only interrupt during an eval

.circleci/config.yml

@@ -0,0 +1,418 @@
+version: 2.1
+
+orbs:
+  apple: ml-explore/pr-approval@0.1.0
+
+parameters:
+  nightly_build:
+    type: boolean
+    default: false
+  weekly_build:
+    type: boolean
+    default: false
+  test_release:
+    type: boolean
+    default: false
+  linux_release:
+    type: boolean
+    default: false
+
+jobs:
+  build_documentation:
+    parameters:
+      upload-docs:
+        type: boolean
+        default: false
+    macos:
+      xcode: "15.2.0"
+    resource_class: macos.m1.medium.gen1
+    steps:
+      - checkout
+      - run:
+          name: Install
+          command: |
+            brew install python@3.9
+            brew install doxygen
+            python3.9 -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install -r docs/requirements.txt
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install . -v
+      - when:
+          condition:
+            not: << parameters.upload-docs >>
+          steps:
+            - run:
+               name: Build documentation
+               command: |
+                 source env/bin/activate
+                 cd docs && doxygen && make html O=-W
+      - when:
+          condition: << parameters.upload-docs >>
+          steps:
+            - add_ssh_keys:
+                fingerprints:
+                  - "SHA256:OhcVVMovbT0pkgMeiVRyxMnjV9R2t+hKBsNcuxq9h+0"
+            - run:
+               name: Upload documentation
+               command: |
+                 source env/bin/activate
+                 git config user.email "mlx@group.apple.com"
+                 git config user.name "CircleCI Docs"
+                 git checkout gh-pages
+                 git rebase main
+                 cd docs
+                 git rm -rf build/html
+                 doxygen && make html O=-W
+                 git add -f build/html
+                 git commit -m "rebase"
+                 git push -f origin gh-pages
+
+  linux_build_and_test:
+    docker:
+      - image: cimg/python:3.9
+
+    steps:
+      - checkout
+      - run:
+          name: Run style checks
+          command: |
+            pip install pre-commit
+            pre-commit run --all
+            if ! git diff --quiet; then echo 'Style checks failed, please install pre-commit and run pre-commit run --all and push the change'; exit 1; fi
+      - run:
+          name: Install dependencies
+          command: |
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install numpy
+            sudo apt-get update
+            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
+      - run:
+          name: Install Python package
+          command: |
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF
+              CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python3 setup.py build_ext --inplace
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF \
+              CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python3 setup.py develop
+      - run:
+          name: Generate package stubs
+          command: |
+            echo "stubs"
+            pip install typing_extensions
+            python setup.py generate_stubs 
+      - run:
+          name: Run Python tests
+          command: |
+            python3 -m unittest discover python/tests -v
+      - run:
+          name: Build CPP only
+          command: |
+            mkdir -p build && cd build 
+            cmake .. -DMLX_BUILD_METAL=OFF -DCMAKE_BUILD_TYPE=DEBUG
+            make -j `nproc`
+      - run:
+          name: Run CPP tests
+          command: ./build/tests/tests
+
+  mac_build_and_test:
+    parameters:
+      xcode_version:
+        type: string
+        default: "15.2.0"
+    macos:
+      xcode: << parameters.xcode_version >>
+    resource_class: macos.m1.medium.gen1
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: |
+            brew install python@3.9
+            brew install openmpi
+            python3.9 -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install numpy
+            pip install torch
+            pip install tensorflow
+            pip install unittest-xml-reporting
+      - run:
+          name: Install Python package
+          command: |
+            source env/bin/activate
+            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+            CMAKE_ARGS="CMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+              pip install -e . -v
+      - run:
+          name: Generate package stubs
+          command: |
+            source env/bin/activate
+            pip install typing_extensions
+            python setup.py generate_stubs 
+      - run:
+          name: Run Python tests
+          command: |
+            source env/bin/activate
+            LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
+            LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
+            mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
+      - run:
+          name: Build example extension
+          command: |
+            source env/bin/activate
+            cd examples/extensions
+            pip install -r requirements.txt
+            python setup.py build_ext -j8
+      - store_test_results:
+          path: test-results
+      - run:
+          name: Build CPP only
+          command: |
+            source env/bin/activate
+            mkdir -p build && cd build && cmake .. && make -j `sysctl -n hw.ncpu`
+      - run:
+          name: Run CPP tests
+          command: |
+            DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 ./build/tests/tests
+      - run:
+          name: Build small binary
+          command: |
+            source env/bin/activate
+            cd build/
+            cmake .. -DCMAKE_BUILD_TYPE=MinSizeRel \
+              -DBUILD_SHARED_LIBS=ON \
+              -DMLX_BUILD_CPU=OFF \
+              -DMLX_BUILD_SAFETENSORS=OFF \
+              -DMLX_BUILD_GGUF=OFF \
+              -DMLX_METAL_JIT=ON
+            make -j `sysctl -n hw.ncpu`
+      - run:
+          name: Run Python tests with JIT
+          command: |
+            source env/bin/activate
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
+              pip install -e . -v
+            LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
+              METAL_DEBUG_ERROR_MODE=0 \
+              python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
+
+  build_release:
+    parameters:
+      python_version:
+        type: string
+        default: "3.9"
+      xcode_version:
+        type: string
+        default: "15.2.0"
+      build_env:
+        type: string
+        default: ""
+    macos:
+      xcode: << parameters.xcode_version >>
+    resource_class: macos.m1.medium.gen1
+    steps:
+      - checkout
+      - run:
+          name: Install dependencies
+          command: |
+            brew install python@<< parameters.python_version >>
+            brew install openmpi
+            python<< parameters.python_version >> -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install --upgrade setuptools
+            pip install numpy
+            pip install twine
+            pip install build
+      - run:
+          name: Install Python package
+          command: |
+            source env/bin/activate
+            DEV_RELEASE=1 \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              pip install . -v
+      - run:
+          name: Generate package stubs
+          command: |
+            source env/bin/activate
+            pip install typing_extensions
+            python setup.py generate_stubs 
+      - run:
+          name: Build Python package
+          command: |
+            source env/bin/activate
+            << parameters.build_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              python -m build -w
+      - when:
+          condition: << parameters.build_env >>
+          steps:
+            - run:
+                name: Upload package
+                command: |
+                  source env/bin/activate
+                  twine upload dist/*
+      - store_artifacts:
+          path: dist/
+
+  build_linux_release:
+    parameters:
+      python_version:
+        type: string
+        default: "3.9"
+      extra_env:
+        type: string
+        default: "DEV_RELEASE=1"
+    docker:
+      - image: ubuntu:20.04
+    steps:
+      - checkout
+      - run:
+          name: Build wheel
+          command: |
+            PYTHON=python<< parameters.python_version >>
+            apt-get update
+            apt-get upgrade -y
+            DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata
+            apt-get install -y apt-utils
+            apt-get install -y software-properties-common
+            add-apt-repository -y ppa:deadsnakes/ppa
+            apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
+            apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            apt-get install -y build-essential git
+            $PYTHON -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install --upgrade setuptools
+            pip install numpy
+            pip install auditwheel
+            pip install patchelf
+            pip install build
+            pip install twine
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              pip install . -v
+            pip install typing_extensions
+            python setup.py generate_stubs 
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python -m build --wheel
+            auditwheel show dist/*
+            auditwheel repair dist/* --plat manylinux_2_31_x86_64
+      - run:
+          name: Upload package
+          command: |
+            source env/bin/activate
+            twine upload wheelhouse/*
+      - store_artifacts:
+          path: wheelhouse/
+
+workflows:
+  build_and_test:
+    when:
+      and:
+        - matches:
+            pattern: "^(?!pull/)[-\\w]+$"
+            value: << pipeline.git.branch >>
+        - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
+        - not: << pipeline.parameters.test_release >>
+    jobs:
+      - mac_build_and_test:
+          matrix:
+            parameters:
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+      - linux_build_and_test
+      - build_documentation 
+
+  build_pypi_release:
+    when:
+      and:
+        - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
+        - not: << pipeline.parameters.test_release >>
+    jobs:
+      - build_release:
+          filters:
+            tags:
+              only: /^v.*/
+            branches:
+              ignore: /.*/
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              xcode_version: ["15.0.0", "15.2.0"]
+              build_env: ["PYPI_RELEASE=1"]
+      - build_documentation:
+          filters:
+            tags:
+              only: /^v.*/
+            branches:
+              ignore: /.*/
+          upload-docs: true
+
+  prb:
+    when:
+      matches:
+        pattern: "^pull/\\d+(/head)?$"
+        value: << pipeline.git.branch >>
+    jobs:
+      - hold:
+          type: approval
+      - apple/authenticate:
+          context: pr-approval
+      - mac_build_and_test:
+          requires: [ hold ]
+          matrix:
+            parameters:
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+      - linux_build_and_test:
+          requires: [ hold ]
+  nightly_build:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.nightly_build >>
+    jobs:
+      - build_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              xcode_version: ["15.0.0", "15.2.0"]
+  weekly_build:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.weekly_build >>
+    jobs:
+      - build_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              build_env: ["DEV_RELEASE=1"]
+  linux_test_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.linux_release >>
+    jobs:
+      - build_linux_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              extra_env: ["PYPI_RELEASE=1"]

.github/actions/build-cuda-release/action.yml

@@ -1,24 +0,0 @@
-name: 'Build CUDA wheel'
-description: 'Build CUDA wheel'
-
-inputs:
-  arch:
-    description: 'Platform architecture tag'
-    required: true
-    type: choice
-    options:
-      - x86_64
-      - aarch64
-
-runs:
-  using: "composite"
-  steps:
-    - name: Build package
-      shell: bash
-      env:
-        CMAKE_ARGS: -DMLX_BUILD_CUDA=ON
-      run: |
-        pip install auditwheel build patchelf setuptools
-        python setup.py clean --all
-        MLX_BUILD_STAGE=2 python -m build -w
-        bash python/scripts/repair_cuda.sh ${{ inputs.arch }}

.github/actions/build-docs/action.yml

@@ -1,38 +0,0 @@
-name: 'Build Documentation'
-description: 'Build documentation'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Setup machine
-      uses: ./.github/actions/setup-linux
-
-    - name: Install dependencies
-      shell: bash
-      run: |
-        sudo apt-get install -y doxygen
-        source .venv/bin/activate
-        pip install -r docs/requirements.txt
-        pip install . -v
-  
-    - name: Build documentation
-      shell: bash
-      run: |
-        source .venv/bin/activate
-        cd docs
-        doxygen
-        make html O=-W
-    
-    - name: Create artifact tar
-      shell: bash
-      run: tar -cf artifact.tar -C docs --dereference build/html index.html
-
-    # Do it manually because upload-pages-artifact requires gtar
-    - name: Upload artifact
-      id: upload-artifact
-      uses: actions/upload-artifact@v5
-      with:
-        name: github-pages
-        path: artifact.tar
-        retention-days: 1
-        if-no-files-found: error

.github/actions/build-linux-release/action.yml

@@ -1,40 +0,0 @@
-name: 'Build Linux wheel'
-description: 'Build Linux wheel'
-
-inputs:
-  build-backend:
-    description: 'Build the backend mlx-cpu package'
-    type: boolean
-    required: false
-    default: false
-  arch:
-    description: 'Platform architecture tag'
-    required: true
-    type: choice
-    options:
-      - x86_64
-      - aarch64
-
-runs:
-  using: "composite"
-  steps:
-    - name: Generate package stubs
-      shell: bash
-      run: |
-        pip install -e ".[dev]" -v
-        pip install typing_extensions
-        python setup.py generate_stubs
-    - name: Build Python package
-      shell: bash
-      run: |
-        pip install auditwheel patchelf build
-        python setup.py clean --all
-        MLX_BUILD_STAGE=1 python -m build -w
-        bash python/scripts/repair_linux.sh ${{ inputs.arch }}
-    - name: Build backend package
-      if: ${{ inputs.build-backend }}
-      shell: bash
-      run: |
-        python setup.py clean --all
-        MLX_BUILD_STAGE=2 python -m build -w
-        auditwheel repair dist/mlx_cpu*.whl --plat manylinux_2_35_${{ inputs.arch }}

.github/actions/build-linux/action.yml

@@ -1,41 +0,0 @@
-name: 'Build and Test on Linux'
-
-inputs:
-  toolkit:
-    description: 'The toolkit to build with'
-    required: false
-    default: 'cpu'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Install Python package
-      id: python_build
-      shell: sh
-      env:
-        DEBUG: 1
-        CMAKE_ARGS: >-
-          -DCMAKE_COMPILE_WARNING_AS_ERROR=ON
-          -DMLX_BUILD_CUDA=${{ startsWith(inputs.toolkit, 'cuda') && 'ON' || 'OFF' }}
-      run: |
-        if ${{ startsWith(inputs.toolkit, 'cuda') && runner.arch == 'arm64' }} ; then
-          # There is no GPU in arm64 runner, use a common arch.
-          CMAKE_ARGS="$CMAKE_ARGS -DMLX_CUDA_ARCHITECTURES=90a"
-          # Can not build tests when the built executables can not run.
-          CMAKE_ARGS="$CMAKE_ARGS -DMLX_BUILD_TESTS=OFF"
-        fi
-        pip install --no-build-isolation -e ".[dev]" -v
-        # Pass the CMAKE_ARGS to following steps.
-        echo CMAKE_ARGS="$CMAKE_ARGS" >> $GITHUB_OUTPUT
-
-    - name: Generate package stubs
-      shell: sh
-      run: |
-        pip install typing_extensions
-        python setup.py generate_stubs
-
-    - name: Build CPP only
-      shell: bash
-      run: |
-        cmake . -B build -DCMAKE_BUILD_TYPE=Debug ${{ steps.python_build.outputs.CMAKE_ARGS }}
-        cmake --build build -j $(nproc)

.github/actions/build-macos-release/action.yml

@@ -1,34 +0,0 @@
-name: 'Build macOS release'
-description: 'Build MLX releases macOS'
-
-inputs:
-  macos-target:
-    description: 'macOS build target'
-    required: false
-    default: '15.0'
-  build-backend:
-    description: 'Build the backend mlx-metal package'
-    type: boolean
-    required: false
-    default: false
-
-runs:
-  using: "composite"
-  steps:
-    - name: Build Python package
-      shell: bash -l {0}
-      env:
-        MACOSX_DEPLOYMENT_TARGET: ${{ inputs.macos-target }}
-      run: |
-        pip install build
-        python setup.py clean --all
-        MLX_BUILD_STAGE=1 python -m build -w
-
-    - name: Build backend package
-      if: ${{ inputs.build-backend }}
-      shell: bash -l {0}
-      env:
-        MACOSX_DEPLOYMENT_TARGET: ${{ inputs.macos-target }}
-      run: |
-        python setup.py clean --all
-        MLX_BUILD_STAGE=2 python -m build -w

.github/actions/build-macos/action.yml

@@ -1,88 +0,0 @@
-name: 'Build and Test on macOS'
-description: 'Build and test MLX on macOS'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Install dependencies
-      env:
-        DEBUG: 1
-        CMAKE_ARGS: "-DCMAKE_COMPILE_WARNING_AS_ERROR=ON"
-      shell: bash -l {0}
-      run: |
-        pip install --upgrade pip
-        pip install cmake setuptools nanobind==2.4.0
-        pip install -e . -v
-
-    - name: Generate package stubs
-      shell: bash -l {0}
-      run: |
-        pip install typing_extensions
-        python setup.py generate_stubs
-
-    - name: Install tests dependencies
-      shell: bash -l {0}
-      run: |
-        pip install numpy torch tensorflow unittest-xml-reporting
-
-    - name: Run Python tests
-      shell: bash -l {0}
-      env:
-        LOW_MEMORY: 1
-      run: |
-        DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
-        DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
-        mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
-        mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
-        if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
-    
-    - name: Build example extension
-      shell: bash -l {0}
-      run: |
-        cd examples/extensions
-        pip install -r requirements.txt
-        python setup.py build_ext --inplace
-        python test.py
-    
-    - name: Build CPP only
-      shell: bash -l {0}
-      run: |
-        mkdir -p build
-        cd build
-        cmake ..
-        make -j $(sysctl -n hw.ncpu)
-    
-    - name: Run CPP tests
-      shell: bash -l {0}
-      env:
-        DEVICE: gpu
-        METAL_DEVICE_WRAPPER_TYPE: 1
-        METAL_DEBUG_ERROR_MODE: 0
-      run: ./build/tests/tests
-    
-    - name: Build small binary with JIT
-      shell: bash -l {0}
-      run: |
-        mkdir -p build
-        cd build
-        cmake .. -DCMAKE_BUILD_TYPE=MinSizeRel \
-          -DBUILD_SHARED_LIBS=ON \
-          -DMLX_BUILD_CPU=OFF \
-          -DMLX_BUILD_SAFETENSORS=OFF \
-          -DMLX_BUILD_GGUF=OFF \
-          -DMLX_METAL_JIT=ON
-        make -j $(sysctl -n hw.ncpu)
-    
-    - name: Run Python tests with JIT
-      shell: bash -l {0}
-      env:
-        LOW_MEMORY: 1
-        DEVICE: gpu
-        METAL_DEVICE_WRAPPER_TYPE: 1
-        METAL_DEBUG_ERROR_MODE: 0
-      run: |
-        CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
-          pip install -e . -v
-        python -m xmlrunner discover \
-            -v python/tests \
-            -o test-results/gpu_jit

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

@@ -1,87 +0,0 @@
-name: 'Setup Linux Environment'
-description: 'Install dependencies for Linux builds'
-
-inputs:
-  toolkit:
-    description: 'Which toolkit to install'
-    required: false
-    default: 'cpu'
-  python-version:
-    description: 'Version of python to set up'
-    required: false
-    default: '3.10'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Use ccache
-      if: ${{ runner.arch == 'x86_64' }}
-      uses: hendrikmuhs/ccache-action@v1.2
-      with:
-        key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ inputs.toolkit }}-py${{ inputs.python-version }}
-        max-size: 1GB
-
-    - name: Install common dependencies
-      shell: bash
-      run: |
-        sudo apt-get update
-        sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev zip
-
-    - uses: actions/setup-python@v6
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - name: Setup Python venv
-      shell: bash
-      run: |
-        python -m venv .venv
-        source .venv/bin/activate
-        pip install setuptools cmake nanobind==2.4.0
-        echo PATH=$PATH >> $GITHUB_ENV
-        # Make cmake search .venv for nanobind
-        echo PYTHONPATH=`python -c 'import sys; print(sys.path[-1])'` >> $GITHUB_ENV
-
-    - name: Install MPI
-      shell: bash
-      run: sudo apt-get install -y openmpi-bin openmpi-common libopenmpi-dev
-
-    - name: Install CUDA toolkit
-      if: ${{ startsWith(inputs.toolkit, 'cuda') }}
-      shell: bash
-      env:
-        # Note: the CI machine does not meet CUDA 13's driver requirement.
-        # Compatibility matrix:
-        # https://docs.nvidia.com/deeplearning/cudnn/backend/latest/reference/support-matrix.html
-        PACKAGES: |
-          {
-            "cuda-12.6": "libcudnn9-dev-cuda-12 cuda-toolkit-12-6",
-            "cuda-12.9": "libcudnn9-dev-cuda-12 cuda-toolkit-12-9",
-            "cuda-13.0": "libcudnn9-dev-cuda-13 cuda-toolkit-13-0"
-          }
-      run: |
-        # The CUDA binaries are hosted in the "sbsa" repo, the "arm64" repo is
-        # Jetson specific. SBSA means Arm Server Base System Architecture.
-        ARCH=${{ runner.arch == 'arm64' && 'sbsa' || 'x86_64' }}
-        wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/$ARCH/cuda-keyring_1.1-1_all.deb
-        sudo dpkg -i cuda-keyring_1.1-1_all.deb
-        sudo apt-get update
-        sudo apt-get install -y \
-            libnccl2 libnccl-dev \
-            ${{ fromJson(env.PACKAGES)[inputs.toolkit] }}
-        echo "/usr/local/${{ inputs.toolkit }}/bin" >> $GITHUB_PATH
-
-    - name: CUDA packages and driver report
-      if: ${{ startsWith(inputs.toolkit, 'cuda') }}
-      shell: bash
-      run: |
-        sudo apt-get install -y ubuntu-drivers-common dkms
-        echo "NVIDIA Driver Packages Available:"
-        sudo ubuntu-drivers list --gpgpu
-        echo "NVIDIA Driver Version:"
-        cat /proc/driver/nvidia/version || echo "nvidia driver not found"
-        echo "Installed NVIDIA and CUDA packages:"
-        dpkg -l | egrep "cuda|nvidia" -i
-        echo "DKMS Status:"
-        dkms status || echo "dkms not found"
-        echo "NVIDIA-SMI Status:"
-        nvidia-smi || echo "nvidia-smi not found"

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

@@ -1,24 +0,0 @@
-name: 'Setup macOS Environment'
-description: 'Install dependencies for macOS builds'
-
-inputs:
-  python-version:
-    description: 'Python version to use'
-    required: false
-    default: '3.10'
-
-runs:
-  using: "composite"
-  steps:
-    - name: Install Homebrew packages
-      shell: sh
-      run: /opt/homebrew/bin/brew install openmpi
-    
-    - name: Verify MetalToolchain installed
-      shell: bash
-      run: xcodebuild -showComponent MetalToolchain
-
-    - uses: conda-incubator/setup-miniconda@v3
-      with:
-        miniconda-version: "latest"
-        python-version: ${{ inputs.python-version }}

.github/actions/test-linux/action.yml

@@ -1,69 +0,0 @@
-name: 'Run Linux tests'
-
-inputs:
-  has-gpu:
-    description: 'Run GPU tests'
-    required: false
-    default: false
-
-runs:
-  using: "composite"
-  steps:
-    - name: Run MPI tests
-      shell: bash
-      run: |
-        echo "::group::MPI tests"
-        mpirun --bind-to none --allow-run-as-root -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
-        echo "::endgroup::"
-
-    - name: Run distributed tests
-      if: ${{ inputs.has-gpu == 'false' }}
-      shell: bash
-      run: |
-        echo "::group::Distributed tests"
-        mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
-        if grep -Fq '[WARN]' stderr.log ; then
-          grep -F '[WARN]' stderr.log
-          echo "Distributed ring test failed";
-          exit 1;
-        fi
-        echo "::endgroup::"
-
-    - name: Run Python tests - CPU
-      if: ${{ inputs.has-gpu == 'false' }}
-      shell: bash
-      env:
-        DEVICE: cpu
-      run: |
-        echo "::group::Python tests - CPU"
-        python -m unittest discover python/tests -v
-        echo "::endgroup::"
-
-    - name: Run Python tests - GPU
-      if: ${{ inputs.has-gpu == 'true' }}
-      shell: bash
-      env:
-        DEVICE: gpu
-      run: |
-        echo "::group::Python tests - GPU"
-        python -m tests discover python/tests -v
-        echo "::endgroup::"
-
-    - name: Run CPP tests - CPU
-      shell: bash
-      env:
-        DEVICE: cpu
-      run: |
-        echo "::group::CPP tests - CPU"
-        ./build/tests/tests
-        echo "::endgroup::"
-
-    - name: Run CPP tests - GPU
-      if: ${{ inputs.has-gpu == 'true' }}
-      shell: bash
-      env:
-        DEVICE: gpu
-      run: |
-        echo "::group::CPP tests - GPU"
-        ./build/tests/tests -sfe="*fft_tests.cpp,*linalg_tests.cpp"
-        echo "::endgroup::"

.github/dependabot.yml

@@ -1,6 +0,0 @@
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"

