Fix the sig_handler check

.circleci/config.yml

@@ -7,6 +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:
@@ -15,8 +24,8 @@ jobs:
         type: boolean
         default: false
     macos:
-      xcode: "16.2.0"
-    resource_class: m2pro.medium
+      xcode: "15.2.0"
+    resource_class: macos.m1.medium.gen1
     steps:
       - checkout
       - run:
@@ -29,7 +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 >>
@@ -61,9 +70,9 @@ jobs:
                  git push -f origin gh-pages
 
   linux_build_and_test:
-    machine:
-      image: ubuntu-2204:current
-      resource_class: large
+    docker:
+      - image: cimg/python:3.9
+
     steps:
       - checkout
       - run:
@@ -75,34 +84,34 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            sudo apt-get update
-            sudo apt-get upgrade -y
             pip install --upgrade cmake
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
+            pip install nanobind==2.4.0
+            pip install numpy
+            sudo apt-get update
+            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
       - run:
           name: Install Python package
           command: |
-            pip install -e ".[dev]"
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python3 setup.py build_ext --inplace
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python3 setup.py develop
       - run:
           name: Generate package stubs
           command: |
             echo "stubs"
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
-            python -m unittest discover python/tests -v
-            mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
-            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
+            python3 -m unittest discover python/tests -v
       - run:
           name: Build CPP only
           command: |
-            mkdir -p build && cd build
+            mkdir -p build && cd build 
             cmake .. -DMLX_BUILD_METAL=OFF -DCMAKE_BUILD_TYPE=DEBUG
             make -j `nproc`
       - run:
@@ -113,15 +122,10 @@ jobs:
     parameters:
       xcode_version:
         type: string
-        default: "16.2.0"
-      macosx_deployment_target:
-        type: string
-        default: ""
+        default: "15.2.0"
     macos:
       xcode: << parameters.xcode_version >>
-    environment:
-      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    resource_class: m2pro.medium
+    resource_class: macos.m1.medium.gen1
     steps:
       - checkout
       - run:
@@ -142,14 +146,13 @@ jobs:
           name: Install Python package
           command: |
             source env/bin/activate
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              pip install -e . -v
+            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install -e . -v
       - run:
           name: Generate package stubs
           command: |
             source env/bin/activate
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
@@ -157,8 +160,7 @@ jobs:
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
             mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
-            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
       - run:
           name: Build example extension
           command: |
@@ -193,34 +195,13 @@ jobs:
           name: Run Python tests with JIT
           command: |
             source env/bin/activate
-            CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
               pip install -e . -v
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
               METAL_DEBUG_ERROR_MODE=0 \
               python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
 
-  cuda_build_and_test:
-    machine:
-      image: linux-cuda-12:2023.11.1
-      resource_class: gpu.nvidia.small.gen2
-    steps:
-      - checkout
-      - run:
-          name: Install Python package
-          command: |
-            sudo apt-get update
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            python3 -m venv env
-            source env/bin/activate
-            CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              pip install -e ".[dev]"
-      - run:
-          name: Run Python tests
-          command: |
-            source env/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
-
   build_release:
     parameters:
       python_version:
@@ -228,18 +209,13 @@ jobs:
         default: "3.9"
       xcode_version:
         type: string
-        default: "16.2.0"
+        default: "15.2.0"
       build_env:
         type: string
         default: ""
-      macosx_deployment_target:
-        type: string
-        default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: m2pro.medium
-    environment:
-      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
+    resource_class: macos.m1.medium.gen1
     steps:
       - checkout
       - run:
@@ -260,30 +236,22 @@ jobs:
           name: Install Python package
           command: |
             source env/bin/activate
-            env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
+            DEV_RELEASE=1 \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
               pip install . -v
       - run:
           name: Generate package stubs
           command: |
             source env/bin/activate
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Build Python package
           command: |
             source env/bin/activate
-            python setup.py clean --all
-            << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  source env/bin/activate
-                  python setup.py clean --all
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 python -m build -w
+            << parameters.build_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              python -m build -w
       - when:
           condition: << parameters.build_env >>
           steps:
@@ -300,100 +268,52 @@ jobs:
       python_version:
         type: string
         default: "3.9"
-      build_env:
+      extra_env:
         type: string
-        default: ""
-    machine:
-      image: ubuntu-2204:current
-      resource_class: large
+        default: "DEV_RELEASE=1"
+    docker:
+      - image: ubuntu:20.04
     steps:
       - checkout
       - run:
           name: Build wheel
           command: |
             PYTHON=python<< parameters.python_version >>
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            sudo apt-get update
-            sudo apt-get upgrade -y
-            TZ=Etc/UTC sudo apt-get -y install tzdata
-            sudo apt-get install -y apt-utils
-            sudo apt-get install -y software-properties-common
-            sudo add-apt-repository -y ppa:deadsnakes/ppa
-            sudo apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install -y build-essential git
+            apt-get update
+            apt-get upgrade -y
+            DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata
+            apt-get install -y apt-utils
+            apt-get install -y software-properties-common
+            add-apt-repository -y ppa:deadsnakes/ppa
+            apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
+            apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            apt-get install -y build-essential git
             $PYTHON -m venv env
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install --upgrade setuptools
+            pip install numpy
             pip install auditwheel
             pip install patchelf
             pip install build
             pip install twine
-            << parameters.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
-            MLX_BUILD_STAGE=1 << parameters.build_env >> python -m build -w
-            bash python/scripts/repair_linux.sh
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  source env/bin/activate
-                  python setup.py clean --all
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 \
-                    python -m build -w
-                  auditwheel repair dist/mlx_cpu*.whl --plat manylinux_2_35_x86_64
-      - when:
-          condition: << parameters.build_env >>
-          steps:
-            - run:
-                name: Upload packages
-                command: |
-                  source env/bin/activate
-                  twine upload wheelhouse/*.whl
-      - store_artifacts:
-          path: wheelhouse/
-
-  build_cuda_release:
-    parameters:
-      build_env:
-        type: string
-        default: ""
-    machine:
-      image: linux-cuda-12:2024.11.1
-      resource_class: gpu.nvidia.small.gen2
-    steps:
-      - checkout
+            python setup.py generate_stubs 
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python -m build --wheel
+            auditwheel show dist/*
+            auditwheel repair dist/* --plat manylinux_2_31_x86_64
       - run:
-          name: Build wheel
+          name: Upload package
           command: |
-            sudo apt-get update
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install zip
-            python -m venv env
             source env/bin/activate
-            pip install auditwheel
-            pip install patchelf
-            pip install build
-            pip install twine
-            << 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: |
-                  source env/bin/activate
-                  twine upload wheelhouse/*.whl
+            twine upload wheelhouse/*
       - store_artifacts:
           path: wheelhouse/
 
@@ -405,19 +325,22 @@ 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", "14.0"]
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
       - linux_build_and_test
-      - cuda_build_and_test 
       - build_documentation 
 
   build_pypi_release:
     when:
       and:
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
+        - not: << pipeline.parameters.test_release >>
     jobs:
       - build_release:
           filters:
@@ -428,70 +351,8 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
+              xcode_version: ["15.0.0", "15.2.0"]
               build_env: ["PYPI_RELEASE=1"]
-              xcode_version: ["16.2.0", "15.0.0"]
-            exclude:
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-                build_env: "PYPI_RELEASE=1"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-                build_env: "PYPI_RELEASE=1"
       - build_documentation:
           filters:
             tags:
@@ -499,25 +360,6 @@ 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:
@@ -533,11 +375,9 @@ workflows:
           requires: [ hold ]
           matrix:
             parameters:
-              macosx_deployment_target: ["13.5", "14.0"]
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
       - linux_build_and_test:
           requires: [ hold ]
-      - cuda_build_and_test:
-          requires: [ hold ]
   nightly_build:
     when:
       and:
@@ -548,56 +388,27 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              xcode_version: ["16.2.0", "15.0.0"]
-            exclude:
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "13.5"
-                xcode_version: "16.2.0"
-                python_version: "3.13"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "14.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.9"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.10"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.11"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.12"
-              - macosx_deployment_target: "15.0"
-                xcode_version: "15.0.0"
-                python_version: "3.13"
+              xcode_version: ["15.0.0", "15.2.0"]
+  weekly_build:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.weekly_build >>
+    jobs:
+      - build_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              xcode_version: ["15.0.0", "15.2.0", "16.0.0"]
+              build_env: ["DEV_RELEASE=1"]
+  linux_test_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.linux_release >>
+    jobs:
       - build_linux_release:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-      - build_cuda_release
+              extra_env: ["PYPI_RELEASE=1"]

.gitignore

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

ACKNOWLEDGMENTS.md

@@ -19,7 +19,6 @@ 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.
 
 <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" />

CMakeLists.txt

@@ -9,7 +9,6 @@ if(NOT MLX_VERSION)
   string(REGEX MATCH "#define MLX_VERSION_PATCH ([0-9]+)" _ "${_mlx_h_version}")
   set(_patch ${CMAKE_MATCH_1})
   set(MLX_PROJECT_VERSION "${_major}.${_minor}.${_patch}")
-  set(MLX_VERSION ${MLX_PROJECT_VERSION})
 else()
   string(REGEX REPLACE "^([0-9]+\.[0-9]+\.[0-9]+).*" "\\1" MLX_PROJECT_VERSION
                        ${MLX_VERSION})
@@ -22,7 +21,7 @@ project(
 
 # ----------------------------- Setup -----------------------------
 set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
-set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 set(CMAKE_INSTALL_MESSAGE NEVER)
@@ -34,7 +33,6 @@ 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)
@@ -43,6 +41,8 @@ option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
+add_compile_definitions("MLX_VERSION=${MLX_VERSION}")
+
 # --------------------- Processor tests -------------------------
 message(
   STATUS
@@ -64,8 +64,10 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
       message(WARNING "Building for x86_64 arch is not officially supported.")
     endif()
   endif()
+
 else()
   set(MLX_BUILD_METAL OFF)
+  message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
 endif()
 
 # ----------------------------- Lib -----------------------------
@@ -75,6 +77,7 @@ include(FetchContent)
 cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
+set_target_properties(mlx PROPERTIES COMPILE_WARNING_AS_ERROR ON)
 
 if(MLX_BUILD_METAL)
   set(METAL_LIB "-framework Metal")
@@ -82,10 +85,6 @@ if(MLX_BUILD_METAL)
   set(QUARTZ_LIB "-framework QuartzCore")
 endif()
 
-if(MLX_BUILD_CUDA)
-  enable_language(CUDA)
-endif()
-
 if(MLX_BUILD_METAL AND NOT METAL_LIB)
   message(STATUS "Metal not found. Unable to build GPU")
   set(MLX_BUILD_METAL OFF)
@@ -215,6 +214,24 @@ else()
   set(MLX_BUILD_ACCELERATE OFF)
 endif()
 
+find_package(MPI)
+if(MPI_FOUND)
+  execute_process(
+    COMMAND zsh "-c" "mpirun --version"
+    OUTPUT_VARIABLE MPI_VERSION
+    ERROR_QUIET)
+  if(${MPI_VERSION} MATCHES ".*Open MPI.*")
+    target_include_directories(mlx PRIVATE ${MPI_INCLUDE_PATH})
+  elseif(MPI_VERSION STREQUAL "")
+    set(MPI_FOUND FALSE)
+    message(
+      WARNING "MPI found but mpirun is not available. Building without MPI.")
+  else()
+    set(MPI_FOUND FALSE)
+    message(WARNING "MPI which is not OpenMPI found. Building without MPI.")
+  endif()
+endif()
+
 message(STATUS "Downloading json")
 FetchContent_Declare(
   json
@@ -229,9 +246,6 @@ target_include_directories(
   mlx PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
              $<INSTALL_INTERFACE:include>)
 
-# Do not add mlx_EXPORTS define for shared library.
-set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
-
 FetchContent_Declare(
   fmt
   GIT_REPOSITORY https://github.com/fmtlib/fmt.git

CONTRIBUTING.md

@@ -5,26 +5,26 @@ possible.
 
 ## Pull Requests
 
-1. Fork and submit pull requests to the repo.
+1. Fork and submit pull requests to the repo. 
 2. If you've added code that should be tested, add tests.
 3. If a change is likely to impact efficiency, run some of the benchmarks before
    and after the change. Examples of benchmarks can be found in `benchmarks/python/`.
 4. If you've changed APIs, update the documentation.
-5. Every PR should have passing tests and at least one review.
+5. Every PR should have passing tests and at least one review. 
 6. For code formatting install `pre-commit` using something like `pip install pre-commit` and run `pre-commit install`.
    This should install hooks for running `black` and `clang-format` to ensure
    consistent style for C++ and python code.
-
+ 
    You can also run the formatters manually as follows:
-
-   ```shell
-   clang-format -i file.cpp
-   ```
-
-   ```shell
-   black file.py
-   ```
-
+ 
+     ```
+     clang-format -i file.cpp
+     ```
+ 
+     ```
+     black file.py
+     ```
+ 
    or run `pre-commit run --all-files` to check all files in the repo.
 
 ## Issues

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

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/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"
-    if torch.cuda.is_available():
-        device = "cuda"
-    if args.cpu:
-        device = "cpu"
+    device = "cpu" if args.cpu else "mps"
 
     types = args.dtype
     if not types:
@@ -475,8 +460,5 @@ if __name__ == "__main__":
     elif args.benchmark == "selu":
         print(bench(selu, x))
 
-    elif args.benchmark == "sum_and_add":
-        print(bench(sum_and_add, axis, *xs))
-
     else:
         raise ValueError(f"Unknown benchmark `{args.benchmark}`.")

benchmarks/python/conv_unaligned_bench.py

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

benchmarks/python/gather_bench.py

@@ -1,6 +1,7 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import argparse
+from time import time
 
 import mlx.core as mx
 import torch

benchmarks/python/gather_mm_bench.py

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

benchmarks/python/gather_qmm_bench.py

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

benchmarks/python/layer_norm_bench.py

@@ -1,7 +1,5 @@
 # Copyright © 2023-2024 Apple Inc.
 
-from functools import partial
-
 import mlx.core as mx
 import mlx.nn as nn
 from time_utils import time_fn
@@ -20,63 +18,51 @@ def layer_norm(x, w, b, eps):
     return y
 
 
-def time_layer_norm(N, dt):
-    L = 1024
+def time_layer_norm():
     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)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
-    y = 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=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    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):
-        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):
+    def layer_norm_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_grad_loop, g2, x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b)
+    time_fn(layer_norm_loop, g1, x, w, b)
+    time_fn(layer_norm_loop, g2, x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g1), 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)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
-    y = 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=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    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_grad_x_loop, g2, x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g1), x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g2), x)
+    time_fn(layer_norm_loop, g1, x)
+    time_fn(layer_norm_loop, g2, x)
+    time_fn(layer_norm_loop, mx.compile(g1), x)
+    time_fn(layer_norm_loop, mx.compile(g2), x)
 
 
 if __name__ == "__main__":
-    for dt in [mx.float32, mx.float16, mx.bfloat16]:
-        for n in [1024, 2048, 4096, 8192, 8192 + 1024]:
-            print(dt, n)
-            time_layer_norm(n, dt)
+    time_layer_norm()

benchmarks/python/sdpa_bench.py

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

benchmarks/python/single_ops.py

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

cmake/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
-# for this specific library. If not provided, uses global MLX_METAL_DEBUG.
+# files (like headers)
 #
 # clang format on
 
 macro(mlx_build_metallib)
   # Parse args
-  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY DEBUG)
+  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY)
   set(multiValueArgs SOURCES INCLUDE_DIRS DEPS)
   cmake_parse_arguments(MTLLIB "" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -27,10 +26,6 @@ macro(mlx_build_metallib)
 
   # Collect compile options
   set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math -Wno-c++17-extensions)
-  if(MLX_METAL_DEBUG OR MTLLIB_DEBUG)
-    set(MTLLIB_COMPILE_OPTIONS ${MTLLIB_COMPILE_OPTIONS} -gline-tables-only
-                               -frecord-sources)
-  endif()
 
   # Prepare metallib build command
   add_custom_command(

docs/Doxyfile

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

docs/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

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
-   signature is generated automatically.
+    We are only required to pass the body of the Metal kernel in ``source``.
 
 The full function signature will be generated using:
 
@@ -86,51 +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
-<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
-function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
-``threadgroup`` size threadgroups.  For optimal performance, each thread group
-dimension should be less than or equal to the corresponding grid dimension.
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
+This means we will launch ``mx.prod(grid)`` threads, subdivided into ``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
-generated code for debugging purposes.
+Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
 
 Using Shape/Strides
 -------------------
 
-:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
-is ``True`` by default. This will copy the array inputs if needed
-before the kernel is launched to ensure that the memory layout is row
-contiguous.  Generally this makes writing the kernel easier, since we don't
-have to worry about gaps or the ordering of the dims when indexing.
+``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
+This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
+Generally this makes writing the kernel easier, since we don't 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
-``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
-present in ``source``. We can then use MLX's built in indexing utils to fetch
-the right elements for each thread.
+If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
+input array ``a`` if any are present in ``source``.
+We can then use MLX's built in indexing utils to fetch the right elements for each thread.
 
-Let's convert ``myexp`` above to support arbitrarily strided arrays without
-relying on a copy from ``ensure_row_contiguous``:
+Let's convert ``myexp`` above to support arbitrarily strided arrays without 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
-  )
 
   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)],
@@ -157,139 +142,137 @@ We'll start with the following MLX implementation using standard ops:
 
 .. code-block:: python
 
-  def grid_sample_ref(x, grid):
-      N, H_in, W_in, _ = x.shape
-      ix = ((grid[..., 0] + 1) * W_in - 1) / 2
-      iy = ((grid[..., 1] + 1) * H_in - 1) / 2
+    def grid_sample_ref(x, grid):
+        N, H_in, W_in, _ = x.shape
+        ix = ((grid[..., 0] + 1) * W_in - 1) / 2
+        iy = ((grid[..., 1] + 1) * H_in - 1) / 2
 
-      ix_nw = mx.floor(ix).astype(mx.int32)
-      iy_nw = mx.floor(iy).astype(mx.int32)
+        ix_nw = mx.floor(ix).astype(mx.int32)
+        iy_nw = mx.floor(iy).astype(mx.int32)
 
-      ix_ne = ix_nw + 1
-      iy_ne = iy_nw
+        ix_ne = ix_nw + 1
+        iy_ne = iy_nw
 
-      ix_sw = ix_nw
-      iy_sw = iy_nw + 1
+        ix_sw = ix_nw
+        iy_sw = iy_nw + 1
 
-      ix_se = ix_nw + 1
-      iy_se = iy_nw + 1
+        ix_se = ix_nw + 1
+        iy_se = iy_nw + 1
 
-      nw = (ix_se - ix)    * (iy_se - iy)
-      ne = (ix    - ix_sw) * (iy_sw - iy)
-      sw = (ix_ne - ix)    * (iy    - iy_ne)
-      se = (ix    - ix_nw) * (iy    - iy_nw)
+        nw = (ix_se - ix)    * (iy_se - iy)
+        ne = (ix    - ix_sw) * (iy_sw - iy)
+        sw = (ix_ne - ix)    * (iy    - iy_ne)
+        se = (ix    - ix_nw) * (iy    - iy_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_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
-      I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
+        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_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
+        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_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_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= 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_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)
 
-      I_nw *= mask_nw[..., None]
-      I_ne *= mask_ne[..., None]
-      I_sw *= mask_sw[..., None]
-      I_se *= mask_se[..., None]
+        I_nw *= mask_nw[..., None]
+        I_ne *= mask_ne[..., None]
+        I_sw *= mask_sw[..., 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 = """
-      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];
+    @mx.custom_function
+    def grid_sample(x, grid):
 
-      int w_stride = C;
-      int h_stride = W * w_stride;
-      int b_stride = H * h_stride;
+        assert x.ndim == 4, "`x` must be 4D."
+        assert grid.ndim == 4, "`grid` must be 4D."
 
-      uint grid_idx = elem / C * 2;
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
+        out_shape = (B, gN, gM, C)
 
-      int ix_nw = floor(ix);
-      int iy_nw = floor(iy);
+        assert D == 2, "Last dim of `grid` must be size 2."
 
-      int ix_ne = ix_nw + 1;
-      int iy_ne = iy_nw;
+        source = """
+            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 iy_sw = iy_nw + 1;
+            int w_stride = C;
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_se = ix_nw + 1;
-      int iy_se = iy_nw + 1;
+            uint grid_idx = elem / C * 2;
+            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);
-      T ne = (ix    - ix_sw) * (iy_sw - iy);
-      T sw = (ix_ne - ix)    * (iy    - iy_ne);
-      T se = (ix    - ix_nw) * (iy    - iy_nw);
+            int ix_nw = floor(ix);
+            int iy_nw = floor(iy);
 
-      int batch_idx = elem / C / gH / gW * b_stride;
-      int channel_idx = elem % C;
-      int base_idx = batch_idx + channel_idx;
+            int ix_ne = ix_nw + 1;
+            int iy_ne = iy_nw;
 
-      T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
-      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];
+            int ix_sw = ix_nw;
+            int iy_sw = iy_nw + 1;
 
-      I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
-      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;
+            int ix_se = ix_nw + 1;
+            int iy_se = iy_nw + 1;
 
-      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(
-      name="grid_sample",
-      input_names=["x", "grid"],
-      output_names=["out"],
-      source=source,
-  )
+            int batch_idx = elem / C / gH / gW * b_stride;
+            int channel_idx = elem % C;
+            int base_idx = batch_idx + channel_idx;
 
-  @mx.custom_function
-  def grid_sample(x, grid):
+            T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
+            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."
-      assert grid.ndim == 4, "`grid` must be 4D."
+            I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
+            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
-      _, gN, gM, D = grid.shape
-      out_shape = (B, gN, gM, C)
-
-      assert D == 2, "Last dim of `grid` must be size 2."
-
-      outputs = kernel(
-          inputs=[x, grid],
-          template=[("T", x.dtype)],
-          output_shapes=[out_shape],
-          output_dtypes=[x.dtype],
-          grid=(np.prod(out_shape), 1, 1),
-          threadgroup=(256, 1, 1),
-      )
-      return outputs[0]
+            out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
+        """
+        kernel = mx.fast.metal_kernel(
+            name="grid_sample",
+            input_names=["x", "grid"],
+            output_names=["out"],
+            source=source,
+        )
+        outputs = kernel(
+            inputs=[x, grid],
+            template=[("T", x.dtype)],
+            output_shapes=[out_shape],
+            output_dtypes=[x.dtype],
+            grid=(np.prod(out_shape), 1, 1),
+            threadgroup=(256, 1, 1),
+        )
+        return outputs[0]
 
 For a reasonably sized input such as:
 
 .. code-block:: python
 
-  x.shape = (8, 1024, 1024, 64)
-  grid.shape = (8, 256, 256, 2)
+    x.shape = (8, 1024, 1024, 64)
+    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
-define its custom vjp transform so MLX can differentiate it.
+Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
+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 = """
-      uint elem = thread_position_in_grid.x;
-      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;
+    @grid_sample.vjp
+    def grid_sample_vjp(primals, cotangent, _):
+        x, grid = primals
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
 
-      int gH = grid_shape[1];
-      int gW = grid_shape[2];
+        assert D == 2, "Last dim of `grid` must be size 2."
 
-      int w_stride = C;
-      int h_stride = W * w_stride;
-      int b_stride = H * h_stride;
+        source = """
+            uint elem = thread_position_in_grid.x;
+            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;
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+            int gH = grid_shape[1];
+            int gW = grid_shape[2];
 
-      int ix_nw = floor(ix);
-      int iy_nw = floor(iy);
+            int w_stride = C;
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_ne = ix_nw + 1;
-      int iy_ne = iy_nw;
+            uint grid_idx = elem / C_padded * 2;
+            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 iy_sw = iy_nw + 1;
+            int ix_nw = floor(ix);
+            int iy_nw = floor(iy);
 
-      int ix_se = ix_nw + 1;
-      int iy_se = iy_nw + 1;
+            int ix_ne = ix_nw + 1;
+            int iy_ne = iy_nw;
 
-      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);
+            int ix_sw = ix_nw;
+            int iy_sw = iy_nw + 1;
 
-      int batch_idx = elem / C_padded / gH / gW * b_stride;
-      int channel_idx = elem % C_padded;
-      int base_idx = batch_idx + channel_idx;
+            int ix_se = ix_nw + 1;
+            int iy_se = iy_nw + 1;
 
-      T gix = T(0);
-      T giy = T(0);
-      if (channel_idx < C) {
-          int cot_index = elem / C_padded * C + channel_idx;
-          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 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);
 
-              T I_nw = x[offset];
-              gix -= I_nw * (iy_se - iy) * cot;
-              giy -= I_nw * (ix_se - ix) * cot;
-          }
-          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);
+            int batch_idx = elem / C_padded / gH / gW * b_stride;
+            int channel_idx = elem % C_padded;
+            int base_idx = batch_idx + channel_idx;
 
-              T I_ne = x[offset];
-              gix += I_ne * (iy_sw - iy) * 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 = T(0);
+            T giy = T(0);
+            if (channel_idx < C) {
+                int cot_index = elem / C_padded * C + channel_idx;
+                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_sw = x[offset];
-              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);
+                    T I_nw = x[offset];
+                    gix -= I_nw * (iy_se - iy) * cot;
+                    giy -= I_nw * (ix_se - ix) * cot;
+                }
+                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_se = x[offset];
-              gix += I_se * (iy - iy_nw) * cot;
-              giy += I_se * (ix - ix_nw) * cot;
-          }
-      }
+                    T I_ne = x[offset];
+                    gix += I_ne * (iy_sw - iy) * 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 giy_mult = H / 2;
+                    T I_sw = x[offset];
+                    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.
-      // This is much faster than relying purely on atomics.
-      gix = simd_sum(gix);
-      giy = simd_sum(giy);
+                    T I_se = x[offset];
+                    gix += I_se * (iy - iy_nw) * cot;
+                    giy += I_se * (ix - ix_nw) * cot;
+                }
+            }
 
-      if (thread_index_in_simdgroup == 0) {
-          atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
-          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,
-  )
+            T gix_mult = W / 2;
+            T giy_mult = H / 2;
 
-  @grid_sample.vjp
-  def grid_sample_vjp(primals, cotangent, _):
-      x, grid = primals
-      B, _, _, C = x.shape
-      _, gN, gM, D = grid.shape
+            // Reduce across each simdgroup first.
+            // This is much faster than relying purely on atomics.
+            gix = simd_sum(gix);
+            giy = simd_sum(giy);
 
-      assert D == 2, "Last dim of `grid` must be size 2."
-
-      # pad the output channels to simd group size
-      # so that our `simd_sum`s don't overlap.
-      simdgroup_size = 32
-      C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
-      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]
+            if (thread_index_in_simdgroup == 0) {
+                atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
+                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,
+        )
+        # pad the output channels to simd group size
+        # so that our `simd_sum`s don't overlap.
+        simdgroup_size = 32
+        C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
+        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

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

docs/src/index.rst

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

docs/src/install.rst

@@ -23,24 +23,13 @@ To install from PyPI you 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 use on any Linux platform with CUDA 12
-and SM 7.0 (Volta) and up. To install MLX with CUDA support, run:
+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
 
-CPU-only (Linux)
-^^^^^^^^^^^^^^^^
-
-For a CPU-only version of MLX that runs on Linux use:
-
-.. code-block:: shell
-
-    pip install "mlx[cpu]"
 
 Troubleshooting
 ^^^^^^^^^^^^^^^
@@ -76,8 +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>`_:
 
@@ -89,20 +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:
 
@@ -120,8 +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
@@ -200,7 +185,6 @@ should point to the path to the built metal library.
 
       xcrun -sdk macosx --show-sdk-version
 
-
 Binary Size Minimization
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -229,50 +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 -y
-
-
-When building either the Python or C++ APIs make sure to pass the cmake flag
-``MLX_BUILD_CUDA=ON``. For example, to build the Python API run:
-
-.. code-block:: shell
-
-  CMAKE_ARGS="-DMLX_BUILD_CUDA=ON" pip install -e ".[dev]"
-
-To build the C++ package run:
-
-.. code-block:: shell
-
-   mkdir -p build && cd build
-   cmake .. -DMLX_BUILD_CUDA=ON && make -j
-
-
 Troubleshooting
 ^^^^^^^^^^^^^^^
 

docs/src/python/array.rst

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

docs/src/python/fft.rst

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

docs/src/python/linalg.rst

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

docs/src/python/memory_management.rst

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

docs/src/python/metal.rst

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

docs/src/python/ops.rst

@@ -36,12 +36,10 @@ Operations
    bitwise_or
    bitwise_xor
    block_masked_mm
-   broadcast_arrays
    broadcast_to
    ceil
    clip
    concatenate
-   contiguous
    conj
    conjugate
    convolve
@@ -103,7 +101,6 @@ Operations
    log10
    log1p
    logaddexp
-   logcumsumexp
    logical_not
    logical_and
    logical_or

docs/src/python/optimizers/common_optimizers.rst

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

docs/src/python/transforms.rst

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

docs/src/usage/indexing.rst

@@ -107,16 +107,6 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
-
-Note, 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

@@ -72,7 +72,9 @@ void axpby_impl(
     float alpha_,
     float beta_,
     mx::Stream stream) {
-  out.set_data(mx::allocator::malloc(out.nbytes()));
+  // Allocate the output with `malloc_or_wait` which synchronously allocates
+  // memory, potentially waiting if the system is under memory pressure
+  out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
 
   // Get the CPU command encoder and register input and output arrays
   auto& encoder = mx::cpu::get_command_encoder(stream);
@@ -158,12 +160,12 @@ void Axpby::eval_gpu(
   // Allocate output memory with strides based on specialization
   if (contiguous_kernel) {
     out.set_data(
-        mx::allocator::malloc(x.data_size() * out.itemsize()),
+        mx::allocator::malloc_or_wait(x.data_size() * out.itemsize()),
         x.data_size(),
         x.strides(),
         x.flags());
   } else {
-    out.set_data(mx::allocator::malloc(out.nbytes()));
+    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)
@@ -172,11 +174,11 @@ void Axpby::eval_gpu(
   kname << (contiguous_kernel ? "contiguous_" : "general_");
   kname << type_to_name(out);
 
-  // Load the metal library
-  auto lib = d.get_library("mlx_ext");
+  // Make sure the metal library is available
+  d.register_library("mlx_ext");
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname.str(), 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. */
-  const char* name() const override {
-    return "Axpby";
+  /** Print the primitive. */
+  void print(std::ostream& os) override {
+    os << "Axpby";
   }
 
   /** Equivalence check **/

mlx/CMakeLists.txt

@@ -5,7 +5,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/compile.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/dtype.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/dtype_utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/export.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/einsum.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
@@ -18,13 +17,9 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/transforms.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/linalg.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
-# Define MLX_VERSION only in the version.cpp file.
-add_library(mlx_version OBJECT ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
-target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
-target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
-
 if(MSVC)
   # Disable some MSVC warnings to speed up compilation.
   target_compile_options(mlx PUBLIC /wd4068 /wd4244 /wd4267 /wd4804)
@@ -49,19 +44,5 @@ add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/io)
 if(MLX_BUILD_METAL)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/metal)
 else()
-  target_sources(mlx
-                 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(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_gpu)
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_metal)
 endif()

mlx/allocator.cpp

@@ -4,11 +4,12 @@
 #include <sstream>
 
 #include "mlx/allocator.h"
+#include "mlx/scheduler.h"
 
 namespace mlx::core::allocator {
 
 Buffer malloc(size_t size) {
-  auto buffer = allocator().malloc(size);
+  auto buffer = allocator().malloc(size, /* allow_swap */ true);
   if (size && !buffer.ptr()) {
     std::ostringstream msg;
     msg << "[malloc] Unable to allocate " << size << " bytes.";
@@ -21,4 +22,45 @@ void free(Buffer buffer) {
   allocator().free(buffer);
 }
 
+Buffer CommonAllocator::malloc(size_t size, bool) {
+  void* ptr = std::malloc(size + sizeof(size_t));
+  if (ptr != nullptr) {
+    *static_cast<size_t*>(ptr) = size;
+  }
+  return Buffer{ptr};
+}
+
+void CommonAllocator::free(Buffer buffer) {
+  std::free(buffer.ptr());
+}
+
+size_t CommonAllocator::size(Buffer buffer) const {
+  if (buffer.ptr() == nullptr) {
+    return 0;
+  }
+  return *static_cast<size_t*>(buffer.ptr());
+}
+
+Buffer malloc_or_wait(size_t size) {
+  auto buffer = allocator().malloc(size);
+
+  while (size && !buffer.ptr() && scheduler::n_active_tasks() > 0) {
+    scheduler::wait_for_one();
+    buffer = allocator().malloc(size);
+  }
+
+  // Try swapping if needed
+  if (size && !buffer.ptr()) {
+    buffer = allocator().malloc(size, /* allow_swap = */ true);
+  }
+
+  if (size && !buffer.ptr()) {
+    std::ostringstream msg;
+    msg << "[malloc_or_wait] Unable to allocate " << size << " bytes.";
+    throw std::runtime_error(msg.str());
+  }
+
+  return buffer;
+}
+
 } // namespace mlx::core::allocator

mlx/allocator.h

@@ -32,10 +32,14 @@ Buffer malloc(size_t size);
 
 void free(Buffer buffer);
 
+// Wait for running tasks to finish and free up memory
+// if allocation fails
+Buffer malloc_or_wait(size_t size);
+
 class Allocator {
   /** Abstract base class for a memory allocator. */
  public:
-  virtual Buffer malloc(size_t size) = 0;
+  virtual Buffer malloc(size_t size, bool allow_swap = false) = 0;
   virtual void free(Buffer buffer) = 0;
   virtual size_t size(Buffer buffer) const = 0;
 
@@ -49,4 +53,16 @@ class Allocator {
 
 Allocator& allocator();
 
+class CommonAllocator : public Allocator {
+  /** A general CPU allocator. */
+ public:
+  virtual Buffer malloc(size_t size, bool allow_swap = false) override;
+  virtual void free(Buffer buffer) override;
+  virtual size_t size(Buffer buffer) const override;
+
+ private:
+  CommonAllocator() = default;
+  friend Allocator& allocator();
+};
+
 } // namespace mlx::core::allocator

mlx/array.cpp

@@ -56,18 +56,6 @@ std::vector<array> array::make_arrays(
   return outputs;
 }
 
-array array::unsafe_weak_copy(const array& other) {
-  auto cpy = array(other.shape(), other.dtype(), nullptr, {});
-  cpy.set_data(
-      other.buffer(),
-      other.data_size(),
-      other.strides(),
-      other.flags(),
-      [](auto) {});
-  cpy.array_desc_->data_ptr = other.array_desc_->data_ptr;
-  return cpy;
-}
-
 array::array(std::initializer_list<float> data)
     : array_desc_(std::make_shared<ArrayDesc>(
           Shape{static_cast<ShapeElem>(data.size())},

mlx/array.h

@@ -199,13 +199,6 @@ class array {
       const std::shared_ptr<Primitive>& primitive,
       const std::vector<array>& inputs);
 
-  /**
-   * Get a new array that refers to the same data as the input but with a
-   * non-owning pointer to it. Note the array is detached from the graph and has
-   * no inputs, siblings or primitive.
-   */
-  static array unsafe_weak_copy(const array& other);
-
   /** A unique identifier for an array. */
   std::uintptr_t id() const {
     return reinterpret_cast<std::uintptr_t>(array_desc_.get());
@@ -224,10 +217,6 @@ 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);
     }
@@ -343,11 +332,11 @@ class array {
     return allocator::allocator().size(buffer());
   }
 
-  // Return the shared pointer to the array::Data struct
-  const std::shared_ptr<Data>& data_shared_ptr() const {
+  // Return a copy of the shared pointer
+  // to the array::Data struct
+  std::shared_ptr<Data> data_shared_ptr() const {
     return array_desc_->data;
   }
-
   // Return a raw pointer to the arrays data
   template <typename T>
   T* data() {
@@ -360,7 +349,7 @@ 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/CMakeLists.txt

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

mlx/backend/common/binary.h

@@ -44,14 +44,14 @@ inline void set_binary_op_output_data(
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
       out.set_data(
-          allocator::malloc(out.itemsize()), 1, a.strides(), a.flags());
+          allocator::malloc_or_wait(out.itemsize()), 1, a.strides(), a.flags());
       break;
     case BinaryOpType::ScalarVector:
       if (b_donatable) {
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc(b.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(b.data_size() * out.itemsize()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -62,7 +62,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(a);
       } else {
         out.set_data(
-            allocator::malloc(a.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -75,7 +75,7 @@ inline void set_binary_op_output_data(
         out.copy_shared_buffer(b);
       } else {
         out.set_data(
-            allocator::malloc(a.data_size() * out.itemsize()),
+            allocator::malloc_or_wait(a.data_size() * out.itemsize()),
             a.data_size(),
             a.strides(),
             a.flags());
@@ -88,7 +88,7 @@ inline void set_binary_op_output_data(
           b_donatable && b.flags().row_contiguous && b.size() == out.size()) {
         out.copy_shared_buffer(b);
       } else {
-        out.set_data(allocator::malloc(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
       }
       break;
   }

mlx/backend/common/broadcasting.cpp

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

mlx/backend/common/broadcasting.h

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

mlx/backend/common/buffer_cache.h

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

mlx/backend/common/common.cpp

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

mlx/backend/common/compiled.cpp

@@ -1,7 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/graph_utils.h"
+#include "mlx/primitives.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
@@ -14,8 +15,6 @@ void print_constant(std::ostream& os, const array& x) {
       return print_float_constant<float16_t>(os, x);
     case bfloat16:
       return print_float_constant<bfloat16_t>(os, x);
-    case float64:
-      return print_float_constant<double>(os, x);
     case complex64:
       return print_complex_constant<complex64_t>(os, x);
     case int8:
@@ -52,8 +51,6 @@ std::string get_type_string(Dtype d) {
       return "float16_t";
     case bfloat16:
       return "bfloat16_t";
-    case float64:
-      return "double";
     case complex64:
       return "complex64_t";
     case bool_:
@@ -82,6 +79,55 @@ std::string get_type_string(Dtype d) {
   }
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids) {
+  NodeNamer namer;
+  std::ostringstream os;
+  std::ostringstream constant_hasher;
+
+  // Fill the input names. This is not really necessary, I just like having A,
+  // B, C, ... as the inputs.
+  for (auto& x : inputs) {
+    namer.get_name(x);
+  }
+
+  // The primitives describing the tape. For unary and binary primitives this
+  // must be enough to describe the full computation.
+  for (auto& a : tape) {
+    // name and type of output
+    os << namer.get_name(a) << kindof(a.dtype()) << a.itemsize();
+    // computation performed
+    a.primitive().print(os);
+    // name of inputs to the function
+    for (auto& inp : a.inputs()) {
+      os << namer.get_name(inp);
+    }
+  }
+  os << "_";
+
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      os << "C";
+      print_constant(constant_hasher, x);
+    } else {
+      os << (is_scalar(x) ? "S" : "V");
+    }
+  }
+  os << "_";
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      continue;
+    }
+    os << kindof(x.dtype()) << x.itemsize();
+  }
+  os << "_" << std::hash<std::string>{}(constant_hasher.str());
+
+  return os.str();
+}
+
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
     const Shape& shape) {
@@ -113,7 +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
@@ -140,7 +188,7 @@ void compiled_allocate_outputs(
     }
     for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
-          allocator::malloc(data_size * outputs[o].itemsize()),
+          allocator::malloc_or_wait(data_size * outputs[o].itemsize()),
           data_size,
           strides,
           flags);
@@ -156,86 +204,16 @@ void compiled_allocate_outputs(
       // - Not a constant
       if (in.flags().row_contiguous && in.size() == outputs[o].size() &&
           in.itemsize() == outputs[o].itemsize() && in.is_donatable() &&
-          is_constant(i)) {
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o].copy_shared_buffer(
             in, outputs[o].strides(), in.flags(), in.data_size());
         o++;
       }
     }
     for (; o < outputs.size(); ++o) {
-      outputs[o].set_data(allocator::malloc(outputs[o].nbytes()));
+      outputs[o].set_data(allocator::malloc_or_wait(outputs[o].nbytes()));
     }
   }
 }
 
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant) {
-  const Shape& shape = out.shape();
-  bool contiguous = compiled_check_contiguity(inputs, shape);
-  if (contiguous) {
-    return {true, shape, {}};
-  }
-
-  std::vector<Strides> strides_vec{out.strides()};
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    // Skip constants.
-    if (is_constant(i)) {
-      continue;
-    }
-
-    // Skip scalar inputs.
-    const auto& x = inputs[i];
-    if (is_scalar(x)) {
-      continue;
-    }
-
-    // Broadcast the inputs to the output shape.
-    Strides xstrides;
-    size_t j = 0;
-    for (; j < shape.size() - x.ndim(); ++j) {
-      if (shape[j] == 1) {
-        xstrides.push_back(out.strides()[j]);
-      } else {
-        xstrides.push_back(0);
-      }
-    }
-    for (size_t i = 0; i < x.ndim(); ++i, ++j) {
-      if (x.shape(i) == 1) {
-        if (shape[j] == 1) {
-          xstrides.push_back(out.strides()[j]);
-        } else {
-          xstrides.push_back(0);
-        }
-      } else {
-        xstrides.push_back(x.strides()[i]);
-      }
-    }
-    strides_vec.push_back(std::move(xstrides));
-  }
-
-  auto tup = collapse_contiguous_dims(shape, strides_vec, INT32_MAX);
-  return {false, std::move(std::get<0>(tup)), std::move(std::get<1>(tup))};
-}
-
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
-    bool contiguous) {
-  if (contiguous) {
-    size_t max_size = 0;
-    for (const auto& in : inputs) {
-      max_size = std::max(max_size, in.data_size());
-    }
-    return max_size > UINT32_MAX;
-  } else {
-    size_t max_size = 0;
-    for (const auto& o : outputs) {
-      max_size = std::max(max_size, o.size());
-    }
-    return max_size > UINT32_MAX;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/common/compiled.h

@@ -1,8 +1,9 @@
 // Copyright © 2023-2024 Apple Inc.
 #pragma once
 
-#include <functional>
 #include <iomanip>
+#include <sstream>
+#include <unordered_set>
 
 #include "mlx/array.h"
 #include "mlx/primitives.h"
@@ -13,17 +14,19 @@ inline bool is_static_cast(const Primitive& p) {
   return (typeid(p) == typeid(Broadcast) || typeid(p) == typeid(AsType));
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids);
+
 std::string get_type_string(Dtype d);
 
 template <typename T>
 void print_float_constant(std::ostream& os, const array& x) {
   auto old_precision = os.precision();
-  if constexpr (std::is_same_v<T, double>) {
-    os << std::setprecision(std::numeric_limits<double>::digits10 + 1);
-  } else {
-    os << std::setprecision(std::numeric_limits<float>::digits10 + 1);
-  }
-  os << x.item<T>() << std::setprecision(old_precision);
+  os << std::setprecision(std::numeric_limits<float>::digits10 + 1)
+     << 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,
-    bool contiguous);
-
-// 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,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
     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 {
 
@@ -26,19 +26,19 @@ 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 {
       out.set_data(
-          allocator::malloc(in.data_size() * out.itemsize()),
+          allocator::malloc_or_wait(in.data_size() * out.itemsize()),
           in.data_size(),
           in.strides(),
           in.flags());
       return false;
     }
   } else {
-    out.set_data(allocator::malloc(out.nbytes()));
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
     return false;
   }
 }

mlx/backend/common/hadamard.h

@@ -99,11 +99,7 @@ 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};
 }
 
-} // namespace mlx::core
+} // namespace mlx::core

mlx/backend/common/load.cpp

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

mlx/backend/common/matmul.h

@@ -1,67 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/utils.h"
-
-#include <sstream>
-
-namespace mlx::core {
-
-inline std::tuple<Shape, Strides, Strides> collapse_batches(
-    const array& a,
-    const array& b) {
-  if (a.ndim() == 2) {
-    return {{1}, {0}, {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 {{1}, {0}, {0}, {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,
-    Strides strides,
+    const array& x,
     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

@@ -48,12 +48,12 @@ inline void set_ternary_op_output_data(
   switch (topt) {
     case TernaryOpType::ScalarScalarScalar:
       out.set_data(
-          allocator::malloc(out.itemsize()), 1, b.strides(), b.flags());
+          allocator::malloc_or_wait(out.itemsize()), 1, b.strides(), b.flags());
       break;
     case TernaryOpType::VectorVectorVector:
       if (!(maybe_donate(a) || maybe_donate(b) || maybe_donate(c))) {
         out.set_data(
-            allocator::malloc(out.itemsize() * b.data_size()),
+            allocator::malloc_or_wait(out.itemsize() * b.data_size()),
             b.data_size(),
             b.strides(),
             b.flags());
@@ -64,7 +64,7 @@ inline void set_ternary_op_output_data(
       if (!((a.flags().row_contiguous && maybe_donate(a)) ||
             (b.flags().row_contiguous && maybe_donate(b)) ||
             (c.flags().row_contiguous && maybe_donate(c)))) {
-        out.set_data(allocator::malloc(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
       }
       break;
   }

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 std::vector<T> remove_index(std::vector<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
-          ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/binary.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/eig.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/encoder.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
@@ -60,7 +58,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/select.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/threefry.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -76,8 +73,8 @@ target_sources(
 if(MLX_BUILD_ACCELERATE)
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/bnns.cpp)
 else()
-  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_fp16.cpp
-                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simd_bf16.cpp)
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_fp16.cpp
+                             ${CMAKE_CURRENT_SOURCE_DIR}/gemms/no_bf16.cpp)
 endif()
 
 if(IOS)

mlx/backend/cpu/arg_reduce.cpp

@@ -11,24 +11,43 @@ namespace mlx::core {
 namespace {
 
 template <typename InT, typename OpT>
-void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
+void arg_reduce(
+    const array& in,
+    array& out,
+    const OpT& op,
+    int axis,
+    Stream stream) {
   auto axis_size = in.shape()[axis];
   auto axis_stride = in.strides()[axis];
-  Strides strides = remove_index(in.strides(), axis);
-  Shape shape = remove_index(in.shape(), axis);
+  Strides strides = in.strides();
+  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>();
 
-  for (uint32_t i = 0; i < out.size(); ++i) {
-    auto loc = elem_to_loc(i, shape, strides);
-    auto local_in_ptr = in_ptr + loc;
-    uint32_t ind_v = 0;
-    InT v = (*local_in_ptr);
-    for (uint32_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
-      op(j, (*local_in_ptr), &ind_v, &v);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+  encoder.dispatch([in_ptr,
+                    out_ptr,
+                    axis_size,
+                    axis_stride,
+                    op = std::move(op),
+                    shape = std::move(shape),
+                    strides = std::move(strides),
+                    size = out.size()]() {
+    for (uint32_t i = 0; i < size; ++i) {
+      auto loc = elem_to_loc(i, shape, strides);
+      auto local_in_ptr = in_ptr + loc;
+      uint32_t ind_v = 0;
+      InT v = (*local_in_ptr);
+      for (uint32_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
+        op(j, (*local_in_ptr), &ind_v, &v);
+      }
+      out_ptr[i] = ind_v;
     }
-    out_ptr[i] = ind_v;
-  }
+  });
 }
 
 template <typename InT>
@@ -36,7 +55,8 @@ void arg_reduce_dispatch(
     const array& in,
     array& out,
     ArgReduce::ReduceType rtype,
-    int axis) {
+    int axis,
+    Stream stream) {
   switch (rtype) {
     case ArgReduce::ArgMin: {
       auto op = [](auto ind_x, auto x, auto ind_y, auto y) {
@@ -45,7 +65,7 @@ void arg_reduce_dispatch(
           (*ind_y) = ind_x;
         }
       };
-      arg_reduce<InT>(in, out, op, axis);
+      arg_reduce<InT>(in, out, op, axis, stream);
       break;
     }
     case ArgReduce::ArgMax: {
@@ -55,7 +75,7 @@ void arg_reduce_dispatch(
           (*ind_y) = ind_x;
         }
       };
-      arg_reduce<InT>(in, out, op, axis);
+      arg_reduce<InT>(in, out, op, axis, stream);
       break;
     }
   }
@@ -66,59 +86,52 @@ void arg_reduce_dispatch(
 void ArgReduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  encoder.dispatch([in = array::unsafe_weak_copy(in),
-                    out = array::unsafe_weak_copy(out),
-                    reduce_type_ = reduce_type_,
-                    axis_ = axis_]() mutable {
-    switch (in.dtype()) {
-      case bool_:
-        arg_reduce_dispatch<bool>(in, out, reduce_type_, axis_);
-        break;
-      case uint8:
-        arg_reduce_dispatch<uint8_t>(in, out, reduce_type_, axis_);
-        break;
-      case uint16:
-        arg_reduce_dispatch<uint16_t>(in, out, reduce_type_, axis_);
-        break;
-      case uint32:
-        arg_reduce_dispatch<uint32_t>(in, out, reduce_type_, axis_);
-        break;
-      case uint64:
-        arg_reduce_dispatch<uint64_t>(in, out, reduce_type_, axis_);
-        break;
-      case int8:
-        arg_reduce_dispatch<int8_t>(in, out, reduce_type_, axis_);
-        break;
-      case int16:
-        arg_reduce_dispatch<int16_t>(in, out, reduce_type_, axis_);
-        break;
-      case int32:
-        arg_reduce_dispatch<int32_t>(in, out, reduce_type_, axis_);
-        break;
-      case int64:
-        arg_reduce_dispatch<int64_t>(in, out, reduce_type_, axis_);
-        break;
-      case float16:
-        arg_reduce_dispatch<float16_t>(in, out, reduce_type_, axis_);
-        break;
-      case float32:
-        arg_reduce_dispatch<float>(in, out, reduce_type_, axis_);
-        break;
-      case bfloat16:
-        arg_reduce_dispatch<bfloat16_t>(in, out, reduce_type_, axis_);
-        break;
-      case float64:
-        arg_reduce_dispatch<double>(in, out, reduce_type_, axis_);
-        break;
-      case complex64:
-        arg_reduce_dispatch<complex64_t>(in, out, reduce_type_, axis_);
-        break;
-    }
-  });
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
+  switch (in.dtype()) {
+    case bool_:
+      arg_reduce_dispatch<bool>(in, out, reduce_type_, axis_, stream());
+      break;
+    case uint8:
+      arg_reduce_dispatch<uint8_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case uint16:
+      arg_reduce_dispatch<uint16_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case uint32:
+      arg_reduce_dispatch<uint32_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case uint64:
+      arg_reduce_dispatch<uint64_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case int8:
+      arg_reduce_dispatch<int8_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case int16:
+      arg_reduce_dispatch<int16_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case int32:
+      arg_reduce_dispatch<int32_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case int64:
+      arg_reduce_dispatch<int64_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case float16:
+      arg_reduce_dispatch<float16_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case float32:
+      arg_reduce_dispatch<float>(in, out, reduce_type_, axis_, stream());
+      break;
+    case bfloat16:
+      arg_reduce_dispatch<bfloat16_t>(in, out, reduce_type_, axis_, stream());
+      break;
+    case float64:
+      arg_reduce_dispatch<double>(in, out, reduce_type_, axis_, stream());
+      break;
+    case complex64:
+      arg_reduce_dispatch<complex64_t>(in, out, reduce_type_, axis_, stream());
+      break;
+  }
 }
 
 } // namespace mlx::core

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

@@ -8,7 +8,6 @@
 #include "mlx/backend/cpu/binary.h"
 #include "mlx/backend/cpu/binary_ops.h"
 #include "mlx/backend/cpu/binary_two.h"
-#include "mlx/backend/cpu/encoder.h"
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
@@ -17,221 +16,51 @@ namespace mlx::core {
 namespace {
 
 template <typename Op>
-void binary(const array& a, const array& b, array& out, Op op, Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        binary_op<bool, Op>(a, b, out, bopt);
-        break;
-      case uint8:
-        binary_op<uint8_t, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, Op>(a, b, out, bopt);
-        break;
-      case float16:
-        binary_op<float16_t, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, Op>(a, b, out, bopt);
-        break;
-    }
-  });
-}
-
-template <typename Op>
-void comparison_op(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (a.dtype()) {
-      case bool_:
-        binary_op<bool, bool, Op>(a, b, out, bopt);
-        break;
-      case uint8:
-        binary_op<uint8_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, bool, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, bool, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, bool, Op>(a, b, out, bopt);
-        break;
-      case float16:
-        binary_op<float16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, bool, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, bool, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, bool, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, bool, Op>(a, b, out, bopt);
-        break;
-    }
-  });
-}
-
-template <typename Op>
-void binary_float(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case float16:
-        binary_op<float16_t, Op>(a, b, out, bopt);
-        break;
-      case float32:
-        binary_op<float, Op>(a, b, out, bopt);
-        break;
-      case float64:
-        binary_op<double, Op>(a, b, out, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t, Op>(a, b, out, bopt);
-        break;
-      case complex64:
-        binary_op<complex64_t, Op>(a, b, out, bopt);
-        break;
-      default:
-        throw std::runtime_error(
-            "[binary_float] Only supports floating point types.");
-    }
-  });
-}
-
-template <typename Op>
-void binary_int(
-    const array& a,
-    const array& b,
-    array& out,
-    Op op,
-    Stream stream) {
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out = array::unsafe_weak_copy(out),
-                    bopt]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        binary_op<bool, Op>(a, b, out, bopt);
-      case uint8:
-        binary_op<uint8_t, Op>(a, b, out, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t, Op>(a, b, out, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t, Op>(a, b, out, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t, Op>(a, b, out, bopt);
-        break;
-      case int8:
-        binary_op<int8_t, Op>(a, b, out, bopt);
-        break;
-      case int16:
-        binary_op<int16_t, Op>(a, b, out, bopt);
-        break;
-      case int32:
-        binary_op<int32_t, Op>(a, b, out, bopt);
-        break;
-      case int64:
-        binary_op<int64_t, Op>(a, b, out, bopt);
-        break;
-      default:
-        throw std::runtime_error("[binary_int] Type not supported");
-        break;
-    }
-  });
+void comparison_op(const array& a, const array& b, array& out) {
+  switch (a.dtype()) {
+    case bool_:
+      binary_op<bool, bool, Op>(a, b, out);
+      break;
+    case uint8:
+      binary_op<uint8_t, bool, Op>(a, b, out);
+      break;
+    case uint16:
+      binary_op<uint16_t, bool, Op>(a, b, out);
+      break;
+    case uint32:
+      binary_op<uint32_t, bool, Op>(a, b, out);
+      break;
+    case uint64:
+      binary_op<uint64_t, bool, Op>(a, b, out);
+      break;
+    case int8:
+      binary_op<int8_t, bool, Op>(a, b, out);
+      break;
+    case int16:
+      binary_op<int16_t, bool, Op>(a, b, out);
+      break;
+    case int32:
+      binary_op<int32_t, bool, Op>(a, b, out);
+      break;
+    case int64:
+      binary_op<int64_t, bool, Op>(a, b, out);
+      break;
+    case float16:
+      binary_op<float16_t, bool, Op>(a, b, out);
+      break;
+    case float32:
+      binary_op<float, bool, Op>(a, b, out);
+      break;
+    case float64:
+      binary_op<double, bool, Op>(a, b, out);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t, bool, Op>(a, b, out);
+      break;
+    case complex64:
+      binary_op<complex64_t, bool, Op>(a, b, out);
+      break;
+  }
 }
 
 } // namespace
@@ -240,7 +69,7 @@ void Add::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Add(), stream());
+  binary(a, b, out, detail::Add());
 }
 
 void DivMod::eval_cpu(
@@ -249,89 +78,70 @@ void DivMod::eval_cpu(
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  auto bopt = get_binary_op_type(a, b);
-  auto& out_a = outputs[0];
-  auto& out_b = outputs[1];
-  set_binary_op_output_data(a, b, out_a, bopt);
-  set_binary_op_output_data(a, b, out_b, bopt);
-
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out_a);
-  encoder.set_output_array(out_b);
-
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    out_a = array::unsafe_weak_copy(out_a),
-                    out_b = array::unsafe_weak_copy(out_b),
-                    bopt]() mutable {
-    auto integral_op = [](auto x, auto y) {
-      return std::make_pair(x / y, x % y);
-    };
-    auto float_op = [](auto x, auto y) {
-      return std::make_pair(std::trunc(x / y), std::fmod(x, y));
-    };
-
-    switch (out_a.dtype()) {
-      case bool_:
-        binary_op<bool>(a, b, out_a, out_b, integral_op, bopt);
-      case uint8:
-        binary_op<uint8_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case uint16:
-        binary_op<uint16_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case uint32:
-        binary_op<uint32_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case uint64:
-        binary_op<uint64_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case int8:
-        binary_op<int8_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case int16:
-        binary_op<int16_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case int32:
-        binary_op<int32_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case int64:
-        binary_op<int64_t>(a, b, out_a, out_b, integral_op, bopt);
-        break;
-      case float16:
-        binary_op<float16_t>(a, b, out_a, out_b, float_op, bopt);
-        break;
-      case float32:
-        binary_op<float>(a, b, out_a, out_b, float_op, bopt);
-        break;
-      case float64:
-        binary_op<double>(a, b, out_a, out_b, float_op, bopt);
-        break;
-      case bfloat16:
-        binary_op<bfloat16_t>(a, b, out_a, out_b, float_op, bopt);
-        break;
-      case complex64:
-        // Should never get here
-        throw std::runtime_error("[DivMod] Complex type not supported");
-        break;
-    }
-  });
+  auto integral_op = [](auto x, auto y) {
+    return std::make_pair(x / y, x % y);
+  };
+  auto float_op = [](auto x, auto y) {
+    return std::make_pair(std::trunc(x / y), std::fmod(x, y));
+  };
+  switch (outputs[0].dtype()) {
+    case bool_:
+      binary_op<bool>(a, b, outputs, integral_op);
+    case uint8:
+      binary_op<uint8_t>(a, b, outputs, integral_op);
+      break;
+    case uint16:
+      binary_op<uint16_t>(a, b, outputs, integral_op);
+      break;
+    case uint32:
+      binary_op<uint32_t>(a, b, outputs, integral_op);
+      break;
+    case uint64:
+      binary_op<uint64_t>(a, b, outputs, integral_op);
+      break;
+    case int8:
+      binary_op<int8_t>(a, b, outputs, integral_op);
+      break;
+    case int16:
+      binary_op<int16_t>(a, b, outputs, integral_op);
+      break;
+    case int32:
+      binary_op<int32_t>(a, b, outputs, integral_op);
+      break;
+    case int64:
+      binary_op<int64_t>(a, b, outputs, integral_op);
+      break;
+    case float16:
+      binary_op<float16_t>(a, b, outputs, float_op);
+      break;
+    case float32:
+      binary_op<float>(a, b, outputs, float_op);
+      break;
+    case float64:
+      binary_op<double>(a, b, outputs, float_op);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, outputs, float_op);
+      break;
+    case complex64:
+      // Should never get here
+      throw std::runtime_error("[DivMod] Complex type not supported");
+      break;
+  }
 }
 
 void Divide::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Divide(), stream());
+  binary(a, b, out, detail::Divide());
 }
 
 void Remainder::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Remainder(), stream());
+  binary(a, b, out, detail::Remainder());
 }
 
 void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -339,143 +149,181 @@ void Equal::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& a = inputs[0];
   auto& b = inputs[1];
   if (equal_nan_) {
-    auto bopt = get_binary_op_type(a, b);
-    set_binary_op_output_data(a, b, out, bopt);
-
-    auto& encoder = cpu::get_command_encoder(stream());
-    encoder.set_input_array(a);
-    encoder.set_input_array(b);
-    encoder.set_output_array(out);
-    encoder.dispatch([a = array::unsafe_weak_copy(a),
-                      b = array::unsafe_weak_copy(b),
-                      out = array::unsafe_weak_copy(out),
-                      bopt]() mutable {
-      switch (a.dtype()) {
-        case float16:
-          binary_op<float16_t, bool, detail::NaNEqual>(a, b, out, bopt);
-          break;
-        case float32:
-          binary_op<float, bool, detail::NaNEqual>(a, b, out, bopt);
-          break;
-        case float64:
-          binary_op<double, bool, detail::NaNEqual>(a, b, out, bopt);
-          break;
-        case bfloat16:
-          binary_op<bfloat16_t, bool, detail::NaNEqual>(a, b, out, bopt);
-          break;
-        case complex64:
-          binary_op<complex64_t, bool, detail::NaNEqual>(a, b, out, bopt);
-          break;
-        default:
-          throw std::runtime_error(
-              "[NanEqual::eval_cpu] Only for floating point types.");
-      }
-    });
+    switch (a.dtype()) {
+      case float16:
+        binary_op<float16_t, bool, detail::NaNEqual>(a, b, out);
+        break;
+      case float32:
+        binary_op<float, bool, detail::NaNEqual>(a, b, out);
+        break;
+      case float64:
+        binary_op<double, bool, detail::NaNEqual>(a, b, out);
+        break;
+      case bfloat16:
+        binary_op<bfloat16_t, bool, detail::NaNEqual>(a, b, out);
+        break;
+      case complex64:
+        binary_op<complex64_t, bool, detail::NaNEqual>(a, b, out);
+        break;
+      default:
+        throw std::runtime_error(
+            "[NanEqual::eval_cpu] Only for floating point types.");
+    }
   } else {
-    comparison_op(a, b, out, detail::Equal(), stream());
+    comparison_op<detail::Equal>(a, b, out);
   }
 }
 
 void Greater::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::Greater(), stream());
+  comparison_op<detail::Greater>(inputs[0], inputs[1], out);
 }
 
 void GreaterEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::GreaterEqual(), stream());
+  comparison_op<detail::GreaterEqual>(inputs[0], inputs[1], out);
 }
 
 void Less::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::Less(), stream());
+  comparison_op<detail::Less>(inputs[0], inputs[1], out);
 }
 
 void LessEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::LessEqual(), stream());
+  comparison_op<detail::LessEqual>(inputs[0], inputs[1], out);
 }
 
 void LogAddExp::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary_float(a, b, out, detail::LogAddExp(), stream());
+  switch (out.dtype()) {
+    case float16:
+      binary_op<float16_t, detail::LogAddExp>(a, b, out);
+      break;
+    case float32:
+      binary_op<float, detail::LogAddExp>(a, b, out);
+      break;
+    case float64:
+      binary_op<double, detail::LogAddExp>(a, b, out);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t, detail::LogAddExp>(a, b, out);
+      break;
+    default:
+      throw std::runtime_error(
+          "[LogAddExp::eval_cpu] Only supports non-complex floating point types.");
+  }
 }
 
 void LogicalAnd::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalAnd requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary(in1, in2, out, detail::LogicalAnd(), stream());
+  binary(in1, in2, out, detail::LogicalAnd());
 }
 
 void LogicalOr::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2); // LogicalOr requires two input arrays
   auto& in1 = inputs[0];
   auto& in2 = inputs[1];
-  binary(in1, in2, out, detail::LogicalOr(), stream());
+  binary(in1, in2, out, detail::LogicalOr());
 }
 
 void Maximum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Maximum(), stream());
+  binary(a, b, out, detail::Maximum());
 }
 
 void Minimum::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Minimum(), stream());
+  binary(a, b, out, detail::Minimum());
 }
 
 void Multiply::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Multiply(), stream());
+  binary(a, b, out, detail::Multiply());
 }
 
 void NotEqual::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
-  comparison_op(inputs[0], inputs[1], out, detail::NotEqual(), stream());
+  comparison_op<detail::NotEqual>(inputs[0], inputs[1], out);
 }
 
 void Power::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Power(), stream());
+  binary(a, b, out, detail::Power());
 }
 
 void Subtract::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
-  binary(a, b, out, detail::Subtract(), stream());
+  binary(a, b, out, detail::Subtract());
 }
 
 void BitwiseBinary::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
   auto& b = inputs[1];
+  auto dispatch_type = [&a, &b, &out](auto op) {
+    switch (out.dtype()) {
+      case bool_:
+        binary_op<bool>(a, b, out, op);
+      case uint8:
+        binary_op<uint8_t>(a, b, out, op);
+        break;
+      case uint16:
+        binary_op<uint16_t>(a, b, out, op);
+        break;
+      case uint32:
+        binary_op<uint32_t>(a, b, out, op);
+        break;
+      case uint64:
+        binary_op<uint64_t>(a, b, out, op);
+        break;
+      case int8:
+        binary_op<int8_t>(a, b, out, op);
+        break;
+      case int16:
+        binary_op<int16_t>(a, b, out, op);
+        break;
+      case int32:
+        binary_op<int32_t>(a, b, out, op);
+        break;
+      case int64:
+        binary_op<int64_t>(a, b, out, op);
+        break;
+      default:
+        throw std::runtime_error(
+            "[BitwiseBinary::eval_cpu] Type not supported");
+        break;
+    }
+  };
   switch (op_) {
     case BitwiseBinary::And:
-      binary_int(a, b, out, detail::BitwiseAnd(), stream());
+      dispatch_type(detail::BitwiseAnd());
       break;
     case BitwiseBinary::Or:
-      binary_int(a, b, out, detail::BitwiseOr(), stream());
+      dispatch_type(detail::BitwiseOr());
       break;
     case BitwiseBinary::Xor:
-      binary_int(a, b, out, detail::BitwiseXor(), stream());
+      dispatch_type(detail::BitwiseXor());
       break;
     case BitwiseBinary::LeftShift:
-      binary_int(a, b, out, detail::LeftShift(), stream());
+      dispatch_type(detail::LeftShift());
       break;
     case BitwiseBinary::RightShift:
-      binary_int(a, b, out, detail::RightShift(), stream());
+      dispatch_type(detail::RightShift());
       break;
   }
 }
@@ -484,7 +332,23 @@ void ArcTan2::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 2);
   const auto& a = inputs[0];
   const auto& b = inputs[1];
-  binary_float(a, b, out, detail::ArcTan2(), stream());
+  switch (out.dtype()) {
+    case float16:
+      binary_op<float16_t>(a, b, out, detail::ArcTan2());
+      break;
+    case float32:
+      binary_op<float>(a, b, out, detail::ArcTan2());
+      break;
+    case float64:
+      binary_op<double>(a, b, out, detail::ArcTan2());
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, out, detail::ArcTan2());
+      break;
+    default:
+      throw std::runtime_error(
+          "[ArcTan2::eval_cpu] Only supports non-complex floating point types.");
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary.h

@@ -3,9 +3,12 @@
 #pragma once
 #include <cassert>
 
+#include "mlx/allocator.h"
 #include "mlx/array.h"
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/cpu/encoder.h"
+#include "mlx/primitives.h"
 
 #include "mlx/backend/cpu/simd/simd.h"
 
@@ -149,145 +152,218 @@ void binary_op_dispatch_dims(
 }
 
 template <typename T, typename U, typename Op>
-void binary_op(const array& a, const array& b, array& out, BinaryOpType bopt) {
+void binary_op(const array& a, const array& b, array& out) {
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, out, bopt);
+
   // The full computation is scalar scalar so call the base op once
   auto a_ptr = a.data<T>();
   auto b_ptr = b.data<T>();
 
   auto out_ptr = out.data<U>();
-  if (bopt == BinaryOpType::ScalarScalar) {
-    *out_ptr = Op{}(*a_ptr, *b_ptr);
-    return;
-  }
-
-  // The full computation is scalar vector so delegate to the op
-  if (bopt == BinaryOpType::ScalarVector) {
-    ScalarVector<Op>{}(a_ptr, b_ptr, out_ptr, b.data_size());
-    return;
-  }
-
-  // The full computation is vector scalar so delegate to the op
-  if (bopt == BinaryOpType::VectorScalar) {
-    VectorScalar<Op>{}(a_ptr, b_ptr, out_ptr, a.data_size());
-    return;
-  }
-
-  // The full computation is vector vector so delegate to the op
-  if (bopt == BinaryOpType::VectorVector) {
-    VectorVector<Op>{}(a_ptr, b_ptr, out_ptr, a.size());
-    return;
-  }
-
-  // General computation so let's try to optimize
-  auto [new_shape, new_strides] = collapse_contiguous_dims(
-      a.shape(), {a.strides(), b.strides(), out.strides()});
-  auto& a_strides = new_strides[0];
-  auto& b_strides = new_strides[1];
-  auto& strides = new_strides[2];
-
-  // Get the left-most dim such that the array is row contiguous after
-  auto leftmost_rc_dim = [&strides](const auto& arr_strides) {
-    int d = arr_strides.size() - 1;
-    for (; d >= 0 && arr_strides[d] == strides[d]; d--) {
+  auto& encoder = cpu::get_command_encoder(out.primitive().stream());
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  encoder.dispatch([bopt,
+                    a_ptr,
+                    b_ptr,
+                    out_ptr,
+                    a_data_size = a.data_size(),
+                    b_data_size = b.data_size(),
+                    size = a.size(),
+                    shape = a.shape(),
+                    a_strides = a.strides(),
+                    b_strides = b.strides(),
+                    strides = out.strides()]() mutable {
+    if (bopt == BinaryOpType::ScalarScalar) {
+      *out_ptr = Op{}(*a_ptr, *b_ptr);
+      return;
     }
-    return d + 1;
-  };
-  auto a_rc_dim = leftmost_rc_dim(a_strides);
-  auto b_rc_dim = leftmost_rc_dim(b_strides);
 
-  // Get the left-most dim such that the array is a broadcasted "scalar" after
-  auto leftmost_s_dim = [](const auto& arr_strides) {
-    int d = arr_strides.size() - 1;
-    for (; d >= 0 && arr_strides[d] == 0; d--) {
+    // The full computation is scalar vector so delegate to the op
+    if (bopt == BinaryOpType::ScalarVector) {
+      ScalarVector<Op>{}(a_ptr, b_ptr, out_ptr, b_data_size);
+      return;
     }
-    return d + 1;
-  };
-  auto a_s_dim = leftmost_s_dim(a_strides);
-  auto b_s_dim = leftmost_s_dim(b_strides);
 
-  auto ndim = new_shape.size();
+    // The full computation is vector scalar so delegate to the op
+    if (bopt == BinaryOpType::VectorScalar) {
+      VectorScalar<Op>{}(a_ptr, b_ptr, out_ptr, a_data_size);
+      return;
+    }
 
-  // Case 1: LxM and FxM where L and F are broadcastable and M is row
-  // contiguous
-  int dim = ndim;
-  if (int d = std::max(a_rc_dim, b_rc_dim); d < ndim) {
-    bopt = BinaryOpType::VectorVector;
-    dim = d;
-    // Case 2: LxM and Fx1 where L and F are broadcastable and M is row
+    // The full computation is vector vector so delegate to the op
+    if (bopt == BinaryOpType::VectorVector) {
+      VectorVector<Op>{}(a_ptr, b_ptr, out_ptr, size);
+      return;
+    }
+
+    // General computation so let's try to optimize
+    auto [new_shape, new_strides] = collapse_contiguous_dims(
+        shape,
+        {std::move(a_strides), std::move(b_strides), std::move(strides)});
+    a_strides = new_strides[0];
+    b_strides = new_strides[1];
+    strides = new_strides[2];
+
+    // Get the left-most dim such that the array is row contiguous after
+    auto leftmost_rc_dim = [&strides](const auto& arr_strides) {
+      int d = arr_strides.size() - 1;
+      for (; d >= 0 && arr_strides[d] == strides[d]; d--) {
+      }
+      return d + 1;
+    };
+    auto a_rc_dim = leftmost_rc_dim(a_strides);
+    auto b_rc_dim = leftmost_rc_dim(b_strides);
+
+    // Get the left-most dim such that the array is a broadcasted "scalar" after
+    auto leftmost_s_dim = [](const auto& arr_strides) {
+      int d = arr_strides.size() - 1;
+      for (; d >= 0 && arr_strides[d] == 0; d--) {
+      }
+      return d + 1;
+    };
+    auto a_s_dim = leftmost_s_dim(a_strides);
+    auto b_s_dim = leftmost_s_dim(b_strides);
+
+    auto ndim = new_shape.size();
+
+    // Case 1: LxM and FxM where L and F are broadcastable and M is row
     // contiguous
-  } else if (int d = std::max(a_rc_dim, b_s_dim); d < ndim) {
-    bopt = BinaryOpType::VectorScalar;
-    dim = d;
-    // Case 3: Lx1 and FxM where L and F are broadcastable and M is row
-    // contiguous
-  } else if (int d = std::max(a_s_dim, b_rc_dim); d < ndim) {
-    bopt = BinaryOpType::ScalarVector;
-    dim = d;
-  }
+    int dim = ndim;
+    if (int d = std::max(a_rc_dim, b_rc_dim); d < ndim) {
+      bopt = BinaryOpType::VectorVector;
+      dim = d;
+      // Case 2: LxM and Fx1 where L and F are broadcastable and M is row
+      // contiguous
+    } else if (int d = std::max(a_rc_dim, b_s_dim); d < ndim) {
+      bopt = BinaryOpType::VectorScalar;
+      dim = d;
+      // Case 3: Lx1 and FxM where L and F are broadcastable and M is row
+      // contiguous
+    } else if (int d = std::max(a_s_dim, b_rc_dim); d < ndim) {
+      bopt = BinaryOpType::ScalarVector;
+      dim = d;
+    }
 
-  // Can be sure dim > 0 since otherwise we would have used one of the fully
-  // contiguous methods above. Except for the case that the flags do not
-  // correspond to the underlying contiguity.
-  if (dim == 0 || strides[dim - 1] < 16) {
-    bopt = BinaryOpType::General;
-    dim = ndim;
-  }
+    // Can be sure dim > 0 since otherwise we would have used one of the fully
+    // contiguous methods above. Except for the case that the flags do not
+    // correspond to the underlying contiguity.
+    if (dim == 0 || strides[dim - 1] < 16) {
+      bopt = BinaryOpType::General;
+      dim = ndim;
+    }
 
-  switch (bopt) {
-    case BinaryOpType::VectorVector:
-      binary_op_dispatch_dims<T, U, true, VectorVector<Op>>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          dim,
-          a.size(),
-          new_shape,
-          a_strides,
-          b_strides,
-          strides);
-      break;
-    case BinaryOpType::VectorScalar:
-      binary_op_dispatch_dims<T, U, true, VectorScalar<Op>>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          dim,
-          a.size(),
-          new_shape,
-          a_strides,
-          b_strides,
-          strides);
-      break;
-    case BinaryOpType::ScalarVector:
-      binary_op_dispatch_dims<T, U, true, ScalarVector<Op>>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          dim,
-          a.size(),
-          new_shape,
-          a_strides,
-          b_strides,
-          strides);
-      break;
-    default:
-      binary_op_dispatch_dims<T, U, false, Op>(
-          a_ptr,
-          b_ptr,
-          out_ptr,
-          dim,
-          a.size(),
-          new_shape,
-          a_strides,
-          b_strides,
-          strides);
-      break;
-  }
+    switch (bopt) {
+      case BinaryOpType::VectorVector:
+        binary_op_dispatch_dims<T, U, true, VectorVector<Op>>(
+            a_ptr,
+            b_ptr,
+            out_ptr,
+            dim,
+            size,
+            new_shape,
+            a_strides,
+            b_strides,
+            strides);
+        break;
+      case BinaryOpType::VectorScalar:
+        binary_op_dispatch_dims<T, U, true, VectorScalar<Op>>(
+            a_ptr,
+            b_ptr,
+            out_ptr,
+            dim,
+            size,
+            new_shape,
+            a_strides,
+            b_strides,
+            strides);
+        break;
+      case BinaryOpType::ScalarVector:
+        binary_op_dispatch_dims<T, U, true, ScalarVector<Op>>(
+            a_ptr,
+            b_ptr,
+            out_ptr,
+            dim,
+            size,
+            new_shape,
+            a_strides,
+            b_strides,
+            strides);
+        break;
+      default:
+        binary_op_dispatch_dims<T, U, false, Op>(
+            a_ptr,
+            b_ptr,
+            out_ptr,
+            dim,
+            size,
+            new_shape,
+            a_strides,
+            b_strides,
+            strides);
+        break;
+    }
+  });
 }
 
 template <typename T, typename Op>
-void binary_op(const array& a, const array& b, array& out, BinaryOpType bopt) {
-  binary_op<T, T, Op>(a, b, out, bopt);
+void binary_op(const array& a, const array& b, array& out) {
+  binary_op<T, T, Op>(a, b, out);
+}
+
+template <typename T, typename Op>
+void binary_op(const array& a, const array& b, array& out, Op op) {
+  binary_op<T, T, Op>(a, b, out);
+}
+
+template <typename Op>
+void binary(const array& a, const array& b, array& out, Op op) {
+  switch (out.dtype()) {
+    case bool_:
+      binary_op<bool, Op>(a, b, out);
+      break;
+    case uint8:
+      binary_op<uint8_t, Op>(a, b, out);
+      break;
+    case uint16:
+      binary_op<uint16_t, Op>(a, b, out);
+      break;
+    case uint32:
+      binary_op<uint32_t, Op>(a, b, out);
+      break;
+    case uint64:
+      binary_op<uint64_t, Op>(a, b, out);
+      break;
+    case int8:
+      binary_op<int8_t, Op>(a, b, out);
+      break;
+    case int16:
+      binary_op<int16_t, Op>(a, b, out);
+      break;
+    case int32:
+      binary_op<int32_t, Op>(a, b, out);
+      break;
+    case int64:
+      binary_op<int64_t, Op>(a, b, out);
+      break;
+    case float16:
+      binary_op<float16_t, Op>(a, b, out);
+      break;
+    case float32:
+      binary_op<float, Op>(a, b, out);
+      break;
+    case float64:
+      binary_op<double, Op>(a, b, out);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t, Op>(a, b, out);
+      break;
+    case complex64:
+      binary_op<complex64_t, Op>(a, b, out);
+      break;
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/binary_two.h

@@ -4,6 +4,8 @@
 
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/binary.h"
+#include "mlx/backend/cpu/encoder.h"
+#include "mlx/primitives.h"
 
 namespace mlx::core {
 
@@ -55,7 +57,14 @@ void binary_op_dispatch_dims(
     const array& b,
     array& out_a,
     array& out_b,
+    Stream stream,
     Op op) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out_a);
+  encoder.set_output_array(out_b);
+
   auto [shape, strides] = collapse_contiguous_dims(
       a.shape(), {a.strides(), b.strides(), out_a.strides()});
   const T* a_ptr = a.data<T>();
@@ -63,101 +72,197 @@ void binary_op_dispatch_dims(
   U* out_a_ptr = out_a.data<U>();
   U* out_b_ptr = out_b.data<U>();
 
-  const auto& a_strides = strides[0];
-  const auto& b_strides = strides[1];
-  const auto& out_strides = strides[2];
-  int ndim = shape.size();
-  switch (ndim) {
-    case 1:
-      binary_op_dims<T, U, Op, 1>(
-          a_ptr,
-          b_ptr,
-          out_a_ptr,
-          out_b_ptr,
-          op,
-          shape,
-          a_strides,
-          b_strides,
-          out_strides,
-          0);
-      return;
-    case 2:
-      binary_op_dims<T, U, Op, 2>(
-          a_ptr,
-          b_ptr,
-          out_a_ptr,
-          out_b_ptr,
-          op,
-          shape,
-          a_strides,
-          b_strides,
-          out_strides,
-          0);
-      return;
-  }
+  encoder.dispatch([a_ptr,
+                    b_ptr,
+                    out_a_ptr,
+                    out_b_ptr,
+                    size = a.size(),
+                    shape = std::move(shape),
+                    strides = std::move(strides),
+                    op = std::move(op)]() {
+    const auto& a_strides = strides[0];
+    const auto& b_strides = strides[1];
+    const auto& out_strides = strides[2];
+    int ndim = shape.size();
+    switch (ndim) {
+      case 1:
+        binary_op_dims<T, U, Op, 1>(
+            a_ptr,
+            b_ptr,
+            out_a_ptr,
+            out_b_ptr,
+            op,
+            shape,
+            a_strides,
+            b_strides,
+            out_strides,
+            0);
+        return;
+      case 2:
+        binary_op_dims<T, U, Op, 2>(
+            a_ptr,
+            b_ptr,
+            out_a_ptr,
+            out_b_ptr,
+            op,
+            shape,
+            a_strides,
+            b_strides,
+            out_strides,
+            0);
+        return;
+    }
 
-  ContiguousIterator a_it(shape, a_strides, ndim - 2);
-  ContiguousIterator b_it(shape, b_strides, ndim - 2);
-  auto stride = out_strides[ndim - 3];
-  for (size_t elem = 0; elem < a.size(); elem += stride) {
-    binary_op_dims<T, U, Op, 2>(
-        a_ptr + a_it.loc,
-        b_ptr + b_it.loc,
-        out_a_ptr + elem,
-        out_b_ptr + elem,
-        op,
-        shape,
-        a_strides,
-        b_strides,
-        out_strides,
-        ndim - 2);
-    a_it.step();
-    b_it.step();
-  }
+    ContiguousIterator a_it(shape, a_strides, ndim - 2);
+    ContiguousIterator b_it(shape, b_strides, ndim - 2);
+    auto stride = out_strides[ndim - 3];
+    for (size_t elem = 0; elem < size; elem += stride) {
+      binary_op_dims<T, U, Op, 2>(
+          a_ptr + a_it.loc,
+          b_ptr + b_it.loc,
+          out_a_ptr + elem,
+          out_b_ptr + elem,
+          op,
+          shape,
+          a_strides,
+          b_strides,
+          out_strides,
+          ndim - 2);
+      a_it.step();
+      b_it.step();
+    }
+  });
 }
 
 template <typename T, typename U = T, typename Op>
 void binary_op(
     const array& a,
     const array& b,
-    array& out_a,
-    array& out_b,
-    Op op,
-    BinaryOpType bopt) {
+    std::vector<array>& outputs,
+    Op op) {
+  auto bopt = get_binary_op_type(a, b);
+  auto& out_a = outputs[0];
+  auto& out_b = outputs[1];
+  set_binary_op_output_data(a, b, out_a, bopt);
+  set_binary_op_output_data(a, b, out_b, bopt);
+
+  auto stream = out_a.primitive().stream();
   // The full computation is scalar scalar so call the base op once
   if (bopt == BinaryOpType::General) {
-    binary_op_dispatch_dims<T, U, Op>(a, b, out_a, out_b, op);
+    binary_op_dispatch_dims<T, U, Op>(a, b, out_a, out_b, stream, op);
     return;
   }
 
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out_a);
+  encoder.set_output_array(out_b);
+
   auto a_ptr = a.data<T>();
   auto b_ptr = b.data<T>();
   auto out_a_ptr = out_a.data<U>();
   auto out_b_ptr = out_b.data<U>();
   if (bopt == BinaryOpType::ScalarScalar) {
-    std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
+    encoder.dispatch(
+        [a_ptr, b_ptr, out_a_ptr, out_b_ptr, op = std::move(op)]() mutable {
+          std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
+        });
   } else if (bopt == BinaryOpType::ScalarVector) {
-    for (size_t i = 0; i < b.data_size(); ++i) {
-      std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
-      out_a_ptr++;
-      out_b_ptr++;
-      b_ptr++;
-    }
+    encoder.dispatch([a_ptr,
+                      b_ptr,
+                      out_a_ptr,
+                      out_b_ptr,
+                      size = b.size(),
+                      op = std::move(op)]() mutable {
+      for (size_t i = 0; i < size; ++i) {
+        std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
+        out_a_ptr++;
+        out_b_ptr++;
+        b_ptr++;
+      }
+    });
   } else if (bopt == BinaryOpType::VectorScalar) {
-    for (size_t i = 0; i < a.data_size(); ++i) {
-      std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
-      out_a_ptr++;
-      out_b_ptr++;
-      a_ptr++;
-    }
+    encoder.dispatch([a_ptr,
+                      b_ptr,
+                      out_a_ptr,
+                      out_b_ptr,
+                      size = a.size(),
+                      op = std::move(op)]() mutable {
+      for (size_t i = 0; i < size; ++i) {
+        std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
+        out_a_ptr++;
+        out_b_ptr++;
+        a_ptr++;
+      }
+    });
   } else { // VectorVector
-    for (size_t i = 0; i < a.size(); ++i) {
-      std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
-      out_a_ptr++;
-      out_b_ptr++;
-      a_ptr++;
-      b_ptr++;
-    }
+    encoder.dispatch([a_ptr,
+                      b_ptr,
+                      out_a_ptr,
+                      out_b_ptr,
+                      size = a.size(),
+                      op = std::move(op)]() mutable {
+      for (size_t i = 0; i < size; ++i) {
+        std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
+        out_a_ptr++;
+        out_b_ptr++;
+        a_ptr++;
+        b_ptr++;
+      }
+    });
+  }
+}
+
+template <typename Op>
+void binary(
+    const array& a,
+    const array& b,
+    std::vector<array>& outputs,
+    Op op) {
+  switch (outputs[0].dtype()) {
+    case bool_:
+      binary_op<bool>(a, b, outputs, op);
+      break;
+    case uint8:
+      binary_op<uint8_t>(a, b, outputs, op);
+      break;
+    case uint16:
+      binary_op<uint16_t>(a, b, outputs, op);
+      break;
+    case uint32:
+      binary_op<uint32_t>(a, b, outputs, op);
+      break;
+    case uint64:
+      binary_op<uint64_t>(a, b, outputs, op);
+      break;
+    case int8:
+      binary_op<int8_t>(a, b, outputs, op);
+      break;
+    case int16:
+      binary_op<int16_t>(a, b, outputs, op);
+      break;
+    case int32:
+      binary_op<int32_t>(a, b, outputs, op);
+      break;
+    case int64:
+      binary_op<int64_t>(a, b, outputs, op);
+      break;
+    case float16:
+      binary_op<float16_t>(a, b, outputs, op);
+      break;
+    case float32:
+      binary_op<float>(a, b, outputs, op);
+      break;
+    case float64:
+      binary_op<double>(a, b, outputs, op);
+      break;
+    case bfloat16:
+      binary_op<bfloat16_t>(a, b, outputs, op);
+      break;
+    case complex64:
+      binary_op<complex64_t>(a, b, outputs, op);
+      break;
   }
 }
 

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

@@ -40,10 +40,7 @@ struct CompilerCache {
   std::shared_mutex mtx;
 };
 
-static CompilerCache& cache() {
-  static CompilerCache cache_;
-  return cache_;
-};
+static CompilerCache cache{};
 
 // GPU compile is always available if the GPU is available and since we are in
 // this file CPU compile is also available.
@@ -59,16 +56,14 @@ void* compile(
     const std::string& kernel_name,
     const std::function<std::string(void)>& source_builder) {
   {
-    std::shared_lock lock(cache().mtx);
-    if (auto it = cache().kernels.find(kernel_name);
-        it != cache().kernels.end()) {
+    std::shared_lock lock(cache.mtx);
+    if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
       return it->second;
     }
   }
 
-  std::unique_lock lock(cache().mtx);
-  if (auto it = cache().kernels.find(kernel_name);
-      it != cache().kernels.end()) {
+  std::unique_lock lock(cache.mtx);
+  if (auto it = cache.kernels.find(kernel_name); it != cache.kernels.end()) {
     return it->second;
   }
   std::string source_code = source_builder();
@@ -125,10 +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 "
@@ -136,7 +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;
 }
 
@@ -146,9 +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
@@ -161,15 +165,14 @@ inline void build_kernel(
 
   // Add the input arguments
   int cnt = 0;
-  for (size_t i = 0; i < inputs.size(); ++i) {
+  for (auto& x : inputs) {
+    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;
@@ -203,11 +206,10 @@ inline void build_kernel(
   }
 
   // Read the inputs in tmps
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    const auto& x = inputs[i];
+  for (auto& x : inputs) {
     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;
@@ -231,7 +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]) << ", ";
@@ -257,9 +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) {
-        const auto& x = inputs[i];
-        if (is_constant(i) || is_scalar(x)) {
+      for (auto& x : inputs) {
+        if (is_constant(x) || is_scalar(x)) {
           continue;
         }
         auto& xname = namer.get_name(x);
@@ -281,37 +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.
-  auto [contiguous, shape, strides] =
-      compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
-
-  // Collect function input arguments.
+  // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;
-  int strides_index = 1;
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    if (is_constant_(i)) {
+  std::vector<std::vector<size_t>> strides;
+  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)) {
-      args.push_back(strides[strides_index++].data());
+
+    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;
@@ -321,7 +350,7 @@ void Compiled::eval_cpu(
         inputs_,
         outputs_,
         tape_,
-        is_constant_,
+        constant_ids_,
         contiguous,
         ndim);
     // Close extern "C"
@@ -329,22 +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);
   }
+  Shape out_shape;
   if (!contiguous) {
-    args.push_back((void*)shape.data());
+    out_shape = outputs[0].shape();
+    args.push_back((void*)out_shape.data());
   } else {
     args.push_back((void*)outputs[0].data_size());
   }
   auto fun = (void (*)(void**))fn_ptr;
-  encoder.dispatch([fun,
-                    args = std::move(args),
-                    strides = std::move(strides),
-                    shape = std::move(shape)]() mutable { fun(args.data()); });
+  encoder.dispatch(
+      [fun,
+       args = std::move(args),
+       strides = std::move(strides),
+       out_shape = std::move(out_shape)]() mutable { fun(args.data()); });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/copy.cpp

@@ -13,20 +13,29 @@ namespace mlx::core {
 namespace {
 
 template <typename SrcT, typename DstT>
-void copy_single(const array& src, array& dst) {
+void copy_single(const array& src, array& dst, Stream stream) {
   auto src_ptr = src.data<SrcT>();
   auto dst_ptr = dst.data<DstT>();
-  auto size = dst.size();
-  auto val = static_cast<DstT>(src_ptr[0]);
-  std::fill_n(dst_ptr, size, val);
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(src);
+  encoder.set_output_array(dst);
+  encoder.dispatch([src_ptr, dst_ptr, size = dst.size()]() {
+    auto val = static_cast<DstT>(src_ptr[0]);
+    std::fill_n(dst_ptr, size, val);
+  });
 }
 
 template <typename SrcT, typename DstT>
-void copy_vector(const array& src, array& dst) {
+void copy_vector(const array& src, array& dst, Stream stream) {
   auto src_ptr = src.data<SrcT>();
   auto dst_ptr = dst.data<DstT>();
-  auto size = src.data_size();
-  std::copy(src_ptr, src_ptr + size, dst_ptr);
+  size_t size = src.data_size();
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(src);
+  encoder.set_output_array(dst);
+  encoder.dispatch([src_ptr, dst_ptr, size = src.data_size()]() {
+    std::copy(src_ptr, src_ptr + size, dst_ptr);
+  });
 }
 
 template <typename SrcT, typename DstT, int D>
@@ -57,6 +66,7 @@ template <typename SrcT, typename DstT>
 void copy_general_general(
     const array& src,
     array& dst,
+    Stream stream,
     const Shape& data_shape,
     const Strides& i_strides,
     const Strides& o_strides,
@@ -70,17 +80,47 @@ void copy_general_general(
       dynamic_i_offset ? dynamic_i_offset->data<int64_t>() : nullptr;
   auto o_offset_ptr =
       dynamic_o_offset ? dynamic_o_offset->data<int64_t>() : nullptr;
-  auto size = src.size();
-  if (data_shape.empty()) {
-    auto val = static_cast<DstT>(*src_ptr);
-    *dst_ptr = val;
-    return;
-  }
-  auto [shape, strides] =
-      collapse_contiguous_dims(data_shape, {i_strides, o_strides});
 
-  int ndim = shape.size();
-  if (ndim < 3) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(src);
+  encoder.set_output_array(dst);
+  encoder.dispatch([src_ptr,
+                    dst_ptr,
+                    size = src.size(),
+                    data_shape = data_shape,
+                    i_strides = i_strides,
+                    o_strides = o_strides,
+                    i_offset_ptr,
+                    o_offset_ptr]() mutable {
+    if (data_shape.empty()) {
+      auto val = static_cast<DstT>(*src_ptr);
+      *dst_ptr = val;
+      return;
+    }
+    auto [shape, strides] =
+        collapse_contiguous_dims(data_shape, {i_strides, o_strides});
+
+    int ndim = shape.size();
+    if (ndim < 3) {
+      if (i_offset_ptr) {
+        src_ptr += i_offset_ptr[0];
+      }
+      if (o_offset_ptr) {
+        dst_ptr += o_offset_ptr[0];
+      }
+
+      if (ndim == 1) {
+        copy_dims<SrcT, DstT, 1>(
+            src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+      } else if (ndim == 2) {
+        copy_dims<SrcT, DstT, 2>(
+            src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+      } else if (ndim == 3) {
+        copy_dims<SrcT, DstT, 3>(
+            src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+      }
+      return;
+    }
     if (i_offset_ptr) {
       src_ptr += i_offset_ptr[0];
     }
@@ -88,47 +128,30 @@ void copy_general_general(
       dst_ptr += o_offset_ptr[0];
     }
 
-    if (ndim == 1) {
-      copy_dims<SrcT, DstT, 1>(
-          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
-    } else if (ndim == 2) {
-      copy_dims<SrcT, DstT, 2>(
-          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
-    } else if (ndim == 3) {
+    ContiguousIterator in(shape, strides[0], ndim - 3);
+    ContiguousIterator out(shape, strides[1], ndim - 3);
+    auto stride = std::accumulate(
+        shape.end() - 3, shape.end(), 1, std::multiplies<int64_t>());
+    for (int64_t elem = 0; elem < size; elem += stride) {
       copy_dims<SrcT, DstT, 3>(
-          src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
+          src_ptr + in.loc,
+          dst_ptr + out.loc,
+          shape,
+          strides[0],
+          strides[1],
+          ndim - 3);
+      in.step();
+      out.step();
     }
-    return;
-  }
-  if (i_offset_ptr) {
-    src_ptr += i_offset_ptr[0];
-  }
-  if (o_offset_ptr) {
-    dst_ptr += o_offset_ptr[0];
-  }
-
-  ContiguousIterator in(shape, strides[0], ndim - 3);
-  ContiguousIterator out(shape, strides[1], ndim - 3);
-  auto stride = std::accumulate(
-      shape.end() - 3, shape.end(), 1, std::multiplies<int64_t>());
-  for (int64_t elem = 0; elem < size; elem += stride) {
-    copy_dims<SrcT, DstT, 3>(
-        src_ptr + in.loc,
-        dst_ptr + out.loc,
-        shape,
-        strides[0],
-        strides[1],
-        ndim - 3);
-    in.step();
-    out.step();
-  }
+  });
 }
 
 template <typename SrcT, typename DstT>
-inline void copy_general_general(const array& src, array& dst) {
+inline void copy_general_general(const array& src, array& dst, Stream stream) {
   copy_general_general<SrcT, DstT>(
       src,
       dst,
+      stream,
       src.shape(),
       src.strides(),
       dst.strides(),
@@ -142,6 +165,7 @@ template <typename SrcT, typename DstT>
 void copy_general(
     const array& src,
     array& dst,
+    Stream stream,
     const Shape& data_shape,
     const Strides& i_strides,
     const Strides&,
@@ -152,6 +176,7 @@ void copy_general(
   copy_general_general<SrcT, DstT>(
       src,
       dst,
+      stream,
       data_shape,
       i_strides,
       make_contiguous_strides(data_shape),
@@ -162,10 +187,11 @@ void copy_general(
 }
 
 template <typename SrcT, typename DstT>
-inline void copy_general(const array& src, array& dst) {
+inline void copy_general(const array& src, array& dst, Stream stream) {
   copy_general_general<SrcT, DstT>(
       src,
       dst,
+      stream,
       src.shape(),
       src.strides(),
       make_contiguous_strides(src.shape()),
@@ -176,67 +202,84 @@ inline void copy_general(const array& src, array& dst) {
 }
 
 template <typename SrcT, typename DstT, typename... Args>
-void copy(const array& src, array& dst, CopyType ctype, Args&&... args) {
+void copy(
+    const array& src,
+    array& dst,
+    CopyType ctype,
+    Stream stream,
+    Args&&... args) {
   switch (ctype) {
     case CopyType::Scalar:
-      copy_single<SrcT, DstT>(src, dst);
+      copy_single<SrcT, DstT>(src, dst, stream);
       return;
     case CopyType::Vector:
-      copy_vector<SrcT, DstT>(src, dst);
+      copy_vector<SrcT, DstT>(src, dst, stream);
       return;
     case CopyType::General:
-      copy_general<SrcT, DstT>(src, dst, std::forward<Args>(args)...);
+      copy_general<SrcT, DstT>(src, dst, stream, std::forward<Args>(args)...);
       return;
     case CopyType::GeneralGeneral:
-      copy_general_general<SrcT, DstT>(src, dst, std::forward<Args>(args)...);
+      copy_general_general<SrcT, DstT>(
+          src, dst, stream, std::forward<Args>(args)...);
       return;
   }
 }
 
 template <typename SrcT, typename... Args>
-void copy(const array& src, array& dst, CopyType ctype, Args&&... args) {
+void copy(
+    const array& src,
+    array& dst,
+    CopyType ctype,
+    Stream stream,
+    Args&&... args) {
   switch (dst.dtype()) {
     case bool_:
-      copy<SrcT, bool>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, bool>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint8:
-      copy<SrcT, uint8_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, uint8_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint16:
-      copy<SrcT, uint16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, uint16_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint32:
-      copy<SrcT, uint32_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, uint32_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint64:
-      copy<SrcT, uint64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, uint64_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int8:
-      copy<SrcT, int8_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, int8_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int16:
-      copy<SrcT, int16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, int16_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int32:
-      copy<SrcT, int32_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, int32_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int64:
-      copy<SrcT, int64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, int64_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float16:
-      copy<SrcT, float16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, float16_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float32:
-      copy<SrcT, float>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, float>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float64:
-      copy<SrcT, double>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, double>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case bfloat16:
-      copy<SrcT, bfloat16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, bfloat16_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case complex64:
-      copy<SrcT, complex64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<SrcT, complex64_t>(
+          src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
   }
 }
@@ -246,70 +289,61 @@ inline void copy_inplace_dispatch(
     const array& src,
     array& dst,
     CopyType ctype,
+    Stream stream,
     Args&&... args) {
   switch (src.dtype()) {
     case bool_:
-      copy<bool>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<bool>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint8:
-      copy<uint8_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<uint8_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint16:
-      copy<uint16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<uint16_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint32:
-      copy<uint32_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<uint32_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case uint64:
-      copy<uint64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<uint64_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int8:
-      copy<int8_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<int8_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int16:
-      copy<int16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<int16_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int32:
-      copy<int32_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<int32_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case int64:
-      copy<int64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<int64_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float16:
-      copy<float16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<float16_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float32:
-      copy<float>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<float>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case float64:
-      copy<double>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<double>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case bfloat16:
-      copy<bfloat16_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<bfloat16_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
     case complex64:
-      copy<complex64_t>(src, dst, ctype, std::forward<Args>(args)...);
+      copy<complex64_t>(src, dst, ctype, stream, std::forward<Args>(args)...);
       break;
   }
 }
 
 } // namespace
 
-void copy_cpu_inplace(
-    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);
-  encoder.dispatch(
-      [src = array::unsafe_weak_copy(src),
-       dst = array::unsafe_weak_copy(dst),
-       ctype]() mutable { copy_inplace_dispatch(src, dst, ctype); });
+void copy_inplace(const array& src, array& dst, CopyType ctype, Stream stream) {
+  copy_inplace_dispatch(src, dst, ctype, stream);
 }
 
-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 +353,10 @@ 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,
@@ -334,47 +368,26 @@ void copy_cpu_inplace(
     Stream stream,
     const std::optional<array>& dynamic_i_offset, /* = std::nullopt */
     const std::optional<array>& dynamic_o_offset /* = std::nullopt */) {
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(src);
-  encoder.set_output_array(dst);
-  auto weak_copy_if_set = [](auto x) -> std::optional<array> {
-    if (x) {
-      return array::unsafe_weak_copy(*x);
-    } else {
-      return std::nullopt;
-    }
-  };
-  encoder.dispatch(
-      [src = array::unsafe_weak_copy(src),
-       dst = array::unsafe_weak_copy(dst),
-       data_shape,
-       i_strides,
-       o_strides,
-       i_offset,
-       o_offset,
-       ctype,
-       dynamic_i_offset = weak_copy_if_set(dynamic_i_offset),
-       dynamic_o_offset = weak_copy_if_set(dynamic_o_offset)]() mutable {
-        switch (ctype) {
-          case CopyType::General:
-          case CopyType::GeneralGeneral:
-            copy_inplace_dispatch(
-                src,
-                dst,
-                ctype,
-                data_shape,
-                i_strides,
-                o_strides,
-                i_offset,
-                o_offset,
-                dynamic_i_offset,
-                dynamic_o_offset);
-            break;
-          case CopyType::Scalar:
-          case CopyType::Vector:
-            copy_inplace_dispatch(src, dst, ctype);
-        }
-      });
+  switch (ctype) {
+    case CopyType::General:
+    case CopyType::GeneralGeneral:
+      copy_inplace_dispatch(
+          src,
+          dst,
+          ctype,
+          stream,
+          data_shape,
+          i_strides,
+          o_strides,
+          i_offset,
+          o_offset,
+          dynamic_i_offset,
+          dynamic_o_offset);
+      break;
+    case CopyType::Scalar:
+    case CopyType::Vector:
+      copy_inplace_dispatch(src, dst, ctype, stream);
+  }
 }
 
 } // 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_cpu_inplace(
-    const array& src,
-    array& dst,
-    CopyType ctype,
-    Stream stream);
+void copy(const array& src, array& dst, CopyType ctype, Stream stream);
+void copy_inplace(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,

mlx/backend/cpu/distributed.cpp

@@ -14,7 +14,7 @@ std::pair<array, bool> ensure_row_contiguous(const array& arr, Stream stream) {
     return {arr, false};
   } else {
     array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-    copy_cpu(arr, arr_copy, CopyType::General, stream);
+    copy(arr, arr_copy, CopyType::General, stream);
     return {arr_copy, true};
   }
 };
@@ -30,12 +30,12 @@ void AllReduce::eval_cpu(
       if (in.is_donatable()) {
         out.copy_shared_buffer(in);
       } else {
-        out.set_data(allocator::malloc(out.nbytes()));
+        out.set_data(allocator::malloc_or_wait(out.nbytes()));
       }
       return in;
     } else {
       array arr_copy(in.shape(), in.dtype(), nullptr, {});
-      copy_cpu(in, arr_copy, CopyType::General, s);
+      copy(in, arr_copy, CopyType::General, s);
       out.copy_shared_buffer(arr_copy);
       return arr_copy;
     }
@@ -46,15 +46,8 @@ void AllReduce::eval_cpu(
     case Sum:
       distributed::detail::all_sum(group(), in, outputs[0], stream());
       break;
-    case Max:
-      distributed::detail::all_max(group(), in, outputs[0], stream());
-      break;
-    case Min:
-      distributed::detail::all_min(group(), in, outputs[0], stream());
-      break;
     default:
-      throw std::runtime_error(
-          "Only all reduce sum, min and max are supported for now");
+      throw std::runtime_error("Only all reduce sum is supported for now");
   }
 }
 
@@ -65,7 +58,7 @@ void AllGather::eval_cpu(
   assert(outputs.size() == 1);
 
   auto [in, copied] = ensure_row_contiguous(inputs[0], stream());
-  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
+  outputs[0].set_data(allocator::malloc_or_wait(outputs[0].nbytes()));
   distributed::detail::all_gather(group(), in, outputs[0], stream());
   if (copied) {
     auto& enc = cpu::get_command_encoder(stream());
@@ -94,7 +87,7 @@ void Recv::eval_cpu(
   assert(inputs.size() == 0);
   assert(outputs.size() == 1);
 
-  outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
+  outputs[0].set_data(allocator::malloc_or_wait(outputs[0].nbytes()));
   distributed::detail::recv(group(), outputs[0], src_, stream());
 }
 

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 +33,50 @@ 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_or_wait(sizeof(T) * lwork)};
+    auto iwork_buf =
+        array::Data{allocator::malloc_or_wait(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());
       }
     }
@@ -194,9 +98,9 @@ void Eigh::eval_cpu(
       ? outputs[1]
       : array(a.shape(), a.dtype(), nullptr, {});
 
-  values.set_data(allocator::malloc(values.nbytes()));
+  values.set_data(allocator::malloc_or_wait(values.nbytes()));
 
-  copy_cpu(
+  copy(
       a,
       vectors,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
@@ -228,10 +132,6 @@ 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/encoder.h

@@ -9,9 +9,6 @@
 
 namespace mlx::core::cpu {
 
-// Number of dispatches per scheduler task
-constexpr int DISPATCHES_PER_TASK = 10;
-
 struct CommandEncoder {
   CommandEncoder(Stream stream) : stream_(stream) {}
 
@@ -42,24 +39,13 @@ struct CommandEncoder {
 
   template <class F, class... Args>
   void dispatch(F&& f, Args&&... args) {
-    num_ops_ = (num_ops_ + 1) % DISPATCHES_PER_TASK;
     auto task = std::bind(std::forward<F>(f), std::forward<Args>(args)...);
-    if (num_ops_ == 0) {
-      scheduler::notify_new_task(stream_);
-      auto task_wrap = [s = stream_, task = std::move(task)]() mutable {
-        task();
-        scheduler::notify_task_completion(s);
-      };
-      scheduler::enqueue(stream_, std::move(task_wrap));
-    } else {
-      scheduler::enqueue(stream_, std::move(task));
-    }
+    scheduler::enqueue(stream_, std::move(task));
   }
 
  private:
   Stream stream_;
   std::vector<array> temporaries_;
-  int num_ops_{0};
 };
 
 CommandEncoder& get_command_encoder(Stream stream);

mlx/backend/cpu/eval.cpp

@@ -33,8 +33,12 @@ void eval(array& arr) {
     buffers.erase(it);
   }
   auto& encoder = cpu::get_command_encoder(s);
-  encoder.dispatch([buffers = std::move(buffers),
-                    temps = std::move(encoder.temporaries())]() {});
+  scheduler::notify_new_task(s);
+  encoder.dispatch([s,
+                    buffers = std::move(buffers),
+                    temps = std::move(encoder.temporaries())]() {
+    scheduler::notify_task_completion(s);
+  });
 }
 
 } // namespace mlx::core::cpu

mlx/backend/cpu/fft.cpp

@@ -22,7 +22,7 @@ void FFT::eval_cpu(const std::vector<array>& inputs, array& out) {
     s *= out.itemsize();
   }
 
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   std::vector<size_t> shape;
   if (out.dtype() == float32) {

mlx/backend/cpu/gemms/no_bf16.cpp

@@ -0,0 +1,27 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cpu/gemm.h"
+
+namespace mlx::core {
+
+template <>
+void matmul<bfloat16_t>(
+    const bfloat16_t*,
+    const bfloat16_t*,
+    bfloat16_t*,
+    bool,
+    bool,
+    size_t,
+    size_t,
+    size_t,
+    float,
+    float,
+    size_t,
+    const Shape&,
+    const Strides&,
+    const Shape&,
+    const Strides&) {
+  throw std::runtime_error("[Matmul::eval_cpu] bfloat16 not supported.");
+}
+
+} // namespace mlx::core

mlx/backend/cpu/gemms/no_fp16.cpp

@@ -0,0 +1,27 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cpu/gemm.h"
+
+namespace mlx::core {
+
+template <>
+void matmul<float16_t>(
+    const float16_t*,
+    const float16_t*,
+    float16_t*,
+    bool,
+    bool,
+    size_t,
+    size_t,
+    size_t,
+    float,
+    float,
+    size_t,
+    const Shape&,
+    const Strides&,
+    const Shape&,
+    const Strides&) {
+  throw std::runtime_error("[Matmul::eval_cpu] float16 not supported.");
+}
+
+} // namespace mlx::core

mlx/backend/cpu/gemms/simd_bf16.cpp

@@ -1,45 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cpu/gemm.h"
-#include "mlx/backend/cpu/gemms/simd_gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<bfloat16_t>(
-    const bfloat16_t* a,
-    const bfloat16_t* b,
-    bfloat16_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];
-  for (int i = 0; i < batch_size; ++i) {
-    simd_gemm<bfloat16_t, float>(
-        a + elem_to_loc(M * K * i, a_shape, a_strides),
-        b + elem_to_loc(K * N * i, b_shape, b_strides),
-        out + M * N * i,
-        a_transposed,
-        b_transposed,
-        M,
-        N,
-        K,
-        alpha,
-        beta);
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/simd_fp16.cpp

@@ -1,45 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cpu/gemm.h"
-#include "mlx/backend/cpu/gemms/simd_gemm.h"
-
-namespace mlx::core {
-
-template <>
-void matmul<float16_t>(
-    const float16_t* a,
-    const float16_t* b,
-    float16_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];
-  for (int i = 0; i < batch_size; ++i) {
-    simd_gemm<float16_t, float>(
-        a + elem_to_loc(M * K * i, a_shape, a_strides),
-        b + elem_to_loc(K * N * i, b_shape, b_strides),
-        out + M * N * i,
-        a_transposed,
-        b_transposed,
-        M,
-        N,
-        K,
-        alpha,
-        beta);
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cpu/gemms/simd_gemm.h

@@ -1,139 +0,0 @@
-// Copyright © 2025 Apple Inc.
-#pragma once
-
-#include "mlx/backend/cpu/simd/simd.h"
-
-namespace mlx::core {
-
-inline int ceildiv(int a, int b) {
-  return (a + b - 1) / b;
-}
-
-template <int block_size, typename T, typename AccT>
-void load_block(
-    const T* in,
-    AccT* out,
-    int M,
-    int N,
-    int i,
-    int j,
-    bool transpose) {
-  if (transpose) {
-    for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
-      for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
-        out[jj * block_size + ii] =
-            in[(i * block_size + ii) * N + j * block_size + jj];
-      }
-    }
-  } else {
-    for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
-      for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
-        out[ii * block_size + jj] =
-            in[(i * block_size + ii) * N + j * block_size + jj];
-      }
-    }
-  }
-}
-
-template <typename T, typename AccT>
-void simd_gemm(
-    const T* a,
-    const T* b,
-    T* c,
-    bool a_trans,
-    bool b_trans,
-    int M,
-    int N,
-    int K,
-    float alpha,
-    float beta) {
-  constexpr int block_size = 16;
-  constexpr int simd_size = simd::max_size<AccT>;
-  static_assert(
-      (block_size % simd_size) == 0,
-      "Block size must be divisible by SIMD size");
-
-  int last_k_block_size = K - block_size * (K / block_size);
-  int last_k_simd_block = (last_k_block_size / simd_size) * simd_size;
-  for (int i = 0; i < ceildiv(M, block_size); i++) {
-    for (int j = 0; j < ceildiv(N, block_size); j++) {
-      AccT c_block[block_size * block_size] = {0.0};
-      AccT a_block[block_size * block_size];
-      AccT b_block[block_size * block_size];
-
-      int k = 0;
-      for (; k < K / block_size; k++) {
-        // Load a and b blocks
-        if (a_trans) {
-          load_block<block_size>(a, a_block, K, M, k, i, true);
-        } else {
-          load_block<block_size>(a, a_block, M, K, i, k, false);
-        }
-        if (b_trans) {
-          load_block<block_size>(b, b_block, N, K, j, k, false);
-        } else {
-          load_block<block_size>(b, b_block, K, N, k, j, true);
-        }
-
-        // Multiply and accumulate
-        for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
-          for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
-            for (int kk = 0; kk < block_size; kk += simd_size) {
-              auto av =
-                  simd::load<AccT, simd_size>(a_block + ii * block_size + kk);
-              auto bv =
-                  simd::load<AccT, simd_size>(b_block + jj * block_size + kk);
-              c_block[ii * block_size + jj] += simd::sum(av * bv);
-            }
-          }
-        }
-      }
-      if (last_k_block_size) {
-        // Load a and b blocks
-        if (a_trans) {
-          load_block<block_size>(a, a_block, K, M, k, i, true);
-        } else {
-          load_block<block_size>(a, a_block, M, K, i, k, false);
-        }
-        if (b_trans) {
-          load_block<block_size>(b, b_block, N, K, j, k, false);
-        } else {
-          load_block<block_size>(b, b_block, K, N, k, j, true);
-        }
-
-        // Multiply and accumulate
-        for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
-          for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
-            int kk = 0;
-            for (; kk < last_k_simd_block; kk += simd_size) {
-              auto av =
-                  simd::load<AccT, simd_size>(a_block + ii * block_size + kk);
-              auto bv =
-                  simd::load<AccT, simd_size>(b_block + jj * block_size + kk);
-              c_block[ii * block_size + jj] += simd::sum(av * bv);
-            }
-            for (; kk < last_k_block_size; ++kk) {
-              c_block[ii * block_size + jj] +=
-                  a_block[ii * block_size + kk] * b_block[jj * block_size + kk];
-            }
-          }
-        }
-      }
-
-      // Store
-      for (int ii = 0; ii < block_size && i * block_size + ii < M; ++ii) {
-        for (int jj = 0; jj < block_size && j * block_size + jj < N; ++jj) {
-          auto c_idx = (i * block_size + ii) * N + j * block_size + jj;
-          if (beta != 0) {
-            c[c_idx] = static_cast<T>(
-                alpha * c_block[ii * block_size + jj] + beta * c[c_idx]);
-          } else {
-            c[c_idx] = static_cast<T>(alpha * c_block[ii * block_size + jj]);
-          }
-        }
-      }
-    }
-  }
-}
-
-} // 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/inverse.cpp

@@ -11,7 +11,7 @@ namespace mlx::core {
 template <typename T>
 void general_inv(T* inv, int N) {
   int info;
-  auto ipiv = array::Data{allocator::malloc(sizeof(int) * N)};
+  auto ipiv = array::Data{allocator::malloc_or_wait(sizeof(int) * N)};
   // Compute LU factorization.
   getrf<T>(
       /* m = */ &N,
@@ -49,7 +49,7 @@ void general_inv(T* inv, int N) {
   }
 
   const int lwork = workspace_size;
-  auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
+  auto scratch = array::Data{allocator::malloc_or_wait(sizeof(T) * lwork)};
 
   // Compute inverse.
   getri<T>(
@@ -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/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())
-#define lapack_complex_float_imag(z) ((z).imag())
-#define lapack_complex_double_real(z) ((z).real())
-#define lapack_complex_double_imag(z) ((z).imag())
+#endif
 
 #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_REAL(orgqr)
-INSTANTIATE_LAPACK_REAL(syevd)
-INSTANTIATE_LAPACK_REAL(geev)
-INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesvdx)
-INSTANTIATE_LAPACK_REAL(getrf)
-INSTANTIATE_LAPACK_REAL(getri)
-INSTANTIATE_LAPACK_REAL(trtri)
-
-#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)
+INSTANTIATE_LAPACK_TYPES(geqrf)
+INSTANTIATE_LAPACK_TYPES(orgqr)
+INSTANTIATE_LAPACK_TYPES(syevd)
+INSTANTIATE_LAPACK_TYPES(potrf)
+INSTANTIATE_LAPACK_TYPES(gesvdx)
+INSTANTIATE_LAPACK_TYPES(getrf)
+INSTANTIATE_LAPACK_TYPES(getri)
+INSTANTIATE_LAPACK_TYPES(trtri)

mlx/backend/cpu/logsumexp.cpp

@@ -1,140 +0,0 @@
-// Copyright © 2023-2024 Apple Inc.
-
-#include <cassert>
-#include <cmath>
-
-#include "mlx/backend/cpu/copy.h"
-#include "mlx/backend/cpu/encoder.h"
-#include "mlx/backend/cpu/simd/simd.h"
-#include "mlx/primitives.h"
-#include "mlx/types/limits.h"
-
-namespace mlx::core {
-
-namespace {
-
-using namespace mlx::core::simd;
-
-template <typename T, typename AccT>
-void logsumexp(const array& in, array& out, Stream stream) {
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-
-  const T* in_ptr = in.data<T>();
-  T* out_ptr = out.data<T>();
-
-  int M = in.shape().back();
-  int L = in.data_size() / M;
-
-  encoder.dispatch([in_ptr, out_ptr, M, L]() mutable {
-    constexpr int N = std::min(max_size<AccT>, max_size<T>);
-
-    const T* current_in_ptr;
-
-    for (int i = 0; i < L; i++, in_ptr += M, out_ptr += 1) {
-      // Find the maximum
-      current_in_ptr = in_ptr;
-      Simd<AccT, N> vmaximum(-numeric_limits<AccT>::infinity());
-      size_t s = M;
-      while (s >= N) {
-        Simd<AccT, N> vals = load<T, N>(current_in_ptr);
-        vmaximum = maximum(vals, vmaximum);
-        current_in_ptr += N;
-        s -= N;
-      }
-
-      AccT maximum = max(vmaximum);
-      while (s-- > 0) {
-        maximum = std::max(maximum, static_cast<AccT>(*current_in_ptr));
-        current_in_ptr++;
-      }
-
-      // Compute the normalizer and the exponentials
-      Simd<AccT, N> vnormalizer(0.0);
-      current_in_ptr = in_ptr;
-      s = M;
-      while (s >= N) {
-        Simd<AccT, N> vexp = load<T, N>(current_in_ptr);
-        vexp = exp(vexp - maximum);
-        vnormalizer = vnormalizer + vexp;
-        current_in_ptr += N;
-        s -= N;
-      }
-      AccT normalizer = sum(vnormalizer);
-      while (s-- > 0) {
-        AccT _exp = std::exp(*current_in_ptr - maximum);
-        normalizer += _exp;
-        current_in_ptr++;
-      }
-      // Normalize
-      *out_ptr = std::isinf(maximum)
-          ? static_cast<T>(maximum)
-          : static_cast<T>(std::log(normalizer) + maximum);
-    }
-  });
-}
-
-} // namespace
-
-void LogSumExp::eval_cpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-
-  // Make sure that the last dimension is contiguous
-  auto s = stream();
-  auto& encoder = cpu::get_command_encoder(s);
-  auto ensure_contiguous = [&s, &encoder](const array& x) {
-    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
-      return x;
-    } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, x_copy, CopyType::General, s);
-      encoder.add_temporary(x_copy);
-      return x_copy;
-    }
-  };
-
-  auto in = ensure_contiguous(inputs[0]);
-  if (in.flags().row_contiguous) {
-    out.set_data(allocator::malloc(out.nbytes()));
-  } else {
-    auto n = in.shape(-1);
-    auto flags = in.flags();
-    auto strides = in.strides();
-    for (auto& s : strides) {
-      s /= n;
-    }
-    bool col_contig = strides[0] == 1;
-    for (int i = 1; col_contig && i < strides.size(); ++i) {
-      col_contig &=
-          (out.shape(i) == 1 || strides[i - 1] == out.shape(i) * strides[i]);
-    }
-    flags.col_contiguous = col_contig;
-    out.set_data(
-        allocator::malloc(in.nbytes() / n),
-        in.data_size() / n,
-        std::move(strides),
-        flags);
-  }
-
-  switch (in.dtype()) {
-    case float32:
-      logsumexp<float, float>(in, out, stream());
-      break;
-    case float16:
-      logsumexp<float16_t, float>(in, out, stream());
-      break;
-    case bfloat16:
-      logsumexp<bfloat16_t, float>(in, out, stream());
-      break;
-    case float64:
-      logsumexp<double, double>(in, out, stream());
-      break;
-    default:
-      throw std::runtime_error(
-          "[logsumexp] only supports floating point types");
-      break;
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cpu/luf.cpp

@@ -30,8 +30,9 @@ void luf_impl(
   auto strides = lu.strides();
   strides[ndim - 1] = M;
   strides[ndim - 2] = 1;
-  lu.set_data(allocator::malloc(lu.nbytes()), lu.nbytes(), strides, flags);
-  copy_cpu_inplace(
+  lu.set_data(
+      allocator::malloc_or_wait(lu.nbytes()), lu.nbytes(), strides, flags);
+  copy_inplace(
       a,
       lu,
       a.shape(),
@@ -43,8 +44,8 @@ void luf_impl(
       stream);
 
   auto a_ptr = lu.data<T>();
-  pivots.set_data(allocator::malloc(pivots.nbytes()));
-  row_indices.set_data(allocator::malloc(row_indices.nbytes()));
+  pivots.set_data(allocator::malloc_or_wait(pivots.nbytes()));
+  row_indices.set_data(allocator::malloc_or_wait(row_indices.nbytes()));
   auto pivots_ptr = pivots.data<uint32_t>();
   auto row_indices_ptr = row_indices.data<uint32_t>();
   size_t num_matrices = a.size() / (M * N);

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) {
@@ -112,7 +59,7 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[BlockMaskedMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
@@ -124,20 +71,20 @@ 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_cpu(arr, arr_copy, CopyType::General, s);
+          copy(arr, arr_copy, CopyType::General, s);
           int64_t stx = arr.shape(-1);
           return std::make_tuple(false, stx, arr_copy, true);
         }
@@ -371,7 +318,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[GatherMM::eval] Currently only supports float32.");
   }
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
@@ -386,7 +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());
     }
@@ -490,121 +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());
     }
@@ -115,7 +115,7 @@ void matmul_general(
 }
 
 void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
   if (inputs[0].shape(-1) == 0) {
     auto& encoder = cpu::get_command_encoder(stream());
     encoder.set_output_array(out);
@@ -132,20 +132,14 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[AddMM::eval_cpu] Currently only supports float32.");
   }
-  if (out.size() == 0) {
-    out.set_data(allocator::malloc(out.nbytes()));
-    return;
-  }
 
   // Fill output with C
   auto& c = inputs[2];
   CopyType ctype = c.data_size() == 1
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
-  copy_cpu(c, out, ctype, stream());
-  if (inputs[0].shape(-1) == 0) {
-    return;
-  }
+  copy(c, out, ctype, stream());
+
   matmul_general(inputs[0], inputs[1], out, stream(), alpha_, beta_);
 }
 

mlx/backend/cpu/primitives.cpp

@@ -21,8 +21,8 @@ namespace mlx::core {
 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());
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    copy_inplace(in, out, CopyType::General, out.primitive().stream());
   } else {
     shared_buffer_reshape(in, out_strides, out);
   }
@@ -39,7 +39,7 @@ static std::pair<array, bool> compute_dynamic_offset(
   if (donate) {
     offset.copy_shared_buffer(indices);
   } else {
-    offset.set_data(allocator::malloc(offset.itemsize()));
+    offset.set_data(allocator::malloc_or_wait(offset.itemsize()));
   }
 
   auto& encoder = cpu::get_command_encoder(stream);
@@ -124,7 +124,7 @@ void Transpose::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 void Arange::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 0);
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
   switch (out.dtype()) {
     case bool_:
       throw std::runtime_error("Bool type unsupported for arange.");
@@ -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) {
@@ -186,7 +186,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
   }
   std::partial_sum(sizes.cbegin(), sizes.cend(), sizes.begin());
 
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   auto strides = out.strides();
   auto flags = out.flags();
@@ -198,20 +198,18 @@ 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());
   }
 }
 
 void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  constexpr size_t extra_bytes = 16384;
-  if (in.buffer_size() <= out.nbytes() + extra_bytes &&
-      (in.flags().row_contiguous ||
-       (allow_col_major_ && in.flags().col_contiguous))) {
+  if (in.flags().row_contiguous ||
+      (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 +233,7 @@ 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 +249,7 @@ 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 +264,7 @@ 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) {
@@ -278,7 +276,7 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   size_t elems_per_key = out.size() / num_keys;
   size_t bytes_per_key = out.itemsize() * elems_per_key;
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   auto kptr = inputs[0].data<uint32_t>();
   auto cptr = out.data<char>();
@@ -337,10 +335,10 @@ void DynamicSlice::eval_cpu(const std::vector<array>& inputs, array& out) {
     return;
   }
   auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(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 +370,11 @@ 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 +410,14 @@ 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(),
@@ -452,13 +450,13 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
   } else {
     auto tmp = array(
         in.shape(), in.dtype() == bool_ ? uint8 : in.dtype(), nullptr, {});
-    tmp.set_data(allocator::malloc(tmp.nbytes()));
+    tmp.set_data(allocator::malloc_or_wait(tmp.nbytes()));
     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

@@ -25,11 +25,12 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   auto strides = in.strides();
   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);
+  in.set_data(
+      allocator::malloc_or_wait(in.nbytes()), in.nbytes(), strides, flags);
+  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()));
+  q.set_data(allocator::malloc_or_wait(q.nbytes()));
+  r.set_data(allocator::malloc_or_wait(r.nbytes()));
 
   auto in_ptr = in.data<T>();
   auto r_ptr = r.data<T>();
@@ -40,7 +41,8 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   encoder.set_output_array(r);
   encoder.dispatch([in_ptr, q_ptr, r_ptr, M, N, lda, num_matrices]() {
     int num_reflectors = std::min(M, N);
-    auto tau = allocator::malloc(sizeof(T) * num_matrices * num_reflectors);
+    auto tau =
+        allocator::malloc_or_wait(sizeof(T) * num_matrices * num_reflectors);
 
     T optimal_work;
     int lwork = -1;
@@ -51,7 +53,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
 
     // Update workspace size
     lwork = optimal_work;
-    auto work = allocator::malloc(sizeof(T) * lwork);
+    auto work = allocator::malloc_or_wait(sizeof(T) * lwork);
 
     // Loop over matrices
     for (int i = 0; i < num_matrices; ++i) {
@@ -94,7 +96,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
         &lwork,
         &info);
     lwork = optimal_work;
-    work = allocator::malloc(sizeof(T) * lwork);
+    work = allocator::malloc_or_wait(sizeof(T) * lwork);
 
     // Loop over matrices
     for (int i = 0; i < num_matrices; ++i) {

mlx/backend/cpu/quantized.cpp

@@ -13,18 +13,9 @@ namespace mlx::core {
 
 namespace {
 
-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);
@@ -34,16 +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] =
@@ -65,8 +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 bytes_per_pack = get_bytes_per_pack(bits);
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / 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++) {
@@ -84,7 +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)
@@ -123,9 +104,8 @@ void _qmm_t(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-
-  constexpr int pack_factor = get_pack_factor(bits, 8);
-  constexpr int bytes_per_pack = get_bytes_per_pack(bits);
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / 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++) {
@@ -141,7 +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)
@@ -324,10 +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);
@@ -350,7 +326,8 @@ void _qmm_dispatch_typed(
     const array& biases,
     int bits,
     int group_size,
-    bool transposed_w) {
+    bool transposed_w,
+    Stream stream) {
   int K = x.shape(-1);
   int M = x.ndim() > 1 ? x.shape(-2) : 1;
   int N = out.shape(-1);
@@ -358,25 +335,56 @@ void _qmm_dispatch_typed(
   int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
   int batch_size = x.size() / (K * M);
 
+  auto& encoder = cpu::get_command_encoder(stream);
+  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);
+
   auto out_ptr = out.data<T>();
   auto x_ptr = x.data<T>();
   auto w_ptr = w.data<uint32_t>();
   auto scales_ptr = scales.data<T>();
   auto biases_ptr = biases.data<T>();
-  for (int i = 0; i < batch_size; i++) {
-    _qmm_dispatch_typed<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()),
-        biases_ptr + elem_to_loc(i * g_els, biases.shape(), biases.strides()),
-        M,
-        N,
-        K,
-        bits,
-        group_size,
-        transposed_w);
-  }
+
+  encoder.dispatch([out_ptr,
+                    x_ptr,
+                    w_ptr,
+                    scales_ptr,
+                    biases_ptr,
+                    x_shape = x.shape(),
+                    x_strides = x.strides(),
+                    w_shape = w.shape(),
+                    w_strides = w.strides(),
+                    scales_shape = scales.shape(),
+                    scales_strides = scales.strides(),
+                    biases_shape = biases.shape(),
+                    biases_strides = biases.strides(),
+                    w_els,
+                    g_els,
+                    batch_size,
+                    M,
+                    N,
+                    K,
+                    bits,
+                    group_size,
+                    transposed_w] {
+    for (int i = 0; i < batch_size; i++) {
+      _qmm_dispatch_typed<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),
+          biases_ptr + elem_to_loc(i * g_els, biases_shape, biases_strides),
+          M,
+          N,
+          K,
+          bits,
+          group_size,
+          transposed_w);
+    }
+  });
 }
 
 void _qmm_dispatch(
@@ -387,19 +395,20 @@ void _qmm_dispatch(
     const array& biases,
     int bits,
     int group_size,
-    bool transposed_w) {
+    bool transposed_w,
+    Stream stream) {
   switch (x.dtype()) {
     case float32:
       _qmm_dispatch_typed<float>(
-          out, x, w, scales, biases, bits, group_size, transposed_w);
+          out, x, w, scales, biases, bits, group_size, transposed_w, stream);
       break;
     case float16:
       _qmm_dispatch_typed<float16_t>(
-          out, x, w, scales, biases, bits, group_size, transposed_w);
+          out, x, w, scales, biases, bits, group_size, transposed_w, stream);
       break;
     case bfloat16:
       _qmm_dispatch_typed<bfloat16_t>(
-          out, x, w, scales, biases, bits, group_size, transposed_w);
+          out, x, w, scales, biases, bits, group_size, transposed_w, stream);
       break;
     default:
       throw std::invalid_argument(
@@ -418,7 +427,8 @@ void _bs_qmm_dispatch_typed(
     const array& rhs_indices,
     int bits,
     int group_size,
-    bool transposed_w) {
+    bool transposed_w,
+    Stream stream) {
   int K = x.shape(-1);
   int M = x.shape(-2);
   int N = out.shape(-1);
@@ -426,6 +436,15 @@ void _bs_qmm_dispatch_typed(
   int w_els = w.shape(-1) * w.shape(-2);
   int g_els = scales.shape(-1) * scales.shape(-2);
 
+  auto& encoder = cpu::get_command_encoder(stream);
+  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);
+
   auto out_ptr = out.data<T>();
   auto x_ptr = x.data<T>();
   auto w_ptr = w.data<uint32_t>();
@@ -434,26 +453,53 @@ void _bs_qmm_dispatch_typed(
   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())];
-    _qmm_dispatch_typed<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()),
-        biases_ptr +
-            elem_to_loc(w_idx * g_els, biases.shape(), biases.strides()),
-        M,
-        N,
-        K,
-        bits,
-        group_size,
-        transposed_w);
-  }
+  encoder.dispatch([out_ptr,
+                    x_ptr,
+                    w_ptr,
+                    scales_ptr,
+                    biases_ptr,
+                    lhs_indices_ptr,
+                    rhs_indices_ptr,
+                    x_shape = x.shape(),
+                    x_strides = x.strides(),
+                    w_shape = w.shape(),
+                    w_strides = w.strides(),
+                    scales_shape = scales.shape(),
+                    scales_strides = scales.strides(),
+                    biases_shape = biases.shape(),
+                    biases_strides = biases.strides(),
+                    lhs_indices_shape = lhs_indices.shape(),
+                    lhs_indices_strides = lhs_indices.strides(),
+                    rhs_indices_shape = rhs_indices.shape(),
+                    rhs_indices_strides = rhs_indices.strides(),
+                    w_els,
+                    g_els,
+                    indices_size = lhs_indices.size(),
+                    M,
+                    N,
+                    K,
+                    bits,
+                    group_size,
+                    transposed_w]() {
+    for (int i = 0; i < 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)];
+      _qmm_dispatch_typed<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),
+          biases_ptr + elem_to_loc(w_idx * g_els, biases_shape, biases_strides),
+          M,
+          N,
+          K,
+          bits,
+          group_size,
+          transposed_w);
+    }
+  });
 }
 
 void _bs_qmm_dispatch(
@@ -466,7 +512,8 @@ void _bs_qmm_dispatch(
     const array& rhs_indices,
     int bits,
     int group_size,
-    bool transposed_w) {
+    bool transposed_w,
+    Stream stream) {
   switch (x.dtype()) {
     case float32:
       _bs_qmm_dispatch_typed<float>(
@@ -479,7 +526,8 @@ void _bs_qmm_dispatch(
           rhs_indices,
           bits,
           group_size,
-          transposed_w);
+          transposed_w,
+          stream);
       break;
     case float16:
       _bs_qmm_dispatch_typed<float16_t>(
@@ -492,7 +540,8 @@ void _bs_qmm_dispatch(
           rhs_indices,
           bits,
           group_size,
-          transposed_w);
+          transposed_w,
+          stream);
       break;
     case bfloat16:
       _bs_qmm_dispatch_typed<bfloat16_t>(
@@ -505,7 +554,8 @@ void _bs_qmm_dispatch(
           rhs_indices,
           bits,
           group_size,
-          transposed_w);
+          transposed_w,
+          stream);
       break;
     default:
       throw std::invalid_argument(
@@ -529,7 +579,7 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
       return 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);
       return temps.back();
     }
   };
@@ -539,25 +589,11 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous(scales_pre);
   auto biases = ensure_row_contiguous(biases_pre);
 
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.add_temporaries(std::move(temps));
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_input_array(scales);
-  encoder.set_input_array(biases);
-  encoder.set_output_array(out);
-  encoder.dispatch([out = array::unsafe_weak_copy(out),
-                    x = array::unsafe_weak_copy(x),
-                    w = array::unsafe_weak_copy(w),
-                    scales = array::unsafe_weak_copy(scales),
-                    biases = array::unsafe_weak_copy(biases),
-                    group_size_ = group_size_,
-                    bits_ = bits_,
-                    transpose_ = transpose_]() mutable {
-    _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
-  });
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  _qmm_dispatch(
+      out, x, w, scales, biases, group_size_, bits_, transpose_, stream());
+  auto& enc = cpu::get_command_encoder(stream());
+  enc.add_temporaries(std::move(temps));
 }
 
 void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -579,7 +615,7 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       return 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);
       return temps.back();
     }
   };
@@ -589,39 +625,21 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto scales = ensure_row_contiguous_last_dims(scales_pre);
   auto biases = ensure_row_contiguous_last_dims(biases_pre);
 
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.add_temporaries(std::move(temps));
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_input_array(scales);
-  encoder.set_input_array(biases);
-  encoder.set_input_array(lhs_indices);
-  encoder.set_input_array(rhs_indices);
-  encoder.set_output_array(out);
-  encoder.dispatch([out = array::unsafe_weak_copy(out),
-                    x = array::unsafe_weak_copy(x),
-                    w = array::unsafe_weak_copy(w),
-                    scales = array::unsafe_weak_copy(scales),
-                    biases = array::unsafe_weak_copy(biases),
-                    lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                    rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                    group_size_ = group_size_,
-                    bits_ = bits_,
-                    transpose_ = transpose_]() mutable {
-    _bs_qmm_dispatch(
-        out,
-        x,
-        w,
-        scales,
-        biases,
-        lhs_indices,
-        rhs_indices,
-        group_size_,
-        bits_,
-        transpose_);
-  });
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+  _bs_qmm_dispatch(
+      out,
+      x,
+      w,
+      scales,
+      biases,
+      lhs_indices,
+      rhs_indices,
+      group_size_,
+      bits_,
+      transpose_,
+      stream());
+  auto& enc = cpu::get_command_encoder(stream());
+  enc.add_temporaries(std::move(temps));
 }
 
 template <typename T, typename U>
@@ -637,8 +655,9 @@ 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 bytes_per_pack = get_bytes_per_pack(bits);
+  int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / 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;
 
@@ -663,21 +682,15 @@ 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;
@@ -696,13 +709,27 @@ void dispatch_quantize(
     array& scales,
     array& biases,
     int bits,
-    int group_size) {
+    int group_size,
+    Stream stream) {
   auto w_ptr = w.data<T>();
   auto out_ptr = out.data<U>();
   auto scales_ptr = scales.data<T>();
   auto biases_ptr = biases.data<T>();
-  quantize<T, U>(
-      w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(w);
+  encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
+  encoder.set_output_array(out);
+  encoder.dispatch([w_ptr,
+                    out_ptr,
+                    scales_ptr,
+                    biases_ptr,
+                    bits,
+                    group_size,
+                    w_size = w.size()]() {
+    quantize<T, U>(
+        w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w_size);
+  });
 }
 
 void fast::AffineQuantize::eval_cpu(
@@ -713,62 +740,50 @@ void fast::AffineQuantize::eval_cpu(
       return std::make_pair(arr, false);
     } else {
       array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_copy, CopyType::General, s);
+      copy(arr, arr_copy, CopyType::General, s);
       return std::make_pair(arr_copy, true);
     }
   };
 
   auto [w, copied] = ensure_row_contiguous(inputs[0]);
   auto& out = outputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   auto& scales = outputs[1];
   auto& biases = outputs[2];
-  scales.set_data(allocator::malloc(scales.nbytes()));
-  biases.set_data(allocator::malloc(biases.nbytes()));
-  auto& encoder = cpu::get_command_encoder(stream());
-  if (copied) {
-    encoder.add_temporary(w);
-  }
-  encoder.set_input_array(w);
-  encoder.set_input_array(scales);
-  encoder.set_input_array(biases);
-  encoder.set_output_array(out);
-  encoder.dispatch([w = array::unsafe_weak_copy(w),
-                    out = array::unsafe_weak_copy(out),
-                    scales = array::unsafe_weak_copy(scales),
-                    biases = array::unsafe_weak_copy(biases),
-                    group_size_ = group_size_,
-                    bits_ = bits_]() mutable {
-    if (w.dtype() == float16) {
-      if (is_power_of_2(bits_)) {
-        dispatch_quantize<float16_t, uint32_t>(
-            w, out, scales, biases, bits_, group_size_);
-      } else {
-        dispatch_quantize<float16_t, uint8_t>(
-            w, out, scales, biases, bits_, group_size_);
-      }
-    } else if (w.dtype() == bfloat16) {
-      if (is_power_of_2(bits_)) {
-        dispatch_quantize<bfloat16_t, uint32_t>(
-            w, out, scales, biases, bits_, group_size_);
-      } else {
-        dispatch_quantize<bfloat16_t, uint8_t>(
-            w, out, scales, biases, bits_, group_size_);
-      }
-    } else if (w.dtype() == float32) {
-      if (is_power_of_2(bits_)) {
-        dispatch_quantize<float, uint32_t>(
-            w, out, scales, biases, bits_, group_size_);
-      } else {
-        dispatch_quantize<float, uint8_t>(
-            w, out, scales, biases, bits_, group_size_);
-      }
+  scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
+  biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
+  if (w.dtype() == float16) {
+    if (is_power_of_2(bits_)) {
+      dispatch_quantize<float16_t, uint32_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
     } else {
-      throw std::runtime_error(
-          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+      dispatch_quantize<float16_t, uint8_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
     }
-  });
+  } else if (w.dtype() == bfloat16) {
+    if (is_power_of_2(bits_)) {
+      dispatch_quantize<bfloat16_t, uint32_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
+    } else {
+      dispatch_quantize<bfloat16_t, uint8_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
+    }
+  } else if (w.dtype() == float32) {
+    if (is_power_of_2(bits_)) {
+      dispatch_quantize<float, uint32_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
+    } else {
+      dispatch_quantize<float, uint8_t>(
+          w, out, scales, biases, bits_, group_size_, stream());
+    }
+  } else {
+    throw std::runtime_error(
+        "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
+  }
+  if (copied) {
+    cpu::get_command_encoder(stream()).add_temporary(w);
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/reduce.cpp

@@ -140,23 +140,34 @@ void reduction_op(
     const array& x,
     array& out,
     const std::vector<int>& axes,
-    U init) {
+    U init,
+    Stream stream) {
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
   ReductionPlan plan = get_reduction_plan(x, axes);
 
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(x);
+  encoder.set_output_array(out);
+
   auto in_ptr = x.data<T>();
   auto out_ptr = out.data<U>();
   if (plan.type == ContiguousAllReduce) {
-    *out_ptr = init;
-    contiguous_reduce(in_ptr, out_ptr, x.size(), Op{}, init);
+    encoder.dispatch([in_ptr, out_ptr, init, size = x.size()]() {
+      *out_ptr = init;
+      contiguous_reduce(in_ptr, out_ptr, size, Op{}, init);
+    });
     return;
   }
 
   if (plan.type == ContiguousReduce && plan.shape.size() == 1) {
     int reduction_size = plan.shape[0];
-    for (int i = 0; i < out.size(); i++, out_ptr++, in_ptr += reduction_size) {
-      *out_ptr = init;
-      contiguous_reduce(in_ptr, out_ptr, reduction_size, Op{}, init);
-    }
+    encoder.dispatch(
+        [in_ptr, out_ptr, init, reduction_size, size = out.size()]() mutable {
+          for (int i = 0; i < size; i++, out_ptr++, in_ptr += reduction_size) {
+            *out_ptr = init;
+            contiguous_reduce(in_ptr, out_ptr, reduction_size, Op{}, init);
+          }
+        });
     return;
   }
 
@@ -167,29 +178,40 @@ void reduction_op(
     // Unrolling the following loop (and implementing it in order for
     // ContiguousReduce) should hold extra performance boost.
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
-    if (plan.shape.size() == 0) {
-      for (int i = 0; i < out.size(); i++, out_ptr++) {
-        int offset = elem_to_loc(i, shape, strides);
-        *out_ptr = init;
-        contiguous_reduce(in_ptr + offset, out_ptr, reduction_size, Op{}, init);
+
+    encoder.dispatch([in_ptr,
+                      out_ptr,
+                      init,
+                      reduction_size,
+                      size = out.size(),
+                      plan = std::move(plan),
+                      shape = std::move(shape),
+                      strides = std::move(strides)]() mutable {
+      if (plan.shape.size() == 0) {
+        for (int i = 0; i < size; i++, out_ptr++) {
+          int offset = elem_to_loc(i, shape, strides);
+          *out_ptr = init;
+          contiguous_reduce(
+              in_ptr + offset, out_ptr, reduction_size, Op{}, init);
+        }
+      } else {
+        for (int i = 0; i < size; i++, out_ptr++) {
+          int offset = elem_to_loc(i, shape, strides);
+          *out_ptr = init;
+          nd_loop(
+              [&](int extra_offset) {
+                contiguous_reduce(
+                    in_ptr + offset + extra_offset,
+                    out_ptr,
+                    reduction_size,
+                    Op{},
+                    init);
+              },
+              plan.shape,
+              plan.strides);
+        }
       }
-    } else {
-      for (int i = 0; i < out.size(); i++, out_ptr++) {
-        int offset = elem_to_loc(i, shape, strides);
-        *out_ptr = init;
-        nd_loop(
-            [&](int extra_offset) {
-              contiguous_reduce(
-                  in_ptr + offset + extra_offset,
-                  out_ptr,
-                  reduction_size,
-                  Op{},
-                  init);
-            },
-            plan.shape,
-            plan.strides);
-      }
-    }
+    });
     return;
   }
 
@@ -198,12 +220,20 @@ void reduction_op(
     size_t reduction_stride = plan.strides.back();
     plan.shape.pop_back();
     plan.strides.pop_back();
-    for (int i = 0; i < out.size(); i += reduction_stride) {
-      std::fill_n(out_ptr, reduction_stride, init);
-      strided_reduce(in_ptr, out_ptr, reduction_size, reduction_stride, Op{});
-      in_ptr += reduction_stride * reduction_size;
-      out_ptr += reduction_stride;
-    }
+
+    encoder.dispatch([in_ptr,
+                      out_ptr,
+                      init,
+                      reduction_size,
+                      reduction_stride,
+                      size = out.size()]() mutable {
+      for (int i = 0; i < size; i += reduction_stride) {
+        std::fill_n(out_ptr, reduction_stride, init);
+        strided_reduce(in_ptr, out_ptr, reduction_size, reduction_stride, Op{});
+        in_ptr += reduction_stride * reduction_size;
+        out_ptr += reduction_stride;
+      }
+    });
     return;
   }
 
@@ -215,49 +245,67 @@ void reduction_op(
     plan.strides.pop_back();
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
 
-    if (plan.shape.size() == 0) {
-      for (int i = 0; i < out.size(); i += reduction_stride) {
-        int offset = elem_to_loc(i, shape, strides);
-        std::fill_n(out_ptr, reduction_stride, init);
-        strided_reduce(
-            in_ptr + offset, out_ptr, reduction_size, reduction_stride, Op{});
-        out_ptr += reduction_stride;
+    encoder.dispatch([in_ptr,
+                      out_ptr,
+                      init,
+                      reduction_size,
+                      reduction_stride,
+                      size = out.size(),
+                      plan = std::move(plan),
+                      shape = std::move(shape),
+                      strides = std::move(strides)]() mutable {
+      if (plan.shape.size() == 0) {
+        for (int i = 0; i < size; i += reduction_stride) {
+          int offset = elem_to_loc(i, shape, strides);
+          std::fill_n(out_ptr, reduction_stride, init);
+          strided_reduce(
+              in_ptr + offset, out_ptr, reduction_size, reduction_stride, Op{});
+          out_ptr += reduction_stride;
+        }
+      } else {
+        for (int i = 0; i < size; i += reduction_stride) {
+          int offset = elem_to_loc(i, shape, strides);
+          std::fill_n(out_ptr, reduction_stride, init);
+          nd_loop(
+              [&](int extra_offset) {
+                strided_reduce(
+                    in_ptr + offset + extra_offset,
+                    out_ptr,
+                    reduction_size,
+                    reduction_stride,
+                    Op{});
+              },
+              plan.shape,
+              plan.strides);
+          out_ptr += reduction_stride;
+        }
       }
-    } else {
-      for (int i = 0; i < out.size(); i += reduction_stride) {
-        int offset = elem_to_loc(i, shape, strides);
-        std::fill_n(out_ptr, reduction_stride, init);
-        nd_loop(
-            [&](int extra_offset) {
-              strided_reduce(
-                  in_ptr + offset + extra_offset,
-                  out_ptr,
-                  reduction_size,
-                  reduction_stride,
-                  Op{});
-            },
-            plan.shape,
-            plan.strides);
-        out_ptr += reduction_stride;
-      }
-    }
+    });
     return;
   }
 
   if (plan.type == GeneralReduce) {
     auto [shape, strides] = shapes_without_reduction_axes(x, axes);
 
-    for (int i = 0; i < out.size(); i++, out_ptr++) {
-      int offset = elem_to_loc(i, shape, strides);
-      U val = init;
-      nd_loop(
-          [&](int extra_offset) {
-            val = Op{}(val, *(in_ptr + offset + extra_offset));
-          },
-          plan.shape,
-          plan.strides);
-      *out_ptr = val;
-    }
+    encoder.dispatch([in_ptr,
+                      out_ptr,
+                      init,
+                      size = out.size(),
+                      plan = std::move(plan),
+                      shape = std::move(shape),
+                      strides = std::move(strides)]() mutable {
+      for (int i = 0; i < size; i++, out_ptr++) {
+        int offset = elem_to_loc(i, shape, strides);
+        U val = init;
+        nd_loop(
+            [&](int extra_offset) {
+              val = Op{}(val, *(in_ptr + offset + extra_offset));
+            },
+            plan.shape,
+            plan.strides);
+        *out_ptr = val;
+      }
+    });
   }
 }
 
@@ -325,15 +373,7 @@ struct MaxReduce {
   };
 
   template <int N, typename T>
-  std::enable_if_t<std::is_integral_v<T>, 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);
-    }
+  T operator()(simd::Simd<T, N> x) {
     return simd::max(x);
   };
 };
@@ -350,15 +390,7 @@ struct MinReduce {
   };
 
   template <int N, typename T>
-  std::enable_if_t<std::is_integral_v<T>, 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);
-    }
+  T operator()(simd::Simd<T, N> x) {
     return simd::min(x);
   };
 };
@@ -402,11 +434,12 @@ void reduce_dispatch_and_or(
     const array& in,
     array& out,
     Reduce::ReduceType rtype,
-    const std::vector<int>& axes) {
+    const std::vector<int>& axes,
+    Stream stream) {
   if (rtype == Reduce::And) {
-    reduction_op<InT, bool, AndReduce>(in, out, axes, true);
+    reduction_op<InT, bool, AndReduce>(in, out, axes, true, stream);
   } else {
-    reduction_op<InT, bool, OrReduce>(in, out, axes, false);
+    reduction_op<InT, bool, OrReduce>(in, out, axes, false, stream);
   }
 }
 
@@ -415,18 +448,19 @@ void reduce_dispatch_sum_prod(
     const array& in,
     array& out,
     Reduce::ReduceType rtype,
-    const std::vector<int>& axes) {
+    const std::vector<int>& axes,
+    Stream stream) {
   if (rtype == Reduce::Sum) {
     if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t, SumReduce>(in, out, axes, 0);
+      reduction_op<InT, int32_t, SumReduce>(in, out, axes, 0, stream);
     } else {
-      reduction_op<InT, InT, SumReduce>(in, out, axes, 0);
+      reduction_op<InT, InT, SumReduce>(in, out, axes, 0, stream);
     }
   } else {
     if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t, ProdReduce>(in, out, axes, 1);
+      reduction_op<InT, int32_t, ProdReduce>(in, out, axes, 1, stream);
     } else {
-      reduction_op<InT, InT, ProdReduce>(in, out, axes, 1);
+      reduction_op<InT, InT, ProdReduce>(in, out, axes, 1, stream);
     }
   }
 }
@@ -436,144 +470,162 @@ void reduce_dispatch_min_max(
     const array& in,
     array& out,
     Reduce::ReduceType rtype,
-    const std::vector<int>& axes) {
+    const std::vector<int>& axes,
+    Stream stream) {
   if (rtype == Reduce::Max) {
     auto init = Limits<InT>::min;
-    reduction_op<InT, InT, MaxReduce>(in, out, axes, init);
+    reduction_op<InT, InT, MaxReduce>(in, out, axes, init, stream);
   } else {
     auto init = Limits<InT>::max;
-    reduction_op<InT, InT, MinReduce>(in, out, axes, init);
+    reduction_op<InT, InT, MinReduce>(in, out, axes, init, stream);
   }
 }
 
 void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  encoder.dispatch([in = array::unsafe_weak_copy(in),
-                    out = array::unsafe_weak_copy(out),
-                    reduce_type_ = reduce_type_,
-                    axes_ = axes_]() mutable {
-    switch (reduce_type_) {
-      case Reduce::And:
-      case Reduce::Or: {
-        switch (in.dtype()) {
-          case bool_:
-          case uint8:
-          case int8:
-            reduce_dispatch_and_or<int8_t>(in, out, reduce_type_, axes_);
-            break;
-          case int16:
-          case uint16:
-          case float16:
-          case bfloat16:
-            reduce_dispatch_and_or<int16_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint32:
-          case int32:
-          case float32:
-            reduce_dispatch_and_or<int32_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint64:
-          case int64:
-          case float64:
-          case complex64:
-            reduce_dispatch_and_or<int64_t>(in, out, reduce_type_, axes_);
-            break;
-        }
-        break;
-      }
-      case Reduce::Sum:
-      case Reduce::Prod: {
-        switch (in.dtype()) {
-          case bool_:
-          case uint8:
-          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:
-            reduce_dispatch_sum_prod<float16_t>(in, out, reduce_type_, axes_);
-            break;
-          case bfloat16:
-            reduce_dispatch_sum_prod<bfloat16_t>(in, out, reduce_type_, axes_);
-            break;
-          case float32:
-            reduce_dispatch_sum_prod<float>(in, out, reduce_type_, axes_);
-            break;
-          case float64:
-            reduce_dispatch_sum_prod<double>(in, out, reduce_type_, axes_);
-            break;
-          case complex64:
-            reduce_dispatch_sum_prod<complex64_t>(in, out, reduce_type_, axes_);
-            break;
-        }
-        break;
-      }
-      case Reduce::Max:
-      case Reduce::Min: {
-        switch (in.dtype()) {
-          case bool_:
-            reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_);
-            break;
-          case uint8:
-            reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint16:
-            reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint32:
-            reduce_dispatch_min_max<uint32_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint64:
-            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_);
-            break;
-          case int16:
-            reduce_dispatch_min_max<int16_t>(in, out, reduce_type_, axes_);
-            break;
-          case int32:
-            reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);
-            break;
-          case int64:
-            reduce_dispatch_min_max<int64_t>(in, out, reduce_type_, axes_);
-            break;
-          case float16:
-            reduce_dispatch_min_max<float16_t>(in, out, reduce_type_, axes_);
-            break;
-          case float32:
-            reduce_dispatch_min_max<float>(in, out, reduce_type_, axes_);
-            break;
-          case float64:
-            reduce_dispatch_min_max<double>(in, out, reduce_type_, axes_);
-            break;
-          case bfloat16:
-            reduce_dispatch_min_max<bfloat16_t>(in, out, reduce_type_, axes_);
-            break;
-          case complex64:
-            reduce_dispatch_min_max<complex64_t>(in, out, reduce_type_, axes_);
-            break;
-        }
-        break;
+  switch (reduce_type_) {
+    case Reduce::And:
+    case Reduce::Or: {
+      switch (in.dtype()) {
+        case bool_:
+        case uint8:
+        case int8:
+          reduce_dispatch_and_or<int8_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int16:
+        case uint16:
+        case float16:
+        case bfloat16:
+          reduce_dispatch_and_or<int16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case uint32:
+        case int32:
+        case float32:
+          reduce_dispatch_and_or<int32_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case uint64:
+        case int64:
+        case float64:
+        case complex64:
+          reduce_dispatch_and_or<int64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
       }
+      break;
     }
-  });
+    case Reduce::Sum:
+    case Reduce::Prod: {
+      switch (in.dtype()) {
+        case bool_:
+        case uint8:
+        case int8:
+          reduce_dispatch_sum_prod<int8_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int16:
+        case uint16:
+          reduce_dispatch_sum_prod<int16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int32:
+        case uint32:
+          reduce_dispatch_sum_prod<int32_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int64:
+        case uint64:
+          reduce_dispatch_sum_prod<int64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float16:
+          reduce_dispatch_sum_prod<float16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case bfloat16:
+          reduce_dispatch_sum_prod<bfloat16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float32:
+          reduce_dispatch_sum_prod<float>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float64:
+          reduce_dispatch_sum_prod<double>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case complex64:
+          reduce_dispatch_sum_prod<complex64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+      }
+      break;
+    }
+    case Reduce::Max:
+    case Reduce::Min: {
+      switch (in.dtype()) {
+        case bool_:
+          reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_, stream());
+          break;
+        case uint8:
+          reduce_dispatch_min_max<uint8_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case uint16:
+          reduce_dispatch_min_max<uint16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case uint32:
+          reduce_dispatch_min_max<uint32_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case uint64:
+          reduce_dispatch_min_max<uint64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int8:
+          reduce_dispatch_min_max<uint8_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int16:
+          reduce_dispatch_min_max<uint16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int32:
+          reduce_dispatch_min_max<int32_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case int64:
+          reduce_dispatch_min_max<int64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float16:
+          reduce_dispatch_min_max<float16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float32:
+          reduce_dispatch_min_max<float>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case float64:
+          reduce_dispatch_min_max<double>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case bfloat16:
+          reduce_dispatch_min_max<bfloat16_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+        case complex64:
+          reduce_dispatch_min_max<complex64_t>(
+              in, out, reduce_type_, axes_, stream());
+          break;
+      }
+      break;
+    }
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/scan.cpp

@@ -3,7 +3,6 @@
 #include <cassert>
 
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/cpu/binary_ops.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
@@ -161,29 +160,38 @@ void scan_op(
     bool reverse,
     bool inclusive,
     const Op& op,
-    U init) {
+    U init,
+    Stream stream) {
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
   if (in.flags().row_contiguous) {
     if (in.strides()[axis] == 1) {
-      contiguous_scan(
-          in.data<T>(),
-          out.data<U>(),
-          in.size() / in.shape(axis),
-          in.shape(axis),
-          reverse,
-          inclusive,
-          op,
-          init);
+      encoder.dispatch([in_ptr = in.data<T>(),
+                        out_ptr = out.data<U>(),
+                        count = in.size() / in.shape(axis),
+                        stride = in.shape(axis),
+                        reverse,
+                        inclusive,
+                        op = std::move(op),
+                        init]() {
+        contiguous_scan(
+            in_ptr, out_ptr, count, stride, reverse, inclusive, op, init);
+      });
     } else {
-      strided_scan(
-          in.data<T>(),
-          out.data<U>(),
-          in.size() / in.shape(axis) / in.strides()[axis],
-          in.shape(axis),
-          in.strides()[axis],
-          reverse,
-          inclusive,
-          op,
-          init);
+      encoder.dispatch([in_ptr = in.data<T>(),
+                        out_ptr = out.data<U>(),
+                        count = in.size() / in.shape(axis) / in.strides()[axis],
+                        size = in.shape(axis),
+                        stride = in.strides()[axis],
+                        reverse,
+                        inclusive,
+                        op = std::move(op),
+                        init]() {
+        strided_scan(
+            in_ptr, out_ptr, count, size, stride, reverse, inclusive, op, init);
+      });
     }
   } else {
     throw std::runtime_error("Scan op supports only contiguous inputs");
@@ -197,18 +205,19 @@ void scan_dispatch(
     array& out,
     int axis,
     bool reverse,
-    bool inclusive) {
+    bool inclusive,
+    Stream stream) {
   switch (rtype) {
     case Scan::Sum: {
       auto op = [](U y, T x) { return y + x; };
       auto init = static_cast<U>(0);
-      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init, stream);
       break;
     }
     case Scan::Prod: {
       auto op = [](U y, T x) { return y * x; };
       auto init = static_cast<U>(1);
-      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init, stream);
       break;
     }
     case Scan::Min: {
@@ -216,7 +225,7 @@ void scan_dispatch(
       auto init = (issubdtype(in.dtype(), floating))
           ? static_cast<U>(std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::max();
-      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init, stream);
       break;
     }
     case Scan::Max: {
@@ -224,17 +233,7 @@ void scan_dispatch(
       auto init = (issubdtype(in.dtype(), floating))
           ? static_cast<U>(-std::numeric_limits<float>::infinity())
           : std::numeric_limits<U>::min();
-      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
-      break;
-    }
-    case Scan::LogAddExp: {
-      auto op = [](U a, T b) {
-        return detail::LogAddExp{}(a, static_cast<U>(b));
-      };
-      auto init = (issubdtype(in.dtype(), floating))
-          ? static_cast<U>(-std::numeric_limits<float>::infinity())
-          : std::numeric_limits<U>::min();
-      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init);
+      scan_op<T, U>(in, out, axis, reverse, inclusive, op, init, stream);
       break;
     }
   }
@@ -245,96 +244,88 @@ void scan_dispatch(
 void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
 
-  auto& encoder = cpu::get_command_encoder(stream());
-
   // Ensure contiguity
   auto in = inputs[0];
+  bool copied = false;
   if (!in.flags().row_contiguous) {
     array arr_copy(in.shape(), in.dtype(), nullptr, {});
-    copy_cpu(in, arr_copy, CopyType::General, stream());
+    copy(in, arr_copy, CopyType::General, stream());
     in = arr_copy;
-    encoder.add_temporary(arr_copy);
+    copied = true;
   }
-  out.set_data(allocator::malloc(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  encoder.dispatch([in = array::unsafe_weak_copy(in),
-                    out = array::unsafe_weak_copy(out),
-                    axis_ = axis_,
-                    reduce_type_ = reduce_type_,
-                    reverse_ = reverse_,
-                    inclusive_ = inclusive_]() mutable {
-    switch (in.dtype()) {
-      case bool_: {
-        // We could do a full dtype x dtype switch but this is the only case
-        // where we accumulate in a different type, for now.
-        //
-        // TODO: If we add the option to accumulate floats in higher precision
-        //       floats perhaps we should add the full all-to-all dispatch.
-        if (reduce_type_ == Scan::Sum && out.dtype() == int32) {
-          scan_dispatch<bool, int32_t>(
-              reduce_type_, in, out, axis_, reverse_, inclusive_);
-        } else {
-          scan_dispatch<bool, bool>(
-              reduce_type_, in, out, axis_, reverse_, inclusive_);
-        }
-        break;
+  switch (in.dtype()) {
+    case bool_: {
+      // We could do a full dtype x dtype switch but this is the only case
+      // where we accumulate in a different type, for now.
+      //
+      // TODO: If we add the option to accumulate floats in higher precision
+      //       floats perhaps we should add the full all-to-all dispatch.
+      if (reduce_type_ == Scan::Sum && out.dtype() == int32) {
+        scan_dispatch<bool, int32_t>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      } else {
+        scan_dispatch<bool, bool>(
+            reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
       }
-      case uint8:
-        scan_dispatch<uint8_t, uint8_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case uint16:
-        scan_dispatch<uint16_t, uint16_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case uint32:
-        scan_dispatch<uint32_t, uint32_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case uint64:
-        scan_dispatch<uint64_t, uint64_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case int8:
-        scan_dispatch<int8_t, int8_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case int16:
-        scan_dispatch<int16_t, int16_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case int32:
-        scan_dispatch<int32_t, int32_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case int64:
-        scan_dispatch<int64_t, int64_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case float16:
-        scan_dispatch<float16_t, float16_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case float32:
-        scan_dispatch<float, float>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case float64:
-        scan_dispatch<double, double>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case bfloat16:
-        scan_dispatch<bfloat16_t, bfloat16_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
-      case complex64:
-        scan_dispatch<complex64_t, complex64_t>(
-            reduce_type_, in, out, axis_, reverse_, inclusive_);
-        break;
+      break;
     }
-  });
+    case uint8:
+      scan_dispatch<uint8_t, uint8_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case uint16:
+      scan_dispatch<uint16_t, uint16_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case uint32:
+      scan_dispatch<uint32_t, uint32_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case uint64:
+      scan_dispatch<uint64_t, uint64_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case int8:
+      scan_dispatch<int8_t, int8_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case int16:
+      scan_dispatch<int16_t, int16_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case int32:
+      scan_dispatch<int32_t, int32_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case int64:
+      scan_dispatch<int64_t, int64_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case float16:
+      scan_dispatch<float16_t, float16_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case float32:
+      scan_dispatch<float, float>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case float64:
+      scan_dispatch<double, double>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case bfloat16:
+      scan_dispatch<bfloat16_t, bfloat16_t>(
+          reduce_type_, in, out, axis_, reverse_, inclusive_, stream());
+      break;
+    case complex64:
+      throw std::runtime_error("Scan ops do not support complex types yet");
+      break;
+  }
+  if (copied) {
+    cpu::get_command_encoder(stream()).add_temporary(std::move(in));
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cpu/select.cpp

@@ -16,70 +16,51 @@ void select_op(
     const array& b,
     const array& c,
     array& out,
-    Op op,
-    Stream stream) {
-  TernaryOpType topt = get_ternary_op_type(a, b, c);
-  set_ternary_op_output_data(a, b, c, out, topt);
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    c = array::unsafe_weak_copy(c),
-                    out = array::unsafe_weak_copy(out),
-                    op,
-                    topt]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        ternary_op<bool, bool, bool, bool>(a, b, c, out, op, topt);
-        break;
-      case uint8:
-        ternary_op<bool, uint8_t, uint8_t, uint8_t>(a, b, c, out, op, topt);
-        break;
-      case uint16:
-        ternary_op<bool, uint16_t, uint16_t, uint16_t>(a, b, c, out, op, topt);
-        break;
-      case uint32:
-        ternary_op<bool, uint32_t, uint32_t, uint32_t>(a, b, c, out, op, topt);
-        break;
-      case uint64:
-        ternary_op<bool, uint64_t, uint64_t, uint64_t>(a, b, c, out, op, topt);
-        break;
-      case int8:
-        ternary_op<bool, int8_t, int8_t, int8_t>(a, b, c, out, op, topt);
-        break;
-      case int16:
-        ternary_op<bool, int16_t, int16_t, int16_t>(a, b, c, out, op, topt);
-        break;
-      case int32:
-        ternary_op<bool, int32_t, int32_t, int32_t>(a, b, c, out, op, topt);
-        break;
-      case int64:
-        ternary_op<bool, int64_t, int64_t, int64_t>(a, b, c, out, op, topt);
-        break;
-      case float16:
-        ternary_op<bool, float16_t, float16_t, float16_t>(
-            a, b, c, out, op, topt);
-        break;
-      case float32:
-        ternary_op<bool, float, float, float>(a, b, c, out, op, topt);
-        break;
-      case float64:
-        ternary_op<bool, double, double, double>(a, b, c, out, op, topt);
-        break;
-      case bfloat16:
-        ternary_op<bool, bfloat16_t, bfloat16_t, bfloat16_t>(
-            a, b, c, out, op, topt);
-        break;
-      case complex64:
-        ternary_op<bool, complex64_t, complex64_t, complex64_t>(
-            a, b, c, out, op, topt);
-        break;
-    }
-  });
+    Op op) {
+  switch (out.dtype()) {
+    case bool_:
+      ternary_op<bool, bool, bool, bool>(a, b, c, out, op);
+      break;
+    case uint8:
+      ternary_op<bool, uint8_t, uint8_t, uint8_t>(a, b, c, out, op);
+      break;
+    case uint16:
+      ternary_op<bool, uint16_t, uint16_t, uint16_t>(a, b, c, out, op);
+      break;
+    case uint32:
+      ternary_op<bool, uint32_t, uint32_t, uint32_t>(a, b, c, out, op);
+      break;
+    case uint64:
+      ternary_op<bool, uint64_t, uint64_t, uint64_t>(a, b, c, out, op);
+      break;
+    case int8:
+      ternary_op<bool, int8_t, int8_t, int8_t>(a, b, c, out, op);
+      break;
+    case int16:
+      ternary_op<bool, int16_t, int16_t, int16_t>(a, b, c, out, op);
+      break;
+    case int32:
+      ternary_op<bool, int32_t, int32_t, int32_t>(a, b, c, out, op);
+      break;
+    case int64:
+      ternary_op<bool, int64_t, int64_t, int64_t>(a, b, c, out, op);
+      break;
+    case float16:
+      ternary_op<bool, float16_t, float16_t, float16_t>(a, b, c, out, op);
+      break;
+    case float32:
+      ternary_op<bool, float, float, float>(a, b, c, out, op);
+      break;
+    case float64:
+      ternary_op<bool, double, double, double>(a, b, c, out, op);
+      break;
+    case bfloat16:
+      ternary_op<bool, bfloat16_t, bfloat16_t, bfloat16_t>(a, b, c, out, op);
+      break;
+    case complex64:
+      ternary_op<bool, complex64_t, complex64_t, complex64_t>(a, b, c, out, op);
+      break;
+  }
 }
 
 } // namespace
@@ -89,7 +70,7 @@ void Select::eval_cpu(const std::vector<array>& inputs, array& out) {
   const auto& condition = inputs[0];
   const auto& a = inputs[1];
   const auto& b = inputs[2];
-  select_op(condition, a, b, out, detail::Select(), stream());
+  select_op(condition, a, b, out, detail::Select());
 }
 
 } // namespace mlx::core

mlx/backend/cpu/simd/accelerate_fp16_simd.h

@@ -17,7 +17,7 @@ struct ScalarT<float16_t, N> {
 #endif
 
 template <>
-inline constexpr int max_size<float16_t> = N;
+static constexpr int max_size<float16_t> = N;
 
 #define SIMD_FP16_DEFAULT_UNARY(op)                    \
   template <>                                          \

mlx/backend/cpu/simd/accelerate_simd.h

@@ -83,25 +83,25 @@ struct Simd {
 // Values chosen based on benchmarks on M3 Max
 // TODO: consider choosing these more optimally
 template <>
-inline constexpr int max_size<int8_t> = 16;
+static constexpr int max_size<int8_t> = 16;
 template <>
-inline constexpr int max_size<int16_t> = 16;
+static constexpr int max_size<int16_t> = 16;
 template <>
-inline constexpr int max_size<int> = 8;
+static constexpr int max_size<int> = 8;
 template <>
-inline constexpr int max_size<int64_t> = 4;
+static constexpr int max_size<int64_t> = 4;
 template <>
-inline constexpr int max_size<uint8_t> = 16;
+static constexpr int max_size<uint8_t> = 16;
 template <>
-inline constexpr int max_size<uint16_t> = 16;
+static constexpr int max_size<uint16_t> = 16;
 template <>
-inline constexpr int max_size<uint32_t> = 8;
+static constexpr int max_size<uint32_t> = 8;
 template <>
-inline constexpr int max_size<uint64_t> = 4;
+static constexpr int max_size<uint64_t> = 4;
 template <>
-inline constexpr int max_size<float> = 8;
+static constexpr int max_size<float> = 8;
 template <>
-inline constexpr int max_size<double> = 4;
+static constexpr int max_size<double> = 4;
 
 #define SIMD_DEFAULT_UNARY(name, op) \
   template <typename T, int N>       \

mlx/backend/cpu/simd/base_simd.h

@@ -87,45 +87,14 @@ DEFAULT_UNARY(cosh, std::cosh)
 DEFAULT_UNARY(expm1, std::expm1)
 DEFAULT_UNARY(floor, std::floor)
 DEFAULT_UNARY(log, std::log)
+DEFAULT_UNARY(log2, std::log2)
 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>) {
-    auto out = std::log(in.value);
-    auto scale = decltype(out.real())(M_LN2);
-    return Simd<T, 1>{T{out.real() / scale, out.imag() / scale}};
-  } else {
-    return Simd<T, 1>{std::log2(in.value)};
-  }
-}
-
 template <typename T>
 Simd<T, 1> operator~(Simd<T, 1> in) {
   return ~in.value;

mlx/backend/cpu/softmax.cpp

@@ -119,12 +119,17 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   // Make sure that the last dimension is contiguous
   auto set_output = [s = stream(), &out](const array& x) {
-    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
+    bool no_copy = x.strides()[x.ndim() - 1] == 1;
+    if (x.ndim() > 1) {
+      auto s = x.strides()[x.ndim() - 2];
+      no_copy &= (s == 0 || s == x.shape().back());
+    }
+    if (no_copy) {
       if (x.is_donatable()) {
         out.copy_shared_buffer(x);
       } else {
         out.set_data(
-            allocator::malloc(x.data_size() * x.itemsize()),
+            allocator::malloc_or_wait(x.data_size() * x.itemsize()),
             x.data_size(),
             x.strides(),
             x.flags());
@@ -132,7 +137,7 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
       return x;
     } else {
       array x_copy(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, x_copy, CopyType::General, s);
+      copy(x, x_copy, CopyType::General, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }
@@ -141,6 +146,18 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto in = set_output(inputs[0]);
 
   switch (in.dtype()) {
+    case bool_:
+    case uint8:
+    case uint16:
+    case uint32:
+    case uint64:
+    case int8:
+    case int16:
+    case int32:
+    case int64:
+      throw std::runtime_error(
+          "Softmax is defined only for floating point types");
+      break;
     case float32:
       softmax<float, float>(in, out, stream());
       break;
@@ -161,9 +178,9 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
     case float64:
       softmax<double, double>(in, out, stream());
       break;
-    default:
-      throw std::runtime_error(
-          "[softmax] Only defined for floating point types.");
+    case complex64:
+      throw std::invalid_argument(
+          "[Softmax] Not yet implemented for complex64");
       break;
   }
 }

mlx/backend/cpu/sort.cpp

@@ -105,11 +105,15 @@ struct StridedIterator {
 };
 
 template <typename T>
-void sort(array& out, int axis) {
+void sort(const array& in, array& out, int axis, Stream stream) {
+  // Copy input to output
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
+  copy(in, out, ctype, stream);
+
   // Get axis, shape and stride info
-  axis = axis < 0 ? axis + out.ndim() : axis;
-  size_t in_size = out.size();
-  size_t n_rows = in_size / out.shape(axis);
+  axis = axis < 0 ? axis + in.ndim() : axis;
+  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
+  size_t n_rows = in_size / in.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -123,20 +127,30 @@ void sort(array& out, int axis) {
   // Perform sorting in place
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
-  auto out_ptr = out.data<T>();
-  for (int i = 0; i < n_rows; i++) {
-    T* data_ptr = out_ptr + src_it.loc;
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(out);
+  encoder.dispatch([out_ptr = out.data<T>(),
+                    src_it = std::move(src_it),
+                    n_rows,
+                    axis_size,
+                    axis_stride]() mutable {
+    for (int i = 0; i < n_rows; i++) {
+      T* data_ptr = out_ptr + src_it.loc;
 
-    StridedIterator st(data_ptr, axis_stride, 0);
-    StridedIterator ed(data_ptr, axis_stride, axis_size);
+      StridedIterator st(data_ptr, axis_stride, 0);
+      StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::stable_sort(st, ed);
-    src_it.step();
-  }
+      std::stable_sort(st, ed);
+      src_it.step();
+    }
+  });
 }
 
 template <typename T, typename IdxT = uint32_t>
-void argsort(const array& in, array& out, int axis) {
+void argsort(const array& in, array& out, int axis, Stream stream) {
+  // Allocate output
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
   size_t n_rows = in.size() / in.shape(axis);
@@ -162,69 +176,99 @@ void argsort(const array& in, array& out, int axis) {
       in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
   ContiguousIterator out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
-  auto in_ptr = in.data<T>();
-  auto out_ptr = out.data<IdxT>();
-  for (int i = 0; i < n_rows; i++) {
-    const T* data_ptr = in_ptr + in_it.loc;
-    IdxT* idx_ptr = out_ptr + out_it.loc;
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_input_array(out);
+  encoder.dispatch([in_ptr = in.data<T>(),
+                    out_ptr = out.data<IdxT>(),
+                    in_it = std::move(in_it),
+                    out_it = std::move(out_it),
+                    n_rows,
+                    axis_size,
+                    in_stride,
+                    out_stride]() mutable {
+    for (int i = 0; i < n_rows; i++) {
+      const T* data_ptr = in_ptr + in_it.loc;
+      IdxT* idx_ptr = out_ptr + out_it.loc;
 
-    in_it.step();
-    out_it.step();
+      in_it.step();
+      out_it.step();
 
-    StridedIterator st_(idx_ptr, out_stride, 0);
-    StridedIterator ed_(idx_ptr, out_stride, axis_size);
+      StridedIterator st_(idx_ptr, out_stride, 0);
+      StridedIterator ed_(idx_ptr, out_stride, axis_size);
 
-    // Initialize with iota
-    std::iota(st_, ed_, IdxT(0));
+      // Initialize with iota
+      std::iota(st_, ed_, IdxT(0));
 
-    // Sort according to vals
-    StridedIterator st(idx_ptr, out_stride, 0);
-    StridedIterator ed(idx_ptr, out_stride, axis_size);
+      // Sort according to vals
+      StridedIterator st(idx_ptr, out_stride, 0);
+      StridedIterator ed(idx_ptr, out_stride, axis_size);
 
-    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];
-      return v1 < v2 || (v1 == v2 && a < b);
-    });
-  }
+      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];
+        return v1 < v2 || (v1 == v2 && a < b);
+      });
+    }
+  });
 }
 
 template <typename T>
-void partition(array& out, int axis, int kth) {
-  // Get axis, shape and stride info
-  axis = axis < 0 ? axis + out.ndim() : axis;
-  size_t in_size = out.size();
-  size_t n_rows = in_size / out.shape(axis);
+void partition(const array& in, array& out, int axis, int kth, Stream stream) {
+  // Copy input to output
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
+  copy(in, out, ctype, stream);
 
-  auto remaining_shape = out.shape();
+  // Get axis, shape and stride info
+  axis = axis < 0 ? axis + in.ndim() : axis;
+  size_t in_size = in.flags().contiguous ? in.data_size() : in.size();
+  size_t n_rows = in_size / in.shape(axis);
+
+  auto remaining_shape = in.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
 
-  auto remaining_strides = out.strides();
+  auto remaining_strides = in.strides();
   remaining_strides.erase(remaining_strides.begin() + axis);
 
-  auto axis_stride = out.strides()[axis];
-  int axis_size = out.shape(axis);
+  auto axis_stride = in.strides()[axis];
+  int axis_size = in.shape(axis);
 
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition in place
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
-  auto out_ptr = out.data<T>();
-  for (int i = 0; i < n_rows; i++) {
-    T* data_ptr = out_ptr + src_it.loc;
-    src_it.step();
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_output_array(out);
+  encoder.dispatch([out_ptr = out.data<T>(),
+                    src_it = std::move(src_it),
+                    n_rows,
+                    axis_size,
+                    axis_stride,
+                    kth]() mutable {
+    for (int i = 0; i < n_rows; i++) {
+      T* data_ptr = out_ptr + src_it.loc;
+      src_it.step();
 
-    StridedIterator st(data_ptr, axis_stride, 0);
-    StridedIterator md(data_ptr, axis_stride, kth);
-    StridedIterator ed(data_ptr, axis_stride, axis_size);
+      StridedIterator st(data_ptr, axis_stride, 0);
+      StridedIterator md(data_ptr, axis_stride, kth);
+      StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::nth_element(st, md, ed);
-  }
+      std::nth_element(st, md, ed);
+    }
+  });
 }
 
 template <typename T, typename IdxT = uint32_t>
-void argpartition(const array& in, array& out, int axis, int kth) {
+void argpartition(
+    const array& in,
+    array& out,
+    int axis,
+    int kth,
+    Stream stream) {
+  // Allocate output
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
+
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
   size_t n_rows = in.size() / in.shape(axis);
@@ -253,32 +297,42 @@ void argpartition(const array& in, array& out, int axis, int kth) {
   ContiguousIterator out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
 
-  auto in_ptr = in.data<T>();
-  auto out_ptr = out.data<IdxT>();
+  auto& encoder = cpu::get_command_encoder(stream);
+  encoder.set_input_array(in);
+  encoder.set_input_array(out);
+  encoder.dispatch([in_ptr = in.data<T>(),
+                    out_ptr = out.data<IdxT>(),
+                    in_it = std::move(in_it),
+                    out_it = std::move(out_it),
+                    n_rows,
+                    axis_size,
+                    in_stride,
+                    out_stride,
+                    kth]() mutable {
+    for (int i = 0; i < n_rows; i++) {
+      const T* data_ptr = in_ptr + in_it.loc;
+      IdxT* idx_ptr = out_ptr + out_it.loc;
+      in_it.step();
+      out_it.step();
 
-  for (int i = 0; i < n_rows; i++) {
-    const T* data_ptr = in_ptr + in_it.loc;
-    IdxT* idx_ptr = out_ptr + out_it.loc;
-    in_it.step();
-    out_it.step();
+      StridedIterator st_(idx_ptr, out_stride, 0);
+      StridedIterator ed_(idx_ptr, out_stride, axis_size);
 
-    StridedIterator st_(idx_ptr, out_stride, 0);
-    StridedIterator ed_(idx_ptr, out_stride, axis_size);
+      // Initialize with iota
+      std::iota(st_, ed_, IdxT(0));
 
-    // Initialize with iota
-    std::iota(st_, ed_, IdxT(0));
+      // Sort according to vals
+      StridedIterator st(idx_ptr, out_stride, 0);
+      StridedIterator md(idx_ptr, out_stride, kth);
+      StridedIterator ed(idx_ptr, out_stride, axis_size);
 
-    // Sort according to vals
-    StridedIterator st(idx_ptr, out_stride, 0);
-    StridedIterator md(idx_ptr, out_stride, kth);
-    StridedIterator ed(idx_ptr, out_stride, axis_size);
-
-    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];
-      return v1 < v2 || (v1 == v2 && a < b);
-    });
-  }
+      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];
+        return v1 < v2 || (v1 == v2 && a < b);
+      });
+    }
+  });
 }
 
 } // namespace
@@ -287,188 +341,144 @@ void ArgSort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  // Allocate output
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.set_input_array(in);
-  encoder.set_input_array(out);
-  encoder.dispatch([in = array::unsafe_weak_copy(in),
-                    out = array::unsafe_weak_copy(out),
-                    axis_ = axis_]() mutable {
-    switch (in.dtype()) {
-      case bool_:
-        return argsort<bool>(in, out, axis_);
-      case uint8:
-        return argsort<uint8_t>(in, out, axis_);
-      case uint16:
-        return argsort<uint16_t>(in, out, axis_);
-      case uint32:
-        return argsort<uint32_t>(in, out, axis_);
-      case uint64:
-        return argsort<uint64_t>(in, out, axis_);
-      case int8:
-        return argsort<int8_t>(in, out, axis_);
-      case int16:
-        return argsort<int16_t>(in, out, axis_);
-      case int32:
-        return argsort<int32_t>(in, out, axis_);
-      case int64:
-        return argsort<int64_t>(in, out, axis_);
-      case float32:
-        return argsort<float>(in, out, axis_);
-      case float64:
-        return argsort<double>(in, out, axis_);
-      case float16:
-        return argsort<float16_t>(in, out, axis_);
-      case bfloat16:
-        return argsort<bfloat16_t>(in, out, axis_);
-      case complex64:
-        return argsort<complex64_t>(in, out, axis_);
-    }
-  });
+  switch (in.dtype()) {
+    case bool_:
+      return argsort<bool>(in, out, axis_, stream());
+    case uint8:
+      return argsort<uint8_t>(in, out, axis_, stream());
+    case uint16:
+      return argsort<uint16_t>(in, out, axis_, stream());
+    case uint32:
+      return argsort<uint32_t>(in, out, axis_, stream());
+    case uint64:
+      return argsort<uint64_t>(in, out, axis_, stream());
+    case int8:
+      return argsort<int8_t>(in, out, axis_, stream());
+    case int16:
+      return argsort<int16_t>(in, out, axis_, stream());
+    case int32:
+      return argsort<int32_t>(in, out, axis_, stream());
+    case int64:
+      return argsort<int64_t>(in, out, axis_, stream());
+    case float32:
+      return argsort<float>(in, out, axis_, stream());
+    case float64:
+      return argsort<double>(in, out, axis_, stream());
+    case float16:
+      return argsort<float16_t>(in, out, axis_, stream());
+    case bfloat16:
+      return argsort<bfloat16_t>(in, out, axis_, stream());
+    case complex64:
+      return argsort<complex64_t>(in, out, axis_, stream());
+  }
 }
 
 void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
-      ? CopyType::Vector
-      : 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_ = axis_]() mutable {
-        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_);
-        }
-      });
+  switch (in.dtype()) {
+    case bool_:
+      return sort<bool>(in, out, axis_, stream());
+    case uint8:
+      return sort<uint8_t>(in, out, axis_, stream());
+    case uint16:
+      return sort<uint16_t>(in, out, axis_, stream());
+    case uint32:
+      return sort<uint32_t>(in, out, axis_, stream());
+    case uint64:
+      return sort<uint64_t>(in, out, axis_, stream());
+    case int8:
+      return sort<int8_t>(in, out, axis_, stream());
+    case int16:
+      return sort<int16_t>(in, out, axis_, stream());
+    case int32:
+      return sort<int32_t>(in, out, axis_, stream());
+    case int64:
+      return sort<int64_t>(in, out, axis_, stream());
+    case float32:
+      return sort<float>(in, out, axis_, stream());
+    case float64:
+      return sort<double>(in, out, axis_, stream());
+    case float16:
+      return sort<float16_t>(in, out, axis_, stream());
+    case bfloat16:
+      return sort<bfloat16_t>(in, out, axis_, stream());
+    case complex64:
+      return sort<complex64_t>(in, out, axis_, stream());
+  }
 }
 
 void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  // Allocate output
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cpu::get_command_encoder(stream());
-  encoder.set_input_array(in);
-  encoder.set_input_array(out);
-  encoder.dispatch([in = array::unsafe_weak_copy(in),
-                    out = array::unsafe_weak_copy(out),
-                    axis_ = axis_,
-                    kth_ = kth_]() mutable {
-    switch (in.dtype()) {
-      case bool_:
-        return argpartition<bool>(in, out, axis_, kth_);
-      case uint8:
-        return argpartition<uint8_t>(in, out, axis_, kth_);
-      case uint16:
-        return argpartition<uint16_t>(in, out, axis_, kth_);
-      case uint32:
-        return argpartition<uint32_t>(in, out, axis_, kth_);
-      case uint64:
-        return argpartition<uint64_t>(in, out, axis_, kth_);
-      case int8:
-        return argpartition<int8_t>(in, out, axis_, kth_);
-      case int16:
-        return argpartition<int16_t>(in, out, axis_, kth_);
-      case int32:
-        return argpartition<int32_t>(in, out, axis_, kth_);
-      case int64:
-        return argpartition<int64_t>(in, out, axis_, kth_);
-      case float32:
-        return argpartition<float>(in, out, axis_, kth_);
-      case float64:
-        return argpartition<double>(in, out, axis_, kth_);
-      case float16:
-        return argpartition<float16_t>(in, out, axis_, kth_);
-      case bfloat16:
-        return argpartition<bfloat16_t>(in, out, axis_, kth_);
-      case complex64:
-        return argpartition<complex64_t>(in, out, axis_, kth_);
-    }
-  });
+  switch (in.dtype()) {
+    case bool_:
+      return argpartition<bool>(in, out, axis_, kth_, stream());
+    case uint8:
+      return argpartition<uint8_t>(in, out, axis_, kth_, stream());
+    case uint16:
+      return argpartition<uint16_t>(in, out, axis_, kth_, stream());
+    case uint32:
+      return argpartition<uint32_t>(in, out, axis_, kth_, stream());
+    case uint64:
+      return argpartition<uint64_t>(in, out, axis_, kth_, stream());
+    case int8:
+      return argpartition<int8_t>(in, out, axis_, kth_, stream());
+    case int16:
+      return argpartition<int16_t>(in, out, axis_, kth_, stream());
+    case int32:
+      return argpartition<int32_t>(in, out, axis_, kth_, stream());
+    case int64:
+      return argpartition<int64_t>(in, out, axis_, kth_, stream());
+    case float32:
+      return argpartition<float>(in, out, axis_, kth_, stream());
+    case float64:
+      return argpartition<double>(in, out, axis_, kth_, stream());
+    case float16:
+      return argpartition<float16_t>(in, out, axis_, kth_, stream());
+    case bfloat16:
+      return argpartition<bfloat16_t>(in, out, axis_, kth_, stream());
+    case complex64:
+      return argpartition<complex64_t>(in, out, axis_, kth_, stream());
+  }
 }
 
 void Partition::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
-      ? CopyType::Vector
-      : 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_ = axis_,
-                    kth_ = kth_]() mutable {
-    switch (out.dtype()) {
-      case bool_:
-        return partition<bool>(out, axis_, kth_);
-      case uint8:
-        return partition<uint8_t>(out, axis_, kth_);
-      case uint16:
-        return partition<uint16_t>(out, axis_, kth_);
-      case uint32:
-        return partition<uint32_t>(out, axis_, kth_);
-      case uint64:
-        return partition<uint64_t>(out, axis_, kth_);
-      case int8:
-        return partition<int8_t>(out, axis_, kth_);
-      case int16:
-        return partition<int16_t>(out, axis_, kth_);
-      case int32:
-        return partition<int32_t>(out, axis_, kth_);
-      case int64:
-        return partition<int64_t>(out, axis_, kth_);
-      case float32:
-        return partition<float>(out, axis_, kth_);
-      case float64:
-        return partition<double>(out, axis_, kth_);
-      case float16:
-        return partition<float16_t>(out, axis_, kth_);
-      case bfloat16:
-        return partition<bfloat16_t>(out, axis_, kth_);
-      case complex64:
-        return partition<complex64_t>(out, axis_, kth_);
-    }
-  });
+  switch (in.dtype()) {
+    case bool_:
+      return partition<bool>(in, out, axis_, kth_, stream());
+    case uint8:
+      return partition<uint8_t>(in, out, axis_, kth_, stream());
+    case uint16:
+      return partition<uint16_t>(in, out, axis_, kth_, stream());
+    case uint32:
+      return partition<uint32_t>(in, out, axis_, kth_, stream());
+    case uint64:
+      return partition<uint64_t>(in, out, axis_, kth_, stream());
+    case int8:
+      return partition<int8_t>(in, out, axis_, kth_, stream());
+    case int16:
+      return partition<int16_t>(in, out, axis_, kth_, stream());
+    case int32:
+      return partition<int32_t>(in, out, axis_, kth_, stream());
+    case int64:
+      return partition<int64_t>(in, out, axis_, kth_, stream());
+    case float32:
+      return partition<float>(in, out, axis_, kth_, stream());
+    case float64:
+      return partition<double>(in, out, axis_, kth_, stream());
+    case float16:
+      return partition<float16_t>(in, out, axis_, kth_, stream());
+    case bfloat16:
+      return partition<bfloat16_t>(in, out, axis_, kth_, stream());
+    case complex64:
+      return partition<complex64_t>(in, out, axis_, kth_, stream());
+  }
 }
 
 } // namespace mlx::core

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,
@@ -50,9 +50,9 @@ void svd_impl(
     array& s = outputs[1];
     array& vt = outputs[2];
 
-    u.set_data(allocator::malloc(u.nbytes()));
-    s.set_data(allocator::malloc(s.nbytes()));
-    vt.set_data(allocator::malloc(vt.nbytes()));
+    u.set_data(allocator::malloc_or_wait(u.nbytes()));
+    s.set_data(allocator::malloc_or_wait(s.nbytes()));
+    vt.set_data(allocator::malloc_or_wait(vt.nbytes()));
 
     encoder.set_output_array(u);
     encoder.set_output_array(s);
@@ -64,7 +64,7 @@ void svd_impl(
   } else {
     array& s = outputs[0];
 
-    s.set_data(allocator::malloc(s.nbytes()));
+    s.set_data(allocator::malloc_or_wait(s.nbytes()));
 
     encoder.set_output_array(s);
 
@@ -91,7 +91,7 @@ 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) * 12 * K)};
+    auto iwork = array::Data{allocator::malloc_or_wait(sizeof(int) * 12 * K)};
 
     static const int lwork_query = -1;
 
@@ -132,7 +132,7 @@ void svd_impl(
     }
 
     const int lwork = workspace_dimension;
-    auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    auto scratch = array::Data{allocator::malloc_or_wait(sizeof(T) * lwork)};
 
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {