Fixed mma and working dequant

.circleci/config.yml

@@ -7,9 +7,6 @@ parameters:
   nightly_build:
     type: boolean
     default: false
-  test_release:
-    type: boolean
-    default: false
 
 jobs:
   build_documentation:
@@ -18,17 +15,16 @@ jobs:
         type: boolean
         default: false
     macos:
-      xcode: "26.0.0"
-    resource_class: m4pro.medium
+      xcode: "16.2.0"
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
           name: Install
           command: |
-            xcodebuild -downloadComponent MetalToolchain
-            brew install python@3.10
+            brew install python@3.9
             brew install doxygen
-            python3.10 -m venv env
+            python3.9 -m venv env
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
@@ -82,25 +78,23 @@ jobs:
             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
-            curl -LsSf https://astral.sh/uv/install.sh | sh
       - run:
           name: Install Python package
           command: |
-            uv venv
-            uv pip install cmake
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              uv pip install -e ".[dev]" -v
+            pip install -e ".[dev]"
       - run:
           name: Generate package stubs
           command: |
-            uv pip install typing_extensions
-            uv run --no-project setup.py generate_stubs
+            echo "stubs"
+            pip install typing_extensions
+            python setup.py generate_stubs
       - run:
           name: Run Python tests
           command: |
-            source .venv/bin/activate
             python -m unittest discover python/tests -v
             mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
             mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
@@ -108,7 +102,6 @@ jobs:
       - run:
           name: Build CPP only
           command: |
-            source .venv/bin/activate
             mkdir -p build && cd build
             cmake .. -DMLX_BUILD_METAL=OFF -DCMAKE_BUILD_TYPE=DEBUG
             make -j `nproc`
@@ -120,7 +113,7 @@ jobs:
     parameters:
       xcode_version:
         type: string
-        default: "26.0.0"
+        default: "16.2.0"
       macosx_deployment_target:
         type: string
         default: ""
@@ -128,37 +121,39 @@ jobs:
       xcode: << parameters.xcode_version >>
     environment:
       MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
     steps:
       - checkout
       - run:
           name: Install dependencies
           command: |
-            xcodebuild -downloadComponent MetalToolchain
-            HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 \
-              brew install openmpi uv
+            brew install python@3.9
+            brew install openmpi
+            python3.9 -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
+            pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install numpy
+            pip install torch
+            pip install tensorflow
+            pip install unittest-xml-reporting
       - run:
           name: Install Python package
           command: |
-            uv venv --python 3.10
-            uv pip install \
-              nanobind==2.4.0 \
-              cmake \
-              numpy \
-              torch \
-              tensorflow \
-              unittest-xml-reporting
+            source env/bin/activate
             DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              uv pip install -e . -v
+              pip install -e . -v
       - run:
           name: Generate package stubs
           command: |
-            uv pip install typing_extensions
-            uv run --no-project setup.py generate_stubs
+            source env/bin/activate
+            pip install typing_extensions
+            python setup.py generate_stubs
       - run:
           name: Run Python tests
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
             mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
@@ -167,17 +162,16 @@ jobs:
       - run:
           name: Build example extension
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             cd examples/extensions
-            uv pip install -r requirements.txt
-            uv run --no-project setup.py build_ext --inplace
-            uv run --no-project python test.py
+            pip install -r requirements.txt
+            python setup.py build_ext -j8
       - store_test_results:
           path: test-results
       - run:
           name: Build CPP only
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             mkdir -p build && cd build && cmake .. && make -j `sysctl -n hw.ncpu`
       - run:
           name: Run CPP tests
@@ -186,7 +180,7 @@ jobs:
       - run:
           name: Build small binary
           command: |
-            source .venv/bin/activate
+            source env/bin/activate
             cd build/
             cmake .. -DCMAKE_BUILD_TYPE=MinSizeRel \
               -DBUILD_SHARED_LIBS=ON \
@@ -198,85 +192,43 @@ jobs:
       - run:
           name: Run Python tests with JIT
           command: |
+            source env/bin/activate
             CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
-              uv pip install -e . -v
+              pip install -e . -v
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
               METAL_DEBUG_ERROR_MODE=0 \
-              uv run --no-project python -m xmlrunner discover \
-                -v python/tests \
-                -o test-results/gpu_jit
+              python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
 
   cuda_build_and_test:
-    parameters:
-      image_date:
-        type: string
-        default: "2023.11.1"
     machine:
-      image: "linux-cuda-12:<< parameters.image_date >>"
+      image: linux-cuda-12:2023.11.1
       resource_class: gpu.nvidia.small.gen2
     steps:
       - checkout
-      - restore_cache:
-          keys:
-            - cuda-<< parameters.image_date >>-{{ arch }}-
-      - run:
-          name: Install dependencies
-          command: |
-            sudo apt-get update
-            sudo apt-get install libcudnn9-dev-cuda-12
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install libnccl2 libnccl-dev
-            curl -sL https://github.com/ccache/ccache/releases/download/v4.11.3/ccache-4.11.3-linux-x86_64.tar.xz | tar xJf -
-            sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
-            rm -rf ccache-4.11.3-linux-x86_64
-            curl -LsSf https://astral.sh/uv/install.sh | sh
-      - run:
-          name: Set CCache size
-          command: ccache --max-size 1G
       - run:
           name: Install Python package
           command: |
-            uv venv
-            uv pip install cmake
-            DEBUG=1 CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              uv pip install -e ".[dev]" -v
+            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 .venv/bin/activate
+            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
-      - run:
-          name: Build CPP only
-          command: |
-            source .venv/bin/activate
-            cmake . -B build \
-              -DMLX_BUILD_CUDA=ON \
-              -DCMAKE_CUDA_COMPILER=`which nvcc` \
-              -DCMAKE_BUILD_TYPE=DEBUG
-            cmake --build build -j `nproc`
-      - run:
-          name: Run CPP tests
-          command: ./build/tests/tests -sfe="*fft_tests.cpp,*linalg_tests.cpp"
-      - run:
-          name: CCache report
-          command: |
-            ccache --show-stats
-            ccache --zero-stats
-            ccache --cleanup
-      - save_cache:
-          key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}
-          paths:
-            - /home/circleci/.cache/ccache
 
   build_release:
     parameters:
       python_version:
         type: string
-        default: "3.10"
+        default: "3.9"
       xcode_version:
         type: string
-        default: "26.0.0"
+        default: "16.2.0"
       build_env:
         type: string
         default: ""
@@ -285,7 +237,7 @@ jobs:
         default: ""
     macos:
       xcode: << parameters.xcode_version >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
     environment:
       MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
     steps:
@@ -293,15 +245,11 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            xcodebuild -downloadComponent MetalToolchain
-            mkdir -p ~/miniconda3
-            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
-            bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-            rm ~/miniconda3/miniconda.sh
-            source ~/miniconda3/bin/activate
-            conda init --all
-            conda create -n env python=<< parameters.python_version >> -y
-            conda activate env
+            brew install python@<< parameters.python_version >>
+            brew install openmpi
+            python<< parameters.python_version >> -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
             pip install --upgrade cmake
             pip install nanobind==2.4.0
             pip install --upgrade setuptools
@@ -311,29 +259,29 @@ jobs:
       - run:
           name: Install Python package
           command: |
-            conda activate env
+            source env/bin/activate
             env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
               pip install . -v
       - run:
           name: Generate package stubs
           command: |
-            conda activate env
+            source env/bin/activate
             pip install typing_extensions
             python setup.py generate_stubs
       - run:
           name: Build Python package
           command: |
-            conda activate env
+            source env/bin/activate
             python setup.py clean --all
             << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
       - when:
           condition:
-            equal: ["3.10", << parameters.python_version >>]
+            equal: ["3.9", << parameters.python_version >>]
           steps:
             - run:
                 name: Build common package
                 command: |
-                  conda activate env
+                  source env/bin/activate
                   python setup.py clean --all
                   << parameters.build_env >> MLX_BUILD_STAGE=2 python -m build -w
       - when:
@@ -342,7 +290,7 @@ jobs:
             - run:
                 name: Upload package
                 command: |
-                  conda activate env
+                  source env/bin/activate
                   twine upload dist/*
       - store_artifacts:
           path: dist/
@@ -351,7 +299,7 @@ jobs:
     parameters:
       python_version:
         type: string
-        default: "3.10"
+        default: "3.9"
       build_env:
         type: string
         default: ""
@@ -367,10 +315,14 @@ jobs:
             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
             $PYTHON -m venv env
             source env/bin/activate
             pip install --upgrade pip
@@ -387,7 +339,7 @@ jobs:
             bash python/scripts/repair_linux.sh
       - when:
           condition:
-            equal: ["3.10", << parameters.python_version >>]
+            equal: ["3.9", << parameters.python_version >>]
           steps:
             - run:
                 name: Build common package
@@ -414,27 +366,22 @@ jobs:
         type: string
         default: ""
     machine:
-      image: ubuntu-2204:current
-      resource_class: xlarge
+      image: linux-cuda-12:2024.11.1
+      resource_class: gpu.nvidia.small.gen2
     steps:
       - checkout
       - run:
           name: Build wheel
           command: |
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
-            sudo dpkg -i cuda-keyring_1.1-1_all.deb
             sudo apt-get update
-            sudo apt-get install cuda-toolkit-12-9 libcudnn9-dev-cuda-12
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
             sudo apt-get install zip
+            python -m venv env
+            source env/bin/activate
             pip install auditwheel
             pip install patchelf
             pip install build
             pip install twine
-            export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
-            export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
             << parameters.build_env >> MLX_BUILD_STAGE=2 \
               CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
               python -m build -w
@@ -445,6 +392,7 @@ jobs:
             - run:
                 name: Upload package
                 command: |
+                  source env/bin/activate
                   twine upload wheelhouse/*.whl
       - store_artifacts:
           path: wheelhouse/
@@ -457,24 +405,19 @@ workflows:
             pattern: "^(?!pull/)[-\\w]+$"
             value: << pipeline.git.branch >>
         - not: << pipeline.parameters.nightly_build >>
-        - not: << pipeline.parameters.test_release >>
     jobs:
       - mac_build_and_test:
           matrix:
             parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
-      - cuda_build_and_test:
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
+      - cuda_build_and_test 
       - build_documentation 
 
   build_pypi_release:
     when:
       and:
         - not: << pipeline.parameters.nightly_build >>
-        - not: << pipeline.parameters.test_release >>
     jobs:
       - build_release:
           filters:
@@ -484,10 +427,71 @@ workflows:
               ignore: /.*/
           matrix:
             parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               macosx_deployment_target: ["13.5", "14.0", "15.0"]
               build_env: ["PYPI_RELEASE=1"]
-              xcode_version: ["26.0.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
       - build_documentation:
           filters:
             tags:
@@ -503,7 +507,7 @@ workflows:
               ignore: /.*/
           matrix:
             parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               build_env: ["PYPI_RELEASE=1"]
       - build_cuda_release:
           filters:
@@ -529,14 +533,11 @@ workflows:
           requires: [ hold ]
           matrix:
             parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test:
           requires: [ hold ]
       - cuda_build_and_test:
           requires: [ hold ]
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
   nightly_build:
     when:
       and:
@@ -546,34 +547,57 @@ workflows:
       - build_release:
           matrix:
             parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              xcode_version: ["26.0.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
       - build_linux_release:
           matrix:
             parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
       - build_cuda_release
-
-  build_dev_release:
-    when:
-      and:
-        - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.test_release >>
-    jobs:
-      - build_release:
-          matrix:
-            parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
-              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              build_env: ["DEV_RELEASE=1"]
-              xcode_version: ["26.0.0"]
-      - build_linux_release:
-          matrix:
-            parameters:
-              python_version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
-              build_env: ["DEV_RELEASE=1"]
-      - build_cuda_release:
-          matrix:
-            parameters:
-              build_env: ["DEV_RELEASE=1"]

ACKNOWLEDGMENTS.md

@@ -19,17 +19,12 @@ MLX was developed with contributions from the following individuals:
 - Gleb Pobudzey: Added the `where` primitive, and groups in 1D and 2D convolutions.
 - Paul Paczuski: Improved stability of BCE loss calculation
 - Max-Heinrich Laves: Added `conv_transpose1d`, `conv_transpose2d`, and `conv_transpose3d` ops.
-- Gökdeniz Gülmez: Added the `Muon (MomentUm Orthogonalized by Newton-schulz)` optimizer, and the `ReLU²` activation function.
+- 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" />
 </a>
 
-# Organizations
-
-MLX has received contributions from the following companies:
-- NVIDIA Corporation & Affiliates
-
 # Third-Party Software
 
 MLX leverages several third-party software, listed here together with

CMakeLists.txt

@@ -20,17 +20,12 @@ project(
   LANGUAGES C CXX
   VERSION ${MLX_PROJECT_VERSION})
 
-if(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
-  add_compile_options(-Wall -Wextra)
-endif()
-
 # ----------------------------- Setup -----------------------------
 set(CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
 set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 set(CMAKE_INSTALL_MESSAGE NEVER)
-set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
 # ----------------------------- Configuration -----------------------------
 option(MLX_BUILD_TESTS "Build tests for mlx" ON)
@@ -46,9 +41,7 @@ option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
 option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
 option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
-option(MLX_USE_CCACHE "Use CCache for compilation cache when available" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
-option(USE_SYSTEM_FMT "Use system's provided fmt library" OFF)
 
 # --------------------- Processor tests -------------------------
 message(
@@ -75,15 +68,6 @@ else()
   set(MLX_BUILD_METAL OFF)
 endif()
 
-if(MLX_USE_CCACHE)
-  find_program(CCACHE_PROGRAM ccache)
-  if(CCACHE_PROGRAM)
-    set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-  endif()
-endif()
-
 # ----------------------------- Lib -----------------------------
 
 include(FetchContent)
@@ -92,21 +76,22 @@ cmake_policy(SET CMP0135 NEW)
 
 add_library(mlx)
 
+if(MLX_BUILD_METAL)
+  set(METAL_LIB "-framework Metal")
+  set(FOUNDATION_LIB "-framework Foundation")
+  set(QUARTZ_LIB "-framework QuartzCore")
+endif()
+
 if(MLX_BUILD_CUDA)
   enable_language(CUDA)
 endif()
 
-if(MLX_BUILD_METAL)
-  find_library(METAL_LIB Metal)
-  find_library(FOUNDATION_LIB Foundation)
-  find_library(QUARTZ_LIB QuartzCore)
-  if(METAL_LIB)
-    message(STATUS "Metal found ${METAL_LIB}")
-  else()
-    message(
-      FATAL_ERROR
-        "Metal not found. Set MLX_BUILD_METAL=OFF to build without GPU")
-  endif()
+if(MLX_BUILD_METAL AND NOT METAL_LIB)
+  message(STATUS "Metal not found. Unable to build GPU")
+  set(MLX_BUILD_METAL OFF)
+  set(MLX_METAL_DEBUG OFF)
+elseif(MLX_BUILD_METAL)
+  message(STATUS "Building METAL sources")
 
   if(MLX_METAL_DEBUG)
     add_compile_definitions(MLX_METAL_DEBUG)
@@ -115,8 +100,7 @@ if(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION
-    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
 
   if(${MACOS_SDK_VERSION} LESS 14.0)
     message(
@@ -145,12 +129,6 @@ if(MLX_BUILD_METAL)
   target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
 endif()
 
-if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-  # With newer clang/gcc versions following libs are implicitly linked, but when
-  # building on old distributions they need to be explicitly listed.
-  target_link_libraries(mlx PRIVATE dl pthread)
-endif()
-
 if(WIN32)
   if(MSVC)
     # GGUF does not build with MSVC.
@@ -178,7 +156,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
     set(MLX_BUILD_ACCELERATE ON)
   else()
-    message(STATUS "Accelerate not found, using default backend.")
+    message(STATUS "Accelerate or arm neon not found, using default backend.")
     set(MLX_BUILD_ACCELERATE OFF)
   endif()
 
@@ -254,16 +232,12 @@ target_include_directories(
 # Do not add mlx_EXPORTS define for shared library.
 set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
 
-if(USE_SYSTEM_FMT)
-  find_package(fmt REQUIRED)
-else()
-  FetchContent_Declare(
-    fmt
-    GIT_REPOSITORY https://github.com/fmtlib/fmt.git
-    GIT_TAG 10.2.1
-    EXCLUDE_FROM_ALL)
-  FetchContent_MakeAvailable(fmt)
-endif()
+FetchContent_Declare(
+  fmt
+  GIT_REPOSITORY https://github.com/fmtlib/fmt.git
+  GIT_TAG 10.2.1
+  EXCLUDE_FROM_ALL)
+FetchContent_MakeAvailable(fmt)
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:fmt::fmt-header-only>)
 
 if(MLX_BUILD_PYTHON_BINDINGS)

README.md

@@ -2,7 +2,7 @@
 
 [**Quickstart**](#quickstart) | [**Installation**](#installation) |
 [**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
-[**Examples**](#examples)
+[**Examples**](#examples) 
 
 [![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)
 
@@ -11,37 +11,37 @@ brought to you by Apple machine learning research.
 
 Some key features of MLX include:
 
-- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
+ - **Familiar APIs**: MLX has a Python API that closely follows NumPy.  MLX
    also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
    [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
-   the Python API. MLX has higher-level packages like `mlx.nn` and
+   the Python API.  MLX has higher-level packages like `mlx.nn` and
    `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
    more complex models.
 
-- **Composable function transformations**: MLX supports composable function
-  transformations for automatic differentiation, automatic vectorization,
-  and computation graph optimization.
+ - **Composable function transformations**: MLX supports composable function
+   transformations for automatic differentiation, automatic vectorization,
+   and computation graph optimization.
 
-- **Lazy computation**: Computations in MLX are lazy. Arrays are only
-  materialized when needed.
+ - **Lazy computation**: Computations in MLX are lazy. Arrays are only
+   materialized when needed.
 
-- **Dynamic graph construction**: Computation graphs in MLX are constructed
-  dynamically. Changing the shapes of function arguments does not trigger
-  slow compilations, and debugging is simple and intuitive.
+ - **Dynamic graph construction**: Computation graphs in MLX are constructed
+   dynamically. Changing the shapes of function arguments does not trigger
+   slow compilations, and debugging is simple and intuitive.
 
-- **Multi-device**: Operations can run on any of the supported devices
-  (currently the CPU and the GPU).
+ - **Multi-device**: Operations can run on any of the supported devices
+   (currently the CPU and the GPU).
 
-- **Unified memory**: A notable difference from MLX and other frameworks
-  is the *unified memory model*. Arrays in MLX live in shared memory.
-  Operations on MLX arrays can be performed on any of the supported
-  device types without transferring data.
+ - **Unified memory**: A notable difference from MLX and other frameworks
+   is the *unified memory model*. Arrays in MLX live in shared memory.
+   Operations on MLX arrays can be performed on any of the supported
+   device types without transferring data.
 
 MLX is designed by machine learning researchers for machine learning
 researchers. The framework is intended to be user-friendly, but still efficient
 to train and deploy models. The design of the framework itself is also
 conceptually simple. We intend to make it easy for researchers to extend and
-improve MLX with the goal of quickly exploring new ideas.
+improve MLX with the goal of quickly exploring new ideas. 
 
 The design of MLX is inspired by frameworks like
 [NumPy](https://numpy.org/doc/stable/index.html),
@@ -68,30 +68,25 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
-macOS, run:
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
 
