Merge branch 'master' into call_inline

cl_compiler instead of compiler because overriding Compiled.compiler seems more confusing

.github/actions/process-replay/action.yml

@@ -11,5 +11,5 @@ runs:
         git fetch origin $CURRENT_SHA
         export COMMIT_MESSAGE=$(git show -s --format=%B "$CURRENT_SHA")
         export CURRENT_HEAD=$(git rev-parse HEAD)
-        cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && IGNORE_OOB=1 PYTHONPATH=. python3 process_replay.py
+        cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && CHECK_OOB=0 PYTHONPATH=. python3 process_replay.py
         git checkout $CURRENT_HEAD  # restore to branch

.github/actions/setup-tinygrad/action.yml

@@ -56,7 +56,15 @@ runs:
 
     # **** Caching packages ****
 
+    - name: Cache Python packages (PR)
+      if: github.event_name == 'pull_request'
+      id: restore-venv-pr
+      uses: actions/cache/restore@v4
+      with:
+        path: ${{ github.workspace }}/.venv
+        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
     - name: Cache Python packages
+      if: github.event_name != 'pull_request'
       id: restore-venv
       uses: actions/cache@v4
       with:
@@ -65,23 +73,23 @@ runs:
 
     # **** Caching downloads ****
 
-    - name: Cache downloads (Linux)
-      if: inputs.key != '' && runner.os == 'Linux'
+    - name: Cache downloads (PR)
+      if: inputs.key != '' && github.event_name == 'pull_request'
+      uses: actions/cache/restore@v4
+      with:
+        path: ${{ runner.os == 'Linux' && '~/.cache/tinygrad/downloads/' || '~/Library/Caches/tinygrad/downloads/' }}
+        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}
+    - name: Cache downloads
+      if: inputs.key != '' && github.event_name != 'pull_request'
       uses: actions/cache@v4
       with:
-        path: ~/.cache/tinygrad/downloads/
-        key: downloads-cache-${{ inputs.key }}-${{ 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 }}
+        path: ${{ runner.os == 'Linux' && '~/.cache/tinygrad/downloads/' || '~/Library/Caches/tinygrad/downloads/' }}
+        key: downloads-${{ github.job }}-${{ inputs.key }}-${{ env.CACHE_VERSION }}
 
     # **** Python deps ****
 
     - name: Install dependencies in venv (with extra)
-      if: inputs.deps != '' && steps.restore-venv.outputs.cache-hit != 'true'
+      if: inputs.deps != '' && steps.restore-venv-pr.outputs.cache-hit != 'true' && steps.restore-venv.outputs.cache-hit != 'true'
       shell: bash
       run: |
         python -m venv .venv
@@ -92,7 +100,7 @@ runs:
         fi
         python -m pip install -e ".[${{ inputs.deps }}]" ${{ inputs.pydeps }} --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/
     - name: Install dependencies in venv (without extra)
-      if: inputs.deps == '' && steps.restore-venv.outputs.cache-hit != 'true'
+      if: inputs.deps == '' && steps.restore-venv-pr.outputs.cache-hit != 'true' && steps.restore-venv.outputs.cache-hit != 'true'
       shell: bash
       run: |
         python -m venv .venv
@@ -182,8 +190,14 @@ runs:
         echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
         echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
+    - name: Cache apt (PR)
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true') && github.event_name == 'pull_request'
+      uses: actions/cache/restore@v4
+      with:
+        path: /var/cache/apt/archives/
+        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
     - name: Cache apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true') && github.event_name != 'pull_request'
       uses: actions/cache@v4
       with:
         path: /var/cache/apt/archives/
@@ -239,8 +253,17 @@ runs:
         ln -s /opt/homebrew/opt/[email protected] /opt/homebrew/opt/boost || true
         ln -s /opt/homebrew/opt/boost/lib/libboost_atomic-mt.dylib /opt/homebrew/opt/boost/lib/libboost_atomic.dylib || true
         ln -s /opt/homebrew/opt/boost/lib/libboost_thread-mt.dylib /opt/homebrew/opt/boost/lib/libboost_thread.dylib || true
+    - name: Cache gpuocelot (PR)
+      if: inputs.ocelot == 'true' && github.event_name == 'pull_request'
+      id: cache-build-pr
+      uses: actions/cache/restore@v4
+      env:
+        cache-name: cache-gpuocelot-build-1
+      with:
+        path: ${{ github.workspace }}/gpuocelot/ocelot
+        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.CACHE_VERSION }}
     - name: Cache gpuocelot
-      if: inputs.ocelot == 'true'
+      if: inputs.ocelot == 'true' && github.event_name != 'pull_request'
       id: cache-build
       uses: actions/cache@v4
       env:
@@ -249,7 +272,7 @@ runs:
         path: ${{ github.workspace }}/gpuocelot/ocelot
         key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.CACHE_VERSION }}
     - name: Clone/compile gpuocelot
-      if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
+      if: inputs.ocelot == 'true' && steps.cache-build-pr.outputs.cache-hit != 'true' && steps.cache-build.outputs.cache-hit != 'true'
       shell: bash
       run: |
         git clone --recurse-submodules https://github.com/gpuocelot/gpuocelot.git ${{ github.workspace }}/gpuocelot
@@ -298,7 +321,7 @@ runs:
     - name: Install mesa (linux)
       if: inputs.mesa == 'true' && runner.os == 'Linux'
       shell: bash
-      run: sudo curl -fL 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

.github/workflows/autogen.yml

@@ -14,10 +14,12 @@ on:
     paths:
     - 'tinygrad/runtime/autogen/**/*'
     - 'tinygrad/runtime/support/autogen.py'
+    - '.github/workflows/autogen.yml'
   workflow_dispatch:
     paths:
     - 'tinygrad/runtime/autogen/**/*'
     - 'tinygrad/runtime/support/autogen.py'
+    - '.github/workflows/autogen.yml'
 
 jobs:
   autogen:
@@ -38,105 +40,36 @@ jobs:
         mesa: 'true'
         pydeps: 'pyyaml mako'
     - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends libclang-20-dev llvm-20-dev hip-dev libusb-1.0-0-dev
-    - name: Verify OpenCL autogen
+      run: sudo apt-get install -y --no-install-recommends libclang-20-dev llvm-20-dev hip-dev libusb-1.0-0-dev libdrm-dev
+    - name: Regenerate autogen files
       run: |
-        mv tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
+        find tinygrad/runtime/autogen -type f -name "*.py" -not -name "__init__.py" -not -name "comgr_3.py" -not -name "metal.py" -not -name "iokit.py" -not -name "corefoundation.py" -not -name "libclang.py" -delete
         python3 -c "from tinygrad.runtime.autogen import opencl"
-        diff /tmp/opencl.py.bak tinygrad/runtime/autogen/opencl.py
-    - name: Verify CUDA autogen
-      run: |
-        mv tinygrad/runtime/autogen/cuda.py /tmp/cuda.py.bak
-        mv tinygrad/runtime/autogen/nvrtc.py /tmp/nvrtc.py.bak
-        mv tinygrad/runtime/autogen/nvjitlink.py /tmp/nvjitlink.py.bak
-        mv tinygrad/runtime/autogen/nv_570.py /tmp/nv_570.py.bak
-        mv tinygrad/runtime/autogen/nv.py /tmp/nv.py.bak
-        python3 -c "from tinygrad.runtime.autogen import cuda, nvrtc, nvjitlink, nv_570, nv"
-        diff /tmp/cuda.py.bak tinygrad/runtime/autogen/cuda.py
-        diff /tmp/nvrtc.py.bak tinygrad/runtime/autogen/nvrtc.py
-        diff /tmp/nvjitlink.py.bak tinygrad/runtime/autogen/nvjitlink.py
-        diff /tmp/nv_570.py.bak tinygrad/runtime/autogen/nv_570.py
-        diff /tmp/nv.py.bak tinygrad/runtime/autogen/nv.py
-    - name: Verify AMD autogen
-      run: |
-        mv tinygrad/runtime/autogen/comgr.py /tmp/comgr.py.bak
-        mv tinygrad/runtime/autogen/hsa.py /tmp/hsa.py.bak
-        mv tinygrad/runtime/autogen/hip.py /tmp/hip.py.bak
-        mv tinygrad/runtime/autogen/amd_gpu.py /tmp/amd_gpu.py.bak
-        mv tinygrad/runtime/autogen/sqtt.py /tmp/sqtt.py.bak
-        mv tinygrad/runtime/autogen/rocprof.py /tmp/rocprof.py.bak
-        mv tinygrad/runtime/autogen/am/am.py /tmp/am_am.py.bak
-        mv tinygrad/runtime/autogen/am/pm4_soc15.py /tmp/am_pm4_soc15.py.bak
-        mv tinygrad/runtime/autogen/am/pm4_nv.py /tmp/am_pm4_nv.py.bak
-        mv tinygrad/runtime/autogen/am/sdma_4_0_0.py /tmp/am_sdma_4_0_0.py.bak
-        mv tinygrad/runtime/autogen/am/sdma_5_0_0.py /tmp/am_sdma_5_0_0.py.bak
-        mv tinygrad/runtime/autogen/am/sdma_6_0_0.py /tmp/am_sdma_6_0_0.py.bak
-        mv tinygrad/runtime/autogen/am/smu_v13_0_0.py /tmp/am_smu_v13_0_0.py.bak
-        mv tinygrad/runtime/autogen/am/smu_v14_0_2.py /tmp/am_smu_v14_0_2.py.bak
-        python3 -c "from tinygrad.runtime.autogen import comgr, hsa, hip, amd_gpu, sqtt, rocprof; from tinygrad.runtime.autogen.am import am, pm4_soc15, pm4_nv, sdma_4_0_0, sdma_5_0_0, sdma_6_0_0, smu_v13_0_0, smu_v14_0_2"
-        diff /tmp/comgr.py.bak tinygrad/runtime/autogen/comgr.py
-        diff /tmp/hsa.py.bak tinygrad/runtime/autogen/hsa.py
-        diff /tmp/hip.py.bak tinygrad/runtime/autogen/hip.py
-        diff /tmp/amd_gpu.py.bak tinygrad/runtime/autogen/amd_gpu.py
-        diff /tmp/sqtt.py.bak tinygrad/runtime/autogen/sqtt.py
-        diff /tmp/rocprof.py.bak tinygrad/runtime/autogen/rocprof.py
-        diff /tmp/am_am.py.bak tinygrad/runtime/autogen/am/am.py
-        diff /tmp/am_pm4_soc15.py.bak tinygrad/runtime/autogen/am/pm4_soc15.py
-        diff /tmp/am_pm4_nv.py.bak tinygrad/runtime/autogen/am/pm4_nv.py
-        diff /tmp/am_sdma_4_0_0.py.bak tinygrad/runtime/autogen/am/sdma_4_0_0.py
-        diff /tmp/am_sdma_5_0_0.py.bak tinygrad/runtime/autogen/am/sdma_5_0_0.py
-        diff /tmp/am_sdma_6_0_0.py.bak tinygrad/runtime/autogen/am/sdma_6_0_0.py
-        diff /tmp/am_smu_v13_0_0.py.bak tinygrad/runtime/autogen/am/smu_v13_0_0.py
-        diff /tmp/am_smu_v14_0_2.py.bak tinygrad/runtime/autogen/am/smu_v14_0_2.py
-    - name: Verify Linux autogen
-      run: |
-        mv tinygrad/runtime/autogen/libc.py /tmp/libc.py.bak
-        mv tinygrad/runtime/autogen/kfd.py /tmp/kfd.py.bak
-        mv tinygrad/runtime/autogen/io_uring.py /tmp/io_uring.py.bak
-        mv tinygrad/runtime/autogen/ib.py /tmp/ib.py.bak
-        mv tinygrad/runtime/autogen/pci.py /tmp/pci.py.bak
-        mv tinygrad/runtime/autogen/vfio.py /tmp/vfio.py.bak
+        python3 -c "from tinygrad.runtime.autogen import cuda, nvrtc, nvjitlink, nv_570, nv_580, nv"
+        python3 -c "from tinygrad.runtime.autogen import comgr, hsa, hip, amd_gpu, sqtt, rocprof, amdgpu_kd, amdgpu_drm"
+        python3 -c "from tinygrad.runtime.autogen.am import am, pm4_soc15, pm4_nv, sdma_4_0_0, sdma_5_0_0, sdma_6_0_0, smu_v13_0_0, smu_v13_0_6, smu_v14_0_2"
         python3 -c "from tinygrad.runtime.autogen import libc, kfd, io_uring, ib, pci, vfio"
-        diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.py
-        diff /tmp/kfd.py.bak tinygrad/runtime/autogen/kfd.py
-        diff /tmp/io_uring.py.bak tinygrad/runtime/autogen/io_uring.py
-        diff /tmp/ib.py.bak tinygrad/runtime/autogen/ib.py
-        diff /tmp/pci.py.bak tinygrad/runtime/autogen/pci.py
-        diff /tmp/vfio.py.bak tinygrad/runtime/autogen/vfio.py
-    - name: Verify LLVM autogen
-      run: |
-        mv tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
         python3 -c "from tinygrad.runtime.autogen import llvm"
-        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
-    - name: Verify WebGPU autogen
-      run: |
-        mv tinygrad/runtime/autogen/webgpu.py /tmp/webgpu.py.bak
         python3 -c "from tinygrad.runtime.autogen import webgpu"
-        diff /tmp/webgpu.py.bak tinygrad/runtime/autogen/webgpu.py
-    - 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"
-        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: |
-        mv tinygrad/runtime/autogen/libusb.py /tmp/libusb.py.bak
+        python3 -c "from tinygrad.runtime.autogen import kgsl, qcom_dsp"
         python3 -c "from tinygrad.runtime.autogen import libusb"
-        diff /tmp/libusb.py.bak tinygrad/runtime/autogen/libusb.py
-    - name: Verify mesa autogen
-      run: |
-        mv tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
         python3 -c "from tinygrad.runtime.autogen import mesa"
-        diff /tmp/mesa.py.bak tinygrad/runtime/autogen/mesa.py
-    - name: Verify libclang autogen
-      run: |
-        cp tinygrad/runtime/autogen/libclang.py /tmp/libclang.py.bak
+        python3 -c "from tinygrad.runtime.autogen import avcodec"
         REGEN=1 python3 -c "from tinygrad.runtime.autogen import libclang"
-        diff /tmp/libclang.py.bak tinygrad/runtime/autogen/libclang.py
+    - name: Check for differences
+      run: |
+        if ! git diff --quiet; then
+          git diff > autogen-ubuntu.patch
+          echo "Autogen files out of date. Apply patch from: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
+          exit 1
+        fi
+    - name: Upload patch artifact
+      if: failure()
+      uses: actions/upload-artifact@v4
+      with:
+        name: autogen-ubuntu-patch
+        path: autogen-ubuntu.patch
+
   autogen-mac:
     name: In-tree Autogen (macos)
     runs-on: macos-14
@@ -148,11 +81,24 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         llvm: 'true'
-    - name: Verify macos autogen
+    - name: Regenerate autogen files
       run: |
-        mv tinygrad/runtime/autogen/metal.py /tmp/metal.py.bak
-        LIBCLANG_PATH=/opt/homebrew/opt/llvm@20/lib/libclang.dylib python3 -c "from tinygrad.runtime.autogen import metal"
-        diff /tmp/metal.py.bak tinygrad/runtime/autogen/metal.py
+        rm tinygrad/runtime/autogen/metal.py tinygrad/runtime/autogen/iokit.py tinygrad/runtime/autogen/corefoundation.py
+        python3 -c "from tinygrad.runtime.autogen import metal, iokit, corefoundation"
+    - name: Check for differences
+      run: |
+        if ! git diff --quiet; then
+          git diff > autogen-macos.patch
+          echo "Autogen files out of date. Apply patch from: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
+          exit 1
+        fi
+    - name: Upload patch artifact
+      if: failure()
+      uses: actions/upload-artifact@v4
+      with:
+        name: autogen-macos-patch
+        path: autogen-macos.patch
+
   autogen-comgr-3:
     name: In-tree Autogen (comgr 3)
     runs-on: ubuntu-24.04
@@ -171,8 +117,20 @@ jobs:
         echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
         sudo apt -qq update || true
         sudo apt-get install -y --no-install-recommends libclang-20-dev comgr
-    - name: Verify comgr (3) autogen
+    - name: Regenerate autogen files
       run: |
-        mv tinygrad/runtime/autogen/comgr_3.py /tmp/comgr_3.py.bak
+        rm tinygrad/runtime/autogen/comgr_3.py
         python3 -c "from tinygrad.runtime.autogen import comgr_3"
-        diff /tmp/comgr_3.py.bak tinygrad/runtime/autogen/comgr_3.py
+    - name: Check for differences
+      run: |
+        if ! git diff --quiet; then
+          git diff > autogen-comgr3.patch
+          echo "Autogen files out of date. Apply patch from: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}#artifacts"
+          exit 1
+        fi
+    - name: Upload patch artifact
+      if: failure()
+      uses: actions/upload-artifact@v4
+      with:
+        name: autogen-comgr3-patch
+        path: autogen-comgr3.patch

.github/workflows/benchmark.yml

@@ -14,14 +14,50 @@ on:
       - update_benchmark
       - update_benchmark_staging
   workflow_dispatch:
-    inputs:
-      run_process_replay:
-        description: "Run process replay tests"
-        required: false
-        default: false
-        type: boolean
 
 jobs:
+  # the goal of this test is to replicate a normal person on a laptop running the test
+  # no process replay, no benchmarks, no CI, just a normal laptop person
+  # the 3 minute timeout should not be raised
+  testmacpytest:
+    name: Mac pytest
+    runs-on: [self-hosted, macOS]
+    timeout-minutes: 3
+    defaults:
+      run:
+        shell: bash -e -o pipefail {0}
+    if: github.repository_owner == 'tinygrad'
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v4
+    # brew install uv
+    - name: setup python environment
+      run: |
+        rm -rf /tmp/tinygrad_pytest_ci
+        uv venv /tmp/tinygrad_pytest_ci
+        source /tmp/tinygrad_pytest_ci/bin/activate
+        uv pip install .[testing]
+    - name: setup staging db
+      run: |
+        echo "CACHEDB=/tmp/pytest-db-ci.db" >> $GITHUB_ENV
+        rm -f /tmp/pytest-db-ci*
+    # TODO: remove this step once all old caches are migrated
+    - name: Migrate old huggingface cache (symlinks break onnxruntime 1.24+)
+      run: |
+        cd ~/Library/Caches/tinygrad/downloads/models 2>/dev/null || exit 0
+        for old_dir in models--*; do
+          [ -d "$old_dir" ] || continue
+          repo_id=$(echo "$old_dir" | sed 's/models--//; s/--/\//g')
+          snapshot=$(ls -1 "$old_dir/snapshots" 2>/dev/null | head -1)
+          [ -n "$snapshot" ] || continue
+          mkdir -p "$repo_id"
+          cp -RLn "$old_dir/snapshots/$snapshot/"* "$repo_id/" 2>/dev/null || true
+        done
+    - name: Run pytest -nauto
+      run: |
+        source /tmp/tinygrad_pytest_ci/bin/activate
+        pytest -nauto --durations=20
+
   testmacbenchmark:
     name: Mac Benchmark
     env:
@@ -39,6 +75,7 @@ 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
@@ -54,19 +91,19 @@ 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
     - 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
     - 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
+      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=4500 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing
     # process replay can't capture this, the graph is too large
     - 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
+      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
     - 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
+      run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py
     - name: Test tensor cores
       run: METAL=1 python3.11 test/opt/test_tensor_cores.py
     - name: Test AMX tensor cores
@@ -76,54 +113,84 @@ jobs:
         DEBUG=2 CPU=1 CPU_LLVM=0 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
         DEBUG=2 CPU=1 CPU_LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
     - name: Run Tensor Core GEMM (float)
-      run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
+      run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (half)
-      run: DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_half.txt
+      run: DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3.11 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (bfloat16)
-      run: DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3.11 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
+      run: DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3.11 extra/gemm/simple_matmul.py
     - name: Fuzz Padded Tensor Core GEMM
       run: METAL=1 M_START=6 M_STOP=10 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=6 K_STOP=24 K_STEP=1 TC_OPT=2 DEBUG=2 python3.11 ./extra/gemm/fuzz_matmul.py
     - name: Run LLaMA
       run: |
-        BENCHMARK_LOG=llama_nojit JIT=0 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama JIT=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        BENCHMARK_LOG=llama_nojit JIT=0 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=llama JIT=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run LLaMA with BEAM
-      run: BENCHMARK_LOG=llama_beam JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
+      run: BENCHMARK_LOG=llama_beam JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run quantized LLaMA
       run: |
-        BENCHMARK_LOG=llama_int8 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8 | tee llama_int8.txt
-        BENCHMARK_LOG=llama_nf4 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4 | tee llama_nf4.txt
+        BENCHMARK_LOG=llama_int8 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize int8
+        BENCHMARK_LOG=llama_nf4 python3.11 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing --quantize nf4
     - name: Run quantized LLaMA3
       run: |
-        BENCHMARK_LOG=llama3_int8 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize int8 | tee llama3_int8.txt
-        BENCHMARK_LOG=llama3_nf4 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize nf4 | tee llama3_nf4.txt
+        BENCHMARK_LOG=llama3_int8 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize int8
+        BENCHMARK_LOG=llama3_nf4 python3.11 examples/llama3.py --size 8B --temperature 0 --benchmark --quantize nf4
     #- name: Run LLaMA 7B on 4 (virtual) GPUs
-    #  run: python3.11 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
+    #  run: python3.11 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing
     - name: Run GPT2
       run: |
-        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=13 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=13 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
+      run: BENCHMARK_LOG=gpt2_half_beam HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing
     - name: Run OLMoE
       run: BENCHMARK_LOG=olmoe python3.11 examples/olmoe.py
     - name: Train MNIST
-      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
+      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py
 
     # NOTE: this is failing in CI. it is not failing on my machine and I don't really have a way to debug it
     # the error is "RuntimeError: Internal Error (0000000e:Internal Error)"
     #- name: Run 10 CIFAR training steps
-    #  run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=3000 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
+    #  run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=3000 STEPS=10 python3.11 examples/hlb_cifar10.py
     #- name: Run 10 CIFAR training steps w HALF
-    #  run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=3000 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
+    #  run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=3000 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py
 
     #- name: Run 10 CIFAR training steps w BF16
-    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py
     # 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
+    #   run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py
+    - uses: actions/upload-artifact@v4
+      with:
+        name: Speed (Mac)
+        path: |
+          onnx_inference_speed.csv
+    - 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: Kill stale pids
+      run: |
+        PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
+        PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
     - name: UsbGPU boot time
       run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
     - name: UsbGPU tiny tests
@@ -132,38 +199,10 @@ jobs:
       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)
-        path: |
-          onnx_inference_speed.csv
-          torch_speed.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama_int8.txt
-          llama_nf4.txt
-          llama3_int8.txt
-          llama3_nf4.txt
-          llama_four_gpu.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.txt
-          matmul.txt
-          matmul_half.txt
-          matmul_bfloat16.txt
-          sd.txt
-          sd_no_fp16.txt
-          sdv2.txt
-          sdxl.txt
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          train_cifar_wino.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=. python3.11 process_replay.py
+    - 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
@@ -197,7 +236,7 @@ jobs:
     - name: Run model inference benchmark
       run: NV=1 CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
     - name: Test speed vs torch
-      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
+      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
     - name: Test speed vs theoretical
       run: NV=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 benchmark allreduce
@@ -208,79 +247,58 @@ jobs:
         NV=1 NV_PTX=1 ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
     - name: Run Tensor Core GEMM (CUDA)
       run: |
-        CUDA=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul.txt
-        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_bfloat16.txt
-        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee matmul_tf32.txt
-        CUDA=1 SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_fp8.txt
+        CUDA=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+        CUDA=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+        CUDA=1 SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
+        CUDA=1 SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (PTX)
-      run: NV=1 NV_PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_ptx.txt
+      run: NV=1 NV_PTX=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (NV)
-      run: NV=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py | tee matmul_nv.txt
+      run: NV=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Test NV=1
       run: DEBUG=2 NV=1 python -m pytest -rA test/test_tiny.py
     - name: Test CUDA=1
       run: DEBUG=2 CUDA=1 python -m pytest -rA test/test_tiny.py
     - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
     # TODO: too slow
     # - name: Run SDXL
-    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing
     - name: Run LLaMA
       run: |
-        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
-        BENCHMARK_LOG=llama NV=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=llama NV=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run LLaMA with BEAM
-      run: BENCHMARK_LOG=llama_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
+      run: BENCHMARK_LOG=llama_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     # - name: Run LLaMA 7B on 4 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
+    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing
     # - name: Run LLaMA 7B on 6 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
+    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
     - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
+      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
+      run: BENCHMARK_LOG=llama3_beam_4gpu NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     - name: Run quantized LLaMA3
-      run: BENCHMARK_LOG=llama3_fp8 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --temperature 0 --benchmark --quantize fp8 | tee llama3_fp8.txt
+      run: BENCHMARK_LOG=llama3_fp8 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --temperature 0 --benchmark --quantize fp8
     # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
+    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     # - name: Run LLaMA-2 70B
-    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 MAX_CONTEXT=256 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
+    #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 MAX_CONTEXT=256 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
     - name: Run Mixtral 8x7B
-      run: time BENCHMARK_LOG=mixtral NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
+      run: time BENCHMARK_LOG=mixtral NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/mixtral.py --temperature 0 --count 10 --timing
     - name: Run GPT2
       run: |
-        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=4 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=4 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=6 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=6 python3 examples/gpt2.py --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
+      run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (NVIDIA)
         path: |
           onnx_inference_speed.csv
-          torch_speed.txt
-          matmul.txt
-          matmul_bfloat16.txt
-          matmul_tf32.txt
-          matmul_ptx.txt
-          matmul_nv.txt
-          sd.txt
-          sdxl.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama3_beam.txt
-          llama3_four_gpu.txt
-          llama3_six_gpu.txt
-          llama3_fp8.txt
-          llama_2_70B.txt
-          mixtral.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.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
 
@@ -319,46 +337,30 @@ jobs:
     # - 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
+      run: VALIDATE=1 MAX_FRAMES=100 JITBEAM=1 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
+      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
     - 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
+    - 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
+    - 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
     # - 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
+    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
+    - 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
+    - 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
     - 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
+    - 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
     - 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
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (NVIDIA Training)
-        path: |
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          train_cifar_wino.txt
-          train_cifar_one_gpu.txt
-          train_cifar_six_gpu.txt
-          train_resnet.txt
-          train_resnet_one_gpu.txt
-          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
     - 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
 
@@ -373,10 +375,12 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
+    - name: Setcap to python
+      run: ./extra/amdpci/setup_python_cap.sh
+    - name: Remove amd modules
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+    - name: Kill stale pids
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
     #- name: Insert amdgpu
     #  run: sudo modprobe amdgpu
     - name: Symlink models and datasets
@@ -410,16 +414,18 @@ jobs:
     #- name: Test speed vs torch
     #  run: |
     #    python3 -c "import torch; print(torch.__version__)"
-    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
+    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
     - 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
     - name: Test AMD=1
       run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
     #- name: Test HIP=1
@@ -434,62 +440,39 @@ jobs:
     - name: Test AM warm start time
       run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
     - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=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
+      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=550 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
+    - 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
     - 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
-        BENCHMARK_LOG=llama AMD=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+        BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=llama AMD=1 JIT=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run LLaMA 7B with BEAM
-      run: BENCHMARK_LOG=llama_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_beam.txt
+      run: BENCHMARK_LOG=llama_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing
     # - name: Run LLaMA 7B on 4 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_four_gpu.txt
+    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 10 --temperature 0  --timing
     # - name: Run LLaMA 7B on 6 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_six_gpu.txt
+    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 1 --size 7B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
     - name: Run LLaMA-3 8B BEAM
-      run: BENCHMARK_LOG=llama3_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
+      run: BENCHMARK_LOG=llama3_beam AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
+      run: BENCHMARK_LOG=llama3_beam_4gpu AMD=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
+    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
     #- name: Restore amdgpu
     #  run: sudo modprobe amdgpu
     # - name: Run LLaMA-2 70B
-    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing | tee llama_2_70B.txt
+    #   run: AMD=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
     - name: Run Mixtral 8x7B
-      run: time BENCHMARK_LOG=mixtral AMD=1 python3 examples/mixtral.py --temperature 0 --count 10 --timing | tee mixtral.txt
+      run: time BENCHMARK_LOG=mixtral AMD=1 python3 examples/mixtral.py --temperature 0 --count 10 --timing
     - name: Run GPT2
       run: |
-        BENCHMARK_LOG=gpt2_nojit AMD=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2_nojit AMD=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
+        BENCHMARK_LOG=gpt2 AMD=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half AMD=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half AMD=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing
     - name: Run GPT2 w HALF/BEAM
-      run: BENCHMARK_LOG=gpt2_half_beam AMD=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD)
-        path: |
-          onnx_inference_speed.csv
-          torch_speed.txt
-          llama_unjitted.txt
-          llama_jitted.txt
-          llama_beam.txt
-          llama3_beam.txt
-          llama3_four_gpu.txt
-          llama3_six_gpu.txt
-          llama_2_70B.txt
-          gpt2_unjitted.txt
-          gpt2_jitted.txt
-          gpt2_half.txt
-          gpt2_half_beam.txt
-          matmul.txt
-          matmul_amd.txt
-          sd.txt
-          sdxl.txt
-          mixtral.txt
+      run: BENCHMARK_LOG=gpt2_half_beam AMD=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing
     - 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
 
@@ -504,10 +487,12 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
+    - name: Setcap to python
+      run: ./extra/amdpci/setup_python_cap.sh
+    - name: Remove amd modules
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+    - name: Kill stale pids
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -526,35 +511,22 @@ jobs:
     - name: reset process replay
       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
+      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
     - 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
+    - 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
     # - name: Run 10 CIFAR training steps w BF16
-    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py
     # 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
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD Training)
-        path: |
-          beautiful_mnist.txt
-          train_cifar.txt
-          train_cifar_half.txt
-          train_cifar_bf16.txt
-          train_cifar_wino.txt
-          train_cifar_one_gpu.txt
-          train_cifar_six_gpu.txt
-          train_cifar_six_gpu_remote.txt
+    #   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
+    - 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
+    - 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
+    - name: Test full tinyfs load
+      run: TINYFS_ENDPOINT=10.0.52.11:6767 PYTHONPATH=. python extra/tinyfs/fetch_file.py --hash d734f5e3be9f1e9d863bfaa4fc6c1ef2 --len 175866113 --dest mapping.json --check
     - 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
 
@@ -569,10 +541,12 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
+    - name: Setcap to python
+      run: ./extra/amdpci/setup_python_cap.sh
+    - name: Remove amd modules
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
+    - name: Kill stale pids
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -592,20 +566,13 @@ 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
+    - 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
     - 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
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AMD MLPerf)
-        path: |
-          train_resnet.txt
-          train_resnet_one_gpu.txt
-          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
     - 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
 
@@ -627,24 +594,22 @@ 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
+      run: BENCHMARK_LOG=openpilot_0_10_0_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=3 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: IMAGE=1 openpilot compile3 0.10.1 driving_vision
+      run: BENCHMARK_LOG=image_1_openpilot_0_10_1_vision PYTHONPATH="." 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
+      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=3 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
+      run: BENCHMARK_LOG=openpilot_0_10_1_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/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/dmonitoring_model.onnx
     - name: benchmark MobileNetV2 on DSP
       run: |
         # generate quantized weights
@@ -667,10 +632,12 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
+    - name: Setcap to python
+      run: ./extra/amdpci/setup_python_cap.sh
     - name: Remove amd modules
-      run: ./extra/hcq/hcq_smi.py amd rmmod
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
     - name: Kill stale pids
-      run: ./extra/hcq/hcq_smi.py amd kill_pids
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -699,7 +666,7 @@ jobs:
     #     AMD=1 AMD_LLVM=1 python3 test/test_linearizer.py test/opt/test_tensor_cores.py
     #     AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
     - name: Run Tensor Core GEMM (AMD)
-      run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee am_matmul_amd.txt
+      run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
     - name: Test AMD=1
       run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
     - name: Test DISK copy time
@@ -708,23 +675,13 @@ 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
     # - 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
+    #   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
     - name: Run 10 MLPerf Bert training steps (1 gpu)
       # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps AMD=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 am_train_bert_one_gpu.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AM Driver)
-        path: |
-          am_matmul_amd.txt
-          am_train_cifar_one_gpu.txt
-          am_train_resnet_one_gpu.txt
-          am_train_bert_one_gpu.txt
+      run: BENCHMARK_LOG=bert_10steps AMD=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
     - 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
 
@@ -739,10 +696,12 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
+    - name: Setcap to python
+      run: ./extra/amdpci/setup_python_cap.sh
     - name: Remove nv modules
-      run: ./extra/hcq/hcq_smi.py nv rmmod
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv rmmod
     - name: Kill stale pids
-      run: ./extra/hcq/hcq_smi.py nv kill_pids
+      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -771,22 +730,13 @@ jobs:
         NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
         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
+      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0
+    - 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
+    - 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
     - 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
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (NV Driver)
-        path: |
-          nv_llama3_beam.txt
-          nv_train_cifar_one_gpu.txt
-          nv_train_resnet_one_gpu.txt
-          nv_train_bert_one_gpu.txt
+      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
     - 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

.github/workflows/test.yml

@@ -1,10 +1,11 @@
 name: Unit Tests
 env:
   # increment this when downloads substantially change to avoid the internet
-  CACHE_VERSION: '13'
+  CACHE_VERSION: '16'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
   PYTHONPATH: ${{ github.workspace }}
+  CHECK_OOB: 1
 
 on:
   push:
@@ -25,17 +26,19 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: llvm-speed
-        deps: testing_minimal
+        deps: testing_unit
         llvm: 'true'
     - name: Speed Test
-      run: CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: CPU=1 CPU_LLVM=1 THREADS=0 python3 test/speed/external_test_speed_v_torch.py
     - name: Speed Test (BEAM=2)
-      run: BEAM=2 CPU=1 CPU_LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: BEAM=2 CPU=1 CPU_LLVM=1 THREADS=0 python3 test/speed/external_test_speed_v_torch.py
 
   docs:
     name: Docs
     runs-on: ubuntu-22.04
     timeout-minutes: 10
+    env:
+      CHECK_OOB: 0
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
@@ -71,9 +74,7 @@ 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
@@ -97,23 +98,19 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_minimal
+        deps: testing_unit
         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: custom tests
+      run: python3 -m pytest -n auto extra/torch_backend/test.py --durations=20
     - 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
@@ -137,7 +134,7 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_minimal
+        deps: testing_unit
         llvm: 'true'
     - name: Install ninja
       run: |
@@ -159,7 +156,7 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: be-minimal
-        deps: testing_minimal
+        deps: testing_unit
     - name: Test dtype with Python emulator
       run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
     - name: Test ops with Python emulator
@@ -210,18 +207,17 @@ jobs:
       run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/opt/test_tensor_cores.py
     - name: Test device flop counts
       run: |
-        DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        DEBUG=2 EMULATE=AMD PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        DEBUG=2 EMULATE=CUDA PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
+        DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=AMD PYTHON=1 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=CUDA PYTHON=1 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/null/test_uops_stats.py TestUOpsStats.test_simple_matmul
 
   linter:
     name: Linters
     runs-on: ubuntu-latest
     timeout-minutes: 10
 
-    # TODO: run the pre-commit hook to replace a lot of this
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
@@ -233,18 +229,20 @@ jobs:
         deps: linting
     - name: Lint bad-indentation and trailing-whitespace with pylint
       run: python -m pylint --disable=all -e W0311 -e C0303 --jobs=0 --indent-string='  ' --recursive=y .
-    - name: Lint with ruff
+    - name: Run pre-commit linting hooks
+      run: SKIP=tiny,tests,example pre-commit run --all-files
+    - name: Lint additional files with ruff
       run: |
-        pip3 install --upgrade --force-reinstall ruff==0.11.0
-        python3 -m ruff check .
         python3 -m ruff check examples/mlperf/ --ignore E501
-    - name: Run mypy
+        python3 -m ruff check extra/thunder/tiny/ --ignore E501 --ignore F841 --ignore E722
+        python3 -m ruff check extra/torch_backend/backend.py
+    - name: Run mypy with lineprecision report
       run: |
-        python -m mypy --strict-equality --lineprecision-report .
+        python -m mypy --lineprecision-report .
+        grep -v autogen lineprecision.txt | awk 'NR>2 {lines+=$2; precise+=$3; imprecise+=$4; any+=$5; empty+=$6} END {t=lines-empty; printf "TOTAL: %d lines, %d precise (%.1f%%), %d imprecise (%.1f%%), %d any (%.1f%%)\n", t, precise, 100*precise/t, imprecise, 100*imprecise/t, any, 100*any/t}'
         cat lineprecision.txt
-    # broken because of UPatAny
-    #- name: Run TYPED=1
-    #  run: TYPED=1 python -c "import tinygrad"
+    - name: Run TYPED=1
+      run: CHECK_OOB=0 DEV=CPU TYPED=1 python test/test_tiny.py
 
   unittest:
     name: Unit Tests
@@ -257,22 +255,30 @@ jobs:
     - name: Setup Environment
       uses: ./.github/actions/setup-tinygrad
       with:
-        key: unittest-12
-        pydeps: "pillow numpy ftfy regex"
+        key: unittest-13
+        pydeps: "pillow ftfy regex pre-commit"
         deps: testing_unit
+        llvm: 'true'
+        amd: 'true'
+    - name: Run pre-commit test hooks
+      run: SKIP=ruff,mypy pre-commit run --all-files
     - 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/null/test_device.py TestRunAsModule.test_module_runs
+        CPU=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Run NULL backend tests
+      run: NULL=1 python -m pytest -n=auto test/null/ --durations=20
     - 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
+      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step
     # TODO: too slow
     # - name: Run SDXL on NULL backend
     #   run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
     - name: Run Clip tests for SD MLPerf on NULL backend
       run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
     - name: Run AMD emulated BERT training on NULL backend
-      run: EMULATE=AMD_RDNA4 NULL=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: EMULATE=AMD_RDNA4 NULL=1 NULL_ALLOW_COPYOUT=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
     # TODO: support fake weights
     #- name: Run LLaMA 7B on 4 fake devices
     #  run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
@@ -289,8 +295,8 @@ 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:
@@ -310,7 +316,7 @@ jobs:
         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: SPEC=2 pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore=test/null --ignore test/test_custom_kernel.py --ignore test/unit/test_hashing.py --timeout 60 -k "not test_setitem_big" --splits 2 --group ${{ matrix.group }}
 
   fuzzing:
     name: Fuzzing
@@ -344,7 +350,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: gpu-image
-          deps: testing_minimal
+          deps: testing_unit
           opencl: 'true'
       - name: Test CL IMAGE=2 ops
         run: |
@@ -365,7 +371,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: gen-dataset
-          deps: testing_minimal
+          deps: testing
           opencl: 'true'
       - name: Generate Dataset
         run: CL=1 extra/optimization/generate_dataset.sh
@@ -420,7 +426,7 @@ jobs:
         with:
           key: onnxoptc
           deps: testing
-          python-version: '3.11'
+          python-version: '3.12'
           llvm: 'true'
       - name: Test ONNX (CPU)
         run: CPU=1 CPU_LLVM=0 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
@@ -447,8 +453,8 @@ jobs:
         with:
           key: onnxoptl
           deps: testing
-          pydeps: "tensorflow==2.15.1 tensorflow_addons"
-          python-version: '3.11'
+          pydeps: "tensorflow==2.19"
+          python-version: '3.12'
           opencl: 'true'
       - name: Test ONNX (CL)
         run: CL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
@@ -461,11 +467,11 @@ jobs:
       - name: Test MLPerf stuff
         run: CL=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
       - name: NULL=1 beautiful_mnist_multigpu
-        run: NULL=1 python examples/beautiful_mnist_multigpu.py
+        run: NULL=1 NULL_ALLOW_COPYOUT=1 python examples/beautiful_mnist_multigpu.py
       - 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
+        run: NULL=1 NULL_ALLOW_COPYOUT=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
       - 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 NULL_ALLOW_COPYOUT=1 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=1 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -473,6 +479,8 @@ jobs:
     name: Test LLM
     runs-on: ubuntu-24.04
     timeout-minutes: 15
+    env:
+      CHECK_OOB: 0
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -520,7 +528,7 @@ jobs:
       with:
         key: metal
         deps: testing
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Test models (Metal)
       run: METAL=1 python -m pytest -n=auto test/models --durations=20
     - name: Test LLaMA compile speed
@@ -539,7 +547,7 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: devectorize-minimal
-        deps: testing_minimal
+        deps: testing_unit
         pydeps: "pillow"
         llvm: "true"
     - name: Test LLVM=1 DEVECTORIZE=0
@@ -560,8 +568,8 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: dsp-minimal
-        deps: testing_minimal
-        pydeps: "onnx==1.18.0 onnxruntime pillow"
+        deps: testing_unit
+        pydeps: "onnx==1.18.0 onnxruntime ml_dtypes"
         llvm: "true"
     - name: Set up Docker Buildx
       uses: docker/setup-buildx-action@v3
@@ -573,7 +581,7 @@ jobs:
         load: true
         tags: qemu-hexagon:latest
         cache-from: type=gha
-        cache-to: type=gha,mode=min
+        cache-to: ${{ github.event_name != 'pull_request' && 'type=gha,mode=min' || '' }}
     - name: Set MOCKDSP env
       run: printf "MOCKDSP=1" >> $GITHUB_ENV
     - name: Run test_tiny on DSP
@@ -594,8 +602,8 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: webgpu-minimal
-        deps: testing_minimal
-        python-version: '3.11'
+        deps: testing_unit
+        python-version: '3.12'
         webgpu: 'true'
     - name: Check Device.DEFAULT (WEBGPU) and print some source
       run: |
@@ -603,9 +611,7 @@ jobs:
         WEBGPU=1 DEBUG=4 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
     - name: Run selected webgpu tests
       run: |
-          WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit \
-          --ignore=test/test_copy_speed.py --ignore=test/test_rearrange_einops.py \
-          --ignore=test/test_fuzz_shape_ops.py --durations=20
+          WEBGPU=1 WEBGPU_BACKEND="WGPUBackendType_Vulkan" python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
     - name: Run process replay tests
       uses: ./.github/actions/process-replay
 
