kimi linear working

llm: reuse exact recurrent prefixes
llm: fully warm recurrent serving at startup
2026-08-20 08:26:06 +00:00 · 2026-08-10 15:37:20 +00:00 · 2026-08-10 14:06:15 +00:00 · 2026-08-10 14:06:15 +00:00 · 2026-08-10 14:06:15 +00:00 · 2026-08-10 14:06:15 +00:00
137 changed files with 5531 additions and 1782 deletions
@@ -94,6 +94,7 @@ 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
@@ -104,6 +105,9 @@ 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
@@ -145,6 +149,7 @@ 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
@@ -155,6 +160,9 @@ 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: |
@@ -194,6 +202,7 @@ 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
@@ -204,6 +213,9 @@ 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
@@ -240,6 +252,7 @@ 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
@@ -250,6 +263,9 @@ 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: |
@@ -292,6 +308,9 @@ 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: |
@@ -0,0 +1,213 @@
+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
@@ -219,8 +219,8 @@ jobs:
      run: python3 test/external/external_benchmark_schedule.py
    - name: Run process replay tests
      uses: ./.github/actions/process-replay
-    - name: Repo line count < 25000 lines
-      run: MAX_LINE_COUNT=25000 python sz.py
+    - name: Repo line count <= 26000 lines
+      run: MAX_LINE_COUNT=26000 python sz.py

  spec:
    strategy:
@@ -294,7 +294,7 @@ 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=54 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=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
          # IMAGE_PITCH_ALIGNMENT=64 matches adreno 630
      - name: Test openpilot CL compile fp32 (test correctness)
        run: |
@@ -585,8 +585,11 @@ 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
+        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 test/opt/test_tensor_cores.py --durations=20
      - name: Run disk copy tests
        run: python -m pytest test/unit/test_disk_tensor.py -k test_copy_from_disk
      - name: Run TRANSCENDENTAL math
@@ -629,165 +632,6 @@ jobs:
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

-# ****** 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 --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
-
 # ****** Compile-only Tests ******

  compiletests:
@@ -824,33 +668,3 @@ 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
@@ -0,0 +1,224 @@
+# Kimi K3 on 8× MI350X
+
+This branch targets text generation directly from the official `moonshotai/Kimi-K3` checkpoint at `/raid/weights/kimi-k3`. It intentionally ignores the vision tower and multimodal projector. The checkpoint remains in its official 96-shard format; the loader never converts, rewrites, or creates a second 1.56 TB copy.
+
+The checked TP8 layout consumes 196.78 GB (183.27 GiB) of text weights per GPU. The compressed MLA cache adds 28.99 GB (27 GiB) per GPU at the full 1,048,576-token context, leaving approximately 62.23 GB of each nominal 288 GB MI350X for execution buffers and allocator overhead. Start much smaller.
+
+## Resume the current optimization session
+
+Work on branch `kimi_slop`. It was cleanly rebased onto `origin/kimi_slop` commit `553bdf68e` on 2026-08-10. The retained K3 commits after that base are `1b3732a6e`, `c6ac4961d`, `1d8620471`, `224bac031`, `f53f0e7e7`, and `2b1b8c22a`; verify the current hashes with `git log` because a later rebase may rewrite them. Before starting any benchmark, check that the worktree is clean and that no model process remains:
+
+```sh
+git status --short --branch
+git log --oneline --decorate -10
+pgrep -af 'tinygrad.llm.cli|benchmark_kimi_k3' || true
+```
+
+The active acceptance target is **more than 100 tok/s decode, more than 200 tok/s prefill, and less than 180 seconds cold startup** on TP8/gfx950. None is currently met. The authoritative official-checkpoint baseline is 389.84 seconds startup, 38.65 tok/s prefill, and 6.25 tok/s decode. The 1.56 TB checkpoint has a measured 6.9 GB/s single-XFS-NVMe read ceiling, giving a roughly 227-second physical cold-read floor; meeting the startup target therefore also requires a faster storage path, not only loader code.
+
+Use the fake-weight, one-layer loop for development. Do not repeatedly load the official checkpoint while optimizing:
+
+```sh
+DEV=AMD python extra/benchmark_kimi_k3_fake.py --mode attention --iterations 30
+DEV=AMD python extra/benchmark_kimi_k3_fake.py --mode block --iterations 30
+PROFILE=1 DEV=AMD python extra/benchmark_kimi_k3_fake.py --mode block --iterations 5
+```
+
+The clean retained baseline is about 0.630 ms per attention layer and 1.37 ms per complete block, with fake initialization taking about 0.9/2 seconds respectively after the rebase. Since K3 has 93 sequential blocks, a 100 tok/s projection requires at most approximately 0.108 ms per complete block. Only run another 96-shard official validation after a candidate produces a large whole-block gain, remains finite and deterministic, and passes a direct numerical comparison. Test one candidate at a time and remove failed experiments before moving on.
+
+The immediate bottleneck is launch and synchronization granularity: an official four-token decode profile contained 6,304 kernel events, while packed expert work was only a small fraction of total GPU time. Continue with whole-component or whole-block fusion/replay work, not isolated expert microkernels. The latest fake-loop A/B retested the previously rejected dual gate/up and weighted-down MFMA prototypes: 1.374 ms baseline versus 1.375 ms fused, so they were removed again. A fused whole-core KDA recurrence was also slower in the exact fake attention gate (0.665 versus 0.633 ms) and must not be restored unchanged.
+
+Preserve these invariants when official validation resumes: use `/raid/weights/kimi-k3` directly, keep all 96 shards byte-for-byte untouched, run only one model process, begin at context 128, verify all eight devices are `gfx950`, and preserve the first failure instead of retrying over it. The most recent preserved official failure from a rejected KDA experiment was the invalid sequence `[198, 163840, 163840, 163840]`; token 163840 is outside the valid vocabulary. The retained path before that experiment produced deterministic in-range replay.
+
+After a synthetic candidate passes, run correctness and performance in this order: NULL gfx950 compile coverage, focused tests with `-n12` where supported, TP8 fake numerical comparison, official context-128 deterministic tokens, load/prefill/decode timing, and then context admission at 4K, 32K, 131K, and 262K. Run `python -m mypy tinygrad/` and `python -m ruff check .` when those tools are installed. Read `tinygrad/viz/README.md` before inspecting rewrite or device profiles.
+
+## Before renting the machine
+
+- Keep the existing 96 shards in `/raid/weights/kimi-k3`; no additional model-sized free space is required. Leave ordinary headroom for logs and temporary files.
+- The host should have roughly 3 TB RAM, in line with AMD's MI350X platform guidance. The loader itself is streaming and must not need checkpoint-sized RAM.
+- Use a recent kernel/ROCm stack supported by the host vendor, although tinygrad uses its own AMD userspace driver when `DEV=AMD`.
+- Clone this exact commit/branch and keep the official checkpoint directory separate from the repository.
+
+Validate the existing directory without modifying it:
+
+```sh
+python examples/kimi_k3_prepare.py /raid/weights/kimi-k3 --context 4096
+```
+
+For a metadata-only preflight, place the official `config.json` and `model.safetensors.index.json` in a directory and run:
+
+```sh
+python examples/kimi_k3_prepare.py /raid/weights/kimi-k3 --metadata-only
+```
+
+## Hardware admission checks
+
+Do these before loading weights. Stop if any device is missing or reports a different architecture.
+
+```sh
+lspci -d 1002:75a0
+amd-smi list
+DEV=AMD DEBUG=2 python - <<'PY'
+from tinygrad import Device
+for i in range(8):
+  dev = Device[f"AMD:{i}"]
+  print(i, dev.arch)
+PY
+```
+
+Expected architecture: `gfx950` on all eight devices. Then run the small TP8 graph tests:
+
+```sh
+python -m pytest test/unit/test_llm_k3.py test/null/test_kimi_k3.py -q -n12
+DEV=NULL:HIP:gfx950 NULL_ALLOW_COPYOUT=1 python -m pytest \
+  test/unit/test_llm_k3.py::TestKimiK3::test_chunked_recurrent_generate -q -n1
+DEV=AMD python examples/kimi_k3_smoke.py --devices 8
+```
+
+The last two commands are deliberately small. They compile CDNA4 kernels and then exercise the complete TP8 topology without loading the checkpoint.
+
+For performance iteration, use the exact-width fake-weight harness before another official load:
+
+```sh
+DEV=AMD python extra/benchmark_kimi_k3_fake.py --mode attention --iterations 20
+DEV=AMD python extra/benchmark_kimi_k3_fake.py --mode block --iterations 20
+```
+
+It retains K3's 7,168-wide residual stream, 12,288-wide KDA state, 96 heads, 128×128 recurrent matrices, TP8 layouts, top-k 16 routing, packed MXFP4 expert shapes, collectives, and decode JIT, but uses one layer and 16 fake experts. Fake attention weights initialize in about 0.9 seconds and the full block in about 3 seconds. The retained path measured 0.630 ms per fake attention layer and 1.367 ms per complete fake block, projecting about 7.87 tok/s across 93 identical blocks versus 6.25 tok/s for the official heterogeneous model. Treat this as a candidate admission benchmark, not a correctness substitute for official weights.
+
+## First official load
+
+Start at a short context so cache allocation and compilation are bounded. The loader reads disk-backed safetensors, TP-shards every destination before realizing it, and drops each source shard/projection immediately afterward.
+
+```sh
+/usr/bin/time -v env DEV=AMD DEBUG=1 python -m tinygrad.llm.cli \
+  --model /raid/weights/kimi-k3 --devices 8 --max_context 128 </dev/null 2>&1 | tee kimi-k3-load.log
+```
+
+Watch host RAM, swap, HBM, temperatures, and XGMI traffic from a second terminal. Do not start with a one-million-token cache. If loading fails, preserve the first exception and the last loader progress line; do not retry with a larger host-side cache.
+
+## Correctness and performance sequence
+
+1. Load with context 128 and generate one token.
+2. Repeat a fixed prompt twice and confirm token-for-token deterministic greedy output.
+3. Compare the first several greedy tokens against the official Transformers implementation at temperature zero.
+4. Benchmark decode only after two warm-up tokens.
+5. Benchmark prefill at 128, 512, 2K, and 8K tokens. Increase context only while HBM and compile time remain healthy.
+6. Use `VIZ=1` plus `python -m tinygrad.viz.cli` to inspect kernels; use `VIZ=2` only for short SQTT captures because it adds overhead.
+
+Example decode benchmark:
+
+```sh
+DEV=AMD DEBUG=1 python -m tinygrad.llm.cli --model /raid/weights/kimi-k3 \
+  --devices 8 --max_context 4096 --warmup --benchmark 20
+```
+
+## MI350X validation results (2026-08-10)
+
+The official directory was audited in place: 96 shards, 497,220 indexed tensors, 497,052 language tensors, and 1,560,860,324,864 total bytes. All eight devices reported `gfx950`. No checkpoint file was converted, copied, or modified, and every model run used a single process. The actual text tower is 1,559,965,606,912 bytes; its checked TP8 layout is 196,784,397,312 bytes per GPU.
+
+The preserved first full-checkpoint error was an `A_log` shape mismatch, `(128,) -> (96, 1)`. K3 stores one decay value per 128-wide KDA channel, not one per head. The loader now keeps this field replicated and applies the official channel-wise broadcast. A numerical unit test covers the distinction from the older head-wise Kimi Linear behavior.
+
+Load speed was fixed before generation. The original loader opened thousands of individual expert tensors and independently realized eight strided TP slices. The MI350 path now does the following without changing the checkpoint:
+
+- parses safetensor headers selectively, constructing disk-backed tensors only for the 2,460 non-expert entries consumed by that pass instead of materializing metadata objects for every expert entry twice;
+- copies contiguous axis-zero shards and replicas directly into their final device buffers;
+- reads a replicated tensor once and fans it out over XGMI instead of issuing eight identical direct reads (14.31 GB less RAID traffic);
+- stages an inner-axis tensor once and schedules all eight TP slices together;
+- reads each layer's contiguous 15.72 GB expert region once, reorders its lexicographically stored expert records on GPU 0, and realizes all six packed/scale destinations together;
+- retains only final MultiBuffer identities, drops the reorder graph, and flushes the 15.72 GB staging allocation before the next layer.
+
+One real expert layer leaves exactly 1,965,293,568 bytes resident on each GPU and zero bytes in the GPU-0 allocator cache. Complete context-128 loads measured 527.20 seconds before the final staging cleanup and 490.05/489.59 seconds afterward. Peak host RSS for the unprofiled correctness run was 2.11 GiB with zero swap. RAID variability produced later loads from 489.06 to 532.85 seconds.
+
+The selective-metadata and bounded-GC pass reduced non-expert loading from 125.77 to 57.77 seconds. A subsequent full official context-128 load completed in 411.49 seconds, 78.10 seconds (16.0%) faster than the 489.59-second baseline. It read the 96 shards in place with 1,049,688 KiB peak host RSS and zero swap; no weight payload was converted, copied, or modified. Direct-I/O probes measured approximately 6.9 GB/s aggregate for both one and eight concurrent 1 GiB reads. At that rate the 1.56 TB checkpoint has a roughly 227-second cold-read lower bound, so this RAID cannot meet a true cold sub-three-minute startup regardless of loader overhead.
+
+Expert staging graphs are acyclic and are released by reference counting after each layer, so the loader now suppresses unnecessary cyclic-collector scans only around that loop and restores its prior state on every exit. A quiet context-128 load then completed in 391.54 seconds, 30.14 seconds (7.1%) faster than the immediately preceding 421.68-second run, with 1.04 GiB peak RSS and zero swap, although storage variability contributes to run-to-run timing. The host used for these measurements actually mounts `/raid` from one 3.5 TB XFS NVMe, not a multi-drive RAID; shard 28 has 218 extents and live reads fell to roughly 160 MB/s there. This storage layout, plus the physical checkpoint size, remains the limiting cold-start constraint. The weights were not defragmented, copied, or modified.
+
+The fixed XTML prompt `Reply with exactly: OK` encodes to 93 tokens. After excluding the cold JIT capture from replay comparison, two greedy runs produced the identical eight-token sequence:
+
+```text
+[9545, 59991, 10580, 14404, 9545, 59991, 9545, 59991]
+```
+
+At context 128, steady prefill was 14.32 seconds (6.49 tok/s) and eight-token decode was 2.27 seconds (3.53 tok/s, 283.3 ms/token). The same first tokens remained stable at every admitted context. These rates are much lower than the planning estimates below and should be treated as the current measured baseline.
+
+The retained gfx950 serving pass enables the validated wave64 recurrent prefill kernel with 128-token chunks, uses exact BF16 decode projections, combines the routed/shared final TP partials into one collective, and tiles four adjacent packed-expert outputs during multi-token execution. On the same 93-token prompt, two replay trials produced the identical sequence `[198, 92652, 220, 80225]`. Prefill replay measured 2.418--2.482 seconds (37.47--38.46 tok/s), and eight-token decode measured 1.294 seconds (6.18 tok/s, 161.81 ms/token). Peak RSS was 2.77 GiB with zero swap. The packed prefill tile changes floating-point reduction order: direct official-layer comparison against the original kernel had maximum differences of 0.015625 for gate and 0.0078125 for down, and the end-to-end greedy sequence was stable across replay.
+
+A subsequent gfx950 decode pass split the 7,168-wide replicated BF16 projections across eight waves per 16 output channels and used CDNA4 BF16 MFMA, with one FP32 LDS reduction at the end. It is enabled only for batch-one/token-one replicated projections whose dimensions satisfy the hardware tile; prefill, the FP32 router, and the output-sharded 12,288-wide KDA gate remain unchanged. The official retained path uses it for MLA q-a/kv-a and KDA f-a. Isolated TP8 measurements improved replicated 128/576-output projections by about 16--18%; applying it to the already output-sharded KDA gate was slower and was rejected. Random-shape comparison against the generic graph had maximum/mean absolute BF16 differences of 2.0/0.1114 because the split changes reduction order. Against a serial FP32 accumulation rounded once to BF16, the 7,168-to-1,536 kernel was bit-exact in the tested sample.
+
+The final official context-128 validation loaded in 389.84 seconds with 2.71 GiB peak RSS and zero swap. Two replay trials produced the identical four-token sequence `[198, 59675, 9817, 12519]`; prefill remained 2.406 seconds (38.65 tok/s), while eight-token decode improved to 1.280 seconds (6.25 tok/s, 160.00 ms/token). A one-wave MFMA variant and a full-wave fused decode recurrence were both rejected: the former delivered 6.02 tok/s, and the latter 6.179 tok/s, while both changed the greedy sequence without a useful speed gain.
+
+A final load-first experiment increased the disk-to-HBM io_uring queue depth from one to the 32 existing bounded 2 MiB staging buffers. On a direct 1 GiB read from fragmented shard 28 it measured 6.834 GB/s versus 6.832 GB/s for the original path, so the change was rejected. The subsequent unmodified official 96-shard load completed in 389.48 seconds, confirming both the prior result and the single-NVMe lower bound. Peak RSS was 2.75 GiB with zero swap.
+
+Two direct packed-expert MFMA prototypes were also rejected after that load. A fused gate/up kernel was about 29% faster in isolation at the TP8-local shape, and a routed-down kernel which combined projection, probability weighting, and route reduction measured 1.45 ms versus 2.42 ms in isolation. End-to-end, however, stable replay produced `[198, 2338, 2127, 148297]`, prefill measured 38.87 tok/s, and decode measured 6.263 tok/s. That is indistinguishable from the retained 38.65/6.25 tok/s path while changing floating-point reduction order, so neither kernel was retained.
+
+A whole-core KDA decode experiment fused convolution, Q/K normalization, channel decay, recurrence, RMS normalization, output gating, and four persistent state updates. Its raw kernel replayed in about 109 microseconds per local KDA layer and matched a one-step synthetic reference within `9.77e-4` output and `8.13e-4` state maximum error. The exact-width fake-layer gate caught that it was slower than the retained attention path (0.665 versus 0.633 ms/layer). The already-running official validation was stopped after its first invalid greedy sequence, `[198, 163840, 163840, 163840]`, where 163840 is outside the checkpoint's vocabulary. The kernel was rejected and removed.
+
+| Maximum context | Load | Short-prompt replay | Result |
+|---:|---:|---:|---|
+| 128 | 489.59s | 14.32s | stable 8-token replay |
+| 4,096 | 489.06s | 14.32s | stable replay, zero swap |
+| 32,768 | 532.85s | 14.33s | stable replay, zero swap |
+| 131,072 | 520.91s | 14.37s | stable first token, zero swap |
+| 262,144 | 497.34s | 14.41s | stable first token, zero swap |
+
+These are maximum-context/cache admission tests with the same 93-token prompt, not full-length 32K/131K/262K prefills. The full cache allocation path was exercised, but filling those contexts remains a separate long-running throughput test.
+
+Runtime profiling bracketed four steady decode tokens. It recorded 6,304 kernel events and about 474--478 ms of summed GPU work across the eight devices inside a roughly 1.5-second profiled wall interval. The packed `mxfp4_expert_linear_wave64` kernels accounted for only about 22.5 ms summed; the largest families were small 1,792-wide reductions. This identifies launch/synchronization granularity as the immediate MI350 bottleneck rather than packed-weight bandwidth. `JIT_BATCH_SIZE=64` produced the same original 3.53 tok/s as 32. A gfx950 fused MXFP8 QDQ experiment was bit-exact but slower on the real device (about 95 microseconds versus 57--64 microseconds), so it was rejected. Combining the routed and shared final TP partials removed one collective per routed decode layer and helped raise unprofiled decode to 6.18 tok/s, but the remaining sequential launch boundaries still dominate.
+
+The checkpoint's bundled Transformers code was used as the architectural reference for channel decay and tensor mapping. A full independent Transformers/vLLM token comparison was not run on this host because the required `compressed_tensors`/serving backend is not installed; deterministic tinygrad replay and the numerical KDA, loader-layout, NULL gfx950 compile, and real TP8 smoke tests are the completed correctness gates.
+
+## Known hardware-only gate
+
+The correctness path now consumes packed MXFP4 expert weights directly on gfx950 with a wave64 software-decode kernel, so it does not create selected-expert BF16 weight expansions. MXFP8 activation quantization is still emulated. tinygrad has gfx950/CDNA4 BF16 and FP8 matrix-core support, but this branch does not yet have a hardware-validated native MXFP4×MXFP8 expert GEMM. Expect the first run to be a correctness bring-up, not production throughput. Capture profiles on MI350X before changing the representation: native FP4 work cannot be validated faithfully on the available gfx1100 cards.
+
+Recurrent prefill is fused. The gfx950 wave-parallel kernel was compared directly with the portable graph at the official per-GPU shape through 128 tokens: maximum core/state differences remained below `8e-6`/`1e-6`, outputs were finite, and replay was about 2.7 ms versus about 8 ms for the portable kernel in the isolated test. Full K3 therefore uses 128-token recurrent chunks on gfx950. Chunk size remains part of the numerical configuration because different reduction orders can select different final greedy tokens.
+
+The following serving changes apply to the official K3 path: recurrent-state reset graph capture, direct AMD scalar readback without rebuilding a scheduler graph, materialized gate/up boundaries, separate greedy decode JITs, K3's uncorrected routed probability semantics, gfx950 KDA Q/K/V and exact BF16 partial projections, one combined routed/shared final collective, a gfx950 greedy output-head kernel, the wave64 packed-expert path, and the multi-token four-output packed tile. Software MXFP8 remains in use.
+
+After hardware admission on MI350X, profile before porting those kernels. The likely implementation order is:
+
+1. A native packed MXFP4×MXFP8 grouped expert GEMM using CDNA4 matrix instructions.
+2. A wave64/MFMA KDA Q/K/V decode projection.
+3. Combined routed/shared down-projection TP partials so each layer performs one XGMI all-reduce.
+4. A CDNA4 output-head matvec and router matvec if they remain visible in the profile.
+
+Every port needs a direct numerical comparison with the generic graph and an end-to-end greedy-token comparison before performance measurements. The wave64 packed-expert kernel has compile coverage through `NULL:HIP:gfx950`; numerical and performance validation still require real MI350X hardware. None of the remaining gfx11-only kernels should be enabled on gfx950 by changing only the architecture guard.
+
+## MI350X performance expectation
+
+Treat the first rental as bring-up, not a guaranteed throughput run. The loader reads every official expert tensor once into a transient GPU-0 staging buffer (at most one packed projection), then redistributes TP8 slices over the GPU fabric; it does not generate files or require checkpoint-sized host RAM. A reasonable planning range for the full text model on eight MI350X cards is 3–8 minutes to stream and TP-shard the 1.56 TB checkpoint, 150–400 tok/s for initial short/medium prefill, and 25–60 tok/s decode with the software packed-expert path. After a native CDNA4 MXFP4×MXFP8 grouped expert kernel, wave64/MFMA recurrent projections, and XGMI collective tuning, 500+ tok/s prefill and roughly 80–150 tok/s decode are plausible targets. These ranges are engineering estimates, not measurements.
+
+The nominal HBM bandwidth is not the main uncertainty: eight MI350X devices have enough aggregate bandwidth for K3's active weights. Utilization is limited by 93 sequential layers, small routed projections, and synchronization after TP input-sharded projections. Record actual HBM and XGMI counters before deciding whether the next port should target matrix instructions or collective count.
+
+The official checkpoint also contains MoonViT-V2 and multimodal projector weights. They are skipped by the text loader. Image input remains a separate implementation and validation task.
+
+## Local TP4 performance baseline
+
+The pre-rental benchmark uses the converted `Kimi-Linear-48B-A3B-Instruct-MXFP4-v2` checkpoint on four gfx1100 GPUs. It is a useful regression test for the KDA/MLA/MoE text path, not a projection of K3 throughput on MI350X.
+
+```sh
+DEV=AMD JIT_BATCH_SIZE=64 python extra/benchmark_kimi.py \
+  /raid/models/Kimi-Linear-48B-A3B-Instruct-MXFP4-v2 \
+  --devices 4 --max-context 128 --prompt-tokens 32 --decode-tokens 32 --chunk-size 32
+```
+
+Results from 2026-08-10:
+
+- load from RAID: 44.28s for the 29.27 GB checkpoint
+- first 32-token prefill includes roughly 10s of compilation/capture
+- steady fresh-prompt prefill replay: 0.118s, 270.20 tok/s
+- steady context-32 decode replay: 101.82 tok/s, 9.82 ms/token
+- peak host RSS: 729.9 MiB; swap was not used
+
+The load, prefill, and decode targets are all met in the bounded prompt-32 run. Decode improved from 23.03 tok/s to 101.82 tok/s. The retained greedy output was checked across 32 decode steps; rejected half-wave and unrounded recurrent reductions were faster but diverged and eventually collapsed to a repeated token.
+
+Fully warmed HTTP serving was also measured with `--max_context 4096`. Startup, including weight load, capture, and replay of both serving shapes, took 113.73s. After a two-turn cache test, the first aligned 64-token request reported 271 tok/s prefill and 101 tok/s decode over 64 generated tokens. A 99-token prompt reported 254 tok/s prefill and 99 tok/s decode; decode falls slightly as MLA context grows.
+
+Recurrent serving uses only the captured 32-token prefill graph and captured single-token graph. Warmup uses two consecutive chunks so both initial and nonzero-position prefill execution are ready before the socket opens. A prompt tail shorter than 32 tokens runs through the single-token graph instead of compiling a new static shape, so no request-time JIT capture is required. Exact extensions reuse recurrent and KV state—the live second turn logged `in: 18 + 15`—while divergent prompts reset both safely. Very short prompts can report less than 200 aggregate prefill tok/s because fixed reset and single-token costs dominate; aligned and medium/long prompts exercise the 200+ tok/s prefill path.
+
+Four 7900 XTX cards provide 96 GB aggregate VRAM and about 3.84 TB/s aggregate physical memory bandwidth. Their nominal aggregate vector FP16 rate is about 245.6 TFLOP/s, or about 492 TFLOP/s through matrix instructions. Kimi Linear activates roughly 3.107B parameters per token; a simple active-weight accounting gives approximately 4.05 GB/token and an optimistic bandwidth-only ceiling near 948 tok/s. The measured decode rate is much lower because this MoE decode workload is a collection of small matrix-vector operations plus PCIe collectives, not one ideal streaming kernel.
+
+The generic loader currently rereads logical TP shards and accounts for roughly 227 GB of disk traffic for a TP4 load. RAID bandwidth hides that inefficiency locally, but a direct one-pass shard loader remains worthwhile before slow remote storage is used. It was not retained here because the attempted direct-shard graph exposed an unresolved scheduler/renderer edge; correctness and bounded memory take priority over avoiding the redundant reads.
+
+Different chunk sizes can choose a different final token because their matrix kernels use different floating-point reduction orders. Each measured shape was repeatable between cold and captured execution. For official K3 validation, compare logits/tokens against the reference at one fixed chunk size and greedy settings rather than requiring bitwise agreement between performance shapes.
@@ -0,0 +1,9 @@
+import argparse
+from tinygrad.llm.kimi import convert_kimi
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(description="Convert official Kimi-Linear-48B-A3B BF16 weights to tinygrad MXFP4/BF16")
+  parser.add_argument("source", help="downloaded moonshotai/Kimi-Linear-48B-A3B-Instruct directory")
+  parser.add_argument("output", help="output directory")
+  args = parser.parse_args()
+  convert_kimi(args.source, args.output)
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+"""Cheap preflight for an official moonshotai/Kimi-K3 checkout. Does not load model weights."""
+import argparse, json, pathlib, shutil
+from tinygrad.llm.kimi_k3 import KIMI_K3_TP8_BYTES_PER_GPU, audit_kimi_k3_checkpoint
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("model_dir", type=pathlib.Path)
+  parser.add_argument("--metadata-only", action="store_true", help="permit absent weight shards")
+  parser.add_argument("--context", type=int, default=4096, help="context length used for the memory estimate")
+  args = parser.parse_args()
+  stats = audit_kimi_k3_checkpoint(args.model_dir, require_shards=not args.metadata_only)
+  if not 1 <= args.context <= 1_048_576: raise ValueError("--context must be between 1 and 1048576")
+
+  # K3 has 24 MLA layers. Each token stores the 512-value compressed latent plus 64 RoPE values in BF16.
+  per_gpu_weights = KIMI_K3_TP8_BYTES_PER_GPU
+  mla_cache = 24 * args.context * (512 + 64) * 2
+  hbm = 288_000_000_000
+  print(json.dumps(stats, indent=2))
+  print(f"exact text weights/GPU under this TP8 layout: {per_gpu_weights/1e9:.2f} GB ({per_gpu_weights/2**30:.2f} GiB)")
+  print(f"replicated MLA cache/GPU at {args.context:,} tokens: {mla_cache/1e9:.2f} GB ({mla_cache/2**30:.2f} GiB)")
+  print(f"nominal MI350X headroom before runtime buffers: {(hbm-per_gpu_weights-mla_cache)/1e9:.2f} GB")
+  if not args.metadata_only:
+    usage = shutil.disk_usage(args.model_dir)
+    print(f"filesystem free space: {usage.free/1e9:.2f} GB")
+
+if __name__ == "__main__": main()
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+"""Run a reduced, architecture-complete K3 prefill/decode on tensor-parallel devices."""
+import argparse, time
+from tinygrad import Tensor, Device, dtypes, nn
+from tinygrad.llm.kimi_k3 import _shard_kimi_k3, kimi_k3_smoke_config
+from tinygrad.llm.model import Transformer
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--devices", type=int, default=8)
+  args = parser.parse_args()
+  if args.devices not in (1, 2, 4, 8): raise ValueError("the K3 admission smoke test supports 1, 2, 4, or 8 devices")
+  devices = tuple(f"AMD:{i}" for i in range(args.devices))
+  model = Transformer(kimi_k3_smoke_config())
+  for name,value in nn.state.get_state_dict(model).items():
+    fill = 127 if name.endswith("weight_scale") else 0
+    dtype = value.dtype if value.dtype is dtypes.uint8 else dtypes.bfloat16
+    value.replace(Tensor.full(value.shape, fill, dtype=dtype, device="CPU"))
+  _shard_kimi_k3(model, devices)
+  temperature = Tensor([0.0], device=devices)
+  for label,tokens,start in (("prefill", [[1, 2]], 0), ("decode", [[3]], 2), ("decode replay", [[4]], 3)):
+    begin = time.perf_counter()
+    out = model(Tensor(tokens, dtype=dtypes.int32, device=devices), start, temperature).realize()
+    for device in devices: Device[device].synchronize()
+    print(f"{label}: shape={out.shape}, {time.perf_counter()-begin:.3f}s")
+
+if __name__ == "__main__": main()
@@ -1282,7 +1282,7 @@ def train_bert():
        previous_step = i

 def train_llama3():
-  from examples.mlperf.models.flat_llama import FlatTransformer, apply_grad, FP8_DTYPE, MXFP8
+  from examples.mlperf.models.flat_llama import FlatTransformer, apply_grad, FP8_DTYPE, MXFP8, MXFP4
  from examples.llama3 import MODEL_PARAMS
  from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
  from examples.mlperf.optim import GradAccClipAdamW, clip_grads
@@ -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] 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_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_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 +1458,12 @@ def train_llama3():

  # realize everything here
  if optim.master_params: Tensor.realize(*optim.master_params)
-  Tensor.realize(*optim.params, *fp8_inv_scales, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
+  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)

  @TinyJit
  def minibatch(tokens:Tensor):
-    for nxt in fp8_next_amax: nxt.assign(0)
-    for nxt in fp8_next_grad_amax: nxt.assign(0)
+    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)
@@ -1477,8 +1477,8 @@ def train_llama3():
    for g, new_g in zip(grads, loss.gradient(*optim.params)):
      apply_grad(g, new_g.uop)

-    loss_cpu = loss.flatten().float().to("CPU")
-    return loss_cpu.realize(*grads, *fp8_amax, *fp8_next_amax, *fp8_grad_amax, *fp8_next_grad_amax)
+    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)

  @TinyJit
  def optim_step():
@@ -1487,14 +1487,14 @@ def train_llama3():
    scheduler.step()

    for g in grads: g.assign(0)
-    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)
+    model.update_amax()

    lr_cpu = optim.lr.float().to("CPU")
    grad_norm_cpu = grad_norm.float().to("CPU")
-    Tensor.realize(lr_cpu, grad_norm_cpu, *grads, *fp8_inv_scales, *fp8_amax, *fp8_grad_amax)
+    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)

-    return lr_cpu, grad_norm_cpu
+    return lr_cpu, grad_norm_cpu, loss_cpu

  @TinyJit
  @Context(TRAINING=0)
@@ -1549,8 +1549,8 @@ def train_llama3():
      st = time.perf_counter()

      stopped = False
-      losses, data_time, dev_time = [], 0, 0
-      for _ in range(grad_acc if i >= 2 else 1):
+      data_time, dev_time = 0, 0
+      for _ in range(accum_steps:=grad_acc if i >= 2 else 1):
        ist = time.perf_counter()
        try: tokens = next(train_iter)
        except StopIteration:
@@ -1558,16 +1558,15 @@ def train_llama3():
          break
        mst = time.perf_counter()
        data_time += mst - ist
-        losses.append(minibatch(tokens).item())
+        minibatch(tokens)
        dev_time += time.perf_counter() - mst
      if stopped: break

      gt = time.perf_counter()
      ret = optim_step()
-      lr, grad_norm = ret[0].item(), ret[1].item()
+      lr, grad_norm, loss = ret[0].item(), ret[1].item(), ret[2].item() / accum_steps
      et = time.perf_counter()

-      loss = sum(losses) / len(losses)
      optim_time = et - gt
      dev_time += optim_time
      step_time = et - st
@@ -1579,7 +1578,7 @@ 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 * 4.6e15)) * 100
+      mfu = ((6 * num_params * SEQLEN * GBS) / (dev_time * device_count * (9.2e15 if MXFP4 else 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")
@@ -83,8 +83,8 @@ def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_sca
      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

-def norm_quantize_matmul(x:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, eps:float, amax_x:Tensor,
-                         next_amax_x:Tensor, grad_amax_state:Tensor, next_grad_amax_state:Tensor):
+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:
    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)
@@ -96,8 +96,8 @@ def norm_quantize_matmul(x:Tensor, norm:Tensor, w:Tensor, w_inv_scale:Tensor, ep
                     next_grad_amax_state=next_grad_amax_state, next_amax_x=next_amax_x)
  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,
-                             next_amax_x:Tensor, grad_amax_state:Tensor|None=None, next_grad_amax_state:Tensor|None=None):
+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:
    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)
@@ -111,9 +111,14 @@ def add_norm_quantize_matmul(x:Tensor, residual:Tensor, norm:Tensor, w:Tensor, w
  return out, h, x_normed, rrms, ret

 def silu_w13_quantize_matmul(x_w13:Tensor, w2:Tensor, s_2:Tensor,
-                             amax_x2:Tensor, next_amax_x2:Tensor,
-                             grad_amax_xw13:Tensor, next_grad_amax_xw13:Tensor,
-                             grad_amax_xout:Tensor, next_grad_amax_xout: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:
    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,
@@ -164,14 +169,15 @@ 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_layers)] for name in names}
-    self._fp8_next_amax = {name: [_amax() for _ in range(n_layers)] for name in names}
+    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}
    grad_names = ["xqkv", "xo", "xout"]
    grad_names += ["xw1", "xw3"] if SPLIT_W13 else ["xw13"]
-    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}
+    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}
    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}
@@ -195,9 +201,10 @@ 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, amax_xo:Tensor, s_qkv:Tensor, s_o:Tensor,
-                next_amax_xqkv:Tensor, next_amax_xo:Tensor,
-                grad_amax_xqkv:Tensor, grad_amax_xo:Tensor, next_grad_amax_xqkv:Tensor, next_grad_amax_xo: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):
    bsz, seqlen, _ = x.shape
    saves = []

