops import Variable
-from tinygrad.runtime.ops_hsa import HSADevice, PROFILE, Profiler
-from tinygrad.engine.realize import ExecItem, BufferXfer, CompiledRunner
-from tinygrad.engine.jit import MultiGraphRunner, GraphException
-import tinygrad.runtime.autogen.hsa as hsa
-from tinygrad.runtime.support.hsa import check, AQLQueue, AQL_PACKET_SIZE, EMPTY_SIGNAL
-
-def dedup_signals(signals): return [hsa.hsa_signal_t(hndl) for hndl in set([x.handle for x in signals if isinstance(x, hsa.hsa_signal_t)])]
-
-class VirtAQLQueue(AQLQueue):
-  def __init__(self, device, sz):
-    self.device = device
-    self.virt_queue = (hsa.hsa_kernel_dispatch_packet_t * sz)()
-    self.queue_base = self.write_addr = ctypes.addressof(self.virt_queue)
-    self.packets_count = 0
-    self.available_packet_slots = sz
-  def _wait_queue(self, need_packets=1): assert False, f"VirtQueue is too small to handle {self.packets_count+need_packets} packets!"
-  def _submit_packet(self):
-    self.write_addr += AQL_PACKET_SIZE
-    self.packets_count += 1
-    self.available_packet_slots -= 1
-
-class HSAGraph(MultiGraphRunner):
-  def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], var_vals: Dict[str, int]):
-    super().__init__(jit_cache, input_rawbuffers, var_vals)
-
-    # Check all jit items are compatible.
-    compiled_devices = set()
-    for ji in self.jit_cache:
-      if isinstance(ji.prg, CompiledRunner): compiled_devices.add(ji.prg.dev)
-      elif isinstance(ji.prg, BufferXfer):
-        for x in ji.bufs[0:2]: compiled_devices.add(Device[cast(Buffer, x).device])
-      else: raise GraphException
-    if any(not isinstance(d, HSADevice) for d in compiled_devices): raise GraphException
-
-    self.devices: List[HSADevice] = list(compiled_devices) #type:ignore
-
-    # Allocate kernel args.
-    kernargs_size: Dict[Compiled, int] = collections.defaultdict(int)
-    for ji in self.jit_cache:
-      if isinstance(ji.prg, CompiledRunner): kernargs_size[ji.prg.dev] += round_up(ctypes.sizeof(ji.prg._prg.args_struct_t), 16)
-    kernargs_ptrs: Dict[Compiled, int] = {dev:dev.allocator._alloc(sz, BufferSpec()) for dev,sz in kernargs_size.items()}
-
-    # Fill initial arguments.
-    self.ji_kargs_structs: Dict[int, ctypes.Structure] = {}
-    for j,ji in enumerate(self.jit_cache):
-      if not isinstance(ji.prg, CompiledRunner): continue
-      self.ji_kargs_structs[j] = ji.prg._prg.args_struct_t.from_address(kernargs_ptrs[ji.prg.dev])
-      kernargs_ptrs[ji.prg.dev] += round_up(ctypes.sizeof(ji.prg._prg.args_struct_t), 16)
-      for i in range(len(ji.bufs)): self.ji_kargs_structs[j].__setattr__(f'f{i}', cast(Buffer, ji.bufs[i])._buf)
-      for i in range(len(ji.prg.p.vars)): self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[ji.prg.p.vars[i].expr])
-
-    # Build queues.
-    self.virt_aql_queues: Dict[Compiled, VirtAQLQueue] = {dev:VirtAQLQueue(dev, 2*len(self.jit_cache)+16) for dev in self.devices}
-    self.packets = {}
-    self.transfers = []
-    self.ji_to_transfer: Dict[int, int] = {} # faster to store transfers as list and update using this mapping table.
-    self.signals_to_reset: List[hsa.hsa_signal_t] = []
-    self.signals_to_devices: Dict[ctypes.c_uint64, List[HSADevice]] = {}
-    self.profile_info: Dict[Compiled, List[Tuple[Any, ...]]] = collections.defaultdict(list)
-
-    # Special packet to wait for the world.
-    self.kickoff_signals: Dict[HSADevice, hsa.hsa_signal_t] = {dev:self.alloc_signal(reset_on_start=True) for dev in self.devices}
-    for dev in self.devices: self.virt_aql_queues[dev].submit_barrier([], self.kickoff_signals[dev])
-
-    for j,ji in enumerate(self.jit_cache):
-      if isinstance(ji.prg, CompiledRunner):
-        wait_signals = self.access_resources(ji.bufs, ji.prg.p.outs, new_dependency=j, sync_with_aql_packets=False)
-        for i in range(0, len(wait_signals), 5):
-          self.virt_aql_queues[ji.prg.dev].submit_barrier(wait_signals[i:i+5])
-        self.packets[j] = hsa.hsa_kernel_dispatch_packet_t.from_address(self.virt_aql_queues[ji.prg.dev].write_addr)
-
-        sync_signal = self.alloc_signal(reset_on_start=True) if PROFILE else None
-        self.virt_aql_queues[ji.prg.dev].submit_kernel(ji.prg._prg, *ji.prg.p.launch_dims(var_vals), #type:ignore
-                                                          ctypes.addressof(self.ji_kargs_structs[j]), completion_signal=sync_signal)
-        if PROFILE: self.profile_info[ji.prg.dev].append((sync_signal, ji.prg._prg.name, False))
-      elif isinstance(ji.prg, BufferXfer):
-        dest, src = [cast(Buffer, x) for x in ji.bufs[0:2]]
-        dest_dev, src_dev = cast(HSADevice, Device[dest.device]), cast(HSADevice, Device[src.device])
-        sync_signal = self.alloc_signal(reset_on_start=True, wait_on=[dest_dev, src_dev])
-
-        wait_signals = self.access_resources([dest, src], write=[0], new_dependency=sync_signal, sync_with_aql_packets=True)
-        self.transfers.append([dest._buf, dest_dev.agent, src._buf, src_dev.agent, dest.nbytes, len(wait_signals),
-                              (hsa.hsa_signal_t*len(wait_signals))(*wait_signals), sync_signal, hsa.HSA_AMD_SDMA_ENGINE_0, True])
-        self.ji_to_transfer[j] = len(self.transfers) - 1
-        if PROFILE: self.profile_info[src_dev].append((sync_signal, f"transfer: HSA:{src_dev.device_id} -> HSA:{dest_dev.device_id}", True))
-
-    # Wait for all active signals to finish the graph
-    wait_signals_to_finish: Dict[HSADevice, List[hsa.hsa_signal_t]] = collections.defaultdict(list)
-    for v in dedup_signals(list(self.w_dependency_map.values()) + list(itertools.chain.from_iterable(self.r_dependency_map.values()))):
-      for dev in self.signals_to_devices[v.handle]:
-        wait_signals_to_finish[dev].append(v)
-
-    self.finish_signal = init_c_var(hsa.hsa_signal_t(), lambda x: check(hsa.hsa_amd_signal_create(1, 0, None, 0, ctypes.byref(x))))
-    for dev in self.devices:
-      wait_signals = wait_signals_to_finish[dev]
-      for i in range(0, max(1, len(wait_signals)), 5):
-        self.virt_aql_queues[dev].submit_barrier(wait_signals[i:i+5], completion_signal=self.finish_signal if i+5>=len(wait_signals) else None)
-
-    # Zero signals to allow graph to start and execute.
