gpt fixes

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

@@ -137,7 +137,6 @@ runs:
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true')
       shell: bash
       run: |
-        sudo mkdir -p /var/cache/apt/archives
         sudo chown -R $USER:$USER /var/cache/apt/archives
 
         echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
@@ -215,7 +214,6 @@ runs:
           sudo apt-get -y --allow-unauthenticated --no-install-recommends install ${{ steps.apt-pkgs.outputs.pkgs }}
         fi
 
-        sudo mkdir -p /var/cache/apt/archives
         sudo chown -R $USER:$USER /var/cache/apt/archives/
 
     - name: Add clang to PATH (Linux)

.github/workflows/benchmark.yml

@@ -94,7 +94,6 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -105,9 +104,6 @@ jobs:
         ./extra/amdpci/setup_python_cap.sh
         ./extra/hcq/hcq_smi.py amd rmmod
         ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Setup (NV)
-      if: ${{ matrix.dev == 'NV' }}
-      run: sudo lsof -tQ /dev/nvidia* | { xargs -r sudo kill -9 || true; }
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -121,10 +117,10 @@ jobs:
       run: python3 test/external/process_replay/reset.py
     - name: Run llama3.2
       run: BENCHMARK_LOG=llama32_3b-f16 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m llama3.2:3b-f16 --benchmark --warmup
-    - name: Run qwen3.6
-      # qwen3.6:35b-a3b doesn't fit on mac
+    - name: Run qwen3.5
+      # qwen3.5:35b-a3b doesn't fit on mac
       if: ${{ matrix.dev != 'METAL' }}
-      run: BENCHMARK_LOG=qwen36_35b-a3b JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m qwen3.6:35b-a3b --benchmark --warmup
+      run: BENCHMARK_LOG=qwen35_35b-a3b JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m qwen3.5:35b-a3b --benchmark --warmup
     - name: Run olmoe
       # just metal for now
       if: ${{ matrix.dev == 'METAL' }}
@@ -149,7 +145,6 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -160,9 +155,6 @@ jobs:
         ./extra/amdpci/setup_python_cap.sh
         ./extra/hcq/hcq_smi.py amd rmmod
         ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Setup (NV)
-      if: ${{ matrix.dev == 'NV' }}
-      run: sudo lsof -tQ /dev/nvidia* | { xargs -r sudo kill -9 || true; }
     - name: setup staging db
       if: github.ref == 'refs/heads/update_benchmark_staging'
       run: |
@@ -202,7 +194,6 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -213,9 +204,6 @@ jobs:
         ./extra/amdpci/setup_python_cap.sh
         ./extra/hcq/hcq_smi.py amd rmmod
         ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Setup (NV)
-      if: ${{ matrix.dev == 'NV' }}
-      run: sudo lsof -tQ /dev/nvidia* | { xargs -r sudo kill -9 || true; }
     - name: Symlink models and datasets
       run: |
         mkdir -p extra/datasets
@@ -252,7 +240,6 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -263,9 +250,6 @@ jobs:
         ./extra/amdpci/setup_python_cap.sh
         ./extra/hcq/hcq_smi.py amd rmmod
         ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Setup (NV)
-      if: ${{ matrix.dev == 'NV' }}
-      run: sudo lsof -tQ /dev/nvidia* | { xargs -r sudo kill -9 || true; }
     - name: setup staging db
       if: github.ref == 'refs/heads/update_benchmark_staging'
       run: |
@@ -308,9 +292,6 @@ jobs:
         ./extra/amdpci/setup_python_cap.sh
         ./extra/hcq/hcq_smi.py amd rmmod
         ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Setup (NV)
-      if: ${{ matrix.dev == 'NV' }}
-      run: sudo lsof -tQ /dev/nvidia* | { xargs -r sudo kill -9 || true; }
     - name: setup staging db
       if: github.ref == 'refs/heads/update_benchmark_staging'
       run: |
@@ -421,35 +402,6 @@ jobs:
       run: PYTHONPATH=. DEV=PCI+NV:NAK python3.11 test/test_tiny.py
 
   testcommalatest:
-    name: comma Benchmark (0.11.2)
-    runs-on: [self-hosted, Linux, comma]
-    timeout-minutes: 12
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - 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: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: openpilot compile3 0.11.2 supercombo
-      run: BENCHMARK_LOG=openpilot_0_11_2_supercombo PYTHONPATH="." ASSERT_MIN_STEP_TIME=26 DEV=QCOM FLOAT16=1 IMAGE=1 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/433f85f956837606ad1f1cbee4aa7e2158ad23c768dea914b20436c97232741b
-    - name: openpilot compile3 0.11.2 supercombo (from pickle)
-      run: BENCHMARK_LOG=openpilot_0_11_2_supercombo_run_pickle RUN_PICKLE=1 PYTHONPATH="." ASSERT_MIN_STEP_TIME=26 DEV=QCOM taskset -c 4-7 python3 examples/openpilot/compile3.py
-    - name: IR3 openpilot compile3 0.11.2 supercombo
-      run: BENCHMARK_LOG=ir3_openpilot_0_11_2_supercombo PYTHONPATH="." ASSERT_MIN_STEP_TIME=41 DEV=QCOM:IR3 FLOAT16=1 IMAGE=1 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/433f85f956837606ad1f1cbee4aa7e2158ad23c768dea914b20436c97232741b
-    - name: openpilot compile3 0.11.2 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_11_2_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=11 DEV=QCOM FLOAT16=1 IMAGE=1 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/3e7b31dfbc0a5234f1baf196513b77fc6af12204b8a8ffe8ee0417e48352f316
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testcommaold:
     name: comma Benchmark (0.11.0)
     runs-on: [self-hosted, Linux, comma]
     timeout-minutes: 12
@@ -480,6 +432,35 @@ jobs:
     - name: Run process replay tests
       uses: ./.github/actions/process-replay
 
+  testcommaold:
+    name: comma Benchmark (0.10.1)
+    runs-on: [self-hosted, Linux, comma]
+    timeout-minutes: 12
+    defaults:
+      run:
+        shell: bash -e -o pipefail {0}
+    if: github.repository_owner == 'tinygrad'
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - 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: reset process replay
+      run: test/external/process_replay/reset.py
+    - name: DEBUG=2 openpilot compile3 0.10.1 driving_vision
+      run: PYTHONPATH="." DEBUG=2 DEV=QCOM FLOAT16=1 IMAGE=1 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
+    - name: openpilot compile3 0.10.1 driving_vision
+      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=17 DEV=QCOM FLOAT16=1 IMAGE=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_policy
+      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=3.2 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_policy.onnx
+    - name: openpilot compile3 0.10.1 dmonitoring
+      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=11 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/dmonitoring_model.onnx
+    - name: Run process replay tests
+      uses: ./.github/actions/process-replay
+
   testqualcommdsp:
     name: DSP Benchmark
     runs-on: [self-hosted, Linux, comma4]
@@ -534,14 +515,12 @@ jobs:
       run: |
         echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
         rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: openpilot compile3 big_driving_supercombo
-      run: BENCHMARK_LOG=usbgpu_openpilot_big_driving_supercombo PICKLE_OOB=1 PYTHONPATH="." TC_OPT=2 GMMU=0 DEV=USB+AMD:LLVM ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/10926f2c0911821ca0e72439c1c3bf3ec11f0a08789aa14b7ee8f25379b2afa4 openpilot.pkl
-    - name: openpilot load_pickle big_driving_supercombo
-      run: BENCHMARK_LOG=usbgpu_openpilot_big_driving_supercombo_load_pickle PICKLE_OOB=1 PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_LOAD_TIME=25 python3 examples/openpilot/load_pickle.py openpilot.pkl
-    - name: openpilot run_pickle big_driving_supercombo
-      run: BENCHMARK_LOG=usbgpu_openpilot_big_driving_supercombo_run_pickle RUN_PICKLE=1 PICKLE_OOB=1 PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py - openpilot.pkl
-    - name: Test copy speeds
-      run: SIZE=64e6 PYTHONPATH=. GMMU=0 DEV=USB+AMD python3 test/external/external_test_usb_asm24.py TestDevCopySpeeds
+    - name: openpilot compile3 0.10.1 driving_vision
+      run: BENCHMARK_LOG=usbgpu_openpilot_0_10_1_vision PYTHONPATH="." GMMU=0 DEV=USB+AMD:LLVM ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/720392c9a5b986981fdbed1bb8c47a6c5573a50e/selfdrive/modeld/models/driving_vision.onnx
+    - name: openpilot load_pickle 0.10.1 driving_vision
+      run: BENCHMARK_LOG=usbgpu_openpilot_0_10_1_vision_load_pickle PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_LOAD_TIME=15 python3 examples/openpilot/load_pickle.py
+    - name: openpilot run_pickle 0.10.1 driving_vision
+      run: BENCHMARK_LOG=usbgpu_openpilot_0_10_1_vision_run_pickle RUN_PICKLE=1 PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py
 
   driverbenchmarks:
     name: PCI Driver Benchmark (DEV=${{ matrix.dev }})

.github/workflows/platform.yml

@@ -1,213 +0,0 @@
-name: Platform Tests
-env:
-  # increment this when downloads substantially change to avoid the internet
-  CACHE_VERSION: '19'
-  CAPTURE_PROCESS_REPLAY: ${{ github.event_name == 'pull_request' && contains(github.event.pull_request.title, '[pr]') && '1' || '0' }}
-  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  PYTHONPATH: ${{ github.workspace }}
-  CHECK_OOB: 1
-
-on:
-  push:
-    branches:
-      - master
-  pull_request:
-  workflow_dispatch:
-
-concurrency:
-  group: platform-${{ github.event_name }}-${{ github.event_name == 'pull_request' && github.event.pull_request.number || github.run_id }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
-jobs:
-
-# ****** OSX Tests ******
-
-  unittestmacos:
-    name: MacOS (unit)
-    runs-on: macos-26
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: unittest-macos
-        deps: testing_unit
-    - name: Run unit tests
-      run: DEV=METAL python -m pytest -n=auto test/unit/ --durations=20
-    - name: Test tensor core ops (fake)
-      run: DEV=METAL DEBUG=3 TC=2 python test/backend/test_ops.py TestOps.test_gemm
-    - name: Test tensor core ops (real)
-      run: DEV=METAL DEBUG=3 python test/backend/test_ops.py TestOps.test_big_gemm
-    - name: Test Beam Search
-      run: DEV=METAL IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
-    - name: Test Device Specific
-      run: DEV=METAL python3 -m pytest test/device/test_metal.py
-    #- name: Fuzz Test linearizer
-    #  run: DEV=METAL DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  unittestmacosmock:
-    name: MacOS (unit, mock)
-    runs-on: macos-26
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: unittest-macos-mock
-        deps: testing_unit
-        amd: 'true'
-        ocelot: 'true'
-    - name: Run NULL backend tests
-      run: SPEC=2 DEV=NULL python -m pytest -n=auto test/null/ --durations=20
-    - name: Run pytest (amd)
-      env:
-        DEV: MOCKKFD+AMD
-        FORWARD_ONLY: 1
-      run: |
-        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
-    - name: Run pytest (ptx)
-      env:
-        DEV: "MOCK+NV:PTX"
-        FORWARD_ONLY: 1
-        # TODO: failing due to library loading error
-        CAPTURE_PROCESS_REPLAY: 0
-      run: |
-        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py \
-          test/testextra/test_hevc.py::TestHevc::test_hevc_decode_compile --durations=20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testmetal:
-    strategy:
-      fail-fast: false
-      matrix:
-        group: [1, 2]
-    name: MacOS (DEV=METAL) (${{ matrix.group }})
-    runs-on: macos-26
-    timeout-minutes: 20
-    env:
-      DEV: METAL
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: macos-metal
-        deps: testing_unit
-    - name: Check Device.DEFAULT and print some source
-      run: |
-        python -c "from tinygrad import Device; assert Device.DEFAULT == 'METAL'"
-        DEBUG=4 python test/test_tiny.py TestTiny.test_plus
-    - name: Run backend tests
-      run: python -m pytest -n=auto test/backend --durations=20 --splits 2 --group ${{ matrix.group }}
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testmacos:
-    strategy:
-      fail-fast: false
-      matrix:
-        dev:
-          - 'CPU:CLANG'
-          - 'CPU:LLVM'
-          - 'CPU:LVP'
-          - 'WEBGPU'
-
-    name: MacOS (DEV=${{ matrix.dev }})
-    runs-on: macos-26
-    timeout-minutes: 20
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: macos-${{ matrix.dev }}
-        deps: "testing_unit${{ contains(matrix.dev, 'LVP') && ' mesa' || '' }}"
-        llvm: ${{ contains(matrix.dev, 'LLVM') || contains(matrix.dev, 'LVP') }}
-        webgpu: ${{ matrix.dev == 'WEBGPU' }}
-    - name: Set env
-      run: printf "DEV=${{ matrix.dev }}${{ matrix.dev == 'CPU:CLANG' && '\nCPU_COUNT=2' || '' }}" >> $GITHUB_ENV
-    - name: Check Device.DEFAULT and print some source
-      run: |
-        python -c "from tinygrad import Device; from tinygrad.helpers import Target; assert Device.DEFAULT == Target.parse('${{ matrix.dev }}').device"
-        DEBUG=4 python test/test_tiny.py TestTiny.test_plus
-    - name: Run test_tiny
-      run: python -m pytest -n=auto test/test_tiny.py --durations=20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-# ****** Windows Tests ******
-
-  testwindows:
-    strategy:
-      fail-fast: false
-      matrix:
-        dev:
-          - 'CPU:CLANG'
-          - 'CPU:LLVM'
-          - 'CPU:X86'
-          - 'WEBGPU'
-
-    name: Windows (DEV=${{ matrix.dev }})
-    runs-on: windows-2025
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v6
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: windows-${{ matrix.dev }}-minimal
-          deps: testing_unit
-          pydeps: ${{ matrix.dev == 'WEBGPU' && 'dawn-python' || '' }}
-      - name: Set env
-        shell: bash
-        run: printf "DEV=${{ matrix.dev }}${{ matrix.dev == 'CPU:CLANG' && '\nCPU_COUNT=2' || '' }}" >> $GITHUB_ENV
-      - name: Check Device.DEFAULT and print some source
-        shell: bash
-        run: |
-          python -c "from tinygrad import Device; from tinygrad.helpers import Target; assert Device.DEFAULT == Target.parse('${{ matrix.dev }}').device"
-          DEBUG=4 python test/test_tiny.py TestTiny.test_plus
-      - name: Run test_tiny
-        shell: bash
-        run: python -m pytest -n=auto test/test_tiny.py --durations=20
-
-
-  qcomclcompiletests:
-    name: Compile-only (QCOM CL)
-    runs-on: ubuntu-24.04-arm
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v6
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: compile-qcomcl
-          deps: testing_unit
-          tinydreno: 'true'
-      - name: Set env
-        shell: bash
-        run: printf "DEV=NULL:QCOMCL:a630\nNULL_ALLOW_COPYOUT=1" >> $GITHUB_ENV
-      - name: Run test_ops
-        shell: bash
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
-          DEBUG=4 python3 test/backend/test_ops.py TestOps.test_add
-          python -m pytest -n=auto test/backend/test_ops.py --durations=20
-      - name: Run test_ops (IMAGE)
-        shell: bash
-        env:
-          IMAGE: 1
-          DEV: "NULL:QCOMCL:a630,IMAGE_PITCH_ALIGNMENT=64"
-        run: |
-          DEBUG=4 python test/backend/test_ops.py TestOps.test_gemm | grep read_imagef
-          python -m pytest -n=auto test/backend/test_ops.py --durations=20

.github/workflows/szdiff.yml

@@ -14,15 +14,12 @@ jobs:
     outputs:
       branchstat: ${{ steps.brstat.outputs.stat}}
     steps:
-      - name: Check code from PR branch
+      - name: Check code from PR branch 
         uses: actions/checkout@v6
         with:
           repository: ${{ github.event.pull_request.head.repo.full_name }}
           ref: ${{ github.event.pull_request.head.sha }}
           fetch-depth: 0
-          # PR code is only inspected with git rev-list, never executed
-          allow-unsafe-pr-checkout: true
-          persist-credentials: false
       - name: Check whether branch is up-to-date
         id: brstat
         run: |
@@ -54,9 +51,6 @@ jobs:
           repository: ${{ github.event.pull_request.head.repo.full_name }}
           ref: ${{ github.event.pull_request.head.sha }}
           path: pr
-          # PR code is only line-counted by master's sz.py, never executed
-          allow-unsafe-pr-checkout: true
-          persist-credentials: false
         # the base default to tinygrad master and cannot be other fork branch for security purpose
       - name: Checkout code from tinygrad master
         uses: actions/checkout@v6

.github/workflows/test.yml

@@ -167,7 +167,6 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: unittest-13
-        python-version: '3.11'
         deps: testing_unit
         llvm: 'true'
         amd: 'true'
@@ -177,14 +176,13 @@ jobs:
       run: |
         DEV=NULL python3 -m unittest test.backend.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step
         DEV=NULL VIZ=1 python3 -m pytest -n=auto test/null/test_viz.py
-        DEBUG=7 python -m tinygrad.viz.cli --json | jq empty
     # TODO: too slow
     # - name: Run SDXL on NULL backend
     #   run: DEV=NULL DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
     - name: Run Clip tests for SD MLPerf on NULL backend
       run: DEV=NULL 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: DEV=NULL::gfx1201 NULL_ALLOW_COPYOUT=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: DEV=NULL::gfx1201 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: DEV=NULL python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
@@ -201,7 +199,6 @@ jobs:
       uses: ./.github/actions/setup-tinygrad
       with:
         key: unittest-13
-        python-version: '3.11'
         pydeps: "pre-commit"
         deps: testing_unit
         llvm: 'true'
@@ -219,8 +216,8 @@ jobs:
       run: python3 test/external/external_benchmark_schedule.py
     - name: Run process replay tests
       uses: ./.github/actions/process-replay
-    - name: Repo line count <= 26000 lines
-      run: MAX_LINE_COUNT=26000 python sz.py
+    - name: Repo line count < 25000 lines
+      run: MAX_LINE_COUNT=25000 python sz.py
 
   spec:
     strategy:
@@ -294,7 +291,7 @@ jobs:
           llvm: 'true'
       - name: Test openpilot model kernel count and gate usage
         run: |
-          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1361 ALLOWED_GATED_READ_IMAGE=38 FLOAT16=1 DEV="CL::IMAGE_PITCH_ALIGNMENT=64" IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1391 ALLOWED_GATED_READ_IMAGE=58 FLOAT16=1 DEV="CL::IMAGE_PITCH_ALIGNMENT=64" IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
           # IMAGE_PITCH_ALIGNMENT=64 matches adreno 630
       - name: Test openpilot CL compile fp32 (test correctness)
         run: |
@@ -353,8 +350,6 @@ jobs:
         run: DEV=NULL 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: DEV=NULL 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: Test gpt-oss training
-        run: DEV=NULL NULL_ALLOW_COPYOUT=1 SAMPLES=32 BS=2 SEQLEN=128 GRADIENT_ACC_STEPS=1 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 MXFP8=1 VOCAB_SIZE=32000 LAYERS=2 EXPERTS=4 MODEL=gptoss PYTHONPATH=. python3 examples/mlperf/model_train.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -483,7 +478,7 @@ jobs:
           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 -y llvm-21 llvm-21-tools cloc
+          sudo apt-get install llvm-21 llvm-21-tools cloc
       - name: Install rocprof-trace-decoder
         run: sudo PYTHONPATH="." ./extra/sqtt/install_rocprof_decoder.py
       - name: Run AMD renderer tests
@@ -521,11 +516,12 @@ jobs:
       - name: Run HCQ2 tests
         run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/test_tiny.py
       - name: Run HCQ2 multi-device tests
-        run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python -m pytest -n=auto test/backend/test_multitensor.py
+        run: |
+          HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/unit/test_multitensor.py \
+            TestMultiTensor.test_simple_add TestMultiTensor.test_shard_reduce \
+            TestMultiTensor.test_backward_sum TestMultiTensor.test_matmul_shard_0_0
       - name: Run HCQ2 JIT tests
         run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/unit/test_jit.py
-      - name: Run HCQ2 unit tests
-        run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python -m pytest test/device/test_hcq2.py
 
   testmockam:
     name: Linux (am)
@@ -584,9 +580,6 @@ jobs:
         run: |
           python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['AMD'], Device.DEFAULT"
           DEBUG=5 FORWARD_ONLY=1 python3 test/test_tiny.py TestTiny.test_plus
-      - name: Run MXFP4 Llama training on NULL backend
-        if: ${{ matrix.backend == 'amd' && matrix.arch == 'gfx950' }}
-        run: PYTHONPATH=. DEV=NULL:HIP:gfx950 MXFP4=1 LLAMA_LAYERS=2 BENCHMARK=3 NULL_ALLOW_COPYOUT=1 NO_HIPCC=1 ROCM_PATH=/opt/rocm JITBEAM=0 examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/profile.sh
       - name: Run pytest (amd)
         run: python -m pytest -n=auto test/backend/test_ops.py test/backend/test_dtype.py test/backend/test_dtype_alu.py test/backend/test_linearizer.py test/backend/test_randomness.py test/backend/test_jit.py test/backend/test_graph.py test/backend/test_multitensor.py test/device/test_hcq.py test/external/external_test_am.py test/backend/test_asm_gemm.py::TestAsmGEMM --durations=20
       - name: Run disk copy tests
@@ -631,6 +624,150 @@ jobs:
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
+# ****** OSX Tests ******
+
+  unittestmacos:
+    name: MacOS (unit)
+    runs-on: &macos macos-26
+    timeout-minutes: 20
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: unittest-macos
+        deps: testing_unit
+        amd: 'true'
+        ocelot: 'true'
+    - name: Run unit tests
+      run: DEV=METAL python -m pytest -n=auto test/unit/ --durations=20
+    - name: Run NULL backend tests
+      run: SPEC=2 DEV=NULL python -m pytest -n=auto test/null/ --durations=20
+    - name: Test tensor core ops (fake)
+      run: DEV=METAL DEBUG=3 TC=2 python test/backend/test_ops.py TestOps.test_gemm
+    - name: Test tensor core ops (real)
+      run: DEV=METAL DEBUG=3 python test/backend/test_ops.py TestOps.test_big_gemm
+    - name: Test Beam Search
+      run: DEV=METAL IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
+    - name: Test Device Specific
+      run: DEV=METAL python3 -m pytest test/device/test_metal.py
+    #- name: Fuzz Test linearizer
+    #  run: DEV=METAL DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
+    - name: Run pytest (amd)
+      env:
+        DEV: MOCKKFD+AMD
+        FORWARD_ONLY: 1
+      run: |
+        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
+    - name: Run pytest (ptx)
+      env:
+        DEV: "MOCK+NV:PTX"
+        FORWARD_ONLY: 1
+        # TODO: failing due to library loading error
+        CAPTURE_PROCESS_REPLAY: 0
+      run: |
+        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
+    - name: Run process replay tests
+      uses: ./.github/actions/process-replay
+
+  testmetal:
+    strategy:
+      fail-fast: false
+      matrix:
+        group: [1, 2]
+    name: MacOS (DEV=METAL) (${{ matrix.group }})
+    runs-on: *macos
+    timeout-minutes: 20
+    env:
+      DEV: METAL
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: macos-metal
+        deps: testing_unit
+    - name: Check Device.DEFAULT and print some source
+      run: |
+        python -c "from tinygrad import Device; assert Device.DEFAULT == 'METAL'"
+        DEBUG=4 python test/test_tiny.py TestTiny.test_plus
+    - name: Run backend tests
+      run: python -m pytest -n=auto test/backend --durations=20 --splits 2 --group ${{ matrix.group }}
+    - name: Run process replay tests
+      uses: ./.github/actions/process-replay
+
+  testmacos:
+    strategy:
+      fail-fast: false
+      matrix:
+        dev:
+          - 'CPU:CLANG'
+          - 'CPU:LLVM'
+          - 'CPU:LVP'
+          - 'WEBGPU'
+
+    name: MacOS (DEV=${{ matrix.dev }})
+    runs-on: *macos
+    timeout-minutes: 20
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: macos-${{ matrix.dev }}
+        deps: "testing_unit${{ contains(matrix.dev, 'LVP') && ' mesa' || '' }}"
+        llvm: ${{ contains(matrix.dev, 'LLVM') || contains(matrix.dev, 'LVP') }}
+        webgpu: ${{ matrix.dev == 'WEBGPU' }}
+    - name: Set env
+      run: printf "DEV=${{ matrix.dev }}${{ matrix.dev == 'CPU:CLANG' && '\nCPU_COUNT=2' || '' }}" >> $GITHUB_ENV
+    - name: Check Device.DEFAULT and print some source
+      run: |
+        python -c "from tinygrad import Device; from tinygrad.helpers import Target; assert Device.DEFAULT == Target.parse('${{ matrix.dev }}').device"
+        DEBUG=4 python test/test_tiny.py TestTiny.test_plus
+    - name: Run test_tiny
+      run: python -m pytest -n=auto test/test_tiny.py --durations=20
+    - name: Run process replay tests
+      uses: ./.github/actions/process-replay
+
+# ****** Windows Tests ******
+
+  testwindows:
+    strategy:
+      fail-fast: false
+      matrix:
+        dev:
+          - 'CPU:CLANG'
+          - 'CPU:LLVM'
+          - 'CPU:X86'
+          - 'WEBGPU'
+
+    name: Windows (DEV=${{ matrix.dev }})
+    runs-on: windows-2025
+    timeout-minutes: 15
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v6
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: windows-${{ matrix.dev }}-minimal
+          deps: testing_unit
+          pydeps: ${{ matrix.dev == 'WEBGPU' && 'dawn-python' || '' }}
+      - name: Set env
+        shell: bash
+        run: printf "DEV=${{ matrix.dev }}${{ matrix.dev == 'CPU:CLANG' && '\nCPU_COUNT=2' || '' }}" >> $GITHUB_ENV
+      - name: Check Device.DEFAULT and print some source
+        shell: bash
+        run: |
+          python -c "from tinygrad import Device; from tinygrad.helpers import Target; assert Device.DEFAULT == Target.parse('${{ matrix.dev }}').device"
+          DEBUG=4 python test/test_tiny.py TestTiny.test_plus
+      - name: Run test_tiny
+        shell: bash
+        run: python -m pytest -n=auto test/test_tiny.py --durations=20
+
 # ****** Compile-only Tests ******
 
   compiletests:
@@ -667,3 +804,33 @@ jobs:
         run: |
           DEBUG=4 python3 test/backend/test_ops.py TestOps.test_gemm | grep image_load
           python -m pytest -n=auto test/backend/test_ops.py --durations=20
+  qcomclcompiletests:
+    name: Compile-only (QCOM CL)
+    runs-on: ubuntu-24.04-arm
+    timeout-minutes: 15
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v6
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: compile-qcomcl
+          deps: testing_unit
+          tinydreno: 'true'
+      - name: Set env
+        shell: bash
+        run: printf "DEV=NULL:QCOMCL:a630\nNULL_ALLOW_COPYOUT=1" >> $GITHUB_ENV
+      - name: Run test_ops
+        shell: bash
+        run: |
+          python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
+          DEBUG=4 python3 test/backend/test_ops.py TestOps.test_add
+          python -m pytest -n=auto test/backend/test_ops.py --durations=20
+      - name: Run test_ops (IMAGE)
+        shell: bash
+        env:
+          IMAGE: 1
+          DEV: "NULL:QCOMCL:a630,IMAGE_PITCH_ALIGNMENT=64"
+        run: |
+          DEBUG=4 python test/backend/test_ops.py TestOps.test_gemm | grep read_imagef
+          python -m pytest -n=auto test/backend/test_ops.py --durations=20

AGENTS.md

@@ -3,4 +3,3 @@
 - Run tests with `-n12` for speed (e.g. `python -m pytest test/null/test_dtype.py -x -q -n12`)
 - Run `python -m mypy tinygrad/` to typecheck
 - Run `python -m ruff check .` to lint
-- Read `./tinygrad/viz/README.md` for profiling and debugging rewrite rules

conftest.py

@@ -1,10 +0,0 @@
-import os, pytest, signal, threading
-
-@pytest.hookimpl(wrapper=True)
-def pytest_runtest_call(item):
-  t = threading.Timer(int(os.getenv("TEST_TIMEOUT", 300)), os.kill, args=(os.getpid(), signal.SIGABRT))
-  t.start()
-  try: yield
-  finally:
-    t.cancel()
-    t.join()

docs/abstractions4.py

@@ -88,7 +88,7 @@ def example_3_custom_uop(a:Tensor, correct):
 
     # store all the per lane accumulators to LOCAL
     local_accs = UOp.placeholder((LCLS,), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL)
-    local_accs = local_accs.after(local_accs[lane].store(acc[0]))
+    local_accs = local_accs.after(local_accs[lane].store(acc[0]).barrier())
 
     # accumulate LOCALs into a single per CU accumulator
     late_reduce_loop = UOp.range(LCLS, 3, AxisType.REDUCE)

docs/dtypes.md

@@ -1,8 +1,7 @@
 ::: tinygrad.dtype.DType
 
-::: tinygrad.dtype.DTypes
+::: tinygrad.dtype.dtypes
     options:
-        heading: dtypes
         members: true
         members_order: source
         show_labels: false

examples/beautiful_cifar.py

@@ -9,7 +9,8 @@ from extra.lr_scheduler import OneCycleLR
 GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]
 
 # override tinygrad defaults
-Context(DEFAULT_FLOAT=dtypes.half, FUSE_OPTIM=1).__enter__()
+dtypes.default_float = dtypes.half
+Context(FUSE_OPTIM=1).__enter__()
 
 # from https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
 batchsize = getenv("BS", 1024)

examples/beautiful_mnist.py

@@ -1,6 +1,6 @@
 # model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
 from typing import Callable
-from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Context
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, function, Context
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
@@ -15,6 +15,7 @@ class Model:
       nn.BatchNorm(64), Tensor.max_pool2d,
       lambda x: x.flatten(1), nn.Linear(576, 10)]
 
+  @function
   def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
 
   @TinyJit

examples/gpt2.py

@@ -22,6 +22,10 @@ class Attention:
     self.head_dim = dim // n_heads
 
   def __call__(self, x:Tensor, start_pos:Variable, mask:Optional[Tensor]) -> Tensor:
+    if mask is not None or start_pos.val == 0:
+      # no symbolic shape qkv when consuming prompts
+      start_pos = start_pos.val
+
     if HALF: x = x.half()
     xqkv = self.c_attn(x).reshape(None, None, 3, self.n_heads, self.head_dim)
     xq, xk, xv = [xqkv[:, :, i, :, :] for i in range(3)]
@@ -34,8 +38,12 @@ class Attention:
     # update the cache
     self.cache_kv[:, :, start_pos:start_pos+seqlen, :, :].assign(Tensor.stack(xk, xv)).realize()
 
-    keys = self.cache_kv[0][:, :start_pos+seqlen, :, :]
-    values = self.cache_kv[1][:, :start_pos+seqlen, :, :]
+    if start_pos > 0:
+      keys = self.cache_kv[0][:, :start_pos+seqlen, :, :]
+      values = self.cache_kv[1][:, :start_pos+seqlen, :, :]
+    else:
+      keys = xk
+      values = xv
 
     xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
     return self.c_proj(xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2).reshape(bsz, seqlen, self.dim))
@@ -78,14 +86,15 @@ class Transformer:
       seqlen = tokens.shape[1]
       tok_emb = self.wte(tokens)
 
-    # start_pos is a bound Variable, so everything below it stays symbolic
-    pos_emb = self.wpe(self.allpos.shrink((None, (start_pos, start_pos+seqlen))))
+    # not symbolic when consuming the prompt
+    selected_pos = (0, seqlen) if start_pos.val == 0 else (start_pos, start_pos+1)
+    pos_emb = self.wpe(self.allpos.shrink((None, selected_pos)))
 
     h = tok_emb + pos_emb
 
     if HALF: h = h.half()
 
-    mask = Tensor.full((1, 1, seqlen, start_pos+seqlen), float("-inf"), dtype=h.dtype).triu(start_pos+1) if seqlen > 1 else None
+    mask = Tensor.full((1, 1, seqlen, start_pos.val+seqlen), float("-inf"), dtype=h.dtype).triu(start_pos.val+1) if seqlen > 1 else None
 
     for hi in self.h: h = hi(h, start_pos, mask)
 

examples/mlperf/dataloader.py

@@ -1,11 +1,11 @@
 import os, random, pickle, queue, struct, math, functools, hashlib, time
 from typing import List
 from pathlib import Path
-from multiprocessing import Queue, Process, shared_memory, connection, Lock
+from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count
 
 import numpy as np
 from tinygrad import dtypes, Tensor
-from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX, NUM_CPU_THREADS
+from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX
 from tinygrad.nn.state import TensorIO
 
 ### ResNet
@@ -131,7 +131,7 @@ def batch_load_resnet(batch_size=64, val=False, shuffle=True, seed=None, pad_fir
     else: X = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:/dev/shm/{shm_name}")
     Y = [None] * (batch_size*BATCH_COUNT)
 
-    for _ in range(NUM_CPU_THREADS.value):
+    for _ in range(cpu_count()):
       p = Process(target=loader_process, args=(q_in, q_out, X, seed))
       p.daemon = True
       p.start()
@@ -212,7 +212,7 @@ def batch_load_train_bert(BS:int, seed:int|None=None):
     rng.shuffle(fs)
     train_files.append(fs.pop(0))
 
-  cycle_length = min(NUM_CPU_THREADS.value, len(train_files))
+  cycle_length = min(getenv("NUM_CPU_THREADS", min(os.cpu_count(), 8)), len(train_files))
   assert cycle_length > 0, "cycle_length must be greater than 0"
 
   dataset = InterleavedDataset(train_files, cycle_length)
@@ -301,7 +301,7 @@ def batch_load_unet3d(preprocessed_dataset_dir:Path, batch_size:int=6, val:bool=
     X = Tensor.empty(*sz, dtype=dtypes.float32, device=f"disk:/dev/shm/{shm_name_x}")
     Y = Tensor.empty(*sz, dtype=dtypes.uint8, device=f"disk:/dev/shm/{shm_name_y}")
 
-    for _ in range(NUM_CPU_THREADS.value):
+    for _ in range(cpu_count()):
       proc = Process(target=load_unet3d_data, args=(preprocessed_dataset_dir, seed, queue_in, queue_out, X, Y))
       proc.daemon = True
       proc.start()
@@ -437,7 +437,7 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
   dataset_iter = iter(image_ids)
 
   try:
-    for _ in range(NUM_CPU_THREADS.value):
+    for _ in range(cpu_count()):
       proc = Process(
         target=load_retinanet_data,
         args=(base_dir, val, queue_in, queue_out, imgs, boxes, labels),

examples/mlperf/model_train.py

@@ -1282,10 +1282,10 @@ def train_bert():
         previous_step = i
 
 def train_llama3():
-  from examples.mlperf.models.flat_llama import FlatTransformer, apply_grad, FP8_DTYPE, MXFP8, MXFP4
+  from examples.mlperf.models.flat_llama import FlatTransformer, apply_grad, FP8_DTYPE, MXFP8
   from examples.llama3 import MODEL_PARAMS
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
-  from examples.mlperf.optim import GradAccClipAdamW, clip_grads
+  from examples.mlperf.optim import GradAccClipAdamW
 
   INITMLPERF = getenv("INITMLPERF")
   RUNMLPERF = getenv("RUNMLPERF")
@@ -1434,9 +1434,9 @@ def train_llama3():
     load_state_dict(scheduler, safe_load(fn), realize=False)
 
   fp8_amax = [t for ts in model._fp8_amax.values() for t in ts]
-  fp8_next_amax = [t for ts in model._fp8_next_amax.values() for t in ts]
-  fp8_grad_amax = [t for ts in model._fp8_grad_amax.values() for t in ts]
-  fp8_next_grad_amax = [t for ts in model._fp8_next_grad_amax.values() for t in ts]
+  fp8_next_amax = [t for ts in model._fp8_next_amax.values() for t in ts] if hasattr(model, "_fp8_next_amax") else []
+  fp8_grad_amax = [t for ts in model._fp8_grad_amax.values() for t in ts] if hasattr(model, "_fp8_grad_amax") else []
+  fp8_next_grad_amax = [t for ts in model._fp8_next_grad_amax.values() for t in ts] if hasattr(model, "_fp8_next_grad_amax") else []
   fp8_inv_scales = list(model._fp8_inv_scale.values()) + list(model._fp8_next_inv_scale.values())
 
   from tinygrad.nn.state import get_state_dict
@@ -1458,12 +1458,10 @@ def train_llama3():
 
   # realize everything here
   if optim.master_params: Tensor.realize(*optim.master_params)
-  loss_acc = Tensor.zeros(1, dtype=dtypes.float32, device=device)
-  Tensor.realize(loss_acc, *optim.params, *fp8_inv_scales, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
+  Tensor.realize(*optim.params, *fp8_inv_scales, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
 
   @TinyJit
   def minibatch(tokens:Tensor):
-    model.reset_amax()
     if is_dp: tokens = tokens.to(None).shard(device, 0)
     if is_mp: tokens = tokens.shard(device)
     if not is_sharding: tokens = tokens.to(None)
@@ -1477,24 +1475,23 @@ def train_llama3():
     for g, new_g in zip(grads, loss.gradient(*optim.params)):
       apply_grad(g, new_g.uop)
 
-    loss_acc.assign(loss_acc + loss.flatten().float())
-    return loss_acc.realize(*grads, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
+    loss_cpu = loss.flatten().float().to("CPU")
+    return loss_cpu.realize(*grads, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
 
   @TinyJit
   def optim_step():
-    grad_norm = clip_grads(grads, grad_acc, 1.0)
-    optim.fstep(grads, grad_norm)
+    grad_norm = optim.fstep(grads)
     scheduler.step()
 
     for g in grads: g.assign(0)
-    model.update_amax()
+    for cur, nxt in zip(fp8_amax, fp8_next_amax): cur.assign(nxt)
+    for cur, nxt in zip(fp8_grad_amax, fp8_next_grad_amax): cur.assign(nxt)
 
     lr_cpu = optim.lr.float().to("CPU")
     grad_norm_cpu = grad_norm.float().to("CPU")
-    loss_cpu = loss_acc.to("CPU")
-    Tensor.realize(lr_cpu, grad_norm_cpu, loss_cpu, loss_acc.assign(0), *grads, *fp8_inv_scales, *fp8_amax, *fp8_grad_amax)
+    Tensor.realize(lr_cpu, grad_norm_cpu, *grads, *fp8_inv_scales, *fp8_amax, *fp8_grad_amax)
 
-    return lr_cpu, grad_norm_cpu, loss_cpu
+    return lr_cpu, grad_norm_cpu
 
   @TinyJit
   @Context(TRAINING=0)
@@ -1549,8 +1546,8 @@ def train_llama3():
       st = time.perf_counter()
 
       stopped = False
-      data_time, dev_time = 0, 0
-      for _ in range(accum_steps:=grad_acc if i >= 2 else 1):
+      losses, data_time, dev_time = [], 0, 0
+      for _ in range(grad_acc if i >= 2 else 1):
         ist = time.perf_counter()
         try: tokens = next(train_iter)
         except StopIteration:
@@ -1558,15 +1555,16 @@ def train_llama3():
           break
         mst = time.perf_counter()
         data_time += mst - ist
-        minibatch(tokens)
+        losses.append(minibatch(tokens).item())
         dev_time += time.perf_counter() - mst
       if stopped: break
 
       gt = time.perf_counter()
       ret = optim_step()
-      lr, grad_norm, loss = ret[0].item(), ret[1].item(), ret[2].item() / accum_steps
+      lr, grad_norm = ret[0].item(), ret[1].item()
       et = time.perf_counter()
 
+      loss = sum(losses) / len(losses)
       optim_time = et - gt
       dev_time += optim_time
       step_time = et - st
@@ -1578,7 +1576,7 @@ def train_llama3():
 
       mem_gb = GlobalCounters.mem_used / 1e9
       gflops = GlobalCounters.global_ops / 1e9 / dev_time
-      mfu = ((6 * num_params * SEQLEN * GBS) / (dev_time * device_count * (9.2e15 if MXFP4 else 4.6e15))) * 100
+      mfu = ((6 * num_params * SEQLEN * GBS) / (dev_time * device_count * 4.6e15)) * 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, {grad_norm:.6f} grad_norm, {mem_gb:.2f} GB used, {gflops:9.2f} GFLOPS, {mfu:5.2f}% MFU")
@@ -1667,7 +1665,7 @@ def train_llama3():
 def train_gptoss():
   from examples.mlperf.models.gpt_oss import GPTOSS, GPT_OSS_20B, apply_grad, FP8_DTYPE
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
-  from examples.mlperf.optim import GradAccClipAdamW, GradAccClipAdamWGroup, clip_grads
+  from examples.mlperf.optim import GradAccClipAdamW
 
   BENCHMARK = getenv("BENCHMARK")
 
@@ -1710,10 +1708,9 @@ def train_gptoss():
     wandb.init(config=config, **wandb_args, project="MLPerf-gpt-oss")
 
   model_params = GPT_OSS_20B
-  model_params['vocab_size'] = getenv("VOCAB_SIZE", 128256)
+  model_params['vocab_size'] = 128256
   real_vocab_size = model_params['vocab_size']
   if (layers:=getenv("LAYERS")) != 0: model_params['n_layers'] = layers
-  if (experts:=getenv("EXPERTS")) != 0: model_params['n_experts'] = experts
   print(f"model parameters: {model_params}")
 
   model = GPTOSS(**model_params, max_context=SEQLEN)
@@ -1734,12 +1731,7 @@ def train_gptoss():
   is_offload_optim = bool(getenv("OFFLOAD_OPTIM"))
   is_fake_offload = Device.DEFAULT == "NULL"
   optim_device = ("CPU" if not is_fake_offload else "NULL:99") if is_offload_optim else None
-  params_wd = [p for p in params if p.ndim >= 3]
-  params_no_wd = [p for p in params if p.ndim < 3]
-  optim = GradAccClipAdamWGroup(
-    GradAccClipAdamW(params_wd, lr=0.0, b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay, grad_acc=grad_acc, device=optim_device),
-    GradAccClipAdamW(params_no_wd, lr=0.0, b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=0.0, grad_acc=grad_acc, device=optim_device),
-  )
+  optim = GradAccClipAdamW(params, lr=0.0, b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay, grad_acc=grad_acc, device=optim_device)
 
   for p in optim.params:
     grad_dtype = dtypes.bfloat16 if p.dtype == FP8_DTYPE else p.dtype
@@ -1748,10 +1740,7 @@ def train_gptoss():
 
   from extra.gemm.cdna_asm_gemm import _mx_block_scale
   model_state = get_state_dict(model)
-  def _scale_key(n):
-    if "." in n and (c:=f"{(b:=n.rsplit('.',1))[0]}_scale.{b[1]}") in model_state: return c
-    return f"{n}_scale"
-  fp8_scale_names = {n: _scale_key(n) for n, t in model_state.items() if t.dtype == FP8_DTYPE}
+  fp8_scale_names = {n: f"{n}_scale" for n, t in model_state.items() if t.dtype == FP8_DTYPE}
   fp8_inv_scales = [model_state[sname] for sname in fp8_scale_names.values()]
   for wname, sname in fp8_scale_names.items():
     w, scale = model_state[wname], model_state[sname]
@@ -1784,8 +1773,7 @@ def train_gptoss():
 
   @TinyJit
   def optim_step():
-    grad_norm = clip_grads(grads, grad_acc, 1.0)
-    optim.fstep(grads, grad_norm)
+    grad_norm = optim.fstep(grads)
     scheduler.step()
 
     for g in grads: g.assign(0)

examples/mlperf/models/flat_llama.py

@@ -25,7 +25,6 @@ FUSED_SILU_W13 = getenv("FUSED_SILU_W13", 0)
 SPLIT_W13 = getenv("SPLIT_W13", 0)
 COLUMNWISE_WEIGHT_SCALE = getenv("COLUMNWISE_WEIGHT_SCALE", 0)
 MXFP8 = getenv("MXFP8", 0)
-MXFP4 = getenv("MXFP4", 0)
 
 FP8_DTYPE = dtypes.fp8e4m3
 FP8_GRAD_DTYPE = dtypes.fp8e5m2
@@ -38,18 +37,13 @@ def quantize_fp8(x:Tensor, amax_state:Tensor|None=None):
   return x_clamped.cast(FP8_DTYPE), scale.float().reciprocal(), new_amax
 
 def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_scale:Tensor|None=None,
-           x_fp8:Tensor|None=None, grad_amax_state:Tensor|None=None, next_grad_amax_state:Tensor|None=None, x_prequant_mx:tuple|None=None,
-           next_amax_x:Tensor|None=None) -> tuple[Tensor,...]:
+           x_fp8:Tensor|None=None, x_new_amax:Tensor|None=None,
+           grad_amax_state:Tensor|None=None, next_grad_amax_state:Tensor|None=None, x_prequant_mx:tuple|None=None) -> tuple[Tensor,...]:
   if not fp8:
     if ASM_GEMM:
       from extra.gemm.cdna_asm_gemm import can_use_asm_gemm, asm_gemm
       if can_use_asm_gemm(x, w.T): return (asm_gemm(x, w.T),)
     return (x @ w.T,)
-  if MXFP4:
-    assert x is not None, "MXFP4 matmul requires an unquantized input"
-    from extra.gemm.cdna_asm_gemm import asm_gemm, can_use_asm_gemm
-    if can_use_asm_gemm(x, w.T): return (asm_gemm(x, w.T, mxfp4=True),)
-    return (x @ w.T,)
   assert w_inv_scale is not None, "fp8 matmul requires w_inv_scale (weights must be stored in fp8 with per-tensor scale)"
   if MXFP8:
     from extra.gemm.cdna_asm_gemm import asm_gemm, quantize_mxfp8, mx_pack, can_use_asm_gemm, _mx_block_scale
@@ -62,14 +56,13 @@ def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_sca
     else:
       x_phys = (x_q.cast(dtypes.bfloat16) * _mx_block_scale(x_e8)).reshape(*l_shape, x_q.shape[-1])
       out = x_phys @ (w.cast(dtypes.bfloat16) * _mx_block_scale(w_inv_scale)).T
-    return out, x_q
+    return out, (amax_x.detach() if amax_x is not None else None), x_q
   if x_fp8 is None:
-    if FUSED_INPUT_QUANTIZE:
+    if FUSED_INPUT_QUANTIZE and amax_x is not None:
       from extra.llama_kernels.quantize_fp8_delayed import quantize_fp8_delayed
-      x_fp8, _ = quantize_fp8_delayed(x, amax_x, next_amax_x, FP8_DTYPE)
+      x_fp8, _, x_new_amax, _ = quantize_fp8_delayed(x, amax_x, FP8_DTYPE)
     else:
-      x_fp8, _, new_amax_x = quantize_fp8(x, amax_state=amax_x)
-      next_amax_x.assign(new_amax_x)
+      x_fp8, _, x_new_amax = quantize_fp8(x, amax_state=amax_x)
   if ASM_GEMM:
     from extra.gemm.cdna_asm_gemm import can_use_asm_gemm, asm_gemm
     if can_use_asm_gemm(x_fp8, w.T):
@@ -80,56 +73,51 @@ def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_sca
       else:
         out = asm_gemm(x_fp8, w.T, x_scale=amax_x, w_scale=w_inv_scale, grad_amax_state=grad_amax_state,
                        next_grad_amax_state=next_grad_amax_state)
-      return out, x_fp8
-  return (x_fp8.dot(w.T, dtype=dtypes.float) * ((amax_x.float() + 1e-8) / FP8_MAX) * w_inv_scale).cast(dtypes.bfloat16), x_fp8
+      return out, x_new_amax, x_fp8
+  return (x_fp8.dot(w.T, dtype=dtypes.float) * ((amax_x.float() + 1e-8) / FP8_MAX) * w_inv_scale).cast(dtypes.bfloat16), x_new_amax, x_fp8
 
-def norm_quantize_matmul(x:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, eps:float, amax_x:Tensor|None,
-                         next_amax_x:Tensor|None, grad_amax_state:Tensor|None, next_grad_amax_state:Tensor|None):
-  if FUSED_ADD_NORM_MUL_QUANTIZE and not MXFP4:
+def norm_quantize_matmul(x:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, eps:float, amax_x:Tensor,
+                         grad_amax_state:Tensor, next_grad_amax_state:Tensor):
+  if FUSED_ADD_NORM_MUL_QUANTIZE:
     from extra.llama_kernels.fused_rmsnorm_mul_quantize_fp8 import fused_rmsnorm_mul_quantize_fp8
-    x_fp8, x_normed, rrms = fused_rmsnorm_mul_quantize_fp8(x, norm, amax_x, eps, FP8_DTYPE, next_amax_x)
-    out, *ret = matmul(None, w, w_inv_scale=w_inv_scale, x_fp8=x_fp8, amax_x=amax_x,
+    x_fp8, new_amax, x_normed, rrms = fused_rmsnorm_mul_quantize_fp8(x, norm, amax_x, eps, FP8_DTYPE)
+    out, *ret = matmul(None, w, w_inv_scale=w_inv_scale, x_fp8=x_fp8, amax_x=amax_x, x_new_amax=new_amax,
                        grad_amax_state=grad_amax_state, next_grad_amax_state=next_grad_amax_state)
     return out, x_normed, rrms, ret
   x_normed, rrms = rmsnorm(x, eps)
   out, *ret = matmul(x_normed * norm, w, amax_x=amax_x, w_inv_scale=w_inv_scale, grad_amax_state=grad_amax_state,
-                     next_grad_amax_state=next_grad_amax_state, next_amax_x=next_amax_x)
+                     next_grad_amax_state=next_grad_amax_state)
   return out, x_normed, rrms, ret
 
-def add_norm_quantize_matmul(x:Tensor, residual:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, eps:float, amax_x:Tensor|None,
-                             next_amax_x:Tensor|None, grad_amax_state:Tensor|None=None, next_grad_amax_state:Tensor|None=None):
-  if FUSED_ADD_NORM_MUL_QUANTIZE and not MXFP4:
+def add_norm_quantize_matmul(x:Tensor, residual:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, eps:float, amax_x:Tensor,
+                             grad_amax_state:Tensor|None=None, next_grad_amax_state:Tensor|None=None):
+  if FUSED_ADD_NORM_MUL_QUANTIZE:
     from extra.llama_kernels.fused_rmsnorm_mul_quantize_fp8 import fused_add_rmsnorm_mul_quantize_fp8
-    x_fp8, h, x_normed, rrms = fused_add_rmsnorm_mul_quantize_fp8(x, residual, norm, amax_x, eps, FP8_DTYPE, next_amax_x)
-    out, *ret = matmul(None, w, w_inv_scale=w_inv_scale, x_fp8=x_fp8, amax_x=amax_x,
+    x_fp8, new_amax, h, x_normed, rrms = fused_add_rmsnorm_mul_quantize_fp8(x, residual, norm, amax_x, eps, FP8_DTYPE)
+    out, *ret = matmul(None, w, w_inv_scale=w_inv_scale, x_fp8=x_fp8, amax_x=amax_x, x_new_amax=new_amax,
                        grad_amax_state=grad_amax_state, next_grad_amax_state=next_grad_amax_state)
     return out, h, x_normed, rrms, ret
   h = x + residual
   x_normed, rrms = rmsnorm(h, eps)
   out, *ret = matmul(x_normed * norm, w, amax_x=amax_x, w_inv_scale=w_inv_scale, grad_amax_state=grad_amax_state,
-                     next_grad_amax_state=next_grad_amax_state, next_amax_x=next_amax_x)
+                     next_grad_amax_state=next_grad_amax_state)
   return out, h, x_normed, rrms, ret
 
 def silu_w13_quantize_matmul(x_w13:Tensor, w2:Tensor, s_2:Tensor,
-                             amax_x2:Tensor|None, next_amax_x2:Tensor|None,
-                             grad_amax_xw13:Tensor|None, next_grad_amax_xw13:Tensor|None,
-                             grad_amax_xout:Tensor|None, next_grad_amax_xout:Tensor|None):
-  if FUSED_SILU_W13 and MXFP4:
-    from extra.llama_kernels.swiglu import swiglu
-    out, *ret = matmul(swiglu(x_w13), w2, amax_x=amax_x2, w_inv_scale=s_2, grad_amax_state=grad_amax_xout,
-                       next_grad_amax_state=next_grad_amax_xout, next_amax_x=next_amax_x2)
-    return out, ret
-  if FUSED_SILU_W13 and not MXFP4:
+                             amax_x2:Tensor,
+                             grad_amax_xw13:Tensor, next_grad_amax_xw13:Tensor,
+                             grad_amax_xout:Tensor, next_grad_amax_xout:Tensor):
+  if FUSED_SILU_W13:
     from extra.llama_kernels.cast_amax import fused_quantize_fp8_w13
-    x2_fp8 = fused_quantize_fp8_w13(x_w13, amax_x2, FP8_DTYPE, grad_amax_state=grad_amax_xw13,
-                                                 next_grad_amax_state=next_grad_amax_xw13, amax_out=next_amax_x2)
-    out, *ret = matmul(None, w2, w_inv_scale=s_2, x_fp8=x2_fp8, amax_x=amax_x2,
+    x2_fp8, new_amax_x2 = fused_quantize_fp8_w13(x_w13, amax_x2, FP8_DTYPE, grad_amax_state=grad_amax_xw13,
+                                                 next_grad_amax_state=next_grad_amax_xw13)
+    out, *ret = matmul(None, w2, w_inv_scale=s_2, x_fp8=x2_fp8, amax_x=amax_x2, x_new_amax=new_amax_x2,
                        grad_amax_state=grad_amax_xout, next_grad_amax_state=next_grad_amax_xout)
     return out, ret
   hidden = x_w13.shape[-1] // 2
   x_w1, x_w3 = x_w13[..., :hidden], x_w13[..., hidden:]
   out, *ret = matmul(x_w1.silu() * x_w3, w2, amax_x=amax_x2, w_inv_scale=s_2, grad_amax_state=grad_amax_xout,
-                     next_grad_amax_state=next_grad_amax_xout, next_amax_x=next_amax_x2)
+                     next_grad_amax_state=next_grad_amax_xout)
   return out, ret
 
 class FlatTransformer:
@@ -169,15 +157,14 @@ class FlatTransformer:
     self.freqs_cis = precompute_freqs_cis(dim // n_heads, max_context * 2, rope_theta).clone().is_param_(False)
 
     def _amax(): return Tensor.full((), FP8_MAX, dtype=dtypes.float32).contiguous().is_param_(False)
-    n_amax = 0 if MXFP4 else n_layers
     names = ["xqkv", "xo", "x2"]
     names += ["x1", "x3"] if SPLIT_W13 else ["x13"]
-    self._fp8_amax = {name: [_amax() for _ in range(n_amax)] for name in names}
-    self._fp8_next_amax = {name: [_amax() for _ in range(n_amax)] for name in names}
+    self._fp8_amax = {name: [_amax() for _ in range(n_layers)] for name in names}
+    self._fp8_next_amax = {name: [_amax() for _ in range(n_layers)] for name in names}
     grad_names = ["xqkv", "xo", "xout"]
     grad_names += ["xw1", "xw3"] if SPLIT_W13 else ["xw13"]
-    self._fp8_grad_amax = {name: [_amax() for _ in range(n_amax)] for name in grad_names}
-    self._fp8_next_grad_amax = {name: [_amax() for _ in range(n_amax)] for name in grad_names}
+    self._fp8_grad_amax = {name: [_amax() for _ in range(n_layers)] for name in grad_names}
+    self._fp8_next_grad_amax = {name: [_amax() for _ in range(n_layers)] for name in grad_names}
     w_scales = [("wqkv", s_qkv), ("wo", s_o), ("w2", s_2)]
     w_scales += [("w1", s_1), ("w3", s_3)] if SPLIT_W13 else [("w13", s_13)]
     self._fp8_inv_scale = {name: (s if MXFP8 else s.float()).contiguous().is_param_(False) for name, s in w_scales}
@@ -191,9 +178,6 @@ class FlatTransformer:
       from extra.gemm.cdna_asm_gemm import quantize_mxfp8
       w_q, w_e8, _ = quantize_mxfp8(w.reshape(self.n_layers * out_features, in_features))
       return w_q.reshape(self.n_layers, out_features, in_features), w_e8.reshape(self.n_layers, out_features, in_features // 32)
-    if MXFP4:
-      # FP4 is produced dynamically so optimizer updates always start from the current BF16 weight.
-      return w.cast(dtypes.bfloat16), Tensor.ones(self.n_layers)
     amax = (w.abs().max(axis=2) if COLUMNWISE_WEIGHT_SCALE else w.abs().flatten(1).max(1)).detach()
     scale = FP8_MAX / (amax + 1e-8)
     inv_scale = (amax + 1e-8) / FP8_MAX
@@ -201,16 +185,15 @@ class FlatTransformer:
     return (w * scale_b).clamp(-FP8_MAX, FP8_MAX).cast(FP8_DTYPE), inv_scale
 
   def attention(self, x:Tensor, freqs_cis:Tensor, *, attention_norm:Tensor, wqkv:Tensor, wo:Tensor,
-                amax_xqkv:Tensor|None, amax_xo:Tensor|None, s_qkv:Tensor, s_o:Tensor,
-                next_amax_xqkv:Tensor|None, next_amax_xo:Tensor|None,
-                grad_amax_xqkv:Tensor|None, grad_amax_xo:Tensor|None,
-                next_grad_amax_xqkv:Tensor|None, next_grad_amax_xo:Tensor|None):
+                amax_xqkv:Tensor, amax_xo:Tensor, s_qkv:Tensor, s_o:Tensor,
+                grad_amax_xqkv:Tensor, grad_amax_xo:Tensor, next_grad_amax_xqkv:Tensor, next_grad_amax_xo:Tensor):
     bsz, seqlen, _ = x.shape
-    saves = []
+    amaxs, saves = [], []
 
-    xqkv, x_normed, rrms, s = norm_quantize_matmul(x, attention_norm, wqkv, s_qkv, self.norm_eps,
+    xqkv, x_normed, rrms, (new_amax, *s) = norm_quantize_matmul(x, attention_norm, wqkv, s_qkv, self.norm_eps,
                                                                   amax_x=amax_xqkv, grad_amax_state=grad_amax_xqkv,
-                                                                  next_grad_amax_state=next_grad_amax_xqkv, next_amax_x=next_amax_xqkv)
+                                                                  next_grad_amax_state=next_grad_amax_xqkv)
+    amaxs.append(new_amax)
     saves.extend([x_normed, rrms, *s, xqkv])
     if getenv("HK_FLASH_ATTENTION"):
       from extra.thunder.amd.fa import flash_attention, fused_qkv_rope
@@ -228,62 +211,64 @@ class FlatTransformer:
       attn = xq.scaled_dot_product_attention(xk, xv, is_causal=True, enable_gqa=True).transpose(1, 2)
     attn = attn.reshape(bsz, seqlen, -1)
 
-    out, *s = matmul(attn, wo, amax_x=amax_xo, w_inv_scale=s_o, grad_amax_state=grad_amax_xo,
-                               next_grad_amax_state=next_grad_amax_xo, next_amax_x=next_amax_xo)
+    out, new_amax, *s = matmul(attn, wo, amax_x=amax_xo, w_inv_scale=s_o, grad_amax_state=grad_amax_xo,
+                               next_grad_amax_state=next_grad_amax_xo)
+    amaxs.append(new_amax)
     saves.extend([*s, out])
-    return out, saves
+    return out, amaxs, saves
 
   def feed_forward(self, x:Tensor, residual:Tensor, **kwargs):
-    saves = []
+    amaxs, saves = [], []
 
     if SPLIT_W13:
       h = x + residual
       x_normed, rrms = rmsnorm(h, self.norm_eps)
       saves.extend([x_normed, rrms])
       inp = x_normed * kwargs["ffn_norm"]
-      x_w1, *s = matmul(inp, kwargs["w1"], amax_x=kwargs["amax_x1"], w_inv_scale=kwargs["s_1"],
-                                  grad_amax_state=kwargs["grad_amax_xw1"], next_grad_amax_state=kwargs["next_grad_amax_xw1"],
-                                  next_amax_x=kwargs["next_amax_x1"])
+      x_w1, new_amax, *s = matmul(inp, kwargs["w1"], amax_x=kwargs["amax_x1"], w_inv_scale=kwargs["s_1"],
+                                  grad_amax_state=kwargs["grad_amax_xw1"], next_grad_amax_state=kwargs["next_grad_amax_xw1"])
+      amaxs.append(new_amax)
       saves.extend([*s, x_w1])
-      x_w3, *s = matmul(inp, kwargs["w3"], amax_x=kwargs["amax_x3"], w_inv_scale=kwargs["s_3"],
-                                  grad_amax_state=kwargs["grad_amax_xw3"], next_grad_amax_state=kwargs["next_grad_amax_xw3"],
-                                  next_amax_x=kwargs["next_amax_x3"])
+      x_w3, new_amax, *s = matmul(inp, kwargs["w3"], amax_x=kwargs["amax_x3"], w_inv_scale=kwargs["s_3"],
+                                  grad_amax_state=kwargs["grad_amax_xw3"], next_grad_amax_state=kwargs["next_grad_amax_xw3"])
+      amaxs.append(new_amax)
       saves.extend([*s, x_w3])
       if FUSED_SILU_W13 and MXFP8:
         from extra.llama_kernels.fused_silu_mul_quantize_mxfp8 import fused_silu_mul_quantize_mxfp8
         aq, ae8, asi = fused_silu_mul_quantize_mxfp8(x_w1.reshape(-1, x_w1.shape[-1]), x_w3.reshape(-1, x_w3.shape[-1]))
-        out, *s = matmul(None, kwargs["w2"], x_prequant_mx=(aq, ae8, asi), amax_x=kwargs["amax_x2"],
-                         w_inv_scale=kwargs["s_2"], grad_amax_state=kwargs["grad_amax_xout"],
-                         next_grad_amax_state=kwargs["next_grad_amax_xout"], next_amax_x=kwargs["next_amax_x2"])
+        out, new_amax, *s = matmul(None, kwargs["w2"], x_prequant_mx=(aq, ae8, asi), amax_x=kwargs["amax_x2"],
+                                   w_inv_scale=kwargs["s_2"], grad_amax_state=kwargs["grad_amax_xout"],
+                                   next_grad_amax_state=kwargs["next_grad_amax_xout"])
         out = out.reshape(*x_w1.shape[:-1], kwargs["w2"].shape[0])
       else:
-        out, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
-                         grad_amax_state=kwargs["grad_amax_xout"], next_grad_amax_state=kwargs["next_grad_amax_xout"],
-                         next_amax_x=kwargs["next_amax_x2"])
+        out, new_amax, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
+                                   grad_amax_state=kwargs["grad_amax_xout"], next_grad_amax_state=kwargs["next_grad_amax_xout"])
+      amaxs.append(new_amax)
       saves.extend([*s, out])
     else:
-      x_w13, h, x_normed, rrms, s = add_norm_quantize_matmul(x, residual, kwargs["ffn_norm"], kwargs["w13"], kwargs["s_13"],
+      x_w13, h, x_normed, rrms, (new_amax, *s) = add_norm_quantize_matmul(x, residual, kwargs["ffn_norm"], kwargs["w13"], kwargs["s_13"],
                                                                           self.norm_eps, amax_x=kwargs["amax_x13"],
-                                                                          next_amax_x=kwargs["next_amax_x13"],
                                                                           grad_amax_state=kwargs["grad_amax_xw13"],
                                                                           next_grad_amax_state=kwargs["next_grad_amax_xw13"])
+      amaxs.append(new_amax)
       saves.extend([x_normed, rrms, *s, x_w13])
-      out, s = silu_w13_quantize_matmul(x_w13, kwargs["w2"], kwargs["s_2"], amax_x2=kwargs["amax_x2"],
-                                                     next_amax_x2=kwargs["next_amax_x2"],
+      out, (new_amax, *s) = silu_w13_quantize_matmul(x_w13, kwargs["w2"], kwargs["s_2"], amax_x2=kwargs["amax_x2"],
                                                      grad_amax_xw13=kwargs["grad_amax_xw13"],
                                                      next_grad_amax_xw13=kwargs["next_grad_amax_xw13"],
                                                      grad_amax_xout=kwargs["grad_amax_xout"],
                                                      next_grad_amax_xout=kwargs["next_grad_amax_xout"])
+      amaxs.append(new_amax)
       saves.extend([*s, out])
-    return out, h, saves
+    return out, h, amaxs, saves
 
   @function(precompile=True, precompile_backward=True)
   def run_layer(self, x:Tensor, freqs_cis:Tensor, attn_kwargs:dict, ffn_kwargs:dict, save:bool=True):
-    attn, attn_saves = self.attention(x, freqs_cis, **attn_kwargs)
-    ffn, h, ffn_saves = self.feed_forward(x, attn, **ffn_kwargs)
+    attn, attn_amaxs, attn_saves = self.attention(x, freqs_cis, **attn_kwargs)
+    ffn, h, ffn_amaxs, ffn_saves = self.feed_forward(x, attn, **ffn_kwargs)
     h = h + ffn
-    if save: return (h, *attn_saves, *ffn_saves)
-    else: return (h,)
+    amaxs = tuple(a.detach() for a in (*attn_amaxs, *ffn_amaxs))
+    if save: return (h, *amaxs, *attn_saves, *ffn_saves)
+    else: return (h, *amaxs)
 
   def shard(self, device:tuple[str, ...], mp:bool=False):
     from tinygrad.nn.state import get_parameters
@@ -326,34 +311,29 @@ class FlatTransformer:
           for i in range(len(amax_dict[name])):
             amax_dict[name][i] = amax_dict[name][i].to(device).contiguous().is_param_(False)
 
