Double buffer keys for vector sdpa

.circleci/config.yml

@@ -7,9 +7,15 @@ parameters:
   nightly_build:
     type: boolean
     default: false
+  weekly_build:
+    type: boolean
+    default: false
   test_release:
     type: boolean
     default: false
+  linux_release:
+    type: boolean
+    default: false
 
 jobs:
   build_documentation:
@@ -18,14 +24,13 @@ jobs:
         type: boolean
         default: false
     macos:
-      xcode: "26.0.0"
+      xcode: "16.2.0"
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
           name: Install
           command: |
-            xcodebuild -downloadComponent MetalToolchain
             brew install python@3.9
             brew install doxygen
             python3.9 -m venv env
@@ -33,7 +38,7 @@ jobs:
             pip install --upgrade pip
             pip install --upgrade cmake
             pip install -r docs/requirements.txt
-            pip install . -v
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install . -v
       - when:
           condition:
             not: << parameters.upload-docs >>
@@ -65,9 +70,9 @@ jobs:
                  git push -f origin gh-pages
 
   linux_build_and_test:
-    machine:
+    docker:
-      image: ubuntu-2204:current
+      - image: cimg/python:3.9
-      resource_class: large
+
     steps:
       - checkout
       - run:
@@ -79,36 +84,36 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            export DEBIAN_FRONTEND=noninteractive
+            pip install --upgrade cmake
-            export NEEDRESTART_MODE=a
+            pip install nanobind==2.4.0
+            pip install numpy
             sudo apt-get update
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            sudo apt-get install 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
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
-            uv pip install cmake
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+              python3 setup.py build_ext --inplace
-              uv pip install -e ".[dev]" -v
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python3 setup.py develop
       - run:
           name: Generate package stubs
           command: |
-            uv pip install typing_extensions
+            echo "stubs"
-            uv run --no-project setup.py generate_stubs
+            pip install typing_extensions
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
-            source .venv/bin/activate
+            python3 -m unittest discover python/tests -v
-            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)
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
-            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`
@@ -120,7 +125,7 @@ jobs:
     parameters:
       xcode_version:
         type: string
-        default: "26.0.0"
+        default: "16.2.0"
       macosx_deployment_target:
         type: string
         default: ""
@@ -128,56 +133,57 @@ jobs:
       xcode: << parameters.xcode_version >>
     environment:
       MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
           name: Install dependencies
           command: |
-            xcodebuild -downloadComponent MetalToolchain
+            brew install python@3.9
-            HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 \
+            brew install openmpi
-              brew install openmpi uv
+            python3.9 -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install numpy
+            pip install torch
+            pip install tensorflow
+            pip install unittest-xml-reporting
       - run:
           name: Install Python package
           command: |
-            uv venv --python 3.9
+            source env/bin/activate
-            uv pip install \
+            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
-              nanobind==2.4.0 \
+            CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              cmake \
+              pip install -e . -v
-              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
+            source env/bin/activate
-            uv run --no-project setup.py generate_stubs
+            pip install typing_extensions
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
             mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
-            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
       - run:
           name: Build example extension
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             cd examples/extensions
-            uv pip install -r requirements.txt
+            pip install -r requirements.txt
-            uv run --no-project setup.py build_ext --inplace
+            python setup.py build_ext -j8
-            uv run --no-project python test.py
       - store_test_results:
           path: test-results
       - run:
           name: Build CPP only
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             mkdir -p build && cd build && cmake .. && make -j `sysctl -n hw.ncpu`
       - run:
           name: Run CPP tests
@@ -186,7 +192,7 @@ jobs:
       - run:
           name: Build small binary
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             cd build/
             cmake .. -DCMAKE_BUILD_TYPE=MinSizeRel \
               -DBUILD_SHARED_LIBS=ON \
@@ -198,76 +204,13 @@ jobs:
       - run:
           name: Run Python tests with JIT
           command: |
-            CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
+            source env/bin/activate
-              uv pip install -e . -v
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
+              pip install -e . -v
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
               METAL_DEBUG_ERROR_MODE=0 \
-              uv run --no-project python -m xmlrunner discover \
+              python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
-                -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
-            sudo apt-get install libnccl2 libnccl-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: Set CCache size
-          command: ccache --max-size 1G
-      - run:
-          name: Install Python package
-          command: |
-            uv venv
-            uv pip install cmake
-            DEBUG=1 CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=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: Build CPP only
-          command: |
-            source .venv/bin/activate
-            cmake . -B build \
-              -DMLX_BUILD_CUDA=ON \
-              -DCMAKE_CUDA_COMPILER=`which nvcc` \
-              -DCMAKE_BUILD_TYPE=DEBUG
-            cmake --build build -j `nproc`
-      - run:
-          name: Run CPP tests
-          command: ./build/tests/tests -sfe="*fft_tests.cpp,*linalg_tests.cpp"
-      - run:
-          name: CCache report
-          command: |
-            ccache --show-stats
-            ccache --zero-stats
-            ccache --cleanup
-      - save_cache:
-          key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}
-          paths:
-            - /home/circleci/.cache/ccache
 
   build_release:
     parameters:
@@ -276,7 +219,7 @@ jobs:
         default: "3.9"
       xcode_version:
         type: string
-        default: "26.0.0"
+        default: "16.2.0"
       build_env:
         type: string
         default: ""
@@ -285,7 +228,7 @@ jobs:
         default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
     environment:
       MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
     steps:
@@ -293,15 +236,11 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            xcodebuild -downloadComponent MetalToolchain
+            brew install python@<< parameters.python_version >>
-            mkdir -p ~/miniconda3
+            brew install openmpi
-            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
+            python<< parameters.python_version >> -m venv env
-            bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+            source env/bin/activate
-            rm ~/miniconda3/miniconda.sh
+            pip install --upgrade pip
-            source ~/miniconda3/bin/activate
-            conda init --all
-            conda create -n env python=<< parameters.python_version >> -y
-            conda activate env
             pip install --upgrade cmake
             pip install nanobind==2.4.0
             pip install --upgrade setuptools
@@ -311,38 +250,30 @@ jobs:
       - run:
           name: Install Python package
           command: |
-            conda activate env
+            source env/bin/activate
             env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
               pip install . -v
       - run:
           name: Generate package stubs
           command: |
-            conda activate env
+            source env/bin/activate
             pip install typing_extensions
             python setup.py generate_stubs 
       - run:
           name: Build Python package
           command: |
-            conda activate env
+            source env/bin/activate
-            python setup.py clean --all
+            << parameters.build_env >> \
-            << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
-      - when:
+              python -m build -w
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  conda activate env
-                  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: |
-                  conda activate env
+                  source env/bin/activate
                   twine upload dist/*
       - store_artifacts:
           path: dist/
@@ -352,100 +283,52 @@ jobs:
       python_version:
         type: string
         default: "3.9"
-      build_env:
+      extra_env:
         type: string
-        default: ""
+        default: "DEV_RELEASE=1"
-    machine:
+    docker:
-      image: ubuntu-2204:current
+      - image: ubuntu:20.04
-      resource_class: large
     steps:
       - checkout
       - run:
           name: Build wheel
           command: |
             PYTHON=python<< parameters.python_version >>
-            export DEBIAN_FRONTEND=noninteractive
+            apt-get update
-            export NEEDRESTART_MODE=a
+            apt-get upgrade -y
-            sudo apt-get update
+            DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata
-            TZ=Etc/UTC sudo apt-get -y install tzdata
+            apt-get install -y apt-utils
-            sudo add-apt-repository -y ppa:deadsnakes/ppa
+            apt-get install -y software-properties-common
-            sudo apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
+            add-apt-repository -y ppa:deadsnakes/ppa
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
+            apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            apt-get install -y build-essential git
             $PYTHON -m venv env
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install --upgrade setuptools
+            pip install numpy
             pip install auditwheel
             pip install patchelf
             pip install build
             pip install twine
-            << parameters.build_env >> pip install ".[dev]" -v
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              pip install . -v
             pip install typing_extensions
             python setup.py generate_stubs 
-            python setup.py clean --all
+            << parameters.extra_env >> \
-            MLX_BUILD_STAGE=1 << parameters.build_env >> python -m build -w
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
-            bash python/scripts/repair_linux.sh
+              python -m build --wheel
-      - when:
+            auditwheel show dist/*
-          condition:
+            auditwheel repair dist/* --plat manylinux_2_31_x86_64
-            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: xlarge
-    steps:
-      - checkout
       - run:
-          name: Build wheel
+          name: Upload package
           command: |
-            export DEBIAN_FRONTEND=noninteractive
+            source env/bin/activate
-            export NEEDRESTART_MODE=a
+            twine upload wheelhouse/*
-            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/
 
@@ -457,23 +340,21 @@ workflows:
             pattern: "^(?!pull/)[-\\w]+$"
             value: << pipeline.git.branch >>
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
         - not: << pipeline.parameters.test_release >>
     jobs:
       - mac_build_and_test:
           matrix:
             parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              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.weekly_build >>
         - not: << pipeline.parameters.test_release >>
     jobs:
       - build_release:
@@ -487,7 +368,68 @@ workflows:
               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: ["26.0.0"]
+              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:
@@ -495,25 +437,6 @@ workflows:
             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:
@@ -529,14 +452,9 @@ workflows:
           requires: [ hold ]
           matrix:
             parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              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:
@@ -548,18 +466,58 @@ workflows:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              xcode_version: ["26.0.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
-      - build_linux_release:
+            exclude:
-          matrix:
+              - macosx_deployment_target: "13.5"
-            parameters:
+                xcode_version: "16.2.0"
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+                python_version: "3.9"
-      - build_cuda_release
+              - macosx_deployment_target: "13.5"
-
+                xcode_version: "16.2.0"
-  build_dev_release:
+                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"
+  weekly_build:
     when:
       and:
         - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.test_release >>
+        - << pipeline.parameters.weekly_build >>
     jobs:
       - build_release:
           matrix:
@@ -567,13 +525,76 @@ workflows:
               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: ["26.0.0"]
+              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"
+  linux_test_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.linux_release >>
+    jobs:
       - build_linux_release:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              build_env: ["DEV_RELEASE=1"]
+              extra_env: ["PYPI_RELEASE=1"]
-      - build_cuda_release:
-          matrix:
-            parameters:
-              build_env: ["DEV_RELEASE=1"]

.gitignore

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

ACKNOWLEDGMENTS.md

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

CMakeLists.txt

@@ -26,7 +26,6 @@ set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 set(CMAKE_INSTALL_MESSAGE NEVER)
-set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 # ----------------------------- Configuration -----------------------------
 option(MLX_BUILD_TESTS "Build tests for mlx" ON)
@@ -35,16 +34,13 @@ option(MLX_BUILD_BENCHMARKS "Build benchmarks for mlx" OFF)
 option(MLX_BUILD_PYTHON_BINDINGS "Build python bindings for mlx" OFF)
 option(MLX_BUILD_METAL "Build metal backend" ON)
 option(MLX_BUILD_CPU "Build cpu backend" ON)
-option(MLX_BUILD_CUDA "Build cuda backend" OFF)
 option(MLX_METAL_DEBUG "Enhance metal debug workflow" OFF)
 option(MLX_ENABLE_X64_MAC "Enable building for x64 macOS" OFF)
 option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
 option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
 option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
-option(MLX_USE_CCACHE "Use CCache for compilation cache when available" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
-option(USE_SYSTEM_FMT "Use system's provided fmt library" OFF)
 
 # --------------------- Processor tests -------------------------
 message(
@@ -67,17 +63,10 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
       message(WARNING "Building for x86_64 arch is not officially supported.")
     endif()
   endif()
+
 else()
   set(MLX_BUILD_METAL OFF)
-endif()
+  message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
-
-if(MLX_USE_CCACHE)
-  find_program(CCACHE_PROGRAM ccache)
-  if(CCACHE_PROGRAM)
-    set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-  endif()
 endif()
 
 # ----------------------------- Lib -----------------------------
@@ -88,21 +77,18 @@ cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
 
-if(MLX_BUILD_CUDA)
+if(MLX_BUILD_METAL)
-  enable_language(CUDA)
+  set(METAL_LIB "-framework Metal")
+  set(FOUNDATION_LIB "-framework Foundation")
+  set(QUARTZ_LIB "-framework QuartzCore")
 endif()
 
-if(MLX_BUILD_METAL)
+if(MLX_BUILD_METAL AND NOT METAL_LIB)
-  find_library(METAL_LIB Metal)
+  message(STATUS "Metal not found. Unable to build GPU")
-  find_library(FOUNDATION_LIB Foundation)
+  set(MLX_BUILD_METAL OFF)
-  find_library(QUARTZ_LIB QuartzCore)
+  set(MLX_METAL_DEBUG OFF)
-  if(METAL_LIB)
+elseif(MLX_BUILD_METAL)
-    message(STATUS "Metal found ${METAL_LIB}")
+  message(STATUS "Building METAL sources")
-  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)
@@ -111,8 +97,7 @@ if(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION
+    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
-    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
 
   if(${MACOS_SDK_VERSION} LESS 14.0)
     message(
@@ -141,12 +126,6 @@ if(MLX_BUILD_METAL)
   target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
 endif()
 
-if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-  # With newer clang/gcc versions following libs are implicitly linked, but when
-  # building on old distributions they need to be explicitly listed.
-  target_link_libraries(mlx PRIVATE dl pthread)
-endif()
-
 if(WIN32)
   if(MSVC)
     # GGUF does not build with MSVC.
@@ -174,7 +153,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
     set(MLX_BUILD_ACCELERATE ON)
   else()
-    message(STATUS "Accelerate not found, using default backend.")
+    message(STATUS "Accelerate or arm neon not found, using default backend.")
     set(MLX_BUILD_ACCELERATE OFF)
   endif()
 
@@ -247,19 +226,12 @@ target_include_directories(
   mlx PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
              $<INSTALL_INTERFACE:include>)
 
-# Do not add mlx_EXPORTS define for shared library.
+FetchContent_Declare(
-set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
+  fmt
-
+  GIT_REPOSITORY https://github.com/fmtlib/fmt.git
-if(USE_SYSTEM_FMT)
+  GIT_TAG 10.2.1
-  find_package(fmt REQUIRED)
+  EXCLUDE_FROM_ALL)
-else()
+FetchContent_MakeAvailable(fmt)
-  FetchContent_Declare(
-    fmt
-    GIT_REPOSITORY https://github.com/fmtlib/fmt.git
-    GIT_TAG 10.2.1
-    EXCLUDE_FROM_ALL)
-  FetchContent_MakeAvailable(fmt)
-endif()
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:fmt::fmt-header-only>)
 
 if(MLX_BUILD_PYTHON_BINDINGS)

MANIFEST.in

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

README.md

@@ -11,31 +11,31 @@ 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
+   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
+ - **Composable function transformations**: MLX supports composable function
-  transformations for automatic differentiation, automatic vectorization,
+   transformations for automatic differentiation, automatic vectorization,
-  and computation graph optimization.
+   and computation graph optimization.
 
-- **Lazy computation**: Computations in MLX are lazy. Arrays are only
+ - **Lazy computation**: Computations in MLX are lazy. Arrays are only
-  materialized when needed.
+   materialized when needed.
 
-- **Dynamic graph construction**: Computation graphs in MLX are constructed
+ - **Dynamic graph construction**: Computation graphs in MLX are constructed
-  dynamically. Changing the shapes of function arguments does not trigger
+   dynamically. Changing the shapes of function arguments does not trigger
-  slow compilations, and debugging is simple and intuitive.
+   slow compilations, and debugging is simple and intuitive.
 
-- **Multi-device**: Operations can run on any of the supported devices
+ - **Multi-device**: Operations can run on any of the supported devices
-  (currently the CPU and the GPU).
+   (currently the CPU and the GPU).
 