-```bash
+**With `pip`**:
+
+```
 pip install mlx
 ```
 
-To install the CUDA backend on Linux, run:
+**With `conda`**:
 
-```bash
-pip install mlx[cuda]
 ```
-
-To install a CPU-only Linux package, run:
-
-```bash
-pip install mlx[cpu]
+conda install -c conda-forge mlx
 ```
 
 Checkout the
 [documentation](https://ml-explore.github.io/mlx/build/html/install.html#)
 for more information on building the C++ and Python APIs from source.
 
-## Contributing
+## Contributing 
 
 Check out the [contribution guidelines](https://github.com/ml-explore/mlx/tree/main/CONTRIBUTING.md) for more information
 on contributing to MLX. See the
@@ -110,7 +105,7 @@ Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
 MLX useful in your research and wish to cite it, please use the following
 BibTex entry:
 
-```text
+```
 @software{mlx2023,
   author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
   title = {{MLX}: Efficient and flexible machine learning on Apple silicon},

benchmarks/python/blas/bench_gemm.py

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

benchmarks/python/blas/bench_gemv.py

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

cmake/FindNCCL.cmake

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

docs/requirements.txt

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

docs/src/conf.py

@@ -18,7 +18,6 @@ release = version
 # -- General configuration ---------------------------------------------------
 
 extensions = [
-    "sphinx_copybutton",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",

docs/src/dev/custom_metal_kernels.rst

@@ -127,8 +127,7 @@ relying on a copy from ``ensure_row_contiguous``:
       name="myexp_strided",
       input_names=["inp"],
       output_names=["out"],
-      source=source,
-      ensure_row_contiguous=False,
+      source=source
   )
 
   def exp_elementwise(a: mx.array):
@@ -139,6 +138,7 @@ relying on a copy from ``ensure_row_contiguous``:
           threadgroup=(256, 1, 1),
           output_shapes=[a.shape],
           output_dtypes=[a.dtype],
+          ensure_row_contiguous=False,
       )
       return outputs[0]
 

docs/src/dev/extensions.rst

@@ -394,14 +394,14 @@ below.
         out.set_data(allocator::malloc(out.nbytes()));
 
         // Resolve name of kernel
-        std::stream kname;
-        kname = "axpby_general_" + type_to_name(out);
+        std::ostringstream kname;
+        kname << "axpby_" << "general_" << type_to_name(out);
 
         // Load the metal library
-        auto lib = d.get_library("mlx_ext", current_binary_dir());
+        auto lib = d.get_library("mlx_ext");
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname, lib);
+        auto kernel = d.get_kernel(kname.str(), lib);
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);

docs/src/index.rst

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

docs/src/install.rst

@@ -13,10 +13,10 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI your system must meet the following requirements:
+To install from PyPI you must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
-- Using a native Python >= 3.10
+- Using a native Python >= 3.9
 - macOS >= 13.5
 
 .. note::
@@ -26,21 +26,12 @@ To install from PyPI your system must meet the following requirements:
 CUDA
 ^^^^
 
-MLX has a CUDA backend which you can install with:
+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:
 
 .. code-block:: shell
 
-    pip install mlx[cuda]
-
-To install the CUDA package from PyPi your system must meet the following
-requirements:
-
-- Nvidia architecture >= SM 7.0 (Volta)
-- Nvidia driver >= 550.54.14
-- CUDA toolkit >= 12.0
-- Linux distribution with glibc >= 2.35
-- Python >= 3.10
-
+    pip install "mlx[cuda]"
 
 CPU-only (Linux)
 ^^^^^^^^^^^^^^^^
@@ -49,14 +40,7 @@ For a CPU-only version of MLX that runs on Linux use:
 
 .. code-block:: shell
 
-    pip install mlx[cpu]
-
-To install the CPU-only package from PyPi your system must meet the following
-requirements:
-
-- Linux distribution with glibc >= 2.35
-- Python >= 3.10
-
+    pip install "mlx[cpu]"
 
 Troubleshooting
 ^^^^^^^^^^^^^^^
@@ -271,7 +255,7 @@ and the CUDA toolkit. For example on Ubuntu, run the following:
    dpkg -i cuda-keyring_1.1-1_all.deb
    apt-get update -y
    apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev libcudnn9-dev-cuda-12 -y
+   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

docs/src/python/cuda.rst

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

docs/src/python/fast.rst

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

docs/src/python/nn/functions.rst

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

docs/src/python/nn/layers.rst

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

docs/src/python/ops.rst

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

docs/src/python/optimizers.rst

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

docs/src/usage/compile.rst

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

docs/src/usage/distributed.rst

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

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++).
+front-end (e.g. C++). 
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting
+Basics of Exporting 
 -------------------
 
 Let's start with a simple example:
-
+ 
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-
+   
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-
+  
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
-
-   params = tree_flatten(model.parameters(), destination={})
+ 
+   params = dict(tree_flatten(model.parameters()))
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -164,13 +164,13 @@ to export a function which can be used for inputs with variable shapes:
 
 .. code-block:: python
 
-  mx.export_function("fun.mlxfn", mx.abs, mx.array([0.0]), shapeless=True)
+  mx.export_function("fun.mlxfn", mx.abs, mx.array(0.0), shapeless=True)
   imported_abs = mx.import_function("fun.mlxfn")
 
   # Ok
-  out, = imported_abs(mx.array([-1.0]))
-
-  # Also ok
+  out, = imported_abs(mx.array(-1.0))
+  
+  # Also ok 
   out, = imported_abs(mx.array([-1.0, -2.0]))
 
 With ``shapeless=False`` (which is the default), the second call to
@@ -197,7 +197,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   def fun(x, y=None):
       constant = mx.array(3.0)
       if y is not None:
-        x += y
+        x += y 
       return x + constant
 
   with mx.exporter("fun.mlxfn", fun) as exporter:
@@ -215,7 +215,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   print(out)
 
 In the above example the function constant data, (i.e. ``constant``), is only
-saved once.
+saved once. 
 
 Transformations with Imported Functions
 ---------------------------------------
@@ -238,7 +238,7 @@ on imported functions just like regular Python functions:
   # Prints: array(1, dtype=float32)
   print(dfdx(x))
 
-  # Compile the imported function
+  # Compile the imported function 
   mx.compile(imported_fun)
   # Prints: array(0, dtype=float32)
   print(compiled_fun(x)[0])
@@ -275,7 +275,7 @@ Import and run the function in C++ with only a few lines of code:
   // Prints: array(2, dtype=float32)
   std::cout << outputs[0] << std::endl;
 
-Imported functions can be transformed in C++ just like in Python. Use
+Imported functions can be transformed in C++ just like in Python. Use 
 ``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
 mx::array>`` for keyword arguments when calling imported functions in C++.
 

docs/src/usage/indexing.rst

@@ -107,20 +107,8 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
-Note that unlike NumPy, slicing an array creates a copy, not a view. So
-mutating it does not mutate the original array:
 
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> b = a[:]
-  >>> b[2] = 0
-  >>> b
-  array([1, 2, 0], dtype=int32)
-  >>> a
-  array([1, 2, 3], dtype=int32)
-
-Also unlike NumPy, updates to the same location are nondeterministic:
+Note, unlike NumPy, updates to the same location are nondeterministic:
 
 .. code-block:: shell
 

examples/cpp/tutorial.cpp

@@ -14,17 +14,14 @@ void array_basics() {
   // Get the value out of it:
   auto s = x.item<float>();
   assert(s == 1.0);
-  (void)s;
 
   // Scalars have a size of 1:
-  int64_t size = x.size();
+  size_t size = x.size();
   assert(size == 1);
-  (void)size;
 
   // Scalars have 0 dimensions:
   int ndim = x.ndim();
   assert(ndim == 0);
-  (void)ndim;
 
   // The shape should be an empty vector:
   auto shape = x.shape();
@@ -33,7 +30,6 @@ void array_basics() {
   // The datatype should be float32:
   auto dtype = x.dtype();
   assert(dtype == mx::float32);
-  (void)dtype;
 
   // Specify the dtype when constructing the array:
   x = mx::array(1, mx::int32);

examples/extensions/axpby/axpby.cpp

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

examples/extensions/requirements.txt

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

examples/extensions/test.py

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

mlx/array.cpp

@@ -44,11 +44,11 @@ std::vector<array> array::make_arrays(
     const std::shared_ptr<Primitive>& primitive,
     const std::vector<array>& inputs) {
   std::vector<array> outputs;
-  for (int i = 0; i < std::ssize(shapes); ++i) {
+  for (size_t i = 0; i < shapes.size(); ++i) {
     outputs.emplace_back(std::move(shapes[i]), dtypes[i], primitive, inputs);
   }
   // For each node in |outputs|, its siblings are the other nodes.
-  for (int i = 0; i < std::ssize(outputs); ++i) {
+  for (size_t i = 0; i < outputs.size(); ++i) {
     auto siblings = outputs;
     siblings.erase(siblings.begin() + i);
     outputs[i].set_siblings(std::move(siblings), i);
@@ -145,9 +145,8 @@ void array::set_data(allocator::Buffer buffer, Deleter d) {
   array_desc_->data_size = size();
   array_desc_->flags.contiguous = true;
   array_desc_->flags.row_contiguous = true;
-  auto max_dim =
-      static_cast<int64_t>(*std::max_element(shape().begin(), shape().end()));
-  array_desc_->flags.col_contiguous = size() <= 1 || size() == max_dim;
+  auto max_dim = std::max_element(shape().begin(), shape().end());
+  array_desc_->flags.col_contiguous = size() <= 1 || size() == *max_dim;
 }
 
 void array::set_data(
@@ -193,7 +192,7 @@ array::~array() {
   }
 
   // Break circular reference for non-detached arrays with siblings
-  if (auto n = std::ssize(siblings()); n > 0) {
+  if (auto n = siblings().size(); n > 0) {
     bool do_detach = true;
     // If all siblings have siblings.size() references except
     // the one we are currently destroying (which has siblings.size() + 1)
@@ -242,8 +241,8 @@ array::ArrayDesc::ArrayDesc(
     std::vector<array> inputs)
     : shape(std::move(shape)),
       dtype(dtype),
-      primitive(std::move(primitive)),
       status(Status::unscheduled),
+      primitive(std::move(primitive)),
       inputs(std::move(inputs)) {
   init();
 }
@@ -275,7 +274,7 @@ array::ArrayDesc::~ArrayDesc() {
     ad.inputs.clear();
     for (auto& [_, a] : input_map) {
       bool is_deletable =
-          (a.array_desc_.use_count() <= std::ssize(a.siblings()) + 1);
+          (a.array_desc_.use_count() <= a.siblings().size() + 1);
       // An array with siblings is deletable only if all of its siblings
       // are deletable
       for (auto& s : a.siblings()) {
@@ -284,7 +283,7 @@ array::ArrayDesc::~ArrayDesc() {
         }
         int is_input = (input_map.find(s.id()) != input_map.end());
         is_deletable &=
-            s.array_desc_.use_count() <= std::ssize(a.siblings()) + is_input;
+            s.array_desc_.use_count() <= a.siblings().size() + is_input;
       }
       if (is_deletable) {
         for_deletion.push_back(std::move(a.array_desc_));

mlx/array.h

@@ -10,7 +10,6 @@
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
 #include "mlx/event.h"
-#include "mlx/small_vector.h"
 
 namespace mlx::core {
 
@@ -19,8 +18,8 @@ class Primitive;
 
 using Deleter = std::function<void(allocator::Buffer)>;
 using ShapeElem = int32_t;
-using Shape = SmallVector<ShapeElem>;
-using Strides = SmallVector<int64_t>;
+using Shape = std::vector<ShapeElem>;
+using Strides = std::vector<int64_t>;
 
 class array {
   /* An array is really a node in a graph. It contains a shared ArrayDesc
@@ -81,22 +80,22 @@ class array {
   }
 
   /** The size of the array's datatype in bytes. */
-  int itemsize() const {
+  size_t itemsize() const {
     return size_of(dtype());
   }
 
   /** The number of elements in the array. */
-  int64_t size() const {
+  size_t size() const {
     return array_desc_->size;
   }
 
   /** The number of bytes in the array. */
-  int64_t nbytes() const {
+  size_t nbytes() const {
     return size() * itemsize();
   }
 
   /** The number of dimensions of the array. */
-  int ndim() const {
+  size_t ndim() const {
     return array_desc_->shape.size();
   }
 
@@ -329,7 +328,7 @@ class array {
    * corresponding to ``arr[-1, -1, ...]``) then ``data_size = last - first``.
    * Note, ``data_size`` is in units of ``item_size`` (not bytes).
    **/
-  int64_t data_size() const {
+  size_t data_size() const {
     return array_desc_->data_size;
   }
 
@@ -340,7 +339,7 @@ class array {
     return array_desc_->data->buffer;
   }
 
-  int64_t buffer_size() const {
+  size_t buffer_size() const {
     return allocator::allocator().size(buffer());
   }
 
@@ -530,7 +529,7 @@ array::array(
     Shape shape,
     Dtype dtype /* = TypeToDtype<T>() */)
     : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
-  if (std::ssize(data) != size()) {
+  if (data.size() != size()) {
     throw std::invalid_argument(
         "Data size and provided shape mismatch in array construction.");
   }

mlx/backend/common/common.cpp

@@ -21,8 +21,8 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
 
   // Compute the flags given the shape and strides
   bool row_contiguous = true, col_contiguous = true;
-  int64_t r = 1, c = 1;
-  for (int i = std::ssize(strides_) - 1, j = 0; i >= 0; i--, j++) {
+  size_t r = 1, c = 1;
+  for (int i = strides_.size() - 1, j = 0; i >= 0; i--, j++) {
     row_contiguous &= (r == strides_[i]) || (shape_[i] == 1);
     col_contiguous &= (c == strides_[j]) || (shape_[j] == 1);
     r *= shape_[i];
@@ -60,8 +60,7 @@ void CustomTransforms::eval(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   assert(inputs.size() > outputs.size());
-  for (int i = 0, j = std::ssize(inputs) - std::ssize(outputs);
-       i < std::ssize(outputs);
+  for (int i = 0, j = inputs.size() - outputs.size(); i < outputs.size();
        i++, j++) {
     outputs[i].copy_shared_buffer(inputs[j]);
   }
@@ -71,7 +70,7 @@ void Depends::eval(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   assert(inputs.size() > outputs.size());
-  for (int i = 0; i < std::ssize(outputs); i++) {
+  for (int i = 0; i < outputs.size(); i++) {
     outputs[i].copy_shared_buffer(inputs[i]);
   }
 }
@@ -207,11 +206,11 @@ void Split::eval(
 
   auto compute_new_flags = [](const auto& shape,
                               const auto& strides,
-                              int64_t in_data_size,
+                              size_t in_data_size,
                               auto flags) {
-    int64_t data_size = 1;
-    int64_t f_stride = 1;
-    int64_t b_stride = 1;
+    size_t data_size = 1;
+    size_t f_stride = 1;
+    size_t b_stride = 1;
     flags.row_contiguous = true;
     flags.col_contiguous = true;
     for (int i = 0, ri = shape.size() - 1; ri >= 0; i++, ri--) {
@@ -241,7 +240,7 @@ void Split::eval(
 
   std::vector<int> indices(1, 0);
   indices.insert(indices.end(), indices_.begin(), indices_.end());
-  for (int i = 0; i < std::ssize(indices); i++) {
+  for (int i = 0; i < indices.size(); i++) {
     size_t offset = indices[i] * in.strides()[axis_];
     auto [new_flags, data_size] = compute_new_flags(
         outputs[i].shape(), in.strides(), in.data_size(), in.flags());
@@ -255,7 +254,7 @@ void Squeeze::eval(const std::vector<array>& inputs, array& out) {
   const auto& in = inputs[0];
   Strides strides;
   for (int i = 0, j = 0; i < in.ndim(); ++i) {
-    if (j < std::ssize(axes_) && i == axes_[j]) {
+    if (j < axes_.size() && i == axes_[j]) {
       j++;
     } else {
       strides.push_back(in.strides(i));
@@ -273,7 +272,7 @@ void Transpose::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   Strides out_strides(out.ndim());
   auto& in = inputs[0];
-  for (int ax = 0; ax < std::ssize(axes_); ++ax) {
+  for (int ax = 0; ax < axes_.size(); ++ax) {
     out_strides[ax] = in.strides()[axes_[ax]];
   }
 

mlx/backend/common/compiled.cpp

@@ -120,7 +120,7 @@ void compiled_allocate_outputs(
     Strides strides;
     size_t data_size;
     array::Flags flags;
-    for (int i = 0; i < std::ssize(inputs) && o < std::ssize(outputs); ++i) {
+    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
       auto& in = inputs[i];
       // Conditions for donation
       // - Correct size
@@ -138,7 +138,7 @@ void compiled_allocate_outputs(
         data_size = in.data_size();
       }
     }
-    for (; o < std::ssize(outputs); ++o) {
+    for (; o < outputs.size(); ++o) {
       outputs[o].set_data(
           allocator::malloc(data_size * outputs[o].itemsize()),
           data_size,
@@ -147,7 +147,7 @@ void compiled_allocate_outputs(
     }
   } else {
     int o = 0;
-    for (int i = 0; i < std::ssize(inputs) && o < std::ssize(outputs); ++i) {
+    for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {
       auto& in = inputs[i];
       // Conditions for donation
       // - Row contiguous
@@ -162,7 +162,7 @@ void compiled_allocate_outputs(
         o++;
       }
     }
-    for (; o < std::ssize(outputs); ++o) {
+    for (; o < outputs.size(); ++o) {
       outputs[o].set_data(allocator::malloc(outputs[o].nbytes()));
     }
   }
@@ -193,7 +193,7 @@ std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
 
     // Broadcast the inputs to the output shape.
     Strides xstrides;
-    int j = 0;
+    size_t j = 0;
     for (; j < shape.size() - x.ndim(); ++j) {
       if (shape[j] == 1) {
         xstrides.push_back(out.strides()[j]);
@@ -201,7 +201,7 @@ std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
         xstrides.push_back(0);
       }
     }
-    for (int i = 0; i < x.ndim(); ++i, ++j) {