.github/scripts/setup+build-cpp-linux-fedora-container.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-set -ex
-
-# [Setup] Install dependencies inside the container.
-dnf update -y
-dnf install -y \
-  blas-devel \
-  lapack-devel \
-  openblas-devel \
-  make \
-  cmake \
-  clang \
-  git
-dnf clean all
-
-# [C++] CI Build Sanity Check: Verifies code compilation, not for release.
-export CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON"
-export DEBUG=1
-export CMAKE_C_COMPILER=/usr/bin/clang
-export CMAKE_CXX_COMPILER=/usr/bin/clang++
-
-mkdir -p build
-pushd build
-cmake .. -DMLX_BUILD_METAL=OFF -DCMAKE_BUILD_TYPE=DEBUG
-make -j $(nproc)
-./tests/tests
-popd

.github/workflows/build_and_test.yml

@@ -1,108 +0,0 @@
-name: Build and Test
-
-on:
-  pull_request:
-  push:
-    branches:
-      - main
-      # For testing CI without starting a pull request:
-      - test/*
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
-
-jobs:
-  check_lint:
-    name: Check Lint
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v6
-      - uses: pre-commit/action@v3.0.1
-
-  linux_build_and_test:
-    name: Linux (cpu, ${{ matrix.arch }})
-    needs: check_lint
-    strategy:
-      fail-fast: false
-      matrix:
-        arch: ['x86_64', 'aarch64']
-    runs-on: ${{ matrix.arch == 'x86_64' && 'ubuntu-22.04' || 'ubuntu-22.04-arm' }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-      - uses: ./.github/actions/build-linux
-      - uses: ./.github/actions/test-linux
-
-  cuda_build_and_test:
-    name: Linux (${{ matrix.toolkit }}, ${{ matrix.arch }})
-    if: github.repository == 'ml-explore/mlx'
-    needs: check_lint
-    strategy:
-      fail-fast: false
-      matrix:
-        arch: ['x86_64', 'aarch64']
-        toolkit: ['cuda-12.6', 'cuda-12.9']
-    runs-on: ${{ matrix.arch == 'x86_64' && 'gpu-t4-4-core' || 'ubuntu-22.04-arm' }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-        with:
-          toolkit: ${{ matrix.toolkit }}
-      - uses: ./.github/actions/build-linux
-        with:
-          toolkit: ${{ matrix.toolkit }}
-      - uses: ./.github/actions/test-linux
-        if: matrix.arch == 'x86_64'
-        with:
-          has-gpu: true
-
-  mac_build_and_test:
-    name: macOS (${{ matrix.macos-target }})
-    if: github.repository == 'ml-explore/mlx'
-    strategy:
-      matrix:
-        macos-target: ["14.0", "15.0"]
-    runs-on: [self-hosted, macos]
-    env:
-      MACOSX_DEPLOYMENT_TARGET: ${{ matrix.macos-target }}
-    needs: check_lint
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-macos
-      - uses: ./.github/actions/build-macos
-
-  build_documentation:
-    name: Build Documentation
-    if: github.repository == 'ml-explore/mlx'
-    runs-on: ubuntu-22.04
-    needs: check_lint
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/build-docs
-
-  linux_fedora_build_cpp:
-    name: Linux Fedora (${{ matrix.arch }})
-    needs: check_lint
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - host: ubuntu-22.04
-            arch: x86_64
-          - host: ubuntu-22.04-arm
-            arch: aarch64
-
-    runs-on: ${{ matrix.host }}
-    container:
-      image: fedora:42
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-
-      - name: CPP Build Test - No Release
-        run: |
-          bash ./.github/scripts/setup+build-cpp-linux-fedora-container.sh

.github/workflows/documentation.yml

@@ -1,28 +0,0 @@
-name: Documentation
-
-on:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  build:
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/build-docs
-      
-  deploy:
-    needs: build
-    permissions:
-      pages: write
-      id-token: write
-    runs-on: ubuntu-latest
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    steps:
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4

.github/workflows/nightly.yml

@@ -1,96 +0,0 @@
-name: Nightly Build
-
-on:
-  schedule:
-    - cron: 33 6 * * 1-5
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  build_linux_release:
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.10", "3.14"]
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-      - uses: ./.github/actions/build-linux-release
-        with:
-          build-backend: ${{ matrix.python-version == '3.10' }}
-          arch: "x86_64"
-      - name: Upload mlx artifacts
-        uses: actions/upload-artifact@v5
-        with:
-          name: linux-wheels-${{ matrix.python_version }}
-          path: wheelhouse/mlx-*.whl
-          retention-days: 7
-      - name: Upload mlx-cpu artifacts
-        if: matrix.python_version == '3.10'
-        uses: actions/upload-artifact@v5
-        with:
-          name: mlx-cpu
-          path: wheelhouse/mlx_cpu-*.whl
-          retention-days: 7
-
-  build_linux_with_tests:
-    strategy:
-      fail-fast: false
-      matrix:
-        python_version: ["3.11", "3.12", "3.13", "3.14"]
-        runner:
-          - ubuntu-22.04
-          - ubuntu-22.04-arm
-    runs-on: ${{ matrix.runner }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-        with:
-          python-version: ${{ matrix.python_version }}
-      - uses: ./.github/actions/build-linux
-      - uses: ./.github/actions/test-linux
-
-  build_mac_release:
-    if: github.repository == 'ml-explore/mlx'
-    strategy:
-      matrix:
-        python-version: ["3.10", "3.13"]
-    runs-on: [self-hosted, macos]
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-macos
-        with:
-          python-version: ${{ matrix.python-version }}
-      - uses: ./.github/actions/build-macos
-      - name: Build macOS 15 package
-        uses: ./.github/actions/build-macos-release
-        with:
-          macos-target: 15.0
-          build-backend: ${{ matrix.python-version == '3.10' }}
-      - name: Build macOS 14 package
-        uses: ./.github/actions/build-macos-release
-        with:
-          macos-target: 14.0
-          build-backend: ${{ matrix.python-version == '3.10' }}
-
-  build_cuda_release:
-    if: github.repository == 'ml-explore/mlx'
-    runs-on: ubuntu-22-large
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-        with:
-          toolkit: 'cuda-12.9'
-      - name: Build Python package
-        uses: ./.github/actions/build-cuda-release
-        with:
-          toolkit: 'cuda-12.9'
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v5
-        with:
-          name: mlx-cuda
-          path: wheelhouse/mlx_cuda-*.whl
-          retention-days: 7

.github/workflows/pull_request.yml

@@ -0,0 +1,20 @@
+on:
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  check_lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pre-commit black isort clang-format
+      - name: Run lint
+        run: |
+          pre-commit run --all-files

.github/workflows/release.yml

@@ -1,244 +0,0 @@
-name: PyPI Release
-
-on:
-  push:
-    tags:
-      - 'v*'
-  workflow_dispatch:
-    inputs:
-      dev_release:
-        description: "Do a dev release or regular release"
-        required: true
-        default: "false"
-
-permissions:
-  contents: read
-
-jobs:
-  setup:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Set publishing variables
-        run: echo "Publishing setup complete"
-
-  build_documentation:
-    if: github.repository == 'ml-explore/mlx'
-    runs-on: ubuntu-22.04
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/build-docs
-    
-  deploy_documentation:
-    needs: build_documentation
-    permissions:
-      pages: write
-      id-token: write
-    runs-on: ubuntu-latest
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    steps:
-      - name: Deploy to GitHub Pages
-        id: deployment
-        uses: actions/deploy-pages@v4
-
-  build_linux_release:
-    if: github.repository == 'ml-explore/mlx'
-    strategy:
-      matrix:
-        python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
-        arch: ['x86_64', 'aarch64']
-    runs-on: ${{ matrix.arch == 'x86_64' && 'ubuntu-22.04' || 'ubuntu-22.04-arm' }}
-    env:
-      PYPI_RELEASE: 1
-      DEV_RELEASE: ${{ github.event.inputs.dev_release == 'true' && 1 || 0 }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-        with:
-          python-version: ${{ matrix.python_version }}
-      - uses: ./.github/actions/build-linux-release
-        with:
-          build-backend: ${{ matrix.python-version == '3.10' }}
-          arch: ${{ matrix.arch }}
-      - name: Upload MLX artifacts
-        uses: actions/upload-artifact@v5
-        with:
-          overwrite: true
-          name: linux-wheels-${{ matrix.python_version }}-${{ matrix.arch }}
-          path: wheelhouse/mlx-*.whl
-      - name: Upload CPU artifacts
-        if: matrix.python_version == '3.10'
-        uses: actions/upload-artifact@v5
-        with:
-          overwrite: true
-          name: mlx-cpu-${{ matrix.arch }}
-          path: wheelhouse/mlx_cpu-*.whl
-  
-  build_mac_release:
-    if: github.repository == 'ml-explore/mlx'
-    strategy:
-      matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
-    runs-on: [self-hosted, macos]
-    env:
-      PYPI_RELEASE: 1
-      DEV_RELEASE: ${{ github.event.inputs.dev_release == 'true' && 1 || 0 }}
-
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-macos
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        shell: bash -l {0}
-        run: |
-          pip install --upgrade pip
-          pip install cmake setuptools nanobind==2.4.0
-          pip install -e . -v
-      - name: Generate package stubs
-        shell: bash -l {0}
-        run: |
-          pip install typing_extensions
-          python setup.py generate_stubs
-      - name: Build macOS 14 package
-        uses: ./.github/actions/build-macos-release
-        with:
-          macos-target: 14.0
-          build-backend: ${{ matrix.python-version == '3.10' }}
-      - name: Build macOS 15 package
-        uses: ./.github/actions/build-macos-release
-        with:
-          macos-target: 15.0
-          build-backend: ${{ matrix.python-version == '3.10' }}
-      - name: Upload MLX artifacts
-        uses: actions/upload-artifact@v5
-        with:
-          overwrite: true
-          name: mac-wheels-${{ matrix.python-version }}
-          path: dist/mlx-*.whl
-      - name: Upload Metal artifacts
-        if: matrix.python-version == '3.10'
-        uses: actions/upload-artifact@v5
-        with:
-          overwrite: true
-          name: mlx-metal
-          path: dist/mlx_metal-*.whl
-
-  build_cuda_release:
-    if: github.repository == 'ml-explore/mlx'
-    strategy:
-      matrix:
-        arch: ['x86_64', 'aarch64']
-        toolkit: ['cuda-12.9', 'cuda-13.0']
-    runs-on: ${{ matrix.arch == 'x86_64' && 'ubuntu-22-large' || 'ubuntu-22-large-arm' }}
-    env:
-      PYPI_RELEASE: 1
-      DEV_RELEASE: ${{ github.event.inputs.dev_release == 'true' && 1 || 0 }}
-    steps:
-      - uses: actions/checkout@v6
-      - uses: ./.github/actions/setup-linux
-        with:
-          toolkit: ${{ matrix.toolkit }}
-      - name: Build Python package
-        uses: ./.github/actions/build-cuda-release
-        with:
-          arch: ${{ matrix.arch }}
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v5
-        with:
-          overwrite: true
-          name: mlx-cuda
-          path: wheelhouse/mlx_cuda-*.whl
-
-  pypi-publish:
-    name: Upload release to PyPI
-    runs-on: ubuntu-latest
-    needs: [setup, build_linux_release, build_mac_release]
-    permissions:
-      id-token: write
-    environment:
-      name: pypi
-      url: https://pypi.org/p/mlx
-    steps:
-      - uses: actions/download-artifact@v6
-        with:
-          pattern: linux-wheels-*
-          merge-multiple: true
-          path: dist
-      - uses: actions/download-artifact@v6
-        with:
-          pattern: mac-wheels-*
-          merge-multiple: true
-          path: dist
-      - name: Display structure of downloaded files
-        run: ls -R dist
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://upload.pypi.org/legacy/
-  
-  pypi-publish-cuda:
-    name: Upload CUDA release to PyPI
-    runs-on: ubuntu-latest
-    needs: [setup, build_cuda_release]
-    permissions:
-      id-token: write
-    environment:
-      name: pypi
-      url: https://pypi.org/p/mlx-cuda
-    steps:
-      - uses: actions/download-artifact@v6
-        with:
-          name: mlx-cuda
-          path: dist
-      - name: Display structure of downloaded files
-        run: ls -R dist
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://upload.pypi.org/legacy/
-
-  pypi-publish-cpu:
-    name: Upload CPU release to PyPI
-    runs-on: ubuntu-latest
-    needs: [setup, build_linux_release]
-    permissions:
-      id-token: write
-    environment:
-      name: pypi
-      url: https://pypi.org/p/mlx-cpu
-    steps:
-      - uses: actions/download-artifact@v6
-        with:
-          pattern: mlx-cpu-*
-          merge-multiple: true
-          path: dist
-      - name: Display structure of downloaded files
-        run: ls -R dist
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://upload.pypi.org/legacy/
-
-  pypi-publish-metal:
-    name: Upload Metal release to PyPI
-    runs-on: ubuntu-latest
-    needs: [setup, build_mac_release]
-    permissions:
-      id-token: write
-    environment:
-      name: pypi
-      url: https://pypi.org/p/mlx-metal
-    steps:
-      - uses: actions/download-artifact@v6
-        with:
-          name: mlx-metal
-          path: dist
-      - name: Display structure of downloaded files
-        run: ls -R dist
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository-url: https://upload.pypi.org/legacy/

.gitignore

@@ -36,7 +36,6 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-uv.lock
 
 # vim
 *.swp

.pre-commit-config.yaml

@@ -1,10 +1,4 @@
 repos:
--   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v6.0.0
-    hooks:
-    -   id: check-yaml
-    # -   id: end-of-file-fixer
-    # -   id: trailing-whitespace
 -   repo: https://github.com/pre-commit/mirrors-clang-format
     rev: v19.1.7
     hooks:

ACKNOWLEDGMENTS.md

@@ -19,17 +19,11 @@ MLX was developed with contributions from the following individuals:
 - Gleb Pobudzey: Added the `where` primitive, and groups in 1D and 2D convolutions.
 - Paul Paczuski: Improved stability of BCE loss calculation
 - Max-Heinrich Laves: Added `conv_transpose1d`, `conv_transpose2d`, and `conv_transpose3d` ops.
-- Gökdeniz Gülmez: Added the `Muon (MomentUm Orthogonalized by Newton-schulz)` optimizer, and the `ReLU²` activation function.
 
 <a href="https://github.com/ml-explore/mlx/graphs/contributors">
   <img class="dark-light" src="https://contrib.rocks/image?repo=ml-explore/mlx&anon=0&columns=20&max=100&r=true" />
 </a>
 
-# Organizations
-
-MLX has received contributions from the following companies:
-- NVIDIA Corporation & Affiliates
-
 # Third-Party Software
 
 MLX leverages several third-party software, listed here together with

CMakeLists.txt

@@ -26,7 +26,6 @@ set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 set(CMAKE_INSTALL_MESSAGE NEVER)
-set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 # ----------------------------- Configuration -----------------------------
 option(MLX_BUILD_TESTS "Build tests for mlx" ON)
@@ -35,16 +34,13 @@ option(MLX_BUILD_BENCHMARKS "Build benchmarks for mlx" OFF)
 option(MLX_BUILD_PYTHON_BINDINGS "Build python bindings for mlx" OFF)
 option(MLX_BUILD_METAL "Build metal backend" ON)
 option(MLX_BUILD_CPU "Build cpu backend" ON)
-option(MLX_BUILD_CUDA "Build cuda backend" OFF)
 option(MLX_METAL_DEBUG "Enhance metal debug workflow" OFF)
 option(MLX_ENABLE_X64_MAC "Enable building for x64 macOS" OFF)
 option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
 option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
 option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
-option(MLX_USE_CCACHE "Use CCache for compilation cache when available" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
-option(USE_SYSTEM_FMT "Use system's provided fmt library" OFF)
 
 # --------------------- Processor tests -------------------------
 message(
@@ -67,18 +63,10 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
       message(WARNING "Building for x86_64 arch is not officially supported.")
     endif()
   endif()
+
 else()
   set(MLX_BUILD_METAL OFF)
-endif()
+  message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
-
-if(MLX_USE_CCACHE)
-  find_program(CCACHE_PROGRAM ccache)
-  if(CCACHE_PROGRAM)
-    message(STATUS "Found CCache: ${CCACHE_PROGRAM}")
-    set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-  endif()
 endif()
 
 # ----------------------------- Lib -----------------------------
@@ -89,27 +77,19 @@ cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
 
-# Supress warnings: note: parameter passing for argument of type
-# ‘std::pair<float, float>’ when C++17 is enabled changed to match C++14 in GCC
-# 10.1
-target_compile_options(mlx PRIVATE -Wno-psabi)
-
-if(MLX_BUILD_CUDA)
-  enable_language(CUDA)
-endif()
-
 if(MLX_BUILD_METAL)
-  find_library(METAL_LIB Metal)
+  set(METAL_LIB "-framework Metal")
-  find_library(FOUNDATION_LIB Foundation)
+  set(FOUNDATION_LIB "-framework Foundation")
-  find_library(QUARTZ_LIB QuartzCore)
+  set(QUARTZ_LIB "-framework QuartzCore")
-  if(METAL_LIB)
-    message(STATUS "Metal found ${METAL_LIB}")
-  else()
-    message(
-      FATAL_ERROR
-        "Metal not found. Set MLX_BUILD_METAL=OFF to build without GPU")
 endif()
 
+if(MLX_BUILD_METAL AND NOT METAL_LIB)
+  message(STATUS "Metal not found. Unable to build GPU")
+  set(MLX_BUILD_METAL OFF)
+  set(MLX_METAL_DEBUG OFF)
+elseif(MLX_BUILD_METAL)
+  message(STATUS "Building METAL sources")
+
   if(MLX_METAL_DEBUG)
     add_compile_definitions(MLX_METAL_DEBUG)
   endif()
@@ -117,8 +97,7 @@ if(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION
+    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
-    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
 
   if(${MACOS_SDK_VERSION} LESS 14.0)
     message(
@@ -128,12 +107,9 @@ if(MLX_BUILD_METAL)
   message(STATUS "Building with macOS SDK version ${MACOS_SDK_VERSION}")
 
   set(METAL_CPP_URL
-      https://developer.apple.com/metal/cpp/files/metal-cpp_26.zip)
+      https://developer.apple.com/metal/cpp/files/metal-cpp_macOS15_iOS18.zip)
 
   if(NOT CMAKE_OSX_DEPLOYMENT_TARGET STREQUAL "")
-    if(${CMAKE_OSX_DEPLOYMENT_TARGET} LESS 14.0)
-      message(FATAL_ERROR "MLX requires macOS >= 14.0")
-    endif()
     set(XCRUN_FLAGS "-mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET}")
   endif()
   execute_process(
@@ -142,6 +118,7 @@ if(MLX_BUILD_METAL)
       "echo \"__METAL_VERSION__\" | xcrun -sdk macosx metal ${XCRUN_FLAGS} -E -x metal -P - | tail -1 | tr -d '\n'"
     OUTPUT_VARIABLE MLX_METAL_VERSION COMMAND_ERROR_IS_FATAL ANY)
   FetchContent_Declare(metal_cpp URL ${METAL_CPP_URL})
+
   FetchContent_MakeAvailable(metal_cpp)
   target_include_directories(
     mlx PUBLIC $<BUILD_INTERFACE:${metal_cpp_SOURCE_DIR}>
@@ -149,12 +126,6 @@ if(MLX_BUILD_METAL)
   target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
 endif()
 
-if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-  # With newer clang/gcc versions following libs are implicitly linked, but when
-  # building on old distributions they need to be explicitly listed.
-  target_link_libraries(mlx PRIVATE dl pthread)
-endif()
-
 if(WIN32)
   if(MSVC)
     # GGUF does not build with MSVC.
@@ -182,7 +153,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
     set(MLX_BUILD_ACCELERATE ON)
   else()
-    message(STATUS "Accelerate not found, using default backend.")
+    message(STATUS "Accelerate or arm neon not found, using default backend.")
     set(MLX_BUILD_ACCELERATE OFF)
   endif()
 
@@ -241,6 +212,24 @@ else()
   set(MLX_BUILD_ACCELERATE OFF)
 endif()
 
+find_package(MPI)
+if(MPI_FOUND)
+  execute_process(
+    COMMAND zsh "-c" "mpirun --version"
+    OUTPUT_VARIABLE MPI_VERSION
+    ERROR_QUIET)
+  if(${MPI_VERSION} MATCHES ".*Open MPI.*")
+    target_include_directories(mlx PRIVATE ${MPI_INCLUDE_PATH})
+  elseif(MPI_VERSION STREQUAL "")
+    set(MPI_FOUND FALSE)
+    message(
+      WARNING "MPI found but mpirun is not available. Building without MPI.")
+  else()
+    set(MPI_FOUND FALSE)
+    message(WARNING "MPI which is not OpenMPI found. Building without MPI.")
+  endif()
+endif()
+
 message(STATUS "Downloading json")
 FetchContent_Declare(
   json
@@ -255,19 +244,12 @@ target_include_directories(
   mlx PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
              $<INSTALL_INTERFACE:include>)
 
-# Do not add mlx_EXPORTS define for shared library.
-set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
-
-if(USE_SYSTEM_FMT)
-  find_package(fmt REQUIRED)
-else()
 FetchContent_Declare(
   fmt
   GIT_REPOSITORY https://github.com/fmtlib/fmt.git
   GIT_TAG 10.2.1
   EXCLUDE_FROM_ALL)
 FetchContent_MakeAvailable(fmt)
-endif()
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:fmt::fmt-header-only>)
 
 if(MLX_BUILD_PYTHON_BINDINGS)

CONTRIBUTING.md

@@ -17,11 +17,11 @@ possible.
  
