Improve the ring backend initialization

.circleci/config.yml

@@ -7,9 +7,18 @@ parameters:
   nightly_build:
     type: boolean
     default: false
+  weekly_build:
+    type: boolean
+    default: false
   test_release:
     type: boolean
     default: false
+  linux_release:
+    type: boolean
+    default: false
+  cuda_release:
+    type: boolean
+    default: false
 
 jobs:
   build_documentation:
@@ -64,9 +73,9 @@ jobs:
                  git push -f origin gh-pages
 
   linux_build_and_test:
-    machine:
+    docker:
-      image: ubuntu-2204:current
+      - image: cimg/python:3.9
-      resource_class: large
+
     steps:
       - checkout
       - run:
@@ -78,17 +87,19 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            export DEBIAN_FRONTEND=noninteractive
-            export NEEDRESTART_MODE=a
-            sudo apt-get update
-            sudo apt-get upgrade -y
             pip install --upgrade cmake
-            sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
+            pip install nanobind==2.4.0
+            pip install numpy
+            sudo apt-get update
+            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
             sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
       - run:
           name: Install Python package
           command: |
-            pip install -e ".[dev]"
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              python3 setup.py build_ext --inplace
+            CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              python3 setup.py develop
       - run:
           name: Generate package stubs
           command: |
@@ -98,10 +109,9 @@ jobs:
       - run:
           name: Run Python tests
           command: |
-            python -m unittest discover python/tests -v
+            python3 -m unittest discover python/tests -v
             mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
-            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
       - run:
           name: Build CPP only
           command: |
@@ -160,8 +170,7 @@ jobs:
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
             mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
-            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
+            mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py
-            if $(grep "\[WARN\]" stderr.log); then echo "Distributed ring test failed"; exit 1; fi
       - run:
           name: Build example extension
           command: |
@@ -203,51 +212,26 @@ jobs:
               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:default
       resource_class: gpu.nvidia.small.gen2
     steps:
       - checkout
-      - restore_cache:
-          keys:
-            - cuda-<< parameters.image_date >>-{{ arch }}-
-      - run:
-          name: Install dependencies
-          command: |
-            sudo apt-get update
-            sudo apt-get install libcudnn9-dev-cuda-12
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            curl -sL https://github.com/ccache/ccache/releases/download/v4.11.3/ccache-4.11.3-linux-x86_64.tar.xz | tar xJf -
-            sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
-            rm -rf ccache-4.11.3-linux-x86_64
       - run:
           name: Install Python package
           command: |
-            python3 -m venv env
+            sudo apt-get update
+            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
+            python -m venv env
             source env/bin/activate
             CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              pip install -e ".[dev]" -v
+              pip install -e ".[dev]"
       - run:
           name: Run Python tests
           command: |
             source env/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m unittest discover python/tests -v
             LOW_MEMORY=1 DEVICE=gpu python -m tests discover python/tests -v
-      - run:
-          name: CCache report
-          command: |
-            ccache --show-stats
-            ccache --zero-stats
-            ccache --max-size 400MB
-            ccache --cleanup
-      - save_cache:
-          key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}
-          paths:
-            - /home/circleci/.cache/ccache
 
   build_release:
     parameters:
@@ -300,18 +284,7 @@ jobs:
           name: Build Python package
           command: |
             source env/bin/activate
-            python setup.py clean --all
+            << parameters.build_env >> python -m build -w
-            << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
-            - run:
-                name: Build common package
-                command: |
-                  source env/bin/activate
-                  python setup.py clean --all
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 python -m build -w
       - when:
           condition: << parameters.build_env >>
           steps:
@@ -328,103 +301,87 @@ jobs:
       python_version:
         type: string
         default: "3.9"
-      build_env:
+      extra_env:
         type: string
-        default: ""
+        default: "DEV_RELEASE=1"
-    machine:
+    docker:
-      image: ubuntu-2204:current
+      - image: ubuntu:20.04
-      resource_class: large
     steps:
       - checkout
       - run:
           name: Build wheel
           command: |
             PYTHON=python<< parameters.python_version >>
-            export DEBIAN_FRONTEND=noninteractive
+            apt-get update
-            export NEEDRESTART_MODE=a
+            apt-get upgrade -y
-            sudo apt-get update
+            DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata
-            sudo apt-get upgrade -y
+            apt-get install -y apt-utils
-            TZ=Etc/UTC sudo apt-get -y install tzdata
+            apt-get install -y software-properties-common
-            sudo apt-get install -y apt-utils
+            add-apt-repository -y ppa:deadsnakes/ppa
-            sudo apt-get install -y software-properties-common
+            apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
-            sudo add-apt-repository -y ppa:deadsnakes/ppa
+            apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
+            apt-get install -y build-essential git
-            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
             pip install --upgrade cmake
+            pip install nanobind==2.4.0
+            pip install --upgrade setuptools
+            pip install numpy
             pip install auditwheel
             pip install patchelf
             pip install build
             pip install twine
-            << parameters.build_env >> pip install ".[dev]" -v
+            << parameters.extra_env >> pip install . -v
             pip install typing_extensions
             python setup.py generate_stubs
-            python setup.py clean --all
+            << parameters.extra_env >> python -m build --wheel
-            MLX_BUILD_STAGE=1 << parameters.build_env >> python -m build -w
+            auditwheel show dist/*
-            bash python/scripts/repair_linux.sh
+            auditwheel repair dist/* --plat manylinux_2_31_x86_64
-      - when:
-          condition:
-            equal: ["3.9", << parameters.python_version >>]
-          steps:
       - run:
-                name: Build common package
+          name: Upload package
           command: |
             source env/bin/activate
-                  python setup.py clean --all
+            twine upload wheelhouse/*
-                  << parameters.build_env >> MLX_BUILD_STAGE=2 \
-                    python -m build -w
-                  auditwheel repair dist/mlx_cpu*.whl --plat manylinux_2_35_x86_64
-      - when:
-          condition: << parameters.build_env >>
-          steps:
-            - run:
-                name: Upload packages
-                command: |
-                  source env/bin/activate
-                  twine upload wheelhouse/*.whl
       - store_artifacts:
           path: wheelhouse/
 
   build_cuda_release:
     parameters:
-      build_env:
+      python_version:
         type: string
-        default: ""
+        default: "3.9"
+      extra_env:
+        type: string
+        default: "DEV_RELEASE=1"
     machine:
-      image: ubuntu-2204:current
+      image: linux-cuda-12:default
-      resource_class: large
+      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}}
+            << parameters.extra_env >> \
-            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
+              pip install ".[dev]" -v
+            python setup.py generate_stubs
+            << parameters.extra_env >> \
+              CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
+              python -m build --wheel
             bash python/scripts/repair_cuda.sh
-      - when:
-          condition: << parameters.build_env >>
-          steps:
       - run:
           name: Upload package
           command: |
+            source env/bin/activate
             twine upload wheelhouse/*.whl
       - store_artifacts:
           path: wheelhouse/
@@ -437,6 +394,7 @@ workflows:
             pattern: "^(?!pull/)[-\\w]+$"
             value: << pipeline.git.branch >>
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
         - not: << pipeline.parameters.test_release >>
     jobs:
       - mac_build_and_test:
@@ -444,16 +402,14 @@ workflows:
             parameters:
               macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
-      - cuda_build_and_test:
+      - cuda_build_and_test 
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
       - build_documentation 
 
   build_pypi_release:
     when:
       and:
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.weekly_build >>
         - not: << pipeline.parameters.test_release >>
     jobs:
       - build_release:
@@ -545,16 +501,7 @@ workflows:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              build_env: ["PYPI_RELEASE=1"]
+              extra_env: ["PYPI_RELEASE=1"]
-      - build_cuda_release:
-          filters:
-            tags:
-              only: /^v.*/
-            branches:
-              ignore: /.*/
-          matrix:
-            parameters:
-              build_env: ["PYPI_RELEASE=1"]
 
   prb:
     when:
@@ -575,9 +522,6 @@ workflows:
           requires: [ hold ]
       - cuda_build_and_test:
           requires: [ hold ]
-          matrix:
-            parameters:
-              image_date: ["2023.11.1", "2025.05.1"]
   nightly_build:
     when:
       and:
@@ -636,17 +580,11 @@ workflows:
               - macosx_deployment_target: "15.0"
                 xcode_version: "15.0.0"
                 python_version: "3.13"
-      - build_linux_release:
+  weekly_build:
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-      - build_cuda_release
-
-  build_dev_release:
     when:
       and:
         - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.test_release >>
+        - << pipeline.parameters.weekly_build >>
     jobs:
       - build_release:
           matrix:
@@ -716,12 +654,25 @@ workflows:
                 xcode_version: "15.0.0"
                 python_version: "3.13"
                 build_env: "DEV_RELEASE=1"
+  linux_test_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.linux_release >>
+    jobs:
       - build_linux_release:
           matrix:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              build_env: ["DEV_RELEASE=1"]
+              extra_env: ["PYPI_RELEASE=1"]
+  cuda_test_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.cuda_release >>
+    jobs:
       - build_cuda_release:
           matrix:
             parameters:
-              build_env: ["DEV_RELEASE=1"]
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              extra_env: ["PYPI_RELEASE=1"]

ACKNOWLEDGMENTS.md

@@ -19,7 +19,6 @@ MLX was developed with contributions from the following individuals:
 - Gleb Pobudzey: Added the `where` primitive, and groups in 1D and 2D convolutions.
 - Paul Paczuski: Improved stability of BCE loss calculation
 - Max-Heinrich Laves: Added `conv_transpose1d`, `conv_transpose2d`, and `conv_transpose3d` ops.
-- Gökdeniz Gülmez: Added the `Muon (MomentUm Orthogonalized by Newton-schulz)` optimizer.
 
 <a href="https://github.com/ml-explore/mlx/graphs/contributors">
   <img class="dark-light" src="https://contrib.rocks/image?repo=ml-explore/mlx&anon=0&columns=20&max=100&r=true" />

CMakeLists.txt

@@ -41,7 +41,6 @@ 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)
 
 # --------------------- Processor tests -------------------------
@@ -65,17 +64,10 @@ if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
       message(WARNING "Building for x86_64 arch is not officially supported.")
     endif()
   endif()
+
 else()
   set(MLX_BUILD_METAL OFF)
-endif()
+  message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
-
-if(MLX_USE_CCACHE)
-  find_program(CCACHE_PROGRAM ccache)
-  if(CCACHE_PROGRAM)
-    set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
-  endif()
 endif()
 
 # ----------------------------- Lib -----------------------------

README.md

@@ -68,23 +68,18 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
-macOS, run:
 
-```bash
+**With `pip`**:
+
+```
 pip install mlx
 ```
 
-To install the CUDA backend on Linux, run:
+**With `conda`**:
 
-```bash
-pip install "mlx[cuda]"
 ```
-
+conda install -c conda-forge mlx
-To install a CPU-only Linux package, run:
-
-```bash
-pip install "mlx[cpu]"
 ```
 
 Checkout the

docs/src/dev/extensions.rst

@@ -138,13 +138,13 @@ more concrete:
         * representing the vectorized computation and the axis which
         * corresponds to the output vectorized dimension.
         */
-        std::pair<std::vector<array>, std::vector<int>> vmap(
+        virtual std::pair<std::vector<array>, std::vector<int>> vmap(
             const std::vector<array>& inputs,
             const std::vector<int>& axes) override;
 
-        /** The name of primitive. */
+        /** Print the primitive. */
-        const char* name() const override {
+        void print(std::ostream& os) override {
-          return "Axpby";
+            os << "Axpby";
         }
 
         /** Equivalence check **/

docs/src/install.rst

@@ -13,7 +13,7 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI your system must meet the following requirements:
+To install from PyPI you must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
 - Using a native Python >= 3.9
@@ -23,39 +23,22 @@ To install from PyPI your system must meet the following requirements:
     MLX is only available on devices running macOS >= 13.5
     It is highly recommended to use macOS 14 (Sonoma)
 
+
+MLX is also available on conda-forge. To install MLX with conda do:
+
+.. code-block:: shell
+
+   conda install conda-forge::mlx
+
 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]"
+    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 >= 3.9
-
-
-CPU-only (Linux)
-^^^^^^^^^^^^^^^^
-
-For a CPU-only version of MLX that runs on Linux use:
-
-.. code-block:: shell
-
-    pip install "mlx[cpu]"
-
-To install the CPU-only package from PyPi your system must meet the following
-requirements:
-
-- Linux distribution with glibc >= 2.35
-- Python >= 3.9
 
 
 Troubleshooting

docs/src/python/optimizers/common_optimizers.rst

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

examples/extensions/axpby/axpby.h

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

mlx/backend/common/utils.cpp

@@ -1,20 +1,14 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <dlfcn.h>
-
 #include "mlx/backend/common/utils.h"
+#include "mlx/primitives.h"
 
 namespace mlx::core {
 
-std::filesystem::path current_binary_dir() {
+std::string get_primitive_string(Primitive* primitive) {
-  static std::filesystem::path binary_dir = []() {
+  std::ostringstream op_t;
-    Dl_info info;
+  primitive->print(op_t);
-    if (!dladdr(reinterpret_cast<void*>(&current_binary_dir), &info)) {
+  return op_t.str();
-      throw std::runtime_error("Unable to get current binary dir.");
-    }
-    return std::filesystem::path(info.dli_fname).parent_path();
-  }();
-  return binary_dir;
 }
 
 std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(

mlx/backend/common/utils.h

@@ -2,7 +2,6 @@
 
 #pragma once
 
-#include <filesystem>
 #include <tuple>
 #include <vector>
 
@@ -10,8 +9,7 @@
 
 namespace mlx::core {
 
-// Return the directory that contains current shared library.
+std::string get_primitive_string(Primitive* primitive);
-std::filesystem::path current_binary_dir();
 
 inline int64_t
 elem_to_loc(int elem, const Shape& shape, const Strides& strides) {

mlx/backend/cpu/cholesky.cpp

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

mlx/backend/cpu/compiled.cpp

@@ -231,7 +231,7 @@ inline void build_kernel(
       os << "static_cast<" << get_type_string(x.dtype()) << ">(tmp_"
          << namer.get_name(x.inputs()[0]) << ");" << std::endl;
     } else {
-      os << x.primitive().name();
+      x.primitive().print(os);
       os << "()(";
       for (int i = 0; i < x.inputs().size() - 1; i++) {
         os << "tmp_" << namer.get_name(x.inputs()[i]) << ", ";

mlx/backend/cpu/conv.cpp

@@ -883,7 +883,7 @@ void explicit_gemm_conv_1D_cpu(
   // Fill with zeros
   std::vector<array> temps;
   temps.push_back(array(0, conv_dtype));
-  copy_cpu(temps.back(), in_padded, CopyType::Scalar, stream);
+  copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
   size_t data_offset = padding_lo[0] * in_padded.strides()[1];
@@ -895,7 +895,7 @@ void explicit_gemm_conv_1D_cpu(
       in_padded_slice.size(),
       data_offset);
   // Copy input values into the slice
-  copy_cpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
+  copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
   temps.push_back(in_padded_slice);
 
   // Make strided view
@@ -920,7 +920,7 @@ void explicit_gemm_conv_1D_cpu(
   // Materialize strided view
   Shape strided_reshape = {N * oH, wH * C};
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
-  copy_cpu(in_strided_view, in_strided, CopyType::General, stream);
+  copy(in_strided_view, in_strided, CopyType::General, stream);
   temps.push_back(in_strided);
 
   // Check wt dtype and prepare
@@ -938,13 +938,13 @@ void explicit_gemm_conv_1D_cpu(
         wt.size(),
         0);
     gemm_wt = array(wt_transpose.shape(), float32, nullptr, {});
-    copy_cpu(wt_transpose, gemm_wt, CopyType::General, stream);
+    copy(wt_transpose, gemm_wt, CopyType::General, stream);
     temps.push_back(gemm_wt);
   } else 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);
+    copy(wt, gemm_wt, ctype, stream);
     temps.push_back(gemm_wt);
   }
 
@@ -991,7 +991,7 @@ void explicit_gemm_conv_1D_cpu(
 
   // Copy results if needed
   if (out.dtype() != float32) {
-    copy_cpu_inplace(gemm_out, out, CopyType::Vector, stream);
+    copy_inplace(gemm_out, out, CopyType::Vector, stream);
   }
   encoder.add_temporaries(std::move(temps));
 }
@@ -1029,7 +1029,7 @@ void explicit_gemm_conv_2D_cpu(
   // Fill with zeros
   std::vector<array> temps;
   temps.push_back(array(0, conv_dtype));
-  copy_cpu(temps.back(), in_padded, CopyType::Scalar, stream);
+  copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
   size_t data_offset = padding_lo[0] * in_padded.strides()[1] +
@@ -1044,7 +1044,7 @@ void explicit_gemm_conv_2D_cpu(
   temps.push_back(in_padded_slice);
 
   // Copy input values into the slice
-  copy_cpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
+  copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
 
   // Make strided view
   Shape strided_shape = {N, oH, oW, wH, wW, C};
@@ -1065,7 +1065,7 @@ void explicit_gemm_conv_2D_cpu(
   // 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);
+  copy(in_strided_view, in_strided, CopyType::General, stream);
   temps.push_back(in_strided);
 
   // Check wt dtype and prepare
@@ -1076,7 +1076,7 @@ void explicit_gemm_conv_2D_cpu(
     auto ctype =
         wt.flags().row_contiguous ? CopyType::Vector : CopyType::General;
     gemm_wt = array(wt.shape(), float32, nullptr, {});
-    copy_cpu(wt, gemm_wt, ctype, stream);
+    copy(wt, gemm_wt, ctype, stream);
     temps.push_back(gemm_wt);
   }
 
@@ -1116,7 +1116,7 @@ void explicit_gemm_conv_2D_cpu(
 
   // Copy results if needed
   if (out.dtype() != float32) {
-    copy_cpu_inplace(gemm_out, out, CopyType::Vector, stream);
+    copy_inplace(gemm_out, out, CopyType::Vector, stream);
   }
   encoder.add_temporaries(std::move(temps));
 }
@@ -1156,7 +1156,7 @@ void explicit_gemm_conv_ND_cpu(
 
   // Fill with zeros
   std::vector<array> temps = {array(0, conv_dtype)};
-  copy_cpu(temps.back(), in_padded, CopyType::Scalar, stream);
+  copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
   size_t data_offset = 0;
@@ -1173,7 +1173,7 @@ void explicit_gemm_conv_ND_cpu(
       data_offset);
 
   // Copy input values into the slice
-  copy_cpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
+  copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral, stream);
   temps.push_back(in_padded_slice);
 
   // Make strided view
@@ -1212,7 +1212,7 @@ void explicit_gemm_conv_ND_cpu(
   }
 
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
-  copy_cpu(in_strided_view, in_strided, CopyType::General, stream);
+  copy(in_strided_view, in_strided, CopyType::General, stream);
   temps.push_back(in_strided);
 
   // Check wt dtype and prepare
@@ -1223,13 +1223,13 @@ void explicit_gemm_conv_ND_cpu(
     auto ctype =
         wt.flags().row_contiguous ? CopyType::Vector : CopyType::General;
     gemm_wt = array(wt.shape(), float32, nullptr, {});
-    copy_cpu(wt, gemm_wt, ctype, stream);
+    copy(wt, gemm_wt, ctype, stream);
     temps.push_back(gemm_wt);
   }
 
   if (flip) {
     auto gemm_wt_ = array(gemm_wt.shape(), float32, nullptr, {});
-    copy_cpu(gemm_wt, gemm_wt_, CopyType::Vector, stream);
+    copy(gemm_wt, gemm_wt_, CopyType::Vector, stream);
     temps.push_back(gemm_wt_);
 
     // Calculate the total size of the spatial dimensions
@@ -1284,7 +1284,7 @@ void explicit_gemm_conv_ND_cpu(
 
   // Copy results if needed
   if (out.dtype() != float32) {
-    copy_cpu_inplace(gemm_out, out, CopyType::Vector, stream);
+    copy_inplace(gemm_out, out, CopyType::Vector, stream);
   }
   encoder.add_temporaries(std::move(temps));
 }

mlx/backend/cpu/copy.cpp

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

mlx/backend/cpu/copy.h

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

mlx/backend/cpu/distributed.cpp

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

mlx/backend/cpu/eig.cpp

@@ -135,7 +135,7 @@ void Eig::eval_cpu(
       : array(a.shape(), complex64, nullptr, {});
 
   auto a_copy = array(a.shape(), a.dtype(), nullptr, {});
-  copy_cpu(
+  copy(
       a,
       a_copy,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,

mlx/backend/cpu/eigh.cpp

@@ -196,7 +196,7 @@ void Eigh::eval_cpu(
 
   values.set_data(allocator::malloc(values.nbytes()));
 
-  copy_cpu(
+  copy(
       a,
       vectors,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,

mlx/backend/cpu/hadamard.cpp

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

mlx/backend/cpu/indexing.cpp

@@ -517,7 +517,7 @@ void Scatter::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy_cpu(src, out, ctype, stream());
+  copy(src, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   std::vector<array> inds;
@@ -686,7 +686,7 @@ void ScatterAxis::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Copy src into out (copy allocates memory for out)
   auto ctype =
       src.flags().row_contiguous ? CopyType::Vector : CopyType::General;
-  copy_cpu(src, out, ctype, stream());
+  copy(src, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_input_array(idx);

mlx/backend/cpu/inverse.cpp

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

mlx/backend/cpu/logsumexp.cpp

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

mlx/backend/cpu/luf.cpp

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

mlx/backend/cpu/masked_mm.cpp

@@ -124,20 +124,21 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
         if (!expand_all && stx == arr.shape(-1) && sty == 1) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy_cpu(arr, arr_copy, CopyType::Vector, s);
+            copy(arr, arr_copy, CopyType::Vector, s);
             return std::make_tuple(false, stx, arr_copy, true);
           }
           return std::make_tuple(false, stx, arr, false);
         } else if (!expand_all && stx == 1 && sty == arr.shape(-2)) {
           if (do_copy) {
             array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-            copy_cpu(arr, arr_copy, CopyType::Vector, s);
+            copy(arr, arr_copy, CopyType::Vector, s);
             return std::make_tuple(true, sty, arr_copy, true);
           }
           return std::make_tuple(true, sty, arr, false);
         } else {
+          array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+          copy(arr, arr_copy, CopyType::General, s);
           int64_t stx = arr.shape(-1);
-          array arr_copy = contiguous_copy_cpu(arr, s);
           return std::make_tuple(false, stx, arr_copy, true);
         }
       };
@@ -385,7 +386,7 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       return std::make_tuple(true, sty, arr);
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
       int64_t stx = arr.shape(-1);
       return std::make_tuple(false, stx, temps.back());
     }
@@ -503,7 +504,7 @@ void SegmentedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       return std::make_tuple(true, sty, x);
     } else {
       array xc(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, xc, CopyType::General, s);
+      copy(x, xc, CopyType::General, s);
       encoder.add_temporary(xc);
       int64_t stx = x.shape(-1);
       return std::make_tuple(false, stx, xc);

mlx/backend/cpu/matmul.cpp

@@ -81,7 +81,7 @@ void matmul_general(
       return std::make_tuple(true, sty, arr);
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, stream);
+      copy(arr, temps.back(), CopyType::General, stream);
       stx = arr.shape(-1);
       return std::make_tuple(false, stx, temps.back());
     }
@@ -142,7 +142,7 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   CopyType ctype = c.data_size() == 1
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
-  copy_cpu(c, out, ctype, stream());
+  copy(c, out, ctype, stream());
   if (inputs[0].shape(-1) == 0) {
     return;
   }

mlx/backend/cpu/primitives.cpp

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

mlx/backend/cpu/qrf.cpp

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

mlx/backend/cpu/quantized.cpp

@@ -529,7 +529,7 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
       return arr;
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
       return temps.back();
     }
   };
@@ -579,7 +579,7 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       return arr;
     } else {
       temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
-      copy_cpu(arr, temps.back(), CopyType::General, s);
+      copy(arr, temps.back(), CopyType::General, s);
       return temps.back();
     }
   };
@@ -712,7 +712,9 @@ void fast::AffineQuantize::eval_cpu(
     if (arr.flags().row_contiguous) {
       return std::make_pair(arr, false);
     } else {
-      return std::make_pair(contiguous_copy_cpu(arr, s), true);
+      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+      copy(arr, arr_copy, CopyType::General, s);
+      return std::make_pair(arr_copy, true);
     }
   };
 

mlx/backend/cpu/scan.cpp

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

mlx/backend/cpu/softmax.cpp

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

mlx/backend/cpu/sort.cpp

@@ -334,10 +334,8 @@ void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-      ? CopyType::Vector
+  copy(in, out, ctype, stream());
-      : CopyType::General;
-  copy_cpu(in, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_output_array(out);
@@ -428,10 +426,8 @@ void Partition::eval_cpu(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
+  CopyType ctype = in.flags().contiguous ? CopyType::Vector : CopyType::General;
-      ? CopyType::Vector
+  copy(in, out, ctype, stream());
-      : CopyType::General;
-  copy_cpu(in, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_output_array(out);

mlx/backend/cpu/svd.cpp

@@ -31,7 +31,7 @@ void svd_impl(
 
   // lapack clobbers the input, so we have to make a copy.
   array in(a.shape(), a.dtype(), nullptr, {});
-  copy_cpu(
+  copy(
       a,
       in,
       a.flags().row_contiguous ? CopyType::Vector : CopyType::General,

mlx/backend/cuda/CMakeLists.txt

@@ -6,7 +6,6 @@
 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
@@ -16,21 +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}/cuda.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${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
@@ -46,17 +42,8 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9.0)
-  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_9.cu)
-else()
-  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_0.cpp)
-endif()
-
 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
 
 # Embed kernel sources in binary for JIT compilation.
@@ -99,18 +86,11 @@ 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
+# Compute capability 7 is required for synchronization between CPU/GPU with
-# and requires drivers released after CUDA 12.4.
+# managed memory. TODO: Add more architectures for potential performance gain.
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
+set(MLX_CUDA_ARCHITECTURES
-  target_compile_options(
+    "70;80"
-    mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--compress-mode=size>")
+    CACHE STRING "CUDA architectures")
-endif()
-
-# Compute capability >= 7.0 is required for synchronization between CPU/GPU with
-# managed memory.
-if(NOT DEFINED MLX_CUDA_ARCHITECTURES)
-  set(MLX_CUDA_ARCHITECTURES "native")
-endif()
 message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
 set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
                                      "${MLX_CUDA_ARCHITECTURES}")
@@ -142,27 +122,6 @@ 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 v1.12.1
-  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>)
-
-# Install CCCL headers for JIT.
-install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
-        DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)

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>
@@ -16,66 +17,14 @@ namespace cu {
 
 constexpr int page_size = 16384;
 
-// Any allocations smaller than this will try to use the small pool
-constexpr int small_block_size = 8;
-
-// The small pool size in bytes. This should be a multiple of the host page
-// size and small_block_size.
-constexpr int small_pool_size = 4 * page_size;
-
-SmallSizePool::SmallSizePool() {
-  auto num_blocks = small_pool_size / small_block_size;
-  buffer_ = new Block[num_blocks];
-
-  next_free_ = buffer_;
-
-  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
-  CHECK_CUDA_ERROR(
-      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, 0));
-
-  auto curr = next_free_;
-  for (size_t i = 1; i < num_blocks; ++i) {
-    curr->next = buffer_ + i;
-    curr = curr->next;
-  }
-  curr->next = nullptr;
-}
-
-SmallSizePool::~SmallSizePool() {
-  CHECK_CUDA_ERROR(cudaFree(data_));
-  delete[] buffer_;
-}
-
-CudaBuffer* SmallSizePool::malloc() {
-  if (next_free_ == nullptr) {
-    return nullptr;
-  }
-  Block* b = next_free_;
-  uint64_t i = next_free_ - buffer_;
-  next_free_ = next_free_->next;
-  b->buf.data = static_cast<char*>(data_) + i * small_block_size;
-  b->buf.size = small_block_size;
-  return &b->buf;
-}
-
-void SmallSizePool::free(CudaBuffer* buf) {
-  auto b = reinterpret_cast<Block*>(buf);
-  b->next = next_free_;
-  next_free_ = b;
-}
-
-bool SmallSizePool::in_pool(CudaBuffer* buf) {
-  constexpr int num_blocks = (small_pool_size / small_block_size);
-  auto b = reinterpret_cast<Block*>(buf);
-  int64_t block_num = b - buffer_;
-  return block_num >= 0 && block_num < num_blocks;
-}
-
 CudaAllocator::CudaAllocator()
     : buffer_cache_(
           page_size,
           [](CudaBuffer* buf) { return buf->size; },
-          [this](CudaBuffer* buf) { cuda_free(buf); }) {
+          [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));
@@ -87,9 +36,7 @@ Buffer CudaAllocator::malloc(size_t size) {
   // Find available buffer from cache.
   auto orig_size = size;
   std::unique_lock lock(mutex_);
-  if (size <= small_block_size) {
+  if (size < page_size) {
-    size = 8;
-  } else if (size < page_size) {
     size = next_power_of_2(size);
   } else {
     size = page_size * ((size + page_size - 1) / page_size);
@@ -97,26 +44,20 @@ 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.
+    // If we have a lot of memory pressure or are over the maximum cache size,
-    int64_t mem_to_free =
+    // try to reclaim memory from the cache.
-        get_active_memory() + get_cache_memory() + size - memory_limit_;
+    size_t mem_required = get_active_memory() + get_cache_memory() + size;
-    if (mem_to_free > 0) {
+    if (mem_required >= memory_limit_) {
-      buffer_cache_.release_cached_buffers(mem_to_free);
+      buffer_cache_.release_cached_buffers(mem_required - memory_limit_);
     }
 
-    // Try the scalar pool first
-    if (size <= small_block_size) {
-      buf = scalar_pool_.malloc();
-    }
     lock.unlock();
-    if (!buf) {
     buf = new CudaBuffer{nullptr, size};
     cudaError_t err = cudaMallocManaged(&buf->data, size);
     if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
       throw std::runtime_error(fmt::format(
           "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
     }
-    }
     lock.lock();
   }
   active_memory_ += size;
@@ -126,6 +67,7 @@ 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};
 }
 
@@ -140,7 +82,9 @@ 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;
   }
 }
 
@@ -152,14 +96,27 @@ size_t CudaAllocator::size(Buffer buffer) const {
   return buf->size;
 }
 
-// This must be called with mutex_ aquired
+void CudaAllocator::register_this_thread() {
-void CudaAllocator::cuda_free(CudaBuffer* buf) {
+  std::lock_guard lock(worker_mutex_);
-  if (scalar_pool_.in_pool(buf)) {
+  allowed_threads_.insert(std::this_thread::get_id());
-    scalar_pool_.free(buf);
+}
-  } else {
+
-    cudaFree(buf->data);
+void CudaAllocator::cuda_free(void* buf) {
-    delete 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;
+    }
+  }
+  cudaFree(buf);
 }
 
 size_t CudaAllocator::get_active_memory() const {

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.
@@ -19,35 +22,21 @@ struct CudaBuffer {
   size_t size;
 };
 
-class SmallSizePool {
- private:
-  union Block {
-    Block* next;
-    CudaBuffer buf;
-  };
-
-  Block* buffer_{nullptr};
-  void* data_{nullptr};
-  Block* next_free_{nullptr};
-
- public:
-  SmallSizePool();
-  ~SmallSizePool();
-
-  SmallSizePool(const SmallSizePool&) = delete;
-  SmallSizePool& operator=(const SmallSizePool&) = delete;
-
-  CudaBuffer* malloc();
-  void free(CudaBuffer* buf);
-  bool in_pool(CudaBuffer* buf);
-};
-
 class CudaAllocator : public allocator::Allocator {
  public:
   Buffer malloc(size_t size) override;
   void free(Buffer buffer) override;
   size_t size(Buffer buffer) const override;
 
+  // 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();
@@ -58,18 +47,19 @@ 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_;
   BufferCache<CudaBuffer> buffer_cache_;
   size_t active_memory_{0};
   size_t peak_memory_{0};
-  SmallSizePool scalar_pool_;
 };
 
 CudaAllocator& allocator();

mlx/backend/cuda/arange.cu

@@ -1,55 +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 <nvtx3/nvtx3.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/transform.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-template <typename T>
-struct Arange {
-  const T start;
-  const T step;
-
-  __device__ T operator()(uint32_t i) const {
-    return start + i * step;
-  }
-};
-
-} // 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);
-
-  auto capture = encoder.capture_context();
-  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    using OutType = cuda_type_t<CTYPE>;
-    CTYPE step =
-        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
-    thrust::transform(
-        cu::thrust_policy(encoder.stream()),
-        thrust::counting_iterator<uint32_t>(0),
-        thrust::counting_iterator<uint32_t>(out.data_size()),
-        thrust::device_pointer_cast(out.data<OutType>()),
-        cu::Arange<OutType>{
-            static_cast<OutType>(start_), static_cast<OutType>(step)});
-  });
-}
-
-} // 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"
@@ -115,7 +115,7 @@ __global__ void arg_reduce_general(
     T vals[N_READS];
     auto tid = r * BLOCK_DIM + block.thread_index().x;
     cub::LoadDirectBlocked(
-        tid, StridedIterator(in + in_idx, axis_stride), vals, axis_size, init);
+        tid, strided_iterator(in + in_idx, axis_stride), vals, axis_size, init);
     best = op.reduce_many(best, vals, tid * N_READS);
   }
 

mlx/backend/cuda/binary.cu

@@ -3,6 +3,7 @@
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/binary_ops.cuh"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
@@ -28,7 +29,7 @@ __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 +50,7 @@ __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 +71,7 @@ __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);
@@ -92,7 +93,7 @@ __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);
@@ -128,7 +129,7 @@ __global__ void binary_g(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc(
+    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]);
   }
@@ -176,7 +177,7 @@ template <typename Op>
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    std::string_view op,
     const Stream& s) {
   assert(inputs.size() > 1);
   const auto& a = inputs[0];
@@ -211,15 +212,12 @@ void binary_op_gpu_inplace(
                 int ndim = shape.size();
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
+                    auto kernel = cu::
+                        binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
                     auto [num_blocks, block_dims] =
-                        get_launch_args(out, large());
+                        get_launch_args(kernel, out, large());
                     encoder.add_kernel_node(
-                        cu::binary_g_nd<
+                        kernel,
-                            Op,
-                            InType,
-                            OutType,
-                            IdxT,
-                            dims_constant()>,
                         num_blocks,
                         block_dims,
                         a.data<InType>(),
@@ -231,9 +229,11 @@ void binary_op_gpu_inplace(
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto [num_blocks, block_dims] = get_launch_args(out, large());
+                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
+                  auto [num_blocks, block_dims] =
+                      get_launch_args(kernel, out, large());
                   encoder.add_kernel_node(
-                      cu::binary_g<Op, InType, OutType, IdxT>,
+                      kernel,
                       num_blocks,
                       block_dims,
                       a.data<InType>(),
@@ -249,7 +249,8 @@ 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>;
@@ -259,7 +260,12 @@ 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,
@@ -285,7 +291,7 @@ template <typename Op>
 void binary_op_gpu(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    std::string_view op,
     const Stream& s) {
   auto& a = inputs[0];
   auto& b = inputs[1];
@@ -298,7 +304,7 @@ void binary_op_gpu(
   void func::eval_gpu(const std::vector<array>& inputs, array& out) {    \
     nvtx3::scoped_range r(#func "::eval_gpu");                           \
     auto& s = out.primitive().stream();                                  \
-    binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
+    binary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
   }
 
 BINARY_GPU(Add)
@@ -322,31 +328,33 @@ 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();
+  auto op = get_primitive_string(this);
   if (equal_nan_) {
-    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
+    binary_op_gpu<cu::NaNEqual>(inputs, out, op, s);
   } else {
-    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
+    binary_op_gpu<cu::Equal>(inputs, out, op, s);
   }
 }
 
 void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
   nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
   auto& s = out.primitive().stream();
+  auto op = get_primitive_string(this);
   switch (op_) {
     case BitwiseBinary::And:
-      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, op, s);
       break;
     case BitwiseBinary::Or:
-      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, op, s);
       break;
     case BitwiseBinary::Xor:
-      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, op, s);
       break;
     case BitwiseBinary::LeftShift:
-      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
+      binary_op_gpu<cu::LeftShift>(inputs, out, op, s);
       break;
     case BitwiseBinary::RightShift:
-      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
+      binary_op_gpu<cu::RightShift>(inputs, out, op, s);
       break;
   }
 }

mlx/backend/cuda/binary_two.cu

@@ -3,6 +3,7 @@
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/binary_ops.cuh"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
@@ -33,8 +34,8 @@ binary_two_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
       auto out = Op{}(a[0], b[0]);
-      out_a_vec[i] = out[0];
+      out_a_vec.val[i] = out[0];
-      out_b_vec[i] = out[1];
+      out_b_vec.val[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -60,9 +61,9 @@ binary_two_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a[0], b_vec[i]);
+      auto out = Op{}(a[0], b_vec.val[i]);
-      out_a_vec[i] = out[0];
+      out_a_vec.val[i] = out[0];
-      out_b_vec[i] = out[1];
+      out_b_vec.val[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -88,9 +89,9 @@ binary_two_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a_vec[i], b[0]);
+      auto out = Op{}(a_vec.val[i], b[0]);
-      out_a_vec[i] = out[0];
+      out_a_vec.val[i] = out[0];
-      out_b_vec[i] = out[1];
+      out_b_vec.val[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -117,9 +118,9 @@ binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a_vec[i], b_vec[i]);
+      auto out = Op{}(a_vec.val[i], b_vec.val[i]);
-      out_a_vec[i] = out[0];
+      out_a_vec.val[i] = out[0];
-      out_b_vec[i] = out[1];
+      out_b_vec.val[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -160,7 +161,7 @@ __global__ void binary_two_g(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc(
+    auto [a_idx, b_idx] = elem_to_loc_4d(
         index, shape.data(), a_strides.data(), b_strides.data(), ndim);
     auto out = Op{}(a[a_idx], b[b_idx]);
     out_a[index] = out[0];
@@ -183,7 +184,7 @@ template <typename Op>
 void binary_two_op_gpu_inplace(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    std::string_view op,
     const Stream& s) {
   assert(inputs.size() > 1);
   const auto& a = inputs[0];
@@ -227,15 +228,16 @@ void binary_two_op_gpu_inplace(
                 int ndim = shape.size();
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto [num_blocks, block_dims] =
+                    auto kernel = cu::binary_two_g_nd<
-                        get_launch_args(out_a, large());
-                    encoder.add_kernel_node(
-                        cu::binary_two_g_nd<
                         Op,
                         InType,
                         OutType,
                         IdxT,
-                            dims_constant()>,
+                        dims_constant()>;
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(kernel, out_a, large());
+                    encoder.add_kernel_node(
+                        kernel,
                         num_blocks,
                         block_dims,
                         a.data<InType>(),
@@ -248,10 +250,11 @@ void binary_two_op_gpu_inplace(
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
+                  auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT>;
                   auto [num_blocks, block_dims] =
-                      get_launch_args(out_a, large());
+                      get_launch_args(kernel, out_a, large());
                   encoder.add_kernel_node(
-                      cu::binary_two_g<Op, InType, OutType, IdxT>,
+                      kernel,
                       num_blocks,
                       block_dims,
                       a.data<InType>(),
@@ -268,7 +271,8 @@ void binary_two_op_gpu_inplace(
         } else {
           dispatch_bool(out_a.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_two_ss<Op, InType, OutType, IdxT, N_READS>;
             if (bopt == BinaryOpType::ScalarVector) {
               kernel = cu::binary_two_sv<Op, InType, OutType, IdxT, N_READS>;
@@ -278,6 +282,7 @@ void binary_two_op_gpu_inplace(
               kernel = cu::binary_two_vv<Op, InType, OutType, IdxT, N_READS>;
             }
             auto [num_blocks, block_dims] = get_launch_args(
+                kernel,
                 out_a.data_size(),
                 out_a.shape(),
                 out_a.strides(),
@@ -309,7 +314,7 @@ template <typename Op>
 void binary_two_op_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    std::string_view op,
     const Stream& s) {
   auto& a = inputs[0];
   auto& b = inputs[1];
@@ -324,7 +329,7 @@ void DivMod::eval_gpu(
     std::vector<array>& outputs) {
   nvtx3::scoped_range r("DivMod::eval_gpu");
   auto& s = outputs[0].primitive().stream();
-  binary_two_op_gpu<cu::DivMod>(inputs, outputs, name(), s);
+  binary_two_op_gpu<cu::DivMod>(inputs, outputs, get_primitive_string(this), s);
 }
 
 } // namespace mlx::core

mlx/backend/cuda/compiled.cpp

@@ -53,10 +53,9 @@ struct FusedKernelBuilder {
 
     // Build function signature.
     if (contiguous) {
-      os += "template <typename IdxT = uint32_t, int work_per_thread = 1>\n";
+      os += "template <typename IdxT = uint32_t>\n";
     } else {
-      os +=
+      os += "template <int NDIM, typename IdxT = uint32_t>\n";
-          "template <int NDIM, typename IdxT = uint32_t, int work_per_thread = 1>\n";
     }
     os += fmt::format("__global__ void {}(\n", kernel_name + name);
     for (size_t i = 0; i < params.size(); ++i) {
@@ -68,46 +67,12 @@ struct FusedKernelBuilder {
     }
     os += ") {\n";
 
-    // Index. For non contiguous kernels we create a separate index
+    // Index.
-    // variable per variable otherwise everyone uses `index`.
     os +=
-        "  IdxT index = cg::this_grid().thread_rank() * work_per_thread;\n"
+        "  IdxT index = cg::this_grid().thread_rank();\n"
         "  if (index >= size) {\n"
         "    return;\n"
         "  }\n";
-    if (!contiguous) {
-      for (size_t i = 0; i < inputs.size(); ++i) {
-        const auto& x = inputs[i];
-        const std::string& xname = namer.get_name(x);
-        if (is_scalar(x) || is_constant(i)) {
-          continue;
-        }
-        os += "  IdxT " + xname + "_idx = 0;\n";
-      }
-      os += "  {\n";
-      os += "    IdxT loc = index;\n";
-      os +=
-          "    #pragma unroll\n"
-          "    for (int i = NDIM - 1; i >= 0; i--) {\n";
-      for (size_t i = 0; i < inputs.size(); ++i) {
-        const auto& x = inputs[i];
-        const std::string& xname = namer.get_name(x);
-        if (is_scalar(x) || is_constant(i)) {
-          continue;
-        }
-        os += "      " + xname + "_idx += (loc \% shape[i]) * IdxT(" + xname +
-            "_strides[i]);\n";
-      }
-      os +=
-          "      loc /= shape[i];\n"
-          "    }\n"
-          "  }\n";
-    }
-
-    // Work loop
-    os +=
-        "\n"
-        "  for (int i = 0; i < work_per_thread && index < size; i++) {\n";
 
     // Read inputs.
     for (size_t i = 0; i < inputs.size(); ++i) {
@@ -124,7 +89,10 @@ struct FusedKernelBuilder {
       } else if (contiguous) {
         value = fmt::format("{}[index]", xname);
       } else {
-        value = fmt::format("{}[{}_idx]", xname, xname);
+        std::string index = fmt::format(
+            "elem_to_loc_nd<NDIM>(index, shape.data(), {}_strides.data())",
+            xname);
+        value = fmt::format("{}[{}]", xname, index);
       }
       os += fmt::format("  {} tmp_{} = {};\n", type, xname, value);
     }
@@ -138,7 +106,9 @@ struct FusedKernelBuilder {
         value = fmt::format(
             "static_cast<{}>(tmp_{})", type, namer.get_name(x.inputs()[0]));
       } else {
-        value = x.primitive().name();
+        std::ostringstream ss;
+        x.primitive().print(ss);
+        value = ss.str();
         value += "{}(";
         for (size_t i = 0; i < x.inputs().size() - 1; ++i) {
           value += fmt::format("tmp_{}, ", namer.get_name(x.inputs()[i]));
@@ -153,22 +123,6 @@ struct FusedKernelBuilder {
       os += fmt::format("  {0}[index] = tmp_{0};\n", namer.get_name(x));
     }
 
-    // End of work loop
-    os +=
-        "\n"
-        "    index++;\n";
-    if (!contiguous) {
-      for (size_t i = 0; i < inputs.size(); ++i) {
-        const auto& x = inputs[i];
-        const std::string& xname = namer.get_name(x);
-        if (is_scalar(x) || is_constant(i)) {
-          continue;
-        }
-        os += "    " + xname + "_idx += " + xname + "_strides[NDIM - 1];\n";
-      }
-    }
-    os += "  }\n";
-
     os += "}\n";
   }
 };
@@ -204,28 +158,15 @@ void Compiled::eval_gpu(
     builder.build("_strided", false);
     builder.os += "\n} // namespace mlx::core::cu\n";
     // Build kernel names.
-    std::vector<std::string> kernel_names;
+    std::vector<std::string> kernel_names = {
-    for (auto work_per_thread : std::array<int, 2>{1, 4}) {
+        fmt::format("mlx::core::cu::{}_contiguous<uint32_t>", lib_name()),
-      kernel_names.push_back(fmt::format(
+        fmt::format("mlx::core::cu::{}_contiguous<int64_t>", lib_name()),
-          "mlx::core::cu::{}_contiguous<uint32_t, {}>",
+    };
-          lib_name(),
-          work_per_thread));
-      kernel_names.push_back(fmt::format(
-          "mlx::core::cu::{}_contiguous<int64_t, {}>",
-          lib_name(),
-          work_per_thread));
     for (int i = 1; i <= MAX_NDIM; ++i) {
       kernel_names.push_back(fmt::format(
-            "mlx::core::cu::{}_strided<{}, uint32_t, {}>",
+          "mlx::core::cu::{}_strided<{}, uint32_t>", lib_name(), i));
-            lib_name(),
+      kernel_names.push_back(
-            i,
+          fmt::format("mlx::core::cu::{}_strided<{}, int64_t>", lib_name(), i));
-            work_per_thread));
-        kernel_names.push_back(fmt::format(
-            "mlx::core::cu::{}_strided<{}, int64_t, {}>",
-            lib_name(),
-            i,
-            work_per_thread));
-      }
     }
     return std::make_pair(std::move(builder.os), std::move(kernel_names));
   });
@@ -268,21 +209,13 @@ void Compiled::eval_gpu(
     args.append<uint32_t>(outputs[0].data_size());
   }
 
-  // Choose work per thread
-  int work_per_thread = 4;
-  if (!contiguous && shape.back() % work_per_thread != 0) {
-    work_per_thread = 1;
-  }
-
   // Launch kernel.
   const char* index_type = large ? "int64_t" : "uint32_t";
   std::string kernel_name = fmt::format("mlx::core::cu::{}", lib_name());
   if (contiguous) {
-    kernel_name +=
+    kernel_name += fmt::format("_contiguous<{}>", index_type);
-        fmt::format("_contiguous<{}, {}>", index_type, work_per_thread);
   } else {
-    kernel_name += fmt::format(
+    kernel_name += fmt::format("_strided<{}, {}>", shape.size(), index_type);
-        "_strided<{}, {}, {}>", shape.size(), index_type, work_per_thread);
   }
   auto& encoder = cu::get_command_encoder(s);
   for (const auto& in : inputs) {
@@ -293,8 +226,7 @@ void Compiled::eval_gpu(
   }
 
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] =
+  auto [num_blocks, block_dims] = get_launch_args(kernel, outputs[0], large);
-      get_launch_args(outputs[0], large, work_per_thread);
   encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }
 

mlx/backend/cuda/conv.cpp

@@ -1,340 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/config.h"
-#include "mlx/backend/cuda/lru_cache.h"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-// cudnn_frontend.h redefines this macro.
-#undef CHECK_CUDA_ERROR
-
-#include <cudnn_frontend.h>
-#include <cudnn_frontend_find_plan.h>
-#include <fmt/format.h>
-#include <nvtx3/nvtx3.hpp>
-
-#include <cassert>
-#include <numeric>
-
-namespace mlx::core {
-
-namespace {
-
-// Not all engines support it so can not use this API now.
-#define MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API 0
-
-struct ConvCacheKey {
-  int device_id;
-  cudnnBackendDescriptorType_t backend_type;
-  cudnnDataType_t cudnn_type;
-  std::array<int, MAX_NDIM> input_shape;
-  std::array<int, MAX_NDIM> filter_shape;
-  std::array<int, MAX_NDIM> padding_lo;
-  std::array<int, MAX_NDIM> padding_hi;
-  std::array<int, MAX_NDIM> stride;
-  std::array<int, MAX_NDIM> dilation;
-  int groups;
-  uint8_t input_alignment;
-  uint8_t filter_alignment;
-  uint8_t output_alignment;
-};
-
-auto& conv_cache() {
-  static LRUBytesKeyCache<ConvCacheKey, cudnn_frontend::ExecutionPlan> cache(
-      /* capacity */ 128);
-  return cache;
-}
-
-template <typename T, typename U>
-inline std::vector<T> convert_vector(const std::vector<U>& vec) {
-  return std::vector<T>(vec.begin(), vec.end());
-}
-
-template <typename T>
-inline std::array<T, MAX_NDIM> fixed_vector(const std::vector<T>& vec) {
-  if (vec.size() > MAX_NDIM) {
-    throw std::runtime_error(
-        fmt::format("ndim can not be larger than {}.", MAX_NDIM));
-  }
-  std::array<T, MAX_NDIM> result = {};
-  std::copy_n(vec.begin(), vec.size(), result.begin());
-  return result;
-}
-
-auto nhwc_to_nchw(const array& x) {
-  auto shape = convert_vector<int64_t>(x.shape());
-  shape.insert(shape.begin() + 1, shape.back());
-  shape.erase(shape.end() - 1);
-  auto strides = convert_vector<int64_t>(x.strides());
-  strides.insert(strides.begin() + 1, strides.back());
-  strides.erase(strides.end() - 1);
-  return std::make_tuple(shape, strides);
-}
-
-inline cudnnDataType_t dtype_to_cudnn_type(Dtype dtype) {
-  switch (dtype) {
-    case int8:
-      return CUDNN_DATA_INT8;
-    case int32:
-      return CUDNN_DATA_INT32;
-    case uint8:
-      return CUDNN_DATA_UINT8;
-    case float16:
-      return CUDNN_DATA_HALF;
-    case bfloat16:
-      return CUDNN_DATA_BFLOAT16;
-    case float32:
-      return CUDNN_DATA_FLOAT;
-    case float64:
-      return CUDNN_DATA_DOUBLE;
-    default:
-      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in Convolution: {}.", dtype_to_string(dtype)));
-  }
-}
-
-inline uint8_t get_alignment(const array& x) {
-  uint8_t alignment = 1;
-  uintptr_t address = reinterpret_cast<uintptr_t>(x.data<void>());
-  for (; alignment < 32; alignment *= 2) {
-    if (address % (alignment * 2)) {
-      return alignment;
-    }
-  }
-  return alignment;
-}
-
-inline cudnn_frontend::Tensor build_tensor(int64_t id, const array& x) {
-  auto [shape, strides] = nhwc_to_nchw(x);
-  return cudnn_frontend::TensorBuilder()
-      .setDim(shape.size(), shape.data())
-      .setStrides(strides.size(), strides.data())
-      .setId(id)
-      .setAlignment(get_alignment(x))
-      .setDataType(dtype_to_cudnn_type(x.dtype()))
-      .build();
-}
-
-cudnn_frontend::EngineConfigList get_engine_configs(
-    cudnnBackendDescriptorType_t backend_type,
-    Dtype dtype,
-    cudnn_frontend::OperationGraph& op_graph,
-    bool use_fallback = false) {
-  cudnn_frontend::GeneratorSource source;
-  if (use_fallback) {
-    source = [&backend_type](cudnn_frontend::OperationGraph& op_graph) {
-      auto fallback = cudnn_frontend::EngineFallbackListBuilder()
-                          .setOperationGraph(op_graph)
-                          .setOperation(backend_type)
-                          .build();
-      return fallback.getFallbackList();
-    };
-  } else {
-    source = [](cudnn_frontend::OperationGraph& op_graph) {
-      auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
-                            .setOperationGraph(op_graph)
-                            .setHeurMode(CUDNN_HEUR_MODE_A)
-                            .build();
-      return heuristics.getEngineConfig(heuristics.getEngineConfigCount());
-    };
-  }
-
-  cudnn_frontend::EngineConfigGenerator generator(1, &source);
-  auto configs = generator.generate_engine_config(op_graph);
-
-  cudnn_frontend::EngineConfigList filtered_configs;
-  cudnn_frontend::filter(configs, filtered_configs, [dtype](auto c) {
-    if (cudnn_frontend::hasNumericalNote<
-            CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(c)) {
-      return true;
-    }
-    if (cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c) &&
-        dtype == float32 && !env::enable_tf32()) {
-      return true;
-    }
-    return false;
-  });
-  return filtered_configs;
-}
-
-bool execute_plan(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    const array& in,
-    const array& wt,
-    array& out) {
-  int workspace_size = plan.getWorkspaceSize();
-  array workspace(allocator::malloc(workspace_size), {workspace_size}, uint8);
-
-  int64_t uids[3] = {'x', 'w', 'y'};
-  void* data_ptrs[3] = {
-      const_cast<void*>(in.data<void>()),
-      const_cast<void*>(wt.data<void>()),
-      out.data<void>(),
-  };
-
-  auto variantPack = cudnn_frontend::VariantPackBuilder()
-                         .setWorkspacePointer(workspace.data<void>())
-                         .setDataPointers(3, data_ptrs)
-                         .setUids(3, uids)
-                         .build();
-
-  auto handle = encoder.device().cudnn_handle();
-  cudnnSetStream(handle, encoder.stream());
-
-#if CUDNN_VERSION >= 90500 && MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API
-  cudaGraph_t graph;
-  cudaGraphCreate(&graph, 0);
-  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
-      &graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
-  if (cudnnBackendPopulateCudaGraph(
-          handle, plan.get_raw_desc(), variantPack.get_raw_desc(), graph) !=
-      CUDNN_STATUS_SUCCESS) {
-    return false;
-  }
-  encoder.add_graph_node(graph);
-#else
-  auto capture = encoder.capture_context();
-  if (cudnnBackendExecute(
-          handle, plan.get_raw_desc(), variantPack.get_raw_desc()) !=
-      CUDNN_STATUS_SUCCESS) {
-    // Discard the captured graph when failed.
-    capture.discard = true;
-    return false;
-  }
-#endif
-
-  encoder.add_temporary(workspace);
-  return true;
-}
-
-bool try_engines(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::EngineConfigList& configs,
-    const ConvCacheKey& cache_key,
-    const std::string& op_graph_tag,
-    const array& in,
-    const array& wt,
-    array& out) {
-  for (auto& config : configs) {
-    try {
-      auto plan = cudnn_frontend::ExecutionPlanBuilder()
-                      .setHandle(encoder.device().cudnn_handle())
-                      .setEngineConfig(config, op_graph_tag)
-                      .build();
-      if (execute_plan(encoder, plan, in, wt, out)) {
-        conv_cache().emplace(cache_key, std::move(plan));
-        return true;
-      }
-    } catch (cudnn_frontend::cudnnException&) {
-    }
-  }
-  return false;
-}
-
-} // namespace
-
-void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Convolution::eval_gpu");
-  if (out.size() == 0) {
-    return;
-  }
-
-  assert(inputs.size() == 2);
-  array in = inputs[0];
-  array wt = inputs[1];
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  // cuDNN requires contiguous input.
-  // TODO: Handle NCHW format specially.
-  if (!in.flags().row_contiguous) {
-    in = contiguous_copy_gpu(in, s);
-    encoder.add_temporary(in);
-  }
-  if (!wt.flags().row_contiguous) {
-    wt = contiguous_copy_gpu(wt, s);
-    encoder.add_temporary(wt);
-  }
-
-  encoder.set_input_array(in);
-  encoder.set_input_array(wt);
-  encoder.set_output_array(out);
-
-  auto backend_type = CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR;
-  auto cudnn_type = dtype_to_cudnn_type(in.dtype());
-
-  // Search cache.
-  ConvCacheKey cache_key{
-      encoder.device().cuda_device(),
-      backend_type,
-      cudnn_type,
-      fixed_vector(in.shape()),
-      fixed_vector(wt.shape()),
-      fixed_vector(padding_lo_),
-      fixed_vector(padding_hi_),
-      fixed_vector(kernel_strides_),
-      fixed_vector(kernel_dilation_),
-      groups_,
-      get_alignment(in),
-      get_alignment(wt),
-      get_alignment(out)};
-  if (auto it = conv_cache().find(cache_key); it != conv_cache().end()) {
-    if (!execute_plan(encoder, it->second, in, wt, out)) {
-      throw std::runtime_error("Cached convolution plan failed to execute.");
-    }
-    return;
-  }
-
-  // Build operation graph.
-  auto compute_data_type = (in.dtype() == float16 || in.dtype() == bfloat16)
-      ? CUDNN_DATA_FLOAT
-      : cudnn_type;
-
-  auto stride = convert_vector<int64_t>(kernel_strides_);
-  auto padding_lo = convert_vector<int64_t>(padding_lo_);
-  auto padding_hi = convert_vector<int64_t>(padding_hi_);
-  auto dilation = convert_vector<int64_t>(kernel_dilation_);
-
-  auto conv_desc = cudnn_frontend::ConvDescBuilder()
-                       .setDataType(compute_data_type)
-                       .setMathMode(CUDNN_CROSS_CORRELATION)
-                       .setNDims(stride.size())
-                       .setStrides(stride.size(), stride.data())
-                       .setPrePadding(padding_lo.size(), padding_lo.data())
-                       .setPostPadding(padding_hi.size(), padding_hi.data())
-                       .setDilation(dilation.size(), dilation.data())
-                       .build();
-
-  auto op = cudnn_frontend::OperationBuilder(backend_type)
-                .setxDesc(build_tensor('x', in))
-                .setwDesc(build_tensor('w', wt))
-                .setyDesc(build_tensor('y', out))
-                .setcDesc(conv_desc)
-                .build();
-
-  std::array<cudnn_frontend::Operation const*, 1> ops = {&op};
-  auto op_graph = cudnn_frontend::OperationGraphBuilder()
-                      .setHandle(encoder.device().cudnn_handle())
-                      .setOperationGraph(ops.size(), ops.data())
-                      .build();
-
-  // Try to run plans based on heuristics.
-  auto configs = get_engine_configs(backend_type, in.dtype(), op_graph);
-  auto op_graph_tag = op_graph.getTag();
-  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
-    return;
-  }
-  // Then try fallback plans.
-  configs = get_engine_configs(backend_type, in.dtype(), op_graph);
-  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
-    return;
-  }
-  throw std::runtime_error("Unable to find an engine for convolution.");
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy_contiguous.cu

@@ -22,7 +22,7 @@ __global__ void copy_s(const In* in, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = cast_to<Out>(in[0]);
+      out_vec.val[i] = cast_to<Out>(in[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -43,7 +43,7 @@ __global__ void copy_v(const In* in, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = cast_to<Out>(in_vec[i]);
+      out_vec.val[i] = cast_to<Out>(in_vec.val[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -65,13 +65,19 @@ void copy_contiguous(
         using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
         using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
         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::copy_s<InType, OutType, IdxT, N_READS>;
         if (ctype == CopyType::Vector) {
           kernel = cu::copy_v<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,

mlx/backend/cuda/copy/copy_general.cu

@@ -37,7 +37,7 @@ __global__ void copy_gg(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc(
+    auto [idx_in, idx_out] = elem_to_loc_4d(
         index, shape.data(), strides_in.data(), strides_out.data(), ndim);
     out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
   }
@@ -71,10 +71,12 @@ void copy_general(
               data_size *= s;
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto ndim_constant) {
-                auto [num_blocks, block_dims] =
+                auto kernel =
-                    get_launch_args(data_size, shape, out.strides(), large());
+                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>;
+                auto [num_blocks, block_dims] = get_launch_args(
+                    kernel, data_size, shape, out.strides(), large());
                 encoder.add_kernel_node(
-                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>,
+                    kernel,
                     num_blocks,
                     block_dims,
                     in_ptr,
@@ -85,10 +87,11 @@ void copy_general(
                     const_param<ndim_constant()>(strides_out));
               });
             } else { // ndim >= 4
-              auto [num_blocks, block_dims] =
+              auto kernel = cu::copy_gg<InType, OutType, IdxT>;
-                  get_launch_args(data_size, shape, out.strides(), large());
+              auto [num_blocks, block_dims] = get_launch_args(
+                  kernel, data_size, shape, out.strides(), large());
               encoder.add_kernel_node(
-                  cu::copy_gg<InType, OutType, IdxT>,
+                  kernel,
                   num_blocks,
                   block_dims,
                   in_ptr,

mlx/backend/cuda/copy/copy_general_dynamic.cu

@@ -41,7 +41,7 @@ __global__ void copy_gg_dynamic(
     const int64_t* offset_out) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc(
+    auto [idx_in, idx_out] = elem_to_loc_4d(
         index, shape.data(), strides_in.data(), strides_out.data(), ndim);
     out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
   }
@@ -74,13 +74,12 @@ void copy_general_dynamic(
             int ndim = shape.size();
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto [num_blocks, block_dims] = get_launch_args(out, large());
+                auto kernel = cu::
+                    copy_gg_dynamic_nd<InType, OutType, IdxT, dims_constant()>;
+                auto [num_blocks, block_dims] =
+                    get_launch_args(kernel, out, large());
                 encoder.add_kernel_node(
-                    cu::copy_gg_dynamic_nd<
+                    kernel,
-                        InType,
-                        OutType,
-                        IdxT,
-                        dims_constant()>,
                     num_blocks,
                     block_dims,
                     in_ptr,
@@ -93,9 +92,11 @@ void copy_general_dynamic(
                     dynamic_offset_out.data<int64_t>());
               });
             } else { // ndim >= 4
-              auto [num_blocks, block_dims] = get_launch_args(out, large());
+              auto kernel = cu::copy_gg_dynamic<InType, OutType, IdxT>;
+              auto [num_blocks, block_dims] =
+                  get_launch_args(kernel, out, large());
               encoder.add_kernel_node(
-                  cu::copy_gg_dynamic<InType, OutType, IdxT>,
+                  kernel,
                   num_blocks,
                   block_dims,
                   in_ptr,

mlx/backend/cuda/copy/copy_general_input.cu

@@ -34,7 +34,7 @@ __global__ void copy_g(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    IdxT idx_in = elem_to_loc(index, shape.data(), strides_in.data(), ndim);
+    IdxT idx_in = elem_to_loc_4d(index, shape.data(), strides_in.data(), ndim);
     out[index] = CastOp<In, Out>{}(in[idx_in]);
   }
 }
@@ -63,9 +63,12 @@ void copy_general_input(
             int ndim = shape.size();
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto [num_blocks, block_dims] = get_launch_args(out, large());
+                auto kernel =
+                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>;
+                auto [num_blocks, block_dims] =
+                    get_launch_args(kernel, out, large());
                 encoder.add_kernel_node(
-                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>,
+                    kernel,
                     num_blocks,
                     block_dims,
                     in_ptr,
@@ -75,9 +78,11 @@ void copy_general_input(
                     const_param<dims_constant()>(strides_in));
               });
             } else { // ndim >= 4
-              auto [num_blocks, block_dims] = get_launch_args(out, large());
+              auto kernel = cu::copy_g<InType, OutType, IdxT>;
+              auto [num_blocks, block_dims] =
+                  get_launch_args(kernel, out, large());
               encoder.add_kernel_node(
-                  cu::copy_g<InType, OutType, IdxT>,
+                  kernel,
                   num_blocks,
                   block_dims,
                   in_ptr,

mlx/backend/cuda/device.cpp

@@ -1,7 +1,6 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/jit_module.h"
 #include "mlx/backend/cuda/worker.h"
 #include "mlx/utils.h"
 
@@ -10,23 +9,12 @@
 #include <future>
 #include <unordered_set>
 
-namespace mlx::core::cu {
+namespace mlx::core {
-
-namespace {
 
 // Can be tuned with MLX_MAX_OPS_PER_BUFFER
 // This should be less than 255
 constexpr int default_max_nodes_per_graph = 20;
 
-#define CHECK_CUDNN_ERROR(cmd) check_cudnn_error(#cmd, (cmd))
-
-void check_cudnn_error(const char* name, cudnnStatus_t err) {
-  if (err != CUDNN_STATUS_SUCCESS) {
-    throw std::runtime_error(
-        fmt::format("{} failed: {}.", name, cudnnGetErrorString(err)));
-  }
-}
-
 int cuda_graph_cache_size() {
   static int cache_size = []() {
     return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
@@ -34,7 +22,7 @@ int cuda_graph_cache_size() {
   return cache_size;
 }
 
-} // namespace
+namespace cu {
 
 Device::Device(int device) : device_(device) {
   CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
@@ -52,18 +40,11 @@ Device::Device(int device) : device_(device) {
   }
   // The cublasLt handle is used by matmul.
   make_current();
-  CHECK_CUBLAS_ERROR(cublasLtCreate(&lt_));
+  cublasLtCreate(&lt_);
-  // The cudnn handle is used by Convolution.
-  CHECK_CUDNN_ERROR(cudnnCreate(&cudnn_));
-
-  // Initialize the jit module cache here ensures it is not
-  // unloaded before any evaluation is done
-  get_jit_module_cache();
 }
 
 Device::~Device() {
-  CHECK_CUDNN_ERROR(cudnnDestroy(cudnn_));
+  cublasLtDestroy(lt_);
-  CHECK_CUBLAS_ERROR(cublasLtDestroy(lt_));
 }
 
 void Device::make_current() {
@@ -85,19 +66,30 @@ CommandEncoder& Device::get_command_encoder(Stream s) {
 }
 
 CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
-  enc.device().make_current();
+  CHECK_CUDA_ERROR(cudaGraphCreate(&graph, 0));
   CHECK_CUDA_ERROR(
       cudaStreamBeginCapture(enc.stream(), cudaStreamCaptureModeGlobal));
 }
 
 CommandEncoder::CaptureContext::~CaptureContext() {
   CHECK_CUDA_ERROR(cudaStreamEndCapture(enc.stream(), &graph));
-  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+  size_t num_nodes;
-      &graph, [](cudaGraph_t* p) { CHECK_CUDA_ERROR(cudaGraphDestroy(*p)); });
+  CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, NULL, &num_nodes));
-  if (discard) {
+  if (num_nodes == 1) {
-    return;
+    cudaGraphNode_t captured_node;
+    CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, &captured_node, &num_nodes));
+    CUDA_KERNEL_NODE_PARAMS params;
+    CHECK_CUDA_ERROR(cuGraphKernelNodeGetParams(captured_node, &params));
+    cudaGraphNode_t node;
+    CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, enc.graph_, NULL, 0, &params));
+    enc.insert_graph_dependencies(GraphNode{node, 'K'});
+  } else {
+    cudaGraphNode_t node;
+    CHECK_CUDA_ERROR(
+        cudaGraphAddChildGraphNode(&node, enc.graph_, NULL, 0, graph));
+    enc.insert_graph_dependencies(GraphNode{node, 'G'});
   }
-  enc.add_graph_node(graph);
+  CHECK_CUDA_ERROR(cudaGraphDestroy(graph));
 }
 
 CommandEncoder::ConcurrentContext::ConcurrentContext(CommandEncoder& enc)
@@ -230,7 +222,10 @@ void CommandEncoder::add_kernel_node(
   kernel_params.gridDim = grid_dim;
   kernel_params.blockDim = block_dim;
   kernel_params.kernelParams = params;
-  add_kernel_node(kernel_params);
+  cudaGraphNode_t node;
+  CHECK_CUDA_ERROR(
+      cudaGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
+  insert_graph_dependencies(GraphNode{node, 'K'});
 }
 
 void CommandEncoder::add_kernel_node(
@@ -247,27 +242,12 @@ void CommandEncoder::add_kernel_node(
   kernel_params.blockDimY = block_dim.y;
   kernel_params.blockDimZ = block_dim.z;
   kernel_params.kernelParams = params;
-  add_kernel_node(kernel_params);
-}
-
-void CommandEncoder::add_kernel_node(const cudaKernelNodeParams& params) {
-  cudaGraphNode_t node;
-  CHECK_CUDA_ERROR(cudaGraphAddKernelNode(&node, graph_, NULL, 0, &params));
-  insert_graph_dependencies(GraphNode{node, 'K'});
-}
-
-void CommandEncoder::add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params) {
   CUgraphNode node;
-  CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, graph_, NULL, 0, &params));
+  CHECK_CUDA_ERROR(
+      cuGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
   insert_graph_dependencies(GraphNode{node, 'K'});
 }
 
-void CommandEncoder::add_graph_node(cudaGraph_t child) {
-  cudaGraphNode_t node;
-  CHECK_CUDA_ERROR(cudaGraphAddChildGraphNode(&node, graph_, NULL, 0, child));
-  insert_graph_dependencies(GraphNode{node, 'G'});
-}
-
 void CommandEncoder::commit() {
   if (!temporaries_.empty()) {
     add_completed_handler([temporaries = std::move(temporaries_)]() {});
@@ -327,6 +307,7 @@ void CommandEncoder::commit() {
   }
 
   // Put completion handlers in a batch.
+  worker_.end_batch();
   worker_.commit(stream_);
 }
 
@@ -335,6 +316,7 @@ void CommandEncoder::synchronize() {
   auto p = std::make_shared<std::promise<void>>();
   std::future<void> f = p->get_future();
   add_completed_handler([p = std::move(p)]() { p->set_value(); });
+  worker_.end_batch();
   commit();
   f.wait();
 }
@@ -352,4 +334,6 @@ CommandEncoder& get_command_encoder(Stream s) {
   return device(s.device).get_command_encoder(s);
 }
 
-} // namespace mlx::core::cu
+} // namespace cu
+
+} // namespace mlx::core

mlx/backend/cuda/device.h

@@ -8,7 +8,6 @@
 
 #include <cublasLt.h>
 #include <cuda.h>
-#include <cudnn.h>
 #include <thrust/execution_policy.h>
 
 #include <unordered_map>
@@ -22,7 +21,6 @@ class CommandEncoder {
     ~CaptureContext();
     cudaGraph_t graph;
     CommandEncoder& enc;
-    bool discard{false};
   };
   struct ConcurrentContext {
     ConcurrentContext(CommandEncoder& enc);
@@ -67,11 +65,6 @@ class CommandEncoder {
   void
   add_kernel_node(void* func, dim3 grid_dim, dim3 block_dim, void** params);
 
-  // Low-level graph helpers.
-  void add_kernel_node(const cudaKernelNodeParams& params);
-  void add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params);
-  void add_graph_node(cudaGraph_t child);
-
   void add_temporary(const array& arr) {
     temporaries_.push_back(arr.data_shared_ptr());
   }
@@ -80,10 +73,6 @@ class CommandEncoder {
   void maybe_commit();
   void commit();
 
-  Device& device() {
-    return device_;
-  }
-
   CudaStream& stream() {
     return stream_;
   }
@@ -148,16 +137,12 @@ class Device {
   cublasLtHandle_t lt_handle() const {
     return lt_;
   }
-  cudnnHandle_t cudnn_handle() const {
-    return cudnn_;
-  }
 
  private:
   int device_;
   int compute_capability_major_;
   int compute_capability_minor_;
   cublasLtHandle_t lt_;
-  cudnnHandle_t cudnn_;
   std::unordered_map<int, CommandEncoder> encoders_;
 };
 

mlx/backend/cuda/device/arange.cuh

@@ -0,0 +1,15 @@
+// Copyright © 2025 Apple Inc.
+
+namespace mlx::core::cu {
+
+template <typename T>
+struct Arange {
+  const T start;
+  const T step;
+
+  __device__ T operator()(uint32_t i) const {
+    return start + i * step;
+  }
+};
+
+} // namespace mlx::core::cu

mlx/backend/cuda/device/atomic_ops.cuh

@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "mlx/backend/cuda/device/complex.cuh"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/device/fp16_math.cuh"
 
 #include <cuda/atomic>
@@ -48,16 +48,25 @@ inline __device__ void atomic_add(__half* out, __half val) {
   atomicAdd(out, val);
 }
 
-inline __device__ void atomic_add(complex64_t* out, complex64_t val) {
+inline __device__ void atomic_add(cuComplex* out, cuComplex val) {
-  atomic_add_general(out, val);
+#if __CUDA_ARCH__ < 900
-}
-
-inline __device__ void atomic_add(__nv_bfloat16* out, __nv_bfloat16 val) {
-#if __CUDA_ARCH__ < 800
   atomic_add_general(out, val);
 #else
   atomicAdd(out, val);
 #endif
 }
 
+inline __device__ void atomic_add(__nv_bfloat16* out, __nv_bfloat16 val) {
+#if __CUDA_ARCH__ < 800
+#if CCCL_VERSION >= 2008000
+  atomic_add_general(out, val);
+#else
+  bool cccl_version_too_old_for_bfloat16_atomic_add = false;
+  assert(cccl_version_too_old_for_bfloat16_atomic_add);
+#endif
+#else
+  atomicAdd(out, val);
+#endif
+}
+
 } // namespace mlx::core::cu

mlx/backend/cuda/device/binary_ops.cuh

@@ -44,7 +44,7 @@ struct Remainder {
       } else {
         return x % y;
       }
-    } else if constexpr (is_complex_v<T>) {
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
       return x % y;
     } else {
       T r = fmod(x, y);
@@ -66,12 +66,14 @@ struct Equal {
 struct NaNEqual {
   template <typename T>
   __device__ bool operator()(T x, T y) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (std::is_same_v<T, cuComplex>) {
       return x == y ||
-          (isnan(x.real()) && isnan(y.real()) && isnan(x.imag()) &&
+          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) && isnan(cuCimagf(x)) &&
-           isnan(y.imag())) ||
+           isnan(cuCimagf(y))) ||
-          (x.real() == y.real() && isnan(x.imag()) && isnan(y.imag())) ||
+          (cuCrealf(x) == cuCrealf(y) && isnan(cuCimagf(x)) &&
-          (isnan(x.real()) && isnan(y.real()) && x.imag() == y.imag());
+           isnan(cuCimagf(y))) ||
+          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) &&
+           cuCimagf(x) == cuCimagf(y));
     } else {
       return x == y || (isnan(x) && isnan(y));
     }
@@ -109,17 +111,17 @@ struct LessEqual {
 struct LogAddExp {
   template <typename T>
   __device__ T operator()(T x, T y) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (isnan(x.real()) || isnan(x.imag()) || isnan(y.real()) ||
+      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x)) || isnan(cuCrealf(y)) ||
-          isnan(y.imag())) {
+          isnan(cuCimagf(y))) {
         return {
             cuda::std::numeric_limits<float>::quiet_NaN(),
             cuda::std::numeric_limits<float>::quiet_NaN()};
       }
-      auto max = x.real() > y.real() ? x : y;
+      auto max = cuCrealf(x) > cuCrealf(y) ? x : y;
-      auto min = x.real() < y.real() ? x : y;
+      auto min = cuCrealf(x) < cuCrealf(y) ? x : y;
-      auto min_real = min.real();
+      auto min_real = cuCrealf(min);
-      auto max_real = max.real();
+      auto max_real = cuCrealf(max);
       if (!isfinite(min_real) && (min_real == max_real)) {
         if (min_real < 0) {
           return min;
@@ -148,8 +150,8 @@ struct Maximum {
   __device__ T operator()(T x, T y) {
     if constexpr (cuda::std::is_integral_v<T>) {
       return max(x, y);
-    } else if constexpr (is_complex_v<T>) {
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (isnan(x.real()) || isnan(x.imag())) {
+      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
         return x;
       }
       return x > y ? x : y;
@@ -167,8 +169,8 @@ struct Minimum {
   __device__ T operator()(T x, T y) {
     if constexpr (cuda::std::is_integral_v<T>) {
       return min(x, y);
-    } else if constexpr (is_complex_v<T>) {
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (isnan(x.real()) || isnan(x.imag())) {
+      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
         return x;
       }
       return x < y ? x : y;
@@ -191,8 +193,8 @@ struct Multiply {
 struct NotEqual {
   template <typename T>
   __device__ bool operator()(T x, T y) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (std::is_same_v<T, cuComplex>) {
-      return x.real() != y.real() || x.imag() != y.imag();
+      return cuCrealf(x) != cuCrealf(y) || cuCimagf(x) != cuCimagf(y);
     } else {
       return x != y;
     }
@@ -212,8 +214,19 @@ struct Power {
         base *= base;
       }
       return res;
-    } else if constexpr (is_complex_v<T>) {
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return pow(base, exp);
+      if (base.y == 0 && base.x == 0) {
+        if (isnan(exp.x) || isnan(exp.y)) {
+          auto nan = cuda::std::numeric_limits<float>::quiet_NaN();
+          return make_cuFloatComplex(nan, nan);
+        }
+        return make_cuFloatComplex(0.0, 0.0);
+      }
+      auto x_theta = atan2f(base.y, base.x);
+      auto x_ln_r = 0.5 * logf(base.x * base.x + base.y * base.y);
+      auto mag = expf(exp.x * x_ln_r - exp.y * x_theta);
+      auto phase = exp.y * x_ln_r + exp.x * x_theta;
+      return make_cuFloatComplex(mag * cosf(phase), mag * sinf(phase));
     } else {
       return powf(base, exp);
     }

mlx/backend/cuda/device/cast_op.cuh

@@ -2,8 +2,7 @@
 
 #pragma once
 
-#include "mlx/backend/cuda/device/complex.cuh"
+#include <cuComplex.h>
-
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 #include <thrust/iterator/transform_iterator.h>
@@ -21,43 +20,50 @@ struct CastOp {
 };
 
 // Castings between complex and boolean.
-template <typename T>
+// TODO: Should make a custom complex type.
-struct CastOp<complex_t<T>, bool> {
+template <>
+struct CastOp<cuComplex, bool> {
   static constexpr bool is_castable = true;
 
-  __device__ bool operator()(complex_t<T> x) {
+  __device__ bool operator()(cuComplex x) {
-    return x.real() != 0 && x.imag() != 0;
+    return x.x != 0 && x.y != 0;
   }
 };
 
-template <typename T>
+template <>
-struct CastOp<bool, complex_t<T>> {
+struct CastOp<bool, cuComplex> {
   static constexpr bool is_castable = true;
 
-  __device__ complex_t<T> operator()(bool x) {
+  __device__ cuComplex operator()(bool x) {
-    return x ? complex_t<T>{1, 1} : complex_t<T>{0, 0};
+    return x ? make_cuFloatComplex(1, 1) : make_cuFloatComplex(0, 0);
   }
 };
 
 // Converting a complex number to real number discards the imaginary part.
-template <typename T, typename DstT>
+template <typename DstT>
-struct CastOp<complex_t<T>, DstT, cuda::std::enable_if_t<!is_complex_v<DstT>>> {
+struct CastOp<
-  static constexpr bool is_castable = cuda::std::is_convertible_v<T, DstT>;
+    cuComplex,
+    DstT,
+    cuda::std::enable_if_t<!cuda::std::is_same_v<cuComplex, DstT>>> {
+  static constexpr bool is_castable = cuda::std::is_convertible_v<float, DstT>;
 
-  __device__ DstT operator()(complex_t<T> x) {
+  __device__ DstT operator()(cuComplex x) {
-    static_assert(!is_complex_v<DstT>);
+    static_assert(!cuda::std::is_same_v<cuComplex, DstT>);
-    return static_cast<DstT>(x.real());
+    return static_cast<DstT>(cuCrealf(x));
   }
 };
 
 // Allow converting a real number to complex number.
-template <typename SrcT, typename T>
+template <typename SrcT>
-struct CastOp<SrcT, complex_t<T>, cuda::std::enable_if_t<!is_complex_v<SrcT>>> {
+struct CastOp<
-  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, T>;
+    SrcT,
+    cuComplex,
+    cuda::std::enable_if_t<!cuda::std::is_same_v<SrcT, cuComplex>>> {
+  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, float>;
 
-  __device__ complex_t<T> operator()(SrcT x) {
+  __device__ cuComplex operator()(SrcT x) {
-    static_assert(!is_complex_v<SrcT>);
+    static_assert(!cuda::std::is_same_v<SrcT, cuComplex>);
-    return complex_t<T>{static_cast<T>(x), 0};
+    return cuComplex{static_cast<float>(x), 0};
   }
 };
 
@@ -82,7 +88,8 @@ struct CastOp<
     SrcT,
     DstT,
     cuda::std::enable_if_t<
-        !cuda::std::is_convertible_v<SrcT, DstT> && !is_complex_v<SrcT> &&
+        !cuda::std::is_convertible_v<SrcT, DstT> &&
+        !cuda::std::is_same_v<SrcT, cuComplex> &&
         (cuda::std::is_same_v<DstT, __half> ||
          cuda::std::is_same_v<DstT, __nv_bfloat16>)>> {
   static constexpr bool is_castable = true;
@@ -97,7 +104,8 @@ struct CastOp<
     SrcT,
     DstT,
     cuda::std::enable_if_t<
-        !cuda::std::is_convertible_v<SrcT, DstT> && !is_complex_v<SrcT> &&
+        !cuda::std::is_convertible_v<SrcT, DstT> &&
+        !cuda::std::is_same_v<DstT, cuComplex> &&
         !cuda::std::is_same_v<DstT, __half> &&
         !cuda::std::is_same_v<DstT, __nv_bfloat16> &&
         (cuda::std::is_same_v<SrcT, __half> ||

mlx/backend/cuda/device/cexpf.cuh

@@ -0,0 +1,138 @@
+// Copyright © 2025 Apple Inc.
+// Copyright © 2008-2013 NVIDIA Corporation
+// Copyright © 2013 Filipe RNC Maia
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Forked from
+// https://github.com/NVIDIA/cccl/blob/main/thrust/thrust/detail/complex/cexpf.h
+
+// TODO: We should use thrust::exp but the thrust header in old CUDA versions
+// can not be used in JIT.
+
+#pragma once
+
+#include <cuComplex.h>
+#include <cuda/std/cstdint>
+
+namespace mlx::core::cu::detail {
+
+using ieee_float_shape_type = union {
+  float value;
+  uint32_t word;
+};
+
+inline __device__ void get_float_word(uint32_t& i, float d) {
+  ieee_float_shape_type gf_u;
+  gf_u.value = (d);
+  (i) = gf_u.word;
+}
+
+inline __device__ void get_float_word(int32_t& i, float d) {
+  ieee_float_shape_type gf_u;
+  gf_u.value = (d);
+  (i) = gf_u.word;
+}
+
+inline __device__ void set_float_word(float& d, uint32_t i) {
+  ieee_float_shape_type sf_u;
+  sf_u.word = (i);
+  (d) = sf_u.value;
+}
+
+inline __device__ float frexp_expf(float x, int* expt) {
+  const uint32_t k = 235;
+  const float kln2 = 162.88958740F;
+
+  float exp_x;
+  uint32_t hx;
+
+  exp_x = expf(x - kln2);
+  get_float_word(hx, exp_x);
+  *expt = (hx >> 23) - (0x7f + 127) + k;
+  set_float_word(exp_x, (hx & 0x7fffff) | ((0x7f + 127) << 23));
+  return exp_x;
+}
+
+inline __device__ cuComplex ldexp_cexpf(cuComplex z, int expt) {
+  float x, y, exp_x, scale1, scale2;
+  int ex_expt, half_expt;
+
+  x = cuCrealf(z);
+  y = cuCimagf(z);
+  exp_x = frexp_expf(x, &ex_expt);
+  expt += ex_expt;
+
+  half_expt = expt / 2;
+  set_float_word(scale1, (0x7f + half_expt) << 23);
+  half_expt = expt - half_expt;
+  set_float_word(scale2, (0x7f + half_expt) << 23);
+
+  return cuComplex{
+      cosf(y) * exp_x * scale1 * scale2, sinf(y) * exp_x * scale1 * scale2};
+}
+
+inline __device__ cuComplex cexpf(const cuComplex& z) {
+  float x, y, exp_x;
+  uint32_t hx, hy;
+
+  const uint32_t exp_ovfl = 0x42b17218, cexp_ovfl = 0x43400074;
+
+  x = cuCrealf(z);
+  y = cuCimagf(z);
+
+  get_float_word(hy, y);
+  hy &= 0x7fffffff;
+
+  /* cexp(x + I 0) = exp(x) + I 0 */
+  if (hy == 0) {
+    return cuComplex{expf(x), y};
+  }
+  get_float_word(hx, x);
+  /* cexp(0 + I y) = cos(y) + I sin(y) */
+  if ((hx & 0x7fffffff) == 0) {
+    return cuComplex{cosf(y), sinf(y)};
+  }
+  if (hy >= 0x7f800000) {
+    if ((hx & 0x7fffffff) != 0x7f800000) {
+      /* cexp(finite|NaN +- I Inf|NaN) = NaN + I NaN */
+      return cuComplex{y - y, y - y};
+    } else if (hx & 0x80000000) {
+      /* cexp(-Inf +- I Inf|NaN) = 0 + I 0 */
+      return cuComplex{0.0, 0.0};
+    } else {
+      /* cexp(+Inf +- I Inf|NaN) = Inf + I NaN */
+      return cuComplex{x, y - y};
+    }
+  }
+
+  if (hx >= exp_ovfl && hx <= cexp_ovfl) {
+    /*
+     * x is between 88.7 and 192, so we must scale to avoid
+     * overflow in expf(x).
+     */
+    return ldexp_cexpf(z, 0);
+  } else {
+    /*
+     * Cases covered here:
+     *  -  x < exp_ovfl and exp(x) won't overflow (common case)
+     *  -  x > cexp_ovfl, so exp(x) * s overflows for all s > 0
+     *  -  x = +-Inf (generated by exp())
+     *  -  x = NaN (spurious inexact exception from y)
+     */
+    exp_x = expf(x);
+    return cuComplex{exp_x * cosf(y), exp_x * sinf(y)};
+  }
+}
+
+} // namespace mlx::core::cu::detail

mlx/backend/cuda/device/complex.cuh

@@ -1,60 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-// Make multiplication and division faster.
-#define LIBCUDACXX_ENABLE_SIMPLIFIED_COMPLEX_OPERATIONS
-
-#include <cuda/std/complex>
-#include <cuda/std/type_traits>
-
-namespace mlx::core::cu {
-
-// TODO: Consider using a faster implementation as cuda::std::complex has to
-// conform to C++ standard.
-template <typename T>
-using complex_t = cuda::std::complex<T>;
-
-using complex64_t = complex_t<float>;
-using complex128_t = complex_t<double>;
-
-template <typename T>
-struct is_complex : cuda::std::false_type {};
-
-template <typename T>
-struct is_complex<cuda::std::complex<T>> : cuda::std::true_type {};
-
-template <typename T>
-inline constexpr bool is_complex_v = is_complex<T>::value;
-
-// cuda::std::complex is missing some operators.
-template <typename T>
-inline __host__ __device__ complex_t<T> operator%(
-    complex_t<T> a,
-    complex_t<T> b) {
-  T r = a.real() - floor(a.real() / b.real()) * b.real();
-  T i = a.imag() - floor(a.imag() / b.imag()) * b.imag();
-  return complex_t<T>{r, i};
-}
-
-template <typename T>
-inline __host__ __device__ bool operator>(complex_t<T> a, complex_t<T> b) {
-  return (a.real() > b.real()) || (a.real() == b.real() && a.imag() > b.imag());
-}
-
-template <typename T>
-inline __host__ __device__ bool operator<(complex_t<T> a, complex_t<T> b) {
-  return operator>(b, a);
-}
-
-template <typename T>
-inline __host__ __device__ bool operator<=(complex_t<T> a, complex_t<T> b) {
-  return !(a > b);
-}
-
-template <typename T>
-inline __host__ __device__ bool operator>=(complex_t<T> a, complex_t<T> b) {
-  return !(a < b);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/cucomplex_math.cuh

@@ -0,0 +1,240 @@
+// Copyright © 2025 Apple Inc.
+// Copyright © 2017-2024 The Simons Foundation, Inc.
+//
+// FINUFFT is licensed under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance with the
+// License.  You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Forked from
+// https://github.com/flatironinstitute/finufft/blob/main/include/cufinufft/contrib/helper_math.h
+
+#pragma once
+
+#include <cuComplex.h>
+
+// This header provides some helper functions for cuComplex types.
+// It mainly wraps existing CUDA implementations to provide operator overloads
+// e.g. cuAdd, cuSub, cuMul, cuDiv, cuCreal, cuCimag, cuCabs, cuCarg, cuConj are
+// all provided by CUDA
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCadd(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCsub(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCmul(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  return cuCdiv(a, b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator%(const cuDoubleComplex& a, const cuDoubleComplex& b) {
+  double r = cuCreal(a) - (floorf(cuCreal(a) / cuCreal(b)) * cuCreal(b));
+  double i = cuCimag(a) - (floorf(cuCimag(a) / cuCimag(b)) * cuCimag(b));
+  return make_cuDoubleComplex(r, i);
+}
+
+__forceinline__ __host__ __device__ bool operator==(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return cuCreal(a) == cuCreal(b) && cuCimag(a) == cuCimag(b);
+}
+
+__forceinline__ __host__ __device__ bool operator!=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return !(a == b);
+}
+
+__forceinline__ __host__ __device__ bool operator>(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  double mag_a = sqrt(cuCreal(a) * cuCreal(a) + cuCimag(a) * cuCimag(a));
+  double mag_b = sqrt(cuCreal(b) * cuCreal(b) + cuCimag(b) * cuCimag(b));
+  return mag_a > mag_b;
+}
+
+__forceinline__ __host__ __device__ bool operator>=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return a > b || a == b;
+}
+
+__forceinline__ __host__ __device__ bool operator<(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return b > a;
+}
+
+__forceinline__ __host__ __device__ bool operator<=(
+    const cuDoubleComplex& a,
+    const cuDoubleComplex& b) {
+  return b > a || a == b;
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) + b, cuCimag(a));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator+(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a + cuCreal(b), cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) - b, cuCimag(a));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator-(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a - cuCreal(b), -cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) * b, cuCimag(a) * b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator*(double a, const cuDoubleComplex& b) {
+  return make_cuDoubleComplex(a * cuCreal(b), a * cuCimag(b));
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(const cuDoubleComplex& a, double b) {
+  return make_cuDoubleComplex(cuCreal(a) / b, cuCimag(a) / b);
+}
+
+__forceinline__ __host__ __device__ cuDoubleComplex
+operator/(double a, const cuDoubleComplex& b) {
+  double denom = cuCreal(b) * cuCreal(b) + cuCimag(b) * cuCimag(b);
+  return make_cuDoubleComplex(
+      (a * cuCreal(b)) / denom, (-a * cuCimag(b)) / denom);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCaddf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCsubf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCmulf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(const cuFloatComplex& a, const cuFloatComplex& b) {
+  return cuCdivf(a, b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator%(const cuFloatComplex& a, const cuFloatComplex& b) {
+  float r = cuCrealf(a) - (floorf(cuCrealf(a) / cuCrealf(b)) * cuCrealf(b));
+  float i = cuCimagf(a) - (floorf(cuCimagf(a) / cuCimagf(b)) * cuCimagf(b));
+  return make_cuFloatComplex(r, i);
+}
+
+__forceinline__ __host__ __device__ bool operator==(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return cuCrealf(a) == cuCrealf(b) && cuCimagf(a) == cuCimagf(b);
+}
+
+__forceinline__ __host__ __device__ bool operator!=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return !(a == b);
+}
+
+__forceinline__ __host__ __device__ bool operator>(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  float mag_a = sqrt(cuCrealf(a) * cuCrealf(a) + cuCimagf(a) * cuCimagf(a));
+  float mag_b = sqrt(cuCrealf(b) * cuCrealf(b) + cuCimagf(b) * cuCimagf(b));
+  return mag_a > mag_b;
+}
+
+__forceinline__ __host__ __device__ bool operator>=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return a > b || a == b;
+}
+
+__forceinline__ __host__ __device__ bool operator<(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return b > a;
+}
+
+__forceinline__ __host__ __device__ bool operator<=(
+    const cuFloatComplex& a,
+    const cuFloatComplex& b) {
+  return b > a || a == b;
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) + b, cuCimagf(a));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator+(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a + cuCrealf(b), cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) - b, cuCimagf(a));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator-(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a - cuCrealf(b), -cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) * b, cuCimagf(a) * b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator*(float a, const cuFloatComplex& b) {
+  return make_cuFloatComplex(a * cuCrealf(b), a * cuCimagf(b));
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(const cuFloatComplex& a, float b) {
+  return make_cuFloatComplex(cuCrealf(a) / b, cuCimagf(a) / b);
+}
+
+__forceinline__ __host__ __device__ cuFloatComplex
+operator/(float a, const cuFloatComplex& b) {
+  float denom = cuCrealf(b) * cuCrealf(b) + cuCimagf(b) * cuCimagf(b);
+  return make_cuFloatComplex(
+      (a * cuCrealf(b)) / denom, (-a * cuCimagf(b)) / denom);
+}

mlx/backend/cuda/device/unary_ops.cuh

@@ -2,6 +2,8 @@
 
 #pragma once
 
+#include "mlx/backend/cuda/device/cexpf.cuh"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/device/fp16_math.cuh"
 #include "mlx/backend/cuda/device/utils.cuh"
 
@@ -14,6 +16,8 @@ struct Abs {
   __device__ T operator()(T x) {
     if constexpr (cuda::std::is_unsigned_v<T>) {
       return x;
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {sqrt(cuCrealf(x) * cuCrealf(x) + cuCimagf(x) * cuCimagf(x)), 0};
     } else {
       return abs(x);
     }
@@ -25,6 +29,8 @@ struct ArcCos {
   __device__ T operator()(T x) {
     return acos(x);
   }
+
+  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcCosh {
@@ -39,6 +45,8 @@ struct ArcSin {
   __device__ T operator()(T x) {
     return asin(x);
   }
+
+  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcSinh {
@@ -53,6 +61,8 @@ struct ArcTan {
   __device__ T operator()(T x) {
     return atan(x);
   }
+
+  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcTanh {
@@ -74,8 +84,6 @@ struct Ceil {
   __device__ T operator()(T x) {
     if constexpr (cuda::std::is_integral_v<T>) {
       return x;
-    } else if constexpr (is_complex_v<T>) {
-      return T{ceil(x.real()), ceil(x.imag())};
     } else {
       return ceil(x);
     }
@@ -83,24 +91,35 @@ struct Ceil {
 };
 
 struct Conjugate {
-  template <typename T>
+  __device__ cuComplex operator()(cuComplex x) {
-  __device__ complex_t<T> operator()(complex_t<T> x) {
+    return {cuCrealf(x), -cuCimagf(x)};
-    return conj(x);
   }
 };
 
 struct Cos {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          cos(cuCrealf(x)) * cosh(cuCimagf(x)),
+          -sin(cuCrealf(x)) * sinh(cuCimagf(x))};
+    } else {
       return cos(x);
     }
+  }
 };
 
 struct Cosh {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          cosh(cuCrealf(x)) * cos(cuCimagf(x)),
+          sinh(cuCrealf(x)) * sin(cuCimagf(x))};
+    } else {
       return cosh(x);
     }
+  }
 };
 
 struct Erf {
@@ -132,8 +151,12 @@ struct ErfInv {
 struct Exp {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return detail::cexpf(x);
+    } else {
       return exp(x);
     }
+  }
 };
 
 struct Expm1 {
@@ -154,8 +177,6 @@ struct Floor {
   __device__ T operator()(T x) {
     if constexpr (cuda::std::is_integral_v<T>) {
       return x;
-    } else if constexpr (is_complex_v<T>) {
-      return T{floor(x.real()), floor(x.imag())};
     } else {
       return floor(x);
     }
@@ -163,25 +184,30 @@ struct Floor {
 };
 
 struct Imag {
-  template <typename T>
+  __device__ float operator()(cuComplex x) {
-  __device__ auto operator()(complex_t<T> x) {
+    return cuCimagf(x);
-    return x.imag();
   }
 };
 
 struct Log {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      auto r = log(cuCrealf(Abs{}(x)));
+      auto i = atan2f(cuCimagf(x), cuCrealf(x));
+      return {r, i};
+    } else {
       return log(x);
     }
+  }
 };
 
 struct Log2 {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
       auto y = Log{}(x);
-      return {y.real() / CUDART_LN2_F, y.imag() / CUDART_LN2_F};
+      return {cuCrealf(y) / CUDART_LN2_F, cuCimagf(y) / CUDART_LN2_F};
     } else {
       return log2(x);
     }
@@ -191,17 +217,23 @@ struct Log2 {
 struct Log10 {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      auto y = Log{}(x);
+      return {cuCrealf(y) / CUDART_LNT_F, cuCimagf(y) / CUDART_LNT_F};
+      return y;
+    } else {
       return log10(x);
     }
+  }
 };
 
 struct Log1p {
   template <typename T>
   __device__ T operator()(T z) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      float x = z.real();
+      float x = cuCrealf(z);
-      float y = z.imag();
+      float y = cuCimagf(z);
-      float zabs = Abs{}(z).real();
+      float zabs = cuCrealf(Abs{}(z));
       float theta = atan2f(y, x + 1);
       if (zabs < 0.5f) {
         float r = x * (2 + x) + y * y;
@@ -228,8 +260,8 @@ struct LogicalNot {
 struct Negative {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return T{0, 0} - x;
+      return 0 - x;
     } else {
       return -x;
     }
@@ -237,17 +269,16 @@ struct Negative {
 };
 
 struct Real {
-  template <typename T>
+  __device__ float operator()(cuComplex x) {
-  __device__ auto operator()(complex_t<T> x) {
+    return cuCrealf(x);
-    return x.real();
   }
 };
 
 struct Round {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {rint(x.real()), rint(x.imag())};
+      return {rint(cuCrealf(x)), rint(cuCimagf(x))};
     } else {
       return rint(x);
     }
@@ -267,8 +298,8 @@ struct Sign {
   __device__ T operator()(T x) {
     if constexpr (cuda::std::is_unsigned_v<T>) {
       return x != 0;
-    } else if constexpr (is_complex_v<T>) {
+    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (x.real() == 0 && x.imag() == 0) {
+      if (cuCrealf(x) == 0 && cuCimagf(x) == 0) {
         return x;
       } else {
         return x / Abs()(x);
@@ -284,15 +315,27 @@ struct Sign {
 struct Sin {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          sin(cuCrealf(x)) * cosh(cuCimagf(x)),
+          cos(cuCrealf(x)) * sinh(cuCimagf(x))};
+    } else {
       return sin(x);
     }
+  }
 };
 
 struct Sinh {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      return {
+          sinh(cuCrealf(x)) * cos(cuCimagf(x)),
+          cosh(cuCrealf(x)) * sin(cuCimagf(x))};
+    } else {
       return sinh(x);
     }
+  }
 };
 
 struct Square {
@@ -307,31 +350,77 @@ struct Sqrt {
   __device__ T operator()(T x) {
     return sqrt(x);
   }
+
+  __device__ cuComplex operator()(cuComplex x) {
+    auto xr = cuCrealf(x);
+    auto xi = cuCimagf(x);
+    if (xr == 0.0f && xi == 0.0f) {
+      return {0.0f, 0.0f};
+    }
+    auto r = cuCrealf(Abs{}(x));
+    auto a = sqrt((r + xr) / 2.0f);
+    auto b_abs = sqrt((r - xr) / 2.0f);
+    auto b = copysign(b_abs, xi);
+    return {a, b};
+  }
 };
 
 struct Rsqrt {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (is_complex_v<T>) {
-      return 1.0f / Sqrt{}(x);
-    } else {
     return rsqrt(x);
   }
+  __device__ cuComplex operator()(cuComplex x) {
+    return 1.0f / Sqrt{}(x);
   }
 };
 
 struct Tan {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      float tan_a = tan(cuCrealf(x));
+      float tanh_b = tanh(cuCimagf(x));
+      float t1 = tan_a * tanh_b;
+      float denom = 1. + t1 * t1;
+      return {(tan_a - tanh_b * t1) / denom, (tanh_b + tan_a * t1) / denom};
+    } else {
       return tan(x);
     }
+  }
 };
 
 struct Tanh {
   template <typename T>
   __device__ T operator()(T x) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
+      float tanh_a = tanh(cuCrealf(x));
+      float tan_b = tan(cuCimagf(x));
+      float t1 = tanh_a * tan_b;
+      float denom = 1. + t1 * t1;
+      return {(tanh_a + tan_b * t1) / denom, (tan_b - tanh_a * t1) / denom};
+    } else {
       return tanh(x);
     }
+  }
+};
+
+inline __device__ cuComplex ArcCos::operator()(cuComplex x) {
+  auto i = cuComplex{0.0, 1.0};
+  auto y = Log{}(x + i * Sqrt{}(1.0 - x * x));
+  return {cuCimagf(y), -cuCrealf(y)};
+};
+
+inline __device__ cuComplex ArcSin::operator()(cuComplex x) {
+  auto i = cuComplex{0.0f, 1.0f};
+  auto y = Log{}(i * x + Sqrt{}(1.0f - x * x));
+  return {cuCimagf(y), -cuCrealf(y)};
+};
+
+inline __device__ cuComplex ArcTan::operator()(cuComplex x) {
+  auto i = cuComplex{0.0f, 1.0f};
+  auto ix = i * x;
+  return (1.0f / cuComplex{0.0f, 2.0f}) * Log{}((1.0f + ix) / (1.0f - ix));
 };
 
 } // namespace mlx::core::cu

mlx/backend/cuda/device/utils.cuh

@@ -8,9 +8,9 @@
 
 #pragma once
 
-#include "mlx/backend/cuda/device/complex.cuh"
 #include "mlx/backend/cuda/device/config.h"
 
+#include <cuComplex.h>
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 #include <cuda/std/array>
@@ -32,119 +32,23 @@ using Strides = cuda::std::array<int64_t, MAX_NDIM>;
 template <typename T, int N>
 struct alignas(sizeof(T) * N) AlignedVector {
   T val[N];
-
-  __device__ T& operator[](int i) {
-    return val[i];
-  }
-
-  __device__ T operator[](int i) const {
-    return val[i];
-  }
 };
 
-template <int N, typename T>
-inline __device__ bool is_aligned(T* x) {
-  return (reinterpret_cast<uintptr_t>(x) % (N * sizeof(T))) == 0;
-}
-
 template <int N, typename T>
 inline __device__ AlignedVector<T, N> load_vector(
     const T* ptr,
     uint32_t offset) {
-  if (is_aligned<N>(ptr)) {
   auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
   return from[offset];
-  } else {
-    AlignedVector<T, N> v;
-#pragma unroll
-    for (int i = 0; i < N; ++i) {
-      v[i] = ptr[offset * N + i];
-    }
-    return v;
-  }
-}
-
-template <int N, typename T, typename SizeT>
-inline __device__ AlignedVector<T, N>
-load_vector(const T* ptr, uint32_t offset, SizeT size, T fallback) {
-  if (is_aligned<N>(ptr) && (offset + 1) * N <= size) {
-    auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
-    return from[offset];
-  } else {
-    AlignedVector<T, N> v;
-#pragma unroll
-    for (int i = 0; i < N; ++i) {
-      v[i] = (N * offset + i) < size ? ptr[offset * N + i] : fallback;
-    }
-    return v;
-  }
-}
-
-template <int N, typename T, typename SizeT>
-inline __device__ AlignedVector<T, N> load_vector(
-    const T* ptr,
-    uint32_t offset,
-    SizeT size,
-    int64_t stride,
-    T fallback) {
-  if (is_aligned<N>(ptr) && stride == 1 && (offset + 1) * N <= size) {
-    auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
-    return from[offset];
-  } else {
-    AlignedVector<T, N> v;
-#pragma unroll
-    for (int i = 0; i < N; ++i) {
-      v[i] =
-          (N * offset + i) < size ? ptr[stride * (offset * N + i)] : fallback;
-    }
-    return v;
-  }
 }
 
 template <int N, typename T>
 inline __device__ void
 store_vector(T* ptr, uint32_t offset, const AlignedVector<T, N>& vec) {
-  if (is_aligned<N>(ptr)) {
   auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
   to[offset] = vec;
-  } else {
-#pragma unroll
-    for (int i = 0; i < N; ++i) {
-      ptr[offset * N + i] = vec[i];
-    }
-  }
 }
 
-template <int N, typename T, typename SizeT>
-inline __device__ void store_vector(
-    T* ptr,
-    uint32_t offset,
-    const AlignedVector<T, N>& vec,
-    SizeT size) {
-  if (is_aligned<N>(ptr) && (offset + 1) * N <= size) {
-    auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
-    to[offset] = vec;
-  } else {
-    for (int i = 0; (offset * N + i) < size && i < N; ++i) {
-      ptr[offset * N + i] = vec[i];
-    }
-  }
-}
-
-// Helper for accessing strided data.
-template <typename T>
-struct StridedIterator {
-  T it;
-  int64_t stride;
-
-  __host__ __device__ StridedIterator(T it, int64_t stride)
-      : it(it), stride(stride) {}
-
-  __host__ __device__ auto operator[](int i) const {
-    return it[i * stride];
-  }
-};
-
 ///////////////////////////////////////////////////////////////////////////////
 // Type limits utils
 ///////////////////////////////////////////////////////////////////////////////
@@ -223,13 +127,13 @@ struct Limits<bool> {
   }
 };
 
-template <typename T>
+template <>
-struct Limits<complex_t<T>> {
+struct Limits<cuComplex> {
-  static constexpr __host__ __device__ complex_t<T> max() {
+  static constexpr __host__ __device__ cuComplex max() {
-    return {Limits<T>::max(), Limits<T>::max()};
+    return {Limits<float>::max(), Limits<float>::max()};
   }
-  static constexpr __host__ __device__ complex_t<T> min() {
+  static constexpr __host__ __device__ cuComplex min() {
-    return {Limits<T>::min(), Limits<T>::min()};
+    return {Limits<float>::min(), Limits<float>::min()};
   }
 };
 
@@ -300,8 +204,20 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_nd(
   return cuda::std::make_tuple(a_loc, b_loc, c_loc);
 }
 
+// Optimized version when ndim is larger than 4.
 template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc(
+inline __host__ __device__ IdxT
+elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
+  IdxT loc = 0;
+  for (int i = ndim - 1; i >= 0; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+template <typename IdxT = int64_t>
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
     IdxT elem,
     const int* shape,
     const int64_t* a_strides,
@@ -319,7 +235,7 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc(
 }
 
 template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc(
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_4d(
     IdxT elem,
     const int* shape,
     const int64_t* a_strides,

mlx/backend/cuda/eval.cpp

@@ -19,6 +19,8 @@ void new_stream(Stream s) {
   cudaFree(nullptr);
   // Ensure the static stream objects get created.
   cu::get_command_encoder(s);
+  // The main thread is safe to free buffers.
+  cu::allocator().register_this_thread();
 }
 
 void eval(array& arr) {

mlx/backend/cuda/event.cu

@@ -90,6 +90,8 @@ bool CudaEvent::completed() const {
 // SharedEvent implementations
 ///////////////////////////////////////////////////////////////////////////////
 
+namespace {
+
 __host__ __device__ void event_wait(SharedEvent::Atomic* ac, uint64_t value) {
   uint64_t current;
   while ((current = ac->load()) < value) {
@@ -110,26 +112,26 @@ __global__ void event_signal_kernel(SharedEvent::Atomic* ac, uint64_t value) {
   event_signal(ac, value);
 }
 
-SharedEvent::Atomic* to_atomic(std::shared_ptr<Buffer> buf) {
+} // namespace
-  return static_cast<SharedEvent::Atomic*>(buf->raw_ptr());
-}
 
 SharedEvent::SharedEvent() {
-  buf_ = std::shared_ptr<Buffer>(
+  // Allocate cuda::atomic on managed memory.
-      new Buffer{allocator().malloc(sizeof(Atomic))}, [](Buffer* ptr) {
+  Atomic* ac;
-        allocator().free(*ptr);
+  CHECK_CUDA_ERROR(cudaMallocManaged(&ac, sizeof(Atomic)));
-        delete ptr;
+  new (ac) Atomic(0);
+  ac_ = std::shared_ptr<Atomic>(ac, [](Atomic* ptr) {
+    ptr->~Atomic();
+    allocator().cuda_free(ptr);
   });
-  *static_cast<uint64_t*>(buf_->raw_ptr()) = 0;
 }
 
 void SharedEvent::wait(uint64_t value) {
   nvtx3::scoped_range r("cu::SharedEvent::wait");
-  event_wait(to_atomic(buf_), value);
+  event_wait(ac_.get(), value);
 }
 
 void SharedEvent::wait(cudaStream_t stream, uint64_t value) {
-  event_wait_kernel<<<1, 1, 0, stream>>>(to_atomic(buf_), value);
+  event_wait_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
 }
 
 void SharedEvent::wait(Stream s, uint64_t value) {
@@ -140,17 +142,17 @@ void SharedEvent::wait(Stream s, uint64_t value) {
     auto& encoder = get_command_encoder(s);
     encoder.commit();
     wait(encoder.stream(), value);
-    encoder.add_completed_handler([buf = buf_]() {});
+    encoder.add_completed_handler([ac = ac_]() {});
   }
 }
 
 void SharedEvent::signal(uint64_t value) {
   nvtx3::scoped_range r("cu::SharedEvent::signal");
-  event_signal(to_atomic(buf_), value);
+  event_signal(ac_.get(), value);
 }
 
 void SharedEvent::signal(cudaStream_t stream, uint64_t value) {
-  event_signal_kernel<<<1, 1, 0, stream>>>(to_atomic(buf_), value);
+  event_signal_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
 }
 
 void SharedEvent::signal(Stream s, uint64_t value) {
@@ -164,18 +166,18 @@ void SharedEvent::signal(Stream s, uint64_t value) {
     auto& encoder = get_command_encoder(s);
     encoder.commit();
     signal(encoder.stream(), value);
-    encoder.add_completed_handler([buf = buf_]() {});
+    encoder.add_completed_handler([ac = ac_]() {});
   }
 }
 
 bool SharedEvent::is_signaled(uint64_t value) const {
   nvtx3::scoped_range r("cu::SharedEvent::is_signaled");
-  return to_atomic(buf_)->load() >= value;
+  return ac_->load() >= value;
 }
 
 uint64_t SharedEvent::value() const {
   nvtx3::scoped_range r("cu::SharedEvent::value");
-  return to_atomic(buf_)->load();
+  return ac_->load();
 }
 
 } // namespace cu

mlx/backend/cuda/event.h

@@ -2,7 +2,6 @@
 
 #pragma once
 
-#include "mlx/allocator.h"
 #include "mlx/stream.h"
 
 #include <cuda_runtime.h>
@@ -56,8 +55,12 @@ class SharedEvent {
   bool is_signaled(uint64_t value) const;
   uint64_t value() const;
 
+  const std::shared_ptr<Atomic>& atomic() const {
+    return ac_;
+  }
+
  private:
-  std::shared_ptr<mlx::core::allocator::Buffer> buf_;
+  std::shared_ptr<Atomic> ac_;
 };
 
 } // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_batched_gemm_12_0.cpp

@@ -1,73 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-
-namespace mlx::core::cu {
-
-void Matmul::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides) {
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  auto nbatch = out.size() / (M_ * N_ * batch_shape.back());
-  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
-  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
-  auto concurrent = encoder.concurrent_context();
-  for (size_t i = 0; i < nbatch; ++i) {
-    run_impl(
-        encoder,
-        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
-        a.data<int8_t>() + a.itemsize() * a_it.loc,
-        b.data<int8_t>() + b.itemsize() * b_it.loc,
-        nullptr);
-    a_it.step();
-    b_it.step();
-  }
-}
-
-void Matmul::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const array& c,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides,
-    const mlx::core::Strides& c_batch_strides,
-    float alpha,
-    float beta) {
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-
-  auto nbatch = out.size() / (M_ * N_ * batch_shape.back());
-  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
-  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
-  ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
-  auto concurrent = encoder.concurrent_context();
-  for (size_t i = 0; i < nbatch; ++i) {
-    run_impl(
-        encoder,
-        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
-        a.data<int8_t>() + a.itemsize() * a_it.loc,
-        b.data<int8_t>() + b.itemsize() * b_it.loc,
-        c.data<int8_t>() + c.itemsize() * c_it.loc,
-        alpha,
-        beta);
-    a_it.step();
-    b_it.step();
-    c_it.step();
-  }
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_batched_gemm_12_9.cu

@@ -1,206 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-__global__ void set_mm_device_pointers(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ Shape batch_shape,
-    const __grid_constant__ Strides a_batch_strides,
-    const __grid_constant__ Strides b_batch_strides,
-    int64_t batch_stride,
-    int batch_ndim,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset] = elem_to_loc(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data(),
-      batch_ndim);
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-__global__ void set_addmm_device_pointers(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* c_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ Shape batch_shape,
-    const __grid_constant__ Strides a_batch_strides,
-    const __grid_constant__ Strides b_batch_strides,
-    const __grid_constant__ Strides c_batch_strides,
-    int64_t batch_stride,
-    int batch_ndim,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset, c_offset] = elem_to_loc(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data(),
-      c_batch_strides.data(),
-      batch_ndim);
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
-  pointers[index + 3 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-void set_pointer_mode(cublasLtMatrixLayout_t desc, int batch_count) {
-  auto batch_mode = CUBLASLT_BATCH_MODE_POINTER_ARRAY;
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc,
-      CUBLASLT_MATRIX_LAYOUT_BATCH_MODE,
-      &batch_mode,
-      sizeof(batch_mode)));
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(int32_t)));
-}
-
-void Matmul::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides) {
-  auto batch_count = out.size() / (M_ * N_);
-  set_pointer_mode(a_desc_, batch_count);
-  set_pointer_mode(b_desc_, batch_count);
-  set_pointer_mode(out_desc_, batch_count);
-
-  // Launch kernel to set device offsets
-  auto pointers = array(
-      allocator::malloc(batch_count * sizeof(uint64_t) * 3),
-      {static_cast<int>(batch_count * 3)},
-      uint64);
-
-  encoder.add_temporary(pointers);
-  int block_size = 512;
-  encoder.set_output_array(pointers);
-
-  encoder.add_kernel_node(
-      cu::set_mm_device_pointers,
-      cuda::ceil_div(pointers.size(), block_size),
-      block_size,
-      pointers.data<int8_t*>(),
-      a.data<int8_t>(),
-      b.data<int8_t>(),
-      out.data<int8_t>(),
-      static_cast<int>(out.dtype().size()),
-      const_param(batch_shape),
-      const_param(a_batch_strides),
-      const_param(b_batch_strides),
-      static_cast<int64_t>(M_) * N_,
-      static_cast<int>(batch_shape.size()),
-      batch_count);
-
-  // Run matmul
-  encoder.set_input_array(pointers);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-
-  auto a_pointers = pointers.data<int8_t*>();
-  auto b_pointers = a_pointers + batch_count;
-  auto out_pointers = b_pointers + batch_count;
-  run_impl(
-      encoder,
-      reinterpret_cast<void*>(out_pointers),
-      reinterpret_cast<void*>(a_pointers),
-      reinterpret_cast<void*>(b_pointers),
-      nullptr);
-}
-
-void Matmul::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const array& c,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides,
-    const mlx::core::Strides& c_batch_strides,
-    float alpha,
-    float beta) {
-  auto batch_count = out.size() / (M_ * N_);
-  set_pointer_mode(a_desc_, batch_count);
-  set_pointer_mode(b_desc_, batch_count);
-  set_pointer_mode(c_desc_, batch_count);
-  set_pointer_mode(out_desc_, batch_count);
-
-  // Launch kernel to set device offsets
-  auto pointers = array(
-      allocator::malloc(batch_count * sizeof(uint64_t) * 4),
-      {static_cast<int>(batch_count * 4)},
-      uint64);
-
-  encoder.add_temporary(pointers);
-  int block_size = 512;
-  encoder.set_output_array(pointers);
-  encoder.add_kernel_node(
-      cu::set_addmm_device_pointers,
-      cuda::ceil_div(pointers.size(), block_size),
-      block_size,
-      pointers.data<int8_t*>(),
-      a.data<int8_t>(),
-      b.data<int8_t>(),
-      c.data<int8_t>(),
-      out.data<int8_t>(),
-      static_cast<int>(out.dtype().size()),
-      const_param(batch_shape),
-      const_param(a_batch_strides),
-      const_param(b_batch_strides),
-      const_param(c_batch_strides),
-      static_cast<int64_t>(M_) * N_,
-      static_cast<int>(batch_shape.size()),
-      batch_count);
-
-  // Run matmul
-  encoder.set_input_array(pointers);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-
-  auto a_pointers = pointers.data<int8_t*>();
-  auto b_pointers = a_pointers + batch_count;
-  auto c_pointers = b_pointers + batch_count;
-  auto out_pointers = c_pointers + batch_count;
-  run_impl(
-      encoder,
-      reinterpret_cast<void*>(out_pointers),
-      reinterpret_cast<void*>(a_pointers),
-      reinterpret_cast<void*>(b_pointers),
-      reinterpret_cast<void*>(c_pointers),
-      alpha,
-      beta);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm.cpp

@@ -1,282 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/utils.h"
-
-#include <fmt/format.h>
-
-namespace mlx::core::cu {
-
-struct CublasPreference {
-  CublasPreference(Device& device) {
-    // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
-    // for Hopper+:
-    // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
-    uint64_t MiB = 1024 * 1024;
-    uint64_t workspace_size =
-        device.compute_capability_major() >= 9 ? 32 * MiB : 4 * MiB;
-
-    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceCreate(&pref_));
-    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceSetAttribute(
-        pref_,
-        CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
-        &workspace_size,
-        sizeof(uint64_t)));
-  }
-
-  ~CublasPreference() {
-    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceDestroy(pref_));
-  }
-
-  cublasLtMatmulPreference_t pref_{nullptr};
-};
-
-cublasLtMatmulPreference_t cublas_preference(Device& device) {
-  static CublasPreference pref(device);
-  return pref.pref_;
-}
-
-cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
-  switch (dtype) {
-    case float16:
-      return CUBLAS_COMPUTE_32F;
-    case bfloat16:
-      return CUBLAS_COMPUTE_32F;
-    case float32:
-      return mlx::core::env::enable_tf32() ? CUBLAS_COMPUTE_32F_FAST_TF32
-                                           : CUBLAS_COMPUTE_32F;
-    case float64:
-    case complex64:
-      return CUBLAS_COMPUTE_64F;
-    default:
-      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
-  }
-}
-
-cudaDataType_t dtype_to_cublas_type(Dtype dtype) {
-  switch (dtype) {
-    case float16:
-      return CUDA_R_16F;
-    case bfloat16:
-      return CUDA_R_16BF;
-    case float32:
-      return CUDA_R_32F;
-    case float64:
-      return CUDA_R_64F;
-    case complex64:
-      return CUDA_C_32F;
-    default:
-      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
-  }
-}
-
-cublasLtMatrixLayout_t create_matrix_layout(
-    cudaDataType_t type,
-    uint64_t rows,
-    uint64_t cols,
-    bool transposed,
-    int64_t ld,
-    int32_t batch_count,
-    int64_t batch_stride) {
-  cublasLtMatrixLayout_t desc;
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutCreate(&desc, type, rows, cols, ld));
-  cublasLtOrder_t order = transposed ? CUBLASLT_ORDER_COL : CUBLASLT_ORDER_ROW;
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order, sizeof(cublasLtOrder_t)));
-  if (batch_count > 1) {
-    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-        desc,
-        CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT,
-        &batch_count,
-        sizeof(int32_t)));
-    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-        desc,
-        CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET,
-        &batch_stride,
-        sizeof(int64_t)));
-  }
-  return desc;
-}
-
-Matmul::Matmul(
-    Device& device,
-    Dtype dtype,
-    bool a_transposed,
-    uint64_t a_rows,
-    uint64_t a_cols,
-    int64_t lda,
-    bool b_transposed,
-    uint64_t b_rows,
-    uint64_t b_cols,
-    int64_t ldb,
-    int32_t batch_count,
-    int64_t a_batch_stride,
-    int64_t b_batch_stride)
-    : handle_(device.lt_handle()),
-      pref_(cublas_preference(device)),
-      M_(a_rows),
-      N_(b_cols) {
-  heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
-
-  auto scale_type = dtype_to_cublas_type(dtype);
-  if (dtype == bfloat16 || dtype == float16) {
-    scale_type = CUDA_R_32F;
-  }
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
-      &matmul_desc_, dtype_to_compute_type(dtype), scale_type));
-  int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_,
-      CUBLASLT_MATMUL_DESC_POINTER_MODE,
-      &pointer_mode,
-      sizeof(int32_t)));
-  cublasOperation_t op = CUBLAS_OP_N;
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_,
-      CUBLASLT_MATMUL_DESC_TRANSA,
-      &op,
-      sizeof(cublasOperation_t)));
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_,
-      CUBLASLT_MATMUL_DESC_TRANSB,
-      &op,
-      sizeof(cublasOperation_t)));
-
-  auto type = dtype_to_cublas_type(dtype);
-  a_desc_ = create_matrix_layout(
-      type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
-  b_desc_ = create_matrix_layout(
-      type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
-  out_desc_ = create_matrix_layout(
-      type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
-}
-
-Matmul::Matmul(
-    Device& device,
-    Dtype dtype,
-    bool a_transposed,
-    uint64_t a_rows,
-    uint64_t a_cols,
-    int64_t lda,
-    bool b_transposed,
-    uint64_t b_rows,
-    uint64_t b_cols,
-    int64_t ldb,
-    int64_t ldc,
-    int32_t batch_count,
-    int64_t a_batch_stride,
-    int64_t b_batch_stride,
-    int64_t c_batch_stride)
-    : Matmul(
-          device,
-          dtype,
-          a_transposed,
-          a_rows,
-          a_cols,
-          lda,
-          b_transposed,
-          b_rows,
-          b_cols,
-          ldb,
-          batch_count,
-          a_batch_stride,
-          b_batch_stride) {
-  auto type = dtype_to_cublas_type(dtype);
-  c_desc_ = create_matrix_layout(
-      type, a_rows, b_cols, false, ldc, batch_count, c_batch_stride);
-}
-
-Matmul::~Matmul() {
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(a_desc_));
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(b_desc_));
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(c_desc_));
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(out_desc_));
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescDestroy(matmul_desc_));
-}
-
-void Matmul::run_impl(
-    cu::CommandEncoder& encoder,
-    void* out,
-    const void* a,
-    const void* b,
-    const void* c,
-    float alpha /* = 1 */,
-    float beta /* = 0 */) {
-  if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
-    int ret = 0;
-    CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
-        handle_,
-        matmul_desc_,
-        a_desc_,
-        b_desc_,
-        out_desc_, // TODO should that be c_desc is it's set?
-        out_desc_,
-        pref_,
-        1,
-        &heuristic_,
-        &ret));
-    if (ret == 0) {
-      throw std::runtime_error("Can not find algorithm for matmul.");
-    }
-  }
-
-  void* workspace_ptr = nullptr;
-  if (heuristic_.workspaceSize > 0) {
-    array workspace(
-        allocator::malloc(heuristic_.workspaceSize),
-        {static_cast<int>(heuristic_.workspaceSize)},
-        int8);
-    encoder.add_temporary(workspace);
-    workspace_ptr = workspace.data<void>();
-  }
-
-  auto capture = encoder.capture_context();
-  CHECK_CUBLAS_ERROR(cublasLtMatmul(
-      handle_,
-      matmul_desc_,
-      &alpha,
-      a,
-      a_desc_,
-      b,
-      b_desc_,
-      &beta,
-      c ? c : out,
-      c ? c_desc_ : out_desc_,
-      out,
-      out_desc_,
-      &heuristic_.algo,
-      workspace_ptr,
-      heuristic_.workspaceSize,
-      encoder.stream()));
-}
-
-void Matmul::run(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const std::optional<array>& c /* = std::nullopt */,
-    float alpha /* = 1 */,
-    float beta /* = 0 */) {
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  if (c) {
-    encoder.set_input_array(*c);
-  }
-  encoder.set_output_array(out);
-
-  run_impl(
-      encoder,
-      out.data<void>(),
-      a.data<void>(),
-      b.data<void>(),
-      c ? c->data<void>() : nullptr,
-      alpha,
-      beta);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm.h

@@ -1,100 +0,0 @@
-// Copyright © 2025 Apple Inc.
-#pragma once
-
-#include "mlx/array.h"
-#include "mlx/backend/cuda/device.h"
-
-#include <cublasLt.h>
-#include <optional>
-
-namespace mlx::core::cu {
-class Matmul {
- public:
-  Matmul(
-      Device& device,
-      Dtype dtype,
-      bool a_transposed,
-      uint64_t a_rows,
-      uint64_t a_cols,
-      int64_t lda,
-      bool b_transposed,
-      uint64_t b_rows,
-      uint64_t b_cols,
-      int64_t ldb,
-      int32_t batch_count,
-      int64_t a_batch_stride,
-      int64_t b_batch_stride);
-
-  Matmul(
-      Device& device,
-      Dtype dtype,
-      bool a_transposed,
-      uint64_t a_rows,
-      uint64_t a_cols,
-      int64_t lda,
-      bool b_transposed,
-      uint64_t b_rows,
-      uint64_t b_cols,
-      int64_t ldb,
-      int64_t ldc,
-      int32_t batch_count,
-      int64_t a_batch_stride,
-      int64_t b_batch_stride,
-      int64_t c_batch_stride);
-
-  ~Matmul();
-
-  void run(
-      cu::CommandEncoder& encoder,
-      array& out,
-      const array& a,
-      const array& b,
-      const std::optional<array>& c = std::nullopt,
-      float alpha = 1,
-      float beta = 0);
-
-  void run_batched(
-      cu::CommandEncoder& encoder,
-      array& out,
-      const array& a,
-      const array& b,
-      const mlx::core::Shape& batch_shape,
-      const mlx::core::Strides& a_batch_strides,
-      const mlx::core::Strides& b_batch_strides);
-
-  void run_batched(
-      cu::CommandEncoder& encoder,
-      array& out,
-      const array& a,
-      const array& b,
-      const array& c,
-      const mlx::core::Shape& batch_shape,
-      const mlx::core::Strides& a_batch_strides,
-      const mlx::core::Strides& b_batch_strides,
-      const mlx::core::Strides& c_batch_strides,
-      float alpha,
-      float beta);
-
- private:
-  void run_impl(
-      cu::CommandEncoder& encoder,
-      void* out,
-      const void* a,
-      const void* b,
-      const void* c,
-      float alpha = 1,
-      float beta = 0);
-
-  uint64_t M_;
-  uint64_t N_;
-  cublasLtMatmulPreference_t pref_{nullptr};
-  cublasLtHandle_t handle_{nullptr};
-  cublasLtMatmulDesc_t matmul_desc_{nullptr};
-  cublasLtMatrixLayout_t a_desc_{nullptr};
-  cublasLtMatrixLayout_t b_desc_{nullptr};
-  cublasLtMatrixLayout_t c_desc_{nullptr};
-  cublasLtMatrixLayout_t out_desc_{nullptr};
-  cublasLtMatmulHeuristicResult_t heuristic_;
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/gemv.cu

@@ -1,166 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/gemms/gemv.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-static constexpr int rows_per_block = 8;
-
-template <typename T, int rows_per_block, int n_per_thread>
-__device__ void
-gemv_impl(const T* mat, const T* vec, T* out, int rows, int cols) {
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  auto g_idx = block.group_index();
-  auto t_idx = block.thread_index();
-  int row = g_idx.x * rows_per_block + t_idx.y;
-
-  if (row < rows) {
-    float sum = 0.0f;
-    for (int col = n_per_thread * warp.thread_rank(); col < cols;
-         col += (WARP_SIZE * n_per_thread)) {
-      auto local_mat = load_vector<n_per_thread>(mat + row * cols + col, 0);
-      auto local_vec = load_vector<n_per_thread>(vec + col, 0);
-#pragma unroll
-      for (int j = 0; j < n_per_thread; ++j) {
-        sum +=
-            static_cast<float>(local_mat[j]) * static_cast<float>(local_vec[j]);
-      }
-    }
-
-    sum = cg::reduce(warp, sum, cg::plus<float>{});
-    if (warp.thread_rank() == 0) {
-      out[row] = static_cast<T>(sum);
-    }
-  }
-}
-
-template <typename T, int rows_per_block, int n_per_thread>
-__global__ void
-gemv_single(const T* mat, const T* vec, T* out, int rows, int cols) {
-  gemv_impl<T, rows_per_block, n_per_thread>(mat, vec, out, rows, cols);
-}
-
-template <typename T, int rows_per_block, int n_per_thread>
-__global__ void gemv_batched(
-    const T* mat,
-    const T* vec,
-    T* out,
-    int rows,
-    int cols,
-    const __grid_constant__ Shape batch_shape,
-    const __grid_constant__ Strides mat_batch_strides,
-    const __grid_constant__ Strides vec_batch_strides,
-    int batch_ndim) {
-  auto block = cg::this_thread_block();
-  auto batch_idx = block.group_index().y;
-  auto [vec_offset, mat_offset] = elem_to_loc(
-      batch_idx,
-      batch_shape.data(),
-      vec_batch_strides.data(),
-      mat_batch_strides.data(),
-      batch_ndim);
-  gemv_impl<T, rows_per_block, n_per_thread>(
-      mat + mat_offset, vec + vec_offset, out + batch_idx * rows, rows, cols);
-}
-
-bool can_use_gemv(int M, int N, int K, bool a_transposed, bool b_transposed) {
-  return K % 32 == 0 && ((M == 1 && b_transposed) || (N == 1 && !a_transposed));
-}
-
-template <typename F>
-void dispatch_n_per_thread(int n_per_thread, F&& f) {
-  switch (n_per_thread) {
-    case 1:
-      f(std::integral_constant<int, 1>{});
-      break;
-    case 2:
-      f(std::integral_constant<int, 2>{});
-      break;
-    case 4:
-      f(std::integral_constant<int, 4>{});
-      break;
-  }
-}
-
-void gemv(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    uint32_t batch_count,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides,
-    CommandEncoder& encoder) {
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "gemv", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    dim3 block_dims{WARP_SIZE, rows_per_block};
-    const DataType* mat;
-    const DataType* vec;
-    int rows;
-    int cols = K;
-    auto mat_strides = const_param(a_batch_strides);
-    auto vec_strides = const_param(b_batch_strides);
-
-    if (M == 1) {
-      mat = b.data<DataType>();
-      vec = a.data<DataType>();
-      rows = N;
-      std::swap(mat_strides, vec_strides);
-    } else {
-      mat = a.data<DataType>();
-      vec = b.data<DataType>();
-      rows = M;
-    }
-    uint32_t num_blocks_x = (rows + rows_per_block - 1) / rows_per_block;
-    int n_per_t = 4;
-    while (K % (n_per_t * WARP_SIZE) != 0) {
-      n_per_t >>= 1;
-    }
-    dispatch_n_per_thread(n_per_t, [&](auto n_per_thread) {
-      if (batch_count == 1) {
-        auto kernel = gemv_single<DataType, rows_per_block, n_per_thread()>;
-        encoder.add_kernel_node(
-            kernel,
-            num_blocks_x,
-            block_dims,
-            mat,
-            vec,
-            out.data<DataType>(),
-            rows,
-            cols);
-      } else {
-        auto kernel = gemv_batched<DataType, rows_per_block, n_per_thread()>;
-        encoder.add_kernel_node(
-            kernel,
-            dim3{num_blocks_x, batch_count},
-            block_dims,
-            mat,
-            vec,
-            out.data<DataType>(),
-            rows,
-            cols,
-            const_param(batch_shape),
-            mat_strides,
-            vec_strides,
-            batch_shape.size());
-      }
-    });
-  });
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/gemv.h

@@ -1,24 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device.h"
-
-namespace mlx::core::cu {
-
-bool can_use_gemv(int M, int N, int K, bool a_transposed, bool b_transposed);
-
-void gemv(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    uint32_t batch_count,
-    const mlx::core::Shape& batch_shape,
-    const mlx::core::Strides& a_batch_strides,
-    const mlx::core::Strides& b_batch_strides,
-    CommandEncoder& encoder);
-
-} // namespace mlx::core::cu

mlx/backend/cuda/indexing.cpp

@@ -128,7 +128,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   encoder.set_output_array(out);
 
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(out, large);
+  auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
   encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }
 
@@ -229,7 +229,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(upd, large);
+  auto [num_blocks, block_dims] = get_launch_args(kernel, upd, large);
   encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }
 
@@ -317,7 +317,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(idx, large);
+  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
   encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }
 
@@ -421,7 +421,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(idx, large);
+  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
   encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }
 

mlx/backend/cuda/iterators/general_iterator.cuh

@@ -0,0 +1,121 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <thrust/iterator/iterator_adaptor.h>
+#include <cuda/std/utility>
+
+#include "mlx/backend/cuda/kernel_utils.cuh"
+
+namespace mlx::core::cu {
+
+// Iterating non-contiguous array.
+template <typename Iterator, typename IdxT = int64_t>
+class general_iterator
+    : public thrust::
+          iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator> {
+ public:
+  using super_t =
+      thrust::iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator>;
+
+  using reference = typename super_t::reference;
+  using difference_type = typename super_t::difference_type;
+
+  __host__ __device__ general_iterator(
+      Iterator it,
+      IdxT index,
+      int ndim,
+      Shape shape,
+      Strides strides)
+      : super_t(it),
+        index_(index),
+        ndim_(ndim),
+        shape_(cuda::std::move(shape)),
+        strides_(cuda::std::move(strides)) {}
+
+  __host__ __device__ IdxT index() const {
+    return index_;
+  }
+
+  __host__ __device__ const Shape& shape() const {
+    return shape_;
+  }
+
+  __host__ __device__ const Strides& strides() const {
+    return strides_;
+  }
+
+ private:
+  friend class thrust::iterator_core_access;
+
+  __host__ __device__ bool equal(const general_iterator& other) const {
+    return this->base() == other.base() && this->index() == other.index();
+  }
+
+  __host__ __device__ void advance(difference_type n) {
+    this->index_ += n;
+  }
+
+  __host__ __device__ void increment() {
+    this->index_ += 1;
+  }
+
+  __host__ __device__ void decrement() {
+    this->index_ -= 1;
+  }
+
+  __host__ __device__ difference_type
+  distance_to(const general_iterator& other) const {
+    _CCCL_ASSERT(
+        this->base() == other.base(),
+        "Underlying iterator must point to same base iterator");
+    return other.index() - this->index();
+  }
+
+  // The dereference is device-only to avoid accidental running in host.
+  __device__ typename super_t::reference dereference() const {
+    IdxT offset = elem_to_loc(index_, shape_.data(), strides_.data(), ndim_);
+    return *(this->base() + offset);
+  }
+
+  IdxT index_;
+  int ndim_;
+  Shape shape_;
+  Strides strides_;
+};
+
+template <typename IdxT, typename Iterator>
+__host__ __device__ auto make_general_iterator(
+    Iterator it,
+    IdxT index,
+    int ndim,
+    Shape shape,
+    Strides strides) {
+  return general_iterator<Iterator, IdxT>(
+      it, index, ndim, cuda::std::move(shape), cuda::std::move(strides));
+}
+
+template <typename IdxT, typename Iterator>
+auto make_general_iterator(
+    Iterator it,
+    const std::vector<int32_t>& shape,
+    const std::vector<int64_t>& strides) {
+  return make_general_iterator<IdxT>(
+      it, 0, shape.size(), const_param(shape), const_param(strides));
+}
+
+template <typename IdxT, typename Iterator>
+auto make_general_iterators(
+    Iterator it,
+    IdxT size,
+    const std::vector<int32_t>& shape,
+    const std::vector<int64_t>& strides) {
+  auto ndim = shape.size();
+  auto shape_arg = const_param(shape);
+  auto strides_arg = const_param(strides);
+  return std::make_pair(
+      make_general_iterator<IdxT>(it, 0, ndim, shape_arg, strides_arg),
+      make_general_iterator<IdxT>(it, size, ndim, shape_arg, strides_arg));
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/iterators/strided_iterator.cuh

@@ -0,0 +1,60 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <thrust/iterator/iterator_adaptor.h>
+#include <thrust/iterator/iterator_facade.h>
+
+namespace mlx::core::cu {
+
+// RandomAccessIterator for strided access to array entries.
+template <typename Iterator, typename Stride = int64_t>
+class strided_iterator
+    : public thrust::
+          iterator_adaptor<strided_iterator<Iterator, Stride>, Iterator> {
+ public:
+  using super_t =
+      thrust::iterator_adaptor<strided_iterator<Iterator, Stride>, Iterator>;
+
+  using reference = typename super_t::reference;
+  using difference_type = typename super_t::difference_type;
+
+  __host__ __device__ strided_iterator(Iterator it, Stride stride)
+      : super_t(it), stride_(stride) {}
+
+  __host__ __device__ Stride stride() const {
+    return stride_;
+  }
+
+ private:
+  friend class thrust::iterator_core_access;
+
+  __host__ __device__ bool equal(const strided_iterator& other) const {
+    return this->base() == other.base();
+  }
+
+  __host__ __device__ void advance(difference_type n) {
+    this->base_reference() += n * stride_;
+  }
+
+  __host__ __device__ void increment() {
+    this->base_reference() += stride_;
+  }
+
+  __host__ __device__ void decrement() {
+    this->base_reference() -= stride_;
+  }
+
+  __host__ __device__ difference_type
+  distance_to(const strided_iterator& other) const {
+    const difference_type dist = other.base() - this->base();
+    _CCCL_ASSERT(
+        dist % stride() == 0,
+        "Underlying iterator difference must be divisible by the stride");
+    return dist / stride();
+  }
+
+  Stride stride_;
+};
+
+} // namespace mlx::core::cu

mlx/backend/cuda/jit_module.cpp

@@ -9,10 +9,10 @@
 #include <cstdlib>
 #include <filesystem>
 #include <fstream>
+#include <unordered_map>
 
 #include <fmt/format.h>
 #include <nvrtc.h>
-#include <unistd.h>
 
 namespace mlx::core::cu {
 
@@ -50,32 +50,6 @@ const std::string& cuda_home() {
   return home;
 }
 
-// Return the location of CCCL headers shipped with the distribution.
-const std::string& cccl_dir() {
-  static std::string dir = []() {
-    std::filesystem::path path;
-#if defined(MLX_CCCL_DIR)
-    // First search the install dir if defined.
-    path = MLX_CCCL_DIR;
-    if (std::filesystem::exists(path)) {
-      return path.string();
-    }
-#endif
-    // Then search dynamically from the dir of libmlx.so file.
-    path = current_binary_dir().parent_path() / "include" / "cccl";
-    if (std::filesystem::exists(path)) {
-      return path.string();
-    }
-    // Finally check the environment variable.
-    path = std::getenv("MLX_CCCL_DIR");
-    if (!path.empty() && std::filesystem::exists(path)) {
-      return path.string();
-    }
-    return std::string();
-  }();
-  return dir;
-}
-
 // Get the cache directory for storing compiled results.
 const std::filesystem::path& ptx_cache_dir() {
   static std::filesystem::path cache = []() -> std::filesystem::path {
@@ -136,8 +110,7 @@ void write_cached_ptx(
     const std::filesystem::path& cache_dir,
     const std::string& module_name,
     const std::vector<char>& ptx,
-    const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
+    const std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
-    const std::string& source_code) {
   if (cache_dir.empty()) {
     return;
   }
@@ -150,9 +123,6 @@ void write_cached_ptx(
   for (const auto& [name, mangled] : ptx_kernels) {
     txt_file << name << "\t" << mangled << std::endl;
   }
-
-  std::ofstream source_file(cache_dir / (module_name + ".cu"));
-  source_file << source_code;
 }
 
 // Return if |device|'s version is not newer than |major|.|minor| version.
@@ -191,8 +161,9 @@ constexpr const char* g_include_names[] = {
     INCLUDE_PREFIX "atomic_ops.cuh",
     INCLUDE_PREFIX "binary_ops.cuh",
     INCLUDE_PREFIX "cast_op.cuh",
+    INCLUDE_PREFIX "cexpf.cuh",
     INCLUDE_PREFIX "config.h",
-    INCLUDE_PREFIX "complex.cuh",
+    INCLUDE_PREFIX "cucomplex_math.cuh",
     INCLUDE_PREFIX "fp16_math.cuh",
     INCLUDE_PREFIX "indexing.cuh",
     INCLUDE_PREFIX "scatter_ops.cuh",
@@ -207,8 +178,9 @@ constexpr const char* g_headers[] = {
     jit_source_atomic_ops,
     jit_source_binary_ops,
     jit_source_cast_op,
+    jit_source_cexpf,
     jit_source_config,
-    jit_source_complex,
+    jit_source_cucomplex_math,
     jit_source_fp16_math,
     jit_source_indexing,
     jit_source_scatter_ops,
@@ -245,24 +217,16 @@ JitModule::JitModule(
     }
 
     // Compile program.
-    std::vector<const char*> args;
     bool use_sass = compiler_supports_device_sass(device);
     std::string compute = fmt::format(
         "--gpu-architecture={}_{}{}",
         use_sass ? "sm" : "compute",
         device.compute_capability_major(),
         device.compute_capability_minor());
-    args.push_back(compute.c_str());
+    std::string include = fmt::format("--include-path={}/include", cuda_home());
-    std::string cccl_include = cccl_dir();
+    const char* args[] = {compute.c_str(), include.c_str()};
-    if (!cccl_include.empty()) {
-      cccl_include = fmt::format("--include-path={}", cccl_include);
-      args.push_back(cccl_include.c_str());
-    }
-    std::string cuda_include =
-        fmt::format("--include-path={}/include", cuda_home());
-    args.push_back(cuda_include.c_str());
     nvrtcResult compile_result =
-        nvrtcCompileProgram(prog, args.size(), args.data());
+        nvrtcCompileProgram(prog, std::size(args), args);
     if (compile_result != NVRTC_SUCCESS) {
       size_t log_size;
       CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
@@ -292,8 +256,7 @@ JitModule::JitModule(
     } else {
       CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
     }
-    write_cached_ptx(
+    write_cached_ptx(ptx_cache_dir(), module_name, ptx, ptx_kernels);
-        ptx_cache_dir(), module_name, ptx, ptx_kernels, source_code);
   }
 
   // Load module.
@@ -329,16 +292,11 @@ CUfunction JitModule::get_kernel(const std::string& kernel_name) {
   return it->second;
 }
 
-std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
-  static std::unordered_map<std::string, JitModule> map;
-  return map;
-}
-
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,
     const KernelBuilder& builder) {
-  auto& map = get_jit_module_cache();
+  static std::unordered_map<std::string, JitModule> map;
   auto it = map.find(name);
   if (it == map.end()) {
     it = map.try_emplace(name, cu::device(device), name, builder).first;

mlx/backend/cuda/jit_module.h

@@ -99,8 +99,6 @@ class JitModule {
   std::unordered_map<std::string, CUfunction> kernels_;
 };
 
-std::unordered_map<std::string, JitModule>& get_jit_module_cache();
-
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,

mlx/backend/cuda/kernel_utils.cu

@@ -30,25 +30,4 @@ std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2) {
   return std::make_pair(dim3(gx, gy, gz), dim3(bx, by, bz));
 }
 
-std::tuple<dim3, uint> get_launch_args(
-    size_t size,
-    const Shape& shape,
-    const Strides& strides,
-    bool large,
-    int work_per_thread) {
-  size_t nthreads = cuda::ceil_div(size, work_per_thread);
-  uint block_dim = 1024;
-  if (block_dim > nthreads) {
-    block_dim = nthreads;
-  }
-  dim3 num_blocks;
-  if (large) {
-    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
-    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
-  } else {
-    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
-  }
-  return std::make_tuple(num_blocks, block_dim);
-}
-
 } // namespace mlx::core

mlx/backend/cuda/kernel_utils.cuh

@@ -11,6 +11,7 @@
 #include "mlx/array.h"
 #include "mlx/backend/cuda/device/utils.cuh"
 
+#include <cuComplex.h>
 #include <cuda.h>
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
@@ -78,7 +79,7 @@ struct CTypeToCudaType<bfloat16_t> {
 
 template <>
 struct CTypeToCudaType<complex64_t> {
-  using type = cu::complex64_t;
+  using type = cuComplex;
 };
 
 template <typename T>
@@ -90,14 +91,10 @@ inline constexpr bool is_floating_v =
     cuda::std::is_same_v<T, float> || cuda::std::is_same_v<T, double> ||
     cuda::std::is_same_v<T, float16_t> || cuda::std::is_same_v<T, bfloat16_t>;
 
-// Type traits for detecting complex numbers.
-template <typename T>
-inline constexpr bool is_complex_v = cuda::std::is_same_v<T, complex64_t> ||
-    cuda::std::is_same_v<T, complex128_t>;
-
 // Type traits for detecting complex or real floating point numbers.
 template <typename T>
-inline constexpr bool is_inexact_v = is_floating_v<T> || is_complex_v<T>;
+inline constexpr bool is_inexact_v =
+    is_floating_v<T> || cuda::std::is_same_v<T, complex64_t>;
 
 // Utility to copy data from vector to array in host.
 template <int NDIM = MAX_NDIM, typename T = int32_t>
@@ -120,19 +117,53 @@ dim3 get_2d_grid_dims(
     size_t divisor);
 std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2);
 
+// Return a block size that achieves maximum potential occupancy for kernel.
+template <typename T>
+inline uint max_occupancy_block_dim(T kernel) {
+  int _, block_dim;
+  if constexpr (std::is_same_v<T, CUfunction>) {
+    CHECK_CUDA_ERROR(
+        cuOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel, 0, 0, 0));
+  } else {
+    CHECK_CUDA_ERROR(
+        cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
+  }
+  return block_dim;
+}
+
 // Get the num_blocks and block_dims that maximize occupancy for |kernel|,
 // assuming each thread handles |work_per_thread| elements of |arr|.
-std::tuple<dim3, uint> get_launch_args(
+template <typename T>
+inline std::tuple<dim3, uint> get_launch_args(
+    T kernel,
     size_t size,
     const Shape& shape,
     const Strides& strides,
     bool large,
-    int work_per_thread = 1);
+    int work_per_thread = 1) {
+  size_t nthreads = cuda::ceil_div(size, work_per_thread);
+  uint block_dim = max_occupancy_block_dim(kernel);
+  if (block_dim > nthreads) {
+    block_dim = nthreads;
+  }
+  dim3 num_blocks;
+  if (large) {
+    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
+    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
+  } else {
+    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
+  }
+  return std::make_tuple(num_blocks, block_dim);
+}
 
-inline std::tuple<dim3, uint>
+template <typename T>
-get_launch_args(const array& arr, bool large, int work_per_thread = 1) {
+inline std::tuple<dim3, uint> get_launch_args(
+    T kernel,
+    const array& arr,
+    bool large,
+    int work_per_thread = 1) {
   return get_launch_args(
-      arr.size(), arr.shape(), arr.strides(), large, work_per_thread);
+      kernel, arr.size(), arr.shape(), arr.strides(), large, work_per_thread);
 }
 
 } // namespace mlx::core

mlx/backend/cuda/layer_norm.cu

@@ -1,6 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/reduce/reduce.cuh"
 #include "mlx/backend/gpu/copy.h"
@@ -10,6 +11,8 @@
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 #include <nvtx3/nvtx3.hpp>
+#include <cub/block/block_load.cuh>
+#include <cub/block/block_reduce.cuh>
 
 namespace mlx::core {
 
@@ -72,11 +75,9 @@ __global__ void layer_norm(
   float sum = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS] = {};
-#pragma unroll
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    for (int i = 0; i < N_READS; ++i) {
+    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
-      sum += static_cast<float>(xn[i]);
-    }
   }
   sum = BlockReduceT{block, temp}.Sum(sum);
 
@@ -87,19 +88,12 @@ __global__ void layer_norm(
   float normalizer = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    if ((index + 1) * N_READS <= axis_size) {
+    T xn[N_READS];
-      auto xn = load_vector<N_READS>(x, index);
+    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-#pragma unroll
     for (int i = 0; i < N_READS; ++i) {
       float t = static_cast<float>(xn[i]) - mean;
       normalizer += t * t;
     }
-    } else {
-      for (int i = index * N_READS; i < axis_size; ++i) {
-        float t = static_cast<float>(x[i]) - mean;
-        normalizer += t * t;
-      }
-    }
   }
   normalizer = BlockReduceT{block, temp}.Sum(normalizer);
   normalizer = rsqrt(normalizer / axis_size + eps);
@@ -107,15 +101,17 @@ __global__ void layer_norm(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS];
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    T wn[N_READS];
-    auto bn = load_vector<N_READS>(b, index, axis_size, b_stride, T(0));
+    T bn[N_READS];
-#pragma unroll
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(b, b_stride), bn, axis_size);
     for (int i = 0; i < N_READS; ++i) {
       float norm = (static_cast<float>(xn[i]) - mean) * normalizer;
       xn[i] = wn[i] * static_cast<T>(norm) + bn[i];
     }
-    store_vector<N_READS>(out, index, xn, axis_size);
+    cub::StoreDirectBlocked(index, out, xn, axis_size);
   }
 }
 
@@ -148,11 +144,9 @@ __global__ void layer_norm_vjp(
   float sum = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS] = {};
-#pragma unroll
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    for (int i = 0; i < N_READS; ++i) {
+    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
-      sum += static_cast<float>(xn[i]);
-    }
   }
   sum = BlockReduceF{block, temp.f}.Sum(sum);
 
@@ -162,29 +156,20 @@ __global__ void layer_norm_vjp(
   // Normalizer.
   float3 factors = {};
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    T xn[N_READS];
+    T wn[N_READS] = {};
+    T gn[N_READS] = {};
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
-
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    if ((index + 1) * N_READS <= axis_size) {
+    for (int i = 0; i < N_READS; i++) {
-      auto xn = load_vector<N_READS>(x, index);
-#pragma unroll
-      for (int i = 0; i < N_READS; ++i) {
       float t = static_cast<float>(xn[i]) - mean;
       float wi = wn[i];
       float gi = gn[i];
       float wg = wi * gi;
       factors = plus_f3(factors, {wg, wg * t, t * t});
     }
-    } else {
-      for (int i = index * N_READS; i < axis_size; ++i) {
-        float t = static_cast<float>(x[i]) - mean;
-        float wi = wn[i];
-        float gi = gn[i];
-        float wg = wi * gi;
-        factors = plus_f3(factors, {wg, wg * t, t * t});
-      }
-    }
   }
   factors = BlockReduceF3{block, temp.f3}.Reduce(factors, plus_f3, {});
   float meanwg = factors.x / axis_size;
@@ -195,10 +180,12 @@ __global__ void layer_norm_vjp(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS];
-    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    T wn[N_READS];
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    T gn[N_READS];
-
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
     for (int i = 0; i < N_READS; i++) {
       float xi = (static_cast<float>(xn[i]) - mean) * normalizer;
       float wi = wn[i];
@@ -208,9 +195,9 @@ __global__ void layer_norm_vjp(
         wn[i] = gi * xi;
       }
     }
-    store_vector<N_READS>(gx, index, xn, axis_size);
+    cub::StoreDirectBlocked(index, gx, xn, axis_size);
     if constexpr (HAS_W) {
-      store_vector<N_READS>(gw, index, wn, axis_size);
+      cub::StoreDirectBlocked(index, gw, wn, axis_size);
     }
   }
 }
@@ -250,7 +237,8 @@ void LayerNorm::eval_gpu(
       }
       return x;
     } else {
-      array x_copy = contiguous_copy_gpu(x, s);
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }
@@ -271,9 +259,9 @@ void LayerNorm::eval_gpu(
   encoder.set_input_array(b);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "layernorm", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr uint32_t N_READS = 4;
-    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::layer_norm<DataType, block_dim(), N_READS>;
       encoder.add_kernel_node(
           kernel,
@@ -307,7 +295,9 @@ void LayerNormVJP::eval_gpu(
       return x;
     }
     copied = true;
-    return contiguous_copy_gpu(x, s);
+    array x_copy(x.shape(), x.dtype(), nullptr, {});
+    copy_gpu(x, x_copy, CopyType::General, s);
+    return x_copy;
   };
   bool donate_x = inputs[0].is_donatable();
   bool donate_g = inputs[3].is_donatable();
@@ -378,10 +368,10 @@ void LayerNormVJP::eval_gpu(
   encoder.set_output_array(gw_temp);
   dispatch_float_types(gx.dtype(), "layernorm_vjp", [&](auto type_tag) {
     dispatch_bool(has_w, [&](auto has_w_constant) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+      constexpr int N_READS = 4;
-      constexpr int N_READS = 16 / sizeof(DataType);
       dispatch_block_dim(
           cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+            using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
             auto kernel = cu::layer_norm_vjp<
                 DataType,
                 has_w_constant.value,

mlx/backend/cuda/logsumexp.cu

@@ -43,19 +43,20 @@ __global__ void logsumexp(const T* in, T* out, int axis_size) {
   AccT maxval = Limits<AccT>::finite_min();
   AccT normalizer = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
+    AccT vals[N_READS];
-    auto vals = load_vector<N_READS>(in, index, axis_size, Limits<T>::min());
+    cub::LoadDirectBlocked(
+        r * BLOCK_DIM + block.thread_rank(),
+        make_cast_iterator<AccT>(in),
+        vals,
+        axis_size,
+        Limits<AccT>::min());
     prevmax = maxval;
-#pragma unroll
+    maxval = max_op(maxval, cub::ThreadReduce(vals, max_op));
-    for (int i = 0; i < N_READS; ++i) {
-      maxval = max_op(maxval, static_cast<AccT>(vals[i]));
-    }
     // Online normalizer calculation for softmax:
     // https://github.com/NVIDIA/online-softmax
     normalizer = normalizer * softmax_exp(prevmax - maxval);
     for (int i = 0; i < N_READS; i++) {
-      normalizer =
+      normalizer = normalizer + softmax_exp(vals[i] - maxval);
-          normalizer + softmax_exp(static_cast<AccT>(vals[i]) - maxval);
     }
   }
 
@@ -107,7 +108,8 @@ void LogSumExp::eval_gpu(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_gpu(x, s);
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
       encoder.add_temporary(x_copy);
       return x_copy;
     }
@@ -142,9 +144,9 @@ void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
   encoder.set_input_array(in);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "logsumexp", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int N_READS = 4;
-    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::logsumexp<DataType, float, block_dim(), N_READS>;
       encoder.add_kernel_node(
           kernel,

mlx/backend/cuda/lru_cache.h

@@ -1,146 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <list>
-#include <unordered_map>
-#include <utility>
-
-namespace mlx::core {
-
-template <
-    typename K,
-    typename V,
-    template <typename...> typename M = std::unordered_map>
-class LRUCache {
- public:
-  using value_type = std::pair<K, V>;
-  using list_type = std::list<value_type>;
-  using iterator = typename list_type::iterator;
-  using const_iterator = typename list_type::const_iterator;
-  using map_type = M<K, iterator>;
-
-  explicit LRUCache(size_t capacity) : capacity_(capacity) {}
-
-  size_t size() const {
-    return map_.size();
-  }
-  size_t capacity() const {
-    return capacity_;
-  }
-  bool empty() const {
-    return vlist_.empty();
-  }
-
-  void resize(size_t new_capacity) {
-    capacity_ = new_capacity;
-    trim();
-  }
-
-  iterator begin() {
-    return vlist_.begin();
-  }
-  const_iterator begin() const {
-    return vlist_.begin();
-  }
-  iterator end() {
-    return vlist_.end();
-  }
-  const_iterator end() const {
-    return vlist_.end();
-  }
-
-  void clear() {
-    map_.clear();
-    vlist_.clear();
-  }
-
-  iterator find(const K& key) {
-    auto it = map_.find(key);
-    if (it == map_.end())
-      return end();
-    vlist_.splice(vlist_.begin(), vlist_, it->second);
-    return it->second;
-  }
-
-  template <typename U>
-  std::pair<iterator, bool> emplace(const K& key, U&& value) {
-    auto it = map_.find(key);
-    if (it != map_.end()) {
-      vlist_.splice(vlist_.begin(), vlist_, it->second);
-      return {it->second, false};
-    }
-
-    vlist_.emplace_front(key, std::forward<U>(value));
-    map_[key] = vlist_.begin();
-
-    trim();
-
-    return {vlist_.begin(), true};
-  }
-
-  iterator erase(iterator pos) {
-    map_.erase(pos->first);
-    return vlist_.erase(pos);
-  }
-
- private:
-  void trim() {
-    while (map_.size() > capacity_) {
-      auto last = std::prev(vlist_.end());
-      map_.erase(last->first);
-      vlist_.pop_back();
-    }
-  }
-
-  list_type vlist_;
-  map_type map_;
-  size_t capacity_;
-};
-
-// Turn a POD struct into a container key by doing bytes compare.
-template <typename T>
-struct BytesKey {
-  T pod;
-  static_assert(std::is_standard_layout_v<T>, "T is not POD");
-
-  BytesKey(T pod) : pod(std::move(pod)) {}
-
-  BytesKey(const BytesKey& other) {
-    memcpy(&pod, &other.pod, sizeof(T));
-  }
-
-  BytesKey(BytesKey&& other) {
-    memcpy(&pod, &other.pod, sizeof(T));
-  }
-
-  bool operator==(const BytesKey& other) const {
-    auto* ptr1 = reinterpret_cast<const uint8_t*>(&pod);
-    auto* ptr2 = reinterpret_cast<const uint8_t*>(&other.pod);
-    return memcmp(ptr1, ptr2, sizeof(T)) == 0;
-  }
-};
-
-// Compute hash according to the bytes value of T.
-template <typename T>
-struct BytesHash {
-  static_assert(std::is_standard_layout_v<T>, "T is not POD");
-
-  size_t operator()(const T& pod) const {
-    auto* ptr = reinterpret_cast<const uint8_t*>(&pod);
-    uint32_t value = 0x811C9DC5;
-    for (int i = 0; i < sizeof(T); ++i) {
-      value ^= ptr[i];
-      value *= 0x01000193;
-    }
-    return value;
-  }
-};
-
-template <typename K, typename V>
-using BytesKeyHashMap = std::unordered_map<K, V, BytesHash<K>>;
-
-template <typename K, typename V>
-using LRUBytesKeyCache = LRUCache<BytesKey<K>, V, BytesKeyHashMap>;
-
-} // namespace mlx::core

mlx/backend/cuda/matmul.cpp

@@ -2,15 +2,275 @@
 
 #include "mlx/backend/common/matmul.h"
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/gemms/gemv.h"
 #include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
+#include "mlx/utils.h"
 
+#include <cublasLt.h>
+#include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>
+
 #include <numeric>
 
 namespace mlx::core {
+
+namespace cu {
+
+#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
+
+void check_cublas_error(const char* name, cublasStatus_t err) {
+  if (err != CUBLAS_STATUS_SUCCESS) {
+    // TODO: Use cublasGetStatusString when it is widely available.
+    throw std::runtime_error(
+        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
+  }
+}
+
+class MatMul {
+ public:
+  MatMul(
+      Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride)
+      : handle_(device.lt_handle()) {
+    heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
+
+    auto scale_type = dtype_to_cuda_type(dtype);
+    if (dtype == bfloat16 || dtype == float16) {
+      scale_type = CUDA_R_32F;
+    }
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
+        &matmul_desc_, dtype_to_compute_type(dtype), scale_type));
+    int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_POINTER_MODE,
+        &pointer_mode,
+        sizeof(int32_t)));
+    cublasOperation_t op = CUBLAS_OP_N;
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_TRANSA,
+        &op,
+        sizeof(cublasOperation_t)));
+    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+        matmul_desc_,
+        CUBLASLT_MATMUL_DESC_TRANSB,
+        &op,
+        sizeof(cublasOperation_t)));
+
+    auto type = dtype_to_cuda_type(dtype);
+    a_desc_ = create_matrix_layout(
+        type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
+    b_desc_ = create_matrix_layout(
+        type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
+    out_desc_ = create_matrix_layout(
+        type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
+
+    // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
+    // for Hopper+:
+    // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
+    uint64_t MiB = 1024 * 1024;
+    uint64_t workspace_size =
+        device.compute_capability_major() >= 9 ? 32 * MiB : 4 * MiB;
+
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceCreate(&pref_));
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceSetAttribute(
+        pref_,
+        CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
+        &workspace_size,
+        sizeof(uint64_t)));
+  }
+
+  MatMul(
+      Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      bool c_transposed,
+      int64_t ldc,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride,
+      int64_t c_batch_stride)
+      : MatMul(
+            device,
+            dtype,
+            a_transposed,
+            a_rows,
+            a_cols,
+            lda,
+            b_transposed,
+            b_rows,
+            b_cols,
+            ldb,
+            batch_count,
+            a_batch_stride,
+            b_batch_stride) {
+    auto type = dtype_to_cuda_type(dtype);
+    c_desc_ = create_matrix_layout(
+        type, a_rows, b_cols, c_transposed, ldc, batch_count, c_batch_stride);
+  }
+
+  ~MatMul() {
+    cublasLtMatrixLayoutDestroy(a_desc_);
+    cublasLtMatrixLayoutDestroy(b_desc_);
+    cublasLtMatrixLayoutDestroy(c_desc_);
+    cublasLtMatrixLayoutDestroy(out_desc_);
+    cublasLtMatmulDescDestroy(matmul_desc_);
+  }
+
+  void run(
+      cu::CommandEncoder& encoder,
+      void* out,
+      void* a,
+      void* b,
+      void* c = nullptr,
+      float alpha = 1,
+      float beta = 0) {
+    if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
+      int ret = 0;
+      CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
+          handle_,
+          matmul_desc_,
+          a_desc_,
+          b_desc_,
+          out_desc_,
+          out_desc_,
+          pref_,
+          1,
+          &heuristic_,
+          &ret));
+      if (ret == 0) {
+        throw std::runtime_error("Can not find algorithm for matmul.");
+      }
+    }
+
+    void* workspace_ptr = nullptr;
+    if (heuristic_.workspaceSize > 0) {
+      array workspace(
+          allocator::malloc(heuristic_.workspaceSize),
+          {static_cast<int>(heuristic_.workspaceSize)},
+          int8);
+      encoder.add_temporary(workspace);
+      workspace_ptr = workspace.data<void>();
+    }
+
+    auto capture = encoder.capture_context();
+    CHECK_CUBLAS_ERROR(cublasLtMatmul(
+        handle_,
+        matmul_desc_,
+        &alpha,
+        a,
+        a_desc_,
+        b,
+        b_desc_,
+        &beta,
+        c ? c : out,
+        c ? c_desc_ : out_desc_,
+        out,
+        out_desc_,
+        &heuristic_.algo,
+        workspace_ptr,
+        heuristic_.workspaceSize,
+        encoder.stream()));
+  }
+
+ private:
+  cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
+    switch (dtype) {
+      case float16:
+        return CUBLAS_COMPUTE_32F;
+      case bfloat16:
+        return CUBLAS_COMPUTE_32F;
+      case float32:
+        return mlx::core::env::enable_tf32() ? CUBLAS_COMPUTE_32F_FAST_TF32
+                                             : CUBLAS_COMPUTE_32F;
+      case float64:
+      case complex64:
+        return CUBLAS_COMPUTE_64F;
+      default:
+        throw std::runtime_error(fmt::format(
+            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
+    }
+  }
+
+  cudaDataType_t dtype_to_cuda_type(Dtype dtype) {
+    switch (dtype) {
+      case float16:
+        return CUDA_R_16F;
+      case bfloat16:
+        return CUDA_R_16BF;
+      case float32:
+        return CUDA_R_32F;
+      case float64:
+        return CUDA_R_64F;
+      case complex64:
+        return CUDA_C_32F;
+      default:
+        throw std::runtime_error(fmt::format(
+            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
+    }
+  }
+
+  cublasLtMatrixLayout_t create_matrix_layout(
+      cudaDataType_t type,
+      uint64_t rows,
+      uint64_t cols,
+      bool transposed,
+      int64_t ld,
+      int32_t batch_count,
+      int64_t batch_stride) {
+    cublasLtMatrixLayout_t desc;
+    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutCreate(&desc, type, rows, cols, ld));
+    cublasLtOrder_t order =
+        transposed ? CUBLASLT_ORDER_COL : CUBLASLT_ORDER_ROW;
+    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+        desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order, sizeof(cublasLtOrder_t)));
+    if (batch_count > 1) {
+      CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+          desc,
+          CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT,
+          &batch_count,
+          sizeof(int32_t)));
+      CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+          desc,
+          CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET,
+          &batch_stride,
+          sizeof(int64_t)));
+    }
+    return desc;
+  }
+
+  cublasLtHandle_t handle_{nullptr};
+  cublasLtMatmulDesc_t matmul_desc_{nullptr};
+  cublasLtMatmulPreference_t pref_{nullptr};
+  cublasLtMatrixLayout_t a_desc_{nullptr};
+  cublasLtMatrixLayout_t b_desc_{nullptr};
+  cublasLtMatrixLayout_t c_desc_{nullptr};
+  cublasLtMatrixLayout_t out_desc_{nullptr};
+  cublasLtMatmulHeuristicResult_t heuristic_;
+};
+
+} // namespace cu
+
 namespace {
 
 std::tuple<bool, int64_t, array>
@@ -22,7 +282,8 @@ check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
   } else if (stx == 1 && sty == arr.shape(-2)) {
     return std::make_tuple(true, sty, arr);
   } else {
-    array arr_copy = contiguous_copy_gpu(arr, s);
+    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+    copy_gpu(arr, arr_copy, CopyType::General, s);
     enc.add_temporary(arr_copy);
     return std::make_tuple(false, arr.shape(-1), arr_copy);
   }
@@ -79,25 +340,10 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     batch_shape = {1};
   }
 
-  if (cu::can_use_gemv(M, N, K, a_transposed, b_transposed)) {
-    cu::gemv(
-        a,
-        b,
-        out,
-        M,
-        N,
-        K,
-        batch_count,
-        batch_shape,
-        a_batch_strides,
-        b_batch_strides,
-        encoder);
-    return;
-  }
-
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
-  cu::Matmul matmul(
+
+  cu::MatMul matmul(
       cu::device(s.device),
       a.dtype(),
       a_transposed,
@@ -112,13 +358,27 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
       a_batch_strides.back(),
       b_batch_strides.back());
 
-  if ((batch_count / batch_shape.back()) == 1) {
+  encoder.set_input_array(a);
-    matmul.run(encoder, out, a, b);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  auto nbatch = batch_count / batch_shape.back();
+  if (nbatch == 1) {
+    matmul.run(encoder, out.data<int8_t>(), a.data<int8_t>(), b.data<int8_t>());
     return;
   }
 
-  matmul.run_batched(
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
-      encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  auto concurrent = encoder.concurrent_context();
+  for (size_t i = 0; i < nbatch; ++i) {
+    matmul.run(
+        encoder,
+        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,
+        a.data<int8_t>() + a.itemsize() * a_it.loc,
+        b.data<int8_t>() + b.itemsize() * b_it.loc);
+    a_it.step();
+    b_it.step();
+  }
 }
 
 void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -129,7 +389,9 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 3);
   auto& a_pre = inputs[0];
   auto& b_pre = inputs[1];
-  auto c = inputs[2];
+  auto& c_pre = inputs[2];
+
+  out.set_data(allocator::malloc(out.nbytes()));
 
   /////////////////////////////////////////////////////////////////////////////
   // Init checks and prep
@@ -142,24 +404,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   // the arrays
   auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
   auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);
-
+  auto [c_transposed, ldc, c] = check_transpose(encoder, s, c_pre);
-  int64_t ldc;
-  {
-    auto stx = c.strides()[c.ndim() - 2];
-    auto sty = c.strides()[c.ndim() - 1];
-    if (sty == 1 && stx == c.shape(-1)) {
-      ldc = stx;
-      out.set_data(allocator::malloc(out.nbytes()));
-    } else if (sty == 1 && stx == 0) {
-      ldc = 0;
-      out.set_data(allocator::malloc(out.nbytes()));
-    } else {
-      // Copy C into out and set C to out
-      ldc = c.shape(-1);
-      copy_gpu(c, out, CopyType::General, s);
-      c = out;
-    }
-  }
 
   /////////////////////////////////////////////////////////////////////////////
   // Check and collapse batch dimensions
@@ -186,7 +431,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
 
-  cu::Matmul matmul(
+  cu::MatMul matmul(
       cu::device(s.device),
       a.dtype(),
       a_transposed,
@@ -197,28 +442,48 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       K,
       N,
       ldb,
+      c_transposed,
       ldc,
       batch_shape.back(),
       a_batch_strides.back(),
       b_batch_strides.back(),
       c_batch_strides.back());
 
-  if ((batch_count / batch_shape.back()) == 1) {
+  encoder.set_input_array(a);
-    matmul.run(encoder, out, a, b, c, alpha_, beta_);
+  encoder.set_input_array(b);
-    return;
+  encoder.set_input_array(c);
-  }
+  encoder.set_output_array(out);
-  matmul.run_batched(
+
+  auto nbatch = batch_count / batch_shape.back();
+  if (nbatch == 1) {
+    matmul.run(
         encoder,
-      out,
+        out.data<int8_t>(),
-      a,
+        a.data<int8_t>(),
-      b,
+        b.data<int8_t>(),
-      c,
+        c.data<int8_t>(),
-      batch_shape,
-      a_batch_strides,
-      b_batch_strides,
-      c_batch_strides,
         alpha_,
         beta_);
+    return;
+  }
+
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
+  auto concurrent = encoder.concurrent_context();
+  for (size_t i = 0; i < nbatch; ++i) {
+    matmul.run(
+        encoder,
+        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,
+        a.data<int8_t>() + a.itemsize() * a_it.loc,
+        b.data<int8_t>() + b.itemsize() * b_it.loc,
+        c.data<int8_t>() + c.itemsize() * c_it.loc,
+        alpha_,
+        beta_);
+    a_it.step();
+    b_it.step();
+    c_it.step();
+  }
 }
 
 } // namespace mlx::core

mlx/backend/cuda/primitives.cu

@@ -1,11 +1,47 @@
 // Copyright © 2025 Apple Inc.
 
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/device/arange.cuh"
+#include "mlx/backend/cuda/device/fp16_math.cuh"
+#include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/distributed/primitives.h"
+#include "mlx/dtype_utils.h"
 #include "mlx/fast_primitives.h"
 #include "mlx/primitives.h"
 
+#include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>
+
+#include <cassert>
+
 namespace mlx::core {
 
+void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Arange::eval_gpu");
+  assert(inputs.size() == 0);
+  out.set_data(allocator::malloc(out.nbytes()));
+  if (out.size() == 0) {
+    return;
+  }
+  auto& encoder = cu::get_command_encoder(stream());
+  encoder.set_output_array(out);
+  auto capture = encoder.capture_context();
+  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
+    using CTYPE = MLX_GET_TYPE(type_tag);
+    using OutType = cuda_type_t<CTYPE>;
+    CTYPE step =
+        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
+    thrust::transform(
+        cu::thrust_policy(encoder.stream()),
+        thrust::counting_iterator<uint32_t>(0),
+        thrust::counting_iterator<uint32_t>(out.data_size()),
+        thrust::device_pointer_cast(out.data<OutType>()),
+        cu::Arange<OutType>{
+            static_cast<OutType>(start_), static_cast<OutType>(step)});
+  });
+}
+
 bool fast::ScaledDotProductAttention::use_fallback(
     const array& q,
     const array& k,
@@ -35,6 +71,7 @@ bool fast::ScaledDotProductAttention::use_fallback(
   }
 
 NO_GPU(BlockMaskedMM)
+NO_GPU(Convolution)
 NO_GPU(DynamicSlice)
 NO_GPU(DynamicSliceUpdate)
 NO_GPU(FFT)
@@ -54,6 +91,7 @@ NO_GPU_MULTI(Eigh)
 
 namespace fast {
 NO_GPU(ScaledDotProductAttention)
+NO_GPU_MULTI(AffineQuantize)
 NO_GPU_MULTI(CustomKernel)
 } // namespace fast
 

mlx/backend/cuda/quantized.cu

@@ -1,382 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/fast_primitives.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <int bits, int wsize = 8>