-- **Unified memory**: A notable difference from MLX and other frameworks
+ - **Unified memory**: A notable difference from MLX and other frameworks
-  is the *unified memory model*. Arrays in MLX live in shared memory.
+   is the *unified memory model*. Arrays in MLX live in shared memory.
-  Operations on MLX arrays can be performed on any of the supported
+   Operations on MLX arrays can be performed on any of the supported
-  device types without transferring data.
+   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
@@ -68,23 +68,18 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
-macOS, run:
 
-```bash
+**With `pip`**:
+
+```
 pip install mlx
 ```
 
-To install the CUDA backend on Linux, run:
+**With `conda`**:
 
-```bash
-pip install mlx[cuda]
 ```
-
+conda install -c conda-forge mlx
-To install a CPU-only Linux package, run:
-
-```bash
-pip install mlx[cpu]
 ```
 
 Checkout the
@@ -110,7 +105,7 @@ Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
 MLX useful in your research and wish to cite it, please use the following
 BibTex entry:
 
-```text
+```
 @software{mlx2023,
   author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
   title = {{MLX}: Efficient and flexible machine learning on Apple silicon},

benchmarks/cpp/irregular_strides.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023 Apple Inc.
 
-#include <cstring>
 #include <iostream>
 #include <sstream>
 

benchmarks/cpp/single_ops.cpp

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

benchmarks/python/blas/bench_gemm.py

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

benchmarks/python/blas/bench_gemv.py

@@ -196,7 +196,7 @@ def bench_with_out_len(ax, out_vec_len, in_vector_lens, dtype, transpose):
 
 
 for transpose in (False, True):
-    for dtype in ("float32", "float16", "complex64"):
+    for dtype in ("float32", "float16"):
         fig, axs = plt.subplots(
             len(in_vec_sizes), 2, figsize=(8.5, 11), layout="constrained"
         )
@@ -215,7 +215,7 @@ for transpose in (False, True):
         fig.suptitle(f"{device_name}: {dtype} {op_name}")
         fig.savefig(
             os.path.join(
-                results_dir, f"{device_name.replace(' ', '_')}_{dtype}_{op_name}.pdf"
+                results_dir, f'{device_name.replace(" ", "_")}_{dtype}_{op_name}.pdf'
             )
         )
         plt.close(fig)

benchmarks/python/comparative/bench_torch.py

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

benchmarks/python/conv_unaligned_bench.py

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

benchmarks/python/layer_norm_bench.py

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

benchmarks/python/sdpa_vector_bench.py

@@ -4,7 +4,7 @@ import math
 import mlx.core as mx
 from time_utils import time_fn
 
-L = 16384
+L = 1024
 H = 32
 H_k = H // 4
 D = 128

benchmarks/python/single_ops.py

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

cmake/FindNCCL.cmake

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

cmake/extension.cmake

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

docs/requirements.txt

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

docs/src/conf.py

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

docs/src/dev/custom_metal_kernels.rst

@@ -8,26 +8,23 @@ MLX supports writing custom Metal kernels through the Python and C++ APIs.
 Simple Example
 --------------
 
-.. currentmodule:: mlx.core
-
 Let's write a custom kernel that computes ``exp`` elementwise:
 
 .. code-block:: python
 
-  source = """
-      uint elem = thread_position_in_grid.x;
-      T tmp = inp[elem];
-      out[elem] = metal::exp(tmp);
-  """
-
-  kernel = mx.fast.metal_kernel(
-      name="myexp",
-      input_names=["inp"],
-      output_names=["out"],
-      source=source,
-  )
-
   def exp_elementwise(a: mx.array):
+      source = """
+          uint elem = thread_position_in_grid.x;
+          T tmp = inp[elem];
+          out[elem] = metal::exp(tmp);
+      """
+
+      kernel = mx.fast.metal_kernel(
+          name="myexp",
+          input_names=["inp"],
+          output_names=["out"],
+          source=source,
+      )
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -42,13 +39,8 @@ Let's write a custom kernel that computes ``exp`` elementwise:
   b = exp_elementwise(a)
   assert mx.allclose(b, mx.exp(a))
 
-Every time you make a kernel, a new Metal library is created and possibly
-JIT compiled. To reduce the overhead from that, build the kernel once with
-:func:`fast.metal_kernel` and then use it many times.
-
 .. note::
-   Only pass the body of the Metal kernel in ``source``. The function
+    We are only required to pass the body of the Metal kernel in ``source``.
-   signature is generated automatically.
 
 The full function signature will be generated using:
 
@@ -86,52 +78,44 @@ Putting this all together, the generated function signature for ``myexp`` is as
 
   template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
 
-Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
-<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
+This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
-function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
+For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
-``threadgroup`` size threadgroups.  For optimal performance, each thread group
-dimension should be less than or equal to the corresponding grid dimension.
 
-Passing ``verbose=True`` to :func:`ast.metal_kernel.__call__` will print the
+Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
-generated code for debugging purposes.
 
 Using Shape/Strides
 -------------------
 
-:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
+``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
-is ``True`` by default. This will copy the array inputs if needed
+This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
-before the kernel is launched to ensure that the memory layout is row
+Generally this makes writing the kernel easier, since we don't have to worry about gaps or the ordering of the dims
-contiguous.  Generally this makes writing the kernel easier, since we don't
+when indexing.
-have to worry about gaps or the ordering of the dims when indexing.
 
-If we want to avoid this copy, :func:`fast.metal_kernel` automatically passes
+If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
-``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
+input array ``a`` if any are present in ``source``.
-present in ``source``. We can then use MLX's built in indexing utils to fetch
+We can then use MLX's built in indexing utils to fetch the right elements for each thread.
-the right elements for each thread.
 
-Let's convert ``myexp`` above to support arbitrarily strided arrays without
+Let's convert ``myexp`` above to support arbitrarily strided arrays without relying on a copy from ``ensure_row_contiguous``:
-relying on a copy from ``ensure_row_contiguous``:
 
 .. code-block:: python
 
-  source = """
-      uint elem = thread_position_in_grid.x;
-      // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
-      uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
-      T tmp = inp[loc];
-      // Output arrays are always row contiguous
-      out[elem] = metal::exp(tmp);
-  """
-
-  kernel = mx.fast.metal_kernel(
-      name="myexp_strided",
-      input_names=["inp"],
-      output_names=["out"],
-      source=source,
-      ensure_row_contiguous=False,
-  )
-
   def exp_elementwise(a: mx.array):
+      source = """
+          uint elem = thread_position_in_grid.x;
+          // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
+          uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
+          T tmp = inp[loc];
+          // Output arrays are always row contiguous
+          out[elem] = metal::exp(tmp);
+      """
+
+      kernel = mx.fast.metal_kernel(
+          name="myexp_strided",
+          input_names=["inp"],
+          output_names=["out"],
+          source=source
+      )
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -139,6 +123,7 @@ relying on a copy from ``ensure_row_contiguous``:
           threadgroup=(256, 1, 1),
           output_shapes=[a.shape],
           output_dtypes=[a.dtype],
+          ensure_row_contiguous=False,
       )
       return outputs[0]
 
@@ -157,139 +142,137 @@ We'll start with the following MLX implementation using standard ops:
 
 .. code-block:: python
 
-  def grid_sample_ref(x, grid):
+    def grid_sample_ref(x, grid):
-      N, H_in, W_in, _ = x.shape
+        N, H_in, W_in, _ = x.shape
-      ix = ((grid[..., 0] + 1) * W_in - 1) / 2
+        ix = ((grid[..., 0] + 1) * W_in - 1) / 2
-      iy = ((grid[..., 1] + 1) * H_in - 1) / 2
+        iy = ((grid[..., 1] + 1) * H_in - 1) / 2
 
-      ix_nw = mx.floor(ix).astype(mx.int32)
+        ix_nw = mx.floor(ix).astype(mx.int32)
-      iy_nw = mx.floor(iy).astype(mx.int32)
+        iy_nw = mx.floor(iy).astype(mx.int32)
 
-      ix_ne = ix_nw + 1
+        ix_ne = ix_nw + 1
-      iy_ne = iy_nw
+        iy_ne = iy_nw
 
-      ix_sw = ix_nw
+        ix_sw = ix_nw
-      iy_sw = iy_nw + 1
+        iy_sw = iy_nw + 1
 
-      ix_se = ix_nw + 1
+        ix_se = ix_nw + 1
-      iy_se = iy_nw + 1
+        iy_se = iy_nw + 1
 
-      nw = (ix_se - ix)    * (iy_se - iy)
+        nw = (ix_se - ix)    * (iy_se - iy)
-      ne = (ix    - ix_sw) * (iy_sw - iy)
+        ne = (ix    - ix_sw) * (iy_sw - iy)
-      sw = (ix_ne - ix)    * (iy    - iy_ne)
+        sw = (ix_ne - ix)    * (iy    - iy_ne)
-      se = (ix    - ix_nw) * (iy    - iy_nw)
+        se = (ix    - ix_nw) * (iy    - iy_nw)
 
-      I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
+        I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
-      I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
+        I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
-      I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
+        I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
-      I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
+        I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
 
-      mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
+        mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
-      mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
+        mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
-      mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
+        mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
-      mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
+        mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
 
-      I_nw *= mask_nw[..., None]
+        I_nw *= mask_nw[..., None]
-      I_ne *= mask_ne[..., None]
+        I_ne *= mask_ne[..., None]
-      I_sw *= mask_sw[..., None]
+        I_sw *= mask_sw[..., None]
-      I_se *= mask_se[..., None]
+        I_se *= mask_se[..., None]
 
-      output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
+        output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
 
-      return output
+        return output
 
-Now let's use :func:`custom_function` together with :func:`fast.metal_kernel`
+Now let's use ``mx.custom_function`` together with ``mx.fast.metal_kernel``
 to write a fast GPU kernel for both the forward and backward passes.
 
 First we'll implement the forward pass as a fused kernel:
 
 .. code-block:: python
 
-  source = """
+    @mx.custom_function
-      uint elem = thread_position_in_grid.x;
+    def grid_sample(x, grid):
-      int H = x_shape[1];
-      int W = x_shape[2];
-      int C = x_shape[3];
-      int gH = grid_shape[1];
-      int gW = grid_shape[2];
 
-      int w_stride = C;
+        assert x.ndim == 4, "`x` must be 4D."
-      int h_stride = W * w_stride;
+        assert grid.ndim == 4, "`grid` must be 4D."
-      int b_stride = H * h_stride;
 
-      uint grid_idx = elem / C * 2;
+        B, _, _, C = x.shape
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+        _, gN, gM, D = grid.shape
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+        out_shape = (B, gN, gM, C)
 
-      int ix_nw = floor(ix);
+        assert D == 2, "Last dim of `grid` must be size 2."
-      int iy_nw = floor(iy);
 
-      int ix_ne = ix_nw + 1;
+        source = """
-      int iy_ne = iy_nw;
+            uint elem = thread_position_in_grid.x;
+            int H = x_shape[1];
+            int W = x_shape[2];
+            int C = x_shape[3];
+            int gH = grid_shape[1];
+            int gW = grid_shape[2];
 
-      int ix_sw = ix_nw;
+            int w_stride = C;
-      int iy_sw = iy_nw + 1;
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_se = ix_nw + 1;
+            uint grid_idx = elem / C * 2;
-      int iy_se = iy_nw + 1;
+            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-      T nw = (ix_se - ix)    * (iy_se - iy);
+            int ix_nw = floor(ix);
-      T ne = (ix    - ix_sw) * (iy_sw - iy);
+            int iy_nw = floor(iy);
-      T sw = (ix_ne - ix)    * (iy    - iy_ne);
-      T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-      int batch_idx = elem / C / gH / gW * b_stride;
+            int ix_ne = ix_nw + 1;
-      int channel_idx = elem % C;
+            int iy_ne = iy_nw;
-      int base_idx = batch_idx + channel_idx;
 
-      T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
+            int ix_sw = ix_nw;
-      T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
+            int iy_sw = iy_nw + 1;
-      T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
-      T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
 
-      I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
+            int ix_se = ix_nw + 1;
-      I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
+            int iy_se = iy_nw + 1;
-      I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
-      I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
 
-      out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
+            T nw = (ix_se - ix)    * (iy_se - iy);
-  """
+            T ne = (ix    - ix_sw) * (iy_sw - iy);
+            T sw = (ix_ne - ix)    * (iy    - iy_ne);
+            T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-  kernel = mx.fast.metal_kernel(
+            int batch_idx = elem / C / gH / gW * b_stride;
-      name="grid_sample",
+            int channel_idx = elem % C;
-      input_names=["x", "grid"],
+            int base_idx = batch_idx + channel_idx;
-      output_names=["out"],
-      source=source,
-  )
 
-  @mx.custom_function
+            T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
-  def grid_sample(x, grid):
+            T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
+            T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
+            T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
 
-      assert x.ndim == 4, "`x` must be 4D."
+            I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
-      assert grid.ndim == 4, "`grid` must be 4D."
+            I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
+            I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
+            I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
 
-      B, _, _, C = x.shape
+            out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
-      _, gN, gM, D = grid.shape
+        """
-      out_shape = (B, gN, gM, C)
+        kernel = mx.fast.metal_kernel(
-
+            name="grid_sample",
-      assert D == 2, "Last dim of `grid` must be size 2."
+            input_names=["x", "grid"],
-
+            output_names=["out"],
-      outputs = kernel(
+            source=source,
-          inputs=[x, grid],
+        )
-          template=[("T", x.dtype)],
+        outputs = kernel(
-          output_shapes=[out_shape],
+            inputs=[x, grid],
-          output_dtypes=[x.dtype],
+            template=[("T", x.dtype)],
-          grid=(np.prod(out_shape), 1, 1),
+            output_shapes=[out_shape],
-          threadgroup=(256, 1, 1),
+            output_dtypes=[x.dtype],
-      )
+            grid=(np.prod(out_shape), 1, 1),
-      return outputs[0]
+            threadgroup=(256, 1, 1),
+        )
+        return outputs[0]
 
 For a reasonably sized input such as:
 
 .. code-block:: python
 
-  x.shape = (8, 1024, 1024, 64)
+    x.shape = (8, 1024, 1024, 64)
-  grid.shape = (8, 256, 256, 2)
+    grid.shape = (8, 256, 256, 2)
 
 On an M1 Max, we see a big performance improvement:
 
@@ -298,11 +281,11 @@ On an M1 Max, we see a big performance improvement:
 Grid Sample VJP
 ---------------
 
-Since we decorated ``grid_sample`` with :func:`custom_function`, we can now
+Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
-define its custom vjp transform so MLX can differentiate it.
+its custom vjp transform so MLX can differentiate it.
 
 The backwards pass requires atomically updating ``x_grad``/``grid_grad`` and so
-requires a few extra :func:`fast.metal_kernel` features:
+requires a few extra ``mx.fast.metal_kernel`` features:
 
 * ``init_value=0``
     Initialize all of the kernel's outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.
@@ -316,129 +299,128 @@ We can then implement the backwards pass as follows:
 
 .. code-block:: python
 
-  source = """
+    @grid_sample.vjp
-      uint elem = thread_position_in_grid.x;
+    def grid_sample_vjp(primals, cotangent, _):
-      int H = x_shape[1];
+        x, grid = primals
-      int W = x_shape[2];
+        B, _, _, C = x.shape
-      int C = x_shape[3];
+        _, gN, gM, D = grid.shape
-      // Pad C to the nearest larger simdgroup size multiple
-      int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
 
-      int gH = grid_shape[1];
+        assert D == 2, "Last dim of `grid` must be size 2."
-      int gW = grid_shape[2];
 
-      int w_stride = C;
+        source = """
-      int h_stride = W * w_stride;
+            uint elem = thread_position_in_grid.x;
-      int b_stride = H * h_stride;
+            int H = x_shape[1];
+            int W = x_shape[2];
+            int C = x_shape[3];
+            // Pad C to the nearest larger simdgroup size multiple
+            int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
 
-      uint grid_idx = elem / C_padded * 2;
+            int gH = grid_shape[1];
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+            int gW = grid_shape[2];
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-      int ix_nw = floor(ix);
+            int w_stride = C;
-      int iy_nw = floor(iy);
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_ne = ix_nw + 1;
+            uint grid_idx = elem / C_padded * 2;
-      int iy_ne = iy_nw;
+            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-      int ix_sw = ix_nw;
+            int ix_nw = floor(ix);
-      int iy_sw = iy_nw + 1;
+            int iy_nw = floor(iy);
 
-      int ix_se = ix_nw + 1;
+            int ix_ne = ix_nw + 1;
-      int iy_se = iy_nw + 1;
+            int iy_ne = iy_nw;
 
-      T nw = (ix_se - ix)    * (iy_se - iy);
+            int ix_sw = ix_nw;
-      T ne = (ix    - ix_sw) * (iy_sw - iy);
+            int iy_sw = iy_nw + 1;