@@ -630,7 +636,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
+          deps: testing_unit
           amd: 'true'
           llvm: ${{ matrix.backend == 'amdllvm' && 'true' }}
       - name: Check Device.DEFAULT and print some source
@@ -641,19 +647,68 @@ jobs:
         if: matrix.backend=='amdllvm'
         run: python test/device/test_amd_llvm.py
       - name: Run pytest (amd)
-        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py --durations=20
+        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py test/testextra/test_cfg_viz.py --durations=20
       - name: Run pytest (amd)
         run: python -m pytest test/external/external_test_am.py --durations=20
       - name: Run TRANSCENDENTAL math
         run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
       - name: Run TestOps.test_add with SQTT
         run: |
-          VIZ=1 PMC=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
-          VIZ=1 SQTT=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
+          VIZ=-2 DEBUG=5 python3 test/test_ops.py TestOps.test_add
           extra/sqtt/rgptool.py create "/tmp/profile.pkl.$USER" -o /tmp/gpu0.rgp
+      - name: Run AMD emulated mmapeak on NULL backend
+        env:
+          AMD: 0
+        run: PYTHONPATH=. NULL=1 EMULATE=AMD python extra/mmapeak/mmapeak.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
+  testamdasm:
+    name: AMD ASM IDE
+    runs-on: ubuntu-24.04
+    timeout-minutes: 20
+    env:
+      AMD: 1
+      PYTHON_REMU: 1
+      MOCKGPU: 1
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: rdna3-emu
+          deps: testing_unit
+          amd: 'true'
+          python-version: '3.14'
+      - name: Verify AMD autogen is up to date
+        run: |
+          python -m extra.assembly.amd.generate
+          git diff --exit-code extra/assembly/amd/autogen/
+      - 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: Install rocprof-trace-decoder
+        run: sudo PYTHONPATH="." ./extra/sqtt/install_sqtt_decoder.py
+      - name: Run RDNA3 emulator tests
+        run: AMD_LLVM=0 python -m pytest -n=auto extra/assembly/amd/ --durations 20
+      - name: Run RDNA3 emulator tests (AMD_LLVM=1)
+        run: AMD_LLVM=1 python -m pytest -n=auto extra/assembly/amd/ --durations 20
+      - name: Run RDNA3 dtype tests
+        run: AMD_LLVM=0 pytest -n=auto test/test_dtype_alu.py test/test_dtype.py --durations 20
+      - name: Run RDNA3 dtype tests (AMD_LLVM=1)
+        run: AMD_LLVM=1 pytest -n=auto test/test_dtype_alu.py test/test_dtype.py --durations 20
+      # TODO: run all once emulator is faster
+      - name: Run RDNA3 ops tests
+        run: SKIP_SLOW_TEST=1 AMD_LLVM=0 pytest -n=auto test/test_ops.py -k "test_sparse_categorical_crossentropy or test_tril or test_nonzero or test_softmax_argmax" --durations 20
+      - name: Run RDNA4 emulator tests
+        run: MOCKGPU_ARCH=rdna4 python -m pytest test/test_tiny.py -v --durations 20
+
   testnvidia:
     strategy:
       fail-fast: false
@@ -673,7 +728,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
+          deps: testing_unit
           cuda: 'true'
           ocelot: 'true'
       - name: Set env
@@ -684,7 +739,9 @@ jobs:
           DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run pytest (cuda)
         # skip multitensor because it's slow
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
+        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --ignore test/test_multitensor.py --durations=20
+      - name: Run TestOps.test_add with PMA
+        run: VIZ=-1 PMA=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -704,7 +761,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: ${{ matrix.backend }}-minimal
-          deps: testing_minimal
+          deps: testing_unit
           opencl: ${{ matrix.backend == 'opencl' && 'true' }}
           llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
           mesa: ${{ matrix.backend == 'lvp' && 'true' }}
@@ -715,77 +772,12 @@ jobs:
           python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
           DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run pytest (${{ matrix.backend }})
-        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
+        run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
       - name: Run TRANSCENDENTAL math
         run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
-  amdremote:
-    name: Linux (remote)
-    runs-on: ubuntu-22.04
-    timeout-minutes: 20
-    env:
-      REMOTE: 1
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: linux-remote
-          deps: testing_minimal
-          amd: 'true'
-          llvm: 'true'
-          opencl: 'true'
-      - name: Start remote server
-        run: |
-          start_server() {
-            systemd-run --user \
-              --unit="$1" \
-              --setenv=REMOTEDEV="$2" \
-              --setenv=MOCKGPU=1 \
-              --setenv=PYTHONPATH=. \
-              --setenv=PORT="$3" \
-              --working-directory="$(pwd)" \
-              python tinygrad/runtime/ops_remote.py
-          }
-
-          start_server "remote-server-amd-1" "AMD" 6667
-          start_server "remote-server-amd-2" "AMD" 6668
-          start_server "remote-server-gpu" "CL" 7667
-          start_server "remote-server-cpu" "CPU" 8667
-      - name: Check Device.DEFAULT and print some source
-        env:
-          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'AMD', Device.default.properties.real_device"
-          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-      - name: Run REMOTE=1 Test (AMD)
-        env:
-          HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py --durations 20
-      - name: Run REMOTE=1 Test (CL)
-        env:
-          HOST: 127.0.0.1:7667*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py --durations 20
-          IMAGE=2 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
-      - name: Run REMOTE=1 Test (CPU)
-        env:
-          HOST: 127.0.0.1:8667*6
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py --durations 20
-      - name: Show remote server logs
-        if: always()
-        run: |
-          journalctl --user -u remote-server-amd-1 --no-pager
-          journalctl --user -u remote-server-amd-2 --no-pager
-          journalctl --user -u remote-server-gpu --no-pager
-          journalctl --user -u remote-server-cpu --no-pager
-
 # ****** OSX Tests ******
 
   testmetal:
@@ -800,13 +792,15 @@ jobs:
       with:
         key: metal
         deps: testing
-        python-version: '3.11'
+        python-version: '3.12'
         amd: 'true'
         cuda: 'true'
         ocelot: 'true'
         llvm: 'true'
     - name: Run unit tests
       run: METAL=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Run NULL backend tests
+      run: NULL=1 python -m pytest -n=auto test/null/ --durations=20
     - name: Run ONNX
       run: METAL=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
     - name: Test tensor core ops (fake)
@@ -883,30 +877,6 @@ 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
@@ -922,8 +892,7 @@ jobs:
         uses: ./.github/actions/setup-tinygrad
         with:
           key: macos-${{ matrix.backend }}-minimal
-          deps: testing_minimal
-          pydeps: "capstone"
+          deps: testing_unit
           llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
           mesa: ${{ matrix.backend == 'lvp' && 'true' }}
       - name: Set env
@@ -933,7 +902,7 @@ jobs:
           python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU','LVP':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
           DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
       - name: Run pytest (${{ matrix.backend }})
-        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
+        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore=test/null --durations=20
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
       - name: Run macOS-specific unit test
@@ -966,9 +935,43 @@ jobs:
       - name: Run unit tests
         if: matrix.backend=='llvm'
         # test_newton_schulz hits RecursionError
-        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
+        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_tar.py --ignore=test/unit/test_linalg.py --durations=20
+      - name: Run NULL backend tests
+        if: matrix.backend=='llvm'
+        shell: bash
+        run: CPU=0 CPU_LLVM=0 NULL=1 python -m pytest -n=auto test/null/ --ignore=test/null/test_elf.py --durations=20
       - name: Run pytest (${{ matrix.backend }})
         shell: bash
         run: |
           python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
           python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20
+
+# ****** Compile-only Tests ******
+
+  compiletests:
+    strategy:
+      fail-fast: false
+      matrix:
+        backend: [ir3, nak]
+    name: Compile-only (${{ 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_unit
+          mesa: ${{ (matrix.backend == 'ir3' || matrix.backend == 'nak') && 'true' }}
+          python-version: '3.12'
+      - name: Set env
+        shell: bash
+        run: printf "NULL=1\nNULL_ALLOW_COPYOUT=1\n${{ matrix.backend == 'ir3' && 'NULL_IR3=1' || matrix.backend == 'nak' && 'NULL_NAK=1' }}" >> $GITHUB_ENV
+      - 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

.gitignore

@@ -58,10 +58,11 @@ weights
 *.lprof
 comgr_*
 *.pkl
+!extra/sqtt/examples/**/*.pkl
 site/
 profile_stats
 *.log
 target
 .mypy_cache
 mutants
-.mutmut-cache
+.mutmut-cache

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
         pass_filenames: false
       - id: mypy
         name: mypy
-        entry: python3 -m mypy tinygrad/ --strict-equality
+        entry: python3 -m mypy
         language: system
         always_run: true
         pass_filenames: false
@@ -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/unit/test_assign.py test/test_tensor.py test/test_jit.py test/unit/test_schedule_cache.py test/null/test_pattern_matcher.py test/null/test_uop_symbolic.py test/unit/test_helpers.py
         language: system
         always_run: true
         pass_filenames: false

CLAUDE.md

@@ -0,0 +1,227 @@
+# 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 Analysis
+
+**Use the right tool:**
+
+- `TRACK_MATCH_STATS=2` - **Profiling**: identify expensive patterns
+- `VIZ=-1` - **Inspection**: see all transformations, what every match pattern does, the before/after diffs
+
+```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.
+
+```bash
+# Save the trace
+VIZ=-1 python test/test_tiny.py TestTiny.test_gemm
+
+# Explore it
+./extra/viz/cli.py --help
+```
+
+## AMD Performance Counter Profiling
+
+Set VIZ to `-2` to save performance counters traces for the AMD backend.
+
+Use the CLI in `./extra/sqtt/roc.py` to explore the trace.

docs/abstractions2.py

@@ -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

docs/abstractions3.py

@@ -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)

docs/developer/developer.md

@@ -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.
 

docs/developer/layout.md

@@ -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

docs/runtime.md

@@ -70,7 +70,7 @@ AMD backend supports several interfaces for communicating with devices:
 
 * `KFD`: uses the amdgpu driver
 * `PCI`: uses the [AM driver](developer/am.md)
-* `USB`: USB3 interafce for asm24xx chips.
+* `USB`: USB3 interface for asm24xx chips.
 
 You can force an interface by setting `AMD_IFACE` to one of these values. In the case of `AMD_IFACE=PCI`, this may unbind your GPU from the amdgpu driver.
 

docs/tensor/elementwise.md

@@ -6,6 +6,7 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.neg
 ::: tinygrad.Tensor.log
 ::: tinygrad.Tensor.log2
+::: tinygrad.Tensor.log10
 ::: tinygrad.Tensor.exp
 ::: tinygrad.Tensor.exp2
 ::: tinygrad.Tensor.sqrt
@@ -87,4 +88,8 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.float
 ::: tinygrad.Tensor.half
 ::: tinygrad.Tensor.int
-::: tinygrad.Tensor.bool
+::: tinygrad.Tensor.bool
+::: tinygrad.Tensor.bfloat16
+::: tinygrad.Tensor.double
+::: tinygrad.Tensor.long
+::: tinygrad.Tensor.short

docs/tensor/movement.md

@@ -27,5 +27,6 @@
 ::: tinygrad.Tensor.flatten
 ::: tinygrad.Tensor.unflatten
 ::: tinygrad.Tensor.diag
+::: tinygrad.Tensor.diagonal
 ::: tinygrad.Tensor.roll
 ::: tinygrad.Tensor.rearrange

docs/tensor/ops.md

@@ -7,6 +7,7 @@
 ::: tinygrad.Tensor.any
 ::: tinygrad.Tensor.all
 ::: tinygrad.Tensor.isclose
+::: tinygrad.Tensor.allclose
 ::: tinygrad.Tensor.mean
 ::: tinygrad.Tensor.var
 ::: tinygrad.Tensor.var_mean
@@ -30,7 +31,9 @@
 ::: tinygrad.Tensor.matmul
 ::: tinygrad.Tensor.einsum
 ::: tinygrad.Tensor.cumsum
+::: tinygrad.Tensor.cumprod
 ::: tinygrad.Tensor.cummax
+::: tinygrad.Tensor.cummin
 ::: tinygrad.Tensor.triu
 ::: tinygrad.Tensor.tril
 ::: tinygrad.Tensor.interpolate
@@ -38,7 +41,9 @@
 ::: tinygrad.Tensor.scatter_reduce
 ::: tinygrad.Tensor.masked_select
 ::: tinygrad.Tensor.masked_fill
+::: tinygrad.Tensor.nonzero
 ::: tinygrad.Tensor.sort
+::: tinygrad.Tensor.argsort
 ::: tinygrad.Tensor.topk
 ::: tinygrad.Tensor.multinomial
 
@@ -56,3 +61,8 @@
 ::: tinygrad.Tensor.sparse_categorical_crossentropy
 ::: tinygrad.Tensor.cross_entropy
 ::: tinygrad.Tensor.nll_loss
+
+## Linear Algebra
+
+::: tinygrad.Tensor.qr
+::: tinygrad.Tensor.svd

eslint.config.mjs

@@ -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,
-];

examples/anthropic_challenge.py

@@ -0,0 +1,196 @@
+from tinygrad import Tensor, dtypes, Context, getenv, UOp, fetch
+from tinygrad.uop.ops import Ops, PatternMatcher, UPat
+from tinygrad.uop.symbolic import symbolic
+from tinygrad.codegen import Renderer
+from tinygrad.codegen.opt import Opt, OptOps
+
+# ************************* implementation of the problem ************************
+
+def myhash(a: Tensor) -> Tensor:
+  a = (a + 0x7ED55D16) + (a << 12)
+  a = (a ^ 0xC761C23C) ^ (a >> 19)
+  a = (a + 0x165667B1) + (a << 5)
+  a = (a + 0xD3A2646C) ^ (a << 9)
+  a = (a + 0xFD7046C5) + (a << 3)
+  a = (a ^ 0xB55A4F09) ^ (a >> 16)
+  return a
+
+def select_with_where_tree(values: Tensor, relative_idx: Tensor) -> Tensor:
+  n = values.shape[0]
+  if n == 1: return values[0].expand(relative_idx.shape)
+
+  mid = n // 2
+  left = select_with_where_tree(values[:mid], relative_idx)
+  right = select_with_where_tree(values[mid:], relative_idx - mid)
+
+  go_left = relative_idx < mid
+  return go_left.where(left, right)
+
+def tree_traversal(forest: Tensor, val: Tensor, height: int, rounds: int, where_tree_threshold=3) -> Tensor:
+  # All walkers start at idx=0
+  idx = Tensor.zeros(val.shape, device=val.device, dtype=dtypes.uint32)
+
+  for r in range(rounds):
+    level = r % (height + 1)
+    level_start = (1 << level) - 1
+    level_size = 1 << level
+
+    if level == 0:
+      # At root (level 0), all walkers are at idx=0
+      # No gather needed, just broadcast the root value
+      node_val = forest[0].expand(val.shape)
+      idx = idx * 0  # Reset to 0
+    elif level <= where_tree_threshold:
+      # Small level: use where-tree
+      level_values = forest[level_start : level_start + level_size]
+      relative_idx = (idx - level_start)
+      node_val = select_with_where_tree(level_values, relative_idx)
+    else:
+      # Large level: use gather
+      node_val = forest.gather(0, idx)
+
+    val = myhash(val ^ node_val)
+    idx = (idx << 1) + (1 + (val & 1))
+
+    # No wrap check needed! At round 10 (level becomes 0), we reset idx above.
+
+  return val.contiguous(arg=(Opt(OptOps.UPCAST, 0, 8),))
+
+# ************************* renderer for VLIW machine *************************
+
+def loop_unrolling(sink:UOp):
+  rng = [x for x in sink.toposort() if x.op is Ops.RANGE]
+  if len(rng) == 0: return None
+  print(f"unrolling loop with size {rng[0].vmax+1}")
+  unrolled_sinks = [sink.substitute({rng[0]:rng[0].const_like(i)}).src[0] for i in range(rng[0].vmax+1)]
+  return UOp.sink(*unrolled_sinks, arg=sink.arg)
+
+global_addrs = []
+vliw_prepare = PatternMatcher([
+  # loop unrolling (should be a part of tinygrad)
+  (UPat(Ops.SINK, name="sink"), loop_unrolling),
+  # cast is fake
+  (UPat(Ops.CAST, name="c"), lambda c: c.src[0]),
+  # rewrites to hardcode the addresses in memory
+  (UPat(Ops.PARAM, name="dg"), lambda dg: UOp.const(dtypes.uint, global_addrs[dg.arg])),
+  # INDEX is just plus
+  (UPat(Ops.INDEX, name="i"), lambda i: i.src[0]+i.src[1]),
+])+symbolic
+
+class VLIWRenderer(Renderer):
+  has_local = False  # TODO: this should be the default / cleaned up
+  # this says this backend supports MULACC + more. decompositions uses this
+  code_for_op: dict = {Ops.MULACC: None, Ops.ADD: "+", Ops.MUL: "*",
+                       Ops.XOR: "^", Ops.AND: "&", Ops.OR: "|",
+                       Ops.SHL: "<<", Ops.SHR: ">>", Ops.CMPLT: "<"}
+  # this matcher runs while still in graph form
+  pre_matcher = vliw_prepare
+
+  def render(self, uops:list[UOp]):
+
+    # TODO: this is a minimal renderer. for low cycle count, make it good
+    # to get speed, you need to add VLIW packing
+    # to get under 1536 regs, you need to add a register allocator
+    # we left the fun parts to you
+
+    print(f"rendering with {len(uops)} uops")
+    reg, inst = 0, []
+    r: dict[UOp, int] = {}
+    for u in uops:
+      assert u.dtype.count in (1,8), "dtype count must be 1 or 8"
+
+      # dumb register allocator
+      if u.op not in {Ops.STORE, Ops.SINK, Ops.GEP}:
+        r[u] = reg
+        reg += u.dtype.count
+
+      # render UOps to instructions
+      match u.op:
+        case Ops.SINK:
+          inst.append({"flow": [("halt",)]})
+        case Ops.CONST:
+          inst.append({"load": [("const", r[u], u.arg)]})
+        case Ops.GEP:
+          # a GEP is just an alias to a special register in the vector
+          r[u] = r[u.src[0]] + u.arg[0]
+        case Ops.VECTORIZE:
+          if all(s == u.src[0] for s in u.src):
+            # if all sources are the same, we can broadcast
+            inst.append({"valu": [("vbroadcast", r[u], r[u.src[0]])]})
+          else:
+            # this is a copy into a contiguous chunk of registers
+            inst.extend({"flow": [("add_imm", r[u]+i, r[s], 0)]} for i,s in enumerate(u.src) if r[s] != r[u]+i)
+        case Ops.LOAD:
+          op = "vload" if u.dtype.count > 1 else "load"
+          inst.append({"load": [(op, r[u], r[u.src[0]])]})
+        case Ops.STORE:
+          op = "vstore" if u.src[1].dtype.count > 1 else "store"
+          inst.append({"store": [(op, r[u.src[0]], r[u.src[1]])]})
+        case Ops.MULACC:
+          assert u.dtype.count == 8
+          inst.append({"valu": [("multiply_add", r[u], r[u.src[0]], r[u.src[1]], r[u.src[2]])]})
+        case Ops.WHERE:
+          assert u.dtype.count == 8
+          inst.append({"flow": [("vselect", r[u], r[u.src[0]], r[u.src[1]], r[u.src[2]])]})
+        case _ if u.op in self.code_for_op:
+          cat = "valu" if u.dtype.count > 1 else "alu"
+          inst.append({cat: [(self.code_for_op[u.op], r[u], r[u.src[0]], r[u.src[1]])]})
+        case _:
+          raise NotImplementedError(f"unhandled op {u.op}")
+    return repr(inst)
+
+# ************************* test and render *************************
+
+import sys, types
+PROBLEM_URL = "https://raw.githubusercontent.com/anthropics/original_performance_takehome/refs/heads/main/tests/frozen_problem.py"
+sys.modules["problem"] = problem = types.ModuleType("problem")
+exec(fetch(PROBLEM_URL).read_text(), problem.__dict__)
+
+if __name__ == "__main__":
+  batch_size = getenv("BS", 256)
+  height = 10
+  rounds = getenv("ROUNDS", 16)
+
+  # build problem
+  tree = problem.Tree.generate(height)
+  inp = problem.Input.generate(tree, batch_size, rounds)
+  mem = problem.build_mem_image(tree, inp)
+  global_addrs.extend([mem[6], mem[6], mem[4]])  # output, input, forest
+
+  # *** verify the kernel in tinygrad compared to reference ***
+
+  forest_t = Tensor(tree.values, dtype=dtypes.uint32)
+  val_t = Tensor(inp.values, dtype=dtypes.uint32)
+
+  if getenv("VERIFY", 1):
+    # verify on normal tinygrad device
+    with Context(PCONTIG=2):
+      out = tree_traversal(forest_t, val_t, height, rounds)
+      val_out = out.tolist()
+    problem.reference_kernel(tree, inp)
+    assert val_out == inp.values
+    print("verification passed")
+
+  # *** render to device ***
+
+  from tinygrad.codegen import get_program
+  with Context(PCONTIG=2, DEVECTORIZE=2, SPEC=0):
+    out = tree_traversal(forest_t, val_t, height, rounds)
+    sink = out.schedule()[-1].ast
+    prg = get_program(sink, VLIWRenderer())
+
+  # *** run on Machine and compare ***
+
+  # NOTE: the scratch size needs to be reduced to 1536 when you have a register allocator
+  src = eval(prg.src)
+  max_regs = max(t[1] for instr in src for v in instr.values() for t in v if len(t) > 1) + 8
+  print(f"{max_regs:5d} regs used" + ("" if max_regs <= 1536 else "       <-- WARNING: TOO MANY REGISTERS, MUST BE <= 1536"))
+  machine = problem.Machine(mem, src, problem.DebugInfo(scratch_map={}), n_cores=1, trace=False, scratch_size=max_regs)
+  machine.run()
+  print(f"ran for {machine.cycle:5d} cycles" + ("" if machine.cycle <= 1363 else "  <-- EVEN CLAUDE GOT 1363"))
+
+  # compare to reference
+  ref_mem = mem.copy()
+  for _ in problem.reference_kernel2(ref_mem, {}): pass
+  assert machine.mem[mem[6]:mem[6]+mem[2]] == ref_mem[mem[6]:mem[6]+mem[2]]
+  print("compare passed!")

examples/audio_helpers.py

@@ -0,0 +1,79 @@
+from typing import Optional
+from tinygrad import Tensor
+from tinygrad.dtype import DTypeLike, dtypes
+import math
+
+# rewritten from numpy
+def rfftfreq(n: int, d: float = 1.0, device=None) -> Tensor:
+  val = 1.0 / (n * d)
+  N = n // 2 + 1
+  results = Tensor.arange(N, device=device)
+  return results * val
+
+# just like in librosa
+def fft_frequencies(sr: float, n_fft: int) -> Tensor:
+  return rfftfreq(n=n_fft, d=1.0 / sr)
+
+def hz_to_mel(freq: Tensor) -> Tensor:
+  # linear part
+  f_min = 0.0
+  f_sp = 200.0 / 3
+  mels = (freq - f_min) / f_sp
+
+  # log-scale part
+  min_log_hz = 1000.0  # beginning of log region (Hz)
+  mask = freq >= min_log_hz
+  return mask.where(((min_log_hz - f_min) / f_sp) + (freq / min_log_hz).log() / (math.log(6.4) / 27.0), mels)
+
+def mel_to_hz(mels: Tensor) -> Tensor:
+  # linear scale
+  f_min = 0.0
+  f_sp = 200.0 / 3
+  freqs = f_min + f_sp * mels
+
+  # nonlinear scale
+  min_log_hz = 1000.0  # beginning of log region (Hz)
+  min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
+  logstep = math.log(6.4) / 27.0  # step size for log region
+
+  log_t = mels >= min_log_mel
+  freqs = log_t.where(min_log_hz * ((logstep * (mels - min_log_mel)).exp()), freqs)
+  return freqs
+
+def mel_frequencies(n_mels: int = 128, *, fmin: float = 0.0, fmax: float = 11025.0) -> Tensor:
+  # center freqs of mel bands - uniformly spaced between limits
+  min_max_mel = hz_to_mel(Tensor([fmin, fmax]))
+
+  mels = Tensor.linspace(min_max_mel[0], min_max_mel[1], n_mels)
+  hz = mel_to_hz(mels)
+  return hz
+
+def mel(
+  *,
+  sr: float,
+  n_fft: int,
+  n_mels: int = 128,
+  fmin: float = 0.0,
+  fmax: Optional[float] = None,
+  dtype: DTypeLike = dtypes.default_float,
+) -> Tensor:
+  if fmax is None:
+    fmax = float(sr) / 2
+
+  n_mels = int(n_mels)
+
+  fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft)  # center freqs of each FFT bin
+  mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax)  # center freqs of mel bands
+
+  fdiff = mel_f[1:] - mel_f[:-1]
+  ramps = mel_f[None].T.expand(-1, fftfreqs.shape[-1]) - fftfreqs
+
+  lower = -ramps[:n_mels] / fdiff[:n_mels][None].T
+  upper = ramps[2 : n_mels + 2] / fdiff[1 : n_mels + 1][None].T
+  weights = lower.minimum(upper).maximum(0)
+
+  # Slaney-style mel is scaled to be approx constant energy per channel
+  enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
+  weights *= enorm[:, None]
+
+  return weights

examples/beautiful_mnist.py

@@ -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()

examples/coder.py

@@ -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("")

examples/conversation.py

@@ -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}")

examples/efficientnet.py

@@ -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)])

examples/flux1.py

@@ -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"))

examples/gradaccum_mnist.py

@@ -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}%")

examples/mask_rcnn.py

@@ -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()

examples/mlperf/dataloader.py

@@ -72,7 +72,7 @@ def loader_process(q_in, q_out, X:Tensor, seed):
       #storage_tensor._copyin(img_tensor.numpy())
 
       # faster
-      X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
+      X[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = img.tobytes()
 
       # ideal
       #X[idx].assign(img.tobytes())   # NOTE: this is slow!
@@ -213,12 +213,13 @@ class InterleavedDataset:
     self.queues[queue_index].queue.extend(load_file(file))
 
 # Reference: https://github.com/mlcommons/training/blob/1c8a098ae3e70962a4f7422c0b0bd35ae639e357/language_model/tensorflow/bert/run_pretraining.py, Line 394
-def batch_load_train_bert(BS:int):
+def batch_load_train_bert(BS:int, seed:int|None=None):
   from extra.datasets.wikipedia import get_wiki_train_files
+  rng = random.Random(seed)
   fs = sorted(get_wiki_train_files())
   train_files = []
   while fs: # TF shuffle
-    random.shuffle(fs)
+    rng.shuffle(fs)
     train_files.append(fs.pop(0))
 
   cycle_length = min(getenv("NUM_CPU_THREADS", min(os.cpu_count(), 8)), len(train_files))
@@ -263,8 +264,8 @@ def load_unet3d_data(preprocessed_dataset_dir, seed, queue_in, queue_out, X:Tens
       x = random_brightness_augmentation(x)
       x = gaussian_noise(x)
 
-    X[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = x.tobytes()
-    Y[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = y.tobytes()
+    X[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = x.tobytes()
+    Y[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = y.tobytes()
 
     queue_out.put(idx)
   queue_out.put(None)
@@ -378,12 +379,12 @@ def load_retinanet_data(base_dir:Path, val:bool, queue_in:Queue, queue_out:Queue
       clipped_match_idxs = np.clip(match_idxs, 0, None)
       clipped_boxes, clipped_labels = tgt["boxes"][clipped_match_idxs], tgt["labels"][clipped_match_idxs]
 
-      boxes[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_boxes.tobytes()
-      labels[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = clipped_labels.tobytes()
-      matches[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = match_idxs.tobytes()
-      anchors[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = anchor.tobytes()
+      boxes[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = clipped_boxes.tobytes()
+      labels[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = clipped_labels.tobytes()
+      matches[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = match_idxs.tobytes()
+      anchors[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = anchor.tobytes()
 
-    imgs[idx].contiguous().realize().uop.base.realized.as_buffer(force_zero_copy=True)[:] = img.tobytes()
+    imgs[idx].contiguous().realize().uop.base.realized.as_memoryview(force_zero_copy=True)[:] = img.tobytes()
 
     queue_out.put(idx)
   queue_out.put(None)
@@ -763,48 +764,26 @@ 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):

examples/mlperf/helpers.py

@@ -219,17 +219,28 @@ def get_mlperf_bert_model():
   config = get_mlperf_bert_config()
   if getenv("DISABLE_DROPOUT", 0):
     config["hidden_dropout_prob"] = config["attention_probs_dropout_prob"] = 0.0
-  return BertForPretraining(**config)
+  model = BertForPretraining(**config)
+  if getenv("FP8_TRAIN"):
+    from extra.fp8.fp8_linear import convert_to_float8_training
+    def module_filter_fn(mod, fqn):
+      if isinstance(mod, LinearBert):
+        skip_layers = [] if (ln:=config["num_hidden_layers"]) <= 2 else ["bert.encoder.layer.0.", f"bert.encoder.layer.{ln-1}"]
+        if mod.weight.shape[-1] >= 1024 and "encoder" in fqn and not any(name in fqn for name in skip_layers):
+          print(f"replacing linear with fp8: {fqn} {mod.weight.shape}")
+          return True
+      return False
+    convert_to_float8_training(model, module_filter_fn)
+  return 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:

examples/mlperf/initializers.py

@@ -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):

examples/mlperf/model_eval.py

@@ -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():

examples/mlperf/model_train.py

@@ -3,7 +3,7 @@ from pathlib import Path
 import multiprocessing
 
 from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
-from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling
+from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling, profile_marker
 from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
 from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
 
@@ -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))
@@ -1041,6 +1008,7 @@ def train_bert():
   config["DISABLE_DROPOUT"] = getenv("DISABLE_DROPOUT", 0)
   config["TRAIN_BEAM"]    = TRAIN_BEAM = getenv("TRAIN_BEAM", BEAM.value)
   config["EVAL_BEAM"]     = EVAL_BEAM  = getenv("EVAL_BEAM", BEAM.value)
+  config["FP8_TRAIN"]     = getenv("FP8_TRAIN", 0)
 
   Tensor.manual_seed(seed)  # seed for weight initialization
 
@@ -1073,8 +1041,8 @@ 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)
 
@@ -1118,7 +1086,7 @@ def train_bert():
   if RUNMLPERF:
     # only load real data with RUNMLPERF
     eval_it = iter(batch_load_val_bert(EVAL_BS))
-    train_it = iter(tqdm(batch_load_train_bert(BS), total=train_steps, disable=BENCHMARK))
+    train_it = iter(tqdm(batch_load_train_bert(BS, seed=seed), total=train_steps, disable=BENCHMARK))
     for _ in range(start_step): next(train_it) # Fast forward
   else:
     # repeat fake data
@@ -1131,12 +1099,38 @@ 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
@@ -1144,21 +1138,17 @@ 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)}"
         loss = loss.item()
-        assert not math.isnan(loss)
+        if not getenv("FP8_TRAIN"): assert not math.isnan(loss)
         lr = lr.item()
 
       cl = time.perf_counter()
@@ -1171,7 +1161,7 @@ def train_bert():
       if WANDB:
         wandb.log({"lr": lr, "train/loss": loss, "train/global_norm": global_norm.item(), "train/step_time": cl - st,
                     "train/python_time": pt - st, "train/data_time": dt - pt, "train/cl_time": cl - dt,
-                    "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*GBS})
+                    "train/mem":GlobalCounters.mem_used / 1e9, "train/GFLOPS": GlobalCounters.global_ops * 1e-9 / (cl - st), "epoch": (i+1)*GBS})
 
       train_data, next_data = next_data, None
       i += 1
@@ -1188,8 +1178,8 @@ 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 +1214,7 @@ 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)
@@ -1296,6 +1286,8 @@ def train_llama3():
   from examples.llama3 import MODEL_PARAMS
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
 
+  BENCHMARK = getenv("BENCHMARK")
+
   config = {}
   BASEDIR            = config["BASEDIR"]                = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
   BS                 = config["BS"]                     = getenv("BS", 16)
@@ -1306,6 +1298,11 @@ def train_llama3():
   TRAIN_ON_VAL       = config["TRAIN_ON_VAL"]           = getenv("TRAIN_ON_VAL", 0)
   SMALL              = config["SMALL"]                  = getenv("SMALL", 0)
   SAMPLES            = config["SAMPLES"]                = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000 * 1152)
+  EVAL_SAMPLES       = config["EVAL_SAMPLES"]           = getenv("EVAL_SAMPLES", 5760 if not SMALL else 1024)
+  MAX_STEPS          = config["MAX_STEPS"]              = getenv("MAX_STEPS", math.ceil(1_200_000 * 1152 / GBS))
+  WARMUP_STEPS       = config["WARMUP_STEPS"]           = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
+  LR                 = config["LR"]                     = getenv("LR", 8e-5 * GBS / 1152)
+  END_LR             = config["END_LR"]                 = getenv("END_LR", 8e-7)
   EVAL_FREQ          = config["EVAL_FREQ"]              = getenv("EVAL_FREQ", 46080)
   EVAL_BS            = config["EVAL_BS"]                = getenv("EVAL_BS", 16)
   EVAL_TARGET        = config["EVAL_TARGET"]            = getenv("EVAL_TARGET", 5.6)
@@ -1319,17 +1316,30 @@ def train_llama3():
   opt_adamw_weight_decay = 0.1
 
   opt_gradient_clip_norm = 1.0
-  opt_learning_rate_warmup_steps = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
-  opt_learning_rate_decay_steps = getenv("MAX_STEPS", math.ceil(1_200_000 * 1152 / GBS)) - opt_learning_rate_warmup_steps
-  opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152)  # NOTE: cannot change for benchmark
-  opt_end_learning_rate = getenv("END_LR", 8e-7)
+  opt_learning_rate_warmup_steps = WARMUP_STEPS
+  opt_learning_rate_decay_steps = MAX_STEPS - opt_learning_rate_warmup_steps
+  opt_base_learning_rate = LR
+  opt_end_learning_rate = END_LR
 
-  # TODO: confirm weights are in bf16
+  Tensor.manual_seed(SEED)  # seed for weight initialization
+
+  # ** 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
+  print(f"model parameters: {model_params}")
+
+  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):
@@ -1361,6 +1371,12 @@ def train_llama3():
 
   optim = AdamW(get_parameters(model), lr=0.0,
                 b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
+
+  # init grads
+  for p in optim.params:
+    p.grad = p.zeros_like().contiguous().realize()
+  grads = [p.grad for p in optim.params]
+
   scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
 
   if resume_ckpt := getenv("RESUME_CKPT"):
@@ -1373,42 +1389,50 @@ def train_llama3():
     load_state_dict(scheduler, safe_load(fn), realize=False)
 
   @TinyJit
-  @Tensor.train()
-  def train_step(model, tokens:Tensor, grad_acc:int):
-    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])
+  def minibatch(tokens:Tensor):
+    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()
+    assert all(p.grad is g for p,g in zip(optim.params, grads))
+    Tensor.realize(loss, *grads)
+    return loss
+
+  @TinyJit
+  def optim_step():
+    for p in optim.params:
+      p.grad.assign(p.grad / grad_acc)
+
     # 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
     if not getenv("DISABLE_GRAD_CLIP_NORM"):
       total_norm = Tensor(0.0, dtype=dtypes.float32, device=optim.params[0].device)
-      for p in optim.params:
-        total_norm += p.grad.float().square().sum()
-      total_norm = total_norm.sqrt().contiguous()
-      for p in optim.params:
-        p.grad = p.grad * (opt_gradient_clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)
+      for g in grads:
+        total_norm += g.float().square().sum()
+      total_norm = total_norm.sqrt().contiguous().realize()
+      for g in grads:
+        g.assign((g * (opt_gradient_clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(g.dtype)).realize()
 
     optim.step()
     scheduler.step()
 
+    for g in grads:
+      g.assign(g.zeros_like().contiguous()).realize()
+
     lr = optim.lr
-    loss.realize(lr)
-    return loss, lr
+    Tensor.realize(lr, *grads)
+
+    return lr
 
   @TinyJit
   @Tensor.train(False)
-  def eval_step(model, tokens:Tensor):
+  def eval_step(tokens:Tensor):
     if (DP := getenv("DP", 1)) > 1:
       device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
       tokens = tokens.shard(device, 0)
@@ -1422,70 +1446,127 @@ 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(EVAL_SAMPLES, SEQLEN, BASEDIR, val=True, small=bool(SMALL))
 
   def get_eval_iter():
-    if getenv("FAKEDATA", 0):
-      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)
+    if eval_dataset is None:
+      return fake_data(EVAL_BS, EVAL_SAMPLES)
+    from examples.mlperf.dataloader import iterate_llama3_dataset
+    return iterate_llama3_dataset(eval_dataset, EVAL_BS)
 
-  iter = get_train_iter()
+  num_params = sum(p.numel() for p in params) - model_params["vocab_size"]*model_params["dim"]
+  train_iter = get_train_iter()
   i, sequences_seen = resume_ckpt, 0
-  for tokens in tqdm(iter, total=SAMPLES//GBS):
-    t = time.perf_counter()
+  step_times = []
+  while i < MAX_STEPS:
     GlobalCounters.reset()
-    loss, lr = train_step(model, tokens, grad_acc)
-    loss = loss.float().item()
+    if getenv("TRAIN", 1):
+      profile_marker(f"train @ {i}")
+      st = time.perf_counter()
 
-    i += 1
-    sequences_seen += tokens.shape[0]
+      stopped = False
+      for _ in range(grad_acc):
+        ist = time.perf_counter()
+        try: tokens = next(train_iter)
+        except StopIteration:
+          stopped = True
+          break
+        dt = time.perf_counter()
+        loss = minibatch(tokens)
+      if stopped: break
 
-    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")
+      gt = time.perf_counter()
+      lr = optim_step()
+      ot = time.perf_counter()
 
-    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)
+      loss = loss.float().item()
+      lr = lr.item()
 
-      tqdm.write("saving optim checkpoint")
-      fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
-      safe_save(get_state_dict(scheduler), fn)
+      et = time.perf_counter()
+      step_time = et - st
+      gbs_time = gt - st
+      optim_time = ot - gt
+      data_time = dt - ist
+      dev_time = step_time - data_time * grad_acc
+      if BENCHMARK: step_times.append(step_time)
 
-    if sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1):
+      i += 1
+      sequences_seen += GBS
+
+      mem_gb = GlobalCounters.mem_used / 1e9
+      gflops = GlobalCounters.global_ops / 1e9 / dev_time
+      mfu = ((6 * num_params * SEQLEN * GBS) / (dev_time * max(getenv("DP", 1), getenv("MP", 1)) * 2.3e15)) * 100
+      tqdm.write(
+          f"{i:5} {step_time:.3f} s step, {gbs_time:.3f} s gbs, {optim_time:.3f} s optim, {data_time:.3f} s data, {loss:.4f} loss, " \
+          f"{lr:.12f} LR, {mem_gb:.2f} GB used, {gflops:9.2f} GFLOPS, {mfu:5.2f}% MFU")
+
+      if WANDB:
+        wandb.log({
+          "lr": lr, "train/loss": loss,
+          "train/step_time": step_time,
+          "train/gbs_time": gbs_time,
+          "train/optim_time": optim_time,
+          "train/dev_time": dev_time,
+          "train/data_time": data_time,
+          "train/mem": mem_gb,
+          "train/GFLOPS": gflops,
+          "train/MFU": mfu,
+          "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 i == BENCHMARK:
+        median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]
+        estimated_total_minutes = int(median_step_time * (SAMPLES // GBS) / 60)
+        print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
+        print(f"epoch global_ops: {GlobalCounters.global_ops:_}, "
+              f"epoch global_mem: {GlobalCounters.global_mem:_}")
+
+    if (sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1)) or (BENCHMARK and i == BENCHMARK):
+      if EVAL_BS == 0: return
       tqdm.write(f"evaluating after {sequences_seen} sequences")
+      profile_marker(f"eval @ {i}")
 
       # run eval
       eval_losses = []
       eval_iter = get_eval_iter()
       tqdm.write(f"evaluating {5760//EVAL_BS} batches of {EVAL_BS} sequences")
 
