hcq2: speed (#17604)

* hcq2: speed

* x

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

@@ -41,12 +41,12 @@ inputs:
     description: "Install LLVM?"
     required: false
     default: 'false'
-  tinydreno:
-    description: "Install tinydreno"
+  qemu:
+    description: "Install qemu?"
     required: false
     default: 'false'
-  qemu:
-    description: "Install qemu"
+  ninja:
+    description: "Install ninja?"
     required: false
     default: 'false'
 runs:
@@ -134,7 +134,7 @@ runs:
 
     # ******************* apt *******************
     - name: Setup apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true')
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true')
       shell: bash
       run: |
         sudo mkdir -p /var/cache/apt/archives
@@ -162,7 +162,7 @@ runs:
         echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-20 main" | sudo tee /etc/apt/sources.list.d/llvm.list
 
     - name: Compute Package List + Hash
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true')
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true')
       id: apt-pkgs
       shell: bash
       run: |
@@ -187,25 +187,29 @@ runs:
         if [[ "${{ inputs.qemu }}" == "true" ]]; then
           pkgs+=" qemu-user-static"
         fi
+        # **** ninja ****
+        if [[ "${{ inputs.ninja }}" == "true" ]]; then
+          pkgs+=" ninja-build"
+        fi
 
         echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
         echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
     - name: Cache apt (PR)
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true') && github.event_name == 'pull_request'
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name == 'pull_request'
       uses: actions/cache/restore@v5
       with:
         path: /var/cache/apt/archives/
         key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
     - name: Cache apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true') && github.event_name != 'pull_request'
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name != 'pull_request'
       uses: actions/cache@v5
       with:
         path: /var/cache/apt/archives/
         key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
 
     - name: Run apt Update + Install
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true')
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true')
       shell: bash
       run: |
         sudo apt -qq update || true
@@ -277,12 +281,6 @@ runs:
       shell: bash
       run: brew install llvm@20
 
-    # *** tinydreno ***
-    - name: Install tinydreno (linux)
-      if: inputs.tinydreno == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: sudo curl -fL https://github.com/sirhcm/tinydreno/raw/refs/heads/master/libllvm-qcom.so -o /usr/lib/libllvm-qcom.so
-
     # *** OpenCL ***
     - name: Install rusticl
       if: inputs.opencl == 'true'

.github/workflows/autogen.yml

@@ -35,7 +35,7 @@ jobs:
         key: 'autogen'
         amd: 'true'
         llvm: 'true'
-        pydeps: 'pyyaml mako'
+        deps: 'autogen'
     - name: Install autogen support packages
       run: sudo apt-get install -y --no-install-recommends libclang-20-dev llvm-20-dev hip-dev libusb-1.0-0-dev libdrm-dev liburing-dev
     - name: Regenerate autogen files

.github/workflows/benchmark.yml

@@ -541,7 +541,7 @@ jobs:
     - name: openpilot run_pickle big_driving_supercombo
       run: BENCHMARK_LOG=usbgpu_openpilot_big_driving_supercombo_run_pickle RUN_PICKLE=1 PICKLE_OOB=1 PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py - openpilot.pkl
     - name: Test copy speeds
-      run: SIZE=64e6 PYTHONPATH=. GMMU=0 DEV=USB+AMD python3 test/external/external_test_usb_asm24.py TestDevCopySpeeds
+      run: SIZE=64000000 PYTHONPATH=. GMMU=0 DEV=USB+AMD python3 test/external/external_test_usb_asm24.py TestDevCopySpeeds
 
   driverbenchmarks:
     name: PCI Driver Benchmark (DEV=${{ matrix.dev }})

.github/workflows/docs.yml

@@ -8,7 +8,7 @@ permissions:
   contents: write
 jobs:
   deploy:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     steps:
       - uses: actions/checkout@v6
       - name: Configure Git Credentials

.github/workflows/platform.yml

@@ -179,35 +179,3 @@ jobs:
       - name: Run test_tiny
         shell: bash
         run: python -m pytest -n=auto test/test_tiny.py --durations=20
-
-
-  qcomclcompiletests:
-    name: Compile-only (QCOM CL)
-    runs-on: ubuntu-24.04-arm
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v6
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: compile-qcomcl
-          deps: testing_unit
-          tinydreno: 'true'
-      - name: Set env
-        shell: bash
-        run: printf "DEV=NULL:QCOMCL:a630\nNULL_ALLOW_COPYOUT=1" >> $GITHUB_ENV
-      - name: Run test_ops
-        shell: bash
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
-          DEBUG=4 python3 test/backend/test_ops.py TestOps.test_add
-          python -m pytest -n=auto test/backend/test_ops.py --durations=20
-      - name: Run test_ops (IMAGE)
-        shell: bash
-        env:
-          IMAGE: 1
-          DEV: "NULL:QCOMCL:a630,IMAGE_PITCH_ALIGNMENT=64"
-        run: |
-          DEBUG=4 python test/backend/test_ops.py TestOps.test_gemm | grep read_imagef
-          python -m pytest -n=auto test/backend/test_ops.py --durations=20

.github/workflows/python-publish.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   deploy:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     steps:
     - uses: actions/checkout@v6
     - name: Set up Python

.github/workflows/szdiff.yml

@@ -10,7 +10,7 @@ concurrency:
 jobs:
   checkbranch:
     name: Check PR Branch status
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     outputs:
       branchstat: ${{ steps.brstat.outputs.stat}}
     steps:
@@ -44,7 +44,7 @@ jobs:
     permissions:
       contents: read
       pull-requests: write
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     needs: checkbranch
     if: needs.checkbranch.outputs.branchstat == 'false'
     steps:
@@ -87,7 +87,7 @@ jobs:
     name: Core Library Line Difference
     permissions:
       pull-requests: write
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     needs: checkbranch
     if: needs.checkbranch.outputs.branchstat == 'true'
     steps:

.github/workflows/test.yml

@@ -21,7 +21,7 @@ concurrency:
 jobs:
   docs:
     name: Docs
-    runs-on: &linux ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 10
     env:
       CHECK_OOB: 0
@@ -31,8 +31,7 @@ jobs:
     - name: Setup Environment
       uses: ./.github/actions/setup-tinygrad
       with:
-        deps: docs
-        pydeps: "capstone torch"
+        deps: "docs testing_minimal"
     - name: Build wheel and show size
       run: |
         uv build --wheel
@@ -61,7 +60,7 @@ jobs:
 
   torchbackend:
     name: Torch Backend Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
     - name: Checkout Code
@@ -73,10 +72,7 @@ jobs:
         deps: testing_unit
         pydeps: "pillow torchvision expecttest"
         llvm: 'true'
-    - name: Install ninja
-      run: |
-        sudo apt update || true
-        sudo apt install -y --no-install-recommends ninja-build
+        ninja: 'true'
     - name: Test ResNet-18
       run: DEBUG=2 python3 extra/torch_backend/example.py
     - name: Test one op in torch tests
@@ -86,9 +82,26 @@ jobs:
     - name: Custom tests
       run: DEV=CPU:LLVM GPUS=4 TINY_BACKEND=1 python3 -m pytest -nauto extra/torch_backend/test.py extra/torch_backend/test_inplace.py extra/torch_backend/test_multigpu.py extra/torch_backend/test_kernel_fusion.py --durations=20
 
+  torchbackendtrain:
+    name: Torch Backend Training
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
+    timeout-minutes: 15
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - name: Setup Environment
+      uses: ./.github/actions/setup-tinygrad
+      with:
+        key: torch-backend-pillow-torchvision-et-pt
+        deps: testing_unit
+        llvm: 'true'
+        ninja: 'true'
+    - name: Test beautiful_mnist in torch with TINY_BACKEND
+      run: STEPS=20 DEV=CPU TARGET_EVAL_ACC_PCT=90.0 MAX_BUFFER_SIZE=0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+
   bepython:
     name: Python Backend
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
     - name: Checkout Code
@@ -126,7 +139,7 @@ jobs:
 
   linter:
     name: Linters
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 10
 
     steps:
@@ -157,7 +170,7 @@ jobs:
 
   nulltest:
     name: Null Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
 
     steps:
@@ -191,7 +204,7 @@ jobs:
 
   unittest:
     name: Unit Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
 
     steps:
@@ -228,7 +241,7 @@ jobs:
       matrix:
         group: [1, 2]
     name: SPEC=2 (${{ matrix.group }})
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
     - name: Checkout Code
@@ -244,7 +257,7 @@ jobs:
 
   fuzzing:
     name: Fuzzing
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 10
     steps:
     - name: Checkout Code
@@ -260,7 +273,7 @@ jobs:
 
   testopenclimage:
     name: CL IMAGE Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
       - name: Checkout Code
@@ -280,7 +293,7 @@ jobs:
 
   testopenpilot:
     name: openpilot Compile Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
       - name: Checkout Code
@@ -309,7 +322,7 @@ jobs:
 
   testonnxcpu:
     name: ONNX (CPU) Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 20
 
     steps:
@@ -328,7 +341,7 @@ jobs:
 
   testoptim:
     name: Optimization Tests
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 20
     steps:
       - name: Checkout Code
@@ -360,7 +373,7 @@ jobs:
 
   testllm:
     name: Test LLM
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     env:
       CHECK_OOB: 0
@@ -387,7 +400,7 @@ jobs:
 
   testmodels:
     name: Models
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
       - name: Checkout Code
@@ -407,7 +420,7 @@ jobs:
 
   testdsp:
     name: Linux (DSP)
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     steps:
     - name: Checkout Code
@@ -435,7 +448,7 @@ jobs:
           - 'WEBGPU'
 
     name: Linux (DEV=${{ matrix.dev }})
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 20
     steps:
     - name: Checkout Code
@@ -461,7 +474,7 @@ jobs:
 
   testamdasm:
     name: AMD ASM IDE
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 20
     env:
       DEV: MOCKKFD+AMD
@@ -507,7 +520,7 @@ jobs:
 
   hcq2:
     name: hcq2
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 5
     steps:
       - name: Checkout Code
@@ -521,10 +534,7 @@ jobs:
       - name: Run HCQ2 tests
         run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/test_tiny.py
       - name: Run HCQ2 multi-device tests
-        run: |
-          HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/unit/test_multitensor.py \
-            TestMultiTensor.test_simple_add TestMultiTensor.test_shard_reduce \
-            TestMultiTensor.test_backward_sum TestMultiTensor.test_matmul_shard_0_0
+        run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python -m pytest -n=auto test/backend/test_multitensor.py
       - name: Run HCQ2 JIT tests
         run: HCQ_RUNTIME_DEV=PYTHON HCQ2=1 DEV=MOCKKFD+AMD FORWARD_ONLY=1 PYTHONPATH=. python test/unit/test_jit.py
       - name: Run HCQ2 unit tests
@@ -532,7 +542,7 @@ jobs:
 
   testmockam:
     name: Linux (am)
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     env:
       DEV: MOCKPCI+AMD
@@ -568,7 +578,7 @@ jobs:
         arch: [gfx1100, gfx1201, gfx950]
 
     name: Linux (${{ matrix.backend }} ${{ matrix.arch }})
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
     env:
       DEV: MOCKKFD+AMD:${{ matrix.backend == 'amdllvm' && 'LLVM' || '' }}:${{ matrix.arch }}
@@ -606,7 +616,7 @@ jobs:
         backend: [ptx, nv]
 
     name: Linux (${{ matrix.backend }})
-    runs-on: *linux
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 20
     env:
       FORWARD_ONLY: 1
@@ -640,10 +650,17 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        backend: [ir3, nak]
-    name: Compile-only (${{ matrix.backend }})
-    runs-on: *linux
+        dev:
+          - 'NULL:IR3:a630'
+          - 'NULL:QCOMCL:a630'
+          - 'NULL:NAK:sm_120'
+    name: Compile-only (DEV=${{ matrix.dev }})
+    runs-on: ${{ github.repository == 'tinygrad/tinygrad' && github.event_name == 'pull_request' && github.event.pull_request.author_association == 'COLLABORATOR' && 'namespace-profile-tinygrad' || 'ubuntu-24.04' }}
     timeout-minutes: 15
+    env:
+      NULL_ALLOW_COPYOUT: 1
+      DEV: ${{ matrix.dev }}${{ contains(matrix.dev, 'a630') && ',IMAGE_PITCH_ALIGNMENT=64' || '' }}
+      IMAGE: ${{ contains(matrix.dev, 'a630') && '1' || '0' }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v6
@@ -652,21 +669,14 @@ jobs:
         with:
           key: compile-${{ matrix.backend }}
           deps: "testing_unit mesa"
-      - name: Set env
+          qemu: ${{ contains(matrix.dev, 'QCOMCL') }}
+      - name: Test IMAGE
         shell: bash
-        run: printf "NULL_ALLOW_COPYOUT=1\n${{ matrix.backend == 'ir3' && 'DEV=NULL:IR3:a630' || matrix.backend == 'nak' && 'DEV=NULL:NAK:sm_120' }}" >> $GITHUB_ENV
+        if: contains(matrix.dev, 'a630')
+        run: DEBUG=7 python3 test/backend/test_ops.py TestOps.test_gemm | grep isam
       - 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)
-        if: matrix.backend == 'ir3'
-        shell: bash
-        env:
-          IMAGE: 1
-          DEV: "NULL:IR3:a630,IMAGE_PITCH_ALIGNMENT=64"
-        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

