Refactoring launcher

Co-authored-by: Awni Hannun <awni@apple.com>

.circleci/config.yml

@@ -1,724 +0,0 @@
-version: 2.1
-
-orbs:
-  apple: ml-explore/pr-approval@0.1.0
-
-parameters:
-  nightly_build:
-    type: boolean
-    default: false
-  test_release:
-    type: boolean
-    default: false
-
-jobs:
-  build_documentation:
-    parameters:
-      upload-docs:
-        type: boolean
-        default: false
-    macos:
-      xcode: "16.2.0"
-    resource_class: m2pro.medium
-    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
-            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:
-    machine:
-      image: ubuntu-2204:current
-      resource_class: large
-    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: |
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            sudo apt-get update
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
-            curl -LsSf https://astral.sh/uv/install.sh | sh
-      - run:
-          name: Install Python package
-          command: |
-            uv venv
-            uv pip install cmake
-            uv pip install -e ".[dev]" -v
-      - run:
-          name: Generate package stubs
-          command: |
-            uv pip install typing_extensions
-            uv run --no-project setup.py generate_stubs
-      - run:
-          name: Run Python tests
-          command: |
-            source .venv/bin/activate
-            python -m unittest discover python/tests -v
-            mpirun --bind-to none -host localhost:8 -np 8 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
-      - run:
-          name: Build CPP only
-          command: |
-            source .venv/bin/activate
-            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: "16.2.0"
-      macosx_deployment_target:
-        type: string
-        default: ""
-    macos:
-      xcode: << parameters.xcode_version >>
-    environment:
-      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    resource_class: m2pro.medium
-    steps:
-      - checkout
-      - run:
-          name: Install dependencies
-          command: |
-            HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 \
-              brew install openmpi uv
-      - run:
-          name: Install Python package
-          command: |
-            uv venv --python 3.9
-            uv pip install \
-              nanobind==2.4.0 \
-              cmake \
-              numpy \
-              torch \
-              tensorflow \
-              unittest-xml-reporting
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              uv pip install -e . -v
-      - run:
-          name: Generate package stubs
-          command: |
-            uv pip install typing_extensions
-            uv run --no-project setup.py generate_stubs
-      - run:
-          name: Run Python tests
-          command: |
-            source .venv/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 -v 2> >(tee -a stderr.log >&2)
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
-      - run:
-          name: Build example extension
-          command: |
-            source .venv/bin/activate
-            cd examples/extensions
-            uv pip install -r requirements.txt
-            uv run --no-project setup.py build_ext --inplace
-            uv run --no-project python test.py
-      - store_test_results:
-          path: test-results
-      - run:
-          name: Build CPP only
-          command: |
-            source .venv/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 .venv/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: |
-            CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
-              uv pip install -e .
-            LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
-              METAL_DEBUG_ERROR_MODE=0 \
-              uv run --no-project python -m xmlrunner discover \
-                -v python/tests \
-                -o test-results/gpu_jit
-
-  cuda_build_and_test:
-    parameters:
-      image_date:
-        type: string
-        default: "2023.11.1"
-    machine:
-      image: "linux-cuda-12:<< parameters.image_date >>"
-      resource_class: gpu.nvidia.small.gen2
-    steps:
-      - checkout
-      - restore_cache:
-          keys:
-            - cuda-<< parameters.image_date >>-{{ arch }}-
-      - run:
-          name: Install dependencies
-          command: |
-            sudo apt-get update
-            sudo apt-get install libcudnn9-dev-cuda-12
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            curl -sL https://github.com/ccache/ccache/releases/download/v4.11.3/ccache-4.11.3-linux-x86_64.tar.xz | tar xJf -
-            sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
-            rm -rf ccache-4.11.3-linux-x86_64
-            curl -LsSf https://astral.sh/uv/install.sh | sh
-      - run:
-          name: Install Python package
-          command: |
-            uv venv
-            CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              uv pip install -e ".[dev]" -v
-      - run:
-          name: Run Python tests
-          command: |
-            source .venv/bin/activate
-            LOW_MEMORY=1 DEVICE=cpu python -m unittest discover python/tests -v
-            LOW_MEMORY=1 DEVICE=gpu python -m tests discover python/tests -v
-      - run:
-          name: CCache report
-          command: |
-            ccache --show-stats
-            ccache --zero-stats
-            ccache --max-size 400MB
-            ccache --cleanup
-      - save_cache:
-          key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}
-          paths:
-            - /home/circleci/.cache/ccache
-
-  build_release:
-    parameters:
-      python_version:
-        type: string
-        default: "3.9"
-      xcode_version:
-        type: string
-        default: "16.2.0"
-      build_env:
-        type: string
-        default: ""
-      macosx_deployment_target:
-        type: string
-        default: ""
-    macos:
-      xcode: << parameters.xcode_version >>
-    resource_class: m2pro.medium
-    environment:
-      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    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
-            env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
-              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
-            python setup.py clean --all
-            << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  source env/bin/activate
-                  python setup.py clean --all
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 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"
-      build_env:
-        type: string
-        default: ""
-    machine:
-      image: ubuntu-2204:current
-      resource_class: large
-    steps:
-      - checkout
-      - run:
-          name: Build wheel
-          command: |
-            PYTHON=python<< parameters.python_version >>
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            sudo apt-get update
-            TZ=Etc/UTC sudo apt-get -y install tzdata
-            sudo add-apt-repository -y ppa:deadsnakes/ppa
-            sudo apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            $PYTHON -m venv env
-            source env/bin/activate
-            pip install --upgrade pip
-            pip install --upgrade cmake
-            pip install auditwheel
-            pip install patchelf
-            pip install build
-            pip install twine
-            << parameters.build_env >> pip install ".[dev]" -v
-            pip install typing_extensions
-            python setup.py generate_stubs
-            python setup.py clean --all
-            MLX_BUILD_STAGE=1 << parameters.build_env >> python -m build -w
-            bash python/scripts/repair_linux.sh
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  source env/bin/activate
-                  python setup.py clean --all
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 \
-                    python -m build -w
-                  auditwheel repair dist/mlx_cpu*.whl --plat manylinux_2_35_x86_64
-      - when:
-          condition: << parameters.build_env >>
-          steps:
-            - run:
-                name: Upload packages
-                command: |
-                  source env/bin/activate
-                  twine upload wheelhouse/*.whl
-      - store_artifacts:
-          path: wheelhouse/
-
-  build_cuda_release:
-    parameters:
-      build_env:
-        type: string
-        default: ""
-    machine:
-      image: ubuntu-2204:current
-      resource_class: large
-    steps:
-      - checkout
-      - run:
-          name: Build wheel
-          command: |
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/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 cuda-toolkit-12-9 libcudnn9-dev-cuda-12
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install zip
-            pip install auditwheel
-            pip install patchelf
-            pip install build
-            pip install twine
-            export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
-            export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-            << parameters.build_env >> MLX_BUILD_STAGE=2 \
-              CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              python -m build -w
-            bash python/scripts/repair_cuda.sh
-      - when:
-          condition: << parameters.build_env >>
-          steps:
-            - run:
-                name: Upload package
-                command: |
-                  twine upload wheelhouse/*.whl
-      - store_artifacts:
-          path: wheelhouse/
-
-workflows:
-  build_and_test:
-    when:
-      and:
-        - matches:
-            pattern: "^(?!pull/)[-\\w]+$"
-            value: << pipeline.git.branch >>
-        - not: << pipeline.parameters.nightly_build >>
-        - not: << pipeline.parameters.test_release >>
-    jobs:
-      - mac_build_and_test:
-          matrix:
-            parameters:
-              macosx_deployment_target: ["13.5", "14.0"]
-      - linux_build_and_test
-      - cuda_build_and_test:
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
-      - build_documentation 
-
-  build_pypi_release:
-    when:
-      and:
-        - not: << pipeline.parameters.nightly_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"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              build_env: ["PYPI_RELEASE=1"]
-              xcode_version: ["16.2.0", "15.0.0"]
-            exclude:
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
-      - build_documentation:
-          filters:
-            tags:
-              only: /^v.*/
-            branches:
-              ignore: /.*/
-          upload-docs: true
-      - build_linux_release:
-          filters:
-            tags:
-              only: /^v.*/
-            branches:
-              ignore: /.*/
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              build_env: ["PYPI_RELEASE=1"]
-      - build_cuda_release:
-          filters:
-            tags:
-              only: /^v.*/
-            branches:
-              ignore: /.*/
-          matrix:
-            parameters:
-              build_env: ["PYPI_RELEASE=1"]
-
-  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:
-              macosx_deployment_target: ["13.5", "14.0"]
-      - linux_build_and_test:
-          requires: [ hold ]
-      - cuda_build_and_test:
-          requires: [ hold ]
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
-  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"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              xcode_version: ["16.2.0", "15.0.0"]
-            exclude:
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.13"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-      - build_linux_release:
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-      - build_cuda_release
-
-  build_dev_release:
-    when:
-      and:
-        - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.test_release >>
-    jobs:
-      - build_release:
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              build_env: ["DEV_RELEASE=1"]
-              xcode_version: ["16.2.0", "15.0.0"]
-            exclude:
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.9"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.10"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.11"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.12"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.13"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "DEV_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "DEV_RELEASE=1"
-      - build_linux_release:
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              build_env: ["DEV_RELEASE=1"]
-      - build_cuda_release:
-          matrix:
-            parameters:
-              build_env: ["DEV_RELEASE=1"]

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

@@ -0,0 +1,15 @@
+name: 'Build CUDA wheel'
+description: 'Build CUDA wheel'
+
+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

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

@@ -0,0 +1,38 @@
+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

@@ -0,0 +1,40 @@
+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

@@ -0,0 +1,41 @@
+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

@@ -0,0 +1,34 @@
+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

@@ -0,0 +1,88 @@
+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

@@ -0,0 +1,86 @@
+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
+      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

@@ -0,0 +1,24 @@
+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

@@ -0,0 +1,69 @@
+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

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

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

@@ -0,0 +1,27 @@
+#!/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

@@ -0,0 +1,108 @@
+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

@@ -0,0 +1,28 @@
+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

@@ -0,0 +1,96 @@
+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

@@ -1,20 +0,0 @@
-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

@@ -0,0 +1,238 @@
+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'
+    runs-on: ubuntu-22-large
+    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: 'cuda-12.9'
+      - name: Build Python package
+        uses: ./.github/actions/build-cuda-release
+      - 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/

.pre-commit-config.yaml

@@ -1,4 +1,10 @@
 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

@@ -19,12 +19,17 @@ 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.
+- 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

@@ -26,6 +26,7 @@ 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)
@@ -73,6 +74,7 @@ endif()
 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}")
@@ -87,22 +89,26 @@ cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
 
-if(MLX_BUILD_METAL)
-  set(METAL_LIB "-framework Metal")
-  set(FOUNDATION_LIB "-framework Foundation")
-  set(QUARTZ_LIB "-framework QuartzCore")
-endif()
+# 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 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_BUILD_METAL)
+  find_library(METAL_LIB Metal)
+  find_library(FOUNDATION_LIB Foundation)
+  find_library(QUARTZ_LIB 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_METAL_DEBUG)
     add_compile_definitions(MLX_METAL_DEBUG)
@@ -111,7 +117,12 @@ elseif(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 COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_SDK_VERSION
+    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
+  execute_process(
+    COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-path"
+    OUTPUT_VARIABLE CMAKE_OSX_SYSROOT
+    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
 
   if(${MACOS_SDK_VERSION} LESS 14.0)
     message(
@@ -121,9 +132,12 @@ elseif(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_macOS15_iOS18.zip)
+      https://developer.apple.com/metal/cpp/files/metal-cpp_26.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(
@@ -132,7 +146,6 @@ elseif(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}>
@@ -140,6 +153,12 @@ elseif(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.
@@ -167,7 +186,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
     set(MLX_BUILD_ACCELERATE ON)
   else()
-    message(STATUS "Accelerate or arm neon not found, using default backend.")
+    message(STATUS "Accelerate not found, using default backend.")
     set(MLX_BUILD_ACCELERATE OFF)
   endif()
 

README.md

@@ -2,7 +2,7 @@
 
 [**Quickstart**](#quickstart) | [**Installation**](#installation) |
 [**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
-[**Examples**](#examples) 
+[**Examples**](#examples)
 
 [![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)
 
@@ -11,37 +11,37 @@ brought to you by Apple machine learning research.
 
 Some key features of MLX include:
 
- - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
+- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
    also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
    [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
    the Python API. MLX has higher-level packages like `mlx.nn` and
    `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
    more complex models.
 
- - **Composable function transformations**: MLX supports composable function
-   transformations for automatic differentiation, automatic vectorization,
-   and computation graph optimization.
+- **Composable function transformations**: MLX supports composable function
+  transformations for automatic differentiation, automatic vectorization,
+  and computation graph optimization.
 
- - **Lazy computation**: Computations in MLX are lazy. Arrays are only
-   materialized when needed.
+- **Lazy computation**: Computations in MLX are lazy. Arrays are only
+  materialized when needed.
 
- - **Dynamic graph construction**: Computation graphs in MLX are constructed
-   dynamically. Changing the shapes of function arguments does not trigger
-   slow compilations, and debugging is simple and intuitive.
+- **Dynamic graph construction**: Computation graphs in MLX are constructed
+  dynamically. Changing the shapes of function arguments does not trigger
+  slow compilations, and debugging is simple and intuitive.
 
- - **Multi-device**: Operations can run on any of the supported devices
-   (currently the CPU and the GPU).
+- **Multi-device**: Operations can run on any of the supported devices
+  (currently the CPU and the GPU).
 
- - **Unified memory**: A notable difference from MLX and other frameworks
-   is the *unified memory model*. Arrays in MLX live in shared memory.
-   Operations on MLX arrays can be performed on any of the supported
-   device types without transferring data.
+- **Unified memory**: A notable difference from MLX and other frameworks
+  is the *unified memory model*. Arrays in MLX live in shared memory.
+  Operations on MLX arrays can be performed on any of the supported
+  device types without transferring data.
 
 MLX is designed by machine learning researchers for machine learning
 researchers. The framework is intended to be user-friendly, but still efficient
 to train and deploy models. The design of the framework itself is also
 conceptually simple. We intend to make it easy for researchers to extend and
-improve MLX with the goal of quickly exploring new ideas. 
+improve MLX with the goal of quickly exploring new ideas.
 
 The design of MLX is inspired by frameworks like
 [NumPy](https://numpy.org/doc/stable/index.html),
@@ -91,7 +91,7 @@ Checkout the
 [documentation](https://ml-explore.github.io/mlx/build/html/install.html#)
 for more information on building the C++ and Python APIs from source.
 
-## Contributing 
+## Contributing
 
 Check out the [contribution guidelines](https://github.com/ml-explore/mlx/tree/main/CONTRIBUTING.md) for more information
 on contributing to MLX. See the
@@ -110,7 +110,7 @@ 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

@@ -75,7 +75,7 @@ void time_irregular_binary_ops_3D() {
 
 void time_irregular_binary_ops_4D() {
   auto device = mx::default_device();
-  std::vector<int> shape = {8, 8, 512, 512};
+  mx::Shape shape = {8, 8, 512, 512};
   auto a = mx::random::uniform(shape);
   auto b = mx::random::uniform(shape);
 
@@ -115,7 +115,7 @@ void time_irregular_binary_ops_4D() {
 
 void time_irregular_reshape() {
   auto device = mx::default_device();
-  std::vector<int> shape;
+  mx::Shape shape;
   auto reshape_fn = [&shape, device](const mx::array& a) {
     return mx::reshape(a, shape, device);
   };
@@ -170,7 +170,7 @@ void time_irregular_astype_1D() {
 void time_irregular_astype_2D() {
   auto device = mx::default_device();
   int size = 2048;
-  std::vector<int> shape = {size, size};
+  mx::Shape shape = {size, size};
 
   auto a = mx::random::uniform(shape);
   TIMEM("2D regular", mx::astype, a, mx::int32, device);

benchmarks/python/blas/bench_gemm.py

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

benchmarks/python/blas/bench_gemv.py

@@ -1,6 +1,5 @@
 # Copyright © 2023 Apple Inc.
 