-      T sw = (ix_ne - ix)    * (iy    - iy_ne);
-      T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-      int batch_idx = elem / C_padded / gH / gW * b_stride;
+            int ix_se = ix_nw + 1;
-      int channel_idx = elem % C_padded;
+            int iy_se = iy_nw + 1;
-      int base_idx = batch_idx + channel_idx;
 
-      T gix = T(0);
+            T nw = (ix_se - ix)    * (iy_se - iy);
-      T giy = T(0);
+            T ne = (ix    - ix_sw) * (iy_sw - iy);
-      if (channel_idx < C) {
+            T sw = (ix_ne - ix)    * (iy    - iy_ne);
-          int cot_index = elem / C_padded * C + channel_idx;
+            T se = (ix    - ix_nw) * (iy    - iy_nw);
-          T cot = cotangent[cot_index];
-          if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
-              int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
 
-              T I_nw = x[offset];
+            int batch_idx = elem / C_padded / gH / gW * b_stride;
-              gix -= I_nw * (iy_se - iy) * cot;
+            int channel_idx = elem % C_padded;
-              giy -= I_nw * (ix_se - ix) * cot;
+            int base_idx = batch_idx + channel_idx;
-          }
-          if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
-              int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
 
-              T I_ne = x[offset];
+            T gix = T(0);
-              gix += I_ne * (iy_sw - iy) * cot;
+            T giy = T(0);
-              giy -= I_ne * (ix - ix_sw) * cot;
+            if (channel_idx < C) {
-          }
+                int cot_index = elem / C_padded * C + channel_idx;
-          if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
+                T cot = cotangent[cot_index];
-              int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
+                if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
-              atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
+                    int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
 
-              T I_sw = x[offset];
+                    T I_nw = x[offset];
-              gix -= I_sw * (iy - iy_ne) * cot;
+                    gix -= I_nw * (iy_se - iy) * cot;
-              giy += I_sw * (ix_ne - ix) * cot;
+                    giy -= I_nw * (ix_se - ix) * cot;
-          }
+                }
-          if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
+                if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
-              int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
+                    int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
+                    atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
 
-              T I_se = x[offset];
+                    T I_ne = x[offset];
-              gix += I_se * (iy - iy_nw) * cot;
+                    gix += I_ne * (iy_sw - iy) * cot;
-              giy += I_se * (ix - ix_nw) * cot;
+                    giy -= I_ne * (ix - ix_sw) * cot;
-          }
+                }
-      }
+                if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
+                    int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
 
-      T gix_mult = W / 2;
+                    T I_sw = x[offset];
-      T giy_mult = H / 2;
+                    gix -= I_sw * (iy - iy_ne) * cot;
+                    giy += I_sw * (ix_ne - ix) * cot;
+                }
+                if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
+                    int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
 
-      // Reduce across each simdgroup first.
+                    T I_se = x[offset];
-      // This is much faster than relying purely on atomics.
+                    gix += I_se * (iy - iy_nw) * cot;
-      gix = simd_sum(gix);
+                    giy += I_se * (ix - ix_nw) * cot;
-      giy = simd_sum(giy);
+                }
+            }
 
-      if (thread_index_in_simdgroup == 0) {
+            T gix_mult = W / 2;
-          atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
+            T giy_mult = H / 2;
-          atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
-      }
-  """
-  kernel = mx.fast.metal_kernel(
-      name="grid_sample_grad",
-      input_names=["x", "grid", "cotangent"],
-      output_names=["x_grad", "grid_grad"],
-      source=source,
-      atomic_outputs=True,
-  )
 
-  @grid_sample.vjp
+            // Reduce across each simdgroup first.
-  def grid_sample_vjp(primals, cotangent, _):
+            // This is much faster than relying purely on atomics.
-      x, grid = primals
+            gix = simd_sum(gix);
-      B, _, _, C = x.shape
+            giy = simd_sum(giy);
-      _, gN, gM, D = grid.shape
 
-      assert D == 2, "Last dim of `grid` must be size 2."
+            if (thread_index_in_simdgroup == 0) {
-
+                atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
-      # pad the output channels to simd group size
+                atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
-      # so that our `simd_sum`s don't overlap.
+            }
-      simdgroup_size = 32
+        """
-      C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
+        kernel = mx.fast.metal_kernel(
-      grid_size = B * gN * gM * C_padded
+            name="grid_sample_grad",
-      outputs = kernel(
+            input_names=["x", "grid", "cotangent"],
-          inputs=[x, grid, cotangent],
+            output_names=["x_grad", "grid_grad"],
-          template=[("T", x.dtype)],
+            source=source,
-          output_shapes=[x.shape, grid.shape],
+            atomic_outputs=True,
-          output_dtypes=[x.dtype, x.dtype],
+        )
-          grid=(grid_size, 1, 1),
+        # pad the output channels to simd group size
-          threadgroup=(256, 1, 1),
+        # so that our `simd_sum`s don't overlap.
-          init_value=0,
+        simdgroup_size = 32
-      )
+        C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
-      return outputs[0], outputs[1]
+        grid_size = B * gN * gM * C_padded
+        outputs = kernel(
+            inputs=[x, grid, cotangent],
+            template=[("T", x.dtype)],
+            output_shapes=[x.shape, grid.shape],
+            output_dtypes=[x.dtype, x.dtype],
+            grid=(grid_size, 1, 1),
+            threadgroup=(256, 1, 1),
+            init_value=0,
+        )
+        return outputs[0], outputs[1]
 
 There's an even larger speed up for the vjp:
 

docs/src/dev/extensions.rst

@@ -138,13 +138,13 @@ more concrete:
         * representing the vectorized computation and the axis which
         * corresponds to the output vectorized dimension.
         */
-        std::pair<std::vector<array>, std::vector<int>> vmap(
+        virtual std::pair<std::vector<array>, std::vector<int>> vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) override;
 
-        /** The name of primitive. */
+        /** Print the primitive. */
-        const char* name() const override {
+        void print(std::ostream& os) override {
-          return "Axpby";
+            os << "Axpby";
         }
 
         /** Equivalence check **/
@@ -394,14 +394,14 @@ below.
         out.set_data(allocator::malloc(out.nbytes()));
 
         // Resolve name of kernel
-        std::stream kname;
+        std::ostringstream kname;
-        kname = "axpby_general_" + type_to_name(out);
+        kname << "axpby_" << "general_" << type_to_name(out);
 
-        // Load the metal library
+        // Make sure the metal library is available
-        auto lib = d.get_library("mlx_ext", current_binary_dir());
+        d.register_library("mlx_ext");
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname, lib);
+        auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);

docs/src/index.rst

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

docs/src/install.rst

@@ -13,7 +13,7 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI your system must meet the following requirements:
+To install from PyPI you must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
 - Using a native Python >= 3.9
@@ -23,39 +23,12 @@ To install from PyPI your system must meet the following requirements:
     MLX is only available on devices running macOS >= 13.5
     It is highly recommended to use macOS 14 (Sonoma)
 
-CUDA
-^^^^
 
-MLX has a CUDA backend which you can install with:
+MLX is also available on conda-forge. To install MLX with conda do:
 
 .. code-block:: shell
 
-    pip install mlx[cuda]
+   conda install conda-forge::mlx
-
-To install the CUDA package from PyPi your system must meet the following
-requirements:
-
-- Nvidia architecture >= SM 7.0 (Volta)
-- Nvidia driver >= 550.54.14
-- CUDA toolkit >= 12.0
-- Linux distribution with glibc >= 2.35
-- Python >= 3.9
-
-
-CPU-only (Linux)
-^^^^^^^^^^^^^^^^
-
-For a CPU-only version of MLX that runs on Linux use:
-
-.. code-block:: shell
-
-    pip install mlx[cpu]
-
-To install the CPU-only package from PyPi your system must meet the following
-requirements:
-
-- Linux distribution with glibc >= 2.35
-- Python >= 3.9
 
 
 Troubleshooting
@@ -92,8 +65,6 @@ Build Requirements
 Python API
 ^^^^^^^^^^
 
-.. _python install:
-
 To build and install the MLX python library from source, first, clone MLX from
 `its GitHub repo <https://github.com/ml-explore/mlx>`_:
 
@@ -105,20 +76,20 @@ Then simply build and install MLX using pip:
 
 .. code-block:: shell
 
-  pip install .
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install .
 
 For developing, install the package with development dependencies, and use an
 editable install:
 
 .. code-block:: shell
 
-  pip install -e ".[dev]"
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install -e ".[dev]"
 
 Once the development dependencies are installed, you can build faster with:
 
 .. code-block:: shell
 
- python setup.py build_ext --inplace
+ CMAKE_BUILD_PARALLEL_LEVEL=8 python setup.py build_ext --inplace
 
 Run the tests with:
 
@@ -136,8 +107,6 @@ IDE:
 C++ API
 ^^^^^^^
 
-.. _cpp install:
-
 Currently, MLX must be built and installed from source.
 
 Similarly to the python library, to build and install the MLX C++ library start
@@ -216,7 +185,6 @@ should point to the path to the built metal library.
 
       xcrun -sdk macosx --show-sdk-version
 
-
 Binary Size Minimization
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -245,50 +213,6 @@ be anwywhere from a few hundred millisecond to a few seconds depending on the
 application. Once a kernel is compiled, it will be cached by the system. The
 Metal kernel cache persists across reboots.
 
-Linux
-^^^^^
-
-To build from source on Linux (CPU only), install the BLAS and LAPACK headers.
-For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   apt-get update -y
-   apt-get install libblas-dev liblapack-dev liblapacke-dev -y
-
-From here follow the instructions to install either the :ref:`Python <python
-install>` or :ref:`C++ <cpp install>` APIs.
-
-CUDA
-^^^^
-
-To build from source on Linux with CUDA, install the BLAS and LAPACK headers
-and the CUDA toolkit. For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-   dpkg -i cuda-keyring_1.1-1_all.deb
-   apt-get update -y
-   apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev libcudnn9-dev-cuda-12 -y
-
-
-When building either the Python or C++ APIs make sure to pass the cmake flag
-``MLX_BUILD_CUDA=ON``. For example, to build the Python API run:
-
-.. code-block:: shell
-
-  CMAKE_ARGS="-DMLX_BUILD_CUDA=ON" pip install -e ".[dev]"
-
-To build the C++ package run:
-
-.. code-block:: shell
-
-   mkdir -p build && cd build
-   cmake .. -DMLX_BUILD_CUDA=ON && make -j
-
-
 Troubleshooting
 ^^^^^^^^^^^^^^^
 

docs/src/python/array.rst

@@ -19,8 +19,6 @@ Array
     array.ndim
     array.shape
     array.size
-    array.real
-    array.imag
     array.abs
     array.all
     array.any

docs/src/python/cuda.rst

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

docs/src/python/fast.rst

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

docs/src/python/fft.rst

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

docs/src/python/linalg.rst

@@ -16,8 +16,6 @@ Linear Algebra
     cross
     qr
     svd
-    eigvals
-    eig
     eigvalsh
     eigh
     lu

docs/src/python/nn/functions.rst

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

docs/src/python/nn/layers.rst

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

docs/src/python/optimizers.rst

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

docs/src/python/optimizers/common_optimizers.rst

@@ -19,4 +19,3 @@ Common Optimizers
    Adamax
    Lion
    MultiOptimizer
-   Muon

docs/src/usage/compile.rst

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

docs/src/usage/distributed.rst

@@ -184,7 +184,7 @@ almost identical to the example above:
 
     def step(model, x, y):
         loss, grads = loss_grad_fn(model, x, y)
-        grads = mx.nn.average_gradients(grads)  # <---- This line was added
+        grads = mlx.nn.average_gradients(grads) # <---- This line was added
         optimizer.update(model, grads)
         return loss
 

docs/src/usage/export.rst

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

docs/src/usage/indexing.rst

@@ -107,28 +107,6 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
-Note that unlike NumPy, slicing an array creates a copy, not a view. So
-mutating it does not mutate the original array:
-
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> b = a[:]
-  >>> b[2] = 0
-  >>> b
-  array([1, 2, 0], dtype=int32)
-  >>> a
-  array([1, 2, 3], dtype=int32)
-
-Also unlike NumPy, updates to the same location are nondeterministic:
-
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> a[[0, 0]] = mx.array([4, 5])
-
-The first element of ``a`` could be ``4`` or ``5``.
-
 Transformations of functions which use in-place updates are allowed and work as
 expected. For example:
 

examples/extensions/axpby/axpby.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023-2025 Apple Inc.
 
-#include <dlfcn.h>
 #include <iostream>
 #include <sstream>
 
@@ -17,19 +16,6 @@
 
 namespace my_ext {
 
-// A helper function to find the location of the current binary on disk.
-// The Metal library ("mlx_ext.mtllib"), should be in the same directory.
-std::string current_binary_dir() {
-  static std::string binary_dir = []() {
-    Dl_info info;
-    if (!dladdr(reinterpret_cast<void*>(&current_binary_dir), &info)) {
-      throw std::runtime_error("Unable to get current binary dir.");
-    }
-    return std::filesystem::path(info.dli_fname).parent_path().string();
-  }();
-  return binary_dir;
-}
-
 ///////////////////////////////////////////////////////////////////////////////
 // Operation Implementation
 ///////////////////////////////////////////////////////////////////////////////
@@ -181,15 +167,16 @@ void Axpby::eval_gpu(
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)
-  std::string kname = "axpby_";
+  std::ostringstream kname;
-  kname += (contiguous_kernel ? "contiguous_" : "general_");
+  kname << "axpby_";
-  kname += type_to_name(out);
+  kname << (contiguous_kernel ? "contiguous_" : "general_");
+  kname << type_to_name(out);
 
-  // Load the metal library
+  // Make sure the metal library is available
-  auto lib = d.get_library("mlx_ext", current_binary_dir());
+  d.register_library("mlx_ext");
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname, lib);
+  auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);

examples/extensions/axpby/axpby.h

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

examples/extensions/requirements.txt

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

examples/extensions/test.py

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

mlx/CMakeLists.txt

@@ -21,7 +21,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
 # Define MLX_VERSION only in the version.cpp file.
-add_library(mlx_version OBJECT ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
+add_library(mlx_version STATIC ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
 target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
 
@@ -49,19 +49,5 @@ add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/io)
 if(MLX_BUILD_METAL)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/metal)
 else()
-  target_sources(mlx
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_metal)
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/no_metal.cpp)
-endif()
-
-if(MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda)
-else()
-  target_sources(mlx
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda/no_cuda.cpp)
-endif()
-
-if(MLX_BUILD_METAL OR MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/gpu)
-else()
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_gpu)
 endif()

mlx/array.h

@@ -10,7 +10,6 @@
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
 #include "mlx/event.h"
-#include "mlx/small_vector.h"
 
 namespace mlx::core {
 
@@ -19,8 +18,8 @@ class Primitive;
 
 using Deleter = std::function<void(allocator::Buffer)>;
 using ShapeElem = int32_t;
-using Shape = SmallVector<ShapeElem>;
+using Shape = std::vector<ShapeElem>;
-using Strides = SmallVector<int64_t>;
+using Strides = std::vector<int64_t>;
 
 class array {
   /* An array is really a node in a graph. It contains a shared ArrayDesc
@@ -225,10 +224,6 @@ class array {
     // Not copyable
     Data(const Data& d) = delete;
     Data& operator=(const Data& d) = delete;
-    Data(Data&& o) : buffer(o.buffer), d(o.d) {
-      o.buffer = allocator::Buffer(nullptr);
-      o.d = [](allocator::Buffer) {};
-    }
     ~Data() {
       d(buffer);
     }
@@ -361,7 +356,7 @@ class array {
   }
 
   enum Status {
-    // The output of a computation which has not been scheduled.
+    // The ouptut of a computation which has not been scheduled.
     // For example, the status of `x` in `auto x = a + b`.
     unscheduled,
 

mlx/backend/common/buffer_cache.h

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

mlx/backend/common/compiled.cpp

@@ -1,7 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/graph_utils.h"
+#include "mlx/primitives.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
@@ -14,8 +15,6 @@ void print_constant(std::ostream& os, const array& x) {
       return print_float_constant<float16_t>(os, x);
     case bfloat16:
       return print_float_constant<bfloat16_t>(os, x);
-    case float64:
-      return print_float_constant<double>(os, x);
     case complex64:
       return print_complex_constant<complex64_t>(os, x);
     case int8:
@@ -52,8 +51,6 @@ std::string get_type_string(Dtype d) {
       return "float16_t";
     case bfloat16:
       return "bfloat16_t";
-    case float64:
-      return "double";
     case complex64:
       return "complex64_t";
     case bool_:
@@ -82,6 +79,55 @@ std::string get_type_string(Dtype d) {
   }
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids) {
+  NodeNamer namer;
+  std::ostringstream os;
+  std::ostringstream constant_hasher;
+
+  // Fill the input names. This is not really necessary, I just like having A,
+  // B, C, ... as the inputs.
+  for (auto& x : inputs) {
+    namer.get_name(x);
+  }
+