README.md

@@ -140,7 +140,7 @@ Documentation along with a quick start guide can be found on the [docs website](
 ```python
 from tinygrad import Tensor
 
-x = Tensor.eye(3)
+x = Tensor.eye(3).clone()  # clone to make it a buffer
 y = Tensor([[2.0,0,-2.0]])
 z = y.matmul(x).sum()
 z.backward()

examples/mlperf/model_train.py

@@ -1742,8 +1742,8 @@ def train_gptoss():
   )
 
   for p in optim.params:
-    grad_dtype = dtypes.bfloat16 if p.dtype == FP8_DTYPE else p.dtype
-    p.grad = p.zeros_like(dtype=grad_dtype).contiguous()
+    p.grad = p.zeros_like(dtype=dtypes.bfloat16 if p.dtype == FP8_DTYPE else p.dtype).contiguous()
+    if getattr(p, "_zero2", False): p.grad = optim.optimizers[0]._zero_shard(p.grad)
   grads = [p.grad for p in optim.params]
 
   from extra.gemm.cdna_asm_gemm import _mx_block_scale

examples/mlperf/models/gpt_oss.py

@@ -12,7 +12,7 @@ from tinygrad.helpers import Timing, colored, GlobalCounters, profile_marker
 from tinygrad.uop.ops import Ops, UOp
 from extra.models.llama import apply_rotary_emb
 from extra.llama_kernels.rmsnorm import rmsnorm
-from extra.gemm.cdna_asm_gemm import _mx_block_scale, _mx_block_scale_3d, quantize_mxfp8
+from extra.gemm.cdna_asm_gemm import _mx_block_scale, _mx_block_scale_3d, quantize_mxfp8, asm_gemm, can_use_asm_gemm
 from extra.gemm.moe_gemm import grouped_mx_gemm
 from extra.gemm.moe_routing import route, dispatch, combine
 
@@ -146,6 +146,7 @@ class GPTOSS:
       return w_q, w_e8.is_param_(False)
     if moe:
       qs = [_one(*shape[1:]) for _ in range(shape[0])]
+      for q in qs: q[0]._zero2 = True  # grad arrives sharded on the expert axis under ZeRO-2 (moe_gemm)
       return [q[0] for q in qs], [q[1] for q in qs]
     return _one(*shape)
 
@@ -182,12 +183,12 @@ class GPTOSS:
     xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
     xq, xk, xv = xq.cast(dtypes.bfloat16), xk.cast(dtypes.bfloat16), xv.cast(dtypes.bfloat16)  # (B,N,H,D)/(B,N,KV,D)
 
-    if sliding:
-      attn = self._sliding_attention(xq, xk, xv, sinks)
-    elif getenv("HK_FLASH_ATTENTION"):
+    if getenv("HK_FLASH_ATTENTION"):
       from extra.thunder.amd.fa import flash_attention
-      attn, *_ = flash_attention(xq, xk, xv, is_causal=True, write_flat=True, sinks=sinks)
+      attn, *_ = flash_attention(xq, xk, xv, is_causal=True, write_flat=True, sinks=sinks, window=self.sliding_window if sliding else 0)
       attn = attn.reshape(bsz, seqlen, self.n_heads * self.head_dim)
+    elif sliding:
+      attn = self._sliding_attention(xq, xk, xv, sinks)
     else:
       xqm = xq.reshape(bsz, seqlen, self.n_kv_heads, self.n_rep, self.head_dim).permute(0, 2, 3, 1, 4)
       xkm, xvm = xk.permute(0, 2, 1, 3).unsqueeze(2), xv.permute(0, 2, 1, 3).unsqueeze(2)
@@ -263,7 +264,11 @@ class GPTOSS:
                         w_down=self.w_down[i], w_down_scale=self.w_down_scale[i], w_down_bias=self.w_down_bias[i])
       h, *_ = self.run_layer(h, freqs_cis, mask_full, i % 2 == 0, attn_kwargs, ffn_kwargs, save=save)
 
-    logits = self.norm(h) @ self.output.T
+    h_normed = self.norm(h)
+    pad = (-self.dim) % 256
+    h_padded, w_padded = h_normed.pad((None, None, (0, pad))), self.output.pad(((0, 0), (0, pad)))
+    if ASM_GEMM and can_use_asm_gemm(h_padded, w_padded.T): logits = asm_gemm(h_padded, w_padded.T)
+    else: logits = h_normed @ self.output.T
     return logits
 
 def _get_pads(uop:UOp) -> list[UOp]:

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

@@ -46,7 +46,7 @@ export DATA_SEED=${DATA_SEED:-5760}
 export JITBEAM=${JITBEAM:-3}
 export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
 
-export FAKEDATA=${FAKEDATA:-1} BENCHMARK=${BENCHMARK:-10}
+export FAKEDATA=${FAKEDATA:-$([[ "$DEV" == NULL:* ]] && echo 1 || echo 0)} BENCHMARK=${BENCHMARK:-10}
 if [ -z "$FULL_LAYERS" ]; then
   export LLAMA_LAYERS=${LLAMA_LAYERS:-2}
 fi

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

@@ -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

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

@@ -11,6 +11,7 @@ export DEVICE_IN_FUNCTION_BUG=1
 export DEBUG=${DEBUG:-2}
 export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
 export ASM_GEMM=${ASM_GEMM:-1}
+export GROUPED_MOE=${GROUPED_MOE:-1}
 export ALL2ALL=${ALL2ALL:-1}
 export LATE_ALLREDUCE=${LATE_ALLREDUCE:-0}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}

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

@@ -11,6 +11,7 @@ export DEVICE_IN_FUNCTION_BUG=1
 export DEBUG=${DEBUG:-0}
 export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
 export ASM_GEMM=${ASM_GEMM:-1}
+export GROUPED_MOE=${GROUPED_MOE:-1}
 export ALL2ALL=${ALL2ALL:-1}
 export LATE_ALLREDUCE=${LATE_ALLREDUCE:-0}
 export ALLREDUCE_CAST=${ALLREDUCE_CAST:-1}

extra/export_model.py

@@ -35,7 +35,7 @@ def compile_net(linear:UOp, output_bufs:List[Buffer]) -> Tuple[Dict[str,str], Li
     return name
 
   for call in iter_kernel_calls(linear):
-    arg_uops = [b for b in call.src[1:] if b.op is not Ops.BIND]
+    arg_uops = [b for b in call.src[1:] if not b.is_bound_var]
     prg = to_program(call.src[0], Device[arg_uops[0].device].renderer)
     info = prg.arg
     functions[info.function_name] = prg.src[2].arg

extra/gemm/cdna_asm_gemm.py

@@ -122,7 +122,8 @@ def custom_mxfp4_gemm(C:UOp, A:UOp, B:UOp, scale_a:UOp, scale_b:UOp, *extra:UOp,
   groups_x, groups_y = UOp.special(ceildiv(N, tile_n), "gidx0"), UOp.special(ceildiv(M, tile_m), "gidx1")
   lds = UOp.placeholder((163840,), dtypes.uint8, 0, AddrSpace.LOCAL)
   sink = UOp.sink(C.base, A.base, B.base, scale_a.base, scale_b.base, *(x.base for x in extra), lds, threads, groups_x, groups_y,
-                  arg=KernelInfo(f"custom_mxfp4_gemm_{M}_{N}_{K}", estimates=Estimates(ops=2*M*N*K)))
+                  arg=KernelInfo(f"mxfp4_gemm_{M}_{N}_{K}",
+                                 estimates=Estimates(ops=2*M*N*K, mem=(M*half_k+N*half_k)*A.dtype.itemsize+M*N*C.dtype.itemsize)))
   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))))
 

extra/gemm/moe_gemm.py

@@ -1,10 +1,32 @@
 import functools, pathlib
 from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
+from tinygrad.helpers import getenv
 from tinygrad.renderer import Estimates
 from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
 from extra.gemm.cdna_asm_gemm import quantize_mxfp8, _mx_block_scale, _mx_block_scale_3d
 
+ZERO_OPTIM = getenv("ZERO_OPTIM", 0)
+
+def reduce_scatter_devaxis(out:Tensor, shard_axis:int=0) -> Tensor:
+  # out: sharded on the device axis, shape (ndev, *rest); return the device-axis sum left sharded on shard_axis.
+  u = out.uop
+  devs, rest = u.device, u.shape[1:]
+  assert rest[shard_axis] % len(devs) == 0, f"reduce_scatter needs even shards: {rest[shard_axis]} % {len(devs)}"
+  # reach the raw per-device buffer below the UNSHARD, keeping the AFTERs so reads stay ordered after the kernel writes
+  node, barriers = u, []
+  while node.op is not Ops.UNSHARD:
+    if node.op is Ops.AFTER: barriers += node.src[1:]
+    node = node.src[0]
+  mbuf = node.src[0].after(*barriers) if barriers else node.src[0]
+  sz = rest[shard_axis] // len(devs)
+  shards = []
+  for i in range(len(devs)):
+    bounds = tuple((0,s) if a != shard_axis else (i*sz,(i+1)*sz) for a,s in enumerate(rest))
+    contribs = [mbuf.mselect(j).reshape(rest).shrink(bounds).copy_to_device(devs[i]) for j in range(len(devs))]
+    shards.append(functools.reduce(lambda a,b: a.alu(Ops.ADD, b), contribs))
+  return Tensor(UOp.mstack(*shards).unshard(shard_axis, UOp.range(len(devs), -1, AxisType.DEVICE)), device=devs)
+
 @functools.cache
 def custom_hk_grouped_mxfp8_gemm(C:UOp, A:UOp, B:UOp, scale_A:UOp, scale_B:UOp, *extra:UOp, dname:str, n_experts:int) -> UOp:
   M, K = A.shape
@@ -58,7 +80,8 @@ def grouped_mx_wgrad(g:Tensor, xg:Tensor, expert_off:Tensor, n_experts:int) -> T
   out = Tensor(inv.uop.unshard(0), device=g.device) if is_multi else inv
   out = Tensor.custom_kernel(out, gT, xT, g_si, x_si, expert_off,
                              fxn=functools.partial(custom_hk_grouped_mxfp8_wgrad, dname=dname, n_experts=n_experts))[0]
-  out = out.sum(0) if is_multi else out.squeeze(0)
+  if is_multi and ZERO_OPTIM: out = reduce_scatter_devaxis(out, 0)
+  else: out = out.sum(0) if is_multi else out.squeeze(0)
   return out.reshape(n_experts, N, K)
 
 def mx_pack_3d(e8:Tensor) -> Tensor:

extra/gemm/triton_nv_matmul.py

@@ -79,7 +79,7 @@ if __name__ == "__main__":
   linear, var_vals = C.linear_with_vars()
   last_call = linear.src[-1]
   ast = last_call.src[0]
-  bufs = [s.buffer for s in last_call.src[1:] if s.op is not Ops.BIND]
+  bufs = [s.buffer for s in last_call.src[1:] if not s.is_bound_var]
 
   src = compiled.asm["ptx"]
   # specify the shared memory here so we don't need to do it dynamically

extra/hcq2/ops_amd2.py

@@ -3,7 +3,7 @@ from typing import cast, Any, Callable
 import os, ctypes, struct, hashlib, functools, importlib, mmap, errno, array, contextlib, sys, weakref, itertools, collections, atexit
 assert sys.platform != 'win32'
 from dataclasses import dataclass
-from tinygrad.runtime.support.hcq2 import HCQ2Compiled, HCQAllocator, HCQ2Buffer, encode_kernargs_clike, make_cmdbuf
+from tinygrad.runtime.support.hcq2 import HCQ2Compiled, HCQAllocator, encode_kernargs_clike, make_cmdbuf
 from tinygrad.runtime.support.hcq2 import make_binary_patch
 from tinygrad.uop.ops import sint, UOp
 from tinygrad.device import Compiled, BufferSpec, Buffer, Device
@@ -179,11 +179,10 @@ class SDMAOps(FastEnum): COPY = auto(); POLL_REGMEM = auto(); FENCE = auto(); TR
 
 def sdma_copy(ctx, call):
   sz = call.src[2].max_numel() * call.src[2].dtype.itemsize
-  src_addr, dst_addr = call.src[2].getaddr(ctx.devs), call.src[1].getaddr(ctx.devs)
-  return call.ins(SDMAOps.COPY, src=tuple(UOp.const(x, dtypes.uint32) for off in range(0, sz, ctx.max_copy_size) for x in (
-    ctx.sdma.SDMA_OP_COPY | ctx.sdma.SDMA_PKT_COPY_LINEAR_HEADER_SUB_OP(ctx.sdma.SDMA_SUBOP_COPY_LINEAR),
-    ctx.sdma.SDMA_PKT_COPY_LINEAR_COUNT_COUNT(min(sz-off, ctx.max_copy_size)-1), 0,
-    *data64_le(src_addr+UOp.const(off, dtypes.uint64)), *data64_le(dst_addr+UOp.const(off, dtypes.uint64)))))
+  hdr = ctx.sdma.SDMA_OP_COPY | ctx.sdma.SDMA_PKT_COPY_LINEAR_HEADER_SUB_OP(ctx.sdma.SDMA_SUBOP_COPY_LINEAR)
+  return call.ins(SDMAOps.COPY, src=tuple(x for off in range(0, sz, ctx.max_copy_size) for x in (
+    *(UOp.const(v, dtypes.uint32) for v in (hdr, ctx.sdma.SDMA_PKT_COPY_LINEAR_COUNT_COUNT(min(sz-off, ctx.max_copy_size)-1), 0)),
+    *(a + UOp.const(off, dtypes.uint64) if off else a for a in (call.src[2].getaddr(ctx.devs), call.src[1].getaddr(ctx.devs))))))
 
 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) \
@@ -290,12 +289,14 @@ class AMDAllocator(HCQAllocator['AMDDevice']):
   def __init__(self, dev:AMDDevice):
     super().__init__(dev, supports_copy_from_disk=dev.has_copy_queue, supports_transfer=dev.has_copy_queue and not dev.is_usb())
 
-  def _alloc(self, size:int, options:BufferSpec) -> HCQ2Buffer:
+  def _alloc(self, size:int, options:BufferSpec) -> HCQBuffer:
     return self.dev.iface.alloc(size, host=options.host, uncached=options.uncached, cpu_access=options.cpu_access or not self.dev.has_copy_queue)
 
   def _do_free(self, opaque, options:BufferSpec): self.dev.iface.free(opaque)
 
-  def _do_map(self, buf:HCQ2Buffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
+  def _do_map(self, buf:HCQBuffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
+
+  def _do_unmap(self, buf:HCQBuffer): self.dev.iface.unmap(buf)
 
 @dataclass
 class AMDQueueDesc:
@@ -388,15 +389,24 @@ class KFDIface:
     return hcqbuf
 
   def free(self, mem):
+    self._unmap(mem)
+    if mem.va_addr: FileIOInterface.munmap(mem.va_addr, mem.size)
+    kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
+
+  def unmap(self, mem):
+    self._unmap(mem)
+    if getattr(mem, '_owns_kfd_handle', False): kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
+
+  def _unmap(self, mem):
     gpus = (ctypes.c_int32 * 1)(self.gpu_id)
     stm = kfd.AMDKFD_IOC_UNMAP_MEMORY_FROM_GPU(self.kfd, handle=mem.meta.handle, device_ids_array_ptr=ctypes.addressof(gpus), n_devices=1)
     assert stm.n_success == 1
-    if mem.owner == self.dev:
-      if mem.va_addr: FileIOInterface.munmap(mem.va_addr, mem.size)
-      kfd.AMDKFD_IOC_FREE_MEMORY_OF_GPU(self.kfd, handle=mem.meta.handle)
 
   def map(self, mem):
-    if mem.owner is not None and mem.owner._is_cpu(): return self.alloc(mem.size, host=True, cpu_addr=mem.va_addr)
+    if mem.owner is not None and mem.owner._is_cpu():
+      mapped = self.alloc(mem.size, host=True, cpu_addr=mem.va_addr)
+      mapped._owns_kfd_handle = True
+      return mapped
 
     c_gpus = (ctypes.c_int32 * 1)(self.gpu_id)
     stm = kfd.AMDKFD_IOC_MAP_MEMORY_TO_GPU(self.kfd, handle=mem.meta.handle, device_ids_array_ptr=ctypes.addressof(c_gpus), n_devices=1)
@@ -468,6 +478,7 @@ class PCIIface(PCIIfaceBase):
 
   def require_profile_mode(self): return True
   def is_wgp_active(self, xcc, se, sa, wgp) -> bool: return True # TODO: account for WGP disablement on some asics.
+  def unmap(self, mem): self.free(mem)
 
   def _compute_props(self):
     self.ip_versions = self.dev_impl.ip_ver
@@ -549,9 +560,7 @@ class AMDDevice(HCQ2Compiled):
   def is_usb(self) -> bool: return False
 
   def __init__(self, device:str=""):
-    self.device_id = int(device.split(":")[1]) if ":" in device else 0
-
-    self.iface = self._select_iface()
+    self.iface = self._select_iface(device)
 
     self.target:tuple[int, ...] = ((trgt:=self.iface.props['gfx_target_version']) // 10000, (trgt // 100) % 100, trgt % 100)
     self.arch = "gfx%d%x%x" % self.target

extra/runbook_digitalocean_mi350x.md

@@ -0,0 +1,217 @@
+# Runbook: Llama 3 8B Training on DigitalOcean MI350X
+
+## Machine Specs
+- 8x MI350X GPUs (gfx950, device ID 75b0), 288GB VRAM each
+- 2TB RAM, 192 CPUs, 2TB disk
+- ROCm 7.14 at `/opt/rocm` (NOT `/opt/rocm-7.1.1` like the submission scripts assume)
+- Python 3.12
+
+## Phase 1: System Setup
+
+### 1.1 Install packages
+```bash
+apt-get update
+apt-get install -y python3-pip python3-venv git tmux rclone clang
+```
+
+### 1.2 Install Python deps
+```bash
+python3 -m pip install --break-system-packages --ignore-installed typing-extensions numpy tqdm wandb tiktoken sentencepiece
+```
+Note: `--ignore-installed typing-extensions` is needed because the base image ships typing-extensions 4.10.0 without a RECORD file, so pip cannot uninstall it.
+
+### 1.3 Install ROCm dev headers
+The base image has ROCm runtime but NOT the HIP dev headers. Need:
+```bash
+apt-get install -y amdrocm-core-dev
+```
+This installs `hip/hip_runtime.h` at `/opt/rocm/core-7.14/include/hip/hip_runtime.h`.
+The symlink `/opt/rocm/include` → `/opt/rocm/core-7.14/include` makes it available at `/opt/rocm/include/hip/hip_runtime.h`.
+
+### 1.4 Configure ROCm comgr
+ROCm 7.14 ships comgr 3.3 at `/opt/rocm/lib/libamd_comgr.so`. tinygrad's DLL loader needs explicit env vars to find it (it searches for `libcomgr.so*` by default, not `libamd_comgr.so*`). Set these in the run command:
+```bash
+export COMGR_PATH=/opt/rocm/lib/libamd_comgr.so
+export COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so
+```
+Also add ROCm libs to ldconfig so comgr's shared library dependencies resolve:
+```bash
+cat > /etc/ld.so.conf.d/rocm.conf << 'EOF'
+/opt/rocm/lib
+/opt/rocm/lib/llvm/lib
+/opt/rocm/lib/rocm_sysdeps/lib
+EOF
+ldconfig
+```
+
+### 1.5 Install geohot tmux config
+```bash
+curl -sL https://raw.githubusercontent.com/geohot/configuration/master/.tmux.conf -o ~/.tmux.conf
+```
+
+### 1.6 Reload amdgpu driver
+tinygrad's HCQ backend needs `/dev/kfd` which is created by the amdgpu kernel driver.
+If the driver was unloaded, reload it:
+```bash
+modprobe amdgpu
+ls /dev/kfd  # should exist
+```
+
+## Phase 2: Clone tinygrad
+```bash
+cd /root
+git clone https://github.com/tinygrad/tinygrad.git
+cd tinygrad
+python3 -m pip install --break-system-packages -e .
+```
+
+## Phase 3: Download C4 Dataset
+
+The C4 data is on the MLCommons Cloudflare R2 bucket in Megatron-LM indexed format.
+
+```bash
+rclone config create mlc-training s3 provider=Cloudflare \
+  access_key_id=76ea42eadb867e854061a1806220ee1e \
+  secret_access_key=a53625c4d45e3ca8ac0df8a353ea3a41ffc3292aa25259addd8b7dc5a6ce2936 \
+  endpoint=c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+
+mkdir -p /raid/datasets/c4-8b
+rclone copy mlc-training:mlcommons-training-wg-public/llama3_1/datasets/c4/llama3_1_8b/ /raid/datasets/c4-8b/ -P
+```
+
+Files downloaded (~85GB total, ~6 minutes):
+- `c4-train.en_6_text_document.bin` (79 GB)
+- `c4-train.en_6_text_document.idx` (870 MB)
+- `c4-validation-91205-samples.en_text_document.bin` (159 MB)
+- `c4-validation-91205-samples.en_text_document.idx` (1.8 MB)
+- `LICENSE.txt`, `NOTICE.txt`
+
+**Wait for rclone to fully complete before starting training.** Starting training while the dataset is still downloading will read a truncated .bin file, causing `ValueError: all input arrays must have the same shape` in the dataloader. The stale `.index_cache` and `.blend_cache` files must also be deleted if this happens:
+```bash
+rm -f /raid/datasets/c4-8b/*.index_cache /raid/datasets/c4-8b/*.blend_cache
+```
+
+## Phase 4: wandb Login
+```bash
+wandb login
+```
+Enter API key from https://wandb.ai/authorize
+
+Alternatively, pass the key directly:
+```bash
+wandb login <API_KEY>
+```
+
+## Phase 5: Run Training
+
+Run training in tmux so it survives SSH disconnects:
+```bash
+tmux new-session -d -s train 'cd /root/tinygrad && COMGR_PATH=/opt/rocm/lib/libamd_comgr.so COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so CC=/opt/rocm/core-7.14/lib/llvm/bin/clang DEV=AMD:HIP ROCM_PATH=/opt/rocm WANDB=1 bash examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_run.sh 2>&1 | tee /root/train.log'
+```
+Attach with `tmux attach -t train`.
+
+### 5.1 Smoke test (beam search, 2 layers, real data)
+Always run beam first to validate the pipeline:
+```bash
+tmux new-session -d -s beam 'cd /root/tinygrad && COMGR_PATH=/opt/rocm/lib/libamd_comgr.so COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so CC=/opt/rocm/core-7.14/lib/llvm/bin/clang DEV=AMD:HIP ROCM_PATH=/opt/rocm bash examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_beam.sh 2>&1 | tee /root/beam.log'
+```
+
+The beam test runs 10 training steps with 2 layers. Expected results:
+- ~0.29s per step after warmup
+- ~700K GFLOPS, ~7% MFU (low because only 2 layers)
+- ~380 GB VRAM used
+- Loss stable at ~12.55 with random init
+
+### 5.2 Full training run
+```bash
+tmux new-session -d -s train 'cd /root/tinygrad && COMGR_PATH=/opt/rocm/lib/libamd_comgr.so COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so CC=/opt/rocm/core-7.14/lib/llvm/bin/clang DEV=AMD:HIP ROCM_PATH=/opt/rocm WANDB=1 bash examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_run.sh 2>&1 | tee /root/train.log'
+```
+
+## Environment Variable Reference
+
+| Variable | Value | Why |
+|---|---|---|
+| `COMGR_PATH` | `/opt/rocm/lib/libamd_comgr.so` | tinygrad's DLL loader needs explicit path to find comgr 3.3 |
+| `COMGR_3_PATH` | `/opt/rocm/lib/libamd_comgr.so` | comgr 3.x uses a separate `comgr_3` module with its own path var |
+| `CC` | `/opt/rocm/core-7.14/lib/llvm/bin/clang` | System clang doesn't know gfx950; must use ROCm's bundled clang |
+| `DEV` | `AMD:HIP` | Force HIPRenderer (comgr-based) over HIPCCRenderer (hipcc subprocess) |
+| `ROCM_PATH` | `/opt/rocm` | Script defaults to `/opt/rocm-7.1.1` which doesn't exist |
+| `WANDB` | `1` | Enable wandb logging (off by default) |
+
+## Architecture
+
+| Component | Source file |
+|---|---|
+| Model | `examples/mlperf/models/flat_llama.py` — FlatTransformer, FP8 MXFP4 weights, fused QKV, flash attention |
+| Trainer | `examples/mlperf/model_train.py` → `train_llama3()` |
+| Optimizer | `examples/mlperf/optim.py` — GradAccClipAdamW, master weights, FP8 re-quant |
+| LR schedule | `examples/mlperf/lr_schedulers.py` — CosineAnnealingLRWithWarmup |
+| Dataloader | `examples/mlperf/dataloader.py` — Megatron-LM indexed bin format |
+| ASM GEMM | `extra/gemm/cdna_asm_gemm.py` — gfx950 MFMA assembly, MXFP4 |
+| Flash attention | `extra/thunder/amd/fa.py` |
+| Fused kernels | `extra/llama_kernels/` — rmsnorm, silu, quantize, fused_ce |
+| GPU driver | `tinygrad/runtime/ops_amd.py` — HCQ, direct KFD ioctl |
+| Renderer | `tinygrad/renderer/cstyle.py` — HIPRenderer for gfx950 |
+| comgr compiler | `tinygrad/runtime/support/compiler_amd.py` — HIPCompiler using comgr 3.3 |
+
+## Troubleshooting
+
+### `'hip/hip_runtime.h' file not found`
+Install `amdrocm-core-dev`:
+```bash
+apt-get install -y amdrocm-core-dev
+```
+
+### `'gfx950' is not a recognized processor` + LLVM crash
+System clang doesn't know gfx950. Set `CC=/opt/rocm/core-7.14/lib/llvm/bin/clang`.
+
+### `comgr not available: try setting COMGR_PATH?`
+Add ROCm libs to ldconfig and set `COMGR_PATH` and `COMGR_3_PATH`:
+```bash
+# /etc/ld.so.conf.d/rocm.conf should contain /opt/rocm/lib paths
+ldconfig
+```
+
+### `comgr not available: try setting COMGR_3_PATH?`
+comgr 3.x uses a separate module. Set `COMGR_3_PATH=/opt/rocm/lib/libamd_comgr.so` too.
+
+### `No such file or directory: 'clang'`
+Install clang: `apt-get install -y clang` (for CPU compilation).
+For gfx950 HIP compilation, comgr (not clang) is used — ensure the ROCm 7.14 comgr 3.3 is properly loaded via `COMGR_PATH` and `COMGR_3_PATH`.
+
+## Appendix: KVM Virtualization Observations
+
+### Virtualization detection
+```
+$ systemd-detect-virt
+kvm
+$ lspci -nn | grep AMD
+83:00.0 ... Device [1002:75b0]
+```
+CPU flags include `hypervisor`. `dmesg` shows `Hypervisor detected: KVM`.
+
+### Working path: amdgpu driver (KFDIface)
+The amdgpu driver loads on boot and binds to all 8 GPUs, creating `/dev/kfd` and 64 renderD nodes (`/dev/dri/renderD128` through `/dev/dri/renderD191`). tinygrad's `KFDIface` enumerates GPUs through `/sys/devices/virtual/kfd/kfd/topology/nodes` and uses `/dev/kfd` for ioctl. No PCI device ID patching is needed — the KFD path does not use `PCIIface` or `AMDev._run_discovery()`.
+
+This is the working configuration. No code changes to tinygrad are required.
+
+### PCIIface path (does not work on this VM)
+For reference, the `PCIIface` path was also explored but does not work in this KVM guest:
+
+- `PCIIface` in `ops_amd.py` does not list device ID `0x75b0`. Adding it allows PCI detection but `AMDev._run_discovery()` fails because the VRAM BAR reads all `0xFF`.
+- This was observed with the GPU unbound from any driver, after PCI reset, and with VFIO bound.
+- VFIO binding (`vfio-pci` with `enable_unsafe_noiommu_mode=1`) succeeded but VRAM BAR still reads all `0xFF`.
+- No IOMMU in guest — `dmesg` has no `AMD-Vi` entries, PCI devices have no `iommu_group` symlink.
+
+### amdgpu driver behavior
+On first boot, amdgpu loaded and bound to all 8 GPUs. On one boot it failed to initialize:
+```
+[  799.780369] amdgpu 0000:83:00.0: Failed to alloc msi vectors
+[  799.781476] amdgpu 0000:83:00.0: sw_init of IP block <vega20_ih> failed -22
+[  799.782724] amdgpu 0000:83:00.0: amdgpu_device_ip_init failed
+[  799.793885] amdgpu 0000:83:00.0: Fatal error during GPU init
+```
+On a subsequent boot, amdgpu initialized successfully (SMU initialized, VRAM ready). After unbinding all 8 GPUs from amdgpu, `rmmod amdgpu` wedged the module (stuck in "Unloading" state in `/proc/modules`), requiring a full VM reboot.
+
+### No fan control
+No `fan*` or `pwm*` hwmon entries exist. Only `temp*`, `power*`, `freq*` are exposed. GPU temps read 56-63°C, power ~265W per GPU.

extra/thunder/amd/fa.py

@@ -110,7 +110,49 @@ def _sharded_empty_like(ref:Tensor, axis:int|None=None) -> Tensor:
   return _sharded_empty(ref.shape, ref, axis)
 
 @functools.cache
-def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink):
+def _windowed_lse(xq:Tensor, xk:Tensor, sinks, W:int) -> Tensor:
+  B, N, H, hd = xq.shape
+  H_KV = xk.shape[2]; R = H // H_KV; nb = N // W; sm = hd ** -0.5
+  q = xq.reshape(B, N, H_KV, R, hd).permute(0, 2, 3, 1, 4).reshape(B, H_KV, R, nb, W, hd).float()
+  k = xk.permute(0, 2, 1, 3).reshape(B, H_KV, 1, nb, W, hd).float()
+  k_prev = k.pad((None, None, None, (1, 0), None, None))[:, :, :, :nb]
+  sc_d = (q @ k.transpose(-1, -2)) * sm
+  sc_p = (q @ k_prev.transpose(-1, -2)) * sm
+  li, lj = Tensor.arange(W).reshape(W, 1), Tensor.arange(W).reshape(1, W)
+  pv = (Tensor.arange(nb).reshape(nb, 1, 1) >= 1)
+  sc_d = (lj <= li).where(sc_d, -float("inf"))
+  sc_p = ((li < lj) & pv).where(sc_p, -float("inf"))
+  m = sc_d.max(-1, keepdim=True).maximum(sc_p.max(-1, keepdim=True))
+  if sinks is not None: m = m.maximum(sinks.reshape(1, H_KV, R, 1, 1, 1).float())
+  denom = (sc_d - m).exp().sum(-1, keepdim=True) + (sc_p - m).exp().sum(-1, keepdim=True)
+  if sinks is not None: denom = denom + (sinks.reshape(1, H_KV, R, 1, 1, 1).float() - m).exp()
+  return (m + denom.log()).reshape(B, H, N).unsqueeze(2)  # (B, H, 1, N), matches saved l_vec
+
+def _windowed_delta(xq:Tensor, xk:Tensor, xv:Tensor, do:Tensor, sinks, W:int) -> Tensor:
+  B, N, H, hd = xq.shape
+  H_KV = xk.shape[2]; R = H // H_KV; nb = N // W; sm = hd ** -0.5
+  q = xq.reshape(B, N, H_KV, R, hd).permute(0, 2, 3, 1, 4).reshape(B, H_KV, R, nb, W, hd).float()
+  k = xk.permute(0, 2, 1, 3).reshape(B, H_KV, 1, nb, W, hd).float()
+  v = xv.permute(0, 2, 1, 3).reshape(B, H_KV, 1, nb, W, hd).float()
+  dob = do.reshape(B, N, H_KV, R, hd).permute(0, 2, 3, 1, 4).reshape(B, H_KV, R, nb, W, hd).float()
+  k_prev = k.pad((None, None, None, (1, 0), None, None))[:, :, :, :nb]
+  v_prev = v.pad((None, None, None, (1, 0), None, None))[:, :, :, :nb]
+  sc_d = (q @ k.transpose(-1, -2)) * sm
+  sc_p = (q @ k_prev.transpose(-1, -2)) * sm
+  li, lj = Tensor.arange(W).reshape(W, 1), Tensor.arange(W).reshape(1, W)
+  pv = (Tensor.arange(nb).reshape(nb, 1, 1) >= 1)
+  sc_d = (lj <= li).where(sc_d, -float("inf"))
+  sc_p = ((li < lj) & pv).where(sc_p, -float("inf"))
+  m = sc_d.max(-1, keepdim=True).maximum(sc_p.max(-1, keepdim=True))
+  if sinks is not None: m = m.maximum(sinks.reshape(1, H_KV, R, 1, 1, 1).float())
+  e_d, e_p = (sc_d - m).exp(), (sc_p - m).exp()
+  denom = e_d.sum(-1, keepdim=True) + e_p.sum(-1, keepdim=True)
+  if sinks is not None: denom = denom + (sinks.reshape(1, H_KV, R, 1, 1, 1).float() - m).exp()
+  o = ((e_d / denom) @ v) + ((e_p / denom) @ v_prev)
+  delta = (dob * o).sum(-1)
+  return delta.reshape(B, H, N).unsqueeze(2)
+
+def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink, window=0):
   def grad(dou:UOp, ker:UOp) -> tuple:
     do = Tensor(dou, device=dou.device)
     attn = Tensor(ker.src[1].after(ker), device=ker.src[1].device)
@@ -118,6 +160,8 @@ def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, sha
     xq = Tensor(ker.src[3], device=ker.src[3].device)
     xk = Tensor(ker.src[4], device=ker.src[4].device)
     xv = Tensor(ker.src[5], device=ker.src[5].device)
+    if window:
+      l_vec = _windowed_lse(xq, xk, Tensor(ker.src[6], device=ker.src[6].device) if has_sink else None, window)
 
     dq = _sharded_empty((B, H, N, D), xq, axis=shard_axis_t)
     GROUP_SIZE = H_local // H_KV_local
@@ -128,8 +172,10 @@ def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, sha
     # delta_vec = (do * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()
     delta_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
     delta_vec, dq = Tensor.custom_kernel(delta_vec, dq, attn, do, fxn=functools.partial(custom_fa_backward_pre, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:2]
+    if window:
+      delta_vec = _windowed_delta(xq, xk, xv, do, Tensor(ker.src[6], device=ker.src[6].device) if has_sink else None, window)
 
-    dq, dk_partial, dv_partial = Tensor.custom_kernel(dq, dk_partial, dv_partial, do, xq, xk, xv, l_vec, delta_vec, fxn=functools.partial(custom_fa_backward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:3]
+    dq, dk_partial, dv_partial = Tensor.custom_kernel(dq, dk_partial, dv_partial, do, xq, xk, xv, l_vec, delta_vec, fxn=functools.partial(custom_fa_backward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D, window=window))[:3]
 
     if D == 64:
       dq = dq.reshape(B, H, N//16, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2).permute(0, 1, 2, 8, 9, 10, 11, 3, 4, 6, 7, 5, 12).reshape(B, H, N, D).transpose(1, 2)
@@ -149,7 +195,7 @@ def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, sha
   return grad
 
 # TODO: remove write_flat once scheduler can remove reshapes between custom_kernel. TestCustomKernel.test_simple_reshape
-def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False, write_flat:bool=False, sinks:Tensor|None=None):
+def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False, write_flat:bool=False, sinks:Tensor|None=None, window:int=0):
   assert attn_mask is None, "attn_mask not supported"
   assert is_causal, "only causal attention supported"
 
@@ -176,18 +222,18 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
   attn = _sharded_empty((B, N, H * D), xq, axis=shard_axis) if write_flat else _sharded_empty_like(xq, axis=shard_axis)
   l_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
 
-  grad = _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink)
+  grad = _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch, has_sink, window=window)
 
   fwd_inputs = (attn, l_vec, xq, xk, xv) + ((sinks,) if has_sink else ())
-  attn, l_vec = Tensor.custom_kernel(*fwd_inputs, fxn=functools.partial(custom_fa_forward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D, has_sink=has_sink), grad_fxn=grad)[:2]
+  attn, l_vec = Tensor.custom_kernel(*fwd_inputs, fxn=functools.partial(custom_fa_forward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D, has_sink=has_sink, window=window), grad_fxn=grad)[:2]
 
   return attn, attn, l_vec
 
 @functools.cache
-def custom_fa_forward(o:UOp, l_vec:UOp, q:UOp, k:UOp, v:UOp, sinks:UOp|None=None, *, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int, has_sink:bool=True):
+def custom_fa_forward(o:UOp, l_vec:UOp, q:UOp, k:UOp, v:UOp, sinks:UOp|None=None, *, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int, has_sink:bool=True, window:int=0):
   code = (pathlib.Path(__file__).parent / "fa_fwd_causal.cpp").read_text()
   compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
-                  f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DATTN_SINK={int(has_sink)}"]
+                  f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DATTN_SINK={int(has_sink)}", f"-DWINDOW={window}"]
 
   Q_BLOCK_SIZE = 32
   NUM_WARPS = 8
@@ -247,10 +293,10 @@ def custom_fa_backward_pre(delta_vec:UOp, dq:UOp, o:UOp, do:UOp, device:str, arc
              src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=code), UOp(Ops.BINARY, arg=lib)))
 
 @functools.cache
-def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_vec:UOp, delta_vec:UOp, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int):
+def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_vec:UOp, delta_vec:UOp, device:str, arch:str, B:int, N:int, H:int, H_KV:int, D:int, window:int=0):
   code = (pathlib.Path(__file__).parent / "fa_bwd_causal.cpp").read_text()
   compile_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "-DKITTENS_CDNA4", "-DHIP_ENABLE_WARP_SYNC_BUILTINS", "-ffast-math",
-                  f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}"]
+                  f"-DATTN_B={B}", f"-DATTN_N={N}", f"-DATTN_H={H}", f"-DATTN_H_KV={H_KV}", f"-DATTN_D={D}", f"-DWINDOW={window}"]
 
   BLOCK_SIZE_KV = 256
   GROUP_SIZE = H // H_KV

extra/thunder/tiny/tk/tiles.py

@@ -209,7 +209,7 @@ class ST:
     return cls(uop, rows, cols, layout, base_shape, ker)
 
   def swizzle(self, row, col):
-    swizzled_offset = self.base_shape.swizzle(row, col, self._uop.dtype.scalar())
+    swizzled_offset = self.base_shape.swizzle(row, col, self._uop.dtype)
 
     row = swizzled_offset // self.base_shape.cols
     col = swizzled_offset % self.base_shape.cols

extra/torch_backend/backend.py

@@ -4,7 +4,7 @@
 # A006 Lambda argument `input` is shadowing a Python builtin
 from tinygrad import Tensor, dtypes, Device
 from tinygrad.uop.ops import Ops, GroupOp
-from tinygrad.helpers import getenv, prod, strides_for_shape, argfix
+from tinygrad.helpers import getenv, prod, strides_for_shape
 import torch.lib
 TORCH_DEBUG = getenv("TORCH_DEBUG")
 import torch, pathlib, operator, functools, weakref
@@ -73,6 +73,12 @@ def wrap_view_op(fn):
     return wrap(ret)
   return _wrap
 
+# NOTE: list assignment raises IndexError on an out of range dim, and the index must be a tuple: a list of all ints is one advanced index
+def _index_dim(self, dim, idx):
+  idxs = [slice(None)] * self.ndim
+  idxs[dim] = idx
+  return self[tuple(idxs)]
+
 view_ops = {
   "aten.view": Tensor.reshape,
   "aten._unsafe_view": Tensor.reshape,  # when are views unsafe, and do we care?
@@ -82,15 +88,13 @@ view_ops = {
   "aten.transpose.int": Tensor.transpose,
   "aten.squeeze.dim": Tensor.squeeze,
   "aten.unsqueeze": Tensor.unsqueeze,
-  "aten.select.int": lambda self, dim, idx: self[(slice(None),) * (dim%self.ndim) + (idx,)],
+  "aten.select.int": _index_dim,
   "aten.permute": Tensor.permute,
   "aten.alias": lambda self: self,
   "aten.diagonal": Tensor.diagonal,
+  "aten.slice.Tensor": lambda self, dim=0, start=None, end=None, step=1: _index_dim(self, dim, slice(start, end, step)),
   }
 
-# torch 2.10 handles this natively
-if tuple(map(int, torch.__version__.split('.')[:2])) < (2, 10): view_ops.update({"aten.detach": Tensor.detach})
-
 for k,v in view_ops.items(): torch.library.impl(k.replace("aten.", "aten::"), "privateuseone")(wrap_view_op(v))
 
 def _get_view_ops(view): return getattr(view, "_view_ops", [])
@@ -99,46 +103,21 @@ def _apply_view_ops(target, ops):
   for fn, args, kwargs in ops: target = fn(target, *args, **kwargs)
   return target
 
-# similar to https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/InferSize.h
-def _reshape_target_shape(shape:tuple[int, ...], args) -> tuple[int, ...]|None:
-  if not (req := argfix(*args)): return None
-  new_shape, infer_idx = [], -1
-  for i, s in enumerate(req):
-    if s is None: s = shape[i] if i < len(shape) else None
-    if not isinstance(s, int): return None
-    if s == -1:
-      if infer_idx != -1: return None
-      infer_idx = len(new_shape)
-    new_shape.append(s)
-  total = prod(shape)
-  if infer_idx != -1:
-    known = prod(x for x in new_shape if x != -1)
-    if known == 0:
-      if total != 0: return None
-      new_shape[infer_idx] = 0
-    else: new_shape[infer_idx] = total // known
-  return tuple(new_shape) if prod(new_shape) == total else None
-
-# TODO: can we get rid of this? only for test_flatten_reshape_add
+# a chain of reshapes is undone by reshaping the value back to the base
 def _try_simple_reshape_view_write(base: Tensor, view: Tensor, val: Tensor) -> bool:
   if not (ops := _get_view_ops(view)): return False
-  shapes = [base.shape]
-  for fn, args, _ in ops:
-    if fn is Tensor.reshape:
-      if not (next_shape := _reshape_target_shape(shapes[-1], args)): return False
-      shapes.append(next_shape)
-  if shapes[-1] != view.shape: return False
-  for s in reversed(shapes[:-1]): val = val.reshape(s)
-  base.assign(val)
+  if any(fn is not Tensor.reshape for fn, _, _ in ops): return False
+  base.assign(val.reshape(base.shape))
   return True
 
 def _view_write(base: Tensor, view: Tensor, value: Tensor) -> None:
   val = value if value.dtype == base.dtype else value.cast(base.dtype)
-  if view.shape == base.shape: return base.assign(val)
   if _try_simple_reshape_view_write(base, view, val): return
   idx_base = Tensor.arange(base.numel(), dtype=dtypes.int32).reshape(base.shape)
   idx_view = _apply_view_ops(idx_base, _get_view_ops(view)).reshape(-1)
-  flat_base = base.reshape(base.numel()).contiguous()
+  # clone, not contiguous: contiguous() on a base that already owns its buffer returns the base itself, and scattering
+  # into that is an in-place write to a buffer other tensors still hold, which setitem refuses
+  flat_base = base.reshape(base.numel()).clone()
   flat_base[idx_view] = val.reshape(-1)
   base.assign(flat_base.reshape(base.shape))
 
@@ -166,11 +145,6 @@ def _index_put_impl_(self, indices, values, accumulate=False, unsafe=False):
 def index_put(self, indices, values, accumulate=False):
   return aten.index_put(self.cpu(), [z.cpu() if isinstance(z, torch.Tensor) else None for z in indices], values.clone().cpu(), accumulate).tiny()
 
-@torch.library.impl("aten::isin.Tensor_Tensor_out", "privateuseone")
-def isin_tensor_tensor_out(x, y, *, assume_unique=False, invert=False, out=None):
-  result = (unwrap(x).unsqueeze(-1) == unwrap(y).flatten()).any(-1)
-  return out.copy_(wrap(~result if invert else result))
-
 @torch.library.impl("aten::randperm.generator_out", "privateuseone")
 def randperm_generator(n, generator=None, out=None):
   if generator is not None: raise NotImplementedError("tinygrad torch backend does not support torch.Generator for randperm")
@@ -231,49 +205,6 @@ def as_strided(tensor:torch.Tensor, size, stride, storage_offset=None):
 def _reshape_alias(tensor:torch.Tensor, size, stride):
   return _as_strided(tensor, size, stride)
 
-@torch.library.impl("aten::empty_strided", "privateuseone")
-def empty_strided(size, stride, dtype=None, layout=None, device=None, pin_memory=False):
-  if TORCH_DEBUG: print(f"empty_strided {size=} {stride=} {dtype=} {layout=} {device=} {pin_memory=}")
-  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device))
-  # TODO: should return with requested strides
-  return wrap(ret)
-
-@torch.library.impl("aten::empty.memory_format", "privateuseone")
-def empty_memory_format(size, dtype=None, layout=None, device=None, pin_memory=False, memory_format=None):
-  if TORCH_DEBUG: print(f"empty.memory_format {size=} {dtype=} {layout=} {device=} {pin_memory=} {memory_format=}")
-  ret = Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device))
-  return wrap(ret)
-
-@torch.library.impl("aten::max_pool2d_with_indices", "privateuseone")
-def max_pool2d_with_indices(self:torch.Tensor, kernel_size:tuple[int, ...], stride=None, padding=0, dilation=1, ceil_mode=False):
-  # TODO: supprt stride [] in tinygrad?
-  if stride is not None and len(stride) == 0: stride = None
-  ret, idx = unwrap(self).max_pool2d(kernel_size, stride, dilation, padding, ceil_mode, return_indices=True)
-  return (wrap(ret), wrap(idx.cast(dtypes.int64)))
-
-@torch.library.impl("aten::max_pool2d_with_indices_backward", "privateuseone")
-def max_pool2d_with_indices_backward(grad_out:torch.Tensor, self:torch.Tensor, kernel_size:tuple[int, ...], stride=None, padding=0, dilation=1, ceil_mode=False, indices=None):
-  return wrap(Tensor.max_unpool2d(unwrap(grad_out), unwrap(indices), output_size=unwrap(self).shape))
-
-@torch.library.impl("aten::max_unpool2d", "privateuseone")
-def max_unpool2d(self:torch.Tensor, indices:torch.Tensor, output_size):
-  return wrap(unwrap(self).max_unpool2d(unwrap(indices), output_size=output_size))
-
-@torch.library.impl("aten::arange", "privateuseone")
-def arange(end, dtype=None, device=None, pin_memory=None):
-  has_float = isinstance(end, float)
-  return wrap(Tensor.arange(0, end, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))
-
-@torch.library.impl("aten::arange.start", "privateuseone")
-def arange_start(start, end, dtype=None, device=None, pin_memory=None):
-  has_float = any(isinstance(x, float) for x in (start, end))
-  return wrap(Tensor.arange(start, end, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))
-
-@torch.library.impl("aten::arange.start_step", "privateuseone")
-def arange_start_step(start, end, step, dtype=None, device=None, pin_memory=None):
-  has_float = any(isinstance(x, float) for x in (start, end, step))
-  return wrap(Tensor.arange(start, end, step, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))
-
 @torch.library.impl("aten::convolution_overrideable", "privateuseone")
 def convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups):
   if TORCH_DEBUG >= 1:
@@ -294,12 +225,27 @@ def convolution_backward_overrideable(grad_out, input, weight, stride, padding,
   grads = out.gradient(*[t for t,m in zip([input, weight, bias], output_mask) if m], gradient=grad_out)
   return tuple([wrap(grads.pop(0)) if m else None for m in output_mask])
 
-@torch.library.impl("aten::slice.Tensor", "privateuseone")
-@wrap_view_op
-def slice_tensor(self, dim=0, start=None, end=None, step=1):
-  slices = [slice(None)] * self.ndim
-  slices[dim] = slice(start, end, step)
-  return self[slices]
+# the functional scatters. without an impl aten falls back to a path that assumes a real storage: "self.has_storage() INTERNAL ASSERT FAILED"
+def _scatter_into(self, src, dim, index):
+  out = unwrap(self).clone()
+  slices = [slice(None)] * out.ndim
+  slices[dim] = index
+  out[slices] = unwrap(src).cast(out.dtype)  # torch casts src to self's dtype, tinygrad setitem demands they already match
+  return wrap(out)
+
+@torch.library.impl("aten::slice_scatter", "privateuseone")
+def slice_scatter(self, src, dim=0, start=None, end=None, step=1): return _scatter_into(self, src, dim, slice(start, end, step))
+
+@torch.library.impl("aten::select_scatter", "privateuseone")
+def select_scatter(self, src, dim, index): return _scatter_into(self, src, dim, index)
+
+@torch.library.impl("aten::diagonal_scatter", "privateuseone")
+def diagonal_scatter(self, src, offset=0, dim1=0, dim2=1):
+  # a diagonal is not one axis, so scatter through the flat indices it picks out
+  base, out = unwrap(self), unwrap(self).clone().reshape(-1)
+  idx = Tensor.arange(base.numel(), dtype=dtypes.int32).reshape(base.shape).diagonal(offset, dim1, dim2).reshape(-1)
+  out[idx] = unwrap(src).cast(base.dtype).reshape(-1)
+  return wrap(out.reshape(base.shape))
 
 @torch.library.impl("aten::slice_backward", "privateuseone")
 def slice_backward(grad_out, input_sizes, dim, start, end, step):
@@ -341,19 +287,14 @@ for dim in [1, 2, 3]:
     torch.library.impl(f"aten::{pad_type}_pad{dim}d", "privateuseone")(functools.partial(pad_forward, mode=mode))
     torch.library.impl(f"aten::{pad_type}_pad{dim}d_backward", "privateuseone")(functools.partial(pad_backward, mode=mode))
 
-def upsample(self, size, align_corners=False, mode=None): return wrap(Tensor.interpolate(unwrap(self), size, mode=mode, align_corners=align_corners))
+# the schemas are all positional: (self, output_size, align_corners, *scales) for linear, (self, output_size, *scales) for nearest.
+def upsample(self, size, *args, mode=None):
+  return wrap(Tensor.interpolate(unwrap(self), size, mode=mode, align_corners=args[0] if mode == "linear" else False))
 for i,pre in enumerate(["", "bi", "tri"]):
   torch.library.impl(f"aten::upsample_{pre}linear{i+1}d", "privateuseone")(functools.partial(upsample, mode="linear"))
   torch.library.impl(f"aten::upsample_nearest{i+1}d", "privateuseone")(functools.partial(upsample, mode="nearest"))
   torch.library.impl(f"aten::_upsample_nearest_exact{i+1}d", "privateuseone")(functools.partial(upsample, mode="nearest-exact"))
 
-@torch.library.impl("aten::scatter_add.out", "privateuseone")
-def scatter_add(self, dim, index, src, out):
-  self, index, src, out_unwrapped = unwrap(self), unwrap(index), unwrap(src), unwrap(out)
-  if self.shape == (): _apply_inplace(out_unwrapped, src)
-  else: _apply_inplace(out_unwrapped, Tensor.scatter_reduce(self, dim, index, src, reduce='sum'))
-  return out
-
 def _copy_between_devices(src, dest, cast_dtype, to_device, non_blocking=False):
   if src.is_tiny and dest.is_tiny:
     src_t, dest_t = unwrap(src), unwrap(dest)
@@ -404,15 +345,11 @@ def sort_values(input, dim=-1, descending=False, stable=True, values=None, indic
   _apply_inplace(unwrap(indices), out_indices.cast(dtypes.int64))
   return values, indices
 
-@torch.library.impl("aten::_linalg_svd", "privateuseone")
-def _linalg_svd(self, full_matrices=False):
-  U, S, Vh = unwrap(self).svd(full_matrices)
-  return wrap(U), wrap(S), wrap(Vh)
-
 # register some decompositions
 from torch._decomp import get_decompositions
 decomps = [
   aten.native_layer_norm_backward,
+  aten.native_group_norm_backward,
   aten.linalg_cross,
   aten.addmm,
   aten.addcmul,
@@ -447,12 +384,20 @@ decomps = [
   aten._softmax_backward_data, aten.embedding_dense_backward,
   aten.linalg_vector_norm,
   aten.binary_cross_entropy, aten.binary_cross_entropy_backward,
+  # the C++ mse/smooth_l1 kernels resize their out tensor, and a tiny tensor has no storage to resize
+  aten.mse_loss, aten.mse_loss_backward,
+  aten.smooth_l1_loss, aten.smooth_l1_loss_backward,
   aten.upsample_nearest2d.out,
+  # NOTE: only the "out" overload, the "vec" one is CompositeImplicitAutograd and overriding it loses the autograd kernel
+  aten.upsample_bicubic2d.out,
+  aten._adaptive_avg_pool2d,
   # activations
   aten.hardswish, aten.hardswish_backward,
   aten.hardtanh, aten.hardtanh_backward,
   aten.gelu, aten.gelu_backward,
-  aten.logical_and,
+  # NOTE: no aten.logical_or here, its decomposition reaches aten.bitwise_or through a path that checks aliasing by
+  # reading storage, which a tiny tensor has none of. it gets a direct impl below instead
+  aten.logical_and, aten.logical_xor,
   aten.randint,
   aten.eye,
   aten.hardsigmoid_backward,
@@ -495,7 +440,7 @@ simple_tensor_methods = [
   # reduce
   "all", "any", "argmax", "argmin", "cumsum", "cumprod",
   # complex
-  "avg_pool2d", "linspace"]
+  "linspace"]
 
 tiny_backend_out = {**{f"aten.{x}.out":getattr(Tensor,x) for x in simple_tensor_methods}, **{
   "aten.add.out": lambda input,other,alpha=1: input+alpha*other,
@@ -540,6 +485,8 @@ tiny_backend_out = {**{f"aten.{x}.out":getattr(Tensor,x) for x in simple_tensor_
   "aten.where.self_out": Tensor.where,
   "aten.prod.int_out": Tensor.prod,
   "aten.scatter.src_out": Tensor.scatter,
+  "aten.scatter_add.out": lambda self,dim,index,src: src if self.shape == () else Tensor.scatter_reduce(self, dim, index, src, reduce="sum"),
+  "aten.isin.Tensor_Tensor_out": lambda x,y,assume_unique=False,invert=False: (x.unsqueeze(-1)==y.flatten()).any(-1) != invert,
   # NOTE: axis=[] in torch means all, change tinygrad?
   "aten.sum.IntList_out": lambda self,axis,keepdim=False,dtype=None:
     self.sum(axis if axis is None or len(axis) else None, keepdim,
@@ -555,10 +502,9 @@ def wrap_out(f):
     assert out.shape == assigned.shape, f"shape mismatch: {assigned.shape} -> {out.shape}"
     assert out.device == assigned.device or out.device is None or assigned.device is None, f"device mismatch: {assigned.device} -> {out.device}"
     assert out.dtype == assigned.dtype, f"dtype mismatch: {assigned.dtype} -> {out.dtype}"
-    # an out= that is a view has to be written through its base, and _apply_inplace gives a deviceless base its buffer first