-import argparse
 import os
 import subprocess
 import time
@@ -196,7 +195,7 @@ def bench_with_out_len(ax, out_vec_len, in_vector_lens, dtype, transpose):
 
 
 for transpose in (False, True):
-    for dtype in ("float32", "float16"):
+    for dtype in ("float32", "float16", "complex64"):
         fig, axs = plt.subplots(
             len(in_vec_sizes), 2, figsize=(8.5, 11), layout="constrained"
         )
@@ -215,7 +214,7 @@ 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/masked_scatter.py

@@ -0,0 +1,212 @@
+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()

cmake/FindNCCL.cmake

@@ -0,0 +1,54 @@
+# 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

@@ -0,0 +1,3 @@
+# 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.

docs/src/dev/custom_metal_kernels.rst

@@ -127,7 +127,8 @@ 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):
@@ -138,7 +139,6 @@ 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]
 

docs/src/index.rst

@@ -70,6 +70,7 @@ are the CPU and GPU.
    python/fft
    python/linalg
    python/metal
+   python/cuda
    python/memory_management
    python/nn
    python/optimizers

docs/src/install.rst

@@ -16,12 +16,11 @@ silicon computer is
 To install from PyPI your system must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
-- Using a native Python >= 3.9
-- macOS >= 13.5
+- Using a native Python >= 3.10
+- macOS >= 14.0
 
 .. note::
-    MLX is only available on devices running macOS >= 13.5
-    It is highly recommended to use macOS 14 (Sonoma)
+    MLX is only available on devices running macOS >= 14.0 and higher.
 
 CUDA
 ^^^^
@@ -39,7 +38,7 @@ requirements:
 - Nvidia driver >= 550.54.14
 - CUDA toolkit >= 12.0
 - Linux distribution with glibc >= 2.35
-- Python >= 3.9
+- Python >= 3.10
 
 
 CPU-only (Linux)
@@ -55,7 +54,7 @@ To install the CPU-only package from PyPi your system must meet the following
 requirements:
 
 - Linux distribution with glibc >= 2.35
-- Python >= 3.9
+- Python >= 3.10
 
 
 Troubleshooting
@@ -271,7 +270,7 @@ and the CUDA toolkit. For example on Ubuntu, run the following:
    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 -y
+   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

docs/src/python/cuda.rst

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

docs/src/python/fast.rst

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

docs/src/python/nn/functions.rst

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

docs/src/python/nn/layers.rst

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

docs/src/python/ops.rst

@@ -112,6 +112,7 @@ Operations
    max
    maximum
    mean
+   median
    meshgrid
    min
    minimum

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(nn.gelu, x)
-  timeit(mx.compile(nn.gelu), x)
+  timeit(gelu, x)
+  timeit(mx.compile(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), state
+      return mx.exp(z)
 
   fun(mx.array(1.0), mx.array(2.0))
   # Prints [array(3, dtype=float32)]

docs/src/usage/distributed.rst

@@ -7,12 +7,13 @@ 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 two different communication backends:
+moment we support three different communication backends:
 
 * `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.
+* A **ring** backend of our own that uses native TCP sockets. It should be
+  faster for thunderbolt connections, but it also works over Ethernet.
+* `nccl <https://developer.nvidia.com/nccl>`_, for use in CUDA environments.
 
 The list of all currently supported operations and their documentation can be
 seen in the :ref:`API docs<distributed>`.
@@ -84,9 +85,8 @@ Selecting Backend
 ^^^^^^^^^^^^^^^^^
 
 You can select the backend you want to use when calling :func:`init` by passing
-one of ``{'any', 'ring', 'mpi'}``. When passing ``any``, MLX will try to
-initialize the ``ring`` backend and if it fails the ``mpi`` backend. If they
-both fail then a singleton group is created.
+one of ``{'any', 'ring', 'mpi', 'nccl'}``. When passing ``any``, MLX will try all
+available backends. If they all fail then a singleton group is created.
 
 .. note::
    After a distributed backend is successfully initialized :func:`init` will
@@ -184,7 +184,7 @@ almost identical to the example above:
 
     def step(model, x, y):
         loss, grads = loss_grad_fn(model, x, y)
-        grads = mlx.nn.average_gradients(grads) # <---- This line was added
+        grads = mx.nn.average_gradients(grads)  # <---- This line was added
         optimizer.update(model, grads)
         return loss
 
@@ -220,7 +220,7 @@ print 4 etc.
 Installing MPI
 ^^^^^^^^^^^^^^
 
-MPI can be installed with Homebrew, using the Anaconda package manager or
+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:
 
@@ -228,14 +228,16 @@ with the Anaconda package manager as follows:
 
     $ conda install conda-forge::openmpi
 
-Installing with Homebrew may require specifying the location of ``libmpi.dyld``
+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``.
+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/ python test.py
+    $ 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
 

docs/src/usage/export.rst

@@ -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

@@ -70,7 +70,8 @@ Differences from NumPy
 
   * Indexing does not perform bounds checking. Indexing out of bounds is
     undefined behavior.
-  * Boolean mask based indexing is not yet supported.
+  * Boolean mask based indexing is supported for assignment only (see
+    :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
@@ -107,8 +108,20 @@ 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:
 
-Note, unlike NumPy, updates to the same location are nondeterministic:
+.. 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
 
@@ -131,3 +144,51 @@ 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.

mlx/allocator.h

@@ -14,7 +14,7 @@ class Buffer {
   void* ptr_;
 
  public:
-  Buffer(void* ptr) : ptr_(ptr) {};
+  explicit Buffer(void* ptr) : ptr_(ptr) {};
 
   // Get the raw data pointer from the buffer
   void* raw_ptr();

mlx/array.cpp

@@ -64,7 +64,7 @@ array array::unsafe_weak_copy(const array& other) {
       other.strides(),
       other.flags(),
       [](auto) {});
-  cpy.array_desc_->data_ptr = other.array_desc_->data_ptr;
+  cpy.array_desc_->offset = other.array_desc_->offset;
   return cpy;
 }
 
@@ -141,7 +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_->data_ptr = buffer.raw_ptr();
+  array_desc_->offset = 0;
   array_desc_->data_size = size();
   array_desc_->flags.contiguous = true;
   array_desc_->flags.row_contiguous = true;
@@ -156,7 +156,7 @@ void array::set_data(
     Flags flags,
     Deleter d) {
   array_desc_->data = std::make_shared<Data>(buffer, d);
-  array_desc_->data_ptr = buffer.raw_ptr();
+  array_desc_->offset = 0;
   array_desc_->data_size = data_size;
   array_desc_->strides = std::move(strides);
   array_desc_->flags = flags;
@@ -167,14 +167,13 @@ void array::copy_shared_buffer(
     const Strides& strides,
     Flags flags,
     size_t data_size,
-    size_t offset /* = 0 */) {
+    int64_t offset /* = 0 */) {
   array_desc_->data = other.array_desc_->data;
   array_desc_->strides = strides;
   array_desc_->flags = flags;
   array_desc_->data_size = data_size;
-  auto char_offset = sizeof(char) * itemsize() * offset;
-  array_desc_->data_ptr = static_cast<void*>(
-      static_cast<char*>(other.array_desc_->data_ptr) + char_offset);
+  array_desc_->offset =
+      sizeof(char) * itemsize() * offset + other.array_desc_->offset;
 }
 
 void array::copy_shared_buffer(const array& other) {
@@ -241,8 +240,8 @@ array::ArrayDesc::ArrayDesc(
     std::vector<array> inputs)
     : shape(std::move(shape)),
       dtype(dtype),
-      status(Status::unscheduled),
       primitive(std::move(primitive)),
+      status(Status::unscheduled),
       inputs(std::move(inputs)) {
   init();
 }

mlx/array.h

@@ -294,6 +294,11 @@ 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;
@@ -349,15 +354,23 @@ class array {
     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 static_cast<T*>(array_desc_->data_ptr);
+    return reinterpret_cast<T*>(
+        (static_cast<char*>(buffer().raw_ptr()) + array_desc_->offset));
   }
 
   template <typename T>
   const T* data() const {
-    return static_cast<T*>(array_desc_->data_ptr);
+    return const_cast<array&>(*this).data<T>();
+  }
+
+  int64_t offset() const {
+    return array_desc_->offset;
   }
 
   enum Status {
@@ -426,7 +439,7 @@ class array {
       const Strides& strides,
       Flags flags,
       size_t data_size,
-      size_t offset = 0);
+      int64_t offset = 0);
 
   void copy_shared_buffer(const array& other);
 
@@ -461,8 +474,8 @@ class array {
     // can share the underlying data buffer.
     std::shared_ptr<Data> data;
 
-    // Properly offset data pointer
-    void* data_ptr{nullptr};
+    // Offset from beginning of data pointer
+    int64_t offset{0};
 
     // The size in elements of the data buffer the array accesses
     size_t data_size;

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(
-          allocator::malloc(out.itemsize()), 1, a.strides(), a.flags());
+      out.set_data(mallocfn(out.itemsize()), 1, a.strides(), a.flags());
       break;
     case BinaryOpType::ScalarVector:
       if (b_donatable) {
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc(b.data_size() * out.itemsize()),
+            mallocfn(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(
-            allocator::malloc(a.data_size() * out.itemsize()),
+            mallocfn(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(
-            allocator::malloc(a.data_size() * out.itemsize()),
+            mallocfn(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(allocator::malloc(out.nbytes()));
+        out.set_data(mallocfn(out.nbytes()));
       }
       break;
   }

mlx/backend/common/broadcasting.cpp

@@ -6,7 +6,7 @@ namespace mlx::core {
 
 void broadcast(const array& in, array& out) {
   if (out.size() == 0) {
-    out.set_data(nullptr);
+    out.set_data(allocator::malloc(0));
     return;
   }
   Strides strides(out.ndim(), 0);

mlx/backend/common/compiled.cpp

@@ -114,7 +114,9 @@ void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
     const std::function<bool(size_t)>& is_constant,
-    bool contiguous) {
+    bool contiguous,
+    const std::function<allocator::Buffer(size_t)>&
+        mallocfn /* = allocator::malloc */) {
   if (contiguous) {
     int o = 0;
     Strides strides;
@@ -140,7 +142,7 @@ void compiled_allocate_outputs(
     }
     for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
-          allocator::malloc(data_size * outputs[o].itemsize()),
+          mallocfn(data_size * outputs[o].itemsize()),
           data_size,
           strides,
           flags);
@@ -163,7 +165,7 @@ void compiled_allocate_outputs(
       }
     }
     for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(allocator::malloc(outputs[o].nbytes()));
+      outputs[o].set_data(mallocfn(outputs[o].nbytes()));
     }
   }
 }

mlx/backend/common/compiled.h

@@ -58,7 +58,9 @@ void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
     const std::function<bool(size_t)>& is_constant,
-    bool contiguous);
+    bool contiguous,
+    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(

mlx/backend/common/copy.h

@@ -22,7 +22,11 @@ enum class CopyType {
   GeneralGeneral
 };
 
-inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
+inline bool set_copy_output_data(
+    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.
@@ -31,14 +35,14 @@ inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
       return true;
     } else {
       out.set_data(
-          allocator::malloc(in.data_size() * out.itemsize()),
+          mallocfn(in.data_size() * out.itemsize()),
           in.data_size(),
           in.strides(),
           in.flags());
       return false;
     }
   } else {
-    out.set_data(allocator::malloc(out.nbytes()));
+    out.set_data(mallocfn(out.nbytes()));
     return false;
   }
 }

mlx/backend/common/matmul.h

@@ -13,7 +13,7 @@ inline std::tuple<Shape, Strides, Strides> collapse_batches(
     const array& a,
     const array& b) {
   if (a.ndim() == 2) {
-    return {{1}, {0}, {0}};
+    return {Shape{1}, Strides{0}, Strides{0}};
   }
 
   Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
@@ -38,7 +38,7 @@ inline std::tuple<Shape, Strides, Strides> collapse_batches(
 inline std::tuple<Shape, Strides, Strides, Strides>
 collapse_batches(const array& a, const array& b, const array& c) {
   if (a.ndim() == 2) {
-    return {{1}, {0}, {0}, {0}};
+    return {Shape{1}, Strides{0}, Strides{0}, Strides{0}};
   }
 
   Shape A_bshape{a.shape().begin(), a.shape().end() - 2};

mlx/backend/common/slicing.cpp

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

@@ -11,6 +11,8 @@ namespace mlx::core {
 enum class TernaryOpType {
   ScalarScalarScalar,
   VectorVectorVector,
+  VectorVectorScalar,
+  VectorScalarVector,
   General,
 };
 
@@ -25,6 +27,14 @@ 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;
   }
@@ -36,7 +46,8 @@ 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);
@@ -47,24 +58,25 @@ inline void set_ternary_op_output_data(
 
   switch (topt) {
     case TernaryOpType::ScalarScalarScalar:
-      out.set_data(
-          allocator::malloc(out.itemsize()), 1, b.strides(), b.flags());
+      out.set_data(mallocfn(out.itemsize()), 1, b.strides(), b.flags());
       break;
     case TernaryOpType::VectorVectorVector:
       if (!(maybe_donate(a) || maybe_donate(b) || maybe_donate(c))) {
         out.set_data(
-            allocator::malloc(out.itemsize() * b.data_size()),
+            mallocfn(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(allocator::malloc(out.nbytes()));
+        out.set_data(mallocfn(out.nbytes()));
       }
       break;
   }

mlx/backend/common/unary.h

@@ -7,19 +7,22 @@
 
 namespace mlx::core {
 
-inline void set_unary_output_data(const array& in, array& out) {
+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(
-          allocator::malloc(in.data_size() * out.itemsize()),
+          mallocfn(in.data_size() * out.itemsize()),
           in.data_size(),
           in.strides(),
           in.flags());
     }
   } else {
-    out.set_data(allocator::malloc(out.nbytes()));
+    out.set_data(mallocfn(out.nbytes()));
   }
 }
 

mlx/backend/common/utils.cpp

@@ -228,31 +228,4 @@ std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2) {
       std::make_tuple(gx, gy, gz), std::make_tuple(bx, by, bz));
 }
 
-array swapaxes_in_eval(const array& x, int axis1, int axis2) {
-  int ndim = x.ndim();
-  if (axis1 < 0) {
-    axis1 += ndim;
-  }
-  if (axis2 < 0) {
-    axis2 += ndim;
-  }
-
-  auto shape = x.shape();
-  std::swap(shape[axis1], shape[axis2]);
-  auto strides = x.strides();
-  std::swap(strides[axis1], strides[axis2]);
-
-  auto [data_size, row_contiguous, col_contiguous] =
-      check_contiguity(shape, strides);
-  bool contiguous = data_size == x.data_size();
-
-  array out(std::move(shape), x.dtype(), nullptr, {});
-  out.copy_shared_buffer(
-      x,
-      std::move(strides),
-      {contiguous, row_contiguous, col_contiguous},
-      x.data_size());
-  return out;
-}
-
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -196,9 +196,6 @@ void shared_buffer_reshape(
     const Strides& out_strides,
     array& out);
 