+  // The primitives describing the tape. For unary and binary primitives this
+  // must be enough to describe the full computation.
+  for (auto& a : tape) {
+    // name and type of output
+    os << namer.get_name(a) << kindof(a.dtype()) << a.itemsize();
+    // computation performed
+    a.primitive().print(os);
+    // name of inputs to the function
+    for (auto& inp : a.inputs()) {
+      os << namer.get_name(inp);
+    }
+  }
+  os << "_";
+
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      os << "C";
+      print_constant(constant_hasher, x);
+    } else {
+      os << (is_scalar(x) ? "S" : "V");
+    }
+  }
+  os << "_";
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      continue;
+    }
+    os << kindof(x.dtype()) << x.itemsize();
+  }
+  os << "_" << std::hash<std::string>{}(constant_hasher.str());
+
+  return os.str();
+}
+
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
     const Shape& shape) {
@@ -113,7 +159,8 @@ bool compiled_check_contiguity(
 void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const std::function<bool(size_t)>& is_constant,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
     bool contiguous) {
   if (contiguous) {
     int o = 0;
@@ -128,7 +175,8 @@ void compiled_allocate_outputs(
       // - Donatable
       // - Not a constant
       if (in.itemsize() == outputs[o].itemsize() && !is_scalar(in) &&
-          in.is_donatable() && is_constant(i)) {
+          in.is_donatable() &&
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o++].copy_shared_buffer(in);
       }
       // Get representative input flags to properly set non-donated outputs
@@ -156,7 +204,7 @@ void compiled_allocate_outputs(
       // - Not a constant
       if (in.flags().row_contiguous && in.size() == outputs[o].size() &&
           in.itemsize() == outputs[o].itemsize() && in.is_donatable() &&
-          is_constant(i)) {
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o].copy_shared_buffer(
             in, outputs[o].strides(), in.flags(), in.data_size());
         o++;
@@ -168,74 +216,4 @@ void compiled_allocate_outputs(
   }
 }
 
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant) {
-  const Shape& shape = out.shape();
-  bool contiguous = compiled_check_contiguity(inputs, shape);
-  if (contiguous) {
-    return {true, shape, {}};
-  }
-
-  std::vector<Strides> strides_vec{out.strides()};
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    // Skip constants.
-    if (is_constant(i)) {
-      continue;
-    }
-
-    // Skip scalar inputs.
-    const auto& x = inputs[i];
-    if (is_scalar(x)) {
-      continue;
-    }
-
-    // Broadcast the inputs to the output shape.
-    Strides xstrides;
-    size_t j = 0;
-    for (; j < shape.size() - x.ndim(); ++j) {
-      if (shape[j] == 1) {
-        xstrides.push_back(out.strides()[j]);
-      } else {
-        xstrides.push_back(0);
-      }
-    }
-    for (size_t i = 0; i < x.ndim(); ++i, ++j) {
-      if (x.shape(i) == 1) {
-        if (shape[j] == 1) {
-          xstrides.push_back(out.strides()[j]);
-        } else {
-          xstrides.push_back(0);
-        }
-      } else {
-        xstrides.push_back(x.strides()[i]);
-      }
-    }
-    strides_vec.push_back(std::move(xstrides));
-  }
-
-  auto tup = collapse_contiguous_dims(shape, strides_vec, INT32_MAX);
-  return {false, std::move(std::get<0>(tup)), std::move(std::get<1>(tup))};
-}
-
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
-    bool contiguous) {
-  if (contiguous) {
-    size_t max_size = 0;
-    for (const auto& in : inputs) {
-      max_size = std::max(max_size, in.data_size());
-    }
-    return max_size > UINT32_MAX;
-  } else {
-    size_t max_size = 0;
-    for (const auto& o : outputs) {
-      max_size = std::max(max_size, o.size());
-    }
-    return max_size > UINT32_MAX;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/common/compiled.h

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

mlx/backend/common/copy.h

@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "mlx/backend/common/utils.h"
+#include "mlx/array.h"
 
 namespace mlx::core {
 
@@ -26,7 +26,7 @@ inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
   if (ctype == CopyType::Vector) {
     // If the input is donateable, we are doing a vector copy and the types
     // have the same size, then the input buffer can hold the output.
-    if (is_donatable(in, out)) {
+    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
       out.copy_shared_buffer(in);
       return true;
     } else {

mlx/backend/common/hadamard.h

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

mlx/backend/common/matmul.h

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

mlx/backend/common/reduce.cpp

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

mlx/backend/common/reduce.h

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

mlx/backend/common/ternary.h

@@ -11,8 +11,6 @@ namespace mlx::core {
 enum class TernaryOpType {
   ScalarScalarScalar,
   VectorVectorVector,
-  VectorVectorScalar,
-  VectorScalarVector,
   General,
 };
 
@@ -27,14 +25,6 @@ get_ternary_op_type(const array& a, const array& b, const array& c) {
       (a.flags().col_contiguous && b.flags().col_contiguous &&
        c.flags().col_contiguous)) {
     topt = TernaryOpType::VectorVectorVector;
-  } else if (
-      b.data_size() == 1 && a.flags().row_contiguous &&
-      c.flags().row_contiguous) {
-    topt = TernaryOpType::VectorScalarVector;
-  } else if (
-      c.data_size() == 1 && a.flags().row_contiguous &&
-      b.flags().row_contiguous) {
-    topt = TernaryOpType::VectorVectorScalar;
   } else {
     topt = TernaryOpType::General;
   }
@@ -69,8 +59,6 @@ inline void set_ternary_op_output_data(
             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)) ||

mlx/backend/common/unary.h

@@ -1,26 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/allocator.h"
-#include "mlx/backend/common/utils.h"
-
-namespace mlx::core {
-
-inline void set_unary_output_data(const array& in, array& out) {
-  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()),
-          in.data_size(),
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
-}
-
-} // namespace mlx::core

mlx/backend/common/utils.cpp

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

mlx/backend/common/utils.h

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

mlx/backend/cpu/CMakeLists.txt

@@ -40,13 +40,11 @@ add_dependencies(mlx cpu_compiled_preamble)
 
 target_sources(
   mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/available.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/eig.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/encoder.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp

mlx/backend/cpu/arg_reduce.cpp

@@ -14,8 +14,10 @@ template <typename InT, typename OpT>
 void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   auto axis_size = in.shape()[axis];
   auto axis_stride = in.strides()[axis];
-  Strides strides = remove_index(in.strides(), axis);
+  Strides strides = in.strides();
-  Shape shape = remove_index(in.shape(), axis);
+  Shape shape = in.shape();
+  strides.erase(strides.begin() + axis);
+  shape.erase(shape.begin() + axis);
   auto in_ptr = in.data<InT>();
   auto out_ptr = out.data<uint32_t>();
 

mlx/backend/cpu/available.cpp

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

mlx/backend/cpu/available.h

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

mlx/backend/cpu/binary.cpp

@@ -172,12 +172,9 @@ void binary_float(
       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.");
+            "[binary_float] Only supports non-complex floating point types.");
     }
   });
 }

mlx/backend/cpu/cholesky.cpp

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

mlx/backend/cpu/compiled.cpp

@@ -15,7 +15,6 @@
 #include "mlx/backend/cpu/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
-#include "mlx/version.h"
 
 namespace mlx::core {
 
@@ -41,10 +40,7 @@ struct CompilerCache {
   std::shared_mutex mtx;
 };
 
-static CompilerCache& cache() {
+static CompilerCache cache{};
-  static CompilerCache cache_;
-  return cache_;
-};
 
 // GPU compile is always available if the GPU is available and since we are in
 // this file CPU compile is also available.
@@ -60,16 +56,14 @@ void* compile(
     const std::string& kernel_name,
     const std::function<std::string(void)>& source_builder) {
   {
-    std::shared_lock lock(cache().mtx);
+    std::shared_lock lock(cache.mtx);
-    if (auto it = cache().kernels.find(kernel_name);
+    if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
-        it != cache().kernels.end()) {
       return it->second;
     }
   }
 
-  std::unique_lock lock(cache().mtx);
+  std::unique_lock lock(cache.mtx);
-  if (auto it = cache().kernels.find(kernel_name);
+  if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
-      it != cache().kernels.end()) {
     return it->second;
   }
   std::string source_code = source_builder();
@@ -95,11 +89,7 @@ void* compile(
     kernel_file_name = kernel_name;
   }
 
-  auto output_dir =
+  auto output_dir = std::filesystem::temp_directory_path();
-      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();
@@ -130,10 +120,10 @@ void* compile(
   }
 
   // load library
-  cache().libs.emplace_back(shared_lib_path);
+  cache.libs.emplace_back(shared_lib_path);
 
   // Load function
-  void* fun = dlsym(cache().libs.back().lib, kernel_name.c_str());
+  void* fun = dlsym(cache.libs.back().lib, kernel_name.c_str());
   if (!fun) {
     std::ostringstream msg;
     msg << "[Compile::eval_cpu] Failed to load compiled function "
@@ -141,7 +131,7 @@ void* compile(
         << dlerror();
     throw std::runtime_error(msg.str());
   }
-  cache().kernels.insert({kernel_name, fun});
+  cache.kernels.insert({kernel_name, fun});
   return fun;
 }
 
@@ -151,9 +141,18 @@ inline void build_kernel(
     const std::vector<array>& inputs,
     const std::vector<array>& outputs,
     const std::vector<array>& tape,
-    const std::function<bool(size_t)>& is_constant,
+    const std::unordered_set<uintptr_t>& constant_ids,
     bool contiguous,
     int ndim) {
+  // All outputs should have the exact same shape and will be row contiguous
+  auto output_shape = outputs[0].shape();
+  auto output_strides = outputs[0].strides();
+
+  // Constants are scalars that are captured by value and cannot change
+  auto is_constant = [&constant_ids](const array& x) {
+    return constant_ids.find(x.id()) != constant_ids.end();
+  };
+
   NodeNamer namer;
 
 #ifdef _MSC_VER
@@ -162,28 +161,25 @@ inline void build_kernel(
 #endif
 
   // Start the kernel
-  os << "void " << kernel_name
+  os << "void " << kernel_name << "(void** args) {" << std::endl;
-     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
 
   // Add the input arguments
   int cnt = 0;
-  int strides_index = 1;
+  for (auto& x : inputs) {
-  for (size_t i = 0; i < inputs.size(); ++i) {
+    auto& xname = namer.get_name(x);
+
     // Skip constants from the input list
-    if (is_constant(i)) {
+    if (is_constant(x)) {
       continue;
     }
 
-    const auto& x = inputs[i];
-    auto& xname = namer.get_name(x);
-
     auto tstr = get_type_string(x.dtype());
     os << "  " << tstr << "* " << xname << " = (" << tstr << "*)args[" << cnt++
        << "];" << std::endl;
     // Scalars and contiguous need no strides
     if (!is_scalar(x) && !contiguous) {
-      os << "  const int64_t* " << xname << "_strides = strides["
+      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
-         << strides_index++ << "];" << std::endl;
+         << "];" << std::endl;
     }
   }
 
@@ -193,8 +189,10 @@ inline void build_kernel(
     os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
        << "*)args[" << cnt++ << "];" << std::endl;
   }
-  // Add output size
+  // Add output strides and shape to extract the indices.
-  if (contiguous) {
+  if (!contiguous) {
+    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
+  } else {
     os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
   }
 
@@ -208,11 +206,10 @@ inline void build_kernel(
   }
 
   // Read the inputs in tmps
-  for (size_t i = 0; i < inputs.size(); ++i) {
+  for (auto& x : inputs) {
-    const auto& x = inputs[i];
     auto& xname = namer.get_name(x);
 
-    if (is_constant(i)) {
+    if (is_constant(x)) {
       os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = ";
       print_constant(os, x);
       os << ";" << std::endl;
@@ -236,7 +233,7 @@ inline void build_kernel(
       os << "static_cast<" << get_type_string(x.dtype()) << ">(tmp_"
          << namer.get_name(x.inputs()[0]) << ");" << std::endl;
     } else {
-      os << x.primitive().name();
+      x.primitive().print(os);
       os << "()(";
       for (int i = 0; i < x.inputs().size() - 1; i++) {
         os << "tmp_" << namer.get_name(x.inputs()[i]) << ", ";
@@ -262,9 +259,8 @@ inline void build_kernel(
   } else {
     for (int d = ndim - 1; d >= 0; --d) {
       // Update pointers
-      for (size_t i = 0; i < inputs.size(); ++i) {
+      for (auto& x : inputs) {
-        const auto& x = inputs[i];
+        if (is_constant(x) || is_scalar(x)) {
-        if (is_constant(i) || is_scalar(x)) {
           continue;
         }
         auto& xname = namer.get_name(x);
@@ -286,33 +282,65 @@ inline void build_kernel(
 void Compiled::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
+  if (kernel_lib_.empty()) {
+    kernel_lib_ = build_lib_name(inputs_, outputs_, tape_, constant_ids_);
+  }
+
+  // Figure out which kernel we are using
+  auto& shape = outputs[0].shape();
+  auto contiguous = compiled_check_contiguity(inputs, shape);
   auto& encoder = cpu::get_command_encoder(stream());
 
-  // Collapse contiguous dims to route to a faster kernel if possible. Also
+  // Handle all broadcasting and collect function input arguments
-  // handle all broadcasting.
-  auto [contiguous, shape, strides] =
-      compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
-
-  // Collect function input arguments.
   std::vector<void*> args;
-  for (size_t i = 0; i < inputs.size(); ++i) {
+  std::vector<std::vector<size_t>> strides;
-    if (is_constant_(i)) {
+  for (int i = 0; i < inputs.size(); i++) {
+    // Skip constants.
+    if (constant_ids_.find(inputs_[i].id()) != constant_ids_.end()) {
       continue;
     }
-    const auto& x = inputs[i];
+    auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
+
+    if (contiguous || is_scalar(x)) {
+      continue;
+    }
+
+    // Broadcast the input to the output shape.
+    std::vector<size_t> xstrides;
+    int j = 0;
+    for (; j < shape.size() - x.ndim(); j++) {
+      if (shape[j] == 1) {
+        xstrides.push_back(outputs[0].strides()[j]);
+      } else {
+        xstrides.push_back(0);
+      }
+    }
+    for (int i = 0; i < x.ndim(); i++, j++) {
+      if (x.shape(i) == 1) {
+        if (shape[j] == 1) {
+          xstrides.push_back(outputs[0].strides()[j]);
+        } else {
+          xstrides.push_back(0);
+        }
+      } else {
+        xstrides.push_back(x.strides()[i]);
+      }
+    }
+    strides.push_back(std::move(xstrides));
+    args.push_back(strides.back().data());
   }
 
   // Get the kernel name from the lib
   int ndim = shape.size();
   auto kernel_name = kernel_lib_ + (contiguous ? "_contiguous" : "_strided_");
   if (!contiguous) {
-    kernel_name += std::to_string(ndim);
+    kernel_name += std::to_string(shape.size());
   }
 
   // Get the function
-  auto fn_ptr = compile(kernel_name, [&, contiguous = contiguous]() {
+  auto fn_ptr = compile(kernel_name, [&]() {
     std::ostringstream kernel;
     kernel << get_kernel_preamble() << std::endl;
     kernel << "extern \"C\"  {" << std::endl;
@@ -322,7 +350,7 @@ void Compiled::eval_cpu(
         inputs_,
         outputs_,
         tape_,
-        is_constant_,
+        constant_ids_,
         contiguous,
         ndim);
     // Close extern "C"
@@ -330,26 +358,26 @@ void Compiled::eval_cpu(
     return kernel.str();
   });
 
-  compiled_allocate_outputs(inputs, outputs, is_constant_, contiguous);
+  compiled_allocate_outputs(
+      inputs, outputs, inputs_, constant_ids_, contiguous);
 
   for (auto& x : outputs) {
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
-  if (contiguous) {
+  Shape out_shape;
+  if (!contiguous) {
+    out_shape = outputs[0].shape();
+    args.push_back((void*)out_shape.data());
+  } else {
     args.push_back((void*)outputs[0].data_size());
   }
-  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
+  auto fun = (void (*)(void**))fn_ptr;
-  encoder.dispatch([fun,
+  encoder.dispatch(
-                    args = std::move(args),
+      [fun,
-                    strides = std::move(strides),
+       args = std::move(args),
-                    shape = std::move(shape)]() mutable {
+       strides = std::move(strides),
-    SmallVector<int64_t*> strides_ptrs;
+       out_shape = std::move(out_shape)]() mutable { fun(args.data()); });
-    for (auto& s : strides) {
-      strides_ptrs.push_back(s.data());
-    }
-    fun(shape.data(), strides_ptrs.data(), args.data());
-  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/copy.cpp

@@ -295,11 +295,7 @@ inline void copy_inplace_dispatch(
 
 } // namespace
 
-void copy_cpu_inplace(
+void copy_inplace(const array& src, array& dst, CopyType ctype, Stream stream) {
-    const array& src,
-    array& dst,
-    CopyType ctype,
-    Stream stream) {
   auto& encoder = cpu::get_command_encoder(stream);
   encoder.set_input_array(src);
   encoder.set_output_array(dst);
@@ -309,7 +305,7 @@ void copy_cpu_inplace(
        ctype]() mutable { copy_inplace_dispatch(src, dst, ctype); });
 }
 
-void copy_cpu(const array& src, array& dst, CopyType ctype, Stream stream) {
+void copy(const array& src, array& dst, CopyType ctype, Stream stream) {
   bool donated = set_copy_output_data(src, dst, ctype);
   if (donated && src.dtype() == dst.dtype()) {
     // If the output has the same type as the input then there is nothing to
@@ -319,10 +315,10 @@ void copy_cpu(const array& src, array& dst, CopyType ctype, Stream stream) {
   if (ctype == CopyType::GeneralGeneral) {
     ctype = CopyType::General;
   }
-  copy_cpu_inplace(src, dst, ctype, stream);
+  copy_inplace(src, dst, ctype, stream);
 }
 
-void copy_cpu_inplace(
+void copy_inplace(
     const array& src,
     array& dst,
     const Shape& data_shape,
@@ -377,10 +373,4 @@ void copy_cpu_inplace(
       });
 }
 
-array contiguous_copy_cpu(const array& arr, Stream stream) {
-  array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-  copy_cpu(arr, arr_copy, CopyType::General, stream);
-  return arr_copy;
-}
-
 } // namespace mlx::core

mlx/backend/cpu/copy.h

@@ -10,14 +10,10 @@
 
 namespace mlx::core {
 
-void copy_cpu(const array& src, array& dst, CopyType ctype, Stream stream);
+void copy(const array& src, array& dst, CopyType ctype, Stream stream);
-void copy_cpu_inplace(
+void copy_inplace(const array& src, array& dst, CopyType ctype, Stream stream);
-    const array& src,
-    array& dst,
-    CopyType ctype,
-    Stream stream);
 
-void copy_cpu_inplace(
+void copy_inplace(
     const array& src,
     array& dst,
     const Shape& data_shape,
@@ -30,7 +26,4 @@ void copy_cpu_inplace(
     const std::optional<array>& dynamic_i_offset = std::nullopt,
     const std::optional<array>& dynamic_o_offset = std::nullopt);
 
-// Return a contiguous array with same shape that copies the data of |arr|.
-array contiguous_copy_cpu(const array& arr, Stream stream);
-
 } // namespace mlx::core

mlx/backend/cpu/distributed.cpp

@@ -13,7 +13,9 @@ std::pair<array, bool> ensure_row_contiguous(const array& arr, Stream stream) {
   if (arr.flags().row_contiguous) {
     return {arr, false};
   } else {
-    return {contiguous_copy_cpu(arr, stream), true};
+    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+    copy(arr, arr_copy, CopyType::General, stream);
+    return {arr_copy, true};
   }
 };
 
@@ -32,7 +34,8 @@ void AllReduce::eval_cpu(
       }
       return in;
     } else {
-      array arr_copy = contiguous_copy_cpu(in, s);
+      array arr_copy(in.shape(), in.dtype(), nullptr, {});
+      copy(in, arr_copy, CopyType::General, s);
       out.copy_shared_buffer(arr_copy);
       return arr_copy;
     }

mlx/backend/cpu/eig.cpp

@@ -1,174 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/allocator.h"
-#include "mlx/array.h"
-#include "mlx/backend/cpu/copy.h"
-#include "mlx/backend/cpu/encoder.h"
-#include "mlx/backend/cpu/lapack.h"
-#include "mlx/linalg.h"
-#include "mlx/primitives.h"
-
-namespace mlx::core {
-
-namespace {
-
-template <typename T>
-void eig_impl(
-    array& a,
-    array& vectors,
-    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& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_output_array(values);
-  OT* vec_ptr = nullptr;
-  if (compute_eigenvectors) {
-    encoder.set_output_array(vectors);
-    vec_ptr = vectors.data<OT>();
-  }
-  encoder.dispatch([a_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)};
-    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]};
-      }
-      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) {
-        std::stringstream msg;
-        msg << "[Eig::eval_cpu] Eigenvalue decomposition failed with error code "
-            << info;
-        throw std::runtime_error(msg.str());
-      }
-    }
-  });
-  encoder.add_temporary(a);
-}
-
-} // namespace
-
-void Eig::eval_cpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  const auto& a = inputs[0];
-  auto& values = outputs[0];
-
-  auto vectors = compute_eigenvectors_
-      ? outputs[1]
-      : array(a.shape(), complex64, nullptr, {});
-
-  auto a_copy = array(a.shape(), a.dtype(), nullptr, {});
-  copy_cpu(
-      a,
-      a_copy,
-      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
-      stream());
-
-  values.set_data(allocator::malloc(values.nbytes()));
-
-  if (compute_eigenvectors_) {
-    // Set the strides and flags so the eigenvectors
-    // are in the columns of the output
-    auto flags = vectors.flags();
-    auto strides = vectors.strides();
-    auto ndim = a.ndim();
-    std::swap(strides[ndim - 1], strides[ndim - 2]);
-
-    if (a.size() > 1) {
-      flags.row_contiguous = false;
-      if (ndim > 2) {
-        flags.col_contiguous = false;
-      } else {
-        flags.col_contiguous = true;
-      }
-    }
-    vectors.set_data(
-        allocator::malloc(vectors.nbytes()), vectors.size(), strides, flags);
-  }
-  switch (a.dtype()) {
-    case float32:
-      eig_impl<float>(a_copy, vectors, values, compute_eigenvectors_, stream());
-      break;
-    default:
-      throw std::runtime_error("[Eig::eval_cpu] only supports float32.");
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cpu/eigh.cpp

@@ -12,133 +12,6 @@ namespace mlx::core {
 
 namespace {
 
-template <typename T, class Enable = void>
-struct EighWork {};
-
-template <typename T>
-struct EighWork<
-    T,
-    typename std::enable_if<std::is_floating_point<T>::value>::type> {
-  using R = T;
-
-  char jobz;
-  char uplo;
-  int N;
-  int lwork;
-  int liwork;
-  int info;
-  std::vector<array::Data> buffers;
-
-  EighWork(char jobz_, char uplo_, int N_)
-      : jobz(jobz_), uplo(uplo_), N(N_), lwork(-1), liwork(-1) {
-    T work;
-    int iwork;
-    syevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        nullptr,
-        &N,
-        nullptr,
-        &work,
-        &lwork,
-        &iwork,
-        &liwork,
-        &info);
-    lwork = static_cast<int>(work);
-    liwork = iwork;
-    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
-    buffers.emplace_back(allocator::malloc(sizeof(int) * liwork));
-  }
-
-  void run(T* vectors, T* values) {
-    syevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        vectors,
-        &N,
-        values,
-        static_cast<T*>(buffers[0].buffer.raw_ptr()),
-        &lwork,
-        static_cast<int*>(buffers[1].buffer.raw_ptr()),
-        &liwork,
-        &info);
-  }
-};
-
-template <>
-struct EighWork<std::complex<float>> {
-  using T = std::complex<float>;
-  using R = float;
-
-  char jobz;
-  char uplo;
-  int N;
-  int lwork;
-  int lrwork;
-  int liwork;
-  int info;
-  std::vector<array::Data> buffers;
-
-  EighWork(char jobz_, char uplo_, int N_)
-      : jobz(jobz_), uplo(uplo_), N(N_), lwork(-1), lrwork(-1), liwork(-1) {
-    T work;
-    R rwork;
-    int iwork;
-    heevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        nullptr,
-        &N,
-        nullptr,
-        &work,
-        &lwork,
-        &rwork,
-        &lrwork,
-        &iwork,
-        &liwork,
-        &info);
-    lwork = static_cast<int>(work.real());
-    lrwork = static_cast<int>(rwork);
-    liwork = iwork;
-    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
-    buffers.emplace_back(allocator::malloc(sizeof(R) * lrwork));
-    buffers.emplace_back(allocator::malloc(sizeof(int) * liwork));
-  }
-
-  void run(T* vectors, R* values) {
-    heevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        vectors,
-        &N,
-        values,
-        static_cast<T*>(buffers[0].buffer.raw_ptr()),
-        &lwork,
-        static_cast<R*>(buffers[1].buffer.raw_ptr()),
-        &lrwork,
-        static_cast<int*>(buffers[2].buffer.raw_ptr()),
-        &liwork,
-        &info);
-    if (jobz == 'V') {
-      // We have pre-transposed the vectors but we also must conjugate them
-      // when they are complex.
-      //
-      // We could vectorize this but it is so fast in comparison to heevd that
-      // it doesn't really matter.
-      for (int i = 0; i < N; i++) {
-        for (int j = 0; j < N; j++) {
-          *vectors = std::conj(*vectors);
-          vectors++;
-        }
-      }
-    }
-  }
-};
-
 template <typename T>
 void eigh_impl(
     array& vectors,
@@ -146,10 +19,8 @@ void eigh_impl(
     const std::string& uplo,
     bool compute_eigenvectors,
     Stream stream) {
-  using R = typename EighWork<T>::R;
-
   auto vec_ptr = vectors.data<T>();
-  auto eig_ptr = values.data<R>();
+  auto eig_ptr = values.data<T>();
   char jobz = compute_eigenvectors ? 'V' : 'N';
 
   auto& encoder = cpu::get_command_encoder(stream);
@@ -162,17 +33,49 @@ void eigh_impl(
                     N = vectors.shape(-1),
                     size = vectors.size()]() mutable {
     // Work query
-    EighWork<T> work(jobz, uplo, N);
+    int lwork = -1;
+    int liwork = -1;
+    int info;
+    {
+      T work;
+      int iwork;
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          nullptr,
+          &N,
+          nullptr,
+          &work,
+          &lwork,
+          &iwork,
+          &liwork,
+          &info);
+      lwork = static_cast<int>(work);
+      liwork = iwork;
+    }
 
-    // Work loop
+    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    auto iwork_buf = array::Data{allocator::malloc(sizeof(int) * liwork)};
     for (size_t i = 0; i < size / (N * N); ++i) {
-      work.run(vec_ptr, eig_ptr);
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          vec_ptr,
+          &N,
+          eig_ptr,
+          static_cast<T*>(work_buf.buffer.raw_ptr()),
+          &lwork,
+          static_cast<int*>(iwork_buf.buffer.raw_ptr()),
+          &liwork,
+          &info);
       vec_ptr += N * N;
       eig_ptr += N;
-      if (work.info != 0) {
+      if (info != 0) {
         std::stringstream msg;
         msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
-            << work.info;
+            << info;
         throw std::runtime_error(msg.str());
       }
     }
@@ -196,7 +99,7 @@ void Eigh::eval_cpu(
 
   values.set_data(allocator::malloc(values.nbytes()));
 
-  copy_cpu(
+  copy(
       a,
       vectors,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
@@ -228,10 +131,6 @@ void Eigh::eval_cpu(
       eigh_impl<double>(
           vectors, values, uplo_, compute_eigenvectors_, stream());
       break;
-    case complex64:
-      eigh_impl<std::complex<float>>(
-          vectors, values, uplo_, compute_eigenvectors_, stream());
-      break;
     default:
       throw std::runtime_error(
           "[Eigh::eval_cpu] only supports float32 or float64.");

mlx/backend/cpu/gemms/cblas.cpp

@@ -88,47 +88,4 @@ 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/hadamard.cpp

@@ -96,7 +96,7 @@ void Hadamard::eval_cpu(const std::vector<array>& inputs, array& out) {
   if (in.flags().row_contiguous && in.is_donatable()) {
     out.copy_shared_buffer(in);
   } else {
-    copy_cpu(
+    copy(
         in,
         out,
         in.flags().row_contiguous ? CopyType::Vector : CopyType::General,

mlx/backend/cpu/indexing.cpp

@@ -257,11 +257,15 @@ void gather_axis(
     const array& ind,
     array& out,
     const int axis) {
-  auto shape = remove_index(ind.shape(), axis);
+  auto strides = ind.strides();
-  ContiguousIterator ind_it(
+  strides.erase(strides.begin() + axis);
-      shape, remove_index(ind.strides(), axis), src.ndim() - 1);
+  auto shape = ind.shape();
-  ContiguousIterator src_it(
+  shape.erase(shape.begin() + axis);
-      shape, remove_index(src.strides(), axis), src.ndim() - 1);
+  ContiguousIterator ind_it(shape, strides, src.ndim() - 1);
+
+  strides = src.strides();
+  strides.erase(strides.begin() + axis);
+  ContiguousIterator src_it(shape, strides, src.ndim() - 1);
 
   auto ind_ptr = ind.data<IdxT>();
   auto src_ptr = src.data<T>();
@@ -517,7 +521,7 @@ void Scatter::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy_cpu(src, out, ctype, stream());
+  copy(src, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   std::vector<array> inds;
@@ -581,11 +585,15 @@ void Scatter::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 template <typename T, typename IdxT, typename OpT>
 void scatter_axis(array& out, const array idx, const array& upd, int axis) {
-  auto shape = remove_index(idx.shape(), axis);
+  auto strides = idx.strides();
-  ContiguousIterator idx_it(
+  strides.erase(strides.begin() + axis);
-      shape, remove_index(idx.strides(), axis), upd.ndim() - 1);
+  auto shape = idx.shape();
-  ContiguousIterator upd_it(
+  shape.erase(shape.begin() + axis);
-      shape, remove_index(upd.strides(), axis), upd.ndim() - 1);
+  ContiguousIterator idx_it(shape, strides, upd.ndim() - 1);
+
+  strides = upd.strides();
+  strides.erase(strides.begin() + axis);
+  ContiguousIterator upd_it(shape, strides, upd.ndim() - 1);
 
   auto idx_ptr = idx.data<IdxT>();
   auto upd_ptr = upd.data<T>();
@@ -686,7 +694,7 @@ void ScatterAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy_cpu(src, out, ctype, stream());
+  copy(src, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(idx);

mlx/backend/cpu/inverse.cpp

@@ -115,7 +115,7 @@ void inverse_impl(
   //   (A⁻¹)ᵀ = (Aᵀ)⁻¹
 
   // The inverse is computed in place, so just copy the input to the output.
-  copy_cpu(
+  copy(
       a,
       inv,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,

mlx/backend/cpu/jit_compiler.cpp

@@ -2,7 +2,6 @@
 
 #include "mlx/backend/cpu/jit_compiler.h"
 
-#include <algorithm>
 #include <sstream>
 #include <vector>
 

mlx/backend/cpu/lapack.h

@@ -2,14 +2,14 @@
 
 #pragma once
 
+// Required for Visual Studio.
+// https://github.com/OpenMathLib/OpenBLAS/blob/develop/docs/install.md
+#ifdef _MSC_VER
 #include <complex>
 #define LAPACK_COMPLEX_CUSTOM
 #define lapack_complex_float std::complex<float>
 #define lapack_complex_double std::complex<double>
-#define lapack_complex_float_real(z) ((z).real())
+#endif
-#define lapack_complex_float_imag(z) ((z).imag())
-#define lapack_complex_double_real(z) ((z).real())
-#define lapack_complex_double_imag(z) ((z).imag())
 
 #ifdef MLX_USE_ACCELERATE
 #include <Accelerate/Accelerate.h>
@@ -32,7 +32,7 @@
 
 #endif
 
-#define INSTANTIATE_LAPACK_REAL(FUNC)                        \
+#define INSTANTIATE_LAPACK_TYPES(FUNC)                       \
   template <typename T, typename... Args>                    \
   void FUNC(Args... args) {                                  \
     if constexpr (std::is_same_v<T, float>) {                \
@@ -42,24 +42,11 @@
     }                                                        \
   }
 
-INSTANTIATE_LAPACK_REAL(geqrf)
+INSTANTIATE_LAPACK_TYPES(geqrf)
-INSTANTIATE_LAPACK_REAL(orgqr)
+INSTANTIATE_LAPACK_TYPES(orgqr)
-INSTANTIATE_LAPACK_REAL(syevd)
+INSTANTIATE_LAPACK_TYPES(syevd)
-INSTANTIATE_LAPACK_REAL(geev)
+INSTANTIATE_LAPACK_TYPES(potrf)
-INSTANTIATE_LAPACK_REAL(potrf)
+INSTANTIATE_LAPACK_TYPES(gesvdx)
-INSTANTIATE_LAPACK_REAL(gesdd)
+INSTANTIATE_LAPACK_TYPES(getrf)
-INSTANTIATE_LAPACK_REAL(getrf)
+INSTANTIATE_LAPACK_TYPES(getri)
-INSTANTIATE_LAPACK_REAL(getri)
+INSTANTIATE_LAPACK_TYPES(trtri)
-INSTANTIATE_LAPACK_REAL(trtri)
-
-#define INSTANTIATE_LAPACK_COMPLEX(FUNC)                            \
-  template <typename T, typename... Args>                           \
-  void FUNC(Args... args) {                                         \
-    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_COMPLEX(heevd)

mlx/backend/cpu/logsumexp.cpp

@@ -87,7 +87,8 @@ void LogSumExp::eval_cpu(const std::vector<array>& inputs, array& out) {
     if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
       return x;
     } else {
-      array x_copy = contiguous_copy_cpu(x, s);
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy(x, x_copy, CopyType::General, s);
       encoder.add_temporary(x_copy);
       return x_copy;
     }

mlx/backend/cpu/luf.cpp

@@ -31,7 +31,7 @@ void luf_impl(
   strides[ndim - 1] = M;
   strides[ndim - 2] = 1;
   lu.set_data(allocator::malloc(lu.nbytes()), lu.nbytes(), strides, flags);
-  copy_cpu_inplace(
+  copy_inplace(
       a,
       lu,
       a.shape(),

mlx/backend/cpu/masked_mm.cpp

@@ -6,7 +6,6 @@
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
-#include "mlx/backend/cpu/gemm.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
@@ -53,58 +52,6 @@ inline void mask_matrix(
   }
 }
 
-template <typename T>
-inline void segmented_mm(
-    const T* a,
-    const T* b,
-    const uint32_t* segments,
-    T* out,
-    bool a_transposed,
-    bool b_transposed,
-    size_t lda,
-    size_t ldb,
-    const Shape& a_shape,
-    const Strides& a_strides,
-    const Shape& b_shape,
-    const Strides& b_strides,
-    size_t num_segments,
-    const Shape& segments_shape,
-    const Strides& segments_strides) {
-  int ndim = a_shape.size();
-  Shape a_copy = a_shape;
-  Shape b_copy = b_shape;
-  int32_t M = a_copy[ndim - 2];
-  int32_t N = b_copy[ndim - 1];
-  for (int i = 0; i < num_segments; i++) {
-    uint32_t k_start =
-        segments[elem_to_loc(2 * i, segments_shape, segments_strides)];
-    uint32_t k_end =
-        segments[elem_to_loc(2 * i + 1, segments_shape, segments_strides)];
-    if (k_end <= k_start) {
-      std::fill_n(out + i * M * N, M * N, T(0));
-      continue;
-    }
-    a_copy[ndim - 1] = k_end - k_start;
-    b_copy[ndim - 2] = k_end - k_start;
-    matmul<T>(
-        a + k_start * a_strides[ndim - 1],
-        b + k_start * b_strides[ndim - 2],
-        out + i * M * N,
-        a_transposed,
-        b_transposed,
-        lda,
-        ldb,
-        N,
-        1.0,
-        0.0,
-        1,
-        a_copy,
-        a_strides,
-        b_copy,
-        b_strides);
-  }
-}
-
 } // namespace
 
 void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -124,20 +71,21 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
         if (!expand_all && stx == arr.shape(-1) && sty == 1) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy_cpu(arr, arr_copy, CopyType::Vector, s);
+            copy(arr, arr_copy, CopyType::Vector, s);
             return std::make_tuple(false, stx, arr_copy, true);
           }
           return std::make_tuple(false, stx, arr, false);
         } else if (!expand_all && stx == 1 && sty == arr.shape(-2)) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy_cpu(arr, arr_copy, CopyType::Vector, s);
+            copy(arr, arr_copy, CopyType::Vector, s);
             return std::make_tuple(true, sty, arr_copy, true);
           }
           return std::make_tuple(true, sty, arr, false);
         } else {
+          array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+          copy(arr, arr_copy, CopyType::General, s);
           int64_t stx = arr.shape(-1);
-          array arr_copy = contiguous_copy_cpu(arr, s);
           return std::make_tuple(false, stx, arr_copy, true);
         }
       };
@@ -385,7 +333,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       return std::make_tuple(true, sty, arr);
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
       int64_t stx = arr.shape(-1);
       return std::make_tuple(false, stx, temps.back());
     }
@@ -489,121 +437,4 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   encoder.add_temporaries(std::move(temps));
 }
 
-void SegmentedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& s = stream();
-  auto& encoder = cpu::get_command_encoder(stream());
-  auto check_transpose = [&s, &encoder](const array& x) {
-    auto stx = x.strides()[x.ndim() - 2];
-    auto sty = x.strides()[x.ndim() - 1];
-    if (stx == x.shape(-1) && sty == 1) {
-      return std::make_tuple(false, stx, x);
-    } else if (stx == 1 && sty == x.shape(-2)) {
-      return std::make_tuple(true, sty, x);
-    } else {
-      array xc(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, xc, CopyType::General, s);
-      encoder.add_temporary(xc);
-      int64_t stx = x.shape(-1);
-      return std::make_tuple(false, stx, xc);
-    }
-  };
-
-  auto [a_transposed, lda, a] = check_transpose(inputs[0]);
-  auto [b_transposed, ldb, b] = check_transpose(inputs[1]);
-  auto& segments = inputs[2];
-
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(segments);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    segments = array::unsafe_weak_copy(segments),
-                    out_ptr = out.data<void>(),
-                    a_transposed = a_transposed,
-                    b_transposed = b_transposed,
-                    lda = lda,
-                    ldb = ldb]() {
-    switch (a.dtype()) {
-      case float64:
-        segmented_mm<double>(
-            a.data<double>(),
-            b.data<double>(),
-            segments.data<uint32_t>(),
-            static_cast<double*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case float32:
-        segmented_mm<float>(
-            a.data<float>(),
-            b.data<float>(),
-            segments.data<uint32_t>(),
-            static_cast<float*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case float16:
-        segmented_mm<float16_t>(
-            a.data<float16_t>(),
-            b.data<float16_t>(),
-            segments.data<uint32_t>(),
-            static_cast<float16_t*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case bfloat16:
-        segmented_mm<bfloat16_t>(
-            a.data<bfloat16_t>(),
-            b.data<bfloat16_t>(),
-            segments.data<uint32_t>(),
-            static_cast<bfloat16_t*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      default:
-        throw std::invalid_argument(
-            "Segmented mm supports only real float types.");
-    }
-  });
-}
-
 } // namespace mlx::core

mlx/backend/cpu/matmul.cpp

@@ -81,7 +81,7 @@ void matmul_general(
       return std::make_tuple(true, sty, arr);
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, stream);
+      copy(arr, temps.back(), CopyType::General, stream);
       stx = arr.shape(-1);
       return std::make_tuple(false, stx, temps.back());
     }
@@ -108,9 +108,6 @@ 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.");
   }
@@ -131,9 +128,9 @@ void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  if (out.size() == 0) {
+  if (out.dtype() != float32) {
-    out.set_data(allocator::malloc(out.nbytes()));
+    throw std::runtime_error(
-    return;
+        "[AddMM::eval_cpu] Currently only supports float32.");
   }
 
   // Fill output with C
@@ -141,10 +138,8 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   CopyType ctype = c.data_size() == 1
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
-  copy_cpu(c, out, ctype, stream());
+  copy(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

@@ -22,7 +22,7 @@ void reshape(const array& in, array& out) {
   auto [copy_necessary, out_strides] = prepare_reshape(in, out);
   if (copy_necessary) {
     out.set_data(allocator::malloc(out.nbytes()));
-    copy_cpu_inplace(in, out, CopyType::General, out.primitive().stream());
+    copy_inplace(in, out, CopyType::General, out.primitive().stream());
   } else {
     shared_buffer_reshape(in, out_strides, out);
   }
@@ -175,7 +175,7 @@ void AsType::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
   CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-  copy_cpu(in, out, ctype, stream());
+  copy(in, out, ctype, stream());
 }
 
 void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -198,7 +198,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
     size_t data_offset = strides[axis_] * sizes[i];
     out_slice.copy_shared_buffer(
         out, strides, flags, out_slice.size(), data_offset);
-    copy_cpu_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, stream());
+    copy_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, stream());
   }
 }
 
@@ -211,7 +211,7 @@ void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
        (allow_col_major_ && in.flags().col_contiguous))) {
     out.copy_shared_buffer(in);
   } else {
-    copy_cpu(in, out, CopyType::General, stream());
+    copy(in, out, CopyType::General, stream());
   }
 }
 
@@ -235,7 +235,7 @@ void Full::eval_cpu(const std::vector<array>& inputs, array& out) {
   } else {
     ctype = CopyType::General;
   }
-  copy_cpu(in, out, ctype, stream());
+  copy(in, out, ctype, stream());
 }
 
 void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -251,7 +251,7 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(val.dtype() == in.dtype() && in.dtype() == out.dtype());
 
   // Fill output with val
-  copy_cpu(val, out, CopyType::Scalar, stream());
+  copy(val, out, CopyType::Scalar, stream());
 
   // Find offset for start of input values
   size_t data_offset = 0;
@@ -266,7 +266,7 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
       out, out.strides(), out.flags(), out_slice.size(), data_offset);
 
   // Copy input values into the slice
-  copy_cpu_inplace(in, out_slice, CopyType::GeneralGeneral, stream());
+  copy_inplace(in, out_slice, CopyType::GeneralGeneral, stream());
 }
 
 void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -340,7 +340,7 @@ void DynamicSlice::eval_cpu(const std::vector<array>& inputs, array& out) {
   out.set_data(allocator::malloc(out.nbytes()));
   auto [in_offset, donated] =
       compute_dynamic_offset(inputs[1], in.strides(), axes_, stream());
-  copy_cpu_inplace(
+  copy_inplace(
       /* const array& src = */ in,
       /* array& dst = */ out,
       /* const Shape& data_shape = */ out.shape(),
@@ -372,11 +372,11 @@ void DynamicSliceUpdate::eval_cpu(
   auto ctype = in.flags().contiguous && in.size() == in.data_size()
       ? CopyType::Vector
       : CopyType::General;
-  copy_cpu(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
+  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
 
   auto [out_offset, donated] =
       compute_dynamic_offset(inputs[2], out.strides(), axes_, stream());
-  copy_cpu_inplace(
+  copy_inplace(
       /* const array& src = */ upd,
       /* array& dst = */ out,
       /* const std::vector<int>& data_shape = */ upd.shape(),
@@ -412,14 +412,14 @@ void SliceUpdate::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto ctype = in.flags().contiguous && in.size() == in.data_size()
       ? CopyType::Vector
       : CopyType::General;
-  copy_cpu(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
+  copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
 
   // Calculate out strides, initial offset and if copy needs to be made
   auto [data_offset, out_strides] =
       prepare_slice(out, start_indices_, strides_);
 
   // Do copy
-  copy_cpu_inplace(
+  copy_inplace(
       /* const array& src = */ upd,
       /* array& dst = */ out,
       /* const std::vector<int>& data_shape = */ upd.shape(),
@@ -456,9 +456,9 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
     if (in.dtype() == bool_) {
       auto in_tmp = array(in.shape(), uint8, nullptr, {});
       in_tmp.copy_shared_buffer(in);
-      copy_cpu_inplace(in_tmp, tmp, CopyType::General, stream());
+      copy_inplace(in_tmp, tmp, CopyType::General, stream());
     } else {
-      copy_cpu_inplace(in, tmp, CopyType::General, stream());
+      copy_inplace(in, tmp, CopyType::General, stream());
     }
 
     auto flags = out.flags();

mlx/backend/cpu/qrf.cpp

@@ -26,7 +26,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   strides[in.ndim() - 2] = 1;
   strides[in.ndim() - 1] = M;
   in.set_data(allocator::malloc(in.nbytes()), in.nbytes(), strides, flags);
-  copy_cpu_inplace(a, in, CopyType::GeneralGeneral, stream);
+  copy_inplace(a, in, CopyType::GeneralGeneral, stream);
   auto& encoder = cpu::get_command_encoder(stream);
   q.set_data(allocator::malloc(q.nbytes()));
   r.set_data(allocator::malloc(r.nbytes()));

mlx/backend/cpu/quantized.cpp

@@ -1,5 +1,7 @@
 // Copyright © 2023 Apple Inc.
 
+#include <cassert>
+
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
@@ -11,47 +13,9 @@ 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);
-}
-
-inline constexpr short get_bytes_per_pack(int bits, int wsize = 8) {
-  auto power_of_2_bits = (bits & (bits - 1)) == 0;
-  return power_of_2_bits ? (wsize / 8) : (bits == 5 ? 5 : 3);
-}
-
 template <typename T, int bits>
 void extract_bits(const uint8_t* w_in, T* w_out) {
-  static_assert(bits == 3 || bits == 5 || bits == 6);
+  assert(bits == 3 || bits == 6);
   if (bits == 3) {
     w_out[0] = static_cast<T>(w_in[0] & 0x7);
     w_out[1] = static_cast<T>((w_in[0] & 0x38) >> 3);
@@ -61,16 +25,6 @@ void extract_bits(const uint8_t* w_in, T* w_out) {
     w_out[5] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0x3) << 1));
     w_out[6] = static_cast<T>((w_in[2] & 0x1c) >> 2);
     w_out[7] = static_cast<T>((w_in[2] & 0xe0) >> 5);
-  } else if (bits == 5) {
-    w_out[0] = static_cast<T>(w_in[0] & 0x1f);
-    w_out[1] = static_cast<T>(((w_in[0] & 0xe0) >> 5) + ((w_in[1] & 0x3) << 3));
-    w_out[2] = static_cast<T>((w_in[1] & 0x7c) >> 2);
-    w_out[3] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0xf) << 1));
-    w_out[4] = static_cast<T>(((w_in[2] & 0xf0) >> 4) + ((w_in[3] & 0x1) << 4));
-    w_out[5] = static_cast<T>((w_in[3] & 0x3e) >> 1);
-    w_out[6] = static_cast<T>(((w_in[3] & 0xc0) >> 6) + ((w_in[4] & 0x7) << 2));
-    w_out[7] = static_cast<T>((w_in[4] & 0xf8) >> 3);
-
   } else if (bits == 6) {
     w_out[0] = static_cast<T>(w_in[0] & 0x3f);
     w_out[1] =
@@ -92,8 +46,8 @@ void _qmm(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = get_pack_factor(bits, 8);
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
-  constexpr int bytes_per_pack = get_bytes_per_pack(bits);
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
   constexpr int packs_in_group = group_size / pack_factor;
 
   for (int m = 0; m < M; m++) {
@@ -111,7 +65,7 @@ void _qmm(
         T scale = *scales_local++;
         T bias = *biases_local++;
         for (int ng = 0; ng < packs_in_group; ng++) {
-          if constexpr (bits == 3 || bits == 5 || bits == 6) {
+          if (bits == 3 || bits == 6) {
             T wl[pack_factor];
             extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
@@ -150,9 +104,8 @@ void _qmm_t(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
-  constexpr int pack_factor = get_pack_factor(bits, 8);
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
-  constexpr int bytes_per_pack = get_bytes_per_pack(bits);
   constexpr int packs_in_group = group_size / pack_factor;
 
   for (int m = 0; m < M; m++) {
@@ -168,7 +121,7 @@ void _qmm_t(
         T bias = *biases_local++;
 
         for (int kw = 0; kw < packs_in_group; kw++) {
-          if constexpr (bits == 3 || bits == 5 || bits == 6) {
+          if (bits == 3 || bits == 6) {
             T wl[pack_factor];
             extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
@@ -351,10 +304,6 @@ void _qmm_dispatch_typed(
       _qmm_dispatch_group<T, 4>(
           result, x, w, scales, biases, M, N, K, group_size, transposed_w);
       break;
-    case 5:
-      _qmm_dispatch_group<T, 5>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
     case 6:
       _qmm_dispatch_group<T, 6>(
           result, x, w, scales, biases, M, N, K, group_size, transposed_w);
@@ -434,231 +383,6 @@ 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 bytes_per_pack = get_bytes_per_pack(4);
-  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 bytes_per_pack = get_bytes_per_pack(4);
-  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,
@@ -765,198 +489,115 @@ 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];
 
-  auto& encoder = cpu::get_command_encoder(stream());
+  std::vector<array> temps;
-  auto ensure_row_contiguous = [s = stream(), &encoder](const array& arr) {
+  auto ensure_row_contiguous = [s = stream(), &temps](const array& arr) {
     if (arr.flags().row_contiguous) {
       return arr;
     } else {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
+      return temps.back();
-      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);
-  if (mode_ == QuantizationMode::Affine) {
+  encoder.dispatch([out = array::unsafe_weak_copy(out),
-    auto biases = ensure_row_contiguous(inputs[3]);
+                    x = array::unsafe_weak_copy(x),
-    encoder.set_input_array(biases);
+                    w = array::unsafe_weak_copy(w),
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                    scales = array::unsafe_weak_copy(scales),
-                      x = array::unsafe_weak_copy(x),
+                    biases = array::unsafe_weak_copy(biases),
-                      w = array::unsafe_weak_copy(w),
+                    group_size_ = group_size_,
-                      scales = array::unsafe_weak_copy(scales),
+                    bits_ = bits_,
-                      biases = array::unsafe_weak_copy(biases),
+                    transpose_ = transpose_]() mutable {
-                      group_size_ = group_size_,
+    _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
-                      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& lhs_indices = inputs[inputs.size() - 2];
+  auto& biases_pre = inputs[3];
-  auto& rhs_indices = inputs[inputs.size() - 1];
+  auto& lhs_indices = inputs[4];
+  auto& rhs_indices = inputs[5];
 
-  auto& encoder = cpu::get_command_encoder(stream());
+  std::vector<array> temps;
   auto ensure_row_contiguous_last_dims = [s = stream(),
-                                          &encoder](const array& arr) {
+                                          &temps](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 {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
+      return temps.back();
-      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);
-  if (mode_ == QuantizationMode::Affine) {
+  encoder.dispatch([out = array::unsafe_weak_copy(out),
-    auto biases = ensure_row_contiguous_last_dims(inputs[3]);
+                    x = array::unsafe_weak_copy(x),
-    encoder.set_input_array(biases);
+                    w = array::unsafe_weak_copy(w),
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
+                    scales = array::unsafe_weak_copy(scales),
-                      x = array::unsafe_weak_copy(x),
+                    biases = array::unsafe_weak_copy(biases),
-                      w = array::unsafe_weak_copy(w),
+                    lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                      scales = array::unsafe_weak_copy(scales),
+                    rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                      biases = array::unsafe_weak_copy(biases),
+                    group_size_ = group_size_,
-                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
+                    bits_ = bits_,
-                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
+                    transpose_ = transpose_]() mutable {
-                      group_size_ = group_size_,
+    _bs_qmm_dispatch(
-                      bits_ = bits_,
+        out,
-                      transpose_ = transpose_]() mutable {
+        x,
-      _bs_qmm_dispatch(
+        w,
-          out,
+        scales,
-          x,
+        biases,
-          w,
+        lhs_indices,
-          scales,
+        rhs_indices,
-          biases,
+        group_size_,
-          lhs_indices,
+        bits_,
-          rhs_indices,
+        transpose_);
-          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>
@@ -972,8 +613,9 @@ void quantize(
   float eps = 1e-7;
 
   bool power_of_2_bits = is_power_of_2(bits);
-  int el_per_int = get_pack_factor(bits, 32);
+  int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
-  int bytes_per_pack = get_bytes_per_pack(bits);
+  // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
+  int bytes_per_pack = power_of_2_bits ? 1 : 3;
   int int_per_group = group_size * bytes_per_pack / el_per_int;
   size_t n_groups = w_size / group_size;
 
@@ -998,21 +640,15 @@ void quantize(
     }
     size_t out_idx = i * int_per_group;
     for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
-      uint64_t out_el = 0;
+      uint32_t out_el = 0;
       for (int k = 0; k < el_per_int; ++k) {
         float w_el = w[w_idx + j * el_per_int + k];
         w_el = std::rint((w_el - bias) / scale);
         w_el = std::min(std::max(w_el, 0.0f), n_bins);
-        out_el |= static_cast<uint64_t>(w_el) << (k * bits);
+        out_el |= static_cast<uint32_t>(w_el) << (k * bits);
       }
       if (power_of_2_bits) {
         out[out_idx + j] = out_el;
-      } else if (bits == 5) {
-        out[out_idx + bytes_per_pack * j] = out_el & 0xff;
-        out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;
-        out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
-        out[out_idx + bytes_per_pack * j + 3] = (out_el & 0xff000000) >> 24;
-        out[out_idx + bytes_per_pack * j + 4] = (out_el & 0xff00000000) >> 32;
       } else {
         out[out_idx + bytes_per_pack * j] = out_el & 0xff;
         out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;
@@ -1040,14 +676,16 @@ void dispatch_quantize(
       w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
 }
 
-void fast::Quantize::eval_cpu(
+void fast::AffineQuantize::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   auto ensure_row_contiguous = [s = stream()](const array& arr) {
     if (arr.flags().row_contiguous) {
       return std::make_pair(arr, false);
     } else {
-      return std::make_pair(contiguous_copy_cpu(arr, s), true);
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy(arr, arr_copy, CopyType::General, s);
+      return std::make_pair(arr_copy, true);
     }
   };
 
@@ -1099,7 +737,7 @@ void fast::Quantize::eval_cpu(
       }
     } else {
       throw std::runtime_error(
-          "[fast::Quantize::eval_cpu] Only supports floating point inputs");
+          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
     }
   });
 }

mlx/backend/cpu/reduce.cpp

@@ -325,15 +325,7 @@ struct MaxReduce {
   };
 
   template <int N, typename T>
-  std::enable_if_t<std::is_integral_v<T>, T> operator()(simd::Simd<T, N> x) {
+  T operator()(simd::Simd<T, N> x) {
-    return simd::max(x);
-  };
-
-  template <int N, typename T>
-  std::enable_if_t<!std::is_integral_v<T>, T> operator()(simd::Simd<T, N> x) {
-    if (simd::any(x != x)) {
-      return static_cast<T>(NAN);
-    }
     return simd::max(x);
   };
 };
@@ -350,15 +342,7 @@ struct MinReduce {
   };
 
   template <int N, typename T>
-  std::enable_if_t<std::is_integral_v<T>, T> operator()(simd::Simd<T, N> x) {
+  T operator()(simd::Simd<T, N> x) {
-    return simd::min(x);
-  };
-
-  template <int N, typename T>
-  std::enable_if_t<!std::is_integral_v<T>, T> operator()(simd::Simd<T, N> x) {
-    if (simd::any(x != x)) {
-      return static_cast<T>(NAN);
-    }
     return simd::min(x);
   };
 };
@@ -491,27 +475,19 @@ void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
         switch (in.dtype()) {
           case bool_:
           case uint8:
-            reduce_dispatch_sum_prod<uint8_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint16:
-            reduce_dispatch_sum_prod<uint16_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint32:
-            reduce_dispatch_sum_prod<uint32_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint64:
-            reduce_dispatch_sum_prod<uint64_t>(in, out, reduce_type_, axes_);
-            break;
           case int8:
             reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
             break;
           case int16:
+          case uint16:
             reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
             break;
           case int32:
+          case uint32:
             reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
             break;
           case int64:
+          case uint64:
             reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
             break;
           case float16:
@@ -551,10 +527,10 @@ void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
             reduce_dispatch_min_max<uint64_t>(in, out, reduce_type_, axes_);
             break;
           case int8:
-            reduce_dispatch_min_max<int8_t>(in, out, reduce_type_, axes_);
+            reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
             break;
           case int16:
-            reduce_dispatch_min_max<int16_t>(in, out, reduce_type_, axes_);
+            reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
             break;
           case int32:
             reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);

mlx/backend/cpu/scan.cpp

@@ -250,8 +250,10 @@ void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Ensure contiguity
   auto in = inputs[0];
   if (!in.flags().row_contiguous) {
-    in = contiguous_copy_cpu(in, stream());
+    array arr_copy(in.shape(), in.dtype(), nullptr, {});
-    encoder.add_temporary(in);
+    copy(in, arr_copy, CopyType::General, stream());
+    in = arr_copy;
+    encoder.add_temporary(arr_copy);
   }
   out.set_data(allocator::malloc(out.nbytes()));
 
@@ -328,8 +330,7 @@ void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
             reduce_type_, in, out, axis_, reverse_, inclusive_);
         break;
       case complex64:
-        scan_dispatch<complex64_t, complex64_t>(
+        throw std::runtime_error("Scan ops do not support complex types yet");
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
         break;
     }
   });

mlx/backend/cpu/simd/accelerate_simd.h

@@ -9,7 +9,7 @@
 
 #include "mlx/backend/cpu/simd/base_simd.h"
 
-// There seems to be a bug in simd/base_simd.h
+// There seems to be a bug in sims/base.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
@@ -234,7 +234,6 @@ 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) {
@@ -252,13 +251,9 @@ 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;
-    // Raising an integer to a negative power is undefined
+    while (any(exp)) {
-    if (any(exp < 0)) {
+      res = select(exp & 1, res * base, res);
-      return 0;
+      base = select(exp, base * base, base);
-    }
-    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

@@ -88,33 +88,12 @@ DEFAULT_UNARY(expm1, std::expm1)
 DEFAULT_UNARY(floor, std::floor)
 DEFAULT_UNARY(log, std::log)
 DEFAULT_UNARY(log10, std::log10)
+DEFAULT_UNARY(log1p, std::log1p)
 DEFAULT_UNARY(sinh, std::sinh)
 DEFAULT_UNARY(sqrt, std::sqrt)
 DEFAULT_UNARY(tan, std::tan)
 DEFAULT_UNARY(tanh, std::tanh)
 
-template <typename T>
-Simd<T, 1> log1p(Simd<T, 1> in) {
-  if constexpr (is_complex<T>) {
-    auto x = in.value.real();
-    auto y = in.value.imag();
-    auto zabs = std::abs(in.value);
-    auto theta = std::atan2(y, x + 1);
-    if (zabs < 0.5) {
-      auto r = x * (2 + x) + y * y;
-      if (r == 0) { // handle underflow
-        return Simd<T, 1>{T{x, theta}};
-      }
-      return Simd<T, 1>{T{((typeof(x))(0.5)) * std::log1p(r), theta}};
-    } else {
-      auto z0 = std::hypot(x + 1, y);
-      return Simd<T, 1>{T{std::log(z0), theta}};
-    }
-  } else {
-    return Simd<T, 1>{std::log1p(in.value)};
-  }
-}
-
 template <typename T>
 Simd<T, 1> log2(Simd<T, 1> in) {
   if constexpr (is_complex<T>) {

mlx/backend/cpu/softmax.cpp

@@ -131,7 +131,8 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
       }
       return x;
     } else {
-      array x_copy = contiguous_copy_cpu(x, s);
+      array x_copy(x.shape(), x.dtype(), nullptr, {});
+      copy(x, x_copy, CopyType::General, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }

mlx/backend/cpu/sort.cpp

@@ -8,25 +8,13 @@
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
-#include "mlx/dtype_utils.h"
+
 #include "mlx/primitives.h"
 
 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;
@@ -142,7 +130,7 @@ 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, nan_aware_less<T>);
+    std::stable_sort(st, ed);
     src_it.step();
   }
 }
@@ -196,15 +184,6 @@ 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);
     });
   }
@@ -240,7 +219,7 @@ 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, nan_aware_less<T>);
+    std::nth_element(st, md, ed);
   }
 }
 
@@ -297,15 +276,6 @@ 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);
     });
   }
@@ -363,24 +333,45 @@ void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  int axis = axis_;
-  if (axis < 0) {
-    axis += in.ndim();
-  }
-
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis] != 0)
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-      ? CopyType::Vector
+  copy(in, out, ctype, stream());
-      : CopyType::General;
-  copy_cpu(in, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_output_array(out);
-  encoder.dispatch([out = array::unsafe_weak_copy(out), axis]() mutable {
+  encoder.dispatch(
-    dispatch_all_types(out.dtype(), [&](auto type_tag) {
+      [out = array::unsafe_weak_copy(out), axis_ = axis_]() mutable {
-      sort<MLX_GET_TYPE(type_tag)>(out, axis);
+        switch (out.dtype()) {
-    });
+          case bool_:
-  });
+            return sort<bool>(out, axis_);
+          case uint8:
+            return sort<uint8_t>(out, axis_);
+          case uint16:
+            return sort<uint16_t>(out, axis_);
+          case uint32:
+            return sort<uint32_t>(out, axis_);
+          case uint64:
+            return sort<uint64_t>(out, axis_);
+          case int8:
+            return sort<int8_t>(out, axis_);
+          case int16:
+            return sort<int16_t>(out, axis_);
+          case int32:
+            return sort<int32_t>(out, axis_);
+          case int64:
+            return sort<int64_t>(out, axis_);
+          case float32:
+            return sort<float>(out, axis_);
+          case float64:
+            return sort<double>(out, axis_);
+          case float16:
+            return sort<float16_t>(out, axis_);
+          case bfloat16:
+            return sort<bfloat16_t>(out, axis_);
+          case complex64:
+            return sort<complex64_t>(out, axis_);
+        }
+      });
 }
 
 void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -435,10 +426,8 @@ void Partition::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-      ? CopyType::Vector
+  copy(in, out, ctype, stream());
-      : CopyType::General;
-  copy_cpu(in, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_output_array(out);

mlx/backend/cpu/svd.cpp

@@ -31,7 +31,7 @@ void svd_impl(
 
   // lapack clobbers the input, so we have to make a copy.
   array in(a.shape(), a.dtype(), nullptr, {});
-  copy_cpu(
+  copy(
       a,
       in,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
@@ -81,7 +81,9 @@ void svd_impl(
     // Vᵀ of shape N x N. (M x M in lapack).
     const int ldvt = M;
 
-    auto jobz = (u_ptr) ? "A" : "N";
+    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;
@@ -89,20 +91,30 @@ void svd_impl(
 
     // 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) * 8 * K)};
+    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.
-    gesdd<T>(
+    gesvdx<T>(
-        /* jobz = */ jobz,
+        /* 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,
@@ -124,13 +136,20 @@ void svd_impl(
 
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {
-      gesdd<T>(
+      gesvdx<T>(
-          /* jobz = */ jobz,
+          /* 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,
@@ -148,6 +167,13 @@ void svd_impl(
         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());
+      }
     }
   });
   encoder.add_temporary(in);

mlx/backend/cpu/unary.h

@@ -2,13 +2,32 @@
 
 #pragma once
 
-#include "mlx/backend/common/unary.h"
+#include "mlx/allocator.h"
+#include "mlx/array.h"
+#include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
 
+void set_unary_output_data(const array& in, array& out) {
+  if (in.flags().contiguous) {
+    if (is_donatable(in, out)) {
+      out.copy_shared_buffer(in);
+    } else {
+      auto size = in.data_size();
+      out.set_data(
+          allocator::malloc(size * out.itemsize()),
+          size,
+          in.strides(),
+          in.flags());
+    }
+  } else {
+    out.set_data(allocator::malloc(out.nbytes()));
+  }
+}
+
 template <typename T, typename U = T, typename Op>
 void unary_op(const T* a, U* out, size_t shape, size_t stride) {
   for (size_t i = 0; i < shape; i += 1) {

mlx/backend/cpu/unary_ops.h

@@ -77,8 +77,7 @@ struct Real {
 struct Sigmoid {
   template <int N, typename T>
   Simd<T, N> operator()(Simd<T, N> x) {
-    auto y = 1.0f / (1.0f + simd::exp(simd::abs(x)));
+    return 1.0f / (1.0f + simd::exp(-x));
-    return simd::select(x < Simd<T, N>{0}, y, Simd<T, N>{1} - y);
   }
   SINGLE()
 };

mlx/backend/cuda/CMakeLists.txt

@@ -1,180 +0,0 @@
-# Filename rules in cuda backend:
-#
-# * Use .cu/.cuh if code contains device code, and .cpp/.h if not.
-# * Device-only code should be put in device/ subdir.
-# * Files in device/ subdir should not include files outside.
-target_sources(
-  mlx
-  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_two.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_contiguous.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
-          ${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}/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}/layer_norm.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/random.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/all_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/col_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/init_reduce.cu
-          ${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.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}/utils.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/affine_quantize.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
-
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/binary)
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/unary)
-
-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)
-else()
-  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_0.cpp)
-endif()
-
-target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
-
-# Embed kernel sources in binary for JIT compilation.
-file(
-  GLOB MLX_JIT_SOURCES
-  RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
-  "${CMAKE_CURRENT_SOURCE_DIR}/device/*.h"
-  "${CMAKE_CURRENT_SOURCE_DIR}/device/*.cuh")
-string(JOIN ":" MLX_JIT_SOURCES_ARG ${MLX_JIT_SOURCES})
-add_custom_command(
-  OUTPUT gen/cuda_jit_sources.h
-  COMMAND
-    ${CMAKE_COMMAND} -DMLX_SOURCE_ROOT=${CMAKE_CURRENT_SOURCE_DIR}
-    -DMLX_JIT_SOURCES=${MLX_JIT_SOURCES_ARG} -P
-    "${CMAKE_CURRENT_SOURCE_DIR}/bin2h.cmake"
-  DEPENDS bin2h.cmake ${MLX_JIT_SOURCES})
-add_custom_target(cuda_jit_sources DEPENDS gen/cuda_jit_sources.h)
-add_dependencies(mlx cuda_jit_sources)
-target_include_directories(mlx PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/gen")
-
-# Enable defining device lambda functions.
-target_compile_options(mlx
-                       PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
-
-# Enable calling host constexpr functions from device. This is needed because
-# the constexpr version of isnan is host only.
-target_compile_options(
-  mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>")
-
-# CUDA 12.8 emits warning #20280-D for copy kernels which is a false positive.
-# Explicitly pass this flag to suppress the warning, it is safe to set it to
-# true but the warning wouldn't be suppressed.
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
-  target_compile_options(
-    mlx
-    PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--static-global-template-stub=false>")
-endif()
-
-# Suppress warning when building for compute capability 7 used by V100.
-target_compile_options(
-  mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--Wno-deprecated-gpu-targets>")
-
-# Use stronger binaries compression. This feature was introduced in CUDA 12.8
-# and requires drivers released after CUDA 12.4.
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
-  target_compile_options(
-    mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--compress-mode=size>")
-endif()
-
-# Compute capability >= 7.0 is required for synchronization between CPU/GPU with
-# managed memory.
-if(NOT DEFINED MLX_CUDA_ARCHITECTURES)
-  set(MLX_CUDA_ARCHITECTURES "native")
-endif()
-message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
-set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
-                                     "${MLX_CUDA_ARCHITECTURES}")
-
-# Use fixed version of CCCL.
-FetchContent_Declare(
-  cccl
-  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")
-
-# Use fixed version of NVTX.
-FetchContent_Declare(
-  nvtx3
-  GIT_REPOSITORY https://github.com/NVIDIA/NVTX.git
-  GIT_TAG v3.1.1
-  GIT_SHALLOW TRUE
-  SOURCE_SUBDIR c EXCLUDE_FROM_ALL)
-FetchContent_MakeAvailable(nvtx3)
-target_link_libraries(mlx PUBLIC $<BUILD_INTERFACE:nvtx3-cpp>)
-
-# Make cuda runtime APIs available in non-cuda files.
-find_package(CUDAToolkit REQUIRED)
-target_include_directories(mlx PRIVATE ${CUDAToolkit_INCLUDE_DIRS})
-
-# Use cublasLt.
-target_link_libraries(mlx PRIVATE CUDA::cublasLt)
-
-# Use NVRTC and driver APIs.
-target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
-
-# Use the frontend APIs of cuDNN.
-FetchContent_Declare(
-  cudnn
-  GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
-  GIT_TAG v1.14.0
-  GIT_SHALLOW TRUE
-  EXCLUDE_FROM_ALL)
-set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
-set(CUDNN_FRONTEND_BUILD_SAMPLES OFF)
-set(CUDNN_FRONTEND_BUILD_TESTS OFF)
-set(CUDNN_FRONTEND_BUILD_PYTHON_BINDINGS OFF)
-FetchContent_MakeAvailable(cudnn)
-target_link_libraries(mlx PRIVATE cudnn_frontend)
-# Link with the actual cuDNN libraries.
-include(${cudnn_frontend_SOURCE_DIR}/cmake/cuDNN.cmake)
-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>)
-# 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)
-
-# Install CCCL headers for JIT.
-install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
-        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)

mlx/backend/cuda/allocator.cpp

@@ -1,270 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/allocator.h"
-#include "mlx/backend/cuda/utils.h"
-#include "mlx/utils.h"
-
-#include <cuda_runtime.h>
-#include <fmt/format.h>
-#include <unistd.h>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-constexpr int page_size = 16384;
-
-// Any allocations smaller than this will try to use the small pool
-constexpr int small_block_size = 8;
-
-// The small pool size in bytes. This should be a multiple of the host page
-// size and small_block_size.
-constexpr int small_pool_size = 4 * page_size;
-
-SmallSizePool::SmallSizePool() {
-  auto num_blocks = small_pool_size / small_block_size;
-  buffer_ = new Block[num_blocks];
-
-  next_free_ = buffer_;
-
-  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
-
-  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) {
-    curr->next = buffer_ + i;
-    curr = curr->next;
-  }
-  curr->next = nullptr;
-}
-
-SmallSizePool::~SmallSizePool() {
-  CHECK_CUDA_ERROR(cudaFree(data_));
-  delete[] buffer_;
-}
-
-CudaBuffer* SmallSizePool::malloc() {
-  if (next_free_ == nullptr) {
-    return nullptr;
-  }
-  Block* b = next_free_;
-  uint64_t i = next_free_ - buffer_;
-  next_free_ = next_free_->next;
-  b->buf.data = static_cast<char*>(data_) + i * small_block_size;
-  b->buf.size = small_block_size;
-  return &b->buf;
-}
-
-void SmallSizePool::free(CudaBuffer* buf) {
-  auto b = reinterpret_cast<Block*>(buf);
-  b->next = next_free_;
-  next_free_ = b;
-}
-
-bool SmallSizePool::in_pool(CudaBuffer* buf) {
-  constexpr int num_blocks = (small_pool_size / small_block_size);
-  auto b = reinterpret_cast<Block*>(buf);
-  int64_t block_num = b - buffer_;
-  return block_num >= 0 && block_num < num_blocks;
-}
-
-CudaAllocator::CudaAllocator()
-    : buffer_cache_(
-          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 * 0.95;
-  max_pool_size_ = memory_limit_;
-}
-
-Buffer CudaAllocator::malloc(size_t size) {
-  // Find available buffer from cache.
-  auto orig_size = size;
-  std::unique_lock lock(mutex_);
-  if (size <= small_block_size) {
-    size = 8;
-  } else if (size < page_size) {
-    size = next_power_of_2(size);
-  } else {
-    size = page_size * ((size + page_size - 1) / page_size);
-  }
-
-  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.
-    int64_t mem_to_free =
-        get_active_memory() + get_cache_memory() + size - memory_limit_;
-    if (mem_to_free > 0) {
-      buffer_cache_.release_cached_buffers(mem_to_free);
-    }
-
-    // Try the scalar pool first
-    if (size <= small_block_size) {
-      buf = scalar_pool_.malloc();
-    }
-    lock.unlock();
-    if (!buf) {
-      buf = new CudaBuffer{nullptr, size};
-      cudaError_t err = cudaMallocManaged(&buf->data, size);
-      if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
-        throw std::runtime_error(fmt::format(
-            "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
-      }
-    }
-    lock.lock();
-  }
-  active_memory_ += 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_);
-  }
-  return Buffer{buf};
-}
-
-void CudaAllocator::free(Buffer buffer) {
-  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
-  if (!buf) {
-    return;
-  }
-
-  std::unique_lock lock(mutex_);
-  active_memory_ -= buf->size;
-  if (get_cache_memory() < max_pool_size_) {
-    buffer_cache_.recycle_to_cache(buf);
-  } else {
-    cuda_free(buf);
-  }
-}
-
-size_t CudaAllocator::size(Buffer buffer) const {
-  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
-  if (!buf) {
-    return 0;
-  }
-  return buf->size;
-}
-
-// This must be called with mutex_ aquired
-void CudaAllocator::cuda_free(CudaBuffer* buf) {
-  if (scalar_pool_.in_pool(buf)) {
-    scalar_pool_.free(buf);
-  } else {
-    cudaFree(buf->data);
-    delete buf;
-  }
-}
-
-size_t CudaAllocator::get_active_memory() const {
-  return active_memory_;
-}
-
-size_t CudaAllocator::get_peak_memory() const {
-  return peak_memory_;
-}
-
-void CudaAllocator::reset_peak_memory() {
-  std::lock_guard lock(mutex_);
-  peak_memory_ = 0;
-}
-
-size_t CudaAllocator::get_memory_limit() {
-  return memory_limit_;
-}
-
-size_t CudaAllocator::set_memory_limit(size_t limit) {
-  std::lock_guard lock(mutex_);
-  std::swap(limit, memory_limit_);
-  return limit;
-}
-
-size_t CudaAllocator::get_cache_memory() const {
-  return buffer_cache_.cache_size();
-}
-
-size_t CudaAllocator::set_cache_limit(size_t limit) {
-  std::lock_guard lk(mutex_);
-  std::swap(limit, max_pool_size_);
-  return limit;
-}
-
-void CudaAllocator::clear_cache() {
-  std::lock_guard lk(mutex_);
-  buffer_cache_.clear();
-}
-
-CudaAllocator& allocator() {
-  // By creating the |allocator_| on heap, the destructor of CudaAllocator
-  // will not be called on exit and buffers in the cache will be leaked. This
-  // can save some time at program exit.
-  static CudaAllocator* allocator_ = new CudaAllocator;
-  return *allocator_;
-}
-
-} // namespace cu
-
-namespace allocator {
-
-Allocator& allocator() {
-  return cu::allocator();
-}
-
-void* Buffer::raw_ptr() {
-  if (!ptr_) {
-    return nullptr;
-  }
-  return static_cast<cu::CudaBuffer*>(ptr_)->data;
-}
-
-} // namespace allocator
-
-size_t get_active_memory() {
-  return cu::allocator().get_active_memory();
-}
-size_t get_peak_memory() {
-  return cu::allocator().get_peak_memory();
-}
-void reset_peak_memory() {
-  return cu::allocator().reset_peak_memory();
-}
-size_t set_memory_limit(size_t limit) {
-  return cu::allocator().set_memory_limit(limit);
-}
-size_t get_memory_limit() {
-  return cu::allocator().get_memory_limit();
-}
-size_t get_cache_memory() {
-  return cu::allocator().get_cache_memory();
-}
-size_t set_cache_limit(size_t limit) {
-  return cu::allocator().set_cache_limit(limit);
-}
-void clear_cache() {
-  cu::allocator().clear_cache();
-}
-
-// Not supported in CUDA.
-size_t set_wired_limit(size_t) {
-  return 0;
-}
-
-} // namespace mlx::core

mlx/backend/cuda/allocator.h

@@ -1,77 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/allocator.h"
-#include "mlx/backend/common/buffer_cache.h"
-
-#include <mutex>
-#include <set>
-#include <utility>
-
-namespace mlx::core::cu {
-
-using allocator::Buffer;
-
-// Stores cuda-managed unified memory.
-struct CudaBuffer {
-  void* data;
-  size_t size;
-};
-
-class SmallSizePool {
- private:
-  union Block {
-    Block* next;
-    CudaBuffer buf;
-  };
-
-  Block* buffer_{nullptr};
-  void* data_{nullptr};
-  Block* next_free_{nullptr};
-
- public:
-  SmallSizePool();
-  ~SmallSizePool();
-
-  SmallSizePool(const SmallSizePool&) = delete;
-  SmallSizePool& operator=(const SmallSizePool&) = delete;
-
-  CudaBuffer* malloc();
-  void free(CudaBuffer* buf);
-  bool in_pool(CudaBuffer* buf);
-};
-
-class CudaAllocator : public allocator::Allocator {
- public:
-  Buffer malloc(size_t size) override;
-  void free(Buffer buffer) override;
-  size_t size(Buffer buffer) const override;
-
-  size_t get_active_memory() const;
-  size_t get_peak_memory() const;
-  void reset_peak_memory();
-  size_t get_memory_limit();
-  size_t set_memory_limit(size_t limit);
-  size_t get_cache_memory() const;
-  size_t set_cache_limit(size_t limit);
-  void clear_cache();
-
- private:
-  void cuda_free(CudaBuffer* buf);
-
-  CudaAllocator();
-  friend CudaAllocator& allocator();
-
-  std::mutex mutex_;
-  size_t memory_limit_;
-  size_t max_pool_size_;
-  BufferCache<CudaBuffer> buffer_cache_;
-  size_t active_memory_{0};
-  size_t peak_memory_{0};
-  SmallSizePool scalar_pool_;
-};
-
-CudaAllocator& allocator();
-
-} // namespace mlx::core::cu

mlx/backend/cuda/arange.cu

@@ -1,69 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-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
-
-void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Arange::eval_gpu");
-  if (out.size() == 0) {
-    return;
-  }
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cu::get_command_encoder(stream());
-  encoder.set_output_array(out);
-
-  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    using OutType = cuda_type_t<CTYPE>;
-    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,
-          out.data<OutType>(),
-          out.data_size(),
-          static_cast<CTYPE>(start_),
-          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_));
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/arg_reduce.cu

@@ -1,188 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T>
-struct IndexValPair {
-  uint32_t index;
-  T val;
-};
-
-template <typename T>
-struct ArgMin {
-  constexpr __device__ T init() {
-    return Limits<T>::max();
-  }
-
-  __device__ IndexValPair<T> operator()(
-      const IndexValPair<T>& best,
-      const IndexValPair<T>& current) {
-    if (best.val > current.val ||
-        (best.val == current.val && best.index > current.index)) {
-      return current;
-    } else {
-      return best;
-    }
-  }
-
-  template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
-    for (int i = 0; i < N; i++) {
-      if (vals[i] < best.val) {
-        best.val = vals[i];
-        best.index = offset + i;
-      }
-    }
-    return best;
-  }
-};
-
-template <typename T>
-struct ArgMax {
-  constexpr __device__ T init() {
-    return Limits<T>::min();
-  }
-
-  __device__ IndexValPair<T> operator()(
-      const IndexValPair<T>& best,
-      const IndexValPair<T>& current) {
-    if (best.val < current.val ||
-        (best.val == current.val && best.index > current.index)) {
-      return current;
-    } else {
-      return best;
-    }
-  }
-
-  template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
-    for (int i = 0; i < N; i++) {
-      if (vals[i] > best.val) {
-        best.val = vals[i];
-        best.index = offset + i;
-      }
-    }
-    return best;
-  }
-};
-
-template <typename T, typename Op, int BLOCK_DIM, int N_READS = 4>
-__global__ void arg_reduce_general(
-    const T* in,
-    uint32_t* out,
-    size_t size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides in_strides,
-    const __grid_constant__ Strides out_strides,
-    int32_t ndim,
-    int64_t axis_stride,
-    int32_t axis_size) {
-  auto block = cg::this_thread_block();
-
-  int64_t index = cg::this_grid().block_rank();
-  if (index >= size) {
-    return;
-  }
-
-  int64_t in_idx = elem_to_loc(index, shape.data(), in_strides.data(), ndim);
-  int64_t out_idx = elem_to_loc(index, shape.data(), out_strides.data(), ndim);
-  in += in_idx;
-
-  Op op;
-  T init = op.init();
-  IndexValPair<T> best{0, init};
-
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto tid = r * BLOCK_DIM + block.thread_index().x;
-    auto vals = load_vector<N_READS>(in, tid, axis_size, axis_stride, init);
-    best = op.reduce_many(best, vals, tid * N_READS);
-  }
-
-  typedef cub::BlockReduce<IndexValPair<T>, BLOCK_DIM> BlockReduceT;
-  __shared__ typename BlockReduceT::TempStorage temp;
-
-  best = BlockReduceT(temp).Reduce(best, op);
-
-  if (block.thread_rank() == 0) {
-    out[out_idx] = best.index;
-  }
-}
-
-} // namespace cu
-
-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();
-
-  // Prepare the shapes, strides and axis arguments.
-  Shape shape = remove_index(in.shape(), axis_);
-  Strides in_strides = remove_index(in.strides(), axis_);
-  Strides out_strides = out.ndim() == in.ndim()
-      ? remove_index(out.strides(), axis_)
-      : out.strides();
-  int64_t axis_stride = in.strides()[axis_];
-  int32_t axis_size = in.shape()[axis_];
-  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) {
-    using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    constexpr uint32_t N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      dim3 num_blocks = get_2d_grid_dims(out.shape(), out.strides());
-      auto kernel =
-          cu::arg_reduce_general<T, cu::ArgMax<T>, block_dim(), N_READS>;
-      if (reduce_type_ == ArgReduce::ArgMin) {
-        kernel = cu::arg_reduce_general<T, cu::ArgMin<T>, block_dim(), N_READS>;
-      }
-      encoder.add_kernel_node(
-          kernel,
-          num_blocks,
-          block_dim(),
-          0,
-          in.data<T>(),
-          out.data<uint32_t>(),
-          out.size(),
-          const_param(shape),
-          const_param(in_strides),
-          const_param(out_strides),
-          ndim,
-          axis_stride,
-          axis_size);
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/bin2h.cmake

@@ -1,150 +0,0 @@
-# Based on: https://github.com/sivachandran/cmake-bin2h
-#
-# Copyright 2020 Sivachandran Paramasivam
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-include(CMakeParseArguments)
-
-# Function to wrap a given string into multiple lines at the given column
-# position.
-#
-# Parameters:
-#
-# * VARIABLE - The name of the CMake variable holding the string.
-# * AT_COLUMN - The column position at which string will be wrapped.
-function(WRAP_STRING)
-  set(oneValueArgs VARIABLE AT_COLUMN)
-  cmake_parse_arguments(WRAP_STRING "${options}" "${oneValueArgs}" "" ${ARGN})
-
-  string(LENGTH ${${WRAP_STRING_VARIABLE}} stringLength)
-  math(EXPR offset "0")
-
-  while(stringLength GREATER 0)
-    if(stringLength GREATER ${WRAP_STRING_AT_COLUMN})
-      math(EXPR length "${WRAP_STRING_AT_COLUMN}")
-    else()
-      math(EXPR length "${stringLength}")
-    endif()
-
-    string(SUBSTRING ${${WRAP_STRING_VARIABLE}} ${offset} ${length} line)
-    set(lines "${lines}\n ${line}")
-
-    math(EXPR stringLength "${stringLength} - ${length}")
-    math(EXPR offset "${offset} + ${length}")
-  endwhile()
-
-  set(${WRAP_STRING_VARIABLE}
-      "${lines}"
-      PARENT_SCOPE)
-endfunction()
-
-# Function to embed contents of a file as byte array in C/C++ header file(.h).
-# The header file will contain a byte array and integer variable holding the
-# size of the array.
-#
-# Parameters:
-#
-# * SOURCE_FILES - The paths of source files whose contents will be embedded in
-#   the header file.
-# * VARIABLE_NAME - The name of the variable for the byte array. The string
-#   "_SIZE" will be append to this name and will be used a variable name for
-#   size variable.
-# * HEADER_FILE - The path of header file.
-# * APPEND - If specified appends to the header file instead of overwriting it
-# * HEADER_NAMESPACE - The namespace, where the array should be located in.
-# * NULL_TERMINATE - If specified a null byte(zero) will be append to the byte
-#   array.
-#
-# Usage:
-#
-# bin2h(SOURCE_FILE "Logo.png" HEADER_FILE "Logo.h" VARIABLE_NAME "LOGO_PNG")
-function(BIN2H)
-  set(options APPEND NULL_TERMINATE)
-  set(oneValueArgs VARIABLE_NAME HEADER_FILE HEADER_NAMESPACE)
-  set(multiValueArgs SOURCE_FILES)
-  cmake_parse_arguments(BIN2H "${options}" "${oneValueArgs}"
-                        "${multiValueArgs}" ${ARGN})
-
-  set(arrayDefinition "")
-  foreach(SOURCE_FILE IN LISTS BIN2H_SOURCE_FILES)
-    # get filename without extension
-    get_filename_component(FILE_NAME_WE ${SOURCE_FILE} NAME_WE)
-    # convert the filename to a valid C identifier
-    string(MAKE_C_IDENTIFIER "${FILE_NAME_WE}" VALID_FILE_NAME)
-
-    # reads source file contents as hex string
-    file(READ ${SOURCE_FILE} hexString HEX)
-
-    # append null
-    if(BIN2H_NULL_TERMINATE)
-      string(APPEND hexString "00")
-    endif()
-
-    # wraps the hex string into multiple lines
-    wrap_string(VARIABLE hexString AT_COLUMN 24)
-
-    # strip the © in source code
-    string(REGEX REPLACE "c2a9" "2020" arrayValues ${hexString})
-
-    string(REGEX REPLACE "([0-9a-f][0-9a-f])" " 0x\\1," arrayValues
-                         ${arrayValues})
-
-    # make a full variable name for the array
-    set(FULL_VARIABLE_NAME "${BIN2H_VARIABLE_NAME}_${VALID_FILE_NAME}")
-
-    # declares byte array and the length variables
-    string(APPEND arrayDefinition
-           "constexpr char ${FULL_VARIABLE_NAME}[] = {${arrayValues}\n};\n\n")
-  endforeach()
-
-  # add namespace wrapper if defined
-  if(DEFINED BIN2H_HEADER_NAMESPACE)
-    set(namespaceStart "namespace ${BIN2H_HEADER_NAMESPACE} {")
-    set(namespaceEnd "} // namespace ${BIN2H_HEADER_NAMESPACE}")
-    set(declarations "${namespaceStart}\n\n${arrayDefinition}${namespaceEnd}\n")
-  endif()
-
-  set(arrayIncludes "#pragma once")
-  string(PREPEND declarations "${arrayIncludes}\n\n")
-
-  if(BIN2H_APPEND)
-    file(APPEND ${BIN2H_HEADER_FILE} "${declarations}")
-  else()
-    file(WRITE ${BIN2H_HEADER_FILE} "${declarations}")
-  endif()
-endfunction()
-
-# ----------------------------- CLI args -----------------------------
-
-string(REPLACE ":" ";" MLX_JIT_SOURCES_LIST ${MLX_JIT_SOURCES})
-foreach(source ${MLX_JIT_SOURCES_LIST})
-  list(APPEND MLX_JIT_SOURCES_ABS "${MLX_SOURCE_ROOT}/${source}")
-endforeach()
-
-bin2h(
-  SOURCE_FILES
-  ${MLX_JIT_SOURCES_ABS}
-  NULL_TERMINATE
-  VARIABLE_NAME
-  "jit_source"
-  HEADER_NAMESPACE
-  "mlx::core"
-  HEADER_FILE
-  "${CMAKE_CURRENT_BINARY_DIR}/gen/cuda_jit_sources.h")

mlx/backend/cuda/binary/CMakeLists.txt

@@ -1,21 +0,0 @@
-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

@@ -1,7 +0,0 @@
-// 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

@@ -1,7 +0,0 @@
-// 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

@@ -1,379 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/binary.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/binary_ops.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (int i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(a[0], b[0]);
-    }
-  } else {
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a[0], b[0]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (IdxT i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(a[0], b[i]);
-    }
-  } else {
-    auto b_vec = load_vector<N_READS>(b, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a[0], b_vec[i]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (IdxT i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(a[i], b[0]);
-    }
-  } else {
-    auto a_vec = load_vector<N_READS>(a, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a_vec[i], b[0]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (IdxT i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(a[i], b[i]);
-    }
-  } else {
-    auto a_vec = load_vector<N_READS>(a, index);
-    auto b_vec = load_vector<N_READS>(b, index);
-
-    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<N_READS>(out, index, out_vec);
-  }
-}
-
-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_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) {
-  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, int N_READS>
-__global__ void binary_g(
-    const In* a,
-    const In* b,
-    Out* out,
-    IdxT size_rest,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides a_strides,
-    const __grid_constant__ Strides b_strides,
-    int ndim) {
-  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>
-constexpr bool supports_binary_op() {
-  if (std::is_same_v<Op, Add> || std::is_same_v<Op, Divide> ||
-      std::is_same_v<Op, Maximum> || std::is_same_v<Op, Minimum> ||
-      std::is_same_v<Op, Multiply> || std::is_same_v<Op, Subtract> ||
-      std::is_same_v<Op, Power> || std::is_same_v<Op, Remainder>) {
-    return std::is_same_v<In, Out>;
-  }
-  if (std::is_same_v<Op, Equal> || std::is_same_v<Op, Greater> ||
-      std::is_same_v<Op, GreaterEqual> || std::is_same_v<Op, Less> ||
-      std::is_same_v<Op, LessEqual> || std::is_same_v<Op, NotEqual>) {
-    return std::is_same_v<Out, bool>;
-  }
-  if (std::is_same_v<Op, LogicalAnd> || std::is_same_v<Op, LogicalOr>) {
-    return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
-  }
-  if (std::is_same_v<Op, NaNEqual>) {
-    return std::is_same_v<Out, bool> && is_inexact_v<In>;
-  }
-  if (std::is_same_v<Op, LogAddExp>) {
-    return std::is_same_v<In, Out> && is_inexact_v<In>;
-  }
-  if (std::is_same_v<Op, ArcTan2>) {
-    return std::is_same_v<In, Out> && is_floating_v<In>;
-  }
-  if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
-      std::is_same_v<Op, BitwiseXor>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In>;
-  }
-  if (std::is_same_v<Op, LeftShift> || std::is_same_v<Op, RightShift>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
-        !std::is_same_v<In, bool>;
-  }
-  return false;
-}
-
-} // namespace cu
-
-template <typename Op>
-void binary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  assert(inputs.size() > 1);
-  const auto& a = inputs[0];
-  const auto& b = inputs[1];
-  if (out.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);
-  dispatch_all_types(a.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        using InType = cuda_type_t<CTYPE_IN>;
-        using OutType = cuda_type_t<CTYPE_OUT>;
-        auto bopt = get_binary_op_type(a, b);
-        if (bopt == BinaryOpType::General) {
-          dispatch_bool(
-              a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
-                  out.data_size() > INT32_MAX,
-              [&](auto large) {
-                using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-                Shape shape;
-                std::vector<Strides> strides;
-                std::tie(shape, strides) = collapse_contiguous_dims(a, b, out);
-                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 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(
-                        kernel,
-                        {num_blocks_x, num_blocks_y},
-                        block_dims,
-                        0,
-                        a.data<InType>(),
-                        b.data<InType>(),
-                        out.data<OutType>(),
-                        rest,
-                        const_param<dims_constant()>(shape),
-                        const_param<dims_constant()>(a_strides),
-                        const_param<dims_constant()>(b_strides));
-                  });
-                } else {
-                  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(
-                      kernel,
-                      {num_blocks_x, num_blocks_y},
-                      block_dims,
-                      0,
-                      a.data<InType>(),
-                      b.data<InType>(),
-                      out.data<OutType>(),
-                      rest,
-                      const_param(shape),
-                      const_param(a_strides),
-                      const_param(b_strides),
-                      ndim);
-                }
-              });
-        } else {
-          dispatch_bool(out.data_size() > UINT32_MAX, [&](auto large) {
-            using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            constexpr int N_READS = 16 / sizeof(InType);
-            auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
-            if (bopt == BinaryOpType::ScalarVector) {
-              kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
-            } else if (bopt == BinaryOpType::VectorScalar) {
-              kernel = cu::binary_vs<Op, InType, OutType, IdxT, N_READS>;
-            } else if (bopt == BinaryOpType::VectorVector) {
-              kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
-            }
-            auto [num_blocks, block_dims] = get_launch_args(
-                out.data_size(), out.shape(), out.strides(), large(), N_READS);
-            encoder.add_kernel_node(
-                kernel,
-                num_blocks,
-                block_dims,
-                0,
-                a.data<InType>(),
-                b.data<InType>(),
-                out.data<OutType>(),
-                out.data_size());
-          });
-        }
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do binary op {} on inputs of {} with result of {}.",
-            op,
-            dtype_to_string(a.dtype()),
-            dtype_to_string(out.dtype())));
-      }
-    });
-  });
-}
-
-template <typename Op>
-void binary_op_gpu(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  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);
-  binary_op_gpu_inplace<Op>(inputs, out, op, s);
-}
-
-#define BINARY_GPU(func)                                              \
-  void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
-    nvtx3::scoped_range r(#func "::eval_gpu");                        \
-    auto& s = out.primitive().stream();                               \
-    binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
-  }
-
-} // namespace mlx::core

mlx/backend/cuda/binary/bitwise_binary.cu

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