-    if canonical_base(out) is not out: return _apply_inplace(out, assigned) or out
-    if out.device is None and assigned.device is not None: out.replace(out.empty_like(device=assigned.device))
-    return out.assign(assigned)
+    # writing out= is an in-place write like any other: through the base if it is a view, refreshing any derived views
+    _apply_inplace(out, assigned)
+    return out
   return _wrap_out
 
 def _inplace_op(t, new_value):
@@ -566,7 +512,14 @@ def _inplace_op(t, new_value):
   else: _apply_inplace(t, new_value)
   return t
 
-tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
+# the three arange overloads are one function at different arity, and dtype/layout/device/pin_memory are keyword only in all of them
+def _arange(*args, dtype=None, **_):
+  return Tensor.arange(*args, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if any(isinstance(x, float) for x in args) else torch.int64)))
+
+def _empty(size, dtype=None, device=None, **_):
+  return Tensor.empty(*size, dtype=_from_torch_dtype(dtype or torch.get_default_dtype()), device=_from_torch_device(device))
+
+tiny_backend = {**tiny_backend_out, **{
   "aten.remainder.Scalar_Tensor": lambda x,y: x%y,
   "aten.floor_divide": lambda x,y: x//y,
   "aten.floor_divide_.Tensor": lambda x,y: x//y,
@@ -579,8 +532,8 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   # inplace ops using replace for fusion
   "aten.zero_": lambda x: x.const_like(0),
   "aten.fill_.Scalar": lambda x, y: x.const_like(y),
-  "aten.add_.Tensor": lambda self, other, alpha=1.0: self + other * alpha,
-  "aten.add_.Scalar": lambda self, other, alpha=1.0: self + other * alpha,
+  "aten.add_.Tensor": lambda self, other, alpha=1: self + other * alpha,
+  "aten.add_.Scalar": lambda self, other, alpha=1: self + other * alpha,
   "aten.mul_.Tensor": lambda self, other: self * other,
   "aten.mul_.Scalar": lambda self, other: self * other,
   # relu doesn't have an out form?
@@ -613,7 +566,9 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   # these don't work in out form, they have size 0
   "aten.abs": Tensor.abs,
   "aten.logical_not": Tensor.logical_not,
-  "aten.logical_or_": lambda x, y: x | y,
+  # compare against zero first: logical_* is bool-valued for any input dtype, while | is bitwise
+  "aten.logical_or": lambda x, y: (x != 0) | (y != 0),
+  "aten.logical_or_": lambda x, y: (x != 0) | (y != 0),
   "aten.multinomial": Tensor.multinomial,
   "aten.masked_fill_.Scalar": lambda self, mask, value: self.masked_fill(mask, value),
   "aten.masked_fill_.Tensor": lambda self, mask, value: self.masked_fill(mask, value),
@@ -622,14 +577,7 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   "aten.masked_select": Tensor.masked_select,
   "aten.all": Tensor.all,
   "aten.sgn": Tensor.sign,
-  "aten.acos": Tensor.acos,
   "aten.any": Tensor.any,
-  "aten.bitwise_not": Tensor.bitwise_not,
-  "aten.argmax": Tensor.argmax,
-  "aten.argmin": Tensor.argmin,
-  "aten.asinh": Tensor.asinh,
-  "aten.mul": Tensor.mul,
-  "aten.atanh": Tensor.atanh,
   "aten.fill_.Tensor": lambda self, value: self.const_like(value.reshape(()).item()),
   "aten.flip": Tensor.flip,
   "aten.scatter_reduce.two": Tensor.scatter_reduce,
@@ -640,10 +588,22 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   "aten.add.Tensor": lambda input,other,alpha=1: input+alpha*other,
   "aten.linspace": lambda start, stop, steps, dtype=None, **kwargs:
     Tensor.linspace(start, stop, steps, **({"dtype": _from_torch_dtype(dtype)} if dtype is not None else {})),
+  # the functional copy_. without an impl the fallback segfaults on a tensor with no storage
+  "aten.copy": lambda self,src,non_blocking=False: src.cast(self.dtype).to(self.device).expand(self.shape),
+  "aten.arange": lambda end, **kwargs: _arange(0, end, **kwargs),
+  "aten.arange.start": _arange,
+  "aten.arange.start_step": _arange,
+  # empty_strided takes the strides and drops them: we always allocate contiguous
+  "aten.empty_strided": lambda size, stride, **kwargs: _empty(size, **kwargs),
+  "aten.empty.memory_format": _empty,
+  # TODO: supprt stride [] in tinygrad?
+  "aten.max_pool2d_with_indices": lambda self,kernel_size,stride=None,padding=0,dilation=1,ceil_mode=False: ((r:=Tensor.max_pool2d(self, kernel_size, stride or None, dilation, padding, ceil_mode, return_indices=True))[0], r[1].cast(dtypes.int64)),
+  "aten.max_pool2d_with_indices_backward": lambda grad_out,self,kernel_size,stride=None,padding=0,dilation=1,ceil_mode=False,indices=None: Tensor.max_unpool2d(grad_out, indices, output_size=self.shape),
+  "aten.max_unpool2d": lambda self,indices,output_size: Tensor.max_unpool2d(self, indices, output_size=output_size),
+  "aten._linalg_svd": lambda self,full_matrices=False: Tensor.svd(self, full_matrices),
   "aten.topk": Tensor.topk,
   "aten.constant_pad_nd": lambda self, padding, value=0.0: self.pad(padding, mode="constant", value=value).contiguous(),
-  # TODO: input contiguous is needed to prevent CFGContext circular dependency assertion for shapes >512 (see test_cumsum_arange_large)
-  "aten.cumsum": lambda self, dim: self.contiguous().cumsum(dim),
+  "aten.cumsum": lambda self, dim: self.cumsum(dim),
   "aten.logsumexp": lambda self, axis, keepdim=False: self.logsumexp(axis[0], keepdim=keepdim),
   "aten.roll": Tensor.roll,
   "aten.logcumsumexp": Tensor.logcumsumexp,
@@ -652,6 +612,7 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
     self.ones_like(**{k: v for k, v in {"dtype": _from_torch_dtype(dtype) if dtype else None,
                                         "device": _from_torch_device(device) if device else None}.items() if v is not None}),
   "aten.max.dim": lambda self, dim, keepdim=False: (self.max(dim, keepdim), self.argmax(dim, keepdim).cast(dtype=dtypes.int64)),
+  "aten.min.dim": lambda self, dim, keepdim=False: (self.min(dim, keepdim), self.argmin(dim, keepdim).cast(dtype=dtypes.int64)),
   "aten.cummax": lambda self, dim: ((r := self.cummax(dim))[0], r[1].cast(dtypes.int64)),
   "aten.cummin": lambda self, dim: ((r := self.cummin(dim))[0], r[1].cast(dtypes.int64)),
   "aten.nonzero": Tensor.nonzero,
@@ -713,15 +674,16 @@ def wrap_inplace_view_op(f):
   return nf
 
 # the aten schema says how an op is called: an inplace view retargets the view, a writable first arg is inplace,
-# and a writable out arg must have come from tiny_backend_out so that wrap_out was applied
+# and a writable out arg gets wrap_out's dtype cast, shape assert, and view write-through
 for k,v in tiny_backend.items():
   name, _, overload = k.removeprefix("aten.").partition(".")
   op = getattr(getattr(aten, name), overload or "default")
   writes = [a.name for a in op._schema.arguments if a.alias_info is not None and a.alias_info.is_write]
   if torch.Tag.inplace_view in op.tags: fxn = wrap_inplace_view_op(v)
   elif writes == [op._schema.arguments[0].name] and op._schema.returns: fxn = wrap_inplace(v)
-  elif not writes or (writes == ["out"] and k in tiny_backend_out): fxn = wrap_fxn(k, v)
-  else: raise RuntimeError(f"{k} writes {writes}: expected an inplace first arg, or an out arg with {k} in tiny_backend_out")
+  elif not writes: fxn = wrap_fxn(k, v)
+  elif writes == ["out"]: fxn = wrap_fxn(k, wrap_out(v))
+  else: raise RuntimeError(f"{k} writes {writes}: unhandled writable arg in schema")
   torch.library.impl(k.replace("aten.", "aten::"), "privateuseone")(fxn)
 
 @torch.library.impl("aten::equal", "privateuseone")

extra/torch_backend/test.py

@@ -83,6 +83,12 @@ class TestTorchBackend(unittest.TestCase):
     torch.add(torch.ones(5, device=device), torch.ones(5, device=device), out=a)
     self.assertEqual(a.detach().storage_offset(), 3)
 
+  def test_out_refreshes_views_of_base(self):
+    a = torch.zeros(4, device=device)
+    v = a[2:]
+    torch.add(torch.ones(4, device=device), torch.ones(4, device=device), out=a)
+    np.testing.assert_equal(v.cpu().numpy(), [2., 2.])
+
   @unittest.expectedFailure  # TODO: storage offset assumes a contiguous source, use UOp.contiguous_view_offset
   def test_storage_offset_non_contiguous_source(self):
     a = torch.arange(12., device=device).reshape(3,4)
@@ -166,6 +172,15 @@ class TestTorchBackend(unittest.TestCase):
     expected = np.array([[1.5, 5.2, 9.0], [13.2, 17.1, 18.4]], dtype=np.float32)
     np.testing.assert_equal(y3.cpu().numpy(), expected)
 
+  def test_argmax_argmin(self):
+    a = torch.arange(12, dtype=torch.float32, device=device).reshape(3, 4)
+    c = a.cpu()
+    for got, want in [(a.argmax(), c.argmax()), (a.argmin(0), c.argmin(0)), (a.argmax(1, keepdim=True), c.argmax(1, keepdim=True)),
+                      (torch.min(a, 1).indices, torch.min(c, 1).indices), (torch.max(a, 1).indices, torch.max(c, 1).indices),
+                      (torch.min(a, 1).values, torch.min(c, 1).values), (torch.min(a, 1, keepdim=True).indices, torch.min(c, 1, keepdim=True).indices)]:
+      self.assertEqual(got.dtype, want.dtype)  # torch's arg reduces are int64, tinygrad's are int32
+      np.testing.assert_equal(got.cpu().numpy(), want.numpy())
+
   def test_isfinite(self):
     a = torch.ones(4, device=device)
     np.testing.assert_equal(torch.isfinite(a).cpu().numpy(), [True, True, True, True])
@@ -373,6 +388,22 @@ class TestTorchBackend(unittest.TestCase):
     for bwd_eps in [1e-5, 0.3]:
       for got, want in zip(run(device, bwd_eps), run("cpu", bwd_eps)): np.testing.assert_allclose(got, want, atol=1e-4, rtol=1e-3)
 
+  def test_groupnorm_backward(self):
+    def run(dev):
+      x = torch.arange(24., device=dev).reshape(2, 4, 3).requires_grad_()
+      w = torch.linspace(0.5, 2.0, 4).to(dev).requires_grad_()
+      torch.nn.functional.group_norm(x, 2, w, torch.zeros(4, device=dev)).square().sum().backward()
+      return x.grad.cpu().numpy(), w.grad.cpu().numpy()
+    for got, want in zip(run(device), run("cpu")): np.testing.assert_allclose(got, want, atol=1e-4, rtol=1e-3)
+
+  def test_mse_smooth_l1_loss_backward(self):
+    def run(dev, loss):
+      x = torch.arange(4., device=dev).requires_grad_()
+      loss(x, torch.ones(4, device=dev)).backward()
+      return x.grad.cpu().numpy()
+    for loss in [torch.nn.functional.mse_loss, torch.nn.functional.smooth_l1_loss]:
+      np.testing.assert_allclose(run(device, loss), run("cpu", loss), atol=1e-6)
+
   def test_batchnorm_unsqueeze(self):
     bn = torch.nn.BatchNorm2d(4).to(device)
     x = torch.randn(8, 4, 3, 3, device=device)
@@ -516,6 +547,15 @@ class TestTorchBackend(unittest.TestCase):
     cpu_res = torch.arange(20, dtype=torch.float32)[::2][1:4].numpy()
     np.testing.assert_equal(torch_res, cpu_res)
 
+  def test_select_out_of_range_dim(self):
+    a = torch.arange(12, dtype=torch.int32, device=device).reshape(3, 4)
+    with self.assertRaises(IndexError): a.select(5, 0)
+
+  def test_select_collapses_the_only_dim(self):
+    a = torch.arange(3, dtype=torch.int32, device=device)
+    self.assertEqual(a.select(0, 1).shape, ())
+    np.testing.assert_equal(a.select(0, 1).cpu().numpy(), 1)
+
   def test_slice_negative_dim(self):
     a = torch.arange(13, dtype=torch.int32, device=device).repeat(8, 1)
     torch_chunks = a.chunk(3, -1)
@@ -796,6 +836,86 @@ class TestTorchBackend(unittest.TestCase):
     np.testing.assert_allclose(w_tiny.grad.cpu().numpy(), w_cpu.grad.numpy(), atol=1e-4, rtol=1e-3)
     np.testing.assert_allclose(b_tiny.grad.cpu().numpy(), b_cpu.grad.numpy(), atol=1e-4, rtol=1e-3)
 
+  def test_write_through_detach_of_unrealized(self):
+    a = torch.empty(4, device=device)
+    a.detach().fill_(3)
+    np.testing.assert_equal(a.cpu().numpy(), [3, 3, 3, 3])
+
+  def test_square_transpose_inplace(self):
+    # a same-shape transpose is not a reshape: writing the transposed values straight back would scramble the base
+    a = torch.tensor([[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]], device=device)
+    a.transpose(0, 1).add_(100)
+    np.testing.assert_equal(a.cpu().numpy(), [[100., 101., 102.], [103., 104., 105.], [106., 107., 108.]])
+
+  def test_interpolate(self):
+    a = torch.arange(4, dtype=torch.float32, device=device).reshape(1, 1, 2, 2)
+    nearest = torch.nn.functional.interpolate(a, scale_factor=2.0)
+    np.testing.assert_equal(nearest.cpu().numpy()[0, 0], [[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]])
+    linear = torch.nn.functional.interpolate(a, size=(4, 4), mode="bilinear", align_corners=False)
+    ref = torch.nn.functional.interpolate(a.cpu(), size=(4, 4), mode="bilinear", align_corners=False)
+    np.testing.assert_allclose(linear.cpu().numpy(), ref.numpy(), rtol=1e-5)
+
+  def test_interpolate_bicubic_area(self):
+    a = torch.arange(32, dtype=torch.float32, device=device).reshape(1, 2, 4, 4)
+    for mode, scale in [("bicubic", 2.0), ("area", 0.5)]:
+      ref = torch.nn.functional.interpolate(a.cpu(), scale_factor=scale, mode=mode)
+      np.testing.assert_allclose(torch.nn.functional.interpolate(a, scale_factor=scale, mode=mode).cpu().numpy(), ref.numpy(), atol=1e-4)
+
+  @unittest.expectedFailure
+  def test_interpolate_bicubic_backward(self):
+    # the forward comes from a decomposition, but aten::upsample_bicubic2d_backward has none (nor does
+    # aten::_adaptive_avg_pool2d_backward, for area), so training through these modes needs a real kernel
+    x = torch.arange(32., dtype=torch.float32, device=device).reshape(1, 2, 4, 4).requires_grad_()
+    torch.nn.functional.interpolate(x, scale_factor=2.0, mode="bicubic").sum().backward()
+
+  @unittest.expectedFailure
+  def test_interpolate_inexact_scale(self):
+    # torch forwards the raw scale_factor, Tensor.interpolate recomputes it from output_size, and they disagree here
+    a = torch.arange(6, dtype=torch.float32, device=device).reshape(1, 1, 2, 3)
+    tiny = torch.nn.functional.interpolate(a, scale_factor=2.5, mode="bilinear")
+    ref = torch.nn.functional.interpolate(a.cpu(), scale_factor=2.5, mode="bilinear")
+    np.testing.assert_allclose(tiny.cpu().numpy(), ref.numpy(), rtol=1e-5)
+
+  def test_logical_or_xor(self):
+    a = torch.tensor([True, True, False, False], device=device)
+    b = torch.tensor([True, False, True, False], device=device)
+    np.testing.assert_equal(torch.logical_or(a, b).cpu().numpy(), [True, True, True, False])
+    np.testing.assert_equal(torch.logical_xor(a, b).cpu().numpy(), [False, True, True, False])
+    # bool-valued whatever the input dtype, so this is not | and ^
+    i, j = torch.tensor([2, 0, 5, 0], device=device), torch.tensor([0, 0, 1, 1], device=device)
+    np.testing.assert_equal(torch.logical_or(i, j).cpu().numpy(), [True, False, True, True])
+    np.testing.assert_equal(torch.logical_xor(i, j).cpu().numpy(), [True, False, False, True])
+
+  def test_slice_scatter(self):
+    # the scatters are functional: they return a new tensor and must leave the one they were given alone
+    a = torch.arange(12, dtype=torch.float32, device=device).reshape(3, 4)
+    out = torch.slice_scatter(a, torch.ones(1, 4, device=device), 0, 0, 1)
+    np.testing.assert_equal(out.cpu().numpy(), [[1, 1, 1, 1], [4, 5, 6, 7], [8, 9, 10, 11]])
+    np.testing.assert_equal(a.cpu().numpy(), np.arange(12, dtype=np.float32).reshape(3, 4))
+
+  def test_slice_scatter_casts_src(self):
+    a = torch.zeros(3, 4, device=device)
+    out = torch.slice_scatter(a, torch.ones(1, 4, dtype=torch.int32, device=device), 0, 0, 1)
+    self.assertEqual(out.dtype, torch.float32)
+    np.testing.assert_equal(out.cpu().numpy()[0], np.ones(4, dtype=np.float32))
+
+  def test_select_scatter(self):
+    a = torch.arange(12, dtype=torch.float32, device=device).reshape(3, 4)
+    out = torch.select_scatter(a, torch.ones(4, device=device), 0, 1)
+    np.testing.assert_equal(out.cpu().numpy(), [[0, 1, 2, 3], [1, 1, 1, 1], [8, 9, 10, 11]])
+
+  def test_diagonal_scatter(self):
+    a = torch.zeros(3, 3, device=device)
+    out = torch.diagonal_scatter(a, torch.arange(3, dtype=torch.float32, device=device))
+    np.testing.assert_equal(out.cpu().numpy(), np.diag([0., 1., 2.]))
+    np.testing.assert_equal(a.cpu().numpy(), np.zeros((3, 3), dtype=np.float32))
+
+  def test_copy_functional(self):
+    # without an impl this segfaults rather than fails: a regression here takes the whole run down
+    a = torch.arange(4, dtype=torch.float32, device=device)
+    out = torch.ops.aten.copy(a, torch.zeros(4, device=device))
+    np.testing.assert_equal(out.cpu().numpy(), [0., 0., 0., 0.])
+    np.testing.assert_equal(a.cpu().numpy(), [0., 1., 2., 3.])
 
 from tinygrad import Tensor
 class TestBackendHelpers(unittest.TestCase):

pyproject.toml

@@ -111,6 +111,10 @@ docs = [
   "numpy",
 ]
 mesa = ["tinymesa==25.2.7.2"]
+autogen = [
+  "pyyaml",
+  "mako",
+]
 
 
 [tool.mutmut]

test/backend/test_const_folding.py

@@ -1,7 +1,7 @@
 import unittest, math
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.dtype import DTYPES_DICT
-from tinygrad.uop.ops import Ops, UOp
+from tinygrad.uop.ops import Ops, UOp, GroupOp
 from tinygrad.codegen.decomp.op import threefry2x32
 import numpy as np
 from test.helpers import not_support_multi_device
@@ -17,7 +17,7 @@ def _check_ast_count(desired_count:int, t:Tensor):
 class TestMovedConstFolding(unittest.TestCase):
   def test_contiguous_deviceless_const(self):
     t = Tensor(UOp.const(2.0, dtypes.float)).contiguous()
-    self.assertIs(t.uop.op, Ops.CONST)
+    self.assertIs(t.uop, UOp.const(2.0, dtypes.float))
     self.assertIsNone(t.uop.device)
 
   def test_add_shrunk_zero(self):
@@ -169,8 +169,8 @@ class TestMultiConstFolding(unittest.TestCase):
 class TestThreefryConstFolding(unittest.TestCase):
   def test_threefry(self):
     # THREEFRY(const,const) folds to a const once decomposed
-    x = threefry2x32(UOp.const(5, dtypes.uint64), UOp.const(10, dtypes.uint64))
-    self.assertIs(x.simplify().op, Ops.CONST)
+    x = threefry2x32(UOp.const(5, dtypes.uint64), UOp.const(10, dtypes.uint64)).simplify()
+    self.assertEqual([u.op for u in x.toposort() if u.op in GroupOp.ALU], [])
 
 class TestTautologicalCompare(unittest.TestCase):
   # without const folding, these would have triggered -Wtautological-compare in clang
@@ -188,7 +188,6 @@ class TestTautologicalCompare(unittest.TestCase):
     np.testing.assert_equal((Tensor(True) < Tensor(False)).numpy(), False)
     np.testing.assert_equal((Tensor(True) < Tensor(True)).numpy(), False)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU doesn't support NaN comparison correctly")
   def test_a_eq_a(self):
     # self eq is always true for int or bool
     a = Tensor([1, 2, 3])

test/backend/test_custom_kernel.py

@@ -4,7 +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
+from test.helpers import assert_kernel_count, KernelCountException
 
 # **** kernels ****
 
@@ -422,9 +422,8 @@ class TestCustomKernel(unittest.TestCase):
       return Tensor.custom_kernel(y, x, fxn=custom_add_one_kernel)[0]
     GlobalCounters.reset()
     y = run(x[0]).realize()
-    # it's copying the input and the output
-    # TODO: subbuffer usage has runtime specific behavior, this will be fixed after the removal of SLICE.
-    assert_kernel_count(2 if y.device in ("CL", "WEBGPU") else 1)
+    # backends that support contiguous views don't launch extra kernels
+    assert_kernel_count(2 if x[0].uop.contiguous_view() is None else 1)
     self.assertEqual(y.tolist(), [1, 2, 3, 4])
 
   @Context(DEV="CPU")
@@ -475,7 +474,7 @@ class TestCustomKernelInput(unittest.TestCase):
     y.realize()
     kernel_count = GlobalCounters.kernel_count
     self.assertEqual(y.tolist(), x.add(1).tolist())
-    self.assertLessEqual(kernel_count, max_kernels)
+    if kernel_count > max_kernels: raise KernelCountException(max_kernels, kernel_count)
     # same test with @function, input is PARAM
     from tinygrad import function
     x0 = Tensor.arange(32).clone("CPU").realize()
@@ -488,7 +487,7 @@ class TestCustomKernelInput(unittest.TestCase):
     y = run(x0).realize()
     kernel_count = GlobalCounters.kernel_count
     self.assertEqual(y.tolist(), mop_fxn(x0).add(1).tolist())
-    self.assertLessEqual(kernel_count, max_kernels)
+    if kernel_count > max_kernels: raise KernelCountException(max_kernels, kernel_count)
 
   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)

test/backend/test_dtype.py

@@ -340,6 +340,9 @@ class TestUint64DType(TestDType):
   DTYPE = dtypes.uint64
   def test_uint64_load(self):
     assert Tensor(2**64 - 1, dtype=dtypes.uint64).numpy() == 2**64 - 1
+  @unittest.skipIf(dtypes.double not in supported_dtypes, "needs float64")
+  def test_uint64_cast_double(self):
+    assert Tensor([2**32 + 1], dtype=dtypes.uint64).cast(dtypes.double).numpy() == 2**32 + 1
 
 @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, PTXRenderer), "PTX does indexing math with longs")
 class TestEmulatedUInt64DType(TestUint64DType):

test/backend/test_isel.py

@@ -7,7 +7,7 @@ from tinygrad.renderer.isa.x86 import X86Renderer, X86Ops
 from tinygrad.renderer.isa import IselContext
 
 # INDEX on a register value with a constant index extracts a single element (the old GEP)
-def lane(y:UOp, i:int) -> UOp: return y.index(UOp.const(i, dtypes.int), dtype=y.dtype.scalar())
+def lane(y:UOp, i:int) -> UOp: return y.index(UOp.cconst(i, dtypes.int), dtype=y.dtype)
 
 @unittest.skipUnless(isinstance(Device[Device.DEFAULT].renderer, X86Renderer), "only x86")
 class TestIselX86(unittest.TestCase):
@@ -46,10 +46,10 @@ class TestIselX86(unittest.TestCase):
   # complex address is [base + index*scale + displacement]
   def test_complex_address(self):
     a = UOp.variable("a", 0, 0, dtypes.int32)
-    load = UOp.param(0, dtypes.int32, (16,)).index(a + 1).load()
+    load = UOp.param(0, dtypes.int32, (16,)).index(a + UOp.cconst(1, dtypes.int32)).load()
     n = self.isel_rewrite(load)
     # displacement is the constant in "a" scaled to the buffer element size, dtype is int8 when the value fits otherwise int32
-    self.assertTrue(n.src[2].op is Ops.CONST and n.src[2].dtype is dtypes.int8 and n.src[2].val == 4)
+    self.assertTrue(n.src[2].dtype is dtypes.int8 and n.src[2].src[0].op is Ops.CONST and n.src[2].src[0].val == 4)
 
 if __name__ == "__main__":
   unittest.main()

test/backend/test_jit.py

@@ -360,7 +360,7 @@ class TestJitGraphSplit(unittest.TestCase):
     self.expect(f, inp, inp_cpu,
       graph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
       multigraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
-      hcqgraph=[self.ji_graph(4)])
+      hcqgraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()]) # cpu is hcq2 now, it does not join hcq graphs
 
   def test_jit_cpu_several(self):
     if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
@@ -377,9 +377,9 @@ class TestJitGraphSplit(unittest.TestCase):
     inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
     inp_cpu = Tensor.randn(10, 10, device="CPU").realize()
     self.expect(f, inp, inp_cpu,
-      graph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
-      multigraph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
-      hcqgraph=[self.ji_graph(5)])
+      graph=[self.ji_graph(2), self.ji_comp(), self.ji_comp(), self.ji_comp()],
+      multigraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp(), self.ji_comp()],
+      hcqgraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp(), self.ji_comp()])
 
   def test_jit_multidev(self):
     if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")

test/backend/test_linearizer.py

@@ -16,8 +16,6 @@ from test.helpers import replace_opts, check_schedule
 from test.backend.test_softmax_fusion import single_kernel_softmax
 MOCKGPU = DEV.interface.startswith("MOCK")
 
-from tinygrad.uop.render import print_uops # noqa: F401 # pylint: disable=unused-import
-
 @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, ISARenderer), "isa backends don't preserve the op spec when lowering")
 class TestLinearizer(unittest.TestCase):
   def test_arg_dedup(self):
@@ -30,7 +28,7 @@ class TestLinearizer(unittest.TestCase):
     c = ((a.shrink(((0, 2),)) - a.shrink(((2, 4),))) - (b.shrink(((0, 2),)) - b.shrink(((2, 4),))))
     linear = c.schedule_linear()
     run_linear(linear)
-    rawbufs = [s.buffer for s in linear.src[-1].src[1:] if s.op is not Ops.BIND]
+    rawbufs = [s.buffer for s in linear.src[-1].src[1:] if not s.is_bound_var]
     assert len(rawbufs) == 3 and set(rawbufs[1:]) == {a.uop.base.realized, b.uop.base.realized}
     np_c = (np_a[:2] - np_a[2:]) - (np_b[:2] - np_b[2:])
     np.testing.assert_allclose(np_c, c.numpy(), atol=1e-4, rtol=1e-4)
@@ -248,11 +246,10 @@ class TestLinearizer(unittest.TestCase):
     uops = tuple(to_program(replace_opts(ast, opt), renderer=Device[Device.DEFAULT].renderer).src[1].src)
     begin_range = [i for i, x in enumerate(uops) if x.op is Ops.RANGE][-1]
     end_range = [i for i, x in enumerate(uops) if x.op is Ops.END][0]
-    for i,u in enumerate(uops): print(i, u.op, [uops.index(s) for s in u.src], u.arg, u.dtype)
     for u in uops:
       if u.op is Ops.STORE and u.src[0].addrspace is AddrSpace.REG:
         if uops.index(u) < begin_range:
-          assert u.src[1].op is Ops.CONST
+          assert u.src[1].op not in GroupOp.ALU
         else:
           assert u.src[1].op in GroupOp.ALU
           assert begin_range < uops.index(u) < end_range
@@ -268,9 +265,9 @@ class TestLinearizer(unittest.TestCase):
     uops = tuple(to_program(replace_opts(ast, []), renderer=Device[Device.DEFAULT].renderer).src[1].src)
     idxs = dedup([uop for uop in uops if uop.op is Ops.SPECIAL])
     idxs = sorted(idxs, key=lambda uop: uop.arg)
-    assert (idxs[0].arg, idxs[0].src[0].val) == ('gidx0', 6), idxs[0]
-    assert (idxs[1].arg, idxs[1].src[0].val) == ('gidx1', 5), idxs[1].arg
-    assert (idxs[2].arg, idxs[2].src[0].val) == ('gidx2', 4), idxs[2].arg
+    assert (idxs[0].arg, idxs[0].src[0].src[0].val) == ('gidx0', 6), idxs[0]
+    assert (idxs[1].arg, idxs[1].src[0].src[0].val) == ('gidx1', 5), idxs[1].arg
+    assert (idxs[2].arg, idxs[2].src[0].src[0].val) == ('gidx2', 4), idxs[2].arg
 
   def test_sum_collapse(self):
     t = Tensor([2]).reshape(1, 1).expand(256, 256).sum()
@@ -411,7 +408,7 @@ def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
   last_call = linear.src[-1]
   ast = last_call.src[0]
   assert ast.op is Ops.SINK, f"helper_realized_ast expects a SINK {last_call}"
-  last_bufs = [s.buffer for s in last_call.src[1:] if s.op is not Ops.BIND]
+  last_bufs = [s.buffer for s in last_call.src[1:] if not s.is_bound_var]
   # now all input buffers in last_call should be realized
   # create fresh buffers for the outputs
   bufs = [Buffer(x.device, x.size, x.dtype).allocate() if i < len(ast.src) else x for i,x in enumerate(last_bufs)]

test/backend/test_multitensor.py

@@ -1,12 +1,12 @@
 import unittest, random
 from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit, dtypes, Variable
-from tinygrad.uop.ops import Ops, UOp, AxisType
+from tinygrad.uop.ops import Ops, UOp, AxisType, graph_rewrite
 from tinygrad.helpers import getenv, prod, Context
 from tinygrad.nn.state import get_parameters
-from tinygrad.engine.realize import run_linear, compile_linear
+from tinygrad.engine.realize import run_linear, compile_linear, pm_beam, pm_compile
 import numpy as np
 from hypothesis import given, strategies as strat, settings
-from test.helpers import not_support_multi_device, needs_second_gpu, slow, call_is_graph, check_schedule, assert_kernel_count
+from test.helpers import not_support_multi_device, needs_second_gpu, slow, call_is_graph, check_schedule, assert_kernel_count, KernelCountException
 
 settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
 settings.load_profile("my_profile")
@@ -79,9 +79,9 @@ class TestMultiTensor(unittest.TestCase):
   def test_shard_beam(self):
     cpu_2 = ("CPU:1", "CPU:2")
     src = Tensor.ones(16).shard(cpu_2, 0).realize()
-    pad = src.to(cpu_2[::-1]).schedule_linear().src[0]
-    with Context(BEAM=1, IGNORE_BEAM_CACHE=1): prg = compile_linear(UOp(Ops.LINEAR, src=(pad,))).src[0].src[0]
-    self.assertNotEqual(prg.src[0].arg.applied_opts, ())
+    lin = UOp(Ops.LINEAR, src=(src.to(cpu_2[::-1]).schedule_linear().src[0],))
+    with Context(BEAM=1, IGNORE_BEAM_CACHE=1): call = graph_rewrite(graph_rewrite(lin, pm_beam, ctx=1, walk=True), pm_compile, walk=True).src[0]
+    self.assertNotEqual(call.src[0].src[0].arg.applied_opts, ())
 
   def test_shard_same_device(self):
     X = Tensor.ones(256).contiguous().realize()
@@ -395,7 +395,7 @@ class TestMultiBufferView(unittest.TestCase):
     linear, var_vals = b_multi.linear_with_vars()
     if all(not d.startswith(("WEBGPU", "CL")) for d in b_multi.device):
       compiled = [call for call in linear.src if call.src[0].op is Ops.SINK]
-      self.assertEqual(len(compiled), 0, f"expected zero compiled kernels, got {len(compiled)}")
+      if len(compiled) != 0: raise KernelCountException(0, len(compiled))
     run_linear(linear, var_vals)
     np.testing.assert_equal(b_multi.numpy(), b_ref.numpy())
 

test/backend/test_ops.py

@@ -720,10 +720,11 @@ class TestOps(unittest.TestCase):
       return torch.autograd.grad(t ** c, t)[0].item()
     for x in [-math.inf, 0, 1, math.inf]:
       for c in [-1, 0, 0.3, 1, 2]:
-        tiny_out = get_tiny_gradient(x, c)
         torch_out = get_torch_gradient(x, c)
+        # the pow backward routes through exp2/log2, whose 0/inf behavior is undefined on WEBGPU
+        if Device.DEFAULT == "WEBGPU" and not math.isfinite(torch_out): continue
+        tiny_out = get_tiny_gradient(x, c)
         if math.isnan(tiny_out):
-          if Device.DEFAULT == "WEBGPU": continue # TODO: WEBGPU issue with nan
           assert math.isnan(torch_out)
         else:
           self.assertAlmostEqual(tiny_out, torch_out, msg=f"{x}, {c}")
@@ -749,6 +750,7 @@ class TestOps(unittest.TestCase):
   def test_exp2_log2_zero_times_negative(self):
     # gallivm's exp2/log2 have "undefined behavior with infs, 0s and nans", so exp2(log2(0)*y) returns 0 instead of inf
     helper_test_op(None, lambda x,y: (x.log2()*y).exp2(), lambda x,y: (x.log2()*y).exp2(), vals=[[0.0], [-0.7]], forward_only=True)
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "pow at 0 routes through exp2/log2, whose 0/inf behavior is undefined on WEBGPU")
   def test_pow_zero_const(self):
     helper_test_op(None, lambda x: x**0.3, vals=[[0.0]])
     helper_test_op(None, lambda x: x**0.0, vals=[[0.0]])
@@ -2162,6 +2164,10 @@ class TestOps(unittest.TestCase):
   def test_roll(self):
     helper_test_op([(2, 4)], lambda x: x.roll(1))
     helper_test_op([(2, 4)], lambda x: x.roll((1,)))
+    helper_test_op([(0,)], lambda x: x.roll(1, 0))
+    helper_test_op([(2, 0, 3)], lambda x: x.roll(1, 0))
+    helper_test_op([(2, 0, 3)], lambda x: x.roll(1, 1))
+    helper_test_op([(2, 0, 3)], lambda x: x.roll(1))
     self.helper_test_exception([(2, 4)], lambda x: x.roll((1, 2)), expected=RuntimeError)
     helper_test_op([(2, 4)], lambda x: x.roll(1, 0))
     helper_test_op([(2, 4)], lambda x: x.roll(-1, 0))

test/backend/test_pickle.py

@@ -2,7 +2,8 @@ import unittest, pickle, types, tracemalloc
 import numpy as np
 from tinygrad import Tensor, Device, TinyJit, Variable, dtypes
 from tinygrad.helpers import GlobalCounters, ContextVar, Context, DEV
-from tinygrad.uop.ops import PatternMatcher, UPat, UOp
+from tinygrad.uop.ops import PatternMatcher, UPat, UOp, deconstruct_function
+from test.helpers import KernelCountException
 
 class TestPickle(unittest.TestCase):
   def test_pickle_code_object(self):
@@ -11,6 +12,11 @@ class TestPickle(unittest.TestCase):
     fxn = types.FunctionType(pickle.loads(code_str), globals())
     self.assertEqual(fxn(2), 4)
 
+  def test_deconstruct_function_nested_comprehension(self):
+    # pre PEP 709, each comprehension is its own code object, so dtypes here is referenced two code objects deep
+    def fxn(): return [[dtypes.int for _ in range(2)] for _ in range(2)]
+    self.assertEqual(types.FunctionType(*deconstruct_function(fxn))(), fxn())
+
   def test_pickle_pattern_matcher(self):
     pm = PatternMatcher([(UPat.cvar('x'), lambda x: x*2)])
     sink = UOp.const(2)
@@ -36,7 +42,7 @@ class TestPickle(unittest.TestCase):
     t2:Tensor = pickle.loads(st)
     np.testing.assert_equal(t_values, t2.numpy())
     # expect at most one COPY kernel
-    self.assertLessEqual(GlobalCounters.kernel_count, 1)
+    if GlobalCounters.kernel_count > 1: raise KernelCountException(1, GlobalCounters.kernel_count)
 
   def test_pickle_realized_tensor_alt(self):
     print("** init")

test/backend/test_quantize_onnx.py

@@ -82,7 +82,7 @@ class TestQuantizeOnnxCPU(unittest.TestCase):
       linear = run_onnx({"input":inp})["output"].schedule_linear()
       prg = to_program(linear.src[-2].src[0], renderer=Device[Device.DEFAULT].renderer)
       daccs = [u for u in tuple(prg.src[1].src) if u.op is Ops.BUFFER and u.addrspace is AddrSpace.REG]
-      assert all(u.dtype.scalar() is dtypes.int for u in daccs)
+      assert all(u.dtype is dtypes.int for u in daccs)
 
 @unittest.skipIf(Device.DEFAULT != "DSP", "only tests for DSP")
 class TestQuantizeOnnx(unittest.TestCase):

test/backend/test_tensor.py

@@ -653,9 +653,19 @@ class TestZeroShapeTensor(unittest.TestCase):
     np.testing.assert_equal(Tensor([[1, 2]]).pad_to(2, 3).numpy(), [[1, 2, 0], [0, 0, 0]])
     np.testing.assert_equal(Tensor([[1, 2]]).pad_to(1, 3).numpy(), [[1, 2, 0]])
     np.testing.assert_equal(Tensor([[1, 2]]).pad_to(None, 3).numpy(), [[1, 2, 0]])
+    np.testing.assert_equal(Tensor([1, 2]).pad_to(4, value=2).numpy(), [1, 2, 2, 2])
+    np.testing.assert_equal(Tensor([[1, 2]]).pad_to(2, 3, value=-1).numpy(), [[1, 2, -1], [-1, -1, -1]])
+    np.testing.assert_equal(Tensor([1, 2]).pad_to(None, value=5).numpy(), [1, 2])  # no-op pad ignores the fill
     with self.assertRaises(ValueError): Tensor([1, 2]).pad_to(2, 3)
     with self.assertRaises(ValueError): Tensor([[1, 2]]).pad_to(3)
 
+  def test_max_shape(self):
+    from tinygrad import UOp
+    t = Tensor.empty(2, UOp.variable('v', 1, 32), 4)
+    self.assertEqual(t.max_shape, (2, 32, 4))
+    self.assertEqual(t.max_numel(), 2*32*4)
+    self.assertEqual(Tensor.empty(2, 3).max_shape, (2, 3))
+
   def test_shrink_into_zero(self):
     t = Tensor.rand(3, 4).realize()
     assert t.shrink((None, (2, 2))).realize().shape == (3, 0)

test/device/test_hcq2.py

@@ -10,5 +10,13 @@ class TestHCQ2(unittest.TestCase):
     with patch.object(Device[Device.DEFAULT], "has_copy_queue", False):
       np.testing.assert_equal(Tensor(np.arange(61, dtype=np.float32)).to(Device.DEFAULT).contiguous().realize().numpy(), np.arange(61))
 
+  def test_overlapping_device_tuples(self):
+    # an op on a wide device tuple followed by an op on an overlapping smaller tuple used to MMU-fault the smaller one
+    d4, d2 = tuple(f"{Device.DEFAULT}:{i}" for i in range(4)), tuple(f"{Device.DEFAULT}:{i}" for i in range(2))
+    ref = Tensor.arange(16).contiguous().realize()
+    Tensor(ref.uop.copy_to_device(d4)).realize()
+    out = Tensor.ones(8).shard(d2, axis=0).contiguous().realize()
+    np.testing.assert_equal(out.numpy(), np.ones(8))
+
 if __name__ == "__main__":
   unittest.main()

test/external/external_benchmark_all2all.py

@@ -0,0 +1,17 @@
+from tinygrad import Device, Tensor, TinyJit, dtypes
+from tinygrad.helpers import Timing, Context
+
+GPUS, DEPTH, SZ = 8, 4, 128 * 2**20
+WARMUP, ITERS = 3, 5
+devs = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS))
+bufs = tuple(Tensor.empty(SZ, dtype=dtypes.uint8, device=dev).contiguous().realize() for _ in range(DEPTH) for dev in devs)
+
+@TinyJit
+def all_to_all(*srcs:Tensor): return Tensor.realize(*(src.to(dst) for i,src in enumerate(srcs) for j,dst in enumerate(devs) if i % GPUS != j))
+
+if __name__ == "__main__":
+  with Context(ALL2ALL=1, JIT_BATCH_SIZE=0):
+    for i in range(-WARMUP, ITERS):
+      with Timing("ALL2ALL ", lambda ns: f" {SZ*GPUS*(GPUS-1)*DEPTH/ns:.2f} GB/s", enabled=i>=0):
+        all_to_all(*bufs)
+        for dev in devs: Device[dev].synchronize()

test/external/external_test_schedule_scaling.py

@@ -1,5 +1,5 @@
-import unittest, time
-from tinygrad import Tensor
+import unittest, time, itertools
+from tinygrad import Tensor, Context
 
 class TestScheduleScaling(unittest.TestCase):
   """Test that .schedule() scales linearly with graph size (no O(n^2) behavior)."""
@@ -130,5 +130,18 @@ class TestScheduleScaling(unittest.TestCase):
       return parts[0].cat(*parts[1:])
     self._assert_linear(concat_chain)
 
+  @Context(DEV="NULL:HIP:gfx1100")
+  def test_custom_kernel_assign_scaling(self):
+    from tinygrad.uop.ops import UOp, Ops, KernelInfo
+    from tinygrad.runtime.autogen.amd.rdna3.ins import s_nop
+    count = itertools.count(0)
+    def custom_kernel_assign(n):
+      def custom_asm(out):
+        return UOp(Ops.PROGRAM, src=(UOp.sink(out, arg=KernelInfo(f"fxn_{next(count)}")),
+                                     UOp(Ops.LINEAR, src=tuple(UOp(Ops.INS, arg=s_nop(i)) for i in range(n*8)))))
+      call = Tensor.custom_kernel(Tensor.empty(1), fxn=custom_asm)[0]
+      return Tensor.cat(*[Tensor.empty(1).assign(call+i) for i in range(n)])
+    self._assert_linear(custom_kernel_assign, n_small=50, n_large=500)
+
 if __name__ == '__main__':
   unittest.main(verbosity=2)

test/external/external_test_usb_asm24.py

@@ -6,7 +6,7 @@ import numpy as np
 class TestDevCopySpeeds(unittest.TestCase):
   @classmethod
   def setUpClass(cls):
-    cls.sz = getenv("SIZE", 2e6)
+    cls.sz = getenv("SIZE", 2000000)
     cls.dev = Device["AMD"]
     if not cls.dev.is_usb(): raise unittest.SkipTest("only test this on USB devices")
 

test/external/external_uop_gc.py

@@ -44,7 +44,7 @@ def realized_matmul():
   z = y.matmul(x)
   Tensor.realize(z)
 def realized_gradient():
-  x = Tensor.eye(3)
+  x = Tensor.eye(3).clone()
   y = Tensor([[2.0,0,-2.0]])
   z = y.matmul(x).sum()
   z.backward()

test/helpers.py

@@ -86,7 +86,9 @@ def assert_jit_cache_len(fxn, expected_len):
   if linear is None or not linear.src:
     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 expected_len and all(call_is_hcq(call) for call in linear.src): # HCQ2: one batch submitter, or fence + reset + merged calls + finalizer
+    from tinygrad.runtime.support.hcq2 import HCQ_RUNTIME_DEV
+    expected_len = 1 if HCQ_RUNTIME_DEV.value == "CPU" else 4
   if call_is_graph(linear.src[0]):
     if len(linear.src) != 1: raise KernelCountException(1, len(linear.src))
     inner = linear.src[0].src[0].src[0]  # LINEAR UOp inside CUSTOM_FUNCTION

test/mockgpu/amd/emu.py

@@ -260,19 +260,6 @@ def _cond(cond, if_true, if_false):
 def _cond_hi16(cond, val: UOp) -> UOp: return _cond(cond, _hi16(val), val)
 def _apply_opsel(val: UOp, sel_bit: int, opsel: int) -> UOp: return _hi16(val) if opsel & (1 << sel_bit) else val
 
-def _set_lane_bit(old: UOp, lane: UOp, val: UOp, exec_mask: UOp) -> UOp:
-  """Set/clear a single bit in a mask based on lane index, respecting exec mask."""
-  if old.dtype in (dtypes.uint64, dtypes.int64):
-    dt = dtypes.uint64
-    mask = UOp.const(1, dt) << lane.cast(dt)
-    new_bit = _to_u32(val).cast(dt) << lane.cast(dt)
-    cleared = old.cast(dt) & (mask ^ UOp.const(0xFFFFFFFFFFFFFFFF, dt))
-    return _lane_active(exec_mask, lane).where(cleared | new_bit, old.cast(dt))
-  mask = _c(1) << lane.cast(dtypes.uint32)
-  new_bit = _to_u32(val) << lane.cast(dtypes.uint32)
-  cleared = old & (mask ^ _c(MASK32))
-  return _lane_active(exec_mask, lane).where(cleared | new_bit, old)
-
 def _val_to_u32(val: UOp) -> UOp:
   """Convert any value to uint32 for storage (bitcast floats, cast ints)."""
   if val.dtype == dtypes.uint32: return val
@@ -532,6 +519,19 @@ class _Ctx:
       return [self.wsgpr_dyn(reg, lo), self.wsgpr_dyn(reg + _c(1), hi)]
     return [self.wsgpr_dyn(reg, val)]
 
+  def wmask_lane_bit(self, reg: UOp, lane: UOp, val: UOp, exec_mask: UOp) -> list[UOp]:
+    """Set/clear bit `lane` of the mask at `reg` from val for exec-active lanes, preserving memory for inactive lanes"""
+    active, bit = _lane_active(exec_mask, lane), _to_u32(val)
+    if self.wave_size <= 32:
+      old = self.rsgpr_dyn(reg)
+      mask = _c(1) << lane.cast(dtypes.uint32)
+      return [self.wsgpr_dyn(reg, active.where((old & (mask ^ _c(MASK32))) | (bit << lane.cast(dtypes.uint32)), old))]
+    off = (lane & _c(31, dtypes.int)).cast(dtypes.uint32)
+    mask = _c(1) << off
+    def half(old: UOp, sel: UOp) -> UOp: return sel.where(active.where((old & (mask ^ _c(MASK32))) | (bit << off), old), old)
+    return [self.wsgpr_dyn(reg, half(self.rsgpr_dyn(reg), lane < _c(32, dtypes.int))),
+            self.wsgpr_dyn(reg + _c(1), half(self.rsgpr_dyn(reg + _c(1)), _c(32, dtypes.int) <= lane))]
+
   def rmask(self, reg: UOp) -> UOp:
     """Read a lane mask (VCC/EXEC). Combines lo/hi for wave64."""
     if self.wave_size > 32: return _u64(self.rsgpr_dyn(reg), self.rsgpr_dyn(reg + _c(1)))
@@ -718,9 +718,7 @@ class _Ctx:
         raw_stores.append(('vgpr_direct', self.vgpr.index(val[0].valid(active)).store(new_val)))
         continue
       if 'D0' in dest and '[laneId]' in dest:
-        old_vcc = self.rmask(_c(VCC_LO.offset))
-        new_vcc = _set_lane_bit(old_vcc, lane, val, exec_mask)
-        raw_stores.extend([('vcc', s) for s in self.wmask(_c(VCC_LO.offset), new_vcc)])
+        raw_stores.extend([('vcc', s) for s in self.wmask_lane_bit(_c(VCC_LO.offset), lane, val, exec_mask)])
       elif dest.startswith('D0'):
         dest_suffix = re.match(r'D0\.(\w+)', dest)
         if dest_suffix is not None:
@@ -1039,13 +1037,11 @@ def _compile_sdwa(inst: irc.VOP1_SDWA | irc.VOP2_SDWA | irc.VOP2_SDWA_SDST | irc
         result = _sdwa_write(old, result, dst_sel, dst_unused)
       stores.append(ctx.wvgpr_dyn(vdst_reg, lane, result, exec_mask))
     elif dest.startswith('VCC'):
-      old_vcc = ctx.rmask(_c(VCC_LO.offset))
-      stores.extend(ctx.wmask(_c(VCC_LO.offset), _set_lane_bit(old_vcc, lane, val, exec_mask)))
+      stores.extend(ctx.wmask_lane_bit(_c(VCC_LO.offset), lane, val, exec_mask))
   if vcc_val is not None:
     # Initialize sdst to 0 before lane loop (old value may be unrelated data), then set lane bits in loop
     init_stores = [ctx.wsgpr_dyn(sdst_off, _c(0)), ctx.wsgpr_dyn(sdst_off + _c(1), _c(0))]
-    old_sdst = ctx.rmask(sdst_off)
-    stores.extend(ctx.wmask(sdst_off, _set_lane_bit(old_sdst, lane, vcc_val, exec_mask)))
+    stores.extend(ctx.wmask_lane_bit(sdst_off, lane, vcc_val, exec_mask))
     if stores:
       return UOp.sink(*init_stores, UOp.sink(*stores).end(lane), *ctx.inc_pc())
     return UOp.sink(*init_stores, *ctx.inc_pc())

test/models/test_whisper.py

@@ -74,6 +74,7 @@ class TestWhisper(unittest.TestCase):
         err
       )
 
+  @slow
   def test_transcribe_file1(self):
     self.assertEqual(transcribe_file(self.model, self.enc, TEST_FILE_1),  TRANSCRIPTION_1)
 
@@ -89,6 +90,7 @@ class TestWhisper(unittest.TestCase):
     self.assertEqual(TRANSCRIPTION_1,  transcriptions[0])
     self.assertEqual(TRANSCRIPTION_2,  transcriptions[1])
 
+  @slow
   def test_transcribe_batch21(self):
     waveforms = [load_file_waveform(TEST_FILE_2), load_file_waveform(TEST_FILE_1)]
     transcriptions = transcribe_waveform(self.model, self.enc, waveforms)

test/null/test_const_folding.py

@@ -1,39 +1,10 @@
 import unittest, itertools, math
-from tinygrad import Tensor, dtypes, Context
-from tinygrad.dtype import DType, ConstType, truncate
+from tinygrad import dtypes, Context
+from tinygrad.dtype import DType, ConstType
 from tinygrad.uop.ops import Ops, UOp
 from test.helpers import full_rewrite
 import numpy as np
 
-def _check_ast_count(desired_count:int, t:Tensor):
-  # NOTE: this has side effect because everything can be scheduled only once
-  linear = t.schedule_linear()
-  asts = [s for s in linear.src if s.src[0].op is Ops.SINK]
-  len(asts)
-  # NOT SUPPORTED ANYMORE
-  #assert len(asts) == desired_count, f"{len(asts)} != {desired_count}"
-
-class TestUnaryOpsConstFolding(unittest.TestCase):
-  def test_all_consts_ops(self):
-    _check_ast_count(0, Tensor.ones(4).exp())
-    _check_ast_count(0, Tensor.ones(4).sqrt())
-    _check_ast_count(0, Tensor.ones(4) + Tensor.ones(4))
-    _check_ast_count(0, Tensor.ones(4) / Tensor.ones(4))
-
-  def test_cast(self):
-    _check_ast_count(0, Tensor.ones(4).cast(dtypes.int16))
-    _check_ast_count(0, Tensor.full(4, fill_value=-1).cast(dtypes.uint16))
-
-  def test_neg_folding(self):
-    _check_ast_count(0, Tensor([1, 2, 3]).mul(-1).neg())
-    _check_ast_count(0, Tensor([1, 2, 3]).neg().mul(-1))
-    _check_ast_count(0, Tensor([1, 2, 3]).neg().neg())
-
-  def test_neg_realized_no_fold(self):
-    x = Tensor.randn(32, 32)
-    x = x.clip(0, 1).realize()
-    _check_ast_count(1, x.neg())
-
 class TestWeakConstFolding(unittest.TestCase):
   def test_weakint_math(self):
     out = (UOp.const(2**40) + UOp.const(2**40)).simplify()
@@ -51,87 +22,18 @@ 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)
-  def test_add_tensor_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) + Tensor.zeros(4))
-  def test_literal_zero_add(self):
-    _check_ast_count(0, 0 + Tensor([1.0, 2, 3, 4]))
-  def test_tensor_zero_add(self):
-    _check_ast_count(0, Tensor.zeros(4) + Tensor([1.0, 2, 3, 4]))
-
-  def test_sub_literal_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) - 0)
-  def test_sub_tensor_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) - Tensor.zeros(4))
-
-  def test_mul_literal_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) * 0)
-  def test_mul_tensor_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) * Tensor.zeros(4))
-  def test_literal_zero_mul(self):
-    _check_ast_count(0, 0 * Tensor([1.0, 2, 3, 4]) * 0)
-  def test_tensor_zero_mul(self):
-    _check_ast_count(0, Tensor.zeros(4) * Tensor([1.0, 2, 3, 4]))
-
-  def test_mul_literal_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) * 1)
-  def test_mul_tensor_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) * Tensor.ones(4))
-  def test_literal_one_mul(self):
-    _check_ast_count(0, 1 * Tensor([1.0, 2, 3, 4]))
-  def test_tensor_one_mul(self):
-    _check_ast_count(0, Tensor.ones(4) * Tensor([1.0, 2, 3, 4]))
-
-  def test_bool_tensor_mul_bool(self):
-    _check_ast_count(0, Tensor([True, False]) * True)
-    _check_ast_count(0, Tensor([True, False]) * False)
-  def test_bool_mul_bool_tensor(self):
-    _check_ast_count(0, True * Tensor([True, False]))
-    _check_ast_count(0, False * Tensor([True, False]))
-
-  def test_div_literal_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) / 1)
-  def test_div_tensor_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) / Tensor.ones(4))
-
-  def test_floordiv_literal_one(self):
-    _check_ast_count(0, Tensor([1, 2, 3, 4]) // 1)
-  def test_floordiv_tensor_one(self):
-    _check_ast_count(0, Tensor([1, 2, 3, 4]) // Tensor.ones(4, dtype=dtypes.int32))
-
-  def test_pow_literal_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** 0)
-  def test_pow_tensor_zero(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** Tensor.zeros(4))
-
-  def test_pow_literal_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** 1)
-  def test_pow_tensor_one(self):
-    _check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** Tensor.ones(4))
-  def test_literal_one_pow(self):
-    _check_ast_count(0, 1 ** Tensor([1.0, 2, 3, 4]))
-  def test_tensor_one_pow(self):
-    _check_ast_count(0, Tensor.ones(4) ** Tensor([1.0, 2, 3, 4]))
-
 class TestBitcastConstFolding(unittest.TestCase):
+  def test_out_of_range_source_value(self):
+    for val, src_dt, dst_dt, bits in ((3000000000, dtypes.int32, dtypes.uint32, 3000000000),
+                                      (70000, dtypes.int16, dtypes.uint16, 4464),
+                                      (-5, dtypes.uint32, dtypes.int32, -5)):
+      self.assertEqual(UOp.const(val, src_dt).bitcast(dst_dt).simplify().val, bits)
+
   def test_scalar_bitcast(self):
     def t(cases: dict[DType, ConstType]):
       for (from_dt, from_v), (to_dt, to_v) in itertools.product(cases.items(), cases.items()):
         if not math.isnan(from_v):
-          r = full_rewrite(UOp.const(from_v, from_dt).bitcast(to_dt).sink()).src[0]
+          r = UOp.const(from_v, from_dt).bitcast(to_dt).simplify()
           self.assertEqual(r.op, Ops.CONST, msg:=f"{from_dt} -> {to_dt} ({from_v} -> {to_v})")
           self.assertEqual(r.dtype, to_dt, msg)
           np.testing.assert_equal(r.val, to_v, msg)
@@ -155,24 +57,9 @@ class TestBitcastConstFolding(unittest.TestCase):
 
   def test_vec_bitcast(self):
     with Context(SPEC=0):
-      srcs = full_rewrite(UOp.const((-1, -2**31, 75), dtypes.int32).bitcast(dtypes.uint32).sink()).src
-    self.assertTrue(all(r.op is Ops.CONST and r.dtype == dtypes.uint32 for r in srcs))
-    self.assertEqual(tuple(x.val for x in srcs), (2**32-1, 2**31, 75))
-
-# folds advance indexing into basic indexing
-class TestIndexingConstFolding(unittest.TestCase):
-  def test_scalar_index(self):
-    t = Tensor.arange(16).float().reshape(1,1,4,4).clone().realize()
-    _check_ast_count(1, t[:,:,Tensor(1),:])
-    _check_ast_count(1, t[:,:,Tensor(1)+2,:])
-    _check_ast_count(1, t[:,:,Tensor(1),Tensor(0)])
-
-  def test_const_tensor_index(self):
-    # TODO: these can be 0, implement const tensor folded indexing
-    t = Tensor.arange(16).float().reshape(1,1,4,4).clone().realize()
-    _check_ast_count(1, t[:,:,Tensor.ones(2,1,dtype=dtypes.int),:])
-    _check_ast_count(1, t[:,:,Tensor.ones(1,2,dtype=dtypes.int)+2,:])
-    _check_ast_count(1, t[:,:,Tensor.ones(1,1,dtype=dtypes.int),Tensor.zeros(2,1,2,dtype=dtypes.int)])
+      result = full_rewrite(UOp.const((-1, -2**31, 75), dtypes.int32).bitcast(dtypes.uint32).sink())
+      expected = full_rewrite(UOp.const((2**32-1, 2**31, 75), dtypes.uint32).sink())
+    self.assertEqual(result.src, expected.src)
 
 if __name__ == '__main__':
   unittest.main()

test/null/test_dtype_spec.py

@@ -51,10 +51,6 @@ class TestHelpers(unittest.TestCase):
     assert dtypes.is_float(dtypes.fp8e4m3)
     assert dtypes.is_float(dtypes.fp8e5m2)
 
-  @given(strat.sampled_from([d for d in DTYPES_DICT.values() if dtypes.is_float(d) or dtypes.is_int(d)]))
-  def test_scalar(self, dtype):
-    assert dtype.scalar() == dtype
-
   def test_from_py(self):
     assert dtypes.from_py(True) == dtypes.bool
     assert dtypes.from_py(Invalid) == dtypes.bool

test/null/test_graph_rewrite.py

@@ -1,8 +1,7 @@
 import unittest, math
 from tinygrad import dtypes
-from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import all_same, Context
-from tinygrad.uop.ops import GroupOp, UOp, Ops, exec_alu, PatternMatcher, TrackedPatternMatcher, UPat
+from tinygrad.uop.ops import GroupOp, UOp, Ops, PatternMatcher, TrackedPatternMatcher, UPat
 from test.helpers import full_rewrite
 from hypothesis import given, strategies as strat
 
@@ -11,125 +10,14 @@ from hypothesis import given, strategies as strat
 def apply_rewrite(expr):
   return full_rewrite(expr.sink()).src[0]
 
-@Context(SPEC=0)
-def apply_rewrite_values(expr):
-  srcs = full_rewrite(expr.sink()).src
-  if len(srcs) == 1:
-    if srcs[0].op is Ops.CONST: return (srcs[0].val,)
-    if srcs[0].op is Ops.STACK: return tuple(s.val for s in srcs[0].src)
-  return tuple(s.val for s in srcs)
-
-def evaluate_uop(uop, variables):
-  if uop.op == Ops.CONST:
-    return uop.val
-  elif uop.op == Ops.PARAM and uop.arg.addrspace is AddrSpace.ALU:
-    return variables[uop.expr]
-  elif uop.op in GroupOp.ALU:
-    src_values = [evaluate_uop(src, variables) for src in uop.src]
-    return exec_alu(uop.op, uop.dtype, src_values)
-  else:
-    raise NotImplementedError(f"Unsupported UOp {uop.op}")
-
-class TestArithmeticSimplifications(unittest.TestCase):
-  def test_full_graph_rewrite_division_by_zero(self):
-    optimized_div_uop = apply_rewrite(UOp.const(10.0) / UOp.const(0.0))
-    self.assertEqual(optimized_div_uop.op, Ops.CONST)
-    self.assertTrue(math.isinf(optimized_div_uop.val) or math.isnan(optimized_div_uop.val))
-
-  def test_full_graph_rewrite_redundant_operations(self):
-    optimized_uop = apply_rewrite((UOp.const(10.0) + UOp.const(0.0)) * UOp.const(1.0))
-    self.assertEqual(optimized_uop.op, Ops.CONST)
-    self.assertEqual(optimized_uop.val, 10.0)
-
-  def test_full_graph_rewrite_large_graph(self):
-    prev_uop = UOp.const(0)
-    for i in range(1, 101):
-      prev_uop += UOp.const(i)
-    optimized_uop = apply_rewrite(prev_uop)
-    self.assertEqual(optimized_uop.op, Ops.CONST)
-    self.assertEqual(optimized_uop.val, sum(range(1, 101)))
-
-  def test_full_graph_rewrite_division_by_one(self):
-    optimized_uop = apply_rewrite(UOp.const(42.0) / UOp.const(1.0))
-    self.assertEqual(optimized_uop.op, Ops.CONST)
-    self.assertEqual(optimized_uop.val, 42.0)
-
-  def test_full_graph_rewrite_modulo_by_one(self):
-    optimized_uop = apply_rewrite(UOp.const(42) % UOp.const(1))
-    self.assertEqual(optimized_uop.op, Ops.CONST)
-    self.assertEqual(optimized_uop.val, 0)
-
-
-class TestFoldingAndReduction(unittest.TestCase):
-  @unittest.skip("reduce is removed now")
-  def test_full_graph_rewrite_constant_reduction_folding(self):
-    const1 = UOp.const(5)
-    const2 = UOp.const(10)
-    const3 = UOp.const(20)
-    optimized_sink = apply_rewrite((const1 + const2 + const3).reduce(Ops.ADD))
-    expected_sum = 5 + 10 + 20
-    self.assertEqual(optimized_sink.val, expected_sum)
-
-  @unittest.skip("reduce is removed now")
-  def test_full_graph_rewrite_reduction_with_unused_range(self):
-    const1 = UOp.const(15)
-    const2 = UOp.const(25)
-    rng = UOp.range(10, idx=0)
-    optimized_sink = apply_rewrite((const1 + const2).reduce(Ops.ADD, rng))
-    expected_sum = 10 * (15 + 25)
-    self.assertEqual(optimized_sink.val, expected_sum)
-
-  @unittest.skip("currently failing")
-  def test_full_graph_rewrite_range_reduction(self):
-    simple_range = UOp.range(5, idx=0)
-    optimized_sink = apply_rewrite(simple_range.reduce(Ops.ADD, simple_range))
-    expected_sum = sum(range(5))
-    self.assertEqual(optimized_sink.val, expected_sum)
-
-  @unittest.skip("currently failing")
-  def test_full_graph_rewrite_simple_reduction_folding(self):
-    simple_range = UOp.range(4, idx=0)
-    add_uop = simple_range + UOp.const(1)
-    optimized_sink = apply_rewrite(add_uop.reduce(Ops.ADD, simple_range))
-    expected_sum = sum(i + 1 for i in range(4))
-    self.assertEqual(optimized_sink.val, expected_sum)
-
-  @unittest.skip("currently failing")
-  def test_full_graph_rewrite_nested_loop_collapse(self):
-    outer_range = UOp.range(8, 0)
-    inner_range = UOp.range(4, 1)
-    expr = (outer_range * 10) + inner_range
-    optimized_reduce_uop = apply_rewrite(expr.reduce(Ops.ADD, outer_range, inner_range))
-    self.assertEqual(optimized_reduce_uop.op, Ops.CONST)
-    self.assertEqual(optimized_reduce_uop.val, sum((i * 10) + j for i in range(8) for j in range(4)))
-
+def const_value(uop:UOp):
+  if uop.op is Ops.CAST: uop = uop.src[0]
+  assert uop.op is Ops.CONST
+  return uop.val
 
 class TestModuloAndDivisionFolding(unittest.TestCase):
-  def test_full_graph_rewrite_modulo_folding_with_define_var(self):
-    # index dtype because div-mod rules only work on index
-    x_var_uop = UOp.variable('x', 0, 100).cast(dtypes.weakint)
-    optimized_mod_uop = apply_rewrite(((x_var_uop * 4) + 2) % 4)
-    self.assertEqual(optimized_mod_uop.op, Ops.CONST)
-    self.assertEqual(optimized_mod_uop.val, 2)
-
-  def test_full_graph_rewrite_division_folding_with_define_var(self):
-    # index dtype because div-mod rules only work on index
-    n_var_uop = UOp.variable('n', 1, 1000).cast(dtypes.weakint)
-    optimized_div_uop = apply_rewrite((n_var_uop * 6) // 3)
-    self.assertEqual(optimized_div_uop.op, Ops.MUL)
-    self.assertEqual(optimized_div_uop.src[1].val, 2)
-
-  def test_full_graph_rewrite_complex_mod_div_folding(self):
-    # index dtype because div-mod rules only work on index
-    k_var_uop = UOp.variable('k', 0, 50).cast(dtypes.weakint)
-    optimized_div_uop = apply_rewrite(((k_var_uop * 12 + 8) % 6) // 2)
-    self.assertEqual(optimized_div_uop.op, Ops.CONST)
-    self.assertEqual(optimized_div_uop.val, 1)
-
   def test_graph_rewrite_div_folding_bug(self):
-    lhs = UOp(Ops.ADD, src=(
-      UOp(Ops.STACK, arg=None, src=(UOp(Ops.SPECIAL, src=(UOp.const(32),), arg='lidx0'),)*4),
-      UOp.const((0, 256, 512, 768))))
+    lhs = UOp.stack(*(UOp.special(32, 'lidx0'),)*4) + UOp.const((0, 256, 512, 768))
     rhs = UOp.const((2,)*4)
     unopt = lhs<rhs
     opt = apply_rewrite(unopt)
@@ -137,74 +25,31 @@ class TestModuloAndDivisionFolding(unittest.TestCase):
     print(opt)
     if opt.op is Ops.STACK: self.assertFalse(all_same(opt.src))
 
-  def test_full_graph_rewrite_modulo_large_divisor(self):
-    # index dtype because div-mod rules only work on index
-    x_var_uop = UOp.variable('x', 1, 5)
-    self.assertIs(apply_rewrite(x_var_uop.cast(dtypes.weakint) % 10).render(simplify=False), x_var_uop.render(simplify=False))
-
-  def test_full_graph_rewrite_division_with_remainder(self):
-    x_var_uop = UOp.variable('x', 7, 9)
-    optimized_sink = apply_rewrite(x_var_uop // 2)
-    for x_value in range(7, 10):
-      self.assertEqual(x_value // 2, evaluate_uop(optimized_sink, {'x': x_value}))
-
-  def test_full_graph_rewrite_complex_mod_div_expression(self):
-    x_var_uop = UOp.variable('x', 1, 10)
-    optimized_sink = apply_rewrite(((x_var_uop * 5) % 3) // 2)
-    for x_value in range(1, 11):
-      original_result = ((x_value * 5) % 3) // 2
-      optimized_result = evaluate_uop(optimized_sink, {'x': x_value})
-      self.assertEqual(original_result, optimized_result)
-
-
 class TestEdgeCasesAndSpecialOperations(unittest.TestCase):
   def test_full_graph_rewrite_transcendental_edge_cases(self):
     optimized_sink = full_rewrite(UOp.const(-1.0).log2().sink(UOp.const(0.0).reciprocal()))
     optimized_log2_neg, optimized_recip_zero = optimized_sink.src
-    self.assertTrue(math.isnan(optimized_log2_neg.val), f"Expected NaN for log2(-1.0), got {optimized_log2_neg.val}")
-    self.assertTrue(math.isinf(optimized_recip_zero.val) and optimized_recip_zero.val > 0,
-                    f"Expected +inf for reciprocal(0.0), got {optimized_recip_zero.val}")
-
-  @unittest.skip("broken")
-  def test_full_graph_rewrite_modulo_negative_dividend(self):
-    x_var_uop = UOp.variable('x', -5, -1)
-    optimized_sink = full_rewrite((x_var_uop % 3).sink())
-    for x_value in range(-5, 0):
-      self.assertEqual(x_value % 3, evaluate_uop(optimized_sink.src[0], {'x': x_value}))
-
-  @unittest.skip("broken")
-  def test_full_graph_rewrite_division_negative_divisor(self):
-    x_var_uop = UOp.variable('x', 1, 5)
-    optimized_sink = full_rewrite((x_var_uop // -2).sink())
-    for x_value in range(1, 6):
-      self.assertEqual(x_value // -2, evaluate_uop(optimized_sink.src[0], {'x': x_value}))
+    log2_neg, recip_zero = const_value(optimized_log2_neg), const_value(optimized_recip_zero)
+    self.assertTrue(math.isnan(log2_neg), f"Expected NaN for log2(-1.0), got {log2_neg}")
+    self.assertTrue(math.isinf(recip_zero) and recip_zero > 0, f"Expected +inf for reciprocal(0.0), got {recip_zero}")
 
 class TestGEPAndVectorizeRewrite(unittest.TestCase):
   def test_gep_single_element_extraction(self):
     # GEP on a vector dtype to extract a single element
     base_vector = UOp.const((1.0, 2.0, 3.0, 4.0))
-    self.assertEqual(apply_rewrite(base_vector.index(2)).val, 3.0)
+    self.assertIs(apply_rewrite(base_vector.index(2)), apply_rewrite(base_vector.src[2]))
 
   def test_gep_tuple_extraction(self):
     # GEP on a vector dtype to extract multiple elements as a vector
     base_vector = UOp.const((1.0, 2.0, 3.0, 4.0))
-    self.assertEqual(list(apply_rewrite_values(UOp.stack(*[base_vector.index(i) for i in (2, 3)]))), [3.0, 4.0])
-
-  def test_gep_on_const_stack(self):
-    # GEP on a const STACK to extract a single element
-    const_stack = UOp.const((1.0, 2.0, 3.0, 4.0))
-    self.assertEqual(apply_rewrite(const_stack.index(2)).val, 3.0)
-
-  def test_gep_tuple_on_const_stack(self):
-    # GEP on a const STACK using a tuple to extract multiple elements
-    const_stack = UOp.const((7.0, 8.0, 9.0, 10.0))
-    self.assertEqual(list(apply_rewrite_values(UOp.stack(*[const_stack.index(i) for i in (1, 3)]))), [8.0, 10.0])
+    self.assertIs(apply_rewrite(UOp.stack(*[base_vector.index(i) for i in (2, 3)])),
+                  apply_rewrite(UOp.stack(base_vector.src[2], base_vector.src[3])))
 
   def test_vectorize_multiple_elements(self):
     # Vectorizing multiple elements using GEP
     base_vector = UOp.const((5.0, 10.0, 15.0, 20.0))
-    vectorized_uop = UOp(Ops.STACK, src=tuple(base_vector.index(i) for i in range(4)))
-    self.assertEqual(list(apply_rewrite_values(vectorized_uop)), [5.0, 10.0, 15.0, 20.0])
+    vectorized_uop = UOp.stack(*(base_vector.index(i) for i in range(4)))
+    self.assertIs(apply_rewrite(vectorized_uop), apply_rewrite(base_vector))
 
 
 import inspect
@@ -256,16 +101,6 @@ class TestSubstitute(unittest.TestCase):
     ret = substitute(ret, {a.sin():b})
     self.assertIs(ret, b.sin())
 
-  # broken due to infinite recursion
-  # NOTE: VIZ hangs and doesn't recover if you click this one
-  @unittest.skip("recursion error no longer raised")
-  def test_assert_inf_recurse(self):
-    a = UOp.variable('a', 0, 10)
-    n1 = a.sin()
-    ret = n1
-    with self.assertRaises(RecursionError):
-      ret = substitute(ret, {n1:n1.sqrt()})
-
   def test_sin_to_sqrt(self):
     a = UOp.variable('a', 0, 10, dtype=dtypes.float)
     n1 = a.sin()

test/null/test_memory_planner.py

@@ -27,10 +27,15 @@ def _make_linear(buffer_lists, copies=None):
     calls.append(UOp(Ops.CALL, src=(src0, *bufs)))
   return UOp(Ops.LINEAR, src=tuple(calls))
 
+def _get_planned_view(buf:UOp) -> tuple[UOp, int, int]|None:
+  view = buf.src[0] if buf.op is Ops.BITCAST else buf
+  if view.op is not Ops.SHRINK or view.src[0].op is not Ops.BUFFER: return None
+  return (arena:=view.src[0]), view.src[1].val * arena.dtype.itemsize, view.src[2].val * arena.dtype.itemsize
+
 def _get_arena(buf, linear, result):
   for orig_si, new_si in zip(linear.src, result.src):
     for orig, new in zip(orig_si.src[1:], new_si.src[1:]):
-      if orig is buf and new.op is Ops.SLICE: return new.src[0]
+      if orig is buf and (planned:=_get_planned_view(new)) is not None: return planned[0]
   return None
 
 def check_assign(buffer_lists, copies=None):
@@ -41,8 +46,8 @@ def check_assign(buffer_lists, copies=None):
   replace_map: dict[int, tuple[UOp, int, int]] = {}
   for orig_si, new_si in zip(linear.src, result.src):
     for orig, new in zip(orig_si.src[1:], new_si.src[1:]):
-      if new.op is Ops.SLICE and id(orig) not in replace_map:
-        replace_map[id(orig)] = (new.src[0], new.src[1].val * new.src[0].dtype.itemsize, new.arg * new.dtype.itemsize)
+      if (planned:=_get_planned_view(new)) is not None and id(orig) not in replace_map:
+        replace_map[id(orig)] = planned
 
   # verify pinned buffers are not planned
   for buf in held_bufs:

test/null/test_mnist_dataset.py

@@ -1,6 +1,7 @@
 import unittest
 from tinygrad.helpers import GlobalCounters
 from tinygrad.nn.datasets import mnist
+from test.helpers import KernelCountException
 
 class TestDataset(unittest.TestCase):
   def test_dataset_is_realized(self):
@@ -8,7 +9,7 @@ class TestDataset(unittest.TestCase):
     X_train[0].contiguous().realize()
     GlobalCounters.reset()
     X_train[0].contiguous().realize()
-    self.assertLessEqual(GlobalCounters.kernel_count, 1)  # 0 if SLICE (zero-copy), 1 otherwise
+    if GlobalCounters.kernel_count > 1: raise KernelCountException(1, GlobalCounters.kernel_count)  # 0 if SLICE (zero-copy), 1 otherwise
 
 if __name__ == '__main__':
   unittest.main()

test/null/test_simplify_valid_idx.py

@@ -15,17 +15,13 @@ def simplify_valid_idx(sink: UOp) -> UOp: return graph_rewrite(sink, sym+pm_move
 def simplify_image_idx(sink: UOp) -> UOp: return graph_rewrite(sink, sym+pm_move_where_on_load+indexing_simplify, name="simplify_image_idx")
 
 def get_gated_load_uop(valid:UOp, idx:UOp):
-  return UOp(Ops.LOAD, src=(
-    UOp.param(0, dtypes.float, (1024,)).index(idx.valid(valid)),
-  ))
+  return UOp.param(0, dtypes.float, (1024,)).index(idx.valid(valid)).load()
 
 def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UOp]):
-  return UOp(Ops.LOAD, src=(
-    UOp.param(0, dtypes.float, image_shape).index(idx[1].valid(valid), idx[0].valid(valid)),
-  ))
+  return UOp.param(0, dtypes.float, image_shape).index(idx[1].valid(valid), idx[0].valid(valid)).load()
 
-def Special(expr, nmax): return UOp(Ops.SPECIAL, src=(UOp.const(nmax),), arg=expr)
-def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
+def Special(expr, nmax): return UOp.special(nmax, expr)
+def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax, param=True)
 def Range(n, nmax): return UOp.range(nmax, n)
 
 class TestValidIdxSimplification(unittest.TestCase):
@@ -512,7 +508,7 @@ class TestDropTrueGate(unittest.TestCase):
     buf = UOp.param(0, dtypes.int, (1,))
     idx = UOp.const(0)
     true_gate = UOp.const(True)
-    index_with_gate = UOp(Ops.INDEX, src=(buf, idx.valid(true_gate)))
+    index_with_gate = buf.index(idx.valid(true_gate))
     # apply the optimization
     result = graph_rewrite(index_with_gate, sym+indexing_simplify)
     # the True valid should be dropped (INDEX should only have 2 sources)
@@ -524,13 +520,17 @@ class TestRangeShrink(unittest.TestCase):
       result = full_rewrite(sink)
     return [u for u in result.toposort() if u.op is Ops.RANGE]
 
+  def assert_range_end(self, ranges:list[UOp], end:int):
+    self.assertEqual(len(ranges), 1)
+    with Context(NOOPT=1, SPEC=0): expected = full_rewrite(UOp.const(end, dtypes.int).sink()).src[0]
+    self.assertIs(ranges[0].src[0], expected)
+
   def test_range_shrink_single_guard(self):
     # range 0..203 guarded by r < 4 everywhere -> shrink to 0..3
     r = Range(0, 204)
     load = get_gated_load_uop(r < UOp.const(4), r)
     ranges = self.get_ranges(load.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 4)
+    self.assert_range_end(ranges, 4)
 
   def test_range_shrink_picks_max_guard(self):
     # two loads guard the same range with r < 4 and r < 8 -> shrink to max(4, 8) = 8
@@ -538,25 +538,22 @@ class TestRangeShrink(unittest.TestCase):
     load1 = get_gated_load_uop(r < UOp.const(4), r)
     load2 = get_gated_load_uop(r < UOp.const(8), r)
     ranges = self.get_ranges(UOp.sink(load1, load2))
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 8)
+    self.assert_range_end(ranges, 8)
 
   def test_range_no_shrink_guard_ge_max(self):
     # guard r < 300 with range max 204 -> no shrink (guard doesn't constrain)
     r = Range(0, 204)
     load = get_gated_load_uop(r < UOp.const(300), r)
     ranges = self.get_ranges(load.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 204)
+    self.assert_range_end(ranges, 204)
 
   def test_range_no_shrink_when_unguarded_elsewhere(self):
     # one load guards r < 4, but another load uses r without a gate -> no shrink
     r = Range(0, 204)
     load1 = get_gated_load_uop(r < UOp.const(4), r)
-    load2 = UOp(Ops.LOAD, src=(UOp.param(1, dtypes.float, (204,)).index(r),))
+    load2 = UOp.param(1, dtypes.float, (204,)).index(r).load()
     ranges = self.get_ranges(UOp.sink(load1, load2))
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 204)
+    self.assert_range_end(ranges, 204)
 
   def test_range_no_shrink_when_used_in_reduce(self):
     # range used in both a gated load AND directly in the reduce expression -> no shrink
@@ -564,8 +561,7 @@ class TestRangeShrink(unittest.TestCase):
     gated_load = get_gated_load_uop(r < UOp.const(4), r)
     red = (r.cast(dtypes.float) + gated_load).reduce(r, arg=Ops.ADD)
     ranges = self.get_ranges(red.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 204)
+    self.assert_range_end(ranges, 204)
 
   def test_range_shrink_to_single_iteration(self):
     # guard r < 1 shrinks range to 1 -> single iteration, range eliminated entirely
@@ -580,8 +576,7 @@ class TestRangeShrink(unittest.TestCase):
     r = Range(0, 204)
     x = (r < 4).where(UOp.const(1.0), Invalid)
     ranges = self.get_ranges(UOp.param(0, dtypes.float, (204,)).index(r).store((r < 4).where(x, Invalid)).sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 4)
+    self.assert_range_end(ranges, 4)
 
   def test_range_shrink_store_where_invalid_flipped(self):
     # above, but flipped
@@ -589,8 +584,7 @@ class TestRangeShrink(unittest.TestCase):
     r = Range(0, 204)
     x = (r < 4).where(UOp.const(1.0), Invalid)
     ranges = self.get_ranges(UOp.param(0, dtypes.float, (204,)).index(r).store((r >= 4).where(Invalid, x)).sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].val, 4)
+    self.assert_range_end(ranges, 4)
 
 if __name__ == '__main__':
   unittest.main()

test/null/test_tensor.py

@@ -69,7 +69,7 @@ class TestIdxUpcast(unittest.TestCase):
     if not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)):
       assert idx.op is Ops.INDEX
       idx_val = idx.src[1]
-      self.assertFalse(idx_val.overflows(idx_val.dtype.scalar()))
+      self.assertFalse(idx_val.overflows(idx_val.dtype))
 
   # use expand to generate kernel that uses large idx
   def do_op_then_assert(self, dtype: DType, dim1, dim2, dim3):
@@ -171,6 +171,11 @@ class TestTensorConstLike(unittest.TestCase):
     t = Tensor.ones(8, 4).shard(("NULL:0", "NULL:1"), axis=0)
     with self.assertRaises(RuntimeError): t.full_like(5, device="NULL")
 
+class TestTensorShape(unittest.TestCase):
+  def test_float_shape_raises(self):
+    for dim in (2.0, 2.5):
+      with self.subTest(dim=dim), self.assertRaisesRegex(RuntimeError, "shape must be int"): Tensor.ones(dim)
+
 class TestTensorDevice(unittest.TestCase):
   def test_create_from_single_device_tuple(self):
     (Tensor([1.0], device=(Device.DEFAULT,)) + Tensor([2.0])).realize()

test/null/test_uop_graph.py

@@ -1,10 +1,9 @@
 import unittest, pytest
 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, KernelInfo
 from tinygrad.uop.symbolic import sym
-from test.helpers import to_uops_list
+from test.helpers import full_rewrite, to_uops_list
 from tinygrad.codegen import full_rewrite_to_sink
 
 simple_pm = PatternMatcher([
@@ -14,43 +13,27 @@ simple_pm = PatternMatcher([
   ((UPat.var('x') + UPat.cvar('c1')) + UPat.cvar('c2'), lambda x,c1,c2: x + (c1.val+c2.val)),
 ])
 
-def const_values(u:UOp):
-  if u.op is Ops.CONST: return (u.val,)
-  if u.op is Ops.STACK: return tuple(x.val for x in u.src)
-  raise AssertionError(f"expected const-like UOp, got {u.op}")
-
 class TestGraphRewriteConst(unittest.TestCase):
   def test_gep_const(self):
     v1 = UOp.const((0,1,2), dtypes.int)
     v2 = v1.index(1)
     ret = graph_rewrite(v2, sym)
-    self.assertEqual(ret.dtype, dtypes.int)
-    self.assertEqual(ret.val, 1)
+    self.assertIs(ret, UOp.const(1, dtypes.int))
 
   def test_add_const(self):
     v1 = UOp.const((0,1,2))
     v2 = UOp.const((5,6,7))
-    ret = graph_rewrite(v1+v2, sym)
-    self.assertEqual(ret.op, Ops.STACK)
-    self.assertEqual(const_values(ret), (5,7,9))
-
-  def test_add_const_lose_v(self):
-    v1 = UOp.const((0,1,2))
-    v2 = UOp.const((2,1,0))
-    ret = graph_rewrite(v1+v2, sym)
-    self.assertEqual(ret.op, Ops.STACK)
-    self.assertEqual(const_values(ret), (2,2,2))
+    self.assertIs(graph_rewrite(v1+v2, sym), UOp.const((5,7,9)))
 
 def xfail_broken_const_wraparound(fn):
   fn = pytest.mark.xfail(reason="const folding does not properly implement modular arithmetic")(fn)
   return unittest.expectedFailure(fn)
 class TestModularWraparound(unittest.TestCase):
   def _test(self, uop:UOp, expected:int):
-    results = to_uops_list([uop])
-    self.assertEqual(len(results), 2)  # +1 for SINK
-    self.assertEqual(results[0].op, Ops.CONST)
-    self.assertEqual(results[0].dtype, uop.dtype)
-    self.assertEqual(results[0].val, expected)
+    result = uop.simplify()
+    self.assertEqual(result.op, Ops.CONST)
+    self.assertEqual(result.dtype, uop.dtype)
+    self.assertEqual(result.val, expected)
 
   @xfail_broken_const_wraparound
   def test_cast(self):
@@ -157,7 +140,7 @@ class TestGraphRewrite(unittest.TestCase):
     self.assertEqual(nout.val, 3.0)
 
   def test_depth_2_fold(self):
-    v = UOp.variable("v", 0, 1, dtypes.float)
+    v = UOp.variable("v", 0, 1, dtypes.float, param=True)
     c1 = UOp.const(1.0)
     c2 = UOp.const(2.0)
     nout = graph_rewrite(v+c1+c2, simple_pm)
@@ -191,63 +174,25 @@ class TestGraphRewrite(unittest.TestCase):
       self.assertEqual(len([x for x in sink.toposort() if x.op is Ops.CONST]), 1)
 
 class TestUOpGraph(unittest.TestCase):
-  def test_add_constant_fold(self):
-    c1 = UOp.const(1.0, dtypes.float)
-    c2 = UOp.const(2.0, dtypes.float)
-    out = c1+c2
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
-    out = uops[-2]
-    self.assertEqual(out.op, Ops.CONST)
-    self.assertEqual(out.val, 3.0)
-
   def test_where_same_fold(self):
     v = UOp.variable('tmp', 0, 1)
     c0 = UOp.const(0)
     vc = v != c0
     c1 = UOp.const(1.0, dtypes.float)
     out = vc.where(c1, c1)
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
-    out = uops[-2]
-    self.assertEqual(out.op, Ops.CONST)
-    self.assertEqual(out.val, 1.0)
+    self.assertIs(out.simplify(), c1)
 
   def test_where_const_fold(self):
     bf = UOp.const(False)
     c1 = UOp.const(1.0, dtypes.float)
     c2 = UOp.const(2.0, dtypes.float)
     out = bf.where(c1, c2)
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
-    out = uops[-2]
-    self.assertEqual(out.op, Ops.CONST)
-    self.assertEqual(out.val, 2.0)
+    self.assertIs(out.simplify(), c2)
 
   def test_const_cast(self):
     bf = UOp.const(False)
     out = bf.cast(dtypes.int)
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
-    out = uops[-2]
-    self.assertEqual(out.op, Ops.CONST)
-    self.assertEqual(out.val, 0)
-
-  def test_const_bitcast(self):
-    bf = UOp.const(1.0, dtypes.float)
-    out = bf.bitcast(dtypes.uint32)
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
-    out = uops[-2]
-    self.assertEqual(out.op, Ops.CONST)
-    self.assertEqual(out.val, 0x3F800000)
-
-  @unittest.expectedFailure
-  def test_const_shape_change_bitcast(self):
-    bf = UOp.const(0x3F).cast(dtypes.uint8)
-    out = bf.bitcast(dtypes.half)
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 2)  # +1 for SINK
+    self.assertIs(full_rewrite(out.sink()).src[0], full_rewrite(UOp.const(0, dtypes.int).sink()).src[0])
 
   def test_devectorize_derives_lane_dtype(self):
     from tinygrad.codegen import do_devectorize
@@ -257,66 +202,11 @@ class TestUOpGraph(unittest.TestCase):
     invalid_lane_mul = next(u for u in out.src[0].toposort() if u.op is Ops.MUL)
     self.assertIs(invalid_lane_mul.dtype, dtypes.bool)
 
-  @unittest.skip("this test isn't valid uops")
-  def test_noop_vectorize_fold(self):
-    d0 = UOp.param(0, dtypes.float, (1,))
-    idx = UOp.const(0)
-    ld = d0.load(idx, dtype=dtypes.float)
-    vec = UOp(Ops.STACK, dtypes.float, (ld,))
-    x = vec.index(0)
-    alu = UOp(Ops.SQRT, src=(x, ))
-    out = UOp(Ops.STORE, src=(d0, idx, alu))
-    uops = to_uops_list([out])
-    self.assertEqual(len([x for x in uops if x.op is Ops.STACK]), 0)
-
-  @unittest.skip("this test isn't valid uops")
-  def test_gep_vec_fold(self):
-    d0 = UOp.param(0, dtypes.float, (1,))
-    d1 = UOp.param(1, dtypes.float, (1,))
-    d2 = UOp.param(2, dtypes.float, (1,))
-    idx = UOp.const(0)
-    def _test_vec(geps, count=4):
-      vec = UOp(Ops.STACK, dtypes.float, geps)
-      out = d0.index(idx).store(vec)
-      uops = to_uops_list([out])
-      if DEBUG >= 4:
-        from tinygrad import Device
-        print(Device[Device.DEFAULT].renderer.render(uops))
-      return uops[-2].src[-1]  # -2 to skip SINK
-
-    # possible
-    val = d1.index(idx).load(dtype=dtypes.float)
-    xyzw = tuple(val.index(i) for i in range(4))
-    self.assertIs(_test_vec(xyzw).op, Ops.LOAD)
-
-    # unaligned
-    val = d1.index(idx).load(dtype=dtypes.float)
-    wzyx = tuple(val.index(i) for i in reversed(range(4)))
-    self.assertIs(_test_vec(wzyx).op, Ops.STACK)
-
-    # different_size
-    val = d1.index(idx).load(dtype=dtypes.float)
-    xy = tuple(val.index(i) for i in range(2))
-    self.assertIs(_test_vec(xy+xy).op, Ops.STACK)
-    val = d1.index(idx).load(dtype=dtypes.float)
-    xy = tuple(val.index(i) for i in range(2))
-    self.assertIs(_test_vec(xy, count=2).op, Ops.STACK)
-
-    # different vals
-    val1 = d1.index(idx).load(dtype=dtypes.float)
-    val2 = d2.index(idx).load(dtype=dtypes.float)
-    xy1 = tuple(val1.index(i) for i in range(2))
-    xy2 = tuple(val2.index(i) for i in range(2))
-    self.assertIs(_test_vec(xy1+xy2).op, Ops.STACK)
-
   def test_gep_vec_const_fold(self):
     for vec_size in [2, 4, 8]:
       consts = [UOp.const(float(i), dtypes.float) for i in range(vec_size)]
-      vec = UOp(Ops.STACK, src=tuple(consts))
-      with Context(SPEC=0):
-        uops = to_uops_list([vec.index(i) for i in range(vec_size)])
-        for uop, const in zip(uops, consts):
-          self.assertEqual(uop, const)
+      vec = UOp.stack(*consts)
+      for i, const in enumerate(consts): self.assertIs(vec.index(i), const)
 
   def test_cast_alu_fold(self):
     d0 = UOp.param(0, dtypes.bool, (1,))
@@ -326,7 +216,7 @@ class TestUOpGraph(unittest.TestCase):
     alu = (ld<1).cast(dtypes.bool)
     out = d0.index(idx).store(alu)
     uops = to_uops_list([out])
-    self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 0)
+    self.assertEqual(len([x for x in uops if x.op is Ops.CAST and x.src[0].op is not Ops.CONST]), 0)
 
   def test_double_cast_fold(self):
     d0 = UOp.param(0, dtypes.float, (1,))
@@ -336,20 +226,15 @@ class TestUOpGraph(unittest.TestCase):
     alu = ld.cast(dtypes.float).cast(dtypes.float)
     out = d0.index(idx).store(alu)
     uops = to_uops_list([out])
-    self.assertEqual(len([x for x in uops if x.op is Ops.CAST]), 1)
+    self.assertEqual(len([x for x in uops if x.op is Ops.CAST and x.src[0].op is not Ops.CONST]), 1)
 
   def test_depth_2_const_fold(self):
-    v = UOp.variable("tmp", 0, 1, dtypes.int)
+    v = UOp.variable("tmp", 0, 1, dtypes.int, param=True)
     c2 = UOp.const(2, dtypes.int)
     c4 = UOp.const(4, dtypes.int)
     vc = v+c2
     out = vc+c4
-    uops = to_uops_list([out])
-    self.assertEqual(len(uops), 5)  # +1 for SINK, +1 for the PARAM shape STACK
-    out = uops[-2]  # -2 to skip SINK
-    self.assertEqual(out.op, Ops.ADD)
-    self.assertEqual(out.src[1].op, Ops.CONST)
-    self.assertEqual(out.src[1].val, 6)
+    self.assertIs(out.simplify(), (v+UOp.const(6, dtypes.int)).simplify())
 
   def test_bitcast_to_same_dtype_fold(self):
     for dt in dtypes.ints + dtypes.floats + (dtypes.bool,):
@@ -360,9 +245,8 @@ class TestUOpGraph(unittest.TestCase):
 
   def test_sub_with_cast_folds(self):
     a = Variable("a", 0, 5)
-    uops = to_uops_list([a.cast(dtypes.int)+(-a).cast(dtypes.int)])
-    assert uops[0] == UOp.const(0, dtypes.int)
-    assert uops[-1].op == Ops.SINK
+    out = a.cast(dtypes.int)+(-a).cast(dtypes.int)
+    self.assertIs(full_rewrite(out.sink()).src[0], full_rewrite(UOp.const(0, dtypes.int).sink()).src[0])
 
   def test_where_on_gated_load_fold(self):
     ridx0 = UOp.range(100, 0)
@@ -371,9 +255,10 @@ class TestUOpGraph(unittest.TestCase):
     w = (ridx0<50).where(ld, 5)
     out = UOp.param(1, dtypes.long, (100,))
     uops = to_uops_list([out.index(ridx0).store(w)])
+    expected = full_rewrite(UOp.const(5, dtypes.long).sink()).src[0]
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: assert u.src[1].val==5
+      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: self.assertIs(u.src[1], expected)
 
   def test_where_on_gated_load_folds_swapped_branches(self):
     ridx0 = UOp.range(100, 0)
@@ -381,9 +266,10 @@ class TestUOpGraph(unittest.TestCase):
     ld = d0.index(ridx0.valid((ridx0<50).logical_not()))
     w = (ridx0<50).where(5, ld)
     uops = to_uops_list([w])
+    expected = full_rewrite(UOp.const(5, dtypes.long).sink()).src[0]
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD: assert u.src[1].val==5
+      if u.op is Ops.LOAD: self.assertIs(u.src[1], expected)
 
   def test_where_on_gated_load_with_cast(self):
     ridx0 = UOp.range(100, 0)
@@ -393,9 +279,10 @@ class TestUOpGraph(unittest.TestCase):
     w = (ridx0<50).where(ld, 5.0)
     out = UOp.param(1, dtypes.float, (100,))
     uops = to_uops_list([out.index(ridx0).store(w)])
+    expected = full_rewrite(UOp.const(5, dtypes.int).sink()).src[0]
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: assert u.src[1].val == 5
+      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: self.assertIs(u.src[1], expected)
 
   def test_where_on_casted_gated_load_extra_cond(self):
     ridx0 = UOp.range(100, 0)
@@ -425,9 +312,10 @@ class TestUOpGraph(unittest.TestCase):
     val = (ridx0<50).where(5, ld)
     st = idx.store(val).end(ridx0)
     uops = to_uops_list([st])
+    expected = full_rewrite(UOp.const(5, dtypes.long).sink()).src[0]
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.STORE: assert u.src[1].val==5
+      if u.op is Ops.STORE: self.assertIs(u.src[1], expected)
 
   def test_load_idx_becomes_int(self):
     # mnist indexing with split reduceop
@@ -501,13 +389,6 @@ class TestUOpGraph(unittest.TestCase):
     # only the second store happens
     self.assertEqual(len([u for u in uops if u.op is Ops.STORE]), 1)
 
-  @unittest.skip("this is a uop type error")
-  def test_asserts_bad_gate(self):
-    glbl0 = UOp.param(0, dtypes.int, (1,))
-    idx = UOp.const(0)
-    bad_gate = UOp.const(1)
-    with self.assertRaises(AssertionError): to_uops_list([UOp(Ops.STORE, src=(glbl0, idx, UOp.const(42), bad_gate))])
-
   def test_after_end(self):
     r = UOp.range(10, 0)
 
@@ -575,7 +456,7 @@ class TestConstBufferize(unittest.TestCase):
     from tinygrad.schedule.rangeify import pm_const_buffer_folding, BufferizeOpts
     c = UOp.const(42.0)
     r1 = UOp.range(3, 0)
-    bufferize_with_range = UOp(Ops.STAGE, src=(c, r1), arg=BufferizeOpts(device="CPU"))
+    bufferize_with_range = c.bufferize(r1, arg=BufferizeOpts(device="CPU"))
     self.assertEqual(len(bufferize_with_range.src), 2)  # const + 1 range
 
     result = graph_rewrite(bufferize_with_range, pm_const_buffer_folding, name='test')
@@ -590,7 +471,7 @@ class TestConstBufferize(unittest.TestCase):
     c = UOp.const(3.14)
     r1 = UOp.range(3, 0)
     r2 = UOp.range(4, 1)
-    bufferize_with_ranges = UOp(Ops.STAGE, src=(c, r1, r2), arg=BufferizeOpts(device="CPU"))
+    bufferize_with_ranges = c.bufferize(r1, r2, arg=BufferizeOpts(device="CPU"))
     self.assertEqual(len(bufferize_with_ranges.src), 3)  # const + 2 ranges
 
     result = graph_rewrite(bufferize_with_ranges, pm_const_buffer_folding, name='test')

test/null/test_uop_symbolic.py

@@ -3,10 +3,10 @@ import unittest, pickle, functools, math
 import z3
 
 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, pm_fold_cast_const, commutative, pm_simplify_valid, pm_move_where_on_load
+from tinygrad.uop.symbolic import sym, commutative, pm_simplify_valid, pm_move_where_on_load
+from tinygrad.uop.weak import pm_cast_weak
 from tinygrad.uop.validate import uops_to_z3
 
 def check_uop_against_string(self, v:UOp, s:str):
@@ -16,7 +16,8 @@ def check_uop_against_string(self, v:UOp, s:str):
   s_eval = graph_rewrite(s_eval, commutative, name="cannonicalize eval")
   self.assertIs(s_eval, v, f"eval did not match simplified: {s_eval} != {v.render()} for {s}")
 
-def Variable(name: str, min_val: ConstType, max_val: ConstType, dtype: DType=dtypes.weakint): return UOp.variable(name,min_val,max_val,dtype)
+def Variable(name: str, min_val: ConstType, max_val: ConstType, dtype: DType=dtypes.weakint):
+  return UOp.variable(name, min_val, max_val, dtype, param=True)
 def uconst(val): return UOp.const(val)
 def usum(ops): return functools.reduce(lambda x,y: x+y, ops)
 def uand(ops): return functools.reduce(lambda x,y: x*y, ops)
@@ -35,7 +36,7 @@ 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+pm_fold_cast_const, name="simplify symbolic uop")
+    v_simplified = graph_rewrite(v, sym+pm_cast_weak, 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)
@@ -442,7 +443,7 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(uand([uconst(1), Variable("a", 0, 1)]), 0, 1, "a")
 
   def test_masked_shr_fold(self):
-    x = UOp.variable('x', 0, 255, dtype=dtypes.uint32)
+    x = UOp.variable('x', 0, 255, dtype=dtypes.uint32, param=True)
     self.helper_test_variable((x & -4) >> 2, 0, 63, "(x>>2)")
 
   def test_bool_or_not_tautology(self):
@@ -483,12 +484,12 @@ class TestSymbolic(unittest.TestCase):
 
   def test_div_drop_small_terms(self):
     # from openpilot, shouldnt simplify
-    gidx0 = UOp.variable("gidx0", 0, 10)
-    gidx1 = UOp.variable("gidx1", 0, 10)
-    lidx0 = UOp.variable("lidx0", 0, 1)
-    lidx1 = UOp.variable("lidx1", 0, 1)
-    ridx1005 = UOp.variable("ridx1005", 0, 2)
-    ridx1006 = UOp.variable("ridx1006", 0, 2)
+    gidx0 = UOp.variable("gidx0", 0, 10, param=True)
+    gidx1 = UOp.variable("gidx1", 0, 10, param=True)
+    lidx0 = UOp.variable("lidx0", 0, 1, param=True)
+    lidx1 = UOp.variable("lidx1", 0, 1, param=True)
+    ridx1005 = UOp.variable("ridx1005", 0, 2, param=True)
+    ridx1006 = UOp.variable("ridx1006", 0, 2, param=True)
     self.helper_test_variable((lidx1+((gidx1*18)+(ridx1005*18)+(lidx0*162))+(gidx0*2)+(ridx1006*2)+-40)//18, -3, 20,
       "(gidx1+ridx1005+lidx0*9+(gidx0+ridx1006+7)//9+-3)")
 
@@ -948,6 +949,11 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(cond.where(u0, u1), 0, 1, "((a<2)!=True)")
     self.helper_test_variable(cond.where(u0, u1).where(u0, u1), 0, 1, "(a<2)")
 
+  def test_equivalent_const_max(self):
+    x = Variable("x", -10, 10)
+    self.helper_test_variable((x < 0).where(0, x), 0, 10, "x.maximum(0)")
+    self.helper_test_variable((0 < x).where(x, 0), 0, 10, "x.maximum(0)")
+
   def test_where_combine(self):
     cond = Variable("x", 0, 3) < 2
     a = Variable("a", 0, 3)
@@ -992,7 +998,7 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(cond.ne(False), 0, 1, "(x<2)")
 
   def test_bitcast_chain(self):
-    a = UOp.variable("a", 0, 3, dtype=dtypes.int32)
+    a = UOp.variable("a", 0, 3, dtype=dtypes.int32, param=True)
     self.assertIs(graph_rewrite(a.bitcast(dtypes.float32).bitcast(a.dtype), sym), a)
 
   def test_negation_in_where(self):
@@ -1008,20 +1014,11 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(-a<-b, False, True, "(b<a)")
 
   def test_where_cast(self):
-    s = Variable("s", 0, 3, dtypes.int)
-    cond = s < 2
+    cond = Variable("s", 0, 3, dtypes.int) < 2
     a = Variable("a", 0, 3, dtypes.int)
-    b = Variable("b", 0, 3, dtypes.int)
-    expr = cond.where(a, b).cast(dtypes.half)
-
-    # TODO: copied from render, render does not support cast
-    glbl = UOp.param(0, dtypes.int, (1,))
-    uops = get_uops(UOp(Ops.STORE, src=(glbl.index(UOp.const(0, dtypes.int)), expr)).sink())
-    rewritten_uop = [uop for uop in uops if uop.op is Ops.STORE][0].src[1]
-
-    # the vars are now scalar PARAMs
-    pvar = {u.expr: u for u in rewritten_uop.toposort() if u.op is Ops.PARAM}
-    self.assertEqual(rewritten_uop, (pvar['s']<UOp.const(2, dtypes.int)).where(pvar['a'].cast(dtypes.half), pvar['b'].cast(dtypes.half)))
+    self.assertIs(graph_rewrite(cond.where(a, a+1).cast(dtypes.half), sym), cond.where(a.cast(dtypes.half), (a+1).cast(dtypes.half)))
+    self.assertIs(graph_rewrite(cond.where(a, uconst(2)).cast(dtypes.half), sym), cond.where(a.cast(dtypes.half), UOp.const(2, dtypes.half)))
+    self.assertIs(graph_rewrite(cond.where(a, UOp.invalid()).cast(dtypes.half), sym), cond.where(a.cast(dtypes.half), UOp.invalid()))
 
   def test_where_merge_branches(self):
     cond1 = Variable("s", 0, 10) < 6
@@ -1175,7 +1172,7 @@ class TestSymbolicVariables(unittest.TestCase):
     assert (a//4 + a//6).variables() == [a]
 
   def test_variable_min_eq_max_bind_folds(self):
-    b = Variable("x", 1, 1).bind(1)
+    b = UOp.variable("x", 1, 1).bind(1)
     s = b.simplify()
     self.assertEqual(s.op, Ops.CONST)
     self.assertEqual(s.val, 1)
@@ -1369,6 +1366,16 @@ class TestInvalidIndex(unittest.TestCase):
     c2 = UOp.const((1, Invalid, 1, 1))
     self.assertIs((c1+c2).simplify(), UOp.const((2, Invalid, Invalid, Invalid)))
 
+  def test_gated_load_keeps_index_valid(self):
+    # the load executes even on gated-off iterations: gated_given_valid must not erase its mask (PADTO OOB shape)
+    buf = UOp.param(0, dtypes.bool, (17,))
+    ridx = Variable("ridx", 0, 31)
+    cond = ridx < 17
+    load = buf.index(ridx.valid(cond))
+    out = graph_rewrite(cond.where(load.where(uconst(2), uconst(0)), UOp.invalid()), sym)
+    idx = next(u for u in out.toposort() if u.op is Ops.INDEX)
+    self.assertIs(idx.src[1].get_valid(), cond.simplify())
+
 class TestStoreLoadFolding(unittest.TestCase):
   """Tests for store(index, load(index)) -> NOOP rule. This rule matches patterns that EMERGE during simplification."""
   def test_store_load_folding(self):

test/null/test_uops.py

@@ -41,11 +41,6 @@ class TestDTypeFromUOp(unittest.TestCase):
     # an explicit (strong) const dtype is legal until the field is removed
     self.assertEqual(UOp.const(3, dtypes.int32).dtype, dtypes.int32)
 
-  def test_weak_dtype_rejected_by_program_spec(self):
-    for weak, concrete, value in ((dtypes.weakint, dtypes.int32, 1), (dtypes.weakfloat, dtypes.float32, 1.0)):
-      with self.assertRaises(RuntimeError): type_verify(UOp.const(value, weak).sink(), spec_program)
-      type_verify(UOp.const(value, concrete).sink(), spec_program)
-
   def test_invalid_stated_dtype(self):
     # UOp.const normalizes a stated dtype away (const_like/full pass their position's); the core constructor does not,
     # and the spec is what rejects a non-bool Invalid
@@ -134,7 +129,7 @@ class TestConstFloatEq(unittest.TestCase):
     self.assertFalse(Invalid != HoldsInvalid())
 
   def test_matchers_agree_on_nan(self):
-    n = UOp.const(math.nan, dtypes.float32)
+    n = UOp.const(math.nan)
     for compiled in (False, True):
       pm = PatternMatcher([(UPat(Ops.CONST, arg=math.nan), lambda: True)], compiled=compiled)
       self.assertTrue(pm.rewrite(n), f"{compiled=}")
@@ -348,10 +343,9 @@ class TestFastIdiv(unittest.TestCase):
   def test_fast_idiv_remove_powers_of_two(self):
     ridx = UOp.range(2**20, 0)
     uops = to_uops_list([ridx//(7*64)], ren=Device[Device.DEFAULT].renderer)
-    ops = [x.op for x in uops]
     # this requires shifting out the powers of two before doing fast_idiv
     # (((ridx0>>6)*18725)>>17) instead of (int)((((long)(ridx0)*1198373)>>29))
-    self.assertNotIn(Ops.CAST, ops)
+    self.assertNotIn(dtypes.long, [x.dtype for x in uops])
 
   @unittest.expectedFailure
   def test_fast_idiv_overflow(self):

test/null/test_validate_oob.py

@@ -1,10 +1,12 @@
 import unittest
-from tinygrad import dtypes, Variable
+from tinygrad import dtypes
 from tinygrad.dtype import AddrSpace
 from tinygrad.helpers import Context
 from tinygrad.uop.ops import Ops, UOp, AxisType
 from test.helpers import to_uops_list
 
+def Variable(name, nmin, nmax): return UOp.variable(name, nmin, nmax, param=True)
+
 class TestValidateOOB(unittest.TestCase):
   """Test z3 validation of index bounds for different ALU ops and patterns."""
 

test/null/test_viz.py

@@ -305,10 +305,10 @@ class TestVizTree(unittest.TestCase):
 
   def test_tree_view(self):
     with save_viz() as viz:
-      a = UOp.variable("a",0,10)
-      b = UOp.variable("b",0,10)
-      c = UOp.variable("c",0,10)
-      d = UOp.variable("d",0,10)
+      a = UOp.variable("a",0,10,param=True)
+      b = UOp.variable("b",0,10,param=True)
+      c = UOp.variable("c",0,10,param=True)
+      d = UOp.variable("d",0,10,param=True)
       sink = UOp.sink(a+b, c+d)
       def tree_rewrite(): return graph_rewrite(sink, root, name="root")
       tree_rewrite()

test/opt/test_gen_float4.py

@@ -10,12 +10,12 @@ from test.helpers import replace_opts
 class TestFloat4(unittest.TestCase):
   @staticmethod
   def count_float4(uops: list[UOp], n=4):
-    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype.scalar() == dtypes.float and uop.shape == (4,)]),
-            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype.scalar() == dtypes.float and uop.shape == (4,)]))
+    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float and uop.shape == (4,)]),
+            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float and uop.shape == (4,)]))
   @staticmethod
   def count_half4(uops: list[UOp]):
-    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype.scalar() == dtypes.half and uop.shape == (4,)]),
-            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype.scalar() == dtypes.half and uop.shape == (4,)]))
+    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half and uop.shape == (4,)]),
+            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half and uop.shape == (4,)]))
 
   def test_float4_basic(self):
     a = Tensor.empty(2, 8).realize()

test/opt/test_kernel_opts.py

@@ -239,6 +239,15 @@ class TestKernelOpts(unittest.TestCase):
     helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],])
     helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])
 
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  @unittest.expectedFailure
+  def test_padto_group_full_unroll_sum(self):
+    a = Tensor.ones(2, 28, 4096, dtype=dtypes.bfloat16).realize()
+    out = ((a * 0.5).float().square()).sum(axis=(0, 2))
+    opts_to_apply = [Opt(OptOps.GROUPTOP, 1, 256), Opt(OptOps.PADTO, 3, 32), Opt(OptOps.UNROLL, 2, 0), Opt(OptOps.UPCAST, 0, 7)]
+    helper_linearizer_opt(out, [opts_to_apply], check_default_opt=False)
+
   def test_padto_sum(self):
     N = 18
     # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension

test/unit/test_allreduce.py

@@ -2,6 +2,7 @@ import unittest
 from tinygrad import Tensor, UOp, dtypes
 from tinygrad.helpers import Context
 from tinygrad.uop.ops import Ops
+from test.helpers import KernelCountException
 
 class TestRingAllReduce(unittest.TestCase):
   def test_schedule_ring(self):
@@ -13,7 +14,7 @@ class TestRingAllReduce(unittest.TestCase):
       copies = [si for si in linear.src if si.src[0].op is Ops.COPY]
       pairs = [(c.src[1].buffer.device, c.src[2].buffer.device) for c in copies]
       # N*(N-1) scatter reduce, and N*(N-1) allgather
-      self.assertEqual(len(pairs), N*(N-1)*2)
+      if len(pairs) != N*(N-1)*2: raise KernelCountException(N*(N-1)*2, len(pairs))
       # copy topology forms a ring
       self.assertEqual(len(set(pairs)), N)
 
@@ -25,8 +26,8 @@ class TestRingAllReduce(unittest.TestCase):
       linear = t.sum(0).mul(2.0).contiguous().linear_with_vars()[0]
       copies = [si for si in linear.src if si.src[0].op is Ops.COPY]
       sinks = [si for si in linear.src if si.src[0].op is Ops.SINK]
-      self.assertEqual(len(copies), 24)
-      self.assertEqual(len(sinks), 26)
+      if len(copies) != 24: raise KernelCountException(24, len(copies))
+      if len(sinks) != 26: raise KernelCountException(26, len(sinks))
 
   @Context(RING=0, ALL2ALL=0)
   def test_schedule_naive(self):
@@ -39,8 +40,8 @@ class TestRingAllReduce(unittest.TestCase):
     sinks = [si for si in linear.src if si.src[0].op is Ops.SINK]
     pairs = [(c.src[1].buffer.device, c.src[2].buffer.device) for c in copies]
 
-    self.assertEqual(len(pairs), N*(N-1))
-    self.assertEqual(len(sinks), 2)
+    if len(pairs) != N*(N-1): raise KernelCountException(N*(N-1), len(pairs))
+    if len(sinks) != 2: raise KernelCountException(2, len(sinks))
     self.assertTrue(all(dst != src for dst, src in pairs))
 
   def test_symbolic_shape(self):
@@ -64,7 +65,7 @@ class TestAllreduceCast(unittest.TestCase):
     with Context(ALLREDUCE_CAST=allreduce_cast, RING=0, SCACHE=0):
       t = Tensor.empty(4, 4, dtype=dtype).shard(ds, axis=0)
       linear = t.sum(0).linear_with_vars()[0]
-      return {si.src[1].buffer.dtype.scalar() for si in linear.src if si.src[0].op is Ops.COPY}
+      return {si.src[1].buffer.dtype for si in linear.src if si.src[0].op is Ops.COPY}
 
   def test_allreduce_cast_bf16(self):
     # with ALLREDUCE_CAST, allreduce copies stay in bfloat16 instead of promoting to float32

test/unit/test_assign.py

@@ -540,6 +540,10 @@ class TestAssign(unittest.TestCase):
     c = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
     c[0:2].bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000], dtype=dtypes.uint32)).realize()
     np.testing.assert_allclose(c.numpy(), [4.0, 3.0, 3.0, 4.0])
+    # without .realize()
+    a = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
+    a.bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000, 0x40000000, 0x3f800000], dtype=dtypes.uint32))
+    np.testing.assert_allclose(a.numpy(), [4.0, 3.0, 2.0, 1.0])
 
   def test_assign_bitcast_different_size(self):
     # assign to a shape-changing bitcast view (only works on DISK currently)

test/unit/test_attention.py

@@ -1,6 +1,6 @@
 import unittest
 import numpy as np
-from tinygrad import Tensor, dtypes
+from tinygrad import Tensor, dtypes, nn
 from tinygrad.llm.model import (
   GatedDeltaNetBlock, SSMConfig, TransformerBlock, TransformerConfig,
   apply_rope as apply_rope_new, precompute_freqs_cis, pairwise_topk,
@@ -45,10 +45,10 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
     return Tensor.linspace(start, stop, int(np.prod(shape)), dtype=dtypes.float32).reshape(*shape)
 
   def _make_config(self, **kwargs):
-    return TransformerConfig(**({"num_blocks":1, "dim":4, "hidden_dim":8, "n_heads":1, "n_kv_heads":1,
-                                 "norm_eps":1e-5, "vocab_size":32, "head_dim":4, "rope_theta":10000.0,
-                                 "rope_dim":4, "v_head_dim":4, "max_context":4, "ssm_layers":(True,),
-                                 "ssm":SSMConfig(conv_kernel=2, state_size=2, group_count=1, time_step_rank=1, inner_size=2)} | kwargs))
+    return TransformerConfig(**({"num_blocks":1, "dim":32, "hidden_dim":64, "n_heads":1, "n_kv_heads":1,
+                                 "norm_eps":1e-5, "vocab_size":32, "head_dim":32, "rope_theta":10000.0,
+                                 "rope_dim":32, "v_head_dim":32, "max_context":4, "ssm_layers":(True,),
+                                 "ssm":SSMConfig(conv_kernel=2, state_size=32, group_count=1, time_step_rank=1, inner_size=32)} | kwargs))
 
   def _make_block(self, config:TransformerConfig) -> GatedDeltaNetBlock:
     block = GatedDeltaNetBlock(config, config.ssm)
@@ -79,6 +79,10 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
       recurrent_state = cache[:, conv_flat:].reshape(cache.shape[0], block.num_v_heads, block.head_v_dim, block.head_v_dim)
       return conv_state, recurrent_state
 
+  def _reset_state(self, block:GatedDeltaNetBlock):
+    Tensor.realize(block.conv_state.assign(block.conv_state.const_like(0)),
+                   block.recurrent_state.assign(block.recurrent_state.const_like(0)))
+
   def _linear_np(self, x:np.ndarray, weight:np.ndarray) -> np.ndarray:
     return x.astype(np.float32) @ weight.T.astype(np.float32)
 
@@ -86,7 +90,7 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
     x_float = x.astype(np.float32)
     return (x_float / np.sqrt((x_float * x_float).mean(axis=-1, keepdims=True) + eps)) * weight.astype(np.float32)
 
-  def _normalize_np(self, x:np.ndarray, eps:float=1e-12) -> np.ndarray:
+  def _normalize_np(self, x:np.ndarray, eps:float=1e-6) -> np.ndarray:
     return x / np.maximum(np.sqrt((x * x).sum(axis=-1, keepdims=True)), eps)
 
   def _softplus_np(self, x:np.ndarray) -> np.ndarray:
@@ -148,6 +152,12 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
     x = Tensor.linspace(-1.0, 1.0, 3 * config.dim, dtype=dtypes.float32).reshape(1, 3, config.dim)
 
     expected_outs, expected_conv, expected_recurrent = self._naive_attention(block, x)
+    out = self._run_attention(block, x, 0)
+    conv_state, recurrent_state = self._cache_views(block)
+    np.testing.assert_allclose(out, np.concatenate(expected_outs, axis=1), rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(conv_state, expected_conv[-1], rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(recurrent_state, expected_recurrent[-1], rtol=1e-3, atol=1e-3)
+    self._reset_state(block)
 
     for step in range(x.shape[1]):
       out = self._run_attention(block, x[:, step:step+1], step)
@@ -163,7 +173,7 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
     prompt = Tensor.linspace(0.75, -0.75, 2 * config.dim, dtype=dtypes.float32).reshape(1, 2, config.dim)
 
     for i in range(warmup.shape[1]): self._run_attention(block, warmup[:, i:i+1], i)
-    Tensor.realize(*block._state_reset_ops())
+    self._reset_state(block)
     expected_outs, expected_conv, expected_recurrent = self._naive_attention(block, prompt)
 
     for step in range(prompt.shape[1]):
@@ -177,18 +187,64 @@ class TestGatedDeltaNetBlock(unittest.TestCase):
                                  err_msg=f"GatedDeltaNet reset recurrent cache mismatch at step {step}")
 
   def test_kda_channel_decay(self):
-    config = self._make_config(n_heads=2, 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.]]])
-    # f_b(f_a(x)) = [1, 2, 3, 4]
+    config = self._make_config(dim=4, hidden_dim=8, n_heads=2, head_dim=4, rope_dim=4, v_head_dim=4,
+      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.], [2., 1., 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._init_state(x)
     initial_state = Tensor.arange(8, dtype=dtypes.float32).reshape(1, 2, 2, 2)
     block.recurrent_state.assign(initial_state).realize()
     block.ssm_a = Tensor([[-1.], [-1.]])
-    block._attention(x, 0).realize()
-    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)
+    block._attention(x, x.shape[1]).realize()
+    alpha = np.exp(-self._softplus_np(np.array([[1, 2, 3, 4], [2, 1, 3, 5]])).reshape(2, 2, 2)).prod(0)
+    np.testing.assert_allclose(block.recurrent_state.numpy(), initial_state.numpy() * alpha[..., None], rtol=1e-5, atol=1e-5)
+
+  def test_kda_prefill_matches_decode(self):
+    config = self._make_config(ssm=SSMConfig(conv_kernel=2, state_size=32, group_count=1, time_step_rank=1, inner_size=32, kda=True))
+    block = GatedDeltaNetBlock(config, config.ssm)
+    for p in nn.state.get_parameters(block):
+      p.replace(self._tensor_linspace(-0.05, 0.05, p.shape) if len(p.shape) > 1 else self._tensor_linspace(0.05, 0.1, p.shape))
+    x = self._tensor_linspace(-0.5, 0.5, (1, 3, config.dim))
+    prefill = self._run_attention(block, x, 0)
+    prefill_conv, prefill_recurrent = self._cache_views(block)
+    self._reset_state(block)
+    decode = np.concatenate([self._run_attention(block, x[:, i:i+1], i) for i in range(3)], axis=1)
+    decode_conv, decode_recurrent = self._cache_views(block)
+    np.testing.assert_allclose(prefill, decode, rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(prefill_conv, decode_conv, rtol=1e-3, atol=1e-3)
+    np.testing.assert_allclose(prefill_recurrent, decode_recurrent, rtol=1e-3, atol=1e-3)
+
+  def test_varied_chunk_sizes_match_decode(self):
+    for kda in (False, True):
+      ssm = SSMConfig(conv_kernel=2, state_size=32, group_count=1, time_step_rank=1, inner_size=32, kda=kda)
+      config = self._make_config(ssm=ssm)
+      if kda:
+        block = GatedDeltaNetBlock(config, config.ssm)
+        for p in nn.state.get_parameters(block):
+          p.replace(self._tensor_linspace(-0.05, 0.05, p.shape) if len(p.shape) > 1 else self._tensor_linspace(0.05, 0.1, p.shape))
+      else: block = self._make_block(config)
+      x = self._tensor_linspace(-0.5, 0.5, (1, 4, config.dim))
+      decode = np.concatenate([self._run_attention(block, x[:, i:i+1], i) for i in range(4)], axis=1)
+      decode_conv, decode_recurrent = self._cache_views(block)
+      for chunking in ([4], [2, 2], [1, 3], [3, 1], [2, 1, 1]):
+        self._reset_state(block)
+        outs, start = [], 0
+        for size in chunking:
+          outs.append(self._run_attention(block, x[:, start:start+size], start))
+          start += size
+        chunked_conv, chunked_recurrent = self._cache_views(block)
+        np.testing.assert_allclose(np.concatenate(outs, axis=1), decode, rtol=1e-3, atol=1e-3, err_msg=f"{kda=} {chunking=}")
+        np.testing.assert_allclose(chunked_conv, decode_conv, rtol=1e-3, atol=1e-3, err_msg=f"{kda=} {chunking=}")
+        np.testing.assert_allclose(chunked_recurrent, decode_recurrent, rtol=1e-3, atol=1e-3, err_msg=f"{kda=} {chunking=}")
+
+  def test_start_zero_resets_realized_state(self):
+    config, x = self._make_config(max_context=3), self._tensor_linspace(-1, 1, (1, 3, 32))
+    block = self._make_block(config)
+    self._run_attention(block, x, 0)
+    restarted = self._run_attention(block, x[:, :2], 0)
+    fresh = self._run_attention(self._make_block(config), x[:, :2], 0)
+    np.testing.assert_allclose(restarted, fresh, rtol=1e-3, atol=1e-3)
 
 class TestPairwiseTopk(unittest.TestCase):
   def test_basic_topk(self):

test/unit/test_dtype_weak.py

@@ -3,11 +3,12 @@ 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
+from tinygrad.uop.ops import UOp, Ops, GroupOp, 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
+from test.helpers import full_rewrite
 
 
 class TestWeakPromotion(unittest.TestCase):
@@ -76,6 +77,11 @@ class TestWeakPromotion(unittest.TestCase):
       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_div_sub_operand_kept_weak(self):
+    a = Tensor.empty(4, dtype=dtypes.float32)
+    for t in (a / 1, a - 0):
+      self.assertEqual(t.uop.src[1].dtype, dtypes.weakfloat)
+
   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):
@@ -88,6 +94,13 @@ class TestWeakPromotion(unittest.TestCase):
       out = Tensor(1.0, dtype=dtypes.float32, device="CPU") / denom
       self.assertAlmostEqual(out.item(), 1 / (70000 + 1e-5), places=10)
 
+  def test_stacked_weak_casts_convert_each_kind(self):
+    # each weak cast is a kind conversion: weakint truncates before weakfloat re-lifts (neither is only a marker)
+    x = Tensor([2.5, -3.7], dtype=dtypes.float32, device="CPU")
+    stacked = x.cast(dtypes.weakint).cast(dtypes.weakfloat)
+    self.assertIs(stacked.dtype, dtypes.weakfloat)
+    self.assertEqual(stacked.tolist(), [2.0, -3.0])
+
   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),
@@ -276,5 +289,18 @@ class TestSignedUint64Weakfloat(unittest.TestCase):
     self.assertAlmostEqual((i64 + u64).sin().item(), math.sin(2), places=5)  # Unary lowers before transcendental
 
 
+class TestNoRedundantWide(unittest.TestCase):
+  def wide_alu(self, t:Tensor) -> int:
+    return sum(sum(1 for u in full_rewrite(call.src[0]).toposort() if u.op in GroupOp.ALU and u.dtype in {dtypes.long, dtypes.ulong})
+               for call in t.schedule_linear().src if call.src[0].op is Ops.SINK)
+
+  def test_unbounded_long_stays_long(self):
+    self.assertGreater(self.wide_alu(Tensor.empty(16, dtype=dtypes.long)*3 + 1), 0)
+
+  def test_fancy_index_has_no_wide_alu(self):
+    j, o = Tensor([0, 1, 2]).reshape(3, 1), Tensor([0, 1]).reshape(1, 2)
+    self.assertEqual(self.wide_alu(Tensor.empty(8, 9, 10, 11, 12)[1, j, 2, o, 2]), 0)
+
+
 if __name__ == "__main__":
   unittest.main()

test/unit/test_function.py

@@ -4,7 +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
+from test.helpers import assert_kernel_count, KernelCountException
 
 class TestFunction(unittest.TestCase):
   def test_simple(self):
@@ -516,7 +516,7 @@ class TestFunctionTuple(unittest.TestCase):
     Tensor.realize(a)
     c = f(a)
 
-    self.assertEqual(count_kernels(c), 1)
+    if count_kernels(c) != 1: raise KernelCountException(1, count_kernels(c))
 
     c.sum().backward()
     Tensor.realize(a.grad)

test/unit/test_gradient.py

@@ -51,6 +51,10 @@ class TestTensorGradient(unittest.TestCase):
     with self.assertRaises(RuntimeError): x.sum().gradient(x)
     with self.assertRaises(RuntimeError): x.float().sum().gradient(x)
 
+  def test_const_target_raise(self):
+    t = Tensor(2.0)
+    with self.assertRaises(RuntimeError): (t * 2.0).gradient(t)
+
   def test_copy_to_device_gradient(self):
     t = Tensor([1.0, 2, 3]).realize()
     t.to("CPU:1").square().sum().backward()
@@ -100,7 +104,7 @@ class TestTensorGradient(unittest.TestCase):
 
   def test_implicit_broadcast_where_gradient(self):
     # WHERE with a bare ()-shape branch: the scalar's gradient counts the positions where it is selected
-    cond, x, w = Tensor([True, False, True]), Tensor([1.0, 2.0, 3.0]), Tensor(4.0)
+    cond, x, w = Tensor([True, False, True]), Tensor([1.0, 2.0, 3.0]), Tensor(4.0, dtype=dtypes.float32)
     dw = Tensor(cond.uop.alu(Ops.WHERE, x.uop, w.uop)).sum().gradient(w)[0]
     self.assertEqual(dw.shape, ())
     self.assertEqual(dw.item(), 1.0)
@@ -109,7 +113,7 @@ class TestTensorGradient(unittest.TestCase):
 
   def test_implicit_broadcast_alu_gradient(self):
     # MUL with a bare ()-shape src, no EXPAND in the graph
-    x, w = Tensor([1.0, 2.0, 3.0]), Tensor(2.0)
+    x, w = Tensor([1.0, 2.0, 3.0]), Tensor(2.0, dtype=dtypes.float32)
     m = x.uop.alu(Ops.MUL, w.uop)
     self.assertIs(m.src[1], w.uop)
     dw = Tensor(m).sum().gradient(w)[0]
@@ -118,7 +122,7 @@ class TestTensorGradient(unittest.TestCase):
 
   def test_implicit_broadcast_intermediate_accumulation(self):
     # s is used directly and through an implicit broadcast edge, each edge's gradient reduces to s's shape before they sum
-    x, p = Tensor([1.0, 2.0, 3.0]), Tensor(0.5)
+    x, p = Tensor([1.0, 2.0, 3.0]), Tensor(0.5, dtype=dtypes.float32)
     s = p.sin()
     z = Tensor(x.uop.alu(Ops.MUL, s.uop)).sum() + s
     dp = z.gradient(p)[0]

test/unit/test_hcq_graph.py

@@ -25,10 +25,10 @@ class TestHCQUnit(unittest.TestCase):
     cpu_call = UOp(Ops.PROGRAM, src=(UOp.sink(),)).call(UOp.new_buffer("CPU", 1, dtypes.float))
     gpu_devs = [d0]
 
-    # local MMIO: GPU works alone and with CPU in batch (cpu_support=True)
+    # CPU uses HCQ2 and is no longer batched into legacy HCQ graphs.
     assert HCQGraph.supports_uop(gpu_devs, gpu_call) is True
-    assert HCQGraph.supports_uop(gpu_devs, cpu_call) is True
-    assert HCQGraph.supports_uop(gpu_devs + [cpu_dev], gpu_call) is True
+    assert HCQGraph.supports_uop(gpu_devs, cpu_call) is False
+    assert HCQGraph.supports_uop(gpu_devs + [cpu_dev], gpu_call) is False
 
     # USB MMIO: GPU-only still works, but CPU batching must be rejected (cpu_support=False)
     orig_view = d0.timeline_signal.base_buf.view

test/unit/test_llm_moe.py

@@ -2,7 +2,7 @@ import unittest
 import numpy as np
 from dataclasses import replace
 from tinygrad import Tensor
-from tinygrad.llm.model import TransformerBlock, TransformerConfig
+from tinygrad.llm.model import ExpertGating, TransformerBlock, TransformerConfig
 
 def _moe_config(dim=8, hidden=16, n_heads=2, num_experts=4, num_experts_per_tok=2):
   return TransformerConfig(
@@ -96,5 +96,32 @@ class TestMoEFeedForward(unittest.TestCase):
     expected = moe_expected + shared_expected
     np.testing.assert_allclose(out.numpy(), expected, rtol=1e-2)
 
+  def test_moe_feed_forward_gating_funcs(self):
+    dim, hidden, n_heads = 8, 16, 2
+    num_experts, k = 4, 2
+    logits = np.array([4.0, 3.0, 0.0, -1.0], dtype=np.float32)
+    def softmax(x):
+      probs = np.exp(x - x.max())
+      return probs / probs.sum()
+    for gating_func in ExpertGating:
+      for norm_topk_prob in (False, True):
+        block = TransformerBlock(replace(_moe_config(dim, hidden, n_heads, num_experts, k),
+                                         expert_gating_func=gating_func, norm_topk_prob=norm_topk_prob))
+        block.ffn_gate_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) for _ in range(num_experts)])
+        block.ffn_up_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) * (i + 1) for i 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((logits / dim)[None, :].repeat(dim, 0).T)
+        out = block._feed_forward(Tensor.ones(1, 1, dim)).numpy()[0, 0, 0]
+
+        if gating_func == ExpertGating.SOFTMAX: selection_scores = softmax(logits)
+        elif gating_func == ExpertGating.SIGMOID: selection_scores = 1 / (1 + np.exp(-logits))
+        elif gating_func == ExpertGating.SOFTMAX_WEIGHT: selection_scores = logits
+        else: selection_scores = np.sqrt(np.logaddexp(0, logits))
+        sel = np.argsort(selection_scores)[-k:]
+        weights = softmax(logits[sel]) if gating_func == ExpertGating.SOFTMAX_WEIGHT else selection_scores[sel]
+        if norm_topk_prob: weights /= weights.sum()
+        expected = (weights * (sel + 1)).sum() / (1 + np.exp(-1))
+        np.testing.assert_allclose(out, expected, rtol=1e-3)
+
 if __name__ == '__main__':
   unittest.main()

test/unit/test_llm_server.py

@@ -13,12 +13,18 @@ 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):
+    def generate(tokens, **kwargs):
       calls.append(tokens)
       yield from (1, 2)
     with patch.object(model, "generate", generate): model.warmup()
     self.assertEqual(calls, [[0], [0]])
 
+  def test_warmup_then_generate_with_default_chunk(self):
+    # warmup must not capture JIT graphs that generate()'s default chunk_size then rejects
+    model = Transformer(TEST_CONFIG)
+    model.warmup()
+    self.assertIsInstance(next(model.generate([5, 6, 7, 8])), int)
+
   def test_first_recurrent_generate_before_state_init(self):
     model = Transformer(TEST_CONFIG)
     model.has_recurrent_block = True
@@ -38,6 +44,15 @@ class TestTransformerGenerate(unittest.TestCase):
       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_divergent_prompt_restarts(self):
+    model, calls = Transformer(TEST_CONFIG), []
+    model.has_recurrent_block, model._cached_tokens = True, [1, 2, 9]
+    def mock_call(self, tokens, start_pos, temperature):
+      calls.append(start_pos)
+      return Tensor([[42]])
+    with patch.object(Transformer, '__call__', mock_call): next(model.generate([1, 2, 10, 11]))
+    self.assertEqual(calls[0], V_START_POS.bind(0))
+
   def test_template_starts_reasoning(self):
     router = StreamRouter(reasoning=True)
     self.assertEqual(list(router.route("reasoning</think>answer")),
@@ -178,6 +193,12 @@ class TestTransformerGenerate(unittest.TestCase):
     # with temperature=2.0, we should see at least 2 distinct outputs across 5 runs
     self.assertGreater(len(runs), 1, "high temperature should produce varied outputs")
 
+  def test_recurrent_temperature_high_produces_variety(self):
+    model = Transformer(TEST_CONFIG)
+    model.has_recurrent_block = True
+    outputs = {model.forward(Tensor([[1]]), 0, Tensor([2.0])).item() for _ in range(5)}
+    self.assertGreater(len(outputs), 1)
+
   def test_temperature_passed_to_forward(self):
     """Temperature from generate should be passed through to __call__."""
     model = Transformer(TEST_CONFIG)

test/unit/test_metal_graph.py

@@ -1,5 +1,4 @@
 import unittest
-from unittest.mock import MagicMock
 from tinygrad import Device
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.dtype import dtypes
@@ -11,36 +10,27 @@ class TestMetalGraph(unittest.TestCase):
     self.MetalGraph = MetalGraph
     self.dev = Device[Device.DEFAULT]
 
-  def metal_buf(self, offset):
-    buf = MagicMock()
-    if offset > 0:
-      buf.op = Ops.SLICE
-      src = MagicMock()
-      src.dtype = dtypes.uint8
-      buf.src = (src, UOp.const(offset))
-      buf.dtype = dtypes.uint8
-    else:
-      buf.op = Ops.BUFFER
-    buf.device = Device.DEFAULT
-    return buf
+  def metal_buf(self, offset, bitcast=False):
+    size = 4 if bitcast else 1
+    buf = UOp.new_buffer(Device.DEFAULT, offset+size, dtypes.uint8)
+    if offset: buf = buf[offset:offset+size]
+    return buf.bitcast(dtypes.float32) if bitcast else buf
 
-  def call(self, *bufs):
-    c = MagicMock()
-    c.src = (MagicMock(op=Ops.PROGRAM),) + tuple(bufs)
-    return c
+  def supports_uop(self, *bufs):
+    return self.MetalGraph.supports_uop([self.dev], UOp(Ops.PROGRAM, src=(UOp.sink(),)).call(*bufs))
 
   def test_supports_uop_normal_offset(self):
-    assert self.MetalGraph.supports_uop([self.dev], self.call(self.metal_buf(0), self.metal_buf(100), self.metal_buf(0xFFFFFFFF))) is True
+    assert self.supports_uop(self.metal_buf(0), self.metal_buf(100), self.metal_buf(0xFFFFFFFF)) is True
 
   def test_supports_uop_overflow_offset(self):
-    assert self.MetalGraph.supports_uop([self.dev], self.call(self.metal_buf(0), self.metal_buf(0x100000000))) is False
+    assert self.supports_uop(self.metal_buf(0), self.metal_buf(0x100000000)) is False
 
-  def test_supports_uop_nonmetal_buf(self):
-    # non-SLICE ops should not be checked for offset
-    buf = MagicMock()
-    buf.op = Ops.BUFFER
-    buf.device = Device.DEFAULT
-    self.MetalGraph.supports_uop([self.dev], self.call(buf))
+  def test_supports_uop_non_view_buf(self):
+    assert self.supports_uop(self.metal_buf(0)) is True
+
+  def test_supports_uop_bitcast(self):
+    assert self.supports_uop(self.metal_buf(0xFFFFFFFF, bitcast=True)) is True
+    assert self.supports_uop(self.metal_buf(0x100000000, bitcast=True)) is False
 
 if __name__ == "__main__":
   unittest.main()

test/unit/test_multitensor.py

@@ -390,6 +390,12 @@ class TestMultiTensor(unittest.TestCase):
     self.assertEqual(out.shape, (rows, 8))
     np.testing.assert_equal(out[:3].to(Device.DEFAULT).numpy(), np.ones((3, 8)))
 
+  def test_symbolic_broadcast_consumed(self):
+    rows = Variable("rows", 1, 4).bind(3)
+    out = (Tensor.ones(rows).to(devices_2) + 1).realize()
+    self.assertEqual(out.shape, (rows,))
+    np.testing.assert_equal(out[:3].to(Device.DEFAULT).numpy(), np.full(3, 2))
+
   def test_multitensor_jit_in_list(self):
     # test MULTI tensor inside a list container - exercises the container unpacking + MULTI unpacking
     @TinyJit

test/unit/test_schedule_cache.py

@@ -1,11 +1,16 @@
 import unittest
 import functools
-from tinygrad import Tensor, Variable, UOp
+from tinygrad import Tensor, Variable, UOp, function
 from tinygrad.uop.ops import KernelInfo
 from tinygrad.schedule import schedule_cache
 
-def custom_set0_kernel(A:UOp, num:int) -> UOp:
-  return A[0].set(num).sink(arg=KernelInfo(f"custom_set0_{num}"))
+def custom_add_kernel(A:UOp, B:UOp, num:int=0) -> UOp:
+  return A[0].set(B[0] + num).sink(arg=KernelInfo(f"custom_add_{num}"))
+
+def custom_add_backward(grad_output:UOp, _) -> tuple[None, UOp]:
+  grad = Tensor.invalids(*grad_output.shape, dtype=grad_output.dtype, device=grad_output.device)
+  grad = Tensor.custom_kernel(grad, Tensor(grad_output, device=grad_output.device), fxn=functools.partial(custom_add_kernel, num=0))[0]
+  return None, grad.uop
 
 class TestScheduleCache(unittest.TestCase):
   def test_bound_variable_reuses_cache(self):
@@ -25,27 +30,27 @@ class TestScheduleCache(unittest.TestCase):
 
   def test_custom_kernel(self):
     for i in range(4):
-      a = Tensor.empty(1)
-      a = Tensor.custom_kernel(a, fxn=functools.partial(custom_set0_kernel, num=i))[0]
+      a, b = Tensor.empty(1), Tensor.ones(1)
+      a = Tensor.custom_kernel(a, b, fxn=functools.partial(custom_add_kernel, num=i))[0]
       a.realize()
-      self.assertEqual(a.item(), i)
+      self.assertEqual(a.item(), i+1)
 
   def test_same_custom_function_reuses_cache(self):
     schedule_cache.clear()
-    fxn = functools.partial(custom_set0_kernel, num=10)
+    fxn = functools.partial(custom_add_kernel, num=10)
 
     # first run
-    a = Tensor.empty(1)
-    a = Tensor.custom_kernel(a, fxn=fxn)[0]
+    a, x = Tensor.empty(1), Tensor.ones(1)
+    a = Tensor.custom_kernel(a, x, fxn=fxn)[0]
     a.realize()
-    self.assertEqual(a.item(), 10)
+    self.assertEqual(a.item(), 11)
     cache_size_after_first = len(schedule_cache)
 
     # second run with same function should reuse cache
-    b = Tensor.empty(1)
-    b = Tensor.custom_kernel(b, fxn=fxn)[0]
+    b, x = Tensor.empty(1), Tensor.ones(1)
+    b = Tensor.custom_kernel(b, x, fxn=fxn)[0]
     b.realize()
-    self.assertEqual(b.item(), 10)
+    self.assertEqual(b.item(), 11)
     self.assertEqual(len(schedule_cache), cache_size_after_first)
 
   def test_simple(self):
@@ -65,5 +70,29 @@ class TestScheduleCache(unittest.TestCase):
       print(num)
     self.assertEqual(len(schedule_cache), start_len_schedule_cache)
 
+  def test_simple_precompile(self):
+    @function(precompile=True, precompile_backward=True)
+    def f(x:Tensor) -> Tensor:
+      out = Tensor.invalids(*x.shape, dtype=x.dtype, device=x.device)
+      out = Tensor.custom_kernel(out, x, fxn=functools.partial(custom_add_kernel, num=10), grad_fxn=custom_add_backward)[0]
+      return out + x
+
+    # warmup
+    x = Tensor.ones(1).realize()
+    out = f(x)
+    out.backward(x)
+    self.assertEqual(out.item(), 12)
+    self.assertEqual(x.grad.item(), 2)
+
+    # use the cache next time function is called
+    start_len_schedule_cache = len(schedule_cache)
+    for _ in range(3):
+      x = Tensor.ones(1).realize()
+      out = f(x)
+      out.backward(x)
+      self.assertEqual(out.item(), 12)
+      self.assertEqual(x.grad.item(), 2)
+    self.assertEqual(len(schedule_cache), start_len_schedule_cache)
+
 if __name__ == "__main__":
   unittest.main()

tinygrad/codegen/__init__.py

@@ -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_fold_cast_const, pm_move_where_on_load, pm_clean_up_group_sink, pm_remove_invalid
+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.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
@@ -153,8 +153,8 @@ devectorizer2 = mop_cleanup+pm_mops+PatternMatcher([
   # unpack WMMA
   (UPat(Ops.WMMA, name="u"), do_stack_wmma),
   # stacked INDEX is many INDEX
-  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.STACK, name="s"))),
-   lambda b,s: UOp.stack(*[b.index(u) for u in s.src])),
+  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.STACK, name="s")), name="x"),
+   lambda b,s,x: UOp.stack(*[x.replace(src=(b,u)) for u in s.src])),
   # INDEX into RESHAPE moves the RESHAPE
   (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.RESHAPE, name="s"))),
    lambda b,s: b.index(s.src[0]).reshape(s.shape)),
@@ -281,6 +281,10 @@ pm_implicit_barriers = PatternMatcher([
   (UPat(Ops.END, name="end"), add_war_barrier),
 ])
 
+pm_casted_consts = PatternMatcher([
+  (UPat(Ops.CONST, dtypes.all, name="c"), lambda c: UOp.cconst(c.val, c.dtype)),
+])
+
 def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
   if VIZ: graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
   if DEBUG >= 5: print(pyrender(ast))
@@ -301,7 +305,7 @@ 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_fold_cast_const+pm_flatten_range, name="initial symbolic")
+    sink = graph_rewrite(sink, sym+pm_flatten_range, name="initial symbolic")
 
     # optimize (schedule) the AST
     sink = graph_rewrite(sink, pm_flatten_range+pm_simplify_ranges, ctx={}, name="simplify ranges")
@@ -346,7 +350,7 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
 
   # lower index dtype
   # NOTE: we need indexing_simplify to remove the cast to long using the Invalid
-  sink = graph_rewrite(sink, symbolic_simple+pm_fold_cast_const+pm_lower_index_dtype+indexing_simplify, ctx={}, name="lower all index dtypes")
+  sink = graph_rewrite(sink, symbolic_simple+pm_lower_index_dtype+indexing_simplify, ctx={}, name="lower all index dtypes")
 
   # final symbolic before decomp
   sink = graph_rewrite(sink, symbolic, name="final symbolic")
@@ -357,7 +361,7 @@ 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+pm_fold_cast_const+get_simplifying_rewrite_patterns(supported_ops)
+  pm_decomp = symbolic_simple+get_simplifying_rewrite_patterns(supported_ops)
   sink = graph_rewrite(sink, pm_decomp, name="early decompositions")
 
   # late decomps + move gates from unrenderable INVALID where
@@ -383,6 +387,10 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
   num_params = len([x for x in sink.toposort() if x.op is Ops.PARAM and x.arg.slot != -1])
   sink = graph_rewrite(sink, pm_number_params, ctx=[num_params], name="number params with -1", walk=True)
 
+  # spell every literal as a casted const CAST(dt, CONST(value))
+  # TODO: remove once consts are always weak
+  sink = graph_rewrite(sink, pm_casted_consts, name="casted consts", walk=True)
+
   if VIZ: graph_rewrite(sink, PatternMatcher([]), name="View Output AST")
   if SPEC: type_verify(sink, spec_program)
 

tinygrad/codegen/decomp/dtype.py

@@ -33,7 +33,8 @@ def l2i(op: Ops, dt: DType, *uops:UOp):
       return (lo:=uops[0].cast(l2i_dt[dt])), (uops[0] / 2**32).cast(l2i_dt[dt]) - ((uops[0] < 0) & lo.ne(0))
     case Ops.CAST if dt in dtypes.floats:
       small = (a1.eq(0) & (a0 >= 0)) | (a1.eq(-1) & (a0 < 0))
-      return small.where(a0.cast(dt), ((a1.cast(dtypes.float32) * (2**32)) + a0.bitcast(dtypes.uint).cast(dtypes.float32)).cast(dt))
+      cdt = dt if dt == dtypes.float64 else dtypes.float32
+      return small.where(a0.cast(dt), ((a1.cast(cdt) * (2**32)) + a0.bitcast(dtypes.uint).cast(cdt)).cast(dt))
     case Ops.CAST: return a0.bitcast(dtypes.uint).cast(dt)
     case Ops.BITCAST: return a0.bitcast(dt), a1.bitcast(dt)
     case Ops.SHL:

tinygrad/codegen/gpudims.py

@@ -57,7 +57,7 @@ def add_gpudims(ctx:Renderer, s:UOp):
 
   # get the idxs
   ki: KernelInfo = s.arg
-  if ctx.has_threads: idxs = [UOp.variable("core_id", 0, int(global_shape[0])-1, dtypes.int).cast(dtypes.weakint)]
+  if ctx.has_threads: idxs = [UOp.variable("core_id", 0, int(global_shape[0])-1, dtypes.int, param=True).cast(dtypes.weakint)]
   elif ki.dont_use_locals:
     assert not local_dims, "can't use locals if there's no local dims"
     idxs = get_grouped_dims("idx", global_shape, ctx.global_max, reverse=True)
@@ -89,7 +89,7 @@ def add_gpudims(ctx:Renderer, s:UOp):
 
 pm_device_to_var = PatternMatcher([
   # the DEVICE axis is not a program axis, it's bound per device at launch. lower it to the _device_num variable (like SPECIAL for devices)
-  (UPat(Ops.RANGE, name="r"), lambda r: UOp.variable("_device_num", 0, r.vmax, dtype=r.dtype) if r.arg[-1] is AxisType.DEVICE else None),
+  (UPat(Ops.RANGE, name="r"), lambda r: UOp.variable("_device_num", 0, r.vmax, dtype=r.dtype, param=True) if r.arg[-1] is AxisType.DEVICE else None),
   # ENDs that closed a DEVICE range no longer close it
   (UPat(Ops.END, name="e"), lambda e: e.replace(src=(e.src[0],)+tuple(s for s in e.src[1:] if s.op is not Ops.PARAM))
    if any(s.op is Ops.PARAM and s.arg.name == '_device_num' for s in e.src[1:]) else None),

tinygrad/codegen/late/coalesce.py

@@ -26,7 +26,7 @@ def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
     # check if idx is out of bound when X is on the wrong side of the bound: X in [c+1, vmax] or [vmin, c-1]
     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)
+      fake = UOp.variable(f"fake{i}", lo, hi, X.dtype, param=True)
       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))
@@ -149,7 +149,7 @@ def memory_coalescing(sink:UOp, ctx:Renderer) -> UOp:
         grp = full_grp[:length]
         # NOTE: we apply the valid again after we determine the length
         offset = offset.valid(valid) if valid is not None else offset
-        idx = UOp(Ops.SHRINK, src=(buf, offset, UOp.const(len(grp)))) if len(grp) > 1 else buf.index(offset)
+        idx = UOp(Ops.SHRINK, src=(buf, offset, UOp.const(len(grp)))) if len(grp) > 1 else buf.index(offset, dtype=offsets[grp[0]][0].src[0].dtype)
         if op == Ops.STORE:
           datas = []
           for i,g in enumerate(grp):

tinygrad/codegen/late/gater.py

@@ -3,7 +3,7 @@ from tinygrad.uop.ops import PatternMatcher, UPat, Ops
 from tinygrad.dtype import Invalid, dtypes
 
 def move_where_load(gate, l, a, w):
-  return l.replace(src=(l.src[0], l.vconst_like(0) if a.is_invalid else
+  return l.replace(src=(l.src[0], l.vconst_like(0) if a.is_invalid else l.const_like(a.val) if a.op is Ops.CONST else
     a.src[0] if a.op is Ops.CAST and a.src[0].dtype == l.dtype else a.cast(l.dtype), l.src[2])).cast(w.dtype)
 
 pm_move_gates_from_index = PatternMatcher([

tinygrad/codegen/late/regalloc.py

@@ -4,7 +4,7 @@ from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat
 from tinygrad.renderer.isa import ISARenderer, Register, greg
 from tinygrad.dtype import dtypes
 
-PSEUDO_OPS = {Ops.CONST, Ops.NOOP, Ops.AFTER, Ops.BARRIER, Ops.GROUP, Ops.STACK}
+PSEUDO_OPS = {Ops.CONST, Ops.CAST, Ops.NOOP, Ops.AFTER, Ops.BARRIER, Ops.GROUP, Ops.STACK}
 
 class LinearScanRegallocContext:
   # returns the uop that defines the virtual register
@@ -52,7 +52,7 @@ class LinearScanRegallocContext:
         # the value of a BUFFER is its 64bit address, XMM registers need 16 bytes
         sz = 16 if v.cons[0].size == 16 else (8 if self.vdef(v).op is Ops.BUFFER else self.vdef(v).dtype.itemsize)
         offset = self.stack_size + (sz - self.stack_size % sz) % sz
-        self.spills[v] = UOp.const(offset, dtypes.int32)
+        self.spills[v] = UOp.cconst(offset, dtypes.int32)
         self.stack_size = offset + sz
       r = alloc(cons if cons is not None else v.cons, i)
       self.insert_before.setdefault(i, []).append((v, r))
@@ -84,7 +84,7 @@ class LinearScanRegallocContext:
 
       # allocate stack array
       if u.op is Ops.BUFFER:
-        self.locals[u] = UOp.const(self.stack_size, dtypes.int32)
+        self.locals[u] = UOp.cconst(self.stack_size, dtypes.int32)
         self.stack_size += u.max_numel() * u.dtype.itemsize
 
       # loop prologue, avoid loading inside the loop
@@ -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.const(ctx.stack_size, sp.dtype)
+    offset = UOp.cconst(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))]
 

tinygrad/codegen/opt/tc.py

@@ -1,6 +1,7 @@
 import math, functools
 from dataclasses import dataclass
 from tinygrad.dtype import DType, dtypes
+from tinygrad.uop.ops import PatternMatcher, UOp, UPat, Ops
 
 @dataclass(frozen=True)
 class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x N)
@@ -135,6 +136,42 @@ amd_cdna4 = amd_cdna_1616128 + amd_cdna_161632 + amd_cdna_161616
 
 def get_amd(arch): return {"gfx942": amd_cdna3, "gfx950": amd_cdna4, "gfx1200": amd_rdna4, "gfx1201": amd_rdna4}.get(arch, amd_rdna3)
 
+pm_validate_wmma_rdna3 = PatternMatcher([
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.int32), lambda x: x.replace(
+    src=(x.src[0].bitcast(dtypes.uint32), x.src[1].bitcast(dtypes.uint32), x.src[2]))
+    if x.src[0].dtype == dtypes.int8 and x.src[0].max_numel() == 16 else None),
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.half), lambda x: UOp(Ops.STACK, src=tuple(x.replace(
+      src=(x.src[0], x.src[1], UOp(Ops.STACK, src=tuple(x.src[2].index(UOp.const(j//2, dtypes.int16))
+      if j%2 == 0 else UOp.const(0.0, x.src[2].dtype)
+      for j in range(x.max_numel()*2)))),
+      arg=(*x.arg[:4], None)).index(UOp.const(i*2, dtypes.int16))
+      for i in range(x.max_numel()))) if x.max_numel() == 8 else None),
+  (UPat(Ops.WMMA, name="x"), lambda x: x.replace(
+    src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
+    if x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 16 else None),
+])
+
+pm_validate_wmma_rdna4 = PatternMatcher([
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.bfloat16), lambda x: x.replace(
+    dtype=dtypes.uint16,
+    src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2].bitcast(dtypes.uint16)))
+      .bitcast(dtypes.bfloat16) if x.max_numel() == 8 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 8 else None),
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
+    lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
+    if x.max_numel() == 8 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 8 else None)
+])
+
+pm_validate_wmma_cdna = PatternMatcher([
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
+    lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint32), x.src[1].bitcast(dtypes.uint32), x.src[2]))
+    if x.arg[0][2] == 128 and x.src[0].dtype.itemsize <= 8 else None),
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
+    lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
+    if x.max_numel() == 4 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 4 else None),
+  (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
+    lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint64), x.src[1].bitcast(dtypes.uint64), x.src[2]))
+    if x.max_numel() == 4 and x.src[0].dtype in dtypes.fp8_ocp and x.src[0].max_numel() == 8 else None),
+])
 # ***** Apple Metal *****
 
 metal = [TensorCore(dims=(8,8,8), threads=32, elements_per_thread=(2,2,2), dtype_in=di, dtype_out=do,

tinygrad/codegen/simplify.py

@@ -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, pm_fold_cast_const, invalid_gate
+from tinygrad.uop.symbolic import symbolic, 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_fold_cast_const+pm_flatten_range, ctx={r0:new_range//s1, r1:new_range%s1},
+        nidx = graph_rewrite(u, _substitute+symbolic+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
@@ -137,7 +137,7 @@ def reduce_collapse(red:UOp, u:UOp, pm:PatternMatcher=pm_reduce_collapse) -> UOp
     for u in included:
       for s in u.src:
         if s in included or s in replaces or s.op in {Ops.CONST, Ops.PARAM, Ops.BUFFER}: continue
-        replaces[s] = UOp.variable(f'in{len(replaces)}', s.vmin, s.vmax, s.dtype)
+        replaces[s] = UOp.variable(f'in{len(replaces)}', s.vmin, s.vmax, s.dtype, param=True)
     collapse_fxn = u.substitute(replaces).reduce(r, arg=Ops.ADD)
     sink = graph_rewrite(collapse_fxn, pm, name="reduce_collapse")
     if not no_range(sink): return None

tinygrad/device.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 from dataclasses import dataclass, replace
 from collections import defaultdict
-from typing import Any, Generic, TypeVar, Iterator, Generator, Self, TYPE_CHECKING
-import importlib, inspect, functools, pathlib, os, contextlib, re, atexit, pickle, decimal
+from typing import Any, Callable, Generic, TypeVar, Iterator, Generator, Self, TYPE_CHECKING
+import importlib, inspect, functools, pathlib, os, contextlib, re, atexit, pickle, decimal, subprocess, struct
 from tinygrad.helpers import LRU, getenv, diskcache_get, diskcache_put, DEBUG, GlobalCounters, PROFILE, temp, colored
 from tinygrad.helpers import Context, CCACHE, ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, cpu_events, ProfileEvent, ProfilePointEvent, suppress_finalizing
 from tinygrad.helpers import select_by_name, select_first_inited, DEV, TracingKey, size_to_str, pluralize, Target, unwrap, round_up
@@ -103,7 +103,7 @@ class Buffer:
   def __init__(self, device:str, size:int, dtype:DType, opaque:Any=None, options:BufferSpec|None=None,
                initial_value:bytes|pickle.PickleBuffer|None=None, uop_refcount=0, base:Buffer|None=None, offset:int=0, preallocate=False):
     assert isinstance(dtype, DType)
-    self.device, self.size, self.dtype, self.options, self.offset, self.allocated_views = device, size, dtype, options, offset, 0
+    self.device, self.size, self.dtype, self.options, self.offset, self.allocated_views = Device.canonicalize(device), size, dtype, options, offset, 0
     self._bufs: dict[str, Any] = {}
     if base is None:
       assert offset == 0, "base buffers can't have offset"
@@ -116,7 +116,7 @@ class Buffer:
         if isinstance(initial_value, pickle.PickleBuffer): initial_value.release()
     else:
       assert base._base is None, "base can't have a base"
-      assert device == base.device, "base must have the same device"
+      assert self.device == base.device, "base must have the same device"
       self._base = base
     if preallocate: self.allocate()
   @property
@@ -133,7 +133,7 @@ class Buffer:
   # check if the underlying buffer is allocated, possibly from the base object
   def is_allocated(self) -> bool: return self.base.is_allocated() if self._base is not None else self.device in self._bufs
   def get_buf(self, device: str) -> Any:
-    if device not in self._bufs:
+    if device not in self._bufs and (device:=Device.canonicalize(device)) not in self._bufs:
       allocator = Device[device].allocator
       if device == self.device: self.ensure_allocated()
       elif self._base is not None: self._bufs[device] = allocator._offset(self._base.get_buf(device), self.nbytes, self.offset)
@@ -310,6 +310,14 @@ class Compiler:
       if self.cachekey is not None: diskcache_put(self.cachekey, src, lib)
     return lib
   def disassemble(self, lib:bytes): pass
+  def server(self, cmd:str, arch:str, *args) -> subprocess.Popen:
+    argv = f"{cmd} {pathlib.Path(__file__).parent}/runtime/support/compileserver.py {type(self).__module__}:{type(self).__name__} {arch}"
+    return subprocess.Popen(argv.split() + [str(a) for a in args], stdout=subprocess.PIPE, stdin=subprocess.PIPE, bufsize=0)
+  def compile_server(self, src:str, proc:subprocess.Popen) -> bytes:
+    unwrap(proc.stdin).write(struct.pack("I", len(src.encode())) + src.encode())
+    if (lib:=unwrap(proc.stdout).read(struct.unpack("I", unwrap(proc.stdout).read(4))[0])): return lib
+    raise CompileError("Compilation Error")
+
 
 @dataclass
 class TinyELF:
@@ -331,17 +339,18 @@ class Program(Generic[DeviceType]):
                wait=False) -> float|None: pass
 
 class Compiled:
+  ifaces:list[Callable] = []
   profile_events:list[ProfileEvent] = [ProfileDeviceEvent("CPU")] # NOTE: CPU is the default device.
 
+  has_copy_queue:bool = True
+
   pm_lower:Any = None
   pm_bufferize:Any = None
 
-  has_copy_queue:bool = True
-
   def __init__(self, device:str, allocator:Allocator, renderers:list[type[Renderer]], runtime:type[Program[Self]]|None, graph=None, arch=None):
     from tinygrad.renderer import Renderer
     self.device, self.allocator, self.runtime_t, self.graph, self.renderers = device, allocator, runtime, graph, renderers or [Renderer]
-    self.arch = arch
+    self.device_id, self.arch = (int(idx) if ":" in device and (idx:=device.split(":")[1]).isdigit() else 0), arch
     self.cached_renderer:dict[Any, Renderer] = {}
 
   @property
@@ -364,11 +373,21 @@ class Compiled:
     return select_first_inited(select_by_name(self.renderers, self._renderer_name, t.renderer, f"{self.device} has no renderer {t.renderer!r}"),
                                f"No renderer for {self.device} is available", self.cached_renderer, t)
 
+  def _select_iface(self, device:str):
+    self.device_id = int(device.split(":")[1]) if ":" in device else 0
+    assert (v:=getenv(k:=f'{type(self).__name__[:-6].upper()}_IFACE', "")) == "",  \
+      f"{k}={v} is deprecated, use DEV={replace(DEV.target(type(self).__name__[:-6]), interface=v)} instead"
+    t = DEV.target(dev:=type(self).__name__[:-6])
+    filtered = select_by_name(self.ifaces, lambda i: i.__name__[:-5], t.interface, f"{dev} has no interface {t.interface!r}")
+    filtered = [i for i in filtered if t.interface.startswith("MOCK") or not i.__name__[:-5].startswith("MOCK")] # never fallback to mock ifaces
+    return select_first_inited([functools.partial(iface, self, self.device_id) for iface in filtered],
+                               f"No interface for {dev}:{self.device_id} is available")
+
   def count(self) -> int:
     """
     Returns the number of physical accelerators available to the runtime.
     """
-    return 1
+    return self.iface.count if hasattr(self, 'iface') else 1
 
   def synchronize(self):
     """
@@ -386,7 +405,7 @@ class Compiled:
     """
     Called at the end of process lifetime to allow the device to finalize.
     """
-    # override this in your device implementation
+    if hasattr(self, 'iface') and hasattr(self.iface, 'device_fini'): self.iface.device_fini()
 
 if PROFILE:
   @atexit.register
@@ -408,7 +427,7 @@ def enumerate_devices_str() -> Generator[str, None, None]:
     ren_results, iface_results = [], []
     try:
       d = Device[device]
-      for iface in [i for i in getattr(d, 'ifaces', []) if not i.__name__.startswith("MOCK")]:
+      for iface in [i for i in d.ifaces if not i.__name__.startswith("MOCK")]:
         try:
           name = iface.__name__[:-5]
           default_text, count = ("(default)", d.count()) if type(d.iface) is iface else (f"(DEV={name}+{device} to make default)", iface(d, 0).count) # type: ignore

tinygrad/dtype.py

@@ -66,7 +66,6 @@ class DType(metaclass=DTypeMetaClass):
   def __reduce__(self): return type(self), tuple(getattr(self, f.name) for f in fields(self))
   def __repr__(self): return f"dtypes.{INVERSE_DTYPES_DICT[self.name]}"
   def __lt__(self, o:DType): return (self.priority, self.bitsize, self.name, self.fmt) < (o.priority, o.bitsize, o.name, o.fmt)