-// Like the swapaxes op but safe to call in eval_gpu.
-array swapaxes_in_eval(const array& x, int axis1, int axis2);
-
 template <typename T>
 inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
   vec.erase(std::next(vec.begin(), index));

mlx/backend/cpu/binary.cpp

@@ -14,233 +14,11 @@
 
 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;
-      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(
-    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(a, b, out, detail::Add(), stream());
+  binary_op_cpu(a, b, out, detail::Add(), stream());
 }
 
 void DivMod::eval_cpu(
@@ -324,14 +102,14 @@ void Divide::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Divide(), stream());
+  binary_op_cpu(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(a, b, out, detail::Remainder(), stream());
+  binary_op_cpu(a, b, out, detail::Remainder(), stream());
 }
 
 void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -372,89 +150,90 @@ void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
       }
     });
   } else {
-    comparison_op(a, b, out, detail::Equal(), stream());
+    comparison_op_cpu(a, b, out, detail::Equal(), stream());
   }
 }
 
 void Greater::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::Greater(), stream());
+  comparison_op_cpu(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(inputs[0], inputs[1], out, detail::GreaterEqual(), stream());
+  comparison_op_cpu(
+      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(inputs[0], inputs[1], out, detail::Less(), stream());
+  comparison_op_cpu(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(inputs[0], inputs[1], out, detail::LessEqual(), stream());
+  comparison_op_cpu(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(a, b, out, detail::LogAddExp(), stream());
+  binary_float_op_cpu(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(in1, in2, out, detail::LogicalAnd(), stream());
+  binary_op_cpu(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(in1, in2, out, detail::LogicalOr(), stream());
+  binary_op_cpu(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(a, b, out, detail::Maximum(), stream());
+  binary_op_cpu(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(a, b, out, detail::Minimum(), stream());
+  binary_op_cpu(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(a, b, out, detail::Multiply(), stream());
+  binary_op_cpu(a, b, out, detail::Multiply(), stream());
 }
 
 void NotEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::NotEqual(), stream());
+  comparison_op_cpu(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(a, b, out, detail::Power(), stream());
+  binary_op_cpu(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(a, b, out, detail::Subtract(), stream());
+  binary_op_cpu(a, b, out, detail::Subtract(), stream());
 }
 
 void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -463,19 +242,19 @@ void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& b = inputs[1];
   switch (op_) {
     case BitwiseBinary::And:
-      binary_int(a, b, out, detail::BitwiseAnd(), stream());
+      binary_int_op_cpu(a, b, out, detail::BitwiseAnd(), stream());
       break;
     case BitwiseBinary::Or:
-      binary_int(a, b, out, detail::BitwiseOr(), stream());
+      binary_int_op_cpu(a, b, out, detail::BitwiseOr(), stream());
       break;
     case BitwiseBinary::Xor:
-      binary_int(a, b, out, detail::BitwiseXor(), stream());
+      binary_int_op_cpu(a, b, out, detail::BitwiseXor(), stream());
       break;
     case BitwiseBinary::LeftShift:
-      binary_int(a, b, out, detail::LeftShift(), stream());
+      binary_int_op_cpu(a, b, out, detail::LeftShift(), stream());
       break;
     case BitwiseBinary::RightShift:
-      binary_int(a, b, out, detail::RightShift(), stream());
+      binary_int_op_cpu(a, b, out, detail::RightShift(), stream());
       break;
   }
 }
@@ -484,7 +263,7 @@ 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(a, b, out, detail::ArcTan2(), stream());
+  binary_float_op_cpu(a, b, out, detail::ArcTan2(), stream());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary.h

@@ -7,6 +7,7 @@
 #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 {
@@ -290,4 +291,227 @@ 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/compiled.cpp

@@ -15,6 +15,7 @@
 #include "mlx/backend/cpu/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
+#include "mlx/version.h"
 
 namespace mlx::core {
 
@@ -94,7 +95,11 @@ void* compile(
     kernel_file_name = kernel_name;
   }
 
-  auto output_dir = std::filesystem::temp_directory_path();
+  auto output_dir =
+      std::filesystem::temp_directory_path() / "mlx" / version() / "cpu";
+  if (!std::filesystem::exists(output_dir)) {
+    std::filesystem::create_directories(output_dir);
+  }
 
   std::string shared_lib_name = "lib" + kernel_file_name + ".so";
   auto shared_lib_path = (output_dir / shared_lib_name).string();
@@ -157,10 +162,12 @@ inline void build_kernel(
 #endif
 
   // Start the kernel
-  os << "void " << kernel_name << "(void** args) {" << std::endl;
+  os << "void " << kernel_name
+     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
 
   // Add the input arguments
   int cnt = 0;
+  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     // Skip constants from the input list
     if (is_constant(i)) {
@@ -175,8 +182,8 @@ inline void build_kernel(
        << "];" << std::endl;
     // Scalars and contiguous need no strides
     if (!is_scalar(x) && !contiguous) {
-      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
-         << "];" << std::endl;
+      os << "  const int64_t* " << xname << "_strides = strides["
+         << strides_index++ << "];" << std::endl;
     }
   }
 
@@ -186,10 +193,8 @@ inline void build_kernel(
     os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
        << "*)args[" << cnt++ << "];" << std::endl;
   }
-  // Add output strides and shape to extract the indices.
-  if (!contiguous) {
-    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
-  } else {
+  // Add output size
+  if (contiguous) {
     os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
   }
 
@@ -288,17 +293,8 @@ void Compiled::eval_cpu(
   auto [contiguous, shape, strides] =
       compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
 
-  // Force allocating shape/strides on heap so we can take their data() first
-  // and then std::move them.
-  // TODO: Refactor code to avoid heap allocation.
-  shape.grow();
-  for (auto& s : strides) {
-    s.grow();
-  }
-
   // Collect function input arguments.
   std::vector<void*> args;
-  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     if (is_constant_(i)) {
       continue;
@@ -306,9 +302,6 @@ void Compiled::eval_cpu(
     const auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
-    if (!contiguous && !is_scalar(x)) {
-      args.push_back(strides[strides_index++].data());
-    }
   }
 
   // Get the kernel name from the lib
@@ -343,16 +336,20 @@ void Compiled::eval_cpu(
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
-  if (!contiguous) {
-    args.push_back((void*)shape.data());
-  } else {
+  if (contiguous) {
     args.push_back((void*)outputs[0].data_size());
   }
-  auto fun = (void (*)(void**))fn_ptr;
+  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
   encoder.dispatch([fun,
                     args = std::move(args),
                     strides = std::move(strides),
-                    shape = std::move(shape)]() mutable { fun(args.data()); });
+                    shape = std::move(shape)]() mutable {
+    SmallVector<int64_t*> strides_ptrs;
+    for (auto& s : strides) {
+      strides_ptrs.push_back(s.data());
+    }
+    fun(shape.data(), strides_ptrs.data(), args.data());
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/conv.cpp

@@ -996,131 +996,6 @@ void explicit_gemm_conv_1D_cpu(
   encoder.add_temporaries(std::move(temps));
 }
 
-void explicit_gemm_conv_2D_cpu(
-    const array& in,
-    const array& wt,
-    array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
-    const std::vector<int>& wt_strides,
-    const std::vector<int>& wt_dilation,
-    Stream stream) {
-  const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
-  const int iH = in.shape(1); // Input spatial dim
-  const int iW = in.shape(2); // Input spatial dim
-  const int oH = out.shape(1); // Output spatial dim
-  const int oW = out.shape(2); // Output spatial dim
-  const int O = wt.shape(0); // Out channels
-  const int C = wt.shape(3); // In channels
-  const int wH = wt.shape(1); // Weight spatial dim
-  const int wW = wt.shape(2); // Weight spatial dim
-
-  auto conv_dtype = out.dtype();
-  auto& encoder = cpu::get_command_encoder(stream);
-
-  // Pad input
-  Shape padded_shape = {
-      N,
-      iH + padding_lo[0] + padding_hi[0],
-      iW + padding_lo[1] + padding_hi[1],
-      C};
-  array in_padded(padded_shape, conv_dtype, nullptr, {});
-
-  // Fill with zeros
-  std::vector<array> temps;
-  temps.push_back(array(0, conv_dtype));
-  copy_cpu(temps.back(), in_padded, CopyType::Scalar, stream);
-
-  // Pick input slice from padded
-  size_t data_offset = padding_lo[0] * in_padded.strides()[1] +
-      padding_lo[1] * in_padded.strides()[2];
-  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
-  in_padded_slice.copy_shared_buffer(
-      in_padded,
-      in_padded.strides(),
-      in_padded.flags(),
-      in_padded_slice.size(),
-      data_offset);
-  temps.push_back(in_padded_slice);
-
-  // Copy input values into the slice
-  copy_cpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
-
-  // Make strided view
-  Shape strided_shape = {N, oH, oW, wH, wW, C};
-
-  Strides strided_strides = {
-      in_padded.strides()[0],
-      in_padded.strides()[1] * wt_strides[0],
-      in_padded.strides()[2] * wt_strides[1],
-      in_padded.strides()[1],
-      in_padded.strides()[2],
-      in_padded.strides()[3]};
-  auto flags = in_padded.flags();
-
-  array in_strided_view(strided_shape, in_padded.dtype(), nullptr, {});
-  in_strided_view.copy_shared_buffer(
-      in_padded, strided_strides, flags, in_strided_view.size(), 0);
-
-  // Materialize strided view
-  Shape strided_reshape = {N * oH * oW, wH * wW * C};
-  array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
-  copy_cpu(in_strided_view, in_strided, CopyType::General, stream);
-  temps.push_back(in_strided);
-
-  // Check wt dtype and prepare
-  auto gemm_wt = wt;
-  auto gemm_out = out;
-
-  if (wt.dtype() != float32 || !wt.flags().row_contiguous) {
-    auto ctype =
-        wt.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-    gemm_wt = array(wt.shape(), float32, nullptr, {});
-    copy_cpu(wt, gemm_wt, ctype, stream);
-    temps.push_back(gemm_wt);
-  }
-
-  if (out.dtype() != float32) {
-    gemm_out = array(out.shape(), float32, nullptr, {});
-    gemm_out.set_data(allocator::malloc(gemm_out.nbytes()));
-    temps.push_back(gemm_out);
-  }
-
-  encoder.set_input_array(in_strided);
-  encoder.set_input_array(gemm_wt);
-  encoder.set_output_array(gemm_out);
-
-  encoder.dispatch([in_strided_ptr = in_strided.data<float>(),
-                    gemm_wt_ptr = gemm_wt.data<float>(),
-                    gemm_out_ptr = gemm_out.data<float>(),
-                    strided_reshape = std::move(strided_reshape),
-                    O]() {
-    // Perform gemm
-    cblas_sgemm(
-        CblasRowMajor,
-        CblasNoTrans, // no trans A
-        CblasTrans, // transB
-        strided_reshape[0], // M
-        O, // N
-        strided_reshape[1], // K
-        1.0f, // alpha
-        in_strided_ptr,
-        strided_reshape[1], // lda
-        gemm_wt_ptr,
-        strided_reshape[1], // ldb
-        0.0f, // beta
-        gemm_out_ptr,
-        O // ldc
-    );
-  });
-
-  // Copy results if needed
-  if (out.dtype() != float32) {
-    copy_cpu_inplace(gemm_out, out, CopyType::Vector, stream);
-  }
-  encoder.add_temporaries(std::move(temps));
-}
-
 void explicit_gemm_conv_ND_cpu(
     const array& in,
     const array& wt,

mlx/backend/cpu/distributed.cpp

@@ -95,4 +95,9 @@ void Recv::eval_cpu(
   distributed::detail::recv(group(), outputs[0], src_, stream());
 }
 
+void ReduceScatter::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  throw std::runtime_error("[ReduceScatter] Not implemented yet.");
+}
 } // namespace mlx::core::distributed

mlx/backend/cpu/eig.cpp

@@ -12,6 +12,167 @@ namespace mlx::core {
 
 namespace {
 
+template <typename T>
+complex64_t to_complex(T r, T i) {
+  return {static_cast<float>(r), static_cast<float>(i)};
+}
+
+template <typename T, class Enable = void>
+struct EigWork {};
+
+template <typename T>
+struct EigWork<
+    T,
+    typename std::enable_if<std::is_floating_point<T>::value>::type> {
+  using O = complex64_t;
+
+  char jobl;
+  char jobr;
+  int N;
+  int lwork;
+  int info;
+  std::vector<array::Data> buffers;
+
+  EigWork(char jobl_, char jobr_, int N_, bool compute_eigenvectors)
+      : jobl(jobl_), jobr(jobr_), N(N_), lwork(-1) {
+    T work;
+    int n_vecs_l = compute_eigenvectors ? N_ : 1;
+    int n_vecs_r = 1;
+    geev<T>(
+        &jobl,
+        &jobr,
+        &N,
+        nullptr,
+        &N,
+        nullptr,
+        nullptr,
+        nullptr,
+        &n_vecs_l,
+        nullptr,
+        &n_vecs_r,
+        &work,
+        &lwork,
+        &info);
+    lwork = static_cast<int>(work);
+
+    buffers.emplace_back(allocator::malloc(sizeof(T) * N * 2));
+    if (compute_eigenvectors) {
+      buffers.emplace_back(allocator::malloc(sizeof(T) * N * N * 2));
+    }
+    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
+  }
+
+  void run(T* a, O* values, O* vectors) {
+    auto eig_tmp = static_cast<T*>(buffers[0].buffer.raw_ptr());
+    T* vec_tmp = nullptr;
+    if (vectors) {
+      vec_tmp = static_cast<T*>(buffers[1].buffer.raw_ptr());
+    }
+    auto work = static_cast<T*>(buffers.back().buffer.raw_ptr());
+
+    int n_vecs_l = vectors ? N : 1;
+    int n_vecs_r = 1;
+    geev<T>(
+        &jobl,
+        &jobr,
+        &N,
+        a,
+        &N,
+        eig_tmp,
+        eig_tmp + N,
+        vectors ? vec_tmp : nullptr,
+        &n_vecs_l,
+        nullptr,
+        &n_vecs_r,
+        work,
+        &lwork,
+        &info);
+
+    for (int i = 0; i < N; ++i) {
+      values[i] = to_complex(eig_tmp[i], eig_tmp[N + i]);
+    }
+
+    if (vectors) {
+      for (int i = 0; i < N; ++i) {
+        if (values[i].imag() != 0) {
+          for (int j = 0; j < N; ++j) {
+            vectors[i * N + j] =
+                to_complex(vec_tmp[i * N + j], -vec_tmp[(i + 1) * N + j]);
+            vectors[(i + 1) * N + j] =
+                to_complex(vec_tmp[i * N + j], vec_tmp[(i + 1) * N + j]);
+          }
+          i += 1;
+        } else {
+          for (int j = 0; j < N; ++j) {
+            vectors[i * N + j] = to_complex(vec_tmp[i * N + j], T(0.0));
+          }
+        }
+      }
+    }
+  }
+};
+
+template <>
+struct EigWork<std::complex<float>> {
+  using T = std::complex<float>;
+  using R = float;
+  using O = T;
+
+  char jobl;
+  char jobr;
+  int N;
+  int lwork;
+  int lrwork;
+  int info;
+  std::vector<array::Data> buffers;
+
+  EigWork(char jobl_, char jobr_, int N_, bool compute_eigenvectors)
+      : jobl(jobl_), jobr(jobr_), N(N_), lwork(-1), lrwork(2 * N_) {
+    T work;
+    R rwork;
+    int n_vecs_l = compute_eigenvectors ? N_ : 1;
+    int n_vecs_r = 1;
+    geev<T>(
+        &jobl,
+        &jobr,
+        &N,
+        nullptr,
+        &N,
+        nullptr,
+        nullptr,
+        &n_vecs_l,
+        nullptr,
+        &n_vecs_r,
+        &work,
+        &lwork,
+        &rwork,
+        &info);
+    lwork = static_cast<int>(work.real());
+    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
+    buffers.emplace_back(allocator::malloc(sizeof(R) * lrwork));
+  }
+
+  void run(T* a, T* values, T* vectors) {
+    int n_vecs_l = vectors ? N : 1;
+    int n_vecs_r = 1;
+    geev<T>(
+        &jobl,
+        &jobr,
+        &N,
+        a,
+        &N,
+        values,
+        vectors,
+        &n_vecs_l,
+        nullptr,
+        &n_vecs_r,
+        static_cast<T*>(buffers[0].buffer.raw_ptr()),
+        &lwork,
+        static_cast<R*>(buffers[1].buffer.raw_ptr()),
+        &info);
+  }
+};
+
 template <typename T>
 void eig_impl(
     array& a,
@@ -19,102 +180,39 @@ void eig_impl(
     array& values,
     bool compute_eigenvectors,
     Stream stream) {
-  using OT = std::complex<T>;
   auto a_ptr = a.data<T>();
-  auto eig_ptr = values.data<OT>();
+  auto val_ptr = values.data<complex64_t>();
 
   auto& encoder = cpu::get_command_encoder(stream);
   encoder.set_input_array(a);
   encoder.set_output_array(values);
-  OT* vec_ptr = nullptr;
+  complex64_t* vec_ptr = nullptr;
   if (compute_eigenvectors) {
     encoder.set_output_array(vectors);
-    vec_ptr = vectors.data<OT>();
+    vec_ptr = vectors.data<complex64_t>();
   }
   encoder.dispatch([a_ptr,
+                    val_ptr,
                     vec_ptr,
-                    eig_ptr,
                     compute_eigenvectors,
                     N = vectors.shape(-1),
                     size = vectors.size()]() mutable {
-    // Work query
     char jobr = 'N';
     char jobl = compute_eigenvectors ? 'V' : 'N';
-    int n_vecs_r = 1;
-    int n_vecs_l = compute_eigenvectors ? N : 1;
-    int lwork = -1;
-    int info;
-    {
-      T work;
-      int iwork;
-      geev<T>(
-          &jobl,
-          &jobr,
-          &N,
-          nullptr,
-          &N,
-          nullptr,
-          nullptr,
-          nullptr,
-          &n_vecs_l,
-          nullptr,
-          &n_vecs_r,
-          &work,
-          &lwork,
-          &info);
-      lwork = static_cast<int>(work);
-    }
 