-  def reset_amax(self):
-    for st in (self._fp8_next_amax, self._fp8_next_grad_amax):
-      for ts in st.values():
-        for t in ts: t.assign(0)
-
-  def update_amax(self):
-    for cur, nxt in ((self._fp8_amax, self._fp8_next_amax), (self._fp8_grad_amax, self._fp8_next_grad_amax)):
-      for name in cur:
-        for c, n in zip(cur[name], nxt[name]): c.assign(n)
-
   def __call__(self, tokens:Tensor, save:bool=True):
     h = self.tok_embeddings(tokens)
     freqs_cis = self.freqs_cis.cast(h.dtype)
     if not getenv("HK_FLASH_ATTENTION"): freqs_cis = freqs_cis[:, :tokens.shape[1], :, :, :]
     a, na, ga, nga, s = self._fp8_amax, self._fp8_next_amax, self._fp8_grad_amax, self._fp8_next_grad_amax, self._fp8_inv_scale
-    def amax_kwargs(i:int, act_names:tuple[str, ...], grad_names:tuple[str, ...]) -> dict[str, Tensor|None]:
-      specs = (("amax_", a, act_names), ("next_amax_", na, act_names), ("grad_amax_", ga, grad_names), ("next_grad_amax_", nga, grad_names))
-      if MXFP4: return dict.fromkeys(f"{prefix}{name}" for prefix, _, names in specs for name in names)
-      return {f"{prefix}{name}":val[name][i] for prefix, val, names in specs for name in names}
     for i in range(self.n_layers):
-      attn_kwargs = dict(attention_norm=self.attention_norm[i], wqkv=self.wqkv[i], wo=self.wo[i], s_qkv=s["wqkv"][i], s_o=s["wo"][i],
-                         **amax_kwargs(i, ("xqkv", "xo"), ("xqkv", "xo")))
-      ffn_kwargs = dict(ffn_norm=self.ffn_norm[i], w2=self.w2[i], s_2=s["w2"][i], **amax_kwargs(i, ("x2",), ("xout",)))
+      attn_kwargs = dict(attention_norm=self.attention_norm[i], wqkv=self.wqkv[i], wo=self.wo[i],
+                         amax_xqkv=a["xqkv"][i], amax_xo=a["xo"][i], s_qkv=s["wqkv"][i], s_o=s["wo"][i],
+                         grad_amax_xqkv=ga["xqkv"][i], grad_amax_xo=ga["xo"][i],
+                         next_grad_amax_xqkv=nga["xqkv"][i], next_grad_amax_xo=nga["xo"][i])
+      ffn_kwargs = dict(ffn_norm=self.ffn_norm[i], w2=self.w2[i],
+                        amax_x2=a["x2"][i], s_2=s["w2"][i], grad_amax_xout=ga["xout"][i], next_grad_amax_xout=nga["xout"][i])
       if SPLIT_W13:
-        ffn_kwargs.update(w1=self.w1[i], w3=self.w3[i], s_1=s["w1"][i], s_3=s["w3"][i], **amax_kwargs(i, ("x1", "x3"), ("xw1", "xw3")))
+        ffn_kwargs.update(w1=self.w1[i], w3=self.w3[i], amax_x1=a["x1"][i], amax_x3=a["x3"][i],
+                          s_1=s["w1"][i], s_3=s["w3"][i], grad_amax_xw1=ga["xw1"][i], grad_amax_xw3=ga["xw3"][i],
+                          next_grad_amax_xw1=nga["xw1"][i], next_grad_amax_xw3=nga["xw3"][i])
       else:
-        ffn_kwargs.update(w13=self.w13[i], s_13=s["w13"][i], **amax_kwargs(i, ("x13",), ("xw13",)))
-      h, *_ = self.run_layer(h, freqs_cis, attn_kwargs, ffn_kwargs, save=save)
+        ffn_kwargs.update(w13=self.w13[i], amax_x13=a["x13"][i], s_13=s["w13"][i], grad_amax_xw13=ga["xw13"][i],
+                          next_grad_amax_xw13=nga["xw13"][i])
+      h, *ret = self.run_layer(h, freqs_cis, attn_kwargs, ffn_kwargs, save=save)
+      amax_names = ["xqkv", "xo"] + (["x1", "x3"] if SPLIT_W13 else ["x13"]) + ["x2"]
+      for name, new_val in zip(amax_names, ret[:len(amax_names)]):
+        na[name][i].assign(new_val)
 
     logits = matmul(self.norm(h), self.output[0], fp8=False)[0]
     return logits
@@ -436,7 +416,6 @@ if __name__ == "__main__":
   @TinyJit
   def fwd_bwd(tokens:Tensor):
     with Timing("python forward: "):
-      model.reset_amax()
       logits = model(tokens[:, :-1], save=llama_size=="8B")
       loss = vocab_mask.where(-1e9, logits).sparse_categorical_crossentropy(tokens[:, 1:])
     with Timing("python backward: "):

examples/mlperf/models/gpt_oss.py

@@ -10,17 +10,13 @@ if __name__ == "__main__":
 from tinygrad import Tensor, nn, function, getenv, dtypes, TinyJit
 from tinygrad.helpers import Timing, colored, GlobalCounters, profile_marker
 from tinygrad.uop.ops import Ops, UOp
-from extra.models.llama import apply_rotary_emb
+from extra.models.llama import apply_rotary_emb, precompute_freqs_cis
 from extra.llama_kernels.rmsnorm import rmsnorm
-from extra.gemm.cdna_asm_gemm import _mx_block_scale, _mx_block_scale_3d, quantize_mxfp8
-from extra.gemm.moe_gemm import grouped_mx_gemm
-from extra.gemm.moe_routing import route, dispatch, combine
+from extra.gemm.cdna_asm_gemm import _mx_block_scale, quantize_mxfp8
 
 FP8_DTYPE = dtypes.fp8e4m3
 FP8_MAX = 448.0
-INIT_STD = 0.02
-ASM_GEMM = getenv("ASM_GEMM", 0)
-
+INIT_STD = 0.008
 
 def _quant_dequant_fwd(x:Tensor) -> Tensor:
   # x (2d bf16) -> bf16 value after an mxfp8 round-trip (1x32 block scaling on the last axis)
@@ -43,18 +39,16 @@ def quant_dequant_mx(x:Tensor) -> Tensor:
   fxn = _quant_dequant_fwd_fxn(x.as_param(0).uop, x.device)
   return Tensor(UOp.maketuple(fxn.uop).call(x.uop, grad_fxn=_quant_dequant_bwd).gettuple(0))
 
-def _mx_scale(e8:Tensor) -> Tensor:
-  return _mx_block_scale(e8) if e8.ndim == 2 else _mx_block_scale_3d(e8)
-
 def _dequant_fwd(w_q:Tensor, w_scale:Tensor) -> Tensor:
-  return w_q.cast(dtypes.bfloat16) * _mx_scale(w_scale)
+  return w_q.cast(dtypes.bfloat16) * _mx_block_scale(w_scale)
 
 @functools.cache
 def _dequant_fwd_fxn(wq_p, ws_p, device):
   return _dequant_fwd(Tensor(wq_p, device=device), Tensor(ws_p, device=device))
 
 def _dequant_bwd(grad:UOp, call:UOp) -> tuple:
-  return (Tensor(grad).cast(dtypes.bfloat16).uop, None)
+  w_scale = Tensor(call.src[2])
+  return ((Tensor(grad).cast(dtypes.bfloat16) * _mx_block_scale(w_scale).cast(dtypes.bfloat16)).uop, None)
 
 def dequant_weight(w_q:Tensor, w_scale:Tensor) -> Tensor:
   fxn = _dequant_fwd_fxn(w_q.as_param(0).uop, w_scale.as_param(1).uop, w_q.device)
@@ -63,45 +57,16 @@ def dequant_weight(w_q:Tensor, w_scale:Tensor) -> Tensor:
 
 def matmul_mx(x:Tensor, w_q:Tensor, w_scale:Tensor) -> Tensor:
   l_shape = x.shape[:-1]
-  if ASM_GEMM:
-    from extra.gemm.cdna_asm_gemm import asm_gemm, can_use_asm_gemm, mx_pack
-    x2, K, N = x.reshape(-1, x.shape[-1]), x.shape[-1], w_q.shape[0]
-    wq, ws = w_q, w_scale
-    if (pad := (-K) % 256):
-      x2 = x2.pad(((0, 0), (0, pad)))
-      wq = wq.pad(((0, 0), (0, pad)))
-      ws = ws.pad(((0, 0), (0, pad // 32)), value=127).cast(dtypes.uint8)
-    if (npad := (-N) % 256):
-      wq = wq.pad(((0, npad), (0, 0)))
-      ws = ws.pad(((0, npad), (0, 0)), value=127).cast(dtypes.uint8)
-    x_q, x_e8, x_si = quantize_mxfp8(x2)
-    if x_si is not None and can_use_asm_gemm(x_q, wq.T):
-      out = asm_gemm(x_q, wq.T, mx=True, mx_scales=(x_si, x_e8, mx_pack(ws), ws), mx_w_stored=True)
-      return (out[:, :N] if npad else out).reshape(*l_shape, N).cast(dtypes.bfloat16)
   x_phys = quant_dequant_mx(x.reshape(-1, x.shape[-1])).reshape(*l_shape, x.shape[-1])
   w_phys = dequant_weight(w_q, w_scale)
   return (x_phys @ w_phys.T).cast(dtypes.bfloat16)
 
-def _pad_to_mult(t:Tensor, axis:int, mult:int=256) -> Tensor:
-  if (r := (-t.shape[axis]) % mult) == 0: return t
-  pads = [(0, 0)] * t.ndim
-  pads[axis] = (0, r)
-  return t.pad(tuple(pads))
-
-def _pad_cols(t:Tensor) -> Tensor: return _pad_to_mult(t, -1)
-def _pad_rows(t:Tensor) -> Tensor: return _pad_to_mult(t, -2)
-
 def swiglu(x:Tensor, limit:float=7.0, alpha:float=1.702) -> Tensor:
   x_glu, x_linear = x[..., ::2], x[..., 1::2]
   x_glu = x_glu.clamp(max_=limit)
   x_linear = x_linear.clamp(-limit, limit)
   return (x_glu * (alpha * x_glu).sigmoid()) * (x_linear + 1)
 
-def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0) -> Tensor:
-  freqs = 1.0 / (theta ** (Tensor.arange(0, dim, 2, dtype=dtypes.float32)[:(dim // 2)] / dim))
-  freqs = Tensor.arange(end, dtype=dtypes.float32).unsqueeze(dim=1) * freqs.unsqueeze(dim=0)
-  return Tensor.stack(freqs.cos(), freqs.sin(), dim=-1).cast(dtypes.default_float).reshape(1, end, 1, dim//2, 2)
-
 class GPTOSS:
   def __init__(self, dim:int, n_layers:int, n_heads:int, n_kv_heads:int, head_dim:int, n_experts:int, experts_per_tok:int,
                intermediate_size:int, vocab_size:int, norm_eps:float=1e-5, rope_theta:int=150000, sliding_window:int=128,
@@ -127,9 +92,9 @@ class GPTOSS:
     self.ffn_norm = Tensor.ones(n_layers, dim).contiguous()
     self.gate = Tensor.normal(n_layers, n_experts, dim, mean=0.0, std=INIT_STD, dtype=dtypes.bfloat16)
     self.gate_bias = Tensor.zeros(n_layers, n_experts, dtype=dtypes.bfloat16).contiguous()
-    self.w_gate_up, self.w_gate_up_scale = self._quant_weight(n_layers, n_experts, intermediate_size * 2, dim, moe=True)
+    self.w_gate_up, self.w_gate_up_scale = self._quant_weight(n_layers, n_experts, intermediate_size * 2, dim)
     self.w_gate_up_bias = Tensor.zeros(n_layers, n_experts, intermediate_size * 2, dtype=dtypes.bfloat16).contiguous()
-    self.w_down, self.w_down_scale = self._quant_weight(n_layers, n_experts, dim, intermediate_size, std=scaled_std, moe=True)
+    self.w_down, self.w_down_scale = self._quant_weight(n_layers, n_experts, dim, intermediate_size, std=scaled_std)
     self.w_down_bias = Tensor.zeros(n_layers, n_experts, dim, dtype=dtypes.bfloat16).contiguous()
 
     # output
@@ -139,40 +104,19 @@ class GPTOSS:
     self.output = Tensor.normal(vocab_size, dim, mean=0.0, std=INIT_STD, dtype=dtypes.bfloat16)
     self.freqs_cis = precompute_freqs_cis(head_dim, max_context * 2, rope_theta).contiguous().is_param_(False)
 
-  def _quant_weight(self, *shape:int, std:float=INIT_STD, moe:bool=False):
-    def _one(*s:int):
-      w = Tensor.zeros(*s) if getenv("ZEROS") else Tensor.normal(*s, mean=0.0, std=std)
-      w_q, w_e8, _ = quantize_mxfp8(_pad_cols(_pad_rows(w)) if moe else w)
-      return w_q, w_e8.is_param_(False)
-    if moe:
-      qs = [_one(*shape[1:]) for _ in range(shape[0])]
-      return [q[0] for q in qs], [q[1] for q in qs]
-    return _one(*shape)
+  def _quant_weight(self, *shape:int, std:float=INIT_STD):
+    w = Tensor.zeros(*shape) if getenv("ZEROS") else Tensor.normal(*shape, mean=0.0, std=std)
+    w_q, w_e8, _ = quantize_mxfp8(w)
+    return w_q, w_e8.is_param_(False)
 
-  def _attn_mask(self, seqlen:int, dtype) -> Tensor:
+  def _attn_mask(self, seqlen:int, sliding:bool, dtype) -> Tensor:
     i, j = Tensor.arange(seqlen).reshape(seqlen, 1), Tensor.arange(seqlen).reshape(1, seqlen)
-    return (j <= i).where(0.0, -1e30).cast(dtype).contiguous()
+    allowed = j <= i
+    if sliding: allowed = allowed & (i - j < self.sliding_window)
+    return allowed.where(0.0, -1e30).cast(dtype).contiguous()
 
-  def _sliding_attention(self, xq:Tensor, xk:Tensor, xv:Tensor, sinks:Tensor) -> Tensor:
-    bsz, seqlen, H, hd = xq.shape
-    KV, R, W = self.n_kv_heads, self.n_rep, self.sliding_window
-    assert seqlen % W == 0, f"seqlen {seqlen} must be a multiple of sliding_window {W} for banded attention"
-    nb = seqlen // W
-    q = xq.reshape(bsz, seqlen, KV, R, hd).permute(0, 2, 3, 1, 4).reshape(bsz, KV, R, nb, W, hd).float()
-    k, v = (x.permute(0, 2, 1, 3).reshape(bsz, KV, 1, nb, W, hd).float() for x in (xk, xv))
-    kk, vv = (x.pad((None, None, None, (1, 0), None, None))[:, :, :, :nb].cat(x, dim=-2) for x in (k, v))
-    sc = (q @ kk.transpose(-1, -2)) * self.sm_scale   # (B,KV,R,nb,W,2W)
-    i, j, pv = Tensor.arange(W).reshape(W, 1), Tensor.arange(2 * W).reshape(1, 2 * W), Tensor.arange(nb).reshape(nb, 1, 1) >= 1
-    sc = ((j > i) & (j <= i + W) & (pv | (j >= W))).where(sc, -float("inf"))
-    sink = sinks.reshape(1, KV, R, 1, 1, 1).float()
-    m = sc.max(-1, keepdim=True).maximum(sink)
-    e = (sc - m).exp()
-    p = (e / (e.sum(-1, keepdim=True) + (sink - m).exp())).cast(dtypes.bfloat16)
-    attn = p @ vv.cast(dtypes.bfloat16)
-    return attn.reshape(bsz, KV, R, seqlen, hd).permute(0, 3, 1, 2, 4).reshape(bsz, seqlen, H * hd)
-
-  def attention(self, x:Tensor, freqs_cis:Tensor, mask:Tensor, sliding:bool, *, attention_norm:Tensor, wqkv:Tensor,
-                wqkv_scale:Tensor, wqkv_bias:Tensor, wo:Tensor, wo_scale:Tensor, wo_bias:Tensor, sinks:Tensor):
+  def attention(self, x:Tensor, freqs_cis:Tensor, mask:Tensor, *, attention_norm:Tensor, wqkv:Tensor, wqkv_scale:Tensor,
+                wqkv_bias:Tensor, wo:Tensor, wo_scale:Tensor, wo_bias:Tensor, sinks:Tensor):
     bsz, seqlen, _ = x.shape
     x_normed, rrms = rmsnorm(x, self.norm_eps)
     qkv = matmul_mx(x_normed * attention_norm, wqkv, wqkv_scale) + wqkv_bias
@@ -180,23 +124,16 @@ class GPTOSS:
     xq = qkv[:, :, :, :self.n_rep].reshape(bsz, seqlen, self.n_heads, self.head_dim)
     xk, xv = qkv[:, :, :, self.n_rep], qkv[:, :, :, self.n_rep + 1]
     xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
-    xq, xk, xv = xq.cast(dtypes.bfloat16), xk.cast(dtypes.bfloat16), xv.cast(dtypes.bfloat16)  # (B,N,H,D)/(B,N,KV,D)
 
-    if sliding:
-      attn = self._sliding_attention(xq, xk, xv, sinks)
-    elif getenv("HK_FLASH_ATTENTION"):
-      from extra.thunder.amd.fa import flash_attention
-      attn, *_ = flash_attention(xq, xk, xv, is_causal=True, write_flat=True, sinks=sinks)
-      attn = attn.reshape(bsz, seqlen, self.n_heads * self.head_dim)
-    else:
-      xqm = xq.reshape(bsz, seqlen, self.n_kv_heads, self.n_rep, self.head_dim).permute(0, 2, 3, 1, 4)
-      xkm, xvm = xk.permute(0, 2, 1, 3).unsqueeze(2), xv.permute(0, 2, 1, 3).unsqueeze(2)
-      scores = (xqm @ xkm.transpose(-2, -1)).float() * self.sm_scale + mask
-      sink = sinks.reshape(1, self.n_kv_heads, self.n_rep, 1, 1).float()
-      m = scores.max(-1, keepdim=True).maximum(sink)
-      e = (scores - m).exp()
-      w = (e / (e.sum(-1, keepdim=True) + (sink - m).exp())).cast(dtypes.bfloat16)
-      attn = (w @ xvm).permute(0, 3, 1, 2, 4).reshape(bsz, seqlen, self.n_heads * self.head_dim)
+    xq = xq.cast(dtypes.bfloat16).reshape(bsz, seqlen, self.n_kv_heads, self.n_rep, self.head_dim).permute(0, 2, 3, 1, 4)
+    xk = xk.cast(dtypes.bfloat16).permute(0, 2, 1, 3).unsqueeze(2)
+    xv = xv.cast(dtypes.bfloat16).permute(0, 2, 1, 3).unsqueeze(2)
+    scores = (xq @ xk.transpose(-2, -1)).float() * self.sm_scale + mask
+    sink = sinks.reshape(1, self.n_kv_heads, self.n_rep, 1, 1).float()
+    m = scores.max(-1, keepdim=True).maximum(sink)
+    e = (scores - m).exp()
+    w = (e / (e.sum(-1, keepdim=True) + (sink - m).exp())).cast(dtypes.bfloat16)
+    attn = (w @ xv).permute(0, 3, 1, 2, 4).reshape(bsz, seqlen, self.n_heads * self.head_dim)
 
     out = matmul_mx(attn, wo, wo_scale) + wo_bias
     return out, [x_normed, rrms, attn]
@@ -206,37 +143,22 @@ class GPTOSS:
                    w_down:Tensor, w_down_scale:Tensor, w_down_bias:Tensor):
     x_normed, rrms = rmsnorm(x, self.norm_eps)
     inp = x_normed * ffn_norm
+
     logits = inp.float() @ gate.float().T + gate_bias.float()
-    dim, inter = self.dim, self.intermediate_size
+    thresh = logits.topk(self.experts_per_tok)[0][..., -1:]
+    weights = (logits >= thresh).where(logits, -float("inf")).softmax(-1)
 
-    if getenv("GROUPED_MOE", 0):
-      bsz, seqlen = x.shape[:2]
-      inp, logits = inp.reshape(-1, dim), logits.reshape(-1, self.n_experts)
-      r = route(logits, self.experts_per_tok, self.n_experts)
-      onehot = r.rows_e.one_hot(self.n_experts).float()
-      xg = dispatch(_pad_cols(inp.cast(dtypes.bfloat16)), r)
-      h = grouped_mx_gemm(xg, (w_gate_up, w_gate_up_scale), r.off)[:, :2*inter] + (onehot @ w_gate_up_bias.float()).cast(dtypes.bfloat16)
-      y = swiglu(h, self.swiglu_limit)
-      z = grouped_mx_gemm(_pad_cols(y.cast(dtypes.bfloat16)), (w_down, w_down_scale), r.off)[:, :dim] \
-          + (onehot @ w_down_bias.float()).cast(dtypes.bfloat16)
-      out = combine(z, r, inp.shape[0], self.experts_per_tok).reshape(bsz, seqlen, dim)
-    else:
-      thresh = logits.topk(self.experts_per_tok)[0][..., -1:]
-      weights = (logits >= thresh).where(logits, -float("inf")).softmax(-1)
-
-      out = None
-      for e in range(self.n_experts):
-        gu_q, gu_s = w_gate_up[e][:2*inter, :dim].contiguous(), w_gate_up_scale[e][:2*inter, :dim//32].contiguous()
-        dn_q, dn_s = w_down[e][:dim, :inter].contiguous(), w_down_scale[e][:dim, :inter//32].contiguous()
-        gate_up = matmul_mx(inp, gu_q, gu_s) + w_gate_up_bias[e]
-        y = (matmul_mx(swiglu(gate_up, self.swiglu_limit), dn_q, dn_s) + w_down_bias[e]).contiguous()
-        contrib = weights[..., e:e+1].cast(y.dtype) * y
-        out = contrib if out is None else out + contrib
+    out = None
+    for e in range(self.n_experts):
+      gate_up = matmul_mx(inp, w_gate_up[e], w_gate_up_scale[e]) + w_gate_up_bias[e]
+      y = (matmul_mx(swiglu(gate_up, self.swiglu_limit), w_down[e], w_down_scale[e]) + w_down_bias[e]).contiguous()
+      contrib = weights[..., e:e+1].cast(y.dtype) * y
+      out = contrib if out is None else out + contrib
     return out, [x_normed, rrms]
 
   @function(precompile=True, precompile_backward=True)
-  def run_layer(self, x:Tensor, freqs_cis:Tensor, mask:Tensor, sliding:bool, attn_kwargs:dict, ffn_kwargs:dict, save:bool=True):
-    attn, attn_saves = self.attention(x, freqs_cis, mask, sliding, **attn_kwargs)
+  def run_layer(self, x:Tensor, freqs_cis:Tensor, mask:Tensor, attn_kwargs:dict, ffn_kwargs:dict, save:bool=True):
+    attn, attn_saves = self.attention(x, freqs_cis, mask, **attn_kwargs)
     h = x + attn
     ffn, ffn_saves = self.feed_forward(h, **ffn_kwargs)
     h = h + ffn
@@ -253,7 +175,8 @@ class GPTOSS:
     h = self.tok_embeddings(tokens)
     bsz, seqlen = tokens.shape
     freqs_cis = self.freqs_cis.cast(h.dtype)[:, :seqlen, :, :, :]
-    mask_full = None if getenv("HK_FLASH_ATTENTION") else self._attn_mask(seqlen, dtypes.float32)
+    mask_full = self._attn_mask(seqlen, False, dtypes.float32)
+    mask_sliding = self._attn_mask(seqlen, True, dtypes.float32)
     for i in range(self.n_layers):
       attn_kwargs = dict(attention_norm=self.attention_norm[i], wqkv=self.wqkv[i], wqkv_scale=self.wqkv_scale[i],
                          wqkv_bias=self.wqkv_bias[i], wo=self.wo[i], wo_scale=self.wo_scale[i], wo_bias=self.wo_bias[i],
@@ -261,7 +184,8 @@ class GPTOSS:
       ffn_kwargs = dict(ffn_norm=self.ffn_norm[i], gate=self.gate[i], gate_bias=self.gate_bias[i],
                         w_gate_up=self.w_gate_up[i], w_gate_up_scale=self.w_gate_up_scale[i], w_gate_up_bias=self.w_gate_up_bias[i],
                         w_down=self.w_down[i], w_down_scale=self.w_down_scale[i], w_down_bias=self.w_down_bias[i])
-      h, *_ = self.run_layer(h, freqs_cis, mask_full, i % 2 == 0, attn_kwargs, ffn_kwargs, save=save)
+      mask = mask_sliding if i % 2 == 0 else mask_full
+      h, *_ = self.run_layer(h, freqs_cis, mask, attn_kwargs, ffn_kwargs, save=save)
 
     logits = self.norm(h) @ self.output.T
     return logits

examples/mlperf/optim.py

@@ -1,8 +1,8 @@
 from tinygrad.tensor import Tensor
 from tinygrad.dtype import dtypes
-from tinygrad.nn.optim import Optimizer, OptimizerGroup
+from tinygrad.nn.optim import Optimizer
 from tinygrad.helpers import FUSE_OPTIM, getenv
-from tinygrad.uop.ops import UOp, Ops, AxisType
+from tinygrad.uop.ops import UOp, Ops
 
 STOCHASTIC_ROUND = getenv("STOCHASTIC_ROUND", 0)
 MASTER_WEIGHTS = getenv("MASTER_WEIGHTS", 0)
@@ -21,12 +21,6 @@ def stochastic_round_bf16(x:Tensor) -> Tensor:
   noise = (noise * 0xFFFF).cast(dtypes.uint32)
   return ((bits + noise) & 0xFFFF0000).bitcast(dtypes.float32).cast(dtypes.bfloat16)
 
-def clip_grads(grads:list[Tensor], grad_acc, clip_norm) -> Tensor:
-  for g in grads: g.assign(g / grad_acc)
-  total_norm = Tensor.stack(*[g.float().square().sum() for g in grads]).sum().sqrt().contiguous()
-  for g in grads: g.assign((g * (clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(g.dtype))
-  return total_norm
-
 class GradAccClipAdamW(Optimizer):
   def __init__(self, params:list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-6, weight_decay=0.0, grad_acc=1, clip_norm=1.0, device=None, fused=FUSE_OPTIM):
     super().__init__(params, lr, device, fused)
@@ -42,29 +36,46 @@ class GradAccClipAdamW(Optimizer):
       self.master_params = None
 
   def _zero_shard(self, t:Tensor) -> Tensor:
-    if not self.zero or t.ndim < 2 or (t.shape[0] % len(self.device)) != 0: return t
-    return Tensor(t.uop._shard(0, UOp.range(len(self.device), -1, AxisType.DEVICE)).unshard(0)).clone()
+    if not self.zero or (t.shape[0] % len(self.device)) != 0: return t
+    return Tensor(t.uop._shard(0, len(self.device)).multi(0)).clone()
 
   def _zero_gather(self, t:Tensor) -> Tensor:
     if not isinstance(t.device, tuple) or t.uop.axis != 0: return t
     n, sz = len(t.device), t.shape[0] // len(t.device)
     return Tensor.cat(*[t[p*sz:(p+1)*sz] for p in range(n)], dim=0)
 
-  def fschedule_step(self, grads:list[Tensor]) -> list[Tensor]:
-    updates, extra = self._step([], grads)
+  def fstep(self, grads:list[Tensor]):
+    if self.fused:
+      out, extra = self._step([], grads)
+      updates = [out[0][self.pos_params[i]:self.pos_params[i+1]].reshape(tt.shape) for i, tt in enumerate(self.params)]
+    else:
+      updates, extra = self._step([], grads)
     for i, tt in enumerate(self.params): tt.assign(self._apply_update(tt, updates[i], self.master_params[i] if self.master_params else None))
+    # collect inv_scale tensors attached to fp8 params (set by _apply_update)
     fp8_inv_scales = [tt._inv_scale for tt in self.params if hasattr(tt, '_inv_scale')]
     fp8_next_inv_scales = [tt._next_inv_scale for tt in self.params if hasattr(tt, '_next_inv_scale')]
-    return extra + self.params + self.buffers + (self.master_params or []) + fp8_inv_scales + fp8_next_inv_scales
+    to_realize = extra+self.params+self.buffers+(self.master_params or [])+fp8_inv_scales+fp8_next_inv_scales
 
-  def fstep(self, grads:list[Tensor], grad_norm:Tensor|None=None):
-    Tensor.realize(*([grad_norm] if grad_norm is not None else []), *self.fschedule_step(grads))
+    Tensor.realize(*to_realize)
+    return extra[-1]
 
   def _step(self, params:list[Tensor], grads:list[Tensor]) -> tuple[list[Tensor], list[Tensor]]:
     grads = list(grads)
 
     for i in range(len(grads)):
       if grads[i].device != self.m[i].device: grads[i] = grads[i].to(self.m[i].device)
+
+    if self.fused:
+      grads[0].assign(grads[0] / self.grad_acc)
+      total_norm = grads[0].float().square().sum().sqrt()
+      grads[0].assign((grads[0] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[0].dtype))
+    else:
+      for i in range(len(grads)):
+        grads[i].assign(grads[i] / self.grad_acc)
+      total_norm = Tensor.stack(*[g.float().square().sum() for g in grads]).sum().sqrt().contiguous()
+      for i in range(len(grads)):
+        grads[i].assign((grads[i] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[i].dtype))
+
     ret = []
     self.b1_t *= self.b1
     self.b2_t *= self.b2
@@ -77,7 +88,7 @@ class GradAccClipAdamW(Optimizer):
       v_hat = v_new / (1.0 - self.b2_t)
       up = m_hat / (v_hat.sqrt() + self.eps)
       ret.append(self.lr * up)
-    return ret, [self.b1_t, self.b2_t] + self.m + self.v
+    return ret, [self.b1_t, self.b2_t] + self.m + self.v + [total_norm]
 
   def _apply_update(self, t:Tensor, up:Tensor, master:Tensor|None=None) -> Tensor:
     w = master if master is not None else t
@@ -85,7 +96,7 @@ class GradAccClipAdamW(Optimizer):
     up = up.float().shard_like(w) + self.lr.to(w.device) * wd * w.detach()
     new_w = w.detach() - up
     if master is not None: master.assign(new_w)
-    if self.zero and not (MXFP8 and t.dtype in dtypes.fp8s): new_w = self._zero_gather(new_w)
+    if self.zero: new_w = self._zero_gather(new_w)
     # when master is offloaded to a different device than the param, results are resharded back onto the param's (sharded) device
     offloaded = master is not None and master.device != t.device
     if STOCHASTIC_ROUND and t.dtype == dtypes.bfloat16:
@@ -95,10 +106,9 @@ class GradAccClipAdamW(Optimizer):
       if MXFP8:
         from extra.gemm.cdna_asm_gemm import quantize_mxfp8
         w_q, w_e8, _ = quantize_mxfp8(new_w.reshape(-1, new_w.shape[-1]))
-        if self.zero: w_q, w_e8 = self._zero_gather(w_q), self._zero_gather(w_e8)
         new_e8 = w_e8.reshape(t._inv_scale.shape)
         t._inv_scale.assign(new_e8.shard_like(t._inv_scale) if offloaded else new_e8)
-        ret = w_q.reshape(t.shape)
+        ret = w_q.reshape(new_w.shape)
         return ret.shard_like(t) if offloaded else ret
       from examples.mlperf.models.flat_llama import FP8_MAX
       if IMMEDIATE_SCALE:
@@ -121,24 +131,3 @@ class GradAccClipAdamW(Optimizer):
       return ret.shard_like(t) if offloaded else ret
     out = new_w.cast(t.dtype)
     return out.shard_like(t) if offloaded else out
-
-class GradAccClipAdamWGroup(OptimizerGroup):
-  def __init__(self, *optimizers:GradAccClipAdamW):
-    super().__init__(*optimizers)
-    for o in self.optimizers[1:]: o.lr = self.optimizers[0].lr
-  def fstep(self, grads:list[Tensor], grad_norm:Tensor|None=None):
-    offset = 0
-    to_realize = []
-    for o in self.optimizers:
-      n = len(o.params)
-      to_realize += o.fschedule_step(grads[offset:offset+n])
-      offset += n
-    Tensor.realize(*to_realize, *([grad_norm] if grad_norm is not None else []))
-  @property
-  def lr(self): return self.optimizers[0].lr
-  @property
-  def device(self): return self.optimizers[0].device
-  @property
-  def master_params(self):
-    mp = [mp for o in self.optimizers for mp in (o.master_params or [])]
-    return mp if mp else None

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_beam.sh

@@ -1,8 +1,8 @@
 #!/usr/bin/env bash
 
 export PYTHONPATH="."
-export ROCM_PATH=${ROCM_PATH:-/opt/rocm-7.1.1}
-export PATH="$ROCM_PATH/bin:$PATH"
+export PATH="/opt/rocm-7.1.1/bin:$PATH"
+export ROCM_PATH="/opt/rocm-7.1.1"
 export DEV=${DEV:-AMD}
 export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
@@ -16,7 +16,7 @@ export USE_ATOMICS=${USE_ATOMICS:-1}
 export ASM_GEMM=${ASM_GEMM:-1}
 export WQKV=${WQKV:-1}
 export MASTER_WEIGHTS=${MASTER_WEIGHTS:-1}
-export MXFP4=${MXFP4:-1}
+export FP8=${FP8:-1}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
 export FAST_CE=${FAST_CE:-1}
 export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
@@ -26,7 +26,7 @@ export FUSED_SILU_W13=${FUSED_SILU_W13:-1}
 export SPLIT_W13=${SPLIT_W13:-0}
 export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-0}
 
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="float32"
+export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
 export DP=${DP:-8} MP=${MP:-1} BS=${BS:-16} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
 export GBS=$((BS * GRADIENT_ACC_STEPS))
 

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_run.sh

@@ -16,7 +16,7 @@ export USE_ATOMICS=${USE_ATOMICS:-1}
 export ASM_GEMM=${ASM_GEMM:-1}
 export WQKV=${WQKV:-1}
 export MASTER_WEIGHTS=${MASTER_WEIGHTS:-1}
-export MXFP4=${MXFP4:-1}
+export FP8=${FP8:-1}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
 export FAST_CE=${FAST_CE:-1}
 export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
@@ -26,7 +26,7 @@ export FUSED_SILU_W13=${FUSED_SILU_W13:-1}
 export SPLIT_W13=${SPLIT_W13:-0}
 export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-0}
 
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="float32"
+export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
 export DP=${DP:-8} MP=${MP:-1} BS=${BS:-16} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
 export GBS=$((BS * GRADIENT_ACC_STEPS))
 

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/profile.sh

@@ -1,6 +1,4 @@
 #!/bin/bash
-set -e
-
 export BENCHMARK=${BENCHMARK:-5}
 export EVAL_BS=0
 VIZ=${VIZ:--1} FULL_LAYERS=1 DEBUG=${DEBUG:--0} examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_beam.sh

examples/mlperf/training_submission_v6.1/tinycorp/benchmarks/gpt_oss/implementations/tinybox_8xMI350X/dev_beam.sh

@@ -10,7 +10,6 @@ export DEVICE_IN_FUNCTION_BUG=1
 
 export DEBUG=${DEBUG:-2}
 export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ASM_GEMM=${ASM_GEMM:-1}
 export ALL2ALL=${ALL2ALL:-1}
 export LATE_ALLREDUCE=${LATE_ALLREDUCE:-0}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}

examples/mlperf/training_submission_v6.1/tinycorp/benchmarks/gpt_oss/implementations/tinybox_8xMI350X/dev_run.sh

@@ -10,7 +10,6 @@ export DEVICE_IN_FUNCTION_BUG=1
 
 export DEBUG=${DEBUG:-0}
 export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ASM_GEMM=${ASM_GEMM:-1}
 export ALL2ALL=${ALL2ALL:-1}
 export LATE_ALLREDUCE=${LATE_ALLREDUCE:-0}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}

examples/openpilot/compile3.py

@@ -1,4 +1,4 @@
-import os, sys, pickle, time, re, tempfile, struct, shutil, io
+import os, sys, pickle, time, re
 import numpy as np
 if "JIT_BATCH_SIZE" not in os.environ: os.environ["JIT_BATCH_SIZE"] = "0"
 
@@ -9,39 +9,6 @@ from tinygrad.nn.onnx import OnnxRunner
 
 OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
 OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
-PICKLE_OOB = getenv("PICKLE_OOB")
-
-def dump_pickle(obj, f):
-  if PICKLE_OOB:
-    # allows pickling when buffers don't fit in (CPU) RAM
-    # from openpilot/selfdrive/modeld/helpers.py
-    with tempfile.TemporaryFile(dir=".") as tmp:
-      def buffer_callback(pb: pickle.PickleBuffer):
-        m = pb.raw()
-        tmp.write(struct.pack('<q', m.nbytes))
-        tmp.write(m)
-        pb.release() # keep peak ram at ~1 buffer
-      stream = io.BytesIO()
-      pickle.Pickler(stream, protocol=5, buffer_callback=buffer_callback).dump(obj)
-      opcodes = stream.getvalue()
-      f.write(struct.pack('<q', len(opcodes)))
-      f.write(opcodes)
-      tmp.seek(0)
-      shutil.copyfileobj(tmp, f)
-  else: pickle.dump(obj, f)
-
-def load_pickle(f):
-  if PICKLE_OOB:
-    # allows unpickling when buffers don't fit in (CPU) RAM
-    # from openpilot/selfdrive/modeld/helpers.py
-    opcodes = f.read(struct.unpack('<q', f.read(8))[0])
-    def buffers():
-      while (h := f.read(8)):
-        pb = pickle.PickleBuffer(bytearray(struct.unpack('<q', h)[0]))
-        f.readinto(pb)
-        yield pb
-    return pickle.load(io.BytesIO(opcodes), buffers=buffers())
-  else: return pickle.load(f)
 
 def compile(onnx_file):
   run_onnx = OnnxRunner(onnx_file)
@@ -61,8 +28,8 @@ def compile(onnx_file):
     inputs = {k:Tensor(v.numpy(), device=Device.DEFAULT).realize() if 'img' in k else v for k,v in inputs.items()}
   print("created tensors")
 
-  @TinyJit(prune=True)
-  def run_onnx_jit(**kwargs): return next(iter(run_onnx({k:v.to(Device.DEFAULT) for k,v in kwargs.items()}).values())).cast('float32')
+  run_onnx_jit = TinyJit(lambda **kwargs:
+                         next(iter(run_onnx({k:v.to(Device.DEFAULT) for k,v in kwargs.items()}).values())).cast('float32'), prune=True)
   for i in range(3):
     GlobalCounters.reset()
     print(f"run {i}")
@@ -98,7 +65,8 @@ def compile(onnx_file):
   if (allowed_gated_read_image:=getenv("ALLOWED_GATED_READ_IMAGE", -1)) != -1:
     assert gated_read_image_count == allowed_gated_read_image, f"different gated read_image! {gated_read_image_count=}, {allowed_gated_read_image=}"
 
-  with open(OUTPUT, "wb") as f: dump_pickle(run_onnx_jit, f)
+  with open(OUTPUT, "wb") as f:
+    pickle.dump(run_onnx_jit, f)
   mdl_sz = os.path.getsize(onnx_file)
   pkl_sz = os.path.getsize(OUTPUT)
   print(f"mdl size is {mdl_sz/1e6:.2f}M")
@@ -168,7 +136,7 @@ def bench(run, inputs):
 
 if __name__ == "__main__":
   if getenv("RUN_PICKLE"):
-    with open(OUTPUT, "rb") as f: pickle_loaded = load_pickle(f)
+    with open(OUTPUT, "rb") as f: pickle_loaded = pickle.load(f)
     inputs = {name: Tensor(Tensor.randn(*view.shape, dtype=dtype).numpy(), device=device)
               for name, (view, _vars, dtype, device) in zip(pickle_loaded.captured.expected_names, pickle_loaded.captured.expected_input_info)}
     test_vs_compile(pickle_loaded, inputs)
@@ -176,7 +144,7 @@ if __name__ == "__main__":
     onnx_file = fetch(OPENPILOT_MODEL)
     inputs, outputs = compile(onnx_file)
 
-    with open(OUTPUT, "rb") as f: pickle_loaded = load_pickle(f)
+    with open(OUTPUT, "rb") as f: pickle_loaded = pickle.load(f)
 
     test_vs_compile(pickle_loaded, inputs, outputs)
     if getenv("SELFTEST"):

examples/openpilot/load_pickle.py

@@ -1,6 +1,5 @@
-import sys
+import sys, pickle
 from extra.bench_log import WallTimeEvent, BenchEvent
-from examples.openpilot.compile3 import load_pickle
 from tinygrad.helpers import getenv
 
 PKL = sys.argv[1] if len(sys.argv) > 1 else "/tmp/openpilot.pkl"
@@ -8,7 +7,7 @@ PKL = sys.argv[1] if len(sys.argv) > 1 else "/tmp/openpilot.pkl"
 load_times = []
 
 for _ in range(10):
-  with WallTimeEvent(BenchEvent.STEP) as wte: load_pickle(open(PKL, 'rb'))
+  with WallTimeEvent(BenchEvent.STEP) as wte: pickle.load(open(PKL, 'rb'))
   load_times.append(wte.time)
   print(f"pickle load: {wte.time:6.2f} s")
 

extra/benchmark_llm.py

@@ -1,31 +0,0 @@
-import argparse, time
-from tinygrad.llm.model import Transformer
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--model", required=True, help="path to gguf model")
-  parser.add_argument("--max-context", type=int, default=8192, help="max context length (default: %(default)s)")
-  parser.add_argument("--prompt-tokens", type=int, default=1024, help="number of prompt tokens (default: %(default)s)")
-  parser.add_argument("--decode-tokens", type=int, default=16, help="number of tokens to decode (default: %(default)s)")
-  parser.add_argument("--chunk-size", type=int, default=32, help="chunk size for prefill (default: %(default)s)")
-  args = parser.parse_args()
-
-  st = time.perf_counter()
-  model, _ = Transformer.from_gguf(args.model, args.max_context)
-  print(f"load {time.perf_counter()-st:.3f}s", flush=True)
-
-  st = time.perf_counter()
-  model.warmup()
-  print(f"warm {time.perf_counter()-st:.3f}s", flush=True)
-
-  prompt = [257] + [1000+i%1000 for i in range(args.prompt_tokens-1)]
-  gen = model.generate(prompt, chunk_size=args.chunk_size)
-  st = time.perf_counter()
-  # first token is time-to-first-token; counted as part of prefill
-  output = [next(gen)]
-  pt = time.perf_counter()
-  print(f"prefill {args.prompt_tokens/(pt-st):.3f} tok/s", flush=True)
-
-  for _ in range(args.decode_tokens): output.append(next(gen))
-  et = time.perf_counter()
-  print(f"decode {args.decode_tokens/(et-pt):.3f} tok/s output {output}", flush=True)

extra/export_model.py

@@ -241,8 +241,8 @@ export default {model_name};
 def export_model(model, target:str, *inputs, model_name: Optional[str] = "model", stream_weights=False):
   assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, f"only {', '.join(EXPORT_SUPPORTED_DEVICE)} are supported"
 
-  # NOTE: NUM_CPU_THREADS=1, since export does not support threading
-  with Context(JIT=2, NUM_CPU_THREADS=1): linear, output_bufs = jit_model(model, *inputs)
+  # NOTE: CPU_COUNT=1, since export does not support threading
+  with Context(JIT=2, CPU_COUNT=1): linear, output_bufs = jit_model(model, *inputs)
   functions, statements, bufs, bufs_to_save = compile_net(linear, output_bufs)
   state = get_state_dict(model)
   weight_names = {(id(b), b.offset, b.size, b.dtype): name for name, x in state.items() if (b:=x.uop.base.realized) is not None}
@@ -264,7 +264,7 @@ def export_model(model, target:str, *inputs, model_name: Optional[str] = "model"
         if getattr(dim, "op", None) is Ops.ADD and len(dim.src) == 2 and \
            any(s.op is Ops.PARAM and s.addrspace is AddrSpace.ALU for s in dim.src) and any(s.op is Ops.CONST for s in dim.src):
           name, val = dim.src if dim.src[1].op is Ops.CONST else reversed(dim.src)
-          global_size[j] = f"_{name.expr}[0] + {val.val}"
+          global_size[j] = f"_{name.expr}[0] + {val.arg}"
 
   prg = ""
   if target == "clang":

extra/f16_decompress.py

@@ -5,10 +5,10 @@ def bit_extract(x: Tensor, e: int, s: int) -> Tensor:
   return (x >> s) & mask
 
 def u16_to_f16(x: Tensor) -> Tensor:
-  sign = bit_extract(x, 15, 15).bool()
+  sign = bit_extract(x, 15, 15).float()
   exponent = bit_extract(x, 14, 10).float()
   fraction = bit_extract(x, 9, 0).float()
-  return sign.where(-1, 1) * exponent.bool().where((exponent - 15.0).exp2() * (1 + fraction / 1024.0), 6.103515625e-5 * (fraction / 1024.0))
+  return sign.where(-1, 1) * exponent.where((exponent - 15.0).exp2() * (1 + fraction / 1024.0), 6.103515625e-5 * (fraction / 1024.0))
 
 def u32_to_f16(oo: Tensor) -> Tensor:
   f1 = u16_to_f16(oo>>16)

extra/fp8/fp8_linear.py

@@ -18,9 +18,9 @@ def custom_matmul(output: UOp, inp: UOp, weight: UOp) -> UOp:
   SEQ = inp.shape[1]
   OUT = weight.shape[0]
   IN = weight.shape[-1]
-  seq_idx = UOp.range(SEQ, 2)
-  out_idx = UOp.range(OUT, 3)
-  batch_idx = UOp.range(output.size//SEQ//OUT, 1)
+  seq_idx = UOp.range(SEQ, 2, AxisType.LOOP)
+  out_idx = UOp.range(OUT, 3, AxisType.LOOP)
+  batch_idx = UOp.range(output.size//SEQ//OUT, 1, AxisType.LOOP)
   reduce_idx = UOp.range(IN, 0, AxisType.REDUCE)
   product = (inp.index((seq_idx*IN+reduce_idx+batch_idx*IN*SEQ)) * weight.index((out_idx*IN+reduce_idx))).cast(dtypes.float)
   reduced = product.reduce(reduce_idx, arg=Ops.ADD)
@@ -53,7 +53,7 @@ class FP8Linear:
     x_fp8, x_scale = quantize_to_fp8(x)
     GPUS = self.weight.device
     if isinstance(GPUS, tuple) and len(GPUS) > 1:
-      y = Tensor(Tensor.empty((batch//len(GPUS), seq, self.weight.shape[0]), dtype=dtypes.float, device=GPUS).uop.unshard(0), device=GPUS)
+      y = Tensor(Tensor.empty((batch//len(GPUS), seq, self.weight.shape[0]), dtype=dtypes.float, device=GPUS).uop.multi(0), device=GPUS)
     else:
       y = Tensor.empty((batch, seq, self.weight.shape[0]), dtype=dtypes.float)
     y = Tensor.custom_kernel(y, x_fp8, w_fp8, fxn=custom_matmul, grad_fxn=custom_matmul_backward)[0]

extra/gemm/amd_copy_matmul.py

@@ -58,8 +58,8 @@ def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
   B_copy = B_local.permute((1,0)) if use_wmma else B_local
   A_store = A_copy.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(a[k_tile].reshape(-1, THREADS_PER_BLOCK)[:, tid])
   B_store = B_copy.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(b[k_tile].reshape(-1, THREADS_PER_BLOCK)[:, tid])
-  # NOTE: no explicit barrier needed, the AFTER on the LOCAL buffers implies it in late codegen
-  A_local, B_local = A_local.after(A_store, B_store), B_local.after(A_store, B_store)
+  barrier = UOp.barrier(A_store, B_store)
+  A_local, B_local = A_local.after(barrier), B_local.after(barrier)
 
   # -- COMPUTE --
   lane_m, lane_n = lane // LANES_PER_WAVE_N, lane % LANES_PER_WAVE_N
@@ -70,8 +70,8 @@ def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
 
   if use_wmma:
     k = UOp.range(BLOCK_K // WMMA_K, 101, AxisType.REDUCE)
-    tile_m = UOp.range(TM // WMMA_ACC, 200)
-    tile_n = UOp.range(TN, 201)
+    tile_m = UOp.range(TM // WMMA_ACC, 200, AxisType.LOOP)
+    tile_n = UOp.range(TN, 201, AxisType.LOOP)
 
     acc_frag = acc.reshape(TM // WMMA_ACC, WMMA_ACC, TN).permute(0,2,1)[tile_m, tile_n]
     a_frag = A_local.reshape(WAVES_M, TM // WMMA_ACC, WMMA_M, BLOCK_K // WMMA_K, WMMA_K)[wave_m, tile_m, lane_n, k]
@@ -96,8 +96,8 @@ def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
     b_frag = b_frag.reshape(1, TN).expand(TM, TN)
     acc_store = acc.store(acc.after(k) + (a_frag * b_frag))
 
-  # store accumulator and loop (the barrier at the end of the loop is implied by the LOCAL buffers stored and loaded in the loop)
-  acc = acc.after(acc_store.end(k).end(k_tile))
+  # store accumulator and loop
+  acc = acc.after(acc_store.end(k).barrier().end(k_tile))
 
   # store accumulator to output (unified)
   c = c.reshape(WAVES_M, TM//UNROLL_M, LANES_PER_WAVE_M, UNROLL_M,

extra/gemm/amd_flash_attention.py

@@ -0,0 +1,203 @@
+from tinygrad import Tensor, UOp, getenv
+from tinygrad.uop.ops import AxisType, KernelInfo, Ops
+from tinygrad.dtype import AddrSpace, dtypes
+from tinygrad.helpers import DEBUG, GlobalCounters, Context
+import math
+
+BLOCK_M, BLOCK_N = 64, 64
+WARP_SIZE = 32
+WMMA_M, WMMA_N, WMMA_K = 16, 16, 16
+WAVES_M, WAVES_N = 4, 1
+LANES_PER_WAVE_M, LANES_PER_WAVE_N = 2, 16
+WMMA_ACC = WMMA_M // LANES_PER_WAVE_M
+THREADS_PER_BLOCK = WARP_SIZE * WAVES_M * WAVES_N
+LDS_PAD = 4  # pad LDS rows to reduce bank conflicts
+
+WMMA_ARG = (WMMA_M, WMMA_N, WMMA_K), 'AMD', 32
+LOG2E = math.log2(math.e)
+
+def warp_shfl_xor(val, offset, lane):
+  """Read val from lane ^ offset using ds_bpermute."""
+  idx = ((lane ^ offset) * 4).cast(dtypes.int)
+  if val.op is Ops.INDEX and val.addrspace == AddrSpace.REG: val = val.load()
+  return UOp(Ops.CUSTOM, dtypes.float, (idx, val),
+             arg="__builtin_bit_cast(float, __builtin_amdgcn_ds_bpermute({0}, __builtin_bit_cast(int, {1})))")
+
+def warp_reduce_max(val, lane):
+  """Tree reduce MAX across LANES_PER_WAVE_N=16 lanes."""
+  for offset in [8, 4, 2, 1]:
+    val = UOp(Ops.MAX, dtypes.float, (val, warp_shfl_xor(val, offset, lane)))
+  return val
+
+def warp_reduce_sum(val, lane):
+  """Tree reduce SUM across LANES_PER_WAVE_N=16 lanes."""
+  for offset in [8, 4, 2, 1]:
+    val = val + warp_shfl_xor(val, offset, lane)
+  return val
+
+def amd_flash_attention(o:UOp, q:UOp, k:UOp, v:UOp) -> UOp:
+  # inputs are (B*H, N, D)
+  BH, N, D = q.shape
+  assert N % BLOCK_M == 0 and N % BLOCK_N == 0, f"N={N} must be divisible by BLOCK_M={BLOCK_M} and BLOCK_N={BLOCK_N}"
+  assert D % WMMA_K == 0 and D % LANES_PER_WAVE_N == 0, f"D={D} must be divisible by WMMA_K={WMMA_K} and LANES_PER_WAVE_N={LANES_PER_WAVE_N}"
+  assert BLOCK_M % (WAVES_M * WMMA_M) == 0 and BLOCK_N % LANES_PER_WAVE_N == 0
+  TM = BLOCK_M // (WAVES_M * LANES_PER_WAVE_M)
+  TN = BLOCK_N // (WAVES_N * LANES_PER_WAVE_N)
+  TD = D // (WAVES_N * LANES_PER_WAVE_N)
+  SCALE = 1.0 / math.sqrt(D)
+
+  block_bh = UOp.range(BH, 0, AxisType.GLOBAL)
+  block_m = UOp.range(N // BLOCK_M, 1, AxisType.GLOBAL)
+
+  q = q.reshape(BH, N//BLOCK_M, BLOCK_M, D)[block_bh, block_m]
+  k = k.reshape(BH, N//BLOCK_N, BLOCK_N, D)[block_bh]
+  v = v.reshape(BH, N//BLOCK_N, BLOCK_N, D)[block_bh]
+  o = o.reshape(BH, N//BLOCK_M, BLOCK_M, D)[block_bh, block_m]
+
+  wave_m = UOp.range(WAVES_M, 2, AxisType.LOCAL)
+  wave_n = UOp.range(WAVES_N, 3, AxisType.LOCAL)
+  lane = UOp.range(WARP_SIZE, -1, AxisType.WARP)
+  tid = (wave_m * WAVES_N + wave_n) * WARP_SIZE + lane
+  lane_m = lane // LANES_PER_WAVE_N
+  lane_n = lane % LANES_PER_WAVE_N
+
+  # LDS allocation: slot 0 = Q then P (shared), slot 1 = K then V
+  # TODO: the memory planner should be able to find this reuse
+  ELEMS_PER_THREAD = BLOCK_M * D // THREADS_PER_BLOCK
+  QP_lds = UOp.placeholder((BLOCK_M, D + LDS_PAD), dtypes.half, slot=0, addrspace=AddrSpace.LOCAL)
+  KV_lds = UOp.placeholder((BLOCK_N, D + LDS_PAD), dtypes.half, slot=1, addrspace=AddrSpace.LOCAL)[:, :D]
+
+  # register state
+  acc = UOp.placeholder((TM, TD), dtypes.float, slot=2, addrspace=AddrSpace.REG)
+  m_i = UOp.placeholder((TM,), dtypes.float, slot=3, addrspace=AddrSpace.REG)
+  l_i = UOp.placeholder((TM,), dtypes.float, slot=4, addrspace=AddrSpace.REG)
+  acc = acc.after(acc.store(acc.const_like(0)))
+  m_i = m_i.after(m_i.store(m_i.const_like(-math.inf)))
+  l_i = l_i.after(l_i.store(l_i.const_like(0)))
+
+  # ====== KV tile loop ======
+  n_tile = UOp.range(N // BLOCK_N, 100, AxisType.REDUCE)
+
+  # load Q + K into LDS (Q reloaded each iteration since P overwrites slot 0)
+  Q_lds = QP_lds[:, :D]
+  Q_store = Q_lds.after(n_tile).reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid].store(
+    q.reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid])
+  K_store = KV_lds.reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid].store(
+    k[n_tile].reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid])
+  qk_load_barrier = UOp.barrier(UOp.group(Q_store, K_store))
+  Q_lds = Q_lds.after(qk_load_barrier)
+  KV_lds_k = KV_lds.after(qk_load_barrier)
+
+  # -- S = Q @ K^T via WMMA (re-init each n_tile) --
+  S_reg = UOp.placeholder((TM, TN), dtypes.float, slot=6, addrspace=AddrSpace.REG)
+  S_reg = S_reg.after(S_reg.after(n_tile).store(S_reg.const_like(0)))
+  k_qk = UOp.range(D // WMMA_K, 101, AxisType.REDUCE)
+  tm1 = UOp.range(TM // WMMA_ACC, 200, AxisType.LOOP)
+  tn1 = UOp.range(TN, 201, AxisType.LOOP)
+  S_frag = S_reg.reshape(TM // WMMA_ACC, WMMA_ACC, TN).permute(0, 2, 1)[tm1, tn1]
+  q_frag = Q_lds.reshape(WAVES_M, TM // WMMA_ACC, WMMA_M, D // WMMA_K, WMMA_K)[wave_m, tm1, lane_n, k_qk]
+  k_frag = KV_lds_k.reshape(WAVES_N, TN, WMMA_N, D // WMMA_K, WMMA_K)[wave_n, tn1, lane_n, k_qk]
+  qk = UOp.wmma(q_frag, k_frag, S_frag.after(k_qk), *WMMA_ARG)
+  qk_done = S_frag.store(qk).end(tm1, tn1).end(k_qk)
+  S_reg = S_reg.after(qk_done)
+
+  # -- softmax in registers with warp shuffles --
+  S_reg = S_reg.after(S_reg.store(S_reg * SCALE))
+
+  # per-thread local row max over TN=4 elements, then warp reduce across 16 lanes
+  m_ij = UOp.placeholder((TM,), dtypes.float, slot=7, addrspace=AddrSpace.REG)
+  m_ij = m_ij.after(m_ij.after(n_tile).store(m_ij.const_like(-math.inf)))
+  rm2 = UOp.range(TN, 261, AxisType.REDUCE)
+  m_ij = m_ij.after(m_ij.store(m_ij.after(rm2).maximum(S_reg[:, rm2])).end(rm2))
+  # warp reduce max (in-place)
+  ri_w = UOp.range(TM, 270, AxisType.LOOP)
+  m_ij = m_ij.after(m_ij[ri_w].store(warp_reduce_max(m_ij[ri_w], lane)).end(ri_w))
+
+  # compute P = exp(S - m_ij) in S_reg
+  S_reg = S_reg.after(S_reg.store(((S_reg - m_ij.reshape(TM, 1).expand(TM, TN)) * LOG2E).exp2()))
+
+  p_local = UOp.placeholder((TM,), dtypes.float, slot=8, addrspace=AddrSpace.REG)
+  p_local = p_local.after(p_local.after(n_tile).store(p_local.const_like(0)))
+  rp2 = UOp.range(TN, 291, AxisType.REDUCE)
+  p_local = p_local.after(p_local.store(p_local.after(rp2) + S_reg[:, rp2]).end(rp2))
+  ri_ws = UOp.range(TM, 295, AxisType.LOOP)
+  p_sum = p_local.after(p_local[ri_ws].store(warp_reduce_sum(p_local[ri_ws], lane)).end(ri_ws))
+
+  # write P = exp(S - m_ij) to P_lds (reuses slot 0, Q no longer needed)
+  P_lds = QP_lds[:, :BLOCK_N]
+  P_write = P_lds.reshape(WAVES_M, TM // WMMA_ACC, WMMA_ACC, LANES_PER_WAVE_M, WAVES_N, TN, LANES_PER_WAVE_N)
+  P_write = P_write.permute((0, 4, 3, 6, 1, 2, 5)).reshape(THREADS_PER_BLOCK, TM, TN)
+  P_store = P_write[tid].store(S_reg.cast(dtypes.half))
+
+  # -- online softmax correction --
+  ri4 = UOp.range(TM, 330, AxisType.LOOP)
+  m_new_val = m_i[ri4].maximum(m_ij[ri4])
+  alpha_val = ((m_i[ri4] - m_new_val) * LOG2E).exp2()
+  beta_val = ((m_ij[ri4] - m_new_val) * LOG2E).exp2()
+  rj4 = UOp.range(TD, 331, AxisType.LOOP)
+  correction = UOp.group(
+    acc[ri4, rj4].store(alpha_val * acc[ri4, rj4]).end(rj4),
+    l_i[ri4].store(alpha_val * l_i[ri4] + beta_val * p_sum[ri4]),
+    m_i[ri4].store(m_new_val),
+  ).end(ri4)
+  acc = acc.after(correction)
+  l_i = l_i.after(correction)
+  m_i = m_i.after(correction)
+
+  # load V into KV_lds (must wait for QK WMMA to finish reading K from KV_lds)
+  V_store = KV_lds.after(qk_done).reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid].store(
+    v[n_tile].reshape(THREADS_PER_BLOCK, ELEMS_PER_THREAD)[tid])
+  pv_barrier = UOp.barrier(UOp.group(P_store, V_store))
+  P_lds = P_lds.after(pv_barrier)
+  KV_lds_v = KV_lds.after(pv_barrier)
+
+  # -- acc += P @ V via WMMA --
+  k_pv = UOp.range(BLOCK_N // WMMA_K, 400, AxisType.REDUCE)
+  tm2 = UOp.range(TM // WMMA_ACC, 401, AxisType.LOOP)
+  tn2 = UOp.range(TD, 402, AxisType.LOOP)
+  acc_frag = acc.reshape(TM // WMMA_ACC, WMMA_ACC, TD).permute(0, 2, 1)[tm2, tn2]
+  p_frag = P_lds.reshape(WAVES_M, TM // WMMA_ACC, WMMA_M, BLOCK_N // WMMA_K, WMMA_K)[wave_m, tm2, lane_n, k_pv]
+  v_frag = KV_lds_v.reshape(WAVES_N, TD, WMMA_N, BLOCK_N // WMMA_K, WMMA_K)[wave_n, tn2, lane_n, k_pv]
+  pv = UOp.wmma(p_frag, v_frag, acc_frag.after(k_pv), *WMMA_ARG)
+
+  # end KV tile loop
+  n_tile_end = acc_frag.store(pv).end(tm2, tn2).end(k_pv).barrier().end(n_tile)
+  acc = acc.after(n_tile_end)
+  l_i = l_i.after(n_tile_end)
+  m_i = m_i.after(n_tile_end)
+
+  # normalize: acc /= l_i
+  acc = acc.after(acc.store(acc * (1 / l_i).reshape(TM, 1).expand(TM, TD)))
+
+  # store output
+  o = o.reshape(WAVES_M, TM // WMMA_ACC, WMMA_ACC, LANES_PER_WAVE_M, WAVES_N, TD, LANES_PER_WAVE_N)
+  o = o.permute((0, 4, 3, 6, 1, 2, 5)).reshape(THREADS_PER_BLOCK, TM, TD)
+  return o[tid].store(acc).end(wave_m, wave_n, lane).end(block_m, block_bh).sink(arg=KernelInfo(opts_to_apply=()))
+
+if __name__ == "__main__":
+  B, H, N, D = getenv("B", 1), getenv("H", 32), getenv("N", 1024), getenv("D", 64)
+  q = Tensor.rand(B, H, N, D).cast(dtypes.half)
+  k = Tensor.rand(B, H, N, D).cast(dtypes.half)
+  v = Tensor.rand(B, H, N, D).cast(dtypes.half)
+  o = Tensor.empty(B, H, N, D, dtype=dtypes.float)
+  with Context(DEBUG=0): Tensor.realize(q, k, v)
+
+  q_flat, k_flat, v_flat, o_flat = q.reshape(B*H, N, D), k.reshape(B*H, N, D), v.reshape(B*H, N, D), o.reshape(B*H, N, D)
+  NUM_RUNS = getenv("CNT", 5)
+  ets = []
+  with Context(DEBUG=2):
+    for _ in range(NUM_RUNS):
+      GlobalCounters.reset()
+      tst = Tensor.custom_kernel(o_flat, q_flat, k_flat, v_flat, fxn=amd_flash_attention)[0].realize()
+      ets.append(GlobalCounters.time_sum_s)
+  print(f"best time: {min(ets)*1e3:.2f}ms")
+
+  if getenv("VERIFY", 1):
+    with Context(DEBUG=0):
+      ref = q.float().scaled_dot_product_attention(k.float(), v.float()).reshape(B*H, N, D).realize()
+      err = (ref - tst).square().mean().item()
+    print(f"mean squared error {err}")
+    if err > 1e-2:
+      raise RuntimeError("flash attention is wrong!")
+    else:
+      print("flash attention is correct!")

extra/gemm/amd_uop_matmul.py

@@ -28,10 +28,10 @@ REG_TILES_PER_WAVE_M = BLOCK_M // (WAVES_PER_BLOCK_M * LANES_PER_WAVE_M * TM)
 assert WAVES_PER_BLOCK_M*REG_TILES_PER_WAVE_M*LANES_PER_WAVE_M*TM == BLOCK_M, "M reshape is wrong"
 assert WAVES_PER_BLOCK_N*REG_TILES_PER_WAVE_N*LANES_PER_WAVE_N*TN == BLOCK_N, "N reshape is wrong"
 
-def rngs_for_shape(shape:tuple[sint, ...], rng:int, axis_type=AxisType.WEAK): return [UOp.range(s, rng+i, axis_type) for i,s in enumerate(shape)]
+def rngs_for_shape(shape:tuple[sint, ...], rng:int, axis_type=AxisType.LOOP): return [UOp.range(s, rng+i, axis_type) for i,s in enumerate(shape)]
 def copy(dest:UOp, src:UOp, rng:int, upcast=False):
   assert dest.shape == src.shape
-  rngs = rngs_for_shape(src.shape, rng, AxisType.UPCAST if upcast else AxisType.WEAK)
+  rngs = rngs_for_shape(src.shape, rng, AxisType.UPCAST if upcast else AxisType.LOOP)
   return dest[*rngs].store(src[*rngs]).end(*rngs)
 
 def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
@@ -66,8 +66,9 @@ def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
   B_local = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
   B_local_store = copy(B_local.reshape(-1, THREADS_PER_BLOCK)[:, tid], b.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=200)
 
-  # NOTE: no explicit barrier needed, the AFTER on the LOCAL buffers implies it in late codegen
-  A_local, B_local = A_local.after(A_local_store, B_local_store), B_local.after(A_local_store, B_local_store)
+  # TODO: can we automate barrier?
+  barrier = UOp.barrier(A_local_store, B_local_store)
+  A_local, B_local = A_local.after(barrier), B_local.after(barrier)
 
   # open inner k range
   k = UOp.range(BLOCK_K, 3, AxisType.REDUCE)
@@ -101,7 +102,7 @@ def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
   sink = c_regs[*rngs].store(c_regs.after(k)[*rngs] + A_col[iter_m, t_m] * B_row[iter_n, t_n]).end(iter_m, iter_n, t_m, t_n)
 
   # Close k, sync, and close K tiles
-  sink = sink.end(k).end(k_tile_range)
+  sink = sink.end(k).barrier().end(k_tile_range)
 