-      for tokens in tqdm(eval_iter, total=5760//EVAL_BS):
-        eval_losses += eval_step(model, tokens).tolist()
+      for j,tokens in tqdm(enumerate(eval_iter), total=EVAL_SAMPLES//EVAL_BS):
+        eval_losses += eval_step(tokens).tolist()
+
+        if BENCHMARK and (j+1) == min(BENCHMARK, EVAL_SAMPLES//EVAL_BS):
+          return
+
       log_perplexity = Tensor(eval_losses).mean().float().item()
 
       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"):
@@ -1564,7 +1645,7 @@ def train_stable_diffusion():
     loss, out_lr = loss.detach().to("CPU"), optimizer.lr.to("CPU")
     Tensor.realize(loss, out_lr)
     return loss, out_lr
-    
+
   # checkpointing takes ~9 minutes without this, and ~1 minute with this
   @TinyJit
   def ckpt_to_cpu():
@@ -1603,7 +1684,7 @@ def train_stable_diffusion():
     if i == 3:
       for _ in range(3): ckpt_to_cpu() # do this at the beginning of run to prevent OOM surprises when checkpointing
       print("BEAM COMPLETE", flush=True) # allows wrapper script to detect BEAM search completion and retry if it failed
-      
+
     total_train_time = time.perf_counter() - train_start_time
     if WANDB:
       wandb.log({"train/loss": loss_item, "train/lr": lr_item, "train/loop_time_prev": loop_time, "train/dl_time": dl_time, "train/step": i,

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_1xMI300X/dev_beam.sh

@@ -4,7 +4,7 @@ export PYTHONPATH="." AMD=1
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
 
-export IGNORE_OOB=1
+export CHECK_OOB=0
 
 export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_beam.sh

@@ -5,7 +5,7 @@ export MODEL="bert"
 export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
 
-export IGNORE_OOB=1
+export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_run.sh

@@ -8,7 +8,7 @@ export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 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 CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh

@@ -11,7 +11,7 @@ export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 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 CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI350X/dev_run_fa.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1 DEBUG=0 JIT=1 FLASH_ATTENTION=1
+export MODEL="bert"
+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 CHECK_OOB=0
+export REWRITE_STACK_LIMIT=5000000
+
+export BEAM=0 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"
+
+export WANDB=1 PARALLEL=0
+
+RUNMLPERF=1 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI350X/dev_run_fp8.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+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 CHECK_OOB=0
+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"
+export BEAM_TIMEOUT_SEC=15
+export FP8_TRAIN=1
+# search
+IGNORE_BEAM_CACHE=1 BENCHMARK=10 BERT_LAYERS=2 RUNMLPERF=0 python3 examples/mlperf/model_train.py
+
+export WANDB=1 PARALLEL=0
+
+RUNMLPERF=1 python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI350X/run_and_time.sh

@@ -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 CHECK_OOB=0
+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

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_beam.sh

@@ -2,9 +2,9 @@
 
 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 CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_run.sh

@@ -2,9 +2,9 @@
 
 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 CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh

@@ -5,9 +5,9 @@ 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 CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_beam.sh

@@ -4,7 +4,7 @@ export PYTHONPATH="." AMD=1
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_run.sh

@@ -4,7 +4,7 @@ export PYTHONPATH="." AMD=1
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh

@@ -7,7 +7,7 @@ export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_red"
 export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=500000
 
 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_beam.sh

@@ -0,0 +1,36 @@
+#!/usr/bin/env bash
+
+export PYTHONPATH="."
+export DEV=${DEV:-AMD}
+export EMULATE="AMD_CDNA4"
+export CHECK_OOB=0
+export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
+
+export DEBUG=${DEBUG:-2}
+export FLASH_ATTENTION=${FLASH_ATTENTION:-1}
+export ALL2ALL=${ALL2ALL:-1}
+export USE_ATOMICS=${USE_ATOMICS:-1}
+export ASM_GEMM=${ASM_GEMM:-1}
+
+export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
+export DP=${DP:-8} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-1}
+export GBS=$((BS * GRADIENT_ACC_STEPS))
+
+export MODEL="llama3"
+export BASEDIR="/raid/datasets/c4-8b/"
+export SMALL=1
+export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
+export EVAL_TARGET=3.3 EVAL_FREQ=12288
+export LR="4e-4" END_LR="4e-5" WARMUP_SAMPLES=256 MAX_STEPS=1200000
+export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
+export SAMPLES=$((MAX_STEPS * GBS))
+export SEQLEN=${SEQLEN:-8192}
+
+export SEED=${SEED:-5760}
+
+export JITBEAM=${JITBEAM:-3}
+export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+
+export FAKEDATA=1 BENCHMARK=10 LLAMA_LAYERS=2
+
+python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run.sh

@@ -0,0 +1,34 @@
+#!/usr/bin/env bash
+
+export PYTHONPATH="."
+export DEV=${DEV:-AMD}
+export EMULATE="AMD_CDNA4"
+export CHECK_OOB=0
+export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
+
+export DEBUG=${DEBUG:-0}
+export FLASH_ATTENTION=${FLASH_ATTENTION:-1}
+export ALL2ALL=${ALL2ALL:-1}
+export USE_ATOMICS=${USE_ATOMICS:-1}
+export ASM_GEMM=${ASM_GEMM:-1}
+
+export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
+export DP=${DP:-8} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-1}
+export GBS=$((BS * GRADIENT_ACC_STEPS))
+
+export MODEL="llama3"
+export BASEDIR="/raid/datasets/c4-8b/"
+export SMALL=1
+export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
+export EVAL_TARGET=3.3 EVAL_FREQ=12288
+export LR="4e-4" END_LR="4e-5" WARMUP_SAMPLES=256 MAX_STEPS=1200000
+export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
+export SAMPLES=$((MAX_STEPS * GBS))
+export SEQLEN=${SEQLEN:-8192}
+
+export SEED=${SEED:-5760}
+
+export JITBEAM=${JITBEAM:-3}
+export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+
+python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/profile.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+export BENCHMARK=5
+export EVAL_BS=0
+export VIZ=${VIZ:--1}
+examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run.sh
+PYTHONPATH="." extra/viz/cli.py --profile --device "AMD" --top 20

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/startup_walltime.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+export BENCHMARK=5
+export EVAL_BS=0
+export FAKEDATA=1
+export NULL_ALLOW_COPYOUT=1
+export HIP_VISIBLE_DEVICES=""
+export DEV=NULL
+export JITBEAM=0
+export LLAMA_LAYERS=${LLAMA_LAYERS:-"2"}
+time examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run.sh

examples/openelm.py

@@ -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)
-
-

examples/other_mnist/beautiful_mnist_mlx.py

@@ -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}%")

examples/rl/lightupbutton.py

@@ -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,
-)

examples/sdv2.py

@@ -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

examples/serious_mnist.py

@@ -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}')

examples/simple_conv_bn.py

@@ -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()

examples/so_vits_svc.py

@@ -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}")

examples/sovits_helpers/preprocess.py

@@ -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()

examples/stable_diffusion.py

@@ -6,7 +6,6 @@ import argparse, time
 from collections import namedtuple
 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, profile_marker
@@ -336,6 +335,7 @@ if __name__ == "__main__":
   print(x.shape)
 
   profile_marker("save image")
+  from PIL import Image
   im = Image.fromarray(x.numpy())
   print(f"saving {args.out}")
   im.save(args.out)

examples/test_pkl_imagenet.py

@@ -7,7 +7,7 @@ if __name__ == "__main__":
   with open(fetch(sys.argv[1]), "rb") as f:
     run_onnx_jit = pickle.load(f)
   input_name = run_onnx_jit.captured.expected_names[0]
-  device = run_onnx_jit.captured.expected_st_vars_dtype_device[0][-1]
+  device = run_onnx_jit.captured.expected_input_info[0][-1]
   print(f"input goes into {input_name=} on {device=}")
   hit = 0
   for i,(img,y) in enumerate(imagenet_dataloader(cnt=getenv("CNT", 100))):

examples/tinychat/tinychat-browser/compile.py

@@ -48,7 +48,7 @@ def prepare_browser_chunks(model):
         weight_metadata = metadata.get(name, default)
         weight_metadata["parts"][part_num] = {"file": i, "file_start_pos": cursor, "size": size}
         metadata[name] = weight_metadata
-        data = bytes(state_dict[name].uop.base.realized.as_buffer())
+        data = bytes(state_dict[name].uop.base.realized.as_memoryview())
         data = data if not offsets else data[offsets[0]:offsets[1]]
         writer.write(data)
         cursor += size

examples/train_efficientnet.py

@@ -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

examples/vit.py

@@ -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])

examples/vits.py

@@ -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}")

examples/webgpu/stable_diffusion/compile.py

@@ -93,7 +93,7 @@ if __name__ == "__main__":
     forward: Any = None
 
   sub_steps = [
-    Step(name = "textModel", input = [Tensor.randn(1, 77)], forward = model.cond_stage_model.transformer.text_model),
+    Step(name = "textModel", input = [Tensor.randint(1, 77, low=0, high=49408, dtype=dtypes.int32)], forward = model.cond_stage_model.transformer.text_model),
     Step(name = "diffusor", input = [Tensor.randn(1, 77, 768), Tensor.randn(1, 77, 768), Tensor.randn(1,4,64,64), Tensor.rand(1), Tensor.randn(1), Tensor.randn(1), Tensor.randn(1)], forward = model),
     Step(name = "decoder", input = [Tensor.randn(1,4,64,64)], forward = model.decode),
     Step(name = "f16tof32", input = [Tensor.randn(2097120, dtype=dtypes.uint32)], forward = u32_to_f16)

examples/whisper.py

@@ -7,6 +7,7 @@ from tinygrad import Tensor, TinyJit, Variable, nn, dtypes
 from tinygrad.nn.state import torch_load, load_state_dict
 from tinygrad.helpers import getenv, fetch
 
+from examples.audio_helpers import mel
 import numpy as np
 import librosa
 
@@ -159,7 +160,7 @@ def prep_audio(waveforms: List[np.ndarray], batch_size: int, truncate=False) ->
 
   stft = librosa.stft(waveforms, n_fft=N_FFT, hop_length=HOP_LENGTH, window='hann', dtype=np.csingle)
   magnitudes = np.absolute(stft[..., :-1]) ** 2
-  mel_spec = librosa.filters.mel(sr=RATE, n_fft=N_FFT, n_mels=N_MELS) @ magnitudes
+  mel_spec = mel(sr=RATE, n_fft=N_FFT, n_mels=N_MELS).numpy() @ magnitudes
 
   log_spec = np.log10(np.clip(mel_spec, 1e-10, None))
   log_spec = np.maximum(log_spec, log_spec.max((1,2), keepdims=True) - 8.0)

extra/amdpci/am_smi.py

@@ -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}]'
 
@@ -88,13 +90,28 @@ 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:"]
+    remove_parts = ["Advanced Micro Devices, Inc. [AMD/ATI]", "VGA compatible controller:", "Processing accelerators:"]
     lspci = subprocess.check_output(["lspci"]).decode("utf-8").splitlines()
     self.lspci = {l.split()[0]: l.split(" ", 1)[1] for l in lspci}
     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.MetricsTableV0_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)|(13,0,12): 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)|(13,0,12): 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)|(13,0,12):
+        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)|(13,0,12): 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)|(13,0,12): 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)|(13,0,12): 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)|(13,0,12): 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)|(13,0,12): 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)|(13,0,12): 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
@@ -212,30 +280,35 @@ class SMICtx:
       device_line = [f"{bold(dev.pcibus)} {trim(self.lspci[dev.pcibus[5:]], col_size - 20)}"] + [pad("", col_size)]
       activity_line = [f"GFX Activity {draw_bar(self.get_gfx_activity(dev, metrics) / 100, activity_line_width)}"] \
                     + [f"MEM Activity {draw_bar(self.get_mem_activity(dev, metrics) / 100, activity_line_width)}"] \
-                    + [f"MEM Usage    {draw_bar((mem_used / mem_total) / 100, activity_line_width, opt_text=mem_fmt)}"] \
+                    + [f"MEM Usage    {draw_bar(mem_used / mem_total, activity_line_width, opt_text=mem_fmt)}"] \
 
       temps_data, temps_data_compact = self.get_temps(dev, metrics), self.get_temps(dev, metrics, compact=True)
       temps_table = ["=== Temps (°C) ==="] + [f"{name:<16}: {color_temp(val)}" for name, val in temps_data.items()]
       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...")

extra/amdpci/hive_reset.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+
+import os
+from tinygrad.helpers import Context
+from tinygrad.runtime.support.system import System, PCIDevice, PCIDevImplBase
+from tinygrad.runtime.support.hcq import FileIOInterface
+from tinygrad.runtime.support.am.amdev import AMDev
+
+if __name__ == "__main__":
+  gpus = System.pci_scan_bus(0x1002, [(0xffff, [0x74a1, 0x75a0])])
+  for gpu in gpus:
+    drv_path = f"/sys/bus/pci/devices/{gpu}/driver"
+    if FileIOInterface.exists(drv_path) and os.path.basename(os.readlink(drv_path)) == "amdgpu":
+      raise RuntimeError(f"amdgpu is bound to {gpu}. Stopping...")
+  pcidevs = [PCIDevice("AM", 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()

extra/amdpci/proclogs.py

@@ -1,48 +1,90 @@
 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, register_objects=None, *, only_xcc0: bool = False):
+  register_map = register_names or {}
+  register_objs = register_objects 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)
+
+  # decode register values into field dicts
+  def decode_value(match):
+    reg_name = match.group(1)
+    xcc_part = match.group(2)  # "xcc=0 " or ""
+    val_str = match.group(3)
+    val = int(val_str, 16)
+
+    reg_obj = register_objs.get(reg_name)
+    if reg_obj is not None and reg_obj.fields:
+      fields = reg_obj.decode(val)
+      # show raw for unaccounted bits
+      accounted = 0
+      for name, (start, end) in reg_obj.fields.items():
+        accounted |= (((1 << (end - start + 1)) - 1) << start)
+      unaccounted = val & ~accounted
+      parts = {k: v for k, v in fields.items() if v != 0}
+      if unaccounted: parts['_raw_unaccounted'] = hex(unaccounted)
+      return f"register {reg_name}, {xcc_part}with value {val_str} {parts}"
+    return match.group(0)
+
+  processed_log = re.sub(r'register (reg\w+), ((?:xcc=\d+ )?)with value (0x[0-9a-fA-F]+)', decode_value, processed_log)
+
+  # 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 = {}
+  reg_objs = {}
   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}"
+      reg_objs[x] = y
 
   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, reg_objs, only_xcc0=only_xcc0)
 
   with open(sys.argv[2], 'w') as f:
     f.write(processed_log)
@@ -51,5 +93,4 @@ if __name__ == '__main__':
   if len(sys.argv) != 3:
     print("Usage: <input_file_path> <output_file_path>")
     sys.exit(1)
-
-  main()
+  main()

extra/assembly/amd/README

@@ -0,0 +1,39 @@
+An integrated environment for AMD GPU assembly and emulation
+
+Test with `PYTHONPATH="." pytest -n12 extra/assembly/amd/`
+`AMD_LLVM=1 PYTHONPATH="." pytest -n12 extra/assembly/amd/`
+
+* pdf.py -- extract assembly format + instruction pseudocode from AMD PDF
+* dsl.py -- helpers for the autogen instruction classes in `__init__.py`. should be standalone with init
+* pcode.py -- pseudocode execution environment. pseudocode should be transformed as little as possible.
+* asm.py -- an asm/disasm function to transform to and from AMD assembly syntax
+* emu.py -- an emulator for RDNA that runs in tinygrad with `AMD=1 MOCKGPU=1 PYTHON_REMU=1`
+
+The code should be as readable and deduplicated as possible. asm and emu shouldn't be required for dsl.
+
+The autogen folder is autogenerated from the AMD PDFs with `python3 -m extra.assembly.amd.pdf --arch all`
+
+test_emu.py has a good set of instruction tests for the emulation, with USE_HW=1 it will compare to real hardware.
+Whenever an instruction is fixed, regression tests should be added here and confirmed with real hardware.
+
+test_llvm.py tests asm/disasm on the LLVM tests, confirming it behaves the same as LLVM.
+
+tinygrad's dtype tests should pass with and without LLVM. they run in about 12 seconds.
+
+`PYTHONPATH="." AMD=1 PYTHON_REMU=1 MOCKGPU=1 AMD_LLVM=0 pytest -n=12 test/test_dtype_alu.py test/test_dtype.py`
+`PYTHONPATH="." AMD=1 PYTHON_REMU=1 MOCKGPU=1 AMD_LLVM=1 pytest -n=12 test/test_dtype_alu.py test/test_dtype.py`
+
+The ops tests also pass, but they are very slow, so you should run them one at a time.
+
+`SKIP_SLOW_TEST=1 PYTHONPATH="." AMD=1 PYTHON_REMU=1 MOCKGPU=1 AMD_LLVM=0 pytest -n=12 test/test_ops.py`
+`SKIP_SLOW_TEST=1 PYTHONPATH="." AMD=1 PYTHON_REMU=1 MOCKGPU=1 AMD_LLVM=1 pytest -n=12 test/test_ops.py`
+
+When something is caught by main tinygrad tests, a local regression test should be added to `extra/assembly/amd/test`.
+While working with tinygrad, you can dump the assembly with `DEBUG=7`. These tests all pass on real hardware
+If a test is failing with `AMD=1 PYTHON_REMU=1 MOCKGPU=1` it's because an instruction is emulated incorrectly.
+You can test without `MOCKGPU=1` to test on real hardware, if it works on real hardware there's a bug in the emulator.
+IMPORTANT: if a test is failing in the emulator, it's an instruction bug. Use DEBUG=7, get the instructions, and debug.
+
+Currently, only RDNA3 is well supported, but when finished, this will support RDNA3+RDNA4+CDNA in ~2000 lines.
+Get line count with `cloc --by-file extra/assembly/amd/*.py`
+

extra/assembly/amd/__init__.py

@@ -0,0 +1,67 @@
+# Instruction format detection and decoding
+from __future__ import annotations
+from extra.assembly.amd.dsl import Inst, FixedBitField, EnumBitField
+
+# SDWA/DPP variant detection: src0 field (bits 0-8) encodes the variant
+# 0xf9 (249) = SDWA, 0xfa (250) = DPP16 for CDNA (GFX9)
+_VARIANT_SRC0 = {"_SDWA_SDST": 0xf9, "_SDWA": 0xf9, "_DPP16": 0xfa}
+
+def _matches(data: bytes, cls: type[Inst]) -> bool:
+  """Check if data matches all FixedBitFields and op is in allowed."""
+  for _, field in cls._fields:
+    dword_idx = field.lo // 32
+    if len(data) < (dword_idx + 1) * 4: return False
+    word = int.from_bytes(data[dword_idx*4:(dword_idx+1)*4], 'little')
+    field_lo = field.lo % 32
+    if isinstance(field, FixedBitField):
+      if ((word >> field_lo) & field.mask) != field.default: return False
+    if isinstance(field, EnumBitField) and field.allowed is not None:
+      try: opcode = field.decode((word >> field_lo) & field.mask)
+      except ValueError: return False  # opcode not in enum
+      if opcode not in field.allowed: return False
+  # Check SDWA/DPP variant based on src0 field (bits 0-8) - only for variant classes
+  name = cls.__name__
+  word = int.from_bytes(data[:4], 'little')
+  for suffix, expected_src0 in _VARIANT_SRC0.items():
+    if name.endswith(suffix): return (word & 0x1ff) == expected_src0
+  return True
+
+# Import instruction classes for each architecture
+from extra.assembly.amd.autogen.rdna3.ins import (VOP1, VOP1_SDST, VOP1_LIT, VOP2, VOP2_LIT, VOP3, VOP3_SDST, VOP3SD, VOP3P, VOPC, VOPD, VINTERP,
+  SOP1, SOP1_LIT, SOP2, SOP2_LIT, SOPC, SOPK, SOPK_LIT, SOPP, SMEM, DS, FLAT, GLOBAL, SCRATCH)
+from extra.assembly.amd.autogen.rdna4.ins import (VOP1 as R4_VOP1, VOP1_SDST as R4_VOP1_SDST, VOP1_LIT as R4_VOP1_LIT,
+  VOP2 as R4_VOP2, VOP2_LIT as R4_VOP2_LIT, VOP3 as R4_VOP3, VOP3_SDST as R4_VOP3_SDST, VOP3SD as R4_VOP3SD, VOP3P as R4_VOP3P,
+  VOPC as R4_VOPC, VOPD as R4_VOPD, VINTERP as R4_VINTERP, SOP1 as R4_SOP1, SOP1_LIT as R4_SOP1_LIT,
+  SOP2 as R4_SOP2, SOP2_LIT as R4_SOP2_LIT, SOPC as R4_SOPC, SOPC_LIT as R4_SOPC_LIT,
+  SOPK as R4_SOPK, SOPK_LIT as R4_SOPK_LIT, SOPP as R4_SOPP,
+  SMEM as R4_SMEM, DS as R4_DS, VFLAT as R4_FLAT, VGLOBAL as R4_GLOBAL, VSCRATCH as R4_SCRATCH)
+from extra.assembly.amd.autogen.cdna.ins import (VOP1 as C_VOP1, VOP1_SDWA as C_VOP1_SDWA, VOP1_DPP16 as C_VOP1_DPP16,
+  VOP2 as C_VOP2, VOP2_LIT as C_VOP2_LIT, VOP2_SDWA as C_VOP2_SDWA, VOP2_DPP16 as C_VOP2_DPP16,
+  VOPC as C_VOPC, VOPC_SDWA_SDST as C_VOPC_SDWA_SDST,
+  VOP3 as C_VOP3, VOP3_SDST as C_VOP3_SDST, VOP3SD as C_VOP3SD, VOP3P as C_VOP3P, VOP3P_MFMA as C_VOP3P_MFMA, VOP3PX2 as C_VOP3PX2,
+  SOP1 as C_SOP1, SOP2 as C_SOP2, SOPC as C_SOPC, SOPK as C_SOPK, SOPK_LIT as C_SOPK_LIT, SOPP as C_SOPP, SMEM as C_SMEM, DS as C_DS,
+  FLAT as C_FLAT, GLOBAL as C_GLOBAL, SCRATCH as C_SCRATCH, MUBUF as C_MUBUF)
+
+# Order matters: more specific encodings first, catch-alls (SOP2, VOP2) last
+# Order: base before _LIT (base matches regular ops, _LIT catches lit-only ops excluded from base)
+_FORMATS = {
+  "rdna3": [VOPD, VOP3P, VINTERP, VOP3SD, VOP3_SDST, VOP3, DS, GLOBAL, SCRATCH, FLAT, SMEM,
+            SOP1, SOP1_LIT, SOP2, SOP2_LIT, SOPC, SOPK, SOPK_LIT, SOPP, VOPC, VOP1_SDST, VOP1, VOP1_LIT, VOP2, VOP2_LIT],
+  "rdna4": [R4_VOPD, R4_VOP3P, R4_VINTERP, R4_VOP3SD, R4_VOP3_SDST, R4_VOP3, R4_DS, R4_GLOBAL, R4_SCRATCH, R4_FLAT, R4_SMEM,
+            R4_SOP1, R4_SOP1_LIT, R4_SOPC, R4_SOPC_LIT, R4_SOPP, R4_SOPK, R4_SOPK_LIT, R4_VOPC, R4_VOP1_SDST, R4_VOP1, R4_VOP1_LIT,
+            R4_SOP2, R4_SOP2_LIT, R4_VOP2, R4_VOP2_LIT],
+  "cdna": [C_VOP3PX2, C_VOP3P_MFMA, C_VOP3P, C_VOP3SD, C_VOP3_SDST, C_VOP3, C_DS, C_GLOBAL, C_SCRATCH, C_FLAT, C_MUBUF, C_SMEM,
+            C_SOP1, C_SOPC, C_SOPP, C_SOPK, C_SOPK_LIT, C_VOPC_SDWA_SDST, C_VOPC,
+            C_VOP1_DPP16, C_VOP1_SDWA, C_VOP1, C_VOP2_DPP16, C_VOP2_SDWA, C_SOP2, C_VOP2, C_VOP2_LIT],
+}
+
+def detect_format(data: bytes, arch: str = "rdna3") -> type[Inst]:
+  """Detect instruction format from machine code bytes."""
+  assert len(data) >= 4, f"need at least 4 bytes, got {len(data)}"
+  for cls in _FORMATS[arch]:
+    if _matches(data, cls): return cls
+  raise ValueError(f"unknown {arch} format word={int.from_bytes(data[:4], 'little'):#010x}")
+
+def decode_inst(data: bytes, arch: str = "rdna3") -> Inst:
+  """Decode machine code bytes into an instruction."""
+  return detect_format(data, arch).from_bytes(data)

extra/assembly/amd/autogen/common.py

@@ -0,0 +1,324 @@
+# autogenerated from AMD ISA XML - do not edit
+from enum import Enum, auto
+
+class ReprEnum(Enum):
+  """Enum with clean repr that roundtrips with eval()."""
+  def __repr__(self): return f"{type(self).__name__}.{self.name}"
+
+class Fmt(Enum):
+  FMT_ANY = auto()
+  FMT_BUF = auto()
+  FMT_IMG = auto()
+  FMT_IMG_BVH = auto()
+  FMT_NUM_B1 = auto()
+  FMT_NUM_B1024 = auto()
+  FMT_NUM_B128 = auto()
+  FMT_NUM_B16 = auto()
+  FMT_NUM_B256 = auto()
+  FMT_NUM_B32 = auto()
+  FMT_NUM_B512 = auto()
+  FMT_NUM_B64 = auto()
+  FMT_NUM_B8 = auto()
+  FMT_NUM_B96 = auto()
+  FMT_NUM_BF16 = auto()
+  FMT_NUM_BF6 = auto()
+  FMT_NUM_BF8 = auto()
+  FMT_NUM_F16 = auto()
+  FMT_NUM_F32 = auto()
+  FMT_NUM_F64 = auto()
+  FMT_NUM_FP4 = auto()
+  FMT_NUM_FP6 = auto()
+  FMT_NUM_FP8 = auto()
+  FMT_NUM_I16 = auto()
+  FMT_NUM_I24 = auto()
+  FMT_NUM_I32 = auto()
+  FMT_NUM_I4 = auto()
+  FMT_NUM_I64 = auto()
+  FMT_NUM_I8 = auto()
+  FMT_NUM_IU4 = auto()
+  FMT_NUM_IU8 = auto()
+  FMT_NUM_M64 = auto()
+  FMT_NUM_PK16_BF16 = auto()
+  FMT_NUM_PK16_BF8 = auto()
+  FMT_NUM_PK16_F16 = auto()
+  FMT_NUM_PK16_F32 = auto()
+  FMT_NUM_PK16_FP8 = auto()
+  FMT_NUM_PK16_I32 = auto()
+  FMT_NUM_PK16_I8 = auto()
+  FMT_NUM_PK2_B16 = auto()
+  FMT_NUM_PK2_B32 = auto()
+  FMT_NUM_PK2_B64 = auto()
+  FMT_NUM_PK2_BF16 = auto()
+  FMT_NUM_PK2_BF8 = auto()
+  FMT_NUM_PK2_F16 = auto()
+  FMT_NUM_PK2_F32 = auto()
+  FMT_NUM_PK2_FP4 = auto()
+  FMT_NUM_PK2_FP8 = auto()
+  FMT_NUM_PK2_I16 = auto()
+  FMT_NUM_PK2_I8 = auto()
+  FMT_NUM_PK2_U16 = auto()
+  FMT_NUM_PK2_U8 = auto()
+  FMT_NUM_PK32_BF16 = auto()
+  FMT_NUM_PK32_BF6 = auto()
+  FMT_NUM_PK32_BF8 = auto()
+  FMT_NUM_PK32_F16 = auto()
+  FMT_NUM_PK32_F32 = auto()
+  FMT_NUM_PK32_FP6 = auto()
+  FMT_NUM_PK32_FP8 = auto()
+  FMT_NUM_PK32_I32 = auto()
+  FMT_NUM_PK32_I8 = auto()
+  FMT_NUM_PK4_B8 = auto()
+  FMT_NUM_PK4_BF16 = auto()
+  FMT_NUM_PK4_BF8 = auto()
+  FMT_NUM_PK4_F16 = auto()
+  FMT_NUM_PK4_F32 = auto()
+  FMT_NUM_PK4_F64 = auto()
+  FMT_NUM_PK4_FP8 = auto()
+  FMT_NUM_PK4_I32 = auto()
+  FMT_NUM_PK4_I8 = auto()
+  FMT_NUM_PK4_IU8 = auto()
+  FMT_NUM_PK4_U8 = auto()
+  FMT_NUM_PK8_B32 = auto()
+  FMT_NUM_PK8_BF16 = auto()
+  FMT_NUM_PK8_BF8 = auto()
+  FMT_NUM_PK8_F16 = auto()
+  FMT_NUM_PK8_FP8 = auto()
+  FMT_NUM_PK8_I4 = auto()
+  FMT_NUM_PK8_I8 = auto()
+  FMT_NUM_PK8_IU4 = auto()
+  FMT_NUM_PK8_U4 = auto()
+  FMT_NUM_PK8_U8 = auto()
+  FMT_NUM_PK_F16 = auto()
+  FMT_NUM_PK_I16 = auto()
+  FMT_NUM_PK_I8 = auto()
+  FMT_NUM_PK_U16 = auto()
+  FMT_NUM_PK_U8 = auto()
+  FMT_NUM_U16 = auto()
+  FMT_NUM_U24 = auto()
+  FMT_NUM_U32 = auto()
+  FMT_NUM_U4 = auto()
+  FMT_NUM_U64 = auto()
+  FMT_NUM_U8 = auto()
+  FMT_RSRC = auto()
+  FMT_RSRC_SCALAR = auto()
+  FMT_RSRC_SCRATCH = auto()
+  FMT_RSRC_SCRATCH_BYTE = auto()
+  FMT_RSRC_SCRATCH_STRIDE = auto()
+  FMT_RSRC_TYPED = auto()
+  FMT_RSRC_TYPED_BYTE = auto()
+  FMT_RSRC_TYPED_SCRATCH = auto()
+  FMT_RSRC_TYPED_STRIDE = auto()
+  FMT_RSRC_VECTOR = auto()
+  FMT_RSRC_VECTOR_BYTE = auto()
+  FMT_RSRC_VECTOR_STRIDE = auto()
+  FMT_SAMP = auto()
+  FMT_WMMA_AB_16X16_BF16 = auto()
+  FMT_WMMA_AB_16X16_BF8 = auto()
+  FMT_WMMA_AB_16X16_F16 = auto()
+  FMT_WMMA_AB_16X16_FP8 = auto()
+  FMT_WMMA_AB_16X16_IU4 = auto()
+  FMT_WMMA_AB_16X16_IU8 = auto()
+  FMT_WMMA_AB_16X32_BF16 = auto()
+  FMT_WMMA_AB_16X32_BF8 = auto()
+  FMT_WMMA_AB_16X32_F16 = auto()
+  FMT_WMMA_AB_16X32_FP8 = auto()
+  FMT_WMMA_AB_16X32_IU4 = auto()
+  FMT_WMMA_AB_16X32_IU8 = auto()
+  FMT_WMMA_AB_16X64_IU4 = auto()
+  FMT_WMMA_AB_BF16 = auto()
+  FMT_WMMA_AB_F16 = auto()
+  FMT_WMMA_AB_IU4 = auto()
+  FMT_WMMA_AB_IU8 = auto()
+  FMT_WMMA_DC_16X16_BF16 = auto()
+  FMT_WMMA_DC_16X16_F16 = auto()
+  FMT_WMMA_DC_16X16_F32 = auto()
+  FMT_WMMA_DC_16X16_I32 = auto()
+  FMT_WMMA_DC_BF16 = auto()
+  FMT_WMMA_DC_F16 = auto()
+  FMT_WMMA_DC_F32 = auto()
+  FMT_WMMA_DC_I32 = auto()
+  FMT_WMMA_INDEX_SET = auto()
+
+FMT_BITS = {
+  Fmt.FMT_ANY: 1,
+  Fmt.FMT_BUF: 64,
+  Fmt.FMT_IMG: 256,
+  Fmt.FMT_IMG_BVH: 128,
+  Fmt.FMT_NUM_B1: 1,
+  Fmt.FMT_NUM_B1024: 1024,
+  Fmt.FMT_NUM_B128: 128,
+  Fmt.FMT_NUM_B16: 16,
+  Fmt.FMT_NUM_B256: 256,
+  Fmt.FMT_NUM_B32: 32,
+  Fmt.FMT_NUM_B512: 512,
+  Fmt.FMT_NUM_B64: 64,
+  Fmt.FMT_NUM_B8: 8,
+  Fmt.FMT_NUM_B96: 96,
+  Fmt.FMT_NUM_BF16: 16,
+  Fmt.FMT_NUM_BF6: 6,
+  Fmt.FMT_NUM_BF8: 8,
+  Fmt.FMT_NUM_F16: 16,
+  Fmt.FMT_NUM_F32: 32,
+  Fmt.FMT_NUM_F64: 64,
+  Fmt.FMT_NUM_FP4: 4,
+  Fmt.FMT_NUM_FP6: 6,
+  Fmt.FMT_NUM_FP8: 8,
+  Fmt.FMT_NUM_I16: 16,
+  Fmt.FMT_NUM_I24: 24,
+  Fmt.FMT_NUM_I32: 32,
+  Fmt.FMT_NUM_I4: 4,
+  Fmt.FMT_NUM_I64: 64,
+  Fmt.FMT_NUM_I8: 8,
+  Fmt.FMT_NUM_IU4: 4,
+  Fmt.FMT_NUM_IU8: 8,
+  Fmt.FMT_NUM_M64: 64,
+  Fmt.FMT_NUM_PK16_BF16: 256,
+  Fmt.FMT_NUM_PK16_BF8: 128,
+  Fmt.FMT_NUM_PK16_F16: 256,
+  Fmt.FMT_NUM_PK16_F32: 512,
+  Fmt.FMT_NUM_PK16_FP8: 128,
+  Fmt.FMT_NUM_PK16_I32: 512,
+  Fmt.FMT_NUM_PK16_I8: 128,
+  Fmt.FMT_NUM_PK2_B16: 32,
+  Fmt.FMT_NUM_PK2_B32: 64,
+  Fmt.FMT_NUM_PK2_B64: 128,
+  Fmt.FMT_NUM_PK2_BF16: 32,
+  Fmt.FMT_NUM_PK2_BF8: 16,
+  Fmt.FMT_NUM_PK2_F16: 32,
+  Fmt.FMT_NUM_PK2_F32: 64,
+  Fmt.FMT_NUM_PK2_FP4: 8,
+  Fmt.FMT_NUM_PK2_FP8: 16,
+  Fmt.FMT_NUM_PK2_I16: 32,
+  Fmt.FMT_NUM_PK2_I8: 16,
+  Fmt.FMT_NUM_PK2_U16: 32,
+  Fmt.FMT_NUM_PK2_U8: 16,
+  Fmt.FMT_NUM_PK32_BF16: 512,
+  Fmt.FMT_NUM_PK32_BF6: 192,
+  Fmt.FMT_NUM_PK32_BF8: 256,
+  Fmt.FMT_NUM_PK32_F16: 512,
+  Fmt.FMT_NUM_PK32_F32: 1024,
+  Fmt.FMT_NUM_PK32_FP6: 192,
+  Fmt.FMT_NUM_PK32_FP8: 256,
+  Fmt.FMT_NUM_PK32_I32: 1024,
+  Fmt.FMT_NUM_PK32_I8: 256,
+  Fmt.FMT_NUM_PK4_B8: 32,
+  Fmt.FMT_NUM_PK4_BF16: 64,
+  Fmt.FMT_NUM_PK4_BF8: 32,
+  Fmt.FMT_NUM_PK4_F16: 64,
+  Fmt.FMT_NUM_PK4_F32: 128,
+  Fmt.FMT_NUM_PK4_F64: 256,
+  Fmt.FMT_NUM_PK4_FP8: 32,
+  Fmt.FMT_NUM_PK4_I32: 128,
+  Fmt.FMT_NUM_PK4_I8: 32,
+  Fmt.FMT_NUM_PK4_IU8: 32,
+  Fmt.FMT_NUM_PK4_U8: 32,
+  Fmt.FMT_NUM_PK8_B32: 256,
+  Fmt.FMT_NUM_PK8_BF16: 128,
+  Fmt.FMT_NUM_PK8_BF8: 64,
+  Fmt.FMT_NUM_PK8_F16: 128,
+  Fmt.FMT_NUM_PK8_FP8: 64,
+  Fmt.FMT_NUM_PK8_I4: 32,
+  Fmt.FMT_NUM_PK8_I8: 64,
+  Fmt.FMT_NUM_PK8_IU4: 32,
+  Fmt.FMT_NUM_PK8_U4: 32,
+  Fmt.FMT_NUM_PK8_U8: 64,
+  Fmt.FMT_NUM_PK_F16: 32,
+  Fmt.FMT_NUM_PK_I16: 32,
+  Fmt.FMT_NUM_PK_I8: 32,
+  Fmt.FMT_NUM_PK_U16: 32,
+  Fmt.FMT_NUM_PK_U8: 32,
+  Fmt.FMT_NUM_U16: 16,
+  Fmt.FMT_NUM_U24: 24,
+  Fmt.FMT_NUM_U32: 32,
+  Fmt.FMT_NUM_U4: 4,
+  Fmt.FMT_NUM_U64: 64,
+  Fmt.FMT_NUM_U8: 8,
+  Fmt.FMT_RSRC: 128,
+  Fmt.FMT_RSRC_SCALAR: 128,
+  Fmt.FMT_RSRC_SCRATCH: 128,
+  Fmt.FMT_RSRC_SCRATCH_BYTE: 128,
+  Fmt.FMT_RSRC_SCRATCH_STRIDE: 128,
+  Fmt.FMT_RSRC_TYPED: 128,
+  Fmt.FMT_RSRC_TYPED_BYTE: 128,
+  Fmt.FMT_RSRC_TYPED_SCRATCH: 128,
+  Fmt.FMT_RSRC_TYPED_STRIDE: 128,
+  Fmt.FMT_RSRC_VECTOR: 128,
+  Fmt.FMT_RSRC_VECTOR_BYTE: 128,
+  Fmt.FMT_RSRC_VECTOR_STRIDE: 128,
+  Fmt.FMT_SAMP: 128,
+  Fmt.FMT_WMMA_AB_16X16_BF16: 128,
+  Fmt.FMT_WMMA_AB_16X16_BF8: 64,
+  Fmt.FMT_WMMA_AB_16X16_F16: 128,
+  Fmt.FMT_WMMA_AB_16X16_FP8: 64,
+  Fmt.FMT_WMMA_AB_16X16_IU4: 32,
+  Fmt.FMT_WMMA_AB_16X16_IU8: 64,
+  Fmt.FMT_WMMA_AB_16X32_BF16: 256,
+  Fmt.FMT_WMMA_AB_16X32_BF8: 128,
+  Fmt.FMT_WMMA_AB_16X32_F16: 256,
+  Fmt.FMT_WMMA_AB_16X32_FP8: 128,
+  Fmt.FMT_WMMA_AB_16X32_IU4: 64,
+  Fmt.FMT_WMMA_AB_16X32_IU8: 128,
+  Fmt.FMT_WMMA_AB_16X64_IU4: 128,
+  Fmt.FMT_WMMA_AB_BF16: 256,
+  Fmt.FMT_WMMA_AB_F16: 256,
+  Fmt.FMT_WMMA_AB_IU4: 64,
+  Fmt.FMT_WMMA_AB_IU8: 128,
+  Fmt.FMT_WMMA_DC_16X16_BF16: 128,
+  Fmt.FMT_WMMA_DC_16X16_F16: 128,
+  Fmt.FMT_WMMA_DC_16X16_F32: 256,
+  Fmt.FMT_WMMA_DC_16X16_I32: 256,
+  Fmt.FMT_WMMA_DC_BF16: 256,
+  Fmt.FMT_WMMA_DC_F16: 256,
+  Fmt.FMT_WMMA_DC_F32: 256,
+  Fmt.FMT_WMMA_DC_I32: 256,
+  Fmt.FMT_WMMA_INDEX_SET: 32,
+}
+
+class OpType(Enum):
+  OPR_ACCVGPR = auto()
+  OPR_ATTR = auto()
+  OPR_CLAUSE = auto()
+  OPR_DELAY = auto()
+  OPR_EXEC = auto()
+  OPR_HWREG = auto()
+  OPR_LABEL = auto()
+  OPR_SDST = auto()
+  OPR_SDST_NULL = auto()
+  OPR_SENDMSG = auto()
+  OPR_SENDMSG_RTN = auto()
+  OPR_SIMM16 = auto()
+  OPR_SIMM24 = auto()
+  OPR_SIMM4 = auto()
+  OPR_SIMM5 = auto()
+  OPR_SIMM8 = auto()
+  OPR_SLEEP = auto()
+  OPR_SMEM_OFFSET = auto()
+  OPR_SMEM_OFFSET_NOK = auto()
+  OPR_SRC = auto()
+  OPR_SRC_ACCVGPR = auto()
+  OPR_SRC_NOLDS = auto()
+  OPR_SRC_NOLIT = auto()
+  OPR_SRC_SIMPLE = auto()
+  OPR_SRC_VGPR = auto()
+  OPR_SRC_VGPR_OR_ACCVGPR = auto()
+  OPR_SRC_VGPR_OR_ACCVGPR_OR_CONST = auto()
+  OPR_SRC_VGPR_OR_INLINE = auto()
+  OPR_SREG = auto()
+  OPR_SREG_LITERAL = auto()
+  OPR_SREG_M0 = auto()
+  OPR_SREG_M0_INL = auto()
+  OPR_SREG_NOVCC = auto()
+  OPR_SSRC = auto()
+  OPR_SSRC_BARRIER_ID = auto()
+  OPR_SSRC_LANESEL = auto()
+  OPR_SSRC_NOLIT = auto()
+  OPR_TGT = auto()
+  OPR_VERSION = auto()
+  OPR_VGPR = auto()
+  OPR_VGPR_OR_ACCVGPR = auto()
+  OPR_VGPR_OR_LDS = auto()
+  OPR_WAITCNT = auto()
+  OPR_WAITCNT_DEPCTR = auto()
+  OPR_WAIT_ALU = auto()
+  OPR_WAIT_EVENT = auto()
+  OPR_WAIT_MEM_DS = auto()