-    auto eig_tmp_data = array::Data{allocator::malloc(sizeof(T) * N * 2)};
-    auto vec_tmp_data =
-        array::Data{allocator::malloc(vec_ptr ? sizeof(T) * N * N * 2 : 0)};
-    auto eig_tmp = static_cast<T*>(eig_tmp_data.buffer.raw_ptr());
-    auto vec_tmp = static_cast<T*>(vec_tmp_data.buffer.raw_ptr());
-    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    EigWork<T> work(jobl, jobr, N, compute_eigenvectors);
+
     for (size_t i = 0; i < size / (N * N); ++i) {
-      geev<T>(
-          &jobl,
-          &jobr,
-          &N,
-          a_ptr,
-          &N,
-          eig_tmp,
-          eig_tmp + N,
-          vec_tmp,
-          &n_vecs_l,
-          nullptr,
-          &n_vecs_r,
-          static_cast<T*>(work_buf.buffer.raw_ptr()),
-          &lwork,
-          &info);
-      for (int i = 0; i < N; ++i) {
-        eig_ptr[i] = {eig_tmp[i], eig_tmp[N + i]};
-      }
+      work.run(a_ptr, val_ptr, vec_ptr);
+      a_ptr += N * N;
+      val_ptr += N;
       if (vec_ptr) {
-        for (int i = 0; i < N; ++i) {
-          if (eig_ptr[i].imag() != 0) {
-            // This vector and the next are a pair
-            for (int j = 0; j < N; ++j) {
-              vec_ptr[i * N + j] = {
-                  vec_tmp[i * N + j], -vec_tmp[(i + 1) * N + j]};
-              vec_ptr[(i + 1) * N + j] = {
-                  vec_tmp[i * N + j], vec_tmp[(i + 1) * N + j]};
-            }
-            i += 1;
-          } else {
-            for (int j = 0; j < N; ++j) {
-              vec_ptr[i * N + j] = {vec_tmp[i * N + j], 0};
-            }
-          }
-        }
         vec_ptr += N * N;
       }
-      a_ptr += N * N;
-      eig_ptr += N;
-      if (info != 0) {
+      if (work.info != 0) {
         std::stringstream msg;
         msg << "[Eig::eval_cpu] Eigenvalue decomposition failed with error code "
-            << info;
+            << work.info;
         throw std::runtime_error(msg.str());
       }
     }
@@ -166,8 +264,17 @@ void Eig::eval_cpu(
     case float32:
       eig_impl<float>(a_copy, vectors, values, compute_eigenvectors_, stream());
       break;
+    case float64:
+      eig_impl<double>(
+          a_copy, vectors, values, compute_eigenvectors_, stream());
+      break;
+    case complex64:
+      eig_impl<std::complex<float>>(
+          a_copy, vectors, values, compute_eigenvectors_, stream());
+      break;
     default:
-      throw std::runtime_error("[Eig::eval_cpu] only supports float32.");
+      throw std::runtime_error(
+          "[Eig::eval_cpu] only supports float32, float64, or complex64.");
   }
 }
 

mlx/backend/cpu/gemms/bnns.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023-2024 Apple Inc.
-
 #include <Accelerate/Accelerate.h>
 
 #include "mlx/array.h"
@@ -49,9 +48,15 @@ void matmul_bnns(
   size_t K = a_shape[ndim - 1];
 
   BNNSDataType bnns_dtype = to_bnns_dtype<T>();
-
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
+  if (beta != 1.0 && beta != 0.0) {
+    // scale the output
+    for (auto i = 0; i < batch_size * M * N; ++i) {
+      out[i] *= beta;
+    }
+    beta = 1.0;
+  }
   const BNNSLayerParametersBroadcastMatMul gemm_params{
       /* float alpha = */ alpha,
       /* float beta = */ beta,

mlx/backend/cpu/gemms/cblas.cpp

@@ -88,4 +88,47 @@ void matmul<double>(
   }
 }
 
+template <>
+void matmul<complex64_t>(
+    const complex64_t* a,
+    const complex64_t* b,
+    complex64_t* out,
+    bool a_transposed,
+    bool b_transposed,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
+    float alpha,
+    float beta,
+    size_t batch_size,
+    const Shape& a_shape,
+    const Strides& a_strides,
+    const Shape& b_shape,
+    const Strides& b_strides) {
+  auto ndim = a_shape.size();
+  size_t M = a_shape[ndim - 2];
+  size_t N = b_shape[ndim - 1];
+  size_t K = a_shape[ndim - 1];
+  auto calpha = static_cast<complex64_t>(alpha);
+  auto cbeta = static_cast<complex64_t>(beta);
+
+  for (int i = 0; i < batch_size; ++i) {
+    cblas_cgemm(
+        CblasRowMajor,
+        a_transposed ? CblasTrans : CblasNoTrans, // transA
+        b_transposed ? CblasTrans : CblasNoTrans, // transB
+        M,
+        N,
+        K,
+        &calpha,
+        a + elem_to_loc(M * K * i, a_shape, a_strides),
+        lda,
+        b + elem_to_loc(K * N * i, b_shape, b_strides),
+        ldb,
+        &cbeta,
+        out + M * N * i,
+        ldc);
+  }
+}
+
 } // namespace mlx::core

mlx/backend/cpu/indexing.cpp

@@ -747,4 +747,108 @@ void ScatterAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
   });
 }
 
+template <typename T>
+void masked_scatter_impl(const array& mask, const array& src, array& out) {
+  ContiguousIterator mask_it(mask);
+  ContiguousIterator src_it(src);
+  ContiguousIterator out_it(out);
+
+  const bool* mask_ptr = mask.data<bool>();
+  const T* src_ptr = src.data<T>();
+  T* dst_ptr = out.data<T>();
+
+  const size_t batch_count = mask.shape(0);
+  const size_t mask_batch_size = mask.size() / batch_count;
+  const size_t src_batch_size = src.size() / batch_count;
+
+  for (uint b = 0; b < batch_count; ++b) {
+    size_t src_consumed = 0;
+    src_it.seek(b * src_batch_size);
+
+    for (size_t i = 0; i < mask_batch_size; ++i) {
+      if (mask_ptr[mask_it.loc]) {
+        if (src_consumed >= src_batch_size) {
+          throw std::runtime_error(
+              "[MaskedScatter::eval_cpu] Source does not have enough elements for mask.");
+        }
+        dst_ptr[out_it.loc] = src_ptr[src_it.loc];
+        src_it.step();
+        ++src_consumed;
+      }
+      mask_it.step();
+      out_it.step();
+    }
+  }
+}
+
+void MaskedScatter::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 3);
+
+  auto& dst = inputs[0];
+  auto& mask = inputs[1];
+  auto& src = inputs[2];
+
+  // Copy src into out (copy allocates memory for out)
+  auto ctype =
+      dst.flags().row_contiguous ? CopyType::Vector : CopyType::General;
+  copy_cpu(dst, out, ctype, stream());
+
+  if (mask.size() == 0) {
+    return;
+  }
+
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(mask);
+  encoder.set_input_array(src);
+  encoder.set_output_array(out);
+  encoder.dispatch([mask = array::unsafe_weak_copy(mask),
+                    src = array::unsafe_weak_copy(src),
+                    out = array::unsafe_weak_copy(out)]() mutable {
+    switch (out.dtype()) {
+      case bool_:
+        masked_scatter_impl<bool>(mask, src, out);
+        break;
+      case uint8:
+        masked_scatter_impl<uint8_t>(mask, src, out);
+        break;
+      case uint16:
+        masked_scatter_impl<uint16_t>(mask, src, out);
+        break;
+      case uint32:
+        masked_scatter_impl<uint32_t>(mask, src, out);
+        break;
+      case uint64:
+        masked_scatter_impl<uint64_t>(mask, src, out);
+        break;
+      case int8:
+        masked_scatter_impl<int8_t>(mask, src, out);
+        break;
+      case int16:
+        masked_scatter_impl<int16_t>(mask, src, out);
+        break;
+      case int32:
+        masked_scatter_impl<int32_t>(mask, src, out);
+        break;
+      case int64:
+        masked_scatter_impl<int64_t>(mask, src, out);
+        break;
+      case float16:
+        masked_scatter_impl<float16_t>(mask, src, out);
+        break;
+      case float32:
+        masked_scatter_impl<float>(mask, src, out);
+        break;
+      case float64:
+        masked_scatter_impl<double>(mask, src, out);
+        break;
+      case bfloat16:
+        masked_scatter_impl<bfloat16_t>(mask, src, out);
+        break;
+      case complex64:
+        masked_scatter_impl<complex64_t>(mask, src, out);
+        break;
+    }
+  });
+}
+
 } // namespace mlx::core

mlx/backend/cpu/lapack.h

@@ -45,9 +45,7 @@
 INSTANTIATE_LAPACK_REAL(geqrf)
 INSTANTIATE_LAPACK_REAL(orgqr)
 INSTANTIATE_LAPACK_REAL(syevd)
-INSTANTIATE_LAPACK_REAL(geev)
 INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesvdx)
 INSTANTIATE_LAPACK_REAL(getrf)
 INSTANTIATE_LAPACK_REAL(getri)
 INSTANTIATE_LAPACK_REAL(trtri)
@@ -63,3 +61,20 @@ INSTANTIATE_LAPACK_REAL(trtri)
   }
 
 INSTANTIATE_LAPACK_COMPLEX(heevd)
+
+#define INSTANTIATE_LAPACK_ALL(FUNC)                                \
+  template <typename T, typename... Args>                           \
+  void FUNC(Args... args) {                                         \
+    if constexpr (std::is_same_v<T, float>) {                       \
+      MLX_LAPACK_FUNC(s##FUNC)(std::forward<Args>(args)...);        \
+    } else if constexpr (std::is_same_v<T, double>) {               \
+      MLX_LAPACK_FUNC(d##FUNC)(std::forward<Args>(args)...);        \
+    } else if constexpr (std::is_same_v<T, std::complex<float>>) {  \
+      MLX_LAPACK_FUNC(c##FUNC)(std::forward<Args>(args)...);        \
+    } else if constexpr (std::is_same_v<T, std::complex<double>>) { \
+      MLX_LAPACK_FUNC(z##FUNC)(std::forward<Args>(args)...);        \
+    }                                                               \
+  }
+
+INSTANTIATE_LAPACK_ALL(geev)
+INSTANTIATE_LAPACK_ALL(gesdd)

mlx/backend/cpu/masked_mm.cpp

@@ -215,18 +215,18 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   encoder.set_input_array(a);
   encoder.set_input_array(b);
-  const void* a_mask_ptr;
-  const void* b_mask_ptr;
-  const void* out_mask_ptr;
+  const void* a_mask_ptr = nullptr;
+  const void* b_mask_ptr = nullptr;
+  const void* out_mask_ptr = nullptr;
   Shape a_mask_shape;
   Shape b_mask_shape;
   Shape out_mask_shape;
   Strides a_mask_strides;
   Strides b_mask_strides;
   Strides out_mask_strides;
-  bool a_mask_bool;
-  bool b_mask_bool;
-  bool out_mask_bool;
+  bool a_mask_bool = false;
+  bool b_mask_bool = false;
+  bool out_mask_bool = false;
   if (has_op_mask) {
     auto& a_mask = inputs[inputs.size() - 2];
     auto& b_mask = inputs[inputs.size() - 1];
@@ -423,7 +423,6 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& rhs_indices = inputs[3];
 
   auto batch_shape = get_batch_dims(out.shape());
-  int batch_ndim = batch_shape.size();
 
   auto batch_shape_A = get_batch_dims(a.shape());
   auto batch_strides_A = get_batch_dims(a.strides());

mlx/backend/cpu/matmul.cpp

@@ -2,6 +2,8 @@
 
 #include <cstring>
 #include "mlx/array.h"
+#include "mlx/backend/cpu/binary.h"
+#include "mlx/backend/cpu/binary_ops.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/gemm.h"
@@ -91,7 +93,6 @@ void matmul_general(
   auto [b_transposed, ldb, b] = check_transpose(b_pre);
   size_t M = a.shape(-2);
   size_t N = b.shape(-1);
-  size_t K = a.shape(-1);
   if (M == 0 || N == 0) {
     return;
   }
@@ -108,6 +109,9 @@ void matmul_general(
   } else if (out.dtype() == float64) {
     matmul_dispatch<double>(
         a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
+  } else if (out.dtype() == complex64) {
+    matmul_dispatch<complex64_t>(
+        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else {
     throw std::runtime_error("[Matmul::eval_cpu] Invalid type.");
   }
@@ -128,24 +132,34 @@ void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  if (out.dtype() != float32) {
-    throw std::runtime_error(
-        "[AddMM::eval_cpu] Currently only supports float32.");
-  }
   if (out.size() == 0) {
     out.set_data(allocator::malloc(out.nbytes()));
     return;
   }
 
+  // Handle empty matrix case (K=0)
+  if (inputs[0].shape(-1) == 0) {
+    auto& c = inputs[2];
+    if (beta_ == 1.0f) {
+      CopyType ctype = c.data_size() == 1
+          ? CopyType::Scalar
+          : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
+      copy_cpu(c, out, ctype, stream());
+    } else {
+      array beta_scalar = array(beta_, c.dtype());
+      auto& encoder = cpu::get_command_encoder(stream());
+      binary_float_op_cpu(c, beta_scalar, out, detail::Multiply(), stream());
+      encoder.add_temporary(std::move(beta_scalar));
+    }
+    return;
+  }
+
   // Fill output with C
   auto& c = inputs[2];
   CopyType ctype = c.data_size() == 1
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
   copy_cpu(c, out, ctype, stream());
-  if (inputs[0].shape(-1) == 0) {
-    return;
-  }
   matmul_general(inputs[0], inputs[1], out, stream(), alpha_, beta_);
 }
 

mlx/backend/cpu/primitives.cpp

@@ -333,7 +333,7 @@ void Reshape::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 void DynamicSlice::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (out.size() == 0) {
-    out.set_data(nullptr);
+    out.set_data(allocator::malloc(0));
     return;
   }
   auto& in = inputs[0];
@@ -361,7 +361,7 @@ void DynamicSliceUpdate::eval_cpu(
     const std::vector<array>& inputs,
     array& out) {
   if (out.size() == 0) {
-    out.set_data(nullptr);
+    out.set_data(allocator::malloc(0));
     return;
   }
 
@@ -396,7 +396,7 @@ void DynamicSliceUpdate::eval_cpu(
 void SliceUpdate::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   if (out.size() == 0) {
-    out.set_data(nullptr);
+    out.set_data(allocator::malloc(0));
     return;
   }
 

mlx/backend/cpu/quantized.cpp

@@ -1,10 +1,11 @@
 // Copyright © 2023 Apple Inc.
 