-    for sig in self.signals_to_reset: hsa.hsa_signal_silent_store_relaxed(sig, 0)
-    hsa.hsa_signal_silent_store_relaxed(self.finish_signal, 0)
-
-  def __call__(self, input_rawbuffers: List[Buffer], var_vals: Dict[str, int], wait=False) -> Optional[float]:
-    # Wait and restore signals
-    hsa.hsa_signal_wait_scacquire(self.finish_signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-    for sig in self.signals_to_reset: hsa.hsa_signal_silent_store_relaxed(sig, 1)
-    hsa.hsa_signal_silent_store_relaxed(self.finish_signal, len(self.devices))
-
-    # Update rawbuffers
-    for (j,i),input_idx in self.input_replace.items():
-      if j in self.ji_kargs_structs:
-        self.ji_kargs_structs[j].__setattr__(f'f{i}', input_rawbuffers[input_idx]._buf)
-      else:
-        if i == 0: self.transfers[self.ji_to_transfer[j]][0] = input_rawbuffers[input_idx]._buf # dest
-        elif i == 1: self.transfers[self.ji_to_transfer[j]][2] = input_rawbuffers[input_idx]._buf # src
-
-    # Update var_vals
-    for j in self.jc_idx_with_updatable_var_vals:
-      for i,v in enumerate(cast(CompiledRunner, self.jit_cache[j].prg).p.vars):
-        self.ji_kargs_structs[j].__setattr__(f'v{i}', var_vals[v.expr])
-
-    # Update launch dims
-    for j in self.jc_idx_with_updatable_launch_dims:
-      gl, lc = cast(CompiledRunner, self.jit_cache[j].prg).p.launch_dims(var_vals)
-      self.packets[j].workgroup_size_x = lc[0]
-      self.packets[j].workgroup_size_y = lc[1]
-      self.packets[j].workgroup_size_z = lc[2]
-      self.packets[j].grid_size_x = gl[0] * lc[0]
-      self.packets[j].grid_size_y = gl[1] * lc[1]
-      self.packets[j].grid_size_z = gl[2] * lc[2]
-
-    for dev in self.devices:
-      dev.flush_hdp()
-      dev.hw_queue.blit_packets(self.virt_aql_queues[dev].queue_base, self.virt_aql_queues[dev].packets_count)
-
-    for transfer_data in self.transfers:
-      check(hsa.hsa_amd_memory_async_copy_on_engine(*transfer_data))
-
-    et = None
-    if wait:
-      st = time.perf_counter()
-      hsa.hsa_signal_wait_scacquire(self.finish_signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-      et = time.perf_counter() - st
-
-    for profdev,profdata in self.profile_info.items(): Profiler.tracked_signals[profdev] += profdata
-    return et
-
-  def alloc_signal(self, reset_on_start=False, wait_on=None):
-    sync_signal = init_c_var(hsa.hsa_signal_t(), lambda x: check(hsa.hsa_amd_signal_create(1, 0, None, 0, ctypes.byref(x))))
-    if reset_on_start: self.signals_to_reset.append(sync_signal)
-    if wait_on is not None: self.signals_to_devices[sync_signal.handle] = wait_on
-    return sync_signal
-
-  def dependency_as_signal(self, dep, sync_with_aql_packets) -> Optional[hsa.hsa_signal_t]:
-    if isinstance(dep, hsa.hsa_signal_t): return dep
-    elif sync_with_aql_packets and isinstance(packet := self.packets.get(dep), hsa.hsa_kernel_dispatch_packet_t):
-      if packet.completion_signal.handle == EMPTY_SIGNAL.handle: packet.completion_signal = self.alloc_signal(reset_on_start=True)
-      return packet.completion_signal
-    return None
-
-  def access_resources(self, rawbufs, write, new_dependency, sync_with_aql_packets=False):
-    rdeps = self._access_resources(rawbufs, write, new_dependency)
-    wait_signals = [self.dependency_as_signal(dep, sync_with_aql_packets=sync_with_aql_packets) for dep in rdeps]
-    if sync_with_aql_packets: wait_signals += [self.kickoff_signals[cast(HSADevice, Device[rawbuf.device])] for rawbuf in rawbufs]
-    return dedup_signals(wait_signals)
@@ -1,275 +0,0 @@
-from __future__ import annotations
-import ctypes, functools, subprocess, io, atexit, collections, json
-from typing import Tuple, TypeVar, List, Dict, Any
-import tinygrad.runtime.autogen.hsa as hsa
-from tinygrad.helpers import DEBUG, init_c_var, from_mv, round_up, to_mv, init_c_struct_t, getenv, PROFILE
-from tinygrad.device import Compiled, Compiler, CompileError, BufferSpec, LRUAllocator
-from tinygrad.renderer.cstyle import HIPRenderer
-from tinygrad.runtime.support.hsa import check, scan_agents, find_memory_pool, AQLQueue
-from tinygrad.runtime.support.hip_comgr import compile_hip
-if getenv("IOCTL"): import extra.hip_gpu_driver.hip_ioctl  # noqa: F401
-
-class HSAProfiler:
-  def __init__(self):
-    self.tracked_signals = collections.defaultdict(list)
-    self.collected_events: List[Tuple[Any, ...]] = []
-    self.copy_timings = hsa.hsa_amd_profiling_async_copy_time_t()
-    self.disp_timings = hsa.hsa_amd_profiling_dispatch_time_t()
-
-  def track(self, signal, device, name, is_copy=False): self.tracked_signals[device].append((signal, name, is_copy))
-  def process(self, device):
-    # Process all tracked signals, should be called before any of tracked signals are reused.
-    for sig,name,is_copy in self.tracked_signals[device]:
-      if is_copy: check(hsa.hsa_amd_profiling_get_async_copy_time(sig, ctypes.byref(timings :=  self.copy_timings)))
-      else: check(hsa.hsa_amd_profiling_get_dispatch_time(device.agent, sig, ctypes.byref(timings := self.disp_timings))) #type:ignore
-      self.collected_events.append((device.device_id, 1 if is_copy else 0, name, timings.start, timings.end))
-    self.tracked_signals.pop(device)
-
-  def save(self, path):
-    mjson = []
-    for i in range(len(HSADevice.devices)):
-      mjson.append({"name": "process_name", "ph": "M", "pid": i, "args": {"name": "HSA"}})
-      mjson.append({"name": "thread_name", "ph": "M", "pid": i, "tid": 0, "args": {"name": "AQL"}})
-      mjson.append({"name": "thread_name", "ph": "M", "pid": i, "tid": 1, "args": {"name": "SDMA"}})
-
-    for dev_id,queue_id,name,st,et in self.collected_events:
-      mjson.append({"name": name, "ph": "B", "pid": dev_id, "tid": queue_id, "ts": st*1e-3})
-      mjson.append({"name": name, "ph": "E", "pid": dev_id, "tid": queue_id, "ts": et*1e-3})
-    with open(path, "w") as f: f.write(json.dumps({"traceEvents": mjson}))
-    print(f"Saved HSA profile to {path}")
-Profiler = HSAProfiler()
-
-class HSACompiler(Compiler):
-  def __init__(self, arch:str):
-    self.arch = arch
-    super().__init__(f"compile_hip_{self.arch}")
-  def compile(self, src:str) -> bytes:
-    try: return compile_hip(src, self.arch)
-    except RuntimeError as e: raise CompileError(e)
-
-class HSAProgram:
-  def __init__(self, device:HSADevice, name:str, lib:bytes):
-    self.device, self.name, self.lib = device, name, lib
-
-    if DEBUG >= 6:
-      asm = subprocess.check_output(["/opt/rocm/llvm/bin/llvm-objdump", '-d', '-'], input=lib)
-      print('\n'.join([x for x in asm.decode('utf-8').split("\n") if 's_code_end' not in x]))
-
-    self.exec = init_c_var(hsa.hsa_executable_t(), lambda x: check(hsa.hsa_executable_create_alt(hsa.HSA_PROFILE_FULL, hsa.HSA_DEFAULT_FLOAT_ROUNDING_MODE_DEFAULT, None, ctypes.byref(x)))) # noqa: E501
-    self.code_reader = init_c_var(hsa.hsa_code_object_reader_t(),
-                                  lambda x: check(hsa.hsa_code_object_reader_create_from_memory(lib, len(lib), ctypes.byref(x))))
-    check(hsa.hsa_executable_load_agent_code_object(self.exec, self.device.agent, self.code_reader, None, None))
-    check(hsa.hsa_executable_freeze(self.exec, None))
-
-    self.kernel = init_c_var(hsa.hsa_executable_symbol_t(), lambda x: check(hsa.hsa_executable_get_symbol_by_name(self.exec, (name+".kd").encode("utf-8"), ctypes.byref(self.device.agent), ctypes.byref(x)))) # noqa: E501
-    self.handle = init_c_var(ctypes.c_uint64(), lambda x: check(hsa.hsa_executable_symbol_get_info(self.kernel, hsa.HSA_EXECUTABLE_SYMBOL_INFO_KERNEL_OBJECT, ctypes.byref(x)))) # noqa: E501
-    self.kernargs_segment_size = init_c_var(ctypes.c_uint32(), lambda x: check(hsa.hsa_executable_symbol_get_info(self.kernel, hsa.HSA_EXECUTABLE_SYMBOL_INFO_KERNEL_KERNARG_SEGMENT_SIZE, ctypes.byref(x)))).value # noqa: E501
-    self.group_segment_size = init_c_var(ctypes.c_uint32(), lambda x: check(hsa.hsa_executable_symbol_get_info(self.kernel, hsa.HSA_EXECUTABLE_SYMBOL_INFO_KERNEL_GROUP_SEGMENT_SIZE, ctypes.byref(x)))).value # noqa: E501
-    self.private_segment_size = init_c_var(ctypes.c_uint32(), lambda x: check(hsa.hsa_executable_symbol_get_info(self.kernel, hsa.HSA_EXECUTABLE_SYMBOL_INFO_KERNEL_PRIVATE_SEGMENT_SIZE, ctypes.byref(x)))).value # noqa: E501
-
-  def __del__(self):
-    self.device.synchronize()
-    if hasattr(self, 'code_reader'): check(hsa.hsa_code_object_reader_destroy(self.code_reader))
-    if hasattr(self, 'exec'): check(hsa.hsa_executable_destroy(self.exec))
-
-  def __call__(self, *args, global_size:Tuple[int,int,int]=(1,1,1), local_size:Tuple[int,int,int]=(1,1,1), vals:Tuple[int, ...]=(), wait=False):
-    if not hasattr(self, "args_struct_t"):
-      self.args_struct_t = init_c_struct_t(tuple([(f'f{i}', ctypes.c_void_p) for i in range(len(args))] +
-                                                 [(f'v{i}', ctypes.c_int) for i in range(len(vals))]))
-      if ctypes.sizeof(self.args_struct_t) != self.kernargs_segment_size:
-        raise RuntimeError(f"HSAProgram.__call__: incorrect args struct size {ctypes.sizeof(self.args_struct_t)} != {self.kernargs_segment_size}")
-
-    kernargs = None
-    if self.kernargs_segment_size > 0:
-      kernargs = self.device.alloc_kernargs(self.kernargs_segment_size)
-      args_st = self.args_struct_t.from_address(kernargs)
-      for i in range(len(args)): args_st.__setattr__(f'f{i}', args[i])
-      for i in range(len(vals)): args_st.__setattr__(f'v{i}', vals[i])
-      self.device.flush_hdp()
-
-    signal = self.device.alloc_signal(reusable=True) if wait or PROFILE else None
-    self.device.hw_queue.submit_kernel(self, global_size, local_size, kernargs, completion_signal=signal)
-    if PROFILE: Profiler.track(signal, self.device, self.name)
-    if wait:
-      hsa.hsa_signal_wait_scacquire(signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-      check(hsa.hsa_amd_profiling_get_dispatch_time(self.device.agent, signal, ctypes.byref(timings := hsa.hsa_amd_profiling_dispatch_time_t())))
-      return (timings.end - timings.start) * self.device.clocks_to_time
-
-T = TypeVar("T")
-CHUNK_SIZE, PAGE_SIZE = 256*1024*1024, 0x1000
-class HSAAllocator(LRUAllocator):
-  def __init__(self, device:HSADevice):
-    self.device = device
-    super().__init__()
-
-  def _alloc(self, size:int, options:BufferSpec):
-    if options.host:
-      check(hsa.hsa_amd_memory_pool_allocate(HSADevice.cpu_mempool, size, 0, ctypes.byref(mem := ctypes.c_void_p())))
-      check(hsa.hsa_amd_agents_allow_access(2, (hsa.hsa_agent_t*2)(HSADevice.cpu_agent, self.device.agent), None, mem))
-      return mem.value
-    c_agents = (hsa.hsa_agent_t * len(HSADevice.agents[hsa.HSA_DEVICE_TYPE_GPU]))(*HSADevice.agents[hsa.HSA_DEVICE_TYPE_GPU])
-    check(hsa.hsa_amd_memory_pool_allocate(self.device.gpu_mempool, size, 0, ctypes.byref(buf := ctypes.c_void_p())))
-    check(hsa.hsa_amd_agents_allow_access(len(HSADevice.agents[hsa.HSA_DEVICE_TYPE_GPU]), c_agents, None, buf))
-    return buf.value
-
-  def _free(self, opaque:T, options:BufferSpec):
-    HSADevice.synchronize_system()
-    check(hsa.hsa_amd_memory_pool_free(opaque))
-
-  def _copyin(self, dest:T, src: memoryview):
-    # Async copyin sync model uses barriers on the main hw queue, since barriers are guaranteed to execute in order with all other packets.