    You can also run the formatters manually as follows:
  
-   ```shell
+     ```
      clang-format -i file.cpp
      ```
  
-   ```shell
+     ```
      black file.py
      ```
  

MANIFEST.in

@@ -1,6 +1,4 @@
 include CMakeLists.txt
-include mlx.pc.in
 recursive-include mlx/ *
-include cmake/*
 include python/src/*
 include python/mlx/py.typed # support type hinting as in PEP-561

README.md

@@ -68,23 +68,18 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
-macOS, run:
 
-```bash
+**With `pip`**:
+
+```
 pip install mlx
 ```
 
-To install the CUDA backend on Linux, run:
+**With `conda`**:
 
-```bash
-pip install mlx[cuda]
 ```
-
+conda install -c conda-forge mlx
-To install a CPU-only Linux package, run:
-
-```bash
-pip install mlx[cpu]
 ```
 
 Checkout the
@@ -110,7 +105,7 @@ Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
 MLX useful in your research and wish to cite it, please use the following
 BibTex entry:
 
-```text
+```
 @software{mlx2023,
   author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
   title = {{MLX}: Efficient and flexible machine learning on Apple silicon},

benchmarks/cpp/irregular_strides.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023 Apple Inc.
 
-#include <cstring>
 #include <iostream>
 #include <sstream>
 
@@ -75,7 +74,7 @@ void time_irregular_binary_ops_3D() {
 
 void time_irregular_binary_ops_4D() {
   auto device = mx::default_device();
-  mx::Shape shape = {8, 8, 512, 512};
+  std::vector<int> shape = {8, 8, 512, 512};
   auto a = mx::random::uniform(shape);
   auto b = mx::random::uniform(shape);
 
@@ -115,7 +114,7 @@ void time_irregular_binary_ops_4D() {
 
 void time_irregular_reshape() {
   auto device = mx::default_device();
-  mx::Shape shape;
+  std::vector<int> shape;
   auto reshape_fn = [&shape, device](const mx::array& a) {
     return mx::reshape(a, shape, device);
   };
@@ -170,7 +169,7 @@ void time_irregular_astype_1D() {
 void time_irregular_astype_2D() {
   auto device = mx::default_device();
   int size = 2048;
-  mx::Shape shape = {size, size};
+  std::vector<int> shape = {size, size};
 
   auto a = mx::random::uniform(shape);
   TIMEM("2D regular", mx::astype, a, mx::int32, device);

benchmarks/cpp/single_ops.cpp

@@ -192,22 +192,6 @@ void time_reductions() {
 
   auto argmin_along_1 = [&a]() { return mx::argmin(a, 1, false); };
   TIME(argmin_along_1);
-
-  auto indices = mx::array({1});
-  auto updates = mx::reshape(mx::array({NAN}), {1, 1, 1});
-  std::vector<int> axes{0};
-  auto b = scatter(a, {indices}, updates, axes);
-  mx::eval(b);
-
-  auto max_along_0 = [&b]() { return mx::max(b, 0, false); };
-  TIME(max_along_0);
-  auto max_along_1 = [&b]() { return mx::max(b, 1, false); };
-  TIME(max_along_1);
-
-  auto min_along_0 = [&b]() { return mx::min(b, 0, false); };
-  TIME(min_along_0);
-  auto min_along_1 = [&b]() { return mx::min(b, 1, false); };
-  TIME(min_along_1);
 }
 
 void time_gather_scatter() {

benchmarks/python/blas/bench_gemm.py

@@ -142,7 +142,9 @@ def bench_shape(B, M, N, K, np_dtype, transpose="nn"):
     t_b = (0, 1, 2) if transpose[1] == "n" else (0, 2, 1)
 
     c_mlx = a_mx.transpose(t_a) @ b_mx.transpose(t_b)
-    c_npy = a_np.transpose(t_a).astype(np_dtype) @ b_np.transpose(t_b).astype(np_dtype)
+    c_npy = a_np.transpose(t_a).astype(np.float32) @ b_np.transpose(t_b).astype(
+        np.float32
+    )
 
     atol = 1e-5 if np_dtype == np.float32 else 1e-4
 
@@ -161,7 +163,7 @@ def get_gflop_count(B, M, N, K):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run gemm benchmarks")
 
-    dtypes = ("float32", "float16", "complex64")
+    dtypes = ("float32", "float16")
     transposes = ("nn", "nt", "tn")
     shapes = (
         (16, 234, 768, 3072),
@@ -185,7 +187,7 @@ if __name__ == "__main__":
                 diff = gflops_mx / gflops_pt - 1.0
 
                 print(
-                    f"{B:3d}, {M:4d}, {N:4d}, {K:4d}, {dtype}, {transpose}, {gflops_pt:05.3f}, {gflops_mx:05.3f}, {100.0 * diff:+5.2f}%"
+                    f"{B:3d}, {M:4d}, {N:4d}, {K:4d}, {dtype}, {transpose}, {gflops_pt:05.3f}, {gflops_mx:05.3f}, {100. * diff:+5.2f}%"
                 )
                 if gflops_pt >= 2.0 * gflops_mx:
                     print("ATTENTION ^^^^^^^")

benchmarks/python/blas/bench_gemv.py

@@ -1,5 +1,6 @@
 # Copyright © 2023 Apple Inc.
 
+import argparse
 import os
 import subprocess
 import time
@@ -195,7 +196,7 @@ def bench_with_out_len(ax, out_vec_len, in_vector_lens, dtype, transpose):
 
 
 for transpose in (False, True):
-    for dtype in ("float32", "float16", "complex64"):
+    for dtype in ("float32", "float16"):
         fig, axs = plt.subplots(
             len(in_vec_sizes), 2, figsize=(8.5, 11), layout="constrained"
         )
@@ -214,7 +215,7 @@ for transpose in (False, True):
         fig.suptitle(f"{device_name}: {dtype} {op_name}")
         fig.savefig(
             os.path.join(
-                results_dir, f"{device_name.replace(' ', '_')}_{dtype}_{op_name}.pdf"
+                results_dir, f'{device_name.replace(" ", "_")}_{dtype}_{op_name}.pdf'
             )
         )
         plt.close(fig)

benchmarks/python/comparative/bench_torch.py

@@ -5,7 +5,6 @@ import os
 import time
 
 import torch
-import torch.cuda
 import torch.mps
 
 
@@ -45,10 +44,8 @@ def bench(f, *args):
 
 
 def sync_if_needed(x):
-    if x.device == torch.device("mps"):
+    if x.device != torch.device("cpu"):
         torch.mps.synchronize()
-    elif x.device == torch.device("cuda"):
-        torch.cuda.synchronize()
 
 
 @torch.no_grad()
@@ -102,14 +99,6 @@ def reduction(op, axis, x):
     sync_if_needed(x)
 
 
-@torch.no_grad()
-def sum_and_add(axis, x, y):
-    z = x.sum(axis=axis, keepdims=True)
-    for i in range(50):
-        z = (z + y).sum(axis=axis, keepdims=True)
-    sync_if_needed(x)
-
-
 @torch.no_grad()
 def softmax(axis, x):
     ys = []
@@ -351,11 +340,7 @@ if __name__ == "__main__":
         args.axis.pop(0)
 
     torch.set_num_threads(1)
-    device = "mps"
+    device = "cpu" if args.cpu else "mps"
-    if torch.cuda.is_available():
-        device = "cuda"
-    if args.cpu:
-        device = "cpu"
 
     types = args.dtype
     if not types:
@@ -475,8 +460,5 @@ if __name__ == "__main__":
     elif args.benchmark == "selu":
         print(bench(selu, x))
 
-    elif args.benchmark == "sum_and_add":
-        print(bench(sum_and_add, axis, *xs))
-
     else:
         raise ValueError(f"Unknown benchmark `{args.benchmark}`.")

benchmarks/python/conv_unaligned_bench.py

@@ -1,107 +0,0 @@
-import math
-import time
-
-import mlx.core as mx
-import numpy as np
-import torch
-
-N_warmup = 10
-N_iter_bench = 100
-N_iter_func = 5
-
-
-def bench(f, a, b):
-    for i in range(N_warmup):
-        f(a, b)
-    torch.mps.synchronize()
-
-    s = time.perf_counter_ns()
-    for i in range(N_iter_bench):
-        f(a, b)
-    e = time.perf_counter_ns()
-    return (e - s) * 1e-9
-
-
-def make_mx_conv_2D(strides=(1, 1), padding=(0, 0), groups=1):
-    def mx_conv_2D(a, b):
-        ys = []
-        for i in range(N_iter_func):
-            y = mx.conv2d(a, b, stride=strides, padding=padding, groups=groups)
-            ys.append(y)
-        mx.eval(ys)
-        return ys
-
-    return mx_conv_2D
-
-
-def make_pt_conv_2D(strides=(1, 1), padding=(0, 0), groups=1):
-    @torch.no_grad()
-    def pt_conv_2D(a, b):
-        ys = []
-        for i in range(N_iter_func):
-            y = torch.conv2d(a, b, stride=strides, padding=padding, groups=groups)
-            ys.append(y)
-        torch.mps.synchronize()
-        return ys
-
-    return pt_conv_2D
-
-
-def bench_shape(N, H, W, C, kH, kW, O, strides, padding, groups, np_dtype):
-    scale = 1.0 / math.sqrt(kH * kH * C)
-    a_np = np.random.uniform(0, 0.5, (N, H, W, C)).astype(np_dtype)
-    b_np = np.random.uniform(-scale, scale, (O, kH, kW, int(C / groups))).astype(
-        np_dtype
-    )
-
-    a_mx = mx.array(a_np)
-    b_mx = mx.array(b_np)
-
-    a_pt = torch.from_numpy(a_np.transpose((0, 3, 1, 2))).to("mps")
-    b_pt = torch.from_numpy(b_np.transpose((0, 3, 1, 2))).to("mps")
-
-    torch.mps.synchronize()
-
-    f_mx = make_mx_conv_2D(strides, padding, groups)
-    f_pt = make_pt_conv_2D(strides, padding, groups)
-
-    time_torch = bench(f_pt, a_pt, b_pt)
-    time_mlx = bench(f_mx, a_mx, b_mx)
-
-    out_mx = mx.conv2d(a_mx, b_mx, stride=strides, padding=padding, groups=groups)
-    out_pt = torch.conv2d(
-        a_pt.to("cpu"), b_pt.to("cpu"), stride=strides, padding=padding, groups=groups
-    )
-    out_pt = torch.permute(out_pt, (0, 2, 3, 1))
-    out_pt = out_pt.numpy(force=True)
-
-    atol = 2e-5 if np_dtype == np.float32 else 1e-4
-
-    if not np.allclose(out_pt, out_mx, atol=atol):
-        print(
-            f"Failed at {(N, H, W, C)}, {(O, kH, kW, C)} [strides = {strides}, padding = {padding}, groups = {groups}] with max(|a - b|) = {np.max(np.abs(out_pt - out_mx))}"
-        )
-
-    return time_mlx, time_torch
-
-
-if __name__ == "__main__":
-    dtype = "float32"
-    shapes = (
-        (4, 32, 32, 21, 3, 3, 128),
-        (4, 32, 32, 21, 3, 3, 37),
-        (4, 32, 32, 370, 3, 3, 370),
-        (4, 32, 32, 370, 7, 7, 128),
-        (2, 320, 640, 21, 7, 7, 21),
-    )
-    for N, H, W, C, kh, kw, O in shapes:
-        time_mlx, time_torch = bench_shape(
-            N, H, W, C, kh, kw, O, (1, 1), (0, 0), 1, dtype
-        )
-        diff = time_torch / time_mlx - 1.0
-
-        print(
-            f"({N}, {H:3d}, {W:3d}, {C:3d}), ({O:3d}, {kh:2d}, {kw:2d}, {C:3d}), {dtype}, {100. * diff:+5.2f}%"
-        )
-        if time_mlx >= 2.0 * time_torch:
-            print("ATTENTION ^^^^^^^")

benchmarks/python/gather_mm_bench.py

@@ -1,74 +0,0 @@
-# Copyright © 2025 Apple Inc.
-
-import mlx.core as mx
-from time_utils import time_fn
-
-N = 1024
-D = 1024
-M = 1024
-E = 32
-I = 4
-
-
-def gather_sort(x, indices):
-    N, M = indices.shape
-    indices = indices.flatten()
-    order = mx.argsort(indices)
-    inv_order = mx.argsort(order)
-    return x.flatten(0, -3)[order // M], indices[order], inv_order
-
-
-def scatter_unsort(x, inv_order, shape=None):
-    x = x[inv_order]
-    if shape is not None:
-        x = mx.unflatten(x, 0, shape)
-    return x
-
-
-def gather_mm_simulate(x, w, indices):
-    x, idx, inv_order = gather_sort(x, indices)
-    for i in range(2):
-        y = mx.concatenate([x[i] @ w[j].T for i, j in enumerate(idx.tolist())], axis=0)
-        x = y[:, None]
-    x = scatter_unsort(x, inv_order, indices.shape)
-    return x
-
-
-def time_gather_mm():
-    x = mx.random.normal((N, 1, 1, D)) / 1024**0.5
-    w1 = mx.random.normal((E, M, D)) / 1024**0.5
-    w2 = mx.random.normal((E, D, M)) / 1024**0.5
-    indices = (mx.random.uniform(shape=(N, I)) * E).astype(mx.uint32)
-    sorted_indices = mx.sort(indices.flatten()).reshape(N, I)
-    mx.eval(x, w1, w2, indices, sorted_indices)
-
-    def gather_mm(x, w1, w2, indices, sort):
-        idx = indices
-        inv_order = None
-        if sort:
-            x, idx, inv_order = gather_sort(x, indices)
-        x = mx.gather_mm(x, w1.swapaxes(-1, -2), rhs_indices=idx, sorted_indices=sort)
-        x = mx.gather_mm(x, w2.swapaxes(-1, -2), rhs_indices=idx, sorted_indices=sort)
-        if sort:
-            x = scatter_unsort(x, inv_order, indices.shape)
-        return x
-
-    time_fn(gather_mm, x, w1, w2, indices, False)
-    time_fn(gather_mm, x, w1, w2, sorted_indices, False)
-    time_fn(gather_mm, x, w1, w2, indices, True)
-
-    x = mx.random.normal((N * I, D)) / 1024**0.5
-    w1 = mx.random.normal((M, D)) / 1024**0.5
-    w2 = mx.random.normal((D, M)) / 1024**0.5
-    mx.eval(x, w1, w2)
-
-    def equivalent_matmul(x, w1, w2):
-        x = x @ w1.T
-        x = x @ w2.T
-        return x
-
-    time_fn(equivalent_matmul, x, w1, w2)
-
-
-if __name__ == "__main__":
-    time_gather_mm()

benchmarks/python/gather_qmm_bench.py

@@ -1,84 +0,0 @@
-# Copyright © 2025 Apple Inc.
-
-import mlx.core as mx
-from time_utils import time_fn
-
-N = 1024
-D = 1024
-M = 1024
-E = 32
-I = 4
-
-
-def gather_sort(x, indices):
-    N, M = indices.shape
-    indices = indices.flatten()
-    order = mx.argsort(indices)
-    inv_order = mx.argsort(order)
-    return x.flatten(0, -3)[order // M], indices[order], inv_order
-
-
-def scatter_unsort(x, inv_order, shape=None):
-    x = x[inv_order]
-    if shape is not None:
-        x = mx.unflatten(x, 0, shape)
-    return x
-
-
-def gather_mm_simulate(x, w, indices):
-    x, idx, inv_order = gather_sort(x, indices)
-    for i in range(2):
-        y = mx.concatenate(
-            [
-                mx.quantized_matmul(x[i], w[0][j], w[1][j], w[2][j], transpose=True)
-                for i, j in enumerate(idx.tolist())
-            ],
-            axis=0,
-        )
-        x = y[:, None]
-    x = scatter_unsort(x, inv_order, indices.shape)
-    return x
-
-
-def time_gather_qmm():
-    x = mx.random.normal((N, 1, 1, D)) / 1024**0.5
-    w1 = mx.random.normal((E, M, D)) / 1024**0.5
-    w2 = mx.random.normal((E, D, M)) / 1024**0.5
-    w1 = mx.quantize(w1)
-    w2 = mx.quantize(w2)
-    indices = (mx.random.uniform(shape=(N, I)) * E).astype(mx.uint32)
-    sorted_indices = mx.sort(indices.flatten()).reshape(N, I)
-    mx.eval(x, w1, w2, indices, sorted_indices)
-
-    def gather_mm(x, w1, w2, indices, sort):
-        idx = indices
-        inv_order = None
-        if sort:
-            x, idx, inv_order = gather_sort(x, indices)
-        x = mx.gather_qmm(x, *w1, transpose=True, rhs_indices=idx, sorted_indices=sort)
-        x = mx.gather_qmm(x, *w2, transpose=True, rhs_indices=idx, sorted_indices=sort)
-        if sort:
-            x = scatter_unsort(x, inv_order, indices.shape)
-        return x
-
-    time_fn(gather_mm, x, w1, w2, indices, False)
-    time_fn(gather_mm, x, w1, w2, sorted_indices, False)
-    time_fn(gather_mm, x, w1, w2, indices, True)
-
-    x = mx.random.normal((N * I, D)) / 1024**0.5
-    w1 = mx.random.normal((M, D)) / 1024**0.5
-    w2 = mx.random.normal((D, M)) / 1024**0.5
-    w1 = mx.quantize(w1)
-    w2 = mx.quantize(w2)
-    mx.eval(x, w1, w2)
-
-    def equivalent_matmul(x, w1, w2):
-        x = mx.quantized_matmul(x, *w1, transpose=True)
-        x = mx.quantized_matmul(x, *w2, transpose=True)
-        return x
-
-    time_fn(equivalent_matmul, x, w1, w2)
-
-
-if __name__ == "__main__":
-    time_gather_qmm()

benchmarks/python/layer_norm_bench.py

@@ -1,7 +1,5 @@
 # Copyright © 2023-2024 Apple Inc.
 
-from functools import partial
-
 import mlx.core as mx
 import mlx.nn as nn
 from time_utils import time_fn
@@ -20,63 +18,51 @@ def layer_norm(x, w, b, eps):
     return y
 
 
-def time_layer_norm(N, dt):
+def time_layer_norm():
-    L = 1024
     f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
     f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0, 1, 2))
     g2 = mx.grad(f2, argnums=(0, 1, 2))
 
-    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
     mx.eval(x, w, b, y)
 
-    def layer_norm_loop(f, x, w, b):
+    def layer_norm_loop(g, x, w, b):
-        for _ in range(32):
-            x = f(x, w, b)
-        return x
-
-    time_fn(layer_norm_loop, partial(layer_norm, eps=1e-5), x, w, b)
-    time_fn(layer_norm_loop, partial(mx.fast.layer_norm, eps=1e-5), x, w, b)
-
-    def layer_norm_grad_loop(g, x, w, b):
         gx, gw, gb = x, w, b
         for _ in range(32):
             gx, gw, gb = g(gx, gw, gb, y)
         return gx, gw, gb
 
-    time_fn(layer_norm_grad_loop, g1, x, w, b)
+    time_fn(layer_norm_loop, g1, x, w, b)
-    time_fn(layer_norm_grad_loop, g2, x, w, b)
+    time_fn(layer_norm_loop, g2, x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
 
     f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
     f2 = lambda x, y: (mx.fast.layer_norm(x, None, None, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0,))
     g2 = mx.grad(f2, argnums=(0,))
 
-    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
-    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
     mx.eval(x, w, b, y)
 
-    def layer_norm_grad_x_loop(g, x):
+    def layer_norm_loop(g, x):
         gx = x
         for _ in range(32):
             gx = g(gx, y)
         return gx
 
-    time_fn(layer_norm_grad_x_loop, g1, x)
+    time_fn(layer_norm_loop, g1, x)
-    time_fn(layer_norm_grad_x_loop, g2, x)
+    time_fn(layer_norm_loop, g2, x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g1), x)
+    time_fn(layer_norm_loop, mx.compile(g1), x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g2), x)
+    time_fn(layer_norm_loop, mx.compile(g2), x)
 
 
 if __name__ == "__main__":
-    for dt in [mx.float32, mx.float16, mx.bfloat16]:
+    time_layer_norm()
-        for n in [1024, 2048, 4096, 8192, 8192 + 1024]:
-            print(dt, n)
-            time_layer_norm(n, dt)

benchmarks/python/masked_scatter.py

@@ -1,212 +0,0 @@
-import math
-import os
-import subprocess
-import time
-from copy import copy
-from functools import partial
-
-import matplotlib.pyplot as plt
-import mlx.core as mx
-import numpy as np
-import torch
-from matplotlib.ticker import FuncFormatter
-
-RESULTS_DIR = "./results"
-
-
-if not os.path.isdir(RESULTS_DIR):
-    os.mkdir(RESULTS_DIR)
-
-DEVICE_NAME = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
-DEVICE_NAME = DEVICE_NAME.decode("utf-8").strip("\n")
-
-TORCH_DEVICE = torch.device(
-    "mps"
-    if torch.backends.mps.is_available()
-    else ("cuda" if torch.cuda.is_available() else "cpu")
-)
-
-
-N_WARMUP = 5
-N_ITER_BENCH = 50
-N_ITER_FUNC = 20
-
-VECTOR_LENGTHS = [4096 * (2**i) for i in range(10)]
-MASK_DENSITIES = [0.01, 0.1, 0.25, 0.5]
-D_TYPES = ("float32", "float16")
-
-
-def _power_of_two_formatter(value, _position):
-    if value <= 0:
-        return ""
-    exponent = int(round(math.log2(value)))
-    if abs(value - (1 << exponent)) / value > 1e-6:
-        return f"{value:g}"
-    return f"$2^{{{exponent}}}$"
-
-
-def torch_sync():
-    if TORCH_DEVICE.type == "cuda":
-        torch.cuda.synchronize()
-    elif TORCH_DEVICE.type == "mps":
-        torch.mps.synchronize()
-
-
-def masked_scatter_mlx(self_arr, mask_arr, src_arr):
-    outs = []
-    for _ in range(N_ITER_FUNC):
-        out = copy(self_arr)
-        out[mask_arr] = src_arr
-        outs.append(out)
-    mx.eval(outs)
-    return outs
-
-
-@torch.no_grad()
-def masked_scatter_torch(self_tensor, mask_tensor, src_tensor):
-    outs = []
-    for _ in range(N_ITER_FUNC):
-        out = self_tensor.clone()
-        out.masked_scatter_(mask_tensor, src_tensor)
-        outs.append(out)
-    torch_sync()
-    return outs
-
-
-def measure(fn):
-    for _ in range(N_WARMUP):
-        fn()
-    start = time.perf_counter_ns()
-    for _ in range(N_ITER_BENCH):
-        fn()
-    end = time.perf_counter_ns()
-    return (end - start) * 1e-9
-
-
-def bytes_touched(length, true_count, item_size):
-    mask_bytes = length
-    self_bytes = length * item_size * 2  # read + write
-    src_bytes = true_count * item_size
-    return (mask_bytes + self_bytes + src_bytes) * N_ITER_FUNC * N_ITER_BENCH
-
-
-def build_case(length, density, np_dtype, torch_dtype):
-    true_count = max(1, int(round(length * density)))
-
-    rng = np.random.default_rng()
-    self_np = rng.normal(0.0, 1.0, length).astype(np_dtype)
-    mask_np = np.zeros(length, dtype=bool)
-    mask_np[:true_count] = True
-    rng.shuffle(mask_np)
-    src_np = rng.normal(0.0, 1.0, true_count).astype(np_dtype)
-
-    self_mlx = mx.array(self_np)
-    mask_mlx = mx.array(mask_np)
-    src_mlx = mx.array(src_np)
-
-    self_torch = torch.from_numpy(self_np).to(device=TORCH_DEVICE, dtype=torch_dtype)
-    mask_torch = torch.from_numpy(mask_np).to(device=TORCH_DEVICE)
-    src_torch = torch.from_numpy(src_np).to(device=TORCH_DEVICE, dtype=torch_dtype)
-
-    # Correctness check once per configuration
-    mx_out = mx.array(self_np)
-    mx_out[mask_mlx] = src_mlx
-    mx.eval(mx_out)
-    torch_out = self_torch.clone()
-    torch_out.masked_scatter_(mask_torch, src_torch)
-
-    atol = 5e-3 if np_dtype == np.float16 else 1e-5
-    if not np.allclose(np.array(mx_out), torch_out.cpu().numpy(), atol=atol):
-        raise AssertionError("masked_scatter results diverged between MLX and Torch")
-
-    return (self_mlx, mask_mlx, src_mlx, self_torch, mask_torch, src_torch, true_count)
-
-
-def bench_case(length, density, dtype):
-    np_dtype = getattr(np, dtype)
-    torch_dtype = getattr(torch, dtype)
-    (
-        self_mlx,
-        mask_mlx,
-        src_mlx,
-        self_torch,
-        mask_torch,
-        src_torch,
-        true_count,
-    ) = build_case(length, density, np_dtype, torch_dtype)
-
-    time_mlx = measure(partial(masked_scatter_mlx, self_mlx, mask_mlx, src_mlx))
-    time_torch = measure(
-        partial(masked_scatter_torch, self_torch, mask_torch, src_torch)
-    )
-
-    total_bytes = bytes_touched(length, true_count, np_dtype().itemsize)
-    bytes_per_gb = float(1024**3)
-    mlx_gbps = (total_bytes / bytes_per_gb) / time_mlx
-    torch_gbps = (total_bytes / bytes_per_gb) / time_torch
-
-    return time_mlx, time_torch, mlx_gbps, torch_gbps
-
-
-def plot_density(ax_perf, ax_speedup, density, dtype):
-    mlx_gbps = []
-    torch_gbps = []
-    mlx_times = []
-    torch_times = []
-
-    for length in VECTOR_LENGTHS:
-        t_mlx, t_torch, gbps_mlx, gbps_torch = bench_case(length, density, dtype)
-        mlx_gbps.append(gbps_mlx)
-        torch_gbps.append(gbps_torch)
-        mlx_times.append(t_mlx)
-        torch_times.append(t_torch)
-
-    ax_perf.plot(VECTOR_LENGTHS, mlx_gbps, "tab:blue", label="MLX")
-    ax_perf.plot(VECTOR_LENGTHS, torch_gbps, "tab:red", label="Torch")
-    ax_perf.set_xscale("log", base=2)
-    ax_perf.set_xticks(VECTOR_LENGTHS)
-    formatter = FuncFormatter(_power_of_two_formatter)
-    ax_perf.xaxis.set_major_formatter(formatter)
-    ax_perf.set_title(f"density={density:.2f}")
-    ax_perf.set_ylabel("GB/s")
-    ax_perf.grid(True, which="both", linestyle=":", alpha=0.4)
-    ax_perf.legend()
-
-    speedup = np.array(torch_times) / np.array(mlx_times)
-    ax_speedup.plot(VECTOR_LENGTHS, speedup, "tab:green")
-    ax_speedup.axhline(1.0, color="tab:gray", linestyle="--")
-    ax_speedup.set_xscale("log", base=2)
-    ax_speedup.set_xticks(VECTOR_LENGTHS)
-    ax_speedup.xaxis.set_major_formatter(formatter)
-    ax_speedup.set_ylabel("Speedup (Torch_t / MLX_t)")
-    ax_speedup.grid(True, which="both", linestyle=":", alpha=0.4)
-
-
-def main():
-    for dtype in D_TYPES:
-        fig, axs = plt.subplots(
-            len(MASK_DENSITIES),
-            2,
-            figsize=(10, 12),
-            layout="constrained",
-            sharex=True,
-        )
-
-        for i, density in enumerate(MASK_DENSITIES):
-            plot_density(axs[i][0], axs[i][1], density, dtype)
-            axs[i][0].set_xlabel("vector length")
-            axs[i][1].set_xlabel("vector length")
-
-        fig.suptitle(
-            f"{DEVICE_NAME.replace('Apple ', '')} ({TORCH_DEVICE.type}) | dtype={dtype}"
-        )
-        output_path = os.path.join(
-            RESULTS_DIR,
-            f"{DEVICE_NAME.replace(' ', '_')}_masked_scatter_{dtype}.pdf",
-        )
-        fig.savefig(output_path)
-        plt.close(fig)
-
-
-if __name__ == "__main__":
-    main()

benchmarks/python/sdpa_bench.py

@@ -28,34 +28,11 @@ def bench(f, *args):