-#include <cassert>
-
+#include "mlx/backend/common/unary.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
+#include "mlx/backend/cpu/unary.h"
+#include "mlx/backend/cpu/unary_ops.h"
 #include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
@@ -13,6 +14,35 @@ namespace mlx::core {
 
 namespace {
 
+const static float MXFP4_LUT[16] = {
+    +0.0f,
+    +0.5f,
+    +1.0f,
+    +1.5f,
+    +2.0f,
+    +3.0f,
+    +4.0f,
+    +6.0f,
+    -0.0f,
+    -0.5f,
+    -1.0f,
+    -1.5f,
+    -2.0f,
+    -3.0f,
+    -4.0f,
+    -6.0f};
+
+template <typename T>
+static inline T dequantize_scale(uint8_t s) {
+  using FOrI = union {
+    bfloat16_t f;
+    uint16_t i;
+  };
+  FOrI out;
+  out.i = (s == 0 ? 0x40 : (static_cast<uint16_t>(s) << 7));
+  return static_cast<T>(out.f);
+}
+
 inline constexpr short get_pack_factor(int bits, int wsize = 8) {
   return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
 }
@@ -407,6 +437,229 @@ void _qmm_dispatch(
   }
 }
 
+template <typename T>
+void mxfp4_qmm(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const uint8_t* scales,
+    int M,
+    int N,
+    int K) {
+  constexpr int group_size = 32;
+  constexpr int pack_factor = get_pack_factor(4, 8);
+  constexpr int packs_in_group = group_size / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint8_t* w_local = (const uint8_t*)w;
+    const uint8_t* scales_local = scales;
+
+    std::fill(result, result + N, 0);
+
+    for (int k = 0; k < K; k++) {
+      T* result_local = result;
+      T xi = *x++;
+
+      for (int n = 0; n < N; n += group_size) {
+        T scale = dequantize_scale<T>(*scales_local++);
+        for (int ng = 0; ng < packs_in_group; ng++) {
+          uint8_t wi = *w_local++;
+#pragma clang loop unroll(full)
+          for (int p = 0; p < pack_factor; p++) {
+            (*result_local++) +=
+                xi * scale * static_cast<T>(MXFP4_LUT[wi & 0xf]);
+            wi >>= 4;
+          }
+        }
+      }
+    }
+
+    result += N;
+  }
+}
+
+template <typename T>
+void mxfp4_qmm_t(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const uint8_t* scales,
+    int M,
+    int N,
+    int K) {
+  constexpr int group_size = 32;
+  constexpr int pack_factor = get_pack_factor(4, 8);
+  constexpr int packs_in_group = group_size / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint8_t* w_local = (const uint8_t*)w;
+    const uint8_t* scales_local = scales;
+
+    for (int n = 0; n < N; n++) {
+      const T* x_local = x;
+      T sum = 0;
+      for (int k = 0; k < K; k += group_size) {
+        T scale = dequantize_scale<T>(*scales_local++);
+
+        T gsum = 0;
+        for (int kw = 0; kw < packs_in_group; kw++) {
+          uint8_t wi = *w_local++;
+#pragma clang loop unroll(full)
+          for (int p = 0; p < pack_factor; p++) {
+            gsum += (*x_local++) * static_cast<T>(MXFP4_LUT[wi & 0xf]);
+            wi >>= 4;
+          }
+        }
+        sum += scale * gsum;
+      }
+      *result = sum;
+      result++;
+    }
+
+    x += K;
+  }
+}
+
+template <int S>
+simd::Simd<float, S> mxfp4_extract_bits_simd(const uint32_t* w) {
+  if constexpr (S == 8) {
+    constexpr std::array<uint32_t, 8> shifts_ = {{0, 4, 8, 12, 16, 20, 24, 28}};
+    auto shifts(*(simd::Simd<uint32_t, S>*)&shifts_);
+    auto wi = simd::Simd<uint32_t, S>(*w);
+    wi = wi >> shifts;
+    wi = wi & 0xf;
+    simd::Simd<float, S> w_out;
+    for (int i = 0; i < S; ++i) {
+      w_out[i] = MXFP4_LUT[wi[i]];
+    }
+    return w_out;
+  } else {
+    // Appease compiler.. but should never get here
+    throw std::runtime_error("Unsupported combination for simd qmm.");
+  }
+}
+
+template <typename T>
+void mxfp4_qmm_t_simd(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const uint8_t* scales,
+    int M,
+    int N,
+    int K) {
+  constexpr int group_size = 32;
+  constexpr int pack_factor = 32 / 4;
+  constexpr int packs_in_group = group_size / pack_factor;
+  constexpr int S = simd::max_size<T>;
+  static_assert(
+      S % pack_factor == 0, "SIMD size must be divisible by pack factor");
+  constexpr int packs_per_simd = S / pack_factor;
+
+  for (int m = 0; m < M; m++) {
+    const uint32_t* w_local = w;
+    const uint8_t* scales_local = scales;
+
+    for (int n = 0; n < N; n++) {
+      simd::Simd<float, S> acc(0);
+      auto x_local = x;
+      for (int k = 0; k < K; k += group_size) {
+        T scale = dequantize_scale<T>(*scales_local++);
+
+        simd::Simd<float, S> g_acc(0);
+        for (int kw = 0; kw < packs_in_group; kw += packs_per_simd) {
+          // Extract bits
+          auto wf = mxfp4_extract_bits_simd<S>(w_local);
+          w_local += packs_per_simd;
+          simd::Simd<float, S> x_simd = simd::load<T, S>(x_local);
+          g_acc = g_acc + x_simd * wf;
+          x_local += S;
+        }
+        acc = acc + scale * g_acc;
+      }
+
+      *result = T(simd::sum(acc));
+      result++;
+    }
+    x += K;
+  }
+}
+
+template <typename T>
+void mxfp4_qmm_dispatch_transpose(
+    T* result,
+    const T* x,
+    const uint32_t* w,
+    const uint8_t* scales,
+    int M,
+    int N,
+    int K,
+    bool transposed_w) {
+  if (transposed_w) {
+    // the simd size must be a multiple of the number of elements per word
+    if constexpr (simd::max_size<T> % 8 == 0) {
+      mxfp4_qmm_t_simd<T>(result, x, w, scales, M, N, K);
+    } else {
+      mxfp4_qmm_t<T>(result, x, w, scales, M, N, K);
+    }
+  } else {
+    mxfp4_qmm<T>(result, x, w, scales, M, N, K);
+  }
+}
+
+template <typename T>
+void mxfp4_qmm_dispatch_typed(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    bool transposed_w) {
+  int K = x.shape(-1);
+  int M = x.ndim() > 1 ? x.shape(-2) : 1;
+  int N = out.shape(-1);
+  int w_els = w.ndim() > 2 ? w.shape(-1) * w.shape(-2) : 0;
+  int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
+  int batch_size = x.size() / (K * M);
+
+  auto out_ptr = out.data<T>();
+  auto x_ptr = x.data<T>();
+  auto w_ptr = w.data<uint32_t>();
+  auto scales_ptr = scales.data<uint8_t>();
+  for (int i = 0; i < batch_size; i++) {
+    mxfp4_qmm_dispatch_transpose<T>(
+        out_ptr + i * M * N,
+        x_ptr + elem_to_loc(i * M * K, x.shape(), x.strides()),
+        w_ptr + elem_to_loc(i * w_els, w.shape(), w.strides()),
+        scales_ptr + elem_to_loc(i * g_els, scales.shape(), scales.strides()),
+        M,
+        N,
+        K,
+        transposed_w);
+  }
+}
+
+void mxfp4_qmm_dispatch(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    bool transposed_w) {
+  switch (x.dtype()) {
+    case bfloat16:
+      mxfp4_qmm_dispatch_typed<bfloat16_t>(out, x, w, scales, transposed_w);
+      break;
+    case float16:
+      mxfp4_qmm_dispatch_typed<float16_t>(out, x, w, scales, transposed_w);
+      break;
+    case float32:
+      mxfp4_qmm_dispatch_typed<float>(out, x, w, scales, transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "[quantized_matmul] only floating types are supported");
+  }
+}
+
 template <typename T>
 void _bs_qmm_dispatch_typed(
     array& out,
@@ -513,115 +766,198 @@ void _bs_qmm_dispatch(
   }
 }
 
+template <typename T>
+void mxfp4_bs_qmm_dispatch_typed(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    const array& lhs_indices,
+    const array& rhs_indices,
+    bool transposed_w) {
+  int K = x.shape(-1);
+  int M = x.shape(-2);
+  int N = out.shape(-1);
+
+  int w_els = w.shape(-1) * w.shape(-2);
+  int g_els = scales.shape(-1) * scales.shape(-2);
+
+  auto out_ptr = out.data<T>();
+  auto x_ptr = x.data<T>();
+  auto w_ptr = w.data<uint32_t>();
+  auto scales_ptr = scales.data<uint8_t>();
+  auto lhs_indices_ptr = lhs_indices.data<uint32_t>();
+  auto rhs_indices_ptr = rhs_indices.data<uint32_t>();
+
+  for (int i = 0; i < lhs_indices.size(); i++) {
+    int x_idx = lhs_indices_ptr[elem_to_loc(
+        i, lhs_indices.shape(), lhs_indices.strides())];
+    int w_idx = rhs_indices_ptr[elem_to_loc(
+        i, rhs_indices.shape(), rhs_indices.strides())];
+    mxfp4_qmm_dispatch_transpose<T>(
+        out_ptr + i * M * N,
+        x_ptr + elem_to_loc(x_idx * M * K, x.shape(), x.strides()),
+        w_ptr + elem_to_loc(w_idx * w_els, w.shape(), w.strides()),
+        scales_ptr +
+            elem_to_loc(w_idx * g_els, scales.shape(), scales.strides()),
+        M,
+        N,
+        K,
+        transposed_w);
+  }
+}
+
+void mxfp4_bs_qmm_dispatch(
+    array& out,
+    const array& x,
+    const array& w,
+    const array& scales,
+    const array& lhs_indices,
+    const array& rhs_indices,
+    bool transposed_w) {
+  switch (x.dtype()) {
+    case float32:
+      mxfp4_bs_qmm_dispatch_typed<float>(
+          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
+      break;
+    case float16:
+      mxfp4_bs_qmm_dispatch_typed<float16_t>(
+          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
+      break;
+    case bfloat16:
+      mxfp4_bs_qmm_dispatch_typed<bfloat16_t>(
+          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
+      break;
+    default:
+      throw std::invalid_argument(
+          "[quantized_matmul] only floating types are supported");
+  }
+}
+
 } // namespace
 
 void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 4);
-
   auto& x_pre = inputs[0];
   auto& w_pre = inputs[1];
   auto& scales_pre = inputs[2];
-  auto& biases_pre = inputs[3];
 
-  std::vector<array> temps;
-  auto ensure_row_contiguous = [s = stream(), &temps](const array& arr) {
+  auto& encoder = cpu::get_command_encoder(stream());
+  auto ensure_row_contiguous = [s = stream(), &encoder](const array& arr) {
     if (arr.flags().row_contiguous) {
       return arr;
     } else {
-      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
-      return temps.back();
+      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
+      copy_cpu(arr, arr_cpy, CopyType::General, s);
+      encoder.add_temporary(arr_cpy);
+      return arr_cpy;
     }
   };
 
   auto x = ensure_row_contiguous(x_pre);
   auto w = ensure_row_contiguous(w_pre);
   auto scales = ensure_row_contiguous(scales_pre);
-  auto biases = ensure_row_contiguous(biases_pre);
 
   out.set_data(allocator::malloc(out.nbytes()));
 
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.add_temporaries(std::move(temps));
   encoder.set_input_array(x);
   encoder.set_input_array(w);
   encoder.set_input_array(scales);
-  encoder.set_input_array(biases);
   encoder.set_output_array(out);
-  encoder.dispatch([out = array::unsafe_weak_copy(out),
-                    x = array::unsafe_weak_copy(x),
-                    w = array::unsafe_weak_copy(w),
-                    scales = array::unsafe_weak_copy(scales),
-                    biases = array::unsafe_weak_copy(biases),
-                    group_size_ = group_size_,
-                    bits_ = bits_,
-                    transpose_ = transpose_]() mutable {
-    _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
-  });
+  if (mode_ == QuantizationMode::Affine) {
+    auto biases = ensure_row_contiguous(inputs[3]);
+    encoder.set_input_array(biases);
+    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                      x = array::unsafe_weak_copy(x),
+                      w = array::unsafe_weak_copy(w),
+                      scales = array::unsafe_weak_copy(scales),
+                      biases = array::unsafe_weak_copy(biases),
+                      group_size_ = group_size_,
+                      bits_ = bits_,
+                      transpose_ = transpose_]() mutable {
+      _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
+    });
+  } else {
+    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                      x = array::unsafe_weak_copy(x),
+                      w = array::unsafe_weak_copy(w),
+                      scales = array::unsafe_weak_copy(scales),
+                      transpose_ = transpose_]() mutable {
+      mxfp4_qmm_dispatch(out, x, w, scales, transpose_);
+    });
+  }
 }
 
 void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 6);
-
   auto& x_pre = inputs[0];
   auto& w_pre = inputs[1];
   auto& scales_pre = inputs[2];
-  auto& biases_pre = inputs[3];
-  auto& lhs_indices = inputs[4];
-  auto& rhs_indices = inputs[5];
+  auto& lhs_indices = inputs[inputs.size() - 2];
+  auto& rhs_indices = inputs[inputs.size() - 1];
 
-  std::vector<array> temps;
+  auto& encoder = cpu::get_command_encoder(stream());
   auto ensure_row_contiguous_last_dims = [s = stream(),
-                                          &temps](const array& arr) {
+                                          &encoder](const array& arr) {
     auto stride_0 = arr.strides()[arr.ndim() - 2];
     auto stride_1 = arr.strides()[arr.ndim() - 1];
     if (stride_0 == arr.shape(-1) && stride_1 == 1) {
       return arr;
     } else {
-      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
-      return temps.back();
+      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
+      copy_cpu(arr, arr_cpy, CopyType::General, s);
+      encoder.add_temporary(arr_cpy);
+      return arr_cpy;
     }
   };
 
   auto x = ensure_row_contiguous_last_dims(x_pre);
   auto w = ensure_row_contiguous_last_dims(w_pre);
   auto scales = ensure_row_contiguous_last_dims(scales_pre);
-  auto biases = ensure_row_contiguous_last_dims(biases_pre);
 
   out.set_data(allocator::malloc(out.nbytes()));
 
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.add_temporaries(std::move(temps));
   encoder.set_input_array(x);
   encoder.set_input_array(w);
   encoder.set_input_array(scales);
-  encoder.set_input_array(biases);
   encoder.set_input_array(lhs_indices);
   encoder.set_input_array(rhs_indices);
   encoder.set_output_array(out);
-  encoder.dispatch([out = array::unsafe_weak_copy(out),
-                    x = array::unsafe_weak_copy(x),
-                    w = array::unsafe_weak_copy(w),
-                    scales = array::unsafe_weak_copy(scales),
-                    biases = array::unsafe_weak_copy(biases),
-                    lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                    rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                    group_size_ = group_size_,
-                    bits_ = bits_,
-                    transpose_ = transpose_]() mutable {
-    _bs_qmm_dispatch(
-        out,
-        x,
-        w,
-        scales,
-        biases,
-        lhs_indices,
-        rhs_indices,
-        group_size_,
-        bits_,
-        transpose_);
-  });
+  if (mode_ == QuantizationMode::Affine) {
+    auto biases = ensure_row_contiguous_last_dims(inputs[3]);
+    encoder.set_input_array(biases);
+    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                      x = array::unsafe_weak_copy(x),
+                      w = array::unsafe_weak_copy(w),
+                      scales = array::unsafe_weak_copy(scales),
+                      biases = array::unsafe_weak_copy(biases),
+                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
+                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
+                      group_size_ = group_size_,
+                      bits_ = bits_,
+                      transpose_ = transpose_]() mutable {
+      _bs_qmm_dispatch(
+          out,
+          x,
+          w,
+          scales,
+          biases,
+          lhs_indices,
+          rhs_indices,
+          group_size_,
+          bits_,
+          transpose_);
+    });
+  } else {
+    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                      x = array::unsafe_weak_copy(x),
+                      w = array::unsafe_weak_copy(w),
+                      scales = array::unsafe_weak_copy(scales),
+                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
+                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
+                      transpose_ = transpose_]() mutable {
+      mxfp4_bs_qmm_dispatch(
+          out, x, w, scales, lhs_indices, rhs_indices, transpose_);
+    });
+  }
 }
 
 template <typename T, typename U>
@@ -705,7 +1041,7 @@ void dispatch_quantize(
       w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
 }
 
-void fast::AffineQuantize::eval_cpu(
+void fast::Quantize::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   auto ensure_row_contiguous = [s = stream()](const array& arr) {
@@ -764,7 +1100,47 @@ void fast::AffineQuantize::eval_cpu(
       }
     } else {
       throw std::runtime_error(
-          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+          "[fast::Quantize::eval_cpu] Only supports floating point inputs");
+    }
+  });
+}
+
+void fast::ConvertFP8::eval_cpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  auto& in = inputs[0];
+  auto& out = outputs[0];
+  set_unary_output_data(in, out);
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.dispatch([in = array::unsafe_weak_copy(in),
+                    out = array::unsafe_weak_copy(out),
+                    to_fp8 = to_fp8_]() mutable {
+    if (to_fp8) {
+      switch (in.dtype()) {
+        case float16:
+          unary_op<float16_t, uint8_t>(in, out, detail::ToFP8());
+          break;
+        case bfloat16:
+          unary_op<bfloat16_t, uint8_t>(in, out, detail::ToFP8());
+          break;
+        default:
+          unary_op<float, uint8_t>(in, out, detail::ToFP8());
+          break;
+      }
+    } else {
+      switch (out.dtype()) {
+        case float16:
+          unary_op<uint8_t, float16_t>(in, out, detail::FromFP8());
+          break;
+        case bfloat16:
+          unary_op<uint8_t, bfloat16_t>(in, out, detail::FromFP8());
+          break;
+        default:
+          unary_op<uint8_t, float>(in, out, detail::FromFP8());
+          break;
+      }
     }
   });
 }

mlx/backend/cpu/simd/accelerate_simd.h

@@ -1,5 +1,6 @@
 #pragma once
 
+#include <arm_neon.h>
 #include <simd/math.h>
 #include <simd/vector.h>
 
@@ -9,7 +10,7 @@
 
 #include "mlx/backend/cpu/simd/base_simd.h"
 