extra/assembly/amd/disasm.py

@@ -0,0 +1,907 @@
+# RDNA3/RDNA4/CDNA disassembler
+from __future__ import annotations
+import re, struct
+from typing import Callable
+from extra.assembly.amd.dsl import Inst, Reg
+
+# Special register mappings for disassembly
+SPECIAL_GPRS = {106: 'vcc_lo', 107: 'vcc_hi', 124: 'null', 125: 'm0', 126: 'exec_lo', 127: 'exec_hi',
+                128: '0', 240: '0.5', 241: '-0.5', 242: '1.0', 243: '-1.0', 244: '2.0', 245: '-2.0', 246: '4.0', 247: '-4.0', 248: '0x3e22f983', 253: 'scc'}
+SPECIAL_GPRS_CDNA = {106: 'vcc_lo', 107: 'vcc_hi', 124: 'm0', 126: 'exec_lo', 127: 'exec_hi',
+                     128: '0', 240: '0.5', 241: '-0.5', 242: '1.0', 243: '-1.0', 244: '2.0', 245: '-2.0', 246: '4.0', 247: '-4.0', 248: '0x3e22f983', 253: 'scc',
+                     102: 'flat_scratch_lo', 103: 'flat_scratch_hi', 104: 'xnack_mask_lo', 105: 'xnack_mask_hi',
+                     251: 'src_vccz', 252: 'src_execz'}
+SPECIAL_PAIRS = {106: 'vcc', 126: 'exec'}
+SPECIAL_PAIRS_CDNA = {106: 'vcc', 126: 'exec', 102: 'flat_scratch', 104: 'xnack_mask'}
+
+def decode_src(v, cdna: bool = False) -> str:
+  """Decode a source operand encoding to its string representation."""
+  v = _unwrap(v)
+  gprs = SPECIAL_GPRS_CDNA if cdna else SPECIAL_GPRS
+  if v in gprs: return gprs[v]
+  if v < 106: return f's{v}'
+  if 108 <= v < 124: return f'ttmp{v - 108}'
+  if 129 <= v <= 192: return str(v - 128)  # positive integers 1-64
+  if 193 <= v <= 208: return str(-(v - 192))  # negative integers -1 to -16
+  if v >= 256: return f'v{v - 256}'
+  return f's{v}'
+
+def _unwrap(v) -> int:
+  """Unwrap Reg to int offset, or return int as-is."""
+  return v.offset if isinstance(v, Reg) else v
+
+def _vi(v) -> int:
+  """Get VGPR index from Reg or int (for v[N] fields that encode as 256+N)."""
+  off = _unwrap(v)
+  return off - 256 if off >= 256 else off
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# LITERAL FORMATTING
+# ═══════════════════════════════════════════════════════════════════════════════
+
+_FLOAT_DEC = {240: 0.5, 241: -0.5, 242: 1.0, 243: -1.0, 244: 2.0, 245: -2.0, 246: 4.0, 247: -4.0}
+
+def _lit(inst, v, neg=0, cdna=None) -> str:
+  """Format literal/inline constant value."""
+  if cdna is None: cdna = _is_cdna(inst)
+  v = _unwrap(v)
+  if v == 255:
+    lit = inst._literal
+    if lit is None: return "0"
+    s = f"0x{lit:x}"
+  elif v in _FLOAT_DEC: s = str(_FLOAT_DEC[v])
+  elif 128 <= v <= 192: s = str(v - 128)
+  elif 193 <= v <= 208: s = str(-(v - 192))
+  elif v < 128: s = decode_src(v, cdna)
+  elif v >= 256: s = f"v{v - 256}"
+  else: s = decode_src(v, cdna)
+  return f"-{s}" if neg else s
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# INSTRUCTION METADATA - fallback functions when inst.num_srcs()/inst.operands unavailable
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def _num_srcs(inst) -> int:
+  """Fallback: get number of source operands from instruction name."""
+  name = getattr(inst, 'op_name', '') or ''
+  n = name.upper()
+  # FMAC/MAC ops are 2-source (dst is implicit accumulator), but FMA/MAD ops are 3-source
+  if 'FMAC' in n or 'V_MAC_' in n: return 2
+  if any(x in n for x in ('FMA', 'MAD', 'CNDMASK', 'BFE', 'BFI', 'LERP', 'MED3', 'SAD', 'DIV_FMAS', 'DIV_FIXUP', 'DIV_SCALE', 'CUBE')): return 3
+  # PERMLANE_VAR ops are 2-source, but PERMLANE (non-VAR) are 3-source
+  if 'PERMLANE' in n and '_VAR' not in n: return 3
+  if any(x in n for x in ('_ADD3', '_LSHL_ADD', '_ADD_LSHL', '_LSHL_OR', '_AND_OR', 'OR3_B32', 'AND_OR_B32', 'ALIGNBIT', 'ALIGNBYTE', 'V_PERM_', 'XOR3', 'XAD', 'MULLIT', 'MINMAX', 'MAXMIN', 'MINIMUMMAXIMUM', 'MAXIMUMMINIMUM', 'MINIMUM3', 'MAXIMUM3', 'MIN3', 'MAX3', 'DOT2', 'CVT_PK_U8_F32', 'DOT4', 'DOT8', 'WMMA', 'SWMMAC')): return 3
+  return 2
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# IMPORTS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+from extra.assembly.amd.autogen.rdna3.ins import (VOP1, VOP1_SDST, VOP1_SDST_LIT, VOP1_LIT, VOP2, VOP2_LIT, VOP3, VOP3_SDST, VOP3_SDST_LIT,
+  VOP3_LIT, VOP3SD, VOP3SD_LIT, VOP3P, VOP3P_LIT, VOPC, VOPC_LIT, VOPD, VOPD_LIT, VINTERP, SOP1, SOP1_LIT, SOP2, SOP2_LIT, SOPC, SOPC_LIT,
+  SOPK, SOPK_LIT, SOPP, SMEM, DS, FLAT, GLOBAL, SCRATCH, VOP2Op, VOPDOp, SOPPOp, HWREG, MSG)
+from extra.assembly.amd.autogen.rdna4.ins import (VOP1 as R4_VOP1, VOP1_SDST as R4_VOP1_SDST, VOP1_SDST_LIT as R4_VOP1_SDST_LIT, VOP1_LIT as R4_VOP1_LIT,
+  VOP2 as R4_VOP2, VOP2_LIT as R4_VOP2_LIT, VOP3 as R4_VOP3, VOP3_SDST as R4_VOP3_SDST, VOP3_SDST_LIT as R4_VOP3_SDST_LIT, VOP3_LIT as R4_VOP3_LIT,
+  VOP3SD as R4_VOP3SD, VOP3SD_LIT as R4_VOP3SD_LIT, VOP3P as R4_VOP3P, VOP3P_LIT as R4_VOP3P_LIT, VOPC as R4_VOPC, VOPC_LIT as R4_VOPC_LIT,
+  VOPD as R4_VOPD, VOPD_LIT as R4_VOPD_LIT, VINTERP as R4_VINTERP, SOP1 as R4_SOP1, SOP1_LIT as R4_SOP1_LIT, SOP2 as R4_SOP2, SOP2_LIT as R4_SOP2_LIT,
+  SOPC as R4_SOPC, SOPC_LIT as R4_SOPC_LIT, SOPK as R4_SOPK, SOPK_LIT as R4_SOPK_LIT, SOPP as R4_SOPP, SMEM as R4_SMEM, DS as R4_DS,
+  VOPDOp as R4_VOPDOp, HWREG as HWREG_RDNA4, VFLAT as R4_FLAT, VGLOBAL as R4_GLOBAL, VSCRATCH as R4_SCRATCH)
+from extra.assembly.amd.autogen.cdna.ins import FLAT as C_FLAT, HWREG as HWREG_CDNA
+
+def _is_cdna(inst: Inst) -> bool: return 'cdna' in inst.__class__.__module__
+def _is_r4(inst: Inst) -> bool: return 'rdna4' in inst.__class__.__module__
+
+# CDNA opcode name aliases for disasm (new name -> old name expected by tests)
+_CDNA_DISASM_ALIASES = {'v_fmac_f64': 'v_mul_legacy_f32', 'v_dot2c_f32_bf16': 'v_mac_f32', 'v_fmamk_f32': 'v_madmk_f32', 'v_fmaak_f32': 'v_madak_f32'}
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# HELPERS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def _reg(p: str, b: int, n: int = 1) -> str: return f"{p}{_unwrap(b)}" if n == 1 else f"{p}[{_unwrap(b)}:{_unwrap(b)+n-1}]"
+def _sreg(b: int, n: int = 1) -> str: return _reg("s", _unwrap(b), n)
+def _vreg(b: int, n: int = 1) -> str: b = _unwrap(b); return _reg("v", b - 256 if b >= 256 else b, n)
+def _areg(b: int, n: int = 1) -> str: b = _unwrap(b); return _reg("a", b - 256 if b >= 256 else b, n)  # accumulator registers for GFX90a
+def _ttmp(b, n: int = 1) -> str | None: b = _unwrap(b); return _reg("ttmp", b - 108, n) if 108 <= b <= 123 else None
+
+def _fmt_sdst(v, n: int = 1, cdna: bool = False) -> str:
+  v = _unwrap(v)
+  if t := _ttmp(v, n): return t
+  pairs = SPECIAL_PAIRS_CDNA if cdna else SPECIAL_PAIRS
+  gprs = SPECIAL_GPRS_CDNA if cdna else SPECIAL_GPRS
+  if n > 1: return pairs.get(v) or gprs.get(v) or _sreg(v, n)  # also check gprs for null/m0
+  return gprs.get(v, f"s{v}")
+
+def _fmt_src(v, n: int = 1, cdna: bool = False) -> str:
+  v = _unwrap(v)
+  if v == 253: return "src_scc"  # SCC as source operand
+  if n == 1: return decode_src(v, cdna)
+  if v >= 256: return _vreg(v, n)
+  if v <= 101: return _sreg(v, n)  # s0-s101 can be pairs, but 102+ are special on CDNA
+  pairs = SPECIAL_PAIRS_CDNA if cdna else SPECIAL_PAIRS
+  if n == 2 and v in pairs: return pairs[v]
+  if v <= 105: return _sreg(v, n)  # s102-s105 regular pairs for RDNA
+  if t := _ttmp(v, n): return t
+  return decode_src(v, cdna)
+
+def _fmt_v16(v, base: int = 256, hi_thresh: int = 384) -> str:
+  v = _unwrap(v)
+  return f"v{(v - base) & 0x7f}.{'h' if v >= hi_thresh else 'l'}"
+
+def _has(op: str, *subs) -> bool: return any(s in op for s in subs)
+def _omod(v: int) -> str: return {1: " mul:2", 2: " mul:4", 3: " div:2"}.get(v, "")
+def _src16(inst, v: int) -> str: v = _unwrap(v); return _fmt_v16(v) if v >= 256 else _lit(inst, v)  # format 16-bit src: vgpr.h/l or literal
+def _mods(*pairs) -> str: return " ".join(m for c, m in pairs if c)
+def _fmt_bits(label: str, val: int, count: int) -> str: return f"{label}:[{','.join(str((val >> i) & 1) for i in range(count))}]"
+
+def _vop3_src(inst, v: int, neg: int, abs_: int, hi: int, n: int, f16: bool) -> str:
+  """Format VOP3 source operand with modifiers."""
+  v = _unwrap(v)
+  if v == 255: s = _lit(inst, v)  # literal constant takes priority
+  elif n > 1: s = _fmt_src(v, n)
+  elif f16 and v >= 256: s = f"v{v - 256}.h" if hi else f"v{v - 256}.l"
+  elif v == 253: s = "src_scc"  # VOP3 sources use src_scc not scc
+  else: s = _lit(inst, v)
+  if abs_: s = f"|{s}|"
+  return f"-{s}" if neg else s
+
+def _opsel_str(opsel: int, n: int, need: bool, is16_d: bool) -> str:
+  """Format op_sel modifier string."""
+  if not need: return ""
+  dst_hi = (opsel >> 3) & 1
+  if n == 1: return f" op_sel:[{opsel & 1},{dst_hi}]"
+  # Use 4-element format if bit 2 is set (src2 selection used) or if 3+ sources
+  if n == 2 and not ((opsel >> 2) & 1): return f" op_sel:[{opsel & 1},{(opsel >> 1) & 1},{dst_hi}]"
+  return f" op_sel:[{opsel & 1},{(opsel >> 1) & 1},{(opsel >> 2) & 1},{dst_hi}]"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# DISASSEMBLER
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def _disasm_vop1(inst: VOP1) -> str:
+  name, cdna = inst.op_name.lower() or f'vop1_op_{inst.op}', _is_cdna(inst)
+  name = name.replace('_e32', '')  # Strip _e32 suffix
+  if any(x in name for x in ('v_nop', 'v_pipeflush', 'v_clrexcp')): return name  # no operands
+  if 'readfirstlane' in name:
+    src = inst.src0.fmt() if inst.src0.offset >= 256 else decode_src(inst.src0.offset, cdna)
+    vdst_off = inst.vdst.offset - 256 if inst.vdst.offset >= 256 else inst.vdst.offset
+    return f"{name} {_fmt_sdst(vdst_off, 1, cdna)}, {src}"
+  bits = inst.canonical_op_bits
+  is16_dst, is16_src = not cdna and bits['d'] == 16, not cdna and bits['s0'] == 16
+  # Format dst
+  if is16_dst: dst = _fmt_v16(inst.vdst)
+  else: dst = inst.vdst.fmt()
+  # Format src
+  if inst.src0.offset == 255: src = _lit(inst, inst.src0)
+  elif is16_src and inst.src0.offset >= 256: src = _fmt_v16(inst.src0)
+  elif inst.src0.sz > 1: src = _fmt_src(inst.src0, inst.src0.sz, cdna)
+  else: src = _lit(inst, inst.src0)
+  return f"{name} {dst}, {src}"
+
+_VOP2_CARRY_OUT = {'v_add_co_u32', 'v_sub_co_u32', 'v_subrev_co_u32'}  # carry out only
+_VOP2_CARRY_INOUT = {'v_addc_co_u32', 'v_subb_co_u32', 'v_subbrev_co_u32'}  # carry in and out (CDNA)
+_VOP2_CARRY_INOUT_RDNA = {'v_add_co_ci_u32', 'v_sub_co_ci_u32', 'v_subrev_co_ci_u32'}  # carry in and out (RDNA)
+def _disasm_vop2(inst: VOP2) -> str:
+  name, cdna = inst.op_name.lower(), _is_cdna(inst)
+  if cdna: name = _CDNA_DISASM_ALIASES.get(name, name)  # apply CDNA aliases
+  suf = "" if cdna or name.endswith('_e32') or (not cdna and inst.op == VOP2Op.V_DOT2ACC_F32_F16_E32) else "_e32"
+  lit = inst._literal
+  is16 = not cdna and inst.canonical_op_bits['d'] == 16
+  # fmaak/madak: dst = src0 * vsrc1 + K, fmamk/madmk: dst = src0 * K + vsrc1
+  if 'fmaak' in name or 'madak' in name or (not cdna and inst.op in (VOP2Op.V_FMAAK_F32_E32, VOP2Op.V_FMAAK_F16_E32)):
+    if lit is None: return f"op_{inst.op.value if hasattr(inst.op, 'value') else inst.op}"
+    if is16: return f"{name}{suf} {_fmt_v16(inst.vdst)}, {_src16(inst, inst.src0)}, {_fmt_v16(inst.vsrc1)}, 0x{lit:x}"
+    return f"{name}{suf} {inst.vdst.fmt()}, {_lit(inst, inst.src0)}, {inst.vsrc1.fmt()}, 0x{lit:x}"
+  if 'fmamk' in name or 'madmk' in name or (not cdna and inst.op in (VOP2Op.V_FMAMK_F32_E32, VOP2Op.V_FMAMK_F16_E32)):
+    if lit is None: return f"op_{inst.op.value if hasattr(inst.op, 'value') else inst.op}"
+    if is16: return f"{name}{suf} {_fmt_v16(inst.vdst)}, {_src16(inst, inst.src0)}, 0x{lit:x}, {_fmt_v16(inst.vsrc1)}"
+    return f"{name}{suf} {inst.vdst.fmt()}, {_lit(inst, inst.src0)}, 0x{lit:x}, {inst.vsrc1.fmt()}"
+  if is16: return f"{name}{suf} {_fmt_v16(inst.vdst)}, {_src16(inst, inst.src0)}, {_fmt_v16(inst.vsrc1)}"
+  vcc = "vcc" if cdna else "vcc_lo"
+  basename = name.replace('_e32', '')
+  if cdna and basename in _VOP2_CARRY_OUT: return f"{name}{suf} {inst.vdst.fmt()}, {vcc}, {_lit(inst, inst.src0)}, {inst.vsrc1.fmt()}"
+  if cdna and basename in _VOP2_CARRY_INOUT: return f"{name}{suf} {inst.vdst.fmt()}, {vcc}, {_lit(inst, inst.src0)}, {inst.vsrc1.fmt()}, {vcc}"
+  if not cdna and basename in _VOP2_CARRY_INOUT_RDNA: return f"{name}{suf} {inst.vdst.fmt()}, {vcc}, {_lit(inst, inst.src0)}, {inst.vsrc1.fmt()}, {vcc}"
+  sn0 = inst.canonical_op_regs.get('s0', 1)
+  if inst.vdst.sz > 1 or sn0 > 1 or inst.vsrc1.sz > 1:
+    src0 = _lit(inst, inst.src0) if inst.src0.offset == 255 else _fmt_src(inst.src0, sn0, cdna)
+    return f"{name.replace('_e32', '')} {inst.vdst.fmt()}, {src0}, {inst.vsrc1.fmt()}"
+  return f"{name}{suf} {inst.vdst.fmt()}, {_lit(inst, inst.src0)}, {inst.vsrc1.fmt()}" + (f", {vcc}" if name == 'v_cndmask_b32' else "")
+
+def _disasm_vopc(inst: VOPC) -> str:
+  name, cdna = inst.op_name.lower(), _is_cdna(inst)
+  bits = inst.canonical_op_bits
+  is16 = bits['s0'] == 16
+  if cdna:
+    s0 = _lit(inst, inst.src0) if inst.src0.offset == 255 else _fmt_src(inst.src0, inst.src0.sz, cdna)
+    return f"{name} vcc, {s0}, {inst.vsrc1.fmt()}"  # CDNA VOPC always outputs vcc
+  # RDNA: v_cmpx_* writes to exec (no vcc), v_cmp_* writes to vcc_lo
+  has_vcc = 'cmpx' not in name
+  s0 = _lit(inst, inst.src0) if inst.src0.offset == 255 else inst.src0.fmt() if inst.src0.sz > 1 else _src16(inst, inst.src0.offset) if is16 else _lit(inst, inst.src0)
+  s1 = inst.vsrc1.fmt() if inst.vsrc1.sz > 1 else _fmt_v16(inst.vsrc1) if is16 else inst.vsrc1.fmt()
+  suf = "" if name.endswith('_e32') else "_e32"
+  return f"{name}{suf} vcc_lo, {s0}, {s1}" if has_vcc else f"{name}{suf} {s0}, {s1}"
+
+NO_ARG_SOPP = {SOPPOp.S_BARRIER, SOPPOp.S_WAKEUP, SOPPOp.S_ICACHE_INV,
+               SOPPOp.S_WAIT_IDLE, SOPPOp.S_ENDPGM_SAVED, SOPPOp.S_CODE_END, SOPPOp.S_ENDPGM_ORDERED_PS_DONE, SOPPOp.S_TTRACEDATA}
+
+def _disasm_sopp(inst: SOPP) -> str:
+  name, cdna = inst.op_name.lower(), _is_cdna(inst)
+  is_rdna4 = _is_r4(inst)
+  # Ops that have no argument when simm16 == 0
+  no_arg_zero = {'s_barrier', 's_wakeup', 's_icache_inv', 's_ttracedata', 's_wait_idle', 's_endpgm_saved',
+                 's_endpgm_ordered_ps_done', 's_code_end'}
+  if name in no_arg_zero: return name if inst.simm16 == 0 else f"{name} {inst.simm16}"
+  if name == 's_endpgm': return name if inst.simm16 == 0 else f"{name} {inst.simm16}"
+  if cdna:
+    if name == 's_waitcnt':
+      # GFX9 format: vmcnt[3:0]=bits[3:0], vmcnt[5:4]=bits[15:14], expcnt=bits[6:4], lgkmcnt=bits[11:8] (4 bits, max 15)
+      vm_lo, exp, lgkm, vm_hi = inst.simm16 & 0xf, (inst.simm16 >> 4) & 0x7, (inst.simm16 >> 8) & 0xf, (inst.simm16 >> 14) & 0x3
+      vm = vm_lo | (vm_hi << 4)
+      p = [f"vmcnt({vm})" if vm != 0x3f else "", f"expcnt({exp})" if exp != 7 else "", f"lgkmcnt({lgkm})" if lgkm != 0xf else ""]
+      return f"s_waitcnt {' '.join(x for x in p if x) or '0'}"
+    if name.startswith(('s_cbranch', 's_branch')): return f"{name} {inst.simm16}"
+    if name == 's_set_gpr_idx_mode':
+      flags = [n for i, n in enumerate(['SRC0', 'SRC1', 'SRC2', 'DST']) if inst.simm16 & (1 << i)]
+      return f"{name} gpr_idx({','.join(flags)})"
+    return f"{name} 0x{inst.simm16:x}" if inst.simm16 else name
+  # RDNA (use name-based checks instead of enum-based for cross-arch compatibility)
+  if name == 's_waitcnt':
+    if is_rdna4:
+      return f"{name} {inst.simm16}" if inst.simm16 else f"{name} 0"
+    vm, exp, lgkm = (inst.simm16 >> 10) & 0x3f, inst.simm16 & 0xf, (inst.simm16 >> 4) & 0x3f
+    p = [f"vmcnt({vm})" if vm != 0x3f else "", f"expcnt({exp})" if exp != 7 else "", f"lgkmcnt({lgkm})" if lgkm != 0x3f else ""]
+    return f"s_waitcnt {' '.join(x for x in p if x) or '0'}"
+  if name == 's_delay_alu':
+    deps = ['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']
+    skips = ['SAME','NEXT','SKIP_1','SKIP_2','SKIP_3','SKIP_4']
+    id0, skip, id1 = inst.simm16 & 0xf, (inst.simm16 >> 4) & 0x7, (inst.simm16 >> 7) & 0xf
+    dep = lambda v: deps[v-1] if 0 < v <= len(deps) else str(v)
+    p = [f"instid0({dep(id0)})" if id0 else "", f"instskip({skips[skip]})" if skip else "", f"instid1({dep(id1)})" if id1 else ""]
+    return f"s_delay_alu {' | '.join(x for x in p if x) or '0'}"
+  if name.startswith(('s_cbranch', 's_branch')): return f"{name} {inst.simm16}"
+  return f"{name} 0x{inst.simm16:x}"
+
+def _disasm_smem(inst: SMEM) -> str:
+  name, cdna = inst.op_name.lower(), _is_cdna(inst)
+  if name in ('s_gl1_inv', 's_dcache_inv', 's_dcache_inv_vol', 's_dcache_wb', 's_dcache_wb_vol', 's_icache_inv'): return name
+  soe, imm = getattr(inst, 'soe', 0) or getattr(inst, 'soffset_en', 0), getattr(inst, 'imm', 1)
+  is_rdna4 = _is_r4(inst)
+  offset = inst.ioffset if is_rdna4 else getattr(inst, 'offset', 0)
+  if cdna:
+    if soe and imm: off_s = f"{decode_src(inst.soffset, cdna)} offset:0x{offset:x}"
+    elif imm: off_s = f"0x{offset:x}"
+    elif offset < 256: off_s = decode_src(offset, cdna)
+    else: off_s = decode_src(inst.soffset, cdna)
+  elif offset and inst.soffset != 124: off_s = f"{decode_src(inst.soffset, cdna)} offset:0x{offset:x}"
+  elif offset: off_s = f"0x{offset:x}"
+  else: off_s = decode_src(inst.soffset, cdna)
+  is_buffer = 'buffer' in name or 's_atc_probe_buffer' == name
+  sbase_idx, sbase_count = _unwrap(inst.sbase), 4 if is_buffer else 2
+  sbase_str = _fmt_src(sbase_idx, sbase_count, cdna) if sbase_count == 2 else _sreg(sbase_idx, sbase_count) if sbase_idx <= 105 else _reg("ttmp", sbase_idx - 108, sbase_count)
+  if name in ('s_atc_probe', 's_atc_probe_buffer'): return f"{name} {_unwrap(inst.sdata)}, {sbase_str}, {off_s}"
+  if 'prefetch' in name:
+    off = getattr(inst, 'ioffset', getattr(inst, 'offset', 0))
+    if off >= 0x800000: off = off - 0x1000000
+    off_s = f"0x{off:x}" if off > 255 else str(off)
+    soff_s = decode_src(inst.soffset, cdna) if inst.soffset != 124 else ("m0" if cdna else "null")
+    if 'pc_rel' in name: return f"{name} {off_s}, {soff_s}, {_unwrap(inst.sdata)}"
+    return f"{name} {sbase_str}, {off_s}, {soff_s}, {_unwrap(inst.sdata)}"
+  # Use get_field_bits for register count
+  dst_n = inst.canonical_op_regs.get('d', 1)
+  th, scope = getattr(inst, 'th', 0), getattr(inst, 'scope', 0)
+  if is_rdna4:  # RDNA4 uses th/scope instead of glc/dlc
+    th_names = ['TH_LOAD_RT', 'TH_LOAD_NT', 'TH_LOAD_HT', 'TH_LOAD_LU']
+    scope_names = ['SCOPE_CU', 'SCOPE_SE', 'SCOPE_DEV', 'SCOPE_SYS']
+    mods = (f" th:{th_names[th]}" if th else "") + (f" scope:{scope_names[scope]}" if scope else "")
+    return f"{name} {_fmt_sdst(inst.sdata, dst_n, cdna)}, {sbase_str}, {off_s}{mods}"
+  if th or scope:
+    th_names = ['TH_LOAD_RT', 'TH_LOAD_NT', 'TH_LOAD_HT', 'TH_LOAD_LU']
+    scope_names = ['SCOPE_CU', 'SCOPE_SE', 'SCOPE_DEV', 'SCOPE_SYS']
+    mods = (f" th:{th_names[th]}" if th else "") + (f" scope:{scope_names[scope]}" if scope else "")
+    return f"{name} {_fmt_sdst(inst.sdata, dst_n, cdna)}, {sbase_str}, {off_s}{mods}"
+  if 'discard' in name: return f"{name} {sbase_str}, {off_s}" + _mods((inst.glc, " glc"), (getattr(inst, 'dlc', 0), " dlc"))
+  if name in ('s_memrealtime', 's_memtime'): return f"{name} {_fmt_sdst(inst.sdata, dst_n, cdna)}"
+  return f"{name} {_fmt_sdst(inst.sdata, dst_n, cdna)}, {sbase_str}, {off_s}" + _mods((inst.glc, " glc"), (getattr(inst, 'dlc', 0), " dlc"))
+
+def _disasm_flat(inst: FLAT) -> str:
+  name, cdna, r4 = inst.op_name.lower(), _is_cdna(inst), _is_r4(inst)
+  acc = getattr(inst, 'acc', 0)
+  reg_fn = _areg if acc else _vreg
+  if r4: seg = 'flat' if (cls_name:=inst.__class__.__name__) == 'VFLAT' else ('global' if cls_name == 'VGLOBAL' else 'scratch')
+  else: seg = ['flat', 'scratch', 'global'][inst.seg] if inst.seg < 3 else 'flat'
+  instr = f"{seg}_{name.split('_', 1)[1] if '_' in name else name}"
+  # Global/scratch uses 13-bit signed offset
+  offset = inst.ioffset if r4 else inst.offset
+  if seg != 'flat':
+    if cdna:
+      # CDNA: bit 12 is sign bit but not in offset field
+      raw = int.from_bytes(inst.to_bytes(), 'little')
+      off_val = offset | ((raw >> 12) & 1) << 12  # get bit 12
+    else:
+      off_val = offset
+    off_val = off_val if off_val < 4096 else off_val - 8192  # sign extend 13-bit
+  else:
+    off_val = offset
+  # Use get_field_bits: data for stores/atomics, d for loads
+  regs = inst.canonical_op_regs
+  w = regs.get('data', regs.get('d', 1)) if 'store' in name or 'atomic' in name else regs.get('d', 1)
+  off_s = f" offset:{off_val}" if off_val else ""
+  if cdna: mods = f"{off_s}{' sc0' if inst.sc0 else ''}{' nt' if inst.nt else ''}{' sc1' if getattr(inst, 'sc1', 0) else ''}"
+  elif r4: mods = f"{off_s}{' scope' if inst.scope else ''}{' th' if inst.th else ''}"
+  else: mods = f"{off_s}{' glc' if inst.glc else ''}{' slc' if inst.slc else ''}{' dlc' if inst.dlc else ''}"
+  if seg == 'flat': saddr_s = ""
+  elif _unwrap(inst.saddr) in (0x7F, 124): saddr_s = ", off"
+  elif seg == 'scratch': saddr_s = f", {decode_src(inst.saddr, cdna)}"
+  elif _unwrap(inst.saddr) in (SPECIAL_PAIRS_CDNA if cdna else SPECIAL_PAIRS): saddr_s = f", {(SPECIAL_PAIRS_CDNA if cdna else SPECIAL_PAIRS)[_unwrap(inst.saddr)]}"
+  elif t := _ttmp(inst.saddr, 2): saddr_s = f", {t}"
+  else: saddr_s = f", {_sreg(inst.saddr, 2) if _unwrap(inst.saddr) < 106 else decode_src(_unwrap(inst.saddr), cdna)}"
+  if 'addtid' in name: return f"{instr} {reg_fn(inst.data if 'store' in name else inst.vdst)}{saddr_s}{mods}"
+  # RDNA4: vaddr instead of addr, vsrc instead of data
+  addr = inst.vaddr if r4 else inst.addr
+  data = inst.vsrc if r4 else inst.data
+  # load_lds_* instructions: vaddr, saddr (no vdst, data goes to LDS)
+  if 'load_lds' in name:
+    addr_w = 1 if seg == 'scratch' or (_unwrap(inst.saddr) not in (0x7F, 124)) else 2
+    addr_s = "off" if not inst.sve and seg == 'scratch' else _vreg(addr, addr_w)
+    return f"{instr} {addr_s}{saddr_s}{mods}"
+  if seg == 'flat': addr_w = 2  # flat always uses 64-bit vaddr
+  elif cdna: addr_w = 1 if seg == 'scratch' or (_unwrap(inst.saddr) not in (0x7F, 124)) else 2
+  else: addr_w = 1 if seg == 'scratch' or (_unwrap(inst.saddr) not in (0x7F, 124)) else 2
+  addr_s = "off" if not inst.sve and seg == 'scratch' else _vreg(addr, addr_w)
+  data_s, vdst_s = reg_fn(data, w), reg_fn(inst.vdst, w // 2 if 'cmpswap' in name else w)
+  if 'atomic' in name:
+    glc_or_sc0 = inst.sc0 if cdna else inst.glc
+    return f"{instr} {vdst_s}, {addr_s}, {data_s}{saddr_s if seg != 'flat' else ''}{mods}" if glc_or_sc0 else f"{instr} {addr_s}, {data_s}{saddr_s if seg != 'flat' else ''}{mods}"
+  if 'store' in name: return f"{instr} {addr_s}, {data_s}{saddr_s}{mods}"
+  return f"{instr} {reg_fn(inst.vdst, w)}, {addr_s}{saddr_s}{mods}"
+
+def _disasm_ds(inst: DS) -> str:
+  op, name = inst.op, inst.op_name.lower()
+  acc = getattr(inst, 'acc', 0)
+  reg_fn = _areg if acc else _vreg
+  gds = " gds" if getattr(inst, 'gds', 0) else ""
+  off = f" offset:{inst.offset0 | (inst.offset1 << 8)}" if inst.offset0 or inst.offset1 else ""
+  off2 = (" offset0:" + str(inst.offset0) if inst.offset0 else "") + (" offset1:" + str(inst.offset1) if inst.offset1 else "")
+  # Use get_field_bits: data for stores/writes/atomics, d for loads
+  regs = inst.canonical_op_regs
+  w = regs.get('data', regs.get('d', 1)) if 'store' in name or 'write' in name or ('load' not in name and 'read' not in name) else regs.get('d', 1)
+  d0, d1, dst, addr = reg_fn(inst.data0, w), reg_fn(inst.data1, w), reg_fn(inst.vdst, w), _vreg(inst.addr)
+
+  if name == 'ds_nop': return name
+  if name == 'ds_bvh_stack_rtn_b32': return f"{name} {_vreg(inst.vdst)}, {addr}, {_vreg(inst.data0)}, {_vreg(inst.data1, 4)}{off}{gds}"
+  if 'bvh_stack_push' in name:
+    d1_regs = 8 if 'push8' in name else 4
+    vdst_regs = 2 if 'pop2' in name else 1
+    vdst_s = _vreg(inst.vdst, vdst_regs) if vdst_regs > 1 else _vreg(inst.vdst)
+    return f"{name} {vdst_s}, {addr}, {_vreg(inst.data0)}, {_vreg(inst.data1, d1_regs)}{off}{gds}"
+  if 'gws_sema' in name and 'sema_br' not in name: return f"{name}{off}{gds}"
+  if 'gws_' in name: return f"{name} {addr}{off}{gds}"
+  if name in ('ds_consume', 'ds_append'): return f"{name} {reg_fn(inst.vdst)}{off}{gds}"
+  if 'gs_reg' in name: return f"{name} {reg_fn(inst.vdst, 2)}, {reg_fn(inst.data0)}{off}{gds}"
+  if '2addr' in name:
+    if 'load' in name: return f"{name} {reg_fn(inst.vdst, regs.get('d', 1))}, {addr}{off2}{gds}"
+    if 'store' in name and 'xchg' not in name: return f"{name} {addr}, {d0}, {d1}{off2}{gds}"
+    return f"{name} {reg_fn(inst.vdst, regs.get('d', 1))}, {addr}, {d0}, {d1}{off2}{gds}"
+  if 'write2' in name: return f"{name} {addr}, {d0}, {d1}{off2}{gds}"
+  if 'read2' in name: return f"{name} {reg_fn(inst.vdst, regs.get('d', 1))}, {addr}{off2}{gds}"
+  if 'xchg2' in name: return f"{name} {reg_fn(inst.vdst, regs.get('d', 1))}, {addr}, {d0}, {d1}{off2}{gds}"
+  if 'load' in name or ('read' in name and 'read2' not in name): return f"{name} {reg_fn(inst.vdst)}{off}{gds}" if 'addtid' in name else f"{name} {dst}, {addr}{off}{gds}"
+  if ('store' in name or 'write' in name) and not _has(name, 'cmp', 'xchg', 'write2'):
+    return f"{name} {reg_fn(inst.data0)}{off}{gds}" if 'addtid' in name else f"{name} {addr}, {d0}{off}{gds}"
+  if 'swizzle' in name or name == 'ds_ordered_count': return f"{name} {reg_fn(inst.vdst)}, {addr}{off}{gds}"
+  if 'permute' in name: return f"{name} {reg_fn(inst.vdst)}, {addr}, {reg_fn(inst.data0)}{off}{gds}"
+  if 'condxchg' in name: return f"{name} {reg_fn(inst.vdst, 2)}, {addr}, {reg_fn(inst.data0, 2)}{off}{gds}"
+  if _has(name, 'cmpst', 'mskor', 'wrap'):
+    return f"{name} {dst}, {addr}, {d0}, {d1}{off}{gds}" if '_rtn' in name else f"{name} {addr}, {d0}, {d1}{off}{gds}"
+  return f"{name} {dst}, {addr}, {d0}{off}{gds}" if '_rtn' in name else f"{name} {addr}, {d0}{off}{gds}"
+
+def _disasm_vop3(inst: VOP3) -> str:
+  op, name = inst.op, inst.op_name.lower()
+  n_up = name.upper()
+  bits = inst.canonical_op_bits
+
+  # RDNA4 v_s_* scalar VOP3 instructions - vdst is SGPR (VGPRField adds 256)
+  if name.startswith('v_s_'):
+    src = _lit(inst, inst.src0) if _unwrap(inst.src0) == 255 else ("src_scc" if _unwrap(inst.src0) == 253 else _fmt_src(inst.src0, max(1, bits['s0'] // 32)))
+    if inst.neg & 1: src = f"-{src}"
+    if inst.abs & 1: src = f"|{src}|"
+    clamp = getattr(inst, 'cm', None) or getattr(inst, 'clmp', 0)
+    vdst_raw = _unwrap(inst.vdst)
+    return f"{name} s{vdst_raw - 256 if vdst_raw >= 256 else vdst_raw}, {src}" + (" clamp" if clamp else "") + _omod(inst.omod)
+
+  # Use get_field_bits for register sizes and 16-bit detection
+  r0, r1, r2 = max(1, bits['s0'] // 32), max(1, bits['s1'] // 32), max(1, bits['s2'] // 32)
+  dn = max(1, bits['d'] // 32)
+  is16_d, is16_s, is16_s2 = bits['d'] == 16, bits['s0'] == 16, bits['s2'] == 16
+
+  s0 = _vop3_src(inst, inst.src0, inst.neg&1, inst.abs&1, inst.opsel&1, r0, is16_s)
+  s1 = _vop3_src(inst, inst.src1, inst.neg&2, inst.abs&2, inst.opsel&2, r1, is16_s)
+  s2 = _vop3_src(inst, inst.src2, inst.neg&4, inst.abs&4, inst.opsel&4, r2, is16_s2)
+
+  # Format destination
+  if 'readlane' in name:
+    vdst_off = inst.vdst.offset - 256 if inst.vdst.offset >= 256 else inst.vdst.offset
+    dst = _fmt_sdst(vdst_off, 1)
+  elif is16_d: dst = f"{inst.vdst.fmt()}.h" if (inst.opsel & 8) else f"{inst.vdst.fmt()}.l"
+  else: dst = inst.vdst.fmt()
+
+  clamp = getattr(inst, 'cm', None) or getattr(inst, 'clmp', 0)
+  cl, om = " clamp" if clamp else "", _omod(inst.omod)
+  nonvgpr_opsel = (inst.src0.offset < 256 and (inst.opsel & 1)) or (inst.src1.offset < 256 and (inst.opsel & 2)) or (inst.src2.offset < 256 and (inst.opsel & 4))
+  need_opsel = nonvgpr_opsel or (inst.opsel and not is16_s)
+
+  op_val = inst.op.value if hasattr(inst.op, 'value') else inst.op
+  e64 = "" if name.endswith('_e64') else "_e64"
+  if op_val < 256:  # VOPC
+    vdst_off = inst.vdst.offset - 256 if inst.vdst.offset >= 256 else inst.vdst.offset
+    return f"{name}{e64} {s0}, {s1}{cl}" if name.startswith('v_cmpx') else f"{name}{e64} {_fmt_sdst(vdst_off, 1)}, {s0}, {s1}{cl}"
+  if op_val < 384:  # VOP2
+    n = inst.num_srcs() or 2
+    os = _opsel_str(inst.opsel, n, need_opsel, is16_d)
+    return f"{name}{e64} {dst}, {s0}, {s1}, {s2}{os}{cl}{om}" if n == 3 else f"{name}{e64} {dst}, {s0}, {s1}{os}{cl}{om}"
+  if op_val < 512:  # VOP1
+    if re.match(r'v_cvt_f32_(bf|fp)8', name) and inst.opsel:
+      os = f" byte_sel:{((inst.opsel & 1) << 1) | ((inst.opsel >> 1) & 1)}"
+    else:
+      os = _opsel_str(inst.opsel, 1, need_opsel, is16_d)