-    self.device.hw_queue.submit_barrier([], sync_signal := self.device.alloc_signal(reusable=True))
-    mem = self._alloc(src.nbytes, BufferSpec(host=True))
-    ctypes.memmove(mem, from_mv(src), src.nbytes)
-    check(hsa.hsa_amd_memory_async_copy_on_engine(dest, self.device.agent, mem, HSADevice.cpu_agent, src.nbytes, 1, ctypes.byref(sync_signal),
-                                                  copy_signal := self.device.alloc_signal(reusable=True), hsa.HSA_AMD_SDMA_ENGINE_0, True))
-    self.device.hw_queue.submit_barrier([copy_signal])
-    self.device.delayed_free.append(mem)
-    if PROFILE: Profiler.track(copy_signal, self.device, f"copyin: CPU -> HSA:{self.device.device_id}", is_copy=True)
-
-  def copy_from_fd(self, dest, fd, offset, size):
-    self.device.hw_queue.submit_barrier([], sync_signal := self.device.alloc_signal(reusable=True))
-
-    if not hasattr(self, 'hb'):
-      self.hb = [self._alloc(CHUNK_SIZE, BufferSpec(host=True)) for _ in range(2)]
-      self.hb_signals = [self.device.alloc_signal(reusable=False) for _ in range(2)]
-      self.hb_polarity = 0
-      self.sdma = [hsa.HSA_AMD_SDMA_ENGINE_0, hsa.HSA_AMD_SDMA_ENGINE_1]
-      for sig in self.hb_signals: hsa.hsa_signal_store_relaxed(sig, 0)
-
-    fo = io.FileIO(fd, "a+b", closefd=False)
-    fo.seek(offset - (minor_offset:=offset % PAGE_SIZE))
-
-    copies_called = 0
-    copied_in = 0
-    for local_offset in range(0, size+minor_offset, CHUNK_SIZE):
-      local_size = min(round_up(size+minor_offset, PAGE_SIZE)-local_offset, CHUNK_SIZE)
-      copy_size = min(local_size-minor_offset, size-copied_in)
-      if copy_size == 0: break
-
-      hsa.hsa_signal_wait_scacquire(self.hb_signals[self.hb_polarity], hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-      self.device.reusable_signals.append(self.hb_signals[self.hb_polarity]) # it's free now and can be reused
-      self.hb_signals[self.hb_polarity] = self.device.alloc_signal(reusable=False)
-
-      fo.readinto(to_mv(self.hb[self.hb_polarity], local_size))
-      check(hsa.hsa_amd_memory_async_copy_on_engine(dest+copied_in, self.device.agent, self.hb[self.hb_polarity]+minor_offset, HSADevice.cpu_agent,
-                                                    copy_size, 1, ctypes.byref(sync_signal), self.hb_signals[self.hb_polarity],
-                                                    self.sdma[self.hb_polarity], True))
-      copied_in += copy_size
-      self.hb_polarity = (self.hb_polarity + 1) % len(self.hb)
-      minor_offset = 0 # only on the first
-      copies_called += 1
-
-    wait_signals = [self.hb_signals[self.hb_polarity - 1]]
-    if copies_called > 1: wait_signals.append(self.hb_signals[self.hb_polarity])
-    self.device.hw_queue.submit_barrier(wait_signals)
-
-  def _copyout(self, dest:memoryview, src:T):
-    HSADevice.synchronize_system()
-    copy_signal = self.device.alloc_signal(reusable=True)
-    c_agents = (hsa.hsa_agent_t*2)(self.device.agent, HSADevice.cpu_agent)
-    check(hsa.hsa_amd_memory_lock_to_pool(from_mv(dest), dest.nbytes, c_agents, 2, HSADevice.cpu_mempool, 0, ctypes.byref(addr:=ctypes.c_void_p())))
-    check(hsa.hsa_amd_memory_async_copy(addr, HSADevice.cpu_agent, src, self.device.agent, dest.nbytes, 0, None, copy_signal))
-    hsa.hsa_signal_wait_scacquire(copy_signal, hsa.HSA_SIGNAL_CONDITION_LT, 1, (1 << 64) - 1, hsa.HSA_WAIT_STATE_ACTIVE)
-    check(hsa.hsa_amd_memory_unlock(from_mv(dest)))
-    if PROFILE: Profiler.track(copy_signal, self.device, f"copyout: HSA:{self.device.device_id} -> CPU", is_copy=True)
-
-  def transfer(self, dest:T, src:T, sz:int, src_dev=None, dest_dev=None):
-    src_dev.hw_queue.submit_barrier([], sync_signal_1 := src_dev.alloc_signal(reusable=True))
-    dest_dev.hw_queue.submit_barrier([], sync_signal_2 := dest_dev.alloc_signal(reusable=True))
-    c_wait_signal = (hsa.hsa_signal_t*2)(sync_signal_1, sync_signal_2)
-    check(hsa.hsa_amd_memory_async_copy_on_engine(dest, dest_dev.agent, src, src_dev.agent, sz, 2, c_wait_signal,
-                                                  copy_signal := dest_dev.alloc_signal(reusable=False), hsa.HSA_AMD_SDMA_ENGINE_0, True))
-    src_dev.hw_queue.submit_barrier([copy_signal])
-    dest_dev.hw_queue.submit_barrier([copy_signal])
-    if PROFILE: Profiler.track(copy_signal, src_dev, f"transfer: HSA:{src_dev.device_id} -> HSA:{dest_dev.device_id}", is_copy=True)
-
-class HSADevice(Compiled):
-  devices: List[HSADevice] = []
-  agents: Dict[int, List[hsa.hsa_agent_t]] = {}
-  cpu_agent: hsa.hsa_agent_t
-  cpu_mempool: hsa.hsa_amd_memory_pool_t
-  def __init__(self, device:str=""):
-    if not HSADevice.agents:
-      check(hsa.hsa_init())
-      atexit.register(hsa_terminate)
-      HSADevice.agents = scan_agents()
-      HSADevice.cpu_agent = HSADevice.agents[hsa.HSA_DEVICE_TYPE_CPU][0]
-      HSADevice.cpu_mempool = find_memory_pool(HSADevice.cpu_agent, segtyp=hsa.HSA_AMD_SEGMENT_GLOBAL, location=hsa.HSA_AMD_MEMORY_POOL_LOCATION_CPU)
-      if PROFILE: check(hsa.hsa_amd_profiling_async_copy_enable(1))
-
-    self.device_id = int(device.split(":")[1]) if ":" in device else 0
-    self.agent = HSADevice.agents[hsa.HSA_DEVICE_TYPE_GPU][self.device_id]
-    self.gpu_mempool = find_memory_pool(self.agent, segtyp=hsa.HSA_AMD_SEGMENT_GLOBAL, location=hsa.HSA_AMD_MEMORY_POOL_LOCATION_GPU)
-    self.hw_queue = AQLQueue(self)
-    HSADevice.devices.append(self)
-
-    check(hsa.hsa_agent_get_info(self.agent, hsa.HSA_AGENT_INFO_NAME, ctypes.byref(agent_name_buf := ctypes.create_string_buffer(256))))
-    self.arch = ctypes.string_at(agent_name_buf).decode()
-
-    check(hsa.hsa_system_get_info(hsa.HSA_SYSTEM_INFO_TIMESTAMP_FREQUENCY, ctypes.byref(gpu_freq := ctypes.c_uint64())))
-    self.clocks_to_time: float = 1 / gpu_freq.value
-
-    check(hsa.hsa_agent_get_info(self.agent, hsa.HSA_AMD_AGENT_INFO_HDP_FLUSH, ctypes.byref(hdp_flush := hsa.hsa_amd_hdp_flush_t())))
-    self.hdp_flush = hdp_flush
-
-    self.delayed_free: List[int] = []
-    self.reusable_signals: List[hsa.hsa_signal_t] = []
-
-    from tinygrad.runtime.graph.hsa import HSAGraph
-    super().__init__(device, HSAAllocator(self), HIPRenderer(), HSACompiler(self.arch), functools.partial(HSAProgram, self), HSAGraph)
-
-    # Finish init: preallocate some signals + space for kernargs
-    self.signal_pool = [init_c_var(hsa.hsa_signal_t(), lambda x: check(hsa.hsa_signal_create(1, 0, None, ctypes.byref(x)))) for _ in range(4096)]
-    self._new_kernargs_region(16 << 20) # initial region size is 16mb
-
-  def synchronize(self):
-    self.hw_queue.wait()
-
-    for sig in self.reusable_signals: hsa.hsa_signal_silent_store_relaxed(sig, 1)
-    self.signal_pool.extend(self.reusable_signals)
-    self.reusable_signals.clear()
-
-    for opaque_to_free in self.delayed_free: check(hsa.hsa_amd_memory_pool_free(opaque_to_free))
-    self.delayed_free.clear()
-
-    self.kernarg_next_addr = self.kernarg_start_addr
-    Profiler.process(self)
-
-  @staticmethod
-  def synchronize_system():
-    for d in HSADevice.devices: d.synchronize()
-
-  def alloc_signal(self, reusable=False):
-    if len(self.signal_pool): signal = self.signal_pool.pop()
-    else: check(hsa.hsa_amd_signal_create(1, 0, None, 0, ctypes.byref(signal := hsa.hsa_signal_t())))
-
-    # reusable means a signal could be reused after synchronize for the device it's allocated from is called.
-    if reusable: self.reusable_signals.append(signal)
-    return signal
-
-  def alloc_kernargs(self, sz):
-    if self.kernarg_next_addr + sz >= self.kernarg_start_addr + self.kernarg_pool_sz: self._new_kernargs_region(int(self.kernarg_pool_sz * 2))
-    result = self.kernarg_next_addr
-    self.kernarg_next_addr = round_up(self.kernarg_next_addr + sz, 16)
-    return result
-
-  def _new_kernargs_region(self, sz:int):
-    if hasattr(self, 'kernarg_start_addr'): self.delayed_free.append(self.kernarg_start_addr)
-    self.kernarg_start_addr: int = self.allocator._alloc(sz, BufferSpec())
-    self.kernarg_next_addr = self.kernarg_start_addr
-    self.kernarg_pool_sz: int = sz
-
-  def flush_hdp(self): self.hdp_flush.HDP_MEM_FLUSH_CNTL[0] = 1
-
-def hsa_terminate():
-  # Need to stop/delete aql queue before hsa shut down, this leads to gpu hangs.
-  for dev in HSADevice.devices:
-    Profiler.process(dev)
-    del dev.hw_queue
-
-  # hsa_shut_down cleans up all hsa-related resources.
-  hsa.hsa_shut_down()
-  HSADevice.synchronize = lambda: None #type:ignore
-  HSAProgram.__del__ = lambda _: None #type:ignore
-  if Profiler.collected_events: Profiler.save("/tmp/profile.json")
@@ -1,127 +0,0 @@
-from typing import Dict, Set
-import yaml
-from tinygrad.codegen.uops import UOpGraph, UOps, UOp
-from tinygrad.uop.ops import BinaryOps
-from tinygrad.dtype import dtypes
-
-def uops_to_rdna(function_name:str, uops:UOpGraph) -> str:
-  replace: Dict[UOp, UOp] = {}
-  seen: Set[UOp] = set()
-  for u in uops:
-    if u in seen: continue
-    seen.add(u)
-    for o,n in replace.items():
-      if o in u.vin and u is not n:
-        u.vin = tuple(n if x == o else x for x in u.vin)
-    # pointer indexing
-    if u.uop in {UOps.LOAD, UOps.STORE} and u.vin[0].dtype.itemsize > 1:
-      val = UOp(UOps.CONST, dtypes.int, tuple(), arg=u.vin[0].dtype.itemsize, insert_at=uops.uops.index(u))
-      ptr = UOp(UOps.ALU, dtypes.int, (u.vin[1], val), arg=BinaryOps.MUL, insert_at=uops.uops.index(u))
-      u.vin = (u.vin[0], ptr) + u.vin[2:]
-  #uops.print()
-
-  args = []
-  ins = []
-
-  v_cnt = 3  # v[0:2] is local_xyz
-  s_cnt = 5  # s[0:1] is the address, s[2:4] is global_xyz
-
-  r: Dict[UOp, str] = {}
-  for u in uops:
-    if u.uop == UOps.SPECIAL:
-      if u.arg.startswith("lidx"):
-        r[u] = f'v{u.src[0].arg}'
-      elif u.arg.startswith("gidx"):
-        r[u] = f's{2+u.src[0].arg}'
-      else:
-        raise NotImplementedError
-    elif u.uop == UOps.CONST:
-      #r[u] = u.arg
-
-      # TODO: sometimes we can use s
-      #r[u] = f"s{s_cnt}"
-      #s_cnt += 1
-      #ins.append(f"s_mov_b32 {r[u]}, {u.arg}")
-
-      r[u] = f"v{v_cnt}"
-      v_cnt += 1
-      ins.append(f"v_mov_b32 {r[u]}, {u.arg}")
-    elif u.uop == UOps.ALU:
-      if u.arg == BinaryOps.ADD:
-        r[u] = f"v{v_cnt}"
-        v_cnt += 1
-        ins.append(f"v_add_f32_e32 {r[u]}, {r[u.vin[0]]}, {r[u.vin[1]]}")
-      elif u.arg == BinaryOps.MUL:
-        r[u] = f"v{v_cnt}"
-        v_cnt += 1
-        if dtypes.is_float(u.dtype):
-          ins.append(f"v_mul_f32_e32 {r[u]}, {r[u.vin[0]]}, {r[u.vin[1]]}")
-        else:
-          ins.append(f"v_mul_u32_u24 {r[u]}, {r[u.vin[0]]}, {r[u.vin[1]]}")
-      else:
-        raise NotImplementedError
-    elif u.uop == UOps.LOAD:
-      r[u] = f"v{v_cnt}"
-      v_cnt += 1
-      ins.append(f"global_load_b32 {r[u]}, {r[u.vin[1]]}, {r[u.vin[0]]}")
-      ins.append("s_waitcnt vmcnt(0)")
-    elif u.uop == UOps.STORE:
-      ins.append(f"global_store_b32 {r[u.vin[1]]}, {r[u.vin[2]]}, {r[u.vin[0]]}")
-    elif u.uop == UOps.DEFINE_GLOBAL:
-      i = u.arg[0]
-      args.append({'.address_space': 'global', '.name': f'buf_{i}', '.offset': i*8, '.size': 8,
-                   '.type_name': u.dtype.name+"*", '.value_kind': 'global_buffer'})
-      s_cnt += s_cnt%2  # skip
-      r[u] = f"s[{s_cnt}:{s_cnt+1}]"
-      s_cnt += 2
-      ins.append(f"s_load_b64 {r[u]}, s[0:1], {i*8}")
-      ins.append("s_waitcnt lgkmcnt(0)")
-    else:
-      raise NotImplementedError(f"can't render {u.uop}")
-
-  # *** boilerplate rendering ***
-
-  metadata = {
-    'amdhsa.kernels': [{'.args': args,
-      '.group_segment_fixed_size': 0, '.kernarg_segment_align': 8, '.kernarg_segment_size': args[-1][".offset"] + args[-1][".size"],
-      '.language': 'OpenCL C', '.language_version': [1, 2], '.max_flat_workgroup_size': 256,
-      '.name': function_name, '.private_segment_fixed_size': 0, '.sgpr_count': s_cnt, '.sgpr_spill_count': 0,
-      '.symbol': f'{function_name}.kd', '.uses_dynamic_stack': False, '.vgpr_count': v_cnt, '.vgpr_spill_count': 0,
-      '.wavefront_size': 32}],
-    'amdhsa.target': 'amdgcn-amd-amdhsa--gfx1100', 'amdhsa.version': [1, 2]}
-
-  boilerplate_start = f"""
-.rodata
-.global {function_name}.kd
-.type {function_name}.kd,STT_OBJECT
-.align 0x10
-.amdhsa_kernel {function_name}"""
-
-  kernel_desc = {
-    '.amdhsa_group_segment_fixed_size': 0, '.amdhsa_private_segment_fixed_size': 0, '.amdhsa_kernarg_size': 0,
-    '.amdhsa_next_free_vgpr': v_cnt,   # this matters!
-    '.amdhsa_reserve_vcc': 0, '.amdhsa_reserve_xnack_mask': 0,
-    '.amdhsa_next_free_sgpr': s_cnt,
-    '.amdhsa_float_round_mode_32': 0, '.amdhsa_float_round_mode_16_64': 0, '.amdhsa_float_denorm_mode_32': 3, '.amdhsa_float_denorm_mode_16_64': 3,
-    '.amdhsa_dx10_clamp': 1, '.amdhsa_ieee_mode': 1, '.amdhsa_fp16_overflow': 0,
-    '.amdhsa_workgroup_processor_mode': 1, '.amdhsa_memory_ordered': 1, '.amdhsa_forward_progress': 0, '.amdhsa_enable_private_segment': 0,
-    '.amdhsa_system_sgpr_workgroup_id_x': 1, '.amdhsa_system_sgpr_workgroup_id_y': 1, '.amdhsa_system_sgpr_workgroup_id_z': 1,
-    '.amdhsa_system_sgpr_workgroup_info': 0, '.amdhsa_system_vgpr_workitem_id': 2, # is amdhsa_system_vgpr_workitem_id real?
-    '.amdhsa_exception_fp_ieee_invalid_op': 0, '.amdhsa_exception_fp_denorm_src': 0,
-    '.amdhsa_exception_fp_ieee_div_zero': 0, '.amdhsa_exception_fp_ieee_overflow': 0, '.amdhsa_exception_fp_ieee_underflow': 0,
-    '.amdhsa_exception_fp_ieee_inexact': 0, '.amdhsa_exception_int_div_zero': 0,
-    '.amdhsa_user_sgpr_dispatch_ptr': 0, '.amdhsa_user_sgpr_queue_ptr': 0, '.amdhsa_user_sgpr_kernarg_segment_ptr': 1,
-    '.amdhsa_user_sgpr_dispatch_id': 0, '.amdhsa_user_sgpr_private_segment_size': 0, '.amdhsa_wavefront_size32': 1, '.amdhsa_uses_dynamic_stack': 0}
-
-  code_start = f""".end_amdhsa_kernel
-.text
-.global {function_name}
-.type {function_name},@function
-.p2align 8
-{function_name}:
-"""
-
-  ins += ['s_sendmsg sendmsg(MSG_DEALLOC_VGPRS)', 's_endpgm', 's_code_end']
-  return ".amdgpu_metadata\n" + yaml.dump(metadata) + ".end_amdgpu_metadata" + \
-         boilerplate_start + "\n" + '\n'.join("%s %d" % x for x in kernel_desc.items()) + "\n" + code_start + \
-         '\n'.join(ins) + f"\n.size {function_name}, .-{function_name}"
@@ -1,131 +0,0 @@
-from typing import Dict, List, Final, Callable, DefaultDict
-from collections import defaultdict
-from tinygrad.uop.ops import UnaryOps, BinaryOps, TernaryOps, Op
-from tinygrad.helpers import DType, PtrDType, dtypes, ImageDType, DEBUG, getenv
-from tinygrad.codegen.opt.kernel import  UOp, Ops
-from triton.compiler import compile as triton_compile
-import linecache
-import math
-import re
-
-triton_dtypes = {dtypes.double: "tl.float64", dtypes.float32: "tl.float32", dtypes.float16: "tl.float16", dtypes.bool: "tl.int1", dtypes.int8: "tl.int8", dtypes.uint8: "tl.uint8", dtypes.int32: "tl.int32", dtypes.int64: "tl.int64", dtypes.uint32: "tl.uint32", dtypes.uint64: "tl.uint64", dtypes.int16: "tl.int16", dtypes.uint16: "tl.uint16"}
-signature_dtypes = {dtypes.double: "fp64",dtypes.float32: "fp32", dtypes.float16: "fp16", dtypes.bool: "i8", dtypes.int8: "i1", dtypes.uint8: "u8", dtypes.int32: "i32", dtypes.int64: "i64", dtypes.uint32: "u32", dtypes.uint64: "u64", dtypes.int16: "i16", dtypes.uint16: "u16"}
-
-def next_power_of_2(x):
-  return 1 << (x - 1).bit_length()
-
-def render_valid(valid):
-  return '(' * (len(valid) -1) + ') and '.join(valid) if len(valid) else 'True'
-
-#NOTE Triton requires matching dimensions for load/store, disable this and see TestOps::test_output_padded_conv_transpose2d fail to compile
-def fill_dims_for_idx(idx, dims):
-  return "(" + idx + "+ (" + (f"0*({'+'.join(d for d in dims)})))") if len(dims) else idx
-
-def get_max(var):
-  if isinstance(var, int): return var
-  return re.sub(r'\[(.*?)\]', '', str(var))[1:-1]
-
-#NOTE can be removed after https://github.com/gpuocelot/gpuocelot/issues/8 gets resolved
-def remove_single_scalar_curly_braces(ptx_code):
-  return '\n'.join([re.sub(r'\{\s*(%\w+)\s*\}', r'\1', line) for line in ptx_code.split('\n')])
-
-def render_const(args,dtype:DType):
-  return (('-' if args<0 else '') + 'tl.where(1,float("inf"),0)') if math.isinf(args) else ('tl.where(1,float("nan"),0)' if math.isnan(args) else f"{int(args)}" if dtypes.is_int(dtype) else str(args))
-
-def render_cast(x:str, dtype:DType, bitcast=False):
-  return f"{x}.to({triton_dtypes[dtype]}, bitcast={bitcast})"
-
-def define_scalar(local_size, dtype, args):
-  if len(local_size) > 0: return f"tl.full(({','.join([str(next_power_of_2(x)) for x in local_size])},),{render_const(args,dtype)}, dtype={triton_dtypes[dtype]})"
-  return render_const(args,dtype)
-
-def uops_to_triton(function_name:str, uops:List[UOp]):
-  local_size: List[int] = []
-  depth = 1
-  signatures, dims, bufs, kernel, valid = [], [], [], [], [] #type: ignore
-
-  c: DefaultDict[str, int] = defaultdict(int)
-  r: Dict[UOp, str] = {}
-  def ssa(u, prefix="t"):
-    nonlocal c, r
-    c[prefix] += 1
-    r[u]=f"{prefix}{c[prefix]-1}"
-    return r[u]
-
-  child_count: DefaultDict[UOp, int] = defaultdict(int)
-  for ru in uops:
-    for v in ru.vin:
-      child_count[v] += 1
-
-  def kk(s): kernel.append("  "*depth+s)
-  code_for_op: Final[Dict[Op, Callable]] = {
-    UnaryOps.EXP2: lambda x,dtype,: f"tl.math.exp2({x})",
-    UnaryOps.LOG2: lambda x,dtype,: f"tl.math.log2({x})",
-    UnaryOps.SIN: lambda x,dtype: f"tl.sin({x})",
-    UnaryOps.SQRT: lambda x,dtype: f"tl.sqrt({x})",
-    UnaryOps.NEG: lambda x,dtype: f"-{x}",
-    BinaryOps.ADD: lambda x,y,dtype: f"({x}+{y})", BinaryOps.SUB: lambda x,y,: f"({x}-{y})",
-    BinaryOps.MUL: lambda x,y,dtype: f"({x}*{y})", BinaryOps.DIV: lambda x,y,: f"({x}/{y})" if y != '0.0' else f"{x}*tl.where({x}==0.0, float('nan'), float('inf'))",
-    BinaryOps.MAX: lambda x,y,dtype: f"tl.maximum({x},{y})",
-    BinaryOps.CMPLT: lambda x,y,dtype: f"({x}<{y})",
-    BinaryOps.MOD: lambda x,y,dtype: f"tl.abs({x})%tl.abs({y})*tl.where({x}<0,-1,1)",
-    TernaryOps.MULACC: lambda x,y,z,dtype: f"(({x}*{y})+{z})",
-    TernaryOps.WHERE: lambda x,y,z,dtype: f"tl.where({x},{y},{z})",
-  }
-  def int_div(x,y): return f"({x}//{y})" if y != '0' else f"{x}*tl.where({x}==0, float('nan'), float('inf'))"
-  for u in uops:
-    uop,dtype,vin,args = u.uop,u.dtype,u.vin,u.arg
-    if uop == Ops.LOOP:
-      kk(f"for {ssa(u, 'ridx')} in range({vin[0].arg}, {r[vin[1]]}):")
-      depth += 1
-    elif uop == Ops.END: depth -= 1
-    elif uop == Ops.ALU:
-      assert dtype is not None
-      val = code_for_op[args](*[r[x] for x in vin])
-      if child_count[u] <=1 or dtypes.is_int(dtype): r[u] = int_div(*[r[x] for x in vin]) if args == BinaryOps.DIV and dtypes.is_int(dtype) else val
-      else: kk(f"{ssa(u, 'alu')} = ({val})")
-    elif uop == Ops.LOAD:
-      assert dtype is not None
-      if len(vin) == 2: kk(f"{ssa(u, 'val')} = {render_cast(f'tl.load({r[vin[0]]} + { fill_dims_for_idx(r[vin[1]], dims)}, mask = {render_valid(valid)})', dtype)}")
-      else: kk(f"{ssa(u, 'val')} = {render_cast(f'tl.where({r[vin[2]]}, tl.load({r[vin[0]]}+{fill_dims_for_idx(r[vin[1]],dims)} , mask={render_valid(valid+[r[vin[2]]])}), 0.0)', dtype)}")
-    elif uop == Ops.DEFINE_REG: kk(f"{ssa(u, 'acc')} = {define_scalar(local_size, dtype, args).replace('//', '/')}")
-    elif uop == Ops.CONST: r[u] = define_scalar([], dtype, args)
-    elif uop == Ops.ASSIGN:
-      kk(f"{r[vin[0]]} = {r[vin[1]].replace('//', '/')}")
-      r[u] = r[vin[0]]
-    elif uop == Ops.STORE:
-      assert not isinstance(dtype, ImageDType), "unimplemented: image store"
-      kk(f"{'if '+r[vin[3]]+': ' if len(vin)>3 else ''}tl.store({r[vin[0]]} + {r[vin[1]]}, {r[vin[2]].replace('//', '/')}, mask = {render_valid(valid)}) ")
-    elif uop == Ops.DEFINE_GLOBAL:
-      bufs.append(args)
-      signatures.append("*" if isinstance(dtype, PtrDType) else "" +  signature_dtypes[dtype])
-      r[u] = args
-    elif uop == Ops.SPECIAL:
-      dims.append(args[1])
-      valid.append(f"{args[1]}<{get_max(args[2])}")
-      if args[1].startswith("g"): kk(f"{args[1]} = tl.program_id({args[0]}) # {args[2]}")
-      elif args[1].startswith("l"):
-        kk(f"{args[1]} = tl.arange({0}, {next_power_of_2(args[2])})")
-        local_size.append(args[2])
-      r[u] = args[1]
-    elif uop == Ops.CAST and dtype is not None: r[u] = render_cast(r[vin[0]], dtype, isinstance(args, tuple) and args[1])
-    else: raise NotImplementedError(f"unimplemented: {uop}")
-
-  prg = f"import triton\nimport triton.language as tl\ntl.core.TRITON_MAX_TENSOR_NUMEL = float('inf')\n@triton.jit\ndef {function_name}("+','.join(bufs)+"):\n"
-  for i, line in enumerate(list(filter(lambda line: "tl.arange" in line, kernel))): kernel[kernel.index(line)] +=  f"[{', '.join([':' if i == j else 'None' for j in range(len(local_size))])}]"
-  prg += "\n".join(kernel)
-
-  acc_local_size = 1
-  for x in local_size: acc_local_size *= next_power_of_2(x)
-  local_size = [acc_local_size] + [1] * (len(local_size) - 1)
-
-  if DEBUG >= 4: print(prg)
-  getlines = linecache.getlines
-  linecache.getlines = lambda filename, module_globals=None: prg.splitlines(keepends=True) if "<triton>" == filename else getlines(filename, module_globals)
-  exec(compile(prg, "<triton>", "exec"), globals()) # pylint: disable=W0122\
-  compiled = triton_compile(globals()[function_name], signature=",".join(signatures), device_type="cuda", debug=False, cc=(35 if getenv("CUDACPU", 0) else None))
-  prg = remove_single_scalar_curly_braces(compiled.asm["ptx"].split(".file")[0].split(".visible .func")[0])
-  max_local_size =  [int(x) for x in prg.split(".maxntid ")[1].split("\n")[0].split(", ")]
-  for i in range(len(local_size)): local_size[i] = min(local_size[i], max_local_size[i])
-
-  return prg, {"shared":compiled.metadata["shared"], "local_size":local_size + [1]*(3-len(local_size))}
@@ -1,199 +0,0 @@
-import json
-import pathlib
-import zipfile
-import numpy as np
-from tinygrad.helpers import fetch
-import pycocotools._mask as _mask
-from examples.mask_rcnn import Masker
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-iou         = _mask.iou
-merge       = _mask.merge
-frPyObjects = _mask.frPyObjects
-
-BASEDIR = pathlib.Path(__file__).parent / "COCO"
-BASEDIR.mkdir(exist_ok=True)
-
-def create_dict(key_row, val_row, rows): return {row[key_row]:row[val_row] for row in rows}
-
-
-if not pathlib.Path(BASEDIR/'val2017').is_dir():
-  fn = fetch('http://images.cocodataset.org/zips/val2017.zip')
-  with zipfile.ZipFile(fn, 'r') as zip_ref:
-    zip_ref.extractall(BASEDIR)
-  fn.unlink()
-
-
-if not pathlib.Path(BASEDIR/'annotations').is_dir():
-  fn = fetch('http://images.cocodataset.org/annotations/annotations_trainval2017.zip')
-  with zipfile.ZipFile(fn, 'r') as zip_ref:
-    zip_ref.extractall(BASEDIR)
-  fn.unlink()
-
-with open(BASEDIR/'annotations/instances_val2017.json', 'r') as f:
-  annotations_raw = json.loads(f.read())
-images = annotations_raw['images']
-categories = annotations_raw['categories']
-annotations = annotations_raw['annotations']
-file_name_to_id = create_dict('file_name', 'id', images)
-id_to_width = create_dict('id', 'width', images)
-id_to_height = create_dict('id', 'height', images)
-json_category_id_to_contiguous_id = {v['id']: i + 1 for i, v in enumerate(categories)}
-contiguous_category_id_to_json_id = {v:k for k,v in json_category_id_to_contiguous_id.items()}
-
-
-def encode(bimask):
-  if len(bimask.shape) == 3:
-    return _mask.encode(bimask)
-  elif len(bimask.shape) == 2:
-    h, w = bimask.shape
-    return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0]
-
-def decode(rleObjs):
-  if type(rleObjs) == list:
-    return _mask.decode(rleObjs)
-  else:
-    return _mask.decode([rleObjs])[:,:,0]
-
-def area(rleObjs):
-  if type(rleObjs) == list:
-    return _mask.area(rleObjs)
-  else:
-    return _mask.area([rleObjs])[0]
-
-def toBbox(rleObjs):
-  if type(rleObjs) == list:
-    return _mask.toBbox(rleObjs)
-  else:
-    return _mask.toBbox([rleObjs])[0]
-
-
-def convert_prediction_to_coco_bbox(file_name, prediction):
-  coco_results = []
-  try:
-    original_id = file_name_to_id[file_name]
-    if len(prediction) == 0:
-      return coco_results
-
-    image_width = id_to_width[original_id]
-    image_height = id_to_height[original_id]
-    prediction = prediction.resize((image_width, image_height))
-    prediction = prediction.convert("xywh")
-
-    boxes = prediction.bbox.numpy().tolist()
-    scores = prediction.get_field("scores").numpy().tolist()
-    labels = prediction.get_field("labels").numpy().tolist()
-
-    mapped_labels = [contiguous_category_id_to_json_id[int(i)] for i in labels]
-
-    coco_results.extend(
-      [
-        {
-          "image_id": original_id,
-          "category_id": mapped_labels[k],
-          "bbox": box,
-          "score": scores[k],
-        }
-          for k, box in enumerate(boxes)
-      ]
-    )
-  except Exception as e:
-    print(file_name, e)
-  return coco_results
-
-masker = Masker(threshold=0.5, padding=1)
-
-def convert_prediction_to_coco_mask(file_name, prediction):
-  coco_results = []
-  try:
-    original_id = file_name_to_id[file_name]
-    if len(prediction) == 0:
-      return coco_results
-
-    image_width = id_to_width[original_id]
-    image_height = id_to_height[original_id]
-    prediction = prediction.resize((image_width, image_height))
-    masks = prediction.get_field("mask")
-
-    scores = prediction.get_field("scores").numpy().tolist()
-    labels = prediction.get_field("labels").numpy().tolist()
-
-    masks = masker([masks], [prediction])[0].numpy()
-
-    rles = [
-      encode(np.array(mask[0, :, :, np.newaxis], order="F"))[0]
-      for mask in masks
-    ]
-    for rle in rles:
-      rle["counts"] = rle["counts"].decode("utf-8")
-
-    mapped_labels = [contiguous_category_id_to_json_id[int(i)] for i in labels]
-
-    coco_results.extend(
-      [
-        {
-          "image_id": original_id,
-          "category_id": mapped_labels[k],
-          "segmentation": rle,
-          "score": scores[k],
-        }
-          for k, rle in enumerate(rles)
-      ]
-    )
-  except Exception as e:
-    print(file_name, e)
-  return coco_results
-
-
-
-def accumulate_predictions_for_coco(coco_results, json_result_file, rm=False):
-  path = pathlib.Path(json_result_file)
-  if rm and path.exists(): path.unlink()
-  with open(path, "a") as f:
-    for s in coco_results:
-      f.write(json.dumps(s))
-      f.write('\n')
-
-def remove_dup(l):
-  seen = set()
-  seen_add = seen.add
-  return [x for x in l if not (x in seen or seen_add(x))]
-
-class NpEncoder(json.JSONEncoder):
-  def default(self, obj):
-    if isinstance(obj, np.integer):
-      return int(obj)
-    if isinstance(obj, np.floating):
-      return float(obj)
-    if isinstance(obj, np.ndarray):
-      return obj.tolist()
-    return super(NpEncoder, self).default(obj)
-
-
-def evaluate_predictions_on_coco(json_result_file, iou_type="bbox"):
-  coco_results = []
-  with open(json_result_file, "r") as f:
-    for line in f:
-      coco_results.append(json.loads(line))
-
-  coco_gt = COCO(str(BASEDIR/'annotations/instances_val2017.json'))
-  set_of_json = remove_dup([json.dumps(d, cls=NpEncoder) for d in coco_results])
-  unique_list = [json.loads(s) for s in set_of_json]
-
-  with open(f'{json_result_file}.flattend', "w") as f:
-    json.dump(unique_list, f)
-
-  coco_dt = coco_gt.loadRes(str(f'{json_result_file}.flattend'))
-  coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
-  coco_eval.evaluate()
-  coco_eval.accumulate()
-  coco_eval.summarize()
-  return coco_eval
-
-def iterate(files, bs=1):
-  batch = []
-  for file in files:
-    batch.append(file)
-    if len(batch) >= bs: yield batch; batch = []
-  if len(batch) > 0: yield batch; batch = []
--- a/Show More
+++ b/Show More