-// There seems to be a bug in sims/base.h
+// There seems to be a bug in simd/base_simd.h
 // __XROS_2_0 is not defined, the expression evaluates
 // to true instead of false setting the SIMD library
 // higher than it should be even on macOS < 15
@@ -200,6 +201,15 @@ SIMD_DEFAULT_COMPARISONS(<=)
 SIMD_DEFAULT_COMPARISONS(==)
 SIMD_DEFAULT_COMPARISONS(!=)
 
+template <typename T, int N>
+Simd<T, N> clz(Simd<T, N> x) {
+  auto a = *(uint32x4_t*)(&x);
+  auto b = *((uint32x4_t*)(&x) + 1);
+  a = vclzq_u32(a);
+  b = vclzq_u32(b);
+  return asd::make_uint8(a, b);
+}
+
 template <typename T, int N>
 Simd<T, N> atan2(Simd<T, N> a, Simd<T, N> b) {
   return asd::atan2(a.value, b.value);
@@ -207,14 +217,20 @@ Simd<T, N> atan2(Simd<T, N> a, Simd<T, N> b) {
 
 template <typename T, int N>
 Simd<T, N> maximum(Simd<T, N> a, Simd<T, N> b) {
-  // TODO add isnan
-  return asd::max(a.value, b.value);
+  auto out = Simd<T, N>(asd::max(a.value, b.value));
+  if constexpr (!std::is_integral_v<T>) {
+    out = select(isnan(b), b, select(isnan(a), a, out));
+  }
+  return out;
 }
 
 template <typename T, int N>
 Simd<T, N> minimum(Simd<T, N> a, Simd<T, N> b) {
-  // TODO add isnan
-  return asd::min(a.value, b.value);
+  auto out = Simd<T, N>(asd::min(a.value, b.value));
+  if constexpr (!std::is_integral_v<T>) {
+    out = select(isnan(b), b, select(isnan(a), a, out));
+  }
+  return out;
 }
 
 template <typename T, int N>
@@ -234,6 +250,7 @@ Simd<T, N> remainder(Simd<T, N> a, Simd<T, N> b) {
 
 template <typename MaskT, typename T1, typename T2, int N>
 Simd<T1, N> select(Simd<MaskT, N> mask, Simd<T1, N> x, Simd<T2, N> y) {
+  static_assert(std::is_same_v<MaskT, bool>);
   if constexpr (sizeof(T1) == 1) {
     return asd::bitselect(y.value, x.value, asd::convert<char>(mask.value));
   } else if constexpr (sizeof(T1) == 2) {
@@ -251,9 +268,13 @@ Simd<T, N> pow(Simd<T, N> base, Simd<T, N> exp) {
     return asd::pow(base.value, exp.value);
   } else {
     Simd<T, N> res = 1;
-    while (any(exp)) {
-      res = select(exp & 1, res * base, res);
-      base = select(exp, base * base, base);
+    // Raising an integer to a negative power is undefined
+    if (any(exp < 0)) {
+      return 0;
+    }
+    while (any(exp > 0)) {
+      res = select((exp & 1) != 0, res * base, res);
+      base = select(exp > 0, base * base, base);
       exp = exp >> 1;
     }
     return res;

mlx/backend/cpu/simd/base_simd.h

@@ -171,6 +171,11 @@ DEFAULT_BINARY(&)
 DEFAULT_BINARY(&&)
 DEFAULT_BINARY(||)
 
+template <typename T>
+Simd<T, 1> clz(Simd<T, 1> x_) {
+  return __builtin_clz(x_.value);
+}
+
 template <typename T>
 Simd<T, 1> remainder(Simd<T, 1> a_, Simd<T, 1> b_) {
   T a = a_.value;

mlx/backend/cpu/sort.cpp

@@ -15,6 +15,18 @@ namespace mlx::core {
 
 namespace {
 
+// NaN-aware comparator that places NaNs at the end
+template <typename T>
+bool nan_aware_less(T a, T b) {
+  if constexpr (std::is_floating_point_v<T> || std::is_same_v<T, complex64_t>) {
+    if (std::isnan(a))
+      return false;
+    if (std::isnan(b))
+      return true;
+  }
+  return a < b;
+}
+
 template <typename T>
 struct StridedIterator {
   using iterator_category = std::random_access_iterator_tag;
@@ -27,7 +39,7 @@ struct StridedIterator {
   StridedIterator() = default;
 
   explicit StridedIterator(T* ptr, int64_t stride, difference_type offset = 0)
-      : ptr_(ptr + offset * stride), stride_(stride) {}
+      : stride_(stride), ptr_(ptr + offset * stride) {}
 
   explicit StridedIterator(array& arr, int axis, difference_type offset = 0)
       : StridedIterator(arr.data<T>(), arr.strides()[axis], offset) {}
@@ -130,7 +142,7 @@ void sort(array& out, int axis) {
     StridedIterator st(data_ptr, axis_stride, 0);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::stable_sort(st, ed);
+    std::stable_sort(st, ed, nan_aware_less<T>);
     src_it.step();
   }
 }
@@ -184,6 +196,15 @@ void argsort(const array& in, array& out, int axis) {
     std::stable_sort(st, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
+
+      // Handle NaNs (place them at the end)
+      if (std::is_floating_point<T>::value) {
+        if (std::isnan(v1))
+          return false;
+        if (std::isnan(v2))
+          return true;
+      }
+
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }
@@ -219,7 +240,7 @@ void partition(array& out, int axis, int kth) {
     StridedIterator md(data_ptr, axis_stride, kth);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::nth_element(st, md, ed);
+    std::nth_element(st, md, ed, nan_aware_less<T>);
   }
 }
 
@@ -276,6 +297,15 @@ void argpartition(const array& in, array& out, int axis, int kth) {
     std::nth_element(st, md, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
+
+      // Handle NaNs (place them at the end)
+      if (std::is_floating_point<T>::value) {
+        if (std::isnan(v1))
+          return false;
+        if (std::isnan(v2))
+          return true;
+      }
+
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }

mlx/backend/cpu/svd.cpp

@@ -8,6 +8,183 @@
 
 namespace mlx::core {
 
+template <typename T, class Enable = void>
+struct SVDWork {};
+
+template <typename T>
+struct SVDWork<
+    T,
+    typename std::enable_if<std::is_floating_point<T>::value>::type> {
+  using R = T;
+
+  int N;
+  int M;
+  int K;
+  int lda;
+  int ldu;
+  int ldvt;
+  char jobz;
+  std::vector<array::Data> buffers;
+  int lwork;
+
+  SVDWork(int N, int M, int K, char jobz)
+      : N(N), M(M), K(K), lda(N), ldu(N), ldvt(M), jobz(jobz) {
+    T workspace_dimension = 0;
+
+    // Will contain the indices of eigenvectors that failed to converge (not
+    // used here but required by lapack).
+    buffers.emplace_back(allocator::malloc(sizeof(int) * 8 * K));
+
+    int lwork_query = -1;
+    int info;
+
+    // Compute workspace size.
+    gesdd<T>(
+        /* jobz = */ &jobz,
+        // M and N are swapped since lapack expects column-major.
+        /* m = */ &N,
+        /* n = */ &M,
+        /* a = */ nullptr,
+        /* lda = */ &lda,
+        /* s = */ nullptr,
+        /* u = */ nullptr,
+        /* ldu = */ &ldu,
+        /* vt = */ nullptr,
+        /* ldvt = */ &ldvt,
+        /* work = */ &workspace_dimension,
+        /* lwork = */ &lwork_query,
+        /* iwork = */ static_cast<int*>(buffers[0].buffer.raw_ptr()),
+        /* info = */ &info);
+
+    if (info != 0) {
+      std::stringstream ss;
+      ss << "[SVD::eval_cpu] workspace calculation failed with code " << info;
+      throw std::runtime_error(ss.str());
+    }
+
+    lwork = workspace_dimension;
+    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
+  }
+
+  void run(T* a, R* s, T* u, T* vt) {
+    int info;
+    gesdd<T>(
+        /* jobz = */ &jobz,
+        // M and N are swapped since lapack expects column-major.
+        /* m = */ &N,
+        /* n = */ &M,
+        /* a = */ a,
+        /* lda = */ &lda,
+        /* s = */ s,
+        // According to the identity above, lapack will write Vᵀᵀ as U.
+        /* u = */ u,
+        /* ldu = */ &ldu,
+        // According to the identity above, lapack will write Uᵀ as Vᵀ.
+        /* vt = */ vt,
+        /* ldvt = */ &ldvt,
+        /* work = */ static_cast<T*>(buffers[1].buffer.raw_ptr()),
+        /* lwork = */ &lwork,
+        /* iwork = */ static_cast<int*>(buffers[0].buffer.raw_ptr()),
+        /* info = */ &info);
+
+    if (info != 0) {
+      std::stringstream ss;
+      ss << "svd_impl: sgesvdx_ failed with code " << info;
+      throw std::runtime_error(ss.str());
+    }
+  }
+};
+
+template <>
+struct SVDWork<std::complex<float>> {
+  using T = std::complex<float>;
+  using R = float;
+
+  int N;
+  int M;
+  int K;
+  int lda;
+  int ldu;
+  int ldvt;
+  char jobz;
+  std::vector<array::Data> buffers;
+  int lwork;
+
+  SVDWork(int N, int M, int K, char jobz)
+      : N(N), M(M), K(K), lda(N), ldu(N), ldvt(M), jobz(jobz) {
+    T workspace_dimension = 0;
+
+    // Will contain the indices of eigenvectors that failed to converge (not
+    // used here but required by lapack).
+    buffers.emplace_back(allocator::malloc(sizeof(int) * 8 * K));
+
+    const int lrwork =
+        jobz == 'A' ? std::max(1, 5 * K * K + 5 * K) : std::max(1, 7 * K);
+    buffers.emplace_back(allocator::malloc(sizeof(float) * lrwork));
+
+    int lwork_query = -1;
+    int work_query = -1;
+    int info;
+
+    // Compute workspace size.
+    gesdd<T>(
+        /* jobz = */ &jobz,
+        // M and N are swapped since lapack expects column-major.
+        /* m = */ &N,
+        /* n = */ &M,
+        /* a = */ nullptr,
+        /* lda = */ &lda,
+        /* s = */ nullptr,
+        /* u = */ nullptr,
+        /* ldu = */ &ldu,
+        /* vt = */ nullptr,
+        /* ldvt = */ &ldvt,
+        /* work = */ &workspace_dimension,
+        /* lwork = */ &lwork_query,
+        /* rwork = */ static_cast<float*>(buffers[1].buffer.raw_ptr()),
+        /* iwork = */ static_cast<int*>(buffers[0].buffer.raw_ptr()),
+        /* info = */ &info);
+
+    if (info != 0) {
+      std::stringstream ss;
+      ss << "[SVD::eval_cpu] workspace calculation failed with code " << info;
+      throw std::runtime_error(ss.str());
+    }
+
+    lwork = workspace_dimension.real();
+    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
+  }
+
+  void run(T* a, R* s, T* u, T* vt) {
+    int info;
+    gesdd<T>(
+        /* jobz = */ &jobz,
+        // M and N are swapped since lapack expects column-major.
+        /* m = */ &N,
+        /* n = */ &M,
+        /* a = */ a,
+        /* lda = */ &lda,
+        /* s = */ s,
+        // According to the identity above, lapack will write Vᵀᵀ as U.
+        /* u = */ u,
+        /* ldu = */ &ldu,
+        // According to the identity above, lapack will write Uᵀ as Vᵀ.
+        /* vt = */ vt,
+        /* ldvt = */ &ldvt,
+        /* work = */ static_cast<T*>(buffers[2].buffer.raw_ptr()),
+        /* lwork = */ &lwork,
+        /* rwork = */ static_cast<float*>(buffers[1].buffer.raw_ptr()),
+        /* iwork = */ static_cast<int*>(buffers[0].buffer.raw_ptr()),
+        /* info = */ &info);
+
+    if (info != 0) {
+      std::stringstream ss;
+      ss << "svd_impl: sgesvdx_ failed with code " << info;
+      throw std::runtime_error(ss.str());
+    }
+  }
+};
+
 template <typename T>
 void svd_impl(
     const array& a,
@@ -27,6 +204,8 @@ void svd_impl(
   const int N = a.shape(-1);
   const int K = std::min(M, N);
 
+  using R = typename SVDWork<T>::R;
+
   size_t num_matrices = a.size() / (M * N);
 
   // lapack clobbers the input, so we have to make a copy.
@@ -42,7 +221,7 @@ void svd_impl(
   encoder.set_input_array(a);
   auto in_ptr = in.data<T>();
   T* u_ptr;
-  T* s_ptr;
+  R* s_ptr;
   T* vt_ptr;
 
   if (compute_uv) {
@@ -58,7 +237,7 @@ void svd_impl(
     encoder.set_output_array(s);
     encoder.set_output_array(vt);
 
-    s_ptr = s.data<T>();
+    s_ptr = s.data<R>();
     u_ptr = u.data<T>();
     vt_ptr = vt.data<T>();
   } else {
@@ -68,124 +247,26 @@ void svd_impl(
 
     encoder.set_output_array(s);
 
-    s_ptr = s.data<T>();
+    s_ptr = s.data<R>();
     u_ptr = nullptr;
     vt_ptr = nullptr;
   }
 
   encoder.dispatch([in_ptr, u_ptr, s_ptr, vt_ptr, M, N, K, num_matrices]() {
-    // A of shape M x N. The leading dimension is N since lapack receives Aᵀ.
-    const int lda = N;
-    // U of shape M x M. (N x N in lapack).
-    const int ldu = N;
-    // Vᵀ of shape N x N. (M x M in lapack).
-    const int ldvt = M;
-
-    auto job_u = (u_ptr) ? "V" : "N";
-    auto job_vt = (u_ptr) ? "V" : "N";
-    static constexpr auto range = "A";
-
-    // Will contain the number of singular values after the call has returned.
-    int ns = 0;
-    T workspace_dimension = 0;
-
-    // Will contain the indices of eigenvectors that failed to converge (not
-    // used here but required by lapack).
-    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
-
-    static const int lwork_query = -1;
-
-    static const int ignored_int = 0;
-    static const T ignored_float = 0;
-
-    int info;
-
-    // Compute workspace size.
-    gesvdx<T>(
-        /* jobu = */ job_u,
-        /* jobvt = */ job_vt,
-        /* range = */ range,
-        // M and N are swapped since lapack expects column-major.
-        /* m = */ &N,
-        /* n = */ &M,
-        /* a = */ nullptr,
-        /* lda = */ &lda,
-        /* vl = */ &ignored_float,
-        /* vu = */ &ignored_float,
-        /* il = */ &ignored_int,
-        /* iu = */ &ignored_int,
-        /* ns = */ &ns,
-        /* s = */ nullptr,
-        /* u = */ nullptr,
-        /* ldu = */ &ldu,
-        /* vt = */ nullptr,
-        /* ldvt = */ &ldvt,
-        /* work = */ &workspace_dimension,
-        /* lwork = */ &lwork_query,
-        /* iwork = */ static_cast<int*>(iwork.buffer.raw_ptr()),
-        /* info = */ &info);
-
-    if (info != 0) {
-      std::stringstream ss;
-      ss << "[SVD::eval_cpu] workspace calculation failed with code " << info;
-      throw std::runtime_error(ss.str());
-    }
-
-    const int lwork = workspace_dimension;
-    auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
-
+    auto jobz = (u_ptr) ? 'A' : 'N';
+    SVDWork<T> svd_work(N, M, K, jobz);
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {
-      gesvdx<T>(
-          /* jobu = */ job_u,
-          /* jobvt = */ job_vt,
-          /* range = */ range,
-          // M and N are swapped since lapack expects column-major.
-          /* m = */ &N,
-          /* n = */ &M,
-          /* a = */ in_ptr + M * N * i,
-          /* lda = */ &lda,
-          /* vl = */ &ignored_float,
-          /* vu = */ &ignored_float,
-          /* il = */ &ignored_int,
-          /* iu = */ &ignored_int,
-          /* ns = */ &ns,
-          /* s = */ s_ptr + K * i,
-          // According to the identity above, lapack will write Vᵀᵀ as U.
-          /* u = */ vt_ptr ? vt_ptr + N * N * i : nullptr,
-          /* ldu = */ &ldu,
-          // According to the identity above, lapack will write Uᵀ as Vᵀ.
-          /* vt = */ u_ptr ? u_ptr + M * M * i : nullptr,
-          /* ldvt = */ &ldvt,
-          /* work = */ static_cast<T*>(scratch.buffer.raw_ptr()),
-          /* lwork = */ &lwork,
-          /* iwork = */ static_cast<int*>(iwork.buffer.raw_ptr()),
-          /* info = */ &info);
-
-      if (info != 0) {
-        std::stringstream ss;
-        ss << "svd_impl: sgesvdx_ failed with code " << info;
-        throw std::runtime_error(ss.str());
-      }
-
-      if (ns != K) {
-        std::stringstream ss;
-        ss << "svd_impl: expected " << K << " singular values, but " << ns
-           << " were computed.";
-        throw std::runtime_error(ss.str());
-      }
+      svd_work.run(
+          in_ptr + M * N * i,
+          s_ptr + K * i,
+          vt_ptr ? vt_ptr + N * N * i : nullptr,
+          u_ptr ? u_ptr + M * M * i : nullptr);
     }
   });
   encoder.add_temporary(in);
 }
 