   # ---------------------------
   # REG -> GLOBAL (epilogue)

extra/gemm/cdna_asm_gemm.py

@@ -1,12 +1,10 @@
-import atexit, functools, math, pathlib
+import atexit, functools, pathlib
 from tinygrad import Tensor, Device, dtypes
-from tinygrad.dtype import AddrSpace
 from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
 from tinygrad.renderer import Estimates
-from tinygrad.helpers import getenv, all_same, DEBUG, ceildiv
+from tinygrad.helpers import getenv, all_same, DEBUG
 from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
 from examples.mlperf.models.flat_llama import FP8_DTYPE, quantize_fp8
-from extra.llama_kernels.quantize_mxfp4 import quantize_mxfp4
 
 TILE_M, TILE_N, TILE_K = 256, 256, 64
 
@@ -74,7 +72,7 @@ def hk_fp8_atb_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, g_amax:Tensor|
     elif b.uop.axis == 2: inv, out_axis = Tensor.invalids(1, M, N // ndev, dtype=dtypes.bfloat16, device=a.device), 2
     elif a.uop.axis == 2: inv, out_axis = Tensor.invalids(1, M // ndev, N, dtype=dtypes.bfloat16, device=a.device), 1
     else: inv, out_axis, reduce_out = Tensor.invalids(1, M, N, dtype=dtypes.bfloat16, device=a.device), 0, True
-    out = Tensor(inv.uop.unshard(out_axis), device=a.device)
+    out = Tensor(inv.uop.multi(out_axis), device=a.device)
     dname = a.device[0]
   else:
     out = Tensor.invalids(1, M, N, dtype=dtypes.bfloat16, device=a.device)
@@ -109,42 +107,6 @@ def custom_hk_mxfp8_gemm(C:UOp, A:UOp, B:UOp, scale_A:UOp, scale_B:UOp, *extra:U
   return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src),
                                UOp(Ops.BINARY, arg=lib)))
 
-# ** MXFP4 GEMM custom kernel
-
-@functools.cache
-def custom_mxfp4_gemm(C:UOp, A:UOp, B:UOp, scale_a:UOp, scale_b:UOp, *extra:UOp, tile_m:int, tile_n:int) -> UOp:
-  from extra.gemm.gemm_mxfp4 import build_kernel
-  M, half_k = math.prod(A.shape[:-1]), A.shape[-1]
-  N, half_k_b = math.prod(B.shape[:-1]), B.shape[-1]
-  K = half_k * 2
-  assert half_k == half_k_b and math.prod(C.shape[:-1]) == M and C.shape[-1] == N
-  threads = UOp.special(256, "lidx0")
-  groups_x, groups_y = UOp.special(ceildiv(N, tile_n), "gidx0"), UOp.special(ceildiv(M, tile_m), "gidx1")
-  lds = UOp.placeholder((163840,), dtypes.uint8, 0, AddrSpace.LOCAL)
-  sink = UOp.sink(C.base, A.base, B.base, scale_a.base, scale_b.base, *(x.base for x in extra), lds, threads, groups_x, groups_y,
-                  arg=KernelInfo(f"custom_mxfp4_gemm_{M}_{N}_{K}", estimates=Estimates(ops=2*M*N*K)))
-  insts = build_kernel(M, N, K, tile_m, tile_n)
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple(UOp(Ops.INS, arg=x) for x in insts))))
-
-def _mxfp4_gemm_quantized(a_q:Tensor, b_q:Tensor, scale_a:Tensor, scale_b:Tensor) -> Tensor:
-  M, half_k = a_q.shape
-  N, half_k_b = b_q.shape
-  assert half_k == half_k_b
-  is_multi = isinstance(a_q.device, tuple)
-  reduce_out = is_multi and (a_q.uop.axis == 1 or b_q.uop.axis == 1)
-  if not is_multi: out = Tensor.invalids(1, M, N, dtype=dtypes.bfloat16, device=a_q.device)
-  elif reduce_out: out = Tensor(Tensor.invalids(1, M, N, dtype=dtypes.bfloat16, device=a_q.device).uop.unshard(0), device=a_q.device)
-  elif a_q.uop.axis == 0:
-    out = Tensor(Tensor.invalids(1, M//len(a_q.device), N, dtype=dtypes.bfloat16, device=a_q.device).uop.unshard(1), device=a_q.device)
-  elif b_q.uop.axis == 0:
-    out = Tensor(Tensor.invalids(1, M, N//len(a_q.device), dtype=dtypes.bfloat16, device=a_q.device).uop.unshard(2), device=a_q.device)
-  else: out = Tensor.invalids(1, M, N, dtype=dtypes.bfloat16, device=a_q.device)
-  tile_m, tile_n = next((tm, tn) for tm, tn in ((256, 256), (192, 256), (128, 512)) if M % tm == N % tn == 0)
-  out = Tensor.custom_kernel(out, a_q, b_q, scale_a, scale_b,
-                             fxn=functools.partial(custom_mxfp4_gemm, tile_m=tile_m, tile_n=tile_n))[0]
-  if reduce_out: out = out.sum(0)
-  return out.squeeze(0)
-
 def quantize_mxfp8(x:Tensor) -> tuple[Tensor, Tensor, Tensor]:
   # 1x32 block scaling along the last axis
   *batch, K = x.shape
@@ -166,11 +128,6 @@ def _mx_block_scale(e8:Tensor) -> Tensor:
   rows, scale_K = e8.shape
   return (e8.cast(dtypes.float32) - 127.0).exp2().reshape(rows, scale_K, 1).expand(rows, scale_K, 32).reshape(rows, scale_K*32)
 
-def _mx_block_scale_3d(e8:Tensor) -> Tensor:
-  # batched (E, rows, scale_K) dequant scale 2^(e8-127) broadcast to (E, rows, scale_K*32)
-  E, rows, scale_K = e8.shape
-  return (e8.cast(dtypes.float32) - 127.0).exp2().reshape(E, rows, scale_K, 1).expand(E, rows, scale_K, 32).reshape(E, rows, scale_K*32)
-
 counters = {"used":0, "todos":[]}
 def todo(msg:str) -> bool: counters["todos"].append(msg); return False
 def _asm_gemm_report():
@@ -209,13 +166,13 @@ def custom_uop_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
   M, K = A.shape[0]*A.shape[1], A.shape[2]
   K2, N = B.shape[(1 if B.ndim == 3 else 0):]
   assert K == K2
-  m = UOp.range(M, 1)
-  n = UOp.range(N, 2)
+  m = UOp.range(M, 1, AxisType.LOOP)
+  n = UOp.range(N, 2, AxisType.LOOP)
   k = UOp.range(K, 0, AxisType.REDUCE)
-  mul = (A.flatten().index((m*UOp.const(K)+k))*
-         B.flatten().index((k*UOp.const(N)+n))).cast(dtypes.float32)
+  mul = (A.flatten().index((m*UOp.const(dtypes.index, K)+k))*
+         B.flatten().index((k*UOp.const(dtypes.index, N)+n))).cast(dtypes.float32)
   red = mul.reduce(k, arg=Ops.ADD, dtype=dtypes.float32).cast(C.dtype)
-  store = C.flatten().index((m*UOp.const(N)+n)).store(red).end(m, n)
+  store = C.flatten().index((m*UOp.const(dtypes.index, N)+n)).store(red).end(m, n)
   return store.sink(arg=KernelInfo(name=f'uop_gemm_{M}_{N}_{K}'))
 
 # ** bf16 A @ B.T kernel in C
@@ -272,7 +229,7 @@ def hk_bf16_atb_gemm(a:Tensor, b:Tensor) -> Tensor:
     elif b.uop.axis == 2: inv, out_axis = Tensor.invalids(1, M, N // ndev, dtype=a.dtype, device=a.device), 2
     elif a.uop.axis == 2: inv, out_axis = Tensor.invalids(1, M // ndev, N, dtype=a.dtype, device=a.device), 1
     else: inv, out_axis, reduce_out = Tensor.invalids(1, M, N, dtype=a.dtype, device=a.device), 0, True
-    out = Tensor(inv.uop.unshard(out_axis), device=a.device)
+    out = Tensor(inv.uop.multi(out_axis), device=a.device)
     dname = a.device[0]
   else:
     out = Tensor.invalids(1, M, N, dtype=a.dtype, device=a.device)
@@ -282,6 +239,7 @@ def hk_bf16_atb_gemm(a:Tensor, b:Tensor) -> Tensor:
   if reduce_out: out = out.sum(0)
   return out.squeeze(0) if out.ndim == 3 else out
 
+
 # ** backward gemm, might use the asm gemm
 
 def custom_gemm_bw(gradient:UOp, kernel:UOp, n_scales:int=2, has_grad_amax:bool=False, has_w_post:bool=False):
@@ -317,7 +275,10 @@ def custom_gemm_bw(gradient:UOp, kernel:UOp, n_scales:int=2, has_grad_amax:bool=
       elif getenv("FUSED_GRAD_QUANTIZE", 0):
         grad_amax_t = Tensor(grad_amax_state, device=a.device)
         g_amax = grad_amax_t
-        g_fp8, _ = quantize_fp8_delayed(g_t, g_amax, Tensor(next_grad_amax_state, device=a.device))
+        g_fp8, _, new_grad_amax, _ = quantize_fp8_delayed(g_t, g_amax)
+        store_effect = next_grad_amax_state.store(new_grad_amax.uop)
+        assert g_fp8.uop.op is Ops.AFTER, f"expected AFTER, got {g_fp8.uop.op}"
+        g_fp8 = Tensor(g_fp8.uop.replace(src=g_fp8.uop.src + (store_effect,)), device=a.device)
       else:
         grad_amax_t = Tensor(grad_amax_state, device=a.device)
         g_amax = grad_amax_t
@@ -378,30 +339,13 @@ def custom_mx_gemm_bw(gradient:UOp, kernel:UOp, has_w_post:bool, w_stored:bool=F
   if wp is not None: grad_b = grad_b / wp.reshape(-1, 1)
   return (None, grad_a.uop, grad_b.uop) + tuple(None for _ in inputs[3:])
 
-# ** mxfp4 gemm backward
-
-def custom_mxfp4_gemm_bw(gradient:UOp, kernel:UOp):
-  inputs = kernel.src[1:]  # out, row operands/scales, BF16 operands, column operands/scales
-  assert len(inputs) == 11
-  a, w = Tensor(inputs[5], device=inputs[5].device), Tensor(inputs[6], device=inputs[6].device)
-  a_col, scale_a_col = Tensor(inputs[7], device=a.device), Tensor(inputs[8], device=a.device)
-  w_col, scale_w_col = Tensor(inputs[9], device=a.device), Tensor(inputs[10], device=a.device)
-  g = Tensor(gradient, device=a.device)[:a.shape[0]].cast(dtypes.bfloat16)
-  g_row, scale_g_row, g_col, scale_g_col = quantize_mxfp4(g, flatten_row=True)
-  grad_a = _mxfp4_gemm_quantized(g_row, w_col, scale_g_row, scale_w_col).reshape(*a.shape[:-1], w.shape[-1])
-  grad_w = _mxfp4_gemm_quantized(g_col, a_col, scale_g_col, scale_a_col).reshape(w.shape)
-  return (None, None, None, None, None, grad_a.uop, grad_w.uop, None, None, None, None)
-
 # ** main gemm function
 
 def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=None, grad_amax_state:Tensor|None=None,
              next_grad_amax_state:Tensor|None=None,
              w_post_scale:Tensor|None=None, mx:bool=False, mx_scales:tuple|None=None, mx_w_stored:bool=False, g_amax:Tensor|None=None,
-             a_pretranspose:Tensor|None=None, mxfp4:bool=False) -> Tensor:
+             a_pretranspose:Tensor|None=None) -> Tensor:
   assert can_use_asm_gemm(a, b), f"{counters['todos'][-1]}"
-  if mxfp4:
-    assert not mx and mx_scales is None, "mxfp4 owns quantization; mx/mx_scales are for mxfp8"
-    assert a.dtype == dtypes.bfloat16, f"cannot quantize {a.dtype} to mxfp4"
   counters["used"] += 1
   unfold_batch = a.ndim == 3 and isinstance(a.device, tuple) and a.uop.axis == 2 and b.uop.axis == 0
   if unfold_batch:
@@ -409,7 +353,7 @@ def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=N
     a = a.reshape(a.shape[0]*a.shape[1], a.shape[2])
   squeeze = a.ndim == 2
   if squeeze: a = a.unsqueeze(0)
-  out_dtype = dtypes.bfloat16 if a.dtype == FP8_DTYPE or mxfp4 else a.dtype
+  out_dtype = dtypes.bfloat16 if a.dtype == FP8_DTYPE else a.dtype
 
   batch, M, K = a.shape
   N = b.shape[1]
@@ -420,11 +364,11 @@ def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=N
 
   if is_multi:
     if n_sharded:
-      out = Tensor(Tensor.invalids(batch, M, N//len(a.device), dtype=out_dtype, device=a.device).uop.unshard(2), device=a.device)
+      out = Tensor(Tensor.invalids(batch, M, N//len(a.device), dtype=out_dtype, device=a.device).uop.multi(2), device=a.device)
     elif m_sharded:
-      out = Tensor(Tensor.invalids(batch, M, N, dtype=out_dtype, device=a.device).uop.unshard(1), device=a.device)
+      out = Tensor(Tensor.invalids(batch, M, N, dtype=out_dtype, device=a.device).uop.multi(1), device=a.device)
     else:
-      out = Tensor(Tensor.invalids(batch//len(a.device) if a.uop.axis==0 else batch, M, N, dtype=out_dtype, device=a.device).uop.unshard(0),
+      out = Tensor(Tensor.invalids(batch//len(a.device) if a.uop.axis==0 else batch, M, N, dtype=out_dtype, device=a.device).uop.multi(0),
                    device=a.device)
   else:
     out = Tensor.invalids(batch, M, N, dtype=out_dtype, device=a.device)
@@ -432,15 +376,7 @@ def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=N
   renderer = Device[dname:=(a.device[0] if is_multi else a.device)].renderer
   dname, arch = dname.split(":")[0], renderer.target.arch
   if arch.startswith("gfx950") and getenv("USE_ASM", 1):
-    if mxfp4:
-      tile_m, tile_n = next((tm, tn) for tm, tn in ((256, 256), (192, 256), (128, 512)) if (batch*M) % tm == N % tn == 0)
-      fxn = functools.partial(custom_mxfp4_gemm, tile_m=tile_m, tile_n=tile_n)
-      w = b.T
-      a_q, scale_a, a_col, scale_a_col = quantize_mxfp4(a, shuffle_col=True)
-      b_q, scale_b, b_col, scale_b_col = quantize_mxfp4(w, shuffle_row=True, shuffle_col=True)
-      out = Tensor.custom_kernel(out, a_q, b_q, scale_a, scale_b, a, w,
-                                 a_col, scale_a_col, b_col, scale_b_col, fxn=fxn, grad_fxn=custom_mxfp4_gemm_bw)[0]
-    elif mx:
+    if mx:
       # mxfp8 1x32 block scaling
       if mx_scales is not None:
         a_si, a_e8, b_si, b_e8 = mx_scales

extra/gemm/mi350x_uop_matmul.py

@@ -79,7 +79,7 @@ def custom_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
   # this is the big accumulator
   acc = UOp.placeholder((BLOCK_N//TC_N, BLOCK_M//TC_M//WARPGROUP_SIZE), dtypes.float, 0, AddrSpace.REG)
   assert acc.size*WARP_SIZE*WARPGROUP_SIZE*4 == BLOCK_M*BLOCK_N
-  acc = acc[init_l:=UOp.range(acc.size, 500)].set(UOp.const((0.0,)*4, dtypes.float), end=init_l)
+  acc = acc[init_l:=UOp.range(acc.size, 500)].set(UOp.const(dtypes.float, (0.0,)*4), end=init_l)
 
   # create locals (note A is permuted, and the stride is changed to avoid bank conflicts)
   def make_locals(slot) -> tuple[UOp, UOp]:

extra/gemm/mi350x_uop_matmul_2.py

@@ -29,7 +29,7 @@ TID_SIZE = WARPGROUP_SIZE*WARP_SIZE
 
 def copy(dest:UOp, src:UOp, rng:int, set=False, upcast=()):
   assert dest.shape == src.shape
-  rngs = [UOp.range(s, rng+i, AxisType.UPCAST if i in upcast else AxisType.WEAK) for i,s in enumerate(src.shape)]
+  rngs = [UOp.range(s, rng+i, AxisType.UPCAST if i in upcast else AxisType.LOOP) for i,s in enumerate(src.shape)]
   copy = dest[*rngs].store(src[*rngs]).end(*rngs)
   return dest.after(copy) if set else copy
 

extra/gemm/moe_gemm.py

@@ -1,111 +0,0 @@
-import functools, pathlib
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo
-from tinygrad.renderer import Estimates
-from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
-from extra.gemm.cdna_asm_gemm import quantize_mxfp8, _mx_block_scale, _mx_block_scale_3d
-
-@functools.cache
-def custom_hk_grouped_mxfp8_gemm(C:UOp, A:UOp, B:UOp, scale_A:UOp, scale_B:UOp, *extra:UOp, dname:str, n_experts:int) -> UOp:
-  M, K = A.shape
-  E, N, K2 = B.shape
-  assert K == K2, f"{A.shape} {B.shape}"
-  assert E == n_experts, f"{E} != {n_experts}"
-  threads = UOp.special(64 * 8, "lidx0")
-  workgroups = UOp.special((M // 256) * (N // 256), "gidx0")
-  sink_inputs = (C.base, A.base, B.base, scale_A.base, scale_B.base, extra[0].base, extra[1].base, extra[2].base, threads, workgroups)
-  sink = UOp.sink(*sink_inputs,
-                  arg=KernelInfo(f"hk_grouped_mxfp8_gemm_{E}_{M}_{N}_{K}",
-                                 estimates=Estimates(ops=2*M*N*K, mem=(M*K+E*N*K)*A.dtype.itemsize+M*N*C.dtype.itemsize)))
-  kittens_path = pathlib.Path(__file__).parent.parent/"thunder"/"amd"
-  src = (kittens_path/"grouped_mxfp8_gemm.cpp").read_text()
-  lib = HIPCCCompiler("gfx950", [f"-I{(kittens_path/'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-ffast-math",
-                                 "-DHIP_ENABLE_WARP_SYNC_BUILTINS", f"-DGEMM_M={M}", f"-DGEMM_N={N}", f"-DGEMM_K={K}",
-                                 f"-DGEMM_E={E}"]).compile_cached(src)
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src),
-                               UOp(Ops.BINARY, arg=lib)))
-
-@functools.cache
-def custom_hk_grouped_mxfp8_wgrad(C:UOp, A:UOp, B:UOp, scale_A:UOp, scale_B:UOp, expert_off:UOp, *, dname:str, n_experts:int) -> UOp:
-  N, M = A.shape
-  K, M2 = B.shape
-  assert M == M2, f"{A.shape} {B.shape}"
-  E = n_experts
-  threads = UOp.special(64 * 8, "lidx0")
-  workgroups = UOp.special(E * (N // 256) * (K // 256), "gidx0")
-  sink = UOp.sink(C.base, A.base, B.base, scale_A.base, scale_B.base, expert_off.base, threads, workgroups,
-                  arg=KernelInfo(f"hk_grouped_mxfp8_wgrad_{E}_{M}_{N}_{K}",
-                                 estimates=Estimates(ops=2*M*N*K, mem=(N*M+K*M)*A.dtype.itemsize+E*N*K*C.dtype.itemsize)))
-  kittens_path = pathlib.Path(__file__).parent.parent/"thunder"/"amd"
-  src = (kittens_path/"grouped_mxfp8_wgrad.cpp").read_text()
-  lib = HIPCCCompiler("gfx950", [f"-I{(kittens_path/'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-ffast-math",
-                                 "-DHIP_ENABLE_WARP_SYNC_BUILTINS", f"-DWGRAD_M={M}", f"-DWGRAD_N={N}", f"-DWGRAD_K={K}",
-                                 f"-DWGRAD_E={E}"]).compile_cached(src)
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src),
-                               UOp(Ops.BINARY, arg=lib)))
-
-def grouped_mx_wgrad(g:Tensor, xg:Tensor, expert_off:Tensor, n_experts:int) -> Tensor:
-  from extra.llama_kernels.transpose_quantize_mxfp8 import transpose_quantize_mxfp8
-  M, N = g.shape
-  M2, K = xg.shape
-  assert M == M2, f"{g.shape} {xg.shape}"
-  assert M % 128 == 0 and N % 256 == 0 and K % 256 == 0, f"wgrad needs M%128,N%256,K%256, got {g.shape} {xg.shape}"
-  gT, _, g_si = transpose_quantize_mxfp8(g.contiguous())
-  xT, _, x_si = transpose_quantize_mxfp8(xg.contiguous())
-  dname = (g.device[0] if isinstance(g.device, tuple) else g.device).split(":")[0]
-  is_multi = isinstance(g.device, tuple)
-  inv = Tensor.invalids(1, n_experts * N, K, dtype=dtypes.bfloat16, device=g.device)
-  out = Tensor(inv.uop.unshard(0), device=g.device) if is_multi else inv
-  out = Tensor.custom_kernel(out, gT, xT, g_si, x_si, expert_off,
-                             fxn=functools.partial(custom_hk_grouped_mxfp8_wgrad, dname=dname, n_experts=n_experts))[0]
-  out = out.sum(0) if is_multi else out.squeeze(0)
-  return out.reshape(n_experts, N, K)
-
-def mx_pack_3d(e8:Tensor) -> Tensor:
-  E, rows, scale_K = e8.shape
-  return e8.reshape(E, rows, scale_K // 4, 4).bitcast(dtypes.uint32).reshape(E, rows, scale_K // 4).permute(0, 2, 1).contiguous()
-
-@functools.cache
-def custom_grouped_mx_gemm_bw(gradient:UOp, kernel:UOp, w_stored:bool=False) -> tuple:
-  inputs = kernel.src[1:]
-  aq = Tensor(inputs[1], device=inputs[1].device)
-  bq = Tensor(inputs[2], device=inputs[2].device)
-  ae8 = Tensor(inputs[5], device=inputs[5].device)
-  be8 = Tensor(inputs[6], device=inputs[6].device)
-  E, N = bq.shape[0], bq.shape[1]
-  M, K = aq.shape
-  g = Tensor(gradient, device=aq.device).reshape(M, N).cast(dtypes.bfloat16)
-  x_phys = (aq.cast(dtypes.bfloat16) * _mx_block_scale(ae8).cast(dtypes.bfloat16))
-  w_phys = (bq.cast(dtypes.bfloat16) * _mx_block_scale_3d(be8).cast(dtypes.bfloat16))
-  expert_off = Tensor(inputs[7], device=inputs[7].device)
-  grad_x = grouped_mx_gemm(g, w_phys.transpose(1, 2), expert_off)
-  grad_w = grouped_mx_wgrad(g, x_phys, expert_off, E)
-  grad_xq = grad_x * _mx_block_scale(ae8).cast(dtypes.bfloat16)
-  grad_wq = grad_w.contiguous() if w_stored else (grad_w * _mx_block_scale_3d(be8).cast(dtypes.bfloat16)).contiguous()
-  return (None, grad_xq.uop, grad_wq.uop) + tuple(None for _ in inputs[3:])
-
-_grouped_bw_stored = functools.partial(custom_grouped_mx_gemm_bw, w_stored=True)
-
-def grouped_mx_gemm(x:Tensor, w:Tensor|tuple[Tensor, Tensor], expert_off:Tensor) -> Tensor:
-  if (pre_quantized := isinstance(w, tuple)):
-    w_q, w_e8 = w
-    E, N, K2 = w_q.shape
-  else:
-    E, N, K2 = w.shape
-  M, K = x.shape
-  assert K == K2, f"shape mismatch {x.shape} {w.shape}"
-  assert M % 256 == 0 and N % 256 == 0 and K % 128 == 0, f"grouped mxfp8 needs M%256,N%256,K%128, got {x.shape} {w.shape}"
-  dname = (x.device[0] if isinstance(x.device, tuple) else x.device).split(":")[0]
-  x_q, x_e8, x_si = quantize_mxfp8(x)
-  if not pre_quantized: w_q, w_e8, _ = quantize_mxfp8(w)
-  w_si = mx_pack_3d(w_e8)
-  xe_in, out_shape = x_e8.reshape(M, K // 32), (M, N)
-  if isinstance(x.device, tuple) and (row_axis := x.uop.axis) is not None:
-    ndev = len(x.device)
-    out = Tensor(Tensor.invalids(*(s // ndev if i == row_axis else s for i, s in enumerate(out_shape)),
-                                 dtype=dtypes.bfloat16, device=x.device).uop.unshard(row_axis), device=x.device)
-  else:
-    out = Tensor.invalids(*out_shape, dtype=dtypes.bfloat16, device=x.device)
-  return Tensor.custom_kernel(out, x_q, w_q, x_si, w_si, xe_in, w_e8, expert_off,
-                              fxn=functools.partial(custom_hk_grouped_mxfp8_gemm, dname=dname, n_experts=E),
-                              grad_fxn=(_grouped_bw_stored if pre_quantized else custom_grouped_mx_gemm_bw))[0]

extra/gemm/moe_routing.py

@@ -1,131 +0,0 @@
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-
-BLOCK_ROW = 256
-
-def _sharded_invalids(shape:tuple[int, ...], dtype, device) -> Tensor:
-  if isinstance(device, tuple):
-    per = Tensor.invalids(shape[0]//len(device), *shape[1:], dtype=dtype, device=device)
-    return Tensor(per.uop.unshard(0), device=device)
-  return Tensor.invalids(*shape, dtype=dtype, device=device)
-
-def _atomic_add(device:str) -> str:
-  return "__hip_atomic_fetch_add({0}, {1}, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);" if device == "AMD" \
-    else "__atomic_fetch_add({0}, {1}, __ATOMIC_RELAXED);"
-
-def _blk_for(D:int) -> int:
-  blk = 64
-  while D % blk: blk //= 2
-  return blk
-
-def _kv_ranges(G, N, D, BLK):
-  g = UOp.range(G, 0)
-  m = UOp.range(N, 1)
-  jo = UOp.range(D // BLK, 2)
-  ji = UOp.range(BLK, 3, AxisType.LOCAL)
-  return g, m, jo * BLK + ji, jo, ji
-
-def _ggather_fwd_kernel(out:UOp, table:UOp, idx:UOp) -> UOp:
-  G, M, D = out.shape
-  g, m, j, jo, ji = _kv_ranges(G, M, D, _blk_for(D))
-  row = idx.index(g, m).cast(dtypes.weakint)
-  val = table.index(g, row, j).load()
-  return out.index(g, m, j).store(val).end(g, m, jo, ji).sink(
-    arg=KernelInfo(name=f"ggather_fwd_{M}_{D}", opts_to_apply=()))
-
-def _ggather_zero_kernel(out:UOp) -> UOp:
-  i = UOp.range(out.numel(), 0)
-  return out.flatten().index(i).store(UOp.const(0.0, out.dtype)).end(i).sink(arg=KernelInfo(name="ggather_zero"))
-
-def _sharded_zeros(shape:tuple[int, ...], dtype, device) -> Tensor:
-  return Tensor.custom_kernel(_sharded_invalids(shape, dtype, device), fxn=_ggather_zero_kernel)[0]
-
-def _ggather_bwd(gradient:UOp, kernel:UOp) -> tuple:
-  _, table_u, idx_u = kernel.src[1:4]
-  dev = table_u.device
-  device = (dev[0] if isinstance(dev, tuple) else dev).split(":")[0]
-  G, R, D = table_u.shape
-  gt = _sharded_zeros((G, R, D), dtypes.float32, dev)
-  go = Tensor(gradient, device=dev)
-  atomic_str = _atomic_add(device)
-  def _bwd_kernel(gtab:UOp, gout:UOp, idx:UOp) -> UOp:
-    Gk, M, Dk = gout.shape
-    g, m, j, jo, ji = _kv_ranges(Gk, M, Dk, _blk_for(Dk))
-    row = idx.index(g, m).cast(dtypes.weakint)
-    val = gout.index(g, m, j).load().cast(dtypes.float32)
-    atomic = UOp(Ops.CUSTOM, dtypes.void, (gtab.index(g, row, j), val), arg=atomic_str)
-    return atomic.end(g, m, jo, ji).sink(arg=KernelInfo(name=f"ggather_bwd_{M}_{Dk}", opts_to_apply=()))
-  grad_table = Tensor.custom_kernel(gt, go, Tensor(idx_u, device=dev), fxn=_bwd_kernel)[0]
-  return (None, grad_table.cast(table_u.dtype).uop, None)
-
-def grouped_gather_rows(table:Tensor, idx:Tensor, n_groups:int) -> Tensor:
-  G, R, D = table.shape
-  M = idx.shape[1]
-  out = _sharded_invalids((G, M, D), table.dtype, table.device)
-  return Tensor.custom_kernel(out, table, idx, fxn=_ggather_fwd_kernel, grad_fxn=_ggather_bwd)[0]
-
-def _gscatter_fwd_kernel(out:UOp, src:UOp, idx:UOp) -> UOp:
-  G, M, D = out.shape
-  k = idx.shape[1] // src.shape[1]
-  g, m, j, jo, ji = _kv_ranges(G, idx.shape[1], D, _blk_for(D))
-  row = idx.index(g, m).cast(dtypes.weakint)
-  val = src.index(g, (m // k).cast(dtypes.weakint), j).load()
-  return out.index(g, row, j).store(val).end(g, m, jo, ji).sink(
-    arg=KernelInfo(name=f"gscatter_fwd_{idx.shape[1]}_{D}", opts_to_apply=()))
-
-def _gscatter_bwd(gradient:UOp, kernel:UOp) -> tuple:
-  _, src_u, idx_u = kernel.src[1:4]
-  dev = src_u.device
-  G, T_l, D = src_u.shape
-  k = idx_u.shape[1] // T_l
-  sel = grouped_gather_rows(Tensor(gradient, device=dev), Tensor(idx_u, device=dev), G)
-  return (None, sel.reshape(G, T_l, k, D).sum(2).cast(src_u.dtype).uop, None)
-
-def grouped_scatter_rows(src:Tensor, idx:Tensor, m_l:int) -> Tensor:
-  G, T_l, D = src.shape
-  zero = _sharded_zeros((G, m_l, D), src.dtype, src.device)
-  return Tensor.custom_kernel(zero, src, idx, fxn=_gscatter_fwd_kernel, grad_fxn=_gscatter_bwd)[0]
-
-def m_max_for(t_local:int, experts_per_tok:int, n_experts:int) -> int:
-  return (-(-t_local * experts_per_tok // BLOCK_ROW) + n_experts) * BLOCK_ROW
-
-class Routing:
-  def __init__(self, weights:Tensor, dest_row:Tensor, off:Tensor, m_l:int, n_groups:int, t_local:int):
-    self.weights, self.dest_row = weights, dest_row
-    self.off = off
-    self.m_l, self.n_groups, self.t_local = m_l, n_groups, t_local
-
-  @property
-  def rows_e(self) -> Tensor:
-    G, E = self.off.shape[0], self.off.shape[1] - 1
-    tr = Tensor.arange(self.m_l // BLOCK_ROW, dtype=dtypes.int32).reshape(1, -1, 1) * BLOCK_ROW
-    tr = tr.shard(self.off.device) if isinstance(self.off.device, tuple) else tr.to(self.off.device)
-    tile_e = ((tr >= self.off[:, :E].reshape(G, 1, E)).sum(-1) - 1).cast(dtypes.int32)
-    return tile_e.reshape(-1, 1).expand(-1, BLOCK_ROW).reshape(-1)
-
-def n_groups_of(t:Tensor) -> int:
-  return len(t.device) if isinstance(t.device, tuple) else 1
-
-def route(logits:Tensor, experts_per_tok:int, n_experts:int) -> Routing:
-  T, E = logits.shape
-  k, G = experts_per_tok, n_groups_of(logits)
-  assert T % G == 0, f"tokens {T} must split across {G} devices"
-  T_l, m_l = T // G, m_max_for(T // G, k, n_experts)
-
-  topv, topi = logits.reshape(G, T_l, E).topk(k)
-  weights = topv.softmax(-1)
-  m = topi.reshape(G, T_l * k).cast(dtypes.int32).one_hot(E).cast(dtypes.int32)
-
-  pad = ((m.sum(1) + (BLOCK_ROW - 1)) // BLOCK_ROW) * BLOCK_ROW
-  off = pad.cumsum(1).pad(((0, 0), (1, 0)))
-  dest_row = ((m.cumsum(1) + off[:, :E].reshape(G, 1, E)) * m).sum(-1).sub(1).cast(dtypes.int32)
-  return Routing(weights, dest_row, off, m_l, G, T_l)
-
-def dispatch(x:Tensor, r:Routing) -> Tensor:
-  G, D = r.n_groups, x.shape[-1]
-  return grouped_scatter_rows(x.reshape(G, r.t_local, D), r.dest_row, r.m_l).reshape(G * r.m_l, D)
-
-def combine(y:Tensor, r:Routing, n_tokens:int, experts_per_tok:int) -> Tensor:
-  G, D, k = r.n_groups, y.shape[-1], experts_per_tok
-  sel = grouped_gather_rows(y.reshape(G, r.m_l, D), r.dest_row, G).reshape(G, r.t_local, k, D)
-  return (sel * r.weights.reshape(G, r.t_local, k, 1).cast(sel.dtype)).sum(2).reshape(n_tokens, D).cast(y.dtype)

extra/gemm_fragment.py

@@ -1,139 +0,0 @@
-"""
-tilelang-style matmul_relu written with tinygrad UOp APIs.
-
-Reference tilelang kernel:
-  @tilelang.jit
-  def matmul_relu(A, B, block_M=64, block_N=64, block_K=64,
-                  dtype=T.float16, accum_dtype=T.float32):
-    M, N, K = T.const('M, N, K')
-    C = T.empty([M, N], dtype)
-    with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
-      A_shared = T.alloc_shared((block_M, block_K), dtype)
-      B_shared = T.alloc_shared((block_K, block_N), dtype)
-      C_local  = T.alloc_fragment((block_M, block_N), accum_dtype)
-      T.clear(C_local)
-      for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
-        T.copy(A[by * block_M, ko * block_K], A_shared)
-        T.copy(B[ko * block_K, bx * block_N], B_shared)
-        T.gemm(A_shared, B_shared, C_local)
-      for i, j in T.Parallel(block_M, block_N):
-        C_local[i, j] = T.max(C_local[i, j], 0)
-      T.copy(C_local, C[by * block_M, bx * block_N])
-    return C
-"""
-
-from tinygrad.dtype import dtypes, AddrSpace, DType
-from tinygrad.uop.ops import UOp, Ops, AxisType, KernelInfo
-from tinygrad.helpers import cdiv, getenv
-from tinygrad.tensor import Tensor
-
-# ---------------------------------------------------------------------------
-# tilelang builtins, expressed with tinygrad UOp APIs
-# ---------------------------------------------------------------------------
-
-def alloc_shared(shape:tuple[int, ...], dtype:DType, slot:int) -> UOp:
-  """T.alloc_shared: one LOCAL buffer shared by all threads in the block."""
-  return UOp.placeholder(tuple(shape), dtype, slot, AddrSpace.LOCAL)
-
-def alloc_fragment(shape:tuple[int, ...], dtype:DType, slot:int, axes:tuple[int, ...], rngs:tuple[UOp, ...]) -> UOp:
-  """T.alloc_fragment: per-thread REG fragment + UNSHARD over the LOCAL thread grid."""
-  assert len(axes) == len(rngs)
-  assert all(tnum.op is Ops.RANGE and tnum.arg[-1] is AxisType.LOCAL for tnum in rngs), "fragments shard over LOCAL ranges"
-  by_axis = dict(zip(axes, rngs))
-  shard_shape = tuple(s // (int(by_axis[i].vmax)+1) if i in by_axis else s for i, s in enumerate(shape))
-  fragment = UOp.placeholder(shard_shape, dtype, slot, AddrSpace.REG)
-  return fragment.unshard(axes, rngs)
-
-# ---------------------------------------------------------------------------
-# GEMM kernel: C = relu(A @ B), float inputs (fp16 or fp32), fp32 fragment accumulator, no WMMA
-# ---------------------------------------------------------------------------
-
-# 64x64 output tile per block, 128 threads as an 8x16 grid; each thread owns an 8x4 fragment sub-tile
-# (the 2-D per-thread layout tilelang infers for this GEMM). The 4 contiguous columns (TN=4) are what
-# let codegen vectorize loads/stores to float4, matching tilelang's lowering exactly.
-BLOCK_M = BLOCK_N = BLOCK_K = 64
-TY = 8
-TX = 16
-THREADS = TY * TX
-TM = BLOCK_M // TY   # fragment rows per thread  (8)
-TN = BLOCK_N // TX   # fragment columns per thread (4)
-
-def matmul_relu_kernel(c:UOp, a:UOp, b:UOp) -> UOp:
-  """C[M, N] = relu(A[M, K] @ B[K, N]) -- one 64x64 tile per block, locals + a 2-D fragment."""
-  M, K = a.shape
-  K2, N = b.shape
-  assert K == K2 and a.dtype == b.dtype == c.dtype and not dtypes.is_int(a.dtype)
-  assert not (K % BLOCK_K or M % BLOCK_M or N % BLOCK_N), "test sizes must be multiples of the block sizes"
-
-  # with T.Kernel(T.ceildiv(N, BLOCK_N), T.ceildiv(M, BLOCK_M), threads=128) as (bx, by):
-  bx = UOp.range(cdiv(N, BLOCK_N), 0, AxisType.GLOBAL)
-  by = UOp.range(cdiv(M, BLOCK_M), 1, AxisType.GLOBAL)
-
-  # 16*8 threads = 128 threads
-  tx = UOp.range(TX, 2, AxisType.LOCAL)
-  ty = UOp.range(TY, 3, AxisType.LOCAL)
-
-  # shared + fragment (regs)
-  A_shared = alloc_shared((BLOCK_M, BLOCK_K), a.dtype, 0)
-  B_shared = alloc_shared((BLOCK_K, BLOCK_N), b.dtype, 1)
-  C_local = alloc_fragment((TM, TY, TX, TN), dtypes.float32, 0, (1, 2), (ty, tx))
-
-  # zero out the regs to start. this is expanded by the devectorizer
-  C_local = C_local.after(C_local.store(0.0))
-
-  # for ko in T.Pipelined(T.ceildiv(K, BLOCK_K), num_stages=3):
-  ko = UOp.range(cdiv(K, BLOCK_K), 6, AxisType.LOOP)
-
-  # index the outer matrices
-  a = a.rearrange("(m bm) (k bk) -> m k bm bk", bm=BLOCK_M, bk=BLOCK_K)[by, ko]
-  b = b.rearrange("(k bk) (n bn) -> k n bk bn", bk=BLOCK_K, bn=BLOCK_N)[ko, bx]
-  c = c.rearrange("(m bm) (n bn) -> m n bm bn", bm=BLOCK_M, bn=BLOCK_N)[by, bx]
-
-  # T.copy: A_shared <- a, B_shared <- b
-  def with_threads(x:UOp): return x.rearrange("(tm ty) (tx tn) -> ty tx tm tn", tm=TM, tn=TN)[ty, tx]
-  A_shared = A_shared.after(with_threads(A_shared).store(with_threads(a)))
-  B_shared = B_shared.after(with_threads(B_shared).store(with_threads(b)))
-
-  # T.gemm(A_shared, B_shared, C_local), no WMMA
-  kk = UOp.range(BLOCK_K, 11, AxisType.LOOP)
-  ir = UOp.range(TM, 12, AxisType.LOOP)
-  jj = UOp.range(TN, 13, AxisType.UPCAST)
-  acc = C_local.after(kk)[ir, ty, tx, jj] + A_shared[ir*TM + ty, kk].cast(dtypes.float32) * B_shared[kk, tx*TN + jj].cast(dtypes.float32)
-  # closing the ko loop here too; codegen adds the barrier so no thread overwrites the tiles while others still read them
-  C_local = C_local[ir, ty, tx, jj].set(acc, end=(kk, ir, jj, ko))
-
-  # c <- C_local (with relu and cast): every thread stores its shard's sub-view of the output tile
-  c_st = c.reshape(C_local.shape).store(C_local.relu().cast(c.dtype))
-
-  # close the locals and globals
-  return c_st.end(tx, ty, bx, by).sink(arg=KernelInfo(name="matmul_relu", opts_to_apply=()))
-
-# ---------------------------------------------------------------------------
-# python wrapper: same signature as the tilelang function
-# ---------------------------------------------------------------------------
-
-def matmul_relu(a:Tensor, b:Tensor) -> Tensor:
-  """C = relu(A @ B), fp16 in/out with an fp32 fragment accumulator."""
-  c = Tensor.empty(a.shape[0], b.shape[1], dtype=a.dtype, device=a.device)
-  return c.custom_kernel(a, b, fxn=matmul_relu_kernel)[0]
-
-# ---------------------------------------------------------------------------
-# test
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-  from tinygrad import Device
-  assert Device[Device.DEFAULT].renderer.has_local, "this GPU-style kernel needs a backend with local memory (LOCAL ranges + barriers)"
-  M = K = N = getenv("N", 256)  # 4x4 grid of 64x64 tiles, 4 K chunks
-  dtype_in = dtypes.half if getenv("HALF") else dtypes.float
-
-  a = Tensor.randn(M, K, dtype=dtype_in).contiguous()
-  b = Tensor.randn(K, N, dtype=dtype_in).contiguous()
-  ref = (a @ b).relu().realize()
-
-  for _ in range(10):
-    out = matmul_relu(a, b).realize()
-
-  import numpy as np
-  np.testing.assert_allclose(out.numpy(), ref.numpy(), atol=1e-1, rtol=1e-2)
-  print("matmul_relu passed!")

extra/hcq2/hcq2.py

@@ -0,0 +1,591 @@
+from __future__ import annotations
+from typing import cast, Callable, TypeVar, Generic, Any
+import struct, functools, time, collections, itertools
+from dataclasses import replace, dataclass
+from tinygrad.helpers import DEV, getenv, select_first_inited, select_by_name, suppress_finalizing, dedup, pluralize
+from tinygrad.helpers import to_tuple, round_up, partition, data64_le, panic, ContextVar
+from tinygrad.device import Device, Buffer, BufferSpec, Compiled, LRUAllocator, MultiBuffer
+from tinygrad.uop.ops import Ops, sint, UOp, UPat, PatternMatcher, KernelInfo, graph_rewrite, track_rewrites, GroupOp
+from tinygrad.uop.symbolic import symbolic
+from tinygrad.dtype import dtypes, truncate
+from tinygrad.runtime.support.hcq import MMIOInterface
+from tinygrad.runtime.support.memory import BumpAllocator
+from tinygrad.renderer import Renderer, Estimates
+from tinygrad.engine.realize import to_program, get_call_arg_uops, get_call_name, get_call_outs_ins, estimate_uop, pm_flatten_linear
+from tinygrad.engine.jit import DepsTracker
+
+HCQDeviceType = TypeVar('HCQDeviceType', bound='HCQ2Compiled')
+
+# *****************
+# 0. helpers
+
+HCQ_RUNTIME_DEV = ContextVar("HCQ_RUNTIME_DEV", "CPU")
+
+HCQ_DEVS = frozenset(("AMD",))
+HCQ_P2P_DEVS = HCQ_DEVS | frozenset(("CPU",))
+HCQ_CACHE_TAGS = frozenset(("program", "systems", "template"))
+
+@dataclass(frozen=True)
+class HCQInfo:
+  name:str
+  estimates:Estimates
+  device:tuple[str, ...]
+  queue:str
+
+  input_idxs:tuple[int, ...] = () # indexes into input_uops used by this call
+  inputs:int|None = None
+
+def all_devices_in(d:Any, c:frozenset[str]) -> bool: return {x.split(":")[0] for x in to_tuple(d)} <= c
+
+def unwrap_after(uop):
+  while uop.op is Ops.AFTER: uop = uop.src[0]
+  return uop
+
+def unwrap_mstack(u):
+  return tuple(x for s in u.src for x in unwrap_mstack(s)) if u.op is Ops.MSTACK else (unwrap_mstack(u.src[0]) if u.op in {Ops.MSELECT, Ops.SLICE} else (u,))
+
+def make_getaddr(u, device=None):
+  if unwrap_after(u).op not in (Ops.BUFFER, Ops.SLICE, Ops.BINARY, Ops.MSTACK, Ops.MSELECT, Ops.PARAM): return u
+  return UOp(Ops.GETADDR, dtypes.uint64, src=(u,), arg=device or to_tuple(u.device)[0])
+
+def make_ins(op, *srcs):
+  return UOp(Ops.INS, dtypes.void, tuple(UOp.const(dtypes.uint32, s) if isinstance(s, int) else s.cast(dtypes.uint32) for s in srcs), op)
+
+def make_placeholder(devs, size:int, dtype, name=None, unique=True) -> UOp:
+  return UOp.param(next(UOp.unique_num) if unique else 0, dtype, shape=(size,), device=devs).rtag(name or "temp")
+
+def make_patch(buf:UOp, off:sint, val:UOp, dtype=None) -> UOp:
+  return buf.index(UOp.const(dtypes.int, off // buf.dtype.itemsize)).store(val.simplify().cast(dtype or buf.dtype))
+
+def make_binary_patch(buf:UOp, blob:bytes) -> UOp:
+  data = UOp(Ops.BINARY, src=(), arg=blob).bitcast(buf.dtype)
+  r = UOp.range(len(blob) // buf.dtype.itemsize, 0, dtype=dtypes.int, src=(buf, data))
+  return buf.index(r).store(data.index(r).load()).end(r)
+
+def make_cmdbuf(lin, devs):
+  blob, patches = b'', []
+  for s in (s for ins in lin.src for s in ins.src):
+    if (ssimp:=s.simplify()).op is not Ops.CONST: patches.append((len(blob), ssimp))
+    blob += struct.pack(f'<{ssimp.dtype.fmt}', ssimp.arg if ssimp.op is Ops.CONST else 0x0)
+  cmdbuf = make_placeholder(devs, len(blob) // 4, dtypes.uint32, name="cmdbuf")
+  return cmdbuf.after(make_binary_patch(cmdbuf, blob), *[make_patch(cmdbuf, off, s) for off, s in patches])
+
+def make_mstack(uops): return uops[0] if len(uops) == 1 else UOp(Ops.MSTACK, uops[0].dtype, tuple(uops))
+
+def make_signal(devs, queue=None, sentinel=False):
+  return make_placeholder(devs, 1, dtypes.uint64, "sentinel_signal" if sentinel else (queue, "timeline_signal") if queue else "timeline_signal", unique=False)
+def make_signal_value(devs, queue=None):
+  return make_placeholder(devs, 1, dtypes.uint64, (queue, "timeline_value") if queue else "timeline_value", unique=False)
+
+def make_submit(*cmds, devs:str|tuple[str, ...], queue:str) -> UOp:
+  return UOp.custom_function("submit_cmdbuf", UOp(Ops.LINEAR, src=tuple(cmds), arg=(to_tuple(devs), queue)))
+def get_submit(ast:UOp) -> UOp: return next(u for u in ast.toposort() if u.op is Ops.CUSTOM_FUNCTION and u.arg == "submit_cmdbuf")
+
+# *****************
+# 0.1. prep: replace buffers with params
+
+def replace_call_buffers(ctx:list[UOp], call:UOp) -> UOp|None:
+  ctx += [s for s in call.src[1:] if s not in ctx and s.op not in (Ops.PARAM, Ops.BIND)]
+  return call.replace(src=call.src[:1] + tuple(s if s.op in (Ops.PARAM, Ops.BIND) else s.param_like(ctx.index(s)) for s in call.src[1:]))
+pm_replace_buffers = PatternMatcher([(UPat(Ops.CALL, name="call"), replace_call_buffers)])
+
+# *****************
+# 1.1. prep: staging copies
+
+def _need_staging(a, b): return all_devices_in(a.device, HCQ_DEVS) and not all_devices_in(b.device, HCQ_P2P_DEVS)
+
+def stage_copy(dst:UOp, src:UOp) -> UOp|None:
+  if not (_need_staging(src, dst) or _need_staging(dst, src)): return None
+
+  stage = UOp.new_buffer("CPU", src.max_numel() * src.dtype.itemsize, dtypes.uint8)
+  return UOp(Ops.LINEAR, src=(src.copy_to_device("CPU").call(stage, src), stage.copy_to_device(dst.device).call(dst, stage)))
+pm_insert_copy_staging = PatternMatcher([(UPat(Ops.CALL, src=(UPat(Ops.COPY), UPat(name="dst"), UPat(name="src"))), stage_copy)])
+
+# *****************
+# 2.1. tag hcq calls
+
+def tag_hcq_call(ctx:itertools.count, call:UOp) -> UOp:
+  if (hcq_devs:=next((b.device for b in call.src[1:] if all_devices_in(b.device, HCQ_DEVS)), None)) is None: return call
+
+  queue = "COMPUTE:0" if call.src[0].op is Ops.PROGRAM else "COPY:0"
+  info = HCQInfo(get_call_name(call, get_call_arg_uops(call)), estimate_uop(call), to_tuple(hcq_devs), queue)
+  return call.replace(arg=replace(call.arg, aux=info)).rtag(next(ctx))
+pm_tag_hcq_calls = PatternMatcher([(UPat(Ops.LINEAR, name="l"), lambda ctx, l: l.replace(src=tuple(tag_hcq_call(ctx, s) for s in l.src)))])
+
+# *****************
+# 2.2. deps tracking
+# device.timeline_signal/value are the per-device schedule epoch. Before a schedule queue accesses memory owned by device N for the first time,
+# it waits for device[N].timeline_signal >= device[N].timeline_value - 1. This orders the schedule after all prior schedules that touched device N.
+#
+# queue.timeline_signal/value are per-queue progress counters used only inside a schedule.
+# Only the owner queue signals its queue.timeline_signal. Values are monotonic.
+#
+# At schedule end, one finalizer queue per touched device[N] waits for every active queue on device[N] to reach its schedule-local
+# final queue.timeline value, then signals device[N].timeline_signal with the schedule's reserved device epoch. After that, buffers/transients
+# for device N from this schedule are safe for the next schedule
+#
+# C programs reserve and bump timeline values, then patch command buffers with the concrete wait/signal values.
+
+class HCQDepsTracker(DepsTracker):
+  @staticmethod
+  def _key(buf:Any) -> tuple[Any, int, int]:
+    return (buf.arg.slot, 0, buf.max_numel() * buf.dtype.itemsize) if isinstance(buf, UOp) else DepsTracker._key(buf)
+
+def make_deps(u:UOp, dep_lanes:list[tuple[UOp, int, int]], nlanes:int) -> UOp:
+  deps:dict[UOp, list[int|None]] = collections.defaultdict(lambda: [None]*nlanes)
+  for dep, dlane, lane in dep_lanes: deps[dep][lane] = dlane
+  return u.after(*deps, arg=tuple(tuple(v) for v in deps.values()))
+
+def sched_sync(ctx:DepsTracker, call:UOp) -> UOp|None:
+  if not isinstance(call.arg.aux, HCQInfo): return None
+
+  refs = get_call_arg_uops(call)
+  outs, _ = get_call_outs_ins(call)
+  devices, queue = call.arg.aux.device, call.arg.aux.queue
+
+  dep_lanes:list[tuple[UOp, int, int]] = []
+  for lane, d in enumerate(devices):
+    lane_refs = [b if b.op is Ops.PARAM else mb.bufs[lane] if isinstance(mb:=b.buffer, MultiBuffer) else mb for b in refs]
+    for dep, dlane in ctx.access_resources(lane_refs, outs, (call, lane)): dep_lanes.append((dep, dlane, lane))
+
+  if devices[0].split(":")[0] in {"AMD", "QCOM"} or queue.startswith("COPY"):
+    dep_lanes = [(dep, dlane, lane) for dep, dlane, lane in dep_lanes if (dep.arg.aux.device[dlane], dep.arg.aux.queue) != (devices[lane], queue)]
+
+  # keep latest dep per (dep device, queue, cur lane)
+  latest = {((dep.arg.aux.device[dlane], dep.arg.aux.queue), lane): (dep, dlane) for dep, dlane, lane in sorted(dep_lanes, key=lambda x: x[0].tag)}
+  return make_deps(call, [(dep, dlane, lane) for (_, lane), (dep, dlane) in latest.items()], len(devices))
+pm_sched_sync = PatternMatcher([(UPat(Ops.CALL, name="call"), sched_sync)])
+
+# *****************
+# 2.3. merge into queues
+
+def _merged_hcq_call(calls:list[UOp]):
+  info = replace(unwrap_after(calls[0]).arg.aux, estimates=sum((unwrap_after(c).arg.aux.estimates for c in calls), start=Estimates()))
+  cmdbuf = make_submit(*calls, devs=info.device, queue=info.queue)
+  return UOp.custom_function("hcq", cmdbuf.sink()).call(name="hcq", aux=info)
+
+def merge_queues(linear:UOp) -> UOp:
+  new_src:list[UOp] = []
+  opened_qs:dict[tuple[tuple[str, ...], str], list[UOp]] = {} # (devs, queue) -> list of calls, kept in submit order
+
+  for call in linear.src:
+    if not isinstance(unwrap_after(call).arg.aux, HCQInfo):
+      new_src += [_merged_hcq_call(opened_qs.pop(k)) for k in list(opened_qs)] + [call]
+      continue
+
+    devices, queue = unwrap_after(call).arg.aux.device, unwrap_after(call).arg.aux.queue
+
+    if (old:=opened_qs.pop((devices, queue), None)) is not None: new_rec = old + [call]
+    else:
+      # no such queue opened: close every open submit on this queue that shares a device, so submit order is kept
+      closing = [k for k in opened_qs if k[1] == queue and set(k[0]) & set(devices)]
+      new_src += [_merged_hcq_call(opened_qs.pop(k)) for k in closing]
+      new_rec = [call]
+    opened_qs[(devices, queue)] = new_rec
+  return linear.replace(src=tuple(new_src + [_merged_hcq_call(c) for c in opened_qs.values()]))
+pm_merge_queues = PatternMatcher([(UPat(Ops.LINEAR, name="linear"), merge_queues)])
+
+# *****************
+# 2.4. finalizer
+
+def add_finalizer(ctx:itertools.count, linear:UOp) -> UOp:
+  # collect by device type
+  parts:dict[str, list[UOp]] = collections.defaultdict(list)
+  for call in linear.src:
+    if (c:=unwrap_after(call)).src[0].op is not Ops.CUSTOM_FUNCTION or c.src[0].arg != "hcq": continue
+    parts[c.arg.aux.device[0].split(':')[0]].append(unwrap_after(get_submit(call).src[0].src[0]))
+
+  nbump = next(ctx)
+  finalizers = []
+  for calls in parts.values():
+    devs = tuple(dedup(d for call in calls for d in unwrap_after(call).arg.aux.device))
+    zero = UOp.const(dtypes.int, 0)
+    tl = make_signal_value(devs)
+
+    # split each (multi-device) call into per-device deps, then store the device timeline value into the device signal after them
+    dep_lanes = [(call, dlane, devs.index(d)) for call in calls for dlane, d in enumerate(unwrap_after(call).arg.aux.device)]
+    store = make_deps(make_signal(devs).store(tl.index(zero)), dep_lanes, len(devs))
+    submit = make_submit(store, devs=devs, queue="COMPUTE:0")
+
+    upd = [(tl, 1)] + [(make_signal_value(devs, queue=qn), nbump) for qn in dedup([unwrap_after(call).arg.aux.queue for call in calls])]
+    patches = [s.after(submit).index(zero, dtype=s.dtype).store(s.index(zero) + inc) for s, inc in upd]
+    finalizers.append(UOp.custom_function("hcq", UOp.barrier(*patches).sink()).call(aux=HCQInfo("hcq finalizer", Estimates(), devs, "COMPUTE:0")))
+  return linear.replace(src=linear.src + tuple(finalizers))
+pm_add_finalizer = PatternMatcher([(UPat(Ops.LINEAR, name="linear"), add_finalizer)])
+
+# *****************
+# 2.5. global sync
+
+def add_global_sync(ctx:set[tuple[str, ...]], submit:UOp, q:UOp) -> UOp|None:
+  if (devs:=q.arg[0]) in ctx: return None
+  ctx.add(devs)
+
+  # some devices from a command buffer might be used for the first time this schedule, so we wait for their global timeline epoch.
+  wait = (make_signal(devs).index(zero:=UOp.const(dtypes.int, 0)).load() >= make_signal_value(devs).index(zero) - 1).wait()
+  return submit.replace(src=(q.replace(src=(UOp(Ops.BARRIER, dtypes.void), wait, *q.src)),))
+pm_add_global_sync = PatternMatcher([(UPat(Ops.CUSTOM_FUNCTION, arg="submit_cmdbuf", src=(UPat(Ops.LINEAR, name="q"),), name="submit"), add_global_sync)])
+
+# *****************
+# 3.1. lower loads/stores
+
+def add_loads(ctx:set[int], submit:UOp, q:UOp) -> UOp|None:
+  cur_devs = q.arg[0]
+  new_src:list[UOp] = []
+  for s in q.src:
+    if s.op is Ops.AFTER:
+      for lanes, dep in zip(s.arg, s.src[1:]):
+        devs, queue = dep.arg.aux.device, dep.arg.aux.queue
+        ctx.add(dep.tag) # mark op to update signal.
+
+        sig = make_mstack([make_signal(d if dl is None else devs[dl], queue=queue, sentinel=dl is None) for dl, d in zip(lanes, cur_devs)])
+        val = make_mstack([make_signal_value(d if dl is None else devs[dl], queue=queue) for dl, d in zip(lanes, cur_devs)]).index(UOp.const(dtypes.int, 0))
+        new_src.append((sig.index(UOp.const(dtypes.int, 0)).load() >= val + dep.tag).wait())
+      s = s.src[0]
+    new_src.append(s)
+  return submit.replace(src=(q.replace(src=tuple(new_src)),))
+pm_add_inner_loads = PatternMatcher([(UPat(Ops.CUSTOM_FUNCTION, arg="submit_cmdbuf", src=(UPat(Ops.LINEAR, name="q"),), name="submit"), add_loads)])
+
+def add_stores(ctx:set[int], submit:UOp, q:UOp) -> UOp|None:
+  devs, queue = q.arg
+  new_src:list[UOp] = []
+  for op in q.src:
+    new_src.append(op)
+    if (sigval:=unwrap_after(op).tag) in ctx:
+      new_src.append(make_signal(devs, queue=queue).store(make_signal_value(devs, queue=queue).index(UOp.const(dtypes.int, 0)) + sigval))
+  return submit.replace(src=(q.replace(src=tuple(new_src)),))
+pm_add_inner_stores = PatternMatcher([(UPat(Ops.CUSTOM_FUNCTION, arg="submit_cmdbuf", src=(UPat(Ops.LINEAR, name="q"),), name="submit"), add_stores)])
+
+# *****************
+# 4.1. hcq lowering: programs
+
+def encode_kernargs_clike(call:UOp, prg:UOp, devs:str|tuple[str, ...]) -> UOp:
+  data, info = prg.arg
+  buf = make_placeholder(devs, data.kernargs_alloc_size // 4, dtypes.uint32, name="kernargs")
+  words = [w for gi in info.globals for w in data64_le(make_getaddr(get_call_arg_uops(call)[gi], devs))] + list(info.vars)
+  return buf.after(*[make_patch(buf, i * 4, w) for i, w in enumerate(words)])
+
+# *****************
+# 4.2. hcq lowering: ops to ir
+
+def encode_cmdbuf(submit:UOp, lin:UOp) -> UOp|None:
+  if (pm:=Device.get_class(lin.arg[0][0]).pm_lower) is None: return None
+  return graph_rewrite(submit, pm, name=f"encode {lin.arg[0]}", enter_calls=True)
+pm_encode_cmdbufs = PatternMatcher([(UPat(Ops.CUSTOM_FUNCTION, arg="submit_cmdbuf", src=(UPat(Ops.LINEAR, name="lin"),), name="submit"), encode_cmdbuf)])
+
+# *****************
+
+def is_value_known_at_link(val:UOp) -> bool:
+  runtime_reads = [u for u in val.toposort() if u.op in (Ops.LOAD, Ops.INDEX)]
+  addressed_bufs = [b for g in val.toposort() if g.op is Ops.GETADDR for b in unwrap_mstack(g.buf_uop)]
+
+  # addr of input params is not known at link time
+  return not runtime_reads and all(b.op is not Ops.PARAM or b.tag is not None for b in addressed_bufs)
+
+def is_link_patch(p:UOp, jit:bool) -> bool:
+  store = p.src[0] if (is_binary_patch:=p.op is Ops.END) else p
+  if not jit: return store.buf_uop.tag == "program"
+  return is_binary_patch or (store.op is Ops.STORE and is_value_known_at_link(store.src[1]))
+
+def trim_link_patches(ctx:tuple[bool, list[UOp]], a:UOp) -> UOp|None:
+  links, kept = partition(a.src[1:], lambda p: is_link_patch(p, ctx[0]))
+
+  # keep all patches from the link-time patches' subtrees in the C code
+  afters = [u for u in UOp.sink(*links).toposort() if u.op is Ops.AFTER]
+  ctx[1].extend(UOp.sink(*links).substitute({p: p.src[0] for p in afters}).src)
+  return a.src[0].after(*kept, *[d for p in afters for d in p.src[1:]]) if links else None
+pm_trim_link_patches = PatternMatcher([(UPat(Ops.AFTER, src=(UPat((Ops.PARAM, Ops.MSTACK)),), allow_any_len=True, name="a"), trim_link_patches)])
+
+def split_patches(ctx:bool, call:UOp) -> UOp|None:
+  body = graph_rewrite(call.src[0], pm_trim_link_patches, ctx=(ctx, lt_patches:=[]), name=f"trim link-time patches ({call.arg.aux.name})")
+
+  lt_srcs = collections.defaultdict(list)
+  for p in lt_patches: lt_srcs[p.buf_uop].append(p)
+  return call.replace(src=(body, *call.src[1:], *[b.after(*ps) for b,ps in lt_srcs.items()]))
+pm_split_patches = PatternMatcher([(UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="hcq"),), name="call", allow_any_len=True), split_patches)])
+
+# *****************
+
+def make_addr_table(call:UOp, gaddrs:list[UOp], name:str) -> tuple[dict[UOp, UOp], tuple[UOp, ...]]:
+  bare = {g: g.replace(src=(unwrap_after(g.src[0]),)) for g in gaddrs}
+
+  order = sorted(dedup(bare.values()), key=lambda g: ((b:=unwrap_mstack(g.buf_uop)[0]).arg.slot, to_tuple(b.tag)))
+  slots, table = {g:i for i,g in enumerate(order)}, make_placeholder(call.arg.aux.device, len(order), dtypes.uint64, name)
+
+  reads = {g: table.after(*g.src[0].src[1:] if g.src[0].op is Ops.AFTER else ()).index(UOp.const(dtypes.int, slots[bare[g]])).load() for g in gaddrs}
+  return reads, (table.after(*[make_patch(table, i * table.dtype.itemsize, addr) for addr, i in slots.items()]),) if slots else ()
+
+def make_blob_bufs(call:UOp, blobs:list[UOp]) -> tuple[dict[UOp, UOp], tuple[UOp, ...]]:
+  bufs = {b: make_placeholder(call.arg.aux.device, b.max_numel(), b.dtype, "template") for b in blobs}
+  return bufs, tuple(buf.after(make_binary_patch(buf, b.src[0].arg)) for b,buf in bufs.items())
+
+def rm_rt_uops(call:UOp) -> UOp|None:
+  if not (rt_uops:=[u for u in call.src[0].toposort() if u.op is Ops.GETADDR or (u.op is Ops.BITCAST and u.src[0].op is Ops.BINARY)]): return None
+  gaddrs, blobs = partition(rt_uops, lambda u: u.op is Ops.GETADDR)
+  inputs, internals = partition(gaddrs, lambda g: all(x.op is Ops.PARAM and x.tag is None for x in unwrap_mstack(g.buf_uop)))
+  runtimes, systems = partition(internals, lambda g: any(x.tag in {"program", "kernargs", "cmdbuf"} for x in unwrap_mstack(g.buf_uop)))
+
+  # exec fills the inputs table with the input addresses every run, so it has no fill patches
+  (reads, _), *tables = [make_addr_table(call, gs, n) for gs,n in ((inputs, "inputs"), (runtimes, "runtime"), (systems, "systems"))] + \
+                        [make_blob_bufs(call, blobs)]
+  reads, fills = reads | {k:v for r,_ in tables for k,v in r.items()}, [f for _,fs in tables for f in fs]
+  return call.replace(src=(call.src[0].substitute(reads), *call.src[1:], *fills),
+                      arg=replace(call.arg, aux=replace(call.arg.aux, input_idxs=tuple(sorted(dedup(g.buf_uop.arg.slot for g in inputs))))))
+pm_rm_rt_uops = PatternMatcher([(UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="hcq"),), name="call", allow_any_len=True), rm_rt_uops)])
+
+# *****************
+
+def replace_params(call:UOp) -> UOp|None:
+  body, variables, param_ops = call.src[0], call.src[0].variables(), {Ops.PARAM, Ops.MSTACK}
+  args = dedup([s for u in body.toposort(gate=lambda u: u.op not in param_ops) for s in u.src if s.op in param_ops and s not in variables])
+
+  patched, refhold = partition(call.src[1:], lambda x: x.src[0] in args)
+  by_root = {p.src[0]: p for p in patched}
+  c_args = [by_root.get(a, a) for a in args]
+
+  sub = {unwrap_after(u): UOp.param(i, u.dtype, shape=unwrap_after(u).shape, device=u.device) for i,u in enumerate(c_args)} | \
+        {v: v.replace(arg=replace(v.arg, slot=-1)) for v in variables if v.op is Ops.PARAM}
+  info = replace(call.arg.aux, inputs=next((i for i,u in enumerate(c_args) if u.tag == "inputs"), None))
+  return call.replace(src=(body.substitute(sub), *c_args, *refhold), arg=replace(call.arg, aux=info)) # TODO: call.after(*refhold)?
+pm_replace_params = PatternMatcher([(UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="hcq"),), name="call", allow_any_len=True), replace_params)])
+
+# *****************
+
+def resolve_getaddr_slice(bv:UOp, g:UOp) -> UOp:
+  base = bv.src[0].after(*g.src[0].src[1:] if g.src[0].op is Ops.AFTER else ())
+  itemsize = bv.src[0].dtype.itemsize if unwrap_after(bv.src[0]).op in (Ops.BUFFER, Ops.SLICE, Ops.MSTACK, Ops.MSELECT) else bv.dtype.itemsize
+  return UOp(Ops.GETADDR, dtypes.uint64, src=(base,), arg=g.arg) + UOp.const(dtypes.uint64, bv.src[1].arg * itemsize)
+
+pm_early_simplify = PatternMatcher([
+  (UPat(Ops.GETADDR, src=(UPat.any(sl:=UPat(Ops.SLICE, name="bv"), sl.after(allow_any_len=True)),), name="g"), resolve_getaddr_slice),
+  (UPat(Ops.INDEX, src=(UPat(Ops.SLICE, name="bv"),), allow_any_len=True, name="x"),
+   lambda bv,x: x.replace(src=(bv.src[0], x.src[1] + bv.src[1].cast(x.src[1].dtype), *x.src[2:]))),
+])
+
+# *****************
+# 5.3. pack placeholders buffers
+
+def pack_hcq_placeholders(call:UOp) -> UOp|None:
+  bufs = [b for b in call.src[0].toposort() if b.op is Ops.PARAM and b.tag in {"scratch", "kernargs"}]
+  offs, sizes = {}, {}
+  for b in bufs:
+    if b.tag == "scratch": sizes[b.tag] = max(sizes.get(b.tag, 0), b.max_numel())
+    else:
+      offs[b] = round_up(sizes.get(b.tag, 0), 128 // b.dtype.itemsize)
+      sizes[b.tag] = offs[b] + b.max_numel()
+  counts = collections.Counter(b.tag for b in bufs)
+  bases = {b.tag:make_placeholder(b.device, sizes[b.tag], b.dtype, b.tag) for b in bufs if counts[b.tag] > 1}
+  subs = {b:UOp(Ops.SLICE, b.dtype, (bases[b.tag], UOp.const(dtypes.index, offs.get(b, 0))), b.max_numel()) for b in bufs if b.tag in bases}
+  return call.replace(src=(call.src[0].substitute(subs, walk=True), *call.src[1:])) if subs else None
+pm_pack_placeholders = PatternMatcher([(UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="hcq"),), name="call", allow_any_len=True), pack_hcq_placeholders)])
+
+# *****************
+# 8. callify hcq programs
+
+pm_callify_hcq = PatternMatcher([(UPat(Ops.CUSTOM_FUNCTION, arg="hcq", src=(UPat(Ops.SINK),), name="cf"),
+  lambda cf: cf.replace(src=(to_program(cf.src[0].replace(arg=KernelInfo("hcq_submit"), tag=1), Device[HCQ_RUNTIME_DEV.value].renderer),)))])
+
+hcq_compile_cache:dict[tuple[bytes, bool], UOp] = {}
+
+@track_rewrites(lambda linear,input_uops,jit,ret: f"HCQ Compile {pluralize('Kernel', len(ret.src))}")
+def hcq_compile(linear:UOp, input_uops:list[UOp]|None=None, jit=False) -> UOp:
+  if input_uops is not None: linear = graph_rewrite(linear, pm_replace_buffers, ctx=input_uops, walk=True, enter_calls=True, name="replace buffer")
+
+  if (final_linear:=(hcq_compile_cache.get(cache_key:=(linear.key, jit)))) is None:
+    # schedule
+    linear = linear.substitute(back_map:={s.param_like(i): s for i,s in enumerate(input_uops)} if input_uops is not None else {}, walk=True)
+    linear = graph_rewrite(linear, pm_insert_copy_staging + pm_flatten_linear, name="insert copy staging")
+    linear = graph_rewrite(linear, pm_tag_hcq_calls, ctx=(enumerator:=itertools.count(0)), walk=True, name="tag hcq calls")
+    linear = graph_rewrite(linear, pm_sched_sync, ctx=HCQDepsTracker(), walk=True, name="schedule sync")
+    linear = linear.substitute({s: p for p, s in back_map.items()}, walk=True)
+    linear = graph_rewrite(linear, pm_merge_queues, walk=True, name="merge queues")
+    linear = graph_rewrite(linear, pm_add_finalizer, ctx=enumerator, walk=True, name="add finalizer")
+    linear = graph_rewrite(linear, pm_add_global_sync, ctx=set(), walk=True, name="add global sync", enter_calls=True)
+
+    # lowering to hcq ir
+    linear = graph_rewrite(linear, pm_add_inner_loads, ctx=(waited:=set()), walk=True, name="add loads", enter_calls=True)
+    linear = graph_rewrite(linear, pm_add_inner_stores, ctx=waited, walk=True, name="add stores", enter_calls=True)
+    linear = graph_rewrite(linear, pm_encode_cmdbufs, walk=True, name="encode cmdbufs", enter_calls=True)
+    linear = graph_rewrite(linear, pm_pack_placeholders, walk=True, name="pack placeholders")
+
+    # pie
+    linear = graph_rewrite(linear, pm_split_patches, ctx=jit, walk=True, name="split rt/lt patches")
+    linear = graph_rewrite(linear, pm_early_simplify + symbolic, bottom_up=False, name="simplify packed placeholders", enter_calls=True)
+    linear = graph_rewrite(linear, pm_rm_rt_uops, walk=True, name="replace rt uops")
+    linear = graph_rewrite(linear, pm_replace_params, walk=True, name="replace with args")
+
+    # and compile it
+    final_linear = hcq_compile_cache[cache_key] = graph_rewrite(linear, pm_callify_hcq, name="callify hcq", enter_calls=True)
+
+  return final_linear
+
+# *****************
+# 6. bufferize placeholders: replace placeholders with real buffers.
+
+def bufferize_buf(ctx:bool, buf:UOp) -> UOp|None:
+  if buf.tag is None: return None
+  return make_mstack(tuple(UOp.from_buffer((dv:=Device[dev]).pm_bufferize.rewrite(buf, ctx=(dv, ctx)), HCQ_RUNTIME_DEV.value) for dev in to_tuple(buf.device)))
+pm_bufferize = PatternMatcher([(UPat(Ops.PARAM, name="buf"), bufferize_buf)])
+
+# *****************
+# 7. resolve patches
+
+def push_stack(op, s): return UOp(Ops.STACK, op.dtype.scalar(),
+  tuple(op.replace(dtype=op.dtype.scalar(), src=tuple(x if y is s else y for y in op.src)) for x in s.src))
+
+def fold_binary(buf:UOp, blob:UOp) -> UOp:
+  for b in (m.bufs if isinstance(m:=buf.buffer, MultiBuffer) else (m,)): b.ensure_allocated()._buf.cpu_view().view(fmt='B')[:len(blob.arg)] = blob.arg
+  return UOp(Ops.NOOP)
+
+def fold_const_store(buf:UOp, off:UOp, val:UOp) -> UOp:
+  for b, v in zip((bs:=mb.bufs if isinstance((mb:=buf.buffer), MultiBuffer) else (mb,)), val.src if val.op is Ops.STACK else (val,)*len(bs)):
+    data = struct.pack(f'<{v.dtype.fmt}', truncate[v.dtype](v.arg))
+    b.ensure_allocated()._buf.cpu_view().view(offset=off.arg * buf.dtype.itemsize, size=len(data), fmt='B')[:] = data
+  return UOp(Ops.NOOP)
+
+def resolve_getaddr(buf:UOp, g:UOp) -> UOp:
+  assert buf.op in (Ops.BUFFER, Ops.MSTACK, Ops.MSELECT), f"{buf.op}"
+
+  devs, b = g.arg, buf.buffer
+  bufs = tuple(cast(Buffer, x.buffer) for x in buf.src) if buf.op is Ops.MSTACK else tuple(b.bufs if isinstance(b, MultiBuffer) else (b,)*len(devs))
+  assert len(bufs) == len(devs), f"can't resolve {len(bufs)} buffers on {len(devs)} devices"
+  addrs = tuple(UOp.const(dtypes.uint64, x.get_buf(d).va_addr) for x, d in zip(bufs, devs))
+  return addrs[0] if len(addrs) == 1 else UOp(Ops.STACK, dtypes.uint64, addrs)
+
+pm_resolve_patches = PatternMatcher([
+  # multi
+  (UPat(GroupOp.ALU, src=[UPat(Ops.STACK, name="s"), UPat(Ops.CONST)], name="op"), push_stack),
+  (UPat(Ops.CAST, src=(UPat(Ops.STACK, name="s"),), name="op"), push_stack),
+
+  # getaddr
+  (UPat(Ops.GETADDR, src=(UPat(name="buf"),), name="g"), resolve_getaddr),
+
+  # folders
+  (UPat(name="buf").index(UPat(Ops.RANGE), allow_any_len=True)
+    .store(UPat.any(UPat(Ops.BINARY, name="blob"), UPat(Ops.BINARY, name="blob").bitcast()).index(UPat(Ops.RANGE), allow_any_len=True).load())
+    .end(UPat(Ops.RANGE)), fold_binary),
+  (UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf").index(UPat.cvar("off"))
+    .store(UPat.any(UPat.cvar("val"), UPat(Ops.STACK, name="val"))), fold_const_store),
+])
+
+pm_assert_no_afters = PatternMatcher([(UPat(Ops.AFTER, name="a"), lambda a: panic(RuntimeError, f"AFTER left at hcq_link: {a.src[0].op}"))])
+
+hcq_link_cache:dict[tuple[bytes, tuple[str, ...]], UOp] = {}
+
+def link_cache_key(a:UOp): return a.key, to_tuple(a.device)
+pm_link_cache = PatternMatcher([(UPat(Ops.AFTER, name="a"), lambda a: hcq_link_cache.get(link_cache_key(a)))])
+
+@track_rewrites(lambda _,jit,ret: f"HCQ Link {pluralize('Kernel', len(ret.src))}")
+def hcq_link(linear:UOp, jit=False) -> UOp:
+  cacheable = {(j,i):a for j,c in enumerate(linear.src) for i,a in enumerate(c.src[1:], 1)
+               if a.op is Ops.AFTER and unwrap_mstack(a.src[0])[0].tag in HCQ_CACHE_TAGS}
+  hits = {a.src[0]:hcq_link_cache[key] for a in cacheable.values() if (key:=link_cache_key(a)) in hcq_link_cache}
+  linear = graph_rewrite(linear, pm_link_cache, name="apply link cache").substitute(hits, walk=True)
+  linear = graph_rewrite(linear, pm_bufferize, ctx=jit, bottom_up=True, walk=True, name="bufferize placeholders")
+  linear = graph_rewrite(linear, pm_resolve_patches + symbolic, bottom_up=False, name="simplify patches")
+  linear = graph_rewrite(linear, pm_assert_no_afters, name="assert no afters")
+  for (j,i),a in cacheable.items(): hcq_link_cache.setdefault(link_cache_key(a), linear.src[j].src[i])
+  return linear
+
+# *****************
+# Device classes
+
+class HCQ2Compiled(Compiled):
+  timestamp_divider: float = 1000.0
+
+  def __init__(self, device:str, allocator:HCQAllocator, compilers:list[type[Renderer]], runtime, can_recover:bool=False, arch=None):
+    self.device_id:int = int(device.split(":")[1]) if ":" in device else 0
+
+    self.pm_bufferize = PatternMatcher([
+      (UPat(Ops.PARAM, tag="timeline_signal"), lambda ctx: ctx[0].timeline_signal()),
+      (UPat(Ops.PARAM, tag="timeline_value"), lambda ctx: ctx[0].timeline_value()),
+      (UPat(Ops.PARAM, tag="sentinel_signal"), lambda ctx: ctx[0].timeline_signal("sentinel", (1 << 64) - 1)),
+      (UPat(Ops.PARAM, name="b"), lambda ctx, b: None if b.tag is None else ctx[0].new_buffer(b, jit=ctx[1]))
+    ])
+
+    super().__init__(device, allocator, compilers, lambda *a, **kw: None, None, arch=arch)
+
+    self.rt_buffer = Buffer(self.device, 64 << 20, dtypes.uint8, options=BufferSpec(uncached=True, cpu_access=True))
+    self.rt_allocator = BumpAllocator(64 << 20, wrap=False)
+
+  def new_buffer(self, b:UOp, jit:bool) -> Buffer:
+    if jit or b.tag in HCQ_CACHE_TAGS: return Buffer(self.device, b.max_numel(), b.dtype, options=BufferSpec(cpu_access=True, nolru=True))
+    return self.rt_buffer.view(b.max_numel(), b.dtype, self.rt_allocator.alloc(b.max_numel() * b.dtype.itemsize, alignment=128))
+
+  @functools.cache
+  def timeline_signal(self, queue:str|None=None, init_value:int=0) -> Buffer:
+    buf = Buffer(self.device, 1, dtypes.uint64, options=BufferSpec(host=True, uncached=True, cpu_access=True), preallocate=True)
+    buf._buf.cpu_view().mv.cast('Q')[0] = init_value
+    return buf
+
+  @functools.cache
+  def timeline_value(self, queue:str|None=None, init_value:int=1) -> Buffer:
+    buf = Buffer("CPU", 1, dtypes.uint64, preallocate=True)
+    buf.as_memoryview(force_zero_copy=True).cast('Q')[0] = init_value
+    return buf
+
+  def synchronize(self, timeout:int|None=None):
+    if not hasattr(self, 'iface'): return
+    sig = self.timeline_signal()._buf.cpu_view().mv.cast('Q')
+    tl = self.timeline_value().as_memoryview(force_zero_copy=True).cast('Q')
+    st = time.perf_counter()
+    while sig[0] < tl[0] - 1:
+      if time.perf_counter() - st > (timeout or 3000) / 1000: self.on_device_hang()
+
+  def device_props(self) -> dict[str,Any]: return {} # to be overridden if needed. dict keys are backend dependent.
+
+  def count(self) -> int: return self.iface.count if hasattr(self, 'iface') else 1
+
+  def _select_iface(self):
+    assert (v:=getenv(k:=f'{type(self).__name__[:-6].upper()}_IFACE', "")) == "",  \
+      f"{k}={v} is deprecated, use DEV={replace(DEV.target(type(self).__name__[:-6]), interface=v)} instead"
+    assert hasattr(self, "ifaces"), "must have ifaces to select an iface"
+    t = DEV.target(dev:=type(self).__name__[:-6])
+    filtered = select_by_name(self.ifaces, lambda i: i.__name__[:-5], t.interface, f"{dev} has no interface {t.interface!r}")
+    filtered = [i for i in filtered if t.interface.startswith("MOCK") or not i.__name__[:-5].startswith("MOCK")] # never fall back to mock ifaces
+    return select_first_inited([functools.partial(cast(Callable, iface), self, self.device_id) for iface in filtered],
+                               f"No interface for {dev}:{self.device_id} is available")
+
+  def _is_cpu(self) -> bool: return hasattr(self, 'device') and self.device.split(":")[0] == "CPU"
+
+  def finalize(self):
+    try: self.synchronize() # try to finalize the device in any case
+    except RuntimeError as e: print(f"{self.device} synchronization failed before finalizing: {e}")
+
+    # if the device has an interface, call device_fini to clean up resources
+    if hasattr(self, 'iface') and hasattr(self.iface, 'device_fini'): self.iface.device_fini()
+
+class HCQ2Buffer:
+  def __init__(self, va_addr:sint, size:int, meta:Any=None, _base:HCQ2Buffer|None=None, view:MMIOInterface|None=None, owner:HCQ2Compiled|None=None):
+    self.va_addr, self.size, self.meta, self._base, self.view, self.owner = va_addr, size, meta, _base, view, owner
+
+  def offset(self, offset:int=0, size:int|None=None) -> HCQ2Buffer:
+    return HCQ2Buffer(self.va_addr+offset, size or (self.size - offset), owner=self.owner, meta=self.meta,
+      _base=self._base or self, view=(self.view.view(offset=offset, size=size) if self.view is not None else None))
+
+  def cpu_view(self) -> MMIOInterface:
+    assert self.view is not None, "buffer has no cpu_view"
+    return self.view
+
+  @property
+  def base(self) -> HCQ2Buffer: return self._base or self
+
+class HCQAllocator(LRUAllocator[HCQDeviceType], Generic[HCQDeviceType]):
+  def _as_buffer(self, buf:HCQ2Buffer) -> memoryview:
+    self.dev.synchronize()
+    return buf.cpu_view().mv
+
+  def _map(self, buf:HCQ2Buffer) -> HCQ2Buffer:
+    if not hasattr(self, '_do_map'): raise NotImplementedError("map failed: no method implemented")
+    return self._do_map(buf)
+
+  @suppress_finalizing
+  def _free(self, buf:HCQ2Buffer, options:BufferSpec|None=None):
+    if options is not None and options.external_ptr is not None: return
+    self.dev.synchronize()
+    if hasattr(self, '_do_free'): self._do_free(buf, options)
+
+  def _unmap(self, mb):
+    self.dev.synchronize()
+    self.dev.iface.free(mb)
+
+  def _offset(self, buf, size:int, offset:int) -> HCQ2Buffer: return buf.offset(offset=offset, size=size)

extra/hcq2/ops_amd2.py

@@ -3,8 +3,8 @@ from typing import cast, Any, Callable
 import os, ctypes, struct, hashlib, functools, importlib, mmap, errno, array, contextlib, sys, weakref, itertools, collections, atexit
 assert sys.platform != 'win32'
 from dataclasses import dataclass
-from tinygrad.runtime.support.hcq2 import HCQ2Compiled, HCQAllocator, HCQ2Buffer, encode_kernargs_clike, make_cmdbuf
-from tinygrad.runtime.support.hcq2 import make_binary_patch
+from extra.hcq2.hcq2 import HCQ2Compiled, HCQAllocator, HCQ2Buffer, encode_kernargs_clike, make_getaddr, make_ins, make_cmdbuf, make_placeholder
+from extra.hcq2.hcq2 import make_binary_patch
 from tinygrad.uop.ops import sint, UOp
 from tinygrad.device import Compiled, BufferSpec, Buffer, Device
 from tinygrad.dtype import dtypes
@@ -36,9 +36,7 @@ class PM4Ops(FastEnum):
   SET_SH_REG = auto(); SET_UCONFIG_REG = auto(); WAIT_REG_MEM = auto(); ACQUIRE_MEM = auto()  # noqa: E702
   RELEASE_MEM = auto(); DISPATCH_DIRECT = auto(); EVENT_WRITE = auto()  # noqa: E702
 
-def pkt3(ctx, op:PM4Ops, *vals):
-  return UOp(Ops.INS, arg=op, src=tuple(UOp.const(x, dtypes.uint32)
-    for x in (ctx.pm4.PACKET3(getattr(ctx.pm4, f"PACKET3_{op.name}"), len(vals) - 1), *vals)))
+def pkt3(ctx, op:PM4Ops, *vals): return make_ins(op, ctx.pm4.PACKET3(getattr(ctx.pm4, f"PACKET3_{op.name}"), len(vals) - 1), *vals)
 
 def wreg(ctx, reg:AMDReg, *args:sint, **kwargs:int):
   if bool(args) == bool(kwargs): raise RuntimeError('One (and only one) of *args or **kwargs must be specified')
@@ -92,26 +90,26 @@ def memory_barrier(ctx):
                  reg_done=getattr(ctx.nbio, f'regBIF_BX_PF{pf}_GPU_HDP_FLUSH_DONE').addr[0], value=0xffffffff),
     acquire_mem(ctx)))
 
-def pm4_wait(ctx, dst, val): return wait_reg_mem(ctx, val, mem=dst.getaddr(ctx.devs))
+def pm4_wait(ctx, x, y): return wait_reg_mem(ctx, y, mem=make_getaddr(x.buf_uop, ctx.devs))
 
 def pm4_barrier(ctx): return memory_barrier(ctx)
 
 def pm4_store(ctx, dst, val):
   if val.op is Ops.BINARY: return None
-  return release_mem(ctx, dst.getaddr(ctx.devs), val, ctx.pm4.data_sel__mec_release_mem__send_32_bit_low,
+  return release_mem(ctx, make_getaddr(dst, ctx.devs), val, ctx.pm4.data_sel__mec_release_mem__send_32_bit_low,
                      ctx.pm4.int_sel__mec_release_mem__send_interrupt_after_write_confirm, cache_flush=True)
 
 def pm4_timestamp(ctx, dst):
-  return release_mem(ctx, dst.getaddr(ctx.devs), 0, ctx.pm4.data_sel__mec_release_mem__send_gpu_clock_counter,
+  return release_mem(ctx, make_getaddr(dst, ctx.devs), 0, ctx.pm4.data_sel__mec_release_mem__send_gpu_clock_counter,
                      ctx.pm4.int_sel__mec_release_mem__none)
 
 def pm4_program(ctx, call, prg):
   data, info = prg.arg
   lib_gpu = prg.src[0]
   args = encode_kernargs_clike(call, prg, ctx.devs)
-  prog_addr = lib_gpu.getaddr(ctx.devs) + data.entry_point_offset
-  scratch_addr = UOp.placeholder((data.private_segment_size,), dtypes.uint8, 0, device=ctx.devs).rtag("scratch").getaddr(ctx.devs)
-  args_addr = args.getaddr(ctx.devs)
+  prog_addr = make_getaddr(lib_gpu, ctx.devs) + data.entry_point_offset
+  scratch_addr = make_getaddr(make_placeholder(ctx.devs, data.private_segment_size, dtypes.uint8, "scratch", unique=False), ctx.devs)
+  args_addr = make_getaddr(args, ctx.devs)
 
   user_regs = []
   if data.enable_private_segment_sgpr:
@@ -140,37 +138,37 @@ def pm4_program(ctx, call, prg):
 pm_pm4_opsel = PatternMatcher([
   (UPat(Ops.CALL, src=(UPat(Ops.PROGRAM, name="prg"),), name="call", allow_any_len=True), pm4_program),
 
-  (UPat(Ops.INS, arg="wait", src=(UPat(name="dst"), UPat(name="val"))), pm4_wait),
-  (UPat(Ops.INS, arg="barrier"), pm4_barrier),
-  (UPat(Ops.INS, arg="timestamp", src=(UPat(name="dst"),)), pm4_timestamp),
-  (UPat(Ops.INS, arg="store", src=(UPat((Ops.BUFFER, Ops.PARAM), name="dst"), UPat(name="val"))), pm4_store),
+  (UPat(Ops.WAIT, src=(UPat.var("x") >= UPat.var("y"),)), pm4_wait),
+  (UPat(Ops.BARRIER), pm4_barrier),
+  (UPat(Ops.CUSTOM_FUNCTION, arg="timestamp", src=(UPat(name="dst"),)), pm4_timestamp),
+  (UPat(Ops.STORE, src=(UPat((Ops.BUFFER, Ops.PARAM), name="dst"), UPat(name="val"))), pm4_store),
 ])
 
-def pm4_submit(ctx, lin):
-  # ensure compute queues are allocated
-  for d in (devs:=ctx.devs): q = Device[d].compute_queue
-  ring, wptr, doorbell, put_ptr = (UOp.placeholder((b.size,), b.dtype, 0, device=devs).rtag(f"COMPUTE:0_{name}")
+def pm4_submit(cmdbuf, devs):
+  size, zero = UOp.const(dtypes.uint32, cmdbuf.nbytes() // dtypes.uint32.itemsize), UOp.const(dtypes.int, 0)
+
+  # the compute queue's ring and its host-side ring/write/put pointers (placeholders, resolved in pm_bufferize)
+  for d in devs: q = Device[d].compute_queue
+  ring, wptr, doorbell, put_ptr = (make_placeholder(devs, b.size, b.dtype, ("COMPUTE:0", name), unique=False)
     for name, b in (("ring", q.ring), ("write_ptr", q.write_ptr), ("doorbell", q.doorbell), ("put_value", q.put_value)))
 
-  # the host fence at the start of the batch guarantees the ib is free to reuse
-  size_dw = sum(len(ins.src) for ins in lin.src)
-  assert size_dw < (1 << 20), f"indirect buffer of {size_dw} dwords doesn't fit one packet"
+  # place the cmdbuf at the ring's write offset, wrapping the ring
+  put = put_ptr.index(zero)
+  next_put = put + size.cast(put.dtype)
+  i = UOp.range(size, 0, dtype=dtypes.int, src=(cmdbuf,))
+  ring_idx = ((put + i.cast(put.dtype)) % q.ring.size).cast(dtypes.int)
 
-  ib = UOp.placeholder((size_dw,), dtypes.uint32, next(UOp.unique_num), device=devs, volatile=True).rtag("cmdbuf")
-  cmdbuf = make_cmdbuf(lin, devs, buf=ib)
+  # copy the cmdbuf into the ring and advance the put/write pointers
+  copy_to_ring = ring.index(ring_idx).store(cmdbuf.index(i).load()).end(i)
+  bump_put_ptr = put_ptr.index(zero).store(next_put)
+  bump_wptr = wptr.index(zero).store(next_put)
 
-  # the ring itself only carries a packet pointing at the ib, wrapping the ring
-  put = put_ptr.index(zero:=UOp.const(0, dtypes.int))
-  pkt = (ctx.pm4.PACKET3(ctx.pm4.PACKET3_INDIRECT_BUFFER, 2), *data64_le(cmdbuf.getaddr(devs)), size_dw | ctx.pm4.INDIRECT_BUFFER_VALID)
-  write_pkt = UOp.barrier(*[ring.index(((put + off) % q.ring.size).cast(dtypes.int)).store(UOp.const(x, dtypes.uint32)) for off,x in enumerate(pkt)])
+  # ring the doorbell once the copy and pointer bumps have landed
+  flush = UOp.barrier(copy_to_ring, bump_put_ptr, bump_wptr)
+  return doorbell.after(flush).index(zero).store(next_put)
 
-  # advance the put/write pointers past the packet
-  bump_put_ptr = put_ptr.index(zero).store(put + len(pkt))
-  bump_wptr = wptr.index(zero).store(put + len(pkt))
-  flush = UOp.barrier(write_pkt, bump_put_ptr, bump_wptr)
-  return doorbell.after(flush).index(zero).store(put + len(pkt))
-
-pm_pm4_submit = PatternMatcher([(UPat(Ops.LINEAR, name="lin"), pm4_submit)])
+pm_pm4_submit = PatternMatcher([(UPat(Ops.LINEAR, name="lin"),
+  lambda lin: pm4_submit(make_cmdbuf(lin, to_tuple(lin.arg[0])), to_tuple(lin.arg[0])))])
 
 # *****************
 # SDMA
@@ -178,46 +176,45 @@ pm_pm4_submit = PatternMatcher([(UPat(Ops.LINEAR, name="lin"), pm4_submit)])
 class SDMAOps(FastEnum): COPY = auto(); POLL_REGMEM = auto(); FENCE = auto(); TRAP = auto(); TIMESTAMP = auto()  # noqa: E702
 
 def sdma_copy(ctx, call):
-  sz = call.src[2].max_numel() * call.src[2].dtype.itemsize
-  src_addr, dst_addr = call.src[2].getaddr(ctx.devs), call.src[1].getaddr(ctx.devs)
-  return call.ins(SDMAOps.COPY, src=tuple(UOp.const(x, dtypes.uint32) for off in range(0, sz, ctx.max_copy_size) for x in (
-    ctx.sdma.SDMA_OP_COPY | ctx.sdma.SDMA_PKT_COPY_LINEAR_HEADER_SUB_OP(ctx.sdma.SDMA_SUBOP_COPY_LINEAR),
-    ctx.sdma.SDMA_PKT_COPY_LINEAR_COUNT_COUNT(min(sz-off, ctx.max_copy_size)-1), 0,
-    *data64_le(src_addr+UOp.const(off, dtypes.uint64)), *data64_le(dst_addr+UOp.const(off, dtypes.uint64)))))
+  dst, src = call.src[1], call.src[2]
+  sz = src.max_numel() * src.dtype.itemsize
+  src_addr, dst_addr = make_getaddr(src, ctx.devs), make_getaddr(dst, ctx.devs)
+  return UOp(Ops.LINEAR, dtypes.void, tuple([make_ins(SDMAOps.COPY,
+     ctx.sdma.SDMA_OP_COPY | ctx.sdma.SDMA_PKT_COPY_LINEAR_HEADER_SUB_OP(ctx.sdma.SDMA_SUBOP_COPY_LINEAR),
+     ctx.sdma.SDMA_PKT_COPY_LINEAR_COUNT_COUNT(min(sz - off, ctx.max_copy_size) - 1), 0,
+     *data64_le(src_addr + off), *data64_le(dst_addr + off)) for off in range(0, sz, ctx.max_copy_size)]))
 
-def sdma_wait(ctx, ins, dst, val):
+def sdma_wait(ctx, x, y):
   op = ctx.sdma.SDMA_OP_POLL_REGMEM | ctx.sdma.SDMA_PKT_POLL_REGMEM_HEADER_FUNC(WAIT_REG_MEM_FUNCTION_GEQ) \
      | ctx.sdma.SDMA_PKT_POLL_REGMEM_HEADER_MEM_POLL(1)
-  return ins.ins(SDMAOps.POLL_REGMEM, src=tuple(UOp.const(x, dtypes.uint32) for x in (
-    op, *data64_le(dst.getaddr(ctx.devs)), val, 0xffffffff,
-    ctx.sdma.SDMA_PKT_POLL_REGMEM_DW5_INTERVAL(0x04) | ctx.sdma.SDMA_PKT_POLL_REGMEM_DW5_RETRY_COUNT(0xfff))))
+  return make_ins(SDMAOps.POLL_REGMEM, op, *data64_le(make_getaddr(x.buf_uop, ctx.devs)), y, 0xffffffff,
+    ctx.sdma.SDMA_PKT_POLL_REGMEM_DW5_INTERVAL(0x04) | ctx.sdma.SDMA_PKT_POLL_REGMEM_DW5_RETRY_COUNT(0xfff))
 
-def sdma_store(ctx, ins, dst, val):
+def sdma_store(ctx, dst, val):
   op = ctx.sdma.SDMA_OP_FENCE | (ctx.sdma.SDMA_PKT_FENCE_HEADER_MTYPE(3) if ctx.target[0] != 9 else 0)
-  return UOp(Ops.LINEAR, src=(
-    ins.ins(SDMAOps.FENCE, src=tuple(UOp.const(x, dtypes.uint32) for x in (op, *data64_le(dst.getaddr(ctx.devs)), val))),
-    ins.ins(SDMAOps.TRAP, src=tuple(UOp.const(x, dtypes.uint32) for x in (ctx.sdma.SDMA_OP_TRAP, 0)))))
+  return UOp(Ops.LINEAR, dtypes.void, (
+    make_ins(SDMAOps.FENCE, op, *data64_le(make_getaddr(dst, ctx.devs)), val), make_ins(SDMAOps.TRAP, ctx.sdma.SDMA_OP_TRAP, 0)))
 
-def sdma_timestamp(ctx, ins, dst):
+def sdma_timestamp(ctx, dst):
   op = ctx.sdma.SDMA_OP_TIMESTAMP | ctx.sdma.SDMA_PKT_TIMESTAMP_GET_HEADER_SUB_OP(ctx.sdma.SDMA_SUBOP_TIMESTAMP_GET_GLOBAL)
-  return ins.ins(SDMAOps.TIMESTAMP, src=tuple(UOp.const(x, dtypes.uint32) for x in (op, *data64_le(dst.getaddr(ctx.devs)))))
+  return make_ins(SDMAOps.TIMESTAMP, op, *data64_le(make_getaddr(dst, ctx.devs)))
 
 pm_sdma_opsel = PatternMatcher([
   (UPat(Ops.CALL, src=(UPat(Ops.COPY),), name="call", allow_any_len=True), sdma_copy),
 
-  (UPat(Ops.INS, arg="barrier"), lambda: UOp(Ops.NOOP, dtypes.void, ())),
-  (UPat(Ops.INS, arg="wait", src=(UPat(name="dst"), UPat(name="val")), name="ins"), sdma_wait),
-  (UPat(Ops.INS, arg="timestamp", src=(UPat(name="dst"),), name="ins"), sdma_timestamp),
-  (UPat(Ops.INS, arg="store", src=(UPat((Ops.BUFFER, Ops.PARAM), name="dst"), UPat(name="val")), name="ins"), sdma_store),
+  (UPat(Ops.BARRIER), lambda: UOp(Ops.NOOP, dtypes.void, ())),
+  (UPat(Ops.WAIT, src=(UPat.var("x") >= UPat.var("y"),)), sdma_wait),
+  (UPat(Ops.CUSTOM_FUNCTION, arg="timestamp", src=(UPat(name="dst"),)), sdma_timestamp),
+  (UPat(Ops.STORE, src=(UPat((Ops.BUFFER, Ops.PARAM), name="dst"), UPat(name="val"))), sdma_store),
 ])
 
 def sdma_submit(cmdbuf, devs):
   # the cmdbuf to submit + the patch writes that fill it
-  size_dw, zero = cmdbuf.nbytes() // dtypes.uint32.itemsize, UOp.const(0, dtypes.int)
+  size_dw, zero = cmdbuf.nbytes() // dtypes.uint32.itemsize, UOp.const(dtypes.int, 0)
 
   # the sdma queue's ring and its host-side ring/write/put pointers
   for d in devs: q = Device[d].sdma_queue(0)
-  ring, wptr, doorbell, put_ptr = (UOp.placeholder((b.size,), b.dtype, 0, device=devs).rtag(f"COPY:0_{name}")
+  ring, wptr, doorbell, put_ptr = (make_placeholder(devs, b.size, b.dtype, ("COPY:0", name), unique=False)
     for name, b in (("ring", q.ring), ("write_ptr", q.write_ptr), ("doorbell", q.doorbell), ("put_value", q.put_value)))
 
   # sdma needs the cmdbuf contiguous: if it won't fit before the ring end, restart at 0 and zero the tail
@@ -229,8 +226,8 @@ def sdma_submit(cmdbuf, devs):
 
   # zero the wrapped tail, then copy the cmdbuf into the ring
   zi = UOp.range(zero_amt_dw, 0, dtype=dtypes.int, src=(cmdbuf,))
-  zero_tail = ring.index(tail_off_dw + zi).store(UOp.const(0, dtypes.uint32)).end(zi)
-  i = UOp.range(UOp.const(size_dw, dtypes.int), 0, dtype=dtypes.int, src=(cmdbuf,))
+  zero_tail = ring.index(tail_off_dw + zi).store(UOp.const(dtypes.uint32, 0)).end(zi)
+  i = UOp.range(UOp.const(dtypes.int, size_dw), 0, dtype=dtypes.int, src=(cmdbuf,))
   copy_to_ring = ring.index(start_dw + i).store(cmdbuf.index(i).load()).end(i)
 
   # advance the put/write pointers past the zeroed tail and the cmdbuf
@@ -243,7 +240,7 @@ def sdma_submit(cmdbuf, devs):
   return doorbell.after(flush).index(zero).store(next_put_b)
 
 pm_sdma_submit = PatternMatcher([(UPat(Ops.LINEAR, name="lin"),
-  lambda ctx, lin: sdma_submit(make_cmdbuf(lin, ctx.devs), ctx.devs))])
+  lambda lin: sdma_submit(make_cmdbuf(lin, to_tuple(lin.arg[0])), to_tuple(lin.arg[0])))])
 
 @dataclass(frozen=True)
 class AMDEncodeCtx:  # encode-time constants for one queue: devs (every cmdbuf address resolves into these) + gfx version + packet/ip modules
@@ -254,7 +251,7 @@ def encode_queue(q:UOp) -> UOp|None:
   d = Device[(devs:=to_tuple(q.arg[0]))[0]]
   ctx = AMDEncodeCtx(devs, d.target, d.pm4, d.sdma, d.soc, d.gc, d.nbio, d.xccs, d.max_copy_size, d.tmpring_size)
   opsel, submit = (pm_pm4_opsel, pm_pm4_submit) if q.arg[1].startswith("COMPUTE") else (pm_sdma_opsel, pm_sdma_submit)
-  return submit.rewrite(graph_rewrite(q, opsel + pm_flatten_linear, walk=True, ctx=ctx, name=f"{q.arg[1]} opsel"), ctx)
+  return submit.rewrite(graph_rewrite(q, opsel + pm_flatten_linear, walk=True, ctx=ctx, name=f"{q.arg[1]} opsel"))
 
 @dataclass(frozen=True)
 class AMDProgramData:
@@ -282,23 +279,21 @@ def amd_build_program(prg:UOp) -> UOp:
       wave32=bool(desc.kernel_code_properties & 0x400), private_segment_size=desc.private_segment_fixed_size, kernargs_segment_size=desc.kernarg_size,
       kernargs_alloc_size=desc.kernarg_size + (ctypes.sizeof(hsa.hsa_kernel_dispatch_packet_t) if edp else 0), enable_dispatch_ptr=edp,
       enable_private_segment_sgpr=desc.kernel_code_properties & hsa.AMD_KERNEL_CODE_PROPERTIES_ENABLE_SGPR_PRIVATE_SEGMENT_BUFFER)
-    buf = UOp.placeholder((len(image),), dtypes.uint8, next(UOp.unique_num), device=prg.device).rtag("program")
+    buf = make_placeholder(prg.device, len(image), dtypes.uint8, "program")
     cached = _amd_program_cache[key] = prg.replace(src=(buf.after(make_binary_patch(buf, bytes(image))),), arg=(data, prg.arg))
   return cached
 
 class AMDAllocator(HCQAllocator['AMDDevice']):
   def __init__(self, dev:AMDDevice):
-    super().__init__(dev, supports_copy_from_disk=dev.has_copy_queue, supports_transfer=dev.has_copy_queue and not dev.is_usb())
+    super().__init__(dev, supports_copy_from_disk=dev.has_sdma_queue, supports_transfer=dev.has_sdma_queue and not dev.is_usb())
 
   def _alloc(self, size:int, options:BufferSpec) -> HCQ2Buffer:
-    return self.dev.iface.alloc(size, host=options.host, uncached=options.uncached, cpu_access=options.cpu_access or not self.dev.has_copy_queue)
+    return self.dev.iface.alloc(size, host=options.host, uncached=options.uncached, cpu_access=options.cpu_access or not self.dev.has_sdma_queue)
 
   def _do_free(self, opaque, options:BufferSpec): self.dev.iface.free(opaque)
 
   def _do_map(self, buf:HCQ2Buffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
 
-  def _do_unmap(self, buf:HCQ2Buffer): self.dev.iface.unmap(buf)
-
 @dataclass
 class AMDQueueDesc:
   ring: Buffer; read_ptr: Buffer; write_ptr: Buffer; doorbell: Buffer; put_value: Buffer  # noqa: E702
@@ -390,24 +385,15 @@ class KFDIface:
     return hcqbuf
 
   def free(self, mem):
-    self._unmap(mem)
-    if mem.va_addr: FileIOInterface.munmap(mem.va_addr, mem.size)
-    kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
-
-  def unmap(self, mem):
-    self._unmap(mem)
-    if getattr(mem, '_owns_kfd_handle', False): kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
-
-  def _unmap(self, mem):
     gpus = (ctypes.c_int32 * 1)(self.gpu_id)
     stm = kfd.AMDKFD_IOC_UNMAP_MEMORY_FROM_GPU(self.kfd, handle=mem.meta.handle, device_ids_array_ptr=ctypes.addressof(gpus), n_devices=1)
     assert stm.n_success == 1
+    if mem.owner == self.dev:
+      if mem.va_addr: FileIOInterface.munmap(mem.va_addr, mem.size)
+      kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
 
   def map(self, mem):
-    if mem.owner is not None and mem.owner._is_cpu():
-      mapped = self.alloc(mem.size, host=True, cpu_addr=mem.va_addr)
-      mapped._owns_kfd_handle = True
-      return mapped
+    if mem.owner is not None and mem.owner._is_cpu(): return self.alloc(mem.size, host=True, cpu_addr=mem.va_addr)
 
     c_gpus = (ctypes.c_int32 * 1)(self.gpu_id)
     stm = kfd.AMDKFD_IOC_MAP_MEMORY_TO_GPU(self.kfd, handle=mem.meta.handle, device_ids_array_ptr=ctypes.addressof(c_gpus), n_devices=1)
@@ -479,7 +465,6 @@ class PCIIface(PCIIfaceBase):
 
   def require_profile_mode(self): return True
   def is_wgp_active(self, xcc, se, sa, wgp) -> bool: return True # TODO: account for WGP disablement on some asics.
-  def unmap(self, mem): self.free(mem)
 
   def _compute_props(self):
     self.ip_versions = self.dev_impl.ip_ver
@@ -520,12 +505,11 @@ class PCIIface(PCIIfaceBase):
     if drain_only: d.iface.dev_impl.ih.drain()
     else: d.iface.dev_impl.ih.interrupt_handler()
 
-    if reset and d.iface.dev_impl.recover(force=True):
+    if reset and d.iface.dev_impl.recover():
       cq = d.compute_queue
       for b in (cq.put_value, cq.read_ptr, cq.write_ptr): b._buf.view.view(fmt='Q')[0] = 0
       d.iface.dev_impl.gfx.setup_ring(*cq.params)
-      d.signal('timeline')._buf.cpu_view().mv.cast('Q')[0] = \
-        d.signal('value', 1).as_memoryview(force_zero_copy=True, no_sync=True).cast('Q')[0] - 1
+      d.timeline_signal()._buf.cpu_view().mv.cast('Q')[0] = d.timeline_value().as_memoryview(force_zero_copy=True).cast('Q')[0] - 1
 
   def sleep(self, timeout):
     if hasattr(self.pci_dev, 'irq_poller') and self.pci_dev.irq_poller is not None and (events_cnt:=len(self.pci_dev.irq_poller.poll(timeout))):
@@ -551,12 +535,9 @@ class AMDDevice(HCQ2Compiled):
   ])
 
   timestamp_divider = 100.0  # AMD GPU clock: ticks/us
-  max_scratch_psize = 0
 
   ifaces = [KFDIface, PCIIface, _mock(KFDIface, "MOCKIface"), _mock(KFDIface), _mock(PCIIface)]
 
-  def device_props(self): return self.iface.props
-
   def is_am(self) -> bool: return isinstance(self.iface, (PCIIface,))
   def is_usb(self) -> bool: return False
 
@@ -593,7 +574,7 @@ class AMDDevice(HCQ2Compiled):
 
     self.max_copy_size = 0x40000000 if self.iface.ip_versions[am.SDMA0_HWIP][0] >= 5 else 0x400000
     self.sdma_queues:dict = {}
-    self.has_copy_queue = not getenv("AMD_DISABLE_SDMA")
+    self.has_sdma_queue = True # self.sdma_queue(0) is not None, TODO: think of this
 
     super().__init__(device, AMDAllocator(self), [HIPRenderer, AMDLLVMRenderer, HIPCCRenderer], None, can_recover=self.is_am(), arch=self.arch)
 
@@ -647,7 +628,10 @@ class AMDDevice(HCQ2Compiled):
 
     qname = f"{'COPY' if queue_type == kfd.KFD_IOC_QUEUE_TYPE_SDMA else 'COMPUTE'}:{idx}"
     self.pm_bufferize = PatternMatcher([
-      (UPat(Ops.PARAM, tag=f"{qname}_{name}"), lambda ctx, b=getattr(queue, name): b) for name in ["ring", "write_ptr", "doorbell", "put_value"]
+      (UPat(Ops.PARAM, tag={(qname, name)}), lambda ctx, b=getattr(queue, name): b) for name in ["ring", "write_ptr", "doorbell", "put_value"]
+    ] + [
+      (UPat(Ops.PARAM, tag={(qname, "timeline_signal")}), lambda ctx, q=qname: ctx[0].timeline_signal(q)),
+      (UPat(Ops.PARAM, tag={(qname, "timeline_value")}), lambda ctx, q=qname: ctx[0].timeline_value(q)),
     ]) + self.pm_bufferize
 
     return queue
@@ -706,7 +690,7 @@ class AMDDevice(HCQ2Compiled):
     return tmpring
 
   def scratch_buffer(self, private_segment_size):
-    AMDDevice.max_scratch_psize = private_segment_size = max(private_segment_size, 128, AMDDevice.max_scratch_psize)
+    private_segment_size = max(private_segment_size, 128)
     if self.max_private_segment_size < private_segment_size:
       lanes_per_wave = 64 # wave64
       mem_alignment_size = 256 if self.target[0] != 9 else 1024

extra/llama_kernels/__init__.py

@@ -20,6 +20,11 @@ def local_abs_max(x:Tensor) -> Tensor:
   fxn = _local_abs_max_fxn(param.uop, x.device)
   return Tensor(fxn[0].uop.call(x.uop).gettuple(0))
 
+def scalar_amax(amax_buf:Tensor) -> Tensor:
+  if isinstance(amax_buf.device, tuple):
+    return local_abs_max(amax_buf).detach()
+  return amax_buf.max().detach()
+
 def shard_shape(shape:tuple, axis:int, ndev:int) -> list:
   s = list(shape)
   s[axis] //= ndev
@@ -31,12 +36,12 @@ def dname_of(device) -> str:
 
 def alloc_like(shape, dtype, device, axis=None) -> Tensor:
   if isinstance(device, tuple) and axis is not None:
-    return Tensor(Tensor.invalids(*shard_shape(shape, axis, len(device)), dtype=dtype, device=device).uop.unshard(axis), device=device)
+    return Tensor(Tensor.invalids(*shard_shape(shape, axis, len(device)), dtype=dtype, device=device).uop.multi(axis), device=device)
   return Tensor.invalids(*shape, dtype=dtype, device=device)
 
 def alloc_local(shape, dtype, device, axis=None) -> Tensor:
   if isinstance(device, tuple) and axis is not None:
-    return Tensor(Tensor.invalids(*shape, dtype=dtype, device=device).uop.unshard(0), device=device)
+    return Tensor(Tensor.invalids(*shape, dtype=dtype, device=device).uop.multi(0), device=device)
   return Tensor.invalids(*shape, dtype=dtype, device=device)
 
 def compile_hip(src:str, defines:list[str]):

extra/llama_kernels/cast_amax/__init__.py

@@ -3,7 +3,7 @@ import functools, pathlib
 from tinygrad import Tensor, dtypes
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
 from tinygrad.renderer import Estimates
-from extra.llama_kernels import NUM_WG, THREADS_PER_WG, compile_cpp, alloc_like, dname_of
+from extra.llama_kernels import NUM_WG, THREADS_PER_WG, compile_cpp, alloc_like, alloc_local, scalar_amax, dname_of
 
 # module-level mailbox: grad_xw13 UOp -> (grad_xw13_fp8 UOp, delayed amax UOp)
 # lets cdna_asm_gemm's bwd reuse the fp8 companion produced by the fused silu_mul bwd kernel
@@ -11,13 +11,13 @@ from extra.llama_kernels import NUM_WG, THREADS_PER_WG, compile_cpp, alloc_like,
 _grad_fp8_mailbox:dict[UOp, tuple[UOp, UOp]] = {}
 
 @functools.cache
-def _custom_fused_bwd_w13(grad_xw13_fp8:UOp, grad_amax_next:UOp, grad_amax:UOp,
+def _custom_fused_bwd_w13(grad_xw13_fp8:UOp, grad_amax_buf:UOp, grad_amax:UOp,
                           xw13:UOp, grad_x2:UOp, amax_state:UOp, grad_amax_state:UOp, dname:str) -> UOp:
   hidden = xw13.shape[2] // 2
   n_elems = xw13.shape[0] * xw13.shape[1] * hidden
   threads, workgroups = UOp.special(THREADS_PER_WG, "lidx0"), UOp.special(NUM_WG, "gidx0")
-  mem = n_elems * 2 * 3 + n_elems * 2 + 4 + 4
-  sink = UOp.sink(grad_xw13_fp8.base, grad_amax_next.base, grad_amax.base,
+  mem = n_elems * 2 * 3 + n_elems * 2 + NUM_WG * 4 + 4
+  sink = UOp.sink(grad_xw13_fp8.base, grad_amax_buf.base, grad_amax.base,
                   xw13.base, grad_x2.base, amax_state.base, grad_amax_state.base, threads, workgroups,
                   arg=KernelInfo(f"fused_silu_mul_bwd_w13_{n_elems}", estimates=Estimates(ops=10*n_elems, mem=mem)))
   src, lib = compile_cpp(pathlib.Path(__file__).parent, "cast_amax_bwd_w13.cpp", n_elems, hidden)
@@ -25,14 +25,14 @@ def _custom_fused_bwd_w13(grad_xw13_fp8:UOp, grad_amax_next:UOp, grad_amax:UOp,
                                UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=lib)))
 
 @functools.cache
-def _custom_fused_cast_amax_w13(fp8_out:UOp, amax_out:UOp, xw13:UOp, amax_state:UOp, grad_amax_state:UOp,
+def _custom_fused_cast_amax_w13(fp8_out:UOp, amax_buf:UOp, xw13:UOp, amax_state:UOp, grad_amax_state:UOp,
                                 next_grad_amax_state:UOp, dname:str) -> UOp:
   # NOTE: grad_amax_state is plumbed through as an unused fwd input so the bwd kernel can read it via kernel.src
   hidden = xw13.shape[2] // 2
   n_elems = xw13.shape[0] * xw13.shape[1] * hidden
   threads, workgroups = UOp.special(THREADS_PER_WG, "lidx0"), UOp.special(NUM_WG, "gidx0")
-  mem = n_elems * 2 * 2 + n_elems + 4
-  sink = UOp.sink(fp8_out.base, amax_out.base, xw13.base, amax_state.base, threads, workgroups,
+  mem = n_elems * 2 * 2 + n_elems + NUM_WG * 4
+  sink = UOp.sink(fp8_out.base, amax_buf.base, xw13.base, amax_state.base, threads, workgroups,
                   arg=KernelInfo(f"fused_silu_mul_cast_amax_w13_{n_elems}", estimates=Estimates(ops=5*n_elems, mem=mem)))
   src, lib = compile_cpp(pathlib.Path(__file__).parent, "cast_amax_fwd_w13.cpp", n_elems, hidden)
   return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)),
@@ -43,23 +43,26 @@ def _fused_quantize_bwd_w13(gradient:UOp, kernel:UOp):
   device = xw13.device
   axis = xw13.axis if isinstance(device, tuple) else None
   grad_xw13_fp8 = alloc_like(xw13.shape, dtypes.fp8e4m3,  device, axis)
-  grad_amax_next = Tensor(next_grad_amax_state, device=device)
+  grad_amax_buf = alloc_local((NUM_WG,), dtypes.float32,  device, axis)
   grad_amax_state_t = Tensor(grad_amax_state, device=device)
   fxn = functools.partial(_custom_fused_bwd_w13, dname=dname_of(device))
   grad_amax = grad_amax_state_t.empty_like()
-  grad_xw13_fp8, grad_amax_next, grad_amax, *_ = Tensor.custom_kernel(
-    grad_xw13_fp8, grad_amax_next, grad_amax,
+  grad_xw13_fp8, grad_amax_buf, grad_amax, *_ = Tensor.custom_kernel(
+    grad_xw13_fp8, grad_amax_buf, grad_amax,
     Tensor(xw13, device=device), Tensor(gradient, device=device).cast(dtypes.bfloat16),
     Tensor(amax_state, device=device), grad_amax_state_t, fxn=fxn)
   grad_xw13_uop = grad_xw13_fp8.uop.cast(dtypes.bfloat16)
+  new_grad_amax = scalar_amax(grad_amax_buf)
+  store_effect = next_grad_amax_state.store(new_grad_amax.uop)
   assert grad_xw13_fp8.uop.op is Ops.AFTER, f"expected AFTER, got {grad_xw13_fp8.uop.op}"
+  grad_xw13_fp8_uop = grad_xw13_fp8.uop.replace(src=grad_xw13_fp8.uop.src + (store_effect,))
   # Stash fp8 companion for cdna_asm_gemm's bwd to attach to grad_a.
-  _grad_fp8_mailbox[grad_xw13_uop] = (grad_xw13_fp8.uop, grad_amax_state_t.uop)
+  _grad_fp8_mailbox[grad_xw13_uop] = (grad_xw13_fp8_uop, grad_amax_state_t.uop)
   return (None, None, grad_xw13_uop, None, None, None)
 
 def fused_quantize_fp8_w13(xw13:Tensor, amax_state:Tensor, fp8_dtype, grad_amax_state:Tensor,
-                           next_grad_amax_state:Tensor, amax_out:Tensor) -> Tensor:
-  # NOTE: silu(xw1)*xw3 -> fp8 + amax over fused xw13 layout. Returns fp8.
+                           next_grad_amax_state:Tensor) -> tuple[Tensor, Tensor]:
+  # NOTE: silu(xw1)*xw3 -> fp8 + amax over fused xw13 layout. Returns (fp8, new_amax)
   # grad_amax_state: delayed amax for grad_xw13 fp8 quantization in the backward.
   assert xw13.dtype == dtypes.bfloat16, f"expected bf16, got {xw13.dtype}"
   MBS, SEQ, H2 = xw13.shape
@@ -67,7 +70,8 @@ def fused_quantize_fp8_w13(xw13:Tensor, amax_state:Tensor, fp8_dtype, grad_amax_
   HIDDEN = H2 // 2
   axis = xw13.uop.axis if isinstance(xw13.device, tuple) else None
   fp8_out  = alloc_like((MBS, SEQ, HIDDEN), fp8_dtype,      xw13.device, axis)
+  amax_buf = alloc_local((NUM_WG,),         dtypes.float32, xw13.device, axis)
   fxn = functools.partial(_custom_fused_cast_amax_w13, dname=dname_of(xw13.device))
-  fp8_out, amax_out, *_ = Tensor.custom_kernel(fp8_out, amax_out, xw13, amax_state, grad_amax_state, next_grad_amax_state,
+  fp8_out, amax_buf, *_ = Tensor.custom_kernel(fp8_out, amax_buf, xw13, amax_state, grad_amax_state, next_grad_amax_state,
                                                 fxn=fxn, grad_fxn=_fused_quantize_bwd_w13)
-  return fp8_out
+  return fp8_out, scalar_amax(amax_buf)

extra/llama_kernels/cast_amax/cast_amax_bwd_w13.cpp

@@ -23,14 +23,14 @@ static_assert(HIDDEN % VEC == 0, "HIDDEN must be divisible by VEC");
 
 // fused silu*mul backward, three outputs in a single HBM pass:
 //   1) fp8  grad_xw13_fp8  — delayed-scale quantize using grad_amax_state (mailbox to matmul bwd)
-//   2) fp32 grad_amax_next — scalar |grad_xw13| via global atomic max
+//   2) fp32 grad_amax_buf  — per-WG partial |grad_xw13|, reduced into next step's grad_amax_state
 //   3) fp32 grad_amax_out  — delayed grad amax used for quantize/GEMM epilogue scale
 // grad_amax_state is read for the fp8 scale. The store of new_grad_amax into grad_amax_state's
 // buffer is built in Python as a separate effect and threaded into grad_a via .after(store).
 extern "C" __global__ __launch_bounds__(THREADS_PER_WG) void
 fused_silu_mul_bwd_w13(
     __hip_fp8_storage_t*  __restrict__ grad_xw13_fp8_out,    // fp8,  2*N_ELEMS
-    float*                __restrict__ grad_amax_next,       // fp32 scalar, initialized to 0 before launch
+    float*                __restrict__ grad_amax_buf,        // fp32, NUM_WG per-WG partials
     float*                __restrict__ grad_amax_out,        // fp32 scalar delayed grad amax
     const __hip_bfloat16* __restrict__ xw13,                 // bf16, 2*N_ELEMS
     const __hip_bfloat16* __restrict__ grad_x2,              // bf16, N_ELEMS
@@ -92,6 +92,5 @@ fused_silu_mul_bwd_w13(
     if (tid < s) sdata[tid] = fmaxf(sdata[tid], sdata[tid + s]);
     __syncthreads();
   }
-  if (tid == 0 && sdata[0] > *grad_amax_next)
-    atomicMax(reinterpret_cast<int32_t*>(grad_amax_next), __float_as_int(sdata[0]));
+  if (tid == 0) grad_amax_buf[wg] = sdata[0];
 }

extra/llama_kernels/cast_amax/cast_amax_fwd_w13.cpp

@@ -24,7 +24,7 @@ static_assert(HIDDEN % VEC == 0, "HIDDEN must be divisible by VEC (so VEC loads
 extern "C" __global__ __launch_bounds__(THREADS_PER_WG) void
 fused_silu_mul_cast_amax_w13(
     __hip_fp8_storage_t*  __restrict__ fp8_out,         // fp8, N_ELEMS
-    float*                __restrict__ amax_out,        // fp32 scalar, initialized to 0 before launch
+    float*                __restrict__ amax_buf,        // fp32, NUM_WG (per-WG amaxes)
     const __hip_bfloat16* __restrict__ xw13,            // bf16, 2*N_ELEMS
     const float*          __restrict__ amax_state)      // fp32 scalar
 {
@@ -67,7 +67,7 @@ fused_silu_mul_cast_amax_w13(
     *reinterpret_cast<uint64_t*>(&fp8_out[base]) = *reinterpret_cast<uint64_t*>(out);
   }
 
-  // LDS tree reduction: per-workgroup amax, then global atomic into the scalar.
+  // LDS tree reduction: per-workgroup amax
   sdata[tid] = local_max;
   __syncthreads();
   for (int s = THREADS_PER_WG / 2; s > 0; s >>= 1) {
@@ -75,5 +75,5 @@ fused_silu_mul_cast_amax_w13(
     __syncthreads();
   }
 
-  if (tid == 0 && sdata[0] > *amax_out) atomicMax(reinterpret_cast<int32_t*>(amax_out), __float_as_int(sdata[0]));
+  if (tid == 0) amax_buf[wg] = sdata[0];
 }

extra/llama_kernels/fused_ce/__init__.py

@@ -16,7 +16,7 @@ def _custom_fused_ce_loss_fwd(loss_out:UOp, max_out:UOp, lse_out:UOp, logits:UOp
   row_lse = (logits[b, s, v_lse].cast(dtypes.float) - row_max).exp().reduce(v_lse, arg=Ops.ADD).log() + row_max
 
   v_smooth = UOp.range(vocab, 3, axis_type=AxisType.REDUCE)
-  target = logits[b, s, targets[row].cast(dtypes.weakint)].cast(dtypes.float)
+  target = logits[b, s, targets[row].cast(dtypes.index)].cast(dtypes.float)
   mean_logits = logits[b, s, v_smooth].cast(dtypes.float).reduce(v_smooth, arg=Ops.ADD) / vocab
   loss = row_lse - (1.0 - label_smoothing) * target - label_smoothing * mean_logits
   stores = UOp.group(loss_out[row].store(loss), max_out[row].store(row_max), lse_out[row].store(row_lse))
@@ -32,7 +32,7 @@ def _custom_fused_ce_loss_bwd(d_logits:UOp, logits:UOp, lse:UOp, targets:UOp, sc
   s = row % seq
 
   prob = (logits[b, s, v].cast(dtypes.float) - lse[row]).exp()
-  target = v.eq(targets[row].cast(dtypes.weakint)).where(1.0 - label_smoothing, 0.0)
+  target = v.eq(targets[row].cast(dtypes.index)).where(1.0 - label_smoothing, 0.0)
   smooth = label_smoothing / vocab
   grad = (prob - target - smooth) * scale[0]
 
@@ -48,7 +48,7 @@ def _fused_ce_loss_bwd(gradient:UOp, kernel:UOp, label_smoothing:float):
     axis = logits_u.axis
     ndev = len(device)
     local_shape = tuple(s//ndev if i == axis else s for i,s in enumerate((MBS, SEQ, VOCAB)))
-    d_logits = Tensor(Tensor.invalids(*local_shape, dtype=dtypes.bfloat16, device=device).uop.unshard(axis), device=device)
+    d_logits = Tensor(Tensor.invalids(*local_shape, dtype=dtypes.bfloat16, device=device).uop.multi(axis), device=device)
     rows_per_dev = local_shape[0] * local_shape[1]
     seq_per_dev = local_shape[1]
   else:
@@ -74,11 +74,11 @@ def fused_ce_loss(logits:Tensor, targets:Tensor, label_smoothing:float=0.1) -> T
     axis = logits.uop.axis
     assert axis in (0, 1), f"unsupported sharding axis={axis} for CE loss"
     ndev = len(logits.device)
-    loss_out = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.unshard(0),
+    loss_out = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.multi(0),
                       device=logits.device)
-    max_out  = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.unshard(0),
+    max_out  = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.multi(0),
                       device=logits.device)
-    lse_out  = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.unshard(0),
+    lse_out  = Tensor(Tensor.invalids(rows // ndev, dtype=dtypes.float32, device=logits.device).uop.multi(0),
                       device=logits.device)
     local_shape = tuple(s//ndev if i == axis else s for i,s in enumerate(logits.shape))
     rows_per_dev = local_shape[0] * local_shape[1]

extra/llama_kernels/fused_rmsnorm_mul_quantize_fp8/__init__.py

@@ -3,19 +3,19 @@ import functools, pathlib
 from tinygrad import Tensor, dtypes
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
 from tinygrad.renderer import Estimates
-from extra.llama_kernels import NUM_WG, THREADS_PER_WG, alloc_like, alloc_local, dname_of, compile_hip
+from extra.llama_kernels import FP8_MAX, NUM_WG, THREADS_PER_WG, alloc_like, alloc_local, scalar_amax, dname_of, compile_hip
 
 def _src() -> str: return (pathlib.Path(__file__).parent/"fused_rmsnorm_mul_quantize_fp8.cpp").read_text()
 def _src_bwd() -> str: return (pathlib.Path(__file__).parent/"fused_rmsnorm_mul_quantize_fp8_bwd.cpp").read_text()
 
 @functools.cache
-def _custom_fwd(fp8_out:UOp, x_normed_out:UOp, rrms_out:UOp, amax_out:UOp,
+def _custom_fwd(fp8_out:UOp, x_normed_out:UOp, rrms_out:UOp, amax_buf:UOp,
                 x:UOp, weight:UOp, amax_state:UOp, dname:str, eps_val:float) -> UOp:
   MBS, SEQ, HIDDEN = x.shape
   n_elems = MBS * SEQ * HIDDEN
   threads, workgroups = UOp.special(THREADS_PER_WG, "lidx0"), UOp.special(NUM_WG, "gidx0")
-  mem = n_elems * 2 + n_elems + MBS * SEQ * 4 + n_elems + HIDDEN * 2 + 4 + 4
-  sink = UOp.sink(fp8_out.base, x_normed_out.base, rrms_out.base, amax_out.base,
+  mem = n_elems * 2 + n_elems + MBS * SEQ * 4 + n_elems + HIDDEN * 2 + NUM_WG * 4 + 4
+  sink = UOp.sink(fp8_out.base, x_normed_out.base, rrms_out.base, amax_buf.base,
                   x.base, weight.base, amax_state.base, threads, workgroups,
                   arg=KernelInfo(f"fused_rmsnorm_mul_quantize_fp8_{n_elems}_h{HIDDEN}_eps{eps_val:.0e}",
                                  estimates=Estimates(ops=6*n_elems, mem=mem)))
@@ -26,13 +26,13 @@ def _custom_fwd(fp8_out:UOp, x_normed_out:UOp, rrms_out:UOp, amax_out:UOp,
                                UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=compile_hip(src, defines))))
 
 @functools.cache
-def _custom_fwd_add(fp8_out:UOp, h_out:UOp, x_normed_out:UOp, rrms_out:UOp, amax_out:UOp,
+def _custom_fwd_add(fp8_out:UOp, h_out:UOp, x_normed_out:UOp, rrms_out:UOp, amax_buf:UOp,
                     x:UOp, residual:UOp, weight:UOp, amax_state:UOp, dname:str, eps_val:float) -> UOp:
   MBS, SEQ, HIDDEN = x.shape
   n_elems = MBS * SEQ * HIDDEN
   threads, workgroups = UOp.special(THREADS_PER_WG, "lidx0"), UOp.special(NUM_WG, "gidx0")
-  mem = n_elems * 2 * 4 + MBS * SEQ * 4 + HIDDEN * 2 + 4 + 4
-  sink = UOp.sink(fp8_out.base, h_out.base, x_normed_out.base, rrms_out.base, amax_out.base,
+  mem = n_elems * 2 * 4 + MBS * SEQ * 4 + HIDDEN * 2 + NUM_WG * 4 + 4
+  sink = UOp.sink(fp8_out.base, h_out.base, x_normed_out.base, rrms_out.base, amax_buf.base,
                   x.base, residual.base, weight.base, amax_state.base, threads, workgroups,
                   arg=KernelInfo(f"fused_add_rmsnorm_mul_quantize_fp8_{n_elems}_h{HIDDEN}_eps{eps_val:.0e}",
                                  estimates=Estimates(ops=7*n_elems, mem=mem)))
@@ -85,7 +85,7 @@ def _bwd_common(fp8_grad_u, h_grad_u, x_u, x_normed_u, rrms_u, weight_u, amax_st
   return grad_total.uop, grad_weight_uop
 
 def _fused_bwd(gradient:UOp, kernel:UOp):
-  # NOTE: fwd inputs (fp8_out, x_normed_out, rrms_out, amax_out, x, weight, amax_state)
+  # NOTE: fwd inputs (fp8_out, x_normed_out, rrms_out, amax_buf, x, weight, amax_state)
   _, x_normed_u, rrms_u, _, x_u, weight_u, amax_state_u = kernel.src[1:]
   grad_x, grad_w = _bwd_common(gradient, None, x_u, x_normed_u, rrms_u, weight_u, amax_state_u, kernel)
   return (None, None, None, None, grad_x, grad_w, None)
@@ -112,9 +112,8 @@ def _fused_add_bwd(*args, **kwargs):
   grad_h, grad_w = _bwd_common(fp8_grad_u, h_grad_u, x_u, x_normed_u, rrms_u, weight_u, amax_state_u, kernel)
   return (None, None, None, None, None, grad_h, grad_h, grad_w, None)
 
-def fused_rmsnorm_mul_quantize_fp8(x:Tensor, weight:Tensor, amax_state:Tensor, eps:float, fp8_dtype,
-                                   amax_out:Tensor) -> tuple[Tensor, Tensor, Tensor]:
-  # NOTE: rmsnorm(x) * weight -> fp8 + amax. Returns (fp8, x_normed, rrms).
+def fused_rmsnorm_mul_quantize_fp8(x:Tensor, weight:Tensor, amax_state:Tensor, eps:float, fp8_dtype) -> tuple[Tensor, Tensor, Tensor, Tensor]:
+  # NOTE: rmsnorm(x) * weight -> fp8 + amax. Returns (fp8, new_amax, x_normed, rrms).
   # x_normed + rrms are saved for the rmsnorm backward (also recomputed here from x regs).
   assert x.dtype == dtypes.bfloat16 and weight.dtype == dtypes.bfloat16
   assert x.shape[-1] == weight.shape[-1], f"HIDDEN mismatch: x={x.shape}, weight={weight.shape}"
@@ -124,15 +123,16 @@ def fused_rmsnorm_mul_quantize_fp8(x:Tensor, weight:Tensor, amax_state:Tensor, e
   fp8_out      = alloc_like((MBS, SEQ, HIDDEN), fp8_dtype,       x.device, axis)
   x_normed_out = alloc_like((MBS, SEQ, HIDDEN), dtypes.bfloat16, x.device, axis)
   rrms_out     = alloc_like((MBS, SEQ),         dtypes.float32,  x.device, axis)
+  amax_buf     = alloc_local((NUM_WG,),         dtypes.float32,  x.device, axis)
   fxn = functools.partial(_custom_fwd, dname=dname_of(x.device), eps_val=eps)
-  fp8_out, x_normed_out, rrms_out, amax_out, *_ = Tensor.custom_kernel(
-    fp8_out, x_normed_out, rrms_out, amax_out, x, weight, amax_state, fxn=fxn, grad_fxn=_fused_bwd)
-  return fp8_out, x_normed_out, rrms_out
+  fp8_out, x_normed_out, rrms_out, amax_buf, *_ = Tensor.custom_kernel(
+    fp8_out, x_normed_out, rrms_out, amax_buf, x, weight, amax_state, fxn=fxn, grad_fxn=_fused_bwd)
+  return fp8_out, scalar_amax(amax_buf), x_normed_out, rrms_out
 
 def fused_add_rmsnorm_mul_quantize_fp8(x:Tensor, residual:Tensor, weight:Tensor, amax_state:Tensor,
-                                       eps:float, fp8_dtype, amax_out:Tensor) -> tuple[Tensor, Tensor, Tensor, Tensor]:
+                                       eps:float, fp8_dtype) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
   # NOTE: h = x + residual; y_normed = rmsnorm(h); fp8 = quantize(y_normed * weight).
-  # Returns (fp8, h, x_normed, rrms). h is also written so downstream can
+  # Returns (fp8, new_amax, h, x_normed, rrms). h is also written so downstream can
   # reuse it without recomputing x+residual — eliminates the separate residual-add kernel.
   assert x.dtype == dtypes.bfloat16 and residual.dtype == dtypes.bfloat16 and weight.dtype == dtypes.bfloat16
   assert x.shape == residual.shape
@@ -143,8 +143,9 @@ def fused_add_rmsnorm_mul_quantize_fp8(x:Tensor, residual:Tensor, weight:Tensor,
   h_out        = alloc_like((MBS, SEQ, HIDDEN), dtypes.bfloat16, x.device, axis)
   x_normed_out = alloc_like((MBS, SEQ, HIDDEN), dtypes.bfloat16, x.device, axis)
   rrms_out     = alloc_like((MBS, SEQ),         dtypes.float32,  x.device, axis)
+  amax_buf     = alloc_local((NUM_WG,),         dtypes.float32,  x.device, axis)
   fxn = functools.partial(_custom_fwd_add, dname=dname_of(x.device), eps_val=eps)
-  fp8_out, h_out, x_normed_out, rrms_out, amax_out, *_ = Tensor.custom_kernel(
-    fp8_out, h_out, x_normed_out, rrms_out, amax_out, x, residual, weight, amax_state,
+  fp8_out, h_out, x_normed_out, rrms_out, amax_buf, *_ = Tensor.custom_kernel(
+    fp8_out, h_out, x_normed_out, rrms_out, amax_buf, x, residual, weight, amax_state,
     fxn=fxn, grad_fxn=_fused_add_bwd)
-  return fp8_out, h_out, x_normed_out, rrms_out
+  return fp8_out, scalar_amax(amax_buf), h_out, x_normed_out, rrms_out

extra/llama_kernels/fused_rmsnorm_mul_quantize_fp8/fused_rmsnorm_mul_quantize_fp8.cpp

@@ -7,7 +7,7 @@
 //   fp8 = fp8_sat(y * (FP8_MAX / amax_state))
 // Also writes:
 //   rrms[row]        — saved for the rmsnorm backward
-//   amax_out         — scalar |y| via global atomic max
+//   amax_buf[wg]     — per-WG |y| partials, reduced later to update amax_state
 //
 // Layout: one WG per row, ROWS_PER_WG rows per WG via grid-stride (ROWS = N_ELEMS / HIDDEN).
 // Each thread handles HIDDEN / THREADS_PER_WG elements per row.
@@ -48,7 +48,7 @@ fused_add_rmsnorm_mul_quantize_fp8(
     __hip_bfloat16*       __restrict__ h_out,           // bf16, ROWS*HIDDEN — x + residual (saved for downstream)
     __hip_bfloat16*       __restrict__ x_normed_out,    // bf16, ROWS*HIDDEN
     float*                __restrict__ rrms_out,        // fp32, ROWS
-    float*                __restrict__ amax_out,        // fp32 scalar, initialized to 0 before launch
+    float*                __restrict__ amax_buf,        // fp32, NUM_WG
     const __hip_bfloat16* __restrict__ x,               // bf16, ROWS*HIDDEN
     const __hip_bfloat16* __restrict__ residual,        // bf16, ROWS*HIDDEN — added into x before rmsnorm
     const __hip_bfloat16* __restrict__ weight,          // bf16, HIDDEN
@@ -60,7 +60,7 @@ fused_rmsnorm_mul_quantize_fp8(
     __hip_fp8_storage_t*  __restrict__ fp8_out,         // fp8, ROWS*HIDDEN
     __hip_bfloat16*       __restrict__ x_normed_out,    // bf16, ROWS*HIDDEN (saved for rmsnorm bwd)
     float*                __restrict__ rrms_out,        // fp32, ROWS (fp32 to match rmsnorm_bwd.cpp expectation)
-    float*                __restrict__ amax_out,        // fp32 scalar, initialized to 0 before launch
+    float*                __restrict__ amax_buf,        // fp32, NUM_WG per-WG partials
     const __hip_bfloat16* __restrict__ x,               // bf16, ROWS*HIDDEN
     const __hip_bfloat16* __restrict__ weight,          // bf16, HIDDEN (per-hidden scale)
     const float*          __restrict__ amax_state)      // fp32 scalar
@@ -144,12 +144,12 @@ fused_rmsnorm_mul_quantize_fp8(
     __syncthreads();  // before next row's sum_sq reduce reuses sdata
   }
 
-  // Final per-WG amax reduce, then global atomic into the scalar.
+  // Final per-WG amax reduce.
   sdata[tid] = local_max;
   __syncthreads();
   for (int s = THREADS_PER_WG / 2; s > 0; s >>= 1) {
     if (tid < s) sdata[tid] = fmaxf(sdata[tid], sdata[tid + s]);
     __syncthreads();
   }
-  if (tid == 0 && sdata[0] > *amax_out) atomicMax(reinterpret_cast<int32_t*>(amax_out), __float_as_int(sdata[0]));
+  if (tid == 0) amax_buf[wg] = sdata[0];
 }

extra/llama_kernels/quantize_fp8_delayed/__init__.py

@@ -3,20 +3,21 @@ from tinygrad import Tensor, dtypes
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import prod
 from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from extra.llama_kernels import FP8_MAX, NUM_WG, THREADS_PER_WG, alloc_like
+from extra.llama_kernels import FP8_MAX, NUM_WG, THREADS_PER_WG, alloc_like, alloc_local, scalar_amax
 
 @functools.cache
-def _custom_quantize_fp8_with_amax(fp8_out:UOp, amax_out:UOp, x:UOp, amax_state:UOp, device=None) -> UOp:
+def _custom_quantize_fp8_with_amax(fp8_out:UOp, amax_partial:UOp, x:UOp, amax_state:UOp) -> UOp:
   VEC = 8
   n_elems = prod(x.shape)
   assert n_elems % (NUM_WG * THREADS_PER_WG * VEC) == 0
+  assert amax_partial.shape[0] == NUM_WG
 
   x = x.reshape(n_elems)
   fp8_out = fp8_out.reshape(n_elems)
 
   wg = UOp.range(NUM_WG, 0, AxisType.GLOBAL)
   tid = UOp.range(THREADS_PER_WG, 1, AxisType.LOCAL)
-  it = UOp.range((n_elems // VEC) // (NUM_WG * THREADS_PER_WG), 2, AxisType.WEAK)
+  it = UOp.range((n_elems // VEC) // (NUM_WG * THREADS_PER_WG), 2, AxisType.LOOP)
   lane = UOp.range(VEC, 3, AxisType.UNROLL)
 
   idx = (((it * NUM_WG + wg) * THREADS_PER_WG + tid) * VEC) + lane
@@ -36,22 +37,17 @@ def _custom_quantize_fp8_with_amax(fp8_out:UOp, amax_out:UOp, x:UOp, amax_state:
   lmax_val = lmax.after(lmax_store.end(it))[0]
 
   lds = UOp.placeholder((THREADS_PER_WG,), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL)
-  lds = lds.after(lds[tid].store(lmax_val))
+  lds = lds.after(lds[tid].store(lmax_val).barrier())
 
   step = THREADS_PER_WG // 2
   while step:
     active = tid < step
     other = lds[(tid + step).valid(active)].load()
-    lds = lds.after(lds[tid.valid(active)].store(lds[tid].maximum(other)))
+    lds = lds.after(lds[tid.valid(active)].store(lds[tid].maximum(other)).barrier())
     step //= 2
 
-  device = device[0].split(":")[0] if isinstance(device, tuple) else device.split(":")[0]
-  if device in {"AMD", "NULL"}: atomic_arg = "if ({2} > {3}) __hip_atomic_fetch_max((int*){0}, {1}, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);"
-  else: raise NotImplementedError(f"no atomic max for device {device}")
-  amax_idx = amax_out.reshape((1,)).index(UOp.const(0))
-  max_val = lds[0].load()
-  atomic = UOp(Ops.CUSTOM, dtypes.void, (amax_idx, max_val.bitcast(dtypes.int32), max_val, amax_idx.load()), arg=atomic_arg)
-  return atomic.end(tid, wg).sink(arg=KernelInfo(f"quantize_fp8_with_amax_{n_elems}", opts_to_apply=()))
+  amax_store = amax_partial[tid.eq(0).where(wg, UOp.invalid())].store(lds[0])
+  return amax_store.end(tid, wg).sink(arg=KernelInfo(f"quantize_fp8_with_amax_{n_elems}", opts_to_apply=()))
 
 @functools.cache
 def _custom_quantize_fp8_scalar(fp8_out:UOp, x:UOp, amax_state:UOp) -> UOp:
@@ -73,19 +69,25 @@ def _quantize_fp8_delayed_bwd(gradient:UOp, kernel:UOp):
   grad_x = (Tensor(gradient, device=device).float() * scale).cast(dtypes.bfloat16)
   return (None, None, grad_x.uop, None)
 
-def quantize_fp8_delayed(x:Tensor, amax_state:Tensor, amax_out:Tensor, fp8_dtype=dtypes.fp8e4m3) -> tuple[Tensor, Tensor]:
-  # NOTE: one-pass bf16 -> fp8 quantize with delayed scaling.
-  # Fused kernel reads x once and writes fp8 + scalar amax via global atomic max.
+def quantize_fp8_delayed(x:Tensor, amax_state:Tensor, fp8_dtype=dtypes.fp8e4m3) -> tuple[Tensor, Tensor, Tensor, UOp]:
+  # NOTE: one-pass bf16 -> fp8 quantize with delayed scaling. Returns (fp8, inv_scale, new_amax, store_effect).
+  # Fused kernel reads x once and writes fp8 + per-WG |x| partials (then a small reduce produces scalar new_amax).
+  # store_effect writes new_amax into amax_state's buffer — the caller must thread it into a realized
+  # output via `.after(store_effect)`. Calling `amax_state.assign(new_amax)` inside a grad_fxn does
+  # NOT work because .assign mutates only the temp Tensor's .uop, not the original layer-owned buffer.
   assert x.dtype == dtypes.bfloat16, f"expected bf16, got {x.dtype}"
   axis = x.uop.axis if isinstance(x.device, tuple) else None
   fp8_out      = alloc_like(x.shape,  fp8_dtype,      x.device, axis)
   n_elems = prod(x.uop.shard_shape)
   assert n_elems % NUM_WG == 0, f"{n_elems=} must divide over {NUM_WG=}"
-  fxn = functools.partial(_custom_quantize_fp8_with_amax, device=x.device)
-  fp8_out, amax_out, *_ = Tensor.custom_kernel(fp8_out, amax_out, x, amax_state,
-                                                fxn=fxn, grad_fxn=_quantize_fp8_delayed_bwd)
+  amax_partial = alloc_local((NUM_WG,), dtypes.float32, x.device, axis)
+  fxn = _custom_quantize_fp8_with_amax
+  fp8_out, amax_partial, *_ = Tensor.custom_kernel(fp8_out, amax_partial, x, amax_state,
+                                                    fxn=fxn, grad_fxn=_quantize_fp8_delayed_bwd)
+  new_amax = scalar_amax(amax_partial)
   inv_scale = (amax_state.float() + 1e-8) / FP8_MAX
-  return fp8_out, inv_scale
+  store_effect = amax_state.uop.store(new_amax.uop)
+  return fp8_out, inv_scale, new_amax, store_effect
 
 def quantize_fp8_scalar(x:Tensor, amax_state:Tensor, fp8_dtype=dtypes.fp8e4m3) -> Tensor:
   # NOTE: pure one-pass bf16 -> fp8 quantize with delayed scalar scale. No amax computation.

extra/llama_kernels/quantize_mxfp4/__init__.py

@@ -1,36 +0,0 @@
-import functools, math, pathlib
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo
-from tinygrad.renderer import Estimates
-from extra.llama_kernels import alloc_like, compile_hip
-
-@functools.cache
-def _custom_quantize_mxfp4(row_fp4:UOp, row_scale:UOp, col_fp4:UOp, col_scale:UOp, x:UOp, *, shuffle_row:bool, shuffle_col:bool) -> UOp:
-  M, N = math.prod(x.shape[:-1]), x.shape[-1]
-  assert M % 256 == 0 and N % 256 == 0, f"MXFP4 quantization requires multiples of 256, got {x.shape}"
-  name = f"quantize_mxfp4_{int(shuffle_row)}_{int(shuffle_col)}_{M}_{N}"
-  mem = M*N*2 + M*N + M*N//16  # read bf16, write row+col fp4 + e8m0
-  outputs = (row_fp4, row_scale, col_fp4, col_scale)
-  sink = UOp.sink(*(o.base for o in outputs), x.base,
-                  *(UOp(Ops.CUSTOM, dtypes.void, (o.base.index(0),), arg="") for o in outputs),
-                  UOp.special(256, "lidx0"), UOp.special(M//128, "gidx0"), UOp.special(N//64, "gidx1"),
-                  arg=KernelInfo(name, estimates=Estimates(ops=12*M*N, mem=mem)))
-  src = (pathlib.Path(__file__).parent/"quantize_mxfp4.cpp").read_text()
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src),
-    UOp(Ops.BINARY, arg=compile_hip(src, [f"-DKERNEL_NAME={name}", f"-DM_DIM={M}", f"-DN_DIM={N}",
-                                             f"-DSHUFFLE_ROWWISE_FP4_VALUE={int(shuffle_row)}",
-                                             f"-DSHUFFLE_COLWISE_FP4_VALUE={int(shuffle_col)}"]))))
-
-def quantize_mxfp4(x:Tensor, *, shuffle_row:bool=False, shuffle_col:bool=False, flatten_row:bool=False) -> tuple[Tensor, Tensor, Tensor, Tensor]:
-  assert x.dtype == dtypes.bfloat16 and x.ndim >= 2, f"expected BF16 matrix, got {x.dtype} {x.shape}"
-  M, N = math.prod(x.shape[:-1]), x.shape[-1]
-  assert M % 256 == 0 and N % 256 == 0, f"MXFP4 quantization requires multiples of 256, got {x.shape}"
-  axis = x.uop.axis if isinstance(x.device, tuple) else None
-  row_axis = 0 if flatten_row and axis is not None else axis
-  col_axis = None if axis is None else (0 if axis == x.ndim-1 else 1)
-  outputs = (alloc_like((M, N//2) if flatten_row else (*x.shape[:-1], N//2), dtypes.uint8, x.device, row_axis),
-             alloc_like((M, N//32) if flatten_row else (*x.shape[:-1], N//32), dtypes.uint8, x.device, row_axis),
-             alloc_like((N, M//2), dtypes.uint8, x.device, col_axis),
-             alloc_like((N, M//32), dtypes.uint8, x.device, col_axis))
-  fxn = functools.partial(_custom_quantize_mxfp4, shuffle_row=shuffle_row, shuffle_col=shuffle_col)
-  return tuple(Tensor.custom_kernel(*outputs, x, fxn=fxn)[:4])

extra/llama_kernels/quantize_mxfp4/quantize_mxfp4.cpp

@@ -1,226 +0,0 @@
-// Copyright (c) 2025-2026, Advanced Micro Devices, Inc. All rights reserved.
-// SPDX-License-Identifier: MIT
-
-#include <hip/hip_runtime.h>
-#include <cstdint>
-
-#if !defined(KERNEL_NAME) || !defined(M_DIM) || !defined(N_DIM) || !defined(SHUFFLE_ROWWISE_FP4_VALUE) || \
-    !defined(SHUFFLE_COLWISE_FP4_VALUE)
-#error kernel dimensions and layouts must be defined
-#endif
-
-namespace {
-
-constexpr int BLOCK = 32;
-constexpr int TILE_M = 128;
-constexpr int TILE_N = 64;
-constexpr int THREADS = 256;
-constexpr int THREADS_PER_ROW = 8;
-constexpr int VALUES_PER_THREAD = 4;
-constexpr int SMEM_STRIDE = BLOCK + 2;
-constexpr int M = M_DIM;
-constexpr int N = N_DIM;
-constexpr int M_PACKED = M / 2;
-constexpr int N_PACKED = N / 2;
-constexpr int M_SCALES = M / BLOCK;
-constexpr int N_SCALES = N / BLOCK;
-constexpr bool SHUFFLE_ROWWISE_FP4 = SHUFFLE_ROWWISE_FP4_VALUE;
-constexpr bool SHUFFLE_COLWISE_FP4 = SHUFFLE_COLWISE_FP4_VALUE;
-
-static_assert(M % 256 == 0 && N % 256 == 0);
-
-struct Quantized4 {
-  uint16_t fp4;
-  uint8_t scale;
-};
-
-__device__ __forceinline__ float swizzle_xor1(float value) {
-  float result;
-  asm volatile("ds_swizzle_b32 %0, %1 offset:0x041f\n\ts_waitcnt lgkmcnt(0)" : "=v"(result) : "v"(value));
-  return result;
-}
-
-__device__ __forceinline__ float swizzle_xor2(float value) {
-  float result;
-  asm volatile("ds_swizzle_b32 %0, %1 offset:0x081f\n\ts_waitcnt lgkmcnt(0)" : "=v"(result) : "v"(value));
-  return result;
-}
-
-__device__ __forceinline__ float swizzle_xor4(float value) {
-  float result;
-  asm volatile("ds_swizzle_b32 %0, %1 offset:0x101f\n\ts_waitcnt lgkmcnt(0)" : "=v"(result) : "v"(value));
-  return result;
-}
-
-__device__ __forceinline__ float max8(float value) {
-  value = fmaxf(value, swizzle_xor4(value));
-  value = fmaxf(value, swizzle_xor2(value));
-  return fmaxf(value, swizzle_xor1(value));
-}
-
-__device__ __forceinline__ float4 load_bf16x4(const uint16_t* values) {
-  const uint32_t lo = *reinterpret_cast<const uint32_t*>(values);
-  const uint32_t hi = *reinterpret_cast<const uint32_t*>(values + 2);
-  return make_float4(__uint_as_float(lo << 16), __uint_as_float(lo & 0xffff0000u),
-                     __uint_as_float(hi << 16), __uint_as_float(hi & 0xffff0000u));
-}
-
-__device__ __forceinline__ void hadamard16(float4& value, int lane) {
-  const float a0 = value.x + value.y, a1 = value.x - value.y;
-  const float a2 = value.z + value.w, a3 = value.z - value.w;
-  value = make_float4(a0 + a2, a1 + a3, a0 - a2, a1 - a3);
-
-  const float4 xor1 = make_float4(swizzle_xor1(value.x), swizzle_xor1(value.y), swizzle_xor1(value.z), swizzle_xor1(value.w));
-  value = lane & 1 ? make_float4(xor1.x - value.x, xor1.y - value.y, xor1.z - value.z, xor1.w - value.w)
-                   : make_float4(xor1.x + value.x, xor1.y + value.y, xor1.z + value.z, xor1.w + value.w);
-
-  const float4 xor2 = make_float4(swizzle_xor2(value.x), swizzle_xor2(value.y), swizzle_xor2(value.z), swizzle_xor2(value.w));
-  value = lane & 2 ? make_float4(xor2.x - value.x, xor2.y - value.y, xor2.z - value.z, xor2.w - value.w)
-                   : make_float4(xor2.x + value.x, xor2.y + value.y, xor2.z + value.z, xor2.w + value.w);
-  value.x *= 0.25f;
-  value.y *= 0.25f;
-  value.z *= 0.25f;
-  value.w *= 0.25f;
-}
-
-__device__ __forceinline__ uint8_t e8m0_scale(float amax, float& scale) {
-  if (amax == 0.0f) {
-    scale = 1.0f;
-    return 127;
-  }
-
-  const uint32_t rounded = (__float_as_uint(amax) + 0x200000u) & 0xff800000u;
-  int exponent = static_cast<int>((rounded >> 23) & 0xff) - 129;
-  exponent = exponent < -127 ? -127 : exponent > 127 ? 127 : exponent;
-  scale = exponent == -127 ? __uint_as_float(0x00400000u) : __uint_as_float(static_cast<uint32_t>(exponent + 127) << 23);
-  return static_cast<uint8_t>(exponent + 127);
-}
-
-__device__ __forceinline__ uint16_t pack_fp4(float4 value, float scale) {
-  uint32_t lo = 0, hi = 0;
-  asm volatile("v_cvt_scalef32_pk_fp4_f32 %0, %1, %2, %3" : "+v"(lo) : "v"(value.x), "v"(value.y), "v"(scale));
-  asm volatile("v_cvt_scalef32_pk_fp4_f32 %0, %1, %2, %3" : "+v"(hi) : "v"(value.z), "v"(value.w), "v"(scale));
-  return static_cast<uint16_t>(lo | (hi << 8));
-}
-
-__device__ __forceinline__ Quantized4 quantize(float4 value, int lane) {
-  hadamard16(value, lane);
-  const float local_max = fmaxf(fmaxf(fabsf(value.x), fabsf(value.y)), fmaxf(fabsf(value.z), fabsf(value.w)));
-  float scale;
-  const uint8_t e8m0 = e8m0_scale(max8(local_max), scale);
-  return {pack_fp4(value, scale), e8m0};
-}
-
-__device__ __forceinline__ void store_scale(uint8_t* output, int row, int col, int cols, uint8_t value) {
-  const int tile = ((row >> 5) * (cols >> 3) + (col >> 3)) << 8;
-  const int offset = ((col & 3) << 6) + ((row & 15) << 2) + (((col >> 2) & 1) << 1) + ((row >> 4) & 1);
-  output[tile + offset] = value;
-}
-
-template<bool Shuffled>
-__device__ __forceinline__ void store_fp4(uint8_t* output, int row, int col, int packed_cols, uint16_t value) {
-  int index = row * packed_cols + col;
-  if constexpr (Shuffled) {
-    const int tile = (row >> 4) * (packed_cols << 4) + (col >> 5) * 512;
-    const int offset = ((col >> 4) & 1) * 256 + (row & 15) * 16 + (col & 15);
-    index = tile + offset;
-  }
-  *reinterpret_cast<uint16_t*>(output + index) = value;
-}
-
-__device__ __forceinline__ void load_tile(uint16_t* tile, const uint16_t* input, int tile_m, int tile_n) {
-  const int row = threadIdx.x / THREADS_PER_ROW;
-  const int col = threadIdx.x % THREADS_PER_ROW * VALUES_PER_THREAD;
-  const uint64_t packed = *reinterpret_cast<const uint64_t*>(input + (tile_m + row) * N + tile_n + col);
-  *reinterpret_cast<uint32_t*>(tile + row * SMEM_STRIDE + col) = static_cast<uint32_t>(packed);
-  *reinterpret_cast<uint32_t*>(tile + row * SMEM_STRIDE + col + 2) = static_cast<uint32_t>(packed >> 32);
-}
-
-__device__ __forceinline__ void quantize_row(uint16_t* tile, uint8_t* fp4_output, uint8_t* scale_output,
-                                             int tile_m, int tile_n, int local_row, int lane) {
-  const int row = tile_m + local_row;
-  const int col = lane * VALUES_PER_THREAD;
-  const Quantized4 result = quantize(load_bf16x4(tile + local_row * SMEM_STRIDE + col), lane);
-  store_fp4<SHUFFLE_ROWWISE_FP4>(fp4_output, row, (tile_n + col) / 2, N_PACKED, result.fp4);
-  if (lane == 0) store_scale(scale_output, row, tile_n / BLOCK, N_SCALES, result.scale);
-}
-
-__device__ __forceinline__ Quantized4 quantize_col(uint16_t* tile, int col, int lane) {
-  const int row = lane * VALUES_PER_THREAD;
-  return quantize(make_float4(
-    __uint_as_float(static_cast<uint32_t>(tile[(row + 0) * SMEM_STRIDE + col]) << 16),
-    __uint_as_float(static_cast<uint32_t>(tile[(row + 1) * SMEM_STRIDE + col]) << 16),
-    __uint_as_float(static_cast<uint32_t>(tile[(row + 2) * SMEM_STRIDE + col]) << 16),
-    __uint_as_float(static_cast<uint32_t>(tile[(row + 3) * SMEM_STRIDE + col]) << 16)), lane);
-}
-
-} // namespace
-
-extern "C" __global__ __launch_bounds__(THREADS, 8)
-void KERNEL_NAME(uint8_t* __restrict__ rowwise_fp4, uint8_t* __restrict__ rowwise_scale,
-                 uint8_t* __restrict__ colwise_fp4, uint8_t* __restrict__ colwise_scale,
-                 const uint16_t* __restrict__ input) {
-  __shared__ uint16_t tile[BLOCK * SMEM_STRIDE];
-  const int tid = threadIdx.x;
-  const int line = tid / THREADS_PER_ROW;
-  const int lane = tid % THREADS_PER_ROW;
-  const int block_m = blockIdx.x * TILE_M;
-  const int block_n = blockIdx.y * TILE_N;
-
-  if constexpr (!SHUFFLE_COLWISE_FP4) {
-    uint16_t col_fp4[TILE_N / BLOCK][TILE_M / BLOCK];
-    uint8_t col_scale[TILE_N / BLOCK][TILE_M / BLOCK];
-
-    for (int chunk_m = 0; chunk_m < TILE_M / BLOCK; chunk_m++) {
-      for (int chunk_n = 0; chunk_n < TILE_N / BLOCK; chunk_n++) {
-        const int tile_m = block_m + chunk_m * BLOCK;
-        const int tile_n = block_n + chunk_n * BLOCK;
-        load_tile(tile, input, tile_m, tile_n);
-        __syncthreads();
-
-        quantize_row(tile, rowwise_fp4, rowwise_scale, tile_m, tile_n, line, lane);
-        const Quantized4 result = quantize_col(tile, line, lane);
-        col_fp4[chunk_n][chunk_m] = result.fp4;
-        col_scale[chunk_n][chunk_m] = result.scale;
-        __syncthreads();
-      }
-    }
-
-    for (int chunk_n = 0; chunk_n < TILE_N / BLOCK; chunk_n++) {
-      for (int chunk_m = 0; chunk_m < TILE_M / BLOCK; chunk_m++)
-        tile[line * BLOCK + chunk_m * THREADS_PER_ROW + lane] = col_fp4[chunk_n][chunk_m];
-      __syncthreads();
-
-      for (int round = 0; round < BLOCK / THREADS_PER_ROW; round++) {
-        const int col = round * THREADS_PER_ROW + tid / BLOCK;
-        const int row_pair = tid % BLOCK;
-        *reinterpret_cast<uint16_t*>(colwise_fp4 + (block_n + chunk_n * BLOCK + col) * M_PACKED + block_m / 2 + row_pair * 2) =
-          tile[col * BLOCK + row_pair];
-      }
-
-      if (lane == 0) {
-        const int col = block_n + chunk_n * BLOCK + line;
-        for (int chunk_m = 0; chunk_m < TILE_M / BLOCK; chunk_m++)
-          store_scale(colwise_scale, col, block_m / BLOCK + chunk_m, M_SCALES, col_scale[chunk_n][chunk_m]);
-      }
-      __syncthreads();
-    }
-  } else {
-    for (int chunk_m = 0; chunk_m < TILE_M / BLOCK; chunk_m++) {
-      for (int chunk_n = 0; chunk_n < TILE_N / BLOCK; chunk_n++) {
-        const int tile_m = block_m + chunk_m * BLOCK;
-        const int tile_n = block_n + chunk_n * BLOCK;
-        load_tile(tile, input, tile_m, tile_n);
-        __syncthreads();
-
-        quantize_row(tile, rowwise_fp4, rowwise_scale, tile_m, tile_n, line, lane);
-        const int row = lane * VALUES_PER_THREAD;
-        const int col = tile_n + line;
-        const Quantized4 result = quantize_col(tile, line, lane);
-        store_fp4<true>(colwise_fp4, col, (tile_m + row) / 2, M_PACKED, result.fp4);
-        if (lane == 0) store_scale(colwise_scale, col, tile_m / BLOCK, M_SCALES, result.scale);
-        __syncthreads();
-      }
-    }
-  }
-}

extra/llama_kernels/swiglu/__init__.py

@@ -1,49 +0,0 @@
-import functools, math
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, KernelInfo
-from tinygrad.renderer import Estimates
-from extra.llama_kernels import alloc_like
-
-LOG2E = 1.4426950408889634
-
-@functools.cache
-def _custom_swiglu(out:UOp, x_w13:UOp) -> UOp:
-  rows, hidden = math.prod(x_w13.shape[:-1]), x_w13.shape[-1]//2
-  n_elems = rows * hidden
-  out, x_w13 = out.reshape(n_elems), x_w13.reshape(rows, 2*hidden)
-  i = UOp.range(n_elems, 0)
-  row, col = i // hidden, i % hidden
-  act, gate = x_w13[row, col].cast(dtypes.float), x_w13[row, hidden+col].cast(dtypes.float)
-  sigmoid = (1.0 + (-LOG2E * act).exp2()).reciprocal()
-  store = out[i].store((act * sigmoid * gate).cast(out.dtype))
-  return store.end(i).sink(arg=KernelInfo(f"swiglu_fwd_{n_elems}", estimates=Estimates(ops=5*n_elems, mem=6*n_elems)))
-
-@functools.cache
-def _custom_swiglu_bwd(grad_out:UOp, x_w13:UOp, grad_act:UOp) -> UOp:
-  rows, hidden = math.prod(x_w13.shape[:-1]), x_w13.shape[-1]//2
-  n_elems = rows * hidden
-  grad_out, x_w13, grad_act = grad_out.reshape(rows, 2*hidden), x_w13.reshape(rows, 2*hidden), grad_act.reshape(n_elems)
-  i = UOp.range(n_elems, 0)
-  row, col = i // hidden, i % hidden
-  act, gate = x_w13[row, col].cast(dtypes.float), x_w13[row, hidden+col].cast(dtypes.float)
-  grad = grad_act[i].cast(dtypes.float)
-  sigmoid = (1.0 + (-LOG2E * act).exp2()).reciprocal()
-  silu = act * sigmoid
-  dact = grad_out[row, col].store((grad * (sigmoid + silu * (1.0 - sigmoid)) * gate).cast(grad_out.dtype))
-  dgate = grad_out.after(dact)[row, hidden+col].store((grad * silu).cast(grad_out.dtype))
-  return dgate.end(i).sink(arg=KernelInfo(f"swiglu_bwd_{n_elems}", estimates=Estimates(ops=10*n_elems, mem=10*n_elems)))
-
-def _swiglu_bwd(gradient:UOp, kernel:UOp):
-  _, x_w13 = kernel.src[1:]
-  axis = x_w13.axis if isinstance(x_w13.device, tuple) else None
-  grad_out = alloc_like(x_w13.shape, dtypes.bfloat16, x_w13.device, axis)
-  grad_out, *_ = Tensor.custom_kernel(grad_out, Tensor(x_w13, device=x_w13.device), Tensor(gradient, device=x_w13.device),
-                                      fxn=_custom_swiglu_bwd)
-  return (None, grad_out.uop)
-
-def swiglu(x_w13:Tensor) -> Tensor:
-  assert x_w13.dtype == dtypes.bfloat16 and x_w13.ndim >= 2 and x_w13.shape[-1] % 32 == 0
-  *prefix, two_k = x_w13.shape
-  axis = x_w13.uop.axis if isinstance(x_w13.device, tuple) else None
-  out = alloc_like((*prefix, two_k//2), dtypes.bfloat16, x_w13.device, axis)
-  return Tensor.custom_kernel(out, x_w13, fxn=_custom_swiglu, grad_fxn=_swiglu_bwd)[0]

extra/qcom_gpu_driver/opencl_ioctl.py

@@ -4,7 +4,7 @@ from hexdump import hexdump
 from copy import deepcopy
 import pathlib, sys
 from tinygrad.helpers import to_mv, getenv
-from tinygrad.runtime.autogen import mesa
+from tinygrad.runtime.autogen import adreno
 sys.path.append(pathlib.Path(__file__).parent.parent.parent.as_posix())
 
 IOCTL = getenv("IOCTL", 0)
@@ -23,7 +23,7 @@ for child in xml.getroot():
 CAPTURED_STATE = {}
 
 REGS = {}
-for k, v in mesa.__dict__.items():
+for k, v in adreno.__dict__.items():
   if k.startswith("REG_") and isinstance(v, int) and v > 1024: REGS[v] = k
 
 from extra.qcom_gpu_driver import msm_kgsl
@@ -42,7 +42,7 @@ def get_struct(argp, stype):
 
 def format_struct(s):
   sdats = []
-  for field_name, *_ in s._fields_:
+  for field_name, *_ in s._real_fields_:
     if field_name in {"__pad", "PADDING_0"}: continue
     dat = getattr(s, field_name)
     if isinstance(dat, int): sdats.append(f"{field_name}:0x{dat:X}")
@@ -96,9 +96,9 @@ def parse_cmd_buf(dat):
         CAPTURED_STATE['LOAD_FRAGS'].append((state_block, state_type, num_unit, dst_off))
 
         if state_block == SB6_CS_SHADER:
-          from tinygrad.runtime.support.compiler_mesa import disas_adreno
+          from extra.disassemblers.adreno import disasm_raw
           if state_type == ST6_SHADER and IOCTL > 3:
-            disas_adreno(get_mem(((vals[2] << 32) | vals[1]), num_unit * 128))
+            disasm_raw(get_mem(((vals[2] << 32) | vals[1]), num_unit * 128))
           if state_type == ST6_CONSTANTS:
             x = get_mem(((vals[2] << 32) | vals[1]), num_unit*4)
             CAPTURED_STATE['constants'] = x[:]
@@ -142,7 +142,7 @@ def parse_cmd_buf(dat):
       vals = struct.unpack("I"*size, dat[ptr+4:ptr+4+4*size])
       if IOCTL > 0: print(f"{ptr:3X} -- typ 4: {size=:3d}, {reg_name}", hprint(vals))
       for vi,v in enumerate(vals): CAPTURED_STATE[offset+vi] = v
-      if offset == mesa.REG_A6XX_SP_CS_CONFIG:
+      if offset == adreno.REG_A6XX_SP_CS_CONFIG:
         val = vals[0]
         if IOCTL > 0:
           print(f"\tBINDLESS_TEX={(val >> 0) & 0b1}")
@@ -215,3 +215,79 @@ def install_hook(c_function, python_function):
 
 libc = ctypes.CDLL(ctypes.util.find_library("libc"))
 install_hook(libc.ioctl, ioctl)
+
+def before_launch():
+  global CAPTURED_STATE
+  CAPTURED_STATE.clear()
+def collect_last_launch_state():
+  global CAPTURED_STATE
+  return deepcopy(CAPTURED_STATE)
+def compare_launch_state(state, good_state):
+  cmp = [
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_NTEX__MASK),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_NSAMP__MASK),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_NIBO__MASK),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_ENABLED),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_BINDLESS_TEX),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_BINDLESS_SAMP),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_BINDLESS_IBO),
+    (adreno.REG_A6XX_SP_CS_CONFIG, adreno.A6XX_SP_CS_CONFIG_BINDLESS_UBO),
+
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_HALFREGFOOTPRINT__MASK),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_FULLREGFOOTPRINT__MASK),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_BRANCHSTACK__MASK),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_FULLREGFOOTPRINT__MASK),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_THREADMODE__MASK),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_EARLYPREAMBLE),
+    (adreno.REG_A6XX_SP_CS_CTRL_REG0, adreno.A6XX_SP_CS_CTRL_REG0_MERGEDREGS),
+
+    (adreno.REG_A6XX_SP_CS_PVT_MEM_PARAM, adreno.A6XX_SP_CS_PVT_MEM_PARAM_MEMSIZEPERITEM__MASK),
+    (adreno.REG_A6XX_SP_CS_PVT_MEM_PARAM, adreno.A6XX_SP_CS_PVT_MEM_PARAM_HWSTACKSIZEPERTHREAD__MASK),
+
+    (adreno.REG_A6XX_SP_CS_UNKNOWN_A9B1, adreno.A6XX_SP_CS_UNKNOWN_A9B1_UNK5),
+    (adreno.REG_A6XX_SP_CS_UNKNOWN_A9B1, adreno.A6XX_SP_CS_UNKNOWN_A9B1_UNK6),
+
+    (adreno.REG_A6XX_SP_CS_BRANCH_COND, 0xffffffff),
+
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_0, adreno.A6XX_HLSQ_CS_NDRANGE_0_KERNELDIM__MASK),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_0, adreno.A6XX_HLSQ_CS_NDRANGE_0_LOCALSIZEX__MASK),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_0, adreno.A6XX_HLSQ_CS_NDRANGE_0_LOCALSIZEY__MASK),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_0, adreno.A6XX_HLSQ_CS_NDRANGE_0_LOCALSIZEZ__MASK),
+
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_1, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_2, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_3, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_4, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_5, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_NDRANGE_6, 0xffffffff),
+
+    (adreno.REG_A6XX_HLSQ_CS_CNTL_0, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_CNTL_1, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_KERNEL_GROUP_X, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_KERNEL_GROUP_Y, 0xffffffff),
+    (adreno.REG_A6XX_HLSQ_CS_KERNEL_GROUP_Z, 0xffffffff),
+  ]
+
+  for x,m in cmp:
+    print(f"Field {REGS[x]}, mask: 0x{m:X} cmp: {state.get(x, 0) & m} vs {good_state.get(x, 0) & m}")
+    if state.get(x, 0) & m != good_state.get(x, 0) & m:
+      return False, f"Field {REGS[x]}, mask: 0x{m:X} mismatch: {state.get(x, 0) & m} vs {good_state.get(x, 0) & m}"
+
+  for n in ['descriptors', 'ibos']:
+    if n not in good_state: continue
+    mv1, mv2 = state.get(n), good_state.get(n)
+
+    if len(mv1) != len(mv2): return False, f"{n}: len mismatch {len(mv1)} != {len(mv2)}"
+    mv1 = memoryview(bytearray(mv1)).cast('I')
+    mv2 = memoryview(bytearray(mv2)).cast('I')
+    for i in range(len(mv2)):
+      if i % 8 == 5 or i % 8 == 4: continue # addresses
+      if mv1[i]!=mv2[i]: return False, f"{n}: content mismatch {i} {mv1[i]} {mv2[i]}"
+
+  for n in ['samplers']:
+    if n not in good_state: continue
+    mv1, mv2 = state.get(n), good_state.get(n)
+    if len(mv1) != len(mv2): return False, f"{n}: len mismatch {len(mv1)} != {len(mv2)}"
+    if any(mv1[i]!=mv2[i] for i in range(len(mv1))): return False, f"{n}: content mismatch"
+
+  return True, "PASS"

extra/runbook_digitalocean_mi350x.md

@@ -1,235 +0,0 @@
-# Runbook: Llama 3 8B Training on DigitalOcean MI350X
-
-## Machine Specs
-- 8x MI350X GPUs (gfx950, device ID 75b0), 288GB VRAM each
-- 2TB RAM, 192 CPUs, 2TB disk
-- ROCm 7.14 at `/opt/rocm` (NOT `/opt/rocm-7.1.1` like the submission scripts assume)
-- Python 3.12
-
-## Phase 1: System Setup
-
-### 1.1 Install packages
-```bash
-apt-get update
-apt-get install -y python3-pip python3-venv git tmux rclone clang
-```
-
-### 1.2 Install Python deps
-```bash
-python3 -m pip install --break-system-packages numpy tqdm wandb tiktoken sentencepiece
-```
-
-### 1.3 Install ROCm dev headers
-The base image has ROCm runtime but NOT the HIP dev headers. Need:
-```bash
-apt-get install -y amdrocm-core-dev
-```
-This installs `hip/hip_runtime.h` at `/opt/rocm/core-7.14/include/hip/hip_runtime.h`.
-The symlink `/opt/rocm/include` → `/opt/rocm/core-7.14/include` makes it available at `/opt/rocm/include/hip/hip_runtime.h`.
-
-### 1.4 Configure ROCm comgr
-ROCm 7.14 ships comgr 3.3 at `/opt/rocm/lib/libamd_comgr.so`. tinygrad's DLL loader needs explicit env vars to find it (it searches for `libcomgr.so*` by default, not `libamd_comgr.so*`). Set these in the run command:
-```bash
-export COMGR_PATH=/opt/rocm/lib/libamd_comgr.so
-export COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so
-```
-Also add ROCm libs to ldconfig so comgr's shared library dependencies resolve:
-```bash
-cat > /etc/ld.so.conf.d/rocm.conf << 'EOF'
-/opt/rocm/lib
-/opt/rocm/lib/llvm/lib
-/opt/rocm/lib/rocm_sysdeps/lib
-EOF
-ldconfig
-```
-
-### 1.5 Install geohot tmux config
-```bash
-curl -sL https://raw.githubusercontent.com/geohot/configuration/master/.tmux.conf -o ~/.tmux.conf
-```
-
-### 1.6 Reload amdgpu driver
-tinygrad's HCQ backend needs `/dev/kfd` which is created by the amdgpu kernel driver.
-If the driver was unloaded, reload it:
-```bash
-modprobe amdgpu
-ls /dev/kfd  # should exist
-```
-
-## Phase 2: Clone tinygrad
-```bash
-cd /root
-git clone https://github.com/tinygrad/tinygrad.git
-cd tinygrad
-python3 -m pip install --break-system-packages -e .
-```
-
-## Phase 3: Download C4 Dataset
-
-The C4 data is on the MLCommons Cloudflare R2 bucket in Megatron-LM indexed format.
-
-```bash
-rclone config create mlc-training s3 provider=Cloudflare \
-  access_key_id=76ea42eadb867e854061a1806220ee1e \
-  secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 \
-  endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
-
-mkdir -p /root/datasets/c4-8b
-rclone copy mlc-training:mlcommons-training-wg-public/llama3_1/datasets/c4/llama3_1_8b/ /root/datasets/c4-8b/ -P
-```
-
-Files downloaded (~85GB total, ~6 minutes):
-- `c4-train.en_6_text_document.bin` (79 GB)
-- `c4-train.en_6_text_document.idx` (870 MB)
-- `c4-validation-91205-samples.en_text_document.bin` (159 MB)
-- `c4-validation-91205-samples.en_text_document.idx` (1.8 MB)
-- `LICENSE.txt`, `NOTICE.txt`
-
-### Symlink for the submission script
-The `dev_run.sh` script hardcodes `BASEDIR="/raid/datasets/c4-8b/"`. Symlink:
-```bash
-mkdir -p /raid/datasets
-ln -s /root/datasets/c4-8b /raid/datasets/c4-8b
-```
-
-## Phase 4: wandb Login
-```bash
-wandb login
-```
-Enter API key from https://wandb.ai/authorize
-
-## Phase 5: Run Training
-
-### 5.1 Smoke test (beam search, 2 layers, fake data)
-Always run beam first to validate the pipeline:
-```bash
-cd /root/tinygrad
-COMGR_PATH=/opt/rocm/lib/libamd_comgr.so \
-COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so \
-CC=/opt/rocm/core-7.14/lib/llvm/bin/clang \
-DEV=AMD:HIP \
-ROCM_PATH=/opt/rocm BASEDIR=/root/datasets/c4-8b/ \
-  bash examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_beam.sh
-```
-
-### 5.2 Full training run
-```bash
-cd /root/tinygrad
-COMGR_PATH=/opt/rocm/lib/libamd_comgr.so \
-COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so \
-CC=/opt/rocm/core-7.14/lib/llvm/bin/clang \
-DEV=AMD:HIP \
-ROCM_PATH=/opt/rocm BASEDIR=/root/datasets/c4-8b/ \
-WANDB=1 \
-  bash examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_run.sh
-```
-
-## Environment Variable Reference
-
-| Variable | Value | Why |
-|---|---|---|
-| `COMGR_PATH` | `/opt/rocm/lib/libamd_comgr.so` | tinygrad's DLL loader needs explicit path to find comgr 3.3 |
-| `COMGR_3_PATH` | `/opt/rocm/lib/libamd_comgr.so` | comgr 3.x uses a separate `comgr_3` module with its own path var |
-| `CC` | `/opt/rocm/core-7.14/lib/llvm/bin/clang` | System clang doesn't know gfx950; must use ROCm's bundled clang |
-| `DEV` | `AMD:HIP` | Force HIPRenderer (comgr-based) over HIPCCRenderer (hipcc subprocess) |
-| `ROCM_PATH` | `/opt/rocm` | Script defaults to `/opt/rocm-7.1.1` which doesn't exist |
-| `BASEDIR` | `/root/datasets/c4-8b/` | Where C4 dataset was downloaded (script hardcodes `/raid/datasets/c4-8b/`) |
-| `WANDB` | `1` | Enable wandb logging (off by default) |
-
-## Architecture
-
-| Component | Source file |
-|---|---|
-| Model | `examples/mlperf/models/flat_llama.py` — FlatTransformer, FP8 MXFP4 weights, fused QKV, flash attention |
-| Trainer | `examples/mlperf/model_train.py` → `train_llama3()` |
-| Optimizer | `examples/mlperf/optim.py` — GradAccClipAdamW, master weights, FP8 re-quant |
-| LR schedule | `examples/mlperf/lr_schedulers.py` — CosineAnnealingLRWithWarmup |
-| Dataloader | `examples/mlperf/dataloader.py` — Megatron-LM indexed bin format |
-| ASM GEMM | `extra/gemm/cdna_asm_gemm.py` — gfx950 MFMA assembly, MXFP4 |
-| Flash attention | `extra/thunder/amd/fa.py` |
-| Fused kernels | `extra/llama_kernels/` — rmsnorm, silu, quantize, fused_ce |
-| GPU driver | `tinygrad/runtime/ops_amd.py` — HCQ, direct KFD ioctl |
-| Renderer | `tinygrad/renderer/cstyle.py` — HIPRenderer for gfx950 |
-| comgr compiler | `tinygrad/runtime/support/compiler_amd.py` — HIPCompiler using comgr 3.3 |
-
-## Troubleshooting
-
-### `'hip/hip_runtime.h' file not found`
-Install `amdrocm-core-dev`:
-```bash
-apt-get install -y amdrocm-core-dev
-```
-
-### `'gfx950' is not a recognized processor` + LLVM crash
-System clang doesn't know gfx950. Set `CC=/opt/rocm/core-7.14/lib/llvm/bin/clang`.
-
-### `comgr not available: try setting COMGR_PATH?`
-Add ROCm libs to ldconfig and set `COMGR_PATH` and `COMGR_3_PATH`:
-```bash
-# /etc/ld.so.conf.d/rocm.conf should contain /opt/rocm/lib paths
-ldconfig
-```
-
-### `comgr not available: try setting COMGR_3_PATH?`
-comgr 3.x uses a separate module. Set `COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so` too.
-
-### `FileNotFoundError: '/raid/datasets/c4-8b/...'`
-Script hardcodes `BASEDIR`. Either symlink or edit the script:
-```bash
-mkdir -p /raid/datasets && ln -s /root/datasets/c4-8b /raid/datasets/c4-8b
-```
-
-### `No such file or directory: 'clang'`
-Install clang: `apt-get install -y clang` (for CPU compilation).
-For gfx950 HIP compilation, comgr (not clang) is used — ensure the ROCm 7.14 comgr 3.3 is properly loaded via `COMGR_PATH` and `COMGR_3_PATH`.
-
-## Appendix: KVM Virtualization Observations
-
-### Virtualization detection
-```
-$ systemd-detect-virt
-kvm
-$ lspci -nn | grep AMD
-83:00.0 ... Device [1002:75b0]
-```
-CPU flags include `hypervisor`. `dmesg` shows `Hypervisor detected: KVM`.
-
-### PCI device ID
-`lspci -v` shows device ID `0x75b0` and subsystem ID `0x75a0`:
-```
-83:00.0 Processing accelerators: ... Device 75b0
-    Subsystem: ... Device 75a0
-```
-tinygrad's `PCIIface` in `ops_amd.py` and `hive_reset.py` did not list `0x75b0`, so the GPU was not found. Adding `0x75b0` to the device ID list in both files fixes the detection.
-
-### amdgpu driver behavior
-On first boot, amdgpu loaded and bound to all 8 GPUs. On one boot it failed to initialize:
-```
-[  799.780369] amdgpu 0000:83:00.0: Failed to alloc msi vectors
-[  799.781476] amdgpu 0000:83:00.0: sw_init of IP block <vega20_ih> failed -22
-[  799.782724] amdgpu 0000:83:00.0: amdgpu_device_ip_init failed
-[  799.793885] amdgpu 0000:83:00.0: Fatal error during GPU init
-```
-On a subsequent boot, amdgpu initialized successfully (SMU initialized, VRAM ready). After unbinding all 8 GPUs from amdgpu, `rmmod amdgpu` wedged the module (stuck in "Unloading" state in `/proc/modules`), requiring a full VM reboot.
-
-### `/dev/kfd`
-`/dev/kfd` exists when amdgpu is loaded. Opening it returns `OSError: [Errno 22] Invalid argument`.
-
-### VRAM BAR reads all 0xFF
-After amdgpu initializes the GPU and is then unbound, reading the VRAM BAR (via `/sys/bus/pci/devices/0000:83:00.0/resource0`) returns all `0xFF` at all offsets — including the discovery table at `vram_size - 64KB`. tinygrad's `AMDev._run_discovery()` fails with `AssertionError: discovery signatures mismatch`.
-
-A PCI reset (`echo 1 > /sys/bus/pci/devices/0000:83:00.0/reset`) did not change the VRAM contents — still all `0xFF`.
-
-VRAM was also all `0xFF` when read via `/dev/mem` at the BAR physical address (`0xa0000000000`).
-
-### VFIO attempt
-Bound the GPU to `vfio-pci` with `enable_unsafe_noiommu_mode=1`. The GPU bound successfully and `/dev/vfio/noiommu-0` appeared. Running tinygrad with `VFIO=1` still failed with the same `discovery signatures mismatch` — VRAM BAR still reads all `0xFF`.
-
-### No IOMMU in guest
-`dmesg` has no `AMD-Vi` entries. PCI devices have no `iommu_group` symlink.
-
-### No fan control
-No `fan*` or `pwm*` hwmon entries exist. Only `temp*`, `power*`, `freq*` are exposed. GPU temps read 56-63°C, power ~265W per GPU.
-
-### Current status: NOT WORKING
-tinygrad's `PCIIface` finds the GPU (after adding `0x75b0`) but `AMDev._run_discovery()` fails because the VRAM discovery table reads all `0xFF`. This was observed with the GPU unbound from any driver, after PCI reset, and with VFIO bound.

extra/thunder/amd/fa.py

@@ -2,7 +2,7 @@ import math, pathlib, functools, struct
 
 from tinygrad import Device, Tensor
 from tinygrad.dtype import DTypeLike, dtypes
-from tinygrad.helpers import DEBUG, getenv
+from tinygrad.helpers import DEBUG
 from tinygrad.renderer import Estimates
 from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
 from tinygrad.runtime.support.elf import elf_loader
@@ -14,7 +14,7 @@ def _sharded_empty(shape:Tensor, ref:Tensor, axis:int|None, dtype:DTypeLike|None
   shard_axis = ref.uop.axis if axis is None else axis
   shape = tuple(s // len(ref.device) if i == shard_axis else s for i, s in enumerate(shape))
   axis = ref.uop.axis if axis is None else axis
-  return Tensor(Tensor.invalids(*shape, dtype=dtype, device=ref.device).uop.unshard(axis), dtype=dtype, device=ref.device)
+  return Tensor(Tensor.invalids(*shape, dtype=dtype, device=ref.device).uop.multi(axis), dtype=dtype, device=ref.device)
 
 @functools.cache
 def custom_fused_qkv_rope_forward(q:UOp, k:UOp, v:UOp, xqkv:UOp, freqs_cis:UOp,
@@ -94,7 +94,7 @@ def fused_qkv_rope(xqkv:Tensor, freqs_cis:Tensor, n_heads:int, n_kv_heads:int, h
   B_local = B // num_devices if is_dp else B
   H_local = n_heads // num_devices if is_mp else n_heads
   H_KV_local = n_kv_heads // num_devices if is_mp else n_kv_heads
-  assert H_local % H_KV_local == 0 and head_dim % 2 == 0 and head_dim <= 512
+  assert (B_local, N, H_local, H_KV_local, head_dim) == (2, 8192, 32, 8, 128)
   single_device = xqkv.device[0] if isinstance(xqkv.device, tuple) else xqkv.device
   arch = Device[single_device].renderer.target.arch
   axis = 0 if is_dp else 2 if is_mp else None
@@ -206,11 +206,10 @@ def custom_fa_forward(o:UOp, l_vec:UOp, q:UOp, k:UOp, v:UOp, sinks:UOp|None=None
                   arg=KernelInfo(name="custom_fa_forward", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
-  if not getenv("NO_HIPCC"):
-    lib = bytearray(lib)
-    rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
-    struct.pack_into('<I', lib, rodata_off, 160000)
-    lib = bytes(lib)
+  lib = bytearray(lib)
+  rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
+  struct.pack_into('<I', lib, rodata_off, 160000)
+  lib = bytes(lib)
 
   return UOp(Ops.PROGRAM,
              src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -237,11 +236,10 @@ def custom_fa_backward_pre(delta_vec:UOp, dq:UOp, o:UOp, do:UOp, device:str, arc
                   arg=KernelInfo(name="custom_fa_backward_pre", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
-  if not getenv("NO_HIPCC"):
-    lib = bytearray(lib)
-    rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
-    struct.pack_into('<I', lib, rodata_off, 160000)
-    lib = bytes(lib)
+  lib = bytearray(lib)
+  rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
+  struct.pack_into('<I', lib, rodata_off, 160000)
+  lib = bytes(lib)
 
   return UOp(Ops.PROGRAM,
              src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -270,11 +268,10 @@ def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_ve
                   arg=KernelInfo(name="custom_fa_backward", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
-  if not getenv("NO_HIPCC"):
-    lib = bytearray(lib)
-    rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
-    struct.pack_into('<I', lib, rodata_off, 160000)
-    lib = bytes(lib)
+  lib = bytearray(lib)
+  rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
+  struct.pack_into('<I', lib, rodata_off, 160000)
+  lib = bytes(lib)
 
   return UOp(Ops.PROGRAM,
              src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -301,11 +298,10 @@ def custom_fa_backward_post(dq_out:UOp, dq_in:UOp, device:str, arch:str, B:int,
                   arg=KernelInfo(name="custom_fa_backward_post", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
-  if not getenv("NO_HIPCC"):
-    lib = bytearray(lib)
-    rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
-    struct.pack_into('<I', lib, rodata_off, 160000)
-    lib = bytes(lib)
+  lib = bytearray(lib)
+  rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
+  struct.pack_into('<I', lib, rodata_off, 160000)
+  lib = bytes(lib)
 
   return UOp(Ops.PROGRAM,
              src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))

extra/thunder/amd/fa_bwd_causal.cpp

@@ -43,10 +43,6 @@ constexpr int SLICE_QO = 32;
 constexpr int DOT_SLICE_QO = 16;
 constexpr int WARP_SIZE_KV = 64; // warp size for KV
 constexpr bool causal = true;
-// WINDOW>0: sliding-window backward (query i sees keys in [i-WINDOW+1, i])
-#ifndef WINDOW
-#define WINDOW 0
-#endif
 
 #define NUM_WARPS 4
 #define NUM_THREADS (kittens::WARP_THREADS * NUM_WARPS)
@@ -92,12 +88,7 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
   const int k_start_min = j_min * WARP_SIZE_KV;
   // first Q step that can overlap this K_span:
   const int first_step = max(0, k_start_min / STEP_QO);
-#if WINDOW
-  // cap the Q loop, padded by 2 masked steps: the epilogue's deferred dq path miscomputes in-window tail queries
-  const int num_steps_per_head = min(total_steps_per_head - first_step, (BLOCK_SIZE_KV + WINDOW) / STEP_QO + 2);
-#else
   const int num_steps_per_head = total_steps_per_head - first_step;
-#endif
   const int num_steps = num_steps_per_head * HEADS_PER_WG;
   const int k_pos = j * WARP_SIZE_KV;
 
@@ -389,13 +380,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             mov<0, 1, neg_inf_v>(P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          // window lower boundary, mirror of the causal edge
-          } else if (q_pos - k_pos == WINDOW) {
-            make_window<0, 0, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -549,8 +533,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -656,13 +638,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             make_causal<0, 1, neg_inf_v>(P_ij, P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            make_window<0, 1, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -816,8 +791,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -922,14 +895,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             // Apply the causal mask to [0, 2] and set [0, 3:4] to -inf
             make_causal<0, 2, neg_inf_v>(P_ij, P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            make_window<0, 2, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -1083,8 +1048,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -1188,15 +1151,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
           } else if (q_pos == k_pos) {
             // Apply the causal mask to [0, 3]
             make_causal<0, 3, neg_inf_v>(P_ij, P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            mov<0, 2, neg_inf_v>(P_ij);
-            make_window<0, 3, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -1349,8 +1303,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[toc][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[toc][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -1476,13 +1428,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             mov<0, 1, neg_inf_v>(P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          // window lower boundary, mirror of the causal edge
-          } else if (q_pos - k_pos == WINDOW) {
-            make_window<0, 0, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -1637,8 +1582,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -1746,13 +1689,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             make_causal<0, 1, neg_inf_v>(P_ij, P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            make_window<0, 1, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -1906,8 +1842,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -2012,14 +1946,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             // Apply the causal mask to [0, 2] and set [0, 3:4] to -inf
             make_causal<0, 2, neg_inf_v>(P_ij, P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            make_window<0, 2, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -2173,8 +2099,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[tic][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -2278,15 +2202,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
           } else if (q_pos == k_pos) {
             // Apply the causal mask to [0, 3]
             make_causal<0, 3, neg_inf_v>(P_ij, P_ij);
-#if WINDOW
-          } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            mov<0, 2, neg_inf_v>(P_ij);
-            make_window<0, 3, neg_inf_v>(P_ij, P_ij);
-          } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
           }
         }
         mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -2439,8 +2354,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         if constexpr (D == 128) load<0, 2>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[toc][0], {0, 0}), Q_i_addr);
         if constexpr (D == 128) load<0, 3>(Q_i, subtile_inplace<DOT_SLICE_QO, D>(Q_i_smem[toc][0], {0, 0}), Q_i_addr);
         mma_AtB<0, 0, 7>(dQ_i_T, K_j_col, dP_ij_bf16_col_T, dQ_i_T);
-        // D=64: wait out MFMA->VALU accumulator hazard on dQ_i_T
-        if constexpr (D == 64) asm volatile("s_nop 15");
         if constexpr (D == 128) mma_AtB<1, 0, 0>(dQ_i_T, K_j_col, dP_ij_bf16_col_T);
         // Load K_j from shared memory to registers
         // load(K_j, subtile_inplace<WARP_SIZE_KV, D>(K_j_smem, {warpid, 0}));
@@ -2558,12 +2471,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             mov<0, 1, neg_inf_v>(P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-        } else if (q_pos - k_pos == WINDOW) {
-            make_window<0, 0, neg_inf_v>(P_ij, P_ij);
-        } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
         }
       }
       mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -2825,13 +2732,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             make_causal<0, 1, neg_inf_v>(P_ij, P_ij);
             mov<0, 2, neg_inf_v>(P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-        } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            make_window<0, 1, neg_inf_v>(P_ij, P_ij);
-        } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
         }
       }
       mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -3088,14 +2988,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
             // Apply the causal mask to [0, 2] and set [0, 3:4] to -inf
             make_causal<0, 2, neg_inf_v>(P_ij, P_ij);
             mov<0, 3, neg_inf_v>(P_ij);
-#if WINDOW
-        } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            make_window<0, 2, neg_inf_v>(P_ij, P_ij);
-        } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
         }
       }
       mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);
@@ -3352,15 +3244,6 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
         } else if (q_pos == k_pos) {
             // Apply the causal mask to [0, 3]
             make_causal<0, 3, neg_inf_v>(P_ij, P_ij);
-#if WINDOW
-        } else if (q_pos - k_pos == WINDOW) {
-            mov<0, 0, neg_inf_v>(P_ij);
-            mov<0, 1, neg_inf_v>(P_ij);
-            mov<0, 2, neg_inf_v>(P_ij);
-            make_window<0, 3, neg_inf_v>(P_ij, P_ij);
-        } else if (q_pos - k_pos > WINDOW) {
-            mov<neg_inf_v>(P_ij);
-#endif
         }
       }
       mul<0, 2>(P_ij, P_ij, P_SCALE_FACTOR);

extra/thunder/amd/fa_fwd_causal.cpp

@@ -34,10 +34,6 @@ constexpr int ATTN_D = 128; // dimension
 constexpr int Q_BLOCK_SIZE = 32; // q block size
 constexpr int KV_BLOCK_SIZE = 64; // kv block size
 constexpr bool causal = true;
-// WINDOW>0: sliding-window attention, query i attends keys in [i-WINDOW+1, i]
-#ifndef WINDOW
-#define WINDOW 0
-#endif
 
 #define NUM_WARPS 8
 #define NUM_THREADS (kittens::WARP_THREADS * NUM_WARPS)
@@ -86,26 +82,11 @@ template<typename T=float, typename L=col_l, typename S=rt_16x32_4_s> using attn
 
 /**********************************************************/
 template<int THR_X, int THR_Y>
-__device__ inline void mask_vec2_imm(uint32_t rel_vgpr, uint32_t rel_hi_vgpr, uint32_t neg_inf_vgpr,
+__device__ inline void mask_vec2_imm(uint32_t rel_vgpr, uint32_t neg_inf_vgpr,
                                      uint32_t& x_ref, uint32_t& y_ref) {
 
     uint64_t x_mask, y_mask;
-#if WINDOW
-    // causal+window in one asm block to not disturb register allocation
-    asm volatile(
-        "v_cmp_lt_i32_e64 %0, %4, %5\n\t"
-        "v_cmp_lt_i32_e64 %1, %4, %7\n\t"
-        "v_cndmask_b32_e64 %2, %2, %6, %0\n\t"
-        "v_cndmask_b32_e64 %3, %3, %6, %1\n\t"
-        "v_cmp_ge_i32_e64 %0, %8, %5\n\t"
-        "v_cmp_ge_i32_e64 %1, %8, %7\n\t"
-        "v_cndmask_b32_e64 %2, %2, %6, %0\n\t"
-        "v_cndmask_b32_e64 %3, %3, %6, %1\n\t"
-        : "=s"(x_mask), "=s"(y_mask), "+v"(x_ref), "+v"(y_ref)
-        : "v"(rel_vgpr), "n"(THR_X), "v"(neg_inf_vgpr), "n"(THR_Y), "v"(rel_hi_vgpr)
-        : "vcc"
-    );
-#else
+    // uint32_t ox, oy;
     asm volatile(
         // x: rel < THR_X ?
         "v_cmp_lt_i32_e64 %0, %6, %7\n\t"
@@ -118,7 +99,7 @@ __device__ inline void mask_vec2_imm(uint32_t rel_vgpr, uint32_t rel_hi_vgpr, ui
           "n"(THR_X), "v"(neg_inf_vgpr), "n"(THR_Y)
         : "vcc"
     );
-#endif
+    // x_ref = ox; y_ref = oy;
 }
 
 template<ducks::rt::col_layout RT>
@@ -141,8 +122,6 @@ __device__ inline void mask_kv_tile(RT &dst, int q_abs, int k_abs, uint32_t neg_
         // (smaller rel ⇒ more "future" keys that must be -inf)
         const int rel0 = q_pos - (k_base + row_base);
         const uint32_t rel = static_cast<uint32_t>(rel0);
-        // rel-WINDOW keeps THR within the inline-constant range
-        const uint32_t rel_hi = static_cast<uint32_t>(rel0 - WINDOW);
 
         #pragma unroll
         for (int j = 0; j < dst.width; ++j) {
@@ -166,14 +145,14 @@ __device__ inline void mask_kv_tile(RT &dst, int q_abs, int k_abs, uint32_t neg_
             //  - reuse a single neg_inf register
             //  - keep VCC live across the pair
             //  - avoid reloading -inf or recomputing rel
-            mask_vec2_imm< 0, 1 >(rel, rel_hi, neg_inf_v, d0x, d0y);
-            mask_vec2_imm< 2, 3 >(rel, rel_hi, neg_inf_v, d1x, d1y);
-            mask_vec2_imm< 8, 9 >(rel, rel_hi, neg_inf_v, d2x, d2y);
-            mask_vec2_imm<10,11 >(rel, rel_hi, neg_inf_v, d3x, d3y);
-            mask_vec2_imm<16,17 >(rel, rel_hi, neg_inf_v, d4x, d4y);
-            mask_vec2_imm<18,19 >(rel, rel_hi, neg_inf_v, d5x, d5y);
-            mask_vec2_imm<24,25 >(rel, rel_hi, neg_inf_v, d6x, d6y);
-            mask_vec2_imm<26,27 >(rel, rel_hi, neg_inf_v, d7x, d7y);
+            mask_vec2_imm< 0, 1 >(rel, neg_inf_v, d0x, d0y);
+            mask_vec2_imm< 2, 3 >(rel, neg_inf_v, d1x, d1y);
+            mask_vec2_imm< 8, 9 >(rel, neg_inf_v, d2x, d2y);
+            mask_vec2_imm<10,11 >(rel, neg_inf_v, d3x, d3y);
+            mask_vec2_imm<16,17 >(rel, neg_inf_v, d4x, d4y);
+            mask_vec2_imm<18,19 >(rel, neg_inf_v, d5x, d5y);
+            mask_vec2_imm<24,25 >(rel, neg_inf_v, d6x, d6y);
+            mask_vec2_imm<26,27 >(rel, neg_inf_v, d7x, d7y);
         }
     }
 }
@@ -222,16 +201,6 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     else max_num_tiles = num_tiles;
     const int q_start_pos = tile_idx * Q_BLOCK_SIZE;
 
-#if WINDOW
-    // start at the first in-window tile; clamp keeps >=4 tiles for the pipeline unroll
-    const int block_min_q = block_tile_idx * NUM_WARPS * Q_BLOCK_SIZE;
-    int min_tile = (block_min_q - WINDOW + 1) / KV_BLOCK_SIZE;
-    if (min_tile < 0) min_tile = 0;
-    if (min_tile > max_num_tiles - 4) min_tile = max(0, max_num_tiles - 4);
-#else
-    constexpr int min_tile = 0;
-#endif
-
     constexpr float TEMPERATURE_SCALE = (D == 128) ? 0.08838834764f*1.44269504089f : 0.125f*1.44269504089f;
     uint32_t neg_inf_v = 0xff800000;
 
@@ -262,7 +231,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     G::prefill_swizzled_offsets<1, false>(k_smem[0], g.Kg, swizzled_offsets_K);
     G::prefill_swizzled_offsets<1, false>(v_smem[0], g.Vg, swizzled_offsets_V);
 
-    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, min_tile, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, 0, head_idx_kv, 0}, swizzled_offsets_K);
     __builtin_amdgcn_s_waitcnt(0);
     __builtin_amdgcn_sched_barrier(0);
     __builtin_amdgcn_s_barrier();
@@ -274,9 +243,9 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     transpose(q_reg_transposed, q_reg);
 
     // All warps then collaboratively load in the first slice of V (V0) and the second slice of K (K1) into shared memory
-    G::load<1, false>(k_smem[1], g.Kg, {batch_idx, min_tile + 1, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[1], g.Kg, {batch_idx, 1, head_idx_kv, 0}, swizzled_offsets_K);
     // All warps then load in the first slice of K (K0)
-    G::load<1, false>(v_smem[0], g.Vg, {batch_idx, min_tile, head_idx_kv, 0}, swizzled_offsets_V);
+    G::load<1, false>(v_smem[0], g.Vg, {batch_idx, 0, head_idx_kv, 0}, swizzled_offsets_V);
     load(k_reg, k_smem[0]);
     __builtin_amdgcn_sched_barrier(0);
     asm volatile("s_waitcnt lgkmcnt(0)");
@@ -290,20 +259,13 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     mma_AtB(att_block[0], k_reg_transposed, q_reg_transposed, att_block[0]);
     __builtin_amdgcn_sched_barrier(0);
     if constexpr (causal) {
-        const int kv_end_pos = (min_tile + 1) * KV_BLOCK_SIZE;
-        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, WINDOW ? 1 : 0)) {
-            mask_kv_tile(att_block[0], tile_idx, min_tile, neg_inf_v, lane);
+        const int kv_end_pos = (1) * KV_BLOCK_SIZE;
+        if (__builtin_expect(q_start_pos < kv_end_pos, 0)) {  // Only mask if needed
+            mask_kv_tile(att_block[0], tile_idx, 0, neg_inf_v, lane);
         }
     }
     // Each warp performs a partial softmax of QK0 (i.e. some of the online softmax up until but not including the second exponential scaling of the attention block likely)
-#if WINDOW
-    // floor the max: min_tile can be fully masked, which would NaN via exp2(-inf - -inf)
-    zero(max_vec_prev);
-    add(max_vec_prev, max_vec_prev, -1e4f);
-    col_max(max_vec, att_block[0], max_vec_prev);
-#else
     col_max(max_vec, att_block[0]);
-#endif
 
     copy(max_vec_prev, max_vec);
     exp2(scale_vec, scale_vec);
@@ -322,25 +284,21 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     // All warps then load in the second slice of K (K1)
     load(k_reg, k_smem[1]);
     // All warps then collaboratively load in the third slice of K (K2) into shared memory
-    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, min_tile + 2, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, 2, head_idx_kv, 0}, swizzled_offsets_K);
     // All warps then collaboratively load in the second slice of V (V1) into shared memory
-    G::load<1, false>(v_smem[1], g.Vg, {batch_idx, min_tile + 1, head_idx_kv, 0}, swizzled_offsets_V);
+    G::load<1, false>(v_smem[1], g.Vg, {batch_idx, 1, head_idx_kv, 0}, swizzled_offsets_V);
     asm volatile("s_waitcnt lgkmcnt(0)");
     asm volatile("s_waitcnt vmcnt(" FA_VM4 ")");
     __builtin_amdgcn_sched_barrier(0);
     __builtin_amdgcn_s_barrier();
 
     // hot loop
-    for (int j = min_tile + 3; j < max_num_tiles - 1; j += 2) {
+    for (int j = 3; j < max_num_tiles - 1; j += 2) {
         // Cluster 0:
         //      QK1
         zero(att_block[1]);
         transpose(k_reg_transposed, k_reg);
         mma_AtB(att_block[1], k_reg_transposed, q_reg_transposed, att_block[1]);
-#if WINDOW
-        // window masks interior tiles that causal skips
-        mask_kv_tile(att_block[1], tile_idx, j - 2, neg_inf_v, lane);
-#endif
         //      Finish softmax for QK0
         exp2(att_block[0].tiles[1][0], att_block[0].tiles[1][0]);
         mul(norm_vec, norm_vec, scale_vec);
@@ -421,7 +379,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
         load(v_reg, v_smem[1]);
         if constexpr (causal) {
             const int kv_end_pos = (j) * KV_BLOCK_SIZE;
-            if (WINDOW || q_start_pos < kv_end_pos) {
+            if (q_start_pos < kv_end_pos) {  // Only mask if needed
                 mask_kv_tile(att_block[0], tile_idx, j - 1, neg_inf_v, lane);
             }
         }
@@ -489,7 +447,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     load(v_reg, v_smem[0]);
     if constexpr (causal) {
         const int kv_end_pos = (max_num_tiles - 2) * KV_BLOCK_SIZE;
-        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, WINDOW ? 1 : 0)) {
+        if (__builtin_expect(q_start_pos < kv_end_pos, 0)) {  // Only mask if needed
             mask_kv_tile(att_block[1], tile_idx, max_num_tiles - 3, neg_inf_v, lane);
         }
     }
@@ -552,7 +510,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     load(v_reg, v_smem[1]);
     if constexpr (causal) {
         const int kv_end_pos = (max_num_tiles - 1) * KV_BLOCK_SIZE;
-        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, 1)) {
+        if (__builtin_expect(q_start_pos < kv_end_pos, 1)) {  // Only mask if needed
             mask_kv_tile(att_block[0], tile_idx, max_num_tiles - 2, neg_inf_v, lane);
         }
     }
@@ -614,7 +572,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     load(v_reg, v_smem[0]);
     if constexpr (causal) {
         const int kv_end_pos = (max_num_tiles) * KV_BLOCK_SIZE;
-        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, 1)) {
+        if (__builtin_expect(q_start_pos < kv_end_pos, 1)) {  // Only mask if needed
             mask_kv_tile(att_block[1], tile_idx, max_num_tiles - 1, neg_inf_v, lane);
         }
     }

extra/thunder/amd/grouped_mxfp8_gemm.cpp

@@ -1,158 +0,0 @@
-#include "kittens.cuh"
-
-using namespace kittens;
-
-#ifndef GEMM_M
-constexpr int GEMM_M = 8192;
-#endif
-#ifndef GEMM_N
-constexpr int GEMM_N = 8192;
-#endif
-#ifndef GEMM_K
-constexpr int GEMM_K = 8192;
-#endif
-#ifndef GEMM_E
-constexpr int GEMM_E = 8;
-#endif
-
-// Kernel
-constexpr int NUM_WARPS  = 8;
-constexpr int WARPS_ROW  = 2;
-constexpr int WARPS_COL  = 4;
-constexpr int BLOCK_ROW  = 256;
-constexpr int BLOCK_COL  = 256;
-constexpr int BLOCK_K    = 128;
-constexpr int HALF_ROW   = BLOCK_ROW / 2;
-constexpr int HALF_COL   = BLOCK_COL / 2;
-constexpr int REG_M      = BLOCK_ROW / WARPS_ROW / 2;
-constexpr int REG_N      = BLOCK_COL / WARPS_COL / 2;
-
-using G = kittens::group<NUM_WARPS>;
-
-__global__ __launch_bounds__(512, 2) void grouped_mxfp8_gemm_kernel(bf16 *C_ptr, fp8e4m3 *A_ptr, fp8e4m3 *B_ptr, fp8e8m0 *scale_A_ptr, fp8e8m0 *scale_B_ptr,
-    const uint8_t *__restrict__ a_e8_unused,
-    const uint8_t *__restrict__ b_e8_unused,
-    const int *__restrict__ expert_off) {
-    constexpr int M = GEMM_M, N = GEMM_N, K = GEMM_K, E = GEMM_E;
-
-    kittens::gl<fp8e4m3, 1, 1, M, K>     A{A_ptr, nullptr, nullptr, nullptr, nullptr};
-    kittens::gl<fp8e4m3, 1, 1, E * N, K> B{B_ptr, nullptr, nullptr, nullptr, nullptr};  // all experts stacked on rows
-    kittens::gl<bf16, 1, 1, M, N>        C{C_ptr, nullptr, nullptr, nullptr, nullptr};
-
-    constexpr int k_iters      = K / BLOCK_K;
-    constexpr int NUM_THREADS  = NUM_WARPS * WARP_THREADS;
-
-    kittens::gl<fp8e8m0, k_iters * (M / BLOCK_ROW), 1, 16, 64>     scale_A_gl{scale_A_ptr, nullptr, nullptr, nullptr, nullptr};
-    kittens::gl<fp8e8m0, E * k_iters * (N / BLOCK_COL), 1, 16, 64> scale_B_gl{scale_B_ptr, nullptr, nullptr, nullptr, nullptr};
-
-    using ST_A     = st_fp8e4m3<HALF_ROW, BLOCK_K, st_16x128_s>;
-    using ST_B     = st_fp8e4m3<HALF_COL, BLOCK_K, st_16x128_s>;
-    using ST_Scale = st<fp8e8m0, 16, 64, st_16x64_s>;
-    using RT_A     = rt_fp8e4m3<REG_M, BLOCK_K>;
-    using RT_B     = rt_fp8e4m3<REG_N, BLOCK_K>;
-    using RT_C     = rt_fl<REG_M, REG_N, col_l, rt_16x16_s>;
-
-    __shared__ ST_A As[2][2];
-    __shared__ ST_B Bs[2][2];
-    __shared__ ST_Scale scale_A_smem[2], scale_B_smem[2];
-
-    RT_A a;
-    RT_B b0, b1;
-    RT_C cA, cB, cC, cD;
-    zero(cA); zero(cB); zero(cC); zero(cD);
-
-    constexpr int tiles_M  = M / BLOCK_ROW;
-    constexpr int tiles_N  = N / BLOCK_COL;
-    const int NUM_XCDS     = 8;
-    const int WGM          = 8;
-    int wgid = chiplet_transform_chunked(blockIdx.x, gridDim.x, NUM_XCDS, WGM * WGM);
-    int num_wgid_in_group = WGM * tiles_N;
-    int group_id     = wgid / num_wgid_in_group;
-    int first_pid_m  = group_id * WGM;
-    int group_size_m = min(tiles_M - first_pid_m, WGM);
-    int block_row    = first_pid_m + ((wgid % num_wgid_in_group) % group_size_m);
-    int block_col    = (wgid % num_wgid_in_group) / group_size_m;
-    int block_m      = block_row * BLOCK_ROW;
-    int block_n      = block_col * BLOCK_COL;
-
-    int e = 0;
-    #pragma unroll
-    for (int i = 1; i < E; i++) e += (expert_off[i] <= block_row * BLOCK_ROW);
-    e = __builtin_amdgcn_readfirstlane(e);
-    const int bcol_base = e * (N / HALF_COL);          // expert base in B row-tile (128-row) units
-    const int sb_base   = e * (k_iters * tiles_N);     // expert base into scale_B batches
-
-    int warp_m = warpid() / WARPS_COL;
-    int warp_n = warpid() % WARPS_COL;
-
-    using T = fp8e4m3;
-    constexpr int bpt      = ST_A::underlying_subtile_bytes_per_thread;
-    constexpr int bpm      = bpt * NUM_THREADS;
-    constexpr int copies_A = HALF_ROW * BLOCK_K * sizeof(T) / bpm;
-    constexpr int copies_B = HALF_COL * BLOCK_K * sizeof(T) / bpm;
-    uint32_t sw_A[copies_A], sw_B[copies_B];
-    G::prefill_swizzled_offsets(As[0][0], A, sw_A);
-    G::prefill_swizzled_offsets(Bs[0][0], B, sw_B);
-
-    const T *a_base = (const T *)&A[{0, 0, 0, 0}];
-    const T *b_base = (const T *)&B[{0, 0, 0, 0}];
-    const int a_row_stride = A.template stride<2>() * sizeof(T);
-    const int b_row_stride = B.template stride<2>() * sizeof(T);
-    i32x4 a_srd = make_srsrc(a_base, (uint32_t)((uint64_t)M * a_row_stride), a_row_stride);
-    i32x4 b_srd = make_srsrc(b_base, (uint32_t)((uint64_t)E * N * b_row_stride), b_row_stride);
-
-    const int wid = warpid() % NUM_WARPS;
-    constexpr int elem_per_warp = (16 / sizeof(T)) * kittens::WARP_THREADS;
-    uint32_t a_lds_00 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[0][0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t a_lds_01 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[0][1].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t a_lds_10 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[1][0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t a_lds_11 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[1][1].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_00 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[0][0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_01 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[0][1].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_10 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[1][0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_11 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[1][1].data[0]) + wid * elem_per_warp * sizeof(T)));
-
-    int a_row_h0 = warp_m * REG_M;
-    int a_row_h1 = HALF_ROW + warp_m * REG_M;
-    int b_row_h0 = warp_n * REG_N;
-    int b_row_h1 = HALF_COL + warp_n * REG_N;
-
-
-    uint32_t a_lds[2][2] = {{a_lds_00, a_lds_01}, {a_lds_10, a_lds_11}};
-    uint32_t b_lds[2][2] = {{b_lds_00, b_lds_01}, {b_lds_10, b_lds_11}};
-
-    #pragma unroll 1
-    for (int kk = 0; kk < k_iters; kk++) {
-        G::load(As[0][0], A, {0, 0, block_row * 2,     kk}, sw_A, a_srd, a_base, __builtin_amdgcn_readfirstlane(a_lds[0][0]));
-        G::load(As[0][1], A, {0, 0, block_row * 2 + 1, kk}, sw_A, a_srd, a_base, __builtin_amdgcn_readfirstlane(a_lds[0][1]));
-        G::load(Bs[0][0], B, {0, 0, bcol_base + block_col * 2,     kk}, sw_B, b_srd, b_base, __builtin_amdgcn_readfirstlane(b_lds[0][0]));
-        G::load(Bs[0][1], B, {0, 0, bcol_base + block_col * 2 + 1, kk}, sw_B, b_srd, b_base, __builtin_amdgcn_readfirstlane(b_lds[0][1]));
-        G::load(scale_A_smem[0], scale_A_gl, {kk * tiles_M + block_row, 0, 0, 0});
-        G::load(scale_B_smem[0], scale_B_gl, {sb_base + kk * tiles_N + block_col, 0, 0, 0});
-        asm volatile("s_waitcnt vmcnt(0)");
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        __builtin_amdgcn_s_barrier();
-
-        fp8e8m0_4 sa_h0 = pack_scales(scale_A_smem[0].data, a_row_h0);
-        fp8e8m0_4 sa_h1 = pack_scales(scale_A_smem[0].data, a_row_h1);
-        fp8e8m0_4 sb_h0 = pack_scales(scale_B_smem[0].data, b_row_h0);
-        fp8e8m0_4 sb_h1 = pack_scales(scale_B_smem[0].data, b_row_h1);
-
-        auto bs0 = subtile_inplace<REG_N, BLOCK_K>(Bs[0][0], {warp_n, 0}); load(b0, bs0);
-        auto bs1 = subtile_inplace<REG_N, BLOCK_K>(Bs[0][1], {warp_n, 0}); load(b1, bs1);
-        auto as0 = subtile_inplace<REG_M, BLOCK_K>(As[0][0], {warp_m, 0}); load(a, as0);
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        mma_ABt_scaled(cA, a, b0, cA, &sa_h0, &sb_h0);
-        mma_ABt_scaled(cB, a, b1, cB, &sa_h0, &sb_h1);
-        auto as1 = subtile_inplace<REG_M, BLOCK_K>(As[0][1], {warp_m, 0}); load(a, as1);
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        mma_ABt_scaled(cC, a, b0, cC, &sa_h1, &sb_h0);
-        mma_ABt_scaled(cD, a, b1, cD, &sa_h1, &sb_h1);
-        __builtin_amdgcn_s_barrier();
-    }
-
-    store(C, cA, {0, 0, block_row * WARPS_ROW * 2 + warp_m, block_col * WARPS_COL * 2 + warp_n});
-    store(C, cB, {0, 0, block_row * WARPS_ROW * 2 + warp_m, block_col * WARPS_COL * 2 + WARPS_COL + warp_n});
-    store(C, cC, {0, 0, block_row * WARPS_ROW * 2 + WARPS_ROW + warp_m, block_col * WARPS_COL * 2 + warp_n});
-    store(C, cD, {0, 0, block_row * WARPS_ROW * 2 + WARPS_ROW + warp_m, block_col * WARPS_COL * 2 + WARPS_COL + warp_n});
-}

extra/thunder/amd/grouped_mxfp8_wgrad.cpp

@@ -1,141 +0,0 @@
-#include "kittens.cuh"
-
-using namespace kittens;
-
-#ifndef WGRAD_M
-constexpr int WGRAD_M = 8192;
-#endif
-#ifndef WGRAD_N
-constexpr int WGRAD_N = 8192;
-#endif
-#ifndef WGRAD_K
-constexpr int WGRAD_K = 8192;
-#endif
-#ifndef WGRAD_E
-constexpr int WGRAD_E = 8;
-#endif
-
-constexpr int NUM_WARPS  = 8;
-constexpr int WARPS_ROW  = 2;
-constexpr int WARPS_COL  = 4;
-constexpr int BLOCK_ROW  = 256;
-constexpr int BLOCK_COL  = 256;
-constexpr int BLOCK_K    = 128;
-constexpr int HALF_ROW   = BLOCK_ROW / 2;
-constexpr int HALF_COL   = BLOCK_COL / 2;
-constexpr int REG_M      = BLOCK_ROW / WARPS_ROW / 2;
-constexpr int REG_N      = BLOCK_COL / WARPS_COL / 2;
-
-using G = kittens::group<NUM_WARPS>;
-
-__global__ __launch_bounds__(512, 2) void grouped_mxfp8_wgrad_kernel(bf16 *C_ptr, fp8e4m3 *A_ptr, fp8e4m3 *B_ptr,
-    fp8e8m0 *scale_A_ptr, fp8e8m0 *scale_B_ptr,
-    const int *__restrict__ expert_off) {
-    constexpr int M = WGRAD_M, N = WGRAD_N, K = WGRAD_K, E = WGRAD_E;
-
-    kittens::gl<fp8e4m3, 1, 1, N, M>     A{A_ptr, nullptr, nullptr, nullptr, nullptr};  // g^T
-    kittens::gl<fp8e4m3, 1, 1, K, M>     B{B_ptr, nullptr, nullptr, nullptr, nullptr};  // x^T
-    kittens::gl<bf16, 1, 1, E * N, K>    C{C_ptr, nullptr, nullptr, nullptr, nullptr};  // grad_w, experts stacked
-
-    constexpr int m_blocks    = M / BLOCK_K;      // 128-wide blocks along the contraction (token) axis
-    constexpr int tiles_N     = N / BLOCK_ROW;
-    constexpr int tiles_K     = K / BLOCK_COL;
-    constexpr int NUM_THREADS = NUM_WARPS * WARP_THREADS;
-
-    kittens::gl<fp8e8m0, m_blocks * tiles_N, 1, 16, 64> scale_A_gl{scale_A_ptr, nullptr, nullptr, nullptr, nullptr};
-    kittens::gl<fp8e8m0, m_blocks * tiles_K, 1, 16, 64> scale_B_gl{scale_B_ptr, nullptr, nullptr, nullptr, nullptr};
-
-    using ST_A     = st_fp8e4m3<HALF_ROW, BLOCK_K, st_16x128_s>;
-    using ST_B     = st_fp8e4m3<HALF_COL, BLOCK_K, st_16x128_s>;
-    using ST_Scale = st<fp8e8m0, 16, 64, st_16x64_s>;
-    using RT_A     = rt_fp8e4m3<REG_M, BLOCK_K>;
-    using RT_B     = rt_fp8e4m3<REG_N, BLOCK_K>;
-    using RT_C     = rt_fl<REG_M, REG_N, col_l, rt_16x16_s>;
-
-    __shared__ ST_A As[2];
-    __shared__ ST_B Bs[2];
-    __shared__ ST_Scale scale_A_smem, scale_B_smem;
-
-    RT_A a;
-    RT_B b0, b1;
-    RT_C cA, cB, cC, cD;
-    zero(cA); zero(cB); zero(cC); zero(cD);
-
-    const int wg        = blockIdx.x;
-    const int e         = wg / (tiles_N * tiles_K);
-    const int rem       = wg % (tiles_N * tiles_K);
-    const int block_row = rem / tiles_K;   // over grad_w rows (N)
-    const int block_col = rem % tiles_K;   // over grad_w cols (K)
-
-    const int o0  = __builtin_amdgcn_readfirstlane(expert_off[e]);
-    const int kk0 = o0 / BLOCK_K;
-    const int nk  = (__builtin_amdgcn_readfirstlane(expert_off[e + 1]) - o0) / BLOCK_K;
-
-    const int warp_m = warpid() / WARPS_COL;
-    const int warp_n = warpid() % WARPS_COL;
-
-    using T = fp8e4m3;
-    constexpr int bpt      = ST_A::underlying_subtile_bytes_per_thread;
-    constexpr int bpm      = bpt * NUM_THREADS;
-    constexpr int copies_A = HALF_ROW * BLOCK_K * sizeof(T) / bpm;
-    constexpr int copies_B = HALF_COL * BLOCK_K * sizeof(T) / bpm;
-    uint32_t sw_A[copies_A], sw_B[copies_B];
-    G::prefill_swizzled_offsets(As[0], A, sw_A);
-    G::prefill_swizzled_offsets(Bs[0], B, sw_B);
-
-    const T *a_base = (const T *)&A[{0, 0, 0, 0}];
-    const T *b_base = (const T *)&B[{0, 0, 0, 0}];
-    const int a_row_stride = A.template stride<2>() * sizeof(T);
-    const int b_row_stride = B.template stride<2>() * sizeof(T);
-    i32x4 a_srd = make_srsrc(a_base, (uint32_t)((uint64_t)N * a_row_stride), a_row_stride);
-    i32x4 b_srd = make_srsrc(b_base, (uint32_t)((uint64_t)K * b_row_stride), b_row_stride);
-
-    const int wid = warpid() % NUM_WARPS;
-    constexpr int elem_per_warp = (16 / sizeof(T)) * kittens::WARP_THREADS;
-    uint32_t a_lds_0 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t a_lds_1 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&As[1].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_0 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[0].data[0]) + wid * elem_per_warp * sizeof(T)));
-    uint32_t b_lds_1 = __builtin_amdgcn_readfirstlane(static_cast<uint32_t>(reinterpret_cast<uintptr_t>(&Bs[1].data[0]) + wid * elem_per_warp * sizeof(T)));
-
-    const int a_row_h0 = warp_m * REG_M;
-    const int a_row_h1 = HALF_ROW + warp_m * REG_M;
-    const int b_row_h0 = warp_n * REG_N;
-    const int b_row_h1 = HALF_COL + warp_n * REG_N;
-
-    #pragma unroll 1
-    for (int t = 0; t < nk; t++) {
-        const int kk = kk0 + t;
-        G::load(As[0], A, {0, 0, block_row * 2,     kk}, sw_A, a_srd, a_base, __builtin_amdgcn_readfirstlane(a_lds_0));
-        G::load(As[1], A, {0, 0, block_row * 2 + 1, kk}, sw_A, a_srd, a_base, __builtin_amdgcn_readfirstlane(a_lds_1));
-        G::load(Bs[0], B, {0, 0, block_col * 2,     kk}, sw_B, b_srd, b_base, __builtin_amdgcn_readfirstlane(b_lds_0));
-        G::load(Bs[1], B, {0, 0, block_col * 2 + 1, kk}, sw_B, b_srd, b_base, __builtin_amdgcn_readfirstlane(b_lds_1));
-        G::load(scale_A_smem, scale_A_gl, {kk * tiles_N + block_row, 0, 0, 0});
-        G::load(scale_B_smem, scale_B_gl, {kk * tiles_K + block_col, 0, 0, 0});
-        asm volatile("s_waitcnt vmcnt(0)");
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        __builtin_amdgcn_s_barrier();
-
-        fp8e8m0_4 sa_h0 = pack_scales(scale_A_smem.data, a_row_h0);
-        fp8e8m0_4 sa_h1 = pack_scales(scale_A_smem.data, a_row_h1);
-        fp8e8m0_4 sb_h0 = pack_scales(scale_B_smem.data, b_row_h0);
-        fp8e8m0_4 sb_h1 = pack_scales(scale_B_smem.data, b_row_h1);
-
-        auto bs0 = subtile_inplace<REG_N, BLOCK_K>(Bs[0], {warp_n, 0}); load(b0, bs0);
-        auto bs1 = subtile_inplace<REG_N, BLOCK_K>(Bs[1], {warp_n, 0}); load(b1, bs1);
-        auto as0 = subtile_inplace<REG_M, BLOCK_K>(As[0], {warp_m, 0}); load(a, as0);
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        mma_ABt_scaled(cA, a, b0, cA, &sa_h0, &sb_h0);
-        mma_ABt_scaled(cB, a, b1, cB, &sa_h0, &sb_h1);
-        auto as1 = subtile_inplace<REG_M, BLOCK_K>(As[1], {warp_m, 0}); load(a, as1);
-        asm volatile("s_waitcnt lgkmcnt(0)");
-        mma_ABt_scaled(cC, a, b0, cC, &sa_h1, &sb_h0);
-        mma_ABt_scaled(cD, a, b1, cD, &sa_h1, &sb_h1);
-        __builtin_amdgcn_s_barrier();
-    }
-
-    const int crow_base = e * (N / REG_M);   // grad_w rows are experts stacked; store coord is in REG_M units
-    store(C, cA, {0, 0, crow_base + block_row * WARPS_ROW * 2 + warp_m,              block_col * WARPS_COL * 2 + warp_n});
-    store(C, cB, {0, 0, crow_base + block_row * WARPS_ROW * 2 + warp_m,              block_col * WARPS_COL * 2 + WARPS_COL + warp_n});
-    store(C, cC, {0, 0, crow_base + block_row * WARPS_ROW * 2 + WARPS_ROW + warp_m,  block_col * WARPS_COL * 2 + warp_n});
-    store(C, cD, {0, 0, crow_base + block_row * WARPS_ROW * 2 + WARPS_ROW + warp_m,  block_col * WARPS_COL * 2 + WARPS_COL + warp_n});
-}

extra/thunder/amd/include/utils.cpp

@@ -97,33 +97,4 @@ __device__ inline static void atomic_pk_add_bf16_with_warpid(const GL &dst, cons
             }(std::make_index_sequence<RT::width>{});
         }.template operator()<Ns>(), ...);
     }(std::make_index_sequence<RT::height>{});
-}
-// make_window: complement of make_causal for the window lower boundary (q_pos-k_pos == WINDOW). masks = ~(causal masks)
-template<int N, int M, int GPR, ducks::art::all T0, ducks::art::all T1>
-__device__ static inline void make_window(T0 &dst, const T1 &src) {
-    static_assert(std::is_same_v<typename T0::T, float> && std::is_same_v<typename T1::T, float>, "Only float to float window mask is supported");
-    static_assert(std::is_same_v<typename T0::layout, typename T1::layout>, "Only same layout is supported");
-    static_assert(std::is_same_v<typename T0::shape, typename T1::shape>, "Only same shape is supported");
-
-    if constexpr (std::is_same_v<typename T0::layout, typename ducks::rt_layout::col> && std::is_same_v<typename T0::shape, typename ducks::rt_shape::rt_16x16>) {
-        using range_type_T0 = ducks::art::get_nth_range_t<typename T0::register_ranges, N * T0::width + M>;
-        using registers_T0 = ducks::art::split_many_t<ducks::art::type_list<range_type_T0>, 1>;
-        using range_type_T1 = ducks::art::get_nth_range_t<typename T1::register_ranges, N * T1::width + M>;
-        using registers_T1 = ducks::art::split_many_t<ducks::art::type_list<range_type_T1>, 1>;
-        static_assert(registers_T0::size == registers_T1::size);
-
-        uint64_t window_mask = 0x1FFF01FF001F0001;
-        macros::v_cndmask_b32_e64<ducks::art::get_nth_range_t<registers_T0, 0>::lo, ducks::art::get_nth_range_t<registers_T1, 0>::lo, GPR>(window_mask);
-
-        window_mask = 0x3FFF03FF003F0003;
-        macros::v_cndmask_b32_e64<ducks::art::get_nth_range_t<registers_T0, 1>::lo, ducks::art::get_nth_range_t<registers_T1, 1>::lo, GPR>(window_mask);
-
-        window_mask = 0x7FFF07FF007F0007;
-        macros::v_cndmask_b32_e64<ducks::art::get_nth_range_t<registers_T0, 2>::lo, ducks::art::get_nth_range_t<registers_T1, 2>::lo, GPR>(window_mask);
-
-        window_mask = 0xFFFF0FFF00FF000F;
-        macros::v_cndmask_b32_e64<ducks::art::get_nth_range_t<registers_T0, 3>::lo, ducks::art::get_nth_range_t<registers_T1, 3>::lo, GPR>(window_mask);
-    } else {
-        static_assert(false, "Unsupported window mask");
-    }
-}
+}

extra/thunder/tiny/fa.py

@@ -16,7 +16,7 @@ def _sharded_empty(shape:Tensor, ref:Tensor, axis:int|None) -> Tensor:
   if not isinstance(ref.device, tuple): return Tensor.empty(*shape, dtype=ref.dtype, device=ref.device)
   shape = tuple(s // len(ref.device) if i == ref.uop.axis else s for i, s in enumerate(shape))
   axis = ref.uop.axis if axis is None else axis
-  return Tensor(Tensor.empty(*shape, dtype=ref.dtype, device=ref.device).uop.unshard(axis), dtype=ref.dtype, device=ref.device)
+  return Tensor(Tensor.empty(*shape, dtype=ref.dtype, device=ref.device).uop.multi(axis), dtype=ref.dtype, device=ref.device)
 
 def _sharded_empty_like(ref:Tensor, axis:int|None=None) -> Tensor:
   return _sharded_empty(ref.shape, ref, axis)

extra/thunder/tiny/tk/group.py

@@ -224,7 +224,7 @@ class Group:
 
       # store to shared memory
       red_local_store = red_local[self.laneid].store(red_reg[0])
-      red_local = red_local.after(red_local_store).reshape(red_local.shape)
+      red_local = red_local.after(red_local_store.barrier()).reshape(red_local.shape)
 
       # reduce from shared memory
       for inner in self.ker.range(3, axis_type=AxisType.REDUCE, track=False):
@@ -258,7 +258,7 @@ class Group:
 
       # store to shared memory
       red_local_store = red_local[self.laneid].store(red_reg[0])
-      red_local = red_local.after(red_local_store).reshape(red_local.shape)
+      red_local = red_local.after(red_local_store.barrier()).reshape(red_local.shape)
 
       # reduce from shared memory
       for inner in self.ker.range(3, axis_type=AxisType.REDUCE, track=False):
@@ -342,7 +342,7 @@ class Group:
           if src.dtype != dst.dtype:
             src_load = src_load.cast(dst.dtype)
           dst_store = dst[*dst_idxs, height, width, srow, scol].store(src_load)
-          dst_store = dst_store.end(height, width, outer, inner)
+          dst_store = dst_store.end(height, width, outer, inner).barrier()
     elif dst.addrspace == AddrSpace.REG and src.addrspace == AddrSpace.GLOBAL and isinstance(dst, RT):
       srcf = src.flatten()
       row_stride = prod(src.shape[axis+1:])

extra/thunder/tiny/tk/kernel.py

@@ -48,14 +48,14 @@ class Kernel(AbstractContextManager):
   @property
   def warpgroup(self): return self.group(4)
 
-  def range(self, start:int, end:int=0, step:int=1, axis_type:AxisType=AxisType.WEAK, track:bool=True):
+  def range(self, start:int, end:int=0, step:int=1, axis_type:AxisType=AxisType.LOOP, track:bool=True):
     if end == 0: start, end = 0, start
     rng = _tk_range(start, end, step, axis_type, self.range_id)
     self.range_id += 1
     if track: self.range_stack.append(rng)
     return rng
 
-  def raw_range(self, end:int=0, axis_type:AxisType=AxisType.WEAK):
+  def raw_range(self, end:int=0, axis_type:AxisType=AxisType.LOOP):
     rng = UOp.range(end, self.range_id, axis_type=axis_type)
     self.range_id += 1
     return rng

extra/torch_backend/backend.py

@@ -3,7 +3,7 @@
 # A002 Function argument `input` is shadowing a Python builtin
 # A006 Lambda argument `input` is shadowing a Python builtin
 from tinygrad import Tensor, dtypes, Device
-from tinygrad.uop.ops import Ops, GroupOp
+from tinygrad.uop.ops import Ops
 from tinygrad.helpers import getenv, prod, strides_for_shape, argfix
 import torch.lib
 TORCH_DEBUG = getenv("TORCH_DEBUG")
@@ -18,16 +18,12 @@ def _to_torch_device(device: str): return torch.device("tiny", int(device.partit
 
 import torch.utils.cpp_extension
 mod = torch.utils.cpp_extension.load(name="custom_device_extension", sources=[str(pathlib.Path(__file__).parent / "wrapped_tensor.cpp")])
-# TODO: this assumes a contiguous source, so PERMUTE/EXPAND/PAD/FLIP are wrong. UOp.contiguous_view_offset does it
-# properly, but it needs a device (these are deviceless)
-alias_ops = GroupOp.Movement | {Ops.BITCAST, Ops.DETACH, Ops.AFTER}
 def calculate_storage_offset(x: Tensor) -> int:
-  offset, u = 0, x.uop
-  while u.op in alias_ops:
-    if u.op is Ops.SHRINK:
+  offset = 0
+  for u in x.uop.toposort():
+    if u.op == Ops.SHRINK:
       u_strides = strides_for_shape(u.src[0].shape)
       for i, (start, _) in enumerate(u.marg): offset += start * u_strides[i]
-    u = u.src[0]
   return offset
 def wrap(x: Tensor, dev: torch.device|None=None) -> torch.Tensor:
   x._strides = strides_for_shape(x.shape) # always recalculate
@@ -224,7 +220,7 @@ def _as_strided(tensor:Tensor, size, stride, storage_offset=0):
 
 @torch.library.impl("aten::as_strided", "privateuseone")
 def as_strided(tensor:torch.Tensor, size, stride, storage_offset=None):
-  if storage_offset is None: storage_offset = tensor.storage_offset()
+  storage_offset = storage_offset or tensor.storage_offset()
   return _as_strided(tensor, size, stride, storage_offset)
 
 @torch.library.impl("aten::_reshape_alias", "privateuseone")
@@ -232,16 +228,16 @@ def _reshape_alias(tensor:torch.Tensor, size, stride):
   return _as_strided(tensor, size, stride)
 
 @torch.library.impl("aten::empty_strided", "privateuseone")
-def empty_strided(size, stride, dtype=None, layout=None, device=None, pin_memory=False):
+def empty_strided(size, stride, dtype, layout=None, device=None, pin_memory=False):
   if TORCH_DEBUG: print(f"empty_strided {size=} {stride=} {dtype=} {layout=} {device=} {pin_memory=}")
-  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device))
+  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype), device=_from_torch_device(device)).contiguous()
   # TODO: should return with requested strides
   return wrap(ret)
 
 @torch.library.impl("aten::empty.memory_format", "privateuseone")
 def empty_memory_format(size, dtype=None, layout=None, device=None, pin_memory=False, memory_format=None):
   if TORCH_DEBUG: print(f"empty.memory_format {size=} {dtype=} {layout=} {device=} {pin_memory=} {memory_format=}")
-  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device))
+  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device)).contiguous()
   return wrap(ret)
 
 @torch.library.impl("aten::max_pool2d_with_indices", "privateuseone")
@@ -555,8 +551,6 @@ def wrap_out(f):
     assert out.shape == assigned.shape, f"shape mismatch: {assigned.shape} -> {out.shape}"
     assert out.device == assigned.device or out.device is None or assigned.device is None, f"device mismatch: {assigned.device} -> {out.device}"
     assert out.dtype == assigned.dtype, f"dtype mismatch: {assigned.dtype} -> {out.dtype}"
-    # an out= that is a view has to be written through its base, and _apply_inplace gives a deviceless base its buffer first
-    if canonical_base(out) is not out: return _apply_inplace(out, assigned) or out
     if out.device is None and assigned.device is not None: out.replace(out.empty_like(device=assigned.device))
     return out.assign(assigned)
   return _wrap_out
@@ -571,10 +565,8 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   "aten.floor_divide": lambda x,y: x//y,
   "aten.floor_divide_.Tensor": lambda x,y: x//y,
   "aten.__lshift__.Scalar": lambda x,y: x<<y,
-  "aten.__lshift__.Tensor": lambda x,y: x<<y,
   "aten.__ilshift__.Scalar": lambda x,y: x<<y,
   "aten.__rshift__.Scalar": lambda x,y: x>>y,
-  "aten.__rshift__.Tensor": lambda x,y: x>>y,
   "aten.__irshift__.Scalar": lambda x,y: x>>y,
   # inplace ops using replace for fusion
   "aten.zero_": lambda x: x.const_like(0),
@@ -658,6 +650,35 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   "aten.unfold": Tensor.unfold,
 }}
 
+# operations that need inplace treatment (use _inplace_op instead of wrap_fxn) AKA return original tensor
+inplace_ops = {
+  "aten.zero_",
+  "aten.fill_.Scalar",
+  "aten.fill_.Tensor",
+  "aten.add_.Tensor",
+  "aten.add_.Scalar",
+  "aten.mul_.Tensor",
+  "aten.mul_.Scalar",
+  "aten.floor_divide_.Tensor",
+  "aten.__ilshift__.Scalar",
+  "aten.__irshift__.Scalar",
+  "aten.relu_",
+  "aten.random_",
+  "aten.random_.from",
+  "aten.uniform_",
+  "aten.normal_",
+  "aten.logical_or_",
+  "aten.masked_fill_.Scalar",
+  "aten.masked_fill_.Tensor",
+}
+
+inplace_view_ops = {
+  "aten.squeeze_.dim",
+  "aten.unsqueeze_",
+  "aten.transpose_",
+  "aten.t_",
+}
+
 def wrap_fxn(k,f):
   def nf(*args, **kwargs):
     if TORCH_DEBUG:
@@ -671,7 +692,7 @@ def wrap_fxn(k,f):
     else: raise RuntimeError(f"unknown output type {type(out)}")
   return nf
 
-def wrap_inplace(f):
+def wrap_inplace(k,f):
   def nf(*args, **kwargs):
     orig = args[0]
     args, kwargs = unwrap_args(args, kwargs)
@@ -679,7 +700,7 @@ def wrap_inplace(f):
     return orig
   return nf
 
-def wrap_inplace_view_op(f):
+def wrap_inplace_view_op(k,f):
   def nf(*args, **kwargs):
     orig = args[0]
     args, kwargs = unwrap_args(args, kwargs)
@@ -712,17 +733,11 @@ def wrap_inplace_view_op(f):
     return orig
   return nf
 
-# the aten schema says how an op is called: an inplace view retargets the view, a writable first arg is inplace,
-# and a writable out arg must have come from tiny_backend_out so that wrap_out was applied
 for k,v in tiny_backend.items():
-  name, _, overload = k.removeprefix("aten.").partition(".")
-  op = getattr(getattr(aten, name), overload or "default")
-  writes = [a.name for a in op._schema.arguments if a.alias_info is not None and a.alias_info.is_write]
-  if torch.Tag.inplace_view in op.tags: fxn = wrap_inplace_view_op(v)
-  elif writes == [op._schema.arguments[0].name] and op._schema.returns: fxn = wrap_inplace(v)
-  elif not writes or (writes == ["out"] and k in tiny_backend_out): fxn = wrap_fxn(k, v)
-  else: raise RuntimeError(f"{k} writes {writes}: expected an inplace first arg, or an out arg with {k} in tiny_backend_out")
-  torch.library.impl(k.replace("aten.", "aten::"), "privateuseone")(fxn)
+  if k in inplace_view_ops: wrapper = wrap_inplace_view_op
+  elif k in inplace_ops: wrapper = wrap_inplace
+  else: wrapper = wrap_fxn
+  torch.library.impl(k.replace("aten.", "aten::"), "privateuseone")(wrapper(k,v))
 
 @torch.library.impl("aten::equal", "privateuseone")
 def equal(x: torch.Tensor, y: torch.Tensor): return (x==y).all().item()
@@ -758,17 +773,21 @@ def native_batch_norm(input, weight, bias, running_mean, running_var, training,
 @torch.library.impl("aten::native_batch_norm_backward", "privateuseone")
 def native_batch_norm_backward(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask):
   grad_out_t, input_t = unwrap(grad_out), unwrap(input)
-  dims, shape = tuple(x for x in range(input_t.ndim) if x != 1), (1, -1) + (1,)*(input_t.ndim-2)
-  # training differentiates the batch stats it was given, eval treats the running stats as constants
-  if train: mean, invstd = unwrap(save_mean), unwrap(save_invstd)
-  else: mean, invstd = unwrap(running_mean), unwrap(running_var).add(eps).rsqrt()
-  xhat = (input_t - mean.reshape(shape)) * invstd.reshape(shape)
-  grad_bias, grad_weight = grad_out_t.sum(axis=dims), (grad_out_t * xhat).sum(axis=dims)
-  grad_input = grad_out_t if not train else \
-    grad_out_t - (grad_bias.reshape(shape) + xhat * grad_weight.reshape(shape)) / (input_t.numel() // input_t.shape[1])
-  grad_input = grad_input * invstd.reshape(shape) * (unwrap(weight).reshape(shape) if weight is not None else 1)
-  return (wrap(grad_input) if output_mask[0] else None, wrap(grad_weight) if output_mask[1] else None,
-          wrap(grad_bias) if output_mask[2] else None)
+  weight_t = unwrap(weight) if weight is not None else None
+  save_mean_t = unwrap(save_mean)
+  save_invstd_t = unwrap(save_invstd)
+  out = input_t.batchnorm(weight_t, None, save_mean_t, save_invstd_t)
+  targets = [t for t, m in zip([input_t, weight_t], output_mask[:2]) if t is not None and m]
+  if targets:
+    grads = out.gradient(*targets, gradient=grad_out_t)
+    grad_input = grads.pop(0) if output_mask[0] else None
+    grad_weight = grads.pop(0) if output_mask[1] and weight_t is not None else None
+  else:
+    grad_input, grad_weight = None, None
+  grad_bias = grad_out_t.sum(axis=tuple(x for x in range(grad_out_t.ndim) if x != 1)) if output_mask[2] else None
+  return (wrap(grad_input) if grad_input is not None else None,
+          wrap(grad_weight) if grad_weight is not None else None,
+          wrap(grad_bias) if grad_bias is not None else None)
 
 # _pad_circular is not CompositeImplicitAutograd (unlike reflect/replicate pad)
 # we need torch.autograd.Function with explicit AutogradPrivateUse1 registration

extra/torch_backend/test.py

@@ -71,48 +71,6 @@ class TestTorchBackend(unittest.TestCase):
     a = a.as_strided((1,1,5,5), (50,50,7,1), storage_offset=21)
     np.testing.assert_equal(a.cpu().numpy().sum(-1), [[[115,150,185,220,255]]])
 
-  def test_storage_offset_of_computed_tensor(self):
-    # a computed result owns its storage, so a slice anywhere in its history must not shift the offset
-    a = torch.arange(8., device=device)
-    self.assertEqual((a[3:]+1).storage_offset(), 0)
-
-  def test_storage_offset_through_aliases(self):
-    a = torch.arange(8., device=device)[3:]
-    self.assertEqual(a.detach().storage_offset(), 3)
-    self.assertEqual(a.view(torch.int32).storage_offset(), 3)
-    torch.add(torch.ones(5, device=device), torch.ones(5, device=device), out=a)
-    self.assertEqual(a.detach().storage_offset(), 3)
-
-  @unittest.expectedFailure  # TODO: storage offset assumes a contiguous source, use UOp.contiguous_view_offset
-  def test_storage_offset_non_contiguous_source(self):
-    a = torch.arange(12., device=device).reshape(3,4)
-    self.assertEqual(a.permute(1,0)[1:].storage_offset(), 1)
-    self.assertEqual(a.flatten()[3:].flip(0).storage_offset(), 0)
-
-  def test_as_strided_explicit_zero_offset(self):
-    # storage_offset=0 is a real offset, not "unspecified": it must not fall back to the input's own offset
-    a = torch.arange(6., device=device)
-    np.testing.assert_equal(a[3:].as_strided((2,), (1,), 0).cpu().numpy(), [0,1])
-    np.testing.assert_equal(a[3:].as_strided((2,), (1,)).cpu().numpy(), [3,4])
-
-  def test_empty_strided_default_dtype(self):
-    self.assertEqual(torch.empty_strided((2,3), (1,2), device=device).dtype, torch.get_default_dtype())
-
-  @unittest.expectedFailure  # TODO: empty_strided ignores the requested strides, the backend treats everything as contiguous
-  def test_empty_strided_honors_strides(self):
-    self.assertEqual(tuple(torch.empty_strided((2,3), (1,2), device=device).stride()), (1,2))
-
-  @unittest.expectedFailure  # TODO: torch refuses an out= that overlaps an input, we compute silently
-  def test_out_overlapping_input_is_rejected(self):
-    x = torch.arange(6., device=device)
-    with self.assertRaises(RuntimeError): torch.add(x[:-1], 10, out=x[1:])
-
-  def test_out_disjoint_input_is_allowed(self):
-    # torch permits an out= that shares a base with an input as long as they do not overlap
-    x, xc = torch.arange(6., device=device), torch.arange(6.)
-    torch.add(x[:3], 10, out=x[3:]); torch.add(xc[:3], 10, out=xc[3:])
-    np.testing.assert_equal(x.cpu().numpy(), xc.numpy())
-
   def test_plus_inplace(self):
     a = torch.ones(4, device=device)
     b = torch.ones(4, device=device)
@@ -358,21 +316,6 @@ class TestTorchBackend(unittest.TestCase):
     assert b.shape == (4, 2, 3)
     np.testing.assert_equal(b.cpu().numpy(), a.cpu().numpy().transpose(2, 0, 1))
 
-  def test_batchnorm_backward_realized_stats(self):
-    # the saved stats are a function of input in training, so grad_input must flow through them even when handed in realized.
-    # the backward eps is unused in training: torch differentiates the save_invstd it was given
-    x0, g0 = torch.randn(8, 4, 3, 3), torch.randn(8, 4, 3, 3)
-    def run(dev, bwd_eps):
-      x, go = x0.to(dev), g0.to(dev)
-      w, b = torch.linspace(0.5, 2.0, 4).to(dev), torch.zeros(4, device=dev)
-      rm, rv = torch.zeros(4, device=dev), torch.ones(4, device=dev)
-      out, sm, si = torch.ops.aten.native_batch_norm(x, w, b, rm, rv, True, 0.1, 1e-5)
-      grads = torch.ops.aten.native_batch_norm_backward(go, x, w, rm, rv, sm.clone().detach(), si.clone().detach(),
-                                                        True, bwd_eps, [True,True,True])
-      return [t.cpu().numpy() for t in grads]
-    for bwd_eps in [1e-5, 0.3]:
-      for got, want in zip(run(device, bwd_eps), run("cpu", bwd_eps)): np.testing.assert_allclose(got, want, atol=1e-4, rtol=1e-3)
-
   def test_batchnorm_unsqueeze(self):
     bn = torch.nn.BatchNorm2d(4).to(device)
     x = torch.randn(8, 4, 3, 3, device=device)
@@ -799,20 +742,6 @@ class TestTorchBackend(unittest.TestCase):
 
 from tinygrad import Tensor
 class TestBackendHelpers(unittest.TestCase):
-  def test_unwrap_rejects_foreign_tensor(self):
-    # unwrap casts to the tiny impl, so a tensor from another backend must be refused rather than reinterpreted
-    with self.assertRaises(RuntimeError): extra.torch_backend.backend.unwrap(torch.ones(4))
-
-  def test_update_metadata_rejects_foreign_tensor(self):
-    # resizing a tensor we don't own would expose memory past its allocation
-    t = torch.ones(4)
-    with self.assertRaises(RuntimeError): extra.torch_backend.backend.mod.update_metadata(t, [8], [1], 0)
-    self.assertEqual(t.shape, (4,))
-
-  def test_unwrap_parameter_and_detached(self):
-    # nn.Parameter and detach rebuild the base OpaqueTensorImpl, which unwrap still has to accept
-    extra.torch_backend.backend.unwrap(torch.nn.Parameter(torch.ones(4, device="tiny")))
-    extra.torch_backend.backend.unwrap(torch.ones(4, device="tiny").detach())
 
   def test_calculate_storage_offset_no_shrink(self):
     t = Tensor.ones(3, 4)

extra/torch_backend/wrapped_tensor.cpp

@@ -124,21 +124,16 @@ at::Tensor wrap_tensor(py::object &py_obj, c10::ScalarType dtype, c10::DeviceInd
     sizes, strides, storage_offset);
 }
 
-// shallow_copy_and_detach (nn.Parameter, aten.detach) rebuilds the base OpaqueTensorImpl, so that is the type every tiny tensor has
-at::OpaqueTensorImpl<std::shared_ptr<c10::SafePyObject>> *tiny_impl(const at::Tensor &tensor) {
-  auto* impl = dynamic_cast<at::OpaqueTensorImpl<std::shared_ptr<c10::SafePyObject>>*>(tensor.unsafeGetTensorImpl());
-  TORCH_CHECK(impl != nullptr, "expected a tiny tensor, got a ", tensor.device().str(), " one. move it with .to(\"tiny\") first");
-  return impl;
-}
-
 py::object unwrap_tensor(const at::Tensor &tensor) {
-  std::shared_ptr<c10::SafePyObject> tiny = tiny_impl(tensor)->opaque_handle();
+  auto* impl = tensor.unsafeGetTensorImpl();
+  auto* opaque_impl = static_cast<at::TinyOpaqueTensorImpl<std::shared_ptr<c10::SafePyObject>>*>(impl);
+  std::shared_ptr<c10::SafePyObject> tiny = opaque_impl->opaque_handle();
   return py::reinterpret_borrow<py::object>(tiny->ptr(getPyInterpreter()));
 }
 
 void update_metadata(const at::Tensor &tensor, const std::vector<int64_t> &sizes,
                      const std::vector<int64_t> &strides, int64_t storage_offset) {
-  auto* impl = tiny_impl(tensor);
+  auto* impl = tensor.unsafeGetTensorImpl();
   impl->set_allow_tensor_metadata_change(true);
   impl->set_sizes_and_strides(sizes, strides, storage_offset);
 }

pyproject.toml

@@ -74,6 +74,7 @@ testing_minimal = [
   "torch==2.9.1",
   "pytest",
   "pytest-xdist",
+  "pytest-timeout",
   "pytest-split",
   "hypothesis>=6.148.9",
   "z3-solver<4.15.4",  # 4.15.4 has a segfault when creating many z3.Context()
@@ -159,6 +160,9 @@ norecursedirs = [
   ".hypothesis",
   ".git",
 ]
+timeout = 300
+timeout_method = "thread"
+timeout_func_only = true
 testpaths = ["test"]
 filterwarnings = [
   # Ignore SWIG warnings from importlib

spec/tinyspec.tex

@@ -29,7 +29,6 @@
 \definecolor{axbrred}{HTML}{E53935}      % GROUP_REDUCE
 \definecolor{axyellow}{HTML}{F9A825}     % UPCAST
 \definecolor{axmagenta}{HTML}{7B1FA2}    % UNROLL
-\definecolor{axgreen}{HTML}{2E7D32}      % DEVICE
 
 \title{tinygrad: a single dialect from Tensor programs to Command Buffers}
 \author{tinygrad, Corp. \\ \texttt{[email protected]}}
@@ -80,7 +79,6 @@ All nodes in the tinygrad graph are \textbf{UOps}. A UOp is a tuple $(\mathrm{op
 \op{Index} & $(T, i_0, i_1, \ldots)$ & --- & Index from left. $()$-shaped $i$ removes dim; $(k,)$-shaped makes it $k$. \\
 \op{Stack} & $(T_0, T_1, \ldots)$ & --- & Join along a newly created leading axis. All shapes must match. \\
 \op{Bitcast} & $(T,)$ & dtype & Reinterpret storage as target dtype; preserve total bytes. \\
-\op{Unshard} & $(T, R_0, R_1, \ldots)$ & axes $(a_0, a_1, \ldots)$ & Concatenate shards of \op{Range} $R_k$ along axis $a_k$; $R_k$ is outer. \\
 \bottomrule
 \end{tabular}
 
@@ -260,7 +258,6 @@ Every UOp has a \textbf{dtype}, \textbf{shape}, \textbf{device}, \textbf{addrspa
 \op{Const} & from arg & $()$ & \textsc{null} & $[v, v]$ \\
 \op{Param} & from arg & from $\mathrm{src}[0]$ & from arg & from src or dtype range \\[3pt]
 Movement ops & $\mathrm{src}[0].\mathrm{dtype}$ & (see op) & $\mathrm{src}[0].\mathrm{device}$ & $\mathrm{src}[0]$ \\
-\op{Unshard} & $\mathrm{src}[0].\mathrm{dtype}$ & $\mathrm{src}[0]$, each $a_k \times n_k$ & $\mathrm{src}[0].\mathrm{device}$ & $\mathrm{src}[0]$ \\
 \op{Reduce} & $\mathrm{src}[0].\mathrm{dtype}$ & remove first $n$ axes & $\mathrm{src}[0].\mathrm{device}$ & dtype range \\[3pt]
 \op{Cast} & from arg & $\mathrm{src}[0].\mathrm{shape}$ & $\mathrm{src}[0].\mathrm{device}$ & clamped to dtype \\
 \op{Bitcast} & from arg & $\mathrm{src}[0].\mathrm{shape}$ & $\mathrm{src}[0].\mathrm{device}$ & dtype range \\
@@ -286,9 +283,9 @@ $[a,A]$, $[b,B]$, $[c,C]$ denote min\_max of $\mathrm{src}[0]$, $\mathrm{src}[1]
 Default \emph{dtype range}: $[\mathrm{dtype\_min},\, \mathrm{dtype\_max}]$.
 
 \medskip
-\textbf{sharding} tracks multi-device sharding as a set of (axis, \op{Range}) pairs. \op{Unshard} defines it: arg is the tuple of sharded axes, one \op{Range} in src per axis (positional: the $k$-th \op{Range} shards the $k$-th axis). \op{Buffer} with $n$-tuple device: sharded on axis $0$ (device dim). The single-axis convenience \textbf{axis} is \textsc{null} unless exactly one axis is sharded.
-\op{Reshape} remaps each sharded axis to preserve its shard boundary. \op{Permute} follows the permutation. \op{Expand} shifts all sharded axes right by $|\mathbf{n}|$.
-\op{Reduce} on a sharded axis drops it. \op{Replicated} on the shard axis $\to$ \textsc{null}. \op{Copy} $\to$ \textsc{null}. ALU ops inherit from sources. Default: \textsc{null}.
+\textbf{axis} tracks the multi-device sharding dimension. \op{Buffer} with $n$-tuple device: axis $= 0$ (device dim).
+\op{Reshape} remaps axis to preserve the shard boundary. \op{Permute} follows the permutation. \op{Expand} shifts axis right by $|\mathbf{n}|$.
+\op{Reduce} on the shard axis $\to$ \textsc{null} (shard axis is among the first $n$ axes). \op{Replicated} on the shard axis $\to$ \textsc{null}. \op{Copy} $\to$ \textsc{null}. ALU ops inherit from sources. Default: \textsc{null}.
 
 %% ============================================================
 \subsection*{Kernel Optimizations (OptOps) \normalfont\small--- schedule-level transforms on kernel ranges}
@@ -300,7 +297,6 @@ Each kernel's iteration space is a set of \op{Range} axes. Every range has an \t
 \toprule
 \textbf{AxisType} & \textbf{Letter} & \textbf{Split from} & \textbf{Direction} & \textbf{Semantics} \\
 \midrule
-{\color{axgreen}\texttt{DEVICE}} & \texttt{d} & --- & --- & Multi-device sharding dimension. \\
 {\color{axblue}\texttt{GLOBAL}} & \texttt{g} & --- & --- & GPU global workgroup dimension. \\
 {\color{axcyan}\texttt{LOCAL}} & \texttt{l} & g, L & inner & Workgroup local dimension (shared memory). \\
 {\color{axbrcyan}\texttt{WARP}} & \texttt{w} & \multicolumn{2}{l}{(created by \op{TC})} & Warp-level lanes for tensor cores. \\
@@ -382,8 +378,6 @@ def scatter_add(T, idx, val):
 Let $D = (d_0, \ldots, d_{n-1})$ be an $n$-tuple device.
 \op{Copy} to an $n$-tuple device reshards with axis $= 0$. \op{Copy} never changes shape.
 
-\textbf{Sharding} splits a tensor along an axis across $n$ devices. It opens a \op{Range} of type \texttt{DEVICE} (a symbolic per-device index $d$), shrinks each device's view to its piece, then closes the range with \op{Unshard}$(T, R, a)$. The result is a logical tensor whose shape along axis $a$ is the full size; each device holds $1/n$ of it. \op{Unshard} is the inverse of sharding --- it marks the boundary between per-device computation and the logical multi-device tensor. The range need not be \texttt{DEVICE}; e.g.\ a \texttt{WARP} range closes the same way, concatenating per-lane shards along $a$ with the range as the outer factor. A tensor may be sharded along several axes at once: \op{Unshard}$(T, R_0, R_1, \ldots;\; a_0, a_1, \ldots)$ carries one \op{Range} per sharded axis, and every movement op maps each sharded axis independently.
-
 \begin{lstlisting}
 # T has shape (s,) on a single device.
 

test/amd/hw/helpers.py

@@ -169,10 +169,11 @@ def run_program_emu(instructions: list, n_lanes: int = 1) -> WaveState:
   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 the AMD runtime."""
-  from tinygrad.device import Device, TinyELF
+  """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 Target, flat_mv
+  from tinygrad.helpers import flat_mv
 
   dev = Device["AMD"]
   compiler = HIPCompiler(dev.arch)  # type: ignore[attr-defined]
@@ -222,7 +223,7 @@ amdhsa.kernels:
 """
 
   lib = compiler.compile(asm_src)
-  prg = dev.runtime(TinyELF(lib, "test", Target("AMD", arch=dev.arch), ()))
+  prg = AMDProgram(dev, "test", lib)  # type: ignore[arg-type]
 
   buf_sz = _out_bytes(n_lanes)
   out_gpu = dev.allocator.alloc(buf_sz)

test/amd/hw/test_cdna_vop3.py

@@ -5,7 +5,7 @@ gfx950 hardware when USE_HW=1.
 """
 import ctypes, struct, unittest
 import tinygrad.runtime.autogen.amd.cdna.ins as cdna
-from tinygrad.helpers import Target, flat_mv
+from tinygrad.helpers import flat_mv
 from tinygrad.renderer.amd.dsl import NULL
 from test.amd.hw.helpers import USE_HW, assemble
 from test.mockgpu.amd.emu import run_asm
@@ -42,7 +42,8 @@ def _run_emu(instructions: list, out_reg: int = 2) -> int:
   return out_buf[0]
 
 def _run_hw(instructions: list, out_reg: int = 2) -> int:
-  from tinygrad.device import Device, TinyELF
+  from tinygrad.device import Device
+  from tinygrad.runtime.ops_amd import AMDProgram
   from tinygrad.runtime.support.compiler_amd import HIPCompiler
 
   dev = Device["AMD"]
@@ -85,7 +86,7 @@ amdhsa.kernels:
 ...
 .end_amdgpu_metadata
 """
-  prg = dev.runtime(TinyELF(HIPCompiler(dev.arch).compile(asm_src), "test", Target("AMD", arch=dev.arch), ()))
+  prg = AMDProgram(dev, "test", HIPCompiler(dev.arch).compile(asm_src))
   prg(global_size=(1, 1, 1), local_size=(LANES, 1, 1), wait=True)
   out = bytearray(LANES * 4)
   dev.allocator._copyout(flat_mv(memoryview(out)), out_gpu)

test/amd/hw/test_dpp.py

@@ -6,7 +6,7 @@ when USE_HW=1.
 """
 import ctypes, unittest
 from tinygrad.runtime.autogen.amd.rdna3.ins import *
-from tinygrad.helpers import Target, flat_mv
+from tinygrad.helpers import flat_mv
 from test.amd.hw.helpers import USE_HW, assemble
 from test.mockgpu.amd.emu import run_asm
 
@@ -37,7 +37,8 @@ def _run_wave64_emu(instructions: list, out_reg: int = 1) -> list[int]:
   return list(out_buf)
 
 def _run_wave64_hw(instructions: list, out_reg: int = 1) -> list[int]:
-  from tinygrad.device import Device, TinyELF
+  from tinygrad.device import Device
+  from tinygrad.runtime.ops_amd import AMDProgram
   from tinygrad.runtime.support.compiler_amd import HIPCompiler
 
   dev = Device["AMD"]
@@ -83,7 +84,7 @@ amdhsa.kernels:
 .end_amdgpu_metadata
 """
   lib = compiler.compile(asm_src)
-  prg = dev.runtime(TinyELF(lib, "test", Target("AMD", arch=dev.arch), ()))
+  prg = AMDProgram(dev, "test", lib)  # type: ignore[arg-type]
   out_gpu = dev.allocator.alloc(WAVE64 * 4)
   prg(out_gpu, global_size=(1, 1, 1), local_size=(WAVE64, 1, 1), wait=True)
   out = bytearray(WAVE64 * 4)

test/amd/hw/test_rdna4_permlane_var.py

@@ -5,7 +5,7 @@ real RDNA4 hardware when USE_HW=1.
 """
 import ctypes, unittest
 import tinygrad.runtime.autogen.amd.rdna4.ins as r4
-from tinygrad.helpers import Target, flat_mv
+from tinygrad.helpers import flat_mv
 from tinygrad.renderer.amd.dsl import NULL
 from test.amd.hw.helpers import USE_HW, assemble
 from test.mockgpu.amd.emu import run_asm
@@ -36,7 +36,8 @@ def _run_emu(instructions: list, out_reg: int = 2) -> list[int]:
   return list(out_buf)
 
 def _run_hw(instructions: list, out_reg: int = 2) -> list[int]:
-  from tinygrad.device import Device, TinyELF
+  from tinygrad.device import Device
+  from tinygrad.runtime.ops_amd import AMDProgram
   from tinygrad.runtime.support.compiler_amd import HIPCompiler
 
   dev = Device['AMD']
@@ -84,7 +85,7 @@ amdhsa.kernels:
 .end_amdgpu_metadata
 """
   lib = compiler.compile(asm_src)
-  prg = dev.runtime(TinyELF(lib, "test", Target("AMD", arch=dev.arch), ()))
+  prg = AMDProgram(dev, 'test', lib)
   out_gpu = dev.allocator.alloc(LANES * 4)
   prg(out_gpu, global_size=(1, 1, 1), local_size=(LANES, 1, 1), wait=True)
   out = bytearray(LANES * 4)

test/amd/hw/test_vopc.py

@@ -471,20 +471,6 @@ class TestCmpFloat(unittest.TestCase):
     st = run_program(instructions, n_lanes=1)
     self.assertEqual(st.vcc & 1, 1, "Expected vcc=1 (1.0 != 2.0)")
 
-  def test_v_cmp_eq_f16_src0_hi(self):
-    """v_cmp_eq_f16 with src0 from high half (true16 384+n encoding)."""
-    cmp = v_cmp_eq_f16_e32(v[0], v[1])
-    cmp._raw += 128  # src0 v[0] -> v[0].h, the dsl can't encode hi-half src0 yet
-    instructions = [
-      s_mov_b32(s[0], 0x42003c00),  # hi=3.0, lo=1.0
-      v_mov_b32_e32(v[0], s[0]),
-      s_mov_b32(s[0], 0x47004200),  # hi=7.0, lo=3.0
-      v_mov_b32_e32(v[1], s[0]),
-      cmp,
-    ]
-    st = run_program(instructions, n_lanes=1)
-    self.assertEqual(st.vcc & 1, 1, "Expected vcc=1 (v0.hi 3.0 == v1.lo 3.0)")
-
   def test_v_cmp_nge_f16_inf_self(self):
     """v_cmp_nge_f16 comparing -inf with itself (unordered less than).
 

test/amd/test_asm_kernel.py

@@ -36,7 +36,7 @@ def custom_add_var(A:UOp, B:UOp) -> UOp:
   A,B = A.flatten(), B.flatten()
   assert A.dtype == dtypes.uint32, f"buffer dtype must be uint32, got {A.dtype}"
   threads = UOp.special(A.numel(), "lidx0")
-  var = UOp.param(2, dtypes.int, vmin_vmax=(0, 10), name="var", addrspace=AddrSpace.ALU)
+  var = UOp.param(2, dtypes.index, vmin_vmax=(0, 10), name="var", addrspace=AddrSpace.ALU)
   insts = [
     s_load_b128(s[4:7], s[0:1]),
     s_load_b32(s[8], s[0:1], offset=0x10), # all threads load the same variable

test/amd/test_emu2_pcode.py

@@ -11,8 +11,8 @@ from tinygrad.runtime.autogen.amd.rdna3.enum import VOP1Op, VOP2Op, SOP2Op, DSOp
 
 def _srcs():
   """Create minimal source variables for pcode parsing."""
-  def u32(v=0): return UOp.const(v, dtypes.uint32)
-  return {'S0': u32(), 'S1': u32(), 'S2': u32(), 'SCC': u32(), 'VCC': UOp.const(0, dtypes.uint64), 'laneId': u32()}
+  def u32(v=0): return UOp.const(dtypes.uint32, v)
+  return {'S0': u32(), 'S1': u32(), 'S2': u32(), 'SCC': u32(), 'VCC': UOp.const(dtypes.uint64, 0), 'laneId': u32()}
 
 class TestBasicParsing(unittest.TestCase):
   """Test basic pcode parsing for common instruction patterns."""
@@ -44,8 +44,8 @@ class TestWithSources(unittest.TestCase):
 
   def test_v_add_f32_with_sources(self):
     """Test V_ADD_F32 with actual float constants."""
-    s0 = UOp.const(0x3f800000, dtypes.uint32)  # 1.0f
-    s1 = UOp.const(0x40000000, dtypes.uint32)  # 2.0f
+    s0 = UOp.const(dtypes.uint32, 0x3f800000)  # 1.0f
+    s1 = UOp.const(dtypes.uint32, 0x40000000)  # 2.0f
     _, assigns = parse_pcode(PCODE[VOP2Op.V_ADD_F32_E32], {'S0': s0, 'S1': s1})
     self.assertEqual(len(assigns), 1)
     dest, val = assigns[0]
@@ -55,8 +55,8 @@ class TestWithSources(unittest.TestCase):
 
   def test_v_mul_f32_with_sources(self):
     """Test V_MUL_F32 with actual float constants."""
-    s0 = UOp.const(0x40000000, dtypes.uint32)  # 2.0f
-    s1 = UOp.const(0x40400000, dtypes.uint32)  # 3.0f
+    s0 = UOp.const(dtypes.uint32, 0x40000000)  # 2.0f
+    s1 = UOp.const(dtypes.uint32, 0x40400000)  # 3.0f
     _, assigns = parse_pcode(PCODE[VOP2Op.V_MUL_F32_E32], {'S0': s0, 'S1': s1})
     self.assertEqual(len(assigns), 1)
     dest, val = assigns[0]
@@ -67,36 +67,36 @@ class TestParseExpr(unittest.TestCase):
 
   def test_integer_literals(self):
     """Test parsing integer literals."""
-    self.assertEqual(parse_expr('0', {}).val, 0)
-    self.assertEqual(parse_expr('42', {}).val, 42)
-    self.assertEqual(parse_expr('42U', {}).val, 42)
+    self.assertEqual(parse_expr('0', {}).arg, 0)
+    self.assertEqual(parse_expr('42', {}).arg, 42)
+    self.assertEqual(parse_expr('42U', {}).arg, 42)
 
   def test_negative_integers(self):
     """Test parsing negative integer literals."""
     result = parse_expr('-1', {})
-    self.assertEqual(result.val, -1)
+    self.assertEqual(result.arg, -1)
     self.assertEqual(result.dtype, dtypes.int)
 
   def test_float_literals(self):
     """Test parsing float literals."""
     result = parse_expr('1.0F', {})
-    self.assertEqual(result.val, 1.0)
+    self.assertEqual(result.arg, 1.0)
     self.assertEqual(result.dtype, dtypes.float32)
 
   def test_hex_literals(self):
     """Test parsing hex literals."""
     result = parse_expr('0xFF', {})
-    self.assertEqual(result.val, 255)
+    self.assertEqual(result.arg, 255)
 
   def test_variable_lookup(self):
     """Test variable lookup in parse_expr."""
-    vrs = {'x': UOp.const(42, dtypes.uint32)}
+    vrs = {'x': UOp.const(dtypes.uint32, 42)}
     result = parse_expr('x', vrs)
-    self.assertEqual(result.val, 42)
+    self.assertEqual(result.arg, 42)
 
   def test_binary_ops(self):
     """Test parsing binary operations."""
-    vrs = {'a': UOp.const(10, dtypes.uint32), 'b': UOp.const(5, dtypes.uint32)}
+    vrs = {'a': UOp.const(dtypes.uint32, 10), 'b': UOp.const(dtypes.uint32, 5)}
 
     # Addition
     result = parse_expr('a + b', vrs)
@@ -105,11 +105,11 @@ class TestParseExpr(unittest.TestCase):
     # Subtraction with constant folding
     result = parse_expr('10 - 5', {})
     self.assertEqual(result.op, Ops.CONST)
-    self.assertEqual(result.val, 5)
+    self.assertEqual(result.arg, 5)
 
   def test_ternary(self):
     """Test parsing ternary expressions."""
-    vrs = {'cond': UOp.const(True), 'a': UOp.const(1, dtypes.uint32), 'b': UOp.const(0, dtypes.uint32)}
+    vrs = {'cond': UOp.const(dtypes.bool, True), 'a': UOp.const(dtypes.uint32, 1), 'b': UOp.const(dtypes.uint32, 0)}
     result = parse_expr('cond ? a : b', vrs)
     self.assertEqual(result.op, Ops.WHERE)
 
@@ -127,7 +127,7 @@ class TestForLoopParsing(unittest.TestCase):
   def test_clz_parsing(self):
     """Test CLZ pcode parsing produces correct structure."""
     pcode = PCODE[VOP1Op.V_CLZ_I32_U32_E32]
-    S0 = UOp.const(0xFFFFFFFF, dtypes.uint32)  # All ones - CLZ should be 0
+    S0 = UOp.const(dtypes.uint32, 0xFFFFFFFF)  # All ones - CLZ should be 0
     _vrs, assigns = parse_pcode(pcode, {'S0': S0})
 
     self.assertEqual(len(assigns), 1)
@@ -139,7 +139,7 @@ class TestForLoopParsing(unittest.TestCase):
   def test_clz_with_zero(self):
     """Test CLZ with input 0 - should return -1."""
     pcode = PCODE[VOP1Op.V_CLZ_I32_U32_E32]
-    S0 = UOp.const(0, dtypes.uint32)
+    S0 = UOp.const(dtypes.uint32, 0)
     _vrs, assigns = parse_pcode(pcode, {'S0': S0})
 
     # Check that the innermost value (default) is -1 (may be wrapped in CAST)
@@ -150,7 +150,7 @@ class TestForLoopParsing(unittest.TestCase):
     # Unwrap CAST if present
     while val.op == Ops.CAST:
       val = val.src[0]
-    self.assertEqual(val.val, -1)
+    self.assertEqual(val.arg, -1)
 
   def test_ctz_parsing(self):
     """Test CTZ pcode parsing."""
@@ -158,7 +158,7 @@ class TestForLoopParsing(unittest.TestCase):
     if pcode is None:
       self.skipTest("V_CTZ_I32_B32_E32 pcode not available")
 
-    S0 = UOp.const(1, dtypes.uint32)  # LSB set - CTZ should be 0
+    S0 = UOp.const(dtypes.uint32, 1)  # LSB set - CTZ should be 0
     _vrs, assigns = parse_pcode(pcode, {'S0': S0})
     self.assertEqual(len(assigns), 1)
 
@@ -169,8 +169,8 @@ class TestDSPcodePatterns(unittest.TestCase):
     """Test GLOBAL_ATOMIC_ADD_F32 keeps memory values in float dtype."""
     vmem = UOp.param(2, dtypes.uint32, (1024,))
     srcs = {
-      'ADDR': UOp.const(0, dtypes.uint64),
-      'DATA': UOp.const(0x3f800000, dtypes.uint32),
+      'ADDR': UOp.const(dtypes.uint64, 0),
+      'DATA': UOp.const(dtypes.uint32, 0x3f800000),
       '_vmem': vmem,
     }
 
@@ -199,8 +199,8 @@ class TestDSPcodePatterns(unittest.TestCase):
     """Test MEM[addr].type read expression parsing."""
     # Create a mock LDS buffer
     lds = UOp.param(3, dtypes.uint32, (16384,))
-    addr = UOp.const(0, dtypes.uint32)
-    vrs = {'_lds': lds, 'ADDR': addr, 'OFFSET': UOp.const(0, dtypes.uint32)}
+    addr = UOp.const(dtypes.uint32, 0)
+    vrs = {'_lds': lds, 'ADDR': addr, 'OFFSET': UOp.const(dtypes.uint32, 0)}
 
     result = parse_expr('MEM[ADDR + OFFSET].b32', vrs)
     # Should be an INDEX operation into LDS
@@ -212,13 +212,13 @@ class TestDSPcodePatterns(unittest.TestCase):
     self.assertIsNotNone(pcode)
     assert pcode is not None
     srcs = {
-      'ADDR': UOp.const(0, dtypes.uint32),
-      'OFFSET0': UOp.const(0, dtypes.uint32),
-      'OFFSET1': UOp.const(1, dtypes.uint32),
-      'DATA': UOp.const(0xAAAAAAAA, dtypes.uint32),
-      'DATA2': UOp.const(0xBBBBBBBB, dtypes.uint32),
+      'ADDR': UOp.const(dtypes.uint32, 0),
+      'OFFSET0': UOp.const(dtypes.uint32, 0),
+      'OFFSET1': UOp.const(dtypes.uint32, 1),
+      'DATA': UOp.const(dtypes.uint32, 0xAAAAAAAA),
+      'DATA2': UOp.const(dtypes.uint32, 0xBBBBBBBB),
     }
-    srcs['laneId'] = UOp.const(0, dtypes.uint32)
+    srcs['laneId'] = UOp.const(dtypes.uint32, 0)
     _, assigns = parse_pcode(pcode, srcs)
     # Should have 2 MEM write assignments
     self.assertEqual(len(assigns), 2)
@@ -235,12 +235,12 @@ class TestDSPcodePatterns(unittest.TestCase):
     assert pcode is not None
     lds = UOp.param(3, dtypes.uint32, (16384,))
     srcs = {
-      'ADDR': UOp.const(0, dtypes.uint32),
-      'OFFSET0': UOp.const(0, dtypes.uint32),
-      'OFFSET1': UOp.const(1, dtypes.uint32),
+      'ADDR': UOp.const(dtypes.uint32, 0),
+      'OFFSET0': UOp.const(dtypes.uint32, 0),
+      'OFFSET1': UOp.const(dtypes.uint32, 1),
       '_lds': lds,
     }
-    srcs['laneId'] = UOp.const(0, dtypes.uint32)
+    srcs['laneId'] = UOp.const(dtypes.uint32, 0)
     _, assigns = parse_pcode(pcode, srcs)
     # Should have 2 RETURN_DATA assignments
     self.assertEqual(len(assigns), 2)
@@ -252,36 +252,36 @@ class TestDSPcodePatterns(unittest.TestCase):
     pcode = PCODE.get(DSOp.DS_STORE_2ADDR_B32)
     assert pcode is not None
     srcs = {
-      'ADDR': UOp.const(100, dtypes.uint32),
-      'OFFSET0': UOp.const(2, dtypes.uint32),
-      'OFFSET1': UOp.const(5, dtypes.uint32),
-      'DATA': UOp.const(0xAAAAAAAA, dtypes.uint32),
-      'DATA2': UOp.const(0xBBBBBBBB, dtypes.uint32),
+      'ADDR': UOp.const(dtypes.uint32, 100),
+      'OFFSET0': UOp.const(dtypes.uint32, 2),
+      'OFFSET1': UOp.const(dtypes.uint32, 5),
+      'DATA': UOp.const(dtypes.uint32, 0xAAAAAAAA),
+      'DATA2': UOp.const(dtypes.uint32, 0xBBBBBBBB),
     }
-    srcs['laneId'] = UOp.const(0, dtypes.uint32)
+    srcs['laneId'] = UOp.const(dtypes.uint32, 0)
     _, assigns = parse_pcode(pcode, srcs)
     # Check addresses: 100 + 2*4 = 108, 100 + 5*4 = 120
     # assigns[i][1] is (addr, val) tuple for MEM writes; mypy sees UOp
-    self.assertEqual(assigns[0][1][0].simplify().val, 108)  # type: ignore[index]
-    self.assertEqual(assigns[1][1][0].simplify().val, 120)  # type: ignore[index]
+    self.assertEqual(assigns[0][1][0].simplify().arg, 108)  # type: ignore[index]
+    self.assertEqual(assigns[1][1][0].simplify().arg, 120)  # type: ignore[index]
 
   def test_ds_store_data_values(self):
     """Test DS_STORE_2ADDR_B32 uses correct data values."""
     pcode = PCODE.get(DSOp.DS_STORE_2ADDR_B32)
     assert pcode is not None
     srcs = {
-      'ADDR': UOp.const(0, dtypes.uint32),
-      'OFFSET0': UOp.const(0, dtypes.uint32),
-      'OFFSET1': UOp.const(1, dtypes.uint32),
-      'DATA': UOp.const(0xAAAAAAAA, dtypes.uint32),
-      'DATA2': UOp.const(0xBBBBBBBB, dtypes.uint32),
+      'ADDR': UOp.const(dtypes.uint32, 0),
+      'OFFSET0': UOp.const(dtypes.uint32, 0),
+      'OFFSET1': UOp.const(dtypes.uint32, 1),
+      'DATA': UOp.const(dtypes.uint32, 0xAAAAAAAA),
+      'DATA2': UOp.const(dtypes.uint32, 0xBBBBBBBB),
     }
-    srcs['laneId'] = UOp.const(0, dtypes.uint32)
+    srcs['laneId'] = UOp.const(dtypes.uint32, 0)
     _, assigns = parse_pcode(pcode, srcs)
     # assigns[i][1] is (addr, val) tuple for MEM writes; mypy sees UOp
     # DATA[31:0] should preserve the value
-    self.assertEqual(assigns[0][1][1].simplify().val, 0xAAAAAAAA)  # type: ignore[index]
-    self.assertEqual(assigns[1][1][1].simplify().val, 0xBBBBBBBB)  # type: ignore[index]
+    self.assertEqual(assigns[0][1][1].simplify().arg, 0xAAAAAAAA)  # type: ignore[index]
+    self.assertEqual(assigns[1][1][1].simplify().arg, 0xBBBBBBBB)  # type: ignore[index]
 
 class TestConditionalParsing(unittest.TestCase):
   """Test conditional (if/elsif/else) pcode parsing."""
@@ -290,9 +290,9 @@ class TestConditionalParsing(unittest.TestCase):
     """Test parsing ternary expression (which becomes WHERE)."""
     # S_CSELECT_B32: D0.u32 = SCC ? S0.u32 : S1.u32
     pcode = PCODE[SOP2Op.S_CSELECT_B32]
-    s0 = UOp.const(10, dtypes.uint32)
-    s1 = UOp.const(20, dtypes.uint32)
-    scc = UOp.const(1, dtypes.uint32)
+    s0 = UOp.const(dtypes.uint32, 10)
+    s1 = UOp.const(dtypes.uint32, 20)
+    scc = UOp.const(dtypes.uint32, 1)
     _vrs, assigns = parse_pcode(pcode, {'S0': s0, 'S1': s1, 'SCC': scc})
     self.assertEqual(len(assigns), 1)
     dest, val = assigns[0]
@@ -305,26 +305,26 @@ class TestConcatWidthParsing(unittest.TestCase):
 
   def test_permlanex16_altrow_concat(self):
     for row, expected in [(0, 1), (1, 0), (2, 3), (3, 2)]:
-      parsed = parse_expr('{ row[1], ~row[0] }', {'row': UOp.const(row, dtypes.uint32)})
-      self.assertEqual(parsed.simplify().val, expected)
+      parsed = parse_expr('{ row[1], ~row[0] }', {'row': UOp.const(dtypes.uint32, row)})
+      self.assertEqual(parsed.simplify().arg, expected)
 
   def test_permlane64_altlane_concat(self):
     for lane, expected in [(0, 32), (1, 33), (31, 63), (32, 0), (63, 31)]:
-      parsed = parse_expr('{ ~lane[5], lane[4:0] }', {'lane': UOp.const(lane, dtypes.uint32)})
-      self.assertEqual(parsed.simplify().val, expected)
+      parsed = parse_expr('{ ~lane[5], lane[4:0] }', {'lane': UOp.const(dtypes.uint32, lane)})
+      self.assertEqual(parsed.simplify().arg, expected)
 
   def test_permlane64_wave64_pcode_indices(self):
     vgpr = UOp.param(0, dtypes.uint32, (256,))
     srcs = {
-      'SRC0': UOp.const(0, dtypes.uint32),
-      'VDST': UOp.const(1, dtypes.uint32),
-      'EXEC_LO': UOp.const(0xFFFFFFFF, dtypes.uint32),
-      'EXEC': UOp.const(0xFFFFFFFFFFFFFFFF, dtypes.uint64),
+      'SRC0': UOp.const(dtypes.uint32, 0),
+      'VDST': UOp.const(dtypes.uint32, 1),
+      'EXEC_LO': UOp.const(dtypes.uint32, 0xFFFFFFFF),
+      'EXEC': UOp.const(dtypes.uint64, 0xFFFFFFFFFFFFFFFF),
       '_vgpr': vgpr,
       '_wave_size': 64,
-      'S0': UOp.const(0, dtypes.uint32),
-      'S1': UOp.const(0, dtypes.uint32),
-      'S2': UOp.const(0, dtypes.uint32),
+      'S0': UOp.const(dtypes.uint32, 0),
+      'S1': UOp.const(dtypes.uint32, 0),
+      'S2': UOp.const(dtypes.uint32, 0),
     }
 
     def load_idx(v: UOp) -> int:
@@ -333,12 +333,12 @@ class TestConcatWidthParsing(unittest.TestCase):
       self.assertEqual(simp.src[0].op, Ops.INDEX)
       idx = simp.src[0].src[1].simplify()
       self.assertEqual(idx.op, Ops.CONST)
-      return idx.val
+      return idx.arg
 
     _, assigns = parse_pcode(PCODE[VOP1Op.V_PERMLANE64_B32_E32], srcs)
     self.assertEqual(len(assigns), 64)
     for lane, (dst_idx, src_idx) in {0: (64, 32), 31: (95, 63), 32: (96, 0), 63: (127, 31)}.items():
-      self.assertEqual(assigns[lane][1][0].simplify().val, dst_idx)  # type: ignore[index]
+      self.assertEqual(assigns[lane][1][0].simplify().arg, dst_idx)  # type: ignore[index]
       self.assertEqual(load_idx(assigns[lane][1][1]), src_idx)  # type: ignore[index]
 
 class TestAllPcode(unittest.TestCase):
@@ -346,7 +346,7 @@ class TestAllPcode(unittest.TestCase):
 
   def _make_srcs(self):
     """Create dummy source variables for pcode parsing."""
-    u32, u64 = lambda v=0: UOp.const(v, dtypes.uint32), lambda v=0: UOp.const(v, dtypes.uint64)
+    u32, u64 = lambda v=0: UOp.const(dtypes.uint32, v), lambda v=0: UOp.const(dtypes.uint64, v)
     lds = UOp.param(3, dtypes.uint32, (16384,))
     return {'laneId': u32(), 'laneID': u32(), 'S0': u32(), 'S1': u32(), 'S2': u32(), 'S3': u32(), 'SRC0': u32(),
             'D0': u32(), 'D1': u32(), 'DST': u32(), 'VDST': u32(), 'SDST': u32(),
@@ -358,7 +358,7 @@ class TestAllPcode(unittest.TestCase):
             'M0': u32(), 'PC': u64(), 'DENORM': u32(1), 'ROUND_MODE': u32(), 'ROUND_TOWARD_ZERO': u32(),
             'ROUND_NEAREST_EVEN': u32(), 'WAVE_STATUS': u32(),
             'MAX_FLOAT_F32': u32(0x7f7fffff), 'Unsigned': u32(1), 'clampedLOD': u32(),
-            '_lds': lds, '_vmem': lds, '_active': UOp.const(True)}
+            '_lds': lds, '_vmem': lds, '_active': UOp.const(dtypes.bool, True)}
 
   def _parse_all_pcode(self, pcode_dict, arch: str, min_pct: float):
     """Parse all pcode. RuntimeError = parser limitation (ok), other exceptions = real bugs."""

test/amd/test_mockgpu_invalid.py

@@ -7,16 +7,15 @@ class TestMockGPUInvalidInstruction(unittest.TestCase):
     """Test that unsupported instructions raise immediately through the full MOCKGPU stack."""
     test_code = '''
 import struct
-from dataclasses import replace
 from tinygrad import Device, Tensor
 from tinygrad.engine.realize import compile_linear
+from tinygrad.runtime.ops_amd import AMDProgram
 
 dev = Device["AMD"]
 a = Tensor([1.0]).realize()
 b = a + 1
 linear = compile_linear(b.schedule_linear())
-compiled_prg = linear.src[-1].src[0]
-lib = bytearray(compiled_prg.src[3].arg)
+lib = bytearray(linear.src[-1].src[0].src[3].arg)
 
 # Find s_endpgm (0xBFB00000) and replace with V_MOVRELD_B32 (op=66) which has no pcode
 # VOP1 encoding: bits[31:25]=0x7E, op=bits[16:9], so op=66 -> 66<<9 = 0x8400
@@ -28,7 +27,7 @@ for i in range(0, len(lib) - 4, 4):
     break
 assert found, "s_endpgm not found"
 
-patched_prg = dev.runtime(replace(compiled_prg.to_elf(), name="patched", lib=bytes(lib)))
+patched_prg = AMDProgram(dev, "patched", bytes(lib))
 b.uop.buffer.allocate()
 patched_prg(b.uop.buffer._buf, a.uop.buffer._buf, global_size=(1,1,1), local_size=(1,1,1))
 dev.synchronize()

test/amd/test_roundtrip.py

@@ -121,8 +121,7 @@ class TestTinygradKernelRoundtrip(unittest.TestCase):
     arch = self.arch
 
     from tinygrad.runtime.support.elf import elf_loader
-    from tinygrad.runtime.support.compiler_amd import HIPCompiler
-    from tinygrad.runtime.support.compiler_llvm import AMDLLVMCompiler
+    from tinygrad.runtime.support.compiler_amd import HIPCompiler, AMDLLVMCompiler
     from tinygrad.helpers import DEV
 
     kernels, _, _ = get_kernels_from_tinygrad(op_fn)

test/backend/test_arange.py

@@ -4,13 +4,13 @@ from tinygrad import Tensor, GlobalCounters, dtypes, nn, Device, Variable
 from tinygrad.helpers import Context, getenv, DEV
 from tinygrad.engine.realize import run_linear, estimate_uop, compile_linear
 from tinygrad.renderer.ptx import PTXRenderer
-from test.helpers import needs_second_gpu, check_schedule, assert_kernel_count, KernelCountException
+from test.helpers import needs_second_gpu
 
 class TestArange(unittest.TestCase):
   def _get_flops(self, tensor, desired):
     GlobalCounters.reset()
     linear = compile_linear(tensor.schedule_linear())
-    if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
+    self.assertEqual(len(linear.src), 1)
     run_linear(linear)
     np.testing.assert_equal(tensor.numpy(), desired)
     return estimate_uop(linear.src[-1]).ops
@@ -19,12 +19,6 @@ class TestArange(unittest.TestCase):
     self.assertLess(self._get_flops(Tensor.arange(256).clone(), np.arange(256)), 256*4)
     self.assertLess(self._get_flops(Tensor.arange(2560).clone(), np.arange(2560)), 2560*4)
 
-  def test_cat_complexity(self):
-    x = Tensor.arange(2**10) + Tensor.empty((), dtype=dtypes.uint32)
-    out = x.cat(x).cat(Tensor.empty(1, dtype=dtypes.uint32))
-    linear = compile_linear(out.schedule_linear())
-    self.assertLessEqual(estimate_uop(linear.src[-1]).ops, out.numel()*20)
-
   @unittest.skipIf(Device.DEFAULT == "CL", "flaky in CI")
   def test_arange_cumsum(self):
     np.testing.assert_equal(Tensor.arange(513).cumsum(0).numpy(), np.arange(513).cumsum())
@@ -55,7 +49,8 @@ class TestIndexing(unittest.TestCase):
     with Context(NOOPT=1):
       GlobalCounters.reset()
       out = ((Tensor.arange(1,16385)-1)*needle).sum()
-      linear, var_vals = check_schedule(out, 1)
+      linear, var_vals = out.linear_with_vars()
+      self.assertEqual(len(linear.src), 1)
       run_linear(linear, var_vals)
     self.assertEqual(out.item(), 1337)
 
@@ -71,7 +66,8 @@ class TestIndexing(unittest.TestCase):
       reshape_dataset = dataset.T.reshape(1, DDIM, DSET, 1).expand(4, DDIM, DSET, 1)
       full = (rng==idxs).where(reshape_dataset, Tensor.zeros(4, DDIM, DSET, 1, buffer=False))
       X = full.sum(axis=(2,3))
-      linear, var_vals = check_schedule(X, 1)
+      linear, var_vals = X.linear_with_vars()
+      self.assertEqual(len(linear.src), 1)
       run_linear(linear, var_vals)
       assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops}"
     np.testing.assert_allclose(real_index, X.numpy())
@@ -96,7 +92,8 @@ class TestIndexing(unittest.TestCase):
       GlobalCounters.reset()
       X = dataset[idxs]
       assert X.shape == (4,DDIM)
-      linear, var_vals = check_schedule(X, 1)
+      linear, var_vals = X.linear_with_vars()
+      self.assertEqual(len(linear.src), 1)
       run_linear(linear, var_vals)
       assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops}"
     np.testing.assert_allclose(real_index, X.numpy())
@@ -110,7 +107,8 @@ class TestIndexing(unittest.TestCase):
       GlobalCounters.reset()
       X = dataset[idxs]
       assert X.shape == (4,DDIM)
-      linear, var_vals = check_schedule(X, 1)
+      linear, var_vals = X.linear_with_vars()
+      self.assertEqual(len(linear.src), 1)
       run_linear(linear, var_vals)
       assert GlobalCounters.global_ops < 4*DSET, f"too many ops {GlobalCounters.global_ops} != {4*DSET}"
     np.testing.assert_allclose(real_index, X.numpy())
@@ -153,7 +151,7 @@ class TestIndexing(unittest.TestCase):
       GlobalCounters.reset()
       z = emb(x).realize()
       self.assertLessEqual(GlobalCounters.global_ops, op_limit)
-      assert_kernel_count(2)
+      self.assertEqual(GlobalCounters.kernel_count, 2)
     if getenv("CHECK", 1):
       import torch
       with torch.no_grad():
@@ -253,7 +251,7 @@ class TestIndexing(unittest.TestCase):
     xq_rope, _ = apply_rotary_emb(xq, xq, freqs_cis)
     xq_rope.sum().backward()
     linear = compile_linear(wq.grad.schedule_linear())
-    if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
+    assert len(linear.src) == 1, f"expected one kernel for backward, got: {len(linear.src)}"
     bwd_ops = estimate_uop(linear.src[0]).ops
     expected_ops = bs*seqlen*dim*dim*ops_scale
     print(f"rope matmul bwd ({dtype}): {GlobalCounters.kernel_count} kernels, {bwd_ops:,} ops")

test/backend/test_asm_gemm.py

@@ -150,44 +150,6 @@ class TestAsmGEMM(unittest.TestCase):
     with self.assertRaisesRegex(AssertionError, "not a multiple"):
       verify_asm_gemm(1, 256, 1000, 256)
 
-class TestMXFP4(unittest.TestCase):
-  def setUp(self):
-    if not is_cdna4() or DEV.interface.startswith("MOCK"):
-      self.skipTest("requires real amd machine")
-
-  def test_quantize(self):
-    import numpy as np
-    from extra.llama_kernels.quantize_mxfp4 import quantize_mxfp4
-    rng = np.random.default_rng(0)
-    x = np.triu(rng.standard_normal((256, 256), dtype=np.float32))
-    x += np.triu(x, 1).T
-    x[:32, :32] = 0
-    row, row_scale, col, col_scale = quantize_mxfp4(Tensor(x, dtype=dtypes.bfloat16))
-    Tensor.realize(row, row_scale, col, col_scale)
-    row, row_scale = row.numpy(), row_scale.numpy()
-    col, col_scale = col.numpy(), col_scale.numpy()
-    np.testing.assert_array_equal(row, col)
-    np.testing.assert_array_equal(row_scale, col_scale)
-    self.assertTrue(row.any())
-    self.assertTrue((row_scale == 127).any())
-    self.assertTrue((row_scale != 127).any())
-
-  def test_correctness(self):
-    import numpy as np
-    M = N = K = 256
-    rng = np.random.default_rng(1)
-    a = Tensor(rng.standard_normal((M, K), dtype=np.float32), dtype=dtypes.bfloat16)
-    b = Tensor(rng.standard_normal((N, K), dtype=np.float32), dtype=dtypes.bfloat16)
-    out = asm_gemm(a, b.T, mxfp4=True).realize().numpy().astype(np.float32)
-    ref = a.numpy().astype(np.float32) @ b.numpy().astype(np.float32).T
-    self.assertLess(np.linalg.norm(out-ref) / np.linalg.norm(ref), 0.2)
-
-  def test_empty(self):
-    M, N, K = getenv("M", 16384), getenv("N", 4096), getenv("K", 14336)
-    a = Tensor.empty(M, K, dtype=dtypes.bfloat16)
-    b = Tensor.empty(N, K, dtype=dtypes.bfloat16)
-    asm_gemm(a, b.T, mxfp4=True).realize()
-
 # test the Asm GEMM with Llama shapes, only run on the real machine for speed
 
 @unittest.skipUnless(has_hipcc(), "requires hipcc to compile")

test/backend/test_call.py

@@ -1,39 +0,0 @@
-import unittest, ctypes
-from tinygrad import Tensor, UOp
-from tinygrad.device import Device
-from tinygrad.dtype import dtypes
-from tinygrad.renderer.cstyle import CStyleLanguage
-from tinygrad.uop.ops import KernelInfo
-
-def call_out_kernel(F:UOp, C:UOp) -> UOp:
-  call = F[0].load().call(UOp.const(3).cast(dtypes.int), C[0], ret_dtype=dtypes.void)
-  return C.after(call)[1].store(C.after(call)[0].load() + 1).sink(arg=KernelInfo(name="call_out"))
-
-def call_ret_kernel(F:UOp, C:UOp) -> UOp:
-  val = F[0].load().call(UOp.const(21).cast(dtypes.int), ret_dtype=dtypes.int)
-  return C[0].store(val * 2).sink(arg=KernelInfo(name="call_ret"))
-
-@unittest.skipUnless(isinstance(Device["CPU"].renderer, CStyleLanguage), "TODO: CALL is rendered in C style only")
-class TestCall(unittest.TestCase):
-  def test_call_out_param(self):
-    called = []
-    @ctypes.CFUNCTYPE(None, ctypes.c_int, ctypes.POINTER(ctypes.c_int))
-    def fxn(n, out):
-      called.append(n)
-      out[0] = n * 2
-    f = Tensor([ctypes.cast(fxn, ctypes.c_void_p).value], dtype=dtypes.uint64, device="CPU")
-    c = Tensor.empty(2, dtype=dtypes.int, device="CPU")
-    c = Tensor.custom_kernel(f, c, fxn=call_out_kernel)[1]
-    self.assertEqual(c.tolist(), [6, 7])
-    self.assertEqual(called, [3])
-
-  def test_call_ret(self):
-    @ctypes.CFUNCTYPE(ctypes.c_int, ctypes.c_int)
-    def fxn(n): return n + 1
-    f = Tensor([ctypes.cast(fxn, ctypes.c_void_p).value], dtype=dtypes.uint64, device="CPU")
-    c = Tensor.empty(1, dtype=dtypes.int, device="CPU")
-    c = Tensor.custom_kernel(f, c, fxn=call_ret_kernel)[1]
-    c.realize()
-    self.assertEqual(c.item(), 44)
-
-if __name__ == "__main__": unittest.main()

test/backend/test_const_folding.py

@@ -16,7 +16,7 @@ def _check_ast_count(desired_count:int, t:Tensor):
 
 class TestMovedConstFolding(unittest.TestCase):
   def test_contiguous_deviceless_const(self):
-    t = Tensor(UOp.const(2.0, dtypes.float)).contiguous()
+    t = Tensor(UOp.const(dtypes.float, 2.0)).contiguous()
     self.assertIs(t.uop.op, Ops.CONST)
     self.assertIsNone(t.uop.device)
 
@@ -169,7 +169,7 @@ class TestMultiConstFolding(unittest.TestCase):
 class TestThreefryConstFolding(unittest.TestCase):
   def test_threefry(self):
     # THREEFRY(const,const) folds to a const once decomposed
-    x = threefry2x32(UOp.const(5, dtypes.uint64), UOp.const(10, dtypes.uint64))
+    x = threefry2x32(UOp.const(dtypes.uint64, 5), UOp.const(dtypes.uint64, 10))
     self.assertIs(x.simplify().op, Ops.CONST)
 
 class TestTautologicalCompare(unittest.TestCase):

test/backend/test_custom_kernel.py

@@ -1,10 +1,7 @@
 import unittest
 from tinygrad import Tensor, UOp, GlobalCounters, Context, Device
-import numpy as np
 from tinygrad.dtype import AddrSpace, dtypes, Invalid
 from tinygrad.uop.ops import KernelInfo, AxisType, Ops
-from tinygrad.renderer.ptx import PTXRenderer
-from test.helpers import assert_kernel_count
 
 # **** kernels ****
 
@@ -58,7 +55,7 @@ def flip_contract_kernel(dest:UOp, src:UOp):
   return store.end(i, j).sink(arg=KernelInfo(name=f"flip_contract_{dest.numel()}", opts_to_apply=()))
 
 def slice_sum_kernel(dest:UOp, src:UOp):
-  G = UOp.range(src.shape[0], 0, dtype=dtypes.int)
+  G = UOp.range(src.shape[0], 0)
   slice_src = src[G, :]
   reg = UOp.placeholder((1,), dest.dtype, 0, addrspace=AddrSpace.REG)
   reg = reg.after(G)[0].set(0)
@@ -120,20 +117,13 @@ class TestCustomKernel(unittest.TestCase):
     out = c.flatten().tolist()
     assert all(x == 2 for x in out), "all 2"
 
-  def test_duplicate_call_arg(self):
-    x = Tensor.arange(4).clone().realize()
-    x = Tensor.custom_kernel(x, x, fxn=custom_add_one_kernel)[0]
-    # webgpu silently errors when a kernel has duplicate buffer args, so the list stays the same.
-    # https://gpuweb.github.io/gpuweb/#abstract-opdef-encoder-bind-groups-alias-a-writable-resource
-    self.assertEqual(x.tolist(), [1, 2, 3, 4] if Device.DEFAULT != "WEBGPU" else [0, 1, 2, 3])
-
   def test_simple_sharded(self):
     devs = ("CPU:0", "CPU:1")
 
     a = Tensor.ones(16, 16).contiguous().shard(devs, axis=0)
     b = Tensor.ones(16, 16).contiguous().shard(devs, axis=0)
     # ugly construction to get a sharded empty tensor
-    c = Tensor(Tensor.empty(8, 16, device=devs).uop.unshard(0), device=devs)
+    c = Tensor(Tensor.empty(8, 16, device=devs).uop.multi(0), device=devs)
     c = Tensor.custom_kernel(c,a,b, fxn=custom_elementwise_add_kernel)[0]
     out = c.flatten().tolist()
     assert all(x == 2 for x in out), "all 2"
@@ -142,7 +132,7 @@ class TestCustomKernel(unittest.TestCase):
     # PYTHON backend explicitly checks for OOB access for wrong multi shape regression
     devs = ("PYTHON:0", "PYTHON:1")
     a = Tensor.ones(4, 4).contiguous().shard(devs, axis=0)
-    c = Tensor(Tensor.empty(2, 4, device=devs).uop.unshard(0), device=devs)
+    c = Tensor(Tensor.empty(2, 4, device=devs).uop.multi(0), device=devs)
     c = Tensor.custom_kernel(c, a, fxn=custom_add_one_kernel)[0]
     assert (c == 2).all().item()
 
@@ -190,12 +180,6 @@ class TestCustomKernel(unittest.TestCase):
     b = Tensor.custom_kernel(tst, a, fxn=custom_sum)[0]
     self.assertEqual(b.item(), 15)
 
-  def test_sum_outside(self):
-    a = Tensor([1.0, 2, 3, 4, 5])+1
-    tst = Tensor.empty(1)
-    b = Tensor.custom_kernel(tst, a, fxn=custom_sum)[0]
-    self.assertEqual(b.item(), 20)
-
   def test_sum_int(self):
     a = Tensor([1, 2, 3, 4, 5])
     tst = Tensor.empty(1, dtype=a.dtype)
@@ -215,16 +199,18 @@ class TestCustomKernel(unittest.TestCase):
     c = Tensor.empty(N, N)
 
     tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm)[0]
-    self.assertTrue(tst.allclose(a@b, atol=1e-3).item())
+    err = (tst - (a@b)).square().max()
+    self.assertLess(err.item(), 1e-6)
 
   def test_gemm_multi(self):
     devs = ("CPU:0", "CPU:1")
     N = 16
     a = Tensor.randn(N, N).shard_(devs, axis=0)
     b = Tensor.randn(N, N).to(devs)
-    c = Tensor(Tensor.empty(N//2, N, device=devs).uop.unshard(0), device=devs)
+    c = Tensor(Tensor.empty(N//2, N, device=devs).uop.multi(0), device=devs)
     tst = Tensor.custom_kernel(c, a, b, fxn=custom_gemm)[0]
-    self.assertTrue(tst.allclose(a@b, atol=1e-3).item())
+    err = (tst - (a@b)).square().max()
+    self.assertLess(err.item(), 1e-6)
 
   def test_gemm_backward_custom(self): self.test_gemm_backward(True)
   # NOTE: grad_fxn doesn't work with pyrender
@@ -247,9 +233,14 @@ class TestCustomKernel(unittest.TestCase):
     real_grad_a, real_grad_b = a.grad, b.grad
     Tensor.realize(ref, real_grad_a, real_grad_b)
 
-    self.assertTrue(tst.allclose(ref, atol=1e-3).item())
-    self.assertTrue(grad_a.allclose(real_grad_a, atol=1e-3).item())
-    self.assertTrue(grad_b.allclose(real_grad_b, atol=1e-3).item())
+    err = (tst - ref).square().max()
+    self.assertLess(err.item(), 1e-6)
+
+    err = (grad_a - real_grad_a).square().max()
+    self.assertLess(err.item(), 1e-6)
+
+    err = (grad_b - real_grad_b).square().max()
+    self.assertLess(err.item(), 1e-6)
 
   def test_simple_qkv(self):
     N, d = 8, 4
@@ -262,7 +253,8 @@ class TestCustomKernel(unittest.TestCase):
     O_ref = ((Q @ K.T) / (d ** 0.5)) @ V
 
     Tensor.realize(O_custom, O_ref)
-    self.assertTrue(O_custom.allclose(O_ref, atol=1e-3).item())
+    err = (O_custom - O_ref).square().max()
+    self.assertLess(err.item(), 1e-6)
 
   def test_gemm_qkv(self):
     B, N, K_DIM, H_KV, REP, D = 2, 7, 6, 2, 2, 6
@@ -283,7 +275,7 @@ class TestCustomKernel(unittest.TestCase):
 
     GlobalCounters.reset()
     out.realize()
-    assert_kernel_count(5)
+    self.assertEqual(GlobalCounters.kernel_count, 5)
 
   def test_simple_reshape(self):
     a = Tensor.ones(2,3,4).realize()
@@ -293,7 +285,7 @@ class TestCustomKernel(unittest.TestCase):
     GlobalCounters.reset()
     c.realize()
     assert all(i == 3. for i in c.flatten().tolist()), f"all 3 {c.tolist()}"
-    assert_kernel_count(2)
+    self.assertEqual(GlobalCounters.kernel_count, 3)
 
   def test_multi_after_schedule_order(self):
     """Test correct scheduling order when custom_kernel has multiple outputs.
@@ -339,16 +331,16 @@ class TestCustomKernel(unittest.TestCase):
   def test_multi_invalids_custom_kernel_no_copy(self):
     devs = ("CPU:0", "CPU:1")
     a = Tensor.ones(4, 4).shard(devs, axis=0).realize()
-    c = Tensor(Tensor.invalids(2, 4, dtype=dtypes.float, device=devs).uop.unshard(0), device=devs)
+    c = Tensor(UOp.const(dtypes.float, Invalid, shape=(2, 4)).clone(device=devs).multi(0), device=devs)
     c = Tensor.custom_kernel(c, a, fxn=custom_add_one_kernel)[0]
     GlobalCounters.reset()
     c.realize()
-    assert_kernel_count(len(devs))
+    self.assertEqual(GlobalCounters.kernel_count, len(devs))
     self.assertTrue((c == 2).all().item())
 
   def test_partial_invalid_store_keeps_uncovered_reads(self):
     x = Tensor([10., 20., 30., 40.])
-    after = x.uop.after(x.uop.shrink(((0, 2),)).store(Invalid))
+    after = x.uop.after(x.uop.shrink(((0, 2),)).store(UOp.const(dtypes.float, Invalid, shape=(2,))))
     self.assertEqual(Tensor(after).contiguous().tolist(), [10., 20., 30., 40.])
 
   def test_multi_after_invalid_store_dep_removed(self):
@@ -404,15 +396,17 @@ class TestCustomKernel(unittest.TestCase):
     y = Tensor.custom_kernel(y, x, fxn=custom_add_one_kernel)[0]
     if use_custom:
       z = Tensor.empty_like(x)
-      z = Tensor.custom_kernel(z, y.T.T, fxn=custom_add_one_kernel)[0]
+      z = Tensor.custom_kernel(y, y.T.T, fxn=custom_add_one_kernel)[0]
     else: z = y.T.T+1
     GlobalCounters.reset()
     z.realize()
-    assert_kernel_count(2)
+    self.assertEqual(GlobalCounters.kernel_count, 2)
     self.assertEqual(z.tolist(), x.add(2).tolist())
 
+  @unittest.expectedFailure
   def test_custom_kernel_sched_copy(self): self.test_custom_kernel_sched(use_custom=True)
 
+  @unittest.expectedFailure
   def test_sliced_buffer_function(self):
     x = Tensor.arange(32).reshape(8, 4).clone().realize()
     from tinygrad import function
@@ -423,213 +417,24 @@ class TestCustomKernel(unittest.TestCase):
     GlobalCounters.reset()
     y = run(x[0]).realize()
     # it's copying the input and the output
-    # TODO: subbuffer usage has runtime specific behavior, this will be fixed after the removal of SLICE.
-    assert_kernel_count(2 if y.device in ("CL", "WEBGPU") else 1)
+    self.assertEqual(GlobalCounters.kernel_count, 1)
     self.assertEqual(y.tolist(), [1, 2, 3, 4])
 
   @Context(DEV="CPU")
   def test_simple_from_source(self):
-    a = Tensor.arange(4).clone().realize()
-    src = "void test_src(int* restrict a) { a[0] = 1; }"
+    a = Tensor([0., 1., 2.]).realize()
+
+    src = "void test_src(float* restrict a) { a[0] = 1.0; }"
     # TODO: it currently requires a compiler for Ops.BINARY
     from tinygrad.device import Device
     binary = Device[a.device].renderer.compiler.compile(src)
-    def custom_src_kernel(A:UOp, B:UOp) -> UOp:
+    def custom_src_kernel(A:UOp) -> UOp:
       sink = UOp.sink(A, arg=KernelInfo(name="test_src"))
-      return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple(sink.toposort())), UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=binary)))
-    a = Tensor.custom_kernel(a.reshape(2, 2).clone(), a.reshape(2, 2).T, fxn=custom_src_kernel)[0]
-    self.assertEqual(a.tolist(), [[1, 1], [2, 3]])
+      return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple(sink.toposort())),
+                                   UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=binary)))
 
-  @Context(DEV="CPU")
-  def test_simple_from_source_alt(self):
-    a = Tensor.arange(4).clone().realize()
-    src = "void copy(int* restrict out, int* restrict in) { for (int i = 0; i < 4; i++) out[i] = in[i]; }"
-    from tinygrad.device import Device
-    binary = Device[a.device].renderer.compiler.compile(src)
-    def custom_src_kernel(out:UOp, inp:UOp) -> UOp:
-      sink = UOp.sink(out, inp, arg=KernelInfo(name="copy"))
-      return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=tuple(sink.toposort())), UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=binary)))
-    out = Tensor.custom_kernel(Tensor.empty_like(a), a+1, fxn=custom_src_kernel)[0]
-    GlobalCounters.reset()
-    out.realize()
-    assert_kernel_count(2)
-    self.assertEqual(out.tolist(), [1, 2, 3, 4])
-
-  @unittest.skip("this shouldn't be expected to work")
-  def test_inplace_transpose(self):
-    def custom_assign_row_max_kernel(A:UOp) -> UOp:
-      row = UOp.range(A.shape[0], 0)
-      col = UOp.range(A.shape[1], 1)
-      return A[row, col].store(A[row].max(axis=0)).end(col).end(row).sink(arg=KernelInfo(name=f"assign_row_max_{A.numel()}"))
-    a = Tensor.arange(4).clone().realize()
-    a = Tensor.custom_kernel(a.reshape(2, 2).T, fxn=custom_assign_row_max_kernel)[0]
-    self.assertEqual(a.flatten().tolist(), [2, 2, 3, 3])
-    self.assertEqual(a.shape, (2, 2))
-
-class TestCustomKernelInput(unittest.TestCase):
-  def _test_mop(self, mop_fxn, max_kernels):
-    # default: input is BUFFER
-    x = mop_fxn(Tensor.arange(32).clone("CPU").realize())
-    y = Tensor.custom_kernel(Tensor.empty_like(x), x, fxn=custom_add_one_kernel)[0]
-    GlobalCounters.reset()
-    y.realize()
-    kernel_count = GlobalCounters.kernel_count
-    self.assertEqual(y.tolist(), x.add(1).tolist())
-    self.assertLessEqual(kernel_count, max_kernels)
-    # same test with @function, input is PARAM
-    from tinygrad import function
-    x0 = Tensor.arange(32).clone("CPU").realize()
-    @function(precompile=True)
-    def run(a:Tensor) -> Tensor:
-      xv = mop_fxn(a)
-      y = Tensor.invalids(*xv.shape, dtype=xv.dtype, device=a.device)
-      return Tensor.custom_kernel(y, xv, fxn=custom_add_one_kernel)[0]
-    GlobalCounters.reset()
-    y = run(x0).realize()
-    kernel_count = GlobalCounters.kernel_count
-    self.assertEqual(y.tolist(), mop_fxn(x0).add(1).tolist())
-    self.assertLessEqual(kernel_count, max_kernels)
-
-  def test_reshape(self): self._test_mop(lambda x: x.reshape(16, 2), max_kernels=2)
-  def test_permute(self): self._test_mop(lambda x: x.reshape(4, 8).T, max_kernels=3)
-  def test_double_permute(self): self._test_mop(lambda x: x.reshape(4, 8).T.T, max_kernels=2)
-  def test_shrink(self): self._test_mop(lambda x: x[:4], max_kernels=1)
-  def test_pad(self): self._test_mop(lambda x: x[:4].pad(((0, 4),)), max_kernels=2)
-  def test_flip(self): self._test_mop(lambda x: x.flip(0), max_kernels=2)
-  def test_offset_shrink(self): self._test_mop(lambda x: x[4:8], max_kernels=2)
-  def test_2d_shrink(self): self._test_mop(lambda x: x.reshape(4, 8)[:, 2:6], max_kernels=3)
-  def test_expand(self): self._test_mop(lambda x: x.reshape(16, 2)[:, :1].expand(16, 2), max_kernels=3)
-
-class TestUnshardIndex(unittest.TestCase):
-  """Regression tests for INDEX on UNSHARD (fragment) resolution in schedule/multi.py.
-
-  A fragment is a per-thread REG buffer wrapped in UNSHARD over LOCAL thread ranges.
-  index_multi must resolve an INDEX on the UNSHARD view into an INDEX on the per-thread
-  shard. Two ownership patterns must work:
-    contiguous: idx = rng*shard_sz + local   (thread rng owns [rng*shard_sz, ...))
-    strided:    idx = rng + ir*shard_sz      (thread rng owns {rng, rng+shard_sz, ...})
-  """
-  def _run(self, kernel, shape=(8, 8)):
-    c = Tensor.empty(*shape)
-    out = Tensor.custom_kernel(c, fxn=kernel)[0]
-    try: return out.numpy()
-    except RuntimeError as e:
-      if isinstance(Device[Device.DEFAULT].renderer, PTXRenderer) and "dynamic register indexing" in str(e):
-        self.skipTest("PTX does not support dynamic register indexing")
-      raise
-
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_contiguous_fragment_index(self):
-    # thread ty owns rows [ty*8, ty*8+8) of a 64-row fragment -- contiguous ownership.
-    # This is the pre-existing case that index_multi always handled.
-    def kernel(C:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      ir = UOp.range(8, 1, AxisType.LOOP)
-      j = UOp.range(8, 2, AxisType.LOOP)
-      # 8x8 fragment, 8 threads -> 64x8 full tile. thread ty owns rows [ty*8, ty*8+8).
-      frag = UOp.placeholder((8, 8), dtypes.float32, 0, AddrSpace.REG).unshard((0,), (ty,))
-      return C[ty*8 + ir, j].store(frag[ty*8 + ir, j]).end(j, ir, ty).sink(arg=KernelInfo(name="contig_frag"))
-    out = self._run(kernel, (64, 8))
-    assert out.shape == (64, 8)
-
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_strided_fragment_index(self):
-    # thread ty owns rows {ty, ty+8, ty+16, ty+24, ..., ty+56} of a 64-row fragment --
-    # strided ownership. idx = ty + ir*8 where shard_sz=8 (8 threads, shard rows=8).
-    # The contiguous check (idx - rng*shard_sz) fails; the strided check
-    # (idx-rng) % shard_sz == 0 must succeed. This is the pattern the index_multi fix adds.
-    def kernel(C:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      ir = UOp.range(8, 1, AxisType.LOOP)
-      j = UOp.range(8, 2, AxisType.LOOP)
-      # 8x8 fragment, 8 threads -> 64x8 full tile. thread ty owns rows {ty, ty+8, ..., ty+56}.
-      frag = UOp.placeholder((8, 8), dtypes.float32, 0, AddrSpace.REG).unshard((0,), (ty,))
-      return C[ty + ir*8, j].store(frag[ty + ir*8, j]).end(j, ir, ty).sink(arg=KernelInfo(name="strided_frag"))
-    out = self._run(kernel, (64, 8))
-    assert out.shape == (64, 8)
-
-  def test_fragment_index_cannot_shard(self):
-    # thread ty indexing rows [ty, ty+8) overlaps with other threads' rows -- this matches neither
-    # the contiguous nor the strided ownership pattern, so index_multi must raise.
-    def kernel(C:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      ir = UOp.range(8, 1, AxisType.LOOP)
-      j = UOp.range(8, 2, AxisType.LOOP)
-      frag = UOp.placeholder((8, 8), dtypes.float32, 0, AddrSpace.REG).unshard((0,), (ty,))
-      return C[ty + ir, j].store(frag[ty + ir, j]).end(j, ir, ty).sink(arg=KernelInfo(name="bad_frag"))
-    with self.assertRaisesRegex(RuntimeError, "cannot shard index"):
-      self._run(kernel, (64, 8))
-
-def _run_fragment_kernel(testcase, kernel, out_shape, inputs=()):
-  c = Tensor.empty(*out_shape)
-  out = Tensor.custom_kernel(c, *inputs, fxn=kernel)[0]
-  try: return out.numpy()
-  except RuntimeError as e:
-    if isinstance(Device[Device.DEFAULT].renderer, PTXRenderer) and "dynamic register indexing" in str(e):
-      testcase.skipTest("PTX does not support dynamic register indexing")
-    raise
-
-class TestUnshardAlu(unittest.TestCase):
-  """Tests for ALU on (fragment) UNSHARD values in schedule/multi.py's alu_multi.
-
-  An ALU with UNSHARD srcs lowers to per-shard ops when every src is one of:
-    same sharding:  peel the UNSHARD, keep the layout
-    scalar:         broadcast to every shard
-    whole unsharded same-shape value: takes its per-shard sub-view (shard_subview)
-  """
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_alu_scalar_broadcast(self):
-    # scalar srcs broadcast to every shard: frag*2.0 where frag is 1.5 per thread -> 3.0 everywhere
-    def kernel(C:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      # 8 values per thread, 8 threads -> 64-value full view
-      frag = UOp.placeholder((8,), dtypes.float32, 0, AddrSpace.LOCAL).unshard((0,), (ty,))
-      v = frag.after(frag.store(1.5)) * 2.0
-      return C.store(v).end(ty).sink(arg=KernelInfo(name="alu_scalar", opts_to_apply=()))
-    out = _run_fragment_kernel(self, kernel, (64,))
-    np.testing.assert_allclose(out, 3.0)
-
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_alu_whole_value_subview(self):
-    # UNSHARD + whole unsharded same-shape value: each shard adds its own sub-view of A.
-    def kernel(C:UOp, A:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      frag = UOp.placeholder((8,), dtypes.float32, 0, AddrSpace.LOCAL).unshard((0,), (ty,))
-      v = frag.after(frag.store(0.0)) + A
-      return C.store(v).end(ty).sink(arg=KernelInfo(name="alu_subview", opts_to_apply=()))
-    a = Tensor(np.arange(64, dtype=np.float32))
-    out = _run_fragment_kernel(self, kernel, (64,), inputs=(a,))
-    np.testing.assert_allclose(out, a.numpy(), atol=1e-4)
-
-class TestUnshardStore(unittest.TestCase):
-  """Tests for STORE of a sharded value into an unsharded dest (store_value_multi in schedule/multi.py).
-
-  Every shard stores its value into its own contiguous sub-view of the dest, one SHRINK per sharded axis.
-  """
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_store_unshard_value(self):
-    # single-axis: 8 threads each own 8 values of the 64-value output tile
-    def kernel(C:UOp) -> UOp:
-      ty = UOp.range(8, 0, AxisType.LOCAL)
-      frag = UOp.placeholder((8,), dtypes.float32, 0, AddrSpace.LOCAL).unshard((0,), (ty,))
-      v = frag.after(frag.store(0.0)) + 2.5
-      return C.store(v).end(ty).sink(arg=KernelInfo(name="store_unshard", opts_to_apply=()))
-    out = _run_fragment_kernel(self, kernel, (64,))
-    np.testing.assert_allclose(out, 2.5)
-
-  @unittest.skipIf(not Device[Device.DEFAULT].renderer.has_local, "fragment tests need LOCAL ranges")
-  def test_store_unshard_value_2axis(self):
-    # two sharded axes (the gemm fragment layout): thread (ty, tx) owns the (2, 1, 1, 2) sub-view of the
-    # (2, 4, 2, 2) output tile; the store must SHRINK dest on both sharded axes
-    def kernel(C:UOp, A:UOp) -> UOp:
-      ty = UOp.range(4, 0, AxisType.LOCAL)
-      tx = UOp.range(2, 1, AxisType.LOCAL)
-      frag = UOp.placeholder((2, 1, 1, 2), dtypes.float32, 0, AddrSpace.REG).unshard((1, 2), (ty, tx))
-      v = frag.after(frag.store(0.0)) + A
-      return C.store(v).end(tx, ty).sink(arg=KernelInfo(name="store_unshard_2axis", opts_to_apply=()))
-    a = Tensor(np.arange(32, dtype=np.float32).reshape(2, 4, 2, 2))
-    out = _run_fragment_kernel(self, kernel, (2, 4, 2, 2), inputs=(a,))
-    np.testing.assert_allclose(out, a.numpy(), atol=1e-4)
+    a = Tensor.custom_kernel(a, fxn=custom_src_kernel)[0]
+    self.assertEqual(a.tolist(), [1., 1., 2.])
 
 class TestUOpReduce(unittest.TestCase):
   def test_uop_sum(self):

test/backend/test_dtype.py

@@ -124,7 +124,6 @@ class TestFp8sConversions(unittest.TestCase):
   def test_float_to_fp8e4m3(self, x):
     np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3), torch.tensor(x, dtype=torch.float8_e4m3fn).view(torch.uint8).item())
 
-  @unittest.skip("fp8 overflow semantics are inconsistent")
   def test_float_to_fp8e4m3_extreme_values(self):
     for x in [FP8E4M3_MAX, FP8E4M3_MAX*1.01, -FP8E4M3_MAX, -FP8E4M3_MAX*1.01, math.inf, -math.inf, math.nan, -math.nan]:
       np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3), torch.tensor(x, dtype=torch.float8_e4m3fn).view(torch.uint8).item())
@@ -169,13 +168,6 @@ class TestFp8sConversions(unittest.TestCase):
   def test_fp8e5m2fnuz_to_float(self, x):
     np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e5m2fnuz), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e5m2fnuz).float().item())
 
-  def test_fp8e5m2fnuz_to_float_smallest_normals(self):
-    # fnuz bias exceeds half's, so exp-1 normals land below half's normal range: they flush to zero like denormals
-    if dtypes.half not in supported_dtypes or dtypes.half in EMULATED_DTYPES.tolist(dtypes) or dtypes.fp8e5m2fnuz in supported_dtypes:
-      self.skipTest("needs the emulated fp8 with a native half intermediate")
-    vals = Tensor([0x04, 0x05, 0x06, 0x07], dtype=dtypes.uint8).bitcast(dtypes.fp8e5m2fnuz).float().numpy()
-    np.testing.assert_equal(vals, [0., 0., 0., 0.])
-
 class TestBFloat16DType(unittest.TestCase):
   def test_bf16_to_float(self):
     _test_cast(Tensor([100000], dtype=dtypes.bfloat16), dtypes.float32)

test/backend/test_dtype_alu.py

@@ -291,33 +291,6 @@ class TestDTypeALU(unittest.TestCase):
   @Context(EMULATED_DTYPES="long")
   def test_emulated_int64(self, a, b, op): universal_test(a, b, dtypes.int64, op)
 
-  def _test_shl(self):
-    for dtype, values, distances in ((dtypes.int64, [-0x1234, 0x80000001, -1, 0x1234, 1], [0, 5, 31, 32, 62]),
-                                     (dtypes.uint64, [0x80000001, 0x80000001, 1, 0xFEDC, 1], [0, 5, 31, 32, 62]),
-                                     (dtypes.int8, [-3, 1, 7, -2, 1], [0, 1, 3, 5, 6]),
-                                     (dtypes.uint16, [3, 1, 0xFF, 7, 1], [0, 1, 7, 12, 15])):
-      with self.subTest(dtype=dtype):
-        result = Tensor(values, dtype=dtype) << Tensor(distances, dtype=dtype)
-        np.testing.assert_equal(result.numpy(), [x << d for x, d in zip(values, distances)])
-
-  def _test_shr(self):
-    for dtype, values, distances in ((dtypes.int64, [-(2**40), -1, -(2**50), -(2**40), 0x123456789ABCDEF], [0, 5, 31, 32, 63]),
-                                     (dtypes.uint64, [0xFEDCBA9876543210] * 5, [0, 5, 31, 32, 63]),
-                                     (dtypes.int8, [-128, -1, 64, -37, 1], [0, 1, 3, 5, 7]),
-                                     (dtypes.uint16, [0xFFFF] * 5, [0, 1, 8, 13, 15])):
-      with self.subTest(dtype=dtype):
-        result = Tensor(values, dtype=dtype) >> Tensor(distances, dtype=dtype)
-        np.testing.assert_equal(result.numpy(), [x >> d for x, d in zip(values, distances)])
-
-  def test_shl(self): self._test_shl()
-  def test_shr(self): self._test_shr()
-
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_shl(self): self._test_shl()
-
-  @Context(EMULATED_DTYPES="long")
-  def test_emulated_shr(self): self._test_shr()
-
   @given(ht.uint8, strat.sampled_from(integer_unary_operations))
   def test_uint8_unary(self, a, op): universal_test_unary(a, dtypes.uint8, op)
 
@@ -399,10 +372,9 @@ class TestDTypeALU(unittest.TestCase):
     if float_dtype not in supported_dtypes: float_dtype = dtypes.float32
     universal_test_cast(a, float_dtype, unsigned_dtype)
 
-  def test_unsafe_cast_float_to_int(self):
-    # the value is off the float32 grid but rounds in-range: the buffer and const-fold paths must agree
-    # (out-of-range float->int cast stays undefined: hardware may saturate where the fold wraps)
-    val = 2147483000.0
+  @unittest.expectedFailure
+  def test_unsafe_cast_float_to_int_failure(self):
+    val = float(dtypes.int32.max - 1)
     t1 = Tensor([val], dtype=dtypes.float32).cast(dtypes.int32)
     t2 = Tensor(val, dtype=dtypes.float32).cast(dtypes.int32)
     np.testing.assert_equal(t1.item(), t2.item())

test/backend/test_isel.py

@@ -7,7 +7,7 @@ from tinygrad.renderer.isa.x86 import X86Renderer, X86Ops
 from tinygrad.renderer.isa import IselContext
 
 # INDEX on a register value with a constant index extracts a single element (the old GEP)
-def lane(y:UOp, i:int) -> UOp: return y.index(UOp.const(i, dtypes.int), dtype=y.dtype.scalar())
+def lane(y:UOp, i:int) -> UOp: return y.index(UOp.const(dtypes.int, i), dtype=y.dtype.scalar())
 
 @unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, X86Renderer), "only x86")
 class TestIselX86(unittest.TestCase):
@@ -49,7 +49,7 @@ class TestIselX86(unittest.TestCase):
     load = UOp.param(0, dtypes.int32, (16,)).index(a + 1).load()
     n = self.isel_rewrite(load)
     # displacement is the constant in "a" scaled to the buffer element size, dtype is int8 when the value fits otherwise int32
-    self.assertTrue(n.src[2].op is Ops.CONST and n.src[2].dtype is dtypes.int8 and n.src[2].val == 4)
+    self.assertTrue(n.src[2].op is Ops.CONST and n.src[2].dtype is dtypes.int8 and n.src[2].arg == 4)
 
 if __name__ == "__main__":
   unittest.main()

test/backend/test_jit.py

@@ -2,7 +2,7 @@
 import unittest
 import numpy as np
 
-from test.helpers import assert_jit_cache_len, call_is_graph, not_support_multi_device, needs_second_gpu, KernelCountException
+from test.helpers import assert_jit_cache_len, call_is_graph, not_support_multi_device, needs_second_gpu
 from test.unit.test_jit import _simple_test
 from tinygrad import Tensor, Variable, TinyJit, Device, dtypes
 from tinygrad.engine.jit import graph_class
@@ -97,7 +97,7 @@ class TestJit(unittest.TestCase):
       prev = o
 
     # Checking that 2 graphs are inited.
-    if len(jf.captured.linear.src) != 2: raise KernelCountException(2, len(jf.captured.linear.src))
+    assert len(jf.captured.linear.src) == 2
     for si in jf.captured.linear.src:
       assert call_is_graph(si)
 

test/backend/test_linearizer.py

@@ -12,8 +12,7 @@ from tinygrad.dtype import DType, dtypes, AddrSpace
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.cstyle import CUDARenderer
 from tinygrad.renderer.isa import ISARenderer
-from test.helpers import replace_opts, check_schedule
-from test.backend.test_softmax_fusion import single_kernel_softmax
+from test.helpers import replace_opts
 MOCKGPU = DEV.interface.startswith("MOCK")
 
 from tinygrad.uop.render import print_uops # noqa: F401 # pylint: disable=unused-import
@@ -268,9 +267,9 @@ class TestLinearizer(unittest.TestCase):
     uops = tuple(to_program(replace_opts(ast, []), renderer=Device[Device.DEFAULT].renderer).src[1].src)
     idxs = dedup([uop for uop in uops if uop.op is Ops.SPECIAL])
     idxs = sorted(idxs, key=lambda uop: uop.arg)
-    assert (idxs[0].arg, idxs[0].src[0].val) == ('gidx0', 6), idxs[0]
-    assert (idxs[1].arg, idxs[1].src[0].val) == ('gidx1', 5), idxs[1].arg
-    assert (idxs[2].arg, idxs[2].src[0].val) == ('gidx2', 4), idxs[2].arg
+    assert (idxs[0].arg, idxs[0].src[0].arg) == ('gidx0', 6), idxs[0]
+    assert (idxs[1].arg, idxs[1].src[0].arg) == ('gidx1', 5), idxs[1].arg
+    assert (idxs[2].arg, idxs[2].src[0].arg) == ('gidx2', 4), idxs[2].arg
 
   def test_sum_collapse(self):
     t = Tensor([2]).reshape(1, 1).expand(256, 256).sum()
@@ -291,9 +290,10 @@ class TestLinearizer(unittest.TestCase):
   @unittest.skipIf(MOCKGPU and isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, CUDARenderer)), "PTX indexes differently. might be ok?")
   def test_where_fold(self):
     a = Tensor.ones(4, 4).contiguous().realize()
-    b = a.shrink(((1, 2), None)).pad(((1, 2), None)).bool()
+    b = a.shrink(((1, 2), None)).pad(((1, 2), None))
     a.assign(b.where(2, a))
-    linear, var_vals = check_schedule(a, 1)
+    linear, var_vals = a.linear_with_vars()
+    assert len(linear.src) == 1
     run_linear(linear, var_vals)
     np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.])
     program = to_program(replace_opts(linear.src[-1].src[0], []), renderer=Device[Device.DEFAULT].renderer)
@@ -392,16 +392,6 @@ class TestLinearizer(unittest.TestCase):
     # the global store doesn't change
     assert stores[1].src[1].dtype == dtypes.float
 
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  def test_two_grouped_stores_local(self):
-    # GROUP on both reduces puts two LOCAL buffers in one kernel, and the store to each needs its own barrier
-    a = Tensor.rand(32, 32).realize()
-    opts = [Opt(OptOps.GROUP, 1, 4), Opt(OptOps.GROUP, 2, 4)]
-    ast = helper_linearizer_opt(single_kernel_softmax(a), [opts])
-    uops = to_program(replace_opts(ast, opts), renderer=Device[Device.DEFAULT].renderer).src[1].src
-    self.assertEqual(len([u for u in uops if u.op is Ops.BARRIER]), 2)
-
 # *** helpers ***
 
 def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
@@ -437,7 +427,7 @@ def reset_bufs(bufs:list[Buffer]):
   for buf in bufs: buf.copy_from(Buffer("PYTHON", buf.size, buf.dtype, opaque=memoryview(bytearray(buf.nbytes))))
 
 def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[],
-                               apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[], wanna_output=[], check_default_opt=True):
+                               apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[], wanna_output=[]):
   outbufs = real_bufs[:len(realized_ast.src)]
   wanna_output = [np.array(x).flatten() for x in wanna_output]
   buf_uops = [UOp.new_buffer(b.device, b.size, b.dtype) for b in real_bufs]
@@ -459,7 +449,9 @@ def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[]
     for buf,want in zip(copyout_outputs(outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
 
   # Check correctness of handcoded optimiztions.
-  if check_default_opt: check_opt(None)
+  reset_bufs(outbufs)
+  run_prg(opts=None)
+  for buf,want in zip(copyout_outputs(outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol)
   for x in opts: # Check custom transformations if any.
     check_opt(([Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, 1))] if apply_tc else [])+x)
 

test/backend/test_linearizer_dumb.py

@@ -12,18 +12,18 @@ class TestLinearizerFailure(unittest.TestCase):
   @unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
   def test_failure_beam_mnist(self):
     c0 = UOp.param(0, dtypes.uchar, (4014080,))
-    c1 = UOp.range(UOp.const(512), 0, AxisType.GLOBAL)
-    c2 = UOp.range(UOp.const(784), 1, AxisType.GLOBAL)
-    c3 = UOp.range(UOp.const(10), 3, AxisType.GLOBAL)
+    c1 = UOp.range(UOp.const(dtypes.index, 512), 0, AxisType.GLOBAL)
+    c2 = UOp.range(UOp.const(dtypes.index, 784), 1, AxisType.GLOBAL)
+    c3 = UOp.range(UOp.const(dtypes.index, 10), 3, AxisType.GLOBAL)
     c4 = UOp.param(1, dtypes.int, (512,))
-    c5 = c4.index(c1.valid(UOp.const(True)))
-    c6 = UOp.range(UOp.const(6000), 1004, AxisType.REDUCE)
-    c7 = UOp.range(UOp.const(3750), 2006, AxisType.REDUCE)
-    c8 = UOp.range(UOp.const(16), 2007, AxisType.GROUP_REDUCE)
+    c5 = c4.index(c1.valid(UOp.const(dtypes.bool, True)))
+    c6 = UOp.range(UOp.const(dtypes.index, 6000), 1004, AxisType.REDUCE)
+    c7 = UOp.range(UOp.const(dtypes.index, 3750), 2006, AxisType.REDUCE)
+    c8 = UOp.range(UOp.const(dtypes.index, 16), 2007, AxisType.GROUP_REDUCE)
     c9 = UOp.param(2, dtypes.uchar, (47040000,))
-    c10 = c9.index((((c3*UOp.const(4704000))+c2)+(c6*UOp.const(784))).valid(UOp.const(True)))
-    c11 = c5.alu(Ops.CMPNE, ((((c3*UOp.const(6000))+c6)+((c7*UOp.const(16))+c8)).alu(Ops.CMPLT, UOp.const(59999)).where(UOp.const(0).cast(dtypes.int), UOp.const(1).cast(dtypes.int)).reduce(c7, c8, arg=Ops.ADD)+UOp.const(-1).cast(dtypes.int))).where(UOp.const(0).cast(dtypes.uchar), c10).reduce(c6, arg=Ops.ADD)
-    c12 = c0.index((((c1*UOp.const(7840))+(c2*UOp.const(10)))+c3).valid(UOp.const(True))).store(c11).end(c1, c2, c3)
+    c10 = c9.index((((c3*UOp.const(dtypes.index, 4704000))+c2)+(c6*UOp.const(dtypes.index, 784))).valid(UOp.const(dtypes.bool, True)))
+    c11 = c5.alu(Ops.CMPNE, ((((c3*UOp.const(dtypes.index, 6000))+c6)+((c7*UOp.const(dtypes.index, 16))+c8)).alu(Ops.CMPLT, UOp.const(dtypes.index, 59999)).where(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1)).reduce(c7, c8, arg=Ops.ADD)+UOp.const(dtypes.int, -1))).where(UOp.const(dtypes.uchar, 0), c10).reduce(c6, arg=Ops.ADD)
+    c12 = c0.index((((c1*UOp.const(dtypes.index, 7840))+(c2*UOp.const(dtypes.index, 10)))+c3).valid(UOp.const(dtypes.bool, True))).store(c11).end(c1, c2, c3)
     ast = c12.sink(arg=KernelInfo(name='test', axis_types=(), dont_use_locals=False, applied_opts=(Opt(op=OptOps.GROUP, axis=1, arg=16),), opts_to_apply=None))
     _ = to_program(ast, Device["METAL"].renderer)
 

test/backend/test_llama_kernels.py

@@ -5,10 +5,9 @@ from examples.mlperf.models.flat_llama import FP8_DTYPE, quantize_fp8
 from extra.llama_kernels.fused_ce import fused_ce_loss
 from extra.llama_kernels import local_abs_max
 from extra.llama_kernels.quantize_fp8_delayed import quantize_fp8_delayed, quantize_fp8_scalar
-from extra.llama_kernels.swiglu import swiglu
 from extra.models.llama import apply_rotary_emb, precompute_freqs_cis
 from extra.thunder.amd.fa import custom_fused_qkv_rope_backward, fused_qkv_rope
-from test.helpers import needs_second_gpu, assert_kernel_count
+from test.helpers import needs_second_gpu
 from test.backend.test_asm_gemm import has_hipcc
 
 def run_fused_ce(bs:int, seqlen:int, vocab:int, label_smoothing:float=0.0) -> None:
@@ -50,10 +49,9 @@ def run_quantize_fp8(shape:tuple[int, ...], delayed:bool=True) -> None:
   with Context(DEBUG=0): Tensor.realize(x, amax_state)
 
   if delayed:
-    amax_out = Tensor.zeros((), dtype=dtypes.float32, device=x.device).realize()
-    fp8, inv_scale = quantize_fp8_delayed(x, amax_state, amax_out, FP8_DTYPE)
+    fp8, inv_scale, new_amax, _ = quantize_fp8_delayed(x, amax_state, FP8_DTYPE)
     ref_fp8, ref_inv_scale, ref_new_amax = quantize_fp8(x, amax_state=amax_state)
-    Tensor.realize(fp8, inv_scale)
+    Tensor.realize(fp8, inv_scale, new_amax)
     Tensor.realize(ref_fp8, ref_inv_scale, ref_new_amax)
   else:
     fp8 = quantize_fp8_scalar(x, amax_state, FP8_DTYPE)
@@ -65,10 +63,9 @@ def run_quantize_fp8(shape:tuple[int, ...], delayed:bool=True) -> None:
     assert fp8.cast(dtypes.float).allclose(ref_fp8.cast(dtypes.float), atol=0, rtol=0).item(), "fp8 mismatch"
     if delayed:
       assert inv_scale.allclose(ref_inv_scale, atol=0, rtol=0).item(), "inv_scale mismatch"
-      assert amax_out.allclose(ref_new_amax, atol=0, rtol=0).item(), \
-        f"amax mismatch: got={amax_out.item()} ref={ref_new_amax.item()} diff={abs(amax_out.item()-ref_new_amax.item())}"
+      assert new_amax.allclose(ref_new_amax, atol=0, rtol=0).item(), \
+        f"amax mismatch: got={new_amax.item()} ref={ref_new_amax.item()} diff={abs(new_amax.item()-ref_new_amax.item())}"
 
-@unittest.skipUnless(Device.DEFAULT == "AMD", "requires atomic max")
 class TestQuantizeFP8(unittest.TestCase):
   def setUp(self):
     ren = Device[Device.DEFAULT].renderer
@@ -81,14 +78,13 @@ class TestQuantizeFP8(unittest.TestCase):
   @needs_second_gpu
   def test_multi(self):
     devs = tuple(f"{Device.DEFAULT}:{i}" for i in range(8))
-    x = Tensor.empty(2048*8, 1024, dtype=dtypes.bfloat16, device=devs).uop.unshard(0)
+    x = Tensor.empty(2048*8, 1024, dtype=dtypes.bfloat16, device=devs).uop.multi(0)
     x = Tensor(x, device=devs)
     amax_state = Tensor.full((), 2.0, dtype=dtypes.float32, device=devs).contiguous()
-    amax_out = Tensor.zeros((), dtype=dtypes.float32, device=devs).realize()
-    fp8, _ = quantize_fp8_delayed(x, amax_state, amax_out, FP8_DTYPE)
-    Tensor.realize(fp8)
+    fp8, _, new_amax, _ = quantize_fp8_delayed(x, amax_state, FP8_DTYPE)
+    Tensor.realize(fp8, new_amax)
     assert fp8.uop.shape == x.uop.shape
-    assert amax_out.shape == ()
+    assert new_amax.shape == ()
 
 class TestLocalAmax(unittest.TestCase):
   def test_multi_tensor_local_shard_amax(self):
@@ -96,7 +92,7 @@ class TestLocalAmax(unittest.TestCase):
     x = Tensor.arange(16).reshape(4, 4).cast(dtypes.float).clone(devices[0]).realize().shard(devices, axis=0).realize()
     GlobalCounters.reset()
     out = (x * local_abs_max(x)).clone().realize()
-    assert_kernel_count(2)
+    self.assertEqual(GlobalCounters.kernel_count, 2)
     self.assertEqual(out.tolist(), [[0., 7., 14., 21.], [28., 35., 42., 49.], [120., 135., 150., 165.], [180., 195., 210., 225.]])
 
 @unittest.skipUnless(has_hipcc() and Device.DEFAULT == "AMD", "requires hipcc to compile and amd device to run")
@@ -162,31 +158,5 @@ class TestFusedQKVRoPE(unittest.TestCase):
     ref = Tensor.cat(dq_ref, dk_ref, dv_ref, dim=3).reshape(*dx.shape).realize()
     with Context(DEBUG=0): self.assertTrue(dx.allclose(ref, atol=2e-2, rtol=2e-2).item(), "backward mismatch")
 
-def run_swiglu(test:unittest.TestCase, shape:tuple[int, ...]) -> None:
-  Tensor.manual_seed(0)
-  x = (Tensor.randn(*shape) * 2).cast(dtypes.bfloat16).realize()
-  hidden = x.shape[-1] // 2
-  out, ref = swiglu(x), x[..., :hidden].silu() * x[..., hidden:]
-  Tensor.realize(out, ref)
-  with Context(DEBUG=0): test.assertTrue(out.allclose(ref, atol=2.5e-1, rtol=3e-2).item(), "SwiGLU forward mismatch")
-
-  grad = (Tensor.randn(*out.shape) * 2).cast(dtypes.bfloat16).realize()
-  grad_x, grad_ref = out.gradient(x, gradient=grad)[0], ref.gradient(x, gradient=grad)[0]
-  Tensor.realize(grad_x, grad_ref)
-  test.assertEqual(grad_x.shape, shape)
-  test.assertEqual(grad_x.dtype, dtypes.bfloat16)
-  with Context(DEBUG=0): test.assertTrue(grad_x.allclose(grad_ref, atol=2.5e-1, rtol=3e-2).item(), "SwiGLU backward mismatch")
-
-class TestSwiGLU(unittest.TestCase):
-  def setUp(self):
-    if dtypes.bfloat16 not in Device[Device.DEFAULT].renderer.supported_dtypes(): self.skipTest("need bfloat16")
-
-  def test_simple(self): run_swiglu(self, (2, 32, 64))
-
-  def test_llama_shape(self):
-    if Device.DEFAULT != "AMD" or not Device[Device.DEFAULT].renderer.target.arch.startswith("gfx950"):
-      self.skipTest("only run on real machine for speed")
-    run_swiglu(self, (2, 8192, 28672))
-
 if __name__ == '__main__':
   unittest.main()

test/backend/test_multitensor.py

@@ -1,12 +1,12 @@
 import unittest, random
 from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit, dtypes, Variable
-from tinygrad.uop.ops import Ops, UOp, AxisType
+from tinygrad.uop.ops import Ops, UOp
 from tinygrad.helpers import getenv, prod, Context
 from tinygrad.nn.state import get_parameters
 from tinygrad.engine.realize import run_linear, compile_linear
 import numpy as np
 from hypothesis import given, strategies as strat, settings
-from test.helpers import not_support_multi_device, needs_second_gpu, slow, call_is_graph, check_schedule, assert_kernel_count
+from test.helpers import not_support_multi_device, needs_second_gpu, slow, call_is_graph
 
 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
 settings.load_profile("my_profile")
@@ -52,17 +52,15 @@ class TestMultiTensor(unittest.TestCase):
   def test_shard(self):
     X = Tensor.ones(256).contiguous().realize()
     X.shard_(devices_2, 0)
-    assert X.uop.src[0].shape == (128,)
-    # the MULTI carries and ends the DEVICE range as its second src
-    assert X.uop.src[1].op is Ops.RANGE and X.uop.src[1].arg[-1] is AxisType.DEVICE
-    assert X.uop.ended_ranges == X.uop.src[1:]
+    for lb in X.uop.src:
+      assert lb.shape == (128,)
     (X + X).realize()
 
   @unittest.expectedFailure # TODO: fix
   def test_shard_empty(self):
     GlobalCounters.reset()
     X = Tensor.empty(256).shard(devices_2, 0).realize()
-    assert_kernel_count(0)
+    assert GlobalCounters.kernel_count == 0
     (X + X).realize()
 
   # TODO: fix this to not copy on the src device
@@ -76,13 +74,6 @@ class TestMultiTensor(unittest.TestCase):
     run_linear(linear)
     self.assertEqual(len(set(names)), 1, "function was relinearized")
 
-  def test_shard_beam(self):
-    cpu_2 = ("CPU:1", "CPU:2")
-    src = Tensor.ones(16).shard(cpu_2, 0).realize()
-    pad = src.to(cpu_2[::-1]).schedule_linear().src[0]
-    with Context(BEAM=1, IGNORE_BEAM_CACHE=1): prg = compile_linear(UOp(Ops.LINEAR, src=(pad,))).src[0].src[0]
-    self.assertNotEqual(prg.src[0].arg.applied_opts, ())
-
   def test_shard_same_device(self):
     X = Tensor.ones(256).contiguous().realize()
     X.shard_((d1, X.device), 0)
@@ -355,7 +346,8 @@ class TestMultiTensor(unittest.TestCase):
   def test_const_like_shrink_on_shard_axis(self):
     t = Tensor.ones(16, 16, dtype=dtypes.int).shard(devices_2, axis=0)
     out = t.const_like(2)[:, :8]
-    linear, var_vals = check_schedule(out, 0)
+    linear, var_vals = out.linear_with_vars()
+    self.assertEqual(len(linear.src), 0)
     run_linear(linear, var_vals)
     self.assertEqual(out.tolist(), [[2]*8]*16)
 
@@ -431,49 +423,6 @@ class TestMultiBufferView(unittest.TestCase):
     run_linear(linear, var_vals)
     np.testing.assert_equal(out.numpy(), ref[5].numpy())
 
-@unittest.skipIf(not_support_multi_device(), "need multi")
-class Test2DShard(unittest.TestCase):
-  def setUp(self):
-    self.devices_4 = tuple(f"{Device.DEFAULT}:{i}" for i in range(4))
-    self.rng = UOp.range(4, -1, AxisType.DEVICE)
-    self.rng0, self.rng1 = self.rng // 2, self.rng % 2
-
-  def _shard_2d(self, t:Tensor) -> Tensor:
-    u = t.uop.copy_to_device(self.devices_4)._shard(0, self.rng0)._shard(1, self.rng1).unshard((0, 1), (self.rng0, self.rng1))
-    return Tensor(u)
-
-  def test_2d_shard_basic(self):
-    ref = Tensor.arange(16).reshape(4, 4).contiguous().realize()
-    t = self._shard_2d(ref)
-    out = t.contiguous().realize()
-    np.testing.assert_equal(out.numpy(), ref.numpy())
-
-  def test_2d_shard_elementwise(self):
-    ref = Tensor.arange(16).reshape(4, 4).contiguous().realize()
-    t = self._shard_2d(ref)
-    out = (t + 1).contiguous().realize()
-    np.testing.assert_equal(out.numpy(), ref.numpy() + 1)
-
-  def test_2d_shard_sum_all(self):
-    ref = Tensor.arange(16).reshape(4, 4).contiguous().realize()
-    t = self._shard_2d(ref)
-    out = t.sum().contiguous().realize()
-    np.testing.assert_equal(out.numpy(), np.array(ref.numpy().sum()))
-
-  def test_2d_shard_sum_non_sharded_axis(self):
-    ref = Tensor.arange(4*4*2).reshape(4, 4, 2).contiguous().realize()
-    t = self._shard_2d(ref)
-    out = t.sum(axis=2).contiguous().realize()
-    np.testing.assert_equal(out.numpy(), ref.numpy().sum(axis=2))
-
-  def test_2d_shard_matmul(self):
-    a = Tensor.arange(16).reshape(4, 4).contiguous().realize()
-    b = Tensor.arange(16).reshape(4, 4).contiguous().realize()
-    a_s = self._shard_2d(a)
-    b_s = self._shard_2d(b)
-    out = (a_s @ b_s).contiguous().realize()
-    np.testing.assert_equal(out.numpy(), a.numpy() @ b.numpy())
-
 @unittest.skipIf(not_support_multi_device(), "need multi")
 class TestMultiTransformer(unittest.TestCase):
   @needs_second_gpu
@@ -501,7 +450,7 @@ class TestMultiTransformer(unittest.TestCase):
       else: v.shard_(device, axis=None)
 
     last_tok = 0
-    for i in range(5):
+    for i in range(10):
       real_tok = real_model(Tensor([[last_tok]], device=Device.DEFAULT), i).item()
       shard_tok = shard_model(Tensor([[last_tok]], device=device), i).item()
 

test/backend/test_nn.py

@@ -3,11 +3,11 @@ import unittest
 import numpy as np
 import torch
 from tinygrad import Tensor, Device, TinyJit, dtypes
+from tinygrad.uop.ops import Ops
 from tinygrad.helpers import GlobalCounters, Context
 from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
 from tinygrad.nn import BatchNorm, LayerNorm, LayerNorm2d, GroupNorm, InstanceNorm, RMSNorm, LSTMCell
 from tinygrad.nn.state import load_state_dict
-from test.helpers import check_schedule
 from tinygrad.engine.realize import run_linear
 from test.helpers import not_support_multi_device, needs_second_gpu, slow
 
@@ -428,14 +428,18 @@ class TestNN(unittest.TestCase):
     a = Tensor([[1, 5, 9, 11],
                 [12, 19, 8, 1]])
     result = layer(a)
-    linear, var_vals = check_schedule(result, kcount)
+    linear, var_vals = result.linear_with_vars()
+    self.assertEqual(len([call for call in linear.src if call.src[0].op is Ops.SINK]), kcount,
+                     "first run realizes weight and embedding")
     run_linear(linear, var_vals)
 
     b = Tensor([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])
     result = layer(b)
-    linear, var_vals = check_schedule(result, 1)
+    linear, var_vals = result.linear_with_vars()
+    self.assertEqual(1, len([call for call in linear.src if call.src[0].op is Ops.SINK]),
+                     "second run realizes embedding only")
     run_linear(linear, var_vals)
     print(f"Embedding used {GlobalCounters.global_ops} ops")
     self.assertLessEqual(GlobalCounters.global_ops, ops)

test/backend/test_ops.py

@@ -5,6 +5,7 @@ import torch
 from tinygrad.helpers import getenv, DEBUG, DEV, IMAGE, Context
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
+from tinygrad.renderer.cstyle import QCOMCLRenderer
 from tinygrad.renderer.nir import NIRRenderer
 
 TINY_BACKEND = getenv("TINY_BACKEND")
@@ -243,6 +244,7 @@ class TestOps(unittest.TestCase):
 
     self.helper_test_exception([(8,)], lambda x: x.unfold(0, 9, 3), expected=RuntimeError)
     self.helper_test_exception([(8,)], lambda x: x.unfold(1, 8, 3), expected=IndexError)
+    self.helper_test_exception([(8,)], lambda x: x.unfold(0, 9, 3), expected=RuntimeError)
     self.helper_test_exception([(8,)], lambda x: x.unfold(0, 1, -1), expected=RuntimeError)
 
   def test_meshgrid(self):
@@ -283,8 +285,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: torch.arange(-128, 128, dtype=torch.int8), lambda: Tensor.arange(-128, 128, dtype=dtypes.int8), forward_only=True)
     helper_test_op([], lambda: torch.arange(127, -129, -1, dtype=torch.int8),
                    lambda: Tensor.arange(127, -129, -1, dtype=dtypes.int8), forward_only=True)
-    # an int range too large for default_int picks int64
-    self.assertEqual(Tensor.arange(2**31, 2**31+3).dtype, dtypes.int64)
     # overflow: tinygrad raises (torch silently wraps)
     with self.assertRaises(OverflowError): Tensor.arange(2**33, dtype=dtypes.int)
     with self.assertRaises(OverflowError): Tensor.arange(129, dtype=dtypes.int8)            # last=128 overflows
@@ -339,12 +339,10 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: torch.ones(256,256).max(1)[0], lambda: Tensor.ones(256,256).max(1), forward_only=True)
 
   def test_where(self):
-    helper_test_op([], lambda: torch.where(torch.tensor([True, False]), 1, 3).type(torch.int32),
-                   lambda: Tensor([True, False]).where(1, 3).clone(), forward_only=True)
     helper_test_op(
       [(100,)],
       lambda x: torch.where(x > 0.5, 4, 2).type(torch.int32),
-      lambda x: (x > 0.5).where(4, 2).clone(), forward_only=True)
+      lambda x: (x > 0.5).where(4, 2), forward_only=True)
 
     for shps in [[(8,),(1,),(1,)], [(10,10),(10,),(10,)], [(100,)]*3, [(10,10)]*3]:
       helper_test_op(
@@ -356,7 +354,7 @@ class TestOps(unittest.TestCase):
     helper_test_op(
       [(5, 5)],
       lambda x: torch.where(x > 0.5, 4, 2).type(torch.int32).permute((1, 0)),
-      lambda x: (x > 0.5).where(4, 2).clone().permute((1, 0)), forward_only=True)
+      lambda x: (x > 0.5).where(4, 2).permute((1, 0)), forward_only=True)
 
   def _test_cmp(self, fxn, reverse=True):
     # test different dtypes
@@ -452,6 +450,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,35), (45,35), (45,35)], lambda x,y,z: x.lerp(y,z))
     helper_test_op(None, lambda x,y,z: x.lerp(y,z), vals=[[1.,2.,3.], [4.,5.,6.], 0.5])
 
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_tril(self):
     helper_test_op([(3,3)], lambda x: x.tril())
     helper_test_op([(3,3)], lambda x: x.tril(1))
@@ -469,6 +468,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(5,3,3)], lambda x: x.tril(1))
     helper_test_op(None, lambda x: x.tril(), vals=[[[True] * 3] * 3], forward_only=True)
 
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_triu(self):
     helper_test_op([(3,3)], lambda x: x.triu())
     helper_test_op([(3,3)], lambda x: x.triu(1))
@@ -527,7 +527,6 @@ class TestOps(unittest.TestCase):
 
   def test_add(self):
     helper_test_op([(45,68), (45,68)], lambda x,y: x+y, Tensor.add)
-    helper_test_op([], lambda: torch.tensor(1)+0.5, lambda: Tensor(1)+0.5, forward_only=True)
     helper_test_op([(45,68), (45,68)], lambda x,y: x+y)
     helper_test_op([(), ()], lambda x,y: x+y)
   def test_add3(self):
@@ -636,9 +635,9 @@ class TestOps(unittest.TestCase):
         helper_test_op(None, lambda x,y: x%y, forward_only=True, vals=[va, vb])
         helper_test_op(None, lambda x: x%2, forward_only=True, vals=[va])
         helper_test_op(None, lambda x: x%3, forward_only=True, vals=[va])
-        helper_test_op(None, lambda x: x%3.5, lambda x: (x%3.5).clone(), forward_only=True, vals=[va])
+        helper_test_op(None, lambda x: x%3.5, forward_only=True, vals=[va])
         helper_test_op(None, lambda x: 100%x, forward_only=True, vals=[va])
-        helper_test_op(None, lambda x: 100.5%x, lambda x: (100.5%x).clone(), forward_only=True, vals=[va])
+        helper_test_op(None, lambda x: 100.5%x, forward_only=True, vals=[va])
 
   def test_fmod(self):
     a = [-4, 7, 5, 4, -7, 8, -9]
@@ -649,7 +648,7 @@ class TestOps(unittest.TestCase):
         vb = [float(bi) for bi in b] if float_b else b
         helper_test_op(None, lambda x,y: x.fmod(y), forward_only=True, vals=[va, vb])
         helper_test_op(None, lambda x: x.fmod(2), forward_only=True, vals=[va])
-        helper_test_op(None, lambda x: x.fmod(3.5), lambda x: x.fmod(3.5).clone(), forward_only=True, vals=[va])
+        helper_test_op(None, lambda x: x.fmod(3.5), forward_only=True, vals=[va])
 
   def test_mul_naninf(self):
     helper_test_op([(45,65)], lambda x: x*math.inf)
@@ -706,7 +705,7 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x: 0.7**x, vals=[[-2.,-1,0,1,2,3]])
     helper_test_op(None, lambda x: (-2)**x, vals=[[-2.,-1,0,1,2,3]])
     # float to power of int
-    helper_test_op(None, lambda x: 0.7**x, lambda x: (0.7**x).clone(), vals=[[-2,-1,0,1,2,3]], forward_only=True)
+    helper_test_op(None, lambda x: 0.7**x, vals=[[-2,-1,0,1,2,3]], forward_only=True)
 
   @unittest.skipIf(COMPILE_ONLY, "test requires runtime")
   @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, NIRRenderer), "TODO: broken in LVP")
@@ -728,17 +727,6 @@ class TestOps(unittest.TestCase):
         else:
           self.assertAlmostEqual(tiny_out, torch_out, msg=f"{x}, {c}")
 
-  def test_pow_neg_inf_frac_exponent(self):
-    # pow(-inf, 0.3) is +inf, so the gradient 0.3*pow(-inf, -0.7) is 0, never nan
-    helper_test_op(None, lambda x: x**0.3, vals=[[-math.inf]])
-    # is_odd truncates, so it calls 3.3 odd: only the non_int guard keeps pow(-inf, 3.3) from negating to -inf
-    helper_test_op(None, lambda x: x**3.3, vals=[[-math.inf]])
-
-  def test_pow_zero_exponent(self):
-    # x ** 0 is the constant 1 for every x, so the gradient with respect to the base is 0, never nan
-    # TODO: nan ** 0, failed on WEBGPU
-    helper_test_op(None, lambda x,y: x**y, vals=[[-math.inf, math.inf, 0.0], [0.0, 0.0, 0.0]])
-
   def test_pow_zero_tensor(self):
     helper_test_op(None, lambda x,y: x**y, vals=[[0.0], [0.0]])
     # TODO: fix WEBGPU
@@ -783,11 +771,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: torch.tensor([2], dtype=torch.int) ** torch.tensor(-2, dtype=torch.int),
                        lambda: Tensor([2]) ** Tensor(-2), forward_only=True)
 
-  def test_pow_int_base_float_exponent(self):
-    for exponent in (0.5, 1.5, 2.0, -1.0, 0.0):
-      helper_test_op([], lambda: torch.tensor([1, 2, 3, 4], dtype=torch.int) ** exponent,
-                         lambda: (Tensor([1, 2, 3, 4], dtype=dtypes.int32) ** exponent).clone(), forward_only=True)
-
   def test_sqrt(self):
     helper_test_op([(45,65)], lambda x: x.sqrt())
     helper_test_op(None, lambda x: x.sqrt(), vals=[[0.0]])
@@ -806,6 +789,9 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor^0x1337, lambda: ten^0x1337, forward_only=True)
     helper_test_op([], lambda: 0x1337^tor, lambda: 0x1337^ten, forward_only=True)
 
+    self.helper_test_exception([(4), (4)], lambda x,y: x.bitwise_xor(y), expected=RuntimeError)
+
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_and(self):
     data = [[1,-8,1],[32,1,6]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -821,6 +807,9 @@ class TestOps(unittest.TestCase):
 
     helper_test_op(None, lambda x: (1 < x) & (x < 2), forward_only=True, vals=[[1.2, 1.2, 1.2, 3.2]])
 
+    self.helper_test_exception([(4), (4)], lambda x,y: x.bitwise_and(y), expected=RuntimeError)
+
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_or(self):
     data = [[1,-8,1],[32,1,6]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -834,6 +823,8 @@ class TestOps(unittest.TestCase):
     ten0, ten1 = Tensor(data[0], dtype=dtypes.bool), Tensor(data[1], dtype=dtypes.bool)
     helper_test_op([], lambda: tor0|tor1, lambda: ten0|ten1, forward_only=True)
 
+    self.helper_test_exception([(4), (4)], lambda x,y: x.bitwise_or(y), expected=RuntimeError)
+
   def test_bitwise_not(self):
     data = [[1,-8,1],[32,1,6]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -847,6 +838,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor.bitwise_not(), lambda: ten.bitwise_not(), forward_only=True)
     helper_test_op([], lambda: ~tor, lambda: ~ten, forward_only=True)
 
+    self.helper_test_exception([(4)], lambda x: x.bitwise_not(), expected=RuntimeError)
+
   def test_lshift(self):
     data = [[0,1,2],[1<<8,1<<16,1<<31-1]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -855,13 +848,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor << 0, lambda: (ten << 0).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor << 2, lambda: (ten << 2).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor << 31, lambda: (ten << 31).cast(dtypes.int32), forward_only=True)
-    helper_test_op([], lambda: tor << torch.tensor([0,2,4]).int(),
-                   lambda: (ten << Tensor([0,2,4], dtype=dtypes.uint32)).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor.__lshift__(2), lambda: ten.__lshift__(2).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor.bitwise_left_shift(2), lambda: ten.lshift(2).cast(dtypes.int32), forward_only=True)
-    self.helper_test_exception([], lambda: torch.tensor([1.0]) << 2, lambda: Tensor([1.0]) << 2, expected=RuntimeError)
-    self.helper_test_exception([], lambda: tor << torch.tensor([1.0]), lambda: ten << Tensor([1.0]), expected=RuntimeError)
-    self.helper_test_exception([], lambda: tor << 1.0, lambda: ten << 1.0, expected=RuntimeError)
 
   def test_rshift(self):
     data = [[0,1,2],[1<<8,1<<16,1<<31-1]]
@@ -871,12 +859,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor >> 0, lambda: (ten >> 0).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor >> 2, lambda: (ten >> 2).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor >> 31, lambda: (ten >> 31).cast(dtypes.int32), forward_only=True)
-    helper_test_op([], lambda: tor >> torch.tensor([0,2,4]).int(),
-                   lambda: (ten >> Tensor([0,2,4], dtype=dtypes.uint32)).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor.__rshift__(2), lambda: ten.__rshift__(2).cast(dtypes.int32), forward_only=True)
     helper_test_op([], lambda: tor.bitwise_right_shift(2), lambda: ten.rshift(2).cast(dtypes.int32), forward_only=True)
-    self.helper_test_exception([], lambda: torch.tensor([4.0]) >> 1, lambda: Tensor([4.0]) >> 1, expected=RuntimeError)
-    self.helper_test_exception([], lambda: tor >> torch.tensor([1.0]), lambda: ten >> Tensor([1.0]), expected=RuntimeError)
 
   def test_lshift_signed(self):
     data = [[-1, -3, 1, 7], [0, -2147483648, 2147483647, -1]]
@@ -886,7 +870,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor << 2, lambda: ten << 2, forward_only=True)
     helper_test_op([], lambda: tor << 8, lambda: ten << 8, forward_only=True)
     helper_test_op([], lambda: tor << 31, lambda: ten << 31, forward_only=True)
-    helper_test_op([], lambda: tor << torch.tensor([0,2,8,31]).int(), lambda: ten << Tensor([0,2,8,31], dtype=dtypes.int), forward_only=True)
 
   def test_rshift_signed(self):
     data = [[-1, -3, 1, 7], [0, -2147483648, 2147483647, -1]]
@@ -896,7 +879,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([], lambda: tor >> 2, lambda: ten >> 2, forward_only=True)
     helper_test_op([], lambda: tor >> 8, lambda: ten >> 8, forward_only=True)
     helper_test_op([], lambda: tor >> 31, lambda: ten >> 31, forward_only=True)
-    helper_test_op([], lambda: tor >> torch.tensor([0,2,8,31]).int(), lambda: ten >> Tensor([0,2,8,31], dtype=dtypes.int), forward_only=True)
 
   def test_idiv_shift_rewrite_negative(self):
     a = Tensor(-5).div(2, rounding_mode="trunc").item()
@@ -917,7 +899,6 @@ class TestOps(unittest.TestCase):
   @unittest.skipIf(DEV.renderer == "NAK", "MUFU.SIN is not accurate enough")
   def test_cos(self):
     helper_test_op([(45,65)], lambda x: x.cos())
-    helper_test_op([], lambda: torch.tensor(2.0).cos(), lambda: Tensor(2).cos(), forward_only=True)
     helper_test_op([()], lambda x: x.cos())
     if not ((DEV.interface.startswith("MOCK") and Device.DEFAULT == "NV") or Device.DEFAULT == "WEBGPU"):
       helper_test_op(None, lambda x: x.cos(), vals=[[math.nan, math.inf, -math.inf, 0.0]])
@@ -996,8 +977,6 @@ class TestOps(unittest.TestCase):
 
   def test_exp(self):
     helper_test_op([(45,65)], torch.exp, Tensor.exp)
-    # int scalar input: the python coefficients in the decomposition must stay float
-    helper_test_op([], lambda: torch.tensor(2.0).exp(), lambda: Tensor(2).exp(), forward_only=True)
     helper_test_op(None, torch.exp, Tensor.exp, vals=[[math.inf, -math.inf, math.nan]])
     helper_test_op([()], torch.exp, Tensor.exp)
   def test_exp2(self):
@@ -1063,8 +1042,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,65)], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid)
     helper_test_op([()], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid)
   def test_hardsigmoid_extreme(self):
-    helper_test_op([(45,65)], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid, low=300, high=400)
-    helper_test_op([(45,65)], torch.nn.functional.hardsigmoid, Tensor.hardsigmoid, low=-400, high=-300)
+    helper_test_op([(45,65)], torch.sigmoid, Tensor.sigmoid, low=300, high=400)
+    helper_test_op([(45,65)], torch.sigmoid, Tensor.sigmoid, low=-400, high=-300)
   def test_softplus(self):
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
     helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=3), lambda t: Tensor.softplus(t, beta=3), grad_atol=1e-6)
@@ -1244,6 +1223,7 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x: x.type(torch.int32).argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[False, True]])
     helper_test_op(None, lambda x: x.type(torch.int32).argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[True, False]])
 
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_argmin(self):
     # check if it returns the first index for multiple occurrences
     helper_test_op(None, lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[2, 2]])
@@ -1280,20 +1260,23 @@ class TestOps(unittest.TestCase):
                    lambda x: x.sort(descending=True)[1], forward_only=True, vals=[[0, 1] * 9])
 
   def test_argsort(self):
-    helper_test_op([(8,8,6)], lambda x: torch.argsort(x, dim=1, descending=True, stable=True).type(torch.int32),
-                              lambda x: x.argsort(1, True), forward_only=True)
+    for dim in [-1, 0, 1]:
+      for descending in [True, False]:
+        helper_test_op([(8,8,6)], lambda x: torch.argsort(x, dim=dim, descending=descending, stable=True).type(torch.int32),
+                                  lambda x: x.argsort(dim, descending), forward_only=True)
 
   def test_topk(self):
     helper_test_op([(8)], lambda x: x.topk(3).values, lambda x: x.topk(3)[0], forward_only=True)
     helper_test_op([(8)], lambda x: x.topk(3).indices.type(torch.int32), lambda x: x.topk(3)[1], forward_only=True)
-    for dim, largest in [(0, True), (1, False)]:
-      for sorted_ in [True]: # TODO support False
-        helper_test_op([(5,5,4)],
-                        lambda x: x.topk(4, dim, largest, sorted_).values,
-                        lambda x: x.topk(4, dim, largest, sorted_)[0], forward_only=True)
-        helper_test_op([(5,5,4)],
-                        lambda x: x.topk(4, dim, largest, sorted_).indices.type(torch.int32),
-                        lambda x: x.topk(4, dim, largest, sorted_)[1], forward_only=True)
+    for dim in [0, 1, -1]:
+      for largest in [True, False]:
+        for sorted_ in [True]: # TODO support False
+          helper_test_op([(5,5,4)],
+                          lambda x: x.topk(4, dim, largest, sorted_).values,
+                          lambda x: x.topk(4, dim, largest, sorted_)[0], forward_only=True)
+          helper_test_op([(5,5,4)],
+                          lambda x: x.topk(4, dim, largest, sorted_).indices.type(torch.int32),
+                          lambda x: x.topk(4, dim, largest, sorted_)[1], forward_only=True)
     # repeated values
     if not COMPILE_ONLY:
       value, indices = Tensor([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0]).topk(3)
@@ -1535,8 +1518,6 @@ class TestOps(unittest.TestCase):
 
   def test_prod(self):
     helper_test_op(None, lambda x: x.prod(), vals=[[1.0, 2.0, 3.0]])
-    helper_test_op(None, lambda x: x.prod(), vals=[[0.0, 2.0, 3.0]])
-    helper_test_op(None, lambda x: x.prod(), vals=[[0.0, 0.0, 3.0]])
     with Context(NOOPT=1): helper_test_op(None, lambda x: x.prod(), vals=[[1.0, 2.0, 3.0]])
     helper_test_op([(3,4,5,6)], lambda x: x.prod(dim=3), lambda x: x.prod(axis=3))
     helper_test_op([(3,4,5,6)], lambda x: x.prod(dim=1), lambda x: x.prod(axis=1))
@@ -1548,6 +1529,7 @@ class TestOps(unittest.TestCase):
   def test_prod_dtype_arg(self):
     with self.assertRaises(AttributeError): Tensor([1.0, 2.0]).prod(dtype="")
 
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_min(self):
     helper_test_op([(3,3)], lambda x: x.min())
     helper_test_op([(45,3)], lambda x: x.min())
@@ -1587,6 +1569,7 @@ class TestOps(unittest.TestCase):
   def test_any_zero_axis(self):
     helper_test_op([(1,0,3,0,5)], lambda x: x.any(axis=(1,3)), forward_only=True)
 
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_all(self):
     helper_test_op([(3,4,5,6)], lambda x: x.all(), forward_only=True)
     helper_test_op(None, lambda x: x.all(), vals=[[True, True]], forward_only=True)
@@ -1741,8 +1724,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(10,10,10)], lambda x: x.log_softmax(0), atol=1e-7, grad_atol=1e-7)
     helper_test_op([(10,10,10)], lambda x: x.log_softmax(1), atol=1e-7, grad_atol=1e-7)
     helper_test_op([(10,10,10)], lambda x: x.log_softmax(2), atol=1e-7, grad_atol=1e-7)
-  def test_softmin(self):
-    helper_test_op([(45,65)], torch.nn.Softmin(dim=1), Tensor.softmin, atol=1e-7, grad_atol=1e-7)
 
   def test_normalize(self):
     helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x), lambda x: x.normalize(), atol=1e-7, grad_atol=1e-7)
@@ -1920,6 +1901,9 @@ class TestOps(unittest.TestCase):
     helper_test_op([(3,3,3)], lambda x: x[-2:2])
     helper_test_op([(3,3,3)], lambda x: x[-2:-5])
 
+  def test_slice_empty(self):
+    helper_test_op([(10,10)], lambda x: x[1:1])
+
   def test_slice_zero_in_shape(self):
     helper_test_op([(10,10)], lambda x: x[1:1])  # x.shape = (0, 10)
     helper_test_op([(3,3,3)], lambda x: x[-2:-5])  # x.shape = (0, 3, 3)
@@ -2112,6 +2096,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(4,3,1,6)], lambda x: x.squeeze(1))
     helper_test_op([(4,3,6,6)], lambda x: x.squeeze(3))
     self.helper_test_exception([(4,3,6,6)], lambda x: x.squeeze(50), expected=IndexError)
+    self.helper_test_exception([(4,3,6,6)], lambda x: x.squeeze(50), expected=IndexError)
     helper_test_op([(4,3,6,1)], lambda x: x.squeeze(-1))
     helper_test_op([(4,3,6,6)], lambda x: x.squeeze())
     helper_test_op([(1,3,6,6)], lambda x: x.squeeze())
@@ -2384,10 +2369,9 @@ class TestOps(unittest.TestCase):
               lambda x,w: Tensor.conv2d(x,w,groups=groups), grad_rtol=1e-5)
   def test_conv2d(self): self._test_conv2d(bs=1, cin=3)
   @slow_test
-  @unittest.skip("redundant: bs/cout are loop dims, kernel×cin sweep covered by test_conv2d")
   def test_conv2d_bs_4_cin_3(self): self._test_conv2d(bs=4, cin=3, cout=2)
   def test_conv2d_bs_1_cin_1(self): self._test_conv2d(bs=1, cin=1)
-  @unittest.skip("redundant: cin=1 covered by test_conv2d_bs_1_cin_1")
+  @slow_test
   def test_conv2d_bs_4_cin_1(self): self._test_conv2d(bs=4, cin=1)
 
   def test_conv2d_errors(self):
@@ -2507,6 +2491,9 @@ class TestOps(unittest.TestCase):
             helper_test_op([(1,1,n,n), (1,1,k,k)],
               lambda x,w: torch.nn.functional.conv2d(torch.nn.functional.pad(x, p),w),
               lambda x,w: Tensor.conv2d(x,w,padding=p))
+            helper_test_op([(1,1,n,n), (1,1,k,k)],
+              lambda x,w: torch.nn.functional.conv2d(torch.nn.functional.pad(x, p),w),
+              lambda x,w: Tensor.conv2d(x,w,padding=p))
 
   def test_padded_conv2d_p21(self):
     bs,cin,H,W,padding = 4, 3, 3, 3, (2,1)
@@ -2555,7 +2542,7 @@ class TestOps(unittest.TestCase):
 
   @slow_test
   def test_max_pool2d(self):
-    for ksz in [2, (3,3), (3,2), (5,5), (5,1)]:
+    for ksz in [(2,2), (3,3), 2, 3, (3,2), (5,5), (5,1)]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([(32,2,11,28)],
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
@@ -2563,7 +2550,7 @@ class TestOps(unittest.TestCase):
 
   @slow_test
   def test_max_pool2d_padding(self):
-    for ksz in [(3,3), 2, (3,2)]:
+    for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
       for p in [1, (1,0), (0,1)]:
         with self.subTest(kernel_size=ksz, padding=p):
           helper_test_op([(4,2,11,28)],
@@ -2626,7 +2613,7 @@ class TestOps(unittest.TestCase):
 
   def test_max_pool2d_ceil_mode(self):
     shape = (1,1,6,6)
-    for ksz in [(3,3), (3,2), 4]:
+    for ksz in [(3,3), 3, (3,2), 4]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([shape],
           lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz, padding=1, stride=3, ceil_mode=True),
@@ -2706,7 +2693,7 @@ class TestOps(unittest.TestCase):
   @slow_test
   def test_avg_pool2d(self):
     shape = (32,2,11,28)
-    for ksz in [2, (3,3), (3,2), (5,5), (5,1)]:
+    for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([shape],
           lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=ksz),
@@ -2720,7 +2707,7 @@ class TestOps(unittest.TestCase):
   @slow_test
   def test_avg_pool2d_padding(self):
     shape = (32,2,11,28)
-    for ksz in [2, (3,3), (3,2)]:
+    for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
       for p in [1, (1,0), (0,1)]:
         with self.subTest(kernel_size=ksz, padding=p):
           helper_test_op([shape],
@@ -2742,7 +2729,7 @@ class TestOps(unittest.TestCase):
   @slow_test
   def test_avg_pool2d_padding_not_counted(self):
     shape = (32,2,11,28)
-    for ksz in [(3,3), 2, (3,2)]:
+    for ksz in [(2,2), (3,3), 2, 3, (3,2)]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([shape],
           lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=ksz, padding=1, count_include_pad=False),
@@ -2750,7 +2737,7 @@ class TestOps(unittest.TestCase):
 
   def test_avg_pool2d_ceil_mode(self):
     shape = (1,1,6,6)
-    for ksz in [(3,3), (3,2), 4]:
+    for ksz in [(3,3), 3, (3,2), 4]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([shape],
           lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=ksz, padding=1, stride=3, ceil_mode=True),
@@ -2758,7 +2745,7 @@ class TestOps(unittest.TestCase):
 
   def test_avg_pool2d_ceil_mode_padding_not_counted(self):
     shape = (1,1,6,6)
-    for ksz in [(3,3), (3,2), 4]:
+    for ksz in [(3,3), 3, (3,2), 4]:
       with self.subTest(kernel_size=ksz):
         helper_test_op([shape],
           lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=ksz, padding=1, stride=3, ceil_mode=True, count_include_pad=False),
@@ -2960,6 +2947,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[...,c,:,e], lambda x: x[...,k,:,p])
 
   @slow_test
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_dim_collapse_int(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim collapse from int
@@ -2970,6 +2958,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,:,3:11:2,d,0:2], lambda x: x[1,:,3:11:2,o,0:2])
 
   @slow_test
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_dim_inject_none(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim injection from None
@@ -3004,6 +2993,7 @@ class TestOps(unittest.TestCase):
                             lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
 
   @slow_test
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_list_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[((0,),)])
@@ -3015,6 +3005,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,(2,1,0),c,(-2,1,0),e], lambda x: x[i,(2,1,0),k,(-2,1,0),p])
 
   @slow_test
+  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_tuple_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[(((0,),),)], lambda x: x[(((0,),),)])
@@ -3345,7 +3336,6 @@ class TestOps(unittest.TestCase):
   @unittest.skipIf((DEV.interface.startswith("MOCK") or Device.DEFAULT == "PYTHON"), "very slow on MOCKGPU because reduce does not fold")
   @unittest.skipIf(Device.DEFAULT == "WEBGPU", "webgpu runtime issue")
   @unittest.skipIf(Device.DEFAULT == "QCOM", "QCOM fails with: Resource deadlock avoided")
-  @unittest.skipIf(Device.DEFAULT == "CPU" and DEV.renderer == "LVP", "extremely slow with LVP")
   def test_masked_select(self):
     helper_test_op([(32, 10)], lambda x: x.masked_select(x>0.5), lambda x: x.masked_select(x>0.5), forward_only=True)
     helper_test_op([(32, 10)], lambda x: x.masked_select(torch.tensor(True)), lambda x: x.masked_select(Tensor(True)), forward_only=True)

test/backend/test_optim.py

@@ -143,11 +143,12 @@ class TestOptim(unittest.TestCase):
 
   @unittest.skipUnless(dtypes.half in Device[Device.DEFAULT].renderer.supported_dtypes(), "need half")
   def test_mixed_precision(self):
-    self.enterContext(Context(DEFAULT_FLOAT=dtypes.half))
+    old_default_float, dtypes.default_float = dtypes.default_float, dtypes.half
     # weight update would overflow without upcasting
     self._test_sgd(10, {'lr': 1e10}, 1e-6, 3e-4)
     self._test_adam(1, {'lr': 1e10}, 1e-4, 1e-4)
     self._test_adamw(1, {'lr': 1e10}, 1e-4, 1e-4)
+    dtypes.default_float = old_default_float
 
   def test_assert_tensor_train(self):
     t = Tensor.ones((1,1))

test/backend/test_pickle.py

@@ -1,7 +1,7 @@
-import unittest, pickle, types, tracemalloc
+import unittest, pickle, types
 import numpy as np
-from tinygrad import Tensor, Device, TinyJit, Variable, dtypes
-from tinygrad.helpers import GlobalCounters, ContextVar, Context, DEV
+from tinygrad import Tensor, TinyJit, Variable, dtypes
+from tinygrad.helpers import GlobalCounters, ContextVar, Context
 from tinygrad.uop.ops import PatternMatcher, UPat, UOp
 
 class TestPickle(unittest.TestCase):
@@ -13,7 +13,7 @@ class TestPickle(unittest.TestCase):
 
   def test_pickle_pattern_matcher(self):
     pm = PatternMatcher([(UPat.cvar('x'), lambda x: x*2)])
-    sink = UOp.const(2)
+    sink = UOp.const(dtypes.int, 2)
     tt = pm.rewrite(sink)
     pm_str = pickle.dumps(pm)
     pm2 = pickle.loads(pm_str)
@@ -78,22 +78,6 @@ class TestPickle(unittest.TestCase):
     a2:UOp = pickle.loads(s)
     self.assertListEqual(a2.base.realized.as_memoryview().cast("I").tolist(), [0, 1, 2, 3])
 
-  @unittest.skipIf(DEV.interface.startswith("MOCK"), "mock device buffers live in host RAM, not VRAM")
-  def test_pickle_oob_ram(self):
-    N, M = 8, 10**6
-    ts = [Tensor.rand(M, dtype='float32').realize() for _ in range(N)]
-    tracemalloc.start()
-    st = pickle.dumps(ts, protocol=5, buffer_callback=lambda pb: pb.release())
-    self.assertLess(tracemalloc.get_traced_memory()[1], N*M*4)
-    tracemalloc.reset_peak()
-    def make_fake_buffers():
-      for _ in range(N):
-        Device[Device.DEFAULT].synchronize()
-        yield pickle.PickleBuffer(bytearray(M*4))
-    pickle.loads(st, buffers=make_fake_buffers())
-    self.assertLess(tracemalloc.get_traced_memory()[1], N*M*4)
-    tracemalloc.stop()
-
   def test_pickle_unrealized_tensor(self):
     t = Tensor.ones(10, 10)
     st = pickle.dumps(t)

test/backend/test_randomness.py

@@ -1,7 +1,7 @@
 import unittest, math
 
 from tinygrad import dtypes, Tensor, Device
-from tinygrad.helpers import getenv, DEV, Context
+from tinygrad.helpers import getenv, DEV
 from tinygrad.codegen import to_program
 
 from tinygrad.uop.ops import Ops
@@ -232,14 +232,16 @@ class TestRandomness(unittest.TestCase):
   @given(strat.sampled_from([dtypes.float, dtypes.float16, dtypes.bfloat16]))
   def test_randn_finite(self, default_float):
     if default_float not in Device[Device.DEFAULT].renderer.supported_dtypes(): return
+    old_default_float = dtypes.default_float
     # low precision can result in inf from randn
-    self.enterContext(Context(DEFAULT_FLOAT=default_float))
+    dtypes.default_float = default_float
     t = Tensor.randn(64, 64)
     mx = t.max().numpy().item()
     mn = t.min().numpy().item()
     print(f"testing with {default_float=}")
     assert math.isfinite(mx), mx
     assert math.isfinite(mn), mn
+    dtypes.default_float = old_default_float
 
   def test_random_counter_overflow(self):
     device = Device.DEFAULT

test/backend/test_rangeify.py

@@ -1,7 +1,7 @@
 import unittest
-from tinygrad import Tensor, nn, Device, dtypes, Variable
+from tinygrad import Tensor, nn, Device
 from tinygrad.helpers import Context, GlobalCounters, getenv, PCONTIG, DEBUG
-from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops, UOp
+from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops
 from tinygrad.codegen.opt import OptOps, Opt
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.nir import NIRRenderer
@@ -66,18 +66,6 @@ class TestRangeifyAssign(unittest.TestCase):
     self.assertListEqual(lst2, B.permute(1, 0).tolist())
 
 class TestRangeifyEdgeCase(unittest.TestCase):
-  def test_variable_stack_data(self):
-    # a bound-Variable STACK used as data gets ranges from its graph position
-    v = Variable("v", 0, 10).bind(3)
-    t = Tensor(UOp.stack(v, v+1).cast(dtypes.int)) + Tensor.arange(2)
-    self.assertListEqual(t.tolist(), [3, 5])
-
-  def test_variable_data_and_shape(self):
-    # the same Variable has a data edge through CAST and a structural edge through SHRINK
-    v = Variable("shared_v", 1, 10).bind(3)
-    t = Tensor.ones(10)[:v] * Tensor(v.cast(dtypes.float))
-    self.assertEqual(t.sum().item(), 9)
-
   def test_matmul_relu_cat(self):
     a = Tensor.ones(100, 512).contiguous().realize()
     c = Tensor.ones(1, 512).contiguous().realize()

test/backend/test_renderer_failures.py

@@ -8,7 +8,6 @@ from tinygrad.helpers import prod
 from tinygrad.renderer.cstyle import CStyleLanguage
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.wgsl import WGSLRenderer
-from test.helpers import check_schedule
 from tinygrad.runtime.ops_python import PythonRenderer
 from tinygrad.uop.ops import UOp, Ops, KernelInfo, python_alu
 from tinygrad.tensor import Tensor
@@ -25,7 +24,7 @@ def _setup_and_test_alu(alu_op:Ops, input_val:ConstType, *alu_src_uops:UOp):
   dtype = alu_src_uops[0].dtype
   a = UOp.param(0, dtype, (1,))
   b = UOp.param(1, dtype, (1,))
-  idx = UOp.const(0)
+  idx = UOp.const(dtypes.int, 0)
   ld = b.index(idx).load()
   alu = ld.alu(alu_op, *alu_src_uops)
   store = UOp.store(a.index(idx), alu)
@@ -36,7 +35,7 @@ class TestRendererFailures(unittest.TestCase):
   def test_gated_store_with_alu(self):
     a = UOp.param(0, dtypes.int, (4,))
     gate_alu = (lidx0:=UOp.special(4, 'lidx0')).ne(0)
-    gated_alu_store = UOp(Ops.STORE, src=(a.index(lidx0.valid(gate_alu)), UOp.const(1).cast(dtypes.int)))
+    gated_alu_store = UOp(Ops.STORE, src=(a.index(lidx0.valid(gate_alu)), UOp.const(dtypes.int, 1)))
     sink = UOp(Ops.SINK, src=(gated_alu_store,), arg=KernelInfo())
     ret = _test_uop_result([], sink, local_size=[4, 1, 1])[0]
     np.testing.assert_equal(ret, [0, 1, 1, 1])
@@ -46,7 +45,7 @@ class TestRendererFailures(unittest.TestCase):
     a = UOp.param(0, dtypes.int, (8,))
     gate_alu_0 = (lidx0:=UOp.special(4, 'lidx0')).ne(0)
     gate_alu_1 = (lidx1:=UOp.special(2, 'lidx1')).ne(0)
-    gated_alu_store = UOp(Ops.STORE, src=(a.index((lidx0+lidx1*4).valid(gate_alu_0&gate_alu_1)), UOp.const(1).cast(dtypes.int)))
+    gated_alu_store = UOp(Ops.STORE, src=(a.index((lidx0+lidx1*4).valid(gate_alu_0&gate_alu_1)), UOp.const(dtypes.int, 1)))
     sink = UOp(Ops.SINK, src=(gated_alu_store,), arg=KernelInfo())
     ret = _test_uop_result([], sink, local_size=[4, 2, 1])[0]
     np.testing.assert_equal(ret, [0, 0, 0, 0, 0, 1, 1, 1])
@@ -55,14 +54,15 @@ class TestRendererFailures(unittest.TestCase):
 class TestCStyleFailures(unittest.TestCase):
   def test_inline_const_alu(self):
     # CPU doesn't use the max function
-    ret = _setup_and_test_alu(Ops.MAX, 1, UOp.const(dtypes.int.min+1).cast(dtypes.int))
+    ret = _setup_and_test_alu(Ops.MAX, 1, UOp.const(dtypes.int, dtypes.int.min+1))
     self.assertEqual(ret[0], 1)
 
   def _test_src_strip_paren(self, op: Ops, should_strip_paren:bool=True):
     dtype = "bool" if op in (Ops.OR, Ops.XOR, Ops.AND) else None
     ret = Tensor.empty(1, dtype=dtype)
     for _ in range(5): ret = python_alu[op](ret, Tensor.empty(1, dtype=dtype))
-    linear, _ = check_schedule(ret, 1)
+    linear = ret.schedule_linear()
+    assert len(linear.src) == 1
     src = to_program(linear.src[0].src[0], Device[Device.DEFAULT].renderer).src[2].arg
     self.assertEqual("("*5 not in src, should_strip_paren)
 
@@ -80,7 +80,7 @@ class TestWGSLFailures(unittest.TestCase):
   def test_multiply_infinity(self):
     # multiplying a positive constant by infinity should return infinity
     # WGSL pipelines do not handle this reliably, some of which return zero, unless infinity always comes from a read on a dynamic buffer
-    ret = _setup_and_test_alu(Ops.MUL, 5.0, UOp.const(float("inf")).cast(dtypes.float32))
+    ret = _setup_and_test_alu(Ops.MUL, 5.0, UOp.const(dtypes.float32, float("inf")))
     self.assertEqual(ret[0], float("inf"))
 
   # WGSL has a specific select(alt, val, gate) ternary operator instead of gate?val:alt
@@ -104,7 +104,7 @@ class TestPTXFailures(unittest.TestCase):
   def test_gated_store_with_if(self):
     a = UOp.param(0, dtypes.int, (4,))
     gate_alu = (lidx0:=UOp.special(4, 'lidx0')).ne(0)
-    val = UOp.const(1).cast(dtypes.int)
+    val = UOp.const(dtypes.int, 1)
     if_uop = UOp(Ops.IF, src=(gate_alu,))
     gated_alu_store = UOp(Ops.STORE, src=(a.index(lidx0, if_uop), val))
     sink = UOp(Ops.SINK, src=(gated_alu_store,), arg=KernelInfo())

test/backend/test_schedule.py

@@ -6,13 +6,34 @@ import unittest, time
 import numpy as np
 
 from tinygrad import nn, dtypes, Device, Tensor, Variable
-from tinygrad.uop.ops import Ops, UPat
-from tinygrad.helpers import DEV, GlobalCounters, Context, all_same, temp
-from tinygrad.engine.realize import run_linear
-from test.helpers import check_schedule, assert_kernel_count
+from tinygrad.uop.ops import UOp, Ops, UPat
+from tinygrad.helpers import DEBUG, DEV, GlobalCounters, Context, all_same, temp
+from tinygrad.engine.realize import compile_linear, run_linear
 
 supported_dtypes = Device[Device.DEFAULT].renderer.supported_dtypes()
 
+class KernelCountException(Exception): pass
+def check_schedule(t:Tensor|list[Tensor]|UOp, allowed:int, to_prerealize:list[Tensor]|None=None, filter_sink=True):
+  if to_prerealize:
+    with Context(DEBUG=0, TRACK_MATCH_STATS=0): Tensor.realize(*to_prerealize)
+  if isinstance(t, Tensor): linear, var_vals = t.linear_with_vars()
+  elif isinstance(t, list) and isinstance(t[0], Tensor): linear, var_vals = Tensor.linear_with_vars(*t)
+  else:
+    assert isinstance(t, UOp), f"can't schedule {t}"
+    linear, var_vals = Tensor(t).linear_with_vars()
+  kernel_cnt = sum((len(call.device) if isinstance(call.device, tuple) else 1)
+                   for call in linear.src if call.src[0].op is Ops.SINK or not filter_sink)
+  if kernel_cnt != allowed:
+    print(f"SCHEDULE ISSUE, expecting {allowed} got {kernel_cnt}")
+    if DEBUG >= 3:
+      for i,call in enumerate(linear.src):
+        print("kernel", i+1)
+        print(call.src[0])
+    raise KernelCountException(f"{kernel_cnt} != {allowed}")
+  # test compiling the linear
+  compile_linear(linear)
+  return linear, var_vals
+
 def _realize_weights(m):
   for p in nn.state.get_parameters(m): p.realize()
 
@@ -92,9 +113,11 @@ class TestSchedule(unittest.TestCase):
     a2 = mop(a)
     expected = (a+a2).tolist()
     a.assign(a+a2)
-    linear, var_vals = check_schedule(a, expected_kcount)
+    linear, var_vals = a.linear_with_vars()
+    kcount = len(linear.src)
     run_linear(linear, var_vals)
     self.assertListEqual(a.tolist(), expected)
+    self.assertEqual(kcount, expected_kcount)
   def test_setitem_permuted_sched(self): self.test_setitem_sched(lambda x: x.T, 2)
   def test_setitem_paddded_sched(self): self.test_setitem_sched(lambda x: x.shrink_to(4, 1).pad_to(4, 4), 1)
 
@@ -103,9 +126,9 @@ class TestSchedule(unittest.TestCase):
     a = Tensor.arange(16).clone().realize()
     GlobalCounters.reset()
     a[4] = 3
-    assert_kernel_count(0)
+    self.assertEqual(GlobalCounters.kernel_count, 0)
     a.realize()
-    assert_kernel_count(1)
+    self.assertEqual(GlobalCounters.kernel_count, 1)
     self.assertListEqual(a.tolist(), [0, 1, 2, 3, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
 
   def test_no_extra_contiguous_on_setitem_assign_back(self):
@@ -340,10 +363,9 @@ class TestCopyFolding(unittest.TestCase):
 
   def test_one_hot_with_copy(self):
     y = Tensor([1, 2, 3]).to("CPU")
-    x = y.one_hot(10).int()
+    x = y.one_hot(10)
     check_schedule(x, 3, filter_sink=False)
 
-  @unittest.skip("no longer supported")
   def test_late_const_copy_folding(self):
     a = Tensor.arange(3).clone().realize()
     zeros = Tensor.zeros(3, buffer=False).realize()
@@ -384,7 +406,6 @@ class TestCopyFolding(unittest.TestCase):
     assert t.uop.is_realized, f"didn't realize Tensor {t}"
     self.assertListEqual(t.tolist(), [1.,1.,1.,1.])
 
-  @unittest.skip("same-device copies are no-ops")
   def test_self_assign_same_device_copy(self):
     a = Tensor.ones(4, 4).contiguous().realize()
     # use copy_to_device to bypass Tensor.to() shortcircuit and force a real same-device COPY in the graph

test/backend/test_softmax_fusion.py

@@ -4,7 +4,6 @@ from tinygrad import Tensor, GlobalCounters, Context, Device
 from tinygrad.dtype import DTypeLike, dtypes
 from tinygrad.engine.realize import run_linear
 from tinygrad.helpers import DEBUG, get_single_element
-from test.helpers import check_schedule
 
 def single_kernel_softmax(x_in:Tensor, axis=-1, dtype:DTypeLike|None=None) -> Tensor:
   # only support axis =-1
@@ -104,7 +103,8 @@ class TestFuse(unittest.TestCase):
     k = (x @ wk).contiguous()
     v = (x @ wv).contiguous()
     attn = q.scaled_dot_product_attention(k, v)
-    check_schedule(attn, 4) # 3 matmul and 1 attention
+    s = attn.schedule_linear()
+    self.assertEqual(len(s.src), 4) # 3 matmul and 1 attention
 
   @unittest.skip("needs RANGEIFY>1")
   def test_flash_attention(self):

test/backend/test_symbolic_ops.py

@@ -6,15 +6,6 @@ from examples.gpt2 import Attention
 import numpy as np
 
 class TestSymbolicOps(unittest.TestCase):
-  def test_negative_slice(self):
-    a = Tensor.rand(3, 10, 4)
-    for i in range(3, 10):
-      vi = Variable("i", 1, 10).bind(i)
-      # negative int bounds against a symbolic dim must resolve against the size, like slice.indices
-      np.testing.assert_allclose(a[:, :vi][:, -3:-1].numpy(), a[:, :i][:, -3:-1].numpy(), atol=1e-6, rtol=1e-6)
-      np.testing.assert_allclose(a[:, :vi][:, -1:].numpy(), a[:, :i][:, -1:].numpy(), atol=1e-6, rtol=1e-6)
-      np.testing.assert_allclose(a[:, :vi][:, -1].numpy(), a[:, :i][:, -1].numpy(), atol=1e-6, rtol=1e-6)
-
   def test_plus1(self):
     def f(a): return (a+1).realize()
     a = Tensor.rand(3, 10)