     return (e - s) * 1e-9
 
 
-def prepare_inputs(B, qL, kL, D, qH, kH, mask, transpose, dtype):
+def mlx_sdpa_fused_inner(q, k, v, scale):
-    np_dtype = getattr(np, dtype)
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=None)
-
-    shape_q = (B, qL, qH, D) if transpose else (B, qH, qL, D)
-    shape_kv = (B, kL, kH, D) if transpose else (B, kH, kL, D)
-
-    scale = 1.0 / math.sqrt(D)
-
-    q_np = np.random.normal(0.0, 1.0, shape_q).astype(np_dtype)
-    k_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
-    v_np = np.random.normal(0.0, scale, shape_kv).astype(np_dtype)
-
-    q_mx = mx.array(q_np)
-    k_mx = mx.array(k_np)
-    v_mx = mx.array(v_np)
-
-    if mask is not None:
-        if mask == "additive":
-            mask_np = np.random.normal(0.0, 1.0, (B, qH, qL, kL)).astype(np_dtype)
-            mask = mx.array(mask_np)
-        elif mask == "bool":
-            mask_np = np.random.uniform(0.0, 1.0, (B, qH, qL, kL)) < 0.5
-            mask = mx.array(mask_np)
-
-    return q_mx, k_mx, v_mx, scale, mask
 
 
-def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
+def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
     q_dtype = q.dtype
     q = q * mx.array(scale, q_dtype)
     n_q_heads = q.shape[-3]
@@ -64,7 +41,6 @@ def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
 
     B = q.shape[0]
     L = q.shape[2]
-    kL = k.shape[2]
 
     if n_repeats > 1:
         q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
@@ -72,27 +48,10 @@ def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
         v = mx.expand_dims(v, 2)
 
     scores = q @ mx.swapaxes(k, -1, -2)
-
+    if f32softmax:
-    if mask is not None:
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q_dtype)
-
-        if mask == "causal":
-            q_offset = max(0, kL - L)
-            q_indices = mx.arange(q_offset, q_offset + L)
-            k_indices = mx.arange(kL)
-            mask = q_indices[:, None] >= k_indices[None]
-
-        if n_repeats > 1 and mask.ndim >= 3:
-            if mask.shape[-3] == 1:
-                mask = mx.expand_dims(mask, -3)
     else:
-                mask = mx.unflatten(mask, -3, (n_kv_heads, n_repeats))
+        scores = mx.softmax(scores, axis=-1)
-
-        if mask.dtype == mx.bool_:
-            scores = mx.where(mask, scores, -np.float32(np.inf))
-        else:
-            scores += mask
-
-    scores = mx.softmax(scores, axis=-1, precise=True)
 
     out = scores @ v
     if n_repeats > 1:
@@ -101,55 +60,74 @@ def mlx_ref_attn(q, k, v, scale=1.0, mask=None):
     return out
 
 
-def mlx_fused_attn(q, k, v, scale, mask):
+def mlx_spda_unfused(q, k, v, scale, transpose):
-    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=mask)
-
-
-def do_attention(f, q, k, v, scale, mask=None, transpose=False):
-    if transpose:
-        q_t = mx.transpose(q, (0, 2, 1, 3))
-        k_t = mx.transpose(k, (0, 2, 1, 3))
-        v_t = mx.transpose(v, (0, 2, 1, 3))
-        o_t = f(q_t, k_t, v_t, scale=scale, mask=mask)
-        return mx.transpose(o_t, (0, 2, 1, 3))
-    else:
-        return f(q, k, v, scale=scale, mask=mask)
-
-
-def do_attention_bench(f, q, k, v, scale, mask=None, transpose=False):
     q_out = q
+    if transpose:
+        k = mx.transpose(k, (0, 2, 1, 3))
+        v = mx.transpose(v, (0, 2, 1, 3))
 
     for i in range(N_iter_func):
-        q_out = do_attention(f, q_out, k, v, scale, mask=mask, transpose=transpose)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_unfused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
 
     mx.eval(q_out)
     return q_out
 
 
-def bench_shape(
+def mlx_spda_fused(q, k, v, scale, transpose):
-    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, dtype, transpose=True, mask_in=None
+    q_out = q
-):
+    if transpose:
-    q_mx, k_mx, v_mx, scale, mask = prepare_inputs(
+        k = mx.transpose(k, (0, 2, 1, 3))
-        B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, mask_in, transpose, dtype
+        v = mx.transpose(v, (0, 2, 1, 3))
+
+    for i in range(N_iter_func):
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+        q_out = mlx_sdpa_fused_inner(q_out, k, v, scale)
+        if transpose:
+            q_out = mx.transpose(q_out, (0, 2, 1, 3))
+
+    mx.eval(q_out)
+    return q_out
+
+
+def bench_shape(B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose=True):
+    shape_q = (
+        (B, qsl, n_q_heads, head_dim) if transpose else (B, n_q_heads, qsl, head_dim)
+    )
+    shape_kv = (
+        (B, ksl, n_kv_heads, head_dim) if transpose else (B, n_kv_heads, ksl, head_dim)
     )
 
-    time_mlx_unfused = bench(
+    q_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_q).astype(np_dtype)
-        do_attention_bench, mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose
+    k_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
-    )
+    v_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
-    time_mlx_fused = bench(
-        do_attention_bench, mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
-    )
 
-    o_mlx_fused = do_attention(mlx_ref_attn, q_mx, k_mx, v_mx, scale, mask, transpose)
+    scale = math.sqrt(1.0 / head_dim)
-    o_mlx_unfused = do_attention(
-        mlx_fused_attn, q_mx, k_mx, v_mx, scale, mask, transpose
-    )
 
-    atol = 1e-5 if dtype == "float32" else 2e-4
+    q_mx = mx.array(q_np)
+    k_mx = mx.array(k_np)
+    v_mx = mx.array(v_np)
 
-    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol, rtol=atol):
+    time_mlx_unfused = bench(mlx_spda_unfused, q_mx, k_mx, v_mx, scale, transpose)
+    time_mlx_fused = bench(mlx_spda_fused, q_mx, k_mx, v_mx, scale, transpose)
+
+    if transpose:
+        q_mx = mx.transpose(q_mx, (0, 2, 1, 3))
+        k_mx = mx.transpose(k_mx, (0, 2, 1, 3))
+        v_mx = mx.transpose(v_mx, (0, 2, 1, 3))
+
+    o_mlx_fused = mlx_sdpa_fused_inner(q_mx, k_mx, v_mx, scale)
+    o_mlx_unfused = mlx_sdpa_unfused_inner(q_mx, k_mx, v_mx, scale, f32softmax=True)
+
+    atol = 1e-5 if np_dtype == np.float32 else 1e-4
+
+    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol):
         print(
-            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}, mask: {mask_in}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
+            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
         )
 
     return time_mlx_fused, time_mlx_unfused
@@ -173,51 +151,39 @@ if __name__ == "__main__":
           (  1,   128,   128,       64,   32,    32),
           (  1,   256,   256,       64,   32,    32),
           (  1,   512,   512,       64,   32,    32),
-          (  1,  1024,  1024,       64,   32,     8),
+          (  1,  1024,  1024,       64,   32,    32),
-          (  1,  2048,  2048,       64,   32,     8),
+          (  1,  2048,  2048,       64,   32,    32),
-          (  1,  4096,  4096,       64,   32,     8),
+          (  1,  4096,  4096,       64,   32,    32),
     )
 
     shapes_80 = (
         # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,       80,   32,     8),
+          (  1,  1024,  1024,       80,   32,    32),
-          (  1,  2048,  2048,       80,   32,     8),
+          (  1,  2048,  2048,       80,   32,    32),
-          (  1,  4096,  4096,       80,   32,     8),
+          (  1,  4096,  4096,       80,   32,    32),
     )
 
     shapes_128 = (
         # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,      128,   32,     8),
+          (  1,  1024,  1024,      128,   32,    32),
-          (  1,  2048,  2048,      128,   32,     8),
+          (  1,  2048,  2048,      128,   32,    32),
-          (  1,  4096,  4096,      128,   32,     8),
+          (  1,  4096,  4096,      128,   32,    32),
     )
     # fmt: on
 
     shapes = shapes_64 + shapes_80 + shapes_128
 
-    masks = [None, "bool", "causal"]
+    print("  B,   qsl,   ksl, hdim, n_qh, n_kvh, tpose,   dtype, t_unfs, t_fuse, diff%")
-
-    print(
-        "  B,   qsl,   ksl, hdim, n_qh, n_kvh, t,   dtype,     mask, t_unfs, t_fuse, diff%"
-    )
 
     for dtype in dtypes:
         for transpose in transposes:
             for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
-                for mask_in in masks:
+                np_dtype = getattr(np, dtype)
                 time_mlx_fused, time_mlx_unfused = bench_shape(
-                        B,
+                    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose
-                        qsl,
-                        ksl,
-                        head_dim,
-                        n_q_heads,
-                        n_kv_heads,
-                        dtype,
-                        transpose,
-                        mask_in,
                 )
                 diff = time_mlx_unfused / time_mlx_fused - 1.0
                 t_str = 1 if transpose else 0
                 print(
-                        f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:1d}, {dtype}, {str(mask_in):>8}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
+                    f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:5d}, {dtype}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
                 )

benchmarks/python/single_ops.py

@@ -51,20 +51,6 @@ def time_maximum():
     time_fn(mx.maximum, a, b)
 
 
-def time_max():
-    a = mx.random.uniform(shape=(32, 1024, 1024))
-    a[1, 1] = mx.nan
-    mx.eval(a)
-    time_fn(mx.max, a, 0)
-
-
-def time_min():
-    a = mx.random.uniform(shape=(32, 1024, 1024))
-    a[1, 1] = mx.nan
-    mx.eval(a)
-    time_fn(mx.min, a, 0)
-
-
 def time_negative():
     a = mx.random.uniform(shape=(10000, 1000))
     mx.eval(a)
@@ -122,8 +108,6 @@ if __name__ == "__main__":
 
     time_add()
     time_matmul()
-    time_min()
-    time_max()
     time_maximum()
     time_exp()
     time_negative()

cmake/FindNCCL.cmake

@@ -1,54 +0,0 @@
-# FindNCCL.cmake This module finds the NVIDIA NCCL library and its include
-# directories.
-
-set(NCCL_ROOT_DIR
-    $ENV{NCCL_ROOT_DIR}
-    CACHE PATH "Folder contains NVIDIA NCCL")
-
-find_path(
-  NCCL_INCLUDE_DIRS
-  NAMES nccl.h
-  HINTS ${NCCL_INCLUDE_DIR} ${NCCL_ROOT_DIR} ${NCCL_ROOT_DIR}/include
-        ${CUDA_TOOLKIT_ROOT_DIR}/include)
-
-if($ENV{USE_STATIC_NCCL})
-  message(
-    STATUS "USE_STATIC_NCCL detected. Linking against static NCCL library")
-  set(NCCL_LIBNAME "libnccl_static.a")
-else()
-  set(NCCL_LIBNAME "nccl")
-endif()
-
-find_library(
-  NCCL_LIBRARIES
-  NAMES ${NCCL_LIBNAME}
-  HINTS ${NCCL_LIB_DIR}
-        ${NCCL_ROOT_DIR}
-        ${NCCL_ROOT_DIR}/lib
-        ${NCCL_ROOT_DIR}/lib/x86_64-linux-gnu
-        ${NCCL_ROOT_DIR}/lib64
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIRS
-                                  NCCL_LIBRARIES)
-
-if(NCCL_FOUND)
-  set(NCCL_HEADER_FILE "${NCCL_INCLUDE_DIRS}/nccl.h")
-  message(
-    STATUS "Determining NCCL version from the header file: ${NCCL_HEADER_FILE}")
-  file(
-    STRINGS ${NCCL_HEADER_FILE} NCCL_MAJOR_VERSION_DEFINED
-    REGEX "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+[0-9]+.*$"
-    LIMIT_COUNT 1)
-  if(NCCL_MAJOR_VERSION_DEFINED)
-    string(REGEX REPLACE "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+" ""
-                         NCCL_MAJOR_VERSION ${NCCL_MAJOR_VERSION_DEFINED})
-    message(STATUS "NCCL_MAJOR_VERSION: ${NCCL_MAJOR_VERSION}")
-  endif()
-  message(
-    STATUS
-      "Found NCCL (include: ${NCCL_INCLUDE_DIRS}, library: ${NCCL_LIBRARIES})")
-  mark_as_advanced(NCCL_ROOT_DIR NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
-endif()

cmake/Findnvpl.cmake

@@ -1,3 +0,0 @@
-# This file does nothing but to suppress the cmake warning: "By not providing
-# Findnvpl.cmake in CMAKE_MODULE_PATH...", which is caused by the
-# find_package(nvpl) from cmake's builtin FindLAPACK.cmake module.

cmake/extension.cmake

@@ -11,14 +11,13 @@ include(CMakeParseArguments)
 # Args: TARGET: Custom target to be added for the metal library TITLE: Name of
 # the .metallib OUTPUT_DIRECTORY: Where to place ${TITLE}.metallib SOURCES: List
 # of source files INCLUDE_DIRS: List of include dirs DEPS: List of dependency
-# files (like headers) DEBUG: Boolean, if true, enables debug compile options
+# files (like headers)
-# for this specific library. If not provided, uses global MLX_METAL_DEBUG.
 #
 # clang format on
 
 macro(mlx_build_metallib)
   # Parse args
-  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY DEBUG)
+  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY)
   set(multiValueArgs SOURCES INCLUDE_DIRS DEPS)
   cmake_parse_arguments(MTLLIB "" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -27,10 +26,6 @@ macro(mlx_build_metallib)
 
   # Collect compile options
   set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math -Wno-c++17-extensions)
-  if(MLX_METAL_DEBUG OR MTLLIB_DEBUG)
-    set(MTLLIB_COMPILE_OPTIONS ${MTLLIB_COMPILE_OPTIONS} -gline-tables-only
-                               -frecord-sources)
-  endif()
 
   # Prepare metallib build command
   add_custom_command(

docs/Doxyfile

@@ -13,7 +13,7 @@ EXCLUDE_PATTERNS       = */private/*
 CREATE_SUBDIRS         = NO
 FULL_PATH_NAMES        = YES
 RECURSIVE              = YES
-GENERATE_HTML          = NO
+GENERATE_HTML          = YES
 GENERATE_LATEX         = NO
 GENERATE_XML           = YES
 XML_PROGRAMLISTING     = YES

docs/requirements.txt

@@ -1,5 +1,4 @@
 sphinx
 breathe
 sphinx-book-theme
-sphinx-copybutton
 mlx

docs/src/conf.py

@@ -10,7 +10,7 @@ import mlx.core as mx
 # -- Project information -----------------------------------------------------
 
 project = "MLX"
-copyright = "2023, Apple"
+copyright = "2023, MLX Contributors"
 author = "MLX Contributors"
 version = ".".join(mx.__version__.split(".")[:3])
 release = version
@@ -18,7 +18,6 @@ release = version
 # -- General configuration ---------------------------------------------------
 
 extensions = [
-    "sphinx_copybutton",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",

docs/src/dev/custom_metal_kernels.rst

@@ -8,12 +8,11 @@ MLX supports writing custom Metal kernels through the Python and C++ APIs.
 Simple Example
 --------------
 
-.. currentmodule:: mlx.core
-
 Let's write a custom kernel that computes ``exp`` elementwise:
 
 .. code-block:: python
 
+  def exp_elementwise(a: mx.array):
       source = """
           uint elem = thread_position_in_grid.x;
           T tmp = inp[elem];
@@ -26,8 +25,6 @@ Let's write a custom kernel that computes ``exp`` elementwise:
           output_names=["out"],
           source=source,
       )
-
-  def exp_elementwise(a: mx.array):
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -42,13 +39,8 @@ Let's write a custom kernel that computes ``exp`` elementwise:
   b = exp_elementwise(a)
   assert mx.allclose(b, mx.exp(a))
 
-Every time you make a kernel, a new Metal library is created and possibly
-JIT compiled. To reduce the overhead from that, build the kernel once with
-:func:`fast.metal_kernel` and then use it many times.
-
 .. note::
-   Only pass the body of the Metal kernel in ``source``. The function
+    We are only required to pass the body of the Metal kernel in ``source``.
-   signature is generated automatically.
 
 The full function signature will be generated using:
 
@@ -86,34 +78,29 @@ Putting this all together, the generated function signature for ``myexp`` is as
 
   template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
 
-Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
-<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
+This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
-function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
+For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
-``threadgroup`` size threadgroups.  For optimal performance, each thread group
-dimension should be less than or equal to the corresponding grid dimension.
 
-Passing ``verbose=True`` to :func:`ast.metal_kernel.__call__` will print the
+Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
-generated code for debugging purposes.
 
 Using Shape/Strides
 -------------------
 
-:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
+``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
-is ``True`` by default. This will copy the array inputs if needed
+This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
-before the kernel is launched to ensure that the memory layout is row
+Generally this makes writing the kernel easier, since we don't have to worry about gaps or the ordering of the dims
-contiguous.  Generally this makes writing the kernel easier, since we don't
+when indexing.
-have to worry about gaps or the ordering of the dims when indexing.
 
-If we want to avoid this copy, :func:`fast.metal_kernel` automatically passes
+If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
-``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
+input array ``a`` if any are present in ``source``.
-present in ``source``. We can then use MLX's built in indexing utils to fetch
+We can then use MLX's built in indexing utils to fetch the right elements for each thread.
-the right elements for each thread.
 
-Let's convert ``myexp`` above to support arbitrarily strided arrays without
+Let's convert ``myexp`` above to support arbitrarily strided arrays without relying on a copy from ``ensure_row_contiguous``:
-relying on a copy from ``ensure_row_contiguous``:
 
 .. code-block:: python
 
+  def exp_elementwise(a: mx.array):
       source = """
           uint elem = thread_position_in_grid.x;
           // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
@@ -127,11 +114,8 @@ relying on a copy from ``ensure_row_contiguous``:
           name="myexp_strided",
           input_names=["inp"],
           output_names=["out"],
-      source=source,
+          source=source
-      ensure_row_contiguous=False,
       )
-
-  def exp_elementwise(a: mx.array):
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -139,6 +123,7 @@ relying on a copy from ``ensure_row_contiguous``:
           threadgroup=(256, 1, 1),
           output_shapes=[a.shape],
           output_dtypes=[a.dtype],
+          ensure_row_contiguous=False,
       )
       return outputs[0]
 
@@ -198,13 +183,25 @@ We'll start with the following MLX implementation using standard ops:
 
         return output
 
-Now let's use :func:`custom_function` together with :func:`fast.metal_kernel`
+Now let's use ``mx.custom_function`` together with ``mx.fast.metal_kernel``
 to write a fast GPU kernel for both the forward and backward passes.
 
 First we'll implement the forward pass as a fused kernel:
 
 .. code-block:: python
 
+    @mx.custom_function
+    def grid_sample(x, grid):
+
+        assert x.ndim == 4, "`x` must be 4D."
+        assert grid.ndim == 4, "`grid` must be 4D."
+
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
+        out_shape = (B, gN, gM, C)
+
+        assert D == 2, "Last dim of `grid` must be size 2."
+
         source = """
             uint elem = thread_position_in_grid.x;
             int H = x_shape[1];
@@ -254,26 +251,12 @@ First we'll implement the forward pass as a fused kernel:
 
             out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
         """
-
         kernel = mx.fast.metal_kernel(
             name="grid_sample",
             input_names=["x", "grid"],
             output_names=["out"],
             source=source,
         )
-
-  @mx.custom_function
-  def grid_sample(x, grid):
-
-      assert x.ndim == 4, "`x` must be 4D."
-      assert grid.ndim == 4, "`grid` must be 4D."
-
-      B, _, _, C = x.shape
-      _, gN, gM, D = grid.shape
-      out_shape = (B, gN, gM, C)
-
-      assert D == 2, "Last dim of `grid` must be size 2."
-
         outputs = kernel(
             inputs=[x, grid],
             template=[("T", x.dtype)],
@@ -298,11 +281,11 @@ On an M1 Max, we see a big performance improvement:
 Grid Sample VJP
 ---------------
 
-Since we decorated ``grid_sample`` with :func:`custom_function`, we can now
+Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
-define its custom vjp transform so MLX can differentiate it.
+its custom vjp transform so MLX can differentiate it.
 
 The backwards pass requires atomically updating ``x_grad``/``grid_grad`` and so
-requires a few extra :func:`fast.metal_kernel` features:
+requires a few extra ``mx.fast.metal_kernel`` features:
 
 * ``init_value=0``
     Initialize all of the kernel's outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.
@@ -316,6 +299,14 @@ We can then implement the backwards pass as follows:
 
 .. code-block:: python
 
+    @grid_sample.vjp
+    def grid_sample_vjp(primals, cotangent, _):
+        x, grid = primals
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
+
+        assert D == 2, "Last dim of `grid` must be size 2."
+
         source = """
             uint elem = thread_position_in_grid.x;
             int H = x_shape[1];
@@ -415,15 +406,6 @@ We can then implement the backwards pass as follows:
             source=source,
             atomic_outputs=True,
         )
-
-  @grid_sample.vjp
-  def grid_sample_vjp(primals, cotangent, _):
-      x, grid = primals
-      B, _, _, C = x.shape
-      _, gN, gM, D = grid.shape
-
-      assert D == 2, "Last dim of `grid` must be size 2."
-
         # pad the output channels to simd group size
         # so that our `simd_sum`s don't overlap.
         simdgroup_size = 32

docs/src/dev/extensions.rst

@@ -93,9 +93,9 @@ Primitives
 ^^^^^^^^^^^
 
 A :class:`Primitive` is part of the computation graph of an :class:`array`. It
-defines how to create output arrays given input arrays. Further, a
+defines how to create outputs arrays given a input arrays. Further, a
 :class:`Primitive` has methods to run on the CPU or GPU and for function
-transformations such as ``vjp`` and ``jvp``.  Let's go back to our example to be
+transformations such as ``vjp`` and ``jvp``.  Lets go back to our example to be
 more concrete:
 
 .. code-block:: C++
@@ -128,7 +128,7 @@ more concrete:
         /** The vector-Jacobian product. */
         std::vector<array> vjp(
             const std::vector<array>& primals,
-            const std::vector<array>& cotangents,
+            const array& cotan,
             const std::vector<int>& argnums,
             const std::vector<array>& outputs) override;
 
@@ -138,13 +138,13 @@ more concrete:
         * representing the vectorized computation and the axis which
         * corresponds to the output vectorized dimension.
         */
-        std::pair<std::vector<array>, std::vector<int>> vmap(
+        virtual std::pair<std::vector<array>, std::vector<int>> vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) override;
 
-        /** The name of primitive. */
+        /** Print the primitive. */
-        const char* name() const override {
+        void print(std::ostream& os) override {
-          return "Axpby";
+            os << "Axpby";
         }
 
         /** Equivalence check **/
@@ -247,7 +247,9 @@ point-wise. This is captured in the templated function :meth:`axpby_impl`.
       float alpha_,
       float beta_,
       mx::Stream stream) {
-    out.set_data(mx::allocator::malloc(out.nbytes()));
+    // Allocate the output with `malloc_or_wait` which synchronously allocates
+    // memory, potentially waiting if the system is under memory pressure
+    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
 