-template <typename T>
-void compute_svd(
-    const array& a,
-    bool compute_uv,
-    std::vector<array>& outputs,
-    Stream stream) {}
-
 void SVD::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
@@ -196,9 +277,12 @@ void SVD::eval_cpu(
     case float64:
       svd_impl<double>(inputs[0], outputs, compute_uv_, stream());
       break;
+    case complex64:
+      svd_impl<std::complex<float>>(inputs[0], outputs, compute_uv_, stream());
+      break;
     default:
       throw std::runtime_error(
-          "[SVD::eval_cpu] only supports float32 or float64.");
+          "[SVD::eval_cpu] only supports float32, float64, or complex64.");
   }
 }
 

mlx/backend/cpu/unary.h

@@ -24,9 +24,9 @@ void unary_op(const array& a, array& out, Op) {
   auto ndim = a.ndim();
   if (a.flags().contiguous) {
     auto size = a.data_size();
-    constexpr int N = simd::max_size<T>;
+    constexpr int N = std::min(simd::max_size<T>, simd::max_size<U>);
     while (size >= N) {
-      simd::store(dst, Op{}(simd::load<T, N>(src)));
+      simd::store(dst, simd::Simd<U, N>(Op{}(simd::load<T, N>(src))));
       size -= N;
       src += N;
       dst += N;

mlx/backend/cpu/unary_ops.h

@@ -77,7 +77,8 @@ struct Real {
 struct Sigmoid {
   template <int N, typename T>
   Simd<T, N> operator()(Simd<T, N> x) {
-    return 1.0f / (1.0f + simd::exp(-x));
+    auto y = 1.0f / (1.0f + simd::exp(simd::abs(x)));
+    return simd::select(x < Simd<T, N>{0}, y, Simd<T, N>{1} - y);
   }
   SINGLE()
 };
@@ -107,4 +108,73 @@ struct Square {
   SINGLE()
 };
 
+template <int N>
+Simd<float, N> fp32_from_bits(Simd<uint32_t, N> x) {
+  return *(Simd<float, N>*)(&x);
+}
+template <int N>
+Simd<uint32_t, N> fp32_to_bits(Simd<float, N> x) {
+  return *(Simd<uint32_t, N>*)(&x);
+}
+
+struct ToFP8 {
+  template <typename T, int N>
+  Simd<uint8_t, N> operator()(Simd<T, N> f) {
+    uint32_t fp8_max = 543 << 21;
+    auto denorm_mask = Simd<uint32_t, N>(141 << 23);
+    Simd<uint32_t, N> f_bits;
+    Simd<float, N> f32 = f;
+    f_bits = fp32_to_bits(f32);
+    Simd<uint8_t, N> result = 0u;
+    auto sign = f_bits & 0x80000000;
+    f_bits = f_bits ^ sign;
+
+    auto f_bits_low =
+        fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    auto result_low = Simd<uint8_t, N>(f_bits_low - denorm_mask);
+
+    auto mant_odd = Simd<uint8_t, N>((f_bits >> 20) & 1);
+    auto f_bits_high = f_bits + (((uint32_t)(7 - 127) << 23) + 0x7FFFF);
+    f_bits_high = f_bits_high + Simd<uint32_t, N>(mant_odd);
+
+    auto result_high = Simd<uint8_t, N>(f_bits_high >> 20);
+    result = select(f_bits < (121 << 23), result_low, result_high);
+
+    auto result_sat = Simd<uint8_t, N>(0x7E);
+    result = select(f_bits >= fp8_max, result_sat, result);
+    return result | Simd<uint8_t, N>(sign >> 24);
+  }
+
+  template <typename T>
+  uint8_t operator()(T x) {
+    return (*this)(Simd<T, 1>(x)).value;
+  }
+};
+
+struct FromFP8 {
+  template <int N>
+  Simd<float, N> operator()(Simd<uint8_t, N> x) {
+    auto w = Simd<uint32_t, N>(x) << 24;
+    auto sign = w & 0x80000000;
+    auto nonsign = w & 0x7FFFFFFF;
+
+    auto renorm_shift = clz(nonsign);
+    renorm_shift = simd::select(
+        renorm_shift > Simd<uint32_t, N>{4},
+        renorm_shift - Simd<uint32_t, N>{4},
+        Simd<uint32_t, N>{0});
+
+    Simd<int32_t, N> inf_nan_mask =
+        (Simd<int32_t, N>(nonsign + 0x01000000) >> 8) & 0x7F800000;
+    auto zero_mask = Simd<int32_t, N>(nonsign - 1) >> 31;
+    auto result = sign |
+        ((((nonsign << renorm_shift >> 4) + ((0x78 - renorm_shift) << 23)) |
+          inf_nan_mask) &
+         ~zero_mask);
+    return fp32_from_bits(result);
+  }
+  float operator()(uint8_t x) {
+    return (*this)(Simd<uint8_t, 1>(x)).value;
+  }
+};
 } // namespace mlx::core::detail

mlx/backend/cuda/CMakeLists.txt

@@ -8,7 +8,6 @@ target_sources(
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/arange.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/binary_two.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
@@ -17,18 +16,23 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_conv.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_grouped_conv.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/cudnn_utils.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/custom_kernel.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/gemv.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/steel_gemm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel_utils.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/load.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/layer_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
@@ -40,18 +44,28 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/quantized/affine_quantize.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/fp_quantize.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/convert_fp8.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/binary)
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/unary)
+
+# fp4 is not available on < 12.8
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 12.8.0)
+  target_include_directories(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/quantized/)
+endif()
+
 if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9.0)
   target_sources(
     mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_9.cu)
@@ -109,10 +123,21 @@ if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
     mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--compress-mode=size>")
 endif()
 
-# Compute capability >= 7.0 is required for synchronization between CPU/GPU with
-# managed memory.
+# Use native CUDA arch by default.
 if(NOT DEFINED MLX_CUDA_ARCHITECTURES)
-  set(MLX_CUDA_ARCHITECTURES "native")
+  execute_process(
+    COMMAND __nvcc_device_query
+    OUTPUT_VARIABLE MLX_CUDA_ARCHITECTURES
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+  set(UPGRADABLE_ARCHITECTURES "90;100;121")
+  if(MLX_CUDA_ARCHITECTURES STREQUAL "")
+    message(
+      FATAL_ERROR
+        "Can not get native CUDA arch, must set MLX_CUDA_ARCHITECTURES")
+  elseif(MLX_CUDA_ARCHITECTURES IN_LIST UPGRADABLE_ARCHITECTURES)
+    # Use arch-specific compute capability whenever possible.
+    set(MLX_CUDA_ARCHITECTURES "${MLX_CUDA_ARCHITECTURES}a")
+  endif()
 endif()
 message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
 set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
@@ -124,6 +149,7 @@ FetchContent_Declare(
   URL "https://github.com/NVIDIA/cccl/releases/download/v2.8.1/cccl-v2.8.1.zip")
 FetchContent_MakeAvailable(cccl)
 target_include_directories(mlx BEFORE PRIVATE "${cccl_SOURCE_DIR}/include")
+set_target_properties(mlx PROPERTIES CCCL_DIR "${cccl_SOURCE_DIR}/include")
 
 # Use fixed version of NVTX.
 FetchContent_Declare(
@@ -149,7 +175,7 @@ target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 FetchContent_Declare(
   cudnn
   GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
-  GIT_TAG v1.12.1
+  GIT_TAG v1.16.0
   GIT_SHALLOW TRUE
   EXCLUDE_FROM_ALL)
 set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
@@ -165,7 +191,6 @@ target_link_libraries(mlx PRIVATE CUDNN::cudnn_all)
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)
-
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
         DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)

mlx/backend/cuda/allocator.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/allocator.h"
+#include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/utils.h"
 #include "mlx/utils.h"
 
@@ -30,8 +31,20 @@ SmallSizePool::SmallSizePool() {
   next_free_ = buffer_;
 
   CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
-  CHECK_CUDA_ERROR(
-      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, 0));
+
+  int device_count = 0;
+  CHECK_CUDA_ERROR(cudaGetDeviceCount(&device_count));
+  for (int i = 0; i < device_count; ++i) {
+#if CUDART_VERSION >= 13000
+    cudaMemLocation loc;
+    loc.type = cudaMemLocationTypeDevice;
+    loc.id = i;
+#else
+    int loc = i;
+#endif // CUDART_VERSION >= 13000
+    CHECK_CUDA_ERROR(
+        cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetAccessedBy, loc));
+  }
 
   auto curr = next_free_;
   for (size_t i = 1; i < num_blocks; ++i) {
@@ -55,6 +68,7 @@ CudaBuffer* SmallSizePool::malloc() {
   next_free_ = next_free_->next;
   b->buf.data = static_cast<char*>(data_) + i * small_block_size;
   b->buf.size = small_block_size;
+  b->buf.device = -1;
   return &b->buf;
 }
 
@@ -76,16 +90,42 @@ CudaAllocator::CudaAllocator()
           page_size,
           [](CudaBuffer* buf) { return buf->size; },
           [this](CudaBuffer* buf) { cuda_free(buf); }) {
-  // TODO: Set memory limit for multi-device.
   size_t free, total;
   CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
-  memory_limit_ = total * 0.8;
+  memory_limit_ = total * 0.9;
   max_pool_size_ = memory_limit_;
+
+  int device_count = 0;
+  CHECK_CUDA_ERROR(cudaGetDeviceCount(&device_count));
+  int curr;
+  CHECK_CUDA_ERROR(cudaGetDevice(&curr));
+  for (int i = 0; i < device_count; ++i) {
+    CHECK_CUDA_ERROR(cudaSetDevice(i));
+    cudaStream_t s;
+    CHECK_CUDA_ERROR(cudaStreamCreateWithFlags(&s, cudaStreamNonBlocking));
+    free_streams_.push_back(s);
+  }
+  CHECK_CUDA_ERROR(cudaSetDevice(curr));
 }
 
-Buffer CudaAllocator::malloc(size_t size) {
+void copy_to_managed(CudaBuffer& buf) {
+  // TODO maybe make this async on a i/o stream to avoid synchronizing the
+  // device on malloc/and free
+  void* new_data;
+  CHECK_CUDA_ERROR(cudaMallocManaged(&new_data, buf.size));
+  buf.device = -1;
+  CHECK_CUDA_ERROR(cudaMemcpy(new_data, buf.data, buf.size, cudaMemcpyDefault));
+  CHECK_CUDA_ERROR(cudaFree(buf.data));
+  buf.data = new_data;
+}
+
+Buffer
+CudaAllocator::malloc_async(size_t size, int device, cudaStream_t stream) {
+  if (size == 0) {
+    return Buffer{new CudaBuffer{nullptr, 0, -1}};
+  }
+
   // Find available buffer from cache.
-  auto orig_size = size;
   std::unique_lock lock(mutex_);
   if (size <= small_block_size) {
     size = 8;
@@ -95,6 +135,10 @@ Buffer CudaAllocator::malloc(size_t size) {
     size = page_size * ((size + page_size - 1) / page_size);
   }
 
+  if (size <= small_block_size || stream == nullptr) {
+    device = -1;
+  }
+
   CudaBuffer* buf = buffer_cache_.reuse_from_cache(size);
   if (!buf) {
     // If we have a lot of memory pressure try to reclaim memory from the cache.
@@ -110,30 +154,51 @@ Buffer CudaAllocator::malloc(size_t size) {
     }
     lock.unlock();
     if (!buf) {
-      buf = new CudaBuffer{nullptr, size};
-      cudaError_t err = cudaMallocManaged(&buf->data, size);
+      cudaError_t err;
+      void* data = nullptr;
+      if (device == -1) {
+        err = cudaMallocManaged(&data, size);
+      } else {
+        err = cudaMallocAsync(&data, size, stream);
+      }
       if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
         throw std::runtime_error(fmt::format(
             "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
       }
+      if (!data) {
+        return Buffer{nullptr};
+      }
+      buf = new CudaBuffer{data, size, device};
     }
     lock.lock();
   }
-  active_memory_ += size;
+  active_memory_ += buf->size;
   peak_memory_ = std::max(active_memory_, peak_memory_);
 
   // Maintain the cache below the requested limit.
   if (get_cache_memory() > max_pool_size_) {
     buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
+  // Copy to managed here if the buffer is not on the right device
+  if (buf->device >= 0 && buf->device != device) {
+    copy_to_managed(*buf);
+  }
   return Buffer{buf};
 }
 
+Buffer CudaAllocator::malloc(size_t size) {
+  return malloc_async(size, -1, nullptr);
+}
+
 void CudaAllocator::free(Buffer buffer) {
   auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
   if (!buf) {
     return;
   }
+  if (buf->size == 0) {
+    delete buf;
+    return;
+  }
 
   std::unique_lock lock(mutex_);
   active_memory_ -= buf->size;
@@ -157,7 +222,11 @@ void CudaAllocator::cuda_free(CudaBuffer* buf) {
   if (scalar_pool_.in_pool(buf)) {
     scalar_pool_.free(buf);
   } else {
-    cudaFree(buf->data);
+    if (buf->device >= 0) {
+      CHECK_CUDA_ERROR(cudaFreeAsync(buf->data, free_streams_[buf->device]));
+    } else {
+      CHECK_CUDA_ERROR(cudaFree(buf->data));
+    }
     delete buf;
   }
 }
@@ -208,6 +277,17 @@ CudaAllocator& allocator() {
   return *allocator_;
 }
 
+Buffer malloc_async(size_t size, CommandEncoder& encoder) {
+  auto buffer = allocator().malloc_async(
+      size, encoder.device().cuda_device(), encoder.stream());
+  if (size && !buffer.ptr()) {
+    std::ostringstream msg;
+    msg << "[malloc_async] Unable to allocate " << size << " bytes.";
+    throw std::runtime_error(msg.str());
+  }
+  return buffer;
+}
+
 } // namespace cu
 
 namespace allocator {
@@ -220,7 +300,11 @@ void* Buffer::raw_ptr() {
   if (!ptr_) {
     return nullptr;
   }
-  return static_cast<cu::CudaBuffer*>(ptr_)->data;
+  auto& cbuf = *static_cast<cu::CudaBuffer*>(ptr_);
+  if (cbuf.device != -1) {
+    copy_to_managed(cbuf);
+  }
+  return cbuf.data;
 }
 
 } // namespace allocator

mlx/backend/cuda/allocator.h

@@ -4,19 +4,24 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/buffer_cache.h"
+#include "mlx/backend/cuda/cuda_utils.h"
 
+#include <cuda_runtime.h>
 #include <mutex>
 #include <set>
 #include <utility>
 
 namespace mlx::core::cu {
 
+class CommandEncoder;
+
 using allocator::Buffer;
 