+    if 'v_nop' in name or 'v_pipeflush' in name: return f"{name}{e64}"
+    return f"{name}{e64} {dst}, {s0}{os}{cl}{om}"
+  # Native VOP3
+  n = inst.num_srcs() or 2
+  os = f" byte_sel:{inst.opsel >> 2}" if 'cvt_sr' in name and inst.opsel else _opsel_str(inst.opsel, n, need_opsel, is16_d)
+  return f"{name} {dst}, {s0}, {s1}, {s2}{os}{cl}{om}" if n == 3 else f"{name} {dst}, {s0}, {s1}{os}{cl}{om}"
+
+def _disasm_vop3sd(inst: VOP3SD) -> str:
+  name = inst.op_name.lower()
+  def src(reg, neg):
+    s = _lit(inst, reg.offset) if reg.offset == 255 else ("src_scc" if reg.offset == 253 else (reg.fmt() if reg.sz > 1 else _lit(inst, reg.offset)))
+    return f"neg({s})" if neg and reg.offset == 255 else (f"-{s}" if neg else s)
+  s0, s1, s2 = src(inst.src0, inst.neg & 1), src(inst.src1, inst.neg & 2), src(inst.src2, inst.neg & 4)
+  # VOP3SD: _co_ ops (add/sub) without _ci_ have only 2 sources, all others (mad, div_scale, _co_ci_) have 3 sources
+  has_only_two_srcs = '_co_' in name and '_ci_' not in name and 'mad' not in name
+  srcs = f"{s0}, {s1}" if has_only_two_srcs else f"{s0}, {s1}, {s2}"
+  clamp = getattr(inst, 'cm', None) or getattr(inst, 'clmp', 0)
+  return f"{name} {inst.vdst.fmt()}, {_fmt_sdst(inst.sdst, 1)}, {srcs}{' clamp' if clamp else ''}{_omod(inst.omod)}"
+
+def _disasm_vopd(inst: VOPD) -> str:
+  lit = inst._literal
+  op_enum = R4_VOPDOp if _is_r4(inst) else VOPDOp
+  nx, ny = op_enum(inst.opx).name.lower(), op_enum(inst.opy).name.lower()
+  def half(n, vd, s0, vs1):
+    vd, vs1 = _vi(vd), _vi(vs1)
+    if 'mov' in n: return f"{n} v{vd}, {_lit(inst, s0)}"
+    if 'fmamk' in n and lit: return f"{n} v{vd}, {_lit(inst, s0)}, 0x{lit:x}, v{vs1}"
+    if 'fmaak' in n and lit: return f"{n} v{vd}, {_lit(inst, s0)}, v{vs1}, 0x{lit:x}"
+    return f"{n} v{vd}, {_lit(inst, s0)}, v{vs1}"
+  return f"{half(nx, inst.vdstx, inst.srcx0, inst.vsrcx1)} :: {half(ny, inst.vdsty, inst.srcy0, inst.vsrcy1)}"
+
+def _disasm_vop3p(inst: VOP3P) -> str:
+  name = inst.op_name.lower()
+  is_wmma, is_swmmac, n, is_fma_mix = 'wmma' in name, 'swmmac' in name, inst.num_srcs() or 2, 'fma_mix' in name
+  def get_src(reg):
+    return _lit(inst, reg.offset) if reg.offset == 255 else reg.fmt()
+  src0, src1, src2, dst = get_src(inst.src0), get_src(inst.src1), get_src(inst.src2), inst.vdst.fmt()
+  opsel_hi = inst.opsel_hi | (inst.opsel_hi2 << 2)
+  clamp = getattr(inst, 'cm', None) or getattr(inst, 'clmp', 0)
+  if is_fma_mix:
+    def m(s, neg, abs_): return f"-{f'|{s}|' if abs_ else s}" if neg else (f"|{s}|" if abs_ else s)
+    src0, src1, src2 = m(src0, inst.neg & 1, inst.neg_hi & 1), m(src1, inst.neg & 2, inst.neg_hi & 2), m(src2, inst.neg & 4, inst.neg_hi & 4)
+    mods = ([_fmt_bits("op_sel", inst.opsel, n)] if inst.opsel else []) + ([_fmt_bits("op_sel_hi", opsel_hi, n)] if opsel_hi else []) + (["clamp"] if clamp else [])
+  elif is_swmmac:
+    mods = ([f"index_key:{inst.opsel}"] if inst.opsel else []) + ([_fmt_bits("neg_lo", inst.neg, n)] if inst.neg else []) + \
+           ([_fmt_bits("neg_hi", inst.neg_hi, n)] if inst.neg_hi else []) + (["clamp"] if clamp else [])
+  else:
+    opsel_hi_default = 7 if n == 3 else 3
+    mods = ([_fmt_bits("op_sel", inst.opsel, n)] if inst.opsel else []) + ([_fmt_bits("op_sel_hi", opsel_hi, n)] if opsel_hi != opsel_hi_default else []) + \
+           ([_fmt_bits("neg_lo", inst.neg, n)] if inst.neg else []) + ([_fmt_bits("neg_hi", inst.neg_hi, n)] if inst.neg_hi else []) + (["clamp"] if clamp else [])
+  return f"{name} {dst}, {src0}, {src1}, {src2}{' ' + ' '.join(mods) if mods else ''}" if n == 3 else f"{name} {dst}, {src0}, {src1}{' ' + ' '.join(mods) if mods else ''}"
+
+def _disasm_sop1(inst: SOP1) -> str:
+  op, name, cdna = inst.op, inst.op_name.lower(), _is_cdna(inst)
+  # Use get_field_bits for register sizes
+  regs = inst.canonical_op_regs
+  dst_regs, src_regs = regs.get('d', 1), regs.get('s0', 1)
+  src = _lit(inst, inst.ssrc0) if _unwrap(inst.ssrc0) == 255 else _fmt_src(inst.ssrc0, src_regs, cdna)
+  if not cdna:
+    if 'getpc_b64' in name: return f"{name} {_fmt_sdst(inst.sdst, 2)}"
+    if 'setpc_b64' in name or 'rfe_b64' in name: return f"{name} {src}"
+    if 'swappc_b64' in name: return f"{name} {_fmt_sdst(inst.sdst, 2)}, {src}"
+    if 'sendmsg_rtn' in name:
+      v = _unwrap(inst.ssrc0)
+      try: msg_str = MSG(v).name if v != 255 else None  # MSG_RTN_ILLEGAL_MSG (255) not supported by LLVM
+      except ValueError: msg_str = None
+      return f"{name} {_fmt_sdst(inst.sdst, dst_regs)}, sendmsg({msg_str})" if msg_str else f"{name} {_fmt_sdst(inst.sdst, dst_regs)}, 0x{v:x}"
+  sop1_src_only = ('S_ALLOC_VGPR', 'S_SLEEP_VAR', 'S_BARRIER_SIGNAL', 'S_BARRIER_SIGNAL_ISFIRST', 'S_BARRIER_INIT', 'S_BARRIER_JOIN', 'S_SET_GPR_IDX_IDX',
+                   'S_CBRANCH_JOIN')
+  if inst.op_name in sop1_src_only: return f"{name} {src}"
+  if cdna:
+    if 'getpc_b64' in name: return f"{name} {_fmt_sdst(inst.sdst, 2, cdna)}"
+    if 'setpc_b64' in name or 'rfe_b64' in name: return f"{name} {src}"
+    if 'swappc_b64' in name: return f"{name} {_fmt_sdst(inst.sdst, 2, cdna)}, {src}"
+  return f"{name} {_fmt_sdst(inst.sdst, dst_regs, cdna)}, {src}"
+
+def _disasm_sop2(inst: SOP2) -> str:
+  cdna, name = _is_cdna(inst), inst.op_name.lower()
+  lit = inst._literal
+  # Use get_field_bits for register sizes
+  regs = inst.canonical_op_regs
+  dn, s0n, s1n = regs['d'], regs['s0'], regs['s1']
+  s0 = _lit(inst, inst.ssrc0) if _unwrap(inst.ssrc0) == 255 else _fmt_src(inst.ssrc0, s0n, cdna)
+  s1 = _lit(inst, inst.ssrc1) if _unwrap(inst.ssrc1) == 255 else _fmt_src(inst.ssrc1, s1n, cdna)
+  dst = _fmt_sdst(inst.sdst, dn, cdna)
+  if 'fmamk' in name and lit is not None: return f"{name} {dst}, {s0}, 0x{lit:x}, {s1}"
+  if 'fmaak' in name and lit is not None: return f"{name} {dst}, {s0}, {s1}, 0x{lit:x}"
+  if name in ('s_cbranch_g_fork', 's_rfe_restore_b64'): return f"{name} {s0}, {s1}"  # no destination
+  return f"{name} {dst}, {s0}, {s1}"
+
+def _disasm_sopc(inst: SOPC) -> str:
+  cdna, regs, name = _is_cdna(inst), inst.canonical_op_regs, inst.op_name.lower()
+  s0 = _lit(inst, inst.ssrc0) if _unwrap(inst.ssrc0) == 255 else _fmt_src(inst.ssrc0, regs['s0'], cdna)
+  if name == 's_set_gpr_idx_on':
+    imm = _unwrap(inst.ssrc1) & 0xf
+    flags = [n for i, n in enumerate(['SRC0', 'SRC1', 'SRC2', 'DST']) if imm & (1 << i)]
+    return f"{name} {s0}, gpr_idx({','.join(flags)})"
+  s1 = _lit(inst, inst.ssrc1) if _unwrap(inst.ssrc1) == 255 else _fmt_src(inst.ssrc1, regs['s1'], cdna)
+  return f"{name} {s0}, {s1}"
+
+_HWREG_BLACKLIST = {'HW_REG_PC_LO', 'HW_REG_PC_HI', 'HW_REG_IB_DBG1', 'HW_REG_FLUSH_IB', 'HW_REG_SHADER_TBA_LO', 'HW_REG_SHADER_TBA_HI',
+                    'HW_REG_SHADER_FLAT_SCRATCH_LO', 'HW_REG_SHADER_FLAT_SCRATCH_HI', 'HW_REG_SHADER_CYCLES'}
+_HWREG_BLACKLIST_CDNA = {'HW_REG_PC_LO', 'HW_REG_PC_HI', 'HW_REG_IB_DBG1', 'HW_REG_FLUSH_IB', 'HW_REG_SQ_SHADER_TBA_LO', 'HW_REG_SQ_SHADER_TBA_HI',
+                         'HW_REG_SQ_SHADER_TMA_LO', 'HW_REG_SQ_SHADER_TMA_HI', 'HW_REG_SQ_PERF_SNAPSHOT_DATA', 'HW_REG_SQ_PERF_SNAPSHOT_DATA1',
+                         'HW_REG_SQ_PERF_SNAPSHOT_PC_LO', 'HW_REG_SQ_PERF_SNAPSHOT_PC_HI', 'HW_REG_XCC_ID'}
+def _disasm_sopk(inst: SOPK) -> str:
+  op, name, cdna = inst.op, inst.op_name.lower(), _is_cdna(inst)
+  is_rdna4 = _is_r4(inst)
+  hw = HWREG_CDNA if cdna else (HWREG_RDNA4 if is_rdna4 else HWREG)
+  blacklist = _HWREG_BLACKLIST_CDNA if cdna else _HWREG_BLACKLIST
+  def fmt_hwreg(hid, hoff, hsz):
+    try: hr_name = hw(hid).name.replace("HW_REG_WAVE_", "HW_REG_")
+    except ValueError: return f"0x{inst.simm16:x}"
+    if hr_name in blacklist: return f"0x{inst.simm16:x}"
+    return f"hwreg({hr_name})" if hoff == 0 and hsz == 32 else f"hwreg({hr_name}, {hoff}, {hsz})"
+  if name == 's_setreg_imm32_b32':
+    hid, hoff, hsz = inst.simm16 & 0x3f, (inst.simm16 >> 6) & 0x1f, ((inst.simm16 >> 11) & 0x1f) + 1
+    return f"{name} {fmt_hwreg(hid, hoff, hsz)}, 0x{inst._literal:x}"
+  if name == 's_version': return f"{name} 0x{inst.simm16:x}"
+  if name in ('s_setreg_b32', 's_getreg_b32'):
+    hid, hoff, hsz = inst.simm16 & 0x3f, (inst.simm16 >> 6) & 0x1f, ((inst.simm16 >> 11) & 0x1f) + 1
+    hs = fmt_hwreg(hid, hoff, hsz)
+    return f"{name} {hs}, {_fmt_sdst(inst.sdst, 1, cdna)}" if 'setreg' in name else f"{name} {_fmt_sdst(inst.sdst, 1, cdna)}, {hs}"
+  if name in ('s_subvector_loop_begin', 's_subvector_loop_end'):
+    return f"{name} {_fmt_sdst(inst.sdst, 1)}, 0x{inst.simm16:x}"
+  return f"{name} {_fmt_sdst(inst.sdst, inst.canonical_op_regs['d'], cdna)}, 0x{inst.simm16:x}"
+
+def _disasm_vinterp(inst: VINTERP) -> str:
+  mods = _mods((inst.waitexp, f"wait_exp:{inst.waitexp}"), (inst.clmp, "clamp"))
+  return f"{inst.op_name.lower()} {inst.vdst.fmt()}, {_lit(inst, inst.src0, inst.neg & 1)}, {_lit(inst, inst.src1, inst.neg & 2)}, {_lit(inst, inst.src2, inst.neg & 4)}" + (" " + mods if mods else "")
+
+DISASM_HANDLERS: dict[type, Callable[..., str]] = {
+  VOP1: _disasm_vop1, VOP1_SDST: _disasm_vop1, VOP1_SDST_LIT: _disasm_vop1, VOP1_LIT: _disasm_vop1,
+  VOP2: _disasm_vop2, VOP2_LIT: _disasm_vop2, VOPC: _disasm_vopc, VOPC_LIT: _disasm_vopc,
+  VOP3: _disasm_vop3, VOP3_SDST: _disasm_vop3, VOP3_SDST_LIT: _disasm_vop3, VOP3_LIT: _disasm_vop3, VOP3SD: _disasm_vop3sd, VOP3SD_LIT: _disasm_vop3sd,
+  VOPD: _disasm_vopd, VOPD_LIT: _disasm_vopd, VOP3P: _disasm_vop3p, VOP3P_LIT: _disasm_vop3p,
+  VINTERP: _disasm_vinterp, SOPP: _disasm_sopp, SMEM: _disasm_smem, DS: _disasm_ds, FLAT: _disasm_flat, GLOBAL: _disasm_flat, SCRATCH: _disasm_flat,
+  SOP1: _disasm_sop1, SOP1_LIT: _disasm_sop1, SOP2: _disasm_sop2, SOP2_LIT: _disasm_sop2,
+  SOPC: _disasm_sopc, SOPC_LIT: _disasm_sopc, SOPK: _disasm_sopk, SOPK_LIT: _disasm_sopk,
+  # RDNA4
+  R4_VOP1: _disasm_vop1, R4_VOP1_SDST: _disasm_vop1, R4_VOP1_SDST_LIT: _disasm_vop1, R4_VOP1_LIT: _disasm_vop1,
+  R4_VOP2: _disasm_vop2, R4_VOP2_LIT: _disasm_vop2, R4_VOPC: _disasm_vopc, R4_VOPC_LIT: _disasm_vopc,
+  R4_VOP3: _disasm_vop3, R4_VOP3_SDST: _disasm_vop3, R4_VOP3_SDST_LIT: _disasm_vop3, R4_VOP3_LIT: _disasm_vop3,
+  R4_VOP3SD: _disasm_vop3sd, R4_VOP3SD_LIT: _disasm_vop3sd, R4_VOP3P: _disasm_vop3p, R4_VOP3P_LIT: _disasm_vop3p,
+  R4_FLAT: _disasm_flat, R4_GLOBAL: _disasm_flat, R4_SCRATCH: _disasm_flat,
+  R4_VOPD: _disasm_vopd, R4_VOPD_LIT: _disasm_vopd, R4_VINTERP: _disasm_vinterp, R4_SOPP: _disasm_sopp, R4_SMEM: _disasm_smem, R4_DS: _disasm_ds,
+  R4_SOP1: _disasm_sop1, R4_SOP1_LIT: _disasm_sop1, R4_SOP2: _disasm_sop2, R4_SOP2_LIT: _disasm_sop2,
+  R4_SOPC: _disasm_sopc, R4_SOPC_LIT: _disasm_sopc, R4_SOPK: _disasm_sopk, R4_SOPK_LIT: _disasm_sopk}
+
+def disasm(inst: Inst) -> str: return DISASM_HANDLERS[type(inst)](inst)
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# CDNA DISASSEMBLER SUPPORT
+# ═══════════════════════════════════════════════════════════════════════════════
+
+from extra.assembly.amd.autogen.cdna.ins import (VOP1 as CDNA_VOP1, VOP1_LIT as CDNA_VOP1_LIT,
+  VOP1_SDWA as CDNA_VOP1_SDWA, VOP1_DPP16 as CDNA_VOP1_DPP16,
+  VOP2 as CDNA_VOP2, VOP2_LIT as CDNA_VOP2_LIT, VOP2_SDWA as CDNA_VOP2_SDWA, VOP2_DPP16 as CDNA_VOP2_DPP16,
+  VOPC as CDNA_VOPC, VOPC_LIT as CDNA_VOPC_LIT, VOPC_SDWA_SDST as CDNA_VOPC_SDWA_SDST,
+  VOP3 as CDNA_VOP3, VOP3_SDST as CDNA_VOP3_SDST, VOP3SD as CDNA_VOP3SD, VOP3P as CDNA_VOP3P, VOP3P_MFMA as CDNA_VOP3P_MFMA, VOP3PX2 as CDNA_VOP3PX2,
+  SOP1 as CDNA_SOP1, SOP1_LIT as CDNA_SOP1_LIT, SOP2 as CDNA_SOP2, SOP2_LIT as CDNA_SOP2_LIT,
+  SOPC as CDNA_SOPC, SOPC_LIT as CDNA_SOPC_LIT, SOPK as CDNA_SOPK, SOPK_LIT as CDNA_SOPK_LIT,
+  SOPP as CDNA_SOPP, SMEM as CDNA_SMEM, DS as CDNA_DS,
+  FLAT as CDNA_FLAT, GLOBAL as CDNA_GLOBAL, SCRATCH as CDNA_SCRATCH, MUBUF as CDNA_MUBUF)
+
+def _cdna_src(inst, v, neg, abs_=0, n=1):
+  s = _lit(inst, v) if v == 255 else _fmt_src(v, n, cdna=True)
+  if abs_: s = f"|{s}|"
+  return f"neg({s})" if neg and v == 255 else (f"-{s}" if neg else s)
+
+_CDNA_VOP3_ALIASES = {'v_fmac_f64': 'v_mul_legacy_f32', 'v_dot2c_f32_bf16': 'v_mac_f32'}
+
+def _disasm_vop3a(inst) -> str:
+  op_val = inst.op.value if hasattr(inst.op, 'value') else inst.op
+  name = inst.op_name.lower() or f'vop3a_op_{op_val}'
+  n = inst.num_srcs() or _num_srcs(inst)
+  cl, om = " clamp" if inst.clmp else "", _omod(inst.omod)
+  # _sr_ instructions use 4-element op_sel (src2 for byte selection)
+  opsel_n = 3 if '_sr_' in name and n == 2 else n
+  opsel = _opsel_str(inst.opsel, opsel_n, inst.opsel != 0, False)
+  orig_name = name
+  name = _CDNA_VOP3_ALIASES.get(name, name)
+  if name != orig_name:
+    s0, s1 = _cdna_src(inst, inst.src0, inst.neg&1, inst.abs&1, 1), _cdna_src(inst, inst.src1, inst.neg&2, inst.abs&2, 1)
+    s2 = ""
+    dst = _vreg(inst.vdst)
+  else:
+    regs = inst.canonical_op_regs
+    dregs, r0, r1, r2 = regs['d'], regs['s0'], regs['s1'], regs['s2']
+    s0, s1, s2 = _cdna_src(inst, inst.src0, inst.neg&1, inst.abs&1, r0), _cdna_src(inst, inst.src1, inst.neg&2, inst.abs&2, r1), _cdna_src(inst, inst.src2, inst.neg&4, inst.abs&4, r2)
+    dst = _vreg(inst.vdst, dregs) if dregs > 1 else _vreg(inst.vdst)
+  if op_val >= 512:
+    return f"{name} {dst}, {s0}, {s1}, {s2}{opsel}{cl}{om}" if n == 3 else f"{name} {dst}, {s0}, {s1}{opsel}{cl}{om}"
+  if op_val < 256:
+    # VOPC: vdst is actually sdst (SGPR pair), but VGPRField adds 256 to the offset
+    sdst_val = _unwrap(inst.vdst)
+    if sdst_val >= 256: sdst_val -= 256
+    sdst = _fmt_sdst(sdst_val, 2, cdna=True)
+    return f"{name} {sdst}, {s0}, {s1}{cl}"
+  if 320 <= op_val < 512:
+    if name in ('v_nop', 'v_clrexcp', 'v_nop_e64', 'v_clrexcp_e64'): return name.replace('_e64', '')
+    return f"{name} {dst}, {s0}{cl}{om}"
+  if name == 'v_cndmask_b32':
+    s2 = _fmt_src(inst.src2, 2, cdna=True)
+    return f"{name} {dst}, {s0}, {s1}, {s2}{cl}{om}"
+  return f"{name} {dst}, {s0}, {s1}, {s2}{opsel}{cl}{om}" if n == 3 else f"{name} {dst}, {s0}, {s1}{opsel}{cl}{om}"
+
+def _disasm_vop3b(inst) -> str:
+  op_val = inst.op.value if hasattr(inst.op, 'value') else inst.op
+  name, cdna = inst.op_name.lower() or f'vop3b_op_{op_val}', _is_cdna(inst)
+  n = inst.num_srcs() or _num_srcs(inst)
+  regs = inst.canonical_op_regs
+  dregs, r0, r1, r2 = regs['d'], regs['s0'], regs['s1'], regs['s2']
+  s0, s1, s2 = _cdna_src(inst, inst.src0, inst.neg&1, n=r0), _cdna_src(inst, inst.src1, inst.neg&2, n=r1), _cdna_src(inst, inst.src2, inst.neg&4, n=r2)
+  # CDNA VOP3_SDST uses vdst field for sdst (but vdst adds 256), RDNA uses separate sdst field
+  sdst_val = getattr(inst, 'sdst', None)
+  if sdst_val is None and hasattr(inst, 'vdst'):
+    sdst_val = _unwrap(inst.vdst)
+    if sdst_val >= 256: sdst_val -= 256  # VGPRField adds 256, remove it for SGPR
+  # For CDNA VOP3_SDST (VOPC->VOP3), vdst is the scalar dest (sdst), there's no vdst output
+  if cdna and 'v_cmp' in name:
+    sdst = _fmt_sdst(sdst_val, 2, cdna=True)
+    cl, om = " clamp" if inst.clmp else "", _omod(inst.omod)
+    return f"{name} {sdst}, {s0}, {s1}{cl}{om}"
+  dst = _vreg(inst.vdst, dregs) if dregs > 1 else _vreg(inst.vdst)
+  sdst = _fmt_sdst(sdst_val, 2, cdna=cdna)
+  cl, om = " clamp" if inst.clmp else "", _omod(inst.omod)
+  if name in ('v_addc_co_u32', 'v_subb_co_u32', 'v_subbrev_co_u32'):
+    s2 = _fmt_src(inst.src2, 2, cdna=cdna)
+  return f"{name} {dst}, {sdst}, {s0}, {s1}, {s2}{cl}{om}" if n == 3 else f"{name} {dst}, {sdst}, {s0}, {s1}{cl}{om}"
+
+def _disasm_cdna_vop3p(inst) -> str:
+  name, n = inst.op_name.lower(), inst.num_srcs() or 2
+  is_mfma = 'mfma' in name or 'smfmac' in name
+  is_accvgpr = 'accvgpr' in name
+  get_src = lambda v, sc: _lit(inst, v) if v == 255 else _fmt_src(v, sc, cdna=True)
+
+  # Handle accvgpr read/write (accumulator register operations)
+  if is_accvgpr:
+    src0_off = _unwrap(inst.src0)
+    vdst_off = _vi(inst.vdst)
+    if 'read' in name:
+      # v_accvgpr_read_b32 vN, aM - reads from accumulator to VGPR
+      return f"{name}_b32 v{vdst_off}, a{src0_off - 256 if src0_off >= 256 else src0_off}"
+    if 'write' in name:
+      # v_accvgpr_write_b32 aM, src - writes to accumulator from source
+      src = _lit(inst, inst.src0) if src0_off == 255 else (f"v{src0_off - 256}" if src0_off >= 256 else decode_src(src0_off, cdna=True))
+      return f"{name}_b32 a{vdst_off}, {src}"
+
+  # Handle v_mfma_ld_scale_b32 - special 2-operand format: v_mfma_ld_scale_b32 src0, src1
+  if 'ld_scale' in name:
+    src0, src1 = get_src(inst.src0, 1), get_src(inst.src1, 1)
+    mods = ([_fmt_bits("op_sel", inst.opsel, 2)] if inst.opsel else []) + \
+           ([_fmt_bits("op_sel_hi", inst.opsel_hi, 2)] if inst.opsel_hi != 3 else [])
+    return f"{name} {src0}, {src1}{' ' + ' '.join(mods) if mods else ''}"
+
+  # Handle MFMA instructions with accumulator destinations
+  if is_mfma:
+    regs = inst.canonical_op_regs
+    dregs, r0, r1, r2 = regs['d'], regs['s0'], regs['s1'], regs['s2']
+    # Infer register counts from instruction name if not in operands table (e.g., v_mfma_f32_32x32x4_xf32)
+    if dregs == 1:
+      if '32x32' in name: dregs, r0, r1, r2 = 16, 2, 2, 16
+      elif '16x16' in name: dregs, r0, r1, r2 = 4, 2, 2, 4
+    # MFMA reuses VOP3P fields differently: clmp -> acc_cd (dest is acc), opsel_hi -> acc (src1/src2 are acc)
+    # acc field (bits 60-59): bit 0 = src2 is acc (always for MFMA), bit 1 = src1 is acc
+    acc = inst.opsel_hi  # opsel_hi field maps to acc for MFMA
+    acc_cd = inst.clmp   # clmp field maps to acc_cd for MFMA (dest is accumulator)
+    is_smfmac = 'smfmac' in name  # SMFMAC has different operand semantics
+    # Format sources: src0 is always VGPR, src1/src2 depend on acc bits
+    def mfma_src(v, sc, is_acc):
+      v = _unwrap(v)
+      if v == 255: return _lit(inst, v)
+      if 128 <= v <= 208 or 240 <= v <= 248: return _lit(inst, v)
+      base = v - 256 if v >= 256 else v
+      if is_acc: return _areg(base, sc)
+      return _vreg(base, sc)
+    src0 = get_src(inst.src0, r0)  # src0 is always VGPR
+    src1 = mfma_src(inst.src1, r1, acc & 2)  # bit 1 = src1 is acc
+    # For SMFMAC, src2 is always a VGPR index (1 register), not accumulator
+    src2 = _vreg(inst.src2) if is_smfmac else mfma_src(inst.src2, r2, acc_cd)
+    dst = _areg(inst.vdst, dregs) if acc_cd else _vreg(inst.vdst, dregs)
+    # MFMA uses neg:[...] not neg_lo:[...], and doesn't support op_sel_hi or clamp
+    # Only f64 MFMA instructions support neg modifier
+    # f8f6f4 MFMA instructions support cbsz/blgp modifiers
+    mods = []
+    if 'f8f6f4' in name:
+      if inst.neg_hi: mods.append(f"cbsz:{inst.neg_hi}")
+      if inst.neg: mods.append(f"blgp:{inst.neg}")
+    elif inst.neg and 'f64' in name:
+      mods.append(_fmt_bits("neg", inst.neg, n))
+    return f"{name} {dst}, {src0}, {src1}, {src2}{' ' + ' '.join(mods) if mods else ''}"
+
+  # Standard VOP3P instructions
+  src0, src1, src2, dst = get_src(inst.src0, 1), get_src(inst.src1, 1), get_src(inst.src2, 1), _vreg(inst.vdst)
+  opsel_hi = inst.opsel_hi  # CDNA VOP3P only has 2 bits for opsel_hi (no opsel_hi2)
+  opsel_hi_default = 3  # CDNA default is 0b11 (2 bits), not 0b111 like RDNA
+  mods = ([_fmt_bits("op_sel", inst.opsel, n)] if inst.opsel else []) + ([_fmt_bits("op_sel_hi", opsel_hi, n)] if opsel_hi != opsel_hi_default else []) + \
+          ([_fmt_bits("neg_lo", inst.neg, n)] if inst.neg else []) + ([_fmt_bits("neg_hi", inst.neg_hi, n)] if inst.neg_hi else []) + (["clamp"] if inst.clmp else [])
+  return f"{name} {dst}, {src0}, {src1}, {src2}{' ' + ' '.join(mods) if mods else ''}" if n == 3 else f"{name} {dst}, {src0}, {src1}{' ' + ' '.join(mods) if mods else ''}"
+
+def _disasm_mubuf(inst) -> str:
+  name = inst.op_name.lower()
+  # Determine vdata register count from instruction name
+  nregs = 4 if 'xyzw' in name else 3 if 'xyz' in name else 2 if 'xy' in name or 'x2' in name or 'f64' in name or 'dwordx2' in name else 1
+  vdata = _vreg(inst.vdata, nregs)
+  vaddr = _vreg(inst.vaddr) if inst.offen or inst.idxen else None
+  srsrc = str(inst.srsrc)
+  soffset_val = _unwrap(inst.soffset)
+  soffset = f"s{soffset_val}" if soffset_val < 128 else "off"
+  offset = f" offset:{inst.offset}" if inst.offset else ""
+  offen = " offen" if inst.offen else ""
+  idxen = " idxen" if inst.idxen else ""
+  lds = " lds" if inst.lds else ""
+  sc0 = " sc0" if inst.sc0 else ""
+  sc1 = " sc1" if inst.sc1 else ""
+  nt = " nt" if inst.nt else ""
+  # Handle special cases
+  if name in ('buffer_wbl2', 'buffer_inv'):
+    return f"{name}{sc0}{sc1}"
+  if vaddr:
+    return f"{name} {vdata}, {vaddr}, {srsrc}, {soffset}{offen}{idxen}{offset}{sc0}{nt}{sc1}{lds}"
+  return f"{name} {vdata}, off, {srsrc}, {soffset}{offset}{sc0}{nt}{sc1}{lds}"
+
+_SDWA_SEL = {0: 'BYTE_0', 1: 'BYTE_1', 2: 'BYTE_2', 3: 'BYTE_3', 4: 'WORD_0', 5: 'WORD_1', 6: 'DWORD'}
+
+def _disasm_vop1_sdwa(inst) -> str:
+  name = inst.op_name.lower().replace('_e32', '')
+  regs = inst.canonical_op_regs
+  dst = _vreg(inst.vdst, regs['d'])
+  # When s0=1, vsrc0 is SGPR/constant (VGPRField adds 256, so subtract it back)
+  if inst.s0 == 0: src0 = _vreg(inst.vsrc0, regs['s0'])
+  else:
+    raw = _unwrap(inst.vsrc0) - 256  # VGPRField adds 256
+    src0 = decode_src(raw, cdna=True)  # handles SGPRs, constants, specials
+  src0_sel = _SDWA_SEL.get(inst.src0_sel, f'SEL{inst.src0_sel}')
+  mods = []
+  if inst.clmp: mods.append("clamp")
+  if inst.omod == 1: mods.append("mul:2")
+  elif inst.omod == 2: mods.append("mul:4")
+  elif inst.omod == 3: mods.append("div:2")
+  mods.append(f"src0_sel:{src0_sel}")
+  return f"{name}_sdwa {dst}, {src0} {' '.join(mods)}"
+
+def _decode_dpp(dpp: int) -> str:
+  """Decode DPP control value to string."""
+  if dpp < 0x100: return f"quad_perm:[{dpp&3},{(dpp>>2)&3},{(dpp>>4)&3},{(dpp>>6)&3}]"
+  if 0x100 <= dpp <= 0x10f: return f"row_shl:{dpp & 0xf}"
+  if 0x110 <= dpp <= 0x11f: return f"row_shr:{dpp & 0xf}"
+  if 0x120 <= dpp <= 0x12f: return f"row_ror:{dpp & 0xf}"
+  if dpp == 0x130: return "wave_shl:1"
+  if dpp == 0x134: return "wave_rol:1"
+  if dpp == 0x138: return "wave_shr:1"
+  if dpp == 0x13c: return "wave_ror:1"
+  if dpp == 0x140: return "row_mirror"
+  if dpp == 0x141: return "row_half_mirror"
+  if dpp == 0x142: return "row_bcast:15"
+  if dpp == 0x143: return "row_bcast:31"
+  if 0x150 <= dpp <= 0x15f: return f"row_newbcast:{dpp & 0xf}"
+  if 0x160 <= dpp <= 0x16f: return f"row_share:{dpp & 0xf}"
+  if 0x170 <= dpp <= 0x17f: return f"row_xmask:{dpp & 0xf}"
+  return f"dpp:{dpp:#x}"
+
+def _disasm_vop1_dpp(inst) -> str:
+  name = inst.op_name.lower().replace('_e32', '')
+  regs = inst.canonical_op_regs
+  dst, src0 = _vreg(inst.vdst, regs['d']), _vreg(inst.vsrc0, regs['s0'])
+  dpp_str = _decode_dpp(inst.dpp)
+  mods = [dpp_str]
+  if inst.row_mask != 0xf: mods.append(f"row_mask:{inst.row_mask:#x}")
+  if inst.bank_mask != 0xf: mods.append(f"bank_mask:{inst.bank_mask:#x}")
+  if inst.bc: mods.append("bound_ctrl:1")
+  return f"{name}_dpp {dst}, {src0} {' '.join(mods)}"
+
+def _disasm_vop2_sdwa(inst) -> str:
+  name, cdna = inst.op_name.lower().replace('_e32', ''), _is_cdna(inst)
+  regs = inst.canonical_op_regs
+  dst = _vreg(inst.vdst, regs['d'])
+  # When s0/s1=1, vsrc is SGPR/constant (VGPRField adds 256, so subtract it back)
+  src0 = _vreg(inst.vsrc0, regs['s0']) if inst.s0 == 0 else decode_src(_unwrap(inst.vsrc0) - 256, cdna)
+  src1 = _vreg(inst.vsrc1, regs['s1']) if inst.s1 == 0 else decode_src(_unwrap(inst.vsrc1) - 256, cdna)
+  src0_sel = _SDWA_SEL.get(inst.src0_sel, f'SEL{inst.src0_sel}')
+  src1_sel = _SDWA_SEL.get(inst.src1_sel, f'SEL{inst.src1_sel}')
+  mods = []
+  if inst.clmp: mods.append("clamp")
+  if inst.omod == 1: mods.append("mul:2")
+  elif inst.omod == 2: mods.append("mul:4")
+  elif inst.omod == 3: mods.append("div:2")
+  if inst.src0_sel != 6: mods.append(f"src0_sel:{src0_sel}")
+  if inst.src1_sel != 6: mods.append(f"src1_sel:{src1_sel}")
+  mods_str = ' '.join(mods) if mods else ""
+  # CDNA carry instructions and cndmask need vcc operands
+  if cdna and name in _VOP2_CARRY_OUT: return f"{name}_sdwa {dst}, vcc, {src0}, {src1} {mods_str}".strip()
+  if cdna and name in _VOP2_CARRY_INOUT: return f"{name}_sdwa {dst}, vcc, {src0}, {src1}, vcc {mods_str}".strip()
+  if cdna and name == 'v_cndmask_b32': return f"{name}_sdwa {dst}, {src0}, {src1}, vcc {mods_str}".strip()
+  return f"{name}_sdwa {dst}, {src0}, {src1} {mods_str}".strip()
+
+def _disasm_vop2_dpp(inst) -> str:
+  name, cdna = inst.op_name.lower().replace('_e32', ''), _is_cdna(inst)
+  regs = inst.canonical_op_regs
+  dst, src0, src1 = _vreg(inst.vdst, regs['d']), _vreg(inst.vsrc0, regs['s0']), _vreg(inst.vsrc1, regs['s1'])
+  dpp_str = _decode_dpp(inst.dpp)
+  mods = [dpp_str]
+  if inst.row_mask != 0xf: mods.append(f"row_mask:{inst.row_mask:#x}")
+  if inst.bank_mask != 0xf: mods.append(f"bank_mask:{inst.bank_mask:#x}")
+  if inst.bc: mods.append("bound_ctrl:1")
+  # CDNA carry instructions and cndmask need vcc operands
+  if cdna and name in _VOP2_CARRY_OUT: return f"{name}_dpp {dst}, vcc, {src0}, {src1} {' '.join(mods)}"
+  if cdna and name in _VOP2_CARRY_INOUT: return f"{name}_dpp {dst}, vcc, {src0}, {src1}, vcc {' '.join(mods)}"
+  if cdna and name == 'v_cndmask_b32': return f"{name}_dpp {dst}, {src0}, {src1}, vcc {' '.join(mods)}"
+  return f"{name}_dpp {dst}, {src0}, {src1} {' '.join(mods)}"
+
+def _disasm_vopc_sdwa(inst) -> str:
+  name = inst.op_name.lower().replace('_e32', '')
+  regs = inst.canonical_op_regs
+  sdst = _fmt_sdst(inst.sdst, 2, cdna=True)
+  src0 = _vreg(inst.vsrc0, regs['s0']) if getattr(inst, 's0', 0) == 0 else decode_src(_unwrap(inst.vsrc0) - 256, cdna=True)
+  src1 = _vreg(inst.vsrc1, regs['s1']) if getattr(inst, 's1', 0) == 0 else decode_src(_unwrap(inst.vsrc1) - 256, cdna=True)
+  src0_sel = _SDWA_SEL.get(inst.src0_sel, f'SEL{inst.src0_sel}')
+  src1_sel = _SDWA_SEL.get(inst.src1_sel, f'SEL{inst.src1_sel}')
+  mods = []
+  if inst.src0_sel != 6: mods.append(f"src0_sel:{src0_sel}")
+  if inst.src1_sel != 6: mods.append(f"src1_sel:{src1_sel}")
+  return f"{name}_sdwa {sdst}, {src0}, {src1} {' '.join(mods)}".strip()
+
+def _disasm_vop3px2(inst) -> str:
+  """VOP3PX2 disassembler for scaled MFMA instructions."""
+  name = inst.op_name.lower()
+  regs = inst.canonical_op_regs
+  dregs, r2 = regs['d'], regs['s2']
+  # F8F6F4 MFMA: CBSZ selects matrix A format, BLGP selects matrix B format
+  # VGPRs: FP8/BF8(0,1)=8, FP6/BF6(2,3)=6, FP4(4)=4
+  vgprs = {0: 8, 1: 8, 2: 6, 3: 6, 4: 4}
+  r0, r1 = vgprs.get(inst.cbsz, 8), vgprs.get(inst.blgp, 8)
+  def mfma_src(v, sc, is_acc):
+    v = _unwrap(v)
+    if v == 255: return _lit(inst, v)
+    base = v - 256 if v >= 256 else v
+    return _areg(base, sc) if is_acc else _vreg(base, sc)
+  src0, src1, src2 = mfma_src(inst.src0, r0, False), mfma_src(inst.src1, r1, inst.acc & 2), mfma_src(inst.src2, r2, inst.acc_cd)
+  dst = _areg(inst.vdst, dregs) if inst.acc_cd else _vreg(inst.vdst, dregs)
+  scale_src0, scale_src1 = _vreg(inst.scale_src0), _vreg(inst.scale_src1)
+  mods = []
+  if inst.opsel: mods.append(_fmt_bits("op_sel", inst.opsel, 3))
+  if inst.opsel_hi != 0: mods.append(_fmt_bits("op_sel_hi", inst.opsel_hi, 3))
+  if inst.neg: mods.append(_fmt_bits("neg", inst.neg, 3))
+  if inst.cbsz: mods.append(f"cbsz:{inst.cbsz}")
+  if inst.blgp: mods.append(f"blgp:{inst.blgp}")
+  return f"{name} {dst}, {src0}, {src1}, {src2}, {scale_src0}, {scale_src1}{' ' + ' '.join(mods) if mods else ''}"
+
+DISASM_HANDLERS.update({CDNA_VOP1: _disasm_vop1, CDNA_VOP1_LIT: _disasm_vop1,
+  CDNA_VOP1_SDWA: _disasm_vop1_sdwa, CDNA_VOP1_DPP16: _disasm_vop1_dpp,
+  CDNA_VOP2: _disasm_vop2, CDNA_VOP2_LIT: _disasm_vop2,
+  CDNA_VOP2_SDWA: _disasm_vop2_sdwa, CDNA_VOP2_DPP16: _disasm_vop2_dpp,
+  CDNA_VOPC: _disasm_vopc, CDNA_VOPC_LIT: _disasm_vopc, CDNA_VOPC_SDWA_SDST: _disasm_vopc_sdwa,
+  CDNA_SOP1: _disasm_sop1, CDNA_SOP1_LIT: _disasm_sop1, CDNA_SOP2: _disasm_sop2, CDNA_SOP2_LIT: _disasm_sop2,
+  CDNA_SOPC: _disasm_sopc, CDNA_SOPC_LIT: _disasm_sopc, CDNA_SOPK: _disasm_sopk, CDNA_SOPK_LIT: _disasm_sopk, CDNA_SOPP: _disasm_sopp,
+  CDNA_SMEM: _disasm_smem, CDNA_DS: _disasm_ds, CDNA_FLAT: _disasm_flat, CDNA_GLOBAL: _disasm_flat, CDNA_SCRATCH: _disasm_flat,
+  CDNA_VOP3: _disasm_vop3a, CDNA_VOP3_SDST: _disasm_vop3b, CDNA_VOP3SD: _disasm_vop3b, CDNA_VOP3P: _disasm_cdna_vop3p, CDNA_VOP3P_MFMA: _disasm_cdna_vop3p,
+  CDNA_MUBUF: _disasm_mubuf, CDNA_VOP3PX2: _disasm_vop3px2})

extra/assembly/amd/dsl.py

@@ -0,0 +1,450 @@
+# dsl.py - clean DSL for AMD assembly
+from typing import Any
+
+# ══════════════════════════════════════════════════════════════
+# Registers - unified src encoding space (0-511)
+# ══════════════════════════════════════════════════════════════
+
+class Reg:
+  # Register names vary by arch: RDNA has NULL@124/M0@125, CDNA has M0@124/reserved@125
+  # RDNA4 has DPP8@233, CDNA has SDWA@249/DPP@250/VCCZ@251/EXECZ@252
+  _NAMES = {102: "FLAT_SCRATCH_LO", 103: "FLAT_SCRATCH_HI", 104: "XNACK_MASK_LO", 105: "XNACK_MASK_HI",
+            106: "VCC_LO", 107: "VCC_HI", 124: "NULL", 125: "M0", 126: "EXEC_LO", 127: "EXEC_HI",
+            233: "DPP8", 234: "DPP8FI", 235: "SHARED_BASE", 236: "SHARED_LIMIT", 237: "PRIVATE_BASE", 238: "PRIVATE_LIMIT",
+            240: "0.5", 241: "-0.5", 242: "1.0", 243: "-1.0", 244: "2.0", 245: "-2.0", 246: "4.0", 247: "-4.0",
+            248: "INV_2PI", 249: "SDWA", 250: "DPP", 251: "VCCZ", 252: "EXECZ", 253: "SCC", 254: "SRC_LDS_DIRECT", 255: "LIT"}
+  _PAIRS = {106: "VCC", 126: "EXEC"}
+
+  def __init__(self, offset: int = 0, sz: int = 512, *, neg: bool = False, abs_: bool = False, hi: bool = False):
+    self.offset, self.sz = offset, sz
+    self.neg, self.abs_, self.hi = neg, abs_, hi
+
+  def __hash__(self): return hash((self.offset, self.sz, self.neg, self.abs_, self.hi))
+  def __getitem__(self, key):
+    if isinstance(key, slice):
+      start, stop = key.start or 0, key.stop or (self.sz - 1)
+      if start < 0 or stop >= self.sz: raise RuntimeError(f"slice [{start}:{stop}] out of bounds for size {self.sz}")
+      return Reg(self.offset + start, stop - start + 1)
+    if key < 0 or key >= self.sz: raise RuntimeError(f"index {key} out of bounds for size {self.sz}")
+    return Reg(self.offset + key, 1)
+  def __eq__(self, other):
+    if isinstance(other, Reg):
+      return (self.offset == other.offset and self.sz == other.sz and
+              self.neg == other.neg and self.abs_ == other.abs_ and self.hi == other.hi)
+    return NotImplemented
+  def __add__(self, other):
+    if isinstance(other, int): return Reg(self.offset + other, self.sz)
+    return NotImplemented
+  def __neg__(self) -> 'Reg': return Reg(self.offset, self.sz, neg=not self.neg, abs_=self.abs_, hi=self.hi)
+  def __abs__(self) -> 'Reg': return Reg(self.offset, self.sz, neg=self.neg, abs_=True, hi=self.hi)
+  @property
+  def h(self) -> 'Reg': return Reg(self.offset, self.sz, neg=self.neg, abs_=self.abs_, hi=True)
+  @property
+  def l(self) -> 'Reg': return Reg(self.offset, self.sz, neg=self.neg, abs_=self.abs_, hi=False)
+  def fmt(self, sz=None, parens=False, upper=False) -> str:
+    o, sz = self.offset, sz or self.sz
+    l, r = ("[", "]") if parens or sz > 1 else ("", "")  # brackets for multi-reg or when parens=True
+    if 256 <= o < 512: idx = o - 256; base = f"v{l}{idx}{r}" if sz == 1 else f"v[{idx}:{idx + sz - 1}]"
+    elif o < 106: base = f"s{l}{o}{r}" if sz == 1 else f"s[{o}:{o + sz - 1}]"
+    elif sz == 2 and o in self._PAIRS: base = self._PAIRS[o] if upper else self._PAIRS[o].lower()
+    elif o in self._NAMES: base = self._NAMES[o] if upper else self._NAMES[o].lower()  # special regs (any sz)
+    elif 108 <= o < 124: idx = o - 108; base = f"ttmp{l}{idx}{r}" if sz == 1 else f"ttmp[{idx}:{idx + sz - 1}]"
+    elif 128 <= o <= 192: base = str(o - 128)  # inline int constants (0-64)
+    elif 193 <= o <= 208: base = str(-(o - 192))  # inline negative int constants (-1 to -16)
+    else: raise RuntimeError(f"unknown register: offset={o}, sz={sz}")
+    if self.hi: base += ".h"
+    if self.abs_: base = f"abs({base})" if upper else f"|{base}|"
+    if self.neg: base = f"-{base}"
+    return base
+  def __repr__(self): return self.fmt(parens=True, upper=True)
+
+# Full src encoding space
+src = Reg(0, 512)
+
+# Slices for each region (inclusive end)
+s = src[0:105]           # SGPR0-105
+VCC_LO = src[106]
+VCC_HI = src[107]
+VCC = src[106:107]
+ttmp = src[108:123]      # TTMP0-15
+NULL = OFF = src[124]
+M0 = src[125]
+EXEC_LO = src[126]
+EXEC_HI = src[127]
+EXEC = src[126:127]
+# 128: 0, 129-192: integers 1-64, 193-208: integers -1 to -16
+# 240-248: float constants (0.5, -0.5, 1.0, -1.0, 2.0, -2.0, 4.0, -4.0, 1/(2*PI))
+INV_2PI = src[248]
+SDWA = src[249]
+DPP = DPP16 = src[250]
+VCCZ = src[251]
+EXECZ = src[252]
+SCC = src[253]
+SRC_LDS_DIRECT = src[254]
+LIT = src[255]           # literal constant marker
+v = src[256:511]         # VGPR0-255
+
+# ══════════════════════════════════════════════════════════════
+# BitField
+# ══════════════════════════════════════════════════════════════
+
+class _Bits:
+  """Helper for defining bit fields with slice syntax: bits[hi:lo] or bits[n]."""
+  def __getitem__(self, key) -> 'BitField': return BitField(key.start, key.stop) if isinstance(key, slice) else BitField(key, key)
+bits = _Bits()
+
+class BitField:
+  name: str | None
+  def __init__(self, hi: int, lo: int, default: int = 0):
+    self.hi, self.lo, self.default, self.name, self.mask = hi, lo, default, None, (1 << (hi - lo + 1)) - 1
+  def __set_name__(self, owner, name: str): self.name = name
+  def __eq__(self, other) -> 'FixedBitField':  # type: ignore[override]
+    if isinstance(other, int): return FixedBitField(self.hi, self.lo, other)
+    raise TypeError(f"BitField.__eq__ expects int, got {type(other).__name__}")
+  def enum(self, enum_cls) -> 'EnumBitField': return EnumBitField(self.hi, self.lo, enum_cls)
+  def encode(self, val) -> int:
+    assert isinstance(val, int), f"BitField.encode expects int, got {type(val).__name__}"
+    return val
+  def decode(self, val): return val
+  def set(self, raw: int, val) -> int:
+    if val is None: val = self.default
+    encoded = self.encode(val)
+    # Handle signed values: convert negative to 2's complement
+    if encoded < 0: encoded = encoded & self.mask
+    if encoded < 0 or encoded > self.mask: raise RuntimeError(f"field '{self.name}': value {encoded} doesn't fit in {self.hi - self.lo + 1} bits")
+    return (raw & ~(self.mask << self.lo)) | (encoded << self.lo)
+  def __get__(self, obj, objtype=None):
+    if obj is None: return self
+    return self.decode((obj._raw >> self.lo) & self.mask)
+  def __set__(self, obj, val): obj._raw = self.set(obj._raw, val)
+
+class FixedBitField(BitField):
+  def set(self, raw: int, val=None) -> int:
+    assert val is None, f"FixedBitField does not accept values, got {val}"
+    return super().set(raw, self.default)
+
+class EnumBitField(BitField):
+  def __init__(self, hi: int, lo: int, enum_cls, allowed: set | None = None):
+    super().__init__(hi, lo)
+    self._enum = enum_cls
+    self.allowed = allowed  # if set, only these enum values are valid for this encoding
+  def encode(self, val) -> int:
+    if not isinstance(val, self._enum): raise RuntimeError(f"expected {self._enum.__name__}, got {type(val).__name__}")
+    if self.allowed is not None and val not in self.allowed:
+      raise RuntimeError(f"opcode {val.name} not allowed in this encoding")
+    return val.value
+  def decode(self, raw): return self._enum(raw)
+
+# ══════════════════════════════════════════════════════════════
+# Typed fields
+# ══════════════════════════════════════════════════════════════
+
+import struct
+def _f32(f: float) -> int: return struct.unpack('I', struct.pack('f', f))[0]
+
+class SrcField(BitField):
+  _valid_range = (0, 511)  # inclusive
+  _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}
+
+  def __init__(self, hi: int, lo: int, default=s[0]):
+    super().__init__(hi, lo, default)
+    expected_size = self._valid_range[1] - self._valid_range[0] + 1
+    actual_size = 1 << (hi - lo + 1)
+    if actual_size != expected_size:
+      raise RuntimeError(f"{self.__class__.__name__}: field size {hi - lo + 1} bits ({actual_size}) doesn't match range {self._valid_range} ({expected_size})")
+
+  def encode(self, val) -> int:
+    """Encode value. Returns 255 (literal marker) for out-of-range values."""
+    if isinstance(val, Reg): offset = val.offset
+    elif isinstance(val, float): offset = self._FLOAT_ENC.get(val, 255)
+    elif isinstance(val, int) and 0 <= val <= 64: offset = 128 + val
+    elif isinstance(val, int) and -16 <= val < 0: offset = 192 - val
+    elif isinstance(val, int): offset = 255  # literal
+    else: raise TypeError(f"invalid src value {val}")
+    if not (self._valid_range[0] <= offset <= self._valid_range[1]):
+      raise TypeError(f"{self.__class__.__name__}: {val} (offset {offset}) out of range {self._valid_range}")
+    return offset - self._valid_range[0]
+
+  def decode(self, raw): return src[raw + self._valid_range[0]]
+
+  def __get__(self, obj, objtype=None):
+    if obj is None: return self
+    reg = self.decode((obj._raw >> self.lo) & self.mask)
+    # Resize register based on operand info (skip non-resizable special registers)
+    # VCC/EXEC pairs (106, 126), NULL (124), M0 (125), float constants (240-255)
+    if reg.offset not in (124, 125) and not 240 <= reg.offset <= 255:
+      # Map variant field names (vsrc0->src0, vsrc1->src1, etc.) for DPP/SDWA classes
+      assert self.name is not None
+      name = self.name[1:] if self.name.startswith('v') and self.name[1:] in obj.op_regs else self.name
+      if sz := obj.op_regs.get(name, 1): reg = Reg(reg.offset, sz, neg=reg.neg, abs_=reg.abs_, hi=reg.hi)
+    return reg
+
+class VGPRField(SrcField):
+  _valid_range = (256, 511)
+  def __init__(self, hi: int, lo: int, default=v[0]): super().__init__(hi, lo, default)
+  def encode(self, val) -> int:
+    if not isinstance(val, Reg): raise TypeError(f"VGPRField requires Reg, got {type(val).__name__}")
+    # For 8-bit vdst fields in VOP1/VOP2 16-bit ops, bit 7 is opsel for dest half
+    encoded = super().encode(val)
+    if val.hi and (self.hi - self.lo + 1) == 8:
+      if encoded >= 128:
+        raise ValueError(f"VGPRField: v[{encoded}].h not encodable in 8-bit field (v[0:127] only for .h)")
+      encoded |= 0x80
+    return encoded
+class SGPRField(SrcField): _valid_range = (0, 127)
+class SSrcField(SrcField): _valid_range = (0, 255)
+
+class AlignedSGPRField(BitField):
+  """SGPR field with alignment requirement. Encoded as sgpr_index // alignment."""
+  _align: int = 2
+  def encode(self, val):
+    if isinstance(val, int) and val == 0: return 0  # default: encode as s[0]
+    if not isinstance(val, Reg): raise TypeError(f"{self.__class__.__name__} requires Reg, got {type(val).__name__}")
+    if not (0 <= val.offset < 128): raise ValueError(f"{self.__class__.__name__} requires SGPR, got offset {val.offset}")
+    if val.offset & (self._align - 1): raise ValueError(f"{self.__class__.__name__} requires {self._align}-aligned SGPR, got s[{val.offset}]")
+    return val.offset >> (self._align.bit_length() - 1)
+  def decode(self, raw): return src[raw << (self._align.bit_length() - 1)]
+  def __get__(self, obj, objtype=None):
+    if obj is None: return self
+    reg = self.decode((obj._raw >> self.lo) & self.mask)
+    if sz := obj.op_regs.get(self.name, 1): reg = Reg(reg.offset, sz, neg=reg.neg, abs_=reg.abs_, hi=reg.hi)
+    return reg
+
+class SBaseField(AlignedSGPRField): _align = 2
+class SRsrcField(AlignedSGPRField): _align = 4
+
+class VDSTYField(BitField):
+  """VOPD vdsty: encoded = vgpr_idx >> 1. Actual vgpr = (encoded << 1) | ((vdstx & 1) ^ 1)."""
+  def encode(self, val):
+    if not isinstance(val, Reg): raise TypeError(f"VDSTYField requires Reg, got {type(val).__name__}")
+    if not (256 <= val.offset < 512): raise ValueError(f"VDSTYField requires VGPR, got offset {val.offset}")
+    return (val.offset - 256) >> 1
+  def __get__(self, obj, objtype=None):
+    if obj is None: return self
+    raw = (obj._raw >> self.lo) & self.mask
+    vdstx_bit0 = (obj.vdstx.offset - 256) & 1
+    vgpr_idx = (raw << 1) | (vdstx_bit0 ^ 1)
+    return Reg(256 + vgpr_idx, 1)
+
+# ══════════════════════════════════════════════════════════════
+# Operand info from XML
+# ══════════════════════════════════════════════════════════════
+
+import functools
+from extra.assembly.amd.autogen.rdna3.operands import OPERANDS as OPERANDS_RDNA3
+from extra.assembly.amd.autogen.rdna4.operands import OPERANDS as OPERANDS_RDNA4
+from extra.assembly.amd.autogen.cdna.operands import OPERANDS as OPERANDS_CDNA
+OPERANDS = {**OPERANDS_CDNA, **OPERANDS_RDNA3, **OPERANDS_RDNA4}
+
+# ══════════════════════════════════════════════════════════════
+# Inst base class
+# ══════════════════════════════════════════════════════════════
+
+def _needs_literal(val) -> bool:
+  """Check if a value needs a literal constant (can't be encoded inline)."""
+  if val is None or isinstance(val, Reg): return False
+  if isinstance(val, float): return val not in SrcField._FLOAT_ENC
+  if isinstance(val, int): return not (0 <= val <= 64 or -16 <= val < 0)
+  return False
+
+def _get_variant(cls, suffix: str):
+  """Get a variant class by suffix (e.g., '_LIT') via module lookup."""
+  import sys
+  module = sys.modules.get(cls.__module__)
+  return getattr(module, f"{cls.__name__}{suffix}", None) if module else None
+
+def _canonical_name(name: str) -> str | None:
+  """Map operand name to canonical name."""
+  if name in ('src0', 'vsrc0', 'ssrc0'): return 's0'
+  if name in ('src1', 'vsrc1', 'ssrc1'): return 's1'
+  if name == 'src2': return 's2'
+  if name in ('vdst', 'sdst', 'sdata'): return 'd'
+  if name in ('data', 'vdata', 'data0', 'vsrc'): return 'data'
+  return None
+
+class Inst:
+  _fields: list[tuple[str, BitField]]
+  _base_size: int
+
+  def __init_subclass__(cls):
+    # Collect fields from all parent classes, then override with this class's fields
+    inherited = {}
+    for base in reversed(cls.__mro__[1:]):
+      if hasattr(base, '_fields'):
+        inherited.update({name: field for name, field in base._fields})
+    inherited.update({name: val for name, val in cls.__dict__.items() if isinstance(val, BitField)})
+    cls._fields = list(inherited.items())
+    cls._base_size = (max(f.hi for _, f in cls._fields) + 8) // 8
+
+  def __new__(cls, *args, **kwargs):
+    # Auto-upgrade to variant if needed (only for base classes, not variants)
+    if not any(cls.__name__.endswith(sfx) for sfx in ('_LIT', '_DPP16', '_DPP8', '_SDWA', '_SDWA_SDST', '_MFMA')):
+      args_iter = iter(args)
+      for name, field in cls._fields:
+        if isinstance(field, FixedBitField): continue
+        val = kwargs.get(name) if name in kwargs else next(args_iter, None)
+        if not isinstance(field, SrcField): continue
+        if isinstance(val, Reg) and val.offset == 255 and (lit_cls := _get_variant(cls, '_LIT')): return lit_cls(*args, **kwargs)
+        if isinstance(val, Reg) and val.offset == 249:
+          if (sdwa_cls := _get_variant(cls, '_SDWA') or _get_variant(cls, '_SDWA_SDST')): return sdwa_cls(*args, **kwargs)
+        if isinstance(val, Reg) and val.offset == 250 and (dpp_cls := _get_variant(cls, '_DPP16')): return dpp_cls(*args, **kwargs)
+        if _needs_literal(val) and (lit_cls := _get_variant(cls, '_LIT')): return lit_cls(*args, **kwargs)
+    return object.__new__(cls)
+
+  def __init__(self, *args, **kwargs):
+    self._raw = 0
+    # Map positional args to field names (skip FixedBitFields)
+    args_iter = iter(args)
+    vals: dict[str, Any] = {}
+    for name, field in self._fields:
+      if isinstance(field, FixedBitField): vals[name] = None
+      elif name in kwargs: vals[name] = kwargs[name]
+      else: vals[name] = next(args_iter, None)
+    assert not (remaining := list(args_iter)), f"too many positional args: {remaining}"
+    # Extract modifiers from Reg objects and merge into neg/abs/opsel
+    neg_bits, abs_bits, opsel_bits = 0, 0, 0
+    for name, bit in [('src0', 0), ('src1', 1), ('src2', 2)]:
+      if name in vals and isinstance(vals[name], Reg):
+        reg = vals[name]
+        if reg.neg: neg_bits |= (1 << bit)
+        if reg.abs_: abs_bits |= (1 << bit)
+        if reg.hi: opsel_bits |= (1 << bit)
+    if 'vdst' in vals and isinstance(vals['vdst'], Reg) and vals['vdst'].hi:
+      opsel_bits |= (1 << 3)
+    if neg_bits: vals['neg'] = (vals.get('neg') or 0) | neg_bits
+    if abs_bits: vals['abs'] = (vals.get('abs') or 0) | abs_bits
+    if opsel_bits: vals['opsel'] = (vals.get('opsel') or 0) | opsel_bits
+    # For _LIT classes, capture literal value from SrcFields that encode to 255
+    literal_val = None
+    for name, field in self._fields:
+      val = vals[name]
+      if isinstance(field, SrcField) and val is not None and _needs_literal(val):
+        literal_val = _f32(val) if isinstance(val, float) else val & 0xFFFFFFFF
+    if literal_val is not None and 'literal' in vals:
+      vals['literal'] = literal_val
+    # Set all field values
+    for name, field in self._fields:
+      self._raw = field.set(self._raw, vals[name])
+    # Validate register sizes against operand info (skip special registers like NULL, VCC, EXEC, SDWA/DPP markers)
+    for name, expected in self.op_regs.items():
+      if (val := vals.get(name)) is None: continue
+      if isinstance(val, Reg) and val.sz != expected and not (106 <= val.offset <= 127 or 249 <= val.offset <= 255):
+        raise TypeError(f"{name} expects {expected} register(s), got {val.sz}")
+
+  @property
+  def op_name(self) -> str: return getattr(self, 'op').name
+  @property
+  def operands(self) -> dict: return OPERANDS.get(getattr(self, 'op'), {}) if hasattr(self, 'op') else {}
+  def _is_cdna(self) -> bool: return 'cdna' in type(self).__module__
+
+  @functools.cached_property
+  def op_bits(self) -> dict[str, int]:
+    """Get bit widths for each operand field, with WAVE32 and addr/saddr adjustments."""
+    if not hasattr(self, 'op'): return {k: v[1] for k, v in self.operands.items()}
+    bits = {k: v[1] for k, v in self.operands.items()}
+    # RDNA (WAVE32): condition masks, carry flags, and compare results are 32-bit
+    if not self._is_cdna():
+      name = self.op_name.lower()
+      if 'cndmask' in name and 'src2' in bits: bits['src2'] = 32
+      if '_co_ci_' in name and 'src2' in bits: bits['src2'] = 32  # carry-in source
+      # VOP3SD: sdst is always wavefront-size dependent (carry-out or condition mask)
+      if 'VOP3SD' in type(self).__name__ and 'sdst' in bits: bits['sdst'] = 32
+      if 'cmp' in name and 'vdst' in bits: bits['vdst'] = 32
+    # GLOBAL/FLAT: addr is 32-bit if saddr is valid SGPR, 64-bit if saddr is NULL
+    # SCRATCH: addr is always 32-bit (offset from scratch base, not absolute address)
+    if 'addr' in bits and (saddr_field := getattr(type(self), 'saddr', None)) and type(self).__name__ not in ('SCRATCH', 'VSCRATCH'):
+      saddr_val = (self._raw >> saddr_field.lo) & saddr_field.mask  # access _raw directly to avoid recursion
+      bits['addr'] = 64 if saddr_val in (124, 125) else 32  # 124=NULL, 125=M0
+    # MUBUF/MTBUF: vaddr size depends on offen/idxen (1 or 2 regs)
+    if 'vaddr' in bits and hasattr(self, 'offen') and hasattr(self, 'idxen'):
+      bits['vaddr'] = max(1, self.offen + self.idxen) * 32
+    # F8F6F4 MFMA: CBSZ selects matrix A format, BLGP selects matrix B format
+    # VGPRs: FP8/BF8(0,1)=8, FP6/BF6(2,3)=6, FP4(4)=4
+    if 'f8f6f4' in getattr(self, 'op_name', '').lower():
+      # Use explicit fields if available (VOP3PX2), else extract from VOP3P-MAI bit positions
+      cbsz = getattr(self, 'cbsz') if hasattr(type(self), 'cbsz') else (self._raw >> 8) & 0x7
+      blgp = getattr(self, 'blgp') if hasattr(type(self), 'blgp') else (self._raw >> 61) & 0x7
+      vgprs = {0: 8, 1: 8, 2: 6, 3: 6, 4: 4}
+      bits['src0'], bits['src1'] = vgprs.get(cbsz, 8) * 32, vgprs.get(blgp, 8) * 32
+    return bits
+  @property
+  def op_regs(self) -> dict[str, int]:
+    """Get register counts for each operand field."""
+    return {k: max(1, v // 32) for k, v in self.op_bits.items()}
+
+  @functools.cached_property
+  def canonical_op_bits(self) -> dict[str, int]:
+    """Get bit widths with canonical names: {'s0', 's1', 's2', 'd', 'data'}."""
+    bits = {'d': 32, 's0': 32, 's1': 32, 's2': 32, 'data': 32}
+    for name, val in self.op_bits.items():
+      if (cn := _canonical_name(name)): bits[cn] = val
+    return bits
+
+  @functools.cached_property
+  def canonical_operands(self) -> dict:
+    """Get operands with canonical names: {'s0', 's1', 's2', 'd', 'data'}."""
+    result = {}
+    for name, val in self.operands.items():
+      if (cn := _canonical_name(name)): result[cn] = val
+    return result
+
+  @property
+  def canonical_op_regs(self) -> dict[str, int]:
+    """Get register counts with canonical names: {'s0', 's1', 's2', 'd', 'data'}."""
+    return {k: max(1, v // 32) for k, v in self.canonical_op_bits.items()}
+
+  def num_srcs(self) -> int:
+    """Get number of source operands from operand info."""
+    ops = self.operands
+    if 'src2' in ops: return 3
+    if 'src1' in ops or 'vsrc1' in ops or 'ssrc1' in ops: return 2
+    if 'src0' in ops or 'vsrc0' in ops or 'ssrc0' in ops: return 1
+    return 0
+  @classmethod
+  def _size(cls) -> int: return cls._base_size
+  def size(self) -> int: return self._base_size
+  def disasm(self) -> str:
+    from extra.assembly.amd.disasm import disasm
+    return disasm(self)
+
+  def to_bytes(self) -> bytes: return self._raw.to_bytes(self._base_size, 'little')
+
+  @property
+  def _literal(self) -> int | None:
+    """Get the literal value if this instruction has one."""
+    return getattr(self, 'literal', None)
+
+  def _variant_suffix(self) -> str | None:
+    """Check if instruction needs a variant class (_LIT, _DPP8, _DPP16, _SDWA). Returns suffix or None."""
+    cls_name = type(self).__name__
+    # Don't check for variants if we're already a variant class
+    if any(s in cls_name for s in ('_LIT', '_DPP8', '_DPP16', '_SDWA')): return None
+    # VOPD: FMAMK/FMAAK opcodes always require literal (check by name since enum may differ across archs)
+    for name in ('opx', 'opy'):
+      if hasattr(self, name) and any(x in getattr(self, name).name for x in ('FMAMK', 'FMAAK')): return '_LIT'
+    for name, field in self._fields:
+      if isinstance(field, SrcField):
+        off = getattr(self, name).offset
+        if off == 255: return '_LIT'
+        if off == 249: return '_SDWA' if self._is_cdna() else '_DPP8'
+        if off == 250: return '_DPP16'
+    return None
+
+  @classmethod
+  def from_bytes(cls, data: bytes):
+    inst = object.__new__(cls)
+    inst._raw = int.from_bytes(data[:cls._base_size], 'little')
+    # Upgrade to variant class if needed (_LIT, _DPP8, _DPP16, _SDWA)
+    if (suffix := inst._variant_suffix()) and (var_cls := _get_variant(cls, suffix)) is not None:
+      return var_cls.from_bytes(data)
+    return inst
+
+  def __eq__(self, other): return type(self) is type(other) and self._raw == other._raw
+  def __hash__(self): return hash((type(self), self._raw))
+
+  def __repr__(self):
+    # collect (repr, is_default) pairs, strip trailing defaults so repr roundtrips with eval
+    name = self.op.name.lower() if hasattr(self, 'op') else type(self).__name__
+    parts = [(repr(v := getattr(self, n)), v == f.default) for n, f in self._fields if n != 'op' and not isinstance(f, FixedBitField)]
+    while parts and parts[-1][1]: parts.pop()
+    return f"{name}({', '.join(p[0] for p in parts)})"

extra/assembly/amd/generate.py

@@ -0,0 +1,480 @@
+# AMD ISA code generator - generates enum.py, ins.py, operands.py, str_pcode.py
+# Sources: XML from https://gpuopen.com/download/machine-readable-isa/latest/
+#          PDF manuals from AMD documentation
+import re, zlib, xml.etree.ElementTree as ET, zipfile
+from tinygrad.helpers import fetch
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# Configuration
+# ═══════════════════════════════════════════════════════════════════════════════
+
+ARCHS = {
+  "rdna3": {"xml": "amdgpu_isa_rdna3_5.xml", "pdf": "https://docs.amd.com/api/khub/documents/UVVZM22UN7tMUeiW_4ShTQ/content"},
+  "rdna4": {"xml": "amdgpu_isa_rdna4.xml", "pdf": "https://docs.amd.com/api/khub/documents/uQpkEvk3pv~kfAb2x~j4uw/content"},
+  "cdna": {"xml": "amdgpu_isa_cdna4.xml", "pdf": "https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-cdna4-instruction-set-architecture.pdf"},
+}
+XML_URL = "https://gpuopen.com/download/machine-readable-isa/latest/"
+# Map XML encoding names to codebase names
+NAME_MAP = {"VOP3_SDST_ENC": "VOP3SD", "VOP3_SDST_ENC_LIT": "VOP3SD_LIT", "VOP3_SDST_ENC_DPP16": "VOP3SD_DPP16",
+            "VOP3_SDST_ENC_DPP8": "VOP3SD_DPP8", "VOPDXY": "VOPD", "VOPDXY_LIT": "VOPD_LIT", "VDS": "DS"}
+# Instructions missing from XML but present in PDF
+FIXES = {"rdna3": {"SOPK": {22: "S_SUBVECTOR_LOOP_BEGIN", 23: "S_SUBVECTOR_LOOP_END"}, "FLAT": {55: "FLAT_ATOMIC_CSUB_U32"}},
+         "rdna4": {"SOP1": {80: "S_GET_BARRIER_STATE", 81: "S_BARRIER_INIT", 82: "S_BARRIER_JOIN"}, "SOPP": {9: "S_WAITCNT", 21: "S_BARRIER_LEAVE"}},
+         "cdna": {"DS": {152: "DS_GWS_SEMA_RELEASE_ALL", 154: "DS_GWS_SEMA_V", 156: "DS_GWS_SEMA_P"},
+                  "VOP3P": {44: "V_MFMA_LD_SCALE_B32", 62: "V_MFMA_F32_16X16X8_XF32", 63: "V_MFMA_F32_32X32X4_XF32"}}}
+# Fields missing from XML but present in hardware (format: {arch: {encoding: [(name, hi, lo), ...]}})
+FIELD_FIXES = {"cdna": {"VOP3P": [("opsel_hi2", 14, 14)]}}
+# Encoding suffixes to strip (variants we don't generate separate classes for)
+_ENC_SUFFIXES = ("_NSA1",)
+# Encoding suffix to class suffix mapping (for variants we DO generate)
+_ENC_SUFFIX_MAP = {"_INST_LITERAL": "_LIT", "_VOP_DPP16": "_DPP16", "_VOP_DPP": "_DPP16", "_VOP_DPP8": "_DPP8",
+                   "_VOP_SDWA": "_SDWA", "_VOP_SDWA_SDST_ENC": "_SDWA_SDST", "_MFMA": "_MFMA"}
+# Field name normalization
+_FIELD_RENAMES = {"opsel_hi_2": "opsel_hi2", "op_sel_hi_2": "opsel_hi2", "op_sel": "opsel", "bound_ctrl": "bc",
+                  "tgt": "target", "row_en": "row", "unorm": "unrm", "clamp": "clmp", "wait_exp": "waitexp",
+                  "simm32": "literal", "dpp_ctrl": "dpp", "acc_cd": "acc_cd", "acc": "acc",
+                  "dst_sel": "dst_sel", "dst_unused": "dst_unused", "src0_sel": "src0_sel", "src1_sel": "src1_sel"}
+# Encoding variants to skip entirely (NSA is for MIMG graphics instructions)
+_SKIP_ENCODINGS = ("NSA",)
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# XML parsing helpers
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def _strip_enc(name: str) -> str:
+  """Strip ENC_ prefix and normalize encoding suffixes."""
+  name = name.removeprefix("ENC_")
+  for sfx in _ENC_SUFFIXES: name = name.replace(sfx, "")
+  # Process longer suffixes first to avoid partial matches (e.g., _VOP_DPP8 before _VOP_DPP)
+  for old, new in sorted(_ENC_SUFFIX_MAP.items(), key=lambda x: -len(x[0])): name = name.replace(old, new)
+  return name
+
+def _norm_field(name: str) -> str:
+  """Normalize field name to match expected names."""
+  for old, new in _FIELD_RENAMES.items(): name = name.replace(old, new)
+  return name
+
+def _map_flat(enc_name: str, instr_name: str) -> str:
+  """Map FLAT/GLOBAL/SCRATCH encoding to correct enum based on instruction prefix."""
+  if enc_name in ("FLAT_GLBL", "FLAT_GLOBAL"): return "GLOBAL"
+  if enc_name == "FLAT_SCRATCH": return "SCRATCH"
+  if enc_name in ("FLAT", "VFLAT", "VGLOBAL", "VSCRATCH"):
+    v = "V" if enc_name.startswith("V") else ""
+    if instr_name.startswith("GLOBAL_"): return f"{v}GLOBAL"
+    if instr_name.startswith("SCRATCH_"): return f"{v}SCRATCH"
+    return f"{v}FLAT"
+  return enc_name
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# XML parsing
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def parse_xml(filename: str):
+  root = ET.fromstring(zipfile.ZipFile(fetch(XML_URL)).read(filename))
+  encodings, enums, types, fmts, op_types_set = {}, {}, {}, {}, set()
+  # Extract HWREG and MSG enums from OperandTypes
+  op_enum_map = {("OPR_HWREG", "ID"): "HWREG", ("OPR_SENDMSG_RTN", "MSG"): "MSG"}
+  for ot in root.findall(".//OperandTypes/OperandType"):
+    ot_name = ot.findtext("OperandTypeName")
+    for field in ot.findall(".//Field"):
+      if (enum_name := op_enum_map.get((ot_name, field.findtext("FieldName")))):
+        enums[enum_name] = {int(pv.findtext("Value")): pv.findtext("Name").upper() for pv in field.findall(".//PredefinedValue")}
+  # Extract DataFormats with BitCount
+  for df in root.findall("ISA/DataFormats/DataFormat"):
+    name, bits = df.findtext("DataFormatName"), df.findtext("BitCount")
+    if name and bits: fmts[name] = int(bits)
+  # Extract encoding definitions
+  for enc in root.findall("ISA/Encodings/Encoding"):
+    name = enc.findtext("EncodingName")
+    is_base = name.startswith("ENC_") or name in ("VOP3_SDST_ENC", "VOPDXY")
+    is_variant = any(sfx in name for sfx in _ENC_SUFFIX_MAP)
+    if not is_base and not is_variant: continue
+    if any(s in name for s in _SKIP_ENCODINGS): continue
+    fields = [(_norm_field(f.findtext("FieldName").lower()), int(f.find("BitLayout/Range").findtext("BitOffset") or 0) + int(f.find("BitLayout/Range").findtext("BitCount") or 0) - 1,
+               int(f.find("BitLayout/Range").findtext("BitOffset") or 0))
+              for f in enc.findall(".//MicrocodeFormat/BitMap/Field") if f.find("BitLayout/Range") is not None]
+    ident = (enc.findall("EncodingIdentifiers/EncodingIdentifier") or [None])[0]
+    enc_field = next((f for f in fields if f[0] == "encoding"), None)
+    # For multi-dword formats, encoding field may be in higher dword but identifier pattern is always in dword0; use % 32
+    enc_bits = "".join(ident.text[len(ident.text)-1-b] for b in range(enc_field[1] % 32, (enc_field[2] % 32)-1, -1)) if ident is not None and enc_field else None
+    base_name = _strip_enc(name)
+    encodings[NAME_MAP.get(base_name, base_name)] = (fields, enc_bits)
+  # Extract instruction opcodes and operand info
+  # Track which encodings each opcode appears in (for detecting LIT-only ops)
+  opcode_encs: dict[str, dict[int, set[str]]] = {}  # {base_fmt: {opcode: {enc_names}}}
+  for instr in root.findall("ISA/Instructions/Instruction"):
+    name = instr.findtext("InstructionName")
+    for enc in instr.findall("InstructionEncodings/InstructionEncoding"):
+      if enc.findtext("EncodingCondition") != "default": continue
+      base, opcode = _map_flat(_strip_enc(enc.findtext("EncodingName")), name), int(enc.findtext("Opcode") or 0)
+      enc_name = NAME_MAP.get(base, base)
+      # Encoding variants use the same Op enum as the base format
+      base_enum = enc_name
+      for sfx in ("_SDWA_SDST", "_DPP16", "_DPP8", "_SDWA", "_LIT", "_MFMA"):
+        base_enum = base_enum.replace(sfx, "")
+      # Track which encodings this opcode appears in
+      opcode_encs.setdefault(base_enum, {}).setdefault(opcode, set()).add(enc_name)
+      # ADDTID instructions go in both FLAT and GLOBAL enums (pcode uses FLATOp for these)
+      if "ADDTID" in name:
+        if base == "GLOBAL": enums.setdefault("FLAT", {})[opcode] = name
+        elif base == "VGLOBAL": enums.setdefault("VFLAT", {})[opcode] = name
+      enums.setdefault(base_enum, {})[opcode] = name
+      # Extract operand info
+      op_info = {op.findtext("FieldName").lower(): (op.findtext("DataFormatName"), int(op.findtext("OperandSize") or 0), op.findtext("OperandType"))
+                 for op in enc.findall("Operands/Operand") if op.findtext("FieldName")}
+      for fmt, _, otype in op_info.values():
+        if fmt and fmt not in fmts: fmts[fmt] = 0
+        if otype: op_types_set.add(otype)
+      if op_info: types[(name, base_enum)] = op_info
+  # Find opcodes that only exist in a specific variant encoding (no base format version)
+  suffix_only_ops: dict[str, dict[str, set[int]]] = {}  # {suffix: {base_fmt: {opcodes}}}
+  for base_fmt, opcodes in opcode_encs.items():
+    for opcode, encs in opcodes.items():
+      suffix = next((s for s in _ENC_SUFFIX_MAP.values() if all(s in e for e in encs)), None)
+      if suffix is not None: suffix_only_ops.setdefault(suffix, {}).setdefault(base_fmt, set()).add(opcode)
+  return encodings, enums, types, fmts, op_types_set, suffix_only_ops
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# PDF parsing
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def extract_pdf_text(url: str) -> list[list[tuple[float, float, str, str]]]:
+  """Extract positioned text from PDF. Returns list of text elements (x, y, text, font) per page."""
+  data = fetch(url).read_bytes()
+  # Parse xref table to locate objects
+  xref: dict[int, int] = {}
+  pos = int(re.search(rb'startxref\s+(\d+)', data).group(1)) + 4
+  while data[pos:pos+7] != b'trailer':
+    while data[pos:pos+1] in b' \r\n': pos += 1
+    line_end = data.find(b'\n', pos)
+    start_obj, count = map(int, data[pos:line_end].split()[:2])
+    pos = line_end + 1
+    for i in range(count):
+      if data[pos+17:pos+18] == b'n' and (off := int(data[pos:pos+10])) > 0: xref[start_obj + i] = off
+      pos += 20
+
+  def get_stream(n: int) -> bytes:
+    obj = data[xref[n]:data.find(b'endobj', xref[n])]
+    raw = obj[obj.find(b'stream\n') + 7:obj.find(b'\nendstream')]
+    return zlib.decompress(raw) if b'/FlateDecode' in obj else raw
+
+  pages = []
+  for n in sorted(xref):
+    if b'/Type /Page' not in data[xref[n]:xref[n]+500]: continue
+    if not (m := re.search(rb'/Contents (\d+) 0 R', data[xref[n]:xref[n]+500])): continue
+    stream = get_stream(int(m.group(1))).decode('latin-1')
+    elements, font = [], ''
+    for bt in re.finditer(r'BT(.*?)ET', stream, re.S):
+      x, y = 0.0, 0.0
+      for m in re.finditer(r'(/F[\d.]+) [\d.]+ Tf|([\d.+-]+) ([\d.+-]+) Td|[\d.+-]+ [\d.+-]+ [\d.+-]+ [\d.+-]+ ([\d.+-]+) ([\d.+-]+) Tm|<([0-9A-Fa-f]+)>.*?Tj|\[([^\]]+)\] TJ', bt.group(1)):
+        if m.group(1): font = m.group(1)
+        elif m.group(2): x, y = x + float(m.group(2)), y + float(m.group(3))
+        elif m.group(4): x, y = float(m.group(4)), float(m.group(5))
+        elif m.group(6) and (t := bytes.fromhex(m.group(6)).decode('latin-1')).strip(): elements.append((x, y, t, font))
+        elif m.group(7) and (t := ''.join(bytes.fromhex(h).decode('latin-1') for h in re.findall(r'<([0-9A-Fa-f]+)>', m.group(7)))).strip(): elements.append((x, y, t, font))
+    pages.append(sorted(elements, key=lambda e: (-e[1], e[0])))
+  return pages
+
+def extract_pcode(pages: list[list[tuple[float, float, str, str]]], name_to_op: dict[str, int]) -> dict[tuple[str, int], str]:
+  """Extract pseudocode for instructions. Returns {(name, opcode): pseudocode}."""
+  # First pass: find all instruction headers across all pages
+  all_instructions: list[tuple[int, float, str, int]] = []  # (page_idx, y, name, opcode)
+  for page_idx, page in enumerate(pages):
+    by_y: dict[int, list[tuple[float, str]]] = {}
+    for x, y, t, _ in page:
+      by_y.setdefault(round(y), []).append((x, t))
+    for y, items in sorted(by_y.items(), reverse=True):
+      left = [(x, t) for x, t in items if 55 < x < 65]
+      right = [(x, t) for x, t in items if 535 < x < 550]
+      if left and right and left[0][1] in name_to_op and right[0][1].isdigit():
+        all_instructions.append((page_idx, y, left[0][1], int(right[0][1])))
+
+  # Second pass: extract pseudocode between consecutive instructions
+  pcode: dict[tuple[str, int], str] = {}
+  for i, (page_idx, y, name, opcode) in enumerate(all_instructions):
+    if i + 1 < len(all_instructions):
+      next_page, next_y = all_instructions[i + 1][0], all_instructions[i + 1][1]
+    else:
+      next_page, next_y = page_idx, 0
+    # Collect F6 text from current position to next instruction (pseudocode is at x ≈ 69)
+    lines = []
+    for p in range(page_idx, next_page + 1):
+      start_y = y if p == page_idx else 800
+      end_y = next_y if p == next_page else 0
+      lines.extend((p, y2, t) for x, y2, t, f in pages[p] if f in ('/F6.0', '/F7.0') and end_y < y2 < start_y and 60 < x < 80)
+    if lines:
+      sorted_lines = sorted(lines, key=lambda x: (x[0], -x[1]))
+      # Stop at large Y gaps (>30) - indicates section break
+      filtered = [sorted_lines[0]]
+      for j in range(1, len(sorted_lines)):
+        prev_page, prev_y, _ = sorted_lines[j-1]
+        curr_page, curr_y, _ = sorted_lines[j]
+        if curr_page == prev_page and prev_y - curr_y > 30: break
+        if curr_page != prev_page and prev_y > 60 and curr_y < 730: break
+        filtered.append(sorted_lines[j])
+      pcode_lines = [t.replace('Ê', '').strip() for _, _, t in filtered]
+      if pcode_lines: pcode[(name, opcode)] = '\n'.join(pcode_lines)
+  return pcode
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# Code generation
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def write_common(all_fmts, all_op_types, path):
+  lines = ["# autogenerated from AMD ISA XML - do not edit", "from enum import Enum, auto", ""]
+  lines.append("class ReprEnum(Enum):")
+  lines.append('  """Enum with clean repr that roundtrips with eval()."""')
+  lines.append('  def __repr__(self): return f"{type(self).__name__}.{self.name}"')
+  lines.append("")
+  lines.append("class Fmt(Enum):")
+  for fmt in sorted(all_fmts.keys()): lines.append(f"  {fmt} = auto()")
+  lines.append("")
+  lines.append("FMT_BITS = {")
+  for fmt, bits in sorted(all_fmts.items()): lines.append(f"  Fmt.{fmt}: {bits},")
+  lines.append("}")
+  lines.append("")
+  lines.append("class OpType(Enum):")
+  for ot in sorted(all_op_types): lines.append(f"  {ot} = auto()")
+  with open(path, "w") as f: f.write("\n".join(lines))
+
+def write_enum(enums, path):
+  lines = ["# autogenerated from AMD ISA XML - do not edit", "from extra.assembly.amd.autogen.common import ReprEnum, Fmt, FMT_BITS, OpType  # noqa: F401", ""]
+  for name, ops in sorted(enums.items()):
+    if not ops: continue
+    suffix = "_E32" if name in ("VOP1", "VOP2", "VOPC") else "_E64" if name == "VOP3" else ""
+    lines.append(f"class {name}(ReprEnum):" if name in ("HWREG", "MSG") else f"class {name}Op(ReprEnum):")
+    aliases = []
+    for op, mem in sorted(ops.items()):
+      msuf = suffix if name != "VOP3" or op < 512 else ""
+      lines.append(f"  {mem}{msuf} = {op}")
+      if msuf: aliases.append((mem, msuf))
+    for mem, msuf in aliases: lines.append(f"  {mem} = {mem}{msuf}")
+    lines.append("")
+  with open(path, "w") as f: f.write("\n".join(lines))
+
+def write_ins(encodings, enums, suffix_only_ops, types, arch, path):
+  _VGPR_FIELDS = {"vdst", "vdstx", "vsrc0", "vsrc1", "vsrc2", "vsrc3", "vsrcx1", "vsrcy1", "vaddr", "vdata", "data", "data0", "data1", "addr", "vsrc"}
+  _VARIANT_SUFFIXES = ("_LIT", "_DPP16", "_DPP8", "_SDWA_SDST", "_SDWA", "_MFMA")
+  def get_base_fmt(fmt):
+    for sfx in _VARIANT_SUFFIXES: fmt = fmt.replace(sfx, "")
+    return fmt
+  def field_def(name, hi, lo, fmt, enc_bits=None):
+    bits = hi - lo + 1
+    base_fmt = get_base_fmt(fmt)
+    if name == "encoding" and enc_bits: return f"FixedBitField({hi}, {lo}, 0b{enc_bits})"
+    if name == "op" and fmt not in ("DPP", "SDWA"): return f"EnumBitField({hi}, {lo}, {base_fmt}Op)"
+    if name in ("opx", "opy"): return f"EnumBitField({hi}, {lo}, VOPDOp)"
+    if name == "vdsty": return f"VDSTYField({hi}, {lo})"
+    if name in _VGPR_FIELDS and bits == 8: return f"VGPRField({hi}, {lo})"
+    if name == "sbase" and bits == 6: return f"SBaseField({hi}, {lo})"
+    if name in ("srsrc", "ssamp") and bits == 5: return f"SRsrcField({hi}, {lo})"
+    if name in ("sdst", "sdata") and bits == 7: return f"SGPRField({hi}, {lo})"
+    if name in ("soffset", "saddr") and bits == 7: return f"SGPRField({hi}, {lo}, default=NULL)"
+    if name.startswith("ssrc") and bits == 8: return f"SSrcField({hi}, {lo})"
+    if name in ("saddr", "soffset") and bits == 8: return f"SSrcField({hi}, {lo}, default=NULL)"
+    if name.startswith("src") and bits == 9: return f"SrcField({hi}, {lo})"
+    # GLOBAL/SCRATCH: offset is 13-bit signed [12:0], FLAT: 12-bit unsigned (XML has 12-bit for all)
+    if name == "offset" and base_fmt in ("GLOBAL", "SCRATCH"): return f"BitField(12, {lo})"
+    if base_fmt == "VOP3P" and name == "opsel_hi": return f"BitField({hi}, {lo}, default=3)"
+    if base_fmt == "VOP3P" and name == "opsel_hi2": return f"BitField({hi}, {lo}, default=1)"
+    return f"BitField({hi}, {lo})"
+  ORDER = ['encoding', 'op', 'opx', 'opy', 'vdst', 'vdstx', 'vdsty', 'sdst', 'vdata', 'sdata', 'addr', 'vaddr', 'data', 'data0', 'data1',
+           'src0', 'srcx0', 'srcy0', 'vsrc0', 'ssrc0', 'src1', 'vsrc1', 'vsrcx1', 'vsrcy1', 'ssrc1', 'src2', 'vsrc2', 'src3', 'vsrc3',
+           'saddr', 'sbase', 'srsrc', 'ssamp', 'soffset', 'offset', 'simm16', 'literal', 'en', 'target', 'attr', 'attr_chan',
+           'omod', 'neg', 'neg_hi', 'abs', 'clmp', 'opsel', 'opsel_hi', 'waitexp', 'wait_va',
+           'dmask', 'dim', 'seg', 'format', 'offen', 'idxen', 'glc', 'dlc', 'slc', 'tfe', 'unrm', 'done', 'row',
+           'dpp', 'fi', 'bc', 'row_mask', 'bank_mask', 'src0_neg', 'src0_abs', 'src1_neg', 'src1_abs',
+           'cbsz', 'abid', 'acc_cd', 'acc', 'blgp', 'lane_sel_0', 'lane_sel_1', 'lane_sel_2', 'lane_sel_3',
+           'lane_sel_4', 'lane_sel_5', 'lane_sel_6', 'lane_sel_7', 'dst_sel', 'dst_unused', 'src0_sel', 'src1_sel']
+  sort_fields = lambda fields: sorted(fields, key=lambda f: (ORDER.index(f[0]) if f[0] in ORDER else 999, f[2]))
+
+  # Separate base encodings from variants
+  base_encodings, variant_encodings = {}, {}
+  for enc_name, data in encodings.items():
+    base = get_base_fmt(enc_name)
+    if base == enc_name: base_encodings[enc_name] = data
+    else: variant_encodings[enc_name] = data
+
+  # Build sets of ops by their vdst type from operand metadata
+  sdst_opcodes = {}  # ops where vdst is OPR_SREG (writes to SGPR)
+  for fmt, ops in enums.items():
+    for op, name in ops.items():
+      op_types = types.get((name, fmt), {})
+      vdst_type = op_types.get("vdst", (None, None, None))[2]
+      if vdst_type == "OPR_SREG": sdst_opcodes.setdefault(fmt, set()).add(op)
+
+  lines = ["# autogenerated from AMD ISA XML - do not edit", "# ruff: noqa: F401,F403",
+           "from extra.assembly.amd.dsl import *", f"from extra.assembly.amd.autogen.{arch}.enum import *", "import functools", ""]
+
+  def fmt_allowed(op_enum: str, ops: set[int]) -> str:
+    """Format allowed ops as {EnumName.MEMBER, ...}."""
+    names = [f"{op_enum}.{enums[op_enum.removesuffix('Op')][op]}" for op in sorted(ops)]
+    return "{" + ", ".join(names) + "}"
+
+  # Generate base classes first
+  for enc_name, (fields, enc_bits) in sorted(base_encodings.items()):
+    all_ops = set(enums.get(enc_name, {}).keys())
+    # Get suffix-only ops for this format (these can't be used in base class)
+    base_suffix_ops = set().union(*(d.get(enc_name, set()) for d in suffix_only_ops.values()))
+    # Exclude SDST ops from base class (they need VOP1_SDST/VOP3_SDST/VOP3B)
+    base_allowed = all_ops - base_suffix_ops - sdst_opcodes.get(enc_name, set())
+    # RDNA3 FLAT/GLOBAL/SCRATCH share encoding bits, differentiated by seg field
+    # RDNA4 VFLAT/VGLOBAL/VSCRATCH have distinct encoding bits, no seg field needed
+    has_seg_field = any(fn == "seg" for fn, _, _ in fields)
+    if enc_name in ("FLAT", "VFLAT") and has_seg_field:
+      prefix = "V" if enc_name == "VFLAT" else ""
+      for cls, seg, op_enum in [(f"{prefix}FLAT", 0, f"{prefix}FLATOp"), (f"{prefix}GLOBAL", 2, f"{prefix}GLOBALOp"), (f"{prefix}SCRATCH", 1, f"{prefix}SCRATCHOp")]:
+        cls_ops = set(enums.get(cls, {}).keys())
+        lines.append(f"class {cls}(Inst):")
+        for fn, hi, lo in sort_fields(fields):
+          if fn == "seg": lines.append(f"  seg = FixedBitField({hi}, {lo}, {seg})")
+          elif fn == "op": lines.append(f"  op = EnumBitField({hi}, {lo}, {op_enum}, {fmt_allowed(op_enum, cls_ops)})")
+          else: lines.append(f"  {fn} = {field_def(fn, hi, lo, cls, enc_bits)}")
+        lines.append("")
+    elif enc_name not in ("FLAT_GLOBAL", "FLAT_SCRATCH", "FLAT_GLBL", "DPP", "SDWA"):
+      lines.append(f"class {enc_name}(Inst):")
+      for fn, hi, lo in sort_fields(fields):
+        if fn == "op":
+          base_fmt = get_base_fmt(enc_name)
+          lines.append(f"  op = EnumBitField({hi}, {lo}, {base_fmt}Op, {fmt_allowed(f'{base_fmt}Op', base_allowed)})")
+        else:
+          lines.append(f"  {fn} = {field_def(fn, hi, lo, enc_name, enc_bits if fn == 'encoding' else None)}")
+      lines.append("")
+
+  # Generate variant classes that inherit from base (only add extra fields)
+  for enc_name, (fields, enc_bits) in sorted(variant_encodings.items()):
+    base = get_base_fmt(enc_name)
+    if base not in base_encodings: continue  # skip if no base class
+    base_fields = {f[0] for f in base_encodings[base][0]}
+    extra_fields = [(fn, hi, lo) for fn, hi, lo in fields if fn not in base_fields]
+    # Check if this is a suffix-only variant
+    variant_suffix = next((sfx for sfx in _VARIANT_SUFFIXES if enc_name.endswith(sfx)), None)
+    is_suffix_variant = variant_suffix in suffix_only_ops
+    all_ops = set(enums.get(base, {}).keys())
+    if extra_fields or is_suffix_variant:
+      lines.append(f"class {enc_name}({base}):")
+      op_field = next((f for f in base_encodings[base][0] if f[0] == "op"), None)
+      # _LIT classes: override op to allow all opcodes (base excludes lit-only ops)
+      # other classes override op to only suffix-only opcodes
+      if op_field and is_suffix_variant:
+        _, hi, lo = op_field
+        allowed_ops = all_ops if variant_suffix == "_LIT" else suffix_only_ops[variant_suffix][base]
+        lines.append(f"  op = EnumBitField({hi}, {lo}, {base}Op, {fmt_allowed(f'{base}Op', allowed_ops)})")
+      for fn, hi, lo in sort_fields(extra_fields):
+        lines.append(f"  {fn} = {field_def(fn, hi, lo, enc_name)}")
+      lines.append("")
+
+  # SDST variants (special case - redefine vdst field type, restrict to SDST ops)
+  for base, field_hi, field_lo in [("VOP1", 24, 17), ("VOP3", 7, 0)]:
+    if base not in base_encodings: continue
+    sdst_ops = sdst_opcodes.get(base, set())
+    if not sdst_ops: continue
+    # For VOP3, all ops < 256 (compare/cmpx ops) use SDST encoding
+    all_base_ops = set(enums.get(base, {}).keys())
+    if base == "VOP3": sdst_ops = sdst_ops | {op for op in all_base_ops if op < 256}
+    op_field = next((f for f in base_encodings[base][0] if f[0] == "op"), None)
+    lines.append(f"class {base}_SDST({base}):")
+    if op_field:
+      _, hi, lo = op_field
+      lines.append(f"  op = EnumBitField({hi}, {lo}, {base}Op, {fmt_allowed(f'{base}Op', sdst_ops)})")
+    lines.append(f"  vdst = SSrcField({field_hi}, {field_lo})")
+    lines.append("")
+    # SDST_LIT class (for literals with SDST destination) - same ops, just adds literal field
+    lit_enc = variant_encodings.get(f"{base}_LIT")
+    if lit_enc:
+      lit_field = next((f for f in lit_enc[0] if f[0] == "literal"), None)
+      if lit_field:
+        lines.append(f"class {base}_SDST_LIT({base}_SDST):")
+        lines.append(f"  literal = BitField({lit_field[1]}, {lit_field[2]})")
+        lines.append("")
+
+  # Instruction helpers
+  lines.append("# instruction helpers")
+  for fmt, ops in sorted(enums.items()):
+    if fmt not in base_encodings and fmt not in ("GLOBAL", "SCRATCH", "VGLOBAL", "VSCRATCH"): continue
+    suffix = "_E32" if fmt in ("VOP1", "VOP2", "VOPC") else "_E64" if fmt == "VOP3" else ""
+    op_to_suffix = {op:suffix for suffix,ops in suffix_only_ops.items() for op in ops.get(fmt, set())}
+    fmt_sdst_ops = sdst_opcodes.get(fmt, set())
+    for op, name in sorted(ops.items()):
+      msuf = suffix if fmt != "VOP3" or op < 512 else ""
+      # Determine class: SDST variants, suffix-specific variants (e.g., _MFMA, _LIT), or base
+      if fmt == "VOP1" and op in fmt_sdst_ops: cls = "VOP1_SDST"
+      elif fmt == "VOP3" and (op in fmt_sdst_ops or op < 256): cls = "VOP3_SDST"
+      elif op_to_suffix.get(op): cls = f"{fmt}{op_to_suffix[op]}"
+      else: cls = fmt
+      lines.append(f"{name.lower()}{msuf.lower()} = functools.partial({cls}, {fmt}Op.{name}{msuf})")
+  with open(path, "w") as f: f.write("\n".join(lines))
+
+def write_operands(types, enums, arch, path):
+  valid = {(name, fmt) for fmt, ops in enums.items() for name in ops.values()}
+  lines = ["# autogenerated from AMD ISA XML - do not edit",
+           "from extra.assembly.amd.autogen.common import Fmt, OpType",
+           f"from extra.assembly.amd.autogen.{arch}.enum import *", ""]
+  lines.append("# instruction operand info: {Op: {field: (Fmt, size_bits, OpType)}}")
+  lines.append("OPERANDS = {")
+  def fmt_val(v):
+    fmt, size, otype = v
+    return f"({f'Fmt.{fmt}' if fmt else 'None'}, {size}, {f'OpType.{otype}' if otype else 'None'})"
+  for (name, enc_base), fields in sorted(types.items()):
+    if (name, enc_base) not in valid: continue
+    fstr = ", ".join(f'"{k}": {fmt_val(v)}' for k, v in sorted(fields.items()))
+    lines.append(f'  {enc_base}Op.{name}: {{{fstr}}},')
+  lines.append("}")
+  with open(path, "w") as f: f.write("\n".join(lines))
+
+def write_pcode(pcode: dict[tuple[str, int], str], enums: dict[str, dict[int, str]], arch: str, path: str):
+  """Write str_pcode.py file from extracted pseudocode."""
+  entries: list[tuple[str, str, int, str]] = []
+  for fmt_name, ops in enums.items():
+    member_suffix = "_E32" if fmt_name in ("VOP1", "VOP2", "VOPC") else "_E64" if fmt_name == "VOP3" else ""
+    for opcode, name in ops.items():
+      if (name, opcode) in pcode:
+        msuf = member_suffix if fmt_name != "VOP3" or opcode < 512 else ""
+        entries.append((f"{fmt_name}Op", f"{name}{msuf}", opcode, pcode[(name, opcode)]))
+  enum_names = sorted(set(e[0] for e in entries))
+  lines = ["# autogenerated from AMD ISA PDF - do not edit", "# ruff: noqa: E501",
+           f"from extra.assembly.amd.autogen.{arch}.enum import {', '.join(enum_names)}", "", "PCODE = {"]
+  for enum_name, name, opcode, code in sorted(entries, key=lambda x: (x[0], x[2])):
+    lines.append(f"  {enum_name}.{name}: {code!r},")
+  lines.append("}")
+  with open(path, "w") as f: f.write("\n".join(lines))
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# Main
+# ═══════════════════════════════════════════════════════════════════════════════
+
+if __name__ == "__main__":
+  import pathlib
+  all_fmts, all_op_types, arch_data = {}, set(), {}
+  # First pass: parse XML for all architectures
+  for arch, cfg in ARCHS.items():
+    print(f"Parsing XML: {cfg['xml']} -> {arch}")
+    encodings, enums, types, fmts, op_types_set, suffix_only_ops = parse_xml(cfg["xml"])
+    for fmt, ops in FIXES.get(arch, {}).items(): enums.setdefault(fmt, {}).update(ops)
+    for fmt, fields in FIELD_FIXES.get(arch, {}).items():
+      if fmt in encodings: encodings[fmt] = (encodings[fmt][0] + fields, encodings[fmt][1])
+    arch_data[arch] = {"encodings": encodings, "enums": enums, "types": types, "suffix_only_ops": suffix_only_ops}
+    for fmt, bits in fmts.items():
+      assert fmt not in all_fmts or all_fmts[fmt] == bits, f"FMT_BITS mismatch for {fmt}: {all_fmts[fmt]} vs {bits}"
+      all_fmts[fmt] = bits
+    all_op_types.update(op_types_set)
+  # Write common.py
+  common_path = pathlib.Path(__file__).parent / "autogen" / "common.py"
+  write_common(all_fmts, all_op_types, common_path)
+  print(f"Wrote common.py: {len(all_fmts)} formats, {len(all_op_types)} op types")
+  # Write per-arch files from XML
+  for arch, data in arch_data.items():
+    base = pathlib.Path(__file__).parent / "autogen" / arch
+    write_enum(data["enums"], base / "enum.py")
+    write_ins(data["encodings"], data["enums"], data["suffix_only_ops"], data["types"], arch, base / "ins.py")
+    write_operands(data["types"], data["enums"], arch, base / "operands.py")
+    print(f"  {arch}: {len(data['encodings'])} encodings, {sum(len(v) for v in data['enums'].values())} instructions")
+  # Second pass: parse PDFs and write pcode
+  for arch, cfg in ARCHS.items():
+    print(f"Parsing PDF: {arch}...")
+    pages = extract_pdf_text(cfg["pdf"])
+    name_to_op = {name: op for ops in arch_data[arch]["enums"].values() for op, name in ops.items()}
+    pcode = extract_pcode(pages, name_to_op)
+    base = pathlib.Path(__file__).parent / "autogen" / arch
+    write_pcode(pcode, arch_data[arch]["enums"], arch, base / "str_pcode.py")
+    print(f"  {arch}: {len(pcode)} pcode entries")

extra/assembly/amd/sqtt.py

@@ -0,0 +1,458 @@
+"""SQTT (SQ Thread Trace) packet encoder and decoder for AMD GPUs.
+
+This module provides encoding and decoding of raw SQTT byte streams.
+The format is nibble-based with variable-width packets determined by a state machine.
+Uses BitField infrastructure from dsl.py, similar to GPU instruction encoding.
+"""
+from __future__ import annotations
+from typing import Iterator
+from enum import Enum
+from extra.assembly.amd.dsl import BitField, FixedBitField, bits
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# FIELD ENUMS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+class MemSrc(Enum):
+  LDS = 0
+  LDS_ALT = 1
+  VMEM = 2
+  VMEM_ALT = 3
+
+class AluSrc(Enum):
+  NONE = 0
+  SALU = 1
+  VALU = 2
+  VALU_SALU = 3
+
+class InstOp(Enum):
+  """SQTT instruction operation types for RDNA3 (gfx1100).
+
+  Memory ops appear in two ranges depending on which SIMD executes them:
+  - 0x1x-0x2x range: ops on traced SIMD
+  - 0x5x range: ops on other SIMD (OTHER_ prefix)
+
+  GLOBAL memory ops encoding depends on addressing mode AND size:
+  - Loads: 0x21 (saddr=SGPR) or 0x22 (saddr=NULL), all sizes same
+  - Stores: base + size_offset, where VADDR is shifted +1 from SADDR
+    SADDR: 0x24(32) 0x25(64) 0x26(96) 0x27(128)
+    VADDR: 0x25(32) 0x26(64) 0x27(96) 0x28(128)
+
+  OTHER_ range follows same pattern but values overlap differently.
+  """
+  SALU = 0x0
+  SMEM = 0x1
+  JUMP = 0x3              # branch taken
+  JUMP_NO = 0x4           # branch not taken
+  MESSAGE = 0x9
+  VALU_TRANS = 0xb        # transcendental: exp, log, rcp, sqrt, sin, cos
+  VALU_64_SHIFT = 0xd     # 64-bit shifts: lshl, lshr, ashr
+  VALU_MAD64 = 0xe        # 64-bit multiply-add
+  VALU_64 = 0xf           # 64-bit: add, mul, fma, rcp, sqrt, rounding, frexp, div helpers
+  VINTERP = 0x12          # interpolation: v_interp_p10_f32, v_interp_p2_f32
+  BARRIER = 0x13
+
+  # FLAT memory ops on traced SIMD (0x1x range)
+  FLAT_LOAD = 0x1c
+  FLAT_STORE = 0x1d
+  FLAT_STORE_64 = 0x1e
+  FLAT_STORE_96 = 0x1f
+  FLAT_STORE_128 = 0x20
+
+  # GLOBAL memory ops on traced SIMD (0x2x range)
+  GLOBAL_LOAD = 0x21             # saddr=SGPR, all sizes
+  GLOBAL_LOAD_VADDR = 0x22       # saddr=NULL, all sizes
+  GLOBAL_STORE = 0x24            # saddr=SGPR, 32-bit
+  GLOBAL_STORE_64 = 0x25         # saddr=SGPR 64 or saddr=NULL 32
+  GLOBAL_STORE_96 = 0x26         # saddr=SGPR 96 or saddr=NULL 64
+  GLOBAL_STORE_128 = 0x27        # saddr=SGPR 128 or saddr=NULL 96
+  GLOBAL_STORE_VADDR_128 = 0x28  # saddr=NULL, 128-bit
+
+  # LDS ops on traced SIMD
+  LDS_LOAD = 0x29
+  LDS_STORE = 0x2b
+  LDS_STORE_64 = 0x2c
+  LDS_STORE_128 = 0x2e
+
+  # Memory ops on other SIMD (0x5x range)
+  OTHER_LDS_LOAD = 0x50
+  OTHER_LDS_STORE = 0x51
+  OTHER_LDS_STORE_64 = 0x52
+  OTHER_LDS_STORE_128 = 0x54
+  OTHER_FLAT_LOAD = 0x55
+  OTHER_FLAT_STORE = 0x56
+  OTHER_FLAT_STORE_64 = 0x57
+  OTHER_FLAT_STORE_96 = 0x58
+  OTHER_FLAT_STORE_128 = 0x59
+  OTHER_GLOBAL_LOAD = 0x5a             # saddr=SGPR, all sizes
+  OTHER_GLOBAL_LOAD_VADDR = 0x5b       # saddr=NULL or saddr=SGPR store 32
+  OTHER_GLOBAL_STORE_64 = 0x5c         # saddr=SGPR 64 or saddr=NULL 32
+  OTHER_GLOBAL_STORE_96 = 0x5d         # saddr=SGPR 96 or saddr=NULL 64
+  OTHER_GLOBAL_STORE_128 = 0x5e        # saddr=SGPR 128 or saddr=NULL 96
+  OTHER_GLOBAL_STORE_VADDR_128 = 0x5f  # saddr=NULL, 128-bit
+
+  # EXEC-modifying ops (0x7x range)
+  SALU_SAVEEXEC = 0x72    # s_*_saveexec_b32/b64
+  VALU_CMPX = 0x73        # v_cmpx_*
+
+class InstOpL4(Enum):
+  """SQTT instruction operation types for RDNA4 (gfx1200). Different encoding from RDNA3."""
+  # TODO: we need to do discovery of all of these from instructions
+  SALU = 0x0
+  SMEM = 0x1
+  UNK_02 = 0x2
+  JUMP_NO = 0x4
+  UNK_06 = 0x6
+  VMEM = 0x10
+  UNK_11 = 0x11
+  VINTERP = 0x12
+  UNK_14 = 0x14
+  OTHER_VMEM = 0x5e
+  UNK_60 = 0x60
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# PACKET TYPE BASE CLASS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+class PacketType:
+  """Base class for SQTT packet types."""
+  encoding: FixedBitField
+  _raw: int
+  _time: int
+
+  def __init_subclass__(cls, **kwargs):
+    super().__init_subclass__(**kwargs)
+    cls._fields = {k: v for k, v in cls.__dict__.items() if isinstance(v, BitField)}
+    cls._size_nibbles = ((max((f.hi for f in cls._fields.values()), default=0) + 4) // 4)
+
+  @classmethod
+  def from_raw(cls, raw: int, time: int = 0):
+    inst = object.__new__(cls)
+    inst._raw, inst._time = raw, time
+    return inst
+
+  def __repr__(self) -> str:
+    fields_str = ", ".join(f"{k}={getattr(self, k)}" for k in self._fields if not k.startswith('_') and k != 'encoding')
+    return f"{self.__class__.__name__}({fields_str})"
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# TS PACKET TYPE DEFINITIONS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+class TS_DELTA_S8_W3(PacketType):
+  encoding = bits[6:0] == 0b0100001
+  delta = bits[10:8]
+  _padding = bits[63:11]
+
+class TS_DELTA_S8_W3_L4(PacketType):  # Layout 4: 64->72 bits
+  encoding = bits[6:0] == 0b0100001
+  delta = bits[10:8]
+  _padding = bits[71:11]
+
+class TS_DELTA_S5_W3(PacketType):
+  encoding = bits[4:0] == 0b00110
+  delta = bits[7:5]
+  _padding = bits[51:8]
+
+class TS_DELTA_S5_W3_L4(PacketType):  # Layout 4: 52->56 bits
+  encoding = bits[4:0] == 0b00110
+  delta = bits[9:7]
+  _padding = bits[55:10]
+
+class TS_DELTA_SHORT(PacketType):
+  encoding = bits[3:0] == 0b1000
+  delta = bits[7:4]
+
+class TS_DELTA_OR_MARK(PacketType):
+  encoding = bits[6:0] == 0b0000001
+  delta = bits[47:12]
+  bit8 = bits[8:8]
+  bit9 = bits[9:9]
+  @property
+  def is_marker(self) -> bool: return bool(self.bit9 and not self.bit8)
+
+class TS_DELTA_OR_MARK_L4(PacketType):  # Layout 4: 48->64 bits
+  encoding = bits[6:0] == 0b0000001
+  delta = bits[63:12]
+  bit7 = bits[7:7]
+  bit8 = bits[8:8]
+  bit9 = bits[9:9]
+  @property
+  def is_marker(self) -> bool: return bool((self.bit9 and not self.bit8) or self.bit7)
+
+class TS_DELTA_S5_W2(PacketType):
+  encoding = bits[4:0] == 0b11100
+  delta = bits[6:5]
+  _padding = bits[47:7]
+
+class TS_DELTA_S5_W2_L4(PacketType):  # Layout 4: 48->40 bits
+  encoding = bits[4:0] == 0b11100
+  delta = bits[6:5]
+  _padding = bits[39:7]
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# PACKET TYPE DEFINITIONS
+# ═══════════════════════════════════════════════════════════════════════════════
+
+class VALUINST(PacketType):  # exclude: 1 << 2
+  encoding = bits[2:0] == 0b011
+  delta = bits[5:3]
+  flag = bits[6:6]
+  wave = bits[11:7]
+
+class VMEMEXEC(PacketType):  # exclude: 1 << 0
+  encoding = bits[3:0] == 0b1111
+  delta = bits[5:4]
+  src = bits[7:6].enum(MemSrc)
+
+class ALUEXEC(PacketType):  # exclude: 1 << 1
+  encoding = bits[3:0] == 0b1110
+  delta = bits[5:4]
+  src = bits[7:6].enum(AluSrc)
+
+class IMMEDIATE(PacketType):  # exclude: 1 << 5
+  encoding = bits[3:0] == 0b1101
+  delta = bits[6:4]
+  wave = bits[11:7]
+
+class IMMEDIATE_MASK(PacketType):  # exclude: 1 << 5
+  encoding = bits[4:0] == 0b00100
+  delta = bits[7:5]
+  mask = bits[23:8]
+
+class WAVERDY(PacketType):  # exclude: 1 << 3
+  encoding = bits[4:0] == 0b10100
+  delta = bits[7:5]
+  mask = bits[23:8]
+
+class WAVEEND(PacketType):  # exclude: 1 << 4
+  encoding = bits[4:0] == 0b10101
+  delta = bits[7:5]
+  flag7 = bits[8:8]
+  simd = bits[10:9]
+  cu_lo = bits[13:11]
+  wave = bits[19:15]
+  @property
+  def cu(self) -> int: return self.cu_lo | (self.flag7 << 3)
+
+class WAVESTART(PacketType):  # exclude: 1 << 4
+  encoding = bits[4:0] == 0b01100
+  delta = bits[6:5]
+  flag7 = bits[7:7]
+  simd = bits[9:8]
+  cu_lo = bits[12:10]
+  wave = bits[17:13]
+  id7 = bits[31:18]
+  @property
+  def cu(self) -> int: return self.cu_lo | (self.flag7 << 3)
+
+class WAVESTART_L4(PacketType):  # Layout 4 has wave field at different position
+  encoding = bits[4:0] == 0b01100
+  delta = bits[6:5]
+  flag7 = bits[7:7]
+  simd = bits[9:8]
+  cu_lo = bits[12:10]
+  wave = bits[19:15]
+  id7 = bits[31:20]
+  @property
+  def cu(self) -> int: return self.cu_lo | (self.flag7 << 3)
+
+class WAVEALLOC(PacketType):  # exclude: 1 << 10
+  encoding = bits[4:0] == 0b00101
+  delta = bits[7:5]
+  _padding = bits[19:8]
+
+class WAVEALLOC_L4(PacketType):  # Layout 4: 20->24 bits
+  encoding = bits[4:0] == 0b00101
+  delta = bits[7:5]
+  _padding = bits[23:8]
+
+class PERF(PacketType):  # exclude: 1 << 11
+  encoding = bits[4:0] == 0b10110
+  delta = bits[7:5]
+  arg = bits[27:8]
+
+class PERF_L4(PacketType):  # Layout 4: 28->32 bits
+  encoding = bits[4:0] == 0b10110
+  delta = bits[9:7]
+  arg = bits[31:10]
+
+class NOP(PacketType):
+  encoding = bits[3:0] == 0b0000
+  delta = None  # type: ignore
+  _padding = bits[3:0]
+
+class TS_WAVE_STATE(PacketType):
+  encoding = bits[6:0] == 0b1010001
+  delta = bits[15:7]
+  coarse = bits[23:16]
+  @property
+  def wave_interest(self) -> bool: return bool(self.coarse & 1)
+  @property
+  def terminate_all(self) -> bool: return bool(self.coarse & 8)
+
+class EVENT(PacketType):  # exclude: 1 << 7
+  encoding = bits[7:0] == 0b01100001
+  delta = bits[10:8]
+  event = bits[23:11]
+
+class EVENT_BIG(PacketType):
+  encoding = bits[7:0] == 0b11100001
+  delta = bits[10:8]
+  event = bits[31:11]
+
+class REG(PacketType):
+  encoding = bits[3:0] == 0b1001
+  delta = bits[6:4]
+  slot = bits[9:7]
+  hi_byte = bits[15:8]
+  subop = bits[31:16]
+  val32 = bits[63:32]
+  @property
+  def is_config(self) -> bool: return bool(self.hi_byte & 0x80)
+
+class SNAPSHOT(PacketType):
+  encoding = bits[6:0] == 0b1110001
+  delta = bits[9:7]
+  snap = bits[63:10]
+
+class LAYOUT_HEADER(PacketType):
+  encoding = bits[6:0] == 0b0010001
+  delta = None  # type: ignore
+  layout = bits[12:7]
+  simd = bits[14:13]
+  group = bits[17:15]
+  sel_a = bits[31:28]
+  sel_b = bits[36:33]
+  flag4 = bits[59:59]
+  _padding = bits[63:60]
+
+class INST(PacketType):
+  encoding = bits[2:0] == 0b010
+  delta = bits[6:4]
+  flag1 = bits[3:3]
+  flag2 = bits[7:7]
+  wave = bits[12:8]
+  op = bits[19:13].enum(InstOp)
+
+class INST_L4(PacketType):  # Layout 4: different delta position and InstOp encoding
+  encoding = bits[2:0] == 0b010
+  delta = bits[5:3]
+  flag1 = bits[6:6]
+  flag2 = bits[7:7]
+  wave = bits[12:8]
+  op = bits[19:13].enum(InstOpL4)
+
+class UTILCTR(PacketType):
+  encoding = bits[6:0] == 0b0110001
+  delta = bits[8:7]
+  ctr = bits[47:9]
+
+# Packet types with rocprof type IDs as keys
+PACKET_TYPES_L3: dict[int, type[PacketType]] = {
+  1: VALUINST, 2: VMEMEXEC, 3: ALUEXEC, 4: IMMEDIATE, 5: IMMEDIATE_MASK, 6: WAVERDY, 7: TS_DELTA_S8_W3, 8: WAVEEND,
+  9: WAVESTART, 10: TS_DELTA_S5_W2, 11: WAVEALLOC, 12: TS_DELTA_S5_W3, 13: PERF, 14: UTILCTR, 15: TS_DELTA_SHORT,
+  16: NOP, 17: TS_WAVE_STATE, 18: EVENT, 19: EVENT_BIG, 20: REG, 21: SNAPSHOT, 22: TS_DELTA_OR_MARK, 23: LAYOUT_HEADER, 24: INST,
+}
+PACKET_TYPES_L4: dict[int, type[PacketType]] = {
+  **PACKET_TYPES_L3,
+  7: TS_DELTA_S8_W3_L4, 9: WAVESTART_L4, 10: TS_DELTA_S5_W2_L4, 11: WAVEALLOC_L4,
+  12: TS_DELTA_S5_W3_L4, 13: PERF_L4, 22: TS_DELTA_OR_MARK_L4, 24: INST_L4,
+}
+def _build_decode_tables(packet_types: dict[int, type[PacketType]]) -> tuple[dict[int, tuple], bytes]:
+  # Build state table: byte -> opcode. Sort by mask specificity (more bits first), NOP last
+  sorted_types = sorted(packet_types.items(), key=lambda x: (-bin(x[1].encoding.mask).count('1'), x[0] == 16))
+  state_table = bytes(next((op for op, cls in sorted_types if (b & cls.encoding.mask) == cls.encoding.default), 16) for b in range(256))
+  # Build decode info: opcode -> (pkt_cls, nib_count, delta_lo, delta_mask, special_case)
+  # special_case: 0=none, 1=TS_DELTA_OR_MARK (check is_marker), 2=TS_DELTA_SHORT (add 8)
+  decode_info = {}
+  for opcode, pkt_cls in packet_types.items():
+    delta_field = getattr(pkt_cls, 'delta', None)
+    special = {22: 1, 15: 2}.get(opcode, 0)  # TS_DELTA_OR_MARK=22, TS_DELTA_SHORT=15
+    decode_info[opcode] = (pkt_cls, pkt_cls._size_nibbles, delta_field.lo if delta_field else 0, delta_field.mask if delta_field else 0, special)
+  return decode_info, state_table
+
+_DECODE_INFO_L3, _STATE_TABLE_L3 = _build_decode_tables(PACKET_TYPES_L3)
+_DECODE_INFO_L4, _STATE_TABLE_L4 = _build_decode_tables(PACKET_TYPES_L4)
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# DECODER
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def decode(data: bytes) -> Iterator[PacketType]:
+  """Decode raw SQTT blob, yielding packet instances. Auto-detects layout from LAYOUT_HEADER."""
+  n, reg, pos, nib_off, nib_count, time = len(data), 0, 0, 0, 16, 0
+  decode_info, state_table = _DECODE_INFO_L3, _STATE_TABLE_L3  # default to layout 3, will update after seeing LAYOUT_HEADER
+
+  while pos + ((nib_count + nib_off + 1) >> 1) <= n:
+    need = nib_count - nib_off
+    # 1. if unaligned, read high nibble to align
+    if nib_off: reg, pos = (reg >> 4) | ((data[pos] >> 4) << 60), pos + 1
+    # 2. read all full bytes at once
+    if (byte_count := need >> 1):
+      chunk = int.from_bytes(data[pos:pos + byte_count], 'little')
+      reg, pos = (reg >> (byte_count * 8)) | (chunk << (64 - byte_count * 8)), pos + byte_count
+    # 3. if odd, read low nibble
+    if (nib_off := need & 1): reg = (reg >> 4) | ((data[pos] & 0xF) << 60)
+
+    opcode = state_table[reg & 0xFF]
+    pkt_cls, nib_count, delta_lo, delta_mask, special = decode_info[opcode]
+    delta = (reg >> delta_lo) & delta_mask
+    if special == 1:  # TS_DELTA_OR_MARK
+      pkt = pkt_cls.from_raw(reg, 0)  # create packet to check is_marker
+      if pkt.is_marker: delta = 0
+    elif special == 2: delta += 8  # TS_DELTA_SHORT
+    time += delta
+    pkt = pkt_cls.from_raw(reg, time)
+    # detect layout from first LAYOUT_HEADER and switch decode tables if needed
+    # NOTE: CDNA uses a completely different 16-bit header format, not nibbles - not supported here
+    if pkt_cls is LAYOUT_HEADER and pkt.layout == 4:
+      decode_info, state_table = _DECODE_INFO_L4, _STATE_TABLE_L4
+    yield pkt
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# PRINTER
+# ═══════════════════════════════════════════════════════════════════════════════
+
+PACKET_COLORS = {
+  "INST": "WHITE", "VALUINST": "BLACK", "VMEMEXEC": "yellow", "ALUEXEC": "yellow",
+  "IMMEDIATE": "YELLOW", "IMMEDIATE_MASK": "YELLOW", "WAVERDY": "cyan", "WAVEALLOC": "cyan",
+  "WAVEEND": "blue", "WAVESTART": "blue", "PERF": "magenta", "EVENT": "red", "EVENT_BIG": "red",
+  "REG": "green", "LAYOUT_HEADER": "white", "SNAPSHOT": "white", "UTILCTR": "green",
+}
+
+def format_packet(p) -> str:
+  from tinygrad.helpers import colored
+  name = type(p).__name__
+  if isinstance(p, (INST, INST_L4)):
+    op_name = p.op.name if isinstance(p.op, (InstOp, InstOpL4)) else f"0x{p.op:02x}"
+    fields = f"wave={p.wave} op={op_name}" + (" flag1" if p.flag1 else "") + (" flag2" if p.flag2 else "")
+  elif isinstance(p, VALUINST): fields = f"wave={p.wave}" + (" flag" if p.flag else "")
+  elif isinstance(p, ALUEXEC): fields = f"src={p.src.name if isinstance(p.src, AluSrc) else p.src}"
+  elif isinstance(p, VMEMEXEC): fields = f"src={p.src.name if isinstance(p.src, MemSrc) else p.src}"
+  elif isinstance(p, (WAVESTART, WAVESTART_L4, WAVEEND)): fields = f"wave={p.wave} simd={p.simd} cu={p.cu}"
+  elif hasattr(p, '_fields'):
+    filt = {'delta', 'encoding'} if not isinstance(p, (TS_DELTA_OR_MARK, TS_DELTA_OR_MARK_L4)) else {'encoding'}
+    fields = " ".join(f"{k}=0x{getattr(p, k):x}" if k in {'snap', 'val32'} else f"{k}={getattr(p, k)}"
+                      for k in p._fields if not k.startswith('_') and k not in filt)
+  else: fields = ""
+  return f"{p._time:8}: {colored(f'{name:18}', PACKET_COLORS.get(name.replace('_L4', ''), 'white'))} {fields}"
+
+def print_packets(packets) -> None:
+  from tinygrad.helpers import getenv
+  skip = {"NOP", "TS_DELTA_SHORT", "TS_WAVE_STATE", "TS_DELTA_OR_MARK",
+          "TS_DELTA_S5_W2", "TS_DELTA_S5_W3", "TS_DELTA_S8_W3", "REG", "EVENT"} if not getenv("NOSKIP") else {"NOP"}
+  for p in packets:
+    if type(p).__name__.replace("_L4", "") not in skip: print(format_packet(p))
+
+if __name__ == "__main__":
+  import sys, pickle
+  if len(sys.argv) < 2:
+    print("Usage: python sqtt.py <pkl_file>")
+    sys.exit(1)
+  with open(sys.argv[1], "rb") as f:
+    data = pickle.load(f)
+  sqtt_events = [e for e in data if type(e).__name__ == "ProfileSQTTEvent"]
+  for i, event in enumerate(sqtt_events):
+    print(f"\n=== event {i} ===")
+    print_packets(decode(event.blob))

extra/assembly/amd/sqtt_cdna.py

@@ -0,0 +1,161 @@
+"""SQTT (SQ Thread Trace) packet decoder for CDNA/MI300 GPUs.
+
+CDNA uses a completely different 16-bit header format from RDNA's nibble-based encoding.
+"""
+from __future__ import annotations
+from typing import Iterator
+from extra.assembly.amd.dsl import bits
+from extra.assembly.amd.sqtt import PacketType
+
+# CDNA pkt_fmt -> size in bytes (extracted from rocprof hash table)
+CDNA_PKT_SIZES = {0: 2, 1: 8, 2: 8, 3: 4, 4: 2, 5: 6, 6: 2, 7: 2, 8: 2, 9: 2, 10: 2, 11: 8, 12: 6, 13: 4, 14: 8, 15: 6}
+
+class CDNA_DELTA(PacketType):
+  """pkt_fmt=0: 16-bit timestamp delta packet"""
+  encoding = bits[3:0] == 0
+  delta = bits[11:4]      # (data >> 4) & 0xff
+  unk_0 = bits[12:12]     # (data >> 0xc) & 1
+  unk_1 = bits[15:13]     # (data >> 0xd)
+
+class CDNA_TIMESTAMP(PacketType):
+  """pkt_fmt=1: 64-bit timestamp packet (case 0x0)"""
+  encoding = bits[3:0] == 1
+  unk_0 = bits[15:4]
+  timestamp = bits[63:16]   # stored as (data_word >> 0x10) in low 46 bits of local_58
+
+class CDNA_PKT_2(PacketType):
+  """pkt_fmt=2: 64-bit packet (case 0x4)"""
+  encoding = bits[3:0] == 2
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[7:7]       # (data >> 7) + 1 & 1
+  unk_padding = bits[63:8]
+
+class CDNA_WAVESTART(PacketType):
+  """pkt_fmt=3: 32-bit WAVESTART packet (case 0x8)"""
+  encoding = bits[3:0] == 3
+  unk_0 = bits[5:5]       # (data >> 5) & 1
+  unk_1 = bits[9:6]       # (data >> 6) & 0xf
+  wave = bits[13:10]      # (data >> 10) & 0xf
+  simd = bits[15:14]      # (data >> 0xe) & 3
+  cu = bits[17:16]        # (data >> 0x10) & 3
+  unk_5 = bits[19:18]     # (data >> 0x12) & 3
+  unk_6 = bits[28:22]     # (data >> 0x16) & 0x7f
+  unk_padding = bits[31:29]
+
+class CDNA_PKT_4(PacketType):
+  """pkt_fmt=4: 16-bit packet (case 0xc, same as 0x8/0x14)"""
+  encoding = bits[3:0] == 4
+  unk_0 = bits[5:5]       # (data_word >> 5) & 1
+  unk_1 = bits[9:6]       # (data_word >> 6) & 0xf
+  unk_2 = bits[13:10]     # (data_word >> 10) & 0xf
+  unk_3 = bits[15:14]     # (data_word >> 0xe)
+
+class CDNA_PKT_5(PacketType):
+  """pkt_fmt=5: 48-bit packet (case 0x10)"""
+  encoding = bits[3:0] == 5
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[7:7]       # (data >> 7) + 1 & 1
+  unk_2 = bits[15:9]      # (data >> 9) & 0x7f
+  unk_padding = bits[47:16]
+
+class CDNA_WAVEEND(PacketType):
+  """pkt_fmt=6: 16-bit WAVEEND packet (case 0x14, same as 0x8/0xc)"""
+  encoding = bits[3:0] == 6
+  unk_0 = bits[5:5]       # (data_word >> 5) & 1
+  unk_1 = bits[9:6]       # (data_word >> 6) & 0xf
+  wave = bits[13:10]      # (data_word >> 10) & 0xf
+  simd = bits[15:14]      # (data_word >> 0xe)
+
+class CDNA_EXEC(PacketType):
+  """pkt_fmt=10: 16-bit EXEC packet (case 0x24)"""
+  encoding = bits[3:0] == 10
+  unk_0 = bits[8:5]       # (data_word >> 5) & 0xf
+  unk_1 = bits[10:9]      # (data_word >> 9) & 3
+  unk_2 = bits[15:11]     # (data_word >> 0xb)
+
+class CDNA_PKT_11(PacketType):
+  """pkt_fmt=11: 64-bit packet (case 0x28)"""
+  encoding = bits[3:0] == 11
+  unk_0 = bits[8:5]       # (data_word >> 5) & 0xf
+  unk_1 = bits[10:9]      # (data_word >> 9) & 3
+  unk_2 = bits[15:15]     # (data_word >> 0xf) & 1
+  unk_padding = bits[63:16]
+
+class CDNA_INST(PacketType):
+  """pkt_fmt=13: 32-bit INST packet (case 0x30)"""
+  encoding = bits[3:0] == 13
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[9:8]       # (data >> 8) & 3
+  unk_2 = bits[11:10]     # (data >> 10) & 3
+  unk_3 = bits[13:12]     # (data >> 0xc) & 3
+  unk_4 = bits[15:14]     # (data >> 0xe) & 3
+  unk_5 = bits[19:18]     # (data >> 0x12) & 3
+  unk_6 = bits[21:20]     # (data >> 0x14) & 3
+  unk_7 = bits[23:22]     # (data >> 0x16) & 3
+  unk_8 = bits[25:24]     # (data >> 0x18) & 3
+  unk_9 = bits[27:26]     # (data >> 0x1a) & 3
+  unk_padding = bits[31:28]
+
+class CDNA_PKT_14(PacketType):
+  """pkt_fmt=14: 64-bit packet (case 0x34)"""
+  encoding = bits[3:0] == 14
+  unk_0 = bits[5:5]       # (data >> 5) & 1
+  unk_1 = bits[9:6]       # (data >> 6) & 0xf
+  unk_2 = bits[11:10]     # (data >> 10) & 3
+  unk_3 = bits[24:12]     # (data >> 0xc) & 0x1fff
+  unk_4 = bits[37:25]     # (data >> 0x19) & 0x1fff
+  unk_5 = bits[50:38]     # (data >> 0x26) & 0x1fff
+  unk_6 = bits[51:51]     # (data >> 0x33) & 1
+  unk_padding = bits[63:52]
+
+class CDNA_PKT_15(PacketType):
+  """pkt_fmt=15: 48-bit packet (case 0x38, same as 0x10)"""
+  encoding = bits[3:0] == 15
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[7:7]       # (data >> 7) + 1 & 1
+  unk_2 = bits[15:9]      # (data >> 9) & 0x7f
+  unk_padding = bits[47:16]
+
+CDNA_PKT_TYPES: dict[int, type[PacketType]] = {
+  0: CDNA_DELTA, 1: CDNA_TIMESTAMP, 2: CDNA_PKT_2, 3: CDNA_WAVESTART, 4: CDNA_PKT_4,
+  5: CDNA_PKT_5, 6: CDNA_WAVEEND, 10: CDNA_EXEC, 11: CDNA_PKT_11, 13: CDNA_INST, 14: CDNA_PKT_14, 15: CDNA_PKT_15,
+}
+
+# Validate CDNA packet definitions
+for pkt_fmt, pkt_cls in CDNA_PKT_TYPES.items():
+  assert pkt_cls.encoding.default == pkt_fmt, f"{pkt_cls.__name__} encoding {pkt_cls.encoding.default} != pkt_fmt {pkt_fmt}"
+  assert CDNA_PKT_SIZES[pkt_fmt] * 2 == pkt_cls._size_nibbles, f"{pkt_cls.__name__} size {pkt_cls._size_nibbles//2} != {CDNA_PKT_SIZES[pkt_fmt]}"
+
+def decode(data: bytes) -> Iterator[PacketType]:
+  """Decode CDNA SQTT blob using 16-bit header format."""
+  pos, time, ts_offset = 0, 0, None
+  while pos + 2 <= len(data):
+    header = int.from_bytes(data[pos:pos+2], 'little')
+    pkt_fmt = header & 0xf
+    pkt_size = CDNA_PKT_SIZES[pkt_fmt]
+    if pos + pkt_size > len(data): break
+
+    raw = int.from_bytes(data[pos:pos+pkt_size], 'little')
+    # pkt_fmt=0 has delta in bits[11:4], accumulate it
+    if pkt_fmt == 0: time += ((raw >> 4) & 0xff) * 4
+    # pkt_fmt=1 with unk_0=0 is absolute timestamp - use it to anchor time
+    if pkt_fmt == 1 and ((raw >> 4) & 0xfff) == 0:
+      abs_ts = raw >> 16
+      if ts_offset is None: ts_offset = abs_ts - time  # first timestamp: save offset
+      else: time = ((abs_ts - ts_offset) & ~3) - 4     # subsequent: compute time, align to 4, subtract 4
+    pkt_cls = CDNA_PKT_TYPES[pkt_fmt]
+    yield pkt_cls.from_raw(raw, time)
+    pos += pkt_size
+
+if __name__ == "__main__":
+  import sys, pickle
+  if len(sys.argv) < 2:
+    print("Usage: python sqtt_cdna.py <pkl_file>")
+    sys.exit(1)
+  with open(sys.argv[1], "rb") as f:
+    data = pickle.load(f)
+  sqtt_events = [e for e in data if type(e).__name__ == "ProfileSQTTEvent"]
+  for i, event in enumerate(sqtt_events):
+    print(f"\n=== event {i} ===")
+    for pkt in decode(event.blob):
+      print(f"{pkt._time:8}: {pkt}")

extra/assembly/amd/sqttmap.py

@@ -0,0 +1,122 @@
+# maps SQTT trace packets to instructions.
+from dataclasses import dataclass
+from typing import Iterator
+
+from extra.assembly.amd.sqtt import decode, print_packets, INST, VALUINST, IMMEDIATE, WAVESTART, WAVEEND, InstOp, PacketType, IMMEDIATE_MASK
+from extra.assembly.amd.dsl import Inst
+from extra.assembly.amd.autogen.rdna3.ins import SOPP, s_endpgm
+from extra.assembly.amd.autogen.rdna3.enum import SOPPOp
+
+@dataclass(frozen=True)
+class InstructionInfo:
+  pc: int
+  wave: int
+  inst: Inst
+
+def map_insts(data:bytes, lib:bytes, target:int) -> Iterator[tuple[PacketType, InstructionInfo|None]]:
+  """maps SQTT packets to instructions, yields (packet, instruction_info or None)"""
+  # map pcs to insts
+  from tinygrad.viz.serve import amd_decode
+  pc_map = amd_decode(lib, target)
+
+  wave_pc:dict[int, int] = {}
+  # only processing packets on one [CU, SIMD] unit
+  def simd_select(p) -> bool: return getattr(p, "cu", 0) == 0 and getattr(p, "simd", 0) == 0
+  for p in decode(data):
+    if not simd_select(p): continue
+    if isinstance(p, WAVESTART):
+      assert p.wave not in wave_pc, "only one inflight wave per unit"
+      wave_pc[p.wave] = next(iter(pc_map))
+      continue
+    if isinstance(p, WAVEEND):
+      pc = wave_pc.pop(p.wave)
+      yield (p, InstructionInfo(pc, p.wave, s_endpgm()))
+      continue
+    # skip OTHER_ instructions, they don't belong to this unit
+    if isinstance(p, INST) and p.op.name.startswith("OTHER_"): continue
+    if isinstance(p, IMMEDIATE_MASK):
+      # immediate mask may yield multiple times per packet
+      for wave in range(16):
+        if p.mask & (1 << wave):
+          inst = pc_map[pc:=wave_pc[wave]]
+          # can this assert be more strict?
+          assert isinstance(inst, SOPP), f"IMMEDIATE_MASK packet must map to SOPP, got {inst}"
+          wave_pc[wave] += inst.size()
+          yield (p, InstructionInfo(pc, wave, inst))
+      continue
+    if isinstance(p, (VALUINST, INST, IMMEDIATE)):
+      inst = pc_map[pc:=wave_pc[p.wave]]
+      # s_delay_alu doesn't get a packet?
+      if isinstance(inst, SOPP) and inst.op in {SOPPOp.S_DELAY_ALU}:
+        wave_pc[p.wave] += inst.size()
+        inst = pc_map[pc:=wave_pc[p.wave]]
+      # identify a branch instruction, only used for asserts
+      is_branch = isinstance(inst, SOPP) and "BRANCH" in inst.op_name
+      if is_branch: assert isinstance(p, INST) and p.op in {InstOp.JUMP_NO, InstOp.JUMP}, f"branch can only be folowed by jump packets, got {p}"
+      # JUMP handling
+      if isinstance(p, INST) and p.op is InstOp.JUMP:
+        assert is_branch, f"JUMP packet must map to a branch instruction, got {inst}"
+        x = inst.simm16 & 0xffff
+        wave_pc[p.wave] += inst.size() + (x - 0x10000 if x & 0x8000 else x)*4
+      else:
+        if is_branch: assert inst.op != SOPPOp.S_BRANCH, f"S_BRANCH must have a JUMP packet, got {p}"
+        wave_pc[p.wave] += inst.size()
+      yield (p, InstructionInfo(pc, p.wave, inst))
+      continue
+    # for all other packets (VMEMEXEC, ALUEXEC, etc.), yield with None
+    yield (p, None)
+
+# test to compare every packet with the rocprof decoder
+
+def test_rocprof_inst_traces_match(sqtt, prg, target):
+  from tinygrad.viz.serve import amd_decode
+  from extra.sqtt.roc import decode as roc_decode, InstExec
+  addr_table = amd_decode(prg.lib, target)
+  disasm = {addr+prg.base:(inst.disasm(), inst.size()) for addr,inst in addr_table.items()}
+  rctx = roc_decode([sqtt], {prg.tag:disasm})
+  rwaves = rctx.inst_execs.get((sqtt.kern, sqtt.exec_tag), [])
+  rwaves_iter:dict[int, list[Iterator[InstExec]]] = {} # wave unit (0-15) -> list of inst trace iterators for all executions on that unit
+  for w in rwaves: rwaves_iter.setdefault(w.wave_id, []).append(w.unpack_insts())
+
+  passed_insts = 0
+  for pkt, info in map_insts(sqtt.blob, prg.lib, target):
+    if DEBUG >= 2: print_packets([pkt])
+    if info is None: continue
+    if DEBUG >= 2: print(f"{' '*29}{info.inst.disasm()}")
+    rocprof_inst = next(rwaves_iter[info.wave][0])
+    ref_pc = rocprof_inst.pc-prg.base
+    # always check pc matches
+    assert ref_pc == info.pc, f"pc mismatch {ref_pc}:{disasm[rocprof_inst.pc][0]} != {info.pc}:{info.inst.disasm()}"
+    # special handling for s_endpgm, it marks the wave completion.
+    if info.inst == s_endpgm():
+      completed_wave = list(rwaves_iter[info.wave].pop(0))
+      assert len(completed_wave) == 0, f"incomplete instructions in wave {info.wave}"
+    # otherwise the packet timestamp is time + "stall"
+    else:
+      assert pkt._time == rocprof_inst.time+rocprof_inst.stall
+    passed_insts += 1
+
+  for k,v in rwaves_iter.items():
+    assert len(v) == 0, f"incomplete wave {k}"
+
+  if len(rwaves):
+    print(f"passed for {passed_insts} instructions across {len(rwaves)} waves scheduled on {len(rwaves_iter)} wave units")
+
+if __name__ == "__main__":
+  import argparse, pickle, pathlib
+  from tinygrad.helpers import temp, DEBUG
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--profile', type=pathlib.Path, metavar="PATH", help='Path to profile (optional file, default: latest profile)',
+                      default=pathlib.Path(temp("profile.pkl", append_user=True)))
+  parser.add_argument('--kernel', type=str, default=None, metavar="NAME", help='Kernel to focus on (optional name, default: all kernels)')
+  args = parser.parse_args()
+  with open(args.profile, "rb") as f:
+    data = pickle.load(f)
+  sqtt_events = [e for e in data if type(e).__name__ == "ProfileSQTTEvent"]
+  kern_events = {e.tag:e for e in data if type(e).__name__ == "ProfileProgramEvent"}
+  target = next((e for e in data if type(e).__name__ == "ProfileDeviceEvent" and e.device.startswith("AMD"))).props["gfx_target_version"]
+  for e in sqtt_events:
+    if args.kernel is not None and args.kernel != e.kern: continue
+    if not e.itrace: continue
+    print(f"==== {e.kern}")
+    test_rocprof_inst_traces_match(e, kern_events[e.kern], target)

extra/assembly/amd/test/bench_emu.py

@@ -0,0 +1,266 @@
+#!/usr/bin/env python3
+"""Benchmark comparing Python vs Rust RDNA3 emulators on real tinygrad kernels."""
+import ctypes, time, os
+from pathlib import Path
+
+# Set AMD=1 before importing tinygrad
+os.environ["AMD"] = "1"
+
+from extra.assembly.amd.emu import run_asm as python_run_asm, decode_program
+from extra.assembly.amd import decode_inst
+from extra.assembly.amd.autogen.rdna3.ins import SOPP, SOPPOp
+
+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, rsrc2: int, 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, rsrc2)
+
+  # 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, rsrc2)
+    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 profile_instructions(kernel: bytes):
+  """Profile individual instruction compile times."""
+  from extra.assembly.amd.emu import _get_runner, _canonical_runner_cache
+  from tinygrad.helpers import Context
+  _get_runner.cache_clear()
+  _canonical_runner_cache.clear()
+
+  results = []
+  i = 0
+  while i < len(kernel):
+    inst = decode_inst(kernel[i:])
+    if isinstance(inst, SOPP) and inst.op == SOPPOp.S_CODE_END: break
+    inst_bytes = bytes(kernel[i:i + inst.size() + 4])
+    try: inst_str = repr(inst)
+    except Exception: inst_str = f"<{type(inst).__name__}>"
+
+    # Time the full compile (sink + render + compile)
+    start = time.perf_counter()
+    with Context(CCACHE=0):
+      runner, is_new = _get_runner(inst_bytes)
+    compile_time = time.perf_counter() - start
+
+    results.append({
+      'inst_str': inst_str + ('' if is_new else ' [CACHED]'),
+      'compile_ms': compile_time * 1000 if is_new else 0,
+    })
+    i += inst.size()
+
+  return sorted(results, key=lambda x: x['compile_ms'], reverse=True)
+
+def benchmark_python_split(kernel: bytes, global_size, local_size, args_ptr, rsrc2: int, iterations: int = 5):
+  """Benchmark Python emulator with compile and execution times."""
+  from extra.assembly.amd.emu import _get_runner, _canonical_runner_cache
+  from tinygrad.helpers import Context
+  _get_runner.cache_clear()
+  _canonical_runner_cache.clear()
+  decode_program.cache_clear()
+
+  # Measure compile time (decode_program builds sinks, renders, and compiles)
+  compile_start = time.perf_counter()
+  with Context(CCACHE=0):
+    program = decode_program(kernel)
+  compile_time = time.perf_counter() - compile_start
+  n_compiled = len(_canonical_runner_cache)
+
+  # Execution time
+  exec_time = benchmark_emulator("Python", python_run_asm, kernel, global_size, local_size, args_ptr, rsrc2, iterations)
+  return compile_time, exec_time, len(program), n_compiled
+
+def get_tinygrad_kernel(op_name: str) -> tuple[bytes, tuple, tuple, list[int], dict[int, bytes], int] | None:
+  """Get a real tinygrad kernel by operation name. Returns (code, global_size, local_size, buf_sizes, buf_data, rsrc2)."""
+  try:
+    from tinygrad import Tensor
+    from tinygrad.runtime.support.elf import elf_loader
+    from tinygrad.runtime.autogen import hsa
+    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)
+        image = memoryview(bytearray(lib))
+        _, sections, _ = elf_loader(lib)
+        rodata_entry = next((sh.header.sh_addr for sh in sections if sh.name == ".rodata"), -1)
+        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
+            # Extract rsrc2 from ELF (same as ops_amd.py)
+            group_segment_size = image[rodata_entry:rodata_entry+4].cast("I")[0]
+            lds_size = ((group_segment_size + 511) // 512) & 0x1FF
+            code = hsa.amd_kernel_code_t.from_buffer_copy(bytes(image[rodata_entry:rodata_entry+256]) + b'\x00'*256)
+            rsrc2 = code.compute_pgm_rsrc2 | (lds_size << 15)
+            return (bytes(sec.content), tuple(lowered.prg.p.global_size), tuple(lowered.prg.p.local_size), buf_sizes, buf_data, rsrc2)
+    return None
+  except Exception as e:
+    print(f"  Error getting kernel: {e}")
+    return None
+
+TINYGRAD_TESTS = ["add", "mul", "reduce_sum", "softmax", "exp", "sin", "gelu", "matmul_small"]
+
+def main():
+  import argparse
+  parser = argparse.ArgumentParser(description="Benchmark RDNA3 emulators")
+  parser.add_argument("--iterations", type=int, default=3, help="Number of iterations per benchmark")
+  parser.add_argument("--profile", type=str, default=None, help="Profile instructions for a specific kernel (e.g. 'sin')")
+  parser.add_argument("--top", type=int, default=20, help="Number of top instructions to show in profile")
+  args = parser.parse_args()
+
+  # Profile mode: show individual instruction timing
+  if args.profile:
+    kernel_info = get_tinygrad_kernel(args.profile)
+    if kernel_info is None:
+      print(f"Failed to get kernel for '{args.profile}'")
+      return
+    kernel = kernel_info[0]
+    print(f"Profiling instructions for '{args.profile}' kernel...")
+    print("=" * 110)
+    results = profile_instructions(kernel)
+    print(f"{'Instruction':<90} {'Compile(ms)':>12}")
+    print("-" * 110)
+    for r in results[:args.top]:
+      inst = r['inst_str'][:87] + "..." if len(r['inst_str']) > 90 else r['inst_str']
+      print(f"{inst:<90} {r['compile_ms']:>12.3f}")
+    print("-" * 110)
+    total = sum(r['compile_ms'] for r in results)
+    print(f"{'TOTAL':<90} {total:>12.3f}")
+    return
+
+  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 = []
+
+  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, rsrc2 = kernel_info
+    buffers, args_arr, args_ptr, ranges = setup_buffers(buf_sizes, buf_data)
+
+    # Benchmark Python emulator (must be first to measure compile time before cache is populated)
+    py_compile, py_exec, n_insts, n_compiled = benchmark_python_split(kernel, global_size, local_size, args_ptr, rsrc2, args.iterations)
+
+    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_compiled} unique) × {n_workgroups} WGs × {n_threads} threads = {total_work:,} ops")
+    rust_time = benchmark_emulator("Rust", rust_remu.run_asm, kernel, global_size, local_size, args_ptr, rsrc2, args.iterations) if rust_remu else None
+
+    if py_compile is not None:
+      py_exec_rate = total_work / py_exec / 1e6
+      print(f"  Compile:        {py_compile*1000:8.3f} ms  ({n_compiled} unique)")
+      print(f"  Exec:           {py_exec*1000:8.3f} ms  ({py_exec_rate:7.2f} M ops/s)")
+    if rust_time:
+      rust_rate = total_work / rust_time / 1e6
+      speedup = py_exec / rust_time if py_exec else 0
+      print(f"  Rust:           {rust_time*1000:8.3f} ms  ({rust_rate:7.2f} M ops/s)  [{speedup:.1f}x faster]")
+
+    results.append((op_name, n_insts, n_compiled, n_workgroups, py_compile, py_exec, rust_time))
+
+  # Summary table
+  print("\n" + "=" * 110)
+  print("SUMMARY")
+  print("=" * 110)
+  print(f"{'Name':<16} {'Insts':<6} {'Unique':<6} {'WGs':<5} {'Compile (ms)':<14} {'Exec (ms)':<12} {'Rust (ms)':<12} {'Speedup':<10}")
+  print("-" * 110)
+
+  for name, n_insts, n_compiled, n_wgs, py_compile, py_exec, rust_time in results:
+    compile_ms = f"{py_compile*1000:.3f}" if py_compile else "error"
+    exec_ms = f"{py_exec*1000:.3f}" if py_exec else "error"
+    if rust_time:
+      rust_ms = f"{rust_time*1000:.3f}"
+      speedup = f"{py_exec/rust_time:.1f}x" if py_exec else "N/A"
+    else:
+      rust_ms, speedup = "N/A", "N/A"
+    print(f"{name:<16} {n_insts:<6} {n_compiled:<6} {n_wgs:<5} {compile_ms:<14} {exec_ms:<12} {rust_ms:<12} {speedup:<10}")
+
+if __name__ == "__main__":
+  main()

extra/assembly/amd/test/helpers.py

@@ -0,0 +1,38 @@
+"""Shared test helpers for RDNA3 tests."""
+import shutil
+from dataclasses import dataclass
+
+@dataclass
+class KernelInfo:
+  code: bytes
+  src: str
+  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")
+
+ARCH_TO_TARGET:dict[str, list[str]] = {
+  "rdna3":["gfx1100"],
+  "rdna4":["gfx1200"],
+  "cdna":["gfx950", "gfx942"],
+}
+
+TARGET_TO_ARCH:dict[str, str] = {t:arch for arch,targets in ARCH_TO_TARGET.items() for t in targets}
+
+def get_target(arch:str) -> str: return ARCH_TO_TARGET[arch][0]
+
+def get_mattr(arch:str) -> str:
+  return {"rdna3":"+real-true16,+wavefrontsize32", "rdna4":"+real-true16,+wavefrontsize32", "cdna":"+wavefrontsize64"}[arch]

extra/assembly/amd/test/hw/__init__.py

@@ -0,0 +1 @@
+"""Hardware-validated emulator tests for RDNA3 instructions."""

extra/assembly/amd/test/hw/helpers.py

@@ -0,0 +1,281 @@
+"""Test infrastructure for hardware-validated RDNA3 emulator tests.
+
+Uses run_asm() with memory output, so tests can run on both emulator and real hardware.
+Set USE_HW=1 to run on both emulator and hardware, comparing results.
+"""
+import ctypes, math, os, struct
+from extra.assembly.amd.autogen.rdna3.ins import *
+
+from extra.assembly.amd.emu import run_asm
+from extra.assembly.amd.dsl import NULL, SCC, VCC_LO, VCC_HI, EXEC_LO, EXEC_HI, M0
+
+def _i32(f: float) -> int: return struct.unpack('<I', struct.pack('<f', f))[0]
+def _f32(i: int) -> float: return struct.unpack('<f', struct.pack('<I', i & 0xFFFFFFFF))[0]
+
+# f16 conversion helpers
+def f16(i: int) -> float: return struct.unpack('<e', struct.pack('<H', i & 0xFFFF))[0]
+def f32_to_f16(f: float) -> int:
+  f = float(f)
+  if math.isnan(f): return 0x7e00
+  if math.isinf(f): return 0x7c00 if f > 0 else 0xfc00
+  try: return struct.unpack('<H', struct.pack('<e', f))[0]
+  except OverflowError: return 0x7c00 if f > 0 else 0xfc00
+
+# For backwards compatibility with tests using SrcEnum.NULL etc.
+class SrcEnum:
+  NULL = NULL
+  VCC_LO = VCC_LO
+  VCC_HI = VCC_HI
+  EXEC_LO = EXEC_LO
+  EXEC_HI = EXEC_HI
+  SCC = SCC
+  M0 = M0
+  POS_HALF = 0.5
+  NEG_HALF = -0.5
+  POS_ONE = 1.0
+  NEG_ONE = -1.0
+  POS_TWO = 2.0
+  NEG_TWO = -2.0
+  POS_FOUR = 4.0
+  NEG_FOUR = -4.0
+
+VCC = VCC_LO  # For VOP3SD sdst field (VCC_LO is exported from dsl)
+USE_HW = os.environ.get("USE_HW", "0") == "1"
+FLOAT_TOLERANCE = 1e-5
+
+def get_gpu_target() -> tuple[int, int, int]:
+  """Get the GPU target as (major, minor, stepping) tuple."""
+  if not USE_HW: return (0, 0, 0)
+  from tinygrad.device import Device
+  return Device["AMD"].target
+
+def skip_unless_gfx(min_major: int, min_minor: int = 0, reason: str = ""):
+  """Skip test if GPU target is below the minimum required version."""
+  import unittest
+  def decorator(test_func):
+    if not USE_HW: return test_func
+    target = get_gpu_target()
+    if target[0] < min_major or (target[0] == min_major and target[1] < min_minor):
+      return unittest.skip(reason or f"requires gfx{min_major}{min_minor}0+")(test_func)
+    return test_func
+  return decorator
+
+# Output buffer layout: vgpr[16][32], sgpr[16], vcc, scc, exec
+N_VGPRS, N_SGPRS, WAVE_SIZE = 16, 16, 32
+VGPR_BYTES = N_VGPRS * WAVE_SIZE * 4  # 16 regs * 32 lanes * 4 bytes = 2048
+SGPR_BYTES = N_SGPRS * 4  # 16 regs * 4 bytes = 64
+OUT_BYTES = VGPR_BYTES + SGPR_BYTES + 12  # + vcc + scc + exec
+
+# Float conversion helpers
+def f2i(f: float) -> int: return _i32(f)
+def i2f(i: int) -> float: return _f32(i)
+def f2i64(f: float) -> int: return struct.unpack('<Q', struct.pack('<d', f))[0]
+def i642f(i: int) -> float: return struct.unpack('<d', struct.pack('<Q', i))[0]
+
+def assemble(instructions: list) -> bytes:
+  return b''.join(inst.to_bytes() for inst in instructions)
+
+# Simple WaveState class for test output parsing (mirrors emu.py interface for tests)
+class WaveState:
+  def __init__(self):
+    self.vgpr = [[0] * 256 for _ in range(32)]  # vgpr[lane][reg]
+    self.sgpr = [0] * 128
+    self.vcc = 0
+    self.scc = 0
+
+def get_prologue_epilogue(n_lanes: int) -> tuple[list, list]:
+  """Generate prologue and epilogue instructions for state capture."""
+  prologue = [
+    s_mov_b32(s[80], s[0]),
+    s_mov_b32(s[81], s[1]),
+    v_mov_b32_e32(v[255], v[0]),
+  ]
+  for i in range(N_VGPRS):
+    prologue.append(v_mov_b32_e32(v[i], 0))
+  for i in range(N_SGPRS):
+    prologue.append(s_mov_b32(s[i], 0))
+  prologue.append(s_mov_b32(VCC_LO, 0))
+
+  epilogue = [
+    s_mov_b32(s[90], VCC_LO),
+    s_cselect_b32(s[91], 1, 0),
+    # Save EXEC early (before we modify it for VGPR stores)
+    s_mov_b32(s[95], EXEC_LO),
+    # Restore EXEC to all active lanes for VGPR stores (test may have modified EXEC)
+    s_mov_b32(EXEC_LO, (1 << n_lanes) - 1),
+    s_load_b64(s[92:93], s[80:81], 0, soffset=NULL),
+    s_waitcnt(0),  # simm16=0 waits for all
+    v_lshlrev_b32_e32(v[240], 2, v[255]),
+  ]
+  for i in range(N_VGPRS):
+    epilogue.append(global_store_b32(addr=v[240], data=v[i], saddr=s[92:93], offset=i * WAVE_SIZE * 4))
+  epilogue.append(v_mov_b32_e32(v[241], 0))
+  epilogue.append(v_cmp_eq_u32_e32(v[255], v[241]))
+  epilogue.append(s_and_saveexec_b32(s[94], VCC_LO))
+  epilogue.append(v_mov_b32_e32(v[240], 0))
+  for i in range(N_SGPRS):
+    epilogue.append(v_mov_b32_e32(v[243], s[i]))
+    epilogue.append(global_store_b32(addr=v[240], data=v[243], saddr=s[92:93], offset=VGPR_BYTES + i * 4))
+  epilogue.append(v_mov_b32_e32(v[243], s[90]))
+  epilogue.append(global_store_b32(addr=v[240], data=v[243], saddr=s[92:93], offset=VGPR_BYTES + SGPR_BYTES))
+  epilogue.append(v_mov_b32_e32(v[243], s[91]))
+  epilogue.append(global_store_b32(addr=v[240], data=v[243], saddr=s[92:93], offset=VGPR_BYTES + SGPR_BYTES + 4))
+  # Store EXEC (saved earlier in s[95])
+  epilogue.append(v_mov_b32_e32(v[243], s[95]))
+  epilogue.append(global_store_b32(addr=v[240], data=v[243], saddr=s[92:93], offset=VGPR_BYTES + SGPR_BYTES + 8))
+  epilogue.append(s_mov_b32(EXEC_LO, s[94]))
+  epilogue.append(s_endpgm())
+  return prologue, epilogue
+
+def parse_output(out_buf: bytes, n_lanes: int) -> WaveState:
+  """Parse output buffer into WaveState."""
+  st = WaveState()
+  for i in range(N_VGPRS):
+    for lane in range(n_lanes):
+      off = i * WAVE_SIZE * 4 + lane * 4
+      st.vgpr[lane][i] = struct.unpack_from('<I', out_buf, off)[0]
+  for i in range(N_SGPRS):
+    st.sgpr[i] = struct.unpack_from('<I', out_buf, VGPR_BYTES + i * 4)[0]
+  st.vcc = struct.unpack_from('<I', out_buf, VGPR_BYTES + SGPR_BYTES)[0]
+  st.scc = struct.unpack_from('<I', out_buf, VGPR_BYTES + SGPR_BYTES + 4)[0]
+  # Store EXEC in its proper location (index 126)
+  st.sgpr[EXEC_LO.offset] = struct.unpack_from('<I', out_buf, VGPR_BYTES + SGPR_BYTES + 8)[0]
+  return st
+
+def run_program_emu(instructions: list, n_lanes: int = 1) -> WaveState:
+  """Run instructions via emulator run_asm, dump state to memory, return WaveState."""
+  out_buf = (ctypes.c_uint8 * OUT_BYTES)(*([0] * OUT_BYTES))
+  out_addr = ctypes.addressof(out_buf)
+
+  prologue, epilogue = get_prologue_epilogue(n_lanes)
+  code = assemble(prologue + instructions + epilogue)
+
+  args = (ctypes.c_uint64 * 1)(out_addr)
+  args_ptr = ctypes.addressof(args)
+  kernel_buf = (ctypes.c_char * len(code)).from_buffer_copy(code)
+  lib_ptr = ctypes.addressof(kernel_buf)
+
+  # rsrc2: USER_SGPR_COUNT=2, ENABLE_SGPR_WORKGROUP_ID_X/Y/Z=1, LDS_SIZE=128 (64KB)
+  rsrc2 = 0x19c | (128 << 15)
+  scratch_size = 0x10000  # 64KB per lane, matches .amdhsa_private_segment_fixed_size in run_program_hw
+  result = run_asm(lib_ptr, len(code), 1, 1, 1, n_lanes, 1, 1, args_ptr, rsrc2, scratch_size)
+  assert result == 0, f"run_asm failed with {result}"
+
+  return parse_output(bytes(out_buf), n_lanes)
+
+def run_program_hw(instructions: list, n_lanes: int = 1) -> WaveState:
+  """Run instructions on real AMD hardware via HIPCompiler and AMDProgram."""
+  from tinygrad.device import Device
+  from tinygrad.runtime.ops_amd import AMDProgram
+  from tinygrad.runtime.support.compiler_amd import HIPCompiler
+  from tinygrad.helpers import flat_mv
+
+  dev = Device["AMD"]
+  compiler = HIPCompiler(dev.arch)
+
+  prologue, epilogue = get_prologue_epilogue(n_lanes)
+  code = assemble(prologue + instructions + epilogue)
+
+  byte_str = ', '.join(f'0x{b:02x}' for b in code)
+  asm_src = f""".text
+.globl test
+.p2align 8
+.type test,@function
+test:
+.byte {byte_str}
+
+.rodata
+.p2align 6
+.amdhsa_kernel test
+  .amdhsa_next_free_vgpr 256
+  .amdhsa_next_free_sgpr 96
+  .amdhsa_wavefront_size32 1
+  .amdhsa_user_sgpr_kernarg_segment_ptr 1
+  .amdhsa_kernarg_size 8
+  .amdhsa_group_segment_fixed_size 65536
+  .amdhsa_private_segment_fixed_size 65536
+  .amdhsa_enable_private_segment 1
+.end_amdhsa_kernel
+
+.amdgpu_metadata
+---
+amdhsa.version:
+  - 1
+  - 0
+amdhsa.kernels:
+  - .name: test
+    .symbol: test.kd
+    .kernarg_segment_size: 8
+    .group_segment_fixed_size: 65536
+    .private_segment_fixed_size: 65536
+    .kernarg_segment_align: 8
+    .wavefront_size: 32
+    .sgpr_count: 96
+    .vgpr_count: 256
+    .max_flat_workgroup_size: 1024
+...
+.end_amdgpu_metadata
+"""
+
+  lib = compiler.compile(asm_src)
+  prg = AMDProgram(dev, "test", lib)
+
+  out_gpu = dev.allocator.alloc(OUT_BYTES)
+  assert out_gpu.va_addr % 16 == 0, f"buffer not 16-byte aligned: 0x{out_gpu.va_addr:x}"
+  prg(out_gpu, global_size=(1, 1, 1), local_size=(n_lanes, 1, 1), wait=True)
+
+  out_buf = bytearray(OUT_BYTES)
+  dev.allocator._copyout(flat_mv(memoryview(out_buf)), out_gpu)
+
+  return parse_output(bytes(out_buf), n_lanes)
+
+def compare_wave_states(emu_st: WaveState, hw_st: WaveState, n_lanes: int, n_vgprs: int = N_VGPRS, ulp_tolerance: int = 0) -> list[str]:
+  """Compare two WaveStates and return list of differences.
+
+  Args:
+    ulp_tolerance: Allow up to this many ULPs difference for float comparisons (0 = exact match required)
+  """
+  import math
+  diffs = []
+  for i in range(n_vgprs):
+    for lane in range(n_lanes):
+      emu_val = emu_st.vgpr[lane][i]
+      hw_val = hw_st.vgpr[lane][i]
+      if emu_val != hw_val:
+        emu_f, hw_f = _f32(emu_val), _f32(hw_val)
+        if math.isnan(emu_f) and math.isnan(hw_f):
+          continue
+        # Check ULP difference for floats (only for same-sign values)
+        if ulp_tolerance > 0 and (emu_val < 0x80000000) == (hw_val < 0x80000000):
+          ulp_diff = abs(int(emu_val) - int(hw_val))
+          if ulp_diff <= ulp_tolerance:
+            continue
+        diffs.append(f"v[{i}] lane {lane}: emu=0x{emu_val:08x} ({emu_f:.6g}) hw=0x{hw_val:08x} ({hw_f:.6g})")
+  for i in range(N_SGPRS):
+    emu_val = emu_st.sgpr[i]
+    hw_val = hw_st.sgpr[i]
+    if emu_val != hw_val:
+      diffs.append(f"s[{i}]: emu=0x{emu_val:08x} hw=0x{hw_val:08x}")
+  if emu_st.vcc != hw_st.vcc:
+    diffs.append(f"vcc: emu=0x{emu_st.vcc:08x} hw=0x{hw_st.vcc:08x}")
+  if emu_st.scc != hw_st.scc:
+    diffs.append(f"scc: emu={emu_st.scc} hw={hw_st.scc}")
+  return diffs
+
+def run_program(instructions: list, n_lanes: int = 1, ulp_tolerance: int = 0) -> WaveState:
+  """Run instructions and return WaveState.
+
+  If USE_HW=1, runs on both emulator and hardware, compares results, and raises if they differ.
+  Otherwise, runs only on emulator.
+
+  Args:
+    ulp_tolerance: Allow up to this many ULPs difference for float comparisons (0 = exact match required)
+  """
+  emu_st = run_program_emu(instructions, n_lanes)
+  if USE_HW:
+    hw_st = run_program_hw(instructions, n_lanes)
+    diffs = compare_wave_states(emu_st, hw_st, n_lanes, ulp_tolerance=ulp_tolerance)
+    if diffs:
+      raise AssertionError(f"Emulator vs Hardware mismatch:\n" + "\n".join(diffs))
+    return hw_st
+  return emu_st