@@ -319,28 +326,33 @@ 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],
-                         amax_xqkv=a["xqkv"][i], amax_xo=a["xo"][i], s_qkv=s["wqkv"][i], s_o=s["wo"][i],
-                         next_amax_xqkv=na["xqkv"][i], next_amax_xo=na["xo"][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],
-                        next_amax_x2=na["x2"][i])
+      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",)))
      if SPLIT_W13:
-        ffn_kwargs.update(w1=self.w1[i], w3=self.w3[i], amax_x1=a["x1"][i], amax_x3=a["x3"][i],
-                          next_amax_x1=na["x1"][i], next_amax_x3=na["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])
+        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")))
      else:
-        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], next_amax_x13=na["x13"][i])
+        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)

    logits = matmul(self.norm(h), self.output[0], fp8=False)[0]
@@ -424,9 +436,7 @@ if __name__ == "__main__":
  @TinyJit
  def fwd_bwd(tokens:Tensor):
    with Timing("python forward: "):
-      for amax_dict in (model._fp8_next_amax, model._fp8_next_grad_amax):
-        for ts in amax_dict.values():
-          for nxt in ts: nxt.assign(0)
+      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: "):
@@ -1,8 +1,8 @@
 #!/usr/bin/env bash

 export PYTHONPATH="."
-export PATH="/opt/rocm-7.1.1/bin:$PATH"
-export ROCM_PATH="/opt/rocm-7.1.1"
+export ROCM_PATH=${ROCM_PATH:-/opt/rocm-7.1.1}
+export PATH="$ROCM_PATH/bin:$PATH"
 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=${MASTER_WEIGHTS:-1}
-export FP8=${FP8:-1}
+export MXFP4=${MXFP4:-1}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
 export FAST_CE=${FAST_CE:-1}
 export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
@@ -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 FP8=${FP8:-1}
+export MXFP4=${MXFP4:-1}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}
 export FAST_CE=${FAST_CE:-1}
 export FUSED_INPUT_QUANTIZE=${FUSED_INPUT_QUANTIZE:-1}
@@ -1,4 +1,6 @@
 #!/bin/bash
+set -e
+
 export BENCHMARK=${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
@@ -10,6 +10,7 @@ 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}
@@ -10,6 +10,7 @@ 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}
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""Benchmark Kimi-Linear load, prefill, and decode on its TP4 checkpoint."""
+import argparse, resource, time
+from tinygrad import Device, TinyJit
+from tinygrad.helpers import profile_marker
+from tinygrad.llm.kimi import load_kimi
+
+def sync(devices:int) -> None:
+  for i in range(devices): Device[f"AMD:{i}"].synchronize()
+
+def timed_next(gen, devices:int) -> tuple[int, float]:
+  begin = time.perf_counter()
+  token = next(gen)
+  sync(devices)
+  return token, time.perf_counter()-begin
+
+def fresh_generate(model, prompt:list[int], chunk_size:int):
+  # Force recurrent/KV state reset so repeated runs and chunk sweeps measure the entire prompt,
+  # rather than silently reusing the prefix cached by the previous measurement.
+  model._cached_tokens = [-1] * len(prompt)
+  return model.generate(prompt.copy(), chunk_size=chunk_size)
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("model", help="converted Kimi-Linear-48B-A3B MXFP4-v2 directory")
+  parser.add_argument("--devices", type=int, default=4)
+  parser.add_argument("--max-context", type=int, default=128)
+  parser.add_argument("--prompt-tokens", type=int, default=32)
+  parser.add_argument("--decode-tokens", type=int, default=8)
+  parser.add_argument("--chunk-size", type=int, default=32)
+  parser.add_argument("--sweep-chunks", help="comma-separated prefill chunk sizes; uses the fastest for decode")
+  args = parser.parse_args()
+  if args.prompt_tokens < 1 or args.prompt_tokens + args.decode_tokens + 1 > args.max_context:
+    raise ValueError("prompt and decode tokens must fit within --max-context")
+
+  begin = time.perf_counter()
+  model = load_kimi(args.model, max_context=args.max_context, devices=args.devices)
+  sync(args.devices)
+  print(f"load: {time.perf_counter()-begin:.3f}s", flush=True)
+
+  prompt = [1] + [1000+i%1000 for i in range(args.prompt_tokens-1)]
+  chunks = [int(x) for x in args.sweep_chunks.split(",")] if args.sweep_chunks else [args.chunk_size]
+  if any(x < 1 or x > args.prompt_tokens for x in chunks): raise ValueError("prefill chunks must be between 1 and --prompt-tokens")
+  timings:list[tuple[float, int]] = []
+  prefill_jits:dict[int, TinyJit] = {}
+  for chunk in chunks:
+    # Recurrent prefill has a static token dimension. Give each swept shape its own capture;
+    # the rollout JIT remains shared and independently benchmarks chunk 1/decode.
+    if chunk != 1: model.prefill_jit = TinyJit(model.forward)
+    cold = fresh_generate(model, prompt, chunk)
+    first, cold_prefill = timed_next(cold, args.devices)
+    print(f"chunk {chunk}: cold prefill {cold_prefill:.3f}s, token={first}", flush=True)
+    warm = fresh_generate(model, prompt, chunk)
+    warm_first, prefill = timed_next(warm, args.devices)
+    if first != warm_first: raise RuntimeError(f"chunk {chunk} is not repeatable: cold={first}, warm={warm_first}")
+    timings.append((prefill, chunk))
+    if chunk != 1: prefill_jits[chunk] = model.prefill_jit
+    print(f"chunk {chunk}: prefill {prefill:.3f}s ({args.prompt_tokens/prefill:.3f} tok/s), token={first}", flush=True)
+
+  prefill, best_chunk = min(timings)
+  if best_chunk != 1: model.prefill_jit = prefill_jits[best_chunk]
+  warm = fresh_generate(model, prompt, best_chunk)
+  first, replay_prefill = timed_next(warm, args.devices)
+  _, cold_decode = timed_next(warm, args.devices)
+  _, capture_decode = timed_next(warm, args.devices)
+  print(f"selected chunk: {best_chunk}; prefill replay {replay_prefill:.3f}s "
+        f"({args.prompt_tokens/replay_prefill:.3f} tok/s), token={first}", flush=True)
+  print(f"cold decode: {cold_decode:.3f}s", flush=True)
+  print(f"capture decode: {capture_decode:.3f}s", flush=True)
+  profile_marker("kimi decode steady start")
+  begin = time.perf_counter()
+  output = [next(warm) for _ in range(args.decode_tokens)]
+  sync(args.devices)
+  decode = time.perf_counter()-begin
+  profile_marker("kimi decode steady end")
+  print(f"decode: {decode:.3f}s ({args.decode_tokens/decode:.3f} tok/s, {decode/args.decode_tokens*1e3:.3f} ms/tok), output={output}", flush=True)
+  print(f"peak RSS: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024:.1f} MiB", flush=True)
+
+if __name__ == "__main__": main()
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""Bounded correctness and load/prefill/decode benchmark for the official TP8 Kimi K3 checkpoint."""
+import argparse, resource, time
+
+from tinygrad import Device
+from tinygrad.helpers import profile_marker
+from tinygrad.llm.cli import KimiK3Template, SimpleTokenizer
+from tinygrad.llm.kimi_k3 import load_kimi_k3, load_kimi_tokenizer_data
+
+def sync(devices:int) -> None:
+  for i in range(devices): Device[f"AMD:{i}"].synchronize()
+
+def fresh_generate(model, prompt:list[int], chunk_size:int):
+  # Never reuse a prefix or recurrent state across correctness/benchmark trials.
+  model._cached_tokens = [-1] * len(prompt)
+  return model.generate(prompt.copy(), chunk_size=chunk_size, temperature=0.0)
+
+def timed_next(gen, devices:int) -> tuple[int, float]:
+  begin = time.perf_counter()
+  token = next(gen)
+  sync(devices)
+  return token, time.perf_counter()-begin
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("model", help="official unmodified Kimi K3 checkpoint directory")
+  parser.add_argument("--devices", type=int, default=8)
+  parser.add_argument("--max-context", type=int, default=128)
+  parser.add_argument("--prompt", default="Reply with exactly: OK")
+  parser.add_argument("--stable-tokens", type=int, default=8)
+  parser.add_argument("--decode-tokens", type=int, default=8)
+  parser.add_argument("--chunk-size", type=int, default=128)
+  args = parser.parse_args()
+
+  begin = time.perf_counter()
+  model = load_kimi_k3(args.model, max_context=args.max_context, devices=args.devices)
+  sync(args.devices)
+  load_time = time.perf_counter()-begin
+  print(f"load: {load_time:.3f}s", flush=True)
+
+  normal, special, bos, eos = load_kimi_tokenizer_data(args.model)
+  tok = SimpleTokenizer(normal, special, "kimi-k2", bos_id=bos, eos_id=eos, eot_id=eos)
+  rendered = KimiK3Template().render(messages=[{"role":"user", "content":args.prompt}], add_generation_prompt=True)
+  prompt = tok.encode(rendered)
+  needed = len(prompt) + max(args.stable_tokens, args.decode_tokens+3)
+  if needed > args.max_context: raise ValueError(f"prompt and output need {needed} tokens but max context is {args.max_context}")
+  print(f"prompt: {len(prompt)} tokens, chunk={args.chunk_size}", flush=True)
+
+  sequences:list[list[int]] = []
+  # TinyJit executes uncaptured once, captures the second call, and replays from the third call.
+  # Compare two replay paths rather than capture numerics/timing against replay.
+  for trial in range(4):
+    gen = fresh_generate(model, prompt, args.chunk_size)
+    sequence:list[int] = []
+    prefill = 0.0
+    for step in range(args.stable_tokens):
+      token, elapsed = timed_next(gen, args.devices)
+      sequence.append(token)
+      if step == 0: prefill = elapsed
+    if trial >= 2: sequences.append(sequence)
+    label = ("uncaptured warmup", "capture warmup", "stable trial 1", "stable trial 2")[trial]
+    print(f"{label}: prefill={prefill:.3f}s "
+          f"({len(prompt)/prefill:.3f} tok/s), tokens={sequence}", flush=True)
+  if sequences[0] != sequences[1]: raise RuntimeError(f"greedy output is not repeatable: {sequences}")
+  print(f"stable text: {tok.decode(sequences[0])!r}", flush=True)
+
+  gen = fresh_generate(model, prompt, args.chunk_size)
+  profile_marker("kimi k3 steady prefill start")
+  first, prefill = timed_next(gen, args.devices)
+  profile_marker("kimi k3 steady prefill end")
+  warmup = [timed_next(gen, args.devices)[0] for _ in range(2)]
+  profile_marker("kimi k3 steady decode start")
+  begin = time.perf_counter()
+  output = [next(gen) for _ in range(args.decode_tokens)]
+  sync(args.devices)
+  decode = time.perf_counter()-begin
+  profile_marker("kimi k3 steady decode end")
+  print(f"prefill replay: {prefill:.3f}s ({len(prompt)/prefill:.3f} tok/s), token={first}", flush=True)
+  print(f"decode after warmup {warmup}: {decode:.3f}s ({args.decode_tokens/decode:.3f} tok/s, "
+        f"{decode/args.decode_tokens*1e3:.3f} ms/tok), output={output}", flush=True)
+  print(f"peak RSS: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024:.1f} MiB", flush=True)
+
+if __name__ == "__main__": main()
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""Fast exact-shape K3 KDA/layer benchmark using bounded fake weights instead of the 1.56 TB checkpoint."""
+from __future__ import annotations
+import argparse, statistics, time
+from dataclasses import replace
+
+from tinygrad import Device, Tensor, TinyJit, dtypes, nn
+from tinygrad.helpers import profile_marker
+from tinygrad.llm.kimi_k3 import kimi_k3_config
+from tinygrad.llm.model import GatedDeltaNetBlock
+
+def tp_axis(name:str) -> int|None:
+  if "ffn_gate_exps.weight" in name or "ffn_up_exps.weight" in name: return 1
+  if "ffn_gate_exps.weight_scale" in name or "ffn_up_exps.weight_scale" in name: return 1
+  if "ffn_down_exps.weight" in name or "ffn_down_exps.weight_scale" in name: return 2
+  if name.endswith(("ffn_gate_shexp.weight", "ffn_up_shexp.weight")): return 0
+  if name.endswith(("ffn_down_shexp.weight", "ffn_routed_down.weight", "ffn_routed_up.weight", "ssm_out.weight")): return 1
+  if name.endswith(("attn_q.weight", "attn_k.weight", "attn_v.weight", "ssm_g_full.weight", "ssm_f_b.weight", "ssm_beta.weight")): return 0
+  if name.endswith(("ssm_q_conv1d.weight", "ssm_k_conv1d.weight", "ssm_v_conv1d.weight", "ssm_dt.bias")): return 0
+  return None
+
+def fake_value(name:str) -> tuple[int|float, object]:
+  if name.endswith("weight_scale"): return 120, dtypes.uint8
+  if name.endswith("_exps.weight"): return 0x11, dtypes.uint8
+  if name.endswith("ssm_a"): return -0.1, dtypes.float32
+  if name.endswith("ssm_dt.bias"): return 0.1, dtypes.float32
+  if "conv1d.weight" in name: return 0.1, dtypes.float32
+  if name.endswith("exp_probs_b.bias"): return 0.0, dtypes.float32
+  if name.endswith("norm.weight"): return 1.0, dtypes.bfloat16
+  return 0.001, dtypes.bfloat16
+
+def fake_tp_tensor(shape:tuple[int, ...], value:int|float, dtype, devices:tuple[str, ...], axis:int|None) -> Tensor:
+  if axis is not None and shape[axis] % len(devices): raise ValueError(f"shape {shape} is not TP{len(devices)} divisible on axis {axis}")
+  source = Tensor.full(shape, value, dtype=dtype, device=devices[0]).clone().realize()
+  return source.shard(devices, axis=axis).realize()
+
+def sync(devices:tuple[str, ...]) -> None:
+  for device in devices: Device[device].synchronize()
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--devices", type=int, default=8)
+  parser.add_argument("--mode", choices=("attention", "block"), default="attention")
+  parser.add_argument("--iterations", type=int, default=20)
+  args = parser.parse_args()
+  devices = tuple(f"AMD:{i}" for i in range(args.devices))
+  # One exact-width KDA layer, but only 16 fake routed experts. This retains top-k 16 and every
+  # official per-GPU matrix/state shape while keeping fake expert storage below 300 MB per layer.
+  config = replace(kimi_k3_config(4), num_blocks=1, num_experts=16, num_experts_per_tok=16, ssm_layers=(True,),
+                   attn_res_block_size=0)
+  block = GatedDeltaNetBlock(config, config.ssm)
+  begin = time.perf_counter()
+  for name,tensor in nn.state.get_state_dict(block).items():
+    if args.mode == "attention" and name.startswith(("ffn_", "exp_probs_")): continue
+    value, dtype = fake_value(name)
+    tensor.replace(fake_tp_tensor(tuple(int(x) for x in tensor.shape), value, dtype, devices, tp_axis(name)))
+  sync(devices)
+  print(f"fake weights: {time.perf_counter()-begin:.3f}s", flush=True)
+  x_source = (((Tensor.arange(config.dim, dtype=dtypes.float32).reshape(1, 1, config.dim) % 31) / 31) \
+    .cast(dtypes.bfloat16).to(devices[0])).clone().realize()
+  x = x_source.shard(devices, axis=None).realize()
+  block._init_state(x)
+  # Use direct buffer-backed state shards. The production path reaches this form after prefill;
+  # the fake harness begins immediately at decode and must not feed lazy clone graphs to TinyJit.
+  for state,axis in ((block.conv_state_q, 2), (block.conv_state_k, 2), (block.conv_state_v, 2), (block.recurrent_state, 1)):
+    state.replace(Tensor.zeros(*state.shape, dtype=state.dtype, device=devices[0]).shard(devices, axis=axis).realize())
+
+  @TinyJit
+  def run(inp:Tensor) -> Tensor:
+    if args.mode == "attention": return block._attention(block.attn_norm(inp), 0).realize()
+    return block(inp, 0).realize()
+
+  # uncaptured, capture, then replay only
+  run(x); sync(devices)
+  run(x); sync(devices)
+  samples:list[float] = []
+  profile_marker(f"fake K3 {args.mode} start")
+  for _ in range(args.iterations):
+    begin = time.perf_counter(); out = run(x); sync(devices); samples.append((time.perf_counter()-begin)*1e3)
+  profile_marker(f"fake K3 {args.mode} end")
+  print(f"{args.mode}: median={statistics.median(samples):.3f} ms/layer, min={min(samples):.3f} ms/layer, "
+        f"projected_93_layer_rate={1000/(statistics.median(samples)*93):.3f} tok/s, finite={out.float().isfinite().all().item()}")
+
+if __name__ == "__main__": main()
@@ -0,0 +1,31 @@
+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)
@@ -1,203 +0,0 @@
-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])
-  # NOTE: no explicit barrier needed, the AFTER on the LOCAL buffers implies it in late codegen
-  Q_lds = Q_lds.after(UOp.group(Q_store, K_store))
-  KV_lds_k = KV_lds.after(UOp.group(Q_store, K_store))
-
-  # -- 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)
-  tn1 = UOp.range(TN, 201)
-  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)
-  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)
-  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)
-  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)
-  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])
-  # NOTE: no explicit barrier needed, the AFTER on the LOCAL buffers implies it in late codegen
-  P_lds = P_lds.after(UOp.group(P_store, V_store))
-  KV_lds_v = KV_lds.after(UOp.group(P_store, V_store))
-
-  # -- acc += P @ V via WMMA --
-  k_pv = UOp.range(BLOCK_N // WMMA_K, 400, AxisType.REDUCE)
-  tm2 = UOp.range(TM // WMMA_ACC, 401)
-  tn2 = UOp.range(TD, 402)
-  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).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!")
@@ -6,6 +6,7 @@ from tinygrad.renderer import Estimates
 from tinygrad.helpers import getenv, all_same, DEBUG, ceildiv
 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

@@ -125,6 +126,25 @@ def custom_mxfp4_gemm(C:UOp, A:UOp, B:UOp, scale_a:UOp, scale_b:UOp, *extra:UOp,
  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
@@ -137,50 +157,6 @@ def quantize_mxfp8(x:Tensor) -> tuple[Tensor, Tensor, Tensor]:
  packed = mx_pack(e8) if len(batch) == 1 and scale_K % 4 == 0 else None
  return x_clamped.cast(FP8_DTYPE), e8, packed

-def _mxfp4_shuffle_weight(x:Tensor) -> Tensor:
-  # shuffle_weight(x, layout=(16, 16)) on the packed uint8 buffer.
-  if x.ndim == 3:
-    ndev, rows, half_k = x.shape
-    return x.reshape(ndev, rows//16, 16, half_k//32, 2, 16).permute(0, 1, 3, 4, 2, 5).reshape(ndev, rows, half_k).contiguous()
-  rows, half_k = x.shape
-  return x.reshape(rows//16, 16, half_k//32, 2, 16).permute(0, 2, 3, 1, 4).reshape(rows, half_k).contiguous()
-
-def _mxfp4_shuffle_scales(x:Tensor) -> Tensor:
-  # e8m0_shuffle: each 256x8 scale tile is arranged for the raw MFMA scale loads.
-  if x.ndim == 3:
-    ndev, rows, scale_k = x.shape
-    return x.reshape(ndev, rows//32, 2, 16, scale_k//8, 2, 4).permute(0, 1, 4, 6, 3, 5, 2).reshape(ndev, rows, scale_k).contiguous()
-  rows, scale_k = x.shape
-  return x.reshape(rows//32, 2, 16, scale_k//8, 2, 4).permute(0, 3, 5, 2, 4, 1).reshape(rows, scale_k).contiguous()
-
-def quantize_mxfp4(x:Tensor) -> tuple[Tensor, Tensor, Tensor]:
-  # OCP MXFP4: 1x32 blocks, E2M1 values packed low-nibble first, and E8M0 scales.
-  *batch, K = x.shape
-  rows = math.prod(batch)
-  assert x.ndim >= 2 and K % 256 == 0 and rows % 32 == 0, \
-    f"mxfp4 quantization needs rows%32 and K%256, got {x.shape}"
-  xb = x.float().reshape(*batch, K//32, 32)
-  amax = xb.abs().max(axis=-1)
-
-  # even scale rounding: round the fp32 significand before choosing 2^(floor(log2)-2).
-  amax_rounded = ((amax.bitcast(dtypes.uint32) + 0x200000) & 0xFF800000).bitcast(dtypes.float32)
-  scale_exp = (amax_rounded.maximum(2**-126).log2().floor() - 2).clamp(-127, 127)
-  e8 = (scale_exp + 127).cast(dtypes.uint8)
-  scaled = xb * (-scale_exp).exp2().reshape(*batch, K//32, 1)
-
-  mag = scaled.abs()
-  code = sum(x.cast(dtypes.uint8) for x in
-             (mag > .25, mag >= .75, mag > 1.25, mag >= 1.75, mag > 2.5, mag >= 3.5, mag > 5.0))
-  code = code | ((scaled < 0).cast(dtypes.uint8) << 3)
-  code = code.reshape(*batch, K)
-  packed = code[..., 0::2] | (code[..., 1::2] << 4)
-  if isinstance(x.device, tuple) and x.uop.axis == x.ndim-2 and x.shape[x.uop.axis] == len(x.device):
-    axis = x.uop.axis
-    order = (axis, *range(axis), *range(axis+1, e8.ndim))
-    e8_local = e8.permute(order)
-    return packed, e8, _mxfp4_shuffle_scales(e8_local.reshape(e8_local.shape[0], -1, K//32))
-  return packed, e8, _mxfp4_shuffle_scales(e8.reshape(rows, K//32))
-
 def mx_pack(e8:Tensor) -> Tensor:
  rows, scale_K = e8.shape
  return e8.reshape(rows, scale_K // 4, 4).bitcast(dtypes.uint32).reshape(rows, scale_K // 4).permute(1, 0).contiguous()
@@ -405,15 +381,16 @@ def custom_mx_gemm_bw(gradient:UOp, kernel:UOp, has_w_post:bool, w_stored:bool=F
 # ** mxfp4 gemm backward

 def custom_mxfp4_gemm_bw(gradient:UOp, kernel:UOp):
-  # The raw kernel consumes quantized buffers, while the final two inputs retain the BF16 operands for STE gradients.
-  inputs = kernel.src[1:]  # (out, a_q, b_q, scale_a, scale_b, a, w)
-  assert len(inputs) == 7
+  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)
-  grad_a = asm_gemm(g, w, mxfp4=True)
-  a_flat, g_flat = a.reshape(-1, a.shape[-1]), g.reshape(-1, g.shape[-1])
-  grad_w = asm_gemm(g_flat.T, a_flat, mxfp4=True)
-  return (None, None, None, None, None, grad_a.uop, grad_w.uop)
+  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

@@ -459,16 +436,10 @@ def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=N
      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
-      if k_sharded:
-        ndev = len(a.device)
-        a_q, _, scale_a = quantize_mxfp4(a.reshape(batch, M, ndev, K))
-        b_q, _, scale_b = quantize_mxfp4(w.reshape(w.shape[0], ndev, K))
-        b_q = _mxfp4_shuffle_weight(b_q.permute(1, 0, 2))
-      else:
-        a_q, _, scale_a = quantize_mxfp4(a.reshape(batch*M, K))
-        b_q, _, scale_b = quantize_mxfp4(w)
-        a_q, b_q = a_q.reshape(batch, M, K//2).contiguous(), _mxfp4_shuffle_weight(b_q)
-      out = Tensor.custom_kernel(out, a_q, b_q, scale_a, scale_b, a, w, fxn=fxn, grad_fxn=custom_mxfp4_gemm_bw)[0]
+      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:
      # mxfp8 1x32 block scaling
      if mx_scales is not None:
@@ -5,7 +5,8 @@ BLOCK_ROW = 256

 def _sharded_invalids(shape:tuple[int, ...], dtype, device) -> Tensor:
  if isinstance(device, tuple):
-    return Tensor(Tensor.invalids(shape[0] // len(device), *shape[1:], dtype=dtype, device=device).uop.multi(0), device=device)
+    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:
@@ -4,7 +4,7 @@ import os, ctypes, struct, hashlib, functools, importlib, mmap, errno, array, co
 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, make_patches
+from tinygrad.runtime.support.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
@@ -152,19 +152,12 @@ def pm4_submit(ctx, lin):
  ring, wptr, doorbell, put_ptr = (UOp.placeholder((b.size,), b.dtype, 0, device=devs).rtag(f"COMPUTE:0_{name}")
    for name, b in (("ring", q.ring), ("write_ptr", q.write_ptr), ("doorbell", q.doorbell), ("put_value", q.put_value)))

-  # two tail dwords coordinate safe IB reuse: GPU completions and host submits
-  size_dw = sum(len(ins.src) for ins in lin.src) + len(release_mem(ctx, 0, 0).src)
+  # 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"

-  ib = UOp.placeholder((size_dw + 2,), dtypes.uint32, next(UOp.unique_num), device=devs, volatile=True).rtag("cmdbuf")
-  done_idx, submit_idx = UOp.const(size_dw + 0, dtypes.int), UOp.const(size_dw + 1, dtypes.int)
-  init_counters = make_patches(ib, [((size_dw + i) * 4, UOp.const(0, dtypes.uint32)) for i in range(2)]).rtag("link")
-  submitted = (counter:=ib.after(init_counters).index(submit_idx)).load()
-  completed = ib.after(loop:=UOp.loop(0)).index(done_idx).load()
-  ib_free = completed.end(loop, completed != submitted)
-
-  bump_fence = pm4_store(ctx, UOp(Ops.SLICE, dtypes.uint32, (ib, UOp.const(size_dw)), 2), (submitted + 1).cast(dtypes.uint64))
-  cmdbuf = make_cmdbuf(lin.replace(src=lin.src + (bump_fence,)), devs, buf=ib, dep=ib_free)
+  ib = UOp.placeholder((size_dw,), dtypes.uint32, next(UOp.unique_num), device=devs, volatile=True).rtag("cmdbuf")
+  cmdbuf = make_cmdbuf(lin, devs, buf=ib)

  # the ring itself only carries a packet pointing at the ib, wrapping the ring
  put = put_ptr.index(zero:=UOp.const(0, dtypes.int))
@@ -174,7 +167,7 @@ def pm4_submit(ctx, lin):
  # 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, counter.store(submitted + 1))
+  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)])
@@ -189,7 +182,8 @@ def sdma_copy(ctx, call):
  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+off), *data64_le(dst_addr+off))))
+    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)))))

 def sdma_wait(ctx, ins, dst, val):
  op = ctx.sdma.SDMA_OP_POLL_REGMEM | ctx.sdma.SDMA_PKT_POLL_REGMEM_HEADER_FUNC(WAIT_REG_MEM_FUNCTION_GEQ) \
@@ -514,12 +508,12 @@ 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():
+    if reset and d.iface.dev_impl.recover(force=True):
      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.timeline_signal('COMPUTE:0')._buf.cpu_view().mv.cast('Q')[0] = \
-        d.timeline_value('COMPUTE:0').as_memoryview(force_zero_copy=True).cast('Q')[0] - 1
+      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

  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))):
@@ -545,9 +539,12 @@ 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

@@ -639,9 +636,6 @@ 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=f"{qname}_timeline_signal"), lambda ctx, q=qname: ctx[0].timeline_signal(q)),
-      (UPat(Ops.PARAM, tag=f"{qname}_timeline_value"), lambda ctx, q=qname: ctx[0].timeline_value(q)),
    ]) + self.pm_bufferize

    return queue
@@ -700,7 +694,7 @@ class AMDDevice(HCQ2Compiled):
    return tmpring

  def scratch_buffer(self, private_segment_size):
-    private_segment_size = max(private_segment_size, 128)
+    AMDDevice.max_scratch_psize = private_segment_size = max(private_segment_size, 128, AMDDevice.max_scratch_psize)
    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
@@ -0,0 +1,36 @@
+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])
@@ -0,0 +1,226 @@
+// 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();
+      }
+    }
+  }
+}
@@ -0,0 +1,49 @@
+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]
@@ -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
+from tinygrad.helpers import DEBUG, getenv
 from tinygrad.renderer import Estimates
 from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
 from tinygrad.runtime.support.elf import elf_loader
@@ -206,10 +206,11 @@ 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)
-  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)
+  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)

  return UOp(Ops.PROGRAM,
             src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -236,10 +237,11 @@ 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)
-  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)
+  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)

  return UOp(Ops.PROGRAM,
             src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -268,10 +270,11 @@ 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)
-  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)
+  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)

  return UOp(Ops.PROGRAM,
             src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -298,10 +301,11 @@ 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)
-  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)
+  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)

  return UOp(Ops.PROGRAM,
             src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
@@ -43,6 +43,10 @@ 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)
@@ -88,7 +92,12 @@ __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;

@@ -380,6 +389,13 @@ __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);
@@ -533,6 +549,8 @@ __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}));
@@ -638,6 +656,13 @@ __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);
@@ -791,6 +816,8 @@ __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}));
@@ -895,6 +922,14 @@ __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);
@@ -1048,6 +1083,8 @@ __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}));
@@ -1151,6 +1188,15 @@ __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);
@@ -1303,6 +1349,8 @@ __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}));
@@ -1428,6 +1476,13 @@ __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);
@@ -1582,6 +1637,8 @@ __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}));
@@ -1689,6 +1746,13 @@ __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);
@@ -1842,6 +1906,8 @@ __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}));
@@ -1946,6 +2012,14 @@ __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);
@@ -2099,6 +2173,8 @@ __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}));
@@ -2202,6 +2278,15 @@ __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);
@@ -2354,6 +2439,8 @@ __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}));
@@ -2471,6 +2558,12 @@ __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);
@@ -2732,6 +2825,13 @@ __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);
@@ -2988,6 +3088,14 @@ __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);
@@ -3244,6 +3352,15 @@ __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);
@@ -34,6 +34,10 @@ 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)
@@ -82,11 +86,26 @@ 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 neg_inf_vgpr,
+__device__ inline void mask_vec2_imm(uint32_t rel_vgpr, uint32_t rel_hi_vgpr, uint32_t neg_inf_vgpr,
                                     uint32_t& x_ref, uint32_t& y_ref) {

    uint64_t x_mask, y_mask;
-    // uint32_t ox, oy;
+#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
    asm volatile(
        // x: rel < THR_X ?
        "v_cmp_lt_i32_e64 %0, %6, %7\n\t"
@@ -99,7 +118,7 @@ __device__ inline void mask_vec2_imm(uint32_t rel_vgpr, uint32_t neg_inf_vgpr,
          "n"(THR_X), "v"(neg_inf_vgpr), "n"(THR_Y)
        : "vcc"
    );
-    // x_ref = ox; y_ref = oy;
+#endif
 }

 template<ducks::rt::col_layout RT>
@@ -122,6 +141,8 @@ __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) {
@@ -145,14 +166,14 @@ __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, 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);
+            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);
        }
    }
 }
@@ -201,6 +222,16 @@ __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;

@@ -231,7 +262,7 @@ __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, 0, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, min_tile, head_idx_kv, 0}, swizzled_offsets_K);
    __builtin_amdgcn_s_waitcnt(0);
    __builtin_amdgcn_sched_barrier(0);
    __builtin_amdgcn_s_barrier();
@@ -243,9 +274,9 @@ __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, 1, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[1], g.Kg, {batch_idx, min_tile + 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, 0, head_idx_kv, 0}, swizzled_offsets_V);
+    G::load<1, false>(v_smem[0], g.Vg, {batch_idx, min_tile, head_idx_kv, 0}, swizzled_offsets_V);
    load(k_reg, k_smem[0]);
    __builtin_amdgcn_sched_barrier(0);
    asm volatile("s_waitcnt lgkmcnt(0)");
@@ -259,13 +290,20 @@ __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 = (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);
+        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);
        }
    }
    // 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);
@@ -284,21 +322,25 @@ __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, 2, head_idx_kv, 0}, swizzled_offsets_K);
+    G::load<1, false>(k_smem[0], g.Kg, {batch_idx, min_tile + 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, 1, head_idx_kv, 0}, swizzled_offsets_V);
+    G::load<1, false>(v_smem[1], g.Vg, {batch_idx, min_tile + 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 = 3; j < max_num_tiles - 1; j += 2) {
+    for (int j = min_tile + 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);
@@ -379,7 +421,7 @@ __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 (q_start_pos < kv_end_pos) {  // Only mask if needed
+            if (WINDOW || q_start_pos < kv_end_pos) {
                mask_kv_tile(att_block[0], tile_idx, j - 1, neg_inf_v, lane);
            }
        }
@@ -447,7 +489,7 @@ __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(q_start_pos < kv_end_pos, 0)) {  // Only mask if needed
+        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, WINDOW ? 1 : 0)) {
            mask_kv_tile(att_block[1], tile_idx, max_num_tiles - 3, neg_inf_v, lane);
        }
    }
@@ -510,7 +552,7 @@ __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(q_start_pos < kv_end_pos, 1)) {  // Only mask if needed
+        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, 1)) {
            mask_kv_tile(att_block[0], tile_idx, max_num_tiles - 2, neg_inf_v, lane);
        }
    }
@@ -572,7 +614,7 @@ __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(q_start_pos < kv_end_pos, 1)) {  // Only mask if needed
+        if (__builtin_expect(WINDOW || q_start_pos < kv_end_pos, 1)) {
            mask_kv_tile(att_block[1], tile_idx, max_num_tiles - 1, neg_inf_v, lane);
        }
    }
@@ -97,4 +97,33 @@ __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");
+    }
+}
@@ -0,0 +1,74 @@
+#include "kittens.cuh"
+
+using namespace kittens;
+
+#ifndef MATVEC_N
+#define MATVEC_N 1536
+#endif
+#ifndef MATVEC_K
+#define MATVEC_K 7168
+#endif
+
+constexpr int SPLIT_WAVES = 8;
+
+template<int W>
+__device__ __forceinline__ float run_split(const bf16 *A_ptr, const bf16 *B_ptr, int out_base,
+                                           st_bf<16, 32, st_16x32_s> &As,
+                                           st_bf<16, 32, st_16x32_s> &Bs) {
+  constexpr int K = MATVEC_K;
+  rt_bf<16, 32, row_l, rt_16x32_s> A;
+  rt_bf<16, 32, row_l, rt_16x32_s> B;
+  rt_fl<16, 16, col_l, rt_16x16_s> C;
+  zero(C);
+  const int lane = laneid();
+  constexpr int k_begin = W * (K / SPLIT_WAVES), k_end = k_begin + K / SPLIT_WAVES;
+  #pragma unroll 1
+  for (int k = k_begin; k < k_end; k += 32) {
+    #pragma unroll
+    for (int idx = lane; idx < 16 * 32; idx += 64) {
+      const int row = idx / 32, col = idx % 32;
+      *reinterpret_cast<bf16 *>(reinterpret_cast<char *>(&As.data[0]) + As.swizzle({row, col})) = A_ptr[k + col];
+      *reinterpret_cast<bf16 *>(reinterpret_cast<char *>(&Bs.data[0]) + Bs.swizzle({row, col})) =
+        B_ptr[(out_base + row) * K + k + col];
+    }
+    asm volatile("s_waitcnt lgkmcnt(0)");
+    load(A, As);
+    load(B, Bs);
+    asm volatile("s_waitcnt lgkmcnt(0)");
+    mma_ABt(C, A, B, C);
+  }
+  return C.tiles[0][0].data[0].x;
+}
+
+// Eight waves split K for one 16-channel output tile.  Each wave uses MFMA on
+// a repeated activation row, then wave zero reduces the eight FP32 partials.
+__global__ __launch_bounds__(64 * SPLIT_WAVES, 1)
+void hk_bf16_matvec_splitk(bf16 *C_ptr, const bf16 *A_ptr, const bf16 *B_ptr, bf16 *unused) {
+  constexpr int N = MATVEC_N, K = MATVEC_K;
+  static_assert(N % 16 == 0 && K % (32 * SPLIT_WAVES) == 0);
+  __shared__ st_bf<16, 32, st_16x32_s> As[SPLIT_WAVES];
+  __shared__ st_bf<16, 32, st_16x32_s> Bs[SPLIT_WAVES];
+  __shared__ float partial[SPLIT_WAVES][16];
+  const int tid = threadIdx.x, wave = tid / 64, lane = tid & 63;
+  const int out_base = blockIdx.x * 16;
+  float result = 0.0f;
+  switch (wave) {
+    case 0: result = run_split<0>(A_ptr, B_ptr, out_base, As[0], Bs[0]); break;
+    case 1: result = run_split<1>(A_ptr, B_ptr, out_base, As[1], Bs[1]); break;
+    case 2: result = run_split<2>(A_ptr, B_ptr, out_base, As[2], Bs[2]); break;
+    case 3: result = run_split<3>(A_ptr, B_ptr, out_base, As[3], Bs[3]); break;
+    case 4: result = run_split<4>(A_ptr, B_ptr, out_base, As[4], Bs[4]); break;
+    case 5: result = run_split<5>(A_ptr, B_ptr, out_base, As[5], Bs[5]); break;
+    case 6: result = run_split<6>(A_ptr, B_ptr, out_base, As[6], Bs[6]); break;
+    case 7: result = run_split<7>(A_ptr, B_ptr, out_base, As[7], Bs[7]); break;
+  }
+  if (lane < 16) partial[wave][lane] = result;
+  asm volatile("s_waitcnt lgkmcnt(0)");
+  __builtin_amdgcn_s_barrier();
+  if (wave == 0 && lane < 16) {
+    float total = 0.0f;
+    #pragma unroll
+    for (int i = 0; i < SPLIT_WAVES; i++) total += partial[i][lane];
+    C_ptr[out_base + lane] = static_cast<bf16>(total);
+  }
+}
@@ -471,6 +471,20 @@ 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).

@@ -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
+from test.helpers import needs_second_gpu, check_schedule, assert_kernel_count, KernelCountException

 class TestArange(unittest.TestCase):
  def _get_flops(self, tensor, desired):
    GlobalCounters.reset()
    linear = compile_linear(tensor.schedule_linear())
-    self.assertEqual(len(linear.src), 1)
+    if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
    run_linear(linear)
    np.testing.assert_equal(tensor.numpy(), desired)
    return estimate_uop(linear.src[-1]).ops
@@ -55,8 +55,7 @@ class TestIndexing(unittest.TestCase):
    with Context(NOOPT=1):
      GlobalCounters.reset()
      out = ((Tensor.arange(1,16385)-1)*needle).sum()
-      linear, var_vals = out.linear_with_vars()
-      self.assertEqual(len(linear.src), 1)
+      linear, var_vals = check_schedule(out, 1)
      run_linear(linear, var_vals)
    self.assertEqual(out.item(), 1337)

@@ -72,8 +71,7 @@ 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 = X.linear_with_vars()
-      self.assertEqual(len(linear.src), 1)
+      linear, var_vals = check_schedule(X, 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())
@@ -98,8 +96,7 @@ class TestIndexing(unittest.TestCase):
      GlobalCounters.reset()
      X = dataset[idxs]
      assert X.shape == (4,DDIM)
-      linear, var_vals = X.linear_with_vars()
-      self.assertEqual(len(linear.src), 1)
+      linear, var_vals = check_schedule(X, 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())
@@ -113,8 +110,7 @@ class TestIndexing(unittest.TestCase):
      GlobalCounters.reset()
      X = dataset[idxs]
      assert X.shape == (4,DDIM)
-      linear, var_vals = X.linear_with_vars()
-      self.assertEqual(len(linear.src), 1)
+      linear, var_vals = check_schedule(X, 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())
@@ -157,7 +153,7 @@ class TestIndexing(unittest.TestCase):
      GlobalCounters.reset()
      z = emb(x).realize()
      self.assertLessEqual(GlobalCounters.global_ops, op_limit)
-      self.assertEqual(GlobalCounters.kernel_count, 2)
+      assert_kernel_count(2)
    if getenv("CHECK", 1):
      import torch
      with torch.no_grad():
@@ -257,7 +253,7 @@ class TestIndexing(unittest.TestCase):
    xq_rope, _ = apply_rotary_emb(xq, xq, freqs_cis)
    xq_rope.sum().backward()
    linear = compile_linear(wq.grad.schedule_linear())
-    assert len(linear.src) == 1, f"expected one kernel for backward, got: {len(linear.src)}"
+    if len(linear.src) != 1: raise KernelCountException(1, 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")
@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor, Device, dtypes, Context
 from tinygrad.helpers import getenv, system, DEV
-from extra.gemm.cdna_asm_gemm import asm_gemm, hk_bf16_atb_gemm, quantize_mxfp4
+from extra.gemm.cdna_asm_gemm import asm_gemm, hk_bf16_atb_gemm
 from test.helpers import needs_second_gpu
 from examples.mlperf.models.flat_llama import FP8_DTYPE, quantize_fp8, FP8_MAX

@@ -157,13 +157,20 @@ class TestMXFP4(unittest.TestCase):

  def test_quantize(self):
    import numpy as np
-    block = np.array([0, .26, .74, .75, 1.26, 1.75, 2.51, 3.5, 5.1, 6, -6] + [0] * 21, dtype=np.float32)
-    x = Tensor(np.tile(block, (32, 8)), dtype=dtypes.bfloat16)
-    packed, scale, _ = quantize_mxfp4(x)
-    p = packed.numpy()
-    codes = np.stack((p & 0xF, p >> 4), axis=-1).reshape(32, 256)
-    np.testing.assert_array_equal(codes[0, :11], [0, 1, 1, 2, 3, 4, 5, 6, 7, 7, 15])
-    np.testing.assert_array_equal(scale.numpy(), np.full((32, 8), 127, dtype=np.uint8))
+    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
@@ -171,17 +178,9 @@ class TestMXFP4(unittest.TestCase):
    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()
-    # reference gemm
-    a_packed, scale_a, _ = quantize_mxfp4(a)
-    b_packed, scale_b, _ = quantize_mxfp4(b)
-    def unpack(x): return np.stack((x & 0xF, x >> 4), axis=-1).reshape(x.shape[0], -1)
-    code_a, code_b = unpack(a_packed.numpy()), unpack(b_packed.numpy())
-    lut = np.array([0, .5, 1, 1.5, 2, 3, 4, 6, -0., -.5, -1, -1.5, -2, -3, -4, -6], dtype=np.float32)
-    a_dequant = lut[code_a] * np.repeat(np.exp2(scale_a.numpy().astype(np.int16)-127), 32, axis=1)
-    b_dequant = lut[code_b] * np.repeat(np.exp2(scale_b.numpy().astype(np.int16)-127), 32, axis=1)
-    ref = Tensor(a_dequant @ b_dequant.T, dtype=dtypes.bfloat16).realize().numpy()
-    np.testing.assert_array_equal(out.numpy(), ref)
+    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)
@@ -4,6 +4,7 @@ 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 ****

@@ -189,6 +190,12 @@ 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)
@@ -276,7 +283,7 @@ class TestCustomKernel(unittest.TestCase):

    GlobalCounters.reset()
    out.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 5)
+    assert_kernel_count(5)

  def test_simple_reshape(self):
    a = Tensor.ones(2,3,4).realize()
@@ -286,7 +293,7 @@ class TestCustomKernel(unittest.TestCase):
    GlobalCounters.reset()
    c.realize()
    assert all(i == 3. for i in c.flatten().tolist()), f"all 3 {c.tolist()}"
-    self.assertEqual(GlobalCounters.kernel_count, 3)
+    assert_kernel_count(2)

  def test_multi_after_schedule_order(self):
    """Test correct scheduling order when custom_kernel has multiple outputs.
@@ -336,7 +343,7 @@ class TestCustomKernel(unittest.TestCase):
    c = Tensor.custom_kernel(c, a, fxn=custom_add_one_kernel)[0]
    GlobalCounters.reset()
    c.realize()
-    self.assertEqual(GlobalCounters.kernel_count, len(devs))
+    assert_kernel_count(len(devs))
    self.assertTrue((c == 2).all().item())

  def test_partial_invalid_store_keeps_uncovered_reads(self):
@@ -401,13 +408,11 @@ class TestCustomKernel(unittest.TestCase):
    else: z = y.T.T+1
    GlobalCounters.reset()
    z.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_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
@@ -418,7 +423,8 @@ class TestCustomKernel(unittest.TestCase):
    GlobalCounters.reset()
    y = run(x[0]).realize()
    # it's copying the input and the output
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    # 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(y.tolist(), [1, 2, 3, 4])

  @Context(DEV="CPU")
@@ -428,11 +434,37 @@ class TestCustomKernel(unittest.TestCase):
    # 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) -> UOp:
+    def custom_src_kernel(A:UOp, B: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).T, fxn=custom_src_kernel)[0]
-    self.assertEqual(a.tolist(), [[1, 2], [1, 3]])
+    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]])
+
+  @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):
@@ -460,8 +492,8 @@ class TestCustomKernelInput(unittest.TestCase):

  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=3)
-  def test_shrink(self): self._test_mop(lambda x: x[:4], max_kernels=2)
+  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)
@@ -169,6 +169,13 @@ 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)
@@ -399,9 +399,10 @@ class TestDTypeALU(unittest.TestCase):
    if float_dtype not in supported_dtypes: float_dtype = dtypes.float32
    universal_test_cast(a, float_dtype, unsigned_dtype)

-  @unittest.expectedFailure
-  def test_unsafe_cast_float_to_int_failure(self):
-    val = float(dtypes.int32.max - 1)
+  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
    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())
@@ -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
+from test.helpers import assert_jit_cache_len, call_is_graph, not_support_multi_device, needs_second_gpu, KernelCountException
 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.
-    assert len(jf.captured.linear.src) == 2
+    if len(jf.captured.linear.src) != 2: raise KernelCountException(2, len(jf.captured.linear.src))
    for si in jf.captured.linear.src:
      assert call_is_graph(si)

@@ -12,7 +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
+from test.helpers import replace_opts, check_schedule
 from test.backend.test_softmax_fusion import single_kernel_softmax
 MOCKGPU = DEV.interface.startswith("MOCK")

@@ -293,8 +293,7 @@ class TestLinearizer(unittest.TestCase):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.shrink(((1, 2), None)).pad(((1, 2), None)).bool()
    a.assign(b.where(2, a))
-    linear, var_vals = a.linear_with_vars()
-    assert len(linear.src) == 1
+    linear, var_vals = check_schedule(a, 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)
@@ -5,9 +5,10 @@ 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
+from test.helpers import needs_second_gpu, assert_kernel_count
 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:
@@ -95,7 +96,7 @@ 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()
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_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")
@@ -161,5 +162,31 @@ 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()
@@ -6,7 +6,7 @@ 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
+from test.helpers import not_support_multi_device, needs_second_gpu, slow, call_is_graph, check_schedule, assert_kernel_count

 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
 settings.load_profile("my_profile")
@@ -62,7 +62,7 @@ class TestMultiTensor(unittest.TestCase):
  def test_shard_empty(self):
    GlobalCounters.reset()
    X = Tensor.empty(256).shard(devices_2, 0).realize()
-    assert GlobalCounters.kernel_count == 0
+    assert_kernel_count(0)
    (X + X).realize()

  # TODO: fix this to not copy on the src device
@@ -355,8 +355,7 @@ 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 = out.linear_with_vars()
-    self.assertEqual(len(linear.src), 0)
+    linear, var_vals = check_schedule(out, 0)
    run_linear(linear, var_vals)
    self.assertEqual(out.tolist(), [[2]*8]*16)

@@ -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

@@ -428,18 +428,14 @@ class TestNN(unittest.TestCase):
    a = Tensor([[1, 5, 9, 11],
                [12, 19, 8, 1]])
    result = layer(a)
-    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")
+    linear, var_vals = check_schedule(result, kcount)
    run_linear(linear, var_vals)

    b = Tensor([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])
    result = layer(b)
-    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")
+    linear, var_vals = check_schedule(result, 1)
    run_linear(linear, var_vals)
    print(f"Embedding used {GlobalCounters.global_ops} ops")
    self.assertLessEqual(GlobalCounters.global_ops, ops)
@@ -728,6 +728,17 @@ 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
@@ -1524,6 +1535,8 @@ 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))
@@ -8,6 +8,7 @@ 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
@@ -61,8 +62,7 @@ class TestCStyleFailures(unittest.TestCase):
    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 = ret.schedule_linear()
-    assert len(linear.src) == 1
+    linear, _ = check_schedule(ret, 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)

@@ -6,34 +6,13 @@ import unittest, time
 import numpy as np

 from tinygrad import nn, dtypes, Device, Tensor, Variable
-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
+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

 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()

@@ -113,11 +92,9 @@ class TestSchedule(unittest.TestCase):
    a2 = mop(a)
    expected = (a+a2).tolist()
    a.assign(a+a2)
-    linear, var_vals = a.linear_with_vars()
-    kcount = len(linear.src)
+    linear, var_vals = check_schedule(a, expected_kcount)
    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)

@@ -126,9 +103,9 @@ class TestSchedule(unittest.TestCase):
    a = Tensor.arange(16).clone().realize()
    GlobalCounters.reset()
    a[4] = 3
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    a.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_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):
@@ -4,6 +4,7 @@ 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
@@ -103,8 +104,7 @@ class TestFuse(unittest.TestCase):
    k = (x @ wk).contiguous()
    v = (x @ wv).contiguous()
    attn = q.scaled_dot_product_attention(k, v)
-    s = attn.schedule_linear()
-    self.assertEqual(len(s.src), 4) # 3 matmul and 1 attention
+    check_schedule(attn, 4) # 3 matmul and 1 attention

  @unittest.skip("needs RANGEIFY>1")
  def test_flash_attention(self):
@@ -6,6 +6,15 @@ 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)
@@ -2,7 +2,7 @@ import unittest
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.helpers import Context, getenv, DEV, OSX
-from test.backend.test_schedule import check_schedule
+from test.helpers import check_schedule
 from test.backend.test_dtype_alu import ht, dtypes_float
 import numpy as np
 import math
@@ -330,6 +330,7 @@ class TestHCQ(unittest.TestCase):
  # Test profile api
  def test_speed_exec_time(self):
    sig_st, sig_en = TestHCQ.d0.new_signal(), TestHCQ.d0.new_signal()
+    st = time.perf_counter()
    TestHCQ.d0.hw_compute_queue_t().timestamp(sig_st) \
                                   .exec(TestHCQ.runtime, TestHCQ.kernargs_ba_ptr, TestHCQ.prg.arg.global_size, TestHCQ.prg.arg.local_size) \
                                   .timestamp(sig_en) \
@@ -337,11 +338,13 @@ class TestHCQ(unittest.TestCase):

    TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
    TestHCQ.d0.timeline_value += 1
+    host_us = (time.perf_counter() - st) * 1e6

    et = float(sig_en.timestamp - sig_st.timestamp)

    print(f"exec kernel time: {et:.2f} us")
-    assert 0.1 <= et <= (3000000 if MOCKGPU or Device.DEFAULT in {"CPU"} else 100)
+    # emulated devices are only bounded by the host window around submit+wait
+    assert 0.1 <= et <= (host_us if MOCKGPU or Device.DEFAULT in {"CPU"} else 100)

  def test_speed_copy_bandwidth(self):
    if TestHCQ.d0.hw_copy_queue_t is None: self.skipTest("device does not support copy queue")
@@ -352,6 +355,7 @@ class TestHCQ(unittest.TestCase):
    b = Buffer(Device.DEFAULT, SZ, dtypes.uint8, options=BufferSpec(nolru=True)).allocate()

    sig_st, sig_en = TestHCQ.d0.new_signal(), TestHCQ.d0.new_signal()
+    st = time.perf_counter()
    TestHCQ.d0.hw_copy_queue_t().timestamp(sig_st) \
                                .copy(a._buf, b._buf, SZ) \
                                .timestamp(sig_en) \
@@ -359,13 +363,14 @@ class TestHCQ(unittest.TestCase):

    TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
    TestHCQ.d0.timeline_value += 1
+    host_ms = (time.perf_counter() - st) * 1e3

-    et = float(sig_en.timestamp - sig_st.timestamp)
-    et_ms = et / 1e3
+    et_ms = float(sig_en.timestamp - sig_st.timestamp) / 1e3
+    assert 0 < et_ms <= host_ms # timestamps are in us and cover only the copy

    gb_s = ((SZ / 1e9) / et_ms) * 1e3
    print(f"same device copy:  {et_ms:.2f} ms, {gb_s:.2f} GB/s")
-    assert (0.2 if MOCKGPU else 10) <= gb_s <= 1000
+    assert (0 if MOCKGPU else 10) <= gb_s <= 1000

  def test_speed_cross_device_copy_bandwidth(self):
    if TestHCQ.d0.hw_copy_queue_t is None: self.skipTest("device does not support copy queue")
@@ -379,6 +384,7 @@ class TestHCQ(unittest.TestCase):
    TestHCQ.d0.allocator._map(b._buf)

    sig_st, sig_en = TestHCQ.d0.new_signal(), TestHCQ.d0.new_signal()
+    st = time.perf_counter()
    TestHCQ.d0.hw_copy_queue_t().timestamp(sig_st) \
                                .copy(a._buf, b._buf, SZ) \
                                .timestamp(sig_en) \
@@ -386,13 +392,14 @@ class TestHCQ(unittest.TestCase):

    TestHCQ.d0.timeline_signal.wait(TestHCQ.d0.timeline_value)
    TestHCQ.d0.timeline_value += 1
+    host_ms = (time.perf_counter() - st) * 1e3

-    et = float(sig_en.timestamp - sig_st.timestamp)
-    et_ms = et / 1e3
+    et_ms = float(sig_en.timestamp - sig_st.timestamp) / 1e3
+    assert 0 < et_ms <= host_ms # timestamps are in us and cover only the copy

    gb_s = ((SZ / 1e9) / et_ms) * 1e3
    print(f"cross device copy: {et_ms:.2f} ms, {gb_s:.2f} GB/s")
-    assert (0.2 if MOCKGPU else 2) <= gb_s <= 100
+    assert (0 if MOCKGPU else 2) <= gb_s <= 100

  def test_timeline_signal_rollover(self):
    for queue_type in [TestHCQ.d0.hw_compute_queue_t, TestHCQ.d0.hw_copy_queue_t]:
@@ -8,10 +8,11 @@ from tinygrad.tensor import _to_np_dtype
 from tinygrad.codegen import to_program
 from tinygrad.dtype import DType, truncate
 from tinygrad.nn.state import get_parameters
-from tinygrad.helpers import T, Target, DEV
+from tinygrad.helpers import T, Target, DEV, DEBUG, Context, GlobalCounters
 from tinygrad.renderer import Renderer
 from tinygrad.codegen import full_rewrite_to_sink, line_rewrite, pm_linearize_cleanups
 from tinygrad.codegen.late.linearizer import linearize
+from tinygrad.engine.realize import compile_linear

 # decorator to skip slow tests by default, run with RUN_SLOW=1 to include them
 slow = unittest.skipUnless(os.getenv("RUN_SLOW"), "slow test, set RUN_SLOW=1 to run")
@@ -34,6 +35,36 @@ def derandomize_model(model):
    p.replace(Tensor.empty(p.shape, device=p.device, dtype=p.dtype))
    p.realize()

+class KernelCountException(Exception):
+  def __init__(self, expected:int, got:int):
+    self.expected, self.got = expected, got
+    super().__init__(f"expected {expected}, got {got}")
+
+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(allowed, kernel_cnt)
+  # test compiling the linear
+  compile_linear(linear)
+  return linear, var_vals
+
+def assert_kernel_count(expected:int):
+  got = GlobalCounters.kernel_count
+  if got != expected: raise KernelCountException(expected, got)
+
 def call_is_graph(call:UOp) -> bool:
  ast = call.src[0]
  return ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "graph"
@@ -53,15 +84,15 @@ def jit_cache_count(linear:UOp) -> int:
 def assert_jit_cache_len(fxn, expected_len):
  linear = fxn.captured.linear if fxn.captured is not None else None
  if linear is None or not linear.src:
-    assert expected_len == 0, expected_len
+    if expected_len != 0: raise KernelCountException(expected_len, 0)
    return
  if expected_len and all(call_is_hcq(call) for call in linear.src): expected_len = 3 # HCQ2: merged same-queue calls + finalizer + bumps
  if call_is_graph(linear.src[0]):
-    assert len(linear.src) == 1, len(linear.src)
+    if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
    inner = linear.src[0].src[0].src[0]  # LINEAR UOp inside CUSTOM_FUNCTION
-    assert len(inner.src) == expected_len, f"expected {expected_len}, got {len(inner.src)}"
+    if len(inner.src) != expected_len: raise KernelCountException(expected_len, len(inner.src))
  else:
-    assert len(linear.src) == expected_len, f"expected {expected_len}, got {len(linear.src)}"
+    if len(linear.src) != expected_len: raise KernelCountException(expected_len, len(linear.src))

 def min_normal(dt:DType) -> float: return 2.0 ** (2 - (1 << (dtypes.finfo(dt)[0] - 1)))

@@ -109,7 +109,7 @@ def _init_sqtt_encoder():
  _SMEM = (ir3.SMEM, ir4.SMEM, irc.SMEM)
  _VALU = (ir3.VOP1, ir3.VOP2, ir3.VOP3, ir3.VOP3P, ir3.VOPC, ir3.VOPD, ir3.VOP3SD, ir3.VOP3_SDST, ir3.VOP1_SDST,
           ir4.VOP1, ir4.VOP2, ir4.VOP3, ir4.VOP3P, ir4.VOPC, ir4.VOPD, ir4.VOP3SD, ir4.VOP3_SDST, ir4.VOP1_SDST,
-           irc.VOP1, irc.VOP2, irc.VOP3, irc.VOP3P, irc.VOPC, irc.VOP3SD, irc.VOP3_SDST)
+           irc.VOP1, irc.VOP2, irc.VOP3, irc.VOP3P, irc.VOP3PX2, irc.VOPC, irc.VOP3SD, irc.VOP3_SDST)
  _DS = (ir3.DS, ir4.DS, irc.DS)
  _GLOBAL = (ir3.GLOBAL, ir4.VGLOBAL, irc.GLOBAL)
  _FLAT = (ir3.FLAT, ir4.VFLAT, irc.FLAT)
@@ -1150,6 +1150,9 @@ def _compile_vopc(inst: ir3.VOPC|ir3.VOPC_DPP16|ir3.VOP3|ir4.VOPC|ir4.VOPC_DPP16
  def get_cmp_bit(lane) -> UOp:
    lc = lane.cast(dtypes.int) if isinstance(lane, UOp) else _c(lane, dtypes.int)
    s0 = _load_dpp16_src0(ctx, inst, lc, _c(0)) if is_dpp16 else ctx.rsrc_dyn(src0_off, lc, bits['s0'], literal, is_f64)
+    if is_vopc and not isinstance(inst, irc.VOPC) and bits['s0'] == 16 and not is_dpp16:
+      src0_hi = src0_off >= _c(384)
+      s0 = src0_hi.where(_hi16(ctx.rvgpr_dyn(src0_hi.where(src0_off - _c(384), _c(0)), lc)), s0)
    s1 = _cond_hi16(vsrc1_hi, ctx.rsrc_dyn(src1_off, lc, bits['s1'], literal, is_f64)) if bits['s0'] == 16 \
      else ctx.rsrc_dyn(src1_off, lc, bits['s1'], literal, is_f64)
    if bits['s0'] == 16 and opsel: s0, s1 = _apply_opsel(s0, 0, opsel), _apply_opsel(s1, 1, opsel)
@@ -1323,7 +1326,7 @@ def _compile_vop3sd(inst: ir3.VOP3SD | ir4.VOP3SD | irc.VOP3SD, ctx: _Ctx) -> UO
  else:
    return ctx.compile_vop_pcode(inst.op, srcs, lane, vdst_reg, exec_mask, sdst_reg=inst.sdst.offset)

-def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
+def _compile_mfma(inst: irc.VOP3P|irc.VOP3PX2, ctx: _Ctx) -> UOp:
  """CDNA MFMA matrix multiply-accumulate emulation.

  Uses local temp arrays to cache inputs, avoiding aliasing issues when vdst overlaps src0/src1.
@@ -1349,6 +1352,25 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
  src0_is_vgpr = src0_off >= _c(256)
  src1_is_vgpr = src1_off >= _c(256)

+  scaled = isinstance(inst, irc.VOP3PX2)
+  if scaled:
+    assert isinstance(inst, irc.VOP3PX2)
+    # F8F6F4 input formats: 0=FP8(E4M3), 1=BF8(E5M2). FP6/FP4 (2-4) not emulated.
+    src0_fmt, src1_fmt = int(inst.cbsz), int(inst.blgp)
+    if src0_fmt > 1 or src1_fmt > 1: raise RuntimeError(f"unsupported scaled MFMA formats cbsz={src0_fmt} blgp={src1_fmt}")
+    # scale_src0/scale_src1 are source operands pointing at 32-bit registers holding 4 packed E8M0 scale exponents.
+    # The 2-bit opsel/opsel_hi select which byte applies to A/B for this instruction.
+    scale0_off = ctx.inst_field(type(inst).scale_src0)
+    scale1_off = ctx.inst_field(type(inst).scale_src1)
+    sel0, sel1 = int(inst.opsel) & 3, int(inst.opsel_hi) & 3
+    def _scale_exp(off: UOp, sel: int, lane: UOp) -> UOp:
+      sv = ctx.rsrc_dyn(off, lane, 32)
+      byte = (sv >> UOp.const(sel * 8, dtypes.uint32)) & UOp.const(0xFF, dtypes.uint32)
+      return byte.cast(dtypes.int32) - UOp.const(127, dtypes.int32)
+    # combined A*B scale for this lane: 2^(ea-127) * 2^(eb-127)
+    def scale_factor(lane: UOp) -> UOp:
+      return UOp.exp2((_scale_exp(scale0_off, sel0, lane) + _scale_exp(scale1_off, sel1, lane)).cast(dtypes.float32))
+
  m = _re.search(r'(\d+)X(\d+)X(\d+)', op_name)
  if m is None: raise ValueError(f"could not parse MFMA dimensions from {op_name}")
  M, N, K = int(m.group(1)), int(m.group(2)), int(m.group(3))
@@ -1404,7 +1426,18 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
  # The optimizer folds bitcast(uint32→float32) stores to float32 arrays, losing the conversion.
  tmp = UOp.placeholder((n_a_elems + n_b_elems,), dtypes.uint32, slot=0, addrspace=AddrSpace.LOCAL)

-  def cvt_elem(raw: UOp, sub_idx: int) -> UOp:
+  # Per-operand fp8 format ("fp8"=E4M3, "bf8"=E5M2) for A and B
+  if 'F8F6F4' in op_name:
+    assert isinstance(inst, (irc.VOP3P_MFMA, irc.VOP3PX2))
+    _fmts = {0: "fp8", 1: "bf8"}
+    a_fmt, b_fmt = _fmts.get(int(inst.cbsz), "fp8"), _fmts.get(int(inst.blgp), "fp8")
+  elif is_fp8:
+    # A/B formats from name suffix, e.g. V_MFMA_F32_16X16X32_BF8_FP8
+    suffixes = op_name.rsplit('_', 2)[-2:]
+    a_fmt, b_fmt = ("bf8" if sfx == "BF8" else "fp8" for sfx in suffixes)
+  else: a_fmt = b_fmt = "fp8"
+
+  def cvt_elem(raw: UOp, sub_idx: int, fp8_fmt: str = "fp8") -> UOp:
    if is_i8:
      # Extract i8, sign-extend to i32
      byte_val = (raw >> UOp.const(sub_idx * 8, dtypes.uint32)) & UOp.const(0xFF, dtypes.uint32)
@@ -1412,7 +1445,7 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
    elif is_f32_src:
      return raw  # already uint32 (f32 bit pattern)
    elif is_fp8:
-      return ((raw >> UOp.const(sub_idx * 8, dtypes.uint32)) & UOp.const(0xFF, dtypes.uint32)).cast(dtypes.uint32)
+      return _FUNCS[f"{fp8_fmt}_to_f32"](raw >> UOp.const(sub_idx * 8, dtypes.uint32)).bitcast(dtypes.uint32)
    elif is_bf16:
      # bf16→f32 bits: just shift left by 16 (bf16 is upper 16 bits of f32)
      return ((raw >> UOp.const(sub_idx * 16, dtypes.uint32)) & UOp.const(0xFFFF, dtypes.uint32)) << UOp.const(16, dtypes.uint32)
@@ -1454,7 +1487,7 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
    # Read A/B sources. Use rsrc_dyn for inline constants/SGPRs (src_off < 256), rvgpr_dyn for VGPRs (src_off >= 256).
    a_raw = src0_is_vgpr.where(ctx.rvgpr_dyn(src0_r + _c(reg_idx), read_lane),
                                ctx.rsrc_dyn(src0_off, _c(0, dtypes.int), 32))
-    a_val = cvt_elem(a_raw, sub_idx)
+    a_val = cvt_elem(a_raw, sub_idx, a_fmt)
    if M == 4:
      a_idx = grp_idx * UOp.const(M * K, dtypes.int) + mn_idx * UOp.const(K, dtypes.int) + UOp.const(kl, dtypes.int)
    else:
@@ -1463,7 +1496,7 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:

    b_raw = src1_is_vgpr.where(ctx.rvgpr_dyn(src1_r + _c(reg_idx), read_lane),
                                ctx.rsrc_dyn(src1_off, _c(0, dtypes.int), 32))
-    b_val = cvt_elem(b_raw, sub_idx)
+    b_val = cvt_elem(b_raw, sub_idx, b_fmt)
    if M == 4:
      b_idx = b_off + grp_idx * UOp.const(N * K, dtypes.int) + mn_idx * UOp.const(K, dtypes.int) + UOp.const(kl, dtypes.int)
    else:
@@ -1480,6 +1513,17 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
  #   Actually: 16 ACCVGPRs per lane, organized as 4 groups (l//32 gives half, each half has 2 sub-groups) of 4 rows
  tmp2 = tmp.after(read_phase)

+  def _dot_accum(acc: UOp, a_row: UOp, b_row: UOp, lane: UOp) -> UOp:
+    """acc += sum_k A[a_row+k] * B[b_row+k]. For scaled MFMA, only the dot product is scaled: D = dot*scale + C."""
+    def prod(k: int) -> UOp:
+      return tmp2.index(a_row + UOp.const(k, dtypes.int)).bitcast(acc_dt) * tmp2.index(b_row + UOp.const(k, dtypes.int)).bitcast(acc_dt)
+    if not scaled:
+      for k in range(K): acc = acc + prod(k)
+      return acc
+    dot = prod(0)
+    for k in range(1, K): dot = dot + prod(k)
+    return acc + dot * scale_factor(lane)
+
  compute_lane = ctx.range()
  compute_stores = []

@@ -1510,10 +1554,7 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
      else: acc_v = acc_v.bitcast(dtypes.float32)
      acc = src2_is_vgpr.where(acc_v, acc_scalar)

-      for k in range(K):
-        a_val = tmp2.index(m_base * UOp.const(K, dtypes.int) + UOp.const(k, dtypes.int)).bitcast(acc_dt)
-        b_val = tmp2.index(b_off + n_idx * UOp.const(K, dtypes.int) + UOp.const(k, dtypes.int)).bitcast(acc_dt)
-        acc = acc + a_val * b_val
+      acc = _dot_accum(acc, m_base * UOp.const(K, dtypes.int), b_off + n_idx * UOp.const(K, dtypes.int), compute_lane)

      if is_int_out:
        compute_stores.append((ctx.waccvgpr_dyn if use_acc else ctx.wvgpr_dyn)(
@@ -1535,17 +1576,13 @@ def _compile_mfma(inst: irc.VOP3P, ctx: _Ctx) -> UOp:
      if M == 4:
        # 4x4: each group is independent. A/B indexed per-group.
        m_base = c_grp * UOp.const(M * K, dtypes.int) + UOp.const(out_reg * K, dtypes.int)
-        for k in range(K):
-          a_val = tmp2.index(m_base + UOp.const(k, dtypes.int)).bitcast(acc_dt)
-          b_val = tmp2.index(b_off + c_grp * UOp.const(N*K, dtypes.int) + n_idx * UOp.const(K, dtypes.int)+UOp.const(k, dtypes.int)).bitcast(acc_dt)
-          acc = acc + a_val * b_val
+        b_base = b_off + c_grp * UOp.const(N * K, dtypes.int) + n_idx * UOp.const(K, dtypes.int)
      else:
        # 16x16: K is split across groups. Shared MxK/NxK arrays.
        m_base = c_grp * UOp.const(out_per_lane, dtypes.int) + UOp.const(out_reg, dtypes.int)
-        for k in range(K):
-          a_val = tmp2.index(m_base * UOp.const(K, dtypes.int) + UOp.const(k, dtypes.int)).bitcast(acc_dt)
-          b_val = tmp2.index(b_off + n_idx * UOp.const(K, dtypes.int) + UOp.const(k, dtypes.int)).bitcast(acc_dt)
-          acc = acc + a_val * b_val
+        b_base = b_off + n_idx * UOp.const(K, dtypes.int)
+
+      acc = _dot_accum(acc, m_base if M == 4 else m_base * UOp.const(K, dtypes.int), b_base, compute_lane)

      if is_int_out:
        compute_stores.append((ctx.waccvgpr_dyn if use_acc else ctx.wvgpr_dyn)(
@@ -1563,33 +1600,41 @@ def _compile_wmma(inst: ir3.VOP3P | ir4.VOP3P | irc.VOP3P, ctx: _Ctx) -> UOp:
  vdst_reg = ctx.inst_field(type(inst).vdst)
  src0_r = ctx.inst_field(type(inst).src0) - _c(256)
  src1_r = ctx.inst_field(type(inst).src1) - _c(256)
-  src2_r = ctx.inst_field(type(inst).src2) - _c(256)
-  is_f16_output = 'F16_16X16X16_F16' in op_name or 'BF16_16X16X16_BF16' in op_name  # F16/BF16 output vs F32 output
+  src2_r = ctx.inst_field(type(inst).src2)
+  src2_r = (src2_r >= 256).where(src2_r - _c(256), src2_r)
+  output_type = op_name.split("WMMA_", 1)[1].split("_", 1)[0]
  is_bf16 = 'BF16' in op_name
  cvt = _FUNCS['bf16_to_f32'] if is_bf16 else _FUNCS['f16_to_f32']
  is_rdna4 = isinstance(inst, ir4.VOP3P)
-  # read 16x16 F16/BF16 matrix from VGPRs → flat f32 array[row*16+k]
-  def read_f16_val(src, lane, vgpr, half):
+  sz = 8 if "8" in op_name else 16
+  # read matrix from VGPRs → flat f32/i32 array[row*16+k]
+  def gval(src, lane, vgpr, ridx):
    v = ctx.rvgpr_dyn(src + _c(vgpr), UOp.const(lane, dtypes.int))
-    return cvt((v >> UOp.const(16, dtypes.uint32)) if half else (v & UOp.const(0xFFFF, dtypes.uint32)))
+    pkd = v >> UOp.const(ridx * sz, dtypes.uint32) if ridx > 0 else v
+    pkd = pkd & UOp.const((1 << sz) - 1, dtypes.uint32)
+    if "F" in output_type: return cvt(pkd)
+    return (pkd << _c(24, dtypes.uint)).bitcast(dtypes.int32) >> _c(24, dtypes.int32) # sign extend

-  # RDNA3: 16 lanes × 8 VGPRs × 2 halves, k maps linearly
-  # RDNA4: 32 lanes × 4 VGPRs × 2 halves, k bits are scrambled (k[2] goes to lane bit 4)
-  def read_f16_mat(src):
-  # (row, k) → (lane, vgpr, half)
+  # RDNA3 f16/bf16: 16 lanes × 8 VGPRs × 2 halves,    k maps linearly
+  # RDNA3 iu8:      16 lanes × 4 VGPRs × 4 quarters,  k maps linearly
+  # RDNA4:          32 lanes x 4 VGPRS x 2 halves, k bits are scrambled (k[2] goes to lane bit 4)
+  def read_mat(src):
+    n = 32 // sz # values per vgpr
+    # (row, k) → (lane, vgpr, row index)
    def ab_map(i, k):
      elem, lane = ((k & 3) | ((k >> 1) & 4), i + ((k >> 2) & 1) * 16) if is_rdna4 else (k, i)
-      return lane, elem // 2, elem % 2
-    return [read_f16_val(src, *ab_map(row, k)) for row in range(16) for k in range(16)]
-  mat_a, mat_b = read_f16_mat(src0_r), read_f16_mat(src1_r)
+      return lane, elem // n, elem % n
+    return [gval(src, *ab_map(row, k)) for row in range(16) for k in range(16)]
+
+  mat_a, mat_b = read_mat(src0_r), read_mat(src1_r)
  # (row, col) -> (lane, vgpr)
  def d_map(m, n):
    lane_bit, vgpr = (m >> 3, m & 7) if is_rdna4 else (m & 1, m >> 1)
    return n + lane_bit * 16, vgpr
-  if is_f16_output:
+  if output_type in ["F16", "BF16"]:
    # read accumulator C with f16 layout: for RDNA4, pairs of f32 vgprs pack into one f16 vgpr
    # for RDNA3, same layout as f32 but only lo 16 bits used
-    mat_c = [read_f16_val(src2_r, *((lane, vgpr // 2, vgpr % 2) if is_rdna4 else (lane, vgpr, 0)))
+    mat_c = [gval(src2_r, *((lane, vgpr // 2, vgpr % 2) if is_rdna4 else (lane, vgpr, 0)))
             for m in range(16) for n in range(16) for lane, vgpr in [d_map(m, n)]]
    mat_d = [sum(mat_a[r*16+k] * mat_b[c*16+k] for k in range(16)) + mat_c[r*16+c] for r in range(16) for c in range(16)]
    def f32_to_f16_bits(v: UOp) -> UOp: return v.cast(dtypes.half).bitcast(dtypes.uint16).cast(dtypes.uint32)
@@ -1602,18 +1647,22 @@ def _compile_wmma(inst: ir3.VOP3P | ir4.VOP3P | irc.VOP3P, ctx: _Ctx) -> UOp:
    else:  # (rdna3) 1 f16 per VGPR (lo half only)
      stores = [ctx.wvgpr_dyn(vdst_reg + _c(d_map(m, n)[1]), UOp.const(d_map(m, n)[0], dtypes.int), out_cvt(mat_d[m*16+n]), exec_mask)
                for m in range(16) for n in range(16)]
-  else: # f32
-    mat_c = [ctx.rvgpr_dyn(src2_r + _c(d_map(m, n)[1]), UOp.const(d_map(m, n)[0], dtypes.int)).bitcast(dtypes.float32)
+  else: # f32/i32
+    out_dt = dtypes.float32 if output_type == "F32" else dtypes.int32
+    mat_c = [ctx.rvgpr_dyn(src2_r + _c(d_map(m, n)[1]), UOp.const(d_map(m, n)[0], dtypes.int)).bitcast(out_dt)
             for m in range(16) for n in range(16)]
    mat_d = [sum(mat_a[r*16+k] * mat_b[c*16+k] for k in range(16)) + mat_c[r*16+c] for r in range(16) for c in range(16)]
    stores = [ctx.wvgpr_dyn(vdst_reg + _c(d_map(m, n)[1]), UOp.const(d_map(m, n)[0], dtypes.int), mat_d[m*16+n].bitcast(dtypes.uint32), exec_mask)
              for m in range(16) for n in range(16)]
  return UOp.sink(*stores, *ctx.inc_pc())

-def _compile_vop3p(inst: ir3.VOP3P | ir4.VOP3P | irc.VOP3P, ctx: _Ctx) -> UOp:
+def _compile_vop3p(inst: ir3.VOP3P | ir4.VOP3P | irc.VOP3P | irc.VOP3PX2, ctx: _Ctx) -> UOp:
  op_name = _op_name(inst)
-  if 'WMMA' in op_name and ('16X16X16_F16' in op_name or '16X16X16_BF16' in op_name): return _compile_wmma(inst, ctx)
-  if 'MFMA' in op_name and any(f'{s}X{s}X' in op_name for s in ('4', '16', '32')) and isinstance(inst, irc.VOP3P): return _compile_mfma(inst, ctx)
+  if 'WMMA' in op_name:
+    assert not isinstance(inst, irc.VOP3PX2)
+    return _compile_wmma(inst, ctx)
+  if 'MFMA' in op_name and any(f'{s}X{s}X' in op_name for s in ('4', '16', '32')) and isinstance(inst, (irc.VOP3P, irc.VOP3PX2)):
+    return _compile_mfma(inst, ctx)

  # ACCVGPR_WRITE/READ/MOV: copies between VGPR and ACCVGPR register files
  # Detect by checking operand types for ACCVGPR involvement
@@ -2044,7 +2093,7 @@ _INST_HANDLERS: dict[type, Callable[..., UOp]] = {
  irc.SOPP: _compile_sopp, irc.SMEM: _compile_smem, irc.SOP1: _compile_sop, irc.SOP2: _compile_sop, irc.SOPC: _compile_sop, irc.SOPK: _compile_sop,
  irc.VOP1: _compile_vop12, irc.VOP1_DPP16: _compile_vop12, irc.VOP2: _compile_vop12, irc.VOP2_DPP16: _compile_vop12,
  irc.VOPC: _compile_vopc, irc.VOP3: _compile_vop3,
-  irc.VOP3_SDST: _compile_vop3, irc.VOP3SD: _compile_vop3sd, irc.VOP3P: _compile_vop3p,
+  irc.VOP3_SDST: _compile_vop3, irc.VOP3SD: _compile_vop3sd, irc.VOP3P: _compile_vop3p, irc.VOP3PX2: _compile_vop3p,
  irc.VOP1_SDWA: _compile_sdwa, irc.VOP2_SDWA: _compile_sdwa, irc.VOP2_SDWA_SDST: _compile_sdwa, irc.VOPC_SDWA_SDST: _compile_sdwa,
  irc.DS: _compile_mem_op, irc.FLAT: _compile_mem_op, irc.GLOBAL: _compile_mem_op, irc.SCRATCH: _compile_mem_op,
  irc.MUBUF: _compile_mubuf,
@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor, dtypes, TinyJit, UOp
 from tinygrad.llm.model import apply_rope as apply_rope_new, precompute_freqs_cis
-from test.helpers import assert_jit_cache_len
+from test.helpers import assert_jit_cache_len, check_schedule

 def apply_rope(x:Tensor, start_pos:int):
  B, H, T, Hd = x.shape
@@ -16,9 +16,8 @@ class TestAttention(unittest.TestCase):
    k = Tensor.ones(BS, seqlen, dim, dtype=dtypes.half).contiguous().realize()
    v = Tensor.ones(BS, seqlen, dim, dtype=dtypes.half).contiguous().realize()
    attn = q.scaled_dot_product_attention(k, v)
-    sched = attn.schedule_linear()
    # attention has 4 kernels now
-    self.assertEqual(len(sched.src), 4)
+    check_schedule(attn, 4)

  def test_apply_rope_jit_prune(self):
    def rope_fn(x_in, pos): return apply_rope(x_in, pos)
@@ -1,6 +1,6 @@
 import unittest, itertools, math
 from tinygrad import Tensor, dtypes, Context
-from tinygrad.dtype import DType, ConstType
+from tinygrad.dtype import DType, ConstType, truncate
 from tinygrad.uop.ops import Ops, UOp
 from test.helpers import full_rewrite
 import numpy as np
@@ -51,6 +51,17 @@ class TestWeakConstFolding(unittest.TestCase):
  def test_invalid_poison(self):
    self.assertTrue(UOp.invalid().alu(Ops.CDIV, UOp.const(0)).simplify().is_invalid)

+  def test_cast_commits_to_dtype_grid(self):
+    # committing a weak const to a stated width puts the value on that width's grid, same as storage packing and native compilers
+    v = 1/123008  # not representable in float16
+    out = UOp.const(v).cast(dtypes.half).simplify()
+    self.assertEqual((out.op, out.dtype, out.val), (Ops.CONST, dtypes.half, truncate[dtypes.half](v)))
+    self.assertNotEqual(out.val, v)
+    # the grid commit preserves the sign of zero
+    self.assertEqual(math.copysign(1, UOp.const(-0.0).cast(dtypes.half).simplify().val), -1)
+    # observable at tensor level: the const-folded comparison agrees with the committed value
+    self.assertTrue((Tensor(-3.2).cast(dtypes.float32) <= truncate[dtypes.float32](-3.2)).item())
+
 class TestBinaryOpsConstFolding(unittest.TestCase):
  def test_add_literal_zero(self):
    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) + 0)
@@ -208,7 +208,7 @@ class TestGEPAndVectorizeRewrite(unittest.TestCase):


 import inspect
-from tinygrad.uop.ops import graph_rewrite, _substitute, track_rewrites
+from tinygrad.uop.ops import graph_rewrite, _substitute, rewrite_group
 from tinygrad.uop.symbolic import symbolic_simple

 class TestBottomUpRewrite(unittest.TestCase):
@@ -220,7 +220,7 @@ class TestBottomUpRewrite(unittest.TestCase):
    self.assertIs(gt, ret)

 # normally .substitute would be fine, but it's not tracked
-@track_rewrites()
+@rewrite_group()
 def named_substitute(name:str, uop:UOp, rel:dict[UOp, UOp]): return graph_rewrite(uop, _substitute, rel, bottom_up=True)
 def substitute(uop:UOp, rel:dict[UOp, UOp]): return named_substitute(inspect.stack()[1].function, uop, rel)

@@ -307,8 +307,8 @@ class TestRecurse(unittest.TestCase):
  def test_inf_loop(self):
    a = UOp.const(3)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype)),
+      (UPat(Ops.CONST, arg=4, name="x"), lambda x: UOp.const(3, x.dtype)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm)
@@ -316,8 +316,8 @@ class TestRecurse(unittest.TestCase):
  def test_inf_loop_bottom_up(self):
    a = UOp.const(3)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype)),
+      (UPat(Ops.CONST, arg=4, name="x"), lambda x: UOp.const(3, x.dtype)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm, bottom_up=True)
@@ -378,8 +378,8 @@ class TestWalkRewrite(unittest.TestCase):
    """A bouncing pattern applies once and stops instead of looping."""
    a = UOp.const(3)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype)),
+      (UPat(Ops.CONST, arg=4, name="x"), lambda x: UOp.const(3, x.dtype)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm, bottom_up=True)
@@ -456,8 +456,8 @@ class TestWalkRewrite(unittest.TestCase):
    """Bottom-up walk also applies once per node, no fixed-point iteration."""
    a = UOp.const(3)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype)),
+      (UPat(Ops.CONST, arg=4, name="x"), lambda x: UOp.const(3, x.dtype)),
    ])
    ret = graph_rewrite(a, pm, bottom_up=True, walk=True)
    self.assertIs(ret, UOp.const(4))
@@ -511,7 +511,7 @@ class TestWalkRewrite(unittest.TestCase):
    def bpm_match(ctx, x):
      ctx.append((x.val if x.op is Ops.CONST else x.op, "bpm"))
      # rewrite const(1) -> const(10), short-circuiting its subtree
-      if x.op is Ops.CONST and x.val == 1: return x.replace(arg=10)
+      if x.op is Ops.CONST and x.val == 1: return UOp.const(10, x.dtype)
      return None
    def pm_match(ctx, x):
      ctx.append((x.val if x.op is Ops.CONST else x.op, "pm"))
@@ -0,0 +1,30 @@
+import unittest
+from tinygrad import Tensor, dtypes, nn
+from tinygrad.llm.kimi import _shard_kimi
+from tinygrad.llm.model import SSMConfig, Transformer, TransformerConfig
+
+class TestKimiTP4(unittest.TestCase):
+  def test_prefill_and_decode_graph(self):
+    devices = ("NULL:0", "NULL:1", "NULL:2", "NULL:3")
+    config = TransformerConfig(num_blocks=2, dim=32, hidden_dim=128, n_heads=4, n_kv_heads=1, norm_eps=1e-5,
+      vocab_size=64, head_dim=12, rope_theta=10000, rope_dim=4, v_head_dim=8, max_context=4, kv_lora_rank=16,
+      num_experts=8, num_experts_per_tok=2, norm_topk_prob=True, shared_expert_dim=32, ssm_layers=(True, False),
+      ssm=SSMConfig(4, 8, 4, 4, 32, True), shared_expert_gate=False, leading_dense_blocks=1, dense_hidden_dim=64,
+      routed_scaling_factor=2.446, expert_bias=True, expert_mxfp4=True, bf16_activations=True, kda_split_qkv=True)
+    model = Transformer(config)
+    for name, value in nn.state.get_state_dict(model).items():
+      fill = 127 if name.endswith("weight_scale") else 0
+      value.replace(Tensor.full(value.shape, fill, dtype=value.dtype if value.dtype is dtypes.uint8 else dtypes.bfloat16, device="NULL"))
+    _shard_kimi(model, devices)
+
+    temperature = Tensor([0.0], device=devices)
+    prefill = model(Tensor([[1, 2]], dtype=dtypes.int32, device=devices), 0, temperature).realize()
+    model(Tensor([[1, 2]], dtype=dtypes.int32, device=devices), 0, temperature).realize()  # replay prefill JIT
+    decode = model(Tensor([[3]], dtype=dtypes.int32, device=devices), 2, temperature).realize()
+    model(Tensor([[4]], dtype=dtypes.int32, device=devices), 3, temperature).realize()  # replay decode JIT
+    self.assertEqual(prefill.shape, (1, 1))
+    self.assertEqual(decode.shape, (1, 1))
+    self.assertEqual(model.blk[0].recurrent_state.uop.axis, 1)
+    self.assertEqual(model.blk[1].cache_k.dtype, dtypes.bfloat16)
+
+if __name__ == "__main__": unittest.main()
@@ -0,0 +1,29 @@
+import unittest
+from tinygrad import Tensor, dtypes, nn
+from tinygrad.llm.kimi_k3 import _shard_kimi_k3
+from test.unit.test_llm_k3 import small_k3_config
+from tinygrad.llm.model import Transformer
+
+class TestKimiK3TP8(unittest.TestCase):
+  @staticmethod
+  def _model():
+    model = Transformer(small_k3_config())
+    for name,value in nn.state.get_state_dict(model).items():
+      fill = 127 if name.endswith("weight_scale") else 0
+      dtype = value.dtype if value.dtype is dtypes.uint8 else dtypes.bfloat16
+      value.replace(Tensor.full(value.shape, fill, dtype=dtype, device="NULL"))
+    _shard_kimi_k3(model, tuple(f"NULL:{i}" for i in range(8)))
+    return model
+
+  def test_prefill_decode_and_jit_replay(self):
+    devices = tuple(f"NULL:{i}" for i in range(8))
+    model = self._model()
+    temperature = Tensor([0.0], device=devices)
+    self.assertEqual(model(Tensor([[1, 2]], dtype=dtypes.int32, device=devices), 0, temperature).realize().shape, (1, 1))
+    model(Tensor([[1, 2]], dtype=dtypes.int32, device=devices), 0, temperature).realize()
+    self.assertEqual(model(Tensor([[3]], dtype=dtypes.int32, device=devices), 2, temperature).realize().shape, (1, 1))
+    model(Tensor([[4]], dtype=dtypes.int32, device=devices), 3, temperature).realize()
+    self.assertEqual(model.blk[0].recurrent_state.uop.axis, 1)
+    self.assertEqual(model.blk[1].cache_k.dtype, dtypes.bfloat16)
+
+if __name__ == "__main__": unittest.main()
@@ -109,6 +109,28 @@ class TestLLMServer(unittest.TestCase):

    self.assertGreater(len(contents), 0)

+  def test_interrupted_stream_logs_tokens(self):
+    with patch.object(self.mock_model, "generate", side_effect=lambda ids, **kwargs: iter([300, 301, 999])), \
+         patch("tinygrad.llm.serve.stderr_log") as log, patch("tinygrad.llm.serve.colored", side_effect=lambda text, color: text) as color:
+      stream = self.server.RequestHandlerClass.run_model(Mock(server=self.server), [200, 201, 202], "test")
+      next(stream)
+      next(stream)
+      stream.close()
+    interrupt = log.call_args.args[0]
+    self.assertFalse(interrupt.startswith("\n"))
+    self.assertTrue(interrupt.endswith("\n"))
+    self.assertIn("gen:", interrupt)
+    self.assertIn("out:    1", interrupt)
+    self.assertTrue(any(args[0].startswith("total:") and args[1] == "red" for args, _ in color.call_args_list))
+
+  def test_stream_disconnect_closes_source(self):
+    from tinygrad.llm.serve import Handler
+    source, handler = Mock(), Mock()
+    source.__iter__ = Mock(return_value=iter([{}]))
+    handler.wfile.write.side_effect = BrokenPipeError
+    Handler.stream_json(handler, source)
+    source.close.assert_called_once()
+
  def test_non_streaming(self):
    resp = self.client.chat.completions.create(
      model="test-model",
@@ -5,7 +5,7 @@ from tinygrad.nn.state import get_parameters
 from tinygrad.engine.jit import TinyJit
 from tinygrad import Tensor, Device, GlobalCounters, dtypes, Variable
 from tinygrad.helpers import Context
-from test.helpers import slow, jit_cache_count
+from test.helpers import slow, jit_cache_count, KernelCountException
 from extra.lr_scheduler import OneCycleLR
 from test.helpers import derandomize_model

@@ -35,8 +35,9 @@ def helper_test(nm, gen, model, max_memory_allowed, max_kernels_allowed, all_jit
    assert mem_used < max_memory_allowed, f"{nm} used more than {max_memory_allowed:.3f} GB - {mem_used:.3} GB used"
    assert (max_memory_allowed - mem_used) / max_memory_allowed < 0.2, f"{max_memory_allowed:.3f} GB is too far from {mem_used:.3} GB used"
    if kernels_used:
-      assert kernels_used <= max_kernels_allowed, f"{nm} used more than {max_kernels_allowed} kernels, it used {kernels_used}"
-      assert (max_kernels_allowed - kernels_used) / max_kernels_allowed < 0.2, f"{max_kernels_allowed=} is too far from {kernels_used=} used"
+      if kernels_used > max_kernels_allowed: raise KernelCountException(max_kernels_allowed, kernels_used)
+      if (max_kernels_allowed - kernels_used) / max_kernels_allowed >= 0.2:
+        raise KernelCountException(max_kernels_allowed, kernels_used)
    if all_jitted:
      assert kernels_used > 0 and kernels_used == GlobalCounters.kernel_count or (kernels_used <= GlobalCounters.kernel_count and getattr(Device[Device.DEFAULT], "graph", None)), f"only {kernels_used} out of {GlobalCounters.kernel_count} were jitted"  # noqa: E501

@@ -2,32 +2,11 @@
 import gc, unittest, time
 from typing import cast
 from tinygrad import nn, dtypes, Device, Tensor, getenv
-from tinygrad.uop.ops import UOp, Ops, GroupOp, UPat, KernelInfo
-from tinygrad.helpers import DEBUG, GlobalCounters, Context
-from tinygrad.engine.realize import compile_linear, run_linear
-from tinygrad.codegen import to_program
-
-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
+from tinygrad.uop.ops import UOp, Ops, GroupOp, UPat, KernelInfo, AxisType
+from tinygrad.helpers import GlobalCounters, Context
+from tinygrad.engine.realize import run_linear, compile_linear
+from tinygrad.codegen import to_program, full_rewrite_to_sink
+from test.helpers import check_schedule, assert_kernel_count, KernelCountException

 def _realize_weights(m):
  for p in nn.state.get_parameters(m): p.realize()
@@ -143,7 +122,7 @@ class TestSimpleSchedule(unittest.TestCase):
    a = Tensor.empty(16,16).sum(axis=1)
    a1 = a.reshape(4,4)
    a2 = a.reshape(16,1,1)
-    self.assertEqual(len(Tensor.schedule_linear(a1, a2).src), 1)
+    check_schedule([a1, a2], 1)

 class TestSchedule(unittest.TestCase):
  def setUp(self):
@@ -155,8 +134,7 @@ class TestSchedule(unittest.TestCase):
  def test_arange_avgpool2d(self, kcount=1):
    x = Tensor.arange(25).reshape(1,1,5,5).cast(dtypes.float32)
    t = x.avg_pool2d(padding=1).clone()
-    linear, var_vals = t.linear_with_vars()
-    self.assertEqual(len(linear.src), kcount)
+    check_schedule(t, kcount)

  def test_arange_avgpool2d_fused_noopt(self):
    with Context(NOOPT=1): self.test_arange_avgpool2d(kcount=1)
@@ -224,7 +202,7 @@ class TestSchedule(unittest.TestCase):
    GlobalCounters.reset()
    expr = (a/b)/c
    expr.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertLessEqual(GlobalCounters.global_ops, 4*3)

  # NOTE: this is causing "LAZYCACHE=1 incorrectly reuses contiguous const" #4562
@@ -357,6 +335,11 @@ class TestSchedule(unittest.TestCase):
    out1 = a.sum() + b
    check_schedule([out0, out1], 2)

+  def test_reduce_broadcast_not_recomputed(self):
+    a = Tensor.empty(32, 16).realize()
+    out = a-a.mean(axis=0, keepdim=True)
+    check_schedule(out, 2)
+
  def test_scaled_dot_product_attention_multireduce_fusion(self):
    q = Tensor.empty(32,8,16,8).realize()
    k = Tensor.empty(32,8,16,8).realize()
@@ -609,9 +592,7 @@ class TestSchedule(unittest.TestCase):
    img = Tensor.randn(BS, CIN, 64, 64).realize()
    w = Tensor.uniform(16, CIN, 3, 3).realize()
    ret = Tensor.conv2d(img, w).relu().mean().backward()
-    linear, var_vals = Tensor.linear_with_vars(ret, img.grad, w.grad)
-    cnt = len([call for call in linear.src if call.src[0].op is Ops.SINK])
-    assert cnt == allowed, f"expected {allowed} kernels, got {cnt}"
+    check_schedule([ret, img.grad, w.grad], allowed)

  def test_conv2d_half(self): self.test_conv2d(4, dtype=dtypes.half)

@@ -632,7 +613,8 @@ class TestSchedule(unittest.TestCase):
        return len([call for call in linear.src if call.src[0].op is Ops.PROGRAM])

      with Context(IMAGE=1):
-        self.assertEqual(cnt(), 5)
+        got = cnt()
+        if got != 5: raise KernelCountException(5, got)

  def test_image_f16_residual_fusion(self):
    with Context(FLOAT16=1, OPENPILOT_HACKS=1):
@@ -647,7 +629,8 @@ class TestSchedule(unittest.TestCase):
        return len([call for call in linear.src if call.src[0].op is Ops.PROGRAM])

      with Context(IMAGE=1):
-        self.assertEqual(cnt(), 9)
+        got = cnt()
+        if got != 9: raise KernelCountException(9, got)

  def _test_fusion(self, shapes, f, cnt):
    with Context(DEBUG=0, TRACK_MATCH_STATS=0):
@@ -714,6 +697,19 @@ class TestSchedule(unittest.TestCase):
    xt = X[[Tensor([2]), Tensor([1])]]
    check_schedule(xt, 1)

+  def test_split_advanced_indexing_not_recomputed(self):
+    with Context(SPLIT_REDUCEOP=1):
+      X = Tensor.empty(32768, 4).realize()
+      idx = Tensor.randint(4, high=X.shape[0])
+      linear, _ = check_schedule(X[idx], 3, [Tensor._device_rng_counters[idx.device]])
+      # The split's final reduction remains, but the one-hot gather should collapse into a direct indexed load.
+      reduce_kernels = 0
+      for call in linear.src:
+        if call.src[0].op is not Ops.SINK: continue
+        sink = full_rewrite_to_sink(call.src[0], Device[call.device].renderer)
+        reduce_kernels += any(u.op is Ops.RANGE and u.arg[-1] is AxisType.REDUCE for u in sink.toposort())
+      self.assertEqual(reduce_kernels, 1)
+
  def test_push_through_reshape(self):
    x = Tensor.empty(10, 20).realize()
    out = x.argmax(1)
@@ -862,6 +858,65 @@ class TestSchedule(unittest.TestCase):
    x = Tensor.rand(32)
    check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])

+  # **** custom kernel realize tests
+
+  @staticmethod
+  def _copy_fxn(name:str="copy"):
+    def copy_kernel(out:UOp, inp:UOp) -> UOp:
+      i = UOp.range(inp.numel(), 0)
+      return UOp.group(out[i].store(inp[i])).end(i).sink(arg=KernelInfo(name=name))
+    return copy_kernel
+
+  def _copy_call(self, out:Tensor, expr:Tensor, name:str="copy") -> Tensor:
+    # forge a custom kernel call with params and call args, like llm/kernels does (no Tensor.custom_kernel contiguous)
+    params = tuple(UOp.placeholder_like(u, slot=i) for i,u in enumerate((out.uop, expr.uop)))
+    return Tensor(out.uop.after(self._copy_fxn(name)(*params).call(out.uop, expr.uop)))
+
+  def test_custom_kernel_buffer_src(self):
+    # custom kernels need buffers: a buffer input must never add a realize kernel
+    y = Tensor.ones(64).contiguous().realize()
+    out = Tensor.empty_like(y)
+    check_schedule(self._copy_call(out, y), 1)
+
+  def test_custom_kernel_view_src(self):
+    # a RESHAPE over a buffer resolves to the buffer state (RESHAPEs on call args are stripped), no realize kernel
+    y = Tensor.ones(64).contiguous().realize()
+    out = Tensor.empty_like(y)
+    check_schedule(self._copy_call(out, y.reshape(8, 8).reshape(64)), 1)
+
+  def test_custom_kernel_elementwise_src(self):
+    # a computed input is not a buffer state: the call args are unwrapped to their base buffer,
+    # so the compute would be silently dropped. this must raise instead of producing wrong results
+    y = Tensor.ones(64).contiguous().realize()
+    out = Tensor.empty_like(y)
+    check_schedule(self._copy_call(out, y + y), 2)
+
+  def test_custom_kernel_lazy_const_src(self):
+    # a lazy const expression above the call has no buffer at all. this used to crash rangeify with a KeyError
+    x = Tensor.linspace(-1.0, 1.0, 64)
+    out = Tensor.empty_like(x)
+    check_schedule(self._copy_call(out, x), 2)
+
+  def test_custom_kernel_offset_view_src(self):
+    # a SHRINK with an offset over a buffer is not a buffer state either, the offset would be silently dropped
+    y = Tensor.ones(128).contiguous().realize()
+    out = Tensor.empty(64)
+    check_schedule(self._copy_call(out, y[16:80]), 2)
+
+  def test_custom_kernel_computed_src_api(self):
+    # the supported way to pass computed inputs: Tensor.custom_kernel makes inputs contiguous (one realize kernel)
+    y = Tensor.ones(64).contiguous().realize()
+    out = Tensor.empty_like(y)
+    check_schedule(Tensor.custom_kernel(out, y + y, fxn=self._copy_fxn())[0], 2)
+
+  def test_custom_kernel_on_custom_kernel(self):
+    # the output of a custom kernel is a buffer state, chaining custom kernels must not add kernels
+    y = Tensor.ones(64).contiguous().realize()
+    k1 = self._copy_call(Tensor.empty_like(y), y, name="k1")
+    k2 = self._copy_call(Tensor.empty_like(y), k1, name="k2")
+    sched, _ = check_schedule(k2, 2)
+    self.assertEqual([call.src[0].arg.name for call in sched.src], ["k1", "k2"])
+
  def test_empty_is_not_realized(self):
    a = Tensor.empty(10)
    child = a+2
@@ -874,8 +929,7 @@ class TestSchedule(unittest.TestCase):
    t = Tensor.zeros((3, 3)).contiguous().realize()
    v = t[1]  # view - is_realized but not has_buffer_identity
    assert v.uop.is_realized
-    linear, _ = Tensor.linear_with_vars(v)
-    self.assertEqual(len(linear.src), 0)
+    check_schedule(v, 0)

  # NOTE: because empty does not have a lowered kernel if realize is called on a childless empty, it never gets allocated.
  def test_childless_empty_never_allocates(self):
@@ -1457,8 +1511,7 @@ class TestSchedule(unittest.TestCase):
    Tensor.manual_seed(0)
    x = Tensor.randn(4, 12, 64, 64, dtype=dtypes.half).realize()
    out = x.softmax(dtype=dtypes.float)
-    linear = out.schedule_linear()
-    self.assertEqual(len(linear.src), 3)
+    linear, _ = check_schedule(out, 3)
    # max reduction stays in input dtype (no numerical loss), upcast happens after subtracting max
    self.assertEqual(linear.src[0].src[1].dtype, dtypes.half)
    self.assertEqual(linear.src[1].src[1].dtype, dtypes.float)
@@ -1873,8 +1926,7 @@ class TestFusionOp(unittest.TestCase):
    val = 1.0
    a = Tensor(val)
    for _ in range(24): a = Tensor.stack(a, a)[0]
-    linear = a.schedule_linear()
-    self.assertLessEqual(len(linear.src), 1)
+    check_schedule(a, 0)
    self.assertLess(time.perf_counter()-st, 2.0)

  def test_recursive_reshape(self):
@@ -1883,8 +1935,7 @@ class TestFusionOp(unittest.TestCase):
    b = Tensor.empty(16, 2).realize()
    r = a.sum(1)
    for _ in range(24): r = r.reshape(16, 2) + b
-    linear = r.schedule_linear()
-    self.assertEqual(len(linear.src), 1)
+    check_schedule(r, 1)
    self.assertLess(time.perf_counter()-st, 2.0)

 # NOTE: the NULL backend supports SLICE
@@ -2,7 +2,8 @@ import unittest, itertools

 from tinygrad.codegen.late.coalesce import indexing_simplify
 from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import UOp, Ops, graph_rewrite, pm_lower_index_dtype
+from tinygrad.uop.ops import UOp, Ops, graph_rewrite
+from tinygrad.uop.weak import pm_lower_index_dtype
 from tinygrad.uop.symbolic import simplify_valid, sym, pm_move_where_on_load
 from tinygrad.helpers import Context
 from test.helpers import full_rewrite
@@ -332,7 +333,7 @@ class TestImageSimplification(unittest.TestCase):
    load = get_load_image_uop(shape, valid, idx)

    self.check(load,
-               "((((idx2*2)+r0)<11)&((((idx1*8)+r1)<3)!=True))",
+               "(((idx2*2)+r0)<11)",
               "(idx0+(idx1*512+r1*64)+-192)",
               "((((idx2*2)+r0)+(((idx1+((r1+5)//8))+1)//2))+-4)")

@@ -460,7 +461,7 @@ class TestImageSimplification(unittest.TestCase):
      self.check(load, None, "(gidx0+lidx0*1024+r0*1024+lidx1*128+-3168)", "0")
    except AssertionError:
      # TODO: fold valid
-      self.check(load, "(((lidx1<1)!=True)&(((lidx0+r0)<3)!=True)&((lidx0+r0)<19))",
+      self.check(load, "(((lidx1<1)!=True)&((lidx0+r0)<19))",
                       "(gidx0+lidx1*128+(lidx0*1024+r0*1024)+-3168)", "0")

  def test_simplify10(self):
@@ -479,7 +480,7 @@ class TestImageSimplification(unittest.TestCase):
      self.check(load, None, "(lidx2+gidx0*4+lidx0*1024+r0*1024+lidx1*256+-3264)", "0")
    except AssertionError:
      # TODO: fold valid
-      self.check(load, "(((lidx1<1)!=True)&(((lidx0+r0)<3)!=True)&((lidx0+r0)<11))",
+      self.check(load, "(((lidx1<1)!=True)&((lidx0+r0)<11))",
                       "(lidx2+gidx0*4+lidx1*256+(lidx0*1024+r0*1024)+-3264)", "0")

  def test_drop_non_monotonic_window(self):
@@ -1,10 +1,11 @@
 import unittest, pytest
-from tinygrad import dtypes, Variable
+from tinygrad import dtypes, Variable, Device
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import DEBUG, Context
-from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, graph_rewrite, GroupOp, AxisType, broadcast_axes
+from tinygrad.uop.ops import Ops, UOp, UPat, PatternMatcher, graph_rewrite, GroupOp, AxisType, broadcast_axes, KernelInfo
 from tinygrad.uop.symbolic import sym
 from test.helpers import to_uops_list
+from tinygrad.codegen import full_rewrite_to_sink

 simple_pm = PatternMatcher([
  (UPat.cvar('x', dtypes.weakint), lambda x: UOp.const(1.0) + UOp.const(2.0)),
@@ -536,6 +537,15 @@ class TestReduceCollapse(unittest.TestCase):
    # Should become add of two separate reduces
    self.assertEqual(result.op, Ops.ADD)

+  def test_reduce_shapeless_const_unroll(self):
+    """a REDUCE over a shapeless CONST (e.g. x*0 folded late in codegen) must collapse before the expander"""
+    out = UOp.param(0, dtypes.float, (1,))
+    red = UOp.const(3.0).cast(dtypes.float).reduce(UOp.range(4, 0, AxisType.UNROLL), arg=(Ops.ADD, 0))
+    ast = UOp.sink(out.index(UOp.const(0)).store(red)).replace(arg=KernelInfo())
+    uops = full_rewrite_to_sink(ast, Device["CPU"].renderer, optimize=False).toposort()
+    self.assertNotIn(Ops.REDUCE, [u.op for u in uops])
+    self.assertIn(12.0, [u.val for u in uops if u.op is Ops.CONST])
+
 class TestMovementOps(unittest.TestCase):
  def test_pm_mops_partial_reshape_index_removes_reshape(self):
    from tinygrad.schedule.rangeify import pm_mops
@@ -593,7 +603,7 @@ class TestUOpTags(unittest.TestCase):
  def test_inc_by_one(self):
    g = UOp.const(1) + UOp.const(1)
    assert g.ssimplify() == 2
-    pm_plus_1 = PatternMatcher([(UPat(Ops.CONST, name="x"), lambda x: x.replace(arg=x.val+1, tag=1) if x.tag is None else None)])
+    pm_plus_1 = PatternMatcher([(UPat(Ops.CONST, name="x"), lambda x: UOp.const(x.val+1, x.dtype).rtag(1) if x.tag is None else None)])
    pm_strip_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
    g = graph_rewrite(g, pm_plus_1)
    assert g.ssimplify() == 4
@@ -6,7 +6,7 @@ from tinygrad.dtype import dtypes, ConstType, DType, Invalid
 from test.helpers import get_uops
 from tinygrad.uop.ops import UOp, Ops, graph_rewrite, sym_infer
 from tinygrad.uop.spec import spec_shared, type_verify
-from tinygrad.uop.symbolic import sym, commutative, pm_simplify_valid, pm_move_where_on_load
+from tinygrad.uop.symbolic import sym, pm_fold_cast_const, commutative, pm_simplify_valid, pm_move_where_on_load
 from tinygrad.uop.validate import uops_to_z3

 def check_uop_against_string(self, v:UOp, s:str):
@@ -35,7 +35,7 @@ class TestSymbolic(unittest.TestCase):
    self.assertEqual(solver.check(expr1 != expr2), z3.unsat, "simplified expression not equal to original")

  def helper_test_variable(self, v, n, m, s, test_z3:bool=True):
-    v_simplified = graph_rewrite(v, sym, name="simplify symbolic uop")
+    v_simplified = graph_rewrite(v, sym+pm_fold_cast_const, name="simplify symbolic uop")
    if test_z3: self.check_equal_z3(v, v_simplified)
    nmin, nmax = v_simplified.vmin, v_simplified.vmax
    check_uop_against_string(self, v_simplified, s)
@@ -1,6 +1,6 @@
 import unittest, math
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.dtype import dtypes, Invalid
+from tinygrad.dtype import dtypes, Invalid, truncate

 class TestVminVmaxProperties(unittest.TestCase):
  def test_vmin_vmax_constant(self):
@@ -162,6 +162,16 @@ class TestVminVmaxProperties(unittest.TestCase):
    self.assertEqual(x_uint.vmin, dtypes.uint.min)
    self.assertEqual(x_uint.vmax, dtypes.uint.max)

+  def test_vmin_vmax_cast_float_to_int(self):
+    self.assertEqual(UOp.variable('x', -4.5, 4.5, dtypes.float).cast(dtypes.int)._min_max, (-4, 4))
+    self.assertEqual(UOp.const(4.5).cast(dtypes.float).cast(dtypes.int)._min_max, (4, 4))
+    x = UOp.const(4.5).cast(dtypes.float)
+    self.assertIs(x.ne(x.cast(dtypes.int).cast(dtypes.float)).simplify().arg, True)
+
+  def test_vmin_vmax_cast_int_to_float_grid(self):
+    # a cast to float only takes values on the float grid, so its bounds are the source bounds rounded at the destination
+    self.assertEqual(UOp.variable('x', 0, 16777219, dtypes.int).cast(dtypes.float)._min_max, (0.0, 16777220.0))
+
  def test_vmin_vmax_invalid(self):
    i = UOp.invalid()
    self.assertNotEqual(i.vmin, i.vmax)
@@ -311,8 +321,8 @@ class TestVminVmaxVConst(unittest.TestCase):
  def test_vmin_vmax_vconst_with_floats(self):
    # vmin and vmax for a vector constant of float values
    uop = UOp.const((1.5, -3.2, 0.0))
-    self.assertEqual(uop.vmin, -3.2)
-    self.assertEqual(uop.vmax, 1.5)
+    self.assertEqual(uop.vmin, truncate[dtypes.default_float](-3.2))
+    self.assertEqual(uop.vmax, truncate[dtypes.default_float](1.5))

  def test_vmin_vmax_vconst_with_bools(self):
    # vmin and vmax for a vector constant of bool values
@@ -5,7 +5,8 @@ from tinygrad.tensor import Tensor
 from tinygrad.helpers import Timing, Context, cdiv
 from tinygrad.dtype import dtypes, AddrSpace, ConstFloat, Invalid  # noqa: F401
 from tinygrad.device import Device
-from tinygrad.uop.ops import Ops, ParamArg, PatternMatcher, UOp, UPat, dtype_from_uop, exec_alu, graph_rewrite, pm_lower_index_dtype  # noqa: F401  # ParamArg used by eval(str(uop)) roundtrip tests
+from tinygrad.uop.ops import Ops, ParamArg, PatternMatcher, UOp, UPat, dtype_from_uop, exec_alu, graph_rewrite  # noqa: F401  # ParamArg used by eval(str(uop)) roundtrip tests
+from tinygrad.uop.weak import pm_lower_index_dtype
 from tinygrad.uop.spec import spec_program, spec_shared, type_verify
 from tinygrad.uop.symbolic import sym, pm_remove_invalid
 from test.helpers import eval_uop, to_uops_list
@@ -55,11 +56,11 @@ class TestDTypeFromUOp(unittest.TestCase):
    invalid = UOp.invalid()
    self.assertIs(invalid.dtype, dtypes.bool)
    self.assertIs(UOp.const(Invalid, dtypes.float32), invalid)
-    self.assertIs((moved:=invalid.reshape((1,))).cast(dtypes.float32), moved)
    scratch = Tensor.invalids(4, dtype=dtypes.float32)
    self.assertEqual((scratch.dtype, next(u.dtype for u in scratch.uop.toposort() if u.op is Ops.BUFFER), next(u.dtype for u in scratch.uop.toposort()
      if u.is_invalid)), (dtypes.float32, dtypes.float32, dtypes.bool))
    invalid, value = UOp.invalid(), UOp.const(1, dtypes.float32)
+    for u in (UOp.param(0, dtypes.bool, ()).where(value, invalid), value+invalid, UOp.stack(value, invalid)): self.assertIs(u.src[-1], invalid)
    for u in (UOp(Ops.STACK, dtypes.float32, src=(value, invalid)), UOp(Ops.ADD, dtypes.float32, src=(value, invalid)),
              UOp.const(True).where(value, invalid), UOp(Ops.CMPLT, src=(invalid, value)), UOp(Ops.CMPLT, src=(value, invalid)),
              UOp.param(0, dtypes.float32, (4,)).index(invalid)): type_verify(u, spec_shared)
@@ -126,6 +127,12 @@ class TestConstFloatEq(unittest.TestCase):
    self.assertFalse(nan == Invalid)
    self.assertTrue(nan != Invalid)    # __ne__ must defer to the reflected eq, not swallow NotImplemented

+  def test_invalid_eq_defers_to_reflected(self):
+    class HoldsInvalid:  # a carrier that knows it holds Invalid. returning False for foreign types would silence its eq
+      def __eq__(self, other): return other is Invalid
+    self.assertTrue(Invalid == HoldsInvalid())
+    self.assertFalse(Invalid != HoldsInvalid())
+
  def test_matchers_agree_on_nan(self):
    n = UOp.const(math.nan, dtypes.float32)
    for compiled in (False, True):
@@ -447,7 +454,7 @@ class TestUPatHelpers(unittest.TestCase):

 class TestUopsObject(unittest.TestCase):
  def test_timing(self):
-    with Timing("create 10k uops:"): ret = [UOp(Ops.CONST, dtypes.int, arg=10000000+i) for i in range(10000)]
+    with Timing("create 10k uops:"): ret = [UOp.const(10000000+i, dtypes.int) for i in range(10000)]
    assert len(ret) == 10000

  def test_nested(self):
@@ -147,21 +147,21 @@ class TestUOpsStats(unittest.TestCase):
  #MULACC should have the same stats as MUL + ADD
  def test_mulacc(self):
    globl = UOp.param(0, dtypes.int, (3,))
-    o1 = UOp(Ops.CONST, dtypes.int, tuple(), 1)
-    o2 = UOp(Ops.CONST, dtypes.int, tuple(), 2)
+    o1 = UOp.const(1, dtypes.int)
+    o2 = UOp.const(2, dtypes.int)
    u1 = globl.index(o1)
    u2 = globl.index(o2)
-    u3 = UOp(Ops.CONST, dtypes.int, tuple(), 3)
+    u3 = UOp.const(3, dtypes.int)
    u4 = UOp(Ops.MUL, src=(u1,u2))
    u5 = UOp(Ops.ADD, src=(u4,u3))
    uops = tuple(u5.toposort())

    globl = UOp.param(0, dtypes.int, (3,))
-    o1 = UOp(Ops.CONST, dtypes.int, tuple(), 1)
-    o2 = UOp(Ops.CONST, dtypes.int, tuple(), 2)
+    o1 = UOp.const(1, dtypes.int)
+    o2 = UOp.const(2, dtypes.int)
    u1 = globl.index(o1)
    u2 = globl.index(o2)
-    u3 = UOp(Ops.CONST, dtypes.int, tuple(), 3)
+    u3 = UOp.const(3, dtypes.int)
    u4 = UOp(Ops.MULACC, src=(u1,u2,u3))
    uops_fma = tuple(u4.toposort())

@@ -1,11 +1,11 @@
 import unittest
 from tinygrad.helpers import DEBUG, Context
 from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import UPat, track_rewrites, GroupOp, Ops
+from tinygrad.uop.ops import UPat, rewrite_group, GroupOp, Ops
 from tinygrad.uop.upat import _get_code, upat_compile
 import dis

-@track_rewrites()
+@rewrite_group()
 def do_compile(up):
  print("\n***** COMPILE", up)
  match_code = _get_code(up, False)
@@ -3,7 +3,7 @@ from pathlib import Path
 from dataclasses import dataclass
 from typing import Generator

-from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher, graph_rewrite, track_rewrites, profile_matches
+from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher, graph_rewrite, rewrite_group
 from tinygrad.uop.symbolic import sym
 from tinygrad.dtype import dtypes, AddrSpace
 from tinygrad.helpers import colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context, cpu_events, profile_marker
@@ -14,7 +14,7 @@ from tinygrad.uop.ops import tracked_keys, tracked_ctxs, uop_fields, active_rewr
 from tinygrad.viz.serve import load_rewrites, get_full_rewrite, uop_to_json, VizData, get_render, addrspace_colors
 from tinygrad.codegen import do_to_program

-@track_rewrites(name=True)
+@rewrite_group(name=True)
 def exec_rewrite(sink:UOp, pm_lst:list[PatternMatcher], names:None|list[str]=None) -> UOp:
  for i,pm in enumerate(pm_lst):
    sink = graph_rewrite(sink, TrackedPatternMatcher(pm.patterns), name=names[i] if names else None)
@@ -97,7 +97,7 @@ class TestViz(unittest.TestCase):
    # VIZ tracks rewrites up to and including the error
    def count_3(x:UOp):
      assert x.val <= 3
-      return x.replace(arg=x.val+1)
+      return UOp.const(x.val+1, x.dtype)
    err_pm = PatternMatcher([(UPat.cvar("x"), count_3),])
    a = UOp.const(1)
    with save_viz() as viz:
@@ -109,7 +109,7 @@ class TestViz(unittest.TestCase):
  def test_default_name(self):
    with save_viz() as viz:
      a = UOp.variable("a", 1, 10)
-      @track_rewrites()
+      @rewrite_group()
      def name_default(): return graph_rewrite(a, PatternMatcher([]))
      name_default()
    lst = viz.list_items()
@@ -118,7 +118,7 @@ class TestViz(unittest.TestCase):
  # name can also come from a function that returns a string
  def test_dyn_name_fxn(self):
    with save_viz() as viz:
-      @track_rewrites(name=lambda *args,ret,**kwargs: ret.render())
+      @rewrite_group(name=lambda *args,ret,**kwargs: ret.render())
      def name_from_fxn(s:UOp, arg:list|None=None): return graph_rewrite(s, PatternMatcher([]))
      name_from_fxn(UOp.variable("a", 1, 10)+1, arg=["test"])
    lst = viz.list_items()
@@ -128,18 +128,18 @@ class TestViz(unittest.TestCase):
  # name can also come from a function that returns a TracingKey
  def test_tracing_key(self):
    with save_viz() as viz:
-      @track_rewrites(name=lambda inp,ret: TracingKey("custom_name", (inp,)))
+      @rewrite_group(name=lambda inp,ret: TracingKey("custom_name", (inp,)))
      def test(s:UOp): return graph_rewrite(s, PatternMatcher([]))
      test(UOp.variable("a", 1, 10)+1)
    lst = viz.list_items()
    # NOTE: names from TracingKey do not get deduped
    self.assertEqual(lst[0]["name"], "custom_name")

-  def test_nested_track_rewrites(self):
+  def test_nested_rewrite_group(self):
    with save_viz() as viz:
-      @track_rewrites(name=lambda x,ret: TracingKey(f"inner fxn for {x.render()}", (ret,)))
+      @rewrite_group(name=lambda x,ret: TracingKey(f"inner fxn for {x.render()}", (ret,)))
      def inner(x:UOp): return graph_rewrite(x, PatternMatcher([]), name="each")
-      @track_rewrites(name=lambda *args,ret: f"outer rewrite of {len(args)} inputs")
+      @rewrite_group(name=lambda *args,ret: f"outer rewrite of {len(args)} inputs")
      def outer(*xs:tuple[UOp, ...]): return graph_rewrite(UOp.sink(*[inner(x) for x in xs]), PatternMatcher([]), name="all")
      items = ["a", "b", "c"]
      outer(*[UOp.variable(x, 1, 10) for x in items])
@@ -156,13 +156,13 @@ class TestViz(unittest.TestCase):
      self.assertEqual(len(steps), 1)
      self.assertEqual(steps[0]["name"], "each")

-  def test_profile_matches(self):
+  def test_rewrite_group_nested(self):
    with save_viz() as viz:
-      @profile_matches
+      @rewrite_group(new_ctx=False)
      def nested_function(u:UOp):
        for i in range(2): graph_rewrite(u, PatternMatcher([]), name=f"step {i+1}")

-      @track_rewrites()
+      @rewrite_group()
      def main_rewrite(u:UOp):
        graph_rewrite(u, PatternMatcher([]), name="init")
        nested_function(u)
@@ -173,9 +173,9 @@ class TestViz(unittest.TestCase):
    self.assertEqual(steps[1]["name"], "nested_function")
    self.assertEqual(len(steps), 4)

-  def test_profile_matches_invalid_arg(self):
+  def test_rewrite_group_invalid_arg(self):
    with save_viz():
-      @profile_matches
+      @rewrite_group(new_ctx=False)
      def invalid_fxn(arg:str): return graph_rewrite(UOp(Ops.SINK), PatternMatcher([]))
      with self.assertRaisesRegex(AssertionError, "invalid match tracing input"):
        invalid_fxn("test")
@@ -202,8 +202,8 @@ class TestViz(unittest.TestCase):
    a = UOp.const(3)
    b = UOp.const(4)
    pm = PatternMatcher([
-      (UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4)),
-      (UPat(Ops.CONST, arg=4, name="x"), lambda x: x.replace(arg=3)),
+      (UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype)),
+      (UPat(Ops.CONST, arg=4, name="x"), lambda x: UOp.const(3, x.dtype)),
    ])
    with save_viz() as viz:
      # use smaller stack limit for faster test (default is 250000)
@@ -224,7 +224,7 @@ class TestViz(unittest.TestCase):
    list(viz.get_details(0, 0))

  def test_enter_calls_rewrite(self):
-    pm = PatternMatcher([(UPat(Ops.CONST, arg=3, name="x"), lambda x: x.replace(arg=4))])
+    pm = PatternMatcher([(UPat(Ops.CONST, arg=3, name="x"), lambda x: UOp.const(4, x.dtype))])
    with save_viz() as viz:
      inner = UOp.const(3)
      call = UOp(Ops.CALL, src=(UOp(Ops.SINK, src=(inner,)),))
@@ -395,7 +395,7 @@ class TestVizIntegration(unittest.TestCase):
    graph = next(viz.get_details(0, 0))["graph"]
    self.assertEqual(len([n for n in graph.values() if repr(metadata) in n["label"]]), 1)

-  # tracing also works without a track_rewrites context
+  # tracing also works without a rewrite_group context
  # all graph_rewrites get put into the default group
  def test_default_tracing(self):
    with save_viz() as viz:
@@ -407,11 +407,11 @@ class TestVizIntegration(unittest.TestCase):
    self.assertEqual(len(ls), 1)
    self.assertEqual(ls[0]["name"], "default graph_rewrite")

-  # using @track_rewrites organizes function calls into groups
+  # using @rewrite_group organizes function calls into groups
  # and nicely counts function calls.
  def test_group_traces(self):
    with save_viz() as viz:
-      @track_rewrites()
+      @rewrite_group()
      def test(root):
        return graph_rewrite(root, sym)
      test(c:=UOp.const(1))
@@ -420,11 +420,11 @@ class TestVizIntegration(unittest.TestCase):
    self.assertEqual(len(ls), 2)
    for i in range(2): self.assertEqual(ls[i]["name"], f"test n{i+1}")

-  # @track_rewrites always starts a new group.
+  # @rewrite_group always starts a new group.
  def test_group_combined(self):
    with save_viz() as viz:
      def default_test(root): return graph_rewrite(root, sym)
-      tracked_test = track_rewrites()(default_test)
+      tracked_test = rewrite_group()(default_test)
      c = UOp.const(1)
      default_test(c+1) # goes to the default group
      tracked_test(c)   # all rewrites after this go inside the second group.
@@ -1,6 +1,7 @@
 import unittest, sys
 from tinygrad import Tensor, GlobalCounters, dtypes, Context
 from tinygrad.helpers import WINO
+from test.helpers import check_schedule

@unittest.skipIf(sys.platform.startswith("win"), "flaky on Windows")
 class TestWinograd(unittest.TestCase):
@@ -13,7 +14,7 @@ class TestWinograd(unittest.TestCase):
  def test_forward_kernels(self):
    x,w = Tensor.rand(1,4,9,9).realize(), Tensor.rand(4,4,3,3).realize()
    out = Tensor.conv2d(x,w)
-    self.assertEqual(len(out.schedule_linear().src), 4)
+    check_schedule(out, 4)

  def test_backward_counters(self):
    # contiguous_backward on the pooled input keeps the input-transform adjoint out of the overlap accumulation, so
@@ -101,7 +101,8 @@ class TestTensorCores(unittest.TestCase):
      if Device.DEFAULT == "CPU" and DEV.renderer == "LLVM":
        assert "0x201000" in prg.src[2].arg
      elif Device.DEFAULT == "AMD" and DEV.renderer == "LLVM":
-        assert "@llvm.amdgcn.wmma" in prg.src[2].arg
+        # RDNA emits wmma intrinsics, CDNA emits mfma intrinsics
+        assert ("@llvm.amdgcn.wmma" in prg.src[2].arg) or ("@llvm.amdgcn.mfma" in prg.src[2].arg)
      elif Device[Device.DEFAULT].renderer.suffix == "PTX":
        assert "mma.sync.aligned" in prg.src[2].arg
      else:
@@ -181,10 +182,12 @@ class TestTensorCores(unittest.TestCase):
  @unittest.skipIf(Device.DEFAULT == "PYTHON", "slow on EMULATED device")
  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
  def test_tensor_cores_unroll_phi(self):
-    tc = Device[Device.DEFAULT].renderer.tensor_cores[0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    # skip fp8 tcs: the unoptimized ALU baseline quantizes products to fp8 (JAX promotion), which legitimately
+    # differs from the MFMA path (f32 accumulation), so the baseline-vs-TC numerical gate can't hold for fp8.
+    tc = next(tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in not in dtypes.fp8s)
+    x, y = Tensor.rand(64, 64, dtype=tc.dtype_in), Tensor.rand(64, 64, dtype=tc.dtype_in)
    r = x.matmul(y, dtype=tc.dtype_out)
-    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    opts = [Opt(OptOps.UNROLL, 0, 2)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
    for u in tuple(to_program(replace_opts(ast, opts), Device[Device.DEFAULT].renderer).src[1].src):
      if u.op is Ops.WMMA:
@@ -195,10 +198,10 @@ class TestTensorCores(unittest.TestCase):
  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
  @unittest.skipIf(Device.DEFAULT in {"CPU"}, "CPU does not support using a different type for accumulation")
  def test_tensor_cores_unroll_casted_phi(self):
-    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out and tc.dtype_in not in dtypes.fp8s][0]
+    x, y = Tensor.rand(64, 64, dtype=tc.dtype_in), Tensor.rand(64, 64, dtype=tc.dtype_in)
    r = x.matmul(y, dtype=tc.dtype_out)
-    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    opts = [Opt(OptOps.UNROLL, 0, 2)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
    for u in tuple(to_program(replace_opts(ast, opts), Device[Device.DEFAULT].renderer).src[1].src):
      if u.op is Ops.WMMA:
@@ -211,10 +214,10 @@ class TestTensorCores(unittest.TestCase):
  @unittest.skipIf(Device.DEFAULT in {"CPU"}, "CPU does not support using a different type for accumulation")
  def test_tensor_cores_unroll_casted_phi_with_children(self):
    # all STORE children are outside the loop
-    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
-    x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
+    tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out and tc.dtype_in not in dtypes.fp8s][0]
+    x, y = Tensor.rand(64, 64, dtype=tc.dtype_in), Tensor.rand(64, 64, dtype=tc.dtype_in)
    r = x.matmul(y, dtype=tc.dtype_out).relu()
-    opts = [Opt(OptOps.UNROLL, 0, 4)]
+    opts = [Opt(OptOps.UNROLL, 0, 2)]
    ast = helper_linearizer_opt(r, [opts], apply_tc=True, atol=3e-2, rtol=1e-3)
    for u in tuple(to_program(replace_opts(ast, opts), Device[Device.DEFAULT].renderer).src[1].src):
      if u.op is Ops.WMMA:
@@ -39,6 +39,10 @@ class TestTiny(unittest.TestCase):
    out = Tensor.ones(N).contiguous().sum()
    self.assertEqual(out.item(), N)

+  def test_eye(self):
+    out = Tensor.eye(3).flatten()
+    self.assertListEqual(out.tolist(), [1.0,0.0,0.0, 0.0,1.0,0.0, 0.0,0.0,1.0])
+
  def test_gemm(self, N=getenv("GEMM_N", 64), dtype=dtypes.float):
    a = Tensor.ones(N,N, dtype=dtype).contiguous()
    b = Tensor.eye(N, dtype=dtype).clone()
@@ -1,7 +1,9 @@
 import unittest

-from tinygrad import Tensor, Device, dtypes
-from tinygrad.helpers import fetch, round_up
+from tinygrad import Tensor, Device, Variable, dtypes
+from tinygrad.helpers import DEV, fetch, round_up
+from tinygrad.engine.realize import compile_linear
+from tinygrad.uop.ops import Ops
 from extra.hevc.hevc import parse_hevc_file_headers, nv_gpu
 from extra.hevc.decode import hevc_decode

@@ -63,7 +65,7 @@ class TestHevc(unittest.TestCase):
    self.assertEqual(list(frame3.initreflistidxl1), [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    self.assertEqual(list(frame3.RefDiffPicOrderCnts), [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

-  @unittest.skipUnless(Device.DEFAULT == "NV", "NV only")
+  @unittest.skipUnless(Device.DEFAULT == "NV" and not DEV.interface.startswith("MOCK"), "real NV only")
  def test_hevc_decode(self):
    url = "https://github.com/haraschax/filedump/raw/09a497959f7fa6fd8dba501a25f2cdb3a41ecb12/comma_video.hevc"
    dat = fetch(url, headers={"Range": f"bytes=0-{512<<10}"}).read_bytes()
@@ -83,5 +85,22 @@ class TestHevc(unittest.TestCase):
      self.assertEqual(f.dtype, dtypes.uint8)
      self.assertEqual(f.device, "NV")

+  @unittest.skipUnless(Device.DEFAULT == "NV", "NV only")
+  def test_hevc_decode_compile(self):
+    url = "https://github.com/haraschax/filedump/raw/09a497959f7fa6fd8dba501a25f2cdb3a41ecb12/comma_video.hevc"
+    dat = fetch(url, headers={"Range": f"bytes=0-{512<<10}"}).read_bytes()
+
+    opaque, frame_info, _, _, luma_w, luma_h, _ = parse_hevc_file_headers(dat)
+    offset, sz, frame_pos, max_hist, _ = frame_info[1]
+    out_image_size = luma_h + (luma_h + 1) // 2, round_up(luma_w, 64)
+    history = [Tensor.empty(*out_image_size, dtype=dtypes.uint8, device="NV") for _ in range(max_hist)]
+    decoded = Tensor(dat, device="NV")[offset:offset+sz].decode_hevc_frame(
+      Variable("pos", 0, max_hist + 1).bind(frame_pos), out_image_size, opaque[1], history)
+
+    compiled = compile_linear(decoded.linear_with_vars()[0])
+    self.assertTrue(any(call.src[0].op is Ops.PROGRAM for call in compiled.src))
+    encdec_calls = [call for call in compiled.src if call.src[0].op is Ops.CUSTOM_FUNCTION and call.src[0].arg == "encdec"]
+    self.assertEqual(len(encdec_calls), 1)
+
 if __name__ == "__main__":
  unittest.main()
@@ -1,5 +1,5 @@
 import unittest
-from tinygrad import Tensor, dtypes
+from tinygrad import Tensor, UOp, dtypes
 from tinygrad.helpers import Context
 from tinygrad.uop.ops import Ops

@@ -43,6 +43,13 @@ class TestRingAllReduce(unittest.TestCase):
    self.assertEqual(len(sinks), 2)
    self.assertTrue(all(dst != src for dst, src in pairs))

+  def test_symbolic_shape(self):
+    rows = UOp.variable("rows", 1, 4).bind(3)
+    t = Tensor.ones(4, 4).shard(("CPU:0", "CPU:1"), axis=1).realize()
+    out = t[:rows].sum(1).realize()
+    self.assertEqual(out.shape, (rows,))
+    self.assertTrue((out == 4).all().item())
+
  def test_correct_ring(self):
    with Context(RING=2):
      N = 4
@@ -4,6 +4,7 @@ import numpy as np
 from tinygrad import dtypes, Tensor, TinyJit, GlobalCounters, Variable
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.helpers import temp, DEV, Context
+from test.helpers import assert_kernel_count

 N = 200  # has to be bigger than the cache to fail

@@ -42,7 +43,7 @@ class TestAssign(unittest.TestCase):
    # it should copy into the empty buffer
    GlobalCounters.reset()
    c.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)

  def test_assign_slice(self):
    X = Tensor([1,2,3,4]).realize()
@@ -50,7 +51,7 @@ class TestAssign(unittest.TestCase):
    xs.assign(xs+1)
    GlobalCounters.reset()
    self.assertListEqual(X.tolist(), [1,2,4,5])
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)

  def test_assign_slice_alt(self):
    X = Tensor([1,2,3,4]).realize()
@@ -58,7 +59,7 @@ class TestAssign(unittest.TestCase):
    xs1.assign(xs2+1)
    GlobalCounters.reset()
    self.assertListEqual(X.tolist(), [1,4,5,4])
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_kernel_count(2)

  def test_assign_flip(self):
    ref = np.arange(16, dtype=np.float32)
@@ -68,7 +69,7 @@ class TestAssign(unittest.TestCase):
    xs.assign(xs + X)
    ref = ref + ref[::-1]
    np.testing.assert_allclose(X.numpy(), ref)
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_kernel_count(2)

  def test_assign_add(self):
    for T in (1, 2, 10):#, 100): # this crashes in CI, not sure why
@@ -331,14 +332,14 @@ class TestAssign(unittest.TestCase):
    a = (Tensor.arange(16).reshape(4,4).clone().realize() + 1)
    GlobalCounters.reset()
    b.assign(a.contiguous()).realize()
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_kernel_count(2)

  def test_assign_contiguous_permute(self):
    b = Tensor.arange(16).reshape(4,4).clone().realize()
    a = (Tensor.arange(16).reshape(4,4).clone().realize() + 1).permute((1,0))
    GlobalCounters.reset()
    b.assign(a.contiguous()).realize()
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_kernel_count(2)

  def test_permuted_assignment(self):
    a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
@@ -413,7 +414,7 @@ class TestAssign(unittest.TestCase):

    GlobalCounters.reset()
    Tensor.realize(b, c, d)
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    np.testing.assert_allclose(b.numpy(), a.sum(1).numpy()+1)
    np.testing.assert_allclose(c.numpy(), a.sum(1).numpy()+2)
    np.testing.assert_allclose(d.numpy(), a.sum(1).numpy()+3)
@@ -461,7 +462,7 @@ class TestAssign(unittest.TestCase):
    b.assign(r + b)
    c.assign(r + b_perm.contiguous())
    Tensor.realize(b, c)
-    self.assertEqual(GlobalCounters.kernel_count, 2)
+    assert_kernel_count(2)
    np.testing.assert_equal(b.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32))
    np.testing.assert_equal(c.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32).transpose(1, 0))

@@ -471,7 +472,7 @@ class TestAssign(unittest.TestCase):
    a.assign(a + b)
    GlobalCounters.reset()
    a.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    np.testing.assert_equal(a.numpy(), np.ones((4, 4))+np.pad(np.ones((4, 4))[:, 0:2], ((0, 0), (0, 2)), constant_values=2))

  def test_permuted_assignment_masked_view_not_contiguous(self):
@@ -510,7 +511,7 @@ class TestAssign(unittest.TestCase):
    expected[0:10] = expected[50:60].copy()
    GlobalCounters.reset()
    a[0:10].assign(a[50:60]).realize()
-    self.assertEqual(GlobalCounters.kernel_count, 2)  # currently conservative, forces contiguous
+    assert_kernel_count(2)  # currently conservative, forces contiguous
    np.testing.assert_allclose(a.numpy(), expected)

  def test_setitem_half(self):
@@ -630,7 +631,7 @@ class TestAssign(unittest.TestCase):
    GlobalCounters.reset()
    x.realize()
    # N assigns (1 kernel each) producing N kernels total
-    self.assertEqual(GlobalCounters.kernel_count, N)
+    assert_kernel_count(N)

  def test_shared_computation_assign_kernel_count(self):
    """When a .contiguous() is shared between an assign value and the next layer's input (like QKV projection in LLM),
@@ -648,7 +649,7 @@ class TestAssign(unittest.TestCase):
    GlobalCounters.reset()
    caches[-1][:1].contiguous().realize()
    # N matmuls + N assigns + 1 final read = 2*N+1 (AFTER embedding allows full graph scheduling with shared contiguous reuse)
-    self.assertEqual(GlobalCounters.kernel_count, 2*N+1)
+    assert_kernel_count(2*N+1)

  def test_double_assign_from_const(self):
    a = Tensor.empty(2)
@@ -656,7 +657,7 @@ class TestAssign(unittest.TestCase):
    a.assign(Tensor.ones(2, buffer=False))
    GlobalCounters.reset()
    a.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(a.tolist(), [1.,1.])

  def test_assign_deviceless_const(self):
@@ -672,7 +673,7 @@ class TestAssign(unittest.TestCase):
    contig.assign(Tensor([1, 4, 3], dtype=dtypes.int64))
    GlobalCounters.reset()
    base.assign(contig).realize()
-    self.assertEqual(GlobalCounters.kernel_count, 2)  # TODO: first copy is dead, could be 1
+    assert_kernel_count(2)  # TODO: first copy is dead, could be 1
    self.assertEqual(base.tolist(), [1,4,3])

  def test_nested_after_contiguous_store_no_init(self):
@@ -682,7 +683,7 @@ class TestAssign(unittest.TestCase):
    contig.assign(Tensor([1, 4, 3], dtype=dtypes.int64))
    GlobalCounters.reset()
    base.assign(contig).realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(base.tolist(), [1,4,3])

 class TestAssignOrdering(unittest.TestCase):
@@ -1,9 +1,11 @@
 import unittest
+from types import SimpleNamespace
 import numpy as np
-from tinygrad import Tensor, dtypes
+from tinygrad import Tensor, dtypes, nn
+from tinygrad.llm.kimi import _shard_kimi
 from tinygrad.llm.model import (
  GatedDeltaNetBlock, SSMConfig, TransformerBlock, TransformerConfig,
-  apply_rope as apply_rope_new, precompute_freqs_cis, pairwise_topk,
+  apply_rope as apply_rope_new, iterative_topk, l2norm, precompute_freqs_cis, pairwise_topk,
 )

 def apply_rope(x:Tensor, start_pos:int):
@@ -41,6 +43,11 @@ class TestAttention(unittest.TestCase):
    np.testing.assert_allclose(block.cache_kv[0, :, :, :seqlen, :].numpy(), expected.numpy(), rtol=1e-5, atol=1e-5)

 class TestGatedDeltaNetBlock(unittest.TestCase):
+  def test_kda_l2norm_matches_fla(self):
+    x = np.array([[1e-4, -2e-4, 3e-4], [1.0, 2.0, -3.0]], dtype=np.float32)
+    expected = x / np.sqrt((x*x).sum(axis=-1, keepdims=True) + 1e-6)
+    np.testing.assert_allclose(l2norm(Tensor(x)).numpy(), expected, rtol=1e-6, atol=1e-6)
+
  def _tensor_linspace(self, start:float, stop:float, shape:tuple[int, ...]) -> Tensor:
    return Tensor.linspace(start, stop, int(np.prod(shape)), dtype=dtypes.float32).reshape(*shape)

@@ -190,6 +197,87 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
    alpha = np.exp(-self._softplus_np(np.arange(1, 5)).reshape(1, 2, 1, 2))
    np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha, rtol=1e-5, atol=1e-5)

+  def test_kda_safe_gate_decay(self):
+    config = self._make_config(n_heads=2, kda_full_rank_gate=True, kda_gate_lower_bound=-5.0,
+      ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=2, time_step_rank=2, inner_size=4, kda=True))
+    block, x = GatedDeltaNetBlock(config, config.ssm), Tensor([[[1., 2., 0., 0.]]])
+    block.ssm_f_a.weight = Tensor([[1., 0., 0., 0.], [0., 1., 0., 0.]])
+    block.ssm_f_b.weight = Tensor([[1., 0.], [0., 1.], [1., 1.], [2., 1.]])
+    block.ssm_dt["bias"] = Tensor.zeros(4)
+    block.ssm_a = Tensor([[-2.], [-3.]])  # stores -exp(A_log)
+    block._init_state(x)
+    initial_state = Tensor.arange(8, dtype=dtypes.float32).reshape(1, 2, 2, 2)
+    block.recurrent_state.assign(initial_state).realize()
+    block._attention(x, 0).realize()
+    gate_logits = np.arange(1, 5, dtype=np.float32).reshape(1, 2, 2)
+    exp_a = np.array([2., 3.], dtype=np.float32).reshape(1, 2, 1)
+    alpha = np.exp(-5.0 / (1.0 + np.exp(-(exp_a * gate_logits)))).reshape(1, 2, 1, 2)
+    np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha, rtol=2e-5, atol=2e-5)
+
+  def test_kda_per_channel_a(self):
+    config = self._make_config(n_heads=2, kda_full_rank_gate=True, kda_gate_lower_bound=-5.0,
+      ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=2, time_step_rank=2, inner_size=4, kda=True, channel_decay=True))
+    block, x = GatedDeltaNetBlock(config, config.ssm), Tensor([[[1., 2., 0., 0.]]])
+    block.ssm_f_a.weight = Tensor([[1., 0., 0., 0.], [0., 1., 0., 0.]])
+    block.ssm_f_b.weight = Tensor([[1., 0.], [0., 1.], [1., 1.], [2., 1.]])
+    block.ssm_dt["bias"] = Tensor.zeros(4)
+    block.ssm_a = Tensor([[-2.], [-3.]])
+    block._init_state(x)
+    initial_state = Tensor.arange(8, dtype=dtypes.float32).reshape(1, 2, 2, 2)
+    block.recurrent_state.assign(initial_state).realize()
+    block._attention(x, 0).realize()
+    gate_logits = np.arange(1, 5, dtype=np.float32).reshape(1, 2, 2)
+    exp_a = np.array([2., 3.], dtype=np.float32).reshape(1, 1, 2)
+    alpha = np.exp(-5.0 / (1.0 + np.exp(-(exp_a * gate_logits)))).reshape(1, 2, 1, 2)
+    np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha, rtol=2e-5, atol=2e-5)
+
+  def test_kda_chunked_prefill_matches_decode(self):
+    config = self._make_config(max_context=4, n_heads=2,
+      ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=2, time_step_rank=2, inner_size=4, kda=True), kda_split_qkv=True)
+    x = Tensor.linspace(-1, 1, 4*config.dim, dtype=dtypes.float32).reshape(1, 4, config.dim).cast(dtypes.bfloat16)
+
+    chunked = GatedDeltaNetBlock(config, config.ssm)
+    sequential = GatedDeltaNetBlock(config, config.ssm)
+    for value in nn.state.get_state_dict(chunked).values(): value.replace(value.cast(dtypes.bfloat16).realize())
+    sequential_state = nn.state.get_state_dict(sequential)
+    for name, value in nn.state.get_state_dict(chunked).items(): sequential_state[name].replace(value)
+
+    chunked._init_state(x)
+    chunk_out = chunked._attention(x, 0).realize()
+    sequential._init_state(x)
+    seq_out = Tensor.cat(*[sequential._attention(x[:, t:t+1], t).realize() for t in range(x.shape[1])], dim=1).realize()
+
+    np.testing.assert_allclose(chunk_out.numpy(), seq_out.numpy(), rtol=1e-5, atol=1e-5)
+    for name in ("conv_state_q", "conv_state_k", "conv_state_v"):
+      np.testing.assert_allclose(getattr(chunked, name).numpy(), getattr(sequential, name).numpy(), rtol=2e-2, atol=4e-3)
+    np.testing.assert_allclose(chunked.recurrent_state.numpy(), sequential.recurrent_state.numpy(), rtol=2e-3, atol=2e-3)
+
+  def test_kda_tp_final_token_matches_unsharded(self):
+    config = self._make_config(dim=8, hidden_dim=16, n_heads=4, n_kv_heads=4, head_dim=2, rope_dim=2, v_head_dim=2,
+      ssm=SSMConfig(conv_kernel=2, state_size=2, group_count=4, time_step_rank=4, inner_size=8, kda=True), kda_split_qkv=True)
+    single, tp = GatedDeltaNetBlock(config, config.ssm), GatedDeltaNetBlock(config, config.ssm)
+    for name, value in nn.state.get_state_dict(single).items():
+      data = np.full(value.shape, 1.0, np.float32) if "norm.weight" in name else \
+             np.linspace(-0.2, 0.2, value.numel(), dtype=np.float32).reshape(value.shape)
+      if name == "ssm_a": data.fill(-0.1)
+      value.replace(Tensor(data, device="CPU", dtype=dtypes.bfloat16).realize())
+    tp_state = nn.state.get_state_dict(tp)
+    for name, value in nn.state.get_state_dict(single).items(): tp_state[name].replace(value)
+    devices = ("CPU", "CPU:1")
+    _shard_kimi(SimpleNamespace(blk=[tp]), devices)
+
+    x = Tensor(np.linspace(-1, 1, 32, dtype=np.float32).reshape(1, 4, 8), device="CPU", dtype=dtypes.bfloat16)
+    single._init_state(x)
+    expected = single._attention(x, 0).realize()
+    x_tp = x.shard(devices, axis=None)
+    tp._init_state(x_tp)
+    actual = tp._attention(x_tp, 0).realize()
+
+    np.testing.assert_equal(actual.numpy(), expected.numpy())
+    np.testing.assert_equal(tp.recurrent_state.numpy(), single.recurrent_state.numpy())
+    for name in ("conv_state_q", "conv_state_k", "conv_state_v"):
+      np.testing.assert_equal(getattr(tp, name).numpy(), getattr(single, name).numpy())
+
 class TestPairwiseTopk(unittest.TestCase):
  def test_basic_topk(self):
    x = Tensor([[[1.0, 3.0, 2.0, 5.0, 4.0]]])
@@ -213,5 +301,13 @@ class TestPairwiseTopk(unittest.TestCase):
        self.assertEqual(set(sel.numpy()[b, t].tolist()), expected)
        np.testing.assert_allclose(vals.numpy()[b, t], data[b, t][sel.numpy()[b, t]])

+  def test_iterative_matches_numpy(self):
+    rng = np.random.default_rng(42)
+    data = rng.standard_normal((2, 3, 896), dtype=np.float32)
+    vals, sel = iterative_topk(Tensor(data), 16)
+    expected = np.argsort(-data, axis=-1, stable=True)[..., :16]
+    np.testing.assert_equal(sel.numpy(), expected)
+    np.testing.assert_allclose(vals.numpy(), np.take_along_axis(data, expected, axis=-1))
+
 if __name__ == '__main__':
  unittest.main()
@@ -212,6 +212,18 @@ class TestCallSchedule(unittest.TestCase):
    out = f(a, v.bind(5))
    np.testing.assert_allclose(out.numpy(), [5., 10., 15.])

+  def test_precompile_scoped_bind_arg(self):
+    @function(precompile=True)
+    def f(x:Tensor, scale:UOp) -> Tensor: return x * scale
+    a = Tensor.ones(3)
+    x = f(a, UOp.variable("scale_a", 1, 100).bind(2))
+    y = f(a, UOp.variable("scale_b", 1, 100).bind(3))
+    fx = next(u for u in x.uop.toposort() if u.op is Ops.FUNCTION)
+    fy = next(u for u in y.uop.toposort() if u.op is Ops.FUNCTION)
+    self.assertEqual(fx.src[0].key, fy.src[0].key)
+    np.testing.assert_equal(x.numpy(), [2, 2, 2])
+    np.testing.assert_equal(y.numpy(), [3, 3, 3])
+
  def test_precompile_schedule_cache_hit(self):
    """two instances of the same @function should produce identical function body keys (schedule cache hit)"""
    @function(precompile=True)
@@ -347,5 +359,15 @@ class TestCallMultiSharded(unittest.TestCase):
    np.testing.assert_allclose(a.grad.numpy(), b.numpy(), rtol=1e-5)
    np.testing.assert_allclose(b.grad.numpy(), a.numpy(), rtol=1e-5)

+  def test_symbolic_reshape_shard_axis(self):
+    toks = UOp.variable("toks", 1, 2).bind(2)
+    devs = ("CPU:0", "CPU:1")
+    x = Tensor(np.arange(16, dtype=np.float32).reshape(1, 2, 8)).shard(devs, axis=2).realize()
+    @function
+    def f(x:Tensor) -> Tensor: return x.reshape(1, x.shape[1], 2, 4)
+    out = f(x[:, :toks]).realize()
+    self.assertEqual(out.uop.axis, 2)
+    np.testing.assert_equal(out[:1, :2].to(devs[0]).numpy(), np.arange(16, dtype=np.float32).reshape(1, 2, 2, 4))
+
 if __name__ == '__main__':
  unittest.main()
@@ -222,6 +222,12 @@ class TestAutoCastType(unittest.TestCase):
    t.square().mean().backward()
    np.testing.assert_allclose(t.grad.numpy().flatten(), [60000 * 2 / (N*N)] * N*N)

+  @unittest.skipUnless(dtypes.half in supported_dtypes, "need half")
+  def test_var_half_precision_large_n(self):
+    # the element count (70000) exceeds half max (65504): the denominator must not be materialized in half
+    t = Tensor([[0.0, 1.0]], dtype=dtypes.half).expand(35000, 2).contiguous()
+    np.testing.assert_allclose(t.var().numpy(), 0.25, rtol=1e-3)
+
  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "Precision error")
  @unittest.skipUnless(dtypes.half in supported_dtypes, "need half")
  def test_softmax_dtype(self):
@@ -3,7 +3,8 @@ import tempfile, unittest, math
 from tinygrad import Tensor, dtypes, TinyJit
 from tinygrad.helpers import Context
 from tinygrad.dtype import least_upper_float
-from tinygrad.uop.ops import UOp, Ops, dtype_from_uop, graph_rewrite, pm_lower_index_dtype, pm_commit_weak
+from tinygrad.uop.ops import UOp, Ops, dtype_from_uop, graph_rewrite
+from tinygrad.uop.weak import pm_lower_index_dtype, pm_commit_weak
 from tinygrad.uop.symbolic import symbolic_simple
 from tinygrad.uop.spec import spec_shared, type_verify
 from tinygrad.engine.jit import JitError
@@ -44,9 +45,13 @@ class TestWeakPromotion(unittest.TestCase):
    r = Tensor([2], dtype=dtypes.uint8, device="CPU").copysign(Tensor([1], dtype=dtypes.uint32, device="CPU"))
    self.assertEqual((r.dtype, r.tolist()), (dtypes.uint32, [2]))

-  def test_minimum_commits_both_operands(self):
+  def test_minimum_reflects_weak_operand(self):
    r = Tensor(1).minimum(Tensor([2], dtype=dtypes.uint8, device="CPU"))
    self.assertEqual((r.dtype, r.tolist()), (dtypes.uint8, [1]))
+    for dt in dtypes.uints:
+      r = Tensor([dt.max], dtype=dt, device="CPU").minimum(1)
+      self.assertEqual((r.dtype, r.tolist()), (dt, [1]))
+      self.assertNotIn(Ops.CAST, [u.op for u in r._uop.toposort()])

  def test_broadcasted_keeps_const_weak(self):
    # a python scalar stays a bare weak CONST through _broadcasted, lifted only to the KIND of the lub
@@ -58,6 +63,31 @@ class TestWeakPromotion(unittest.TestCase):
    self.assertEqual((x._uop.base.op, x._uop.base.val, x.dtype, x.shape, y.dtype),
                     (Ops.CONST, 1, dtypes.weakfloat, (1,), dtypes.float32))

+  def test_weak_expression_anchors_at_strong_lub(self):
+    # regression test for the HALF bert nan (#17408, reverted in #17409): lub(int32, weakfloat)==weakfloat makes
+    # `loss_mask.sum() + 1e-5` a weakfloat EXPRESSION. Meeting a strong float in a binop must pin it at the lub
+    denom = (Tensor.zeros(912, dtype=dtypes.int32) != Tensor.zeros(912, dtype=dtypes.float32)).sum() + 1e-5
+    self.assertIs(denom.dtype, dtypes.weakfloat)  # the setup: the denominator expression itself is weak
+    x, y = Tensor([2048.0], dtype=dtypes.float32)._broadcasted(denom)
+    self.assertIs(y.dtype, dtypes.float32)
+    recips = [u for u in (x / y)._uop.toposort() if u.op is Ops.RECIPROCAL]
+    self.assertEqual([(u.dtype, u.src[0].dtype) for u in recips], [(dtypes.float32, dtypes.float32)])
+    with Context(DEFAULT_FLOAT=dtypes.float16):
+      committed = graph_rewrite((UOp.const(1).cast(dtypes.int32) + UOp.const(1.0)).cast(dtypes.float32), pm_lower_index_dtype, ctx={})
+    self.assertEqual([u.dtype for u in committed.toposort() if u.op is Ops.ADD], [dtypes.float32])
+
+  def test_cast_weak_expression_commits_at_cast_floor(self):
+    # the floor never narrows: a cast BELOW the default does not pull the compute width down with it
+    with Context(DEFAULT_FLOAT=dtypes.float32):
+      narrowed = graph_rewrite((UOp.const(1.0) + UOp.const(2.0)).cast(dtypes.float16), pm_lower_index_dtype, ctx={})
+    self.assertEqual((narrowed.dtype, narrowed.src[0].dtype), (dtypes.float16, dtypes.float32))
+
+  def test_cast_weak_expression_value_uses_cast_floor(self):
+    with Context(DEFAULT_FLOAT=dtypes.float16):
+      denom = Tensor.ones(1, dtype=dtypes.int32, device="CPU").sum() * 70000 + 1e-5
+      out = Tensor(1.0, dtype=dtypes.float32, device="CPU") / denom
+      self.assertAlmostEqual(out.item(), 1 / (70000 + 1e-5), places=10)
+
  def test_uop_scalar_const_lifts_kind(self):
    for dtype, value, out_dtype, const_dtype in ((dtypes.weakint, 1, dtypes.weakint, dtypes.weakint),
                                                 (dtypes.int32, 1, dtypes.int32, dtypes.weakint),
@@ -4,6 +4,7 @@ from tinygrad.function import function
 from tinygrad import Tensor, GlobalCounters, Device
 from tinygrad.dtype import Invalid
 from tinygrad.uop.ops import UOp, Ops, KernelInfo, ProgramInfo
+from test.helpers import assert_kernel_count

 class TestFunction(unittest.TestCase):
  def test_simple(self):
@@ -618,7 +619,7 @@ class TestFunctionTuple(unittest.TestCase):
    out = f(a)
    GlobalCounters.reset()
    out.realize()
-    self.assertEqual(GlobalCounters.kernel_count, kernel_count)
+    assert_kernel_count(kernel_count)
    np.testing.assert_allclose(out.numpy(), [3., 5., 7., 9.])

  def test_custom_kernel_precompile_further_compute_multi(self): self.test_custom_kernel_precompile_further_compute(multi=True, kernel_count=4)
@@ -0,0 +1,30 @@
+import unittest
+from tinygrad.llm.cli import KimiK3Template
+from tinygrad.llm.serve import StreamRouter
+
+class TestKimiK3Template(unittest.TestCase):
+  def test_simple_text_chat(self):
+    template = KimiK3Template()
+    got = template.render([{"role":"system", "content":"Be concise."}, {"role":"user", "content":"Hello"}])
+    self.assertTrue(got.startswith('<|open|>message role="system" type="thinking-effort"<|sep|>'))
+    self.assertIn('<|open|>message role="user"<|sep|>Hello<|close|>message<|sep|><|end_of_msg|>', got)
+    self.assertTrue(got.endswith('<|open|>message role="assistant"<|sep|><|open|>think<|sep|>'))
+
+  def test_preserves_assistant_thinking(self):
+    got = KimiK3Template().render([{"role":"assistant", "reasoning_content":"why", "content":"answer"}], add_generation_prompt=False)
+    self.assertIn('<|open|>think<|sep|>why<|close|>think<|sep|>', got)
+    self.assertIn('<|open|>response<|sep|>answer<|close|>response<|sep|>', got)
+
+  def test_rejects_unimplemented_modalities(self):
+    with self.assertRaisesRegex(ValueError, "text-only"):
+      KimiK3Template().render([{"role":"user", "content":[{"type":"image", "url":"x"}]}])
+    with self.assertRaisesRegex(ValueError, "tool rendering"):
+      KimiK3Template().render([{"role":"user", "content":"x"}], tools=[{"type":"function"}])
+
+  def test_xtml_stream_router(self):
+    router, routed = StreamRouter(reasoning=True, xtml=True), []
+    for piece in ("rea", "son<|close|>thi", "nk<|sep|><|open|>response<|sep|>ans", "wer<|close|>response<|sep|>"):
+      routed.extend(router.route(piece))
+    self.assertEqual(routed, [("reasoning_content", "rea"), ("reasoning_content", "son"), ("content", "ans"), ("content", "wer")])
+
+if __name__ == "__main__": unittest.main()
@@ -0,0 +1,139 @@
+import tempfile, unittest
+from pathlib import Path
+from dataclasses import replace
+import numpy as np
+from tinygrad import Tensor, dtypes, nn
+from tinygrad.helpers import getenv
+from tinygrad.llm.kernels import bf16_mfma_splitk
+from tinygrad.llm.kimi_k3 import KIMI_K3_FULL_ATTN_LAYERS, KIMI_K3_SSM_LAYERS, KIMI_K3_TEXT_SIZE, KIMI_K3_TP8_BYTES_PER_GPU, \
+  _layer_sources, _load_stacked_experts, _replace, _safe_load_selected, _shard_kimi_k3, _validate_config, kimi_k3_config, kimi_k3_smoke_config
+from tinygrad.llm.model import FFNBlock, Transformer
+
+def small_k3_config(max_context:int=4): return replace(kimi_k3_smoke_config(max_context), num_experts=8)
+
+class TestKimiK3(unittest.TestCase):
+  def test_selective_safetensor_load(self):
+    with tempfile.TemporaryDirectory() as tmp:
+      path = Path(tmp) / "weights.safetensors"
+      nn.state.safe_save({"keep":Tensor.arange(8), "skip":Tensor.arange(16)}, str(path))
+      selected = _safe_load_selected(path, ["keep"])
+      self.assertEqual(list(selected), ["keep"])
+      np.testing.assert_equal(selected["keep"].numpy(), np.arange(8))
+      with self.assertRaisesRegex(ValueError, "missing tensor absent"): _safe_load_selected(path, ["absent"])
+
+  def test_smoke_config_preserves_gfx950_expert_alignment(self):
+    c = kimi_k3_smoke_config()
+    self.assertEqual(c.routed_expert_dim % 64, 0)
+    self.assertEqual((c.hidden_dim // 8) % 64, 0)
+
+  @unittest.skipUnless(getenv("DEV", "") == "NULL:HIP:gfx950", "gfx950 compile coverage")
+  def test_gfx950_mfma_splitk_compile(self):
+    x = Tensor.zeros(1, 1, 256, dtype=dtypes.bfloat16, device="NULL:HIP:gfx950")
+    weight = Tensor.zeros(16, 256, dtype=dtypes.bfloat16, device="NULL:HIP:gfx950")
+    self.assertEqual(bf16_mfma_splitk(x, weight).realize().shape, (1, 1, 16))
+
+  def test_official_config(self):
+    c = kimi_k3_config(1_048_576)
+    self.assertEqual((c.num_blocks, c.dim, c.n_heads, c.num_experts, c.num_experts_per_tok), (93, 7168, 96, 896, 16))
+    self.assertEqual((sum(KIMI_K3_SSM_LAYERS), len(KIMI_K3_FULL_ATTN_LAYERS)), (69, 24))
+    self.assertEqual(KIMI_K3_FULL_ATTN_LAYERS, (*range(3, 93, 4), 92))
+    self.assertEqual((c.routed_expert_dim, c.hidden_dim, c.shared_expert_dim), (3584, 3072, 6144))
+    self.assertTrue(c.route_weights_uncorrected and c.kda_full_rank_gate and c.attn_output_gate)
+    self.assertTrue(c.ssm is not None and c.ssm.channel_decay)
+    self.assertEqual((c.activation_situ_beta, c.activation_situ_linear_beta, c.kda_gate_lower_bound), (4.0, 25.0, -5.0))
+
+  def test_config_rejects_wrong_checkpoint(self):
+    with self.assertRaisesRegex(ValueError, "not the supported official"):
+      _validate_config({"model_type":"kimi_linear", "hidden_size":2304})
+
+  def test_official_mapping_covers_model(self):
+    model = Transformer(kimi_k3_config(1))
+    state = nn.state.get_state_dict(model)
+    targets = {"token_embd.weight", "output_norm.weight", "output.weight", "output_attn_res_norm.weight", "output_attn_res_proj.weight"}
+    for i,is_kda in enumerate(KIMI_K3_SSM_LAYERS):
+      for target in _layer_sources(i, is_kda).values(): targets.update(target.split("|"))
+      if i:
+        for name in ("ffn_gate_exps.weight", "ffn_gate_exps.weight_scale", "ffn_up_exps.weight", "ffn_up_exps.weight_scale",
+                     "ffn_down_exps.weight", "ffn_down_exps.weight_scale"): targets.add(f"blk.{i}.{name}")
+    self.assertEqual(targets, set(state))
+    self.assertEqual(state["blk.1.ffn_gate_exps.weight"].shape, (896, 3072, 1792))
+    self.assertEqual(state["blk.1.ffn_gate_exps.weight_scale"].shape, (896, 3072, 112))
+    self.assertEqual(state["blk.0.ssm_a"].shape, (128, 1))
+    _shard_kimi_k3(model, tuple(f"NULL:{i}" for i in range(8)))
+    total, per_gpu = 0, 0
+    for name,value in state.items():
+      dtype = dtypes.uint8 if name.endswith(("weight_scale", "_exps.weight")) else dtypes.float32 if name.endswith(
+        ("exp_probs_b.bias", "ssm_q_conv1d.weight", "ssm_k_conv1d.weight", "ssm_v_conv1d.weight", "ssm_norm.weight", "ssm_a", "ssm_dt.bias")) \
+        else dtypes.bfloat16
+      size = value.numel() * dtype.itemsize
+      total += size
+      per_gpu += size if value.uop.axis is None else size//8
+    self.assertEqual((total, per_gpu), (KIMI_K3_TEXT_SIZE, KIMI_K3_TP8_BYTES_PER_GPU))
+
+  def test_situ_matches_reference(self):
+    block = FFNBlock(small_k3_config())
+    gate, up = Tensor([[-8., -1., 0., 3.]]), Tensor([[-30., -2., 5., 40.]])
+    got = block._activation(gate, up).numpy()
+    g, u = gate.numpy().astype(np.float32), up.numpy().astype(np.float32)
+    expected = (4*np.tanh(g/4)/(1+np.exp(-g))) * (25*np.tanh(u/25))
+    np.testing.assert_allclose(got, expected, rtol=1e-5, atol=1e-5)
+
+  def test_attention_residual_matches_reference(self):
+    block = FFNBlock(small_k3_config())
+    block.attn_res_norm.weight.assign([1.0+i/16 for i in range(32)])
+    block.attn_res_proj.weight.assign([[(-1.0)**i/8 for i in range(32)]])
+    prefix, residual = Tensor.arange(64).reshape(2, 32).float()/16, Tensor.arange(128).reshape(2, 2, 32).float()/32
+    got = block._apply_attn_res(prefix, residual, block.attn_res_proj, block.attn_res_norm).numpy()
+    v = np.concatenate((residual.numpy(), prefix.numpy()[:, None]), axis=1).astype(np.float32)
+    k = v / np.sqrt(np.mean(v*v, axis=-1, keepdims=True) + 1e-5)
+    scores = np.sum(k * block.attn_res_norm.weight.numpy() * block.attn_res_proj.weight.numpy()[0], axis=-1)
+    probs = np.exp(scores-scores.max(axis=-1, keepdims=True))
+    probs /= probs.sum(axis=-1, keepdims=True)
+    expected = np.matmul(probs[:, None], v).squeeze(1)
+    np.testing.assert_allclose(got, expected, rtol=1e-5, atol=1e-5)
+
+  def test_tp8_schema(self):
+    model = Transformer(small_k3_config())
+    _shard_kimi_k3(model, tuple(f"NULL:{i}" for i in range(8)))
+    state = nn.state.get_state_dict(model)
+    for name,axis in (("token_embd.weight",0), ("blk.1.ffn_gate_exps.weight",1), ("blk.1.ffn_down_exps.weight_scale",2),
+                      ("blk.1.ffn_routed_down.weight",1), ("blk.0.ssm_g_full.weight",0), ("blk.1.attn_q_b.weight",0)):
+      self.assertEqual(state[name].uop.axis, axis, name)
+    self.assertIsNone(state["blk.1.attn_res_norm.weight"].uop.axis)
+    self.assertIsNone(state["blk.1.ffn_routed_norm.weight"].uop.axis)
+    self.assertIsNone(state["blk.0.ssm_a"].uop.axis)
+
+  def test_direct_expert_staging(self):
+    devices = tuple(f"PYTHON:{i}" for i in range(4))
+    sources = [Tensor([[(e*40+r*4+c)&255 for c in range(4)] for r in range(8)], dtype=dtypes.uint8,
+                      device=devices[0]).realize() for e in range(8)]
+    expected = Tensor.stack(*sources).numpy()
+    for axis in (1, 2):
+      dst = Tensor.zeros(8, 8, 4, dtype=dtypes.uint8, device=devices[0]).shard(devices, axis=axis)
+      _load_stacked_experts(dst, sources)
+      np.testing.assert_equal(dst.numpy(), expected)
+
+  def test_direct_tp_replacement(self):
+    devices = tuple(f"PYTHON:{i}" for i in range(4))
+    source = Tensor.arange(64, dtype=dtypes.float32).reshape(8, 8).realize()
+    expected = source.numpy()
+    for axis in (None, 0, 1):
+      dst = Tensor.zeros(8, 8, device="PYTHON").shard(devices, axis=axis)
+      _replace(dst, source)
+      np.testing.assert_equal(dst.numpy(), expected)
+
+  def test_chunked_recurrent_generate(self):
+    model = Transformer(small_k3_config(max_context=8))
+    for name,value in nn.state.get_state_dict(model).items():
+      fill = 127 if name.endswith("weight_scale") else 0
+      value.replace(Tensor.full(value.shape, fill, dtype=value.dtype if value.dtype is dtypes.uint8 else dtypes.bfloat16, device="PYTHON"))
+    self.assertIsInstance(next(model.generate([1], chunk_size=2)), int)
+    prompt = [1, 2, 3, 4]
+    for _ in range(3): self.assertIsInstance(next(model.generate(prompt.copy(), chunk_size=2)), int)
+    self.assertEqual(model.get_start_pos(model._cached_tokens + [42]), len(prompt))
+    self.assertEqual(model.get_start_pos([9, 2, 3, 4, 42]), 0)
+    self.assertIsInstance(next(model.generate([1, 2, 3, 4, 5], chunk_size=3)), int)
+    self.assertEqual(set(model.recurrent_greedy_prefill_jits), {2})
+    self.assertEqual(model._cached_tokens[:4], [1, 2, 3, 4])
+
+if __name__ == "__main__": unittest.main()
@@ -0,0 +1,37 @@
+import unittest
+from tinygrad import dtypes, nn
+from tinygrad.llm.kimi import KIMI_LOGICAL_BYTES, KIMI_SSM_LAYERS, KIMI_TENSOR_COUNT, _shard_kimi, _validate_kimi_state, kimi_config
+from tinygrad.llm.model import Transformer
+
+class TestKimiLinear(unittest.TestCase):
+  def test_architecture_config(self):
+    config = kimi_config(4096)
+    self.assertEqual((config.num_blocks, config.dim, config.n_heads, config.vocab_size), (27, 2304, 32, 163840))
+    self.assertEqual(tuple(i for i, is_kda in enumerate(KIMI_SSM_LAYERS) if not is_kda), (3, 7, 11, 15, 19, 23, 26))
+    self.assertEqual((config.num_experts, config.num_experts_per_tok, config.shared_expert_dim), (256, 8, 1024))
+    self.assertTrue(config.expert_mxfp4 and config.bf16_activations and config.kda_split_qkv)
+    self.assertFalse(config.shared_expert_gate)
+
+  def test_tp4_schema_and_axes(self):
+    model = Transformer(kimi_config(32))
+    state = nn.state.get_state_dict(model)
+    self.assertEqual(len(state), KIMI_TENSOR_COUNT)
+    self.assertNotIn("blk.1.ffn_gate_inp_shexp.weight", state)
+    self.assertEqual(state["blk.1.ffn_gate_exps.weight"].dtype, dtypes.uint8)
+    self.assertEqual(state["blk.1.ffn_gate_exps.weight_scale"].dtype, dtypes.uint8)
+
+    _shard_kimi(model, ("NULL:0", "NULL:1", "NULL:2", "NULL:3"))
+    state = nn.state.get_state_dict(model)
+    for name, axis in (("token_embd.weight", 0), ("blk.1.ffn_gate_exps.weight", 1),
+                       ("blk.1.ffn_down_exps.weight_scale", 2), ("blk.3.attn_k_b.weight", 0)):
+      self.assertEqual(state[name].uop.axis, axis, name)
+    self.assertIsNone(state["blk.1.attn_norm.weight"].uop.axis)
+
+  def test_converted_schema_validation(self):
+    model = Transformer(kimi_config(1))
+    state = {name:value if value.dtype is dtypes.uint8 else value.cast(dtypes.bfloat16)
+             for name,value in nn.state.get_state_dict(model).items()}
+    _validate_kimi_state(model, state)
+    self.assertEqual(sum(value.nbytes() for value in state.values()), KIMI_LOGICAL_BYTES)
+
+if __name__ == "__main__": unittest.main()
@@ -72,6 +72,20 @@ class TestMoEFeedForward(unittest.TestCase):
    expected = (Tensor([1.0]).silu().item() + Tensor([3.0]).silu().item()) / 2
    np.testing.assert_allclose(out.numpy()[0, 0, 0], expected, rtol=1e-2)

+  def test_kimi_correction_bias_affects_route_weights(self):
+    dim, hidden, n_heads, num_experts, k = 8, 16, 2, 4, 2
+    config = replace(_moe_config(dim, hidden, n_heads, num_experts, k), norm_topk_prob=True, expert_bias=True)
+    block = TransformerBlock(config)
+    block.ffn_gate_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) * (i + 1) for i in range(num_experts)])
+    block.ffn_up_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) for _ in range(num_experts)])
+    block.ffn_down_exps.weight = Tensor.stack(*[Tensor.eye(dim, hidden) for _ in range(num_experts)])
+    block.ffn_gate_inp.weight = Tensor.zeros(num_experts, dim)
+    block.exp_probs_b["bias"] = Tensor([0.2, 0.1, 0.0, -0.1])
+
+    out = block._feed_forward(Tensor.ones(1, 1, dim))
+    expected = (Tensor([1.0]).silu().item() * 0.7 + Tensor([2.0]).silu().item() * 0.6) / 1.3
+    np.testing.assert_allclose(out.numpy()[0, 0, 0], expected, rtol=1e-2)
+
  def test_moe_feed_forward_shared_expert(self):
    dim, hidden, n_heads = 8, 16, 2
    num_experts, k = 4, 2
@@ -0,0 +1,68 @@
+import unittest
+import numpy as np
+from tinygrad import Tensor, dtypes
+from tinygrad.llm.quant import MXFP4_VALUES, dequantize_mxfp4, quantize_dequantize_mxfp8, quantize_mxfp4, quantize_mxfp4_cpu
+
+class TestMXFormats(unittest.TestCase):
+  def test_mxfp4_known_codes_and_scale(self):
+    values = np.array(MXFP4_VALUES * 2, dtype=np.float32)
+    packed, scale = quantize_mxfp4(Tensor(values))
+    # Positive and negative zero are numerically identical, so nearest-value encoding canonicalizes to +0.
+    np.testing.assert_array_equal(packed.numpy(), np.array([0x10, 0x32, 0x54, 0x76, 0x90, 0xba, 0xdc, 0xfe] * 2, dtype=np.uint8))
+    np.testing.assert_array_equal(scale.numpy(), np.array([127], dtype=np.uint8))
+    np.testing.assert_array_equal(dequantize_mxfp4(packed, scale, dtypes.float32).numpy(), values)
+
+  def test_mxfp4_block_scales_and_zero(self):
+    x = Tensor(np.array([0.0]*32 + [12.0, -12.0] + [0.0]*30, dtype=np.float32))
+    packed, scale = quantize_mxfp4(x)
+    np.testing.assert_array_equal(scale.numpy(), np.array([127, 128], dtype=np.uint8))
+    np.testing.assert_allclose(dequantize_mxfp4(packed, scale, dtypes.float32).numpy(), x.numpy())
+
+  def test_mxfp4_scale_rounds_amax_over_format_max(self):
+    # OCP E8M0 scale selection rounds log2(amax / 6), rather than flooring the
+    # input exponent. At this boundary the two rules differ by a factor of two.
+    x = Tensor(np.array([8.0] + [0.0]*31, dtype=np.float32))
+    packed, scale = quantize_mxfp4(x)
+    np.testing.assert_array_equal(scale.numpy(), np.array([127], dtype=np.uint8))
+    self.assertEqual(dequantize_mxfp4(packed, scale, dtypes.float32).numpy()[0], 6.0)
+
+  def test_mxfp4_cpu_converter_matches_tensor_path(self):
+    x = Tensor(np.linspace(-13, 13, 64*32, dtype=np.float32).reshape(64, 32))
+    packed, scale = quantize_mxfp4(x)
+    cpu_packed, cpu_scale = quantize_mxfp4_cpu(x)
+    np.testing.assert_array_equal(cpu_packed.numpy(), packed.numpy())
+    np.testing.assert_array_equal(cpu_scale.numpy(), scale.numpy())
+
+  def test_mxfp4_midpoints_round_to_even(self):
+    midpoints = np.array([0.25, 0.75, 1.25, 1.75, 2.5, 3.5, 5.0], dtype=np.float32)
+    x = Tensor(np.pad(np.concatenate((midpoints, -midpoints)), (0, 18)))
+    packed, scale = quantize_mxfp4(x)
+    expected = np.pad(np.array([0, 1, 1, 2, 2, 4, 4, 0, -1, -1, -2, -2, -4, -4], dtype=np.float32), (0, 18))
+    np.testing.assert_array_equal(dequantize_mxfp4(packed, scale, dtypes.float32).numpy(), expected)
+
+  def test_mxfp8_roundtrip_and_dtype(self):
+    # All E4M3-exact values remain exact after extracting a shared exponent.
+    x = Tensor(np.array(([0.0, 0.5, 1.0, 1.5, 2.0, -3.0, 4.0, -6.0] * 4), dtype=np.float32))
+    out = quantize_dequantize_mxfp8(x)
+    self.assertEqual(out.dtype, dtypes.bfloat16)
+    np.testing.assert_array_equal(out.float().numpy(), x.numpy())
+
+  def test_mxfp8_subnormal_and_rounding(self):
+    x = np.zeros(32, dtype=np.float32)
+    x[:5] = [1.0, 1.0625, 1.07, 2**-9, 2**-10]
+    out = quantize_dequantize_mxfp8(Tensor(x), dtype=dtypes.float32).numpy()
+    # amax / 448 rounds to an E8M0 scale of 2**-9, saturating the largest
+    # values while retaining the E4M3 subnormal quantum for this block.
+    np.testing.assert_array_equal(out[:5], [0.875, 0.875, 0.875, 2**-9, 2**-10])
+
+  def test_mxfp8_uses_full_e4m3_range(self):
+    x = np.zeros(32, dtype=np.float32)
+    x[:4] = [448.0, 416.0, 400.0, -448.0]
+    np.testing.assert_array_equal(quantize_dequantize_mxfp8(Tensor(x), dtype=dtypes.float32).numpy()[:4], [448.0, 416.0, 384.0, -448.0])
+
+  def test_mxfp8_scale_rounds_amax_over_format_max(self):
+    x = np.zeros(32, dtype=np.float32)
+    x[0] = 512.0
+    self.assertEqual(quantize_dequantize_mxfp8(Tensor(x), dtype=dtypes.float32).numpy()[0], 448.0)
+
+if __name__ == "__main__": unittest.main()
@@ -1,8 +1,10 @@
 import unittest
+from dataclasses import replace
 from unittest.mock import patch
 from tinygrad import Tensor, UOp
 from tinygrad.schedule import schedule_cache
 from tinygrad.llm.model import Transformer, TransformerConfig
+from tinygrad.llm.serve import StreamRouter, parse_kimi_tool_call

 TEST_CONFIG = TransformerConfig(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
                           norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, rope_dim=32, v_head_dim=32, max_context=32)
@@ -10,12 +12,99 @@ V_START_POS = UOp.variable("start_pos", 0, TEST_CONFIG.max_context-1)
 V_TOKS = UOp.variable("toks", 1, 32)  # 32 is the default chunk_size in generate

 class TestTransformerGenerate(unittest.TestCase):
+  def test_warmup(self):
+    model, calls = Transformer(TEST_CONFIG), []
+    def generate(tokens, temperature):
+      calls.append((tokens, temperature))
+      yield from (1, 2)
+    with patch.object(model, "generate", generate): model.warmup()
+    self.assertEqual(calls, [([0], 0.0), ([0], 0.0)])
+
+  def test_recurrent_warmup_captures_reset_replay(self):
+    model, calls = Transformer(TEST_CONFIG), []
+    model.has_recurrent_block = True
+    state = Tensor.ones(4).realize()
+    model.blk[0]._state_reset_ops = lambda: [state.assign(state.const_like(0))]
+    def generate(tokens, temperature):
+      if calls: model.reset_jit()
+      calls.append((tokens.copy(), temperature))
+      tokens.append(42)
+      yield from (1, 2)
+    with patch.object(model, "generate", generate): model.warmup()
+    prompt = [0] * (TEST_CONFIG.max_context-2)
+    self.assertEqual(calls, [(prompt, 0.0)] * 3 + [(prompt, 1.0)] * 3 + [(prompt + list(range(1, i+1)), 0.0) for i in range(1, 4)])
+    self.assertEqual(model.reset_jit.cnt, 8)
+    cache_size = len(schedule_cache)
+    model.reset_jit()
+    self.assertEqual(len(schedule_cache), cache_size)
+
+  def test_first_recurrent_generate_before_state_init(self):
+    model = Transformer(TEST_CONFIG)
+    model.has_recurrent_block = True
+    with patch.object(Transformer, '__call__', return_value=Tensor([[42]])):
+      self.assertEqual(next(model.generate([0])), 42)
+
+  def test_recurrent_prefill_tail_uses_rollout_shape(self):
+    model = Transformer(TEST_CONFIG)
+    model.has_recurrent_block = True
+    model.config = replace(model.config, recurrent_prefill_chunked=True)
+    calls = []
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
+      calls.append(tokens.shape)
+      return Tensor([[42]])
+    with patch.object(Transformer, '__call__', mock_call): next(model.generate([1, 2, 3, 4, 5, 6], chunk_size=4))
+    self.assertEqual(calls, [(1, 4), (1, 1), (1, 1)])
+
+  def test_recurrent_live_state_reuse(self):
+    model = Transformer(TEST_CONFIG)
+    model.has_recurrent_block = True
+    model._cached_tokens = [1, 2, 3, 4, 5]
+    self.assertEqual(model.get_start_pos([1, 2, 3, 4, 5, 42, 10]), 5)
+    calls = []
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
+      calls.append((tokens.shape, start_pos))
+      return Tensor([[42]])
+    with patch.object(Transformer, '__call__', mock_call):
+      next(model.generate([1, 2, 3, 4, 5, 42, 10]))
+    self.assertEqual(calls, [((1, 1), V_START_POS.bind(5)), ((1, 1), V_START_POS.bind(6))])
+
+  def test_recurrent_prompt_snapshot_reuse(self):
+    model = Transformer(TEST_CONFIG)
+    model.has_recurrent_block = True
+    state, calls = Tensor.ones(4).realize(), []
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
+      calls.append(start_pos)
+      return Tensor([[42]])
+    with patch.object(model, "_state_tensors", return_value=[state]), patch.object(model.blk[0], "_reusable_prefix_len", return_value=0), \
+         patch.object(Transformer, '__call__', mock_call):
+      next(model.generate([1, 2, 3]))
+      state.assign(state.const_like(5)).realize()
+      model._cached_tokens = [1, 2, 3, 9, 9]
+      calls.clear()
+      self.assertEqual(model.get_start_pos([1, 2, 3, 7, 8]), 3)
+      next(model.generate([1, 2, 3, 7, 8]))
+    self.assertEqual(calls, [V_START_POS.bind(3), V_START_POS.bind(4)])
+    self.assertEqual(state.tolist(), [1.0] * 4)
+
+  def test_template_starts_reasoning(self):
+    router = StreamRouter(reasoning=True)
+    self.assertEqual(list(router.route("reasoning</think>answer")),
+                     [("reasoning_content", "reasoning"), ("content", "answer")])
+
+  def test_kimi_tool_call_stream(self):
+    router = StreamRouter()
+    self.assertEqual(list(router.route("before<|tool_calls_section_beg")), [("content", "before")])
+    self.assertEqual(list(router.route("in|><|tool_call_begin|>functions.read:0<|tool_call_argument_begin|>"
+                                      '{"path":"/tmp/x"}<|tool_call_end|><|tool_calls_section_end|>')), [])
+    self.assertEqual(parse_kimi_tool_call("functions.read:0<|tool_call_argument_begin|>{\"path\":\"/tmp/x\"}"),
+                     ("read", {"path":"/tmp/x"}))
+
  def test_kv_cache_reuse(self):
    """Test that generate reuses the KV cache when tokens extend the cached prefix."""
    model = Transformer(TEST_CONFIG)

    captured_inputs = []
-    def mock_call(self, tokens, start_pos, temperature):
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
      captured_inputs.append((tokens.shape, start_pos))
      return Tensor([[42]])

@@ -40,7 +129,7 @@ class TestTransformerGenerate(unittest.TestCase):
    model = Transformer(TEST_CONFIG)

    captured_inputs = []
-    def mock_call(self, tokens, start_pos, temperature):
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
      captured_inputs.append((tokens.shape, start_pos))
      return Tensor([[42]])

@@ -88,7 +177,7 @@ class TestTransformerGenerate(unittest.TestCase):

    def get_prefill_flags(tokens, chunk_size):
      is_prefill = []
-      def mock_call(self, tokens, start_pos, temperature):
+      def mock_call(self, tokens, start_pos, temperature, **kwargs):
        is_prefill.append(resolve(tokens.shape[1] != 1))
        return Tensor([[42]])
      with patch.object(Transformer, '__call__', mock_call):
@@ -149,7 +238,7 @@ class TestTransformerGenerate(unittest.TestCase):
    """Temperature from generate should be passed through to __call__."""
    model = Transformer(TEST_CONFIG)
    captured_temps = []
-    def mock_call(self, tokens, start_pos, temperature):
+    def mock_call(self, tokens, start_pos, temperature, **kwargs):
      captured_temps.append(float(temperature.item()))
      return Tensor([[42]])
    with patch.object(Transformer, '__call__', mock_call):
@@ -2,7 +2,7 @@ import unittest, numpy as np
 from tinygrad import Tensor, Variable, Context, Device, TinyJit, GlobalCounters, dtypes, UOp, nn, getenv
 from tinygrad.nn.state import get_parameters, get_state_dict
 from tinygrad.uop.ops import Ops
-from test.helpers import not_support_multi_device, needs_second_gpu, slow
+from test.helpers import not_support_multi_device, needs_second_gpu, slow, assert_kernel_count, KernelCountException
 from hypothesis import given, strategies as strat, settings

 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
@@ -143,9 +143,8 @@ class TestMultiTensor(unittest.TestCase):
    GlobalCounters.reset()
    with Context(ALLREDUCE_CAST=1, RING=0, ALL2ALL=0):
      tst.realize()
-    kernel_count = GlobalCounters.kernel_count
+    assert_kernel_count(kernel_count)
    np.testing.assert_allclose(tst.numpy(), (a_src.numpy()+b_src.numpy()).sum(0))
-    self.assertEqual(kernel_count, kernel_count)

  def test_allreduce_cast_half_assign(self): self.test_allreduce_cast_half(assign=True, kernel_count=10)

@@ -385,6 +384,12 @@ class TestMultiTensor(unittest.TestCase):
      np.testing.assert_allclose(r.numpy(), np.ones(256)+np.ones(256), atol=1e-4, rtol=1e-5)
    assert jf.captured is not None

+  def test_symbolic_broadcast_copy(self):
+    rows = Variable("rows", 1, 4).bind(3)
+    out = Tensor.ones(rows, 8).to(devices_2).realize()
+    self.assertEqual(out.shape, (rows, 8))
+    np.testing.assert_equal(out[:3].to(Device.DEFAULT).numpy(), np.ones((3, 8)))
+
  def test_multitensor_jit_in_list(self):
    # test MULTI tensor inside a list container - exercises the container unpacking + MULTI unpacking
    @TinyJit
@@ -584,7 +589,7 @@ class TestMultiTensor(unittest.TestCase):
      zeros = Tensor.zeros(3).realize()
    b = a.to(devices_2)*zeros.to(devices_2)
    sched = b.schedule_linear().src
-    self.assertEqual(len(sched), 0)
+    if len(sched) != 0: raise KernelCountException(0, len(sched))
    self.assertListEqual(b.tolist(), [0, 0, 0])

@unittest.skipIf(not_support_multi_device(), "no multi")
@@ -1,15 +1,16 @@
 import unittest
 from tinygrad import Tensor, dtypes, GlobalCounters
+from test.helpers import assert_kernel_count

 class TestSetitemInto(unittest.TestCase):
  def test_setitem_into_unrealized(self):
    GlobalCounters.reset()
    t = Tensor.arange(4, dtype=dtypes.int32).reshape(2, 2)
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    self.assertEqual(GlobalCounters.global_mem, 0)
    self.assertListEqual(t.tolist(), [[0, 1], [5, 5]])

@@ -18,11 +19,11 @@ class TestSetitemInto(unittest.TestCase):
    GlobalCounters.reset()
    a = Tensor.arange(8, dtype=dtypes.int32).reshape(2, 4)
    w = a[0] + a[1]  # unrealized ADD with SHRINK in graph: [4, 6, 8, 10]
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    w[1] = 99
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    w.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    self.assertEqual(GlobalCounters.global_mem, 0)
    self.assertListEqual(w.tolist(), [4, 99, 8, 10])

@@ -30,61 +31,61 @@ class TestSetitemInto(unittest.TestCase):
    GlobalCounters.reset()
    t = Tensor.empty(4, dtype=dtypes.int32)
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(GlobalCounters.global_mem, 4)
    t[1].realize()
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(t[1].item(), 5)

  def test_setitem_into_empty_alu(self):
    GlobalCounters.reset()
    t = Tensor.empty(4, dtype=dtypes.int32) + 1
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertLessEqual(GlobalCounters.global_mem, 32)
    t[1].realize()
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(t[1].item(), 5)

  def test_setitem_into_tensor(self):
    t = Tensor([1, 2, 3, 4], dtype=dtypes.int32).realize()
    GlobalCounters.reset()
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t[1].realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(GlobalCounters.global_mem, 4)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertListEqual(t.tolist(), [1, 5, 3, 4])

  def test_setitem_into_tensor_alu(self):
    t = Tensor([1, 2, 3, 4], dtype=dtypes.int32).realize() + 1
    GlobalCounters.reset()
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t[1].realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertLessEqual(GlobalCounters.global_mem, 32)
    t[1].realize()
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertListEqual(t.tolist(), [2, 5, 4, 5])

  def test_setitem_into_const(self):
    GlobalCounters.reset()
    t = Tensor.ones(4, dtype=dtypes.int32, buffer=False)
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    self.assertEqual(GlobalCounters.global_mem, 0)
    self.assertListEqual(t.tolist(), [1, 5, 1, 1])

@@ -92,9 +93,9 @@ class TestSetitemInto(unittest.TestCase):
    GlobalCounters.reset()
    t = Tensor.ones(4, dtype=dtypes.int32, buffer=False) + 1
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    self.assertEqual(GlobalCounters.global_mem, 0)
    self.assertListEqual(t.tolist(), [2, 5, 2, 2])

@@ -105,18 +106,18 @@ class TestSetitemInto(unittest.TestCase):
    t = Tensor.arange(4, dtype=dtypes.int32)
    self.assertIs(other.uop, t.uop)
    t[1] = 5
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    self.assertListEqual(t.tolist(), [0, 5, 2, 3])

  def test_setitem_slice_const(self):
    t = Tensor.zeros(100, dtype=dtypes.int32).contiguous().realize()
    GlobalCounters.reset()
    t[20:50] = 3
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(GlobalCounters.global_mem, 30*4)  # 30 elements written

  def test_setitem_slice_tensor(self):
@@ -124,18 +125,18 @@ class TestSetitemInto(unittest.TestCase):
    v = Tensor.zeros(30, dtype=dtypes.int32).contiguous().realize()
    GlobalCounters.reset()
    t[20:50] = v
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(GlobalCounters.global_mem, 30*4*2)  # 30 read + 30 written

  def test_setitem_full(self):
    t = Tensor.zeros(100, dtype=dtypes.int32).contiguous().realize()
    GlobalCounters.reset()
    t[:] = 3
-    self.assertEqual(GlobalCounters.kernel_count, 0)
+    assert_kernel_count(0)
    t.realize()
-    self.assertEqual(GlobalCounters.kernel_count, 1)
+    assert_kernel_count(1)
    self.assertEqual(GlobalCounters.global_mem, 100*4)  # full buffer written

 if __name__ == '__main__':
@@ -1,221 +0,0 @@
-from dataclasses import dataclass, field
-from tinygrad.dtype import dtypes, AddrSpace
-from tinygrad.uop.ops import UOp, UPat, PatternMatcher, Ops, GroupOp, ParamArg, graph_rewrite, track_rewrites
-from tinygrad.helpers import VIZ, pluralize, all_int
-
-@dataclass
-class AllocCtx:
-  uop_list: list[UOp] = field(default_factory=list)
-  buffer_map: dict[UOp, UOp] = field(default_factory=dict)
-  bases: set[UOp] = field(default_factory=set)
-  assigns: list[UOp] = field(default_factory=list)
-  replacements: list[UOp] = field(default_factory=list)
-
-def tag_uop(ctx:AllocCtx, x:UOp):
-  if x.tag is not None: return None
-  ctx.uop_list.append(x)
-  return x.replace(tag=(len(ctx.uop_list)-1,))
-
-def disk_like(u:UOp): return isinstance(u.device, str) and u.device.startswith(("DISK", "TINYFS"))
-
-def disk_copy_is_buffer(ctx:AllocCtx, u:UOp):
-  # copies to disk are replaced with the disk buffer
-  if disk_like(u) and u.tag is None:
-    ctx.buffer_map[u] = u.empty_like()
-    return u.rtag(())
-  # all copies from disk/numpy are realized into a real buffer
-  from_creation = isinstance(u.src[0].device, str) and u.src[0].device.startswith(("NPY", "DISK", "PYTHON", "TINYFS"))
-  if from_creation: return tag_uop(ctx, u)
-
-# CONTIGUOUS and AFTER + parents are the only nodes that get updated
-add_tags = PatternMatcher([
-  (UPat(Ops.COPY, name="u"), disk_copy_is_buffer),
-  # no tag on copies that are assigned via STORE+AFTER — merge COPY tag into AFTER
-  (UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat(name="dest"), UPat(Ops.COPY, name="c")))), name="a"),
-   lambda a,c,dest: a.replace(src=(a.src[0], a.src[1].replace(src=(dest, c.rtag(())))), tag=a.tag+c.tag) if a.tag and c.tag else None),
-  (UPat((Ops.CONTIGUOUS, Ops.AFTER), name="x"), tag_uop),
-  (UPat(GroupOp.All, name="x"), lambda ctx,x: tag_uop(ctx,x) if x in ctx.bases else None),
-])
-
-def replace_contig_with_store_after(u:UOp):
-  # can't allocate a buffer for a virtual value
-  if u.is_virtual: return None
-  # if size is 0, remove the contig
-  if 0 in u.shape: return u.src[0]
-  # no real contig for DISK/TINYFS tensors, they are left alone
-  if disk_like(u): return u.rtag(None)
-  buf = u.empty_like()
-  return buf.after(buf.store(u.src[0])).rtag(u.tag)
-
-def replace_store_after_with_contig(u:UOp, src:UOp):
-  assigned_to = u
-  while assigned_to.op in {Ops.BITCAST, Ops.AFTER, Ops.UNSHARD}: assigned_to = assigned_to.src[0].base
-  if assigned_to.op not in {Ops.BUFFER, Ops.SLICE}: return src.contiguous(tag=u.tag)
-
-def _make_buffer_view(src:UOp) -> UOp|None:
-  """If movement ops on src collapse to a contiguous range, return SLICE. Otherwise None."""
-  if (offset := src.contiguous_view_offset()) is None: return None
-  buf = src.base
-  if buf.op is Ops.SLICE:
-    byte_offset = buf.src[1].val * buf.src[0].dtype.itemsize + offset * src.dtype.itemsize
-    buf = buf.src[0]
-    if byte_offset % buf.dtype.itemsize != 0: return None
-    offset = byte_offset // buf.dtype.itemsize
-  return UOp(Ops.SLICE, src.dtype, (buf, UOp.const(offset)), src.numel())
-
-def contiguous_mops_to_view(c:UOp, src:UOp):
-  """MOPS(BUFFER) → SLICE when movement ops collapse to a contiguous range."""
-  buf = src.base
-  if buf.op not in {Ops.BUFFER, Ops.SLICE, Ops.UNSHARD}: return None
-  if src.op is Ops.RESHAPE and src.src[0].op in {Ops.BUFFER, Ops.SLICE} and c.op is not Ops.BITCAST: return None
-  if c.op is not Ops.BITCAST and src.op is Ops.BUFFER: return None
-
-  # no symbolic shape
-  if not all_int(c.shape): return None
-
-  if buf.op is not Ops.UNSHARD and (view := _make_buffer_view(src)) is not None:
-    view = (view.replace(dtype=c.dtype, arg=c.numel()) if c.op is Ops.BITCAST else view).reshape(c.shape)
-    return c.replace(src=(view,)) if c.op is Ops.COPY else view
-
-  # for UNSHARD tensors, use multi_pm to resolve per-shard movement ops, then create SLICE on the resolved result
-  if not isinstance(c.device, str):
-    from tinygrad.schedule.multi import multi_pm
-    resolved = graph_rewrite(src, multi_pm, name="multi_buffer_view")
-    if resolved.op is not Ops.UNSHARD: return None
-    if (view := _make_buffer_view(resolved.src[0])) is None: return None
-    return view.reshape(resolved.src[0].shape).unshard(resolved.arg, resolved.src[1:]).contiguous(tag=c.tag)
-
-  return None
-
-def _precompiled_output_redirect(s:UOp, t:UOp) -> UOp|None:
-  # how output s lands in the caller's buffer t, or None if it must be copied into t
-  # materialize straight into t
-  if s.op is Ops.CONTIGUOUS: return t.after(t.store(s.src[0]))
-  # rebind output storage to t
-  if s.op in {Ops.BUFFER, Ops.UNSHARD} and s.has_buffer_identity(): return t
-  return None
-
-def transform_precompiled_call(c:UOp) -> UOp|None:
-  if not c.arg.precompile: return None
-  assert c.src[0].op is Ops.TUPLE, f"expected TUPLE body for precompiled FUNCTION, got {c.src[0].op}"
-  input_buffers = tuple(x.contiguous() if x.op not in {Ops.AFTER, Ops.BIND} else x for x in c.src[1:])
-
-  # add the outputs to the call
-  srcs = c.src[0].src
-  resolved = [c.gettuple(i) for i in range(len(srcs))]
-  outs = tuple(r.empty_like() for r in resolved)
-  targets = [o.param_like(len(c.src)-1+i).shrink_to(s.shape) for i,(o,s) in enumerate(zip(outs, srcs))]
-
-  subs:dict[UOp, UOp] = {}
-  items:list[UOp] = []
-  for s, t in zip(srcs, targets):
-    after_deps:list[UOp] = []
-    while s.op is Ops.AFTER:
-      after_deps.extend(s.src[1:])
-      s = s.src[0]
-    if (placed := _precompiled_output_redirect(s, t)) is not None and s not in subs:
-      subs[s] = placed
-      items.append(s.after(*after_deps) if after_deps else s)
-    else:
-      items.append(t.after(t.store(s.after(*after_deps))))
-  fxn = UOp.sink(*(x.substitute(subs) for x in items))
-
-  # body switches from TUPLE to SINK, so the node becomes an opaque CALL (not FUNCTION)
-  new_call = UOp(Ops.CALL, src=(fxn, *input_buffers, *outs), arg=c.arg)
-  rets = tuple(o.after(new_call) for o in outs)
-
-  # if the CALL has symbolic shapes, shrink the max-sized output to the actual symbolic shape
-  # NOTE: must use resolved shapes from the FUNCTION (which substitutes PARAMs with external args), not raw body shapes
-  rets = tuple(r.shrink_to(rs.shape) for r,rs in zip(rets, resolved))
-
-  return UOp.maketuple(*rets)
-
-# NOTE: adding rules to here is bad. these all need to run before the schedule cache
-pm_early_transform_tensor_graph = PatternMatcher([
-  # transform precompiled FUNCTIONs into CALLs (body becomes SINK with stores)
-  (UPat(Ops.FUNCTION, name="c"), transform_precompiled_call),
-
-  # resolve TUPLE+GETTUPLE (for precompiled calls)
-  (UPat(Ops.GETTUPLE, src=(UPat(Ops.TUPLE, name="t"),), name="g"), lambda g,t: t.src[g.arg]),
-
-  # fold MOPS+BITCAST over BUFFER/SLICE into SLICE when movement ops collapse to contiguous range
-  (UPat((Ops.BITCAST, Ops.COPY, Ops.CONTIGUOUS), src=(UPat(GroupOp.Movement|{Ops.BUFFER}, name="src"),), name="c"), contiguous_mops_to_view),
-
-  # remove contiguous on movement ops before a copy on disk
-  (UPat(GroupOp.Movement-{Ops.SHRINK, Ops.RESHAPE}, name="x").f(Ops.CONTIGUOUS).f(Ops.COPY, name="copy"), lambda x,copy:
-   copy.replace(src=(x,), tag=None) if isinstance(x.device, str) and x.device.startswith("DISK") else None),
-  # push copy past movement ops to disk
-  (UPat(GroupOp.Movement-{Ops.SHRINK, Ops.RESHAPE}, name="x").f(Ops.COPY, name="copy"), lambda x,copy:
-   x.replace(src=(copy.replace(src=(x.src[0],), tag=None),)+x.src[1:]) \
-   if isinstance(x.device, str) and x.device.startswith("DISK") else None),
-
-  # add CONTIGUOUS to tagged UOps
-  (UPat(GroupOp.All-{Ops.CONTIGUOUS, Ops.AFTER, Ops.STORE}, name="x"),
-   lambda x: None if x.tag is None else x.rtag(None).contiguous(tag=x.tag) if x.tag else x.replace(tag=None)),
-  # remove extra CONTIGUOUS on AFTER (only when target is contiguous)
-  (UPat(Ops.CONTIGUOUS, src=(UPat(Ops.AFTER, name="a"),), name="c"),
-   lambda a,c: a.replace(tag=(a.tag or ())+(c.tag or ())) if a.src[0].has_buffer_identity() else None),
-  # replace AFTER+STORE with CONTIGUOUS when target is not a buffer
-  (UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat(), UPat(name="src")))), name="u"), replace_store_after_with_contig),
-  # replace CONTIGUOUS with STORE+AFTER
-  (UPat(Ops.CONTIGUOUS, name="u"), replace_contig_with_store_after),
-  # remove DETACH/CONTIGUOUS_BACKWARD (allows more contiguous removal)
-  (UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]),
-])
-
-def finalize_after(ctx:AllocCtx, x:UOp):
-  # untagged: record as an assign for the call body
-  if x.tag is None:
-    ctx.assigns.append(x)
-    return None
-  # tagged: untag and map each original pre-rewrite UOp to the stripped buffer; the untagged result is reprocessed as untagged
-  ret = x.replace(tag=None)
-  replace_uop = ret
-  while replace_uop.op is Ops.AFTER: replace_uop = replace_uop.src[0]
-  for t in x.tag:
-    original_uop: UOp = ctx.uop_list[t]
-    ctx.buffer_map[original_uop] = replace_uop.shrink_to(original_uop.shape)
-  return ret
-
-def replace_input_buffer(ctx:AllocCtx, b:UOp):
-  ctx.replacements.append(b)
-  if b.op is Ops.BIND: return b.param_like(len(ctx.replacements)-1)
-  return UOp.param(len(ctx.replacements)-1, b.dtype, b.shape, b.device,
-                   addrspace=b.addrspace if b.addrspace is not None else AddrSpace.GLOBAL)
-
-pm_finalize_call = PatternMatcher([
-  (UPat(Ops.AFTER, name="x"), finalize_after),
-  (UPat(Ops.COPY, name="x"), lambda ctx,x: ctx.assigns.append(x) if isinstance(x.device, str) and x.device.startswith(("DISK", "TINYFS")) else None),
-])
-
-pm_replace_buf = PatternMatcher([
-  # replace BUFFER with PARAM for cache key normalization
-  (UPat(Ops.BUFFER, src=(UPat(),), name="b"), lambda ctx,b:
-   replace_input_buffer(ctx, b) if isinstance(b.arg, ParamArg) and b.addrspace is AddrSpace.GLOBAL else None),
-  # replace SLICE with PARAM. this rewrite is bottom up so BUFFERs we don't need won't be in the input
-  (UPat(Ops.SLICE, src=(UPat(Ops.BUFFER), UPat(Ops.CONST, dtype=dtypes.weakint)), name="b"), replace_input_buffer),
-  # strip value from BIND for cache key normalization, so different values hit same cache
-  (UPat(Ops.BIND, src=(UPat(Ops.PARAM), UPat(Ops.CONST)), name="b"), replace_input_buffer),
-])
-
-@track_rewrites(lambda _,ret: f"Callify {pluralize('Buffer', len(ret[1]))}")
-def transform_to_call(big_sink:UOp) -> tuple[UOp, dict[UOp, UOp]]:
-  if VIZ: graph_rewrite(big_sink, PatternMatcher([]), name="View Tensor Graph")
-  # uop list is a list in the original_sink graph and we can map to the tags later
-  # same predicate as Tensor.realize
-  ctx = AllocCtx(bases={base for x in big_sink.src if not (base:=x.base).is_virtual and not base.has_buffer_identity()
-                        and base.op is not Ops.AFTER and base.addrspace is not AddrSpace.ALU})
-
-  # this rewrite is "read-only", it adds simple things to buffer_map and may sink things on big_sink, bottom_up
-  # this is the only one where we have to be careful to not break the tensor graph
-  big_sink = graph_rewrite(big_sink, add_tags, ctx=ctx, bottom_up=True, name="number the uops")
-
-  # here we can break the tensor graph. this is the only place you need to maintain numbered tags
-  big_sink = graph_rewrite(big_sink, pm_early_transform_tensor_graph, name="early transform tensor graph")
-
-  # here we construct the final buffer_map: as-built nodes -> their final storage. values are never keys
-  graph_rewrite(big_sink, pm_finalize_call, ctx=ctx, name="finalize call")
-  ret = graph_rewrite(UOp.sink(*ctx.assigns), pm_replace_buf, ctx=ctx, bottom_up=True, name="replace bufs").call(*ctx.replacements)
-  assert not any(x in ctx.buffer_map for x in ctx.buffer_map.values())
-  if VIZ: graph_rewrite(ret, PatternMatcher([]), name="View Call")
-  return ret, ctx.buffer_map
@@ -2,8 +2,8 @@ from dataclasses import replace, dataclass
 import itertools, functools
 from tinygrad.helpers import DISABLE_FAST_IDIV, TRANSCENDENTAL, SPEC, DEBUG, VIZ, IMAGE, NOOPT, EMULATED_DTYPES, NOLOCALS, USE_TC
 from tinygrad.helpers import ALLOW_TF32, DEFAULT_FLOAT, DEFAULT_INT, TracingKey, Context, panic
-from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, pm_lower_index_dtype, Ops, UPat, track_rewrites, KernelInfo, ProgramInfo, GroupOp
-from tinygrad.uop.ops import AxisType, pm_commit_weak, pm_cast_weak
+from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, Ops, UPat, rewrite_group, KernelInfo, ProgramInfo, GroupOp, AxisType
+from tinygrad.uop.weak import pm_lower_index_dtype, pm_commit_weak, pm_cast_weak
 from tinygrad.uop.render import pyrender
 from tinygrad.uop.spec import type_verify, spec_tensor, spec_program
 from tinygrad.renderer import Renderer, Estimates
@@ -12,7 +12,7 @@ from tinygrad.dtype import dtypes, AddrSpace

 # import all pattern matchers here
 from tinygrad.codegen.gpudims import pm_add_gpudims
-from tinygrad.uop.symbolic import sym, symbolic_simple, symbolic, pm_move_where_on_load, pm_clean_up_group_sink, pm_remove_invalid
+from tinygrad.uop.symbolic import sym, symbolic_simple, symbolic, pm_fold_cast_const, pm_move_where_on_load, pm_clean_up_group_sink, pm_remove_invalid
 from tinygrad.uop.movement import mop_cleanup
 from tinygrad.codegen.decomp.dtype import pm_dtype_decomps
 from tinygrad.codegen.decomp.op import get_late_rewrite_patterns, get_simplifying_rewrite_patterns
@@ -20,7 +20,7 @@ from tinygrad.codegen.decomp.transcendental import get_transcendental_patterns
 from tinygrad.codegen.late.coalesce import indexing_simplify
 from tinygrad.codegen.opt.postrange import apply_opts
 from tinygrad.codegen.late.gater import pm_move_gates_from_index
-from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse
+from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse, pm_reduce_unparented
 from tinygrad.schedule.multi import multi_pm
 from tinygrad.schedule.rangeify import pm_mops
 from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
@@ -125,8 +125,7 @@ def do_devectorize(b:UOp):
  # broadcasting needs to be already unpacked, Invalid matches any dtype and shape
  if not all(x.shape == b.shape or x.base.is_invalid for x in b.src): return None
  src = []
-  for idx in itertools.product(*[range(x) for x in b.shape]):
-    idx_c = [UOp.const(i) for i in idx]
+  for idx_c in itertools.product(*[[UOp.const(i) for i in range(x)] for x in b.shape]):
    src.append(b.replace(dtype=None, src=tuple(x.base if x.base.is_invalid else x.index(*idx_c) for x in b.src)))
  return UOp.stack(*src).reshape(b.shape) if b.op is not Ops.STORE else UOp.group(*src)

@@ -214,7 +213,7 @@ def reduce_ranges_to_acc(ctx:ReduceContext, r:UOp):
  topo = r.src[0].toposort()
  ended_ranges = flatten([x.ended_ranges for x in topo if x.op is Ops.END])
  input_ranges = tuple(x for x in topo if x.op is Ops.RANGE and x not in r.src[1:] and x not in ended_ranges)
-  acc_init = acc.after(*input_ranges).store(identity_element(r.arg[0], r.dtype))
+  acc_init = acc.after(*input_ranges).store(UOp.const(identity_element(r.arg[0], r.dtype)))
  acc_initted = acc.after(acc_init, *r.src[1:])
  inp = r.src[0].reduce(arg=r.arg) if r.arg[1] else r.src[0]
  acc_out = acc_initted.store(acc_initted.alu(r.arg[0], inp)).end(*r.src[1:]).rtag("mergeable")
@@ -302,7 +301,7 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
    sink = graph_rewrite(sink, pm_split_ranges+pm_flatten_range, ctx={}, name="split ranges")

    # symbolic (NOTE: this is a requirement for pm_simplify_ranges to be correct)
-    sink = graph_rewrite(sink, sym+pm_flatten_range, name="initial symbolic")
+    sink = graph_rewrite(sink, sym+pm_fold_cast_const+pm_flatten_range, name="initial symbolic")

    # optimize (schedule) the AST
    sink = graph_rewrite(sink, pm_flatten_range+pm_simplify_ranges, ctx={}, name="simplify ranges")
@@ -311,7 +310,8 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
    sink = apply_opts(sink, ren, beam=ast.arg.beam)

  # ** expander (expand_rewrite) **
-  sink = graph_rewrite(sink, sym+pm_move_where_on_load+pm_flatten_range, name="postopt symbolic")
+  # reduce_unparented: a REDUCE whose src folded to a CONST (e.g. x*0) has no parented ranges, collapse it before the expander
+  sink = graph_rewrite(sink, sym+pm_move_where_on_load+pm_flatten_range+pm_reduce_unparented, name="postopt symbolic")

  # expand
  sink = graph_rewrite(sink, expander2, ctx=build_range_map(sink), name="expander")
@@ -330,24 +330,23 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
  sink = graph_rewrite(sink, symbolic_simple+pm_expand_broadcast+pm_add_loads, name="*** expand broadcast / add loads")

  # devectorize
-  sink = graph_rewrite(sink, symbolic_simple+devectorizer2, ctx=ren, name="devectorize2")
-
-  # simplify indexing
-  sink = graph_rewrite(sink, indexing_simplify, name="simplify load/store indexing")
+  sink = graph_rewrite(sink, symbolic_simple+devectorizer2+indexing_simplify, ctx=ren, name="devectorize2")

  # some coalescing misses without this
  sink = graph_rewrite(sink, sym, name="early symbolic")

  # do memory coalescing (late)
  sink = memory_coalescing(sink, ren)
-  sink = graph_rewrite(sink, symbolic_simple+ew_devectorizer+pm_simplify_add_image, name="add images", ctx=({}, ren), bottom_up=True)
+  sink = graph_rewrite(sink, symbolic_simple+ew_devectorizer+pm_simplify_add_image,
+                       name="add images", ctx=({}, ren), bottom_up=True)

  # extra symbolic before decomp. crashes without this?
-  sink = graph_rewrite(sink, sym, name="extra symbolic")
+  # NOTE: also run indexing_simplify here, while the index is still weakint and (x+y)*c -> x*c+y*c applies
+  sink = graph_rewrite(sink, sym+indexing_simplify, name="extra symbolic")

  # lower index dtype
  # NOTE: we need indexing_simplify to remove the cast to long using the Invalid
-  sink = graph_rewrite(sink, pm_lower_index_dtype+indexing_simplify, ctx={}, name="lower all index dtypes")
+  sink = graph_rewrite(sink, symbolic_simple+pm_fold_cast_const+pm_lower_index_dtype+indexing_simplify, ctx={}, name="lower all index dtypes")

  # final symbolic before decomp
  sink = graph_rewrite(sink, symbolic, name="final symbolic")
@@ -358,7 +357,7 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:

  # floordiv+mod / dtype decomp (early)
  supported_ops = tuple(ren.code_for_op.keys())
-  pm_decomp = symbolic_simple+get_simplifying_rewrite_patterns(supported_ops)
+  pm_decomp = symbolic_simple+pm_fold_cast_const+get_simplifying_rewrite_patterns(supported_ops)
  sink = graph_rewrite(sink, pm_decomp, name="early decompositions")

  # late decomps + move gates from unrenderable INVALID where
@@ -452,7 +451,7 @@ pm_to_program = PatternMatcher([
  (UPat(Ops.PROGRAM, src=(UPat(), UPat(Ops.LINEAR), UPat(Ops.SOURCE, name="source")), name="prg"), do_compile),
 ])

-@track_rewrites(name=lambda ast,renderer,ret,**kwargs: TracingKey(ret.src[0].arg.name,(ret.src[0].arg.function_name, ast), ret=renderer), replay=True)
+@rewrite_group(name=lambda ast,renderer,ret,**kwargs: TracingKey(ret.src[0].arg.name,(ret.src[0].arg.function_name, ast), ret=renderer), replay=True)
@Context(ALLOW_DEVICE_USAGE=0)
 def do_to_program(ast:UOp, renderer:Renderer) -> UOp:
  """
@@ -78,9 +78,11 @@ def l2i(op: Ops, dt: DType, *uops:UOp):
    case Ops.MAX: return l2i(Ops.WHERE, dt, l2i(Ops.CMPLT, dt, *uops), b0, b1, a0, a1)
    case _: raise NotImplementedError(f"long decomposition of {op} unsupported")

-def split_l2i(op: Ops, dt: DType, *uops:UOp):
-  # l2i does arithmetic on its inputs; rules enter here to split them to 32-bit words first, l2i recurses on itself
-  return l2i(op, dt, *graph_rewrite(UOp.sink(*uops), pm_long_decomp, bottom_up=True).src)
+def split_l2i(ctx:dict, op: Ops, dt: DType, *uops:UOp):
+  # l2i does arithmetic on its inputs; rules enter here to split them to 32-bit words first, l2i recurses on itself.
+  # both word halves of a node ask for the same split, so ctx memos it for the pass
+  if (key:=(op, dt, uops)) not in ctx: ctx[key] = l2i(op, dt, *graph_rewrite(UOp.sink(*uops), pm_long_decomp, ctx=ctx, bottom_up=True).src)
+  return ctx[key]

 # ***** floats *****
 f2f_dt = { f:getattr(dtypes, f"uint{f.bitsize}") for f in dtypes.floats }
@@ -97,7 +99,8 @@ def f2f(v, fr:DType, to:DType, sat=True):
    if fr in dtypes.fp8_fnuz:
      fnuz_nan = sign.ne(0) & nosign.eq(0)
      qnan = shl(shl(1, te) - 1, tm) | shl(1, tm - 1)
-      return fnuz_nan.where(qnan, sign | exp.eq(0).where(0, norm)).bitcast(to)
+      # the fnuz bias can exceed the target's: exp in [1, fb-tb] is normal in fr but lands below to's normal range, so it flushes like a denormal
+      return fnuz_nan.where(qnan, sign | (exp < max(fb - tb, 0) + 1).where(0, norm)).bitcast(to)
    # fp8e4m3 has only one nan
    is_nan = (nosign.eq(shl(1, fm + fe) - 1) if fr == dtypes.fp8e4m3 else exp.eq(shl(1, fe) - 1))
    return (sign | exp.eq(0).where(0, is_nan.where(nan, norm))).bitcast(to)
@@ -139,21 +142,21 @@ pm_long_decomp = PatternMatcher([
  (UPat(Ops.STORE, src=(UPat.var('idx', tuple(l2i_dt.keys())), UPat.var('val')), name='st'), lambda st,idx,val:
   st.replace(src=(idx.rtag((0, dt:=l2i_dt[idx.dtype])), val.rtag((0, dt)))).group(
     st.replace(src=(idx.rtag((1, dt)), val.rtag((1, dt))))) if val.tag is None else None),
-  (UPat(GroupOp.Comparison, src=[UPat.var('a', tuple(l2i_dt.keys())), UPat()], name="x"), lambda a,x:
-   split_l2i(x.op, dt:=l2i_dt[a.dtype], *flatten((s.rtag((0, dt)), s.rtag((1, dt))) for s in x.src))),
-  (UPat(Ops.CAST, tuple(l2i_dt.keys()), src=(UPat.var('a', tuple(l2i_dt.keys())),), name="x"), lambda a,x:
-   split_l2i(Ops.BITCAST, l2i_dt[x.dtype], a.rtag((0, dt:=l2i_dt[a.dtype])), a.rtag((1, dt)))[x.tag[0]]),
-  (UPat(Ops.CAST, tuple(l2i_dt.keys()), src=(UPat.var('a'),), name="x"), lambda a,x:
-   split_l2i(x.op, x.dtype, a)[x.tag[0]] if x.tag is not None else None),
-  (UPat(Ops.CAST, src=(UPat.var('a', tuple(l2i_dt.keys())),), name="x"), lambda a,x:
-   split_l2i(x.op, x.dtype, a.rtag((0, dt:=l2i_dt[a.dtype])), a.rtag((1, dt))) if x.dtype not in l2i_dt and a.tag is None else None),
-  (UPat((Ops.SHL, Ops.SHR), tuple(l2i_dt.keys()), src=(UPat.var('a'), UPat.var('b')), name="x"), lambda a,b,x:
-   split_l2i(x.op, dt:=l2i_dt[x.dtype], a.rtag((0, dt)), a.rtag((1, dt)), b.rtag((0, dt)))[x.tag[0]] if x.tag is not None else None),
-  (UPat(Ops.WHERE, tuple(l2i_dt.keys()), src=(UPat.var('c'), UPat.var('a'), UPat.var('b')), name="x"), lambda a,b,c,x:
-   split_l2i(x.op, dt:=l2i_dt[x.dtype], c, a.rtag((0, dt)), a.rtag((1, dt)), b.rtag((0, dt)), b.rtag((1, dt)))[x.tag[0]]
+  (UPat(GroupOp.Comparison, src=[UPat.var('a', tuple(l2i_dt.keys())), UPat()], name="x"), lambda ctx,a,x:
+   split_l2i(ctx, x.op, dt:=l2i_dt[a.dtype], *flatten((s.rtag((0, dt)), s.rtag((1, dt))) for s in x.src))),
+  (UPat(Ops.CAST, tuple(l2i_dt.keys()), src=(UPat.var('a', tuple(l2i_dt.keys())),), name="x"), lambda ctx,a,x:
+   split_l2i(ctx, Ops.BITCAST, l2i_dt[x.dtype], a.rtag((0, dt:=l2i_dt[a.dtype])), a.rtag((1, dt)))[x.tag[0]]),
+  (UPat(Ops.CAST, tuple(l2i_dt.keys()), src=(UPat.var('a'),), name="x"), lambda ctx,a,x:
+   split_l2i(ctx, x.op, x.dtype, a)[x.tag[0]] if x.tag is not None else None),
+  (UPat(Ops.CAST, src=(UPat.var('a', tuple(l2i_dt.keys())),), name="x"), lambda ctx,a,x:
+   split_l2i(ctx, x.op, x.dtype, a.rtag((0, dt:=l2i_dt[a.dtype])), a.rtag((1, dt))) if x.dtype not in l2i_dt and a.tag is None else None),
+  (UPat((Ops.SHL, Ops.SHR), tuple(l2i_dt.keys()), src=(UPat.var('a'), UPat.var('b')), name="x"), lambda ctx,a,b,x:
+   split_l2i(ctx, x.op, dt:=l2i_dt[x.dtype], a.rtag((0, dt)), a.rtag((1, dt)), b.rtag((0, dt)))[x.tag[0]] if x.tag is not None else None),
+  (UPat(Ops.WHERE, tuple(l2i_dt.keys()), src=(UPat.var('c'), UPat.var('a'), UPat.var('b')), name="x"), lambda ctx,a,b,c,x:
+   split_l2i(ctx, x.op, dt:=l2i_dt[x.dtype], c, a.rtag((0, dt)), a.rtag((1, dt)), b.rtag((0, dt)), b.rtag((1, dt)))[x.tag[0]]
   if x.tag is not None else None),
-  (UPat((*(GroupOp.ALU - GroupOp.Comparison - {Ops.SHL, Ops.SHR, Ops.WHERE}), Ops.BITCAST), tuple(l2i_dt.keys()), name="x"), lambda x:
-   split_l2i(x.op, l2i_dt[x.dtype], *flatten((a.rtag((0, l2i_dt[x.dtype])), a.rtag((1, l2i_dt[x.dtype]))) for a in x.src))[x.tag[0]]
+  (UPat((*(GroupOp.ALU - GroupOp.Comparison - {Ops.SHL, Ops.SHR, Ops.WHERE}), Ops.BITCAST), tuple(l2i_dt.keys()), name="x"), lambda ctx,x:
+   split_l2i(ctx, x.op, l2i_dt[x.dtype], *flatten((a.rtag((0, l2i_dt[x.dtype])), a.rtag((1, l2i_dt[x.dtype]))) for a in x.src))[x.tag[0]]
   if x.tag is not None else None),
  (UPat(Ops.LOAD, tuple(l2i_dt.keys()), src=(UPat.var('idx'),), name='x'), lambda x,idx:
   x.replace(dtype=l2i_dt[x.dtype], src=(reindex(idx, x.tag[0]).replace(dtype=l2i_dt[x.dtype], tag=None),), tag=None) if x.tag is not None else None),
@@ -178,7 +181,9 @@ pm_float_decomp = PatternMatcher([
   f2f(x.bitcast(f2f_dt[ctx[0]]), ctx[0], ctx[1]) if bc.dtype == ctx[0] else None),
  (UPat(Ops.CAST, dtypes.floats, src=(UPat.var("val"),), name="x"), lambda ctx,x,val:
   f2f_clamp(val.cast(ctx[1]), ctx[0]) if x.dtype == ctx[0] else None),
-  (UPat(GroupOp.All-{Ops.BITCAST}, dtypes.floats, name="x"), lambda ctx,x:
+  # a CONST has no srcs to cast, it restates its value at the emulating dtype
+  (UPat(Ops.CONST, dtypes.floats, name="x"), lambda ctx,x: UOp.const(x.val, ctx[1]) if x.dtype == ctx[0] else None),
+  (UPat(GroupOp.All-GroupOp.Defines-{Ops.CAST, Ops.BITCAST, Ops.CONST}, dtypes.floats, name="x"), lambda ctx,x:
   x.replace(dtype=ctx[1], src=tuple(s.cast(ctx[1]) if s.dtype == ctx[0] else s for s in x.src))
   if x.dtype == ctx[0] else None),
  (UPat(Ops.STORE, src=(UPat.var("idx"), UPat(Ops.BITCAST, dtypes.floats, name="val")), name='st'), lambda ctx,st,idx,val:
@@ -195,7 +200,7 @@ def do_dtype_decomps(sink:UOp, ctx:tuple[set[DType], Renderer]) -> UOp:
      to = dtypes.int if fr == dtypes.long else dtypes.half if not _should_emulate(dtypes.half) and fr in dtypes.fp8s else dtypes.float
      if DEBUG >= 2: print(f"emulating {fr} as {to}")
      pm = pm_float_decomp if fr in dtypes.floats else pm_long_decomp
-      sink = graph_rewrite(sink, pm, name=f"decomp {fr} -> {to}", ctx=(fr, to), bottom_up=True)
+      sink = graph_rewrite(sink, pm, name=f"decomp {fr} -> {to}", ctx={} if pm is pm_long_decomp else (fr, to), bottom_up=True)
  ctx[0].clear()
  return sink

@@ -47,17 +47,17 @@ def fast_idiv(ren: Renderer, x: UOp, d: int, dont_cast=False) -> UOp|None:

 def threefry2x32(x: UOp, key: UOp):
  # split x and key from uint64 to two uint32
-  x0, x1 = (x & 0xffffffff).cast(dtypes.uint32), ((x // 2**32) & 0xffffffff).cast(dtypes.uint32)
-  key0, key1 = (key & 0xffffffff).cast(dtypes.uint32), ((key // 2**32) & 0xffffffff).cast(dtypes.uint32)
+  x0, x1 = x.cast(dtypes.uint32), (x >> 32).cast(dtypes.uint32)
+  key0, key1 = key.cast(dtypes.uint32), (key >> 32).cast(dtypes.uint32)

  rotations = [[13, 15, 26, 6], [17, 29, 16, 24]]
  ks = [key1, key0 ^ key1 ^ 0x1BD11BDA, key0]
  xr:list[UOp] = [x0 + ks[-1], x1 + ks[0]]
  for i in range(5):
-    for r in rotations[i % 2]: xr[0], xr[1] = (x0 := xr[0] + xr[1]), x0 ^ ((xr[1] * 2**r) + (xr[1] // 2**(32 - r)))
+    for r in rotations[i % 2]: xr[0], xr[1] = (x0 := xr[0] + xr[1]), x0 ^ ((xr[1] << r) + (xr[1] >> (32 - r)))
    xr = [(xr[0] + ks[i % 3]), (xr[1] + ks[(i + 1) % 3] + i + 1)]

-  return xr[1].cast(dtypes.uint64) * 2**32 | xr[0].cast(dtypes.uint64)
+  return (xr[1].cast(dtypes.uint64) << 32) | xr[0].cast(dtypes.uint64)

 # ***** decomposition patterns *****

@@ -257,12 +257,12 @@ def xlog2(d:UOp) -> UOp:
 def xpow(base:UOp, exponent:UOp) -> UOp:
  # start with b ** e = exp2(e * log2(b))
  ret = (base < 0).where(-base, base).log2().mul(exponent).exp2()
-  # negative base: nan for non-integer exponent, negate for odd integer exponent
+  # negative base: nan for non-integer exponent, negate for odd integer exponent. -inf is never nan, it stays |base| ** exponent
  non_int = exponent != exponent.cast(dtypes.int32).cast(exponent.dtype)
  is_odd = (exponent < 0).where(-exponent, exponent).cast(dtypes.int32).mod(2).cast(dtypes.bool)
-  neg_base = non_int.where(ret.const_like(math.nan), is_odd.where(-ret, ret))
-  # fix 0 ** 0 = 1
-  return (base.eq(0) & exponent.eq(0)).where(ret.const_like(1), (base < 0).where(neg_base, ret))
+  neg_base = non_int.where(base.ne(-math.inf).where(ret.const_like(math.nan), ret), is_odd.where(-ret, ret))
+  # x ** 0 = 1, including 0 ** 0 and inf ** 0
+  return exponent.eq(0).where(ret.const_like(1), (base < 0).where(neg_base, ret))

@functools.cache
 def get_transcendental_patterns(ops:tuple[Ops, ...], force_transcendental:bool) -> PatternMatcher:
@@ -1,8 +1,8 @@
 import itertools, functools
 from collections import defaultdict
 from tinygrad.dtype import dtypes, AddrSpace, Invalid, DType
-from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, shape_to_shape_arg
-from tinygrad.uop.symbolic import uop_given_valid, parse_valid, invalid_gate
+from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, shape_to_shape_arg, graph_rewrite
+from tinygrad.uop.symbolic import uop_given_valid, parse_valid, invalid_gate, sym
 from tinygrad.helpers import getenv, IMAGE, OSX, ceildiv, is_image_shape
 from tinygrad.renderer import Renderer

@@ -27,11 +27,14 @@ def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
    lo, hi = (c + 1, X.vmax) if is_upper_bound else (X.vmin, c - 1)
    if lo <= hi:
      fake = UOp.variable(f"fake{i}", lo, hi, X.dtype)
-      for coord,b in zip(idx.src, (width, height)):
-        rw = coord.substitute({X:fake}).simplify()
-        if rw.vmin >= b or rw.vmax < 0:
-          drop_stmt.append(stmt)
-          break
+      subs = [{X: fake}]
+      # idx may not have X itself, so also substitute a term of X: v -> fake - (X - v)
+      terms = list(X.split_uop(Ops.ADD))
+      v = next((u for u in terms if u.op in GroupOp.Irreducible and u.op is not Ops.CONST), None)
+      if v is not None and (rest:=[u for u in terms if u is not v]): subs.append({v: fake - UOp.usum(*rest)})
+      if any((testidx:=graph_rewrite(coord.substitute(sub), sym)).vmin >= b or testidx.vmax < 0
+             for sub in subs for coord,b in zip(idx.src, (width, height))):
+        drop_stmt.append(stmt)
  return drop_stmt

 def simplify_valid_load(buf:UOp, start_idx:UOp, valid:UOp) -> UOp|None:
@@ -125,7 +125,7 @@ def regalloc_rewrite(ctx:LinearScanRegallocContext, x:UOp):
  # alloc/dealloc stack
  if ctx.stack_size > 0:
    sp = ctx.ren.stack_pointer()
-    offset = UOp(Ops.CONST, sp.dtype, arg=ctx.stack_size)
+    offset = UOp.const(ctx.stack_size, sp.dtype)
    if i == 0: before = [ctx.ren.isel_matcher.rewrite(UOp(Ops.SUB, src=(sp, offset), tag=sp.tag))] + before
    elif i == len(ctx.uops) - 2: before += [ctx.ren.isel_matcher.rewrite(UOp(Ops.ADD, src=(sp, offset), tag=sp.tag))]

@@ -332,9 +332,9 @@ class Scheduler:
  @property
  def group_for_reduces(self) -> int: return len(self.axes_of(AxisType.GROUP_REDUCE))

-def bufs_from_ast(ast:UOp, dname:str) -> list[Buffer]:
+def args_from_ast(ast:UOp, dname:str) -> tuple[list[Buffer], dict[str, int]]:
  glbls = sorted([x for x in ast.backward_slice if x.op is Ops.PARAM and x.arg.slot >= 0], key=lambda x: x.arg.slot)
-  return [Buffer(dname, x.max_numel(), x.dtype) for x in glbls]
+  return [Buffer(dname, x.max_numel(), x.dtype) for x in glbls], {k.expr:int(k.vmax+k.vmin)//2 for k in ast.variables()}

 def apply_opts(ast:UOp, ren:Renderer, beam:int=0) -> UOp:
  if ast.tag is not None: return ast
@@ -344,10 +344,10 @@ def apply_opts(ast:UOp, ren:Renderer, beam:int=0) -> UOp:
    for opt in ast.arg.opts_to_apply: k.apply_opt(opt)
  elif beam >= 1:
    from tinygrad.codegen.opt.search import beam_search
-    rawbufs = bufs_from_ast(ast, ren.target.device)
+    rawbufs, var_vals = args_from_ast(ast, ren.target.device)
    # beam search may open devices
    with Context(ALLOW_DEVICE_USAGE=1):
-      k = beam_search(k, rawbufs, beam, bool(getenv("BEAM_ESTIMATE", 1)))
+      k = beam_search(k, rawbufs, var_vals, beam, bool(getenv("BEAM_ESTIMATE", 1)))
  elif not NOOPT and (ast.arg is None or ast.arg.applied_opts == ()):
    from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
    # NOTE: hand_coded_optimizations doesn't support multiblock opts yet
@@ -1,6 +1,6 @@
 import math, time, multiprocessing, traceback, signal, atexit
 from dataclasses import replace
-from tinygrad.uop.ops import sym_infer, AxisType, UOp
+from tinygrad.uop.ops import sym_infer, AxisType, UOp, Ops
 from tinygrad.uop.render import pyrender
 from tinygrad.device import Device, Buffer
 from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
@@ -62,7 +62,8 @@ def _try_compile(x:tuple[int,Scheduler]) -> tuple[int, tuple[UOp, float]|None]:
  ret = None
  try:
    st = time.perf_counter()
-    prg = to_program(x[1].copy().get_optimized_ast(name_override="test"), x[1].ren)
+    ast, dev = x[1].copy().get_optimized_ast(name_override="test"), x[1].ren.target.device
+    prg = to_program(ast.substitute({p: p.replace(arg=replace(p.arg, device=dev)) for p in ast.toposort() if p.op is Ops.PARAM}), x[1].ren)
    et = time.perf_counter() - st
    uops = prg.src[1].src
    if len(uops) >= (uops_max:=getenv("BEAM_UOPS_MAX", 3000)) > 0:
@@ -111,7 +112,7 @@ def get_kernel_actions(s:Scheduler, include_0=True, max_up:int|None=None) -> dic
  return acted

 beam_pool, BEAM_DEBUG = None, getenv("BEAM_DEBUG")
-def beam_search(s:Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
+def beam_search(s:Scheduler, rawbufs:list[Buffer], var_vals:dict[str,int], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
  global beam_pool
  key = {"ast": s.ast.key, "amt": amt, "allow_test_size": allow_test_size, "device": s.ren.target.device, "suffix": s.ren.suffix}
  if not disable_cache and CACHELEVEL >= 1 and (val:=diskcache_get("beam_search", key)) is not None:
@@ -136,7 +137,6 @@ def beam_search(s:Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True

  try:
    rawbufs = _ensure_buffer_alloc(rawbufs)
-    var_vals: dict[str, int] = {k.expr:int(k.vmax+k.vmin)//2 for k in s.ast.variables()}
    exiting, st = False, time.perf_counter()
    dev = Device[s.ren.target.device]
    while not exiting:
@@ -1,7 +1,7 @@
 import itertools
 from typing import Callable
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, graph_rewrite, _substitute, range_start, AxisType
-from tinygrad.uop.symbolic import symbolic, invalid_gate
+from tinygrad.uop.symbolic import symbolic, pm_fold_cast_const, invalid_gate
 from tinygrad.helpers import partition
 from tinygrad.dtype import dtypes

@@ -32,7 +32,7 @@ def simplify_merge_adjacent(u:UOp) -> UOp|None:
        s0, s1 = r0.src[0], r1.src[0]
        # do the merge
        new_range = r0.replace(src=(s0*s1,))
-        nidx = graph_rewrite(u, _substitute+symbolic+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1},
+        nidx = graph_rewrite(u, _substitute+symbolic+pm_fold_cast_const+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1},
                             name=f"check_merge_{r0.arg[0]}_{r1.arg[0]}")

        # check if it simplifies
@@ -27,7 +27,7 @@ class InvalidType:
  def __new__(cls):
    if cls._instance is None: cls._instance = object.__new__(cls)
    return cls._instance
-  def __eq__(self, other): return self is other
+  def __eq__(self, other): return self is other if isinstance(other, InvalidType) else NotImplemented  # foreign types get the reflected eq
  def __hash__(self): return id(self)
  def __repr__(self): return "Invalid"
  def __reduce__(self): return (InvalidType, ())  # unpickle returns the singleton
@@ -80,7 +80,7 @@ class DType(metaclass=DTypeMetaClass):
    # NOTE: float('nan') != float('nan'), so we canonicalize here
    if isinstance(val, float) and math.isnan(val): val = math.nan
    # int is the default. wrap floats in ConstFloat to distinguish -0.0 from 0.0 in cache
-    return ConstFloat(float(val)) if dtypes.is_float(self) else bool(val) if dtypes.is_bool(self) else int(val)
+    return ConstFloat(truncate.get(self, float)(float(val))) if dtypes.is_float(self) else bool(val) if dtypes.is_bool(self) else int(val)


 class DTypes:
@@ -293,6 +293,12 @@ truncate: dict[DType, Callable] = {dtypes.bool: bool,
  **{getattr(dtypes, n): (lambda x, c=getattr(ctypes, f'c_{n}'): c(x).value)
     for n in ('float', 'double', 'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64')}}

+def bitcast(x, in_dtype:DType, out_dtype:DType):
+  assert in_dtype.itemsize == out_dtype.itemsize, "bitcast itemsize mismatch"
+  packed = struct.pack(storage_fmt_for_dtype(in_dtype), to_storage_scalar(x, in_dtype))
+  out_val = struct.unpack(storage_fmt_for_dtype(out_dtype), packed)[0]
+  return from_storage_scalar(out_val, out_dtype)
+
 # numpy and torch dtype interop

 def _to_np_dtype(dtype:DType) -> type|None:
@@ -4,7 +4,7 @@ from tinygrad.tensor import Tensor, all_tensors
 from tinygrad.helpers import flatten, merge_dicts, DEBUG, Context, BEAM, getenv, JIT, JIT_BATCH_SIZE, dedup, pluralize, VIZ, disable_gc
 from tinygrad.device import Buffer, Compiled, Device, MultiBuffer, DepsTracker
 from tinygrad.dtype import DType
-from tinygrad.uop.ops import UOp, PatternMatcher, Variable, sym_infer, Ops, buffers, track_rewrites, graph_rewrite
+from tinygrad.uop.ops import UOp, PatternMatcher, Variable, sym_infer, Ops, buffers, rewrite_group, graph_rewrite
 from tinygrad.renderer import Estimates
 from tinygrad.engine.realize import capturing, compile_linear, link_linear, run_linear, graph_cache, estimate_uop, get_runtime
 from tinygrad.engine.realize import unwrap_multi, resolve_params, get_call_arg_uops, get_call_outs_ins
@@ -64,14 +64,14 @@ def _copy_input(u:UOp) -> UOp:
  run_linear(UOp(Ops.LINEAR, src=(u.copy_to_device(u.device).call(new:=UOp.new_buffer(u.device, u.max_numel(), u.dtype), u),)))
  return new

-@track_rewrites(lambda linear,held_bufs,input_uops,ret=(): f"JIT {pluralize('call', len(linear.src))}")
+@rewrite_group(lambda linear,held_bufs,input_uops,ret=(): f"JIT {pluralize('call', len(linear.src))}")
 def jit_lower(linear:UOp, held_bufs:set[UOp], input_uops:list[UOp]) -> UOp:
  if VIZ: graph_rewrite(linear, PatternMatcher([]), name="View captured linear")

  # parametrize input buffers: map each input buffer UOp to a PARAM with the correct slot index
  linear = linear.substitute({u: UOp.param(i, u.dtype, u.shape, u.device) for i,u in enumerate(input_uops)}, walk=True)
  linear = memory_plan_rewrite(linear, held_bufs)
-  linear = compile_linear(linear, beam=getenv("JITBEAM", BEAM.value), jit=True)
+  linear = compile_linear(linear, beam=getenv("JITBEAM", BEAM.value))
  if JIT < 2: linear = graph_split_rewrite(linear, max_batch_size=JIT_BATCH_SIZE.value)
  if VIZ: graph_rewrite(linear, PatternMatcher([]), name="View graphed linear")
  return linear
@@ -169,7 +169,7 @@ class CapturedJit(Generic[ReturnType]):
  expected_input_info: list[tuple[UOp, tuple[Variable, ...], DType, str]]  # (view, variables, dtype, device) per input

  @functools.cached_property
-  def linear(self) -> UOp: return link_linear(self._linear, jit=True)
+  def linear(self) -> UOp: return link_linear(self._linear)

  def __reduce__(self): return self.__class__, (self.ret, self._linear, self.expected_names, self.expected_input_info)

@@ -3,12 +3,12 @@ from typing import cast, Iterator, Any, Sequence
 import time, random, itertools, math, contextlib, weakref, array
 from dataclasses import dataclass, replace, field
 from tinygrad.helpers import colored, DEBUG, GlobalCounters, ansilen, all_int, prod, flatten, Context, getenv, to_tuple
-from tinygrad.helpers import BEAM, size_to_str, time_to_str, VALIDATE_WITH_CPU, PROFILE, ProfilePointEvent, cpu_events
+from tinygrad.helpers import BEAM, size_to_str, time_to_str, VALIDATE_WITH_CPU, PROFILE, ProfilePointEvent, cpu_events, wait_cond
 from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, AxisType, sym_infer, buffers, graph_rewrite
 from tinygrad.device import Device, Buffer, MultiBuffer
 from tinygrad.renderer import Estimates
 from tinygrad.codegen import to_program
-from tinygrad.codegen.opt.postrange import bufs_from_ast
+from tinygrad.codegen.opt.postrange import args_from_ast

 # **************** Helpers ****************

@@ -33,7 +33,7 @@ def get_call_name(call:UOp, bufs:Sequence[Buffer|UOp], var_vals:dict[str, int]|N
  if ast.op is Ops.COPY: return colored(f"copy {_uop_sz_to_str(arg_uops[0]):>10}, {_dev_str(bufs[0]):>7s} <- {_dev_str(bufs[1]):7s}", "yellow")
  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "encdec": return colored(f"enc/dec {_uop_sz_to_str(arg_uops[0])}", "yellow")
  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "graph": return colored(f"batched {len(ast.src[0].src)}", "cyan")
-  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "hcq": return call.arg.aux.name
+  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "hcq": return cast(str, call.arg.name)
  raise NotImplementedError("get_call_name is not implemented")

 # **************** Stat ****************
@@ -90,12 +90,13 @@ def optimize_local_size(call:UOp, prg:UOp) -> UOp|None:

  if (local_size:=local_size_cache.get(prg.key)) is None:
    # reuse one loaded runtime across candidates, only launch dims vary
-    bufs, runtime = [b.allocate() for b in bufs_from_ast(prg.src[0], device)], get_runtime(device, prg, cache=False)
+    (bufs, var_vals), runtime = args_from_ast(prg.src[0], device), get_runtime(device, prg, cache=False)
+    bufs  = [b.allocate() for b in bufs]
    def try_exec(local_size):
      try:
        new_gs = tuple(g//l if g%l == 0 else g/l for g,l in zip(prg.arg.global_size, local_size))
        return runtime(*[bufs[i].get_buf(device) for i in prg.arg.globals], global_size=new_gs, local_size=(*local_size,),
-                       vals=prg.arg.vals({}), wait=True)
+                       vals=prg.arg.vals(var_vals), wait=True)
      except Exception: return float('inf')

    MAX_WORKGROUP = 1024
@@ -214,16 +215,22 @@ def exec_hcq(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
    table = call.src[1+inputs].buffer
    for j,dev in enumerate(call.arg.aux.device):
      addrs = array.array('Q', [(b.bufs[j] if isinstance(b, MultiBuffer) else b).get_buf(dev).va_addr for b in bufs])
-      buf = table.bufs[j] if isinstance(table, MultiBuffer) else table
-      buf.ensure_allocated()._buf.cpu_view().view(fmt='Q')[:len(addrs)] = addrs
+      mv = (table.bufs[j] if isinstance(table, MultiBuffer) else table).ensure_allocated()._buf.cpu_view().view(fmt='Q')
+      wait_cond(lambda: mv[0], value=0, timeout_ms=ctx.timeout or getenv("HCQDEV_WAIT_TIMEOUT_MS", 30000), msg=f"{dev} hang detected")
+      mv[:len(addrs)] = addrs

  exec_kernel(replace(ctx, update_stats=False), call, ast)

-  st = time.perf_counter()
-  for d in call.arg.aux.device:
-    with track_stats(ctx, call, d, [], ctx.var_vals):
-      if ctx.wait: Device[d].synchronize()
-  return time.perf_counter() - st
+  tms:list[float|None] = []
+  for e in (aux:=call.arg.aux).prof: cast(Any, Device[e.device]).prof_ents[e.st_id] = e
+  for d in [cast(Any, Device[x]) for x in aux.device]:
+    with track_stats(ctx, call, d.device, [], ctx.var_vals) as et:
+      if ctx.wait:
+        d.synchronize(timeout=ctx.timeout)
+        ts = [d.signal(i)._buf.cpu_view().view(fmt='Q')[0] for e in aux.prof if e.device == d.device for i in (e.st_id, e.en_id)]
+        if ts: et[0] = float(max(ts)-min(ts))/d.timestamp_divider/1e6
+      tms += et
+  return tms[0]

 # flatten LINEAR-in-LINEAR: any nested LINEAR child gets inlined into its parent's src
 pm_flatten_linear = PatternMatcher([
@@ -265,18 +272,18 @@ pm_exec = PatternMatcher([

 if getenv("HCQ2"): from tinygrad.runtime.support.hcq2 import hcq_compile, hcq_link # noqa: E402 # down here, hcq2 imports the helpers above

-def compile_linear(linear:UOp, beam:int|None=None, validate=False, input_uops:list[UOp]|None=None, jit=False) -> UOp:
+def compile_linear(linear:UOp, beam:int|None=None, validate=False, input_uops:list[UOp]|None=None, profile:bool|None=None) -> UOp:
  if validate: linear = graph_rewrite(linear, pm_validate, name="validate", walk=True)
  if (beam_val:=BEAM.value if beam is None else beam) >= 1: linear = graph_rewrite(linear, pm_beam, ctx=beam_val, walk=True)
  linear = graph_rewrite(linear, pm_compile, name="precompile kernels", walk=True)
-  if getenv("HCQ2"): linear = hcq_compile(linear, input_uops, jit=jit)
+  if getenv("HCQ2"): linear = hcq_compile(linear, input_uops, bool(PROFILE) if profile is None else profile)
  return graph_rewrite(linear, pm_optimize_local_size, name="optimize local size", walk=True)

-def link_linear(linear:UOp, jit=False, cache=True) -> UOp: return hcq_link(linear, jit=jit, cache=cache) if getenv("HCQ2") else linear
+def link_linear(linear:UOp, cache=True) -> UOp: return hcq_link(linear, cache=cache) if getenv("HCQ2") else linear

 def run_linear(linear:UOp, var_vals:dict[str, int]|None=None, input_uops:Sequence[UOp]=(), update_stats=True, jit=False, wait=False):
  inputs = list(input_uops)
-  if not jit: linear = link_linear(compile_linear(linear, validate=VALIDATE_WITH_CPU, input_uops=inputs, jit=False))
+  if not jit: linear = link_linear(compile_linear(linear, validate=VALIDATE_WITH_CPU, input_uops=inputs))
  ctx = ExecContext(var_vals or {}, tuple(inputs), update_stats, jit, wait or DEBUG>=2)
  for call in linear.src: pm_exec.rewrite(call, ctx)

@@ -287,4 +294,5 @@ def time_call(call:UOp, var_vals:dict[str, int]|None=None, timeout:int|None=None
      from tinygrad.tensor import Tensor
      with Context(DEBUG=0, BEAM=0, CAPTURING=0, TRACK_MATCH_STATS=0): Tensor.ones(1024, 1024).contiguous().realize(do_update_stats=False)
  ctx = ExecContext(var_vals or {}, update_stats=False, wait=True, timeout=timeout, cache=False)
-  return pm_exec.rewrite(link_linear(compile_linear(UOp(Ops.LINEAR, src=(call,)), beam=0), cache=ctx.cache).src[0], ctx)
+  linear = link_linear(compile_linear(UOp(Ops.LINEAR, src=(call,)), beam=0, profile=True), cache=ctx.cache)
+  return max(pm_exec.rewrite(c, ctx) or 0.0 for c in linear.src)
@@ -251,7 +251,7 @@ DEFAULT_FLOAT, DEFAULT_INT = ContextVar("DEFAULT_FLOAT", "float32"), ContextVar(
 CAPTURE_PROCESS_REPLAY = ContextVar("CAPTURE_PROCESS_REPLAY", 0)
 def _get_cpu_count() -> int:
  # os.process_cpu_count (3.13+) respects cgroup limits
-  if hasattr(os, "process_cpu_count"): return max(1, os.process_cpu_count())
+  if hasattr(os, "process_cpu_count"): return max(1, os.process_cpu_count() or 1)
  # cgroup v2 (containers with --cpus=N)
  try:
    with open("/sys/fs/cgroup/cpu.max") as f:
@@ -21,7 +21,7 @@
    const d = document.createElement('div'); d.className = 'msg'; chat.appendChild(d);
    const r = await fetch('/v1/chat/completions', {method: 'POST', headers: {'Content-Type': 'application/json'},
      body: JSON.stringify({model: 'llama', messages: msgs, stream: true, temperature: 0.7})});
-    let buf = '';
+    let buf = '', txt = '', rsn = '';
    for (const rd = r.body.getReader(), dec = new TextDecoder();;) {
      const {done, value} = await rd.read();
      if (done) break;
@@ -30,9 +30,13 @@
      buf = lines.pop();
      for (const ln of lines)
        if (ln.startsWith('data: ') && !ln.includes('[DONE]'))
-          try { d.textContent += JSON.parse(ln.slice(6)).choices[0]?.delta?.content || '' } catch {}
+          try { const dl = JSON.parse(ln.slice(6)).choices[0]?.delta;
+            if (dl?.reasoning_content) { const s = document.createElement('span'); s.style.color = '#888';
+              s.textContent = dl.reasoning_content; rsn += dl.reasoning_content; d.appendChild(s) }
+            if (dl?.content) { const s = document.createElement('span');
+              s.textContent = dl.content; txt += dl.content; d.appendChild(s) } } catch {}
      chat.scrollTop = chat.scrollHeight;
    }
-    msgs.push({role: 'assistant', content: d.textContent});
+    const m = {role:'assistant', content:txt}; if (rsn) m.reasoning_content = rsn; msgs.push(m);
  }
 </script></body></html>
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
geohot	a0d4a2971b	kimi linear working	2026-08-10 15:37:20 +00:00
geohot	951aaf893b	llm: reuse exact recurrent prefixes	2026-08-10 14:06:15 +00:00
geohot	a455e17539	llm: fully warm recurrent serving at startup	2026-08-10 14:06:15 +00:00
nimlgenandgeohot	778f8aee59	fix hevc (#17477 ) * hevc tests * x	2026-08-10 14:06:15 +00:00
qazalandgeohot	9b347cc3e7	late loss.to("CPU") in llama (#17476 ) * late loss.to("CPU") in llama * acc = 0	2026-08-10 14:06:15 +00:00
qazalandgeohot	32b9149040	llama: custom silu kernels (#17462 ) * start by copying the C * uop kernel * cleanup tests * estimates is part of SPEC	2026-08-10 14:06:15 +00:00
George Hotzandgeohot	bbd4a77351	move platform tests to platform.yml (#17475 ) * ci: split mac/windows/qcom-cl tests into platform.yml Move the 6 jobs that don't run on Linux (4 macos, 1 windows, 1 QCOM CL compile test on arm) out of test.yml into a separate Platform Tests workflow so they run (and can be gated/runners-matched) independently. * ci: gate platform tests to the upstream repo Skip mac/windows/qcom-cl jobs anywhere but tinygrad/tinygrad, so the Platform Tests workflow is disabled on the gitea fork (and any fork). * ci: revert repo gate on platform tests Job-level if is only evaluated by gitea when a runner with matching labels fetches the task; with no mac/windows/arm runners the jobs queue forever. Disable the workflow on the instance instead.	2026-08-10 14:06:15 +00:00
geohot	e103421a12	document Kimi K3 optimization handoff	2026-08-10 06:14:30 -07:00
geohot	2b1b8c22a9	add fast exact-shape Kimi K3 benchmark	2026-08-10 06:13:24 -07:00
geohot	f53f0e7e79	document final Kimi K3 MI350 load profiling	2026-08-10 06:13:24 -07:00
geohot	224bac0318	speed up Kimi K3 decode projections on MI350X	2026-08-10 06:13:24 -07:00
geohot	1d86204718	optimize Kimi K3 serving on MI350X	2026-08-10 06:13:24 -07:00
geohot	c6ac4961d7	speed up Kimi K3 safetensor loading	2026-08-10 06:13:24 -07:00
geohot	1b3732a6ed	speed up Kimi K3 TP8 loading on MI350X	2026-08-10 06:13:24 -07:00
geohot	553bdf68e6	llm: defer recurrent server warmup	2026-08-10 06:59:49 +00:00
geohot	4e1c0166f8	llm: stabilize recurrent serving across requests	2026-08-10 06:43:27 +00:00
geohot	d28f5f261b	llm: keep recurrent single-token shapes static	2026-08-10 05:45:20 +00:00
geohot	14595b9ae8	llm: prepare direct Kimi K3 serving on MI350X	2026-08-10 05:28:18 +00:00
geohot	eaf7822239	llm: accelerate Kimi serving on gfx11	2026-08-10 03:58:44 +00:00
geohot	77e5be99bc	llm: prepare Kimi K3 and accelerate recurrent prefill	2026-08-10 02:03:14 +00:00
geohot	6edb5f9698	get kimi-linear running on 4x7900XTX (codex slop)	2026-08-10 00:38:38 +00:00
nimlgenandGitHub	8c8b43de62	hcq2: fix beam (#17467 ) * fix beam * x	2026-08-09 16:53:47 +03:00
nimlgenandGitHub	e17c21e102	hcq2: timings (#17464 ) * hcq2: timings * Dx * x * x * x * x * align * x	2026-08-08 22:00:32 +03:00
George HotzandGitHub	d4d537c8ae	add SPEC checking for the kernel graph (#17432 ) * add SPEC checking for the kernel graph * skip test * raise there, not None * handwritten * issue with unshard * multi works * and bitcast * fix new tests * fix linear * remove call index * fix shrink * fixes	2026-08-08 10:00:06 -07:00
b1tgandGitHub	abe2256299	fix symbolic sharded reshape (#17463 )	2026-08-08 09:18:02 -07:00
b1tgandGitHub	8c49a7a34b	support symbolic shapes in copy (#17461 ) * pad_to is no-op when same shape * support symbolic shapes in copy	2026-08-08 09:16:59 -07:00
qazalandGitHub	9dd3b8402e	default llama 8b to MXFP4=1 (#17465 )	2026-08-09 00:13:32 +08:00
sirhcmandGitHub	c0d2f9ac0c	nolocals supports variables (#17457 )	2026-08-07 17:54:38 -04:00
nimlgenandGitHub	4c206a52b1	fix ci emu (gpt) (#17437 ) * fix ci emu * x	2026-08-07 22:36:59 +03:00
chenyuandGitHub	4a3b8f6501	better _drop_valid_stmts [pr] (#17454 )	2026-08-07 15:18:35 -04:00
chenyuandGitHub	59b88ea5e2	move pm_fold_cast_const [pr] (#17453 ) move to lower index dtype	2026-08-07 13:31:56 -04:00
chenyuandGitHub	f76422b8af	fix cast to float _min_max [pr] (#17451 )	2026-08-07 11:56:19 -04:00
wozeparrotandGitHub	1827ec57f7	gptoss: fix sharded invalids (#17450 )	2026-08-07 08:42:02 -07:00
nimlgenandGitHub	b6189db8e9	cpu: fix eintr (#17449 )	2026-08-07 17:46:57 +03:00
chenyuandGitHub	73e670c10f	c0+x<c1 -> x < c1-c0 is ints only [pr] (#17448 )	2026-08-07 10:44:01 -04:00
chenyuandGitHub	fca695a36f	clean up reduce MUL gradient (#17447 )	2026-08-07 10:08:04 -04:00
Robert JosephandGitHub	0c96cdc300	fix prod gradients at zero (#17404 )	2026-08-07 09:56:52 -04:00
chenyuandGitHub	baa6148066	fix var of large half input (#17444 ) * fix var of large half input similar to mean, we use sum_acc_dtype for denominator * mypy	2026-08-06 23:14:32 -04:00
George Hotz GitHub qazal	1858f1fd9a	viz: collapse PROGRAM nodes like CALL (codex) (#17438 ) Co-authored-by: qazal <[email protected]>	2026-08-07 11:44:34 +09:00
qazal GitHub geohot	f253c4469d	remove contiguous from custom_kernel (#17149 ) * no user contig on custom_kernel * clean up * non removable * test MXFP4 llama without hipcc * use compiler-free HIPCC renderer in llama CI * move llama coverage to AMD tests * run llama coverage in AMD test matrix * respect configured ROCm path in llama profile * work * clean up * fix * add views back * remove that * update test * test_double_permute one less kernel * test_shrink less kernels --------- Co-authored-by: George Hotz <[email protected]>	2026-08-07 11:20:59 +09:00
chenyuandGitHub	28195d51fb	fix f2f from fp8e5m2fnuz to half (#17442 ) * fix f2f from fp8e5m2fnuz to half * it works if it's supported	2026-08-06 21:19:46 -04:00
chenyuandGitHub	9020a88f03	truncate float in DType.const [pr] (#17439 )	2026-08-06 20:19:08 -04:00
chenyuandGitHub	d8cbc11105	update linear interpolate to use int math for indices (#17441 )	2026-08-06 20:18:39 -04:00
wozeparrotandGitHub	1fd6b1035f	fa: swa support (#17367 )	2026-08-06 08:07:30 -07:00
nimlgenandGitHub	46230e9f17	hcq2: fence inputs (#17436 )	2026-08-06 16:27:12 +03:00
qazalandGitHub	9636dd1a25	test MXFP4 llama without hipcc (#17435 ) * test MXFP4 llama without hipcc * first pythonpath then dev	2026-08-06 17:31:40 +09:00
qazalandGitHub	f258708d7d	llama: custom quantize_mxfp4+transpose kernel (codex) (#17434 ) * llama: custom quantize_mxfp4+transpose kernel (codex) * rename to cpp * inline * cleanup * lds load_bf16x4 * more tests, add Estimates	2026-08-06 16:13:28 +09:00
chenyuandGitHub	28e6ef6937	fix postopt symbolic [pr] (#17433 ) REDUCE with src simplied to const would become unparented	2026-08-06 00:12:30 -04:00
chenyuandGitHub	969df866a3	one less strong dtype const in symbolic [pr] (#17431 )	2026-08-05 23:35:06 -04:00
chenyuandGitHub	7a9cd8e329	move weak function and pm to uop/weak [PR] (#17429 )	2026-08-05 22:27:40 -04:00
George HotzandGitHub	b4372df9c6	revert wrong custom kernel fix (#17427 )	2026-08-05 18:31:35 -07:00
chenyuandGitHub	d51e55aa17	remove some pm_fold_cast_const [pr] (#17426 )	2026-08-05 21:28:38 -04:00
sirhcmandGitHub	be25207a7a	scope variable names inside CALLs (#17424 )	2026-08-05 20:59:09 -04:00
chenyuandGitHub	d726e5f7f3	split pm_fold_cast_const [PR] (#17425 ) need to delete this rule that writes strong typed CONST	2026-08-05 19:56:04 -04:00
George HotzandGitHub	470c032a5e	fix slice + non contig kernels (#17423 ) * movement: resolve negative int slice bounds against symbolic sizes negative int bounds in a slice against a symbolic dim were passed through unresolved, giving wrong views. resolve them against the (possibly symbolic) size, like slice.indices does for int dims * schedule: realize custom kernel inputs that don't resolve to a buffer state rangeify assigns ranges backward from consumers and CALL contributes none, so the subgraph above a custom kernel input gets no ranges unless something in it is realized, and reduce conversion crashes with a KeyError. realize call inputs that don't resolve to a buffer state. only view-only movement ops preserve the underlying buffer: anything computed (ALU, REDUCE, ...) must be realized even if one of its sources resolves to a buffer, since the whole subgraph above the call has no ranges. unwrapping src[0] unconditionally missed const branches hanging off non-src[0] children and silently resolved REDUCEs to their source buffer. includes regression tests for pure const, mixed buffer+const, and view-over-buffer inputs	2026-08-05 16:18:17 -07:00
geohot	a8a8030bc9	add benchmark_llm script	2026-08-05 15:59:51 -07:00
George HotzandGitHub	581bfdd94f	merge track_rewrites and profile_matches into rewrite_group [PR] (#17420 ) * merge track_rewrites and profile_matches into rewrite_group * bug * flip ctx polarity	2026-08-05 14:41:38 -07:00
chenyuandGitHub	07ac911665	few weak and decomp tweaks [PR] (#17419 )	2026-08-05 15:55:04 -04:00
chenyuandGitHub	c2f1e5ae2a	fix weak cast to strong dtype [pr] (#17418 ) weak can mean higher than that strong dtype, so always use that strong dtype is wrong	2026-08-05 15:32:23 -04:00
George HotzandGitHub	757a727808	move callify into tensor (#17416 )	2026-08-05 11:48:56 -07:00
George HotzandGitHub	2cce85a606	chat: display reasoning_content from streamed responses (#17414 ) * chat: display reasoning_content from streamed responses The server's StreamRouter emits reasoning_content deltas for think blocks, but the chat UI was only reading delta.content, silently dropping all reasoning. Now reasoning is shown in gray (#888) and included in the message history sent back to the server. * fix	2026-08-05 10:49:24 -07:00
nimlgenandGitHub	9b27ea8523	hcq2: cleaner (#17413 ) * hcq2: cleaner * x	2026-08-05 19:45:04 +03:00
chenyuandGitHub	6cb419b9b7	regression test for bert nan with weak (#17412 )	2026-08-05 12:14:26 -04:00
nimlgenandGitHub	5b0b68ec55	remove debug from test (#17410 )	2026-08-05 15:47:40 +03:00
qazalandGitHub	ad32bd272b	viz/cli: faster and more complete rewrites print (#17411 ) * viz/cli: faster and more complete matches print * kwargs	2026-08-05 20:02:27 +09:00
nimlgenandGitHub	874d33128b	hcq2 benchmark (#17235 ) * hcq2 in ci? * fix * traning * x * x * x * recover * debug * impler * x * x * x * hcq2: group input scatter plans by destination * hcq2: simplify input scatter tables * x	2026-08-05 10:00:42 +03:00
chenyuandGitHub	3bf9e70b19	Revert "don't cast weak in _broadcasted [pr] (#17408 )" (#17409 ) This reverts commit `b45058b5ec`.	2026-08-05 02:40:33 -04:00
Raine GitHub George Hotz	77e124e455	fix AMD WMMA emulation and test in CI (#17184 ) * fix SPEC=1 test_tensor_cores * implement i32 WMMA for RDNA3, add regression test to CI * gfx950 scaled mfma llvmir fix * detect VOP3PX2 in emu * start cdna4 scaled mfam emu * fix gfx12 llvmir signatures * oops * fix * fix src2 const field extraction (gpt) * scaled mfma fixes cdna4 (kimi) * fp8 out breaks mfma (glm) * fix const signature * another * fix f string for linter * lint * clean * and a final lint * . * fix mypy * skip slow tests on ci * reduce unroll tensor shape -> 64x64 --------- Co-authored-by: George Hotz <[email protected]>	2026-08-04 23:25:32 -07:00
chenyuandGitHub	b45058b5ec	don't cast weak in _broadcasted [pr] (#17408 ) * don't cast weak in _broadcasted [pr] * fine now?	2026-08-05 02:19:31 -04:00
George HotzandGitHub	46f0003776	more KernelCountException (#17407 )	2026-08-04 22:55:38 -07:00
sirhcmandGitHub	de57be1f26	kill nvidia pids at benchmarks start (#17406 )	2026-08-04 23:45:49 -04:00
chenyuandGitHub	9b508dfafc	remove invalid special case in cast [PR] (#17405 )	2026-08-04 23:08:07 -04:00
George HotzandGitHub	e1f42681fa	add new schedule tests + format better (#17402 ) * add new schedule tests + format better * assert_kernel_count	2026-08-04 18:46:38 -07:00
chenyuandGitHub	3eab809e06	update minimum to not create strong type const [PR] (#17401 )	2026-08-04 21:29:24 -04:00
George HotzandGitHub	6122b3c98f	use check_schedule in tests where possible (#17400 )	2026-08-04 18:17:29 -07:00
chenyuandGitHub	f295f9fc99	use weak 0 in convert_pad_to_where_to_keep_behavior_local [pr] (#17398 )	2026-08-04 19:44:34 -04:00
chenyuandGitHub	d79772f057	fix pow on extreme inputs (#17397 ) * fix pow on extreme inputs * WEBGPU	2026-08-04 19:30:43 -04:00
chenyuandGitHub	c1a10e0726	fix _min_max for CAST from float to int [pr] (#17396 ) * fix _min_max for CAST from float to int [pr] * fix	2026-08-04 18:06:50 -04:00
chenyuandGitHub	7b6d2ddf23	more weak const without cast in const_like [PR] (#17395 ) * more weak const without cast in const_like [PR] * that?	2026-08-04 15:10:43 -04:00
George HotzandGitHub	f489f4bc36	add test_eye + color INDEX (#17394 )	2026-08-04 11:08:08 -07:00
wozeparrotandGitHub	80d2073a11	fa: fix dq hazard with D=64 (#17393 )	2026-08-04 09:38:01 -07:00
chenyuandGitHub	85e9440446	keep weak consts weak in symbolic [PR] (#17392 )	2026-08-04 12:16:53 -04:00
chenyuandGitHub	0db63e13cf	update dtype_from_uop for IMAGE INDEX [PR] (#17391 )	2026-08-04 11:50:24 -04:00
chenyuandGitHub	6eedca57ce	dtype_from_uop for CUSTOM, CUSTOMI, PYLITERAL [PR] (#17390 ) these do not change dtype	2026-08-04 11:02:13 -04:00
b1tgandGitHub	0796853845	support symbolic shapes in allreduce (#17364 )	2026-08-04 07:53:12 -07:00
geohot	568bfb6a37	Revert "usb: wait for PCIe link after power on (#17380 )" This reverts commit `104ee90ccf`.	2026-08-04 06:42:26 -07:00
qazalandGitHub	f993228b73	llama: accurate mxfp4 mfu (#17388 ) * llama: accurate mxfp4 mfu * train_llama3 import	2026-08-04 18:41:57 +09:00
qazalandGitHub	c9cd44b751	more custom kernel contig input edge case tests (#17387 )	2026-08-04 18:20:50 +09:00
chenyuandGitHub	13fff4f258	const_like cleanups [PR] (#17386 )	2026-08-03 23:57:20 -04:00
chenyuandGitHub	0170a3075a	use python bitcast in fold_bitcast [PR] (#17385 )	2026-08-03 23:09:53 -04:00
George HotzandGitHub	c21a552f3d	llm: bugfixes + warmup (#17384 )	2026-08-03 18:23:14 -07:00
chenyuandGitHub	67dc02d7e7	bitcast in python for _bits_to_rand [PR] (#17383 ) * bitcast in python for _bits_to_rand [PR] const in mixin would be weak only without width, so not bitcast	2026-08-03 21:14:58 -04:00
George HotzandGitHub	3cb786f447	llm: update test_llm_server tests (#17382 )	2026-08-03 16:06:17 -07:00
geohot	87289a7410	hotfix: revert test_scalar_alu_index, violates spec	2026-08-03 15:25:51 -07:00
George HotzandGitHub	c2625c78cb	scalar ALU index fix + llm: preserve_thinking (#17381 ) * cstyle: scalar ALU index fix, serve: preserve_thinking, test: fix Handler import - cstyle.py: return scalar directly when ALU buffer has 1 element - cli.py: add preserve_thinking param to FallbackTemplate.render - serve.py: pass preserve_thinking=True when rendering chat completions - test_llm_server.py: fix import to use Handler from llm.serve * real fix	2026-08-03 15:10:21 -07:00
chenyuandGitHub	33755a3465	improve threefry codegen [pr] (#17379 ) decomp uint64 can handle part of it	2026-08-03 17:24:25 -04:00
YassineYousfiandGitHub	104ee90ccf	usb: wait for PCIe link after power on (#17380 )	2026-08-03 14:19:05 -07:00
chenyuandGitHub	a2385ae21d	MAX_LINE_COUNT=26000 (#17378 ) oh well	2026-08-03 15:37:58 -04:00
wozeparrotandGitHub	3331944547	gptoss: fix moe routing (#17377 )	2026-08-03 11:16:44 -07:00
nimlgenandGitHub	7c1ce50f63	hcq2: epoch (#17376 ) * hcq2: epoch * x * minor	2026-08-03 16:37:25 +03:00
qazalandGitHub	be5f62d269	llama: refactor amax stuff and skip in fp4 (#17375 )	2026-08-03 20:05:52 +09:00
nimlgenandGitHub	e22935c758	hcq2: inputs table (#17374 ) * revert this * x * simpler * fix	2026-08-03 13:40:43 +03:00
chenyuandGitHub	314df72b5f	deflake test_hcq with MOCKGPU (#17370 ) for MOCKGPU we compare with e2e wall time which would be device agnostic	2026-08-03 13:22:59 +03:00
chenyuandGitHub	23c7813f44	no hard coded dtype int for rangeify debuf [PR] (#17373 )	2026-08-02 20:43:04 -04:00
George HotzandGitHub	05bc7c6994	fix llm reasoning and Linear import (#17372 )	2026-08-02 12:31:52 -07:00
chenyuandGitHub	09dabfe05e	minor pm_float_decomp cleanup [PR] (#17371 ) make the rule order independently correct	2026-08-02 15:17:33 -04:00
chenyuandGitHub	59df317b12	use UOp.const to create new consts [PR] (#17368 ) replace arg won't work with ConstArg	2026-08-02 13:31:55 -04:00
wozeparrotandGitHub	e14cadb1fb	gptoss: set ASM_GEMM (#17363 )	2026-08-02 06:52:36 -07:00
chenyuandGitHub	0258c7fefc	minor argstr and alloc stack cleanup [PR] (#17361 )	2026-08-01 22:37:06 -04:00
George HotzandGitHub	fb607fb990	faster devectorizer with one line (#17360 )	2026-08-01 13:06:35 -07:00
George HotzandGitHub	15c936db01	merge devectorize + indexing (#17354 ) * external benchmark schedule in 5 sec (codex slop) * prune * real? * delete	2026-08-01 11:40:15 -07:00