 // Stores cuda-managed unified memory.
 struct CudaBuffer {
   void* data;
   size_t size;
+  int device; // -1 for managed
 };
 
 class SmallSizePool {
@@ -45,6 +50,7 @@ class SmallSizePool {
 class CudaAllocator : public allocator::Allocator {
  public:
   Buffer malloc(size_t size) override;
+  Buffer malloc_async(size_t size, int device, cudaStream_t stream);
   void free(Buffer buffer) override;
   size_t size(Buffer buffer) const override;
 
@@ -69,9 +75,12 @@ class CudaAllocator : public allocator::Allocator {
   BufferCache<CudaBuffer> buffer_cache_;
   size_t active_memory_{0};
   size_t peak_memory_{0};
+  std::vector<cudaStream_t> free_streams_;
   SmallSizePool scalar_pool_;
 };
 
 CudaAllocator& allocator();
 
+Buffer malloc_async(size_t size, CommandEncoder& encoder);
+
 } // namespace mlx::core::cu

mlx/backend/cuda/arange.cu

@@ -6,23 +6,33 @@
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
 
+#include <cooperative_groups.h>
 #include <nvtx3/nvtx3.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/transform.h>
 
 namespace mlx::core {
 
 namespace cu {
 
-template <typename T>
-struct Arange {
-  const T start;
-  const T step;
+namespace cg = cooperative_groups;
 
-  __device__ T operator()(uint32_t i) const {
-    return start + i * step;
+template <typename T, typename IdxT, int N_WRITES>
+__global__ void arange(T* out, IdxT size, T start, T step) {
+  IdxT index = cg::this_grid().thread_rank();
+
+  if ((index + 1) * N_WRITES > size) {
+    for (IdxT i = index * N_WRITES; i < size; ++i) {
+      out[i] = start + i * step;
+    }
+  } else {
+    AlignedVector<T, N_WRITES> out_vec;
+#pragma unroll
+    for (int i = 0; i < N_WRITES; ++i) {
+      out_vec[i] = start + (index * N_WRITES + i) * step;
+    }
+
+    store_vector<N_WRITES>(out, index, out_vec);
   }
-};
+}
 
 } // namespace cu
 
@@ -31,24 +41,27 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (out.size() == 0) {
     return;
   }
-  out.set_data(allocator::malloc(out.nbytes()));
-
   auto& encoder = cu::get_command_encoder(stream());
+  out.set_data(cu::malloc_async(out.nbytes(), encoder));
   encoder.set_output_array(out);
 
-  auto capture = encoder.capture_context();
   dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
     using CTYPE = MLX_GET_TYPE(type_tag);
     using OutType = cuda_type_t<CTYPE>;
-    CTYPE step =
-        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
-    thrust::transform(
-        cu::thrust_policy(encoder.stream()),
-        thrust::counting_iterator<uint32_t>(0),
-        thrust::counting_iterator<uint32_t>(out.data_size()),
-        thrust::device_pointer_cast(out.data<OutType>()),
-        cu::Arange<OutType>{
-            static_cast<OutType>(start_), static_cast<OutType>(step)});
+    constexpr int N_WRITES = 16 / sizeof(OutType);
+    dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
+      using IdxT = std::conditional_t<large(), int64_t, int32_t>;
+      auto [num_blocks, block_dims] = get_launch_args(out, large(), N_WRITES);
+      encoder.add_kernel_node(
+          cu::arange<OutType, IdxT, N_WRITES>,
+          num_blocks,
+          block_dims,
+          0,
+          gpu_ptr<OutType>(out),
+          out.data_size(),
+          static_cast<CTYPE>(start_),
+          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_));
+    });
   });
 }
 

mlx/backend/cuda/arg_reduce.cu

@@ -140,8 +140,10 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
   nvtx3::scoped_range r("ArgReduce::eval_gpu");
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
+
   auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+  out.set_data(cu::malloc_async(out.nbytes(), encoder));
 
   // Prepare the shapes, strides and axis arguments.
   Shape shape = remove_index(in.shape(), axis_);
@@ -154,7 +156,6 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
   int32_t ndim = shape.size();
 
   // ArgReduce.
-  auto& encoder = cu::get_command_encoder(s);
   encoder.set_input_array(in);
   encoder.set_output_array(out);
   dispatch_real_types(in.dtype(), "ArgReduce", [&](auto type_tag) {
@@ -172,8 +173,8 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
           num_blocks,
           block_dim(),
           0,
-          in.data<T>(),
-          out.data<uint32_t>(),
+          gpu_ptr<T>(in),
+          gpu_ptr<uint32_t>(out),
           out.size(),
           const_param(shape),
           const_param(in_strides),

mlx/backend/cuda/binary/CMakeLists.txt

@@ -0,0 +1,21 @@
+target_sources(
+  mlx
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/add.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arctan2.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bitwise_binary.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/divide.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_and.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_or.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/log_add_exp.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/minimum.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/maximum.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/multiply.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/power.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/remainder.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/not_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/subtract.cu)

mlx/backend/cuda/binary/add.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Add)
+} // namespace mlx::core

mlx/backend/cuda/binary/arctan2.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(ArcTan2)
+} // namespace mlx::core

mlx/backend/cuda/binary/binary.cuh

@@ -99,39 +99,89 @@ __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
+template <
+    typename Op,
+    typename In,
+    typename Out,
+    typename IdxT,
+    int NDIM,
+    int N_READS>
 __global__ void binary_g_nd(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), a_strides.data(), b_strides.data());
-    out[index] = Op{}(a[a_idx], b[b_idx]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto a_stride_x = a_strides[NDIM - 1];
+  auto b_stride_x = b_strides[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT>
+template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
 __global__ void binary_g(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc(
-        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
-    out[index] = Op{}(a[a_idx], b[b_idx]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto a_stride_x = a_strides[ndim - 1];
+  auto b_stride_x = b_strides[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc(
+      index_rest * shape_x,
+      shape.data(),
+      a_strides.data(),
+      b_strides.data(),
+      ndim);
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -209,39 +259,61 @@ void binary_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
+                int work_per_thread = 1;
+                auto dim0 = ndim > 0 ? shape.back() : 1;
+                auto rest = out.size() / dim0;
+                if (dim0 >= 4) {
+                  work_per_thread = 4;
+                }
+                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+                auto block_dims = get_block_dims(dim0, rest, 1);
+                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(out, large());
+                    auto kernel = cu::binary_g_nd<
+                        Op,
+                        InType,
+                        OutType,
+                        IdxT,
+                        dims_constant(),
+                        1>;
+                    if (work_per_thread == 4) {
+                      kernel = cu::binary_g_nd<
+                          Op,
+                          InType,
+                          OutType,
+                          IdxT,
+                          dims_constant(),
+                          4>;
+                    }
                     encoder.add_kernel_node(
-                        cu::binary_g_nd<
-                            Op,
-                            InType,
-                            OutType,
-                            IdxT,
-                            dims_constant()>,
-                        num_blocks,
+                        kernel,
+                        {num_blocks_x, num_blocks_y},
                         block_dims,
                         0,
-                        a.data<InType>(),
-                        b.data<InType>(),
-                        out.data<OutType>(),
-                        out.size(),
+                        gpu_ptr<InType>(a),
+                        gpu_ptr<InType>(b),
+                        gpu_ptr<OutType>(out),
+                        rest,
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto [num_blocks, block_dims] = get_launch_args(out, large());
+                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT, 1>;
+                  if (work_per_thread == 4) {
+                    kernel = cu::binary_g<Op, InType, OutType, IdxT, 4>;
+                  }
                   encoder.add_kernel_node(
-                      cu::binary_g<Op, InType, OutType, IdxT>,
-                      num_blocks,
+                      kernel,
+                      {num_blocks_x, num_blocks_y},
                       block_dims,
                       0,
-                      a.data<InType>(),
-                      b.data<InType>(),
-                      out.data<OutType>(),
-                      out.size(),
+                      gpu_ptr<InType>(a),
+                      gpu_ptr<InType>(b),
+                      gpu_ptr<OutType>(out),
+                      rest,
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -267,9 +339,9 @@ void binary_op_gpu_inplace(
                 num_blocks,
                 block_dims,
                 0,
-                a.data<InType>(),
-                b.data<InType>(),
-                out.data<OutType>(),
+                gpu_ptr<InType>(a),
+                gpu_ptr<InType>(b),
+                gpu_ptr<OutType>(out),
                 out.data_size());
           });
         }
@@ -293,7 +365,10 @@ void binary_op_gpu(
   auto& a = inputs[0];
   auto& b = inputs[1];
   auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
+  auto& encoder = cu::get_command_encoder(s);
+
+  set_binary_op_output_data(
+      a, b, out, bopt, [&](auto n) { return cu::malloc_async(n, encoder); });
   binary_op_gpu_inplace<Op>(inputs, out, op, s);
 }
 
@@ -304,54 +379,4 @@ void binary_op_gpu(
     binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
   }
 
-BINARY_GPU(Add)
-BINARY_GPU(ArcTan2)
-BINARY_GPU(Divide)
-BINARY_GPU(Remainder)
-BINARY_GPU(Greater)
-BINARY_GPU(GreaterEqual)
-BINARY_GPU(Less)
-BINARY_GPU(LessEqual)
-BINARY_GPU(LogicalAnd)
-BINARY_GPU(LogicalOr)
-BINARY_GPU(LogAddExp)
-BINARY_GPU(Maximum)
-BINARY_GPU(Minimum)
-BINARY_GPU(Multiply)
-BINARY_GPU(NotEqual)
-BINARY_GPU(Power)
-BINARY_GPU(Subtract)
-
-void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Equal::eval_gpu");
-  auto& s = out.primitive().stream();
-  if (equal_nan_) {
-    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
-  } else {
-    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
-  }
-}
-
-void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
-  auto& s = out.primitive().stream();
-  switch (op_) {
-    case BitwiseBinary::And:
-      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Or:
-      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Xor:
-      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::LeftShift:
-      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::RightShift:
-      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
-      break;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/cuda/binary/bitwise_binary.cu

@@ -0,0 +1,27 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
+  auto& s = out.primitive().stream();
+  switch (op_) {
+    case BitwiseBinary::And:
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Or:
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Xor:
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::LeftShift:
+      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::RightShift:
+      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
+      break;
+  }
+}
+} // namespace mlx::core

mlx/backend/cuda/binary/divide.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Divide)
+} // namespace mlx::core

mlx/backend/cuda/binary/equal.cu

@@ -0,0 +1,15 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Equal::eval_gpu");
+  auto& s = out.primitive().stream();
+  if (equal_nan_) {
+    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
+  } else {
+    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
+  }
+}
+} // namespace mlx::core

mlx/backend/cuda/binary/greater.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Greater)
+} // namespace mlx::core

mlx/backend/cuda/binary/greater_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(GreaterEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/less.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Less)
+} // namespace mlx::core

mlx/backend/cuda/binary/less_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LessEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/log_add_exp.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogAddExp)
+} // namespace mlx::core

mlx/backend/cuda/binary/logical_and.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogicalAnd)
+} // namespace mlx::core

mlx/backend/cuda/binary/logical_or.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogicalOr)
+} // namespace mlx::core

mlx/backend/cuda/binary/maximum.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Maximum)
+} // namespace mlx::core

mlx/backend/cuda/binary/minimum.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Minimum)
+} // namespace mlx::core

mlx/backend/cuda/binary/multiply.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Multiply)
+} // namespace mlx::core

mlx/backend/cuda/binary/not_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(NotEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/power.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Power)
+} // namespace mlx::core

mlx/backend/cuda/binary/remainder.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Remainder)
+} // namespace mlx::core

mlx/backend/cuda/binary/subtract.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Subtract)
+} // namespace mlx::core

mlx/backend/cuda/binary_two.cu

@@ -127,45 +127,99 @@ binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
+template <
+    typename Op,
+    typename In,
+    typename Out,
+    typename IdxT,
+    int NDIM,
+    int N_READS>
 __global__ void binary_two_g_nd(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), a_strides.data(), b_strides.data());
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto a_stride_x = a_strides[NDIM - 1];
+  auto b_stride_x = b_strides[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec_a;
+  AlignedVector<Out, N_READS> out_vec_b;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    auto out = Op{}(a_vec[i], b_vec[i]);
+    out_vec_a[i] = out[0];
+    out_vec_b[i] = out[1];
+  }
+  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
+  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT>
+template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
 __global__ void binary_two_g(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc(
-        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto a_stride_x = a_strides[ndim - 1];
+  auto b_stride_x = b_strides[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc(
+      index_rest * shape_x,
+      shape.data(),
+      a_strides.data(),
+      b_strides.data(),
+      ndim);
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec_a;
+  AlignedVector<Out, N_READS> out_vec_b;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    auto out = Op{}(a_vec[i], b_vec[i]);
+    out_vec_a[i] = out[0];
+    out_vec_b[i] = out[1];
+  }
+  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
+  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -191,14 +245,16 @@ void binary_two_op_gpu_inplace(
   auto& out_a = outputs[0];
   auto& out_b = outputs[1];
   auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out_a, bopt);
-  set_binary_op_output_data(a, b, out_b, bopt);
+  auto& encoder = cu::get_command_encoder(s);
+  set_binary_op_output_data(
+      a, b, out_a, bopt, [&](auto n) { return cu::malloc_async(n, encoder); });
+  set_binary_op_output_data(
+      a, b, out_b, bopt, [&](auto n) { return cu::malloc_async(n, encoder); });
 
   if (out_a.size() == 0) {
     return;
   }
 
-  auto& encoder = cu::get_command_encoder(s);
   encoder.set_input_array(a);
   encoder.set_input_array(b);
   encoder.set_output_array(out_a);
@@ -225,42 +281,64 @@ void binary_two_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
+                int work_per_thread = 1;
+                auto dim0 = ndim > 0 ? shape.back() : 1;
+                auto rest = out_a.size() / dim0;
+                if (dim0 >= 4) {
+                  work_per_thread = 4;
+                }
+                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+                auto block_dims = get_block_dims(dim0, rest, 1);
+                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
+
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(out_a, large());
+                    auto kernel = cu::binary_two_g_nd<
+                        Op,
+                        InType,
+                        OutType,
+                        IdxT,
+                        dims_constant(),
+                        1>;
+                    if (work_per_thread == 4) {
+                      kernel = cu::binary_two_g_nd<
+                          Op,
+                          InType,
+                          OutType,
+                          IdxT,
+                          dims_constant(),
+                          4>;
+                    }
                     encoder.add_kernel_node(
-                        cu::binary_two_g_nd<
-                            Op,
-                            InType,
-                            OutType,
-                            IdxT,
-                            dims_constant()>,
-                        num_blocks,
+                        kernel,
+                        {num_blocks_x, num_blocks_y},
                         block_dims,
                         0,
-                        a.data<InType>(),
-                        b.data<InType>(),
-                        out_a.data<OutType>(),
-                        out_b.data<OutType>(),
-                        out_a.size(),
+                        gpu_ptr<InType>(a),
+                        gpu_ptr<InType>(b),
+                        gpu_ptr<OutType>(out_a),
+                        gpu_ptr<OutType>(out_b),
+                        rest,
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto [num_blocks, block_dims] =
-                      get_launch_args(out_a, large());
+                  auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 1>;
+                  if (work_per_thread == 4) {
+                    kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 4>;
+                  }
                   encoder.add_kernel_node(
-                      cu::binary_two_g<Op, InType, OutType, IdxT>,
-                      num_blocks,
+                      kernel,
+                      {num_blocks_x, num_blocks_y},
                       block_dims,
                       0,
-                      a.data<InType>(),
-                      b.data<InType>(),
-                      out_a.data<OutType>(),
-                      out_b.data<OutType>(),
-                      out_a.size(),
+                      gpu_ptr<InType>(a),
+                      gpu_ptr<InType>(b),
+                      gpu_ptr<OutType>(out_a),
+                      gpu_ptr<OutType>(out_b),
+                      rest,
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -290,10 +368,10 @@ void binary_two_op_gpu_inplace(
                 num_blocks,
                 block_dims,
                 0,
-                a.data<InType>(),
-                b.data<InType>(),
-                out_a.data<OutType>(),
-                out_b.data<OutType>(),
+                gpu_ptr<InType>(a),
+                gpu_ptr<InType>(b),
+                gpu_ptr<OutType>(out_a),
+                gpu_ptr<OutType>(out_b),
                 out_a.data_size());
           });
         }