+    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]);
@@ -224,13 +224,13 @@ bool compiled_use_large_index(
     const std::vector<array>& outputs,
     bool contiguous) {
   if (contiguous) {
-    int64_t max_size = 0;
+    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 {
-    int64_t max_size = 0;
+    size_t max_size = 0;
     for (const auto& o : outputs) {
       max_size = std::max(max_size, o.size());
     }

mlx/backend/common/load.cpp

@@ -27,7 +27,7 @@ void swap_endianness(uint8_t* data_bytes, size_t N) {
 
 namespace mlx::core {
 
-void Load::eval_cpu(const std::vector<array>& /* inputs */, array& out) {
+void Load::eval_cpu(const std::vector<array>& inputs, array& out) {
   out.set_data(allocator::malloc(out.nbytes()));
   auto read_task = [out_ptr = out.data<char>(),
                     size = out.size(),

mlx/backend/common/matmul.h

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

mlx/backend/common/reduce.cpp

@@ -28,7 +28,7 @@ std::pair<Shape, Strides> shapes_without_reduction_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() && std::ssize(axes) == x.ndim() &&
+  if (x.size() == x.data_size() && axes.size() == x.ndim() &&
       x.flags().contiguous) {
     return ContiguousAllReduce;
   }
@@ -38,7 +38,7 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
     // Merge consecutive axes
     Shape shape = {x.shape(axes[0])};
     Strides strides = {x.strides()[axes[0]]};
-    for (int i = 1; i < std::ssize(axes); i++) {
+    for (int i = 1; i < axes.size(); i++) {
       if (axes[i] - 1 == axes[i - 1] && x.shape(axes[i]) > 1) {
         shape.back() *= x.shape(axes[i]);
         strides.back() = x.strides()[axes[i]];

mlx/backend/common/slicing.cpp

@@ -24,8 +24,8 @@ std::tuple<int64_t, Strides> prepare_slice(
 void shared_buffer_slice(
     const array& in,
     const Strides& out_strides,
-    int64_t data_offset,
-    int64_t data_size,
+    size_t data_offset,
+    size_t data_size,
     array& out) {
   // Compute row/col contiguity
   auto [no_bsx_size, is_row_contiguous, is_col_contiguous] =
@@ -61,7 +61,7 @@ void slice(
   if (data_end < 0) {
     data_end += in.data_size();
   }
-  int64_t data_size = (data_end - data_offset);
+  size_t data_size = (data_end - data_offset);
   shared_buffer_slice(in, inp_strides, data_offset, data_size, out);
 }
 

mlx/backend/common/ternary.h

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

mlx/backend/common/utils.cpp

@@ -28,7 +28,7 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     if (shape[0] != 1) {
       to_collapse.push_back(0);
     }
-    int64_t size = shape[0];
+    size_t size = shape[0];
     for (int i = 1; i < shape.size(); i++) {
       bool contiguous = true;
       size *= shape[i];
@@ -64,7 +64,7 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
       current_shape *= shape[to_collapse[k]];
     }
     out_shape.push_back(current_shape);
-    for (int j = 0; j < std::ssize(strides); j++) {
+    for (int j = 0; j < strides.size(); j++) {
       const auto& st = strides[j];
       out_strides[j].push_back(st[to_collapse[k - 1]]);
     }

mlx/backend/common/utils.h

@@ -162,7 +162,7 @@ struct ContiguousIterator {
 };
 
 inline auto check_contiguity(const Shape& shape, const Strides& strides) {
-  int64_t no_broadcast_data_size = 1;
+  size_t no_broadcast_data_size = 1;
   int64_t f_stride = 1;
   int64_t b_stride = 1;
   bool is_row_contiguous = true;
@@ -183,7 +183,7 @@ inline auto check_contiguity(const Shape& shape, const Strides& strides) {
 }
 
 inline bool is_donatable(const array& in, const array& out) {
-  constexpr int64_t donation_extra = 16384;
+  constexpr size_t donation_extra = 16384;
 
   return in.is_donatable() && in.itemsize() == out.itemsize() &&
       in.buffer_size() <= out.nbytes() + donation_extra;
@@ -197,7 +197,7 @@ void shared_buffer_reshape(
     array& out);
 
 template <typename T>
-inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
+inline std::vector<T> remove_index(std::vector<T> vec, size_t index) {
   vec.erase(std::next(vec.begin(), index));
   return vec;
 }

mlx/backend/cpu/arange.h

@@ -10,7 +10,7 @@ namespace mlx::core {
 namespace {
 
 template <typename T>
-void arange(T start, T next, array& out, int64_t size, Stream stream) {
+void arange(T start, T next, array& out, size_t size, Stream stream) {
   auto ptr = out.data<T>();
   auto step_size = next - start;
   auto& encoder = cpu::get_command_encoder(stream);

mlx/backend/cpu/arg_reduce.cpp

@@ -19,12 +19,12 @@ void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   auto in_ptr = in.data<InT>();
   auto out_ptr = out.data<uint32_t>();
 
-  for (int64_t i = 0; i < out.size(); ++i) {
+  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 (int64_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
+    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;

mlx/backend/cpu/binary.cpp

@@ -17,12 +17,7 @@ namespace mlx::core {
 namespace {
 
 template <typename Op>
-void binary(
-    const array& a,
-    const array& b,
-    array& out,
-    Op /* op */,
-    Stream stream) {
+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);
 
@@ -86,7 +81,7 @@ void comparison_op(
     const array& a,
     const array& b,
     array& out,
-    Op /* op */,
+    Op op,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
@@ -151,7 +146,7 @@ void binary_float(
     const array& a,
     const array& b,
     array& out,
-    Op /* op */,
+    Op op,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
@@ -192,7 +187,7 @@ void binary_int(
     const array& a,
     const array& b,
     array& out,
-    Op /* op */,
+    Op op,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);

mlx/backend/cpu/binary_two.h

@@ -99,7 +99,7 @@ void binary_op_dispatch_dims(
   ContiguousIterator a_it(shape, a_strides, ndim - 2);
   ContiguousIterator b_it(shape, b_strides, ndim - 2);
   auto stride = out_strides[ndim - 3];
-  for (int64_t elem = 0; elem < std::ssize(a); elem += stride) {
+  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,
@@ -137,21 +137,21 @@ void binary_op(
   if (bopt == BinaryOpType::ScalarScalar) {
     std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
   } else if (bopt == BinaryOpType::ScalarVector) {
-    for (int64_t i = 0; i < b.data_size(); ++i) {
+    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++;
     }
   } else if (bopt == BinaryOpType::VectorScalar) {
-    for (int64_t i = 0; i < a.data_size(); ++i) {
+    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++;
     }
   } else { // VectorVector
-    for (int64_t i = 0; i < a.size(); ++i) {
+    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++;

mlx/backend/cpu/cholesky.cpp

@@ -33,8 +33,8 @@ void cholesky_impl(const array& a, array& factor, bool upper, Stream stream) {
                     N = a.shape(-1),
                     size = a.size()]() mutable {
     char uplo = (upper) ? 'L' : 'U';
-    int64_t num_matrices = size / (N * N);
-    for (int64_t i = 0; i < num_matrices; i++) {
+    size_t num_matrices = size / (N * N);
+    for (int i = 0; i < num_matrices; i++) {
       // Compute Cholesky factorization.
       int info;
       potrf<T>(

mlx/backend/cpu/compiled.cpp

@@ -15,7 +15,6 @@
 #include "mlx/backend/cpu/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
-#include "mlx/version.h"
 
 namespace mlx::core {
 
@@ -49,7 +48,7 @@ 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.
 namespace detail {
-bool compile_available_for_device(const Device& /* device */) {
+bool compile_available_for_device(const Device& device) {
   return true;
 }
 
@@ -95,11 +94,7 @@ void* compile(
     kernel_file_name = kernel_name;
   }
 
-  auto output_dir =
-      std::filesystem::temp_directory_path() / "mlx" / version() / "cpu";
-  if (!std::filesystem::exists(output_dir)) {
-    std::filesystem::create_directories(output_dir);
-  }
+  auto output_dir = std::filesystem::temp_directory_path();
 
   std::string shared_lib_name = "lib" + kernel_file_name + ".so";
   auto shared_lib_path = (output_dir / shared_lib_name).string();
@@ -162,13 +157,11 @@ inline void build_kernel(
 #endif
 
   // Start the kernel
-  os << "void " << kernel_name
-     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
+  os << "void " << kernel_name << "(void** args) {" << std::endl;
 
   // Add the input arguments
   int cnt = 0;
-  int strides_index = 1;
-  for (int i = 0; i < std::ssize(inputs); ++i) {
+  for (size_t i = 0; i < inputs.size(); ++i) {
     // Skip constants from the input list
     if (is_constant(i)) {
       continue;
@@ -182,8 +175,8 @@ inline void build_kernel(
        << "];" << std::endl;
     // Scalars and contiguous need no strides
     if (!is_scalar(x) && !contiguous) {
-      os << "  const int64_t* " << xname << "_strides = strides["
-         << strides_index++ << "];" << std::endl;
+      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
+         << "];" << std::endl;
     }
   }
 
@@ -193,8 +186,10 @@ inline void build_kernel(
     os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
        << "*)args[" << cnt++ << "];" << std::endl;
   }
-  // Add output size
-  if (contiguous) {
+  // Add output strides and shape to extract the indices.
+  if (!contiguous) {
+    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
+  } else {
     os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
   }
 
@@ -238,7 +233,7 @@ inline void build_kernel(
     } else {
       os << x.primitive().name();
       os << "()(";
-      for (int i = 0; i < std::ssize(x.inputs()) - 1; i++) {
+      for (int i = 0; i < x.inputs().size() - 1; i++) {
         os << "tmp_" << namer.get_name(x.inputs()[i]) << ", ";
       }
       os << "tmp_" << namer.get_name(x.inputs().back()) << ");" << std::endl;
@@ -295,6 +290,7 @@ void Compiled::eval_cpu(
 
   // Collect function input arguments.
   std::vector<void*> args;
+  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     if (is_constant_(i)) {
       continue;
@@ -302,6 +298,9 @@ void Compiled::eval_cpu(
     const auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
+    if (!contiguous && !is_scalar(x)) {
+      args.push_back(strides[strides_index++].data());
+    }
   }
 
   // Get the kernel name from the lib
@@ -336,20 +335,16 @@ void Compiled::eval_cpu(
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
-  if (contiguous) {
+  if (!contiguous) {
+    args.push_back((void*)shape.data());
+  } else {
     args.push_back((void*)outputs[0].data_size());
   }
-  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
+  auto fun = (void (*)(void**))fn_ptr;
   encoder.dispatch([fun,
                     args = std::move(args),
                     strides = std::move(strides),
-                    shape = std::move(shape)]() mutable {
-    SmallVector<int64_t*> strides_ptrs;
-    for (auto& s : strides) {
-      strides_ptrs.push_back(s.data());
-    }
-    fun(shape.data(), strides_ptrs.data(), args.data());
-  });
+                    shape = std::move(shape)]() mutable { fun(args.data()); });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/conv.cpp

@@ -860,7 +860,7 @@ void explicit_gemm_conv_1D_cpu(
     const std::vector<int>& padding_lo,
     const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
-    const std::vector<int>& /* wt_dilation */,
+    const std::vector<int>& wt_dilation,
     Stream stream) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
   const int iH = in.shape(1); // Input spatial dim
@@ -996,6 +996,131 @@ void explicit_gemm_conv_1D_cpu(
   encoder.add_temporaries(std::move(temps));
 }
 
+void explicit_gemm_conv_2D_cpu(
+    const array& in,
+    const array& wt,
+    array out,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
+    const std::vector<int>& wt_strides,
+    const std::vector<int>& wt_dilation,
+    Stream stream) {
+  const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
+  const int iH = in.shape(1); // Input spatial dim
+  const int iW = in.shape(2); // Input spatial dim
+  const int oH = out.shape(1); // Output spatial dim
+  const int oW = out.shape(2); // Output spatial dim
+  const int O = wt.shape(0); // Out channels
+  const int C = wt.shape(3); // In channels
+  const int wH = wt.shape(1); // Weight spatial dim
+  const int wW = wt.shape(2); // Weight spatial dim
+
+  auto conv_dtype = out.dtype();
+  auto& encoder = cpu::get_command_encoder(stream);
+
+  // Pad input
+  Shape padded_shape = {
+      N,
+      iH + padding_lo[0] + padding_hi[0],
+      iW + padding_lo[1] + padding_hi[1],
+      C};
+  array in_padded(padded_shape, conv_dtype, nullptr, {});
+
+  // Fill with zeros
+  std::vector<array> temps;
+  temps.push_back(array(0, conv_dtype));
+  copy_cpu(temps.back(), in_padded, CopyType::Scalar, stream);
+
+  // Pick input slice from padded
+  size_t data_offset = padding_lo[0] * in_padded.strides()[1] +
+      padding_lo[1] * in_padded.strides()[2];
+  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
+  in_padded_slice.copy_shared_buffer(
+      in_padded,
+      in_padded.strides(),
+      in_padded.flags(),
+      in_padded_slice.size(),
+      data_offset);
+  temps.push_back(in_padded_slice);
+
+  // Copy input values into the slice
+  copy_cpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
+
+  // Make strided view
+  Shape strided_shape = {N, oH, oW, wH, wW, C};
+
+  Strides strided_strides = {
+      in_padded.strides()[0],
+      in_padded.strides()[1] * wt_strides[0],
+      in_padded.strides()[2] * wt_strides[1],
+      in_padded.strides()[1],
+      in_padded.strides()[2],
+      in_padded.strides()[3]};
+  auto flags = in_padded.flags();
+
+  array in_strided_view(strided_shape, in_padded.dtype(), nullptr, {});
+  in_strided_view.copy_shared_buffer(
+      in_padded, strided_strides, flags, in_strided_view.size(), 0);
+
+  // Materialize strided view
+  Shape strided_reshape = {N * oH * oW, wH * wW * C};
+  array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
+  copy_cpu(in_strided_view, in_strided, CopyType::General, stream);
+  temps.push_back(in_strided);
+
+  // Check wt dtype and prepare
+  auto gemm_wt = wt;
+  auto gemm_out = out;
+
+  if (wt.dtype() != float32 || !wt.flags().row_contiguous) {
+    auto ctype =
+        wt.flags().row_contiguous ? CopyType::Vector : CopyType::General;
+    gemm_wt = array(wt.shape(), float32, nullptr, {});
+    copy_cpu(wt, gemm_wt, ctype, stream);
+    temps.push_back(gemm_wt);
+  }
+
+  if (out.dtype() != float32) {
+    gemm_out = array(out.shape(), float32, nullptr, {});
+    gemm_out.set_data(allocator::malloc(gemm_out.nbytes()));
+    temps.push_back(gemm_out);
+  }
+
+  encoder.set_input_array(in_strided);
+  encoder.set_input_array(gemm_wt);
+  encoder.set_output_array(gemm_out);
+
+  encoder.dispatch([in_strided_ptr = in_strided.data<float>(),
+                    gemm_wt_ptr = gemm_wt.data<float>(),
+                    gemm_out_ptr = gemm_out.data<float>(),
+                    strided_reshape = std::move(strided_reshape),
+                    O]() {
+    // Perform gemm
+    cblas_sgemm(
+        CblasRowMajor,
+        CblasNoTrans, // no trans A
+        CblasTrans, // transB
+        strided_reshape[0], // M
+        O, // N
+        strided_reshape[1], // K
+        1.0f, // alpha
+        in_strided_ptr,
+        strided_reshape[1], // lda
+        gemm_wt_ptr,
+        strided_reshape[1], // ldb
+        0.0f, // beta
+        gemm_out_ptr,
+        O // ldc
+    );
+  });
+
+  // Copy results if needed
+  if (out.dtype() != float32) {
+    copy_cpu_inplace(gemm_out, out, CopyType::Vector, stream);
+  }
+  encoder.add_temporaries(std::move(temps));
+}
+
 void explicit_gemm_conv_ND_cpu(
     const array& in,
     const array& wt,
@@ -1003,7 +1128,7 @@ void explicit_gemm_conv_ND_cpu(
     const std::vector<int>& padding_lo,
     const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
-    const std::vector<int>& /* wt_dilation */,
+    const std::vector<int>& wt_dilation,
     const bool flip,
     Stream stream) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
@@ -1023,7 +1148,7 @@ void explicit_gemm_conv_ND_cpu(
   // Pad input
   Shape padded_shape(in.shape().size());
   padded_shape.front() = N;
-  for (int i = 0; i < iDim.size(); i++) {
+  for (size_t i = 0; i < iDim.size(); i++) {
     padded_shape[i + 1] = iDim[i] + padding_lo[i] + padding_hi[i];
   }
   padded_shape.back() = C;
@@ -1054,20 +1179,20 @@ void explicit_gemm_conv_ND_cpu(
   // Make strided view
   Shape strided_shape(oDim.size() + wDim.size() + 2);
   strided_shape.front() = N;
-  for (int i = 0; i < oDim.size(); i++) {
+  for (size_t i = 0; i < oDim.size(); i++) {
     strided_shape[i + 1] = oDim[i];
   }
-  for (int i = 0; i < wDim.size(); i++) {
+  for (size_t i = 0; i < wDim.size(); i++) {
     strided_shape[i + 1 + oDim.size()] = wDim[i];
   }
   strided_shape.back() = C;
 
   Strides strided_strides(in.shape().size() * 2 - 2);
   strided_strides[0] = in_padded.strides()[0];
-  for (int i = 0; i < std::ssize(wt_strides); i++) {
+  for (size_t i = 0; i < wt_strides.size(); i++) {
     strided_strides[i + 1] = in_padded.strides()[i + 1] * wt_strides[i];
   }
-  for (int i = 1; i < std::ssize(in_padded.strides()); i++) {
+  for (size_t i = 1; i < in_padded.strides().size(); i++) {
     strided_strides[i + wt_strides.size()] = in_padded.strides()[i];
   }
 

mlx/backend/cpu/copy.cpp

@@ -377,10 +377,4 @@ void copy_cpu_inplace(
       });
 }
 
-array contiguous_copy_cpu(const array& arr, Stream stream) {
-  array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-  copy_cpu(arr, arr_copy, CopyType::General, stream);
-  return arr_copy;
-}
-
 } // namespace mlx::core

mlx/backend/cpu/copy.h

@@ -30,7 +30,4 @@ void copy_cpu_inplace(
     const std::optional<array>& dynamic_i_offset = std::nullopt,
     const std::optional<array>& dynamic_o_offset = std::nullopt);
 
-// Return a contiguous array with same shape that copies the data of |arr|.
-array contiguous_copy_cpu(const array& arr, Stream stream);
-
 } // namespace mlx::core

mlx/backend/cpu/distributed.cpp

@@ -13,7 +13,9 @@ std::pair<array, bool> ensure_row_contiguous(const array& arr, Stream stream) {
   if (arr.flags().row_contiguous) {
     return {arr, false};
   } else {
-    return {contiguous_copy_cpu(arr, stream), true};
+    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+    copy_cpu(arr, arr_copy, CopyType::General, stream);
+    return {arr_copy, true};
   }
 };
 
@@ -32,7 +34,8 @@ void AllReduce::eval_cpu(
       }
       return in;
     } else {
-      array arr_copy = contiguous_copy_cpu(in, s);
+      array arr_copy(in.shape(), in.dtype(), nullptr, {});
+      copy_cpu(in, arr_copy, CopyType::General, s);
       out.copy_shared_buffer(arr_copy);
       return arr_copy;
     }
@@ -90,7 +93,6 @@ void Recv::eval_cpu(
     std::vector<array>& outputs) {
   assert(inputs.size() == 0);
   assert(outputs.size() == 1);
-  (void)inputs;
 
   outputs[0].set_data(allocator::malloc(outputs[0].nbytes()));
   distributed::detail::recv(group(), outputs[0], src_, stream());

mlx/backend/cpu/eig.cpp

@@ -46,6 +46,7 @@ void eig_impl(
     int info;
     {
       T work;
+      int iwork;
       geev<T>(
           &jobl,
           &jobr,
@@ -70,7 +71,7 @@ void eig_impl(
     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 (int64_t i = 0; i < size / (N * N); ++i) {
+    for (size_t i = 0; i < size / (N * N); ++i) {
       geev<T>(
           &jobl,
           &jobr,

mlx/backend/cpu/eigh.cpp

@@ -165,7 +165,7 @@ void eigh_impl(
     EighWork<T> work(jobz, uplo, N);
 
     // Work loop
-    for (int64_t i = 0; i < size / (N * N); ++i) {
+    for (size_t i = 0; i < size / (N * N); ++i) {
       work.run(vec_ptr, eig_ptr);
       vec_ptr += N * N;
       eig_ptr += N;

mlx/backend/cpu/encoder.h

@@ -20,8 +20,8 @@ struct CommandEncoder {
   CommandEncoder(CommandEncoder&&) = delete;
   CommandEncoder& operator=(CommandEncoder&&) = delete;
 
-  void set_input_array(const array& /* a */) {}
-  void set_output_array(array& /* a */) {}
+  void set_input_array(const array& a) {}
+  void set_output_array(array& a) {}
 
   // Hold onto a temporary until any already scheduled tasks which use it as
   // an input are complete.

mlx/backend/cpu/gemm.h

@@ -12,12 +12,12 @@ void matmul(
     T* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
     float alpha,
     float beta,
-    int64_t batch_size,
+    size_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,

mlx/backend/cpu/gemms/bnns.cpp

@@ -1,4 +1,5 @@
 // Copyright © 2023-2024 Apple Inc.
+
 #include <Accelerate/Accelerate.h>
 
 #include "mlx/array.h"
@@ -34,7 +35,7 @@ void matmul_bnns(
     bool b_transposed,
     size_t lda,
     size_t ldb,
-    size_t /* ldc */,
+    size_t ldc,
     float alpha,
     float beta,
     size_t batch_size,
@@ -48,15 +49,9 @@ void matmul_bnns(
   size_t K = a_shape[ndim - 1];
 
   BNNSDataType bnns_dtype = to_bnns_dtype<T>();
+
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-  if (beta != 1.0 && beta != 0.0) {
-    // scale the output
-    for (size_t i = 0; i < batch_size * M * N; ++i) {
-      out[i] *= beta;
-    }
-    beta = 1.0;
-  }
   const BNNSLayerParametersBroadcastMatMul gemm_params{
       /* float alpha = */ alpha,
       /* float beta = */ beta,
@@ -127,7 +122,7 @@ void matmul_bnns(
   auto bnns_filter =
       BNNSFilterCreateLayerBroadcastMatMul(&gemm_params, nullptr);
 
-  for (size_t i = 0; i < batch_size; ++i) {
+  for (int i = 0; i < batch_size; ++i) {
     BNNSFilterApplyTwoInput(
         bnns_filter,
         reinterpret_cast<const uint8_t*>(
@@ -148,12 +143,12 @@ void matmul<float16_t>(
     float16_t* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
     float alpha,
     float beta,
-    int64_t batch_size,
+    size_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
@@ -183,12 +178,12 @@ void matmul<bfloat16_t>(
     bfloat16_t* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
     float alpha,
     float beta,
-    int64_t batch_size,
+    size_t batch_size,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,

mlx/backend/cpu/gemms/cblas.cpp

@@ -13,20 +13,20 @@ void matmul<float>(
     float* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
     float alpha,
     float beta,
-    int64_t batch_size,
+    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();
-  int64_t M = a_shape[ndim - 2];
-  int64_t N = b_shape[ndim - 1];
-  int64_t K = a_shape[ndim - 1];
+  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) {
     cblas_sgemm(
@@ -54,20 +54,20 @@ void matmul<double>(
     double* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
+    size_t lda,
+    size_t ldb,
+    size_t ldc,
     float alpha,
     float beta,
-    int64_t batch_size,
+    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();
-  int64_t M = a_shape[ndim - 2];
-  int64_t N = b_shape[ndim - 1];
-  int64_t K = a_shape[ndim - 1];
+  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) {
     cblas_dgemm(
@@ -88,47 +88,4 @@ void matmul<double>(
   }
 }
 
-template <>
-void matmul<complex64_t>(
-    const complex64_t* a,
-    const complex64_t* b,
-    complex64_t* out,
-    bool a_transposed,
-    bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
-    float alpha,
-    float beta,
-    int64_t batch_size,
-    const Shape& a_shape,
-    const Strides& a_strides,
-    const Shape& b_shape,
-    const Strides& b_strides) {
-  auto ndim = a_shape.size();
-  int64_t M = a_shape[ndim - 2];
-  int64_t N = b_shape[ndim - 1];
-  int64_t K = a_shape[ndim - 1];
-  auto calpha = static_cast<complex64_t>(alpha);
-  auto cbeta = static_cast<complex64_t>(beta);
-
-  for (int i = 0; i < batch_size; ++i) {
-    cblas_cgemm(
-        CblasRowMajor,
-        a_transposed ? CblasTrans : CblasNoTrans, // transA
-        b_transposed ? CblasTrans : CblasNoTrans, // transB
-        M,
-        N,
-        K,
-        &calpha,
-        a + elem_to_loc(M * K * i, a_shape, a_strides),
-        lda,
-        b + elem_to_loc(K * N * i, b_shape, b_strides),
-        ldb,
-        &cbeta,
-        out + M * N * i,
-        ldc);
-  }
-}
-
 } // namespace mlx::core

mlx/backend/cpu/hadamard.cpp

@@ -11,9 +11,9 @@ namespace mlx::core {
 
 // n = 2^k component
 template <typename T>
-void hadamard_n(T* out, int n, int /* m */, float scale, int64_t size) {
+void hadamard_n(T* out, int n, int m, float scale, size_t size) {
   for (int b = 0; b < size / n; b++) {
-    int64_t loc = b * n;
+    size_t loc = b * n;
     T* data_ptr = out + loc;
     int h = 1;
     int n_over_2 = n / 2;
@@ -37,7 +37,7 @@ void hadamard_n(T* out, int n, int /* m */, float scale, int64_t size) {
 
 // m component
 template <typename T>
-void hadamard_m(T* out, int n, int m, float scale, int64_t size) {
+void hadamard_m(T* out, int n, int m, float scale, size_t size) {
   auto h_matrices = hadamard_matrices();
   auto& matrix = h_matrices[m];
   auto start = 1;
@@ -45,7 +45,7 @@ void hadamard_m(T* out, int n, int m, float scale, int64_t size) {
   std::vector<bool> hmat_vec;
   while (end != std::string_view::npos) {
     auto row = matrix.substr(start, end - start);
-    for (int i = 0; i < std::ssize(row); i++) {
+    for (int i = 0; i < row.length(); i++) {
       hmat_vec.push_back(row[i] == '+');
     }
     start = end + 1;
@@ -53,7 +53,7 @@ void hadamard_m(T* out, int n, int m, float scale, int64_t size) {
   }
 
   for (int b = 0; b < size / m / n; b++) {
-    int64_t loc = b * n * m;
+    size_t loc = b * n * m;
     T* data_ptr = out + loc;
     for (int i = 0; i < n; i++) {
       std::vector<float> out(m);

mlx/backend/cpu/indexing.cpp

@@ -78,7 +78,7 @@ void gather(
     can_copy = true;
 
     // Ignore leading 1s
-    int64_t i = 0;
+    int i = 0;
     for (; i < slice_sizes.size() && slice_sizes[i] == 1; ++i)
       ;
 
@@ -91,7 +91,7 @@ void gather(
     can_copy = true;
 
     // Ignore trailing 1s
-    int64_t i = slice_sizes.size() - 1;
+    int i = slice_sizes.size() - 1;
     for (; i >= 0 && slice_sizes[i] == 1; --i)
       ;
 
@@ -101,11 +101,11 @@ void gather(
       can_copy = (src.shape(i) == slice_sizes[i]);
     }
   }
-  int64_t slice_size = 1;
+  size_t slice_size = 1;
   for (auto s : slice_sizes) {
     slice_size *= s;
   }
-  int64_t ind_size = slice_size == 0 ? 0 : out.size() / slice_size;
+  size_t ind_size = slice_size == 0 ? 0 : out.size() / slice_size;
   const T* src_ptr = src.data<T>();
   T* dst_ptr = out.data<T>();
 
@@ -115,10 +115,10 @@ void gather(
     src_it = ContiguousIterator(slice_sizes, src.strides(), src.ndim());
   }
 
-  int64_t out_idx = 0;
-  for (int64_t idx = 0; idx < ind_size; idx++) {
-    int64_t src_idx = 0;
-    for (int ii = 0; ii < std::ssize(inds); ++ii) {
+  size_t out_idx = 0;
+  for (int idx = 0; idx < ind_size; idx++) {
+    size_t src_idx = 0;
+    for (int ii = 0; ii < inds.size(); ++ii) {
       auto ax = axes[ii];
       auto idx_loc = its[ii].loc;
       its[ii].step();
@@ -134,7 +134,7 @@ void gather(
           src_ptr + src_idx, src_ptr + src_idx + slice_size, dst_ptr + out_idx);
       out_idx += slice_size;
     } else {
-      for (int64_t jj = 0; jj < slice_size; jj++) {
+      for (int jj = 0; jj < slice_size; jj++) {
         dst_ptr[out_idx++] = src_ptr[src_idx + src_it.loc];
         src_it.step();
       }
@@ -403,11 +403,11 @@ void scatter(
     const std::vector<int>& axes) {
   int nind = inds.size();
   auto inds_ndim = updates.ndim() - out.ndim();
-  int64_t n_updates = nind ? inds[0].size() : 1;
+  size_t n_updates = nind ? inds[0].size() : 1;
 
   Shape update_shape(
       updates.shape().begin() + inds_ndim, updates.shape().end());
-  int64_t update_size = 1;
+  size_t update_size = 1;
   for (auto us : update_shape) {
     update_size *= us;
   }
@@ -418,9 +418,9 @@ void scatter(
 
   auto out_ptr = out.data<InT>();
   auto upd_ptr = updates.data<InT>();
-  for (int64_t i = 0; i < n_updates; ++i) {
-    int64_t out_offset = 0;
-    for (int j = 0; j < std::ssize(inds); ++j) {
+  for (int i = 0; i < n_updates; ++i) {
+    size_t out_offset = 0;
+    for (int j = 0; j < inds.size(); ++j) {
       auto ax = axes[j];
       auto idx_loc = its[j].loc;
       its[j].step();
@@ -429,7 +429,7 @@ void scatter(
       out_offset += (idx_val * out.strides()[ax]);
     }
     update_it.seek(i * update_size);
-    for (int64_t j = 0; j < update_size; ++j) {
+    for (int j = 0; j < update_size; ++j) {
       OpT{}(upd_ptr[update_it.loc], out_ptr + out_offset + out_it.loc);
       update_it.step();
       out_it.step();

mlx/backend/cpu/inverse.cpp

@@ -122,7 +122,7 @@ void inverse_impl(
       stream);
 
   const int N = a.shape(-1);
-  const int64_t num_matrices = a.size() / (N * N);
+  const size_t num_matrices = a.size() / (N * N);
 
   auto& encoder = cpu::get_command_encoder(stream);
   encoder.set_output_array(inv);
@@ -130,13 +130,13 @@ void inverse_impl(
   auto inv_ptr = inv.data<T>();
   if (tri) {
     encoder.dispatch([inv_ptr, N, num_matrices, upper]() {
-      for (int64_t i = 0; i < num_matrices; i++) {
+      for (int i = 0; i < num_matrices; i++) {
         tri_inv<T>(inv_ptr + N * N * i, N, upper);
       }
     });
   } else {
     encoder.dispatch([inv_ptr, N, num_matrices]() {
-      for (int64_t i = 0; i < num_matrices; i++) {
+      for (int i = 0; i < num_matrices; i++) {
         general_inv<T>(inv_ptr + N * N * i, N);
       }
     });

mlx/backend/cpu/jit_compiler.cpp

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

mlx/backend/cpu/lapack.h

@@ -47,7 +47,7 @@ INSTANTIATE_LAPACK_REAL(orgqr)
 INSTANTIATE_LAPACK_REAL(syevd)
 INSTANTIATE_LAPACK_REAL(geev)
 INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesdd)
+INSTANTIATE_LAPACK_REAL(gesvdx)
 INSTANTIATE_LAPACK_REAL(getrf)
 INSTANTIATE_LAPACK_REAL(getri)
 INSTANTIATE_LAPACK_REAL(trtri)

mlx/backend/cpu/logsumexp.cpp

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

mlx/backend/cpu/masked_mm.cpp

@@ -25,7 +25,7 @@ inline void mask_matrix(
     const int64_t Y_data_str,
     const int64_t X_mask_str,
     const int64_t Y_mask_str,
-    const int64_t mask_offset) {
+    const size_t mask_offset) {
   int tX = (X + block_size - 1) / block_size;
   int tY = (Y + block_size - 1) / block_size;
 
@@ -61,13 +61,13 @@ inline void segmented_mm(
     T* out,
     bool a_transposed,
     bool b_transposed,
-    int64_t lda,
-    int64_t ldb,
+    size_t lda,
+    size_t ldb,
     const Shape& a_shape,
     const Strides& a_strides,
     const Shape& b_shape,
     const Strides& b_strides,
-    int64_t num_segments,
+    size_t num_segments,
     const Shape& segments_shape,
     const Strides& segments_strides) {
   int ndim = a_shape.size();
@@ -136,8 +136,9 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
           }
           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);
           int64_t stx = arr.shape(-1);
-          array arr_copy = contiguous_copy_cpu(arr, s);
           return std::make_tuple(false, stx, arr_copy, true);
         }
       };
@@ -149,9 +150,9 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto [b_transposed, ldb, b, b_copied] =
       check_transpose(b_pre, has_op_mask, inputs.back().dtype() != bool_);
 
-  int64_t M = a.shape(-2);
-  int64_t N = b.shape(-1);
-  int64_t K = a.shape(-1);
+  size_t M = a.shape(-2);
+  size_t N = b.shape(-1);
+  size_t K = a.shape(-1);
 
   if (M == 0 || N == 0) {
     return;
@@ -172,8 +173,8 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
                        int batch_idx,
                        int X,
                        int Y,
-                       int64_t X_data_str,
-                       int64_t Y_data_str,
+                       size_t X_data_str,
+                       size_t Y_data_str,
                        const Shape& mask_shape,
                        const Strides& mask_strides,
                        bool is_bool) {
@@ -215,18 +216,18 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   encoder.set_input_array(a);
   encoder.set_input_array(b);
-  const void* a_mask_ptr = nullptr;
-  const void* b_mask_ptr = nullptr;
-  const void* out_mask_ptr = nullptr;
+  const void* a_mask_ptr;
+  const void* b_mask_ptr;
+  const void* out_mask_ptr;
   Shape a_mask_shape;
   Shape b_mask_shape;
   Shape out_mask_shape;
   Strides a_mask_strides;
   Strides b_mask_strides;
   Strides out_mask_strides;
-  bool a_mask_bool = false;
-  bool b_mask_bool = false;
-  bool out_mask_bool = false;
+  bool a_mask_bool;
+  bool b_mask_bool;
+  bool out_mask_bool;
   if (has_op_mask) {
     auto& a_mask = inputs[inputs.size() - 2];
     auto& b_mask = inputs[inputs.size() - 1];
@@ -253,7 +254,7 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto a_ptr = a.data<float>();
   auto b_ptr = b.data<float>();
   auto out_ptr = out.data<float>();
-  int64_t num_matrices = out.size() / (M * int64_t(N));
+  size_t num_matrices = out.size() / (M * size_t(N));
   auto ldc = out.shape(-1);
 
   encoder.dispatch([a_ptr,
@@ -394,9 +395,9 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto [a_transposed, lda, a] = check_transpose(a_pre);
   auto [b_transposed, ldb, b] = check_transpose(b_pre);
 
-  int64_t M = a.shape(-2);
-  int64_t N = b.shape(-1);
-  int64_t K = a.shape(-1);
+  size_t M = a.shape(-2);
+  size_t N = b.shape(-1);
+  size_t K = a.shape(-1);
 
   if (M == 0 || N == 0) {
     return;
@@ -413,7 +414,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
 
   // Get batch dims
   auto batch_size_out = out.size() / (M * N);
-  int64_t matrix_stride_out = M * N;
+  size_t matrix_stride_out = M * N;
 
   auto get_batch_dims = [](const auto& v) {
     return decltype(v){v.begin(), v.end() - 2};
@@ -423,6 +424,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& rhs_indices = inputs[3];
 
   auto batch_shape = get_batch_dims(out.shape());
+  int batch_ndim = batch_shape.size();
 
   auto batch_shape_A = get_batch_dims(a.shape());
   auto batch_strides_A = get_batch_dims(a.strides());

mlx/backend/cpu/matmul.cpp

@@ -91,6 +91,7 @@ void matmul_general(
   auto [b_transposed, ldb, b] = check_transpose(b_pre);
   size_t M = a.shape(-2);
   size_t N = b.shape(-1);
+  size_t K = a.shape(-1);
   if (M == 0 || N == 0) {
     return;
   }
@@ -107,9 +108,6 @@ void matmul_general(
   } else if (out.dtype() == float64) {
     matmul_dispatch<double>(
         a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
-  } else if (out.dtype() == complex64) {
-    matmul_dispatch<complex64_t>(
-        a, b, out, a_transposed, b_transposed, lda, ldb, alpha, beta, stream);
   } else {
     throw std::runtime_error("[Matmul::eval_cpu] Invalid type.");
   }
@@ -130,6 +128,10 @@ void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
+  if (out.dtype() != float32) {
+    throw std::runtime_error(
+        "[AddMM::eval_cpu] Currently only supports float32.");
+  }
   if (out.size() == 0) {
     out.set_data(allocator::malloc(out.nbytes()));
     return;

mlx/backend/cpu/primitives.cpp

@@ -48,7 +48,7 @@ static std::pair<array, bool> compute_dynamic_offset(
   auto compute_offset =
       [strides, axes, offset = offset.data<int64_t>()](const auto* indices) {
         int64_t offset_ = 0;
-        for (int i = 0; i < std::ssize(axes); ++i) {
+        for (int i = 0; i < axes.size(); ++i) {
           offset_ += indices[i] * strides[axes[i]];
         }
         offset[0] = offset_;
@@ -124,7 +124,6 @@ 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);
-  (void)inputs;
   out.set_data(allocator::malloc(out.nbytes()));
   switch (out.dtype()) {
     case bool_:
@@ -194,9 +193,9 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
   flags.row_contiguous = false;
   flags.col_contiguous = false;
   flags.contiguous = false;
-  for (int i = 0; i < std::ssize(inputs); i++) {
+  for (int i = 0; i < inputs.size(); i++) {
     array out_slice(inputs[i].shape(), out.dtype(), nullptr, {});
-    int64_t data_offset = strides[axis_] * sizes[i];
+    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());
@@ -206,7 +205,7 @@ void Concatenate::eval_cpu(const std::vector<array>& inputs, array& out) {
 void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  constexpr int64_t extra_bytes = 16384;
+  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))) {
@@ -255,8 +254,8 @@ void Pad::eval_cpu(const std::vector<array>& inputs, array& out) {
   copy_cpu(val, out, CopyType::Scalar, stream());
 
   // Find offset for start of input values
-  int64_t data_offset = 0;
-  for (int i = 0; i < std::ssize(axes_); i++) {
+  size_t data_offset = 0;
+  for (int i = 0; i < axes_.size(); i++) {
     auto ax = axes_[i] < 0 ? out.ndim() + axes_[i] : axes_[i];
     data_offset += out.strides()[ax] * low_pad_size_[i];
   }
@@ -275,10 +274,10 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
   // keys has shape (N1, ..., NK, 2)
   // out has shape (N1, ..., NK, M1, M2, ...)
   auto& keys = inputs[0];
-  int64_t num_keys = keys.size() / 2;
+  size_t num_keys = keys.size() / 2;
 
-  int64_t elems_per_key = out.size() / num_keys;
-  int64_t bytes_per_key = out.itemsize() * elems_per_key;
+  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()));
 
   auto kptr = inputs[0].data<uint32_t>();
@@ -292,8 +291,8 @@ void RandomBits::eval_cpu(const std::vector<array>& inputs, array& out) {
                     num_keys,
                     kshape = keys.shape(),
                     kstrides = keys.strides()]() mutable {
-    int64_t out_skip = (bytes_per_key + 4 - 1) / 4;
-    uintptr_t half_size = out_skip / 2;
+    size_t out_skip = (bytes_per_key + 4 - 1) / 4;
+    auto half_size = out_skip / 2;
     bool even = out_skip % 2 == 0;
     for (int i = 0; i < num_keys; ++i, cptr += bytes_per_key) {
       auto ptr = reinterpret_cast<uint32_t*>(cptr);

mlx/backend/cpu/qrf.cpp

@@ -13,7 +13,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
   const int M = a.shape(-2);
   const int N = a.shape(-1);
   const int lda = M;
-  int64_t num_matrices = a.size() / (M * N);
+  size_t num_matrices = a.size() / (M * N);
 
   // Copy A to inplace input and make it col-contiguous
   array in(a.shape(), a.dtype(), nullptr, {});
@@ -54,7 +54,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     auto work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
-    for (int64_t i = 0; i < num_matrices; ++i) {
+    for (int i = 0; i < num_matrices; ++i) {
       // Solve
       geqrf<T>(
           &M,
@@ -68,7 +68,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     }
     allocator::free(work);
 
-    for (int64_t i = 0; i < num_matrices; ++i) {
+    for (int i = 0; i < num_matrices; ++i) {
       /// num_reflectors x N
       for (int j = 0; j < num_reflectors; ++j) {
         for (int k = 0; k < j; ++k) {
@@ -97,7 +97,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
     work = allocator::malloc(sizeof(T) * lwork);
 
     // Loop over matrices
-    for (int64_t i = 0; i < num_matrices; ++i) {
+    for (int i = 0; i < num_matrices; ++i) {
       // Compute Q
       orgqr<T>(
           &M,
@@ -111,7 +111,7 @@ void qrf_impl(const array& a, array& q, array& r, Stream stream) {
           &info);
     }
 
-    for (int64_t i = 0; i < num_matrices; ++i) {
+    for (int i = 0; i < num_matrices; ++i) {
       // M x num_reflectors
       for (int j = 0; j < M; ++j) {
         for (int k = 0; k < num_reflectors; ++k) {

mlx/backend/cpu/quantized.cpp

@@ -1,5 +1,7 @@
 // Copyright © 2023 Apple Inc.
 
+#include <cassert>
+
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
@@ -11,35 +13,6 @@ namespace mlx::core {
 
 namespace {
 
-const static float MXFP4_LUT[16] = {
-    +0.0f,
-    +0.5f,
-    +1.0f,
-    +1.5f,
-    +2.0f,
-    +3.0f,
-    +4.0f,
-    +6.0f,
-    -0.0f,
-    -0.5f,
-    -1.0f,
-    -1.5f,
-    -2.0f,
-    -3.0f,
-    -4.0f,
-    -6.0f};
-
-template <typename T>
-static inline T dequantize_scale(uint8_t s) {
-  using FOrI = union {
-    bfloat16_t f;
-    uint16_t i;
-  };
-  FOrI out;
-  out.i = (s == 0 ? 0x40 : (static_cast<uint16_t>(s) << 7));
-  return static_cast<T>(out.f);
-}
-
 inline constexpr short get_pack_factor(int bits, int wsize = 8) {
   return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
 }
@@ -434,229 +407,6 @@ void _qmm_dispatch(
   }
 }
 
-template <typename T>
-void mxfp4_qmm(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = get_pack_factor(4, 8);
-  constexpr int packs_in_group = group_size / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
-    const uint8_t* scales_local = scales;
-
-    std::fill(result, result + N, 0);
-
-    for (int k = 0; k < K; k++) {
-      T* result_local = result;
-      T xi = *x++;
-
-      for (int n = 0; n < N; n += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-        for (int ng = 0; ng < packs_in_group; ng++) {
-          uint8_t wi = *w_local++;
-#pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            (*result_local++) +=
-                xi * scale * static_cast<T>(MXFP4_LUT[wi & 0xf]);
-            wi >>= 4;
-          }
-        }
-      }
-    }
-
-    result += N;
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_t(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = get_pack_factor(4, 8);
-  constexpr int packs_in_group = group_size / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
-    const uint8_t* scales_local = scales;
-
-    for (int n = 0; n < N; n++) {
-      const T* x_local = x;
-      T sum = 0;
-      for (int k = 0; k < K; k += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-
-        T gsum = 0;
-        for (int kw = 0; kw < packs_in_group; kw++) {
-          uint8_t wi = *w_local++;
-#pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            gsum += (*x_local++) * static_cast<T>(MXFP4_LUT[wi & 0xf]);
-            wi >>= 4;
-          }
-        }
-        sum += scale * gsum;
-      }
-      *result = sum;
-      result++;
-    }
-
-    x += K;
-  }
-}
-
-template <int S>
-simd::Simd<float, S> mxfp4_extract_bits_simd(const uint32_t* w) {
-  if constexpr (S == 8) {
-    constexpr std::array<uint32_t, 8> shifts_ = {{0, 4, 8, 12, 16, 20, 24, 28}};
-    auto shifts(*(simd::Simd<uint32_t, S>*)&shifts_);
-    auto wi = simd::Simd<uint32_t, S>(*w);
-    wi = wi >> shifts;
-    wi = wi & 0xf;
-    simd::Simd<float, S> w_out;
-    for (int i = 0; i < S; ++i) {
-      w_out[i] = MXFP4_LUT[wi[i]];
-    }
-    return w_out;
-  } else {
-    // Appease compiler.. but should never get here
-    throw std::runtime_error("Unsupported combination for simd qmm.");
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_t_simd(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = 32 / 4;
-  constexpr int packs_in_group = group_size / pack_factor;
-  constexpr int S = simd::max_size<T>;
-  static_assert(
-      S % pack_factor == 0, "SIMD size must be divisible by pack factor");
-  constexpr int packs_per_simd = S / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint32_t* w_local = w;
-    const uint8_t* scales_local = scales;
-
-    for (int n = 0; n < N; n++) {
-      simd::Simd<float, S> acc(0);
-      auto x_local = x;
-      for (int k = 0; k < K; k += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-
-        simd::Simd<float, S> g_acc(0);
-        for (int kw = 0; kw < packs_in_group; kw += packs_per_simd) {
-          // Extract bits
-          auto wf = mxfp4_extract_bits_simd<S>(w_local);
-          w_local += packs_per_simd;
-          simd::Simd<float, S> x_simd = simd::load<T, S>(x_local);
-          g_acc = g_acc + x_simd * wf;
-          x_local += S;
-        }
-        acc = acc + scale * g_acc;
-      }
-
-      *result = T(simd::sum(acc));
-      result++;
-    }
-    x += K;
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_dispatch_transpose(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K,
-    bool transposed_w) {
-  if (transposed_w) {
-    // the simd size must be a multiple of the number of elements per word
-    if constexpr (simd::max_size<T> % 8 == 0) {
-      mxfp4_qmm_t_simd<T>(result, x, w, scales, M, N, K);
-    } else {
-      mxfp4_qmm_t<T>(result, x, w, scales, M, N, K);
-    }
-  } else {
-    mxfp4_qmm<T>(result, x, w, scales, M, N, K);
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_dispatch_typed(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    bool transposed_w) {
-  int K = x.shape(-1);
-  int M = x.ndim() > 1 ? x.shape(-2) : 1;
-  int N = out.shape(-1);
-  int w_els = w.ndim() > 2 ? w.shape(-1) * w.shape(-2) : 0;
-  int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
-  int batch_size = x.size() / (K * M);
-
-  auto out_ptr = out.data<T>();
-  auto x_ptr = x.data<T>();
-  auto w_ptr = w.data<uint32_t>();
-  auto scales_ptr = scales.data<uint8_t>();
-  for (int i = 0; i < batch_size; i++) {
-    mxfp4_qmm_dispatch_transpose<T>(
-        out_ptr + i * M * N,
-        x_ptr + elem_to_loc(i * M * K, x.shape(), x.strides()),
-        w_ptr + elem_to_loc(i * w_els, w.shape(), w.strides()),
-        scales_ptr + elem_to_loc(i * g_els, scales.shape(), scales.strides()),
-        M,
-        N,
-        K,
-        transposed_w);
-  }
-}
-
-void mxfp4_qmm_dispatch(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    bool transposed_w) {
-  switch (x.dtype()) {
-    case bfloat16:
-      mxfp4_qmm_dispatch_typed<bfloat16_t>(out, x, w, scales, transposed_w);
-      break;
-    case float16:
-      mxfp4_qmm_dispatch_typed<float16_t>(out, x, w, scales, transposed_w);
-      break;
-    case float32:
-      mxfp4_qmm_dispatch_typed<float>(out, x, w, scales, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "[quantized_matmul] only floating types are supported");
-  }
-}
-
 template <typename T>
 void _bs_qmm_dispatch_typed(
     array& out,
@@ -763,198 +513,115 @@ void _bs_qmm_dispatch(
   }
 }
 
-template <typename T>
-void mxfp4_bs_qmm_dispatch_typed(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    const array& lhs_indices,
-    const array& rhs_indices,
-    bool transposed_w) {
-  int K = x.shape(-1);
-  int M = x.shape(-2);
-  int N = out.shape(-1);
-
-  int w_els = w.shape(-1) * w.shape(-2);
-  int g_els = scales.shape(-1) * scales.shape(-2);
-
-  auto out_ptr = out.data<T>();
-  auto x_ptr = x.data<T>();
-  auto w_ptr = w.data<uint32_t>();
-  auto scales_ptr = scales.data<uint8_t>();
-  auto lhs_indices_ptr = lhs_indices.data<uint32_t>();
-  auto rhs_indices_ptr = rhs_indices.data<uint32_t>();
-
-  for (int i = 0; i < lhs_indices.size(); i++) {
-    int x_idx = lhs_indices_ptr[elem_to_loc(
-        i, lhs_indices.shape(), lhs_indices.strides())];
-    int w_idx = rhs_indices_ptr[elem_to_loc(
-        i, rhs_indices.shape(), rhs_indices.strides())];
-    mxfp4_qmm_dispatch_transpose<T>(
-        out_ptr + i * M * N,
-        x_ptr + elem_to_loc(x_idx * M * K, x.shape(), x.strides()),
-        w_ptr + elem_to_loc(w_idx * w_els, w.shape(), w.strides()),
-        scales_ptr +
-            elem_to_loc(w_idx * g_els, scales.shape(), scales.strides()),
-        M,
-        N,
-        K,
-        transposed_w);
-  }
-}
-
-void mxfp4_bs_qmm_dispatch(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    const array& lhs_indices,
-    const array& rhs_indices,
-    bool transposed_w) {
-  switch (x.dtype()) {
-    case float32:
-      mxfp4_bs_qmm_dispatch_typed<float>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    case float16:
-      mxfp4_bs_qmm_dispatch_typed<float16_t>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    case bfloat16:
-      mxfp4_bs_qmm_dispatch_typed<bfloat16_t>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "[quantized_matmul] only floating types are supported");
-  }
-}
-
 } // namespace
 
 void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 4);
+
   auto& x_pre = inputs[0];
   auto& w_pre = inputs[1];
   auto& scales_pre = inputs[2];
+  auto& biases_pre = inputs[3];
 
-  auto& encoder = cpu::get_command_encoder(stream());
-  auto ensure_row_contiguous = [s = stream(), &encoder](const array& arr) {
+  std::vector<array> temps;
+  auto ensure_row_contiguous = [s = stream(), &temps](const array& arr) {
     if (arr.flags().row_contiguous) {
       return arr;
     } else {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
-      return arr_cpy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy_cpu(arr, temps.back(), CopyType::General, s);
+      return temps.back();
     }
   };
 
   auto x = ensure_row_contiguous(x_pre);
   auto w = ensure_row_contiguous(w_pre);
   auto scales = ensure_row_contiguous(scales_pre);
+  auto biases = ensure_row_contiguous(biases_pre);
 
   out.set_data(allocator::malloc(out.nbytes()));
 
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
   encoder.set_input_array(x);
   encoder.set_input_array(w);
   encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
   encoder.set_output_array(out);
-  if (mode_ == QuantizationMode::Affine) {
-    auto biases = ensure_row_contiguous(inputs[3]);
-    encoder.set_input_array(biases);
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      biases = array::unsafe_weak_copy(biases),
-                      group_size_ = group_size_,
-                      bits_ = bits_,
-                      transpose_ = transpose_]() mutable {
-      _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
-    });
-  } else {
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      transpose_ = transpose_]() mutable {
-      mxfp4_qmm_dispatch(out, x, w, scales, transpose_);
-    });
-  }
+  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_);
+  });
 }
 
 void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 6);
+
   auto& x_pre = inputs[0];
   auto& w_pre = inputs[1];
   auto& scales_pre = inputs[2];
-  auto& lhs_indices = inputs[inputs.size() - 2];
-  auto& rhs_indices = inputs[inputs.size() - 1];
+  auto& biases_pre = inputs[3];
+  auto& lhs_indices = inputs[4];
+  auto& rhs_indices = inputs[5];
 
-  auto& encoder = cpu::get_command_encoder(stream());
+  std::vector<array> temps;
   auto ensure_row_contiguous_last_dims = [s = stream(),
-                                          &encoder](const array& arr) {
+                                          &temps](const array& arr) {
     auto stride_0 = arr.strides()[arr.ndim() - 2];
     auto stride_1 = arr.strides()[arr.ndim() - 1];
     if (stride_0 == arr.shape(-1) && stride_1 == 1) {
       return arr;
     } else {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
-      return arr_cpy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy_cpu(arr, temps.back(), CopyType::General, s);
+      return temps.back();
     }
   };
 
   auto x = ensure_row_contiguous_last_dims(x_pre);
   auto w = ensure_row_contiguous_last_dims(w_pre);
   auto scales = ensure_row_contiguous_last_dims(scales_pre);
+  auto biases = ensure_row_contiguous_last_dims(biases_pre);
 
   out.set_data(allocator::malloc(out.nbytes()));
 
+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
   encoder.set_input_array(x);
   encoder.set_input_array(w);
   encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
   encoder.set_input_array(lhs_indices);
   encoder.set_input_array(rhs_indices);
   encoder.set_output_array(out);
-  if (mode_ == QuantizationMode::Affine) {
-    auto biases = ensure_row_contiguous_last_dims(inputs[3]);
-    encoder.set_input_array(biases);
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      biases = array::unsafe_weak_copy(biases),
-                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                      group_size_ = group_size_,
-                      bits_ = bits_,
-                      transpose_ = transpose_]() mutable {
-      _bs_qmm_dispatch(
-          out,
-          x,
-          w,
-          scales,
-          biases,
-          lhs_indices,
-          rhs_indices,
-          group_size_,
-          bits_,
-          transpose_);
-    });
-  } else {
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                      transpose_ = transpose_]() mutable {
-      mxfp4_bs_qmm_dispatch(
-          out, x, w, scales, lhs_indices, rhs_indices, transpose_);
-    });
-  }
+  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_);
+  });
 }
 
 template <typename T, typename U>
@@ -1038,14 +705,16 @@ void dispatch_quantize(
       w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
 }
 
-void fast::Quantize::eval_cpu(
+void fast::AffineQuantize::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
   auto ensure_row_contiguous = [s = stream()](const array& arr) {
     if (arr.flags().row_contiguous) {
       return std::make_pair(arr, false);
     } else {
-      return std::make_pair(contiguous_copy_cpu(arr, s), true);
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy_cpu(arr, arr_copy, CopyType::General, s);
+      return std::make_pair(arr_copy, true);
     }
   };
 
@@ -1097,7 +766,7 @@ void fast::Quantize::eval_cpu(
       }
     } else {
       throw std::runtime_error(
-          "[fast::Quantize::eval_cpu] Only supports floating point inputs");
+          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
     }
   });
 }

mlx/backend/cpu/reduce.cpp

@@ -491,27 +491,19 @@ void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
         switch (in.dtype()) {
           case bool_:
           case uint8:
-            reduce_dispatch_sum_prod<uint8_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint16:
-            reduce_dispatch_sum_prod<uint16_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint32:
-            reduce_dispatch_sum_prod<uint32_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint64:
-            reduce_dispatch_sum_prod<uint64_t>(in, out, reduce_type_, axes_);
-            break;
           case int8:
             reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
             break;
           case int16:
+          case uint16:
             reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
             break;
           case int32:
+          case uint32:
             reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
             break;
           case int64:
+          case uint64:
             reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
             break;
           case float16:

mlx/backend/cpu/scan.cpp

@@ -250,8 +250,10 @@ void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Ensure contiguity
   auto in = inputs[0];
   if (!in.flags().row_contiguous) {
-    in = contiguous_copy_cpu(in, stream());
-    encoder.add_temporary(in);
+    array arr_copy(in.shape(), in.dtype(), nullptr, {});
+    copy_cpu(in, arr_copy, CopyType::General, stream());
+    in = arr_copy;
+    encoder.add_temporary(arr_copy);
   }
   out.set_data(allocator::malloc(out.nbytes()));
 

mlx/backend/cpu/simd/accelerate_simd.h

@@ -9,7 +9,7 @@
 
 #include "mlx/backend/cpu/simd/base_simd.h"
 
-// There seems to be a bug in simd/base_simd.h
+// There seems to be a bug in sims/base.h
 // __XROS_2_0 is not defined, the expression evaluates
 // to true instead of false setting the SIMD library
 // higher than it should be even on macOS < 15
@@ -234,7 +234,6 @@ Simd<T, N> remainder(Simd<T, N> a, Simd<T, N> b) {
 
 template <typename MaskT, typename T1, typename T2, int N>
 Simd<T1, N> select(Simd<MaskT, N> mask, Simd<T1, N> x, Simd<T2, N> y) {
-  static_assert(std::is_same_v<MaskT, bool>);
   if constexpr (sizeof(T1) == 1) {
     return asd::bitselect(y.value, x.value, asd::convert<char>(mask.value));
   } else if constexpr (sizeof(T1) == 2) {
@@ -252,13 +251,9 @@ Simd<T, N> pow(Simd<T, N> base, Simd<T, N> exp) {
     return asd::pow(base.value, exp.value);
   } else {
     Simd<T, N> res = 1;
-    // Raising an integer to a negative power is undefined
-    if (any(exp < static_cast<T>(0))) {
-      return 0;
-    }
-    while (any(exp > static_cast<T>(0))) {
-      res = select((exp & 1) != 0, res * base, res);
-      base = select(exp > static_cast<T>(0), base * base, base);
+    while (any(exp)) {
+      res = select(exp & 1, res * base, res);
+      base = select(exp, base * base, base);
       exp = exp >> 1;
     }
     return res;

mlx/backend/cpu/simd/math.h

@@ -79,8 +79,7 @@ Simd<T, N> sincos(Simd<T, N> in) {
 
   // Get the polynom selection mask. There is one polynom for 0 <= x <= Pi/4
   // and another one for Pi/4<x<=Pi/2. Both branches will be computed.
-  auto poly_mask =
-      (emm2 & static_cast<uint32_t>(2)) != static_cast<uint32_t>(0);
+  auto poly_mask = (emm2 & 2) != 0;
 
   // The magic pass: "Extended precision modular arithmetic"
   // x = ((x - y * DP1) - y * DP2) - y * DP3
@@ -88,8 +87,8 @@ Simd<T, N> sincos(Simd<T, N> in) {
   x = fma(y, Simd<float, N>(-2.4187564849853515625e-4f), x);
   x = fma(y, Simd<float, N>(-3.77489497744594108e-8f), x);
 
-  sign_mask_sin = sign_mask_sin ^ ((emm2 & 4) != static_cast<uint32_t>(0));
-  auto sign_mask_cos = ((emm2 - 2) & 4) != static_cast<uint32_t>(0);
+  sign_mask_sin = sign_mask_sin ^ ((emm2 & 4) != 0);
+  auto sign_mask_cos = ((emm2 - 2) & 4) != 0;
 
   // Evaluate the first polynom  (0 <= x <= Pi/4) in y1,
   // and the second polynom      (Pi/4 <= x <= 0) in y2

mlx/backend/cpu/softmax.cpp

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

mlx/backend/cpu/sort.cpp

@@ -8,25 +8,13 @@
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
-#include "mlx/dtype_utils.h"
+
 #include "mlx/primitives.h"
 
 namespace mlx::core {
 
 namespace {
 
-// NaN-aware comparator that places NaNs at the end
-template <typename T>
-bool nan_aware_less(T a, T b) {
-  if constexpr (std::is_floating_point_v<T> || std::is_same_v<T, complex64_t>) {
-    if (std::isnan(a))
-      return false;
-    if (std::isnan(b))
-      return true;
-  }
-  return a < b;
-}
-
 template <typename T>
 struct StridedIterator {
   using iterator_category = std::random_access_iterator_tag;
@@ -39,7 +27,7 @@ struct StridedIterator {
   StridedIterator() = default;
 
   explicit StridedIterator(T* ptr, int64_t stride, difference_type offset = 0)
-      : stride_(stride), ptr_(ptr + offset * stride) {}
+      : ptr_(ptr + offset * stride), stride_(stride) {}
 
   explicit StridedIterator(array& arr, int axis, difference_type offset = 0)
       : StridedIterator(arr.data<T>(), arr.strides()[axis], offset) {}
@@ -120,8 +108,8 @@ template <typename T>
 void sort(array& out, int axis) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + out.ndim() : axis;
-  int64_t in_size = out.size();
-  int64_t n_rows = in_size / out.shape(axis);
+  size_t in_size = out.size();
+  size_t n_rows = in_size / out.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -136,13 +124,13 @@ void sort(array& out, int axis) {
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   auto out_ptr = out.data<T>();
-  for (int64_t i = 0; i < n_rows; i++) {
+  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);
 
-    std::stable_sort(st, ed, nan_aware_less<T>);
+    std::stable_sort(st, ed);
     src_it.step();
   }
 }
@@ -151,7 +139,7 @@ template <typename T, typename IdxT = uint32_t>
 void argsort(const array& in, array& out, int axis) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  int64_t n_rows = in.size() / in.shape(axis);
+  size_t n_rows = in.size() / in.shape(axis);
 
   auto in_remaining_shape = in.shape();
   in_remaining_shape.erase(in_remaining_shape.begin() + axis);
@@ -176,7 +164,7 @@ void argsort(const array& in, array& out, int axis) {
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   auto in_ptr = in.data<T>();
   auto out_ptr = out.data<IdxT>();
-  for (int64_t i = 0; i < n_rows; i++) {
+  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;
 
@@ -196,15 +184,6 @@ void argsort(const array& in, array& out, int axis) {
     std::stable_sort(st, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
-
-      // Handle NaNs (place them at the end)
-      if (std::is_floating_point<T>::value) {
-        if (std::isnan(v1))
-          return false;
-        if (std::isnan(v2))
-          return true;
-      }
-
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }
@@ -214,8 +193,8 @@ template <typename T>
 void partition(array& out, int axis, int kth) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + out.ndim() : axis;
-  int64_t in_size = out.size();
-  int64_t n_rows = in_size / out.shape(axis);
+  size_t in_size = out.size();
+  size_t n_rows = in_size / out.shape(axis);
 
   auto remaining_shape = out.shape();
   remaining_shape.erase(remaining_shape.begin() + axis);
@@ -232,7 +211,7 @@ void partition(array& out, int axis, int kth) {
   ContiguousIterator src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   auto out_ptr = out.data<T>();
-  for (int64_t i = 0; i < n_rows; i++) {
+  for (int i = 0; i < n_rows; i++) {
     T* data_ptr = out_ptr + src_it.loc;
     src_it.step();
 
@@ -240,7 +219,7 @@ void partition(array& out, int axis, int kth) {
     StridedIterator md(data_ptr, axis_stride, kth);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::nth_element(st, md, ed, nan_aware_less<T>);
+    std::nth_element(st, md, ed);
   }
 }
 
@@ -248,7 +227,7 @@ template <typename T, typename IdxT = uint32_t>
 void argpartition(const array& in, array& out, int axis, int kth) {
   // Get axis, shape and stride info
   axis = axis < 0 ? axis + in.ndim() : axis;
-  int64_t n_rows = in.size() / in.shape(axis);
+  size_t n_rows = in.size() / in.shape(axis);
 
   auto in_remaining_shape = in.shape();
   in_remaining_shape.erase(in_remaining_shape.begin() + axis);
@@ -277,7 +256,7 @@ void argpartition(const array& in, array& out, int axis, int kth) {
   auto in_ptr = in.data<T>();
   auto out_ptr = out.data<IdxT>();
 
-  for (int64_t i = 0; i < n_rows; i++) {
+  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();
@@ -297,15 +276,6 @@ void argpartition(const array& in, array& out, int axis, int kth) {
     std::nth_element(st, md, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
-
-      // Handle NaNs (place them at the end)
-      if (std::is_floating_point<T>::value) {
-        if (std::isnan(v1))
-          return false;
-        if (std::isnan(v2))
-          return true;
-      }
-
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }
@@ -363,24 +333,47 @@ void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
-  int axis = axis_;
-  if (axis < 0) {
-    axis += in.ndim();
-  }
-
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis] != 0)
+  CopyType ctype = (in.flags().contiguous && 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]() mutable {
-    dispatch_all_types(out.dtype(), [&](auto type_tag) {
-      sort<MLX_GET_TYPE(type_tag)>(out, axis);
-    });
-  });
+  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_);
+        }
+      });
 }
 
 void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {

mlx/backend/cpu/svd.cpp

@@ -27,7 +27,7 @@ void svd_impl(
   const int N = a.shape(-1);
   const int K = std::min(M, N);
 
-  int64_t num_matrices = a.size() / (M * N);
+  size_t num_matrices = a.size() / (M * N);
 
   // lapack clobbers the input, so we have to make a copy.
   array in(a.shape(), a.dtype(), nullptr, {});
@@ -81,26 +81,40 @@ void svd_impl(
     // Vᵀ of shape N x N. (M x M in lapack).
     const int ldvt = M;
 
-    auto jobz = (u_ptr) ? "A" : "N";
+    auto job_u = (u_ptr) ? "V" : "N";
+    auto job_vt = (u_ptr) ? "V" : "N";
+    static constexpr auto range = "A";
 
+    // Will contain the number of singular values after the call has returned.
+    int ns = 0;
     T workspace_dimension = 0;
 
     // Will contain the indices of eigenvectors that failed to converge (not
     // used here but required by lapack).
-    auto iwork = array::Data{allocator::malloc(sizeof(int) * 8 * K)};
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
 
     static const int lwork_query = -1;
 
+    static const int ignored_int = 0;
+    static const T ignored_float = 0;
+
     int info;
 
     // Compute workspace size.
-    gesdd<T>(
-        /* jobz = */ jobz,
+    gesvdx<T>(
+        /* jobu = */ job_u,
+        /* jobvt = */ job_vt,
+        /* range = */ range,
         // M and N are swapped since lapack expects column-major.
         /* m = */ &N,
         /* n = */ &M,
         /* a = */ nullptr,
         /* lda = */ &lda,
+        /* vl = */ &ignored_float,
+        /* vu = */ &ignored_float,
+        /* il = */ &ignored_int,
+        /* iu = */ &ignored_int,
+        /* ns = */ &ns,
         /* s = */ nullptr,
         /* u = */ nullptr,
         /* ldu = */ &ldu,
@@ -121,14 +135,21 @@ void svd_impl(
     auto scratch = array::Data{allocator::malloc(sizeof(T) * lwork)};
 
     // Loop over matrices.
-    for (int64_t i = 0; i < num_matrices; i++) {
-      gesdd<T>(
-          /* jobz = */ jobz,
+    for (int i = 0; i < num_matrices; i++) {
+      gesvdx<T>(
+          /* jobu = */ job_u,
+          /* jobvt = */ job_vt,
+          /* range = */ range,
           // M and N are swapped since lapack expects column-major.
           /* m = */ &N,
           /* n = */ &M,
           /* a = */ in_ptr + M * N * i,
           /* lda = */ &lda,
+          /* vl = */ &ignored_float,
+          /* vu = */ &ignored_float,
+          /* il = */ &ignored_int,
+          /* iu = */ &ignored_int,
+          /* ns = */ &ns,
           /* s = */ s_ptr + K * i,
           // According to the identity above, lapack will write Vᵀᵀ as U.
           /* u = */ vt_ptr ? vt_ptr + N * N * i : nullptr,
@@ -146,6 +167,13 @@ void svd_impl(
         ss << "svd_impl: sgesvdx_ failed with code " << info;
         throw std::runtime_error(ss.str());
       }
+
+      if (ns != K) {
+        std::stringstream ss;
+        ss << "svd_impl: expected " << K << " singular values, but " << ns
+           << " were computed.";
+        throw std::runtime_error(ss.str());
+      }
     }
   });
   encoder.add_temporary(in);
@@ -153,10 +181,10 @@ void svd_impl(
 
 template <typename T>
 void compute_svd(
-    const array& /* a */,
-    bool /* compute_uv */,
-    std::vector<array>& /* outputs */,
-    Stream /* stream */) {}
+    const array& a,
+    bool compute_uv,
+    std::vector<array>& outputs,
+    Stream stream) {}
 
 void SVD::eval_cpu(
     const std::vector<array>& inputs,

mlx/backend/cpu/ternary.h

@@ -136,7 +136,7 @@ void ternary_op(
   if (topt == TernaryOpType::ScalarScalarScalar) {
     *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
   } else if (topt == TernaryOpType::VectorVectorVector) {
-    for (int64_t i = 0; i < out.size(); ++i) {
+    for (size_t i = 0; i < out.size(); ++i) {
       *out_ptr = op(*a_ptr, *b_ptr, *c_ptr);
       a_ptr++;
       b_ptr++;

mlx/backend/cpu/unary.h

@@ -10,8 +10,8 @@
 namespace mlx::core {
 
 template <typename T, typename U = T, typename Op>
-void unary_op(const T* a, U* out, int64_t shape, int64_t stride) {
-  for (int64_t i = 0; i < shape; i += 1) {
+void unary_op(const T* a, U* out, size_t shape, size_t stride) {
+  for (size_t i = 0; i < shape; i += 1) {
     out[i] = Op{}(*a);
     a += stride;
   }
@@ -38,14 +38,14 @@ void unary_op(const array& a, array& out, Op) {
       src++;
     }
   } else {
-    int64_t shape = ndim > 0 ? a.shape().back() : 1;
-    int64_t stride = ndim > 0 ? a.strides().back() : 1;
+    size_t shape = ndim > 0 ? a.shape().back() : 1;
+    size_t stride = ndim > 0 ? a.strides().back() : 1;
     if (ndim <= 1) {
       unary_op<T, U, Op>(src, dst, shape, stride);
       return;
     }
     auto it = ContiguousIterator(a.shape(), a.strides(), ndim - 1);
-    for (int64_t elem = 0; elem < a.size(); elem += shape) {
+    for (size_t elem = 0; elem < a.size(); elem += shape) {
       unary_op<T, U, Op>(src + it.loc, dst + elem, shape, stride);
       it.step();
     }

mlx/backend/cpu/unary_ops.h

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

mlx/backend/cuda/CMakeLists.txt

@@ -6,8 +6,8 @@
 target_sources(
   mlx
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/arange.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/binary_two.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
@@ -15,26 +15,18 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_conv.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_grouped_conv.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/cudnn_utils.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/custom_kernel.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/gemv.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel_utils.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/layer_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/random.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/all_reduce.cu
@@ -43,28 +35,18 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/quantized/affine_quantize.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/qmm.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/binary)
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/unary)
-
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9.0)
-  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_9.cu)
-else()
-  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_0.cpp)
-endif()
-
 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
 
 # Embed kernel sources in binary for JIT compilation.
@@ -107,18 +89,11 @@ endif()
 target_compile_options(
   mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--Wno-deprecated-gpu-targets>")
 
-# Use stronger binaries compression. This feature was introduced in CUDA 12.8
-# and requires drivers released after CUDA 12.4.
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
-  target_compile_options(
-    mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--compress-mode=size>")
-endif()
-
-# Compute capability >= 7.0 is required for synchronization between CPU/GPU with
-# managed memory.
-if(NOT DEFINED MLX_CUDA_ARCHITECTURES)
-  set(MLX_CUDA_ARCHITECTURES "native")
-endif()
+# Compute capability 7 is required for synchronization between CPU/GPU with
+# managed memory. TODO: Add more architectures for potential performance gain.
+set(MLX_CUDA_ARCHITECTURES
+    "80"
+    CACHE STRING "CUDA architectures")
 message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
 set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
                                      "${MLX_CUDA_ARCHITECTURES}")
@@ -150,31 +125,19 @@ target_link_libraries(mlx PRIVATE CUDA::cublasLt)
 # Use NVRTC and driver APIs.
 target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 
-# Use the frontend APIs of cuDNN.
-FetchContent_Declare(
-  cudnn
-  GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
-  GIT_TAG v1.14.0
-  GIT_SHALLOW TRUE
-  EXCLUDE_FROM_ALL)
-set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
-set(CUDNN_FRONTEND_BUILD_SAMPLES OFF)
-set(CUDNN_FRONTEND_BUILD_TESTS OFF)
-set(CUDNN_FRONTEND_BUILD_PYTHON_BINDINGS OFF)
-FetchContent_MakeAvailable(cudnn)
-target_link_libraries(mlx PRIVATE cudnn_frontend)
-# Link with the actual cuDNN libraries.
-include(${cudnn_frontend_SOURCE_DIR}/cmake/cuDNN.cmake)
-target_link_libraries(mlx PRIVATE CUDNN::cudnn_all)
-
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)
-# Supress warnings: note: parameter passing for argument of type
-# ‘std::pair<float, float>’ when C++17 is enabled changed to match C++14 in GCC
-# 10.1
-target_compile_options(mlx PRIVATE -Wno-psabi)
 
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
         DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)
+
+# Make Thunderkittens available
+FetchContent_Declare(
+  kittens
+  GIT_REPOSITORY https://github.com/HazyResearch/ThunderKittens.git
+  GIT_TAG aaab847f430ed313ed466e64b25b9177babd1db8
+  GIT_SHALLOW TRUE)
+FetchContent_MakeAvailable(kittens)
+target_include_directories(mlx BEFORE PRIVATE "${kittens_SOURCE_DIR}/include")

mlx/backend/cuda/allocator.cpp

@@ -2,6 +2,7 @@
 
 #include "mlx/backend/cuda/allocator.h"
 #include "mlx/backend/cuda/utils.h"
+#include "mlx/backend/cuda/worker.h"
 #include "mlx/utils.h"
 
 #include <cuda_runtime.h>
@@ -24,74 +25,56 @@ constexpr int small_block_size = 8;
 constexpr int small_pool_size = 4 * page_size;
 
 SmallSizePool::SmallSizePool() {
+  CHECK_CUDA_ERROR(cudaMallocManaged(&buffer_, small_pool_size));
+  end_ = reinterpret_cast<void*>(
+      reinterpret_cast<char*>(buffer_) + small_pool_size);
+  next_free_ = reinterpret_cast<Block*>(buffer_);
+
   auto num_blocks = small_pool_size / small_block_size;
-  buffer_ = new Block[num_blocks];
-
-  next_free_ = buffer_;
-
-  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
-
-  int device_count = 0;
-  CHECK_CUDA_ERROR(cudaGetDeviceCount(&device_count));
-  for (int i = 0; i < device_count; ++i) {
-#if CUDART_VERSION >= 13000
-    cudaMemLocation loc;
-    loc.type = cudaMemLocationTypeDevice;
-    loc.id = i;
-#else
-    int loc = i;
-#endif // CUDART_VERSION >= 13000
-    CHECK_CUDA_ERROR(
-        cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetAccessedBy, loc));
-  }
-
   auto curr = next_free_;
-  for (size_t i = 1; i < num_blocks; ++i) {
-    curr->next = buffer_ + i;
+  for (size_t i = 0; i < num_blocks - 1; ++i) {
+    curr->next = reinterpret_cast<Block*>(