-  def scalar(self) -> DType: return self
   @functools.cached_property
   def min(self):
     if dtypes.is_int(self): return 0 if dtypes.is_unsigned(self) else -2**(self.bitsize-1)

tinygrad/engine/jit.py

@@ -44,9 +44,7 @@ def graph_split_rewrite(linear:UOp, max_batch_size:int=0) -> UOp:
     current_batch, current_batch_devs = [], []
 
   for si in linear.src:
-    if si.src[0].op is Ops.SLICE: continue
-
-    devs = dedup([Device[x] for b in si.src[1:] if b.op is not Ops.BIND for x in (b.device if isinstance(b.device, tuple) else (b.device,))])
+    devs = dedup([Device[x] for b in si.src[1:] if not b.is_bound_var for x in (b.device if isinstance(b.device, tuple) else (b.device,))])
     graph_t = graph_class(devs[0]) if devs[0].graph is not None else None
 
     can_graph = graph_t is not None and graph_t.supports_uop(devs, si)
@@ -180,7 +178,7 @@ class CapturedJit(Generic[ReturnType]):
       if call.op is not Ops.CALL: continue
       arg_uops = get_call_arg_uops(call)
       outs, ins = get_call_outs_ins(call)
-      out |= {arg_uops[k] for k in set(outs) - set(ins) if arg_uops[k].op in (Ops.BUFFER, Ops.SLICE)}
+      out |= {b for k in set(outs) - set(ins) if (b:=u if (cv:=(u:=arg_uops[k]).contiguous_view()) is None else cv[0]).op is Ops.BUFFER}
     return out
 
   def __call__(self, input_uops:list[UOp], var_vals:dict[str, int]) -> ReturnType:
@@ -271,10 +269,14 @@ class _TinyJit(Generic[ReturnType]):
         big_linear, onetime_linear = prune_linear(big_linear, set(input_buf_uops))
         if DEBUG >= 1: print(f"pruned from {len(big_linear.src) + len(onetime_linear.src)} -> {len(big_linear.src)} kernels")
         run_linear(onetime_linear, var_vals)
+        del onetime_linear
 
       # hold all buffers reachable from live Tensors (e.g. lazy .grad created during capture), the memory planner can't suballocate those
       held_bufs = set(buffers) | {u for tref in list(all_tensors) if (t:=tref()) is not None for u in t.uop.toposort() if u.op is Ops.BUFFER}
       linear = jit_lower(big_linear, held_bufs, input_buf_uops)
+      # drop the pre-planning graph: it keeps the whole capture-time working set allocated (big_linear) or referenced (held_bufs).
+      # the planned linear only uses the arena/held buffers, so the intermediates must be freed before linking and first exec
+      del big_linear, held_bufs
       self.captured = CapturedJit(ret, linear, names, expected_input_info)
       ret = self.captured(input_buf_uops, var_vals)
     elif self.cnt >= 2:

tinygrad/engine/realize.py

@@ -1,35 +1,42 @@
 from __future__ import annotations
 from typing import cast, Iterator, Any, Sequence
-import time, random, itertools, math, contextlib, weakref, array
+import random, itertools, math, weakref, array, decimal
 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, 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.helpers import BEAM, size_to_str, time_to_str, VALIDATE_WITH_CPU, PROFILE, ProfilePointEvent, cpu_events, perf_counter_us
+from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, AxisType, sym_infer, graph_rewrite
+from tinygrad.device import Device, Buffer, MultiBuffer, ProfileGraphEntry
+from tinygrad.dtype import dtypes
 from tinygrad.renderer import Estimates
 from tinygrad.codegen import to_program
 from tinygrad.codegen.opt.postrange import args_from_ast
 
 # **************** Helpers ****************
 
-def get_call_arg_uops(call:UOp) -> tuple[UOp, ...]: return tuple(s for s in call.src[1:] if s.op is not Ops.BIND)
+def get_call_arg_uops(call:UOp) -> tuple[UOp, ...]: return tuple(s for s in call.src[1:] if not s.is_bound_var)
+def get_call_var_uops(call:UOp, prg:UOp) -> list[UOp]:
+  bound = {s.src[0].expr: s.src[1].src[1] for s in call.src[1:] if s.is_bound_var}
+  return [bound.get(v.expr, v) for v in prg.arg.vars]
 
 def get_call_outs_ins(call:UOp) -> tuple[tuple[int, ...], tuple[int, ...]]:
   ast = call.src[0]
   if ast.op is Ops.PROGRAM: return tuple(ast.arg.outs), tuple(ast.arg.ins)
-  if ast.op in (Ops.COPY, Ops.SLICE): return (0,), (1,)
+  if ast.op is Ops.COPY: return (0,), (1,)
   if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "encdec": return (0,), tuple(range(1, len(get_call_arg_uops(call))))
   return (), ()
 
+def get_call_kernels(call:UOp) -> list[tuple[str, UOp]]:
+  if (ast:=call.src[0]).op is Ops.CUSTOM_FUNCTION and ast.arg == "hcq": return [(d, k) for devs, k, _ in call.arg.aux.kernels for d in devs]
+  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "graph": return [(to_tuple(ast.device)[0], call)]
+  if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "validate": return []
+  return [(d, call) for d in to_tuple(call.src[1].device)]
+
 def get_call_name(call:UOp, bufs:Sequence[Buffer|UOp], var_vals:dict[str, int]|None=None) -> str:
   def _uop_sz_to_str(uop:UOp) -> str: return size_to_str(sym_infer(prod(uop.shape) * uop.dtype.itemsize, var_vals or {}))
   def _dev_str(buf:Buffer|UOp) -> str: return ', '.join(d[:7] for d in to_tuple(buf.device))
 
   ast, arg_uops = call.src[0], get_call_arg_uops(call)
   if ast.op is Ops.PROGRAM: return ast.arg.name
-  if ast.op is Ops.SLICE:
-    offset = ast.src[1].val * arg_uops[1].dtype.itemsize
-    return colored(f"view {_uop_sz_to_str(arg_uops[0]):>10} @ {offset:<10d}", "yellow")
   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")
@@ -39,49 +46,52 @@ def get_call_name(call:UOp, bufs:Sequence[Buffer|UOp], var_vals:dict[str, int]|N
 # **************** Stat ****************
 
 def estimate_uop(call:UOp) -> Estimates:
-  ast = call.src[0]
-  if ast.op is Ops.PROGRAM: return ast.src[0].arg.estimates or Estimates()
+  if (ast:=call.src[0]).op is Ops.PROGRAM: return ast.src[0].arg.estimates or Estimates()
   if ast.op is Ops.COPY or (ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "encdec"):
-    nbytes = prod(call.src[1].shape) * call.src[1].dtype.itemsize
-    return Estimates(lds=nbytes, mem=nbytes)
+    return Estimates(lds=(nbytes:=prod(call.src[1].shape) * call.src[1].dtype.itemsize), mem=nbytes)
   if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "graph": return get_graph_runtime(ast).estimates
   if ast.op is Ops.CUSTOM_FUNCTION and ast.arg == "hcq": return call.arg.aux.estimates
   return Estimates()
 
 first_run_cache:set[bytes] = set()
-@contextlib.contextmanager
-def track_stats(ctx:ExecContext, call:UOp, device:str, bufs:list[Buffer], var_vals:dict[str, int]):
-  if PROFILE:
-    outputs, inputs = get_call_outs_ins(call)
-    cpu_events.append(ProfilePointEvent(device, "exec", len(cpu_events), {"var_vals": var_vals,
-      "bufs": [b.trace_num for b in bufs], "name": get_call_name(call, bufs, var_vals), "outputs": outputs, "inputs": inputs}))
-  et: list[float|None] = [None]
-  if DEBUG >= 2: st = time.perf_counter()
-  yield et
-  if not ctx.update_stats: return
+def track_stats(ctx:ExecContext, call:UOp, st:decimal.Decimal, ets:list[float|None]):
+  if ctx.update_stats:
+    is_hcq = (ast:=call.src[0]).op is Ops.CUSTOM_FUNCTION and ast.arg == "hcq"
+    estimates, n = estimate_uop(call), 1 if is_hcq else len(get_call_kernels(call))
+    GlobalCounters.kernel_count += len(call.arg.aux.kernels) if is_hcq else n
+    GlobalCounters.global_ops += n*sym_infer(estimates.ops, ctx.var_vals)
+    GlobalCounters.global_mem += n*sym_infer(estimates.mem, ctx.var_vals)
+    GlobalCounters.time_sum_s += sum(et for et in ets if et is not None)
+  if DEBUG < 2 and not PROFILE: return
 
-  if DEBUG >= 2 and et[0] is None:
-    Device[device].synchronize()
-    et[0] = time.perf_counter() - st
+  kernels = get_call_kernels(call) # everything below is the per kernel display: exec events for the profiler and DEBUG=2 lines
+  args = resolve_params(call, ctx.input_uops) if kernels and kernels[0][1] is call else []
+  lanes = list(unwrap_multi(call, [args[g] for g in call.src[0].arg.globals] if call.src[0].op is Ops.PROGRAM else args)) if args else []
+  for i, (device, kcall) in enumerate(kernels):
+    et, bufs = ets[i] if i < len(ets) else None, lanes[i][0] if i < len(lanes) else []
+    if PROFILE: # backdate the event to the start of the call, the viz matches a device range with the exec event before it
+      outputs, inputs = get_call_outs_ins(kcall)
+      cpu_events.append(ProfilePointEvent(device, "exec", len(cpu_events), {"var_vals": ctx.var_vals,
+        "bufs": [b.trace_num for b in bufs], "name": get_call_name(kcall, bufs, ctx.var_vals), "outputs": outputs, "inputs": inputs}, ts=st))
+    if DEBUG < 2 or not ctx.update_stats: continue
+    if et is None:
+      Device[device].synchronize()
+      et, st = float(perf_counter_us() - st)*1e-6, perf_counter_us()
+      GlobalCounters.time_sum_s += et
 
-  estimates = estimate_uop(call)
-  GlobalCounters.kernel_count += 1
-  GlobalCounters.global_ops += (op_est:=sym_infer(estimates.ops, var_vals))
-  GlobalCounters.global_mem += (mem_est:=sym_infer(estimates.mem, var_vals))
-  if et[0] is not None: GlobalCounters.time_sum_s += et[0]
-  if DEBUG >= 2:
-    display_name = get_call_name(call, bufs, var_vals)
-    lds_est = sym_infer(estimates.lds, var_vals)
-    header_color = 'magenta' if ctx.jit else ('green' if call.src[0].key not in first_run_cache else None)
-    ptm = colored(time_to_str(et[0], w=9), "yellow" if et[0] > 0.01 else None) if et[0] is not None else ""
-    flops, membw, ldsbw = op_est/(et[0] or 1e-20), mem_est/(et[0] or 1e-20), lds_est/(et[0] or 1e-20)
+    estimates = estimate_uop(kcall)
+    display_name = get_call_name(kcall, bufs, ctx.var_vals)
+    op_est, mem_est, lds_est = (sym_infer(x, ctx.var_vals) for x in (estimates.ops, estimates.mem, estimates.lds))
+    header_color = 'magenta' if ctx.jit else ('green' if kcall.src[0].key not in first_run_cache else None)
+    ptm = colored(time_to_str(et, w=9), "yellow" if et > 0.01 else None) if et is not None else ""
+    flops, membw, ldsbw = op_est/(et or 1e-20), mem_est/(et or 1e-20), lds_est/(et or 1e-20)
     flops_str = f"{flops*1e-9:7.0f} GFLOPS" if flops < 1e14 else colored(f"{flops*1e-12:7.0f} TFLOPS", 'green')
     mem_str = f"{membw*1e-9:4.0f}|{ldsbw*1e-9:<6.0f} GB/s" if membw < 1e13 and ldsbw < 1e15 else \
       colored(f"{membw*1e-12:4.0f}|{ldsbw*1e-12:<6.0f} TB/s", 'green')
     print(f"{colored(f'*** {device[:7]:7s} {GlobalCounters.kernel_count:4d}', header_color)}"+
       f" {display_name+' '*(46-ansilen(display_name))} arg {len(bufs):2d} mem {GlobalCounters.mem_used/1e9:6.2f} GB"+
-      ("" if et[0] is None else f" tm {ptm}/{GlobalCounters.time_sum_s*1e3:9.2f}ms ({flops_str} {mem_str})"))
-    first_run_cache.add(call.src[0].key)
+      ("" if et is None else f" tm {ptm}/{GlobalCounters.time_sum_s*1e3:9.2f}ms ({flops_str} {mem_str})"))
+    first_run_cache.add(kcall.src[0].key)
 
 local_size_cache: dict[bytes, tuple[int, ...]] = {}
 def optimize_local_size(call:UOp, prg:UOp) -> UOp|None:
@@ -140,7 +150,7 @@ class ExecContext:
   cache: bool = True
 
 def _resolve(b:UOp, inputs:tuple[UOp, ...]) -> UOp:
-  if b.op in (Ops.SLICE, Ops.MSELECT) and b.src[0].op is Ops.PARAM: return b.replace(src=(inputs[b.src[0].arg.slot], *b.src[1:]))
+  if b.op in (Ops.MSELECT, Ops.SHRINK) and b.src[0].op is Ops.PARAM: return b.replace(src=(inputs[b.src[0].arg.slot], *b.src[1:]))
   if b.op is Ops.MSTACK: return b.replace(src=tuple(_resolve(x, inputs) for x in b.src))
   return inputs[b.arg.slot] if b.op is Ops.PARAM else b
 def resolve_params(call:UOp, inputs:tuple[UOp, ...]) -> list[UOp]: return [_resolve(b, inputs) for b in get_call_arg_uops(call)]
@@ -154,39 +164,31 @@ def unwrap_multi(call:UOp, resolved:list[UOp]) -> Iterator[tuple[list[Buffer], d
                    for x in call.src[0].toposort())
     for j, per_dev in enumerate(zip(*[cast(MultiBuffer, b).bufs for b in bufs])): yield list(per_dev), {"_device_num": j} if has_dnum else {}
 
-def exec_view(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
-  resolved = resolve_params(call, ctx.input_uops)
-  bufs = [cast(Buffer, b.buffer) for b in resolved]
-  bv = bufs[1].view(resolved[0].max_numel(), ast.dtype, ast.src[1].val*bufs[1].dtype.itemsize)
-  with track_stats(ctx, call, bv.device, [bv, bufs[1]], ctx.var_vals): buffers[resolved[0]] = bv
-  return None
-
-def exec_copy(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
+def exec_copy(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
   for bufs, device_vars in unwrap_multi(call, resolve_params(call, ctx.input_uops)):
     dest, src = bufs[0].ensure_allocated(), bufs[1].ensure_allocated()
-    with track_stats(ctx, call, dest.device, [dest, src], ctx.var_vals):
-      if hasattr(dest.allocator,'_transfer') and dest.allocator.supports_transfer and dest.device.split(":")[0] == src.device.split(":")[0]:
-        dest.allocator._transfer(dest._buf, src._buf, dest.nbytes, src_dev=src.allocator.dev, dest_dev=dest.allocator.dev)
-      elif src.device.startswith("DISK") and getattr(src.allocator.dev, 'fd', None) is not None \
-           and hasattr(dest.allocator, 'copy_from_disk') and src.nbytes >= 4096 and dest.allocator.supports_copy_from_disk:
-        dest.allocator.copy_from_disk(dest._buf, src._buf, src.nbytes)
-      elif hasattr(dest.allocator, '_as_buffer'): src.allocator._copyout(dest.as_memoryview(force_zero_copy=True), src._buf)
-      else: dest.allocator._copyin(dest._buf, src.as_memoryview(allow_zero_copy=True))
-  return None
+    if hasattr(dest.allocator,'_transfer') and dest.allocator.supports_transfer and dest.device.split(":")[0] == src.device.split(":")[0]:
+      dest.allocator._transfer(dest._buf, src._buf, dest.nbytes, src_dev=src.allocator.dev, dest_dev=dest.allocator.dev)
+    elif src.device.startswith("DISK") and getattr(src.allocator.dev, 'fd', None) is not None \
+         and hasattr(dest.allocator, 'copy_from_disk') and src.nbytes >= 4096 and dest.allocator.supports_copy_from_disk:
+      dest.allocator.copy_from_disk(dest._buf, src._buf, src.nbytes)
+    elif hasattr(dest.allocator, '_as_buffer'): src.allocator._copyout(dest.as_memoryview(force_zero_copy=True), src._buf)
+    else: dest.allocator._copyin(dest._buf, src.as_memoryview(allow_zero_copy=True))
+  return []
 
-def exec_kernel(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
-  et = None
-  for device, (bufs, device_vars) in zip(to_tuple(call.src[1].device), unwrap_multi(call, resolve_params(call, ctx.input_uops))):
+def exec_kernel(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
+  ets:list[float|None] = []
+  resolved = resolve_params(call, ctx.input_uops)
+  for device, (bufs, device_vars) in zip(to_tuple(call.src[1].device), unwrap_multi(call, [resolved[i] for i in ast.arg.globals])):
     var_vals = {**ctx.var_vals, **device_vars}
-    prg_bufs = [bufs[i].ensure_allocated() for i in ast.arg.globals]
+    prg_bufs = [b.ensure_allocated() for b in bufs]
     rt = get_runtime(device, ast, cache=ctx.cache)
     global_size, local_size = ast.arg.launch_dims(var_vals)
-    with track_stats(ctx, call, device, prg_bufs, var_vals) as tm:
-      et = tm[0] = rt(*[b.get_buf(device) for b in prg_bufs], global_size=global_size, local_size=local_size, vals=ast.arg.vals(var_vals),
-                       wait=ctx.wait, timeout=ctx.timeout)
-  return et
+    ets.append(rt(*[b.get_buf(device) for b in prg_bufs], global_size=global_size, local_size=local_size, vals=ast.arg.vals(var_vals),
+                  wait=ctx.wait, timeout=ctx.timeout))
+  return ets
 
-def exec_validate(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
+def exec_validate(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
   import numpy as np
   for bufs, device_vars in unwrap_multi(call, resolve_params(call, ctx.input_uops)):
     bufs, dev_bufs = bufs[:len(bufs)//2], bufs[len(bufs)//2:]
@@ -195,42 +197,36 @@ def exec_validate(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
     global_size, local_size = prg.arg.launch_dims(var_vals)
     cpu_rt(*[bufs[i].ensure_allocated()._buf for i in prg.arg.globals], global_size=global_size, local_size=local_size, vals=prg.arg.vals(var_vals))
     for i in prg.arg.outs: np.testing.assert_allclose(dev_bufs[i].ensure_allocated().numpy(), bufs[i].numpy(), rtol=1e-3, atol=1e-3)
-  return None
+  return []
 
-def exec_encdec(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
+def exec_encdec(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
   bufs = [cast(Buffer, b.buffer).ensure_allocated() for b in resolve_params(call, ctx.input_uops)]
   shape, pos_var = tuple(s.val for s in ast.src if s.op is Ops.CONST), ast.variables()[0].expr
-  with track_stats(ctx, call, bufs[0].device, bufs, ctx.var_vals):
-    bufs[0].allocator._encode_decode(bufs[0]._buf, bufs[1]._buf, bufs[2]._buf, [x._buf for x in bufs[3:]], shape, ctx.var_vals[pos_var])
-  return None
+  bufs[0].allocator._encode_decode(bufs[0]._buf, bufs[1]._buf, bufs[2]._buf, [x._buf for x in bufs[3:]], shape, ctx.var_vals[pos_var])
+  return []
 
-def exec_graph(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
-  rt = get_graph_runtime(ast, ctx.input_uops)
-  with track_stats(ctx, call, rt.device, [], ctx.var_vals) as t: t[0] = rt(ctx.input_uops, ctx.var_vals, wait=ctx.wait)
-  return t[0]
+def exec_graph(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
+  return [get_graph_runtime(ast, ctx.input_uops)(ctx.input_uops, ctx.var_vals, wait=ctx.wait)]
 
-def exec_hcq(ctx:ExecContext, call:UOp, ast:UOp) -> float|None:
-  if (inputs:=call.arg.aux.inputs) is not None:
-    bufs = [_resolve(ctx.input_uops[i], ctx.input_uops).buffer for i in call.arg.aux.input_idxs]
-    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])
-      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
+def exec_hcq(ctx:ExecContext, call:UOp, ast:UOp) -> list[float|None]:
+  dev = cast(Any, Device[(info:= call.arg.aux).device[0]])
+  addrs = [(b.bufs[j] if isinstance(b:=_resolve(ctx.input_uops[k], ctx.input_uops).buffer, MultiBuffer) else b).get_buf(dev_name).va_addr
+           for devs, idxs in info.input_idxs for j, dev_name in enumerate(devs) for k in idxs]
+  dev.rt_buffer._buf.cpu_view().view(offset=(base:=dev.rt_allocator.alloc(len(addrs) * 8)), fmt='Q')[:len(addrs)] = array.array('Q', addrs)
 
-  exec_kernel(replace(ctx, update_stats=False), call, ast)
+  if info.inputs is not None:
+    tables = [UOp.from_buffer(dev.rt_buffer.view(len(idxs), dtypes.uint64, base + j*len(idxs)*8), HCQ_RUNTIME_DEV.value)
+              for devs, idxs in info.input_idxs for j in range(len(devs))]
+    call = call.substitute({call.src[1+info.inputs]: UOp.mstack(*tables)})
+  exec_kernel(replace(ctx, var_vals={**ctx.var_vals, "hcq_inputs_ptr": dev.rt_buffer._buf.va_addr + base}), call, ast)
 
-  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]
+  def _prof_tm(device:str, stat_call:UOp, prof:tuple[int, ...]) -> float|None:
+    (d:=cast(Any, Device[device])).prof_ents[prof[0]] = ProfileGraphEntry(device, stat_call.arg.name, *prof)
+    if not ctx.wait: return None
+    d.synchronize(timeout=ctx.timeout)
+    st, en = (d.signal(x)._buf.cpu_view().view(fmt='Q')[0] for x in prof)
+    return float(en-st)/d.timestamp_divider/1e6
+  return [_prof_tm(device, k, prof) for devices, k, prof in info.kernels if prof for device in devices] if PROFILE or ctx.wait else []
 
 # flatten LINEAR-in-LINEAR: any nested LINEAR child gets inlined into its parent's src
 pm_flatten_linear = PatternMatcher([
@@ -261,7 +257,6 @@ pm_optimize_local_size = PatternMatcher([
 ])
 
 pm_exec = PatternMatcher([
-  (UPat(Ops.CALL, src=(UPat(Ops.SLICE, name="ast"),), name="call", allow_any_len=True), exec_view),
   (UPat(Ops.CALL, src=(UPat(Ops.COPY, name="ast"),), name="call", allow_any_len=True), exec_copy),
   (UPat(Ops.CALL, src=(UPat(Ops.PROGRAM, name="ast"),), name="call", allow_any_len=True), exec_kernel),
   (UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="encdec", name="ast"),), name="call", allow_any_len=True), exec_encdec),
@@ -270,14 +265,15 @@ pm_exec = PatternMatcher([
   (UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="validate", name="ast"),), name="call", allow_any_len=True), exec_validate),
 ])
 
-if getenv("HCQ2"): from tinygrad.runtime.support.hcq2 import hcq_compile, hcq_link # noqa: E402 # down here, hcq2 imports the helpers above
+if getenv("HCQ2"): from tinygrad.runtime.support.hcq2 import hcq_compile, hcq_link, HCQ_RUNTIME_DEV # noqa: E402 # down here, hcq2 imports realize
 
 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, bool(PROFILE) if profile is None else profile)
-  return graph_rewrite(linear, pm_optimize_local_size, name="optimize local size", walk=True)
+  linear = graph_rewrite(linear, pm_optimize_local_size, name="optimize local size", walk=True)
+  if getenv("HCQ2"): linear = hcq_compile(linear, input_uops, bool(PROFILE or DEBUG >= 2) if profile is None else profile)
+  return linear
 
 def link_linear(linear:UOp, cache=True) -> UOp: return hcq_link(linear, cache=cache) if getenv("HCQ2") else linear
 
@@ -285,7 +281,7 @@ def run_linear(linear:UOp, var_vals:dict[str, int]|None=None, input_uops:Sequenc
   inputs = list(input_uops)
   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)
+  for call in linear.src: track_stats(ctx, call, perf_counter_us(), pm_exec.rewrite(call, ctx))
 
 def time_call(call:UOp, var_vals:dict[str, int]|None=None, timeout:int|None=None, clear_l2:bool=False) -> float:
   if clear_l2:
@@ -295,4 +291,4 @@ def time_call(call:UOp, var_vals:dict[str, int]|None=None, timeout:int|None=None
       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)
   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)
+  return max(et for c in linear.src for et in pm_exec.rewrite(c, ctx) or [0.0])

tinygrad/function.py

@@ -1,4 +1,5 @@
 import functools, time
+from dataclasses import replace
 from typing import Generic, TypeVar, Callable, cast, overload
 from tinygrad.helpers import Context, dedup, getenv, DEBUG
 from tinygrad.uop.ops import UOp, Ops, graph_rewrite, PatternMatcher, UPat
@@ -12,7 +13,7 @@ def add_to_ctx(ctx, x:UOp):
   return ret
 
 pm_ctx = PatternMatcher([
-  (UPat((Ops.BUFFER, Ops.BIND), name="x"), add_to_ctx),
+  (UPat(Ops.BUFFER, name="x"), add_to_ctx),
   (UPat((Ops.AFTER, Ops.CONTIGUOUS), name="x"),
    lambda ctx,x: add_to_ctx(ctx,x) if not x.op_in_backward_slice_with_self(Ops.PARAM) and x.op_in_backward_slice_with_self(Ops.BUFFER) else None),
 ])
@@ -23,6 +24,10 @@ def invalid_outputs(uret:UOp) -> set[UOp]:
   return {u.src[0].buf_uop for u in uret.backward_slice_with_self
           if u.op is Ops.STORE and u.src[1].base.is_invalid and not u.src[0].buf_uop.is_realized}
 
+def renumber_invalid_outputs(uret:UOp) -> UOp:
+  return uret.substitute({b:b.replace(arg=replace(b.arg, slot=i))
+                          for i,b in enumerate(x for x in uret.toposort(enter_calls=False) if x in invalid_outputs(uret))})
+
 ReturnType = TypeVar('ReturnType')
 class _function(Generic[ReturnType]):
   depth = 0
@@ -65,6 +70,7 @@ class _function(Generic[ReturnType]):
     # the BUFFERs that are left are the implicit inputs
     num_explicit = len(call_uops)
     uret = graph_rewrite(uret, pm_ctx, (call_uops, invalid_outputs(uret)), bottom_up=True, name="get_implicit_inputs")
+    uret = renumber_invalid_outputs(uret)
     name = getattr(self.fxn, '__qualname__', None) or type(self.fxn).__qualname__
     if not self.allow_implicit:
       implicit_buffers = [x for x in call_uops[num_explicit:] if x.op is Ops.BUFFER]

tinygrad/helpers.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 import time
 START_TIME = time.perf_counter()
-import os, functools, platform, re, contextlib, operator, hashlib, pickle, sqlite3, tempfile, pathlib, string, ctypes, sys, gzip, getpass, gc
+import os, functools, re, contextlib, operator, hashlib, pickle, sqlite3, tempfile, pathlib, string, ctypes, sys, gzip, getpass, gc
 from collections import defaultdict
-import subprocess, shutil, math, types, copyreg, inspect, importlib, decimal, itertools, difflib
+import shutil, math, types, copyreg, inspect, importlib, decimal, itertools, difflib
 from dataclasses import dataclass, field, replace
 from typing import ClassVar, Iterable, Any, TypeVar, Callable, Sequence, TypeGuard, Iterator, Generic, Generator, cast, overload
 
@@ -13,8 +13,7 @@ U = TypeVar("U")
 def prod(x:Iterable[T]) -> T|int: return functools.reduce(operator.mul, x, 1)
 
 # NOTE: helpers is not allowed to import from anything else in tinygrad
-OSX, WIN = platform.system() == "Darwin", sys.platform == "win32"
-ARCH_X86 = any(x in platform.processor() for x in ("Intel", "i386", "x86_64"))
+OSX, WIN = sys.platform == "darwin", sys.platform == "win32"
 BASEDIR = pathlib.Path(__file__).parent
 
 # fix colors on Windows, https://stackoverflow.com/questions/12492810/python-how-can-i-make-the-ansi-escape-codes-to-work-also-in-windows
@@ -231,7 +230,7 @@ class _DEV(ContextVar):
 
 DEV, DEBUG, BEAM, NOOPT = _DEV("DEV", ""), ContextVar("DEBUG", 0), ContextVar("BEAM", 0), ContextVar("NOOPT", 0)
 IMAGE, FLOAT16, OPENPILOT_HACKS = ContextVar("IMAGE", 0), ContextVar("FLOAT16", 0), ContextVar("OPENPILOT_HACKS", 0)
-JIT, JIT_BATCH_SIZE = ContextVar("JIT", 2 if OSX and ARCH_X86 else 1), ContextVar("JIT_BATCH_SIZE", 32)
+JIT, JIT_BATCH_SIZE = ContextVar("JIT", 1), ContextVar("JIT_BATCH_SIZE", 32)
 CHUNK_SIZE = 2**20  # TinyFS content-addressed store: blob chunk + hash-tree node granularity
 WINO, CAPTURING, TRACEMETA, NO_COLOR = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1), ContextVar("NO_COLOR", 0)
 TRAINING = ContextVar("TRAINING", 0)
@@ -454,9 +453,9 @@ def _ensure_downloads_dir() -> pathlib.Path:
   if pathlib.Path("/etc/tinybox-release").is_file():
     # try creating dir with sudo
     if not (downloads_dir := pathlib.Path("/raid/downloads")).exists():
-      subprocess.run(["sudo", "mkdir", "-p", downloads_dir], check=True)
-      subprocess.run(["sudo", "chown", "tiny:root", downloads_dir], check=True)
-      subprocess.run(["sudo", "chmod", "775", downloads_dir], check=True)
+      system(f"sudo mkdir -p {downloads_dir}")
+      system(f"sudo chown tiny:root {downloads_dir}")
+      system(f"sudo chmod 775 {downloads_dir}")
     return downloads_dir
   return pathlib.Path(cache_dir) / "downloads"
 
@@ -497,6 +496,7 @@ def fetch_fw(path:str, name:str, sha256:str) -> bytes:
 # *** Exec helpers
 
 def system(cmd:str, **kwargs) -> str:
+  import subprocess
   st = time.perf_counter()
   try: ret = subprocess.check_output(cmd.split(), stderr=subprocess.STDOUT, **kwargs).decode().strip()
   except subprocess.CalledProcessError as e:

tinygrad/llm/model.py

@@ -1,11 +1,17 @@
 from __future__ import annotations
-import functools, itertools, pathlib
+import enum, functools, itertools, pathlib
 from dataclasses import dataclass, replace
-from tinygrad import Tensor, nn, UOp, TinyJit, getenv, function
+from tinygrad import Tensor, nn, UOp, TinyJit, getenv, function, dtypes
 from tinygrad.nn import Linear
 from tinygrad.llm.gguf import gguf_load
 from tinygrad.uop.ops import resolve
 
+class ExpertGating(enum.IntEnum):
+  SOFTMAX = 1
+  SIGMOID = 2
+  SOFTMAX_WEIGHT = 3  # softmax over the top-k selected logits
+  SQRT_SOFTPLUS = 4
+
 @functools.cache
 def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, device:str|None=None) -> Tensor:
   freqs = 1.0 / (theta ** (Tensor.arange(0, dim, 2)[:(dim // 2)] / dim))
@@ -61,6 +67,7 @@ class TransformerConfig:
   num_experts: int = 0
   num_experts_per_tok: int = 0
   norm_topk_prob: bool = False
+  expert_gating_func: ExpertGating = ExpertGating.SOFTMAX
   q_lora_rank: int = 0
   kv_lora_rank: int = 0
   shared_expert_dim: int = 0
@@ -103,14 +110,21 @@ class FFNBlock:
     if hasattr(self, 'ffn_gate_exps'):
       h = x.unsqueeze(2)  # (B, T, 1, D) - add expert dim for broadcasting
       logits = self.ffn_gate_inp(x)
-      if hasattr(self, 'exp_probs_b'):
-        probs = logits.sigmoid()
-        _, sel = pairwise_topk(probs + self.exp_probs_b["bias"], self.config.num_experts_per_tok)
-        probs = probs.gather(-1, sel)
-        if self.config.norm_topk_prob: probs = probs / probs.sum(axis=-1, keepdim=True)
-      else:
-        vals, sel = pairwise_topk(logits, self.config.num_experts_per_tok)
-        probs = vals.softmax(-1) if self.config.norm_topk_prob else logits.softmax(-1).gather(-1, sel)
+      bias = self.exp_probs_b["bias"] if hasattr(self, 'exp_probs_b') else None
+      gating, normalize_topk = self.config.expert_gating_func, self.config.norm_topk_prob
+      # fast path: without selection bias, normalized SOFTMAX is equivalent to SOFTMAX_WEIGHT
+      if gating == ExpertGating.SOFTMAX and bias is None and normalize_topk:
+        gating, normalize_topk = ExpertGating.SOFTMAX_WEIGHT, False
+      if   gating == ExpertGating.SOFTMAX_WEIGHT: scores = logits
+      elif gating == ExpertGating.SOFTMAX:        scores = logits.softmax(-1)
+      elif gating == ExpertGating.SIGMOID:        scores = logits.sigmoid()
+      elif gating == ExpertGating.SQRT_SOFTPLUS:  scores = logits.softplus().sqrt()
+
+      _, sel = pairwise_topk(scores if bias is None else scores + bias, self.config.num_experts_per_tok)
+      probs = scores.gather(-1, sel)
+      # SOFTMAX_WEIGHT applies softmax after top-k selection
+      if gating == ExpertGating.SOFTMAX_WEIGHT: probs = probs.softmax(-1)
+      if normalize_topk: probs = probs / probs.sum(axis=-1, keepdim=True)
       probs = probs * self.config.routed_scaling_factor
       x_down = self.ffn_down_exps(sel, (self.ffn_gate_exps(sel, h).silu() * self.ffn_up_exps(sel, h)).contiguous())  # (B, T, k, D)
       out = (x_down * probs.unsqueeze(-1)).sum(axis=2)  # (B, T, D)
@@ -124,8 +138,6 @@ class FFNBlock:
 
   # given the token-prefix match, return how much cached state this block can still reuse
   def _reusable_prefix_len(self, prefix_len:int, cached_len:int) -> int: return prefix_len
-  # return writes that reset this block's state after a cache mismatch
-  def _state_reset_ops(self) -> list[Tensor]: return []
   def _init_state(self, x:Tensor): raise NotImplementedError
   def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor: raise NotImplementedError
 
@@ -187,8 +199,8 @@ class TransformerBlock(FFNBlock):
 
   def _init_state(self, x:Tensor):
     if not hasattr(self, "cache_kv"):
-      # TODO: how is the dtype of this determined?
-      self.cache_kv = Tensor.empty(2, x.shape[0], self.config.n_kv_heads, self.config.max_context, self.config.head_dim, device=x.device)
+      self.cache_kv = Tensor.empty(2, x.shape[0], self.config.n_kv_heads, self.config.max_context, self.config.head_dim,
+                                   dtype=dtypes.default_float, device=x.device)
       self.freqs_cis = precompute_freqs_cis(self.config.rope_dim, self.config.max_context, self.config.rope_theta, device=x.device)
 
 class MLATransformerBlock(FFNBlock):
@@ -260,45 +272,65 @@ class GatedDeltaNetBlock(FFNBlock):
 
   def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
     B, T, _ = x.shape
-    assert T == 1, "GatedDeltaNetBlock currently only supports T=1"
+    # bind ints to a variable so the reset flag stays a runtime value (it toggles when generation restarts at position 0)
+    start_pos = start_pos if isinstance(start_pos, UOp) else UOp.variable("start_pos", 0, self.config.max_context-1).bind(start_pos)
+    initial = Tensor(start_pos).eq(0)
+    is_kda = hasattr(self, "ssm_g_a")
+    symbolic = isinstance(T, UOp)
+    T_pad = x.max_shape[1]  # symbolic chunks are padded to their max size: one graph serves every size
 
     # input processing
     x = x.half()
-    out_gate = self.ssm_g_b(self.ssm_g_a(x)) if hasattr(self, "ssm_g_a") else self.attn_gate(x)
-    out_gate = out_gate.reshape(B, 1, self.num_v_heads, self.head_v_dim)
-    beta = self.ssm_beta(x).sigmoid().reshape(B, self.num_v_heads, 1, 1)
-    alpha = self.ssm_f_b(self.ssm_f_a(x)) if hasattr(self, "ssm_f_a") else self.ssm_alpha(x)
-    alpha = ((alpha.float() + self.ssm_dt["bias"]).softplus().reshape(B, self.num_v_heads, -1) *
-             self.ssm_a.reshape(1, self.num_v_heads, -1)).exp().unsqueeze(-2)
+    out_gate = self.ssm_g_b(self.ssm_g_a(x)) if is_kda else self.attn_gate(x)
+    out_gate = out_gate.reshape(B, T, self.num_v_heads, self.head_v_dim)
+    beta = self.ssm_beta(x).sigmoid().reshape(B, T, self.num_v_heads)
+    alpha = self.ssm_f_b(self.ssm_f_a(x)) if is_kda else self.ssm_alpha(x)
+    log_alpha = ((alpha.float() + self.ssm_dt["bias"]).softplus().reshape(B, T, self.num_v_heads, -1) *
+                 self.ssm_a.reshape(self.num_v_heads, -1))
 
-    # qkv conv
-    conv_window = self.conv_state.cat(self.attn_qkv(x), dim=1)
-    conv_out = (conv_window * self.ssm_conv1d["weight"].T.unsqueeze(0)).sum(1).silu()
+    # qkv conv, conv_state is reset when starting from position 0
+    conv_state = initial.where(0, self.conv_state)
+    # assemble the conv window in a static-size buffer: [conv_state | qkv rows | zero-pad].
+    # padded steps are exact no-ops: beta=0 (delta rule off), log_alpha=0 (decay 1 after exp)
+    win = Tensor.zeros(B, self.ssm_conv_kernel-1 + T_pad, self.conv_channels).uop
+    win = win.after(win[:, :self.ssm_conv_kernel-1].store(conv_state.cast(win.dtype).uop))
+    win = win.after(win[:, self.ssm_conv_kernel-1:self.ssm_conv_kernel-1+T].store(self.attn_qkv(x).cast(win.dtype).uop))
+    conv_window = Tensor(win)
+    # the last conv_kernel-1 columns of the window become the next conv state
+    conv_state_store = self.conv_state.uop.store(conv_window[:, T:T+self.ssm_conv_kernel-1].cast(self.conv_state.dtype).uop)
+
+    conv_out = functools.reduce(lambda a,b: a+b,
+      (conv_window[:, i:i+T_pad] * self.ssm_conv1d["weight"][:, i] for i in range(self.ssm_conv_kernel))).silu()
+    if symbolic:
+      out_gate = out_gate.pad_to((B, T_pad, self.num_v_heads, self.head_v_dim))
+      beta, log_alpha = beta.pad_to((B, T_pad, self.num_v_heads)), log_alpha.pad_to((B, T_pad, *log_alpha.shape[2:]))
     q, k, v = conv_out.split([self.q_dim, self.q_dim, self.conv_channels - 2*self.q_dim], dim=-1)
-    q = q.reshape(B, self.num_k_heads, self.head_k_dim).normalize(dim=-1).repeat(1, self.num_v_heads//self.num_k_heads, 1)
-    k = k.reshape(B, self.num_k_heads, self.head_k_dim).normalize(dim=-1).repeat(1, self.num_v_heads//self.num_k_heads, 1)
-    v = v.reshape(B, self.num_v_heads, self.head_v_dim)
-    q, k, v = q.mul(self.head_k_dim**-0.5).unsqueeze(-1), k.unsqueeze(-1), v.unsqueeze(-1)
+    qk_eps = 1e-12 if is_kda else 1e-6
+    q, k = (z.reshape(B, T_pad, self.num_k_heads, self.head_k_dim).normalize(dim=-1, eps=qk_eps)
+            .repeat(1, 1, self.num_v_heads//self.num_k_heads, 1) for z in (q, k))
+    v = v.reshape(B, T_pad, self.num_v_heads, self.head_v_dim)
+    # layout the per-step operands to broadcast against the (B, H, V, K) state
+    q, k, v, beta = (z.transpose(1, 2).float() for z in (q, k, v, beta))
+    q, k, v, beta = q.unsqueeze(-2) * self.head_k_dim**-0.5, k.unsqueeze(-2), v.unsqueeze(-1), beta.unsqueeze(-1).unsqueeze(-1)
+    alpha = log_alpha.transpose(1, 2).exp().unsqueeze(-1)  # per-channel decay for kda, per-head otherwise (B, H, T, V|1, 1)
 
-    # recurrent
-    recurrent_state = self.recurrent_state * alpha
-    recurrent_state = recurrent_state + ((v - recurrent_state@k) * beta)@k.transpose(-1, -2)
+    # recurrent: scan over the (padded) tokens, updating the recurrent state. collect the per-step outputs
+    state = Tensor(self.recurrent_state.uop.after(conv_state_store)).float()  # carry the conv write into this graph
+    state = initial.where(0, state)
+    outs = []
+    for t in range(T_pad):
+      s1 = state * alpha[:, :, t]  # decay the state
+      delta = (v[:, :, t] - (s1*k[:, :, t]).sum(-1, keepdim=True)) * beta[:, :, t]  # the delta rule update
+      state = s1 + delta * k[:, :, t]
+      outs.append((state * q[:, :, t]).sum(-1))
 
-    # store the updated state
-    conv_state_store = self.conv_state.uop.store(conv_window[:, 1:, :].cast(self.conv_state.dtype).uop)
-    recurrent_state_store = self.recurrent_state.uop.store(recurrent_state.cast(self.recurrent_state.dtype).uop)
-    recurrent_state = Tensor(self.recurrent_state.uop.after(recurrent_state_store, conv_state_store))
+    # store the updated recurrent state in place, then read the stacked outputs after the write
+    core = Tensor(outs[0].stack(*outs[1:], dim=1).contiguous().uop.after(self.recurrent_state.uop.store(state.cast(self.recurrent_state.dtype).uop)))
 
-    # output
-    core_attn_out = self.ssm_norm((recurrent_state@q).squeeze(-1).reshape(B, 1, self.num_v_heads, self.head_v_dim))
-    out_gate = out_gate.sigmoid() if hasattr(self, "ssm_g_a") else out_gate.silu()
-    return self.ssm_out((core_attn_out * out_gate).reshape(B, 1, -1).cast(x.dtype))
-
-  # recurrent state can't be partially reused after divergence, force a full rebuild
-  def _state_reset_ops(self):
-    return [self.conv_state.assign(self.conv_state.const_like(0)),
-            self.recurrent_state.assign(self.recurrent_state.const_like(0))] if hasattr(self, "conv_state") else []
-  def _reusable_prefix_len(self, prefix_len:int, cached_len:int) -> int: return 0 if prefix_len != cached_len else prefix_len
+    # output; undo the padding before the output projection
+    z = (self.ssm_norm(core) * (out_gate.sigmoid() if is_kda else out_gate.silu())).cast(x.dtype).contiguous()
+    if symbolic: z = z[:, :T]
+    return self.ssm_out(z.reshape(B, T, -1))
 
   def _init_state(self, x):
     if not hasattr(self, "conv_state"):
@@ -326,7 +358,8 @@ class Transformer:
   def forward(self, tokens:Tensor, start_pos:int|UOp, temperature:Tensor) -> Tensor:
     x = self.token_embd(tokens).float()                   # (B, T, D)
     for block in self.blk: x = block(x, start_pos)
-    logits = self.output(self.output_norm(x))[:, -1, :]
+    # only run the output projection on the last token
+    logits = self.output(self.output_norm(x[:, -1:]))[:, -1, :]
     # Gumbel-max trick: argmax(logits/temp - log(-log(uniform))) is equivalent to sampling from softmax(logits/temp)
     return (logits / temperature.maximum(1e-12) - (Tensor.rand_like(logits).maximum(1e-12).log().neg()).log()).argmax(-1, keepdim=True)
 
@@ -397,6 +430,7 @@ class Transformer:
       qk_norm=int(state_dict['blk.0.attn_q_norm.weight'].shape[0]) if 'blk.0.attn_q_norm.weight' in state_dict else 0,
       num_experts=kv.get(f'{arch}.expert_count', 0), num_experts_per_tok=kv.get(f'{arch}.expert_used_count', 0),
       norm_topk_prob=kv.get(f'{arch}.expert_weights_norm', arch in ('qwen3moe', 'qwen35moe', 'kimi-linear')),
+      expert_gating_func=ExpertGating(kv.get(f'{arch}.expert_gating_func', ExpertGating.SOFTMAX)),
       kv_lora_rank=kv_lora_rank, q_lora_rank=kv.get(f'{arch}.attention.q_lora_rank', 0),
       leading_dense_blocks=kv.get(f'{arch}.leading_dense_block_count', 0),
       shared_expert_dim=kv.get(
@@ -420,6 +454,10 @@ class Transformer:
     for _ in range(2): list(zip(range(2), self.generate([0])))
 
   def get_start_pos(self, tokens:list[int]) -> int:
+    # recurrent state can't be partially reused after divergence: reuse it only when tokens extend the cached prefix
+    if self.has_recurrent_block:
+      return len(self._cached_tokens) if self._cached_tokens and len(self._cached_tokens) < len(tokens) \
+        and tokens[:len(self._cached_tokens)] == self._cached_tokens else 0
     prefix_len = sum(1 for _ in itertools.takewhile(lambda ab: ab[0] == ab[1], zip(tokens[:-1], self._cached_tokens)))
     return min(block._reusable_prefix_len(prefix_len, len(self._cached_tokens)) for block in self.blk)
 
@@ -433,7 +471,6 @@ class Transformer:
     t = Tensor(tokens + [0] * (self.max_context - len(tokens)), dtype="int32").reshape(1, self.max_context)
     # recompute start_pos from what's currently valid in the caches
     start_pos = self.get_start_pos(tokens)
-    if start_pos < len(self._cached_tokens) and (resets := [r for b in self.blk for r in b._state_reset_ops()]): Tensor.realize(*resets)
     out, prompt_len = None, len(tokens)
     while len(tokens) < self.max_context:
       n_toks = min(chunk_size, len(tokens) - start_pos)

tinygrad/mixin/elementwise.py

@@ -115,7 +115,8 @@ class ElementwiseMixin(CreationMixin):
     ```
     """
     a, b = self._broadcasted(x, reverse)
-    return a + (-b)
+    # alu, not +: _broadcasted already promoted these, and a second promote would cast -b (only a bare weak CONST is kept weak)
+    return a.alu(Ops.ADD, -b)
 
   def mul(self, x: Self | ConstType, reverse: bool = False) -> Self:
     """
@@ -245,8 +246,9 @@ class ElementwiseMixin(CreationMixin):
     if dtypes.is_int(a.dtype) and dtypes.is_int(b.dtype):
       if rounding_mode == "trunc": return a.alu(Ops.CDIV, b)
       if rounding_mode == "floor": return a.alu(Ops.FLOORDIV, b)
-      a = a.cast(dtypes.default_float)
-    d = a * b.reciprocal()
+    if dtypes.is_int(a.dtype) or a.dtype == dtypes.bool: a = a.cast(dtypes.default_float)
+    # alu, not *: _broadcasted already promoted these, and a second promote would cast 1/b (only a bare weak CONST is kept weak)
+    d = a.alu(Ops.MUL, b.reciprocal())
     if rounding_mode is None: return d
     if rounding_mode == "trunc": return d.trunc()
     if rounding_mode == "floor": return d.floor()

tinygrad/mixin/gradient.py

@@ -3,6 +3,7 @@ import math, dataclasses
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata, broadcast_axes
 from tinygrad.helpers import argsort
 from tinygrad.dtype import sum_acc_dtype
+from tinygrad.function import renumber_invalid_outputs
 
 def reduce_gradient(ctx:UOp, ret:UOp, op:Ops):
   if op == Ops.ADD: return (ctx._broadcast_to(ret.src[0].shape),)
@@ -32,7 +33,7 @@ def call_gradient(ctx:UOp, k:UOp, needed:set[int]) -> tuple[UOp|None, ...]:
   params = {x.arg.slot:x for x in fxn.toposort(enter_calls=False) if x.op == Ops.PARAM}
   grad_args = ctx.src
   root_grad = UOp(Ops.TUPLE, src=tuple(UOp(Ops.NOOP) if g.op is Ops.NOOP else
-    g if g.base.op is Ops.CONST else g.param_like(len(args)+i) for i,g in enumerate(grad_args)))
+    g if g.device is None else g.param_like(len(args)+i) for i,g in enumerate(grad_args)))
   grads = compute_gradient(fxn, root_grad, set(params.values()))
   # for precompiled calls, substitute forward outputs with params so intermediates aren't recomputed
   fwd_subs = {src: src.param_like(len(args)+len(grad_args)+i) for i, src in enumerate(fxn.src)} if k.arg.precompile else {}
@@ -40,6 +41,7 @@ def call_gradient(ctx:UOp, k:UOp, needed:set[int]) -> tuple[UOp|None, ...]:
   # collect needed gradient bodies, compact unused params, create a single backward CALL
   grad_bodies = [(i, grads[p]) for i in needed if (p:=params.get(i)) is not None and p in grads]
   bwd_body = UOp.maketuple(*(gb for _, gb in grad_bodies)).substitute(fwd_subs, walk=True)
+  bwd_body = renumber_invalid_outputs(bwd_body)
   bwd_body, compact_args = _compact_params(bwd_body, (*args, *grad_args, *fwd_outs))
   bwd_call = bwd_body.call(*compact_args, name=(k.arg.name or "")+"_backward", precompile=k.arg.precompile_backward)
   gb_map = {i: idx for idx, (i, _) in enumerate(grad_bodies)}

tinygrad/mixin/movement.py

@@ -46,6 +46,16 @@ class MovementMixin:
     """
     return prod(self.shape)
 
+  @property
+  def max_shape(self) -> tuple[int, ...]:
+    """The shape with every symbolic dimension replaced by its maximum."""
+    from tinygrad.uop.ops import to_max_shape  # deferred: ops.py imports the mixins
+    return to_max_shape(self.shape)
+
+  def max_numel(self) -> int:
+    """The number of elements in `max_shape`."""
+    return prod(self.max_shape)
+
   def size(self, dim:int|None=None) -> sint|tuple[sint, ...]:
     """
     Returns the size of the tensor. If `dim` is specified, return the length along dimension `dim`. Otherwise return the shape of the tensor.
@@ -540,6 +550,7 @@ class MovementMixin:
     if dims is None: return self.flatten().roll(shifts, 0).reshape(self.shape)
     dims, shifts = tuple(self._resolve_dim(d) for d in make_tuple(dims, 1)), make_tuple(shifts, 1)
     if len(dims) != len(shifts): raise RuntimeError(f"{len(dims)=} != {len(shifts)=}")
+    if 0 in self.shape: return self
     shrink_arg: list[tuple[sint, sint]|None] = [None] * self.ndim
     for d, s in zip(dims, shifts): shrink_arg[d] = (delta:=self.shape[d]-s%self.shape[d], delta+self.shape[d])
     return self.repeat(*tuple(2 if i in dims else 1 for i in range(self.ndim))).shrink(tuple(shrink_arg))

tinygrad/mixin/op.py

@@ -289,6 +289,12 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     if value == 0: return base
     return MovementMixin.pad(X.const_like(True, dtypes.bool), pads).where(base, value)
 
+  def pad_to(self, shape, *args, value:ConstType=0) -> Self:
+    # same mask trick as _pad_constant so the fill survives backends that realize PAD as 0-fill
+    ret = MovementMixin.pad_to(self, shape, *args)
+    if value == 0 or ret is self: return ret
+    return MovementMixin.pad_to(self.const_like(True, dtypes.bool), shape, *args).where(ret, value)
+
   def _pad_circular(self, pX:tuple[tuple[sint, sint], ...]) -> Self:
     # shrink first for negative pads, then wrap the non-negative remainder
     X = self.shrink(tuple((-smin(pB,0), smin(pA+sh,sh)) for (pB,pA),sh in zip(pX, self.shape)))
@@ -460,6 +466,7 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     """
     assert gradient is not None or self.shape == tuple(), "when no gradient is provided, backward must be called on a scalar tensor"
     if not (self.is_floating_point() and all(t.is_floating_point() for t in targets)): raise RuntimeError("only float Tensors have gradient")
+    if any(t.dtype in dtypes.weaks for t in targets): raise RuntimeError("cannot take gradient wrt a weak Tensor")
     from tinygrad.mixin.gradient import compute_gradient
     if gradient is None: gradient = self.const_like(1.0)
     target_uops = [t._uop for t in targets]

tinygrad/nn/state.py

@@ -1,4 +1,4 @@
-import json, math, pathlib, zipfile, pickle, tarfile, struct, functools, io, zlib
+import json, math, pathlib, struct, functools, io, zlib
 from collections import OrderedDict
 from typing import Any, Callable, BinaryIO, Iterable, cast
 from tinygrad.tensor import Tensor
@@ -219,6 +219,7 @@ def load_state_dict(model, state_dict:dict[str, Tensor], strict=True, verbose=Tr
 
 @accept_filename
 def zip_extract(t: Tensor) -> dict[str, Tensor]:
+  import zipfile
   files: dict[str, Tensor] = {}
   with zipfile.ZipFile(TensorIO(t), "r") as myzip:
     # sadly, the extra length needs to be read from the local header of each file.
@@ -249,6 +250,7 @@ def tar_extract(t: Tensor) -> dict[str, Tensor]:
   tensors = nn.state.tar_extract(Tensor(pathlib.Path("archive.tar")))
   ```
   """
+  import tarfile
   with tarfile.open(fileobj=TensorIO(t), mode="r") as tar:
     return {member.name:t[member.offset_data:member.offset_data+member.size] for member in tar if member.type == tarfile.REGTYPE}
 
@@ -303,6 +305,7 @@ def torch_load(t:Tensor) -> dict[str, Tensor]:
                "FloatTensor": None, "Parameter": Parameter}
   whitelist = {"torch", "collections", "numpy", "_codecs"}  # NOTE: this is not for security, only speed
   class Dummy: pass
+  import pickle, zipfile, tarfile
   class TorchPickle(pickle.Unpickler):
     def find_class(self, module, name):
       module_root = module.split(".")[0]

tinygrad/renderer/__init__.py

@@ -35,8 +35,8 @@ class Estimates:
         while len(buf.src) and buf.op is not Ops.PARAM: buf = buf.src[0]
         if buf.op is Ops.PARAM:
           # u.src[0] is INDEX, cap at buffer size for re-reads (e.g. matmul)
-          accessed = mem.get((buf, u.op), 0) + u.src[0].max_numel() * u.src[0].dtype.scalar().itemsize * mults
-          mem[(buf, u.op)] = smin(accessed, buf.max_numel() * buf.dtype.scalar().itemsize)
+          accessed = mem.get((buf, u.op), 0) + u.src[0].max_numel() * u.src[0].dtype.itemsize * mults
+          mem[(buf, u.op)] = smin(accessed, buf.max_numel() * buf.dtype.itemsize)
       if u.op is Ops.RANGE:
         mult_stack.append(mults)
         if u.dtype is not dtypes.void:  # unbounded loop, unknown trip count
@@ -47,9 +47,9 @@ class Estimates:
       elif u.op is Ops.SPECIAL: mults *= cast(sint, u.src[0].ssimplify()) # NOTE: we don't push to the mult_stack here, you can't end these
       elif u.op is Ops.PARAM and u.arg.addrspace == AddrSpace.ALU and u.expr == 'core_id': mults *= int(u.vmax) + 1
       elif u.op is Ops.LOAD and u.src[0].addrspace != AddrSpace.REG:
-        lds += u.max_numel() * u.dtype.scalar().itemsize * mults
+        lds += u.max_numel() * u.dtype.itemsize * mults
       elif u.op is Ops.STORE and u.src[0].addrspace != AddrSpace.REG:
-        lds += u.max_numel() * u.src[1].dtype.scalar().itemsize * mults
+        lds += u.max_numel() * u.src[1].dtype.itemsize * mults
       elif u.op in GroupOp.ALU and u not in excluded:
         flops += (mults * (2 if u.op is Ops.MULACC else 1)) * u.max_numel()
       elif u.op is Ops.WMMA and u not in excluded:

tinygrad/renderer/cstyle.py

@@ -7,7 +7,6 @@ from tinygrad.helpers import strip_parens, getenv, prod, dedup, Target, NUM_CPU_
 from tinygrad.dtype import dtypes, DType, AddrSpace, truncate, float_to_bf16
 from tinygrad.renderer import Renderer
 
-
 base_rewrite = PatternMatcher([
   # local/reg buffers
   (UPat(Ops.BUFFER, name="x"), lambda ctx,x: ctx.render_buffer(x)),
@@ -20,6 +19,21 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.IF, name="x"), lambda ctx,x: f"if ({ctx[x.src[0]]}) {{"),
   (UPat((Ops.ENDIF, Ops.END)), lambda ctx: "}"),
 
+  # const
+  (UPat.cvar("c").cast(dtypes.floats, name="x"), lambda ctx,x,c: None if math.isfinite(v:=c.val) else \
+    f"({ctx.render_cast(x, ctx.nan if math.isnan(v) else ctx.infinity if v > 0 else f'-{ctx.infinity}')})"),
+  (UPat.cvar("c").cast(dtypes.float), lambda ctx,c: f"{c.val}f"),
+  (UPat.cvar("c").cast(dtypes.int64), lambda ctx,c: f"{c.val}l"),
+  (UPat.cvar("c").cast(dtypes.uint64, name="x"), lambda ctx,x,c: f"{truncate[x.dtype](c.val)}ul"),
+  (UPat.cvar("c").cast(dtypes.uint32, name="x"), lambda ctx,x,c: f"{truncate[x.dtype](c.val)}u"),
+  (UPat.cvar("c").cast(dtypes.bool), lambda ctx,c: "1" if c.val else "0"),
+  # consts are rendered to larger type and casted
+  (UPat.cvar("c").cast((*dtypes.fp8s, dtypes.bfloat16, dtypes.half), name="x"), lambda ctx,x,c: f"({ctx.render_cast(x, f'{c.val}f')})"),
+  (UPat.cvar("c").cast((dtypes.uint8, dtypes.uint16), name="x"), lambda ctx,x,c: f"({ctx.render_cast(x, f'{c.val}u')})"),
+  (UPat.cvar("c").cast((dtypes.int8, dtypes.int16), name="x"), lambda ctx,x,c: f"({ctx.render_cast(x, str(c.val))})"),
+  # default const render
+  (UPat.cvar("c").cast(), lambda ctx,c: str(c.val)),
+
   # casting
   (UPat(Ops.CAST, name="x"), lambda ctx,x: f"__builtin_convertvector({ctx[x.src[0]]}, {ctx.render_type(x)})" \
     if x.max_numel() > 1 and x.addrspace is AddrSpace.REG else None),
@@ -31,25 +45,9 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.BARRIER), lambda ctx: ctx.barrier),
   (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0]](x.arg[-1])}; /* {(x.src[0]).render()} */"),
 
-  # const
-  (UPat(Ops.CONST, arg=math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x, ctx.infinity)})"),
-  (UPat(Ops.CONST, arg=-math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x, f'-{ctx.infinity}')})"),
-  (UPat(Ops.CONST, dtype=dtypes.floats, name="x"), lambda ctx,x: f"({ctx.render_cast(x, ctx.nan)})" if math.isnan(x.val) else None),
-  (UPat(Ops.CONST, dtype=dtypes.float, name="x"), lambda ctx,x: f"{x.val}f"),
-  (UPat(Ops.CONST, dtype=dtypes.int64, name="x"), lambda ctx,x: f"{x.val}l"),
-  (UPat(Ops.CONST, dtype=dtypes.uint64, name="x"), lambda ctx,x: f"{truncate[x.dtype](x.val)}ul"),
-  (UPat(Ops.CONST, dtype=dtypes.uint32, name="x"), lambda ctx,x: f"{truncate[x.dtype](x.val)}u"),
-  (UPat(Ops.CONST, dtype=dtypes.bool, name="x"), lambda ctx,x: "1" if x.val else "0"),
-  # consts are rendered to larger type and casted
-  (UPat(Ops.CONST, (*dtypes.fp8s, dtypes.bfloat16, dtypes.half), name="x"), lambda ctx,x: f"({ctx.render_cast(x, f'{x.val}f')})"),
-  (UPat(Ops.CONST, (dtypes.uint8, dtypes.uint16), name="x"), lambda ctx,x: f"({ctx.render_cast(x, f'{x.val}u')})"),
-  (UPat(Ops.CONST, (dtypes.int8, dtypes.int16), name="x"), lambda ctx,x: f"({ctx.render_cast(x, str(x.val))})"),
-  # default const render
-  (UPat(Ops.CONST, name="x"), lambda ctx,x: str(x.val)),
-
   # SHRINK/INDEX
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var('idx')), name="x"), lambda ctx,**kwargs: ctx.render_index(**kwargs)),
-  (UPat(Ops.SHRINK, src=(UPat.var("buf"), UPat.var('idx'), UPat.cvar()), name="x"), lambda ctx,**kwargs: ctx.render_index(**kwargs)),
+  (UPat(Ops.SHRINK, src=(UPat.var("buf"), UPat.var('idx'), UPat.cvar().cast()), name="x"), lambda ctx,**kwargs: ctx.render_index(**kwargs)),
   (UPat(Ops.STACK, name="x"),
    lambda ctx,x: f"{ctx.float4.replace('float4', ctx.render_type(x))}" + \
                  f"{ctx.float4_style[0]}{','.join([ctx[y] for y in x.src])}{ctx.float4_style[1]}"),
@@ -107,11 +105,11 @@ def uops_to_dtypes(uops:list[UOp]) -> list[tuple[DType, int]]:
 
 def _wmma_name(u:UOp) -> str:
   # sanitize spaces in DType.name (int8 = "signed char")
-  return f"WMMA_{'_'.join(map(str, u.arg[0]))}_{u.arg[1].name}_{u.dtype.scalar().name}".replace(" ", "_")
+  return f"WMMA_{'_'.join(map(str, u.arg[0]))}_{u.arg[1].name}_{u.dtype.name}".replace(" ", "_")
 
 # (name, dims, dtype_in, dtype_out, device, threads, upcast_sizes)
 def wmma_args(uops:list[UOp]):
-  return dedup((_wmma_name(uop), uop.arg[0], uop.arg[1], uop.dtype.scalar(), *(uop.arg[2:4]),
+  return dedup((_wmma_name(uop), uop.arg[0], uop.arg[1], uop.dtype, *(uop.arg[2:4]),
                tuple(uop.src[i].shape[-1] for i in range(3)))
               for uop in uops if uop.op is Ops.WMMA)
 
@@ -163,8 +161,8 @@ class CStyleLanguage(Renderer):
   def render_index(self, x:UOp, buf:UOp, idx:UOp):
     if buf.addrspace == AddrSpace.ALU:
       # this is lane access in C
-      if idx.op is not Ops.CONST: return f"({self[buf]})[{self[idx]}]"
-      return self[buf]+(f"[{idx.val}]" if buf.max_numel() > self.gep_arr_threshold else f".{'xyzwabcd'[idx.val]}")
+      if not (idx.op is Ops.CAST and idx.src[0].op is Ops.CONST): return f"({self[buf]})[{self[idx]}]"
+      return self[buf]+(f"[{idx.src[0].val}]" if buf.max_numel() > self.gep_arr_threshold else f".{'xyzwabcd'[idx.src[0].val]}")
     return f"({self[buf]}+{strip_parens(self[idx]) if idx.arg == Ops.ADD else self[idx]})"
 
   def render_buffer(self, x:UOp):
@@ -182,8 +180,8 @@ class CStyleLanguage(Renderer):
     if addrspace in (AddrSpace.LOCAL, AddrSpace.GLOBAL) or override_ptr:
       suffix = "*"
     if sz > 1:
-      return prefix + self.type_map.get(scalar:=dtype.scalar(), scalar.name).replace(" ", "_") + str(sz) + suffix
-    return prefix + self.type_map.get(scalar:=dtype.scalar(), scalar.name) + suffix
+      return prefix + self.type_map.get(dtype, dtype.name).replace(" ", "_") + str(sz) + suffix
+    return prefix + self.type_map.get(dtype, dtype.name) + suffix
 
   def render_type(self, u:UOp): return self._render_dtype(u.dtype, u.max_numel(), u.addrspace, shape=u._shape)
   def render_access(self, u:UOp):
@@ -210,7 +208,7 @@ class CStyleLanguage(Renderer):
     c: defaultdict[str, int] = defaultdict(int)
     name = "test"
     for u in uops:
-      if u.op in {Ops.NOOP, Ops.GROUP}: continue
+      if u.op in {Ops.NOOP, Ops.GROUP, Ops.CONST}: continue
       if u.op == Ops.STACK and len(u.src) == 0: continue
       if u.op is Ops.AFTER:
         r[u] = r[u.src[0]]
@@ -228,7 +226,7 @@ class CStyleLanguage(Renderer):
       if u.op is Ops.SPECIAL: r[u] = u.arg
       elif u.op is Ops.RANGE: r[u] = f"{axis_letters[u.arg[-1]]}idx"+range_str(u)
       else:
-        prefix = {Ops.WMMA: "wmma", Ops.CONST: "const", Ops.BUFFER: "buf", Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.STACK: "cast",
+        prefix = {Ops.WMMA: "wmma", Ops.BUFFER: "buf", Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.STACK: "cast",
                   Ops.INDEX: "bidx", Ops.LOAD: "val"}.get(u.op, "alu")
         r[u] = f"{prefix}{c[prefix]}"
 
@@ -236,7 +234,8 @@ class CStyleLanguage(Renderer):
       assert l is not None, f"failed to render {u.op} {u.dtype} {[(x.op,x.dtype) for x in u.src]} {u.arg}"
 
       if u.op in {Ops.ENDIF, Ops.END}: depth -= 1
-      if (u.op is not Ops.CAST or u.max_numel() == 1) and (u.op in {Ops.CONST, Ops.INDEX, Ops.SHRINK, Ops.CUSTOMI} or \
+      if (u.op is not Ops.CAST or u.max_numel() == 1) and ((u.op is Ops.CAST and u.src[0].op is Ops.CONST) or \
+        u.op in {Ops.INDEX, Ops.SHRINK, Ops.CUSTOMI} or \
         (u.op is Ops.LOAD and u.src[0].addrspace == AddrSpace.REG and child_count[u] == 1) or \
         (u.op is Ops.CAST and u.addrspace in (AddrSpace.GLOBAL, AddrSpace.LOCAL)) or \
         (u.op in {Ops.STACK, *(GroupOp.ALU-{Ops.WHERE}), Ops.CAST, Ops.BITCAST} and child_count[u] == 1 and not getenv("EXPAND_SSA"))):
@@ -264,6 +263,7 @@ class ClangRenderer(CStyleLanguage):
   nan = '__builtin_nanf("")'
 
   # language options
+  barrier = "__atomic_thread_fence(__ATOMIC_SEQ_CST);"
   buffer_suffix = " restrict"
   type_map = {dtypes.bool:"_Bool", dtypes.half:"__fp16"}
   code_for_op = {**({k:v for k,v in CStyleLanguage.code_for_op.items() if k not in [Ops.EXP2, Ops.SIN, Ops.LOG2, Ops.TRUNC, Ops.RECIPROCAL]}),
@@ -319,8 +319,7 @@ class OpenCLRenderer(CStyleLanguage):
   string_rewrite = PatternMatcher([
     (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"as_{ctx.render_dtype(x.dtype)}(({ctx.render_dtype(x.src[0].dtype)})({ctx[x.src[0]]}))"),
     # bfloat16 constants need to be rendered as their bit pattern since bf16 is stored as ushort
-    (UPat(Ops.CONST, dtypes.bfloat16, name="x"),
-      lambda ctx,x: f"{(struct.unpack('I', struct.pack('f', float_to_bf16(x.val)))[0] >> 16)}u"),
+    (UPat.cvar("c").cast(dtypes.bfloat16), lambda ctx,c: f"{(struct.unpack('I', struct.pack('f', float_to_bf16(c.val)))[0] >> 16)}u"),
     # load/store image (OpenCL)
     (UPat.var('buf').index(UPat.var('idx_y'), UPat.var('idx_x')), lambda ctx,buf,idx_y,idx_x: f"IMAGE<{ctx[buf]}, {ctx[idx_y]}, {ctx[idx_x]}>"),
     (UPat(Ops.LOAD, dtype=dtypes.float, src=(UPat.var('buf').index(UPat.var('idx_y'), UPat.var('idx_x')), UPat.var("var"), UPat.var("gate"))),
@@ -471,7 +470,7 @@ class CUDARenderer(CStyleLanguage):
 class NVCCRenderer(CUDARenderer):
   def __init__(self, target:Target): super().__init__(target, use_nvcc=True)
 
-def fp8_index(dtype: DType): return (dtypes.fp8e4m3, dtypes.fp8e5m2).index(dtype.scalar())
+def fp8_index(dtype: DType): return (dtypes.fp8e4m3, dtypes.fp8e5m2).index(dtype)
 def _ocml(op): return lambda x,dtype: f"__ocml_{op}_f{ {dtypes.half:16, dtypes.double:64}.get(dtype, 32)}({x})"
 
 class HIPRenderer(CStyleLanguage):
@@ -494,10 +493,9 @@ class HIPRenderer(CStyleLanguage):
         (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{_wmma_name(x)}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]},"
           f" {fp8_index(x.src[0].dtype)}, {fp8_index(x.src[0].dtype)}, 0, 0, 0, 0)" if x.arg[0][2] == 128 else None),
         (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{_wmma_name(x)}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]}, 0, 0, 0)"),
-        (UPat(Ops.CONST, dtypes.fp8s, name="x"), lambda ctx,x: f"f32_to_fp8({ctx.nan}, {fp8_index(x.dtype)})" if math.isnan(x.val) else None),
-        (UPat(Ops.CONST, dtypes.fp8s, arg=math.inf, name="x"), lambda ctx,x: f"f32_to_fp8({ctx.infinity}, {fp8_index(x.dtype)})"),
-        (UPat(Ops.CONST, dtypes.fp8s, arg=-math.inf, name="x"), lambda ctx,x: f"f32_to_fp8(-{ctx.infinity}, {fp8_index(x.dtype)})"),
-        (UPat(Ops.CONST, dtypes.fp8s, name="x"), lambda ctx,x: f"f32_to_fp8({x.val}f, {fp8_index(x.dtype)})"),
+        (UPat.cvar("c").cast(dtypes.fp8s, name="x"), lambda ctx,x,c:
+          f"f32_to_fp8({ctx.nan if math.isnan(v:=c.val) else ctx.infinity if v == math.inf else f'-{ctx.infinity}' if v == -math.inf else f'{v}f'},"
+          f" {fp8_index(x.dtype)})"),
         (UPat(Ops.CAST, dtypes.fp8s, (UPat(dtype=dtypes.float),), name="x",),
           lambda ctx,x: f"f32_to_fp8({ctx[x.src[0]]}, {fp8_index(x.dtype)})"),
         (UPat(Ops.CAST, dtypes.float, (UPat.var("y", dtypes.fp8s),), name="x",),
@@ -538,21 +536,21 @@ class HIPRenderer(CStyleLanguage):
     prefix, ockl = [], []
     type_map = { dtypes.bfloat16: "bf16", dtypes.float: "f32", dtypes.half: "f16", dtypes.fp8e4m3: "_fp8_fp8", dtypes.fp8e5m2: "_bf8_bf8" }
     used_dtypes = uops_to_dtypes(uops)
-    if any(u.op is Ops.CONST and not math.isfinite(u.val) for u in uops):
+    if any(u.op is Ops.CAST and u.src[0].op is Ops.CONST and not math.isfinite(u.src[0].val) for u in uops):
       prefix += ["#define INFINITY (__builtin_inff())", "#define NAN (__builtin_nanf(\"\"))"]
     if any(u.op is Ops.SPECIAL for u in uops):
       prefix.append("typedef long unsigned int size_t;")
       ockl = [(f"__ockl_get_{name}", "unsigned int", "size_t", "const") for name in ["local_id", "group_id", "local_size"]]
     ocml_ops = {Ops.EXP2: ("exp2", "pure"), Ops.LOG2: ("log2", "pure"), Ops.SQRT: ("sqrt", "const"), Ops.SIN: ("sin", ""), Ops.TRUNC: ("trunc", "")}
     ocml = [(f"__ocml_{ocml_ops[op][0]}_f{dt.bitsize}", dt.name, dt.name, ocml_ops[op][1])
-      for op, dt in dedup((u.op, u.dtype.scalar()) for u in uops) if op in ocml_ops and dt in (dtypes.half, dtypes.float, dtypes.double)]
+      for op, dt in dedup((u.op, u.dtype) for u in uops) if op in ocml_ops and dt in (dtypes.half, dtypes.float, dtypes.double)]
     if any(dt == dtypes.bfloat16 for dt, _ in used_dtypes):
       prefix.append(f"typedef {'__bf16' if self.is_cdna4(self.target.arch) else 'unsigned short'} hip_bfloat16;")
     if any(dt == dtypes.half for dt, _ in used_dtypes): prefix.append("#define half _Float16")
     if any(dt in dtypes.fp8s for dt, _ in used_dtypes):
       prefix += ["typedef unsigned char hip_bf8;", "typedef unsigned char hip_fp8;"]
     if any((u.op is Ops.CAST and u.dtype in dtypes.fp8s and u.src[0].dtype == dtypes.float) or
-           (u.op is Ops.CONST and u.dtype in dtypes.fp8s) for u in uops):
+           (u.op is Ops.CAST and u.src[0].op is Ops.CONST and u.dtype in dtypes.fp8s) for u in uops):
       prefix.append("""static inline __attribute__((device)) unsigned char f32_to_fp8(float v, int is_bf8) {
   v = (((*(unsigned*)&v)&0x7F800000)!=0x7F800000)?__builtin_amdgcn_fmed3f(v,is_bf8?57344.0f:448.0f,is_bf8?-57344.0f:-448.0f) : v;
   return (unsigned char)(is_bf8?__builtin_amdgcn_cvt_pk_bf8_f32(v,v,0,false):__builtin_amdgcn_cvt_pk_fp8_f32(v,v,0,false));\n}""")

tinygrad/renderer/isa/x86.py

@@ -165,16 +165,16 @@ def scratch_buffer(elem_dt:DType, count:int, slot:int) -> UOp:
   return UOp.placeholder((count,), elem_dt, slot, AddrSpace.LOCAL)
 
 def gated_load(ctx, addr:UOp, alt:UOp, gate:UOp, x:UOp):
-  local = scratch_buffer(addr.src[0].dtype.scalar(), x.max_numel(), next(ctx))
-  local_idx = local.index(UOp.const(0, dtypes.int32), dtype=dtypes.uint64)
+  local = scratch_buffer(addr.src[0].dtype, x.max_numel(), next(ctx))
+  local_idx = local.index(UOp.cconst(0, dtypes.int32), dtype=dtypes.uint64)
   # the selected address is a 64bit value, the AFTER orders the load after the scratch store and carries the element dtype for the encoder
   sel = gate.where(addr.replace(dtype=dtypes.uint64), local_idx)
   ptr = UOp(Ops.AFTER, addr.dtype, (sel, (local_idx if x.max_numel() == 1 else local).store(alt)))
   return ptr.load(dtype=x.dtype)
 
 def gated_store(addr:UOp, gate:UOp, val:UOp):
-  local = scratch_buffer(addr.src[0].dtype.scalar(), val.max_numel(), -1)
-  sel = gate.where(addr.replace(dtype=dtypes.uint64), local.index(UOp.const(0, dtypes.int32), dtype=dtypes.uint64))
+  local = scratch_buffer(addr.src[0].dtype, val.max_numel(), -1)
+  sel = gate.where(addr.replace(dtype=dtypes.uint64), local.index(UOp.cconst(0, dtypes.int32), dtype=dtypes.uint64))
   return UOp(Ops.AFTER, addr.dtype, (sel,)).store(val)
 
 # legalize the new style graph for isel. NOTE: this runs after the spec is verified, some of these rewrites violate it
@@ -195,7 +195,7 @@ pre_isel_matcher = PatternMatcher([
   # if gate in scalar int cmove is not a comparison need to add one to set the flag
   # NOTE: the 0 is int so the bool gate zero-extends and compares as int (a byte compare renders different kernels)
   (UPat.var("m", dtypes.bool).where(UPat.var("a"), UPat.var("b")),
-   lambda m,a,b: m.ne(UOp.const(0, dtypes.int)).where(a,b) if m.op not in GroupOp.Comparison else None),
+   lambda m,a,b: m.ne(UOp.cconst(0, dtypes.int)).where(a,b) if m.op not in GroupOp.Comparison else None),
 ])
 
 # ***** X86 registers *****
@@ -221,15 +221,15 @@ reg_strs = {"rax": {4:"eax", 2:"ax", 1:"al"}, "rcx": {4:"ecx", 2:"cx", 1:"cl"},
 
 # ***** X86 instruction selection *****
 def base(x:UOp, i:int) -> UOp: return s.src[0] if (s:=x.src[i]).op is Ops.INDEX else s
-def lane(x:UOp, i:int) -> int: return s.src[1].val if (s:=x.src[i]).op is Ops.INDEX else 0
+def lane(x:UOp, i:int) -> int: return s.src[1].src[0].val if (s:=x.src[i]).op is Ops.INDEX else 0
 def to_int(dt:DType): return {dtypes.float16: dtypes.int16, dtypes.float32: dtypes.int32, dtypes.float64: dtypes.int64}[dt]
 def def_reg(dt:DType, reg:Register|None=None) -> UOp: return UOp(Ops.INS, dt, arg=X86Ops.DEFINE, tag=None if reg is None else (reg,))
-def imm(dt:DType, v:int) -> UOp: return UOp.const(truncate[dt](v), dt).rtag()
+def imm(dt:DType, v:int) -> UOp: return UOp.cconst(truncate[dt](v), dt).rtag()
 def to_imm(c:UOp) -> UOp|None:
-  if c.op is not Ops.CONST: return None
-  if c.dtype is dtypes.int64: return imm(dtypes.int32, c.val) if not c.overflows(dtypes.int32) else None
-  if c.dtype is dtypes.uint64: return imm(dtypes.uint32, c.val) if not c.overflows(dtypes.uint32) else None
-  if c.dtype in dtypes.ints+(dtypes.bool,): return imm(c.dtype, c.val)
+  if not (c.op is Ops.CAST and (v:=c.src[0]).op is Ops.CONST): return None
+  if c.dtype is dtypes.int64: return imm(dtypes.int32, v.val) if not v.overflows(dtypes.int32) else None
+  if c.dtype is dtypes.uint64: return imm(dtypes.uint32, v.val) if not v.overflows(dtypes.uint32) else None
+  if c.dtype in dtypes.ints+(dtypes.bool,): return imm(c.dtype, v.val)
   return None
 def cmp(x:UOp) -> UOp:
   if x.src[0].dtype is dtypes.float32: return x.ins(X86Ops.VUCOMISS, dtype=dtypes.void)
@@ -237,7 +237,7 @@ def cmp(x:UOp) -> UOp:
   return x.ins(X86Ops.CMP, dtype=dtypes.void) if (i:=to_imm(x.src[1])) is None else x.ins(X86Ops.CMPi, dtype=dtypes.void, src=(x.src[0], i))
 def vcmp(x:UOp) -> UOp:
   v = imm(dtypes.uint8, {Ops.CMPLT: 1, Ops.CMPNE: 4, Ops.CMPEQ: 0}[x.op])
-  if x.dtype.scalar() is dtypes.float32: return x.ins(X86Ops.VCMPSS if x.max_numel() == 1 else X86Ops.VCMPPS, src=x.src + (v,))
+  if x.dtype is dtypes.float32: return x.ins(X86Ops.VCMPSS if x.max_numel() == 1 else X86Ops.VCMPPS, src=x.src + (v,))
   return x.ins(X86Ops.VCMPSD if x.max_numel() == 1 else X86Ops.VCMPPD, src=x.src + (v,))
 
 # vinsertps xmm2, xmm0, xmm1, imm
@@ -252,7 +252,7 @@ def vinsertps(x:UOp) -> UOp:
 # vpinsq xmm2, xmm0, rax, imm
 # inserts element in rax into any position in xmm0, result is written to xmm2 according to imm
 def vpins(x:UOp) -> UOp:
-  op = {1: X86Ops.VPINSRB, 2: X86Ops.VPINSRW, 4: X86Ops.VPINSRD, 8: X86Ops.VPINSRQ}[x.dtype.scalar().itemsize]
+  op = {1: X86Ops.VPINSRB, 2: X86Ops.VPINSRW, 4: X86Ops.VPINSRD, 8: X86Ops.VPINSRQ}[x.dtype.itemsize]
   return functools.reduce(lambda ret,i: x.ins(op, src=(ret, x.src[i], imm(dtypes.uint8, i))), range(len(x.src)), def_reg(x.dtype))
 
 # we don't call ctx.vreg on the srcs to avoid duplicates, a rewrite will assign the tuple of valid registers to a vreg
@@ -289,8 +289,9 @@ def fold_address(x:UOp) -> tuple[UOp, UOp, UOp, UOp]:
   # buffers are indexed by element, everything else (the stack pointer) by byte
   scale = base.dtype.itemsize if base.op in {Ops.PARAM, Ops.BUFFER, Ops.AFTER} else 1
   sz = imm(dtypes.uint8, base.dtype.itemsize)
-  if idx.op is Ops.ADD and idx.src[1].op is Ops.CONST: return (base, _cast(idx.src[0]), _disp(idx.src[1].val * scale), sz)
-  if idx.op is Ops.CONST: return (base, UOp(Ops.NOOP), _disp(idx.val * scale), sz)
+  if idx.op is Ops.ADD and (c:=idx.src[1]).op is Ops.CAST and c.src[0].op is Ops.CONST:
+    return (base, _cast(idx.src[0]), _disp(c.src[0].val * scale), sz)
+  if idx.op is Ops.CAST and idx.src[0].op is Ops.CONST: return (base, UOp(Ops.NOOP), _disp(idx.src[0].val * scale), sz)
   return (base, _cast(idx), _disp(0), sz)
 
 def abi(ctx:IselContext, x:UOp) -> UOp|None:
@@ -353,7 +354,7 @@ isel_matcher = PatternMatcher([
   # cast of void is a noop
   (UPat.var("y").cast(name="x"), lambda y,x: y if y.dtype == dtypes.void else None),
   # range is lowered to acc, cmp, jmp after regalloc
-  (UPat(Ops.RANGE, src=(UPat.cvar("c"),), allow_any_len=True, name="x"), lambda c,x: x.replace(src=(imm(c.dtype, c.val),) + x.src[1:])),
+  (UPat(Ops.RANGE, src=(UPat.cvar("c").cast(),), allow_any_len=True, name="x"), lambda c,x: x.replace(src=(imm(x.dtype, c.val),) + x.src[1:])),
   (UPat(Ops.RANGE, name="x"), lambda ctx,x: x.replace(tag=(ctx.vreg(WGPR),)) if not isinstance(x.tag, tuple) else None),
   # really all a backedge END is is an IF with a tag referencing the RANGE start label
   (UPat(Ops.END, src=(UPat(), UPat(), UPat(GroupOp.Comparison, name="cond")), name="x"),
@@ -367,10 +368,10 @@ isel_matcher = PatternMatcher([
   # function abi constraints
   (UPat((Ops.PARAM, Ops.SPECIAL), name="x"), abi),
   # constants that can't be immediates, move them to registers
-  (UPat.cvar("x", dtypes.int64s), lambda x: x.ins(X86Ops.MOVABS, src=(imm(x.dtype, x.val),)) if not x.tag else None),
-  (UPat.cvar("x", dtypes.ints+(dtypes.bool,)), lambda x: x.ins(X86Ops.MOVi, src=(imm(x.dtype, x.val),)) if not x.tag else None),
-  (UPat.cvar("x", dtypes.floats), lambda x:
-   UOp.const(struct.unpack((dt:=to_int(x.dtype)).fmt, struct.pack(x.dtype.fmt, x.val))[0], dt).bitcast(x.dtype) if not x.tag else None),
+  (UPat.cvar("c").cast(dtypes.int64s, name="x"), lambda c,x: x.ins(X86Ops.MOVABS, src=(imm(x.dtype, c.val),)) if not x.tag else None),
+  (UPat.cvar("c").cast(dtypes.ints+(dtypes.bool,), name="x"), lambda c,x: x.ins(X86Ops.MOVi, src=(imm(x.dtype, c.val),)) if not x.tag else None),
+  (UPat.cvar("c").cast(dtypes.floats, name="x"), lambda c,x:
+   UOp.cconst(struct.unpack((dt:=to_int(x.dtype)).fmt, struct.pack(x.dtype.fmt, c.val))[0], dt).bitcast(x.dtype) if not x.tag else None),
   # conditional moves that use masks NOTE: these currently assume a mask producing cmp exists
   (UPat.var("m").where(UPat.var("a", dtypes.int8s+dtypes.int16s+dtypes.int32s+(dtypes.int64,)), UPat.var("b")), lambda m,a,b:
    a.ins(X86Ops.VPBLENDVB, src=(b, a, m.replace(dtype=m.src[0].dtype))) if a.max_numel() > 1 else None),
@@ -380,7 +381,7 @@ isel_matcher = PatternMatcher([
    a.ins(X86Ops.VBLENDVPD, src=(b, a, m.replace(dtype=m.src[0].dtype)))),
   # in this case we have a mask producing comparison whose user expects a bool, so we convert to bool
   (UPat(GroupOp.Comparison, dtypes.bool, (UPat.var("y", (dtypes.float32, dtypes.float64)), UPat()), name="x"), lambda y,x:
-   UOp(Ops.AND, src=(x.replace(dtype=y.dtype).bitcast(dt:=to_int(y.dtype)), UOp.const(1, dt))).f(Ops.NOOP, dtype=dtypes.bool)),
+   UOp(Ops.AND, src=(x.replace(dtype=y.dtype).bitcast(dt:=to_int(y.dtype)), UOp.cconst(1, dt))).f(Ops.NOOP, dtype=dtypes.bool)),
   # conditional moves that use flags
   (UPat(Ops.CMPLT, src=(UPat(dtype=dtypes.sints), UPat()), name="m").where(UPat.var("a"), UPat.var("b")), lambda m,a,b:
    a.ins(X86Ops.CMOVL, src=(b, a, cmp(m)))),
@@ -420,15 +421,15 @@ isel_matcher = PatternMatcher([
   (UPat(Ops.STACK, dtypes.float32, name="x"), vinsertps),
   (UPat(Ops.STACK, dtypes.ints+(dtypes.bool,), name="x"), vpins),
   # INDEX on a vector register value extracts a single element
-  (UPat.var("y", dtypes.int8s+(dtypes.bool,)).index(UPat.cvar("c"), name="x"),
+  (UPat.var("y", dtypes.int8s+(dtypes.bool,)).index(UPat.cvar("c").cast(), name="x"),
    lambda y,c,x: x.ins(X86Ops.VPEXTRB, src=(y, imm(dtypes.uint8, c.val))) if _is_vec_xmm(y) else None),
-  (UPat.var("y", dtypes.int16s).index(UPat.cvar("c"), name="x"),
+  (UPat.var("y", dtypes.int16s).index(UPat.cvar("c").cast(), name="x"),
    lambda y,c,x: x.ins(X86Ops.VPEXTRW, src=(y, imm(dtypes.uint8, c.val))) if _is_vec_xmm(y) else None),
-  (UPat.var("y", dtypes.int32s).index(UPat.cvar("c"), name="x"),
+  (UPat.var("y", dtypes.int32s).index(UPat.cvar("c").cast(), name="x"),
    lambda y,c,x: x.ins(X86Ops.VPEXTRD, src=(y, imm(dtypes.uint8, c.val))) if _is_vec_xmm(y) else None),
-  (UPat.var("y", dtypes.int64s).index(UPat.cvar("c"), name="x"),
+  (UPat.var("y", dtypes.int64s).index(UPat.cvar("c").cast(), name="x"),
    lambda y,c,x: x.ins(X86Ops.VPEXTRQ, src=(y, imm(dtypes.uint8, c.val))) if _is_vec_xmm(y) else None),
-  (UPat.var("y", dtypes.floats).index(UPat.cvar("c"), name="x"),
+  (UPat.var("y", dtypes.floats).index(UPat.cvar("c").cast(), name="x"),
    lambda y,c,x: x.ins(X86Ops.VPSRLDQ, src=(y, imm(dtypes.uint8, c.val * x.dtype.itemsize))) if _is_vec_xmm(y) else None),
   # packed bitwise
   ((UPat() & UPat()).named("x"), lambda x: x.ins(X86Ops.VPAND) if x.max_numel() > 1 else None),
@@ -453,15 +454,19 @@ isel_matcher = PatternMatcher([
   # scalar int binary
   ((UPat(dtype=dtypes.ints).alu(Ops.CDIV, UPat())).named("x"), idiv),
   # scalar int binary with immediate
-  (UPat.var("a", dtypes.ints) << UPat.cvar("c"), lambda a,c: a.ins(X86Ops.SHLi, src=(a, imm(dtypes.uint8, c.val)))),
-  (UPat.var("a", dtypes.uints) >> UPat.cvar("c"), lambda a,c: a.ins(X86Ops.SHRi, src=(a, imm(dtypes.uint8, c.val)))),
-  (UPat.var("a", dtypes.sints) >> UPat.cvar("c"), lambda a,c: a.ins(X86Ops.SARi, src=(a, imm(dtypes.uint8, c.val)))),
-  (UPat.var("a", dtypes.ints) + UPat.cvar("c"), lambda a,c: a.ins(X86Ops.ADDi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
-  (UPat.var("a", dtypes.ints) * UPat.cvar("c"), lambda a,c: a.ins(X86Ops.IMULi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
-  (UPat.var("a", dtypes.ints+(dtypes.bool,)) & UPat.cvar("c"), lambda a,c: a.ins(X86Ops.ANDi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
-  (UPat.var("a", dtypes.ints+(dtypes.bool,)) | UPat.cvar("c"), lambda a,c: a.ins(X86Ops.ORi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
-  (UPat.var("a", dtypes.ints+(dtypes.bool,)) ^ UPat.cvar("c"), lambda a,c: a.ins(X86Ops.XORi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
-  (UPat(Ops.SUB, dtypes.ints, (UPat.var("a"), UPat.cvar("c"))), lambda a,c: a.ins(X86Ops.SUBi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat.var("a", dtypes.ints) << UPat.cvar("c").cast(), lambda a,c: a.ins(X86Ops.SHLi, src=(a, imm(dtypes.uint8, c.val)))),
+  (UPat.var("a", dtypes.uints) >> UPat.cvar("c").cast(), lambda a,c: a.ins(X86Ops.SHRi, src=(a, imm(dtypes.uint8, c.val)))),
+  (UPat.var("a", dtypes.sints) >> UPat.cvar("c").cast(), lambda a,c: a.ins(X86Ops.SARi, src=(a, imm(dtypes.uint8, c.val)))),
+  (UPat.var("a", dtypes.ints) + UPat.cvar().cast(name="c"), lambda a,c: a.ins(X86Ops.ADDi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat.var("a", dtypes.ints) * UPat.cvar().cast(name="c"), lambda a,c: a.ins(X86Ops.IMULi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat.var("a", dtypes.ints+(dtypes.bool,)) & UPat.cvar().cast(name="c"),
+   lambda a,c: a.ins(X86Ops.ANDi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat.var("a", dtypes.ints+(dtypes.bool,)) | UPat.cvar().cast(name="c"),
+   lambda a,c: a.ins(X86Ops.ORi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat.var("a", dtypes.ints+(dtypes.bool,)) ^ UPat.cvar().cast(name="c"),
+   lambda a,c: a.ins(X86Ops.XORi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
+  (UPat(Ops.SUB, dtypes.ints, (UPat.var("a"), UPat.cvar().cast(name="c"))),
+   lambda a,c: a.ins(X86Ops.SUBi, src=(a, i)) if (i:=to_imm(c)) is not None else None),
   # scalar int binary with register
   ((UPat(dtype=dtypes.ints) << UPat()).named("x"), lambda x: shift(x, X86Ops.SHL)),
   ((UPat(dtype=dtypes.uints) >> UPat()).named("x"), lambda x: shift(x, X86Ops.SHR)),
@@ -572,7 +577,7 @@ def lower_range(ctx, x:UOp) -> tuple[UOp, list[UOp]]:
   if x.dtype is dtypes.void: return (label, [label])
   else:
     acc = x.ins(X86Ops.MOVi, src=(imm(x.dtype, 0),) + x.src[1:])
-    cmp = UOp(Ops.INS, arg=X86Ops.CMPi if x.src[0].op is Ops.CONST else X86Ops.CMP, src=(acc, x.src[0]))
+    cmp = UOp(Ops.INS, arg=X86Ops.CMPi if x.src[0].op is Ops.CAST else X86Ops.CMP, src=(acc, x.src[0]))
     jump_out = UOp(Ops.INS, arg=X86Ops.JGE, src=(cmp,), tag=f".LOOP_OUT_{loop_label}")
     ctx.loop_label[acc] = loop_label
     return (acc, [acc, label, cmp, jump_out])
@@ -591,7 +596,7 @@ def lower_loop(ctx, x:UOp) -> tuple[UOp, list[UOp]]:
 # final rewrite to match the isa spec
 post_regalloc_matcher = PatternMatcher([
   # rewrite FRAME_INDEX to IMM now that the stack size is known
-  (UPat(Ops.INS, arg=X86Ops.FRAME_INDEX, name="x"), lambda ctx,x: (nx:=x.const_like(ctx.stack_size + x.tag), [nx])),
+  (UPat(Ops.INS, arg=X86Ops.FRAME_INDEX, name="x"), lambda ctx,x: (nx:=UOp.cconst(ctx.stack_size + x.tag, x.dtype), [nx])),
   # expand the cmp here so we can preserve rng src edge to get label from ctx
   (UPat(Ops.INS, arg=X86Ops.LOOP_CMP, name="x"), lower_loop),
   # rewrite RANGE to ACC = 0 -> LABEL -> JUMP if ACC >= loop bound
@@ -614,7 +619,7 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
     rm = cast(Register, greg(rm_uop)).index
     idx = cast(Register, greg(idx_uop)).index if idx_uop is not None and greg(idx_uop) is not None else 4
     # for a memory operand the rm size is the element size from the address, otherwise it's the size of the value in the register
-    rm_sz = sz_uop.val if sz_uop is not None else rm_uop.dtype.itemsize
+    rm_sz = sz_uop.src[0].val if sz_uop is not None else rm_uop.dtype.itemsize
     reg_sz = reg_uop.dtype.itemsize if reg_uop is not None else 0
     sz = reg_sz or rm_sz
 
@@ -647,10 +652,10 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
     # 0b10 -- signals memory access with 32bit displacement
     # 0b11 -- signals no memory access
     if disp_uop is not None:
-      assert disp_uop.op is Ops.CONST, "displacement must be a constant"
+      assert disp_uop.op is Ops.CAST, "displacement must be a literal"
       assert disp_uop.dtype in (dtypes.int8, dtypes.int32), "displacement can only be 1 or 4 byte signed int"
       # rbp/r13 always require a displacement
-      if disp_uop.val != 0 or rm == 0b101: mod = 0b01 if disp_uop.dtype.itemsize == 1 else 0b10
+      if disp_uop.src[0].val != 0 or rm == 0b101: mod = 0b01 if disp_uop.dtype.itemsize == 1 else 0b10
       else: mod = 0b00
     else: mod = 0b11
     # x 0b0 and idx 0b100 means rsp which means no index exists
@@ -664,10 +669,10 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
     # DISP byte
     if mod == 0b01 or mod == 0b10:
       assert disp_uop is not None
-      inst += struct.pack(unwrap(disp_uop.dtype.fmt), disp_uop.val)
+      inst += struct.pack(unwrap(disp_uop.dtype.fmt), disp_uop.src[0].val)
     # IMM byte
     if imm_uop is not None:
-      if imm_uop.op is Ops.CONST: inst += struct.pack(unwrap(imm_uop.dtype.fmt), imm_uop.val)
+      if imm_uop.op is Ops.CAST: inst += struct.pack(unwrap(imm_uop.dtype.fmt), imm_uop.src[0].val)
       elif isinstance(greg(imm_uop), Register): inst += bytes([(greg(imm_uop).index & 0b1111) << 4 | 0b0000])
     return inst
 
@@ -677,13 +682,13 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
   if x.arg in X86GroupOp.WriteMem:
     if len(x.src) > 4: address, rest = x.src[:4], x.src[4:]
     else: address, rest = (x, None, None, None), x.src
-    imm_uop = rest[:1] if rest and rest[0].op is Ops.CONST else (None,)
+    imm_uop = rest[:1] if rest and rest[0].op is Ops.CAST else (None,)
     return _encode(rest[0], *address, *(None, *rest[1:])) if reg is None else _encode(None, *address, *(None, *imm_uop))
 
   if x.arg in X86GroupOp.Rm1st:
     if len(x.src) > 3: address, rest = x.src[:4], x.src[4:]
     else: address, rest = (x.src[0], None, None, None), x.src[1:]
-    imm_uop = rest[:1] if rest and rest[0].op is Ops.CONST else (None,)
+    imm_uop = rest[:1] if rest and rest[0].op is Ops.CAST else (None,)
     return _encode(x, *address, *(None, *imm_uop)) if reg is None else _encode(None, *address, *(x if sel else None, *imm_uop))
 
   if x.arg in X86GroupOp.Rm2nd:
@@ -701,7 +706,7 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
 encodings = {
   # moves
   X86Ops.MOVABS: lambda x:
-   bytes([0b0100 << 4 | 0b1 << 3 | 0b00 << 2 | greg(x).index >> 3, 0xB8 + (greg(x).index & 0b111)]) + struct.pack(x.dtype.fmt, x.src[0].val),
+   bytes([0b0100 << 4 | 0b1 << 3 | 0b00 << 2 | greg(x).index >> 3, 0xB8 + (greg(x).index & 0b111)]) + struct.pack(x.dtype.fmt, x.src[0].src[0].val),
   X86Ops.MOV: lambda x: encode(x, 0x8B), X86Ops.MOVi: lambda x: encode(x, 0xC7, reg=0),
   X86Ops.MOVm: lambda x: encode(x, 0x89), X86Ops.LEA: lambda x: encode(x, 0x8D),
   X86Ops.VMOVSS: lambda x: encode(x, 0x10, pp=2, sel=1), X86Ops.VMOVSSm: lambda x: encode(x, 0x11, pp=2, sel=1),
@@ -724,8 +729,8 @@ encodings = {
   X86Ops.VCVTPS2PD: lambda x: encode(x, 0x5A, pp=0, sel=1), X86Ops.VCVTPD2PS: lambda x: encode(x, 0x5A, pp=1, sel=1),
   X86Ops.VCVTTPS2DQ: lambda x: encode(x, 0x5B, pp=2, sel=1), X86Ops.VCVTTPD2DQ: lambda x: encode(x, 0xE6, pp=1, sel=1),
   # the int src is the 2nd src (the rm field), if it was folded into a memory operand its width is the element size of the address
-  X86Ops.VCVTSI2SS: lambda x: encode(x, 0x2A, pp=2, sel=1, we=(x.src[4].val if len(x.src) > 4 else x.src[1].dtype.itemsize) == 8),
-  X86Ops.VCVTSI2SD: lambda x: encode(x, 0x2A, pp=3, sel=1, we=(x.src[4].val if len(x.src) > 4 else x.src[1].dtype.itemsize) == 8),
+  X86Ops.VCVTSI2SS: lambda x: encode(x, 0x2A, pp=2, sel=1, we=(x.src[4].src[0].val if len(x.src) > 4 else x.src[1].dtype.itemsize) == 8),
+  X86Ops.VCVTSI2SD: lambda x: encode(x, 0x2A, pp=3, sel=1, we=(x.src[4].src[0].val if len(x.src) > 4 else x.src[1].dtype.itemsize) == 8),
   X86Ops.VCVTTSS2SI: lambda x: encode(x, 0x2C, pp=2, sel=1, we=x.dtype.itemsize == 8),
   X86Ops.VCVTTSD2SI: lambda x: encode(x, 0x2C, pp=3, sel=1, we=x.dtype.itemsize == 8),
   # int division
@@ -840,10 +845,10 @@ class X86Renderer(ISARenderer):
     def _format_op(x:UOp) -> str: return f"    {(o[7:-1] if (o:=str(x.arg))[-1] in ('i', 'm') else o[7:]).lower():7s}"
     def _format_operands(x:UOp) -> str:
       def _format(src:tuple[UOp, ...]) -> list[str]:
-        return [str(s.val) if s.op is Ops.CONST else reg_strs[o].get(s.dtype.itemsize, o) if \
+        return [str(s.src[0].val) if s.op is Ops.CAST else reg_strs[o].get(s.dtype.itemsize, o) if \
                 (o:=str(greg(s))) in reg_strs else o for s in src if greg(s) is not None]
       def _mem_adress(base:UOp, idx:UOp, disp:UOp, sz:UOp) -> list[str]:
-        return [f"[{greg(base)}" + (f" + {greg(idx)}*{sz.val}" if greg(idx) else "") + (f" + {disp.val}" if disp.val else "") + "]"]
+        return [f"[{greg(base)}" + (f" + {greg(idx)}*{sz.src[0].val}" if greg(idx) else "") + (f" + {d}" if (d:=disp.src[0].val) else "") + "]"]
 
       if len(x.src) > 4 and x.arg in X86GroupOp.WriteMem: ret = _mem_adress(*x.src[:4]) + _format(x.src[4:])
       elif len(x.src) > 3 and x.arg in X86GroupOp.Rm1st: ret = _format((x,)) + _mem_adress(*x.src[:4]) + _format(x.src[4:])

tinygrad/renderer/llvmir.py

@@ -81,8 +81,8 @@ base_rewrite = PatternMatcher([
   (UPat((Ops.INDEX, Ops.SHRINK), src=(UPat((Ops.BUFFER, Ops.PARAM, Ops.AFTER)),), allow_any_len=True, name="x"), lambda ctx,x:
    f"  {ctx[x]} = getelementptr inbounds {ldt(x.dtype)}, {ldt(x.dtype, ptr=True)} {ctx[x.src[0]]}, {ldt(x.src[1].dtype)} {ctx[x.src[1]]}"),
   # register index
-  (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.cvar("idx")), name="x"), lambda ctx,buf,idx,x:
-   f"  {ctx[x]} = extractelement {ldt(buf.dtype, buf.max_numel())} {ctx[buf]}, i32 {idx.val}" if buf.addrspace == AddrSpace.ALU else None),
+  (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.cvar("c").cast()), name="x"), lambda ctx,buf,c,x:
+   f"  {ctx[x]} = extractelement {ldt(buf.dtype, buf.max_numel())} {ctx[buf]}, i32 {c.val}" if buf.addrspace == AddrSpace.ALU else None),
 
   # load/store
   (UPat(Ops.LOAD, src=(UPat.var("idx"), UPat.var("alt"), UPat.var("mask")), name="x"),
@@ -142,7 +142,7 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.IF, name="x"), lambda ctx,x: f"  br i1 {ctx[x.src[0]]}, label %ifbody_{ctx[x][1:]}, label %ifskip_{ctx[x][1:]}\nifbody_{ctx[x][1:]}:"),
   (UPat(Ops.ENDIF, name="x"), lambda ctx,x: f"  br label %ifskip_{ctx[x.src[0]][1:]}\nifskip_{ctx[x.src[0]][1:]}:"),
 
-  (UPat(Ops.BARRIER), lambda ctx: "")
+  (UPat(Ops.BARRIER), lambda ctx: "  fence seq_cst")
 ])
 
 class LLVMRenderer(Renderer):
@@ -165,7 +165,7 @@ class LLVMRenderer(Renderer):
     local_args: list[str] = []
     name = "test"
     for u in uops:
-      if u.op in {Ops.NOOP, Ops.GROUP}: continue
+      if u.op in {Ops.NOOP, Ops.GROUP, Ops.CONST}: continue
       if u.op is Ops.AFTER:
         r[u] = r[u.src[0]]
         continue
@@ -185,7 +185,7 @@ class LLVMRenderer(Renderer):
           kernel.append(f"  {r[u]} = addrspacecast [{size} x {ldt(u.dtype)}] addrspace(3)* @{r[u][1:]} to [{size} x {ldt(u.dtype)}]*")
         else:
           kernel.append(f"  {r[u]} = alloca [{size} x {ldt(u.dtype)}], align 16")
-      elif u.op is Ops.CONST: r[u] = lconst(u.val, u.dtype)
+      elif u.op is Ops.CAST and u.src[0].op is Ops.CONST: r[u] = lconst(u.src[0].val, u.dtype)
       elif u.op is Ops.CAST and ldt(u.dtype) == ldt(u.src[0].dtype):
         r[u] = r[u.src[0]] # cast from signed to unsigned of the same size is a noop, or pointer cast
       else:
@@ -238,8 +238,8 @@ class AMDLLVMRenderer(LLVMRenderer):
     (UPat(Ops.CAST, dtypes.fp8s, (UPat(dtype=dtypes.float),), name="x",), lambda ctx,x:
       f"  {ctx[x]} = call i8 @f32_to_fp8({ldt(x.src[0].dtype)}  {ctx[x.src[0]]}, i1 {'1' if x.dtype == dtypes.fp8e5m2 else '0'})"),
     (UPat(Ops.CAST, dtypes.float, (UPat.var("y", dtypes.fp8s),), name="x",), lambda ctx,x,y:
-      f"  {ctx[x.src[0]]}_i32 = zext i8 {ctx[x.src[0]]} to i32\n"
-      f"  {ctx[x]} = call float @llvm.amdgcn.cvt.f32.{'bf8' if y.dtype == dtypes.fp8e5m2 else 'fp8'}(i32 {ctx[x.src[0]]}_i32, i32 0)"),
+      f"  {ctx[x]}_i32 = zext i8 {ctx[x.src[0]]} to i32\n"
+      f"  {ctx[x]} = call float @llvm.amdgcn.cvt.f32.{'bf8' if y.dtype == dtypes.fp8e5m2 else 'fp8'}(i32 {ctx[x]}_i32, i32 0)"),
   ]) + base_rewrite
   extra_matcher = LLVMRenderer.extra_matcher + create_non_native_float_pats(dtypes.fp8s) + PatternMatcher([
     # amd llvm intrinsics llvm.log2/llvm.exp2 don't support double
@@ -279,43 +279,9 @@ exit: %packed = phi i32 [%packed_bf8, %do_bf8], [%packed_fp8, %do_fp8]\n  %trunc
       (UPat(Ops.WMMA, name="wmma"), lambda ctx, wmma, rdna4=AMDLLVMRenderer.is_rdna4(target.arch), cdna=self.is_cdna:
         render_wmma_amd(ctx, wmma, cdna, rdna4))
     ])
-    if self.is_cdna:
-      self.extra_matcher += PatternMatcher([
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
-          lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint32), x.src[1].bitcast(dtypes.uint32), x.src[2]))
-          if x.arg[0][2] == 128 and x.src[0].dtype.itemsize <= 8 else None),
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
-          lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
-          if x.max_numel() == 4 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 4 else None),
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
-          lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint64), x.src[1].bitcast(dtypes.uint64), x.src[2]))
-          if x.max_numel() == 4 and x.src[0].dtype in dtypes.fp8_ocp and x.src[0].max_numel() == 8 else None),
-      ])
-    if target.arch in {"gfx1100", "gfx1151"}:
-      self.extra_matcher += PatternMatcher([
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.int32), lambda x: x.replace(
-          src=(x.src[0].bitcast(dtypes.uint32), x.src[1].bitcast(dtypes.uint32), x.src[2]))
-          if x.src[0].dtype == dtypes.int8 and x.src[0].max_numel() == 16 else None),
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.half), lambda x: UOp(Ops.STACK, src=tuple(x.replace(
-          src=(x.src[0], x.src[1], UOp(Ops.STACK, src=tuple(x.src[2].index(UOp.const(j//2, dtypes.int16))
-            if j%2 == 0 else UOp.const(0.0, x.src[2].dtype)
-            for j in range(x.max_numel()*2)))),
-          arg=(*x.arg[:4], None)).index(UOp.const(i*2, dtypes.int16))
-          for i in range(x.max_numel()))) if x.max_numel() == 8 else None),
-        (UPat(Ops.WMMA, name="x"), lambda x: x.replace(
-          src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
-          if x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 16 else None),
-      ])
-    if target.arch in {"gfx1200", "gfx1201"}:
-      self.extra_matcher += PatternMatcher([
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.bfloat16), lambda x: x.replace(
-          dtype=dtypes.uint16,
-          src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2].bitcast(dtypes.uint16)))
-            .bitcast(dtypes.bfloat16) if x.max_numel() == 8 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 8 else None),
-        (UPat(Ops.WMMA, name="x", dtype=dtypes.float),
-          lambda x: x.replace(src=(x.src[0].bitcast(dtypes.uint16), x.src[1].bitcast(dtypes.uint16), x.src[2]))
-          if x.max_numel() == 8 and x.src[0].dtype == dtypes.bfloat16 and x.src[0].max_numel() == 8 else None)
-      ])
+    if self.is_cdna: self.extra_matcher += tc.pm_validate_wmma_cdna
+    if target.arch in {"gfx1100", "gfx1151"}: self.extra_matcher += tc.pm_validate_wmma_rdna3
+    if target.arch in {"gfx1200", "gfx1201"}: self.extra_matcher += tc.pm_validate_wmma_rdna4
 
   def supported_dtypes(self): return {d for d in super().supported_dtypes()
                                       if (d not in dtypes.fp8_ocp or self.target.arch == "gfx950") and d not in dtypes.fp8_fnuz}

tinygrad/renderer/nir.py

@@ -137,14 +137,15 @@ class NIRRenderer(Renderer):
     (UPat(Ops.CAST, (dtypes.uchar, dtypes.ushort), src=(UPat.var("x", dtypes.floats),), name="c"), lambda x,c: x.cast(dtypes.int32).cast(c.dtype)),
     # load/store use pointer arithmetic, and the cast does nothing. NOTE: this doesn't apply to image indexing cause it's 1-D
     (UPat((Ops.INDEX, Ops.SHRINK), src=(UPat.var("buf"), UPat.var("off")), allow_any_len=True, name="x"), lambda x,buf,off: x.replace(
-      src=(buf,off.cast(dtypes.long))+x.src[2:]) if buf.addrspace != AddrSpace.REG and not is_image_shape(buf._shape) else None),
+      src=(buf,UOp.const(off.val, dtypes.long) if off.op is Ops.CONST else off.cast(dtypes.long))+x.src[2:])
+      if buf.addrspace != AddrSpace.REG and not is_image_shape(buf._shape) else None),
     # images need index to be int for nir (coordinates only: the INDEX keeps its access dtype)
     (UPat.var("buf").index(UPat.var("idx_y"), UPat.var("idx_x"), name="x"),
      lambda x,buf,idx_y,idx_x: x.replace(src=(buf, idx_y.cast(dtypes.int), idx_x.cast(dtypes.int)))),
   ])
 
   def_rewrite = PatternMatcher([
-    (UPat(Ops.CONST, name="x"), lambda ctx,x: nimm(ctx.b, x.val, x.dtype)),
+    (UPat.cvar("c").cast(name="x"), lambda ctx,x,c: nimm(ctx.b, c.val, x.dtype)),
     (UPat(Ops.PARAM, name="x"), lambda ctx,x: ctx.param(ctx.b, x, x.dtype.itemsize if x.addrspace is AddrSpace.ALU else 8)),
     (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: nchannel(ctx.b, {'g':ngid, 'l':nlid, 'i': nid}[x.arg[0]](ctx.b), int(x.arg[-1]))),
     (UPat(Ops.STORE, src=(UPat((Ops.INDEX, Ops.SHRINK), src=(UPat.var("buf"),UPat.var("off")), allow_any_len=True), UPat.var("val"))),
@@ -185,16 +186,17 @@ class NIRRenderer(Renderer):
 
   def render(self, uops:list[UOp]):
     self.prerender(uops)
-    for u in [u for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]: self.b.shader.contents.info.workgroup_size[int(u.arg[-1])] = u.src[0].val
+    for u in [u for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]:
+      self.b.shader.contents.info.workgroup_size[int(u.arg[-1])] = u.src[0].src[0].val
     self.r: dict[UOp, Any] = {}
     self.param_idx = 0
     ranges: list[mesa.nir_def|None] = []
 
     for u in uops:
-      if u.op in {Ops.NOOP, Ops.GROUP} or (u.op is Ops.STACK and len(u.src) == 0): pass
+      if u.op in {Ops.NOOP, Ops.GROUP, Ops.CONST} or (u.op is Ops.STACK and len(u.src) == 0): pass
       elif u.op in {Ops.INDEX, Ops.SHRINK}:
         # INDEX on a register value picks the element, memory INDEX is handled in the LOAD/STORE patterns
-        if u.src[0].op not in {Ops.PARAM, Ops.BUFFER, Ops.AFTER}: self.r[u] = nchannel(self.b, self.r[u.src[0]], u.src[1].val)
+        if u.src[0].op not in {Ops.PARAM, Ops.BUFFER, Ops.AFTER}: self.r[u] = nchannel(self.b, self.r[u.src[0]], u.src[1].src[0].val)
       elif u.op is Ops.AFTER:
         self.r[u] = self.r[u.src[0]]
       elif u.op == Ops.SINK:

tinygrad/renderer/ptx.py

@@ -64,7 +64,7 @@ def render_wmma(ctx: "PTXRenderer", wmma: UOp):
 
   for src, regs in zip(wmma.src, ctx.wmma_r):
     for i, reg in enumerate(regs): # pack input and acc registers
-      if (elems_per_reg := 4 // src.dtype.scalar().itemsize) == 1: yield f"mov.b32 {reg}, {ctx.r[src][i]};"
+      if (elems_per_reg := 4 // src.dtype.itemsize) == 1: yield f"mov.b32 {reg}, {ctx.r[src][i]};"
       else: yield f"mov.b32 {reg}, {{{', '.join(ctx.r[src][i * elems_per_reg : (i+1) * elems_per_reg])}}};"
 
   dt_map_in, dt_map_out = {dtypes.float: "tf32", dtypes.half: "f16"}, {dtypes.float: "f32", dtypes.half: "f16"}
@@ -79,8 +79,8 @@ def modifier(a: DType, b: DType): return '.rzi' if dtypes.is_int(a) and dtypes.i
   (a.itemsize < b.itemsize or dtypes.is_int(b) or b == dtypes.bool) else ''
 
 string_rewrite = PatternMatcher([
-  (UPat.cvar("x", dtypes.bool), lambda ctx, x: f"setp.ne.s16 {ctx.r[x]}, {render_val(x.val, x.dtype)}, 0;"),
-  (UPat.cvar("x"), lambda ctx, x: f"mov.b{ctx.types[x.dtype][1:]} {ctx.r[x]}, {render_val(x.val, x.dtype)};"),
+  (UPat.cvar("c").cast(dtypes.bool, name="x"), lambda ctx, x, c: f"setp.ne.s16 {ctx.r[x]}, {render_val(c.val, x.dtype)}, 0;"),
+  (UPat.cvar("c").cast(name="x"), lambda ctx, x, c: f"mov.b{ctx.types[x.dtype][1:]} {ctx.r[x]}, {render_val(c.val, x.dtype)};"),
   (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"mov.u32 %{x.arg}, %{'ctaid' if x.arg[0] == 'g' else 'tid'}.{chr(120+int(x.arg[-1]))};"),
   (UPat(Ops.PARAM, name="x"), lambda ctx, x:
    f"ld.param.{ctx.types[dtypes.ulong] if x.addrspace is AddrSpace.GLOBAL else ctx.mem_types[x.dtype]} {ctx.r[x]}, [data{x.arg.slot}+0];"),
@@ -101,17 +101,17 @@ string_rewrite = PatternMatcher([
      if loc.addrspace == AddrSpace.REG else None),
   (UPat(Ops.STORE, src=(UPat((Ops.INDEX, Ops.SHRINK), name="loc"), UPat.var("var"))),
    lambda ctx, loc, var: f"st.{mem_type(loc)}" + \
-    f"{f'.v{cnt}' if ((cnt:=var.max_numel())>1) else ''}.{ctx.mem_types[var.dtype.scalar()]} " + \
+    f"{f'.v{cnt}' if ((cnt:=var.max_numel())>1) else ''}.{ctx.mem_types[var.dtype]} " + \
     f"[{ctx.r[loc]}+0], {('{' + ', '.join(ctx.r[var]) + '}') if var.max_numel() > 1 else ctx.r[var]};"),
   (UPat(Ops.LOAD, name="x", src=(UPat((Ops.INDEX, Ops.SHRINK), name="loc"), UPat.var("alt"), UPat.var("gate"))),
     lambda ctx, x, loc, alt, gate: flatten([
-    [f"mov.{ctx.mem_types[x.dtype.scalar()]} {v}, {render_val(0, x.dtype.scalar())};" for v in ctx.r[x]],
-    [f"@{ctx.r[gate]} ld.{mem_type(loc)}.v{x.max_numel()}.{ctx.mem_types[x.dtype.scalar()]} {{{', '.join(ctx.r[x])}}}, [{ctx.r[loc]}+0];"]
+    [f"mov.{ctx.mem_types[x.dtype]} {v}, {render_val(0, x.dtype)};" for v in ctx.r[x]],
+    [f"@{ctx.r[gate]} ld.{mem_type(loc)}.v{x.max_numel()}.{ctx.mem_types[x.dtype]} {{{', '.join(ctx.r[x])}}}, [{ctx.r[loc]}+0];"]
   ]) if alt.max_numel() > 1 else [
-    f"@{ctx.r[gate]} ld.{mem_type(loc)}.{ctx.mem_types[x.dtype.scalar()]} {ctx.r[x]}, [{ctx.r[loc]}+0];",
-    f"@!{ctx.r[gate]} mov.b{ctx.types[x.dtype.scalar()][1:]} {ctx.r[x]}, {ctx.r[alt]};"]),
+    f"@{ctx.r[gate]} ld.{mem_type(loc)}.{ctx.mem_types[x.dtype]} {ctx.r[x]}, [{ctx.r[loc]}+0];",
+    f"@!{ctx.r[gate]} mov.b{ctx.types[x.dtype][1:]} {ctx.r[x]}, {ctx.r[alt]};"]),
   (UPat(Ops.LOAD, name="x", src=(UPat((Ops.INDEX, Ops.SHRINK), name="loc"),)),
-    lambda ctx, x, loc: f"ld.{mem_type(loc)}.v{x.max_numel()}.{ctx.mem_types[x.dtype.scalar()]} {{{', '.join(ctx.r[x])}}}, [{ctx.r[loc]}+0];" \
+    lambda ctx, x, loc: f"ld.{mem_type(loc)}.v{x.max_numel()}.{ctx.mem_types[x.dtype]} {{{', '.join(ctx.r[x])}}}, [{ctx.r[loc]}+0];" \
      if x.max_numel() > 1 else f"ld.{mem_type(loc)}.{ctx.mem_types[x.dtype]} {ctx.r[x]}, [{ctx.r[loc]}+0];"),
   # simple
   (UPat(Ops.BUFFER, name="x"), lambda ctx, x: [] if x.addrspace == AddrSpace.REG else [
@@ -186,7 +186,7 @@ class PTXRenderer(Renderer):
 
     name = "test"
     for u in uops:
-      if u.op in {Ops.NOOP, Ops.GROUP}: continue
+      if u.op in {Ops.NOOP, Ops.GROUP, Ops.CONST}: continue
       if u.op is Ops.AFTER:
         self.r[u] = self.r[u.src[0]]
         continue
@@ -197,26 +197,26 @@ class PTXRenderer(Renderer):
         r[u] = [cast(str,r[x]) for x in u.src]
         continue
       if u.op is Ops.BUFFER and u.addrspace == AddrSpace.REG:
-        r[u] = [ssa("reg", u, self.types[u.dtype.scalar()]) for _ in range(u.max_numel())]
+        r[u] = [ssa("reg", u, self.types[u.dtype]) for _ in range(u.max_numel())]
         continue
       if u.op in {Ops.INDEX, Ops.SHRINK, Ops.LOAD} and u.src[0].addrspace in (AddrSpace.REG, AddrSpace.ALU):
         # on REG, INDEX/SHRINK pick the register (must be CONST) and LOAD is a noop
-        if u.op is not Ops.LOAD and u.src[1].op is not Ops.CONST:
+        if u.op is not Ops.LOAD and not (u.src[1].op is Ops.CAST and u.src[1].src[0].op is Ops.CONST):
           raise RuntimeError(f"PTX does not support dynamic register indexing: {u}")
-        r[u] = r[u.src[0]] if u.op is Ops.LOAD else r[u.src[0]][u.src[1].val]
+        r[u] = r[u.src[0]] if u.op is Ops.LOAD else r[u.src[0]][u.src[1].src[0].val]
         continue
       if u.op is Ops.SPECIAL: r[u] = "%" + u.arg
       elif u.op is Ops.LOAD:
-        r[u] = [ssa('val', dtype=self.types[u.dtype.scalar()]) for _ in range(u.max_numel())] if u.max_numel() > 1 else ssa('val', u)
+        r[u] = [ssa('val', dtype=self.types[u.dtype]) for _ in range(u.max_numel())] if u.max_numel() > 1 else ssa('val', u)
       elif u.op is Ops.PARAM: bufs.append((f"data{u.arg.slot}", u))
       elif u.op is Ops.WMMA:
         # registers for packing/unpacking input and acc
-        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.src[0].dtype.scalar().itemsize)],
-                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.src[0].dtype.scalar().itemsize)],
-                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.dtype.scalar().itemsize)]]
-        r[u] = [ssa("wmma", dtype=self.types[u.dtype.scalar()]) for _ in range(u.max_numel())]
+        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.src[0].dtype.itemsize)],
+                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.src[0].dtype.itemsize)],
+                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.dtype.itemsize)]]
+        r[u] = [ssa("wmma", dtype=self.types[u.dtype]) for _ in range(u.max_numel())]
       prefix, dtype = {Ops.CAST: ("cast", None), Ops.BITCAST: ("cast", None), Ops.END: ("pred", "pred"), Ops.RANGE: ("ridx", None),
-        Ops.CONST: ("const", None), Ops.BUFFER: ("local", "u64"), Ops.INDEX: ("bidx", "u64"), Ops.SHRINK: ("bidx", "u64"),
+        Ops.BUFFER: ("local", "u64"), Ops.INDEX: ("bidx", "u64"), Ops.SHRINK: ("bidx", "u64"),
         Ops.PARAM: ("dat", "u64" if u.addrspace is AddrSpace.GLOBAL else None), **{op: ("alu", None) for op in GroupOp.ALU}}.get(u.op, (None, None))
       if u.op is Ops.RANGE and u.dtype == dtypes.void: prefix = None  # loop headers don't have a register
       if prefix: r[u] = ssa(prefix, u, dtype)

tinygrad/renderer/wgsl.py

@@ -50,8 +50,9 @@ wgsl_matcher = PatternMatcher([
   (UPat.store(UPat.var("b"), UPat.var("var"), name="s"), lambda b,var,s: packed_store(b,var) if is_packed(s) else None),
   (UPat.var("a") << UPat.var("b"),lambda a,b:(a.bitcast(dtypes.uint32)<<b.cast(dtypes.uint32)).bitcast(a.dtype) if b.dtype!=dtypes.uint32 else None),
   (UPat.var("x") >> UPat.var("y"), lambda x,y: UOp(Ops.SHR, x.dtype, (x,y.cast(dtypes.uint))) if y.dtype != dtypes.uint else None),
-  # fix nan check: 'a != a -> is_nan()'
-  (UPat.var("a") != UPat.var("a"), is_nan),
+  # fix nan check: 'a != a -> is_nan()'. the decomp rewrites (a != a).logical_not() to CMPEQ, so match both forms
+  (UPat.var("a", dtypes.floats) != UPat.var("a"), is_nan),
+  (UPat.var("a", dtypes.floats).alu(Ops.CMPEQ, UPat.var("a")), lambda a: is_nan(a).ne(True)),
   ])
 
 class WGSLRenderer(CStyleLanguage):
@@ -68,10 +69,10 @@ class WGSLRenderer(CStyleLanguage):
 
   string_rewrite = PatternMatcher([
     (UPat(Ops.NEG, dtypes.uints, src=(UPat.var('x'))), lambda ctx,x: f"(0-{ctx[x]})"),
-    (UPat.cvar("x", dtype=dtypes.bool), lambda x: "true" if x.val else "false"),
-    (UPat(Ops.CONST, dtype=(dtypes.uchar, dtypes.ushort, dtypes.uint32), name="x"),
-     lambda x: f"bitcast<u32>({x.val})" if x.val < 0 else f"{x.val&0xFFFFFFFF}u"),
-    (UPat(Ops.CONST, dtype=dtypes.int32, name="x"), lambda ctx,x: f"{truncate[x.dtype](x.val)}"),
+    (UPat.cvar("c").cast(dtypes.bool), lambda c: "true" if c.val else "false"),
+    (UPat.cvar("c").cast((dtypes.uchar, dtypes.ushort, dtypes.uint32)),
+     lambda c: f"bitcast<u32>({c.val})" if c.val < 0 else f"{c.val&0xFFFFFFFF}u"),
+    (UPat.cvar("c").cast(dtypes.int32, name="x"), lambda ctx,x,c: f"{truncate[x.dtype](c.val)}"),
     (UPat(Ops.BUFFER, name="x"), lambda ctx,x:
      f"var{'<workgroup>' if x.addrspace == AddrSpace.LOCAL else ''} {ctx[x]}: array<{ctx.buf_map(x)},{_packed_size(x)}>;"),
     (UPat(Ops.BITCAST, dtype=dtypes.half, name="x", src=(UPat(dtype=(dtypes.short, dtypes.ushort, dtypes.uint32),),)),

tinygrad/runtime/autogen/__init__.py

@@ -1,4 +1,4 @@
-import glob, importlib, os, pathlib, shutil, subprocess, tarfile, tempfile
+import glob, importlib, os, pathlib, subprocess
 from tinygrad.helpers import fetch, flatten, system, getenv
 
 root = (here:=pathlib.Path(__file__).parent).parents[2]
@@ -31,6 +31,7 @@ def load(name, files, **kwargs):
   if not (f:=(root/(path:=kwargs.pop("path", __name__)).replace('.','/')/f"{name}.py")).exists() or getenv('REGEN'):
     files, kwargs['args'] = files() if callable(files) else files, args() if callable(args:=kwargs.get('args', [])) else args
     if (srcs:=kwargs.pop('srcs', None)):
+      import tempfile, tarfile
       srcpath = (td:=tempfile.TemporaryDirectory(f"autogen-src-{name.replace('/','-')}")).name + "/"
       for src in (srcs if isinstance(srcs, list) else [srcs]):
         if 'tar' in src:
@@ -157,7 +158,7 @@ def __getattr__(nm):
           *[f"python3 src/compiler/nir/nir_{s}_h.py --outdir gen" for s in ["intrinsics", "intrinsics_indices"]]]), cwd=path, shell=True, check=True),
   srcs="https://gitlab.freedesktop.org/mesa/mesa/-/archive/mesa-25.2.7/mesa-25.2.7.tar.gz",
   dll=f"'tinymesa_cpu' if DEV.renderer == 'LVP' else 'tinymesa', {tinymesa_path}, emsg='pip install tinymesa==25.2.7.2'",
-  prolog=["from tinygrad.helpers import DEV", "import gzip, base64, platform, sysconfig, os"],
+  prolog=["from tinygrad.helpers import DEV", "import gzip, base64, sysconfig, os"],
   epilog=lambda path: [system(f"{root}/extra/mesa/lvp_nir_options.sh {path}")])
     case "libclang":
       return load("libclang",

tinygrad/runtime/autogen/mesa.py

@@ -5,7 +5,7 @@ from typing import Literal, TypeAlias
 from tinygrad.runtime.support.c import _IO, _IOW, _IOR, _IOWR
 from tinygrad.runtime.support import c
 from tinygrad.helpers import DEV
-import gzip, base64, platform, sysconfig, os
+import gzip, base64, sysconfig, os
 dll = c.DLL('mesa', 'tinymesa_cpu' if DEV.renderer == 'LVP' else 'tinymesa', os.path.join(sysconfig.get_paths()['platlib'], 'tinymesa'), emsg='pip install tinymesa==25.2.7.2')
 class struct_u_printf_info(c.Struct): pass
 u_printf_info: TypeAlias = struct_u_printf_info