     // Get the CPU command encoder and register input and output arrays
     auto& encoder = mx::cpu::get_command_encoder(stream);
@@ -391,17 +393,17 @@ below.
         auto& d = metal::device(s.device);
 
         // Allocate output memory
-        out.set_data(allocator::malloc(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
         // Resolve name of kernel
-        std::stream kname;
+        std::ostringstream kname;
-        kname = "axpby_general_" + type_to_name(out);
+        kname << "axpby_" << "general_" << type_to_name(out);
 
-        // Load the metal library
+        // Make sure the metal library is available
-        auto lib = d.get_library("mlx_ext", current_binary_dir());
+        d.register_library("mlx_ext");
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname, lib);
+        auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);
@@ -469,7 +471,7 @@ one we just defined:
             const std::vector<array>& tangents,
             const std::vector<int>& argnums) {
         // Forward mode diff that pushes along the tangents
-        // The jvp transform on the primitive can be built with ops
+        // The jvp transform on the primitive can built with ops
         // that are scheduled on the same stream as the primitive
 
         // If argnums = {0}, we only push along x in which case the
@@ -481,7 +483,7 @@ one we just defined:
             auto scale_arr = array(scale, tangents[0].dtype());
             return {multiply(scale_arr, tangents[0], stream())};
         }
-        // If argnums = {0, 1}, we take contributions from both
+        // If, argnums = {0, 1}, we take contributions from both
         // which gives us jvp = tangent_x * alpha + tangent_y * beta
         else {
             return {axpby(tangents[0], tangents[1], alpha_, beta_, stream())};
@@ -735,7 +737,7 @@ Let's look at a simple script and its results:
 
     print(f"c shape: {c.shape}")
     print(f"c dtype: {c.dtype}")
-    print(f"c is correct: {mx.all(c == 6.0).item()}")
+    print(f"c correct: {mx.all(c == 6.0).item()}")
 
 Output:
 
@@ -743,7 +745,7 @@ Output:
 
     c shape: [3, 4]
     c dtype: float32
-    c is correct: True
+    c correctness: True
 
 Results
 ^^^^^^^

docs/src/index.rst

@@ -70,8 +70,6 @@ are the CPU and GPU.
    python/fft
    python/linalg
    python/metal
-   python/cuda
-   python/memory_management
    python/nn
    python/optimizers
    python/distributed

docs/src/install.rst

@@ -13,51 +13,22 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI your system must meet the following requirements:
+To install from PyPI you must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
-- Using a native Python >= 3.10
+- Using a native Python >= 3.9
-- macOS >= 14.0
+- macOS >= 13.5
 
 .. note::
-    MLX is only available on devices running macOS >= 14.0 and higher.
+    MLX is only available on devices running macOS >= 13.5
+    It is highly recommended to use macOS 14 (Sonoma)
 
-CUDA
-^^^^
 
-MLX has a CUDA backend which you can install with:
+MLX is also available on conda-forge. To install MLX with conda do:
 
 .. code-block:: shell
 
-    pip install mlx[cuda12]
+   conda install conda-forge::mlx
-
-
-To install the CUDA package from PyPi your system must meet the following
-requirements:
-
-- Nvidia architecture >= SM 7.5
-- Nvidia driver >= 550.54.14
-- CUDA toolkit >= 12.0
-- Linux distribution with glibc >= 2.35
-- Python >= 3.10
-
-For CUDA 13 use ``pip install mlx[cuda13]``. The CUDA 13 package requires
-an Nvidia driver >= 580 or an appropriate CUDA compatibility package.
-
-CPU-only (Linux)
-^^^^^^^^^^^^^^^^
-
-For a CPU-only version of MLX that runs on Linux use:
-
-.. code-block:: shell
-
-    pip install mlx[cpu]
-
-To install the CPU-only package from PyPi your system must meet the following
-requirements:
-
-- Linux distribution with glibc >= 2.35
-- Python >= 3.10
 
 
 Troubleshooting
@@ -94,8 +65,6 @@ Build Requirements
 Python API
 ^^^^^^^^^^
 
-.. _python install:
-
 To build and install the MLX python library from source, first, clone MLX from
 `its GitHub repo <https://github.com/ml-explore/mlx>`_:
 
@@ -107,20 +76,20 @@ Then simply build and install MLX using pip:
 
 .. code-block:: shell
 
-  pip install .
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install .
 
 For developing, install the package with development dependencies, and use an
 editable install:
 
 .. code-block:: shell
 
-  pip install -e ".[dev]"
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install -e ".[dev]"
 
 Once the development dependencies are installed, you can build faster with:
 
 .. code-block:: shell
 
- python setup.py build_ext --inplace
+ CMAKE_BUILD_PARALLEL_LEVEL=8 python setup.py build_ext --inplace
 
 Run the tests with:
 
@@ -138,8 +107,6 @@ IDE:
 C++ API
 ^^^^^^^
 
-.. _cpp install:
-
 Currently, MLX must be built and installed from source.
 
 Similarly to the python library, to build and install the MLX C++ library start
@@ -218,7 +185,6 @@ should point to the path to the built metal library.
 
       xcrun -sdk macosx --show-sdk-version
 
-
 Binary Size Minimization
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -247,50 +213,6 @@ be anwywhere from a few hundred millisecond to a few seconds depending on the
 application. Once a kernel is compiled, it will be cached by the system. The
 Metal kernel cache persists across reboots.
 
-Linux
-^^^^^
-
-To build from source on Linux (CPU only), install the BLAS and LAPACK headers.
-For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   apt-get update -y
-   apt-get install libblas-dev liblapack-dev liblapacke-dev -y
-
-From here follow the instructions to install either the :ref:`Python <python
-install>` or :ref:`C++ <cpp install>` APIs.
-
-CUDA
-^^^^
-
-To build from source on Linux with CUDA, install the BLAS and LAPACK headers
-and the CUDA toolkit. For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-   dpkg -i cuda-keyring_1.1-1_all.deb
-   apt-get update -y
-   apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev libcudnn9-dev-cuda-12 -y
-
-
-When building either the Python or C++ APIs make sure to pass the cmake flag
-``MLX_BUILD_CUDA=ON``. For example, to build the Python API run:
-
-.. code-block:: shell
-
-  CMAKE_ARGS="-DMLX_BUILD_CUDA=ON" pip install -e ".[dev]"
-
-To build the C++ package run:
-
-.. code-block:: shell
-
-   mkdir -p build && cd build
-   cmake .. -DMLX_BUILD_CUDA=ON && make -j
-
-
 Troubleshooting
 ^^^^^^^^^^^^^^^
 

docs/src/python/array.rst

@@ -19,8 +19,6 @@ Array
     array.ndim
     array.shape
     array.size
-    array.real
-    array.imag
     array.abs
     array.all
     array.any
@@ -40,7 +38,6 @@ Array
     array.log10
     array.log1p
     array.log2
-    array.logcumsumexp
     array.logsumexp
     array.max
     array.mean

docs/src/python/cuda.rst

@@ -1,9 +0,0 @@
-CUDA
-=====
-
-.. currentmodule:: mlx.core.cuda
-
-.. autosummary::
-  :toctree: _autosummary
-
-  is_available

docs/src/python/fast.rst

@@ -13,4 +13,3 @@ Fast
   rope
   scaled_dot_product_attention
   metal_kernel
-  cuda_kernel

docs/src/python/fft.rst

@@ -20,5 +20,3 @@ FFT
   irfft2
   rfftn
   irfftn
-  fftshift
-  ifftshift

docs/src/python/linalg.rst

@@ -16,12 +16,9 @@ Linear Algebra
     cross
     qr
     svd
-    eigvals
-    eig
     eigvalsh
     eigh
     lu
     lu_factor
-    pinv
     solve
     solve_triangular

docs/src/python/memory_management.rst

@@ -1,16 +0,0 @@
-Memory Management
-=================
-
-.. currentmodule:: mlx.core
-
-.. autosummary::
-  :toctree: _autosummary
-
-  get_active_memory
-  get_peak_memory
-  reset_peak_memory
-  get_cache_memory
-  set_memory_limit
-  set_cache_limit
-  set_wired_limit
-  clear_cache

docs/src/python/metal.rst

@@ -8,5 +8,13 @@ Metal
 
   is_available
   device_info
+  get_active_memory
+  get_peak_memory
+  reset_peak_memory
+  get_cache_memory
+  set_memory_limit
+  set_cache_limit
+  set_wired_limit
+  clear_cache
   start_capture
   stop_capture

docs/src/python/nn/functions.rst

@@ -27,7 +27,6 @@ simple functions.
    mish
    prelu
    relu
-   relu2
    relu6
    selu
    sigmoid

docs/src/python/nn/layers.rst

@@ -50,7 +50,6 @@ Layers
    QuantizedLinear
    RMSNorm
    ReLU
-   ReLU2
    ReLU6
    RNN
    RoPE

docs/src/python/ops.rst

@@ -36,12 +36,10 @@ Operations
    bitwise_or
    bitwise_xor
    block_masked_mm
-   broadcast_arrays
    broadcast_to
    ceil
    clip
    concatenate
-   contiguous
    conj
    conjugate
    convolve
@@ -103,7 +101,6 @@ Operations
    log10
    log1p
    logaddexp
-   logcumsumexp
    logical_not
    logical_and
    logical_or
@@ -112,7 +109,6 @@ Operations
    max
    maximum
    mean
-   median
    meshgrid
    min
    minimum

docs/src/python/optimizers.rst

@@ -51,14 +51,14 @@ the saved state. Here's a simple example:
    optimizer.update(model, grads)
 
    # Save the state
-   state = tree_flatten(optimizer.state, destination={})
+   state = tree_flatten(optimizer.state)
-   mx.save_safetensors("optimizer.safetensors", state)
+   mx.save_safetensors("optimizer.safetensors", dict(state))
 
    # Later on, for example when loading from a checkpoint,
    # recreate the optimizer and load the state
    optimizer = optim.Adam(learning_rate=1e-2)
 
-   state = tree_unflatten(mx.load("optimizer.safetensors"))
+   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/python/optimizers/common_optimizers.rst

@@ -18,5 +18,3 @@ Common Optimizers
    AdamW
    Adamax
    Lion
-   MultiOptimizer
-   Muon

docs/src/python/transforms.rst

@@ -9,7 +9,6 @@ Transforms
   :toctree: _autosummary
 
    eval
-   async_eval
    compile
    custom_function
    disable_compile

docs/src/usage/compile.rst

@@ -130,8 +130,8 @@ Now make an array, and benchmark both functions:
 .. code-block:: python
 
   x = mx.random.uniform(shape=(32, 1000, 4096))
-  timeit(gelu, x)
+  timeit(nn.gelu, x)
-  timeit(mx.compile(gelu), x)
+  timeit(mx.compile(nn.gelu), x)
 
 On an M1 Max the times are 15.5 and 3.1 milliseconds. The compiled ``gelu`` is
 five times faster.
@@ -225,7 +225,7 @@ In some cases returning updated state can be pretty inconvenient. Hence,
   def fun(x, y):
       z = x + y
       state.append(z)
-      return mx.exp(z)
+      return mx.exp(z), state
 
   fun(mx.array(1.0), mx.array(2.0))
   # Prints [array(3, dtype=float32)]

docs/src/usage/distributed.rst

@@ -7,29 +7,21 @@ Distributed Communication
 
 MLX supports distributed communication operations that allow the computational cost
 of training or inference to be shared across many physical machines. At the
-moment we support several different communication backends introduced below.
+moment we support two different communication backends:
-
-.. list-table::
-   :widths: 20 80
-   :header-rows: 1
-
-   * - Backend
-     - Description
-   * - :ref:`MPI <mpi_section>`
-     - A full featured and mature distributed communications library.
-   * - :ref:`RING <ring_section>`
-     - Ring all reduce and all gather over TCP sockets. Always available and
-       usually faster than MPI.
-   * - :ref:`JACCL <ring_section>`
-     - Low latency communication with RDMA over thunderbolt. Necessary for
-       things like tensor parallelism.
-   * - :ref:`NCCL <nccl_section>`
-     - The backend of choice for CUDA environments.
 
+* `MPI <https://en.wikipedia.org/wiki/Message_Passing_Interface>`_ a
+  full-featured and mature distributed communications library
+* A **ring** backend of our own that uses native TCP sockets and should be
+  faster for thunderbolt connections.
 
 The list of all currently supported operations and their documentation can be
 seen in the :ref:`API docs<distributed>`.
 
+.. note::
+   Some operations may not be supported or not as fast as they should be.
+   We are adding more and tuning the ones we have as we are figuring out the
+   best way to do distributed computing on Macs using MLX.
+
 Getting Started
 ---------------
 
@@ -92,8 +84,9 @@ Selecting Backend
 ^^^^^^^^^^^^^^^^^
 
 You can select the backend you want to use when calling :func:`init` by passing
-one of ``{'any', 'ring', 'jaccl', 'mpi', 'nccl'}``. When passing ``any``, MLX will try all
+one of ``{'any', 'ring', 'mpi'}``. When passing ``any``, MLX will try to
-available backends. If they all fail then a singleton group is created.
+initialize the ``ring`` backend and if it fails the ``mpi`` backend. If they
+both fail then a singleton group is created.
 
 .. note::
    After a distributed backend is successfully initialized :func:`init` will
@@ -117,8 +110,6 @@ The following examples aim to clarify the backend initialization logic in MLX:
     world_ring = mx.distributed.init(backend="ring")
     world_any = mx.distributed.init()  # same as MPI because it was initialized first!
 
-.. _training_example:
-
 Training Example
 ----------------
 
@@ -193,7 +184,7 @@ almost identical to the example above:
 
     def step(model, x, y):
         loss, grads = loss_grad_fn(model, x, y)
-        grads = mx.nn.average_gradients(grads)  # <---- This line was added
+        grads = mlx.nn.average_gradients(grads) # <---- This line was added
         optimizer.update(model, grads)
         return loss
 
@@ -201,7 +192,80 @@ almost identical to the example above:
         loss = step(model, x, y)
         mx.eval(loss, model.parameters())
 
-.. _ring_section:
+
+Getting Started with MPI
+------------------------
+
+MLX already comes with the ability to "talk" to MPI if it is installed on the
+machine. Launching distributed MLX programs that use MPI can be done with
+``mpirun`` as expected. However, in the following examples we will be using
+``mlx.launch --backend mpi`` which takes care of some nuisances such as setting
+absolute paths for the ``mpirun`` executable and the ``libmpi.dyld`` shared
+library.
+
+The simplest possible usage is the following which, assuming the minimal
+example in the beginning of this page, should result in:
+
+.. code:: shell
+
+    $ mlx.launch --backend mpi -n 2 test.py
+    1 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
+    0 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
+
+The above launches two processes on the same (local) machine and we can see
+both standard output streams. The processes send the array of 1s to each other
+and compute the sum which is printed. Launching with ``mlx.launch -n 4 ...`` would
+print 4 etc.
+
+Installing MPI
+^^^^^^^^^^^^^^
+
+MPI can be installed with Homebrew, using the Anaconda package manager or
+compiled from source. Most of our testing is done using ``openmpi`` installed
+with the Anaconda package manager as follows:
+
+.. code:: shell
+
+    $ conda install conda-forge::openmpi
+
+Installing with Homebrew may require specifying the location of ``libmpi.dyld``
+so that MLX can find it and load it at runtime. This can simply be achieved by
+passing the ``DYLD_LIBRARY_PATH`` environment variable to ``mpirun`` and it is
+done automatically by ``mlx.launch``.
+
+.. code:: shell
+
+    $ mpirun -np 2 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python test.py
+    $ # or simply
+    $ mlx.launch -n 2 test.py
+
+Setting up Remote Hosts
+^^^^^^^^^^^^^^^^^^^^^^^
+
+MPI can automatically connect to remote hosts and set up the communication over
+the network if the remote hosts can be accessed via ssh. A good checklist to
+debug connectivity issues is the following:
+
+* ``ssh hostname`` works from all machines to all machines without asking for
+  password or host confirmation
+* ``mpirun`` is accessible on all machines.
+* Ensure that the ``hostname`` used by MPI is the one that you have configured
+  in the ``.ssh/config`` files on all machines.
+
+Tuning MPI All Reduce
+^^^^^^^^^^^^^^^^^^^^^
+
+.. note::
+
+    For faster all reduce consider using the ring backend either with Thunderbolt
+    connections or over Ethernet.
+
+Configure MPI to use N tcp connections between each host to improve bandwidth
+by passing ``--mca btl_tcp_links N``.
+
+Force MPI to use the most performant network interface by setting ``--mca
+btl_tcp_if_include <iface>`` where ``<iface>`` should be the interface you want
+to use.
 
 Getting Started with Ring
 -------------------------
@@ -252,13 +316,22 @@ utility as follows:
 
 .. code:: shell
 
-   mlx.distributed_config --verbose --hosts host1,host2,host3,host4 --backend ring
+   mlx.distributed_config --verbose --hosts host1,host2,host3,host4
 
 By default the script will attempt to discover the thunderbolt ring and provide
 you with the commands to configure each node as well as the ``hostfile.json``
 to use with ``mlx.launch``. If password-less ``sudo`` is available on the nodes
 then ``--auto-setup`` can be used to configure them automatically.
 
+To validate your connection without configuring anything
+``mlx.distributed_config`` can also plot the ring using DOT format.
+
+.. code:: shell
+
+   mlx.distributed_config --verbose --hosts host1,host2,host3,host4 --dot >ring.dot
+   dot -Tpng ring.dot >ring.png
+   open ring.png
+
 If you want to go through the process manually, the steps are as follows:
 
 * Disable the thunderbolt bridge interface
@@ -269,382 +342,3 @@ If you want to go through the process manually, the steps are as follows:
   and ``en2`` also on node ``i + 1`` then we may assign IPs ``192.168.0.1`` and
   ``192.168.0.2`` respectively to the two nodes. For more details you can see
   the commands prepared by the utility script.
-
-.. _jaccl_section:
-
-Getting Started with RDMA over Thunderbolt
-------------------------------------------
-
-Starting from version 26.2 RDMA over thunderbolt is available in MacOS and
-enables low-latency communication between Macs with thunderbolt 5. MLX provides
-the JACCL backend that uses this functionality to achieve communication latency
-an order of magnitude lower than the ring backend.
-
-.. note::
-
-   The name JACCL (pronounced Jackal) stands for *Jack and Angelos' Collective
-   Communication Library* and it is an obvious pun to Nvidia's NCCL but also
-   tribute to *Jack Beasley* who led the development of RDMA over Thunderbolt
-   at Apple.
-
-Enabling RDMA
-^^^^^^^^^^^^^
-
-Until the feature matures, enabling RDMA over thunderbolt is slightly more
-involved and **cannot** be done remotely even with sudo. In fact, it has to be
-done in macOS recovery:
-
-1. `Start your computer in recovery <https://support.apple.com/en-us/102518>`_.
-2. Open the Terminal by going to Utilities -> Terminal.
-3. Run ``rdma_ctl enable``.
-4. Reboot.
-
-To verify that you have successfully enabled Thunderbolt RDMA you can run
-``ibv_devices`` which should produce something like the following for an M3 Ultra.
-
-.. code-block:: bash
-
-    ~ % ibv_devices
-    device          	   node GUID
-    ------          	----------------
-    rdma_en2        	8096a9d9edbaac05
-    rdma_en3        	8196a9d9edbaac05
-    rdma_en5        	8396a9d9edbaac05
-    rdma_en4        	8296a9d9edbaac05
-    rdma_en6        	8496a9d9edbaac05
-    rdma_en7        	8596a9d9edbaac05
-
-Defining a Mesh
-^^^^^^^^^^^^^^^
-
-The JACCL backend supports only fully connected topologies. Namely, there needs
-to be a thunderbolt cable connecting all pairs of Macs directly. For example, in
-the following topology visualizations, the left one is valid because there is a
-connection from any node to any other node, while for the one on the right M3
-Ultra 1 is not connected to M3 Ultra 2.
-
-.. raw:: html
-
-   <div style="display: flex; text-align: center; align-items: end; font-size: 80%;">
-     <div>
-       <img src="/_static/distributed/m3-ultra-mesh.png" alt="M3 Ultra thunderbolt mesh" style="width: 55%">
-       <p>Fully connected mesh of four M3 Ultra.</p>
-     </div>
-     <div>
-       <img src="/_static/distributed/m3-ultra-mesh-broken.png" alt="M3 Ultra broken thunderbolt mesh" style="width: 55%">
-       <p>Not a valid mesh (M3 Ultra 1 is not connected to M3 Ultra 2).</p>
-     </div>
-   </div>
-
-Similar to the ring backend, the easiest way to use JACCL with MLX is to write
-a JSON hostfile that will be used by ``mlx.launch``. The hostfile needs to contain
-
-- Hostnames to use for launching scripts via ssh
-- An IP for rank 0 that is reachable by all nodes
-- A list of rdma devices that connect each node to each other node
-
-The following JSON defines the valid 4-node mesh from the image above.
-
-.. code-block:: json
-
-    [
-        {
-            "ssh": "m3-ultra-1",
-            "ips": ["123.123.123.1"],
-            "rdma": [null, "rdma_en5", "rdma_en4", "rdma_en3"]
-        },
-        {
-            "ssh": "m3-ultra-2",
-            "ips": [],
-            "rdma": ["rdma_en5", null, "rdma_en3", "rdma_en4"]
-        },
-        {
-            "ssh": "m3-ultra-3",
-            "ips": [],
-            "rdma": ["rdma_en4", "rdma_en3", null, "rdma_en5"]
-        },
-        {
-            "ssh": "m3-ultra-4",
-            "ips": [],
-            "rdma": ["rdma_en3", "rdma_en4", "rdma_en5", null]
-        }
-    ]
-
-Even though TCP/IP is not used when communicating with Thunderbolt RDMA,
-disabling the thunderbolt bridge is still required as well as setting up
-isolated local networks for each thunderbolt connection.
-
-All of the above can be done instead via ``mlx.distributed_config``. This helper
-script will
-
-- ssh into each node
-- extract the thunderbolt connectivity
-- check for a valid mesh
-- provide the commands to configure each node (or run them if sudo is available)
-- generate the hostfile to be used with ``mlx.launch``
-
-Putting it All Together
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-For example launching a distributed MLX script that uses JACCL is fairly simple
-if the nodes are reachable via ssh and have password-less sudo.
-
-First, connect all the thunderbolt cables. Then we can verify the connections
-by using the ``mlx.distributed_config`` script to visualize them.
-
-.. code-block::
-
-   mlx.distributed_config --verbose \
-        --hosts m3-ultra-1,m3-ultra-2,m3-ultra-3,m3-ultra-4 \
-        --over thunderbolt --dot | dot -Tpng | open -f -a Preview
-
-After making sure that everything looks right we can auto-configure the nodes
-and save the hostfile to ``m3-ultra-jaccl.json`` by running:
-
-.. code-block::
-
-   mlx.distributed_config --verbose \
-        --hosts m3-ultra-1,m3-ultra-2,m3-ultra-3,m3-ultra-4 \
-        --over thunderbolt --backend jaccl \
-        --auto-setup --output m3-ultra-jaccl.json
-
-And now we are ready to run a distributed MLX script such as distributed inference
-of a gigantic model using MLX-LM.
-
-.. code-block::
-
-   mlx.launch --verbose --backend jaccl --hostfile m3-ultra-jaccl.json \
-        --env MLX_METAL_FAST_SYNCH=1 -- \  # <--- important
-        /path/to/remote/python -m mlx_lm chat --model mlx-community/DeepSeek-V3.2-8bit --shard
-
-.. note::
-
-   Defining the environment variable ``MLX_METAL_FAST_SYNCH=1`` enables a
-   different, faster way of synchronizing between the GPU and the CPU. It is
-   not specific to the JACCL backend and can be used in all cases where the CPU
-   and GPU need to collaborate for some computation and is pretty critical for
-   low-latency communication since the communication is done by the CPU.
-
-.. _nccl_section:
-
-Getting Started with NCCL
--------------------------
-
-MLX on CUDA environments ships with the ability to talk to `NCCL
-<https://developer.nvidia.com/nccl>`_ which is a high-performance collective
-communication library that supports both multi-gpu and multi-node setups.
-
-For CUDA environments, NCCL is the default backend for ``mlx.launch`` and all
-it takes to run a distributed job is
-
-.. code-block::
-
-   mlx.launch -n 8 test.py
-
-   # perfect for interactive scripts
-   mlx.launch -n 8 python -m mlx_lm chat --model my-model --shard
-
-You can also use ``mlx.launch`` to ssh to a remote node and launch a script
-with the same ease
-
-.. code-block::
-
-   mlx.launch --hosts my-cuda-node -n 8 test.py
-
-In many cases you may not want to use ``mlx.launch`` with the NCCL backend
-because the cluster scheduler will be the one launching the processes. You can
-:ref:`see which environment variables need to be defined <no_mlx_launch>` in
-order for the MLX NCCL backend to be initialized correctly.
-
-.. _mpi_section:
-
-Getting Started with MPI
-------------------------
-
-MLX already comes with the ability to "talk" to `MPI
-<https://en.wikipedia.org/wiki/Message_Passing_Interface>`_ if it is installed
-on the machine. Launching distributed MLX programs that use MPI can be done
-with ``mpirun`` as expected. However, in the following examples we will be
-using ``mlx.launch --backend mpi`` which takes care of some nuisances such as
-setting absolute paths for the ``mpirun`` executable and the ``libmpi.dyld``
-shared library.
-
-The simplest possible usage is the following which, assuming the minimal
-example in the beginning of this page, should result in:
-
-.. code:: shell
-
-    $ mlx.launch --backend mpi -n 2 test.py
-    1 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
-    0 array([2, 2, 2, ..., 2, 2, 2], dtype=float32)
-
-The above launches two processes on the same (local) machine and we can see
-both standard output streams. The processes send the array of 1s to each other
-and compute the sum which is printed. Launching with ``mlx.launch -n 4 ...`` would
-print 4 etc.
-
-Installing MPI
-^^^^^^^^^^^^^^
-
-MPI can be installed with Homebrew, pip, using the Anaconda package manager, or
-compiled from source. Most of our testing is done using ``openmpi`` installed
-with the Anaconda package manager as follows:
-
-.. code:: shell
-
-    $ conda install conda-forge::openmpi
-
-Installing with Homebrew or pip requires specifying the location of ``libmpi.dyld``
-so that MLX can find it and load it at runtime. This can simply be achieved by
-passing the ``DYLD_LIBRARY_PATH`` environment variable to ``mpirun`` and it is
-done automatically by ``mlx.launch``. Some environments use a non-standard
-library filename that can be specified using the ``MPI_LIBNAME`` environment
-variable. This is automatically taken care of by ``mlx.launch`` as well.
-
-.. code:: shell
-
-    $ mpirun -np 2 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ -x MPI_LIBNAME=libmpi.40.dylib python test.py
-    $ # or simply
-    $ mlx.launch -n 2 test.py
-
-Setting up Remote Hosts
-^^^^^^^^^^^^^^^^^^^^^^^
-
-MPI can automatically connect to remote hosts and set up the communication over
-the network if the remote hosts can be accessed via ssh. A good checklist to
-debug connectivity issues is the following:
-
-* ``ssh hostname`` works from all machines to all machines without asking for
-  password or host confirmation
-* ``mpirun`` is accessible on all machines.
-* Ensure that the ``hostname`` used by MPI is the one that you have configured
-  in the ``.ssh/config`` files on all machines.
-
-Tuning MPI All Reduce
-^^^^^^^^^^^^^^^^^^^^^
-
-.. note::
-
-    For faster all reduce consider using the ring backend either with Thunderbolt
-    connections or over Ethernet.
-
-Configure MPI to use N tcp connections between each host to improve bandwidth
-by passing ``--mca btl_tcp_links N``.
-
-Force MPI to use the most performant network interface by setting ``--mca
-btl_tcp_if_include <iface>`` where ``<iface>`` should be the interface you want
-to use.
-
-.. _no_mlx_launch:
-
-Distributed Without ``mlx.launch``
-----------------------------------
-
-None of the implementations of the distributed backends require launching with
-``mlx.launch``. The script simply connects to each host. Starts a process per
-rank and sets up the necessary environment variables before delegating to your
-MLX script. See the :doc:`dedicated documentation page <launching_distributed>`
-for more details.
-
-For many use-cases this will be the easiest way to perform distributed
-computations in MLX. However, there may be reasons that you cannot or should
-not use ``mlx.launch``. A common such case is the use of a scheduler that
-starts all the processes for you on machines undetermined at the time of
-scheduling the job.
-
-Below we list the environment variables required to use each backend.
-
-Ring
-^^^^^^
-
-**MLX_RANK** should contain a single 0-based integer that defines the rank of
-the process.
-
-**MLX_HOSTFILE** should contain the path to a json file that contains IPs and
-ports for each rank to listen to, something like the following:
-
-.. code-block:: json
-
-   [
-     ["123.123.1.1:5000", "123.123.1.2:5000"],
-     ["123.123.2.1:5000", "123.123.2.2:5000"],
-     ["123.123.3.1:5000", "123.123.3.2:5000"],
-     ["123.123.4.1:5000", "123.123.4.2:5000"]
-   ]
-
-**MLX_RING_VERBOSE** is optional and if set to 1 it enables some more logging
-from the distributed backend.
-
-JACCL
-^^^^^
-
-**MLX_RANK** should contain a single 0-based integer that defines the rank of
-the process.
-
-**MLX_JACCL_COORDINATOR** should contain the IP and port that rank 0 can listen
-to all the other ranks connect to in order to establish the RDMA connections.
-
-**MLX_IBV_DEVICES** should contain the path to a json file that contains the
-ibverbs device names that connect each node to each other node, something like
-the following:
-
-.. code-block:: json
-
-   [
-      [null, "rdma_en5", "rdma_en4", "rdma_en3"],
-      ["rdma_en5", null, "rdma_en3", "rdma_en4"],
-      ["rdma_en4", "rdma_en3", null, "rdma_en5"],
-      ["rdma_en3", "rdma_en4", "rdma_en5", null]
-   ]
-
-
-NCCL
-^^^^^
-
-**MLX_RANK** should contain a single 0-based integer that defines the rank of
-the process.
-
-**MLX_WORLD_SIZE** should contain the total number of processes that will be
-launched.
-
-**NCCL_HOST_IP** and **NCCL_PORT** should contain the IP and port that all
-hosts can connect to to establish the NCCL communication.
-
-**CUDA_VISIBLE_DEVICES** should contain the local index of the gpu that
-corresponds to this process.
-
-Of course any `other environment variable
-<https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html>`_ that is
-used by NCCL can be set.
-
-.. _tips_and_tricks:
-
-Tips and Tricks
-----------------
-
-This is a small collection of tips to help you utilize better the distributed
-communication capabilities of MLX.
-
-- *Test locally first.*
-
-  You can use the pattern ``mlx.launch -n2 -- my_script.py`` to run a small
-  scale test on a single node first.
-
-- *Batch your communication.*
-
-  As described in the :ref:`training example <training_example>`, performing a
-  lot of small communication can hurt performance. Copy the approach of
-  :func:`mlx.nn.average_gradients` to gather many small communications in a
-  single large one.
-
-- *Visualize the connectivity.*
-
-  Use ``mlx.distributed_config --hosts h1,h2,h3 --over thunderbolt --dot`` to
-  visualize the connnections and make sure that the cables are connected
-  correctly. See the :ref:`JACCL section <jaccl_section>` for examples.
-
-- *Use the debugger.*
-
-  ``mlx.launch`` is meant for interactive use. It broadcasts stdin to all
-  processes and gathers stdout from all processes. This makes using ``pdb`` a
-  breeze.

docs/src/usage/export.rst

@@ -151,7 +151,7 @@ parameters, pass them as inputs to the ``call`` wrapper:
      model.update(tree_unflatten(list(params.items())))
      return model(x)
  
-   params = tree_flatten(model.parameters(), destination={})
+   params = dict(tree_flatten(model.parameters()))
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -164,11 +164,11 @@ to export a function which can be used for inputs with variable shapes:
 
 .. code-block:: python
 
-  mx.export_function("fun.mlxfn", mx.abs, mx.array([0.0]), shapeless=True)
+  mx.export_function("fun.mlxfn", mx.abs, mx.array(0.0), shapeless=True)
   imported_abs = mx.import_function("fun.mlxfn")
 
   # Ok
-  out, = imported_abs(mx.array([-1.0]))
+  out, = imported_abs(mx.array(-1.0))
   
   # Also ok 
   out, = imported_abs(mx.array([-1.0, -2.0]))

docs/src/usage/indexing.rst

@@ -70,8 +70,7 @@ Differences from NumPy
 
   * Indexing does not perform bounds checking. Indexing out of bounds is
     undefined behavior.
-  * Boolean mask based indexing is supported for assignment only (see
+  * Boolean mask based indexing is not yet supported.
-    :ref:`boolean-mask-assignment`).
 
 The reason for the lack of bounds checking is that exceptions cannot propagate
 from the GPU. Performing bounds checking for array indices before launching the
@@ -108,28 +107,6 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
-Note that unlike NumPy, slicing an array creates a copy, not a view. So
-mutating it does not mutate the original array:
-
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> b = a[:]
-  >>> b[2] = 0
-  >>> b
-  array([1, 2, 0], dtype=int32)
-  >>> a
-  array([1, 2, 3], dtype=int32)
-
-Also unlike NumPy, updates to the same location are nondeterministic:
-
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> a[[0, 0]] = mx.array([4, 5])
-
-The first element of ``a`` could be ``4`` or ``5``.
-
 Transformations of functions which use in-place updates are allowed and work as
 expected. For example:
 
@@ -144,51 +121,3 @@ expected. For example:
 
 In the above ``dfdx`` will have the correct gradient, namely zeros at ``idx``
 and ones elsewhere.
-
-.. _boolean-mask-assignment:
-
-Boolean Mask Assignment
------------------------
-
-MLX supports boolean indices using NumPy syntax. A mask must already be
-a :class:`bool_` MLX :class:`array` or a NumPy ``ndarray`` with ``dtype=bool``.
-Other index types are routed through the standard scatter code.
-
-.. code-block:: shell
-
-   >>> a = mx.array([1.0, 2.0, 3.0])
-   >>> mask = mx.array([True, False, True])
-   >>> updates = mx.array([5.0, 6.0])
-   >>> a[mask] = updates
-   >>> a
-   array([5.0, 2.0, 6.0], dtype=float32)
-
-Scalar assignments broadcast to every ``True`` entry in ``mask``. For non-scalar
-assignments, ``updates`` must provide at least as many elements as there are
-``True`` entries in ``mask``.
-
-.. code-block:: shell
-
-   >>> a = mx.zeros((2, 3))
-   >>> mask = mx.array([[True, False, True],
-                        [False, False, True]])
-   >>> a[mask] = 1.0
-   >>> a
-   array([[1.0, 0.0, 1.0],
-          [0.0, 0.0, 1.0]], dtype=float32)
-
-Boolean masks follow NumPy semantics:
-
-- The mask shape must match the shape of the axes it indexes exactly. The only
-  exception is a scalar boolean mask, which broadcasts to the full array.
-- Any axes not covered by the mask are taken in full.
-
-.. code-block:: shell
-
-   >>> a = mx.arange(1000).reshape(10, 10, 10)
-   >>> a[mx.random.randn(10, 10) > 0.0] = 0  # valid: mask covers axes 0 and 1
-
-The mask of shape ``(10, 10)`` applies to the first two axes, so ``a[mask]``
-selects the 1-D slices ``a[i, j, :]`` where ``mask[i, j]`` is ``True``.
-Shapes such as ``(1, 10, 10)`` or ``(10, 10, 1)`` do not match the indexed
-axes and therefore raise errors.

examples/extensions/axpby/axpby.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023-2025 Apple Inc.
 
-#include <dlfcn.h>
 #include <iostream>
 #include <sstream>
 
@@ -17,19 +16,6 @@
 
 namespace my_ext {
 
-// A helper function to find the location of the current binary on disk.
-// The Metal library ("mlx_ext.mtllib"), should be in the same directory.
-std::string current_binary_dir() {
-  static std::string binary_dir = []() {
-    Dl_info info;
-    if (!dladdr(reinterpret_cast<void*>(&current_binary_dir), &info)) {
-      throw std::runtime_error("Unable to get current binary dir.");
-    }
-    return std::filesystem::path(info.dli_fname).parent_path().string();
-  }();
-  return binary_dir;
-}
-
 ///////////////////////////////////////////////////////////////////////////////
 // Operation Implementation
 ///////////////////////////////////////////////////////////////////////////////
@@ -86,7 +72,9 @@ void axpby_impl(
     float alpha_,
     float beta_,
     mx::Stream stream) {
-  out.set_data(mx::allocator::malloc(out.nbytes()));
+  // Allocate the output with `malloc_or_wait` which synchronously allocates
+  // memory, potentially waiting if the system is under memory pressure
+  out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
 
   // Get the CPU command encoder and register input and output arrays
   auto& encoder = mx::cpu::get_command_encoder(stream);
@@ -172,24 +160,25 @@ void Axpby::eval_gpu(
   // Allocate output memory with strides based on specialization
   if (contiguous_kernel) {
     out.set_data(
-        mx::allocator::malloc(x.data_size() * out.itemsize()),
+        mx::allocator::malloc_or_wait(x.data_size() * out.itemsize()),
         x.data_size(),
         x.strides(),
         x.flags());
   } else {
-    out.set_data(mx::allocator::malloc(out.nbytes()));
+    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)
-  std::string kname = "axpby_";
+  std::ostringstream kname;
-  kname += (contiguous_kernel ? "contiguous_" : "general_");
+  kname << "axpby_";
-  kname += type_to_name(out);
+  kname << (contiguous_kernel ? "contiguous_" : "general_");
+  kname << type_to_name(out);
 
-  // Load the metal library
+  // Make sure the metal library is available
-  auto lib = d.get_library("mlx_ext", current_binary_dir());
+  d.register_library("mlx_ext");
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname, lib);
+  auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);

examples/extensions/axpby/axpby.h

@@ -74,9 +74,9 @@ class Axpby : public mx::Primitive {
       const std::vector<mx::array>& inputs,
       const std::vector<int>& axes) override;
 
-  /** The name of primitive. */
+  /** Print the primitive. */
-  const char* name() const override {
+  void print(std::ostream& os) override {
-    return "Axpby";
+    os << "Axpby";
   }
 
   /** Equivalence check **/

examples/extensions/requirements.txt

@@ -1,4 +1,4 @@
 setuptools>=42
 cmake>=3.25
 mlx>=0.21.0
-nanobind==2.4.0
+nanobind==2.2.0

examples/extensions/test.py

@@ -3,10 +3,8 @@ from mlx_sample_extensions import axpby
 
 a = mx.ones((3, 4))
 b = mx.ones((3, 4))
-c_cpu = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
+c = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
-c_gpu = axpby(a, b, 4.0, 2.0, stream=mx.gpu)
 
-print(f"c shape: {c_cpu.shape}")
+print(f"c shape: {c.shape}")
-print(f"c dtype: {c_cpu.dtype}")
+print(f"c dtype: {c.dtype}")
-print(f"c_cpu correct: {mx.all(c_cpu == 6.0).item()}")
+print(f"c correct: {mx.all(c == 6.0).item()}")
-print(f"c_gpu correct: {mx.all(c_gpu == 6.0).item()}")

mlx/CMakeLists.txt

@@ -1,10 +1,10 @@
 target_sources(
   mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/array.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/compile.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/dtype.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/dtype_utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/export.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/einsum.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
@@ -20,7 +20,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
 # Define MLX_VERSION only in the version.cpp file.
-add_library(mlx_version OBJECT ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
+add_library(mlx_version STATIC ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
 target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
 
@@ -48,19 +48,5 @@ add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/io)
 if(MLX_BUILD_METAL)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/metal)
 else()
-  target_sources(mlx
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_metal)
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/no_metal.cpp)
-endif()
-
-if(MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda)
-else()
-  target_sources(mlx
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda/no_cuda.cpp)
-endif()
-
-if(MLX_BUILD_METAL OR MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/gpu)
-else()
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_gpu)
 endif()

mlx/allocator.cpp

@@ -0,0 +1,66 @@
+// Copyright © 2023 Apple Inc.
+
+#include <cstdlib>
+#include <sstream>
+
+#include "mlx/allocator.h"
+#include "mlx/scheduler.h"
+
+namespace mlx::core::allocator {
+
+Buffer malloc(size_t size) {
+  auto buffer = allocator().malloc(size, /* allow_swap */ true);
+  if (size && !buffer.ptr()) {
+    std::ostringstream msg;
+    msg << "[malloc] Unable to allocate " << size << " bytes.";
+    throw std::runtime_error(msg.str());
+  }
+  return buffer;
+}
+
+void free(Buffer buffer) {
+  allocator().free(buffer);
+}
+
+Buffer CommonAllocator::malloc(size_t size, bool) {
+  void* ptr = std::malloc(size + sizeof(size_t));
+  if (ptr != nullptr) {
+    *static_cast<size_t*>(ptr) = size;
+  }
+  return Buffer{ptr};
+}
+
+void CommonAllocator::free(Buffer buffer) {
+  std::free(buffer.ptr());
+}
+
+size_t CommonAllocator::size(Buffer buffer) const {
+  if (buffer.ptr() == nullptr) {
+    return 0;
+  }
+  return *static_cast<size_t*>(buffer.ptr());
+}
+
+Buffer malloc_or_wait(size_t size) {
+  auto buffer = allocator().malloc(size);
+
+  while (size && !buffer.ptr() && scheduler::n_active_tasks() > 0) {
+    scheduler::wait_for_one();
+    buffer = allocator().malloc(size);
+  }
+
+  // Try swapping if needed
+  if (size && !buffer.ptr()) {
+    buffer = allocator().malloc(size, /* allow_swap = */ true);
+  }
+
+  if (size && !buffer.ptr()) {
+    std::ostringstream msg;
+    msg << "[malloc_or_wait] Unable to allocate " << size << " bytes.";
+    throw std::runtime_error(msg.str());
+  }
+
+  return buffer;
+}
+
+} // namespace mlx::core::allocator

mlx/allocator.h

@@ -14,7 +14,7 @@ class Buffer {
   void* ptr_;
 
  public:
-  explicit Buffer(void* ptr) : ptr_(ptr) {};
+  Buffer(void* ptr) : ptr_(ptr) {};
 
   // Get the raw data pointer from the buffer
   void* raw_ptr();
@@ -28,16 +28,20 @@ class Buffer {
   };
 };
 
+Buffer malloc(size_t size);
+
+void free(Buffer buffer);
+
+// Wait for running tasks to finish and free up memory
+// if allocation fails
+Buffer malloc_or_wait(size_t size);
+
 class Allocator {
   /** Abstract base class for a memory allocator. */
  public:
-  virtual Buffer malloc(size_t size) = 0;
+  virtual Buffer malloc(size_t size, bool allow_swap = false) = 0;
   virtual void free(Buffer buffer) = 0;
   virtual size_t size(Buffer buffer) const = 0;
-  virtual Buffer make_buffer(void* ptr, size_t size) {
-    return Buffer{nullptr};
-  };
-  virtual void release(Buffer buffer) {}
 
   Allocator() = default;
   Allocator(const Allocator& other) = delete;
@@ -49,25 +53,16 @@ class Allocator {
 
 Allocator& allocator();
 
-inline Buffer malloc(size_t size) {
+class CommonAllocator : public Allocator {
-  return allocator().malloc(size);
+  /** A general CPU allocator. */
-}
+ public:
+  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
+  virtual void free(Buffer buffer) override;
+  virtual size_t size(Buffer buffer) const override;
 
-inline void free(Buffer buffer) {
+ private:
-  allocator().free(buffer);
+  CommonAllocator() = default;
-}
+  friend Allocator& allocator();
-
-// Make a Buffer from a raw pointer of the given size without a copy.  If a
-// no-copy conversion is not possible then the returned buffer.ptr() will be
-// nullptr. Any buffer created with this function must be released with
-// release(buffer)
-inline Buffer make_buffer(void* ptr, size_t size) {
-  return allocator().make_buffer(ptr, size);
 };
 
-// Release a buffer from the allocator made with make_buffer
-inline void release(Buffer buffer) {
-  allocator().release(buffer);
-}
-
 } // namespace mlx::core::allocator

mlx/array.cpp

@@ -64,7 +64,7 @@ array array::unsafe_weak_copy(const array& other) {
       other.strides(),
       other.flags(),
       [](auto) {});
-  cpy.array_desc_->offset = other.array_desc_->offset;
+  cpy.array_desc_->data_ptr = other.array_desc_->data_ptr;
   return cpy;
 }
 
@@ -82,28 +82,6 @@ array::array(std::initializer_list<int> data, Dtype dtype)
   init(data.begin());
 }
 
-array::array(
-    void* data,
-    Shape shape,
-    Dtype dtype,
-    const std::function<void(void*)>& deleter)
-    : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
-  auto buffer = allocator::make_buffer(data, nbytes());
-  if (buffer.ptr() == nullptr) {
-    set_data(allocator::malloc(nbytes()));
-    auto ptr = static_cast<char*>(data);
-    std::copy(ptr, ptr + nbytes(), this->data<char>());
-    deleter(data);
-  } else {
-    auto wrapped_deleter = [deleter](allocator::Buffer buffer) {
-      auto ptr = buffer.ptr();
-      allocator::release(buffer);
-      return deleter(ptr);
-    };
-    set_data(buffer, std::move(wrapped_deleter));
-  }
-}
-
 /* Build an array from a shared buffer */
 array::array(allocator::Buffer data, Shape shape, Dtype dtype, Deleter deleter)
     : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
@@ -163,7 +141,7 @@ bool array::is_tracer() const {
 
 void array::set_data(allocator::Buffer buffer, Deleter d) {
   array_desc_->data = std::make_shared<Data>(buffer, d);
-  array_desc_->offset = 0;
+  array_desc_->data_ptr = buffer.raw_ptr();
   array_desc_->data_size = size();
   array_desc_->flags.contiguous = true;
   array_desc_->flags.row_contiguous = true;
@@ -178,7 +156,7 @@ void array::set_data(
     Flags flags,
     Deleter d) {
   array_desc_->data = std::make_shared<Data>(buffer, d);
-  array_desc_->offset = 0;
+  array_desc_->data_ptr = buffer.raw_ptr();
   array_desc_->data_size = data_size;
   array_desc_->strides = std::move(strides);
   array_desc_->flags = flags;
@@ -189,13 +167,14 @@ void array::copy_shared_buffer(
     const Strides& strides,
     Flags flags,
     size_t data_size,
-    int64_t offset /* = 0 */) {
+    size_t offset /* = 0 */) {
   array_desc_->data = other.array_desc_->data;
   array_desc_->strides = strides;
   array_desc_->flags = flags;
   array_desc_->data_size = data_size;
-  array_desc_->offset =
+  auto char_offset = sizeof(char) * itemsize() * offset;
-      sizeof(char) * itemsize() * offset + other.array_desc_->offset;
+  array_desc_->data_ptr = static_cast<void*>(
+      static_cast<char*>(other.array_desc_->data_ptr) + char_offset);
 }
 
 void array::copy_shared_buffer(const array& other) {
@@ -262,8 +241,8 @@ array::ArrayDesc::ArrayDesc(
     std::vector<array> inputs)
     : shape(std::move(shape)),
       dtype(dtype),
-      primitive(std::move(primitive)),
       status(Status::unscheduled),
+      primitive(std::move(primitive)),
       inputs(std::move(inputs)) {
   init();
 }

mlx/array.h

@@ -10,7 +10,6 @@
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
 #include "mlx/event.h"
-#include "mlx/small_vector.h"
 
 namespace mlx::core {
 
@@ -19,8 +18,8 @@ class Primitive;
 
 using Deleter = std::function<void(allocator::Buffer)>;
 using ShapeElem = int32_t;
-using Shape = SmallVector<ShapeElem>;
+using Shape = std::vector<ShapeElem>;
-using Strides = SmallVector<int64_t>;
+using Strides = std::vector<int64_t>;
 
 class array {
   /* An array is really a node in a graph. It contains a shared ArrayDesc
@@ -57,16 +56,6 @@ class array {
       Shape shape,
       Dtype dtype = TypeToDtype<T>());
 
-  /* Build an array from a raw pointer. The constructor will attempt to use the
-   * input data without a copy. The deleter will be called when the array no
-   * longer needs the underlying memory - after the array is destroyed in the
-   * no-copy case and after the copy otherwise. */
-  explicit array(
-      void* data,
-      Shape shape,
-      Dtype dtype,
-      const std::function<void(void*)>& deleter);
-
   /* Build an array from a buffer */
   explicit array(
       allocator::Buffer data,
@@ -235,10 +224,6 @@ class array {
     // Not copyable
     Data(const Data& d) = delete;
     Data& operator=(const Data& d) = delete;
-    Data(Data&& o) : buffer(o.buffer), d(o.d) {
-      o.buffer = allocator::Buffer(nullptr);
-      o.d = [](allocator::Buffer) {};
-    }
     ~Data() {
       d(buffer);
     }
@@ -304,11 +289,6 @@ class array {
     return array_desc_->siblings;
   }
 
-  /** The array's position in the sibling list. */
-  int sibling_position() const {
-    return array_desc_->position;
-  }
-
   void set_siblings(std::vector<array> siblings, uint16_t position) {
     array_desc_->siblings = std::move(siblings);
     array_desc_->position = position;
@@ -359,32 +339,24 @@ class array {
     return allocator::allocator().size(buffer());
   }
 
-  // Return the shared pointer to the array::Data struct
+  // Return a copy of the shared pointer
-  const std::shared_ptr<Data>& data_shared_ptr() const {
+  // to the array::Data struct
+  std::shared_ptr<Data> data_shared_ptr() const {
     return array_desc_->data;
   }
-
+  // Return a raw pointer to the arrays data
-  // Return a raw pointer to the arrays data. This function may do a copy if
-  // the underlying buffer is not accessible on the CPU. When accessing the
-  // data for GPU kernels, be sure to use the correct method / function for the
-  // given backend to access the GPU pointer.
   template <typename T>
   T* data() {
-    return reinterpret_cast<T*>(
+    return static_cast<T*>(array_desc_->data_ptr);
-        (static_cast<char*>(buffer().raw_ptr()) + array_desc_->offset));
   }
 
   template <typename T>
   const T* data() const {
-    return const_cast<array&>(*this).data<T>();
+    return static_cast<T*>(array_desc_->data_ptr);
-  }
-
-  int64_t offset() const {
-    return array_desc_->offset;
   }
 
   enum Status {
-    // The output of a computation which has not been scheduled.
+    // The ouptut of a computation which has not been scheduled.
     // For example, the status of `x` in `auto x = a + b`.
     unscheduled,
 
@@ -449,7 +421,7 @@ class array {
       const Strides& strides,
       Flags flags,
       size_t data_size,
-      int64_t offset = 0);
+      size_t offset = 0);
 
   void copy_shared_buffer(const array& other);
 
@@ -484,8 +456,8 @@ class array {
     // can share the underlying data buffer.
     std::shared_ptr<Data> data;
 
-    // Offset from beginning of data pointer
+    // Properly offset data pointer
-    int64_t offset{0};
+    void* data_ptr{nullptr};
 
     // The size in elements of the data buffer the array accesses
     size_t data_size;

mlx/backend/common/CMakeLists.txt

@@ -1,7 +1,6 @@
 target_sources(
   mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/broadcasting.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/common.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/load.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cpp

mlx/backend/common/binary.h

@@ -38,20 +38,20 @@ inline void set_binary_op_output_data(
     const array& a,
     const array& b,
     array& out,
-    BinaryOpType bopt,
+    BinaryOpType bopt) {
-    std::function<allocator::Buffer(size_t)> mallocfn = allocator::malloc) {
   bool b_donatable = is_donatable(b, out);
   bool a_donatable = is_donatable(a, out);
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
-      out.set_data(mallocfn(out.itemsize()), 1, a.strides(), a.flags());
+      out.set_data(
+          allocator::malloc_or_wait(out.itemsize()), 1, a.strides(), a.flags());
       break;
     case BinaryOpType::ScalarVector:
       if (b_donatable) {
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            mallocfn(b.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(b.data_size() * out.itemsize()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -62,7 +62,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(a);
       } else {
         out.set_data(
-            mallocfn(a.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -75,7 +75,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            mallocfn(a.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -88,7 +88,7 @@ inline void set_binary_op_output_data(
           b_donatable && b.flags().row_contiguous && b.size() == out.size()) {
         out.copy_shared_buffer(b);
       } else {
-        out.set_data(mallocfn(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
       }
       break;
   }

mlx/backend/common/broadcasting.cpp

@@ -1,24 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-
-namespace mlx::core {
-
-void broadcast(const array& in, array& out) {
-  if (out.size() == 0) {
-    out.set_data(allocator::malloc(0));
-    return;
-  }
-  Strides strides(out.ndim(), 0);
-  int diff = out.ndim() - in.ndim();
-  for (int i = in.ndim() - 1; i >= 0; --i) {
-    strides[i + diff] = (in.shape()[i] == 1) ? 0 : in.strides()[i];
-  }
-  auto flags = in.flags();
-  if (out.size() > in.size()) {
-    flags.row_contiguous = flags.col_contiguous = false;
-  }
-  out.copy_shared_buffer(in, strides, flags, in.data_size());
-}
-
-} // namespace mlx::core

mlx/backend/common/broadcasting.h

@@ -1,11 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#pragma once
-
-#include "mlx/array.h"
-
-namespace mlx::core {
-
-void broadcast(const array& in, array& out);
-
-} // namespace mlx::core

mlx/backend/common/buffer_cache.h

@@ -1,157 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <cassert>
-#include <functional>
-#include <map>
-
-namespace mlx::core {
-
-template <typename T>
-class BufferCache {
- public:
-  BufferCache(
-      size_t page_size,
-      std::function<size_t(T*)> get_size,
-      std::function<void(T*)> free)
-      : page_size_(page_size),
-        get_size_(std::move(get_size)),
-        free_(std::move(free)) {}
-
-  ~BufferCache() {
-    clear();
-  }
-
-  BufferCache(const BufferCache&) = delete;
-  BufferCache& operator=(const BufferCache&) = delete;
-
-  T* reuse_from_cache(size_t size) {
-    // Find the closest buffer in pool.
-    auto it = buffer_pool_.lower_bound(size);
-    if (it == buffer_pool_.end() ||
-        it->first >= std::min(2 * size, size + 2 * page_size_)) {
-      return nullptr;
-    }
-
-    // Collect from the cache.
-    T* buf = it->second->buf;
-    pool_size_ -= it->first;
-
-    // Remove from record.
-    remove_from_list(it->second);
-    buffer_pool_.erase(it);
-    return buf;
-  }
-
-  void recycle_to_cache(T* buf) {
-    assert(buf);
-    // Add to cache.
-    BufferHolder* bh = new BufferHolder(buf);
-    add_at_head(bh);
-    size_t size = get_size_(buf);
-    pool_size_ += size;
-    buffer_pool_.emplace(size, bh);
-  }
-
-  int release_cached_buffers(size_t min_bytes_to_free) {
-    if (min_bytes_to_free >= 0.9 * pool_size_) {
-      return clear();
-    } else {
-      int n_release = 0;
-      size_t total_bytes_freed = 0;
-
-      while (tail_ && (total_bytes_freed < min_bytes_to_free)) {
-        // Release buffer.
-        size_t size = get_size_(tail_->buf);
-        total_bytes_freed += size;
-        free_(tail_->buf);
-        n_release++;
-
-        // Remove from record.
-        auto its = buffer_pool_.equal_range(size);
-        auto it = std::find_if(its.first, its.second, [this](const auto& el) {
-          return el.second == tail_;
-        });
-        assert(it != buffer_pool_.end());
-        buffer_pool_.erase(it);
-        remove_from_list(tail_);
-      }
-
-      pool_size_ -= total_bytes_freed;
-      return n_release;
-    }
-  }
-
-  int clear() {
-    int n_release = 0;
-    for (auto& [size, holder] : buffer_pool_) {
-      free_(holder->buf);
-      n_release++;
-      delete holder;
-    }
-    buffer_pool_.clear();
-    pool_size_ = 0;
-    head_ = nullptr;
-    tail_ = nullptr;
-    return n_release;
-  }
-
-  size_t cache_size() const {
-    return pool_size_;
-  }
-
-  size_t page_size() const {
-    return page_size_;
-  }
-
- private:
-  struct BufferHolder {
-   public:
-    explicit BufferHolder(T* buf_) : buf(buf_) {}
-
-    BufferHolder* prev{nullptr};
-    BufferHolder* next{nullptr};
-    T* buf;
-  };
-
-  void add_at_head(BufferHolder* to_add) {
-    if (!head_) {
-      head_ = to_add;
-      tail_ = to_add;
-    } else {
-      head_->prev = to_add;
-      to_add->next = head_;
-      head_ = to_add;
-    }
-  }
-
-  void remove_from_list(BufferHolder* to_remove) {
-    if (to_remove->prev && to_remove->next) { // if middle
-      to_remove->prev->next = to_remove->next;
-      to_remove->next->prev = to_remove->prev;
-    } else if (to_remove->prev && to_remove == tail_) { // if tail
-      tail_ = to_remove->prev;
-      tail_->next = nullptr;
-    } else if (to_remove == head_ && to_remove->next) { // if head
-      head_ = to_remove->next;
-      head_->prev = nullptr;
-    } else if (to_remove == head_ && to_remove == tail_) { // if only element
-      head_ = nullptr;
-      tail_ = nullptr;
-    }
-
-    delete to_remove;
-  }
-
-  std::multimap<size_t, BufferHolder*> buffer_pool_;
-  BufferHolder* head_{nullptr};
-  BufferHolder* tail_{nullptr};
-  size_t pool_size_{0};
-
-  const size_t page_size_;
-  std::function<size_t(T*)> get_size_;
-  std::function<void(T*)> free_;
-};
-
-} // namespace mlx::core

mlx/backend/common/common.cpp

@@ -1,7 +1,6 @@
 // Copyright © 2024 Apple Inc.
 #include <cassert>
 
-#include "mlx/backend/common/broadcasting.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/primitives.h"
 
@@ -43,6 +42,23 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
   return out.copy_shared_buffer(in, strides_, flags, data_size, offset_);
 }
 
+void broadcast(const array& in, array& out) {
+  if (out.size() == 0) {
+    out.set_data(nullptr);
+    return;
+  }
+  Strides strides(out.ndim(), 0);
+  int diff = out.ndim() - in.ndim();
+  for (int i = in.ndim() - 1; i >= 0; --i) {
+    strides[i + diff] = (in.shape()[i] == 1) ? 0 : in.strides()[i];
+  }
+  auto flags = in.flags();
+  if (out.size() > in.size()) {
+    flags.row_contiguous = flags.col_contiguous = false;
+  }
+  out.copy_shared_buffer(in, strides, flags, in.data_size());
+}
+
 void Broadcast::eval(const std::vector<array>& inputs, array& out) {
   broadcast(inputs[0], out);
 }
@@ -87,7 +103,7 @@ void ExpandDims::eval(const std::vector<array>& inputs, array& out) {
 
 void NumberOfElements::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   double numel = 1;
   for (auto ax : axes_) {

mlx/backend/common/compiled.cpp

@@ -1,7 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/graph_utils.h"
+#include "mlx/primitives.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
@@ -14,8 +15,6 @@ void print_constant(std::ostream& os, const array& x) {
       return print_float_constant<float16_t>(os, x);
     case bfloat16:
       return print_float_constant<bfloat16_t>(os, x);
-    case float64:
-      return print_float_constant<double>(os, x);
     case complex64:
       return print_complex_constant<complex64_t>(os, x);
     case int8:
@@ -52,8 +51,6 @@ std::string get_type_string(Dtype d) {
       return "float16_t";
     case bfloat16:
       return "bfloat16_t";
-    case float64:
-      return "double";
     case complex64:
       return "complex64_t";
     case bool_:
@@ -82,6 +79,55 @@ std::string get_type_string(Dtype d) {
   }
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids) {
+  NodeNamer namer;
+  std::ostringstream os;
+  std::ostringstream constant_hasher;
+
+  // Fill the input names. This is not really necessary, I just like having A,
+  // B, C, ... as the inputs.
+  for (auto& x : inputs) {
+    namer.get_name(x);
+  }
+
+  // The primitives describing the tape. For unary and binary primitives this
+  // must be enough to describe the full computation.
+  for (auto& a : tape) {
+    // name and type of output
+    os << namer.get_name(a) << kindof(a.dtype()) << a.itemsize();
+    // computation performed
+    a.primitive().print(os);
+    // name of inputs to the function
+    for (auto& inp : a.inputs()) {
+      os << namer.get_name(inp);
+    }
+  }
+  os << "_";
+
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      os << "C";
+      print_constant(constant_hasher, x);
+    } else {
+      os << (is_scalar(x) ? "S" : "V");
+    }
+  }
+  os << "_";
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      continue;
+    }
+    os << kindof(x.dtype()) << x.itemsize();
+  }
+  os << "_" << std::hash<std::string>{}(constant_hasher.str());
+
+  return os.str();
+}
+
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
     const Shape& shape) {
@@ -113,10 +159,9 @@ bool compiled_check_contiguity(
 void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const std::function<bool(size_t)>& is_constant,
+    const std::vector<array>& inputs_,
-    bool contiguous,
+    const std::unordered_set<uintptr_t>& constant_ids_,
-    const std::function<allocator::Buffer(size_t)>&
+    bool contiguous) {
-        mallocfn /* = allocator::malloc */) {
   if (contiguous) {
     int o = 0;
     Strides strides;
@@ -130,7 +175,8 @@ void compiled_allocate_outputs(
       // - Donatable
       // - Not a constant
       if (in.itemsize() == outputs[o].itemsize() && !is_scalar(in) &&
-          in.is_donatable() && is_constant(i)) {
+          in.is_donatable() &&
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o++].copy_shared_buffer(in);
       }
       // Get representative input flags to properly set non-donated outputs
@@ -142,7 +188,7 @@ void compiled_allocate_outputs(
     }
     for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
-          mallocfn(data_size * outputs[o].itemsize()),
+          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
           data_size,
           strides,
           flags);
@@ -158,86 +204,16 @@ void compiled_allocate_outputs(
       // - Not a constant
       if (in.flags().row_contiguous && in.size() == outputs[o].size() &&
           in.itemsize() == outputs[o].itemsize() && in.is_donatable() &&
-          is_constant(i)) {
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o].copy_shared_buffer(
             in, outputs[o].strides(), in.flags(), in.data_size());
         o++;
       }
     }
     for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(mallocfn(outputs[o].nbytes()));
+      outputs[o].set_data(allocator::malloc_or_wait(outputs[o].nbytes()));
     }
   }
 }
 
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant) {
-  const Shape& shape = out.shape();
-  bool contiguous = compiled_check_contiguity(inputs, shape);
-  if (contiguous) {
-    return {true, shape, {}};
-  }
-
-  std::vector<Strides> strides_vec{out.strides()};
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    // Skip constants.
-    if (is_constant(i)) {
-      continue;
-    }
-
-    // Skip scalar inputs.
-    const auto& x = inputs[i];
-    if (is_scalar(x)) {
-      continue;
-    }
-
-    // Broadcast the inputs to the output shape.
-    Strides xstrides;
-    size_t j = 0;
-    for (; j < shape.size() - x.ndim(); ++j) {
-      if (shape[j] == 1) {
-        xstrides.push_back(out.strides()[j]);
-      } else {
-        xstrides.push_back(0);
-      }
-    }
-    for (size_t i = 0; i < x.ndim(); ++i, ++j) {
-      if (x.shape(i) == 1) {
-        if (shape[j] == 1) {
-          xstrides.push_back(out.strides()[j]);
-        } else {
-          xstrides.push_back(0);
-        }
-      } else {
-        xstrides.push_back(x.strides()[i]);
-      }
-    }
-    strides_vec.push_back(std::move(xstrides));
-  }
-
-  auto tup = collapse_contiguous_dims(shape, strides_vec, INT32_MAX);
-  return {false, std::move(std::get<0>(tup)), std::move(std::get<1>(tup))};
-}
-
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
-    bool contiguous) {
-  if (contiguous) {
-    size_t max_size = 0;
-    for (const auto& in : inputs) {
-      max_size = std::max(max_size, in.data_size());
-    }
-    return max_size > UINT32_MAX;
-  } else {
-    size_t max_size = 0;
-    for (const auto& o : outputs) {
-      max_size = std::max(max_size, o.size());
-    }
-    return max_size > UINT32_MAX;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/common/compiled.h

@@ -1,8 +1,9 @@
 // Copyright © 2023-2024 Apple Inc.
 #pragma once
 
-#include <functional>
 #include <iomanip>
+#include <sstream>
+#include <unordered_set>
 
 #include "mlx/array.h"
 #include "mlx/primitives.h"
@@ -13,17 +14,19 @@ inline bool is_static_cast(const Primitive& p) {
   return (typeid(p) == typeid(Broadcast) || typeid(p) == typeid(AsType));
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids);
+
 std::string get_type_string(Dtype d);
 
 template <typename T>
 void print_float_constant(std::ostream& os, const array& x) {
   auto old_precision = os.precision();
-  if constexpr (std::is_same_v<T, double>) {
+  os << std::setprecision(std::numeric_limits<float>::digits10 + 1)
-    os << std::setprecision(std::numeric_limits<double>::digits10 + 1);
+     << x.item<T>() << std::setprecision(old_precision);
-  } else {
-    os << std::setprecision(std::numeric_limits<float>::digits10 + 1);
-  }
-  os << x.item<T>() << std::setprecision(old_precision);
 }
 
 template <typename T>
@@ -57,21 +60,8 @@ bool compiled_check_contiguity(
 void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const std::function<bool(size_t)>& is_constant,
+    const std::vector<array>& inputs_,
-    bool contiguous,
+    const std::unordered_set<uintptr_t>& constant_ids_,
-    const std::function<allocator::Buffer(size_t)>& mallocfn =
-        allocator::malloc);
-
-// Collapse contiguous dims ignoring scalars and constants.
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant);
-
-// Return whether the kernel should use large index.
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
     bool contiguous);
 
 } // namespace mlx::core

mlx/backend/common/copy.h

@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "mlx/backend/common/utils.h"
+#include "mlx/array.h"
 
 namespace mlx::core {
 
@@ -22,27 +22,23 @@ enum class CopyType {
   GeneralGeneral
 };
 
-inline bool set_copy_output_data(
+inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
-    const array& in,
-    array& out,
-    CopyType ctype,
-    std::function<allocator::Buffer(size_t)> mallocfn = allocator::malloc) {
   if (ctype == CopyType::Vector) {
     // If the input is donateable, we are doing a vector copy and the types
     // have the same size, then the input buffer can hold the output.
-    if (is_donatable(in, out)) {
+    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
       out.copy_shared_buffer(in);
       return true;
     } else {
       out.set_data(
-          mallocfn(in.data_size() * out.itemsize()),
+          allocator::malloc_or_wait(in.data_size() * out.itemsize()),
           in.data_size(),
           in.strides(),
           in.flags());
       return false;
     }
   } else {
-    out.set_data(mallocfn(out.nbytes()));
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
     return false;
   }
 }

mlx/backend/common/hadamard.h

@@ -99,10 +99,6 @@ inline std::pair<int, int> decompose_hadamard(int n) {
           "[hadamard] Only supports n = m*2^k where m in (1, 12, 20, 28).");
     }
   }
-  if (n > (1 << 26)) {
-    throw std::invalid_argument(
-        "[hadamard] Only supports n = m*2^k where k <= 26");
-  }
   return {n, m};
 }
 

mlx/backend/common/load.cpp

@@ -28,7 +28,7 @@ void swap_endianness(uint8_t* data_bytes, size_t N) {
 namespace mlx::core {
 
 void Load::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
   auto read_task = [out_ptr = out.data<char>(),
                     size = out.size(),
                     itemsize = out.itemsize(),

mlx/backend/common/matmul.h

@@ -1,67 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/utils.h"
-
-#include <sstream>
-
-namespace mlx::core {
-
-inline std::tuple<Shape, Strides, Strides> collapse_batches(
-    const array& a,
-    const array& b) {
-  if (a.ndim() == 2) {
-    return {Shape{1}, Strides{0}, Strides{0}};
-  }
-
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] =
-      collapse_contiguous_dims(A_bshape, std::vector{A_bstride, B_bstride});
-
-  auto a_batch_strides = batch_strides[0];
-  auto b_batch_strides = batch_strides[1];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    a_batch_strides.push_back(0);
-    b_batch_strides.push_back(0);
-  }
-
-  return std::make_tuple(batch_shape, a_batch_strides, b_batch_strides);
-}
-
-inline std::tuple<Shape, Strides, Strides, Strides>
-collapse_batches(const array& a, const array& b, const array& c) {
-  if (a.ndim() == 2) {
-    return {Shape{1}, Strides{0}, Strides{0}, Strides{0}};
-  }
-
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-  Strides C_bstride{c.strides().begin(), c.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] = collapse_contiguous_dims(
-      A_bshape, std::vector{A_bstride, B_bstride, C_bstride});
-
-  auto A_batch_stride = batch_strides[0];
-  auto B_batch_stride = batch_strides[1];
-  auto C_batch_stride = batch_strides[2];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    A_batch_stride.push_back(0);
-    B_batch_stride.push_back(0);
-    C_batch_stride.push_back(0);
-  }
-
-  return std::make_tuple(
-      batch_shape, A_batch_stride, B_batch_stride, C_batch_stride);
-}
-
-} // namespace mlx::core

mlx/backend/common/reduce.cpp

@@ -5,9 +5,11 @@
 namespace mlx::core {
 
 std::pair<Shape, Strides> shapes_without_reduction_axes(
-    Shape shape,
+    const array& x,
-    Strides strides,
     const std::vector<int>& axes) {
+  auto shape = x.shape();
+  auto strides = x.strides();
+
   for (int i = axes.size() - 1; i >= 0; i--) {
     int a = axes[i];
     shape.erase(shape.begin() + a);
@@ -17,15 +19,6 @@ std::pair<Shape, Strides> shapes_without_reduction_axes(
   return std::make_pair(shape, strides);
 }
 
-std::pair<Shape, Strides> shapes_without_reduction_axes(
-    const array& x,
-    const std::vector<int>& axes) {
-  auto shape = x.shape();
-  auto strides = x.strides();
-  return shapes_without_reduction_axes(
-      std::move(shape), std::move(strides), axes);
-}
-
 ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
   // The data is all there and we are reducing over everything
   if (x.size() == x.data_size() && axes.size() == x.ndim() &&

mlx/backend/common/reduce.h

@@ -51,9 +51,5 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes);
 std::pair<Shape, Strides> shapes_without_reduction_axes(
     const array& x,
     const std::vector<int>& axes);
-std::pair<Shape, Strides> shapes_without_reduction_axes(
-    Shape shape,
-    Strides strides,
-    const std::vector<int>& axes);
 
 } // namespace mlx::core

mlx/backend/common/slicing.cpp

@@ -14,13 +14,17 @@ std::tuple<int64_t, Strides> prepare_slice(
     data_offset += start_indices[i] * in.strides()[i];
     inp_strides[i] = in.strides()[i] * strides[i];
   }
+  // Normalize the offset
+  if (data_offset < 0) {
+    data_offset += in.data_size();
+  }
   return std::make_tuple(data_offset, inp_strides);
 }
 
 void shared_buffer_slice(
     const array& in,
     const Strides& out_strides,
-    int64_t data_offset,
+    size_t data_offset,
     size_t data_size,
     array& out) {
   // Compute row/col contiguity
@@ -41,30 +45,23 @@ void slice(
     const Shape& start_indices,
     const Shape& strides) {
   if (out.size() == 0) {
-    out.set_data(allocator::malloc(0));
+    out.set_data(nullptr);
     return;
   }
 
   // Calculate out strides, initial offset
   auto [data_offset, inp_strides] = prepare_slice(in, start_indices, strides);
-
+  int64_t data_end = 1;
-  // Get the location of the end based on the inp strides and out.shape()
+  for (int i = 0; i < start_indices.size(); ++i) {
-  int64_t low_idx = 0;
+    if (in.shape()[i] > 1) {
-  int64_t high_idx = 0;
+      auto end_idx = start_indices[i] + out.shape()[i] * strides[i] - 1;
-  for (int i = 0; i < inp_strides.size(); ++i) {
+      data_end += end_idx * in.strides()[i];
-    auto delta = inp_strides[i] * (out.shape()[i] - 1);
-    if (inp_strides[i] > 0) {
-      high_idx += delta;
-    } else {
-      low_idx += delta;
     }
   }
-  int64_t data_size = (high_idx - low_idx) + 1;
+  if (data_end < 0) {
-  if (data_size < 0) {
+    data_end += in.data_size();
-    std::ostringstream msg;
-    msg << "[slice] Computed invalid data size: " << data_size << ".";
-    throw std::runtime_error(msg.str());
   }
+  size_t data_size = (data_end - data_offset);
   shared_buffer_slice(in, inp_strides, data_offset, data_size, out);
 }
 

mlx/backend/common/ternary.h

@@ -11,8 +11,6 @@ namespace mlx::core {
 enum class TernaryOpType {
   ScalarScalarScalar,
   VectorVectorVector,
-  VectorVectorScalar,
-  VectorScalarVector,
   General,
 };
 
@@ -27,14 +25,6 @@ get_ternary_op_type(const array& a, const array& b, const array& c) {
       (a.flags().col_contiguous && b.flags().col_contiguous &&
        c.flags().col_contiguous)) {
     topt = TernaryOpType::VectorVectorVector;
-  } else if (
-      b.data_size() == 1 && a.flags().row_contiguous &&
-      c.flags().row_contiguous) {
-    topt = TernaryOpType::VectorScalarVector;
-  } else if (
-      c.data_size() == 1 && a.flags().row_contiguous &&
-      b.flags().row_contiguous) {
-    topt = TernaryOpType::VectorVectorScalar;
   } else {
     topt = TernaryOpType::General;
   }
@@ -46,8 +36,7 @@ inline void set_ternary_op_output_data(
     const array& b,
     const array& c,
     array& out,
-    TernaryOpType topt,
+    TernaryOpType topt) {
-    std::function<allocator::Buffer(size_t)> mallocfn = allocator::malloc) {
   auto maybe_donate = [&out](const array& x) {
     if (is_donatable(x, out)) {
       out.copy_shared_buffer(x);
@@ -58,25 +47,24 @@ inline void set_ternary_op_output_data(
 
   switch (topt) {
     case TernaryOpType::ScalarScalarScalar:
-      out.set_data(mallocfn(out.itemsize()), 1, b.strides(), b.flags());
+      out.set_data(
+          allocator::malloc_or_wait(out.itemsize()), 1, b.strides(), b.flags());
       break;
     case TernaryOpType::VectorVectorVector:
       if (!(maybe_donate(a) || maybe_donate(b) || maybe_donate(c))) {
         out.set_data(
-            mallocfn(out.itemsize() * b.data_size()),
+            allocator::malloc_or_wait(out.itemsize() * b.data_size()),
             b.data_size(),
             b.strides(),
             b.flags());
       }
       break;
-    case TernaryOpType::VectorVectorScalar:
-    case TernaryOpType::VectorScalarVector:
     case TernaryOpType::General:
       // Try to donate an input which is row_contiguous
       if (!((a.flags().row_contiguous && maybe_donate(a)) ||
             (b.flags().row_contiguous && maybe_donate(b)) ||
             (c.flags().row_contiguous && maybe_donate(c)))) {
-        out.set_data(mallocfn(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
       }
       break;
   }

mlx/backend/common/unary.h

@@ -1,29 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/allocator.h"
-#include "mlx/backend/common/utils.h"
-
-namespace mlx::core {
-
-inline void set_unary_output_data(
-    const array& in,
-    array& out,
-    std::function<allocator::Buffer(size_t)> mallocfn = allocator::malloc) {
-  if (in.flags().contiguous) {
-    if (is_donatable(in, out)) {
-      out.copy_shared_buffer(in);
-    } else {
-      out.set_data(
-          mallocfn(in.data_size() * out.itemsize()),
-          in.data_size(),
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(mallocfn(out.nbytes()));
-  }
-}
-
-} // namespace mlx::core

mlx/backend/common/utils.cpp

@@ -1,22 +1,9 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <dlfcn.h>
-
 #include "mlx/backend/common/utils.h"
 
 namespace mlx::core {
 
-std::filesystem::path current_binary_dir() {
-  static std::filesystem::path binary_dir = []() {
-    Dl_info info;
-    if (!dladdr(reinterpret_cast<void*>(&current_binary_dir), &info)) {
-      throw std::runtime_error("Unable to get current binary dir.");
-    }
-    return std::filesystem::path(info.dli_fname).parent_path();
-  }();
-  return binary_dir;
-}
-
 std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     const Shape& shape,
     const std::vector<Strides>& strides,
@@ -114,118 +101,4 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
   return collapse_contiguous_dims(a.shape(), a.strides(), size_cap);
 }
 
-Dims get_block_dims_common(int dim0, int dim1, int dim2, int pow2 /* = 10 */) {
-  int pows[3] = {0, 0, 0};
-  int sum = 0;
-  while (true) {
-    int presum = sum;
-    // Check all the pows
-    if (dim0 >= (1 << (pows[0] + 1))) {
-      pows[0]++;
-      sum++;
-    }
-    if (sum == 10) {
-      break;
-    }
-    if (dim1 >= (1 << (pows[1] + 1))) {
-      pows[1]++;
-      sum++;
-    }
-    if (sum == 10) {
-      break;
-    }
-    if (dim2 >= (1 << (pows[2] + 1))) {
-      pows[2]++;
-      sum++;
-    }
-    if (sum == presum || sum == pow2) {
-      break;
-    }
-  }
-  return std::make_tuple(1ul << pows[0], 1ul << pows[1], 1ul << pows[2]);
-}
-
-Dims get_2d_grid_dims_common(const Shape& shape, const Strides& strides) {
-  // Dims with strides of 0 are ignored as they
-  // correspond to broadcasted dimensions
-  size_t grid_x = 1;
-  size_t grid_y = 1;
-  for (int i = 0; i < shape.size(); ++i) {
-    if (strides[i] == 0) {
-      continue;
-    }
-    if (grid_x * shape[i] < UINT32_MAX) {
-      grid_x *= shape[i];
-    } else {
-      grid_y *= shape[i];
-    }
-  }
-  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX) {
-    throw std::runtime_error("Unable to safely factor shape.");
-  }
-  if (grid_y > grid_x) {
-    std::swap(grid_x, grid_y);
-  }
-  return std::make_tuple(
-      static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y), 1);
-}
-
-Dims get_2d_grid_dims_common(
-    const Shape& shape,
-    const Strides& strides,
-    size_t divisor) {
-  // Compute the 2d grid dimensions such that the total size of the grid is
-  // divided by divisor.
-  size_t grid_x = 1;
-  size_t grid_y = 1;
-  for (int i = 0; i < shape.size(); ++i) {
-    if (strides[i] == 0) {
-      continue;
-    }
-
-    // No need to add this shape we can just remove it from the divisor.
-    if (divisor % shape[i] == 0) {
-      divisor /= shape[i];
-      continue;
-    }
-
-    if (grid_x * shape[i] < UINT32_MAX) {
-      grid_x *= shape[i];
-    } else {
-      grid_y *= shape[i];
-    }
-
-    if (divisor > 1) {
-      if (grid_x % divisor == 0) {
-        grid_x /= divisor;
-        divisor = 1;
-      } else if (grid_y % divisor == 0) {
-        grid_y /= divisor;
-        divisor = 1;
-      }
-    }
-  }
-  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX) {
-    throw std::runtime_error("Unable to safely factor shape.");
-  }
-  if (grid_y > grid_x) {
-    std::swap(grid_x, grid_y);
-  }
-  if (divisor > 1) {
-    grid_x = ((grid_x + divisor - 1) / divisor) * divisor;
-  }
-  return std::make_tuple(
-      static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y), 1);
-}
-
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2) {
-  auto [bx, by, bz] = get_block_dims_common(dim0, dim1, dim2);
-  auto gx = (dim0 + bx - 1) / bx;
-  auto gy = (dim1 + by - 1) / by;
-  auto gz = (dim2 + bz - 1) / bz;
-
-  return std::make_pair(
-      std::make_tuple(gx, gy, gz), std::make_tuple(bx, by, bz));
-}
-
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -2,17 +2,12 @@
 
 #pragma once
 
-#include <filesystem>
-#include <tuple>
 #include <vector>
 
 #include "mlx/array.h"
 
 namespace mlx::core {
 
-// Return the directory that contains current shared library.
-std::filesystem::path current_binary_dir();
-
 inline int64_t
 elem_to_loc(int elem, const Shape& shape, const Strides& strides) {
   int64_t loc = 0;
@@ -75,31 +70,6 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
     const array& a,
     int64_t size_cap = std::numeric_limits<int32_t>::max());
 
-// Compute the thread block dimensions which fit the given
-// input dimensions.
-// - The thread block dimensions will be powers of two
-// - The thread block size will be less than 2^pow2
-using Dims = std::tuple<uint32_t, uint32_t, uint32_t>;
-Dims get_block_dims_common(int dim0, int dim1, int dim2, int pow2 = 10);
-
-// Computes a 2D grid where each element is < UINT_MAX
-// Assumes:
-// - overall size (product of non-broadcasted dimensions) is < UINT_MAX^2
-// - shape and strides correspond to a contiguous (no holes) but
-//   possibly broadcasted array
-Dims get_2d_grid_dims_common(const Shape& shape, const Strides& strides);
-
-// Same as above but we do an implicit division with divisor.
-// Basically, equivalent to factorizing
-//    Prod(s \forall s in shape if strides[s] > 0) / divisor.
-Dims get_2d_grid_dims_common(
-    const Shape& shape,
-    const Strides& strides,
-    size_t divisor);
-
-// Get both the block and a grid of blocks that covers dim0, dim1 and dim2.
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2);
-
 struct ContiguousIterator {
   inline void step() {
     int dims = shape_.size();
@@ -195,11 +165,4 @@ void shared_buffer_reshape(
     const array& in,
     const Strides& out_strides,
     array& out);
-
-template <typename T>
-inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
-  vec.erase(std::next(vec.begin(), index));
-  return vec;
-}
-
 } // namespace mlx::core

mlx/backend/cpu/CMakeLists.txt

@@ -40,13 +40,11 @@ add_dependencies(mlx cpu_compiled_preamble)
 
 target_sources(
   mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/available.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/eig.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/encoder.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
@@ -60,7 +58,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/select.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/threefry.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -76,8 +73,8 @@ target_sources(
 if(MLX_BUILD_ACCELERATE)
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/bnns.cpp)
 else()
-  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_fp16.cpp
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_fp16.cpp
-                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_bf16.cpp)
+                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_bf16.cpp)
 endif()
 
 if(IOS)

mlx/backend/cpu/arg_reduce.cpp

@@ -14,8 +14,10 @@ template <typename InT, typename OpT>
 void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   auto axis_size = in.shape()[axis];
   auto axis_stride = in.strides()[axis];
-  Strides strides = remove_index(in.strides(), axis);
+  Strides strides = in.strides();
-  Shape shape = remove_index(in.shape(), axis);
+  Shape shape = in.shape();
+  strides.erase(strides.begin() + axis);
+  shape.erase(shape.begin() + axis);
   auto in_ptr = in.data<InT>();
   auto out_ptr = out.data<uint32_t>();
 
@@ -66,7 +68,7 @@ void arg_reduce_dispatch(
 void ArgReduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(in);
   encoder.set_output_array(out);

mlx/backend/cpu/available.cpp

@@ -1,11 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cpu/available.h"
-
-namespace mlx::core::cpu {
-
-bool is_available() {
-  return true;
-}
-
-} // namespace mlx::core::cpu

mlx/backend/cpu/available.h

@@ -1,9 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-namespace mlx::core::cpu {
-
-bool is_available();
-
-} // namespace mlx::core::cpu

mlx/backend/cpu/binary.cpp

@@ -14,11 +14,230 @@
 
 namespace mlx::core {
 
+namespace {
+
+template <typename Op>
+void binary(const array& a, const array& b, array& out, Op op, Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        binary_op<bool, Op>(a, b, out, bopt);
+        break;
+      case uint8:
+        binary_op<uint8_t, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, Op>(a, b, out, bopt);
+        break;
+      case float16:
+        binary_op<float16_t, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, Op>(a, b, out, bopt);
+        break;
+      case complex64:
+        binary_op<complex64_t, Op>(a, b, out, bopt);
+        break;
+    }
+  });
+}
+
+template <typename Op>
+void comparison_op(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (a.dtype()) {
+      case bool_:
+        binary_op<bool, bool, Op>(a, b, out, bopt);
+        break;
+      case uint8:
+        binary_op<uint8_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, bool, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, bool, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, bool, Op>(a, b, out, bopt);
+        break;
+      case float16:
+        binary_op<float16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, bool, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, bool, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, bool, Op>(a, b, out, bopt);
+        break;
+      case complex64:
+        binary_op<complex64_t, bool, Op>(a, b, out, bopt);
+        break;
+    }
+  });
+}
+
+template <typename Op>
+void binary_float(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case float16:
+        binary_op<float16_t, Op>(a, b, out, bopt);
+        break;
+      case float32:
+        binary_op<float, Op>(a, b, out, bopt);
+        break;
+      case float64:
+        binary_op<double, Op>(a, b, out, bopt);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, Op>(a, b, out, bopt);
+        break;
+      default:
+        throw std::runtime_error(
+            "[binary_float] Only supports non-complex floating point types.");
+    }
+  });
+}
+
+template <typename Op>
+void binary_int(
+    const array& a,
+    const array& b,
+    array& out,
+    Op op,
+    Stream stream) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([a = array::unsafe_weak_copy(a),
+                    b = array::unsafe_weak_copy(b),
+                    out = array::unsafe_weak_copy(out),
+                    bopt]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        binary_op<bool, Op>(a, b, out, bopt);
+      case uint8:
+        binary_op<uint8_t, Op>(a, b, out, bopt);
+        break;
+      case uint16:
+        binary_op<uint16_t, Op>(a, b, out, bopt);
+        break;
+      case uint32:
+        binary_op<uint32_t, Op>(a, b, out, bopt);
+        break;
+      case uint64:
+        binary_op<uint64_t, Op>(a, b, out, bopt);
+        break;
+      case int8:
+        binary_op<int8_t, Op>(a, b, out, bopt);
+        break;
+      case int16:
+        binary_op<int16_t, Op>(a, b, out, bopt);
+        break;
+      case int32:
+        binary_op<int32_t, Op>(a, b, out, bopt);
+        break;
+      case int64:
+        binary_op<int64_t, Op>(a, b, out, bopt);
+        break;
+      default:
+        throw std::runtime_error("[binary_int] Type not supported");
+        break;
+    }
+  });
+}
+
+} // namespace
+
 void Add::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Add(), stream());
+  binary(a, b, out, detail::Add(), stream());
 }
 
 void DivMod::eval_cpu(
@@ -102,14 +321,14 @@ void Divide::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Divide(), stream());
+  binary(a, b, out, detail::Divide(), stream());
 }
 
 void Remainder::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Remainder(), stream());
+  binary(a, b, out, detail::Remainder(), stream());
 }
 
 void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -150,90 +369,89 @@ void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
       }
     });
   } else {
-    comparison_op_cpu(a, b, out, detail::Equal(), stream());
+    comparison_op(a, b, out, detail::Equal(), stream());
   }
 }
 
 void Greater::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op_cpu(inputs[0], inputs[1], out, detail::Greater(), stream());
+  comparison_op(inputs[0], inputs[1], out, detail::Greater(), stream());
 }
 
 void GreaterEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op_cpu(
+  comparison_op(inputs[0], inputs[1], out, detail::GreaterEqual(), stream());
-      inputs[0], inputs[1], out, detail::GreaterEqual(), stream());
 }
 
 void Less::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op_cpu(inputs[0], inputs[1], out, detail::Less(), stream());
+  comparison_op(inputs[0], inputs[1], out, detail::Less(), stream());
 }
 
 void LessEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op_cpu(inputs[0], inputs[1], out, detail::LessEqual(), stream());
+  comparison_op(inputs[0], inputs[1], out, detail::LessEqual(), stream());
 }
 
 void LogAddExp::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_float_op_cpu(a, b, out, detail::LogAddExp(), stream());
+  binary_float(a, b, out, detail::LogAddExp(), stream());
 }
 
 void LogicalAnd::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalAnd requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary_op_cpu(in1, in2, out, detail::LogicalAnd(), stream());
+  binary(in1, in2, out, detail::LogicalAnd(), stream());
 }
 
 void LogicalOr::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalOr requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary_op_cpu(in1, in2, out, detail::LogicalOr(), stream());
+  binary(in1, in2, out, detail::LogicalOr(), stream());
 }
 
 void Maximum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Maximum(), stream());
+  binary(a, b, out, detail::Maximum(), stream());
 }
 
 void Minimum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Minimum(), stream());
+  binary(a, b, out, detail::Minimum(), stream());
 }
 
 void Multiply::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Multiply(), stream());
+  binary(a, b, out, detail::Multiply(), stream());
 }
 
 void NotEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op_cpu(inputs[0], inputs[1], out, detail::NotEqual(), stream());
+  comparison_op(inputs[0], inputs[1], out, detail::NotEqual(), stream());
 }
 
 void Power::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Power(), stream());
+  binary(a, b, out, detail::Power(), stream());
 }
 
 void Subtract::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_op_cpu(a, b, out, detail::Subtract(), stream());
+  binary(a, b, out, detail::Subtract(), stream());
 }
 
 void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -242,19 +460,19 @@ void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& b = inputs[1];
   switch (op_) {
     case BitwiseBinary::And:
-      binary_int_op_cpu(a, b, out, detail::BitwiseAnd(), stream());
+      binary_int(a, b, out, detail::BitwiseAnd(), stream());
       break;
     case BitwiseBinary::Or:
-      binary_int_op_cpu(a, b, out, detail::BitwiseOr(), stream());
+      binary_int(a, b, out, detail::BitwiseOr(), stream());
       break;
     case BitwiseBinary::Xor:
-      binary_int_op_cpu(a, b, out, detail::BitwiseXor(), stream());
+      binary_int(a, b, out, detail::BitwiseXor(), stream());
       break;
     case BitwiseBinary::LeftShift:
-      binary_int_op_cpu(a, b, out, detail::LeftShift(), stream());
+      binary_int(a, b, out, detail::LeftShift(), stream());
       break;
     case BitwiseBinary::RightShift:
-      binary_int_op_cpu(a, b, out, detail::RightShift(), stream());
+      binary_int(a, b, out, detail::RightShift(), stream());
       break;
   }
 }
@@ -263,7 +481,7 @@ void ArcTan2::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   const auto& a = inputs[0];
   const auto& b = inputs[1];
-  binary_float_op_cpu(a, b, out, detail::ArcTan2(), stream());
+  binary_float(a, b, out, detail::ArcTan2(), stream());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary.h

@@ -7,7 +7,6 @@
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/common/utils.h"
 
-#include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 
 namespace mlx::core {
@@ -291,227 +290,4 @@ void binary_op(const array& a, const array& b, array& out, BinaryOpType bopt) {
   binary_op<T, T, Op>(a, b, out, bopt);
 }
 
-template <typename Op>
-void binary_op_cpu(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        binary_op<bool, Op>(a, b, out, bopt);
-        break;
-      case uint8:
-        binary_op<uint8_t, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, Op>(a, b, out, bopt);
-        break;
-      case float16:
-        binary_op<float16_t, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, Op>(a, b, out, bopt);
-        break;
-    }
-  });
-}
-
-template <typename Op>
-void comparison_op_cpu(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (a.dtype()) {
-      case bool_:
-        binary_op<bool, bool, Op>(a, b, out, bopt);
-        break;
-      case uint8:
-        binary_op<uint8_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, bool, Op>(a, b, out, bopt);
-        break;
-      case float16:
-        binary_op<float16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, bool, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, bool, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, bool, Op>(a, b, out, bopt);
-        break;
-    }
-  });
-}
-
-template <typename Op>
-void binary_float_op_cpu(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case float16:
-        binary_op<float16_t, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, Op>(a, b, out, bopt);
-        break;
-      default:
-        throw std::runtime_error(
-            "[binary_float] Only supports floating point types.");
-    }
-  });
-}
-
-template <typename Op>
-void binary_int_op_cpu(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        binary_op<bool, Op>(a, b, out, bopt);
-      case uint8:
-        binary_op<uint8_t, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, Op>(a, b, out, bopt);
-        break;
-      default:
-        throw std::runtime_error("[binary_int] Type not supported");
-        break;
-    }
-  });
-}
-
 } // namespace mlx::core

mlx/backend/cpu/cholesky.cpp

@@ -20,7 +20,7 @@ void cholesky_impl(const array& a, array& factor, bool upper, Stream stream) {
 
   // The decomposition is computed in place, so just copy the input to the
   // output.
-  copy_cpu(
+  copy(
       a,
       factor,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,