+        reinterpret_cast<char*>(buffer_) + (i + 1) * small_block_size);
     curr = curr->next;
   }
   curr->next = nullptr;
 }
 
 SmallSizePool::~SmallSizePool() {
-  CHECK_CUDA_ERROR(cudaFree(data_));
-  delete[] buffer_;
+  CHECK_CUDA_ERROR(cudaFree(buffer_));
 }
 
-CudaBuffer* SmallSizePool::malloc() {
+void* SmallSizePool::malloc() {
   if (next_free_ == nullptr) {
     return nullptr;
   }
   Block* b = next_free_;
-  uint64_t i = next_free_ - buffer_;
   next_free_ = next_free_->next;
-  b->buf.data = static_cast<char*>(data_) + i * small_block_size;
-  b->buf.size = small_block_size;
-  return &b->buf;
+  return static_cast<void*>(b);
 }
 
-void SmallSizePool::free(CudaBuffer* buf) {
-  auto b = reinterpret_cast<Block*>(buf);
+void SmallSizePool::free(void* p) {
+  auto b = static_cast<Block*>(p);
   b->next = next_free_;
   next_free_ = b;
 }
 
-bool SmallSizePool::in_pool(CudaBuffer* buf) {
-  constexpr int num_blocks = (small_pool_size / small_block_size);
-  auto b = reinterpret_cast<Block*>(buf);
-  int64_t block_num = b - buffer_;
-  return block_num >= 0 && block_num < num_blocks;
+bool SmallSizePool::in_pool(void* p) {
+  return (p >= buffer_) && (p < end_);
 }
 
 CudaAllocator::CudaAllocator()
     : buffer_cache_(
           page_size,
           [](CudaBuffer* buf) { return buf->size; },
-          [this](CudaBuffer* buf) { cuda_free(buf); }) {
+          [this](CudaBuffer* buf) {
+            cuda_free(buf->data);
+            delete buf;
+          }) {
   // TODO: Set memory limit for multi-device.
   size_t free, total;
   CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
-  memory_limit_ = total * 0.95;
+  memory_limit_ = total * 0.8;
   max_pool_size_ = memory_limit_;
 }
 
@@ -109,26 +92,28 @@ Buffer CudaAllocator::malloc(size_t size) {
 
   CudaBuffer* buf = buffer_cache_.reuse_from_cache(size);
   if (!buf) {
-    // If we have a lot of memory pressure try to reclaim memory from the cache.
-    int64_t mem_to_free =
-        get_active_memory() + get_cache_memory() + size - memory_limit_;
-    if (mem_to_free > 0) {
-      buffer_cache_.release_cached_buffers(mem_to_free);
+    // If we have a lot of memory pressure or are over the maximum cache size,
+    // try to reclaim memory from the cache.
+    size_t mem_required = get_active_memory() + get_cache_memory() + size;
+    if (mem_required >= memory_limit_) {
+      buffer_cache_.release_cached_buffers(mem_required - memory_limit_);
     }
 
+    lock.unlock();
+    buf = new CudaBuffer{nullptr, size};
+
     // Try the scalar pool first
     if (size <= small_block_size) {
-      buf = scalar_pool_.malloc();
+      buf->data = scalar_pool_.malloc();
     }
-    lock.unlock();
-    if (!buf) {
-      buf = new CudaBuffer{nullptr, size};
+    if (!buf->data) {
       cudaError_t err = cudaMallocManaged(&buf->data, size);
       if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
         throw std::runtime_error(fmt::format(
             "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
       }
     }
+
     lock.lock();
   }
   active_memory_ += size;
@@ -138,6 +123,7 @@ Buffer CudaAllocator::malloc(size_t size) {
   if (get_cache_memory() > max_pool_size_) {
     buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
+
   return Buffer{buf};
 }
 
@@ -152,7 +138,9 @@ void CudaAllocator::free(Buffer buffer) {
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
   } else {
-    cuda_free(buf);
+    lock.unlock();
+    cuda_free(buf->data);
+    delete buf;
   }
 }
 
@@ -164,13 +152,30 @@ size_t CudaAllocator::size(Buffer buffer) const {
   return buf->size;
 }
 
-// This must be called with mutex_ aquired
-void CudaAllocator::cuda_free(CudaBuffer* buf) {
+void CudaAllocator::register_this_thread() {
+  std::lock_guard lock(worker_mutex_);
+  allowed_threads_.insert(std::this_thread::get_id());
+}
+
+void CudaAllocator::cuda_free(void* buf) {
+  // If cuda_free() is called from a unregistered thread, reschedule the call to
+  // worker.
+  {
+    std::lock_guard lock(worker_mutex_);
+    if (allowed_threads_.count(std::this_thread::get_id()) == 0) {
+      if (!worker_) {
+        worker_.reset(new Worker);
+      }
+      worker_->add_task([this, buf]() { this->cuda_free(buf); });
+      worker_->end_batch();
+      worker_->commit();
+      return;
+    }
+  }
   if (scalar_pool_.in_pool(buf)) {
     scalar_pool_.free(buf);
   } else {
-    cudaFree(buf->data);
-    delete buf;
+    cudaFree(buf);
   }
 }
 

mlx/backend/cuda/allocator.h

@@ -7,10 +7,13 @@
 
 #include <mutex>
 #include <set>
+#include <thread>
 #include <utility>
 
 namespace mlx::core::cu {
 
+class Worker;
+
 using allocator::Buffer;
 
 // Stores cuda-managed unified memory.
@@ -21,14 +24,13 @@ struct CudaBuffer {
 
 class SmallSizePool {
  private:
-  union Block {
+  struct Block {
     Block* next;
-    CudaBuffer buf;
   };
 
-  Block* buffer_{nullptr};
-  void* data_{nullptr};
+  void* buffer_{nullptr};
   Block* next_free_{nullptr};
+  void* end_{nullptr};
 
  public:
   SmallSizePool();
@@ -37,9 +39,9 @@ class SmallSizePool {
   SmallSizePool(const SmallSizePool&) = delete;
   SmallSizePool& operator=(const SmallSizePool&) = delete;
 
-  CudaBuffer* malloc();
-  void free(CudaBuffer* buf);
-  bool in_pool(CudaBuffer* buf);
+  void* malloc();
+  void free(void* p);
+  bool in_pool(void* p);
 };
 
 class CudaAllocator : public allocator::Allocator {
@@ -48,6 +50,15 @@ class CudaAllocator : public allocator::Allocator {
   void free(Buffer buffer) override;
   size_t size(Buffer buffer) const override;
 
+  // Register current thread as safe to free buffers.
+  // In cuda freeing a buffer implicitly synchronizes stream, and for threads
+  // that may be waited by gpu stream (for example cpu stream threads), freeing
+  // buffers there would result in dead lock.
+  void register_this_thread();
+
+  // Call cudaFree in the safe thread.
+  void cuda_free(void* buf);
+
   size_t get_active_memory() const;
   size_t get_peak_memory() const;
   void reset_peak_memory();
@@ -58,11 +69,13 @@ class CudaAllocator : public allocator::Allocator {
   void clear_cache();
 
  private:
-  void cuda_free(CudaBuffer* buf);
-
   CudaAllocator();
   friend CudaAllocator& allocator();
 
+  std::mutex worker_mutex_;
+  std::unique_ptr<Worker> worker_;
+  std::set<std::thread::id> allowed_threads_;
+
   std::mutex mutex_;
   size_t memory_limit_;
   size_t max_pool_size_;

mlx/backend/cuda/arange.cu

@@ -1,69 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, typename IdxT, int N_WRITES>
-__global__ void arange(T* out, IdxT size, T start, T step) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_WRITES > size) {
-    for (IdxT i = index * N_WRITES; i < size; ++i) {
-      out[i] = start + i * step;
-    }
-  } else {
-    AlignedVector<T, N_WRITES> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_WRITES; ++i) {
-      out_vec[i] = start + (index * N_WRITES + i) * step;
-    }
-
-    store_vector<N_WRITES>(out, index, out_vec);
-  }
-}
-
-} // namespace cu
-
-void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Arange::eval_gpu");
-  if (out.size() == 0) {
-    return;
-  }
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& encoder = cu::get_command_encoder(stream());
-  encoder.set_output_array(out);
-
-  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    using OutType = cuda_type_t<CTYPE>;
-    constexpr int N_WRITES = 16 / sizeof(OutType);
-    dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
-      using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-      auto [num_blocks, block_dims] = get_launch_args(out, large(), N_WRITES);
-      encoder.add_kernel_node(
-          cu::arange<OutType, IdxT, N_WRITES>,
-          num_blocks,
-          block_dims,
-          0,
-          out.data<OutType>(),
-          out.data_size(),
-          static_cast<CTYPE>(start_),
-          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_));
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/arg_reduce.cu

@@ -1,8 +1,8 @@
 // Copyright © 2025 Apple Inc.
-
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/fp16_math.cuh"
+#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
@@ -44,11 +44,8 @@ struct ArgMin {
   }
 
   template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
+  __device__ IndexValPair<T>
+  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
     for (int i = 0; i < N; i++) {
       if (vals[i] < best.val) {
         best.val = vals[i];
@@ -77,11 +74,8 @@ struct ArgMax {
   }
 
   template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
+  __device__ IndexValPair<T>
+  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
     for (int i = 0; i < N; i++) {
       if (vals[i] > best.val) {
         best.val = vals[i];
@@ -112,15 +106,16 @@ __global__ void arg_reduce_general(
 
   int64_t in_idx = elem_to_loc(index, shape.data(), in_strides.data(), ndim);
   int64_t out_idx = elem_to_loc(index, shape.data(), out_strides.data(), ndim);
-  in += in_idx;
 
   Op op;
   T init = op.init();
   IndexValPair<T> best{0, init};
 
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    T vals[N_READS];
     auto tid = r * BLOCK_DIM + block.thread_index().x;
-    auto vals = load_vector<N_READS>(in, tid, axis_size, axis_stride, init);
+    cub::LoadDirectBlocked(
+        tid, strided_iterator(in + in_idx, axis_stride), vals, axis_size, init);
     best = op.reduce_many(best, vals, tid * N_READS);
   }
 
@@ -171,7 +166,6 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
           kernel,
           num_blocks,
           block_dim(),
-          0,
           in.data<T>(),
           out.data<uint32_t>(),
           out.size(),

mlx/backend/cuda/binary.cu

@@ -28,7 +28,7 @@ __global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a[0], b[0]);
+      out_vec.val[i] = Op{}(a[0], b[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -49,7 +49,7 @@ __global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a[0], b_vec[i]);
+      out_vec.val[i] = Op{}(a[0], b_vec.val[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -70,7 +70,7 @@ __global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a_vec[i], b[0]);
+      out_vec.val[i] = Op{}(a_vec.val[i], b[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -92,96 +92,46 @@ __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a_vec[i], b_vec[i]);
+      out_vec.val[i] = Op{}(a_vec.val[i], b_vec.val[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
   }
 }
 
-template <
-    typename Op,
-    typename In,
-    typename Out,
-    typename IdxT,
-    int NDIM,
-    int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
 __global__ void binary_g_nd(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), a_strides.data(), b_strides.data());
+    out[index] = Op{}(a[a_idx], b[b_idx]);
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto a_stride_x = a_strides[NDIM - 1];
-  auto b_stride_x = b_strides[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_g(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_4d(
+        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
+    out[index] = Op{}(a[a_idx], b[b_idx]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto a_stride_x = a_strides[ndim - 1];
-  auto b_stride_x = b_strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc(
-      index_rest * shape_x,
-      shape.data(),
-      a_strides.data(),
-      b_strides.data(),
-      ndim);
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -259,61 +209,36 @@ void binary_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
-                int work_per_thread = 1;
-                auto dim0 = ndim > 0 ? shape.back() : 1;
-                auto rest = out.size() / dim0;
-                if (dim0 >= 4) {
-                  work_per_thread = 4;
-                }
-                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-                auto block_dims = get_block_dims(dim0, rest, 1);
-                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::binary_g_nd<
-                        Op,
-                        InType,
-                        OutType,
-                        IdxT,
-                        dims_constant(),
-                        1>;
-                    if (work_per_thread == 4) {
-                      kernel = cu::binary_g_nd<
-                          Op,
-                          InType,
-                          OutType,
-                          IdxT,
-                          dims_constant(),
-                          4>;
-                    }
+                    auto kernel = cu::
+                        binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(kernel, out, large());
                     encoder.add_kernel_node(
                         kernel,
-                        {num_blocks_x, num_blocks_y},
+                        num_blocks,
                         block_dims,
-                        0,
                         a.data<InType>(),
                         b.data<InType>(),
                         out.data<OutType>(),
-                        rest,
+                        out.size(),
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT, 1>;
-                  if (work_per_thread == 4) {
-                    kernel = cu::binary_g<Op, InType, OutType, IdxT, 4>;
-                  }
+                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
+                  auto [num_blocks, block_dims] =
+                      get_launch_args(kernel, out, large());
                   encoder.add_kernel_node(
                       kernel,
-                      {num_blocks_x, num_blocks_y},
+                      num_blocks,
                       block_dims,
-                      0,
                       a.data<InType>(),
                       b.data<InType>(),
                       out.data<OutType>(),
-                      rest,
+                      out.size(),
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -323,7 +248,8 @@ void binary_op_gpu_inplace(
         } else {
           dispatch_bool(out.data_size() > UINT32_MAX, [&](auto large) {
             using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            constexpr int N_READS = 16 / sizeof(InType);
+            // TODO: Choose optimized value based on type size.
+            constexpr int N_READS = 4;
             auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
             if (bopt == BinaryOpType::ScalarVector) {
               kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
@@ -333,12 +259,16 @@ void binary_op_gpu_inplace(
               kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
             }
             auto [num_blocks, block_dims] = get_launch_args(
-                out.data_size(), out.shape(), out.strides(), large(), N_READS);
+                kernel,
+                out.data_size(),
+                out.shape(),
+                out.strides(),
+                large(),
+                N_READS);
             encoder.add_kernel_node(
                 kernel,
                 num_blocks,
                 block_dims,
-                0,
                 a.data<InType>(),
                 b.data<InType>(),
                 out.data<OutType>(),
@@ -376,4 +306,54 @@ void binary_op_gpu(
     binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
   }
 
+BINARY_GPU(Add)
+BINARY_GPU(ArcTan2)
+BINARY_GPU(Divide)
+BINARY_GPU(Remainder)
+BINARY_GPU(Greater)
+BINARY_GPU(GreaterEqual)
+BINARY_GPU(Less)
+BINARY_GPU(LessEqual)
+BINARY_GPU(LogicalAnd)
+BINARY_GPU(LogicalOr)
+BINARY_GPU(LogAddExp)
+BINARY_GPU(Maximum)
+BINARY_GPU(Minimum)
+BINARY_GPU(Multiply)
+BINARY_GPU(NotEqual)
+BINARY_GPU(Power)
+BINARY_GPU(Subtract)
+
+void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Equal::eval_gpu");
+  auto& s = out.primitive().stream();
+  if (equal_nan_) {
+    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
+  } else {
+    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
+  }
+}
+
+void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
+  auto& s = out.primitive().stream();
+  switch (op_) {
+    case BitwiseBinary::And:
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Or:
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Xor:
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::LeftShift:
+      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::RightShift:
+      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
+      break;
+  }
+}
+
 } // namespace mlx::core

mlx/backend/cuda/binary/CMakeLists.txt

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

mlx/backend/cuda/binary/add.cu

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

mlx/backend/cuda/binary/arctan2.cu

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

mlx/backend/cuda/binary/bitwise_binary.cu

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

mlx/backend/cuda/binary/divide.cu

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

mlx/backend/cuda/binary/equal.cu

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

mlx/backend/cuda/binary/greater.cu

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

mlx/backend/cuda/binary/greater_equal.cu

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

mlx/backend/cuda/binary/less.cu

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

mlx/backend/cuda/binary/less_equal.cu

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

mlx/backend/cuda/binary/log_add_exp.cu

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

mlx/backend/cuda/binary/logical_and.cu

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

mlx/backend/cuda/binary/logical_or.cu

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

mlx/backend/cuda/binary/maximum.cu

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

mlx/backend/cuda/binary/minimum.cu

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

mlx/backend/cuda/binary/multiply.cu

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

mlx/backend/cuda/binary/not_equal.cu

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

mlx/backend/cuda/binary/power.cu

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

mlx/backend/cuda/binary/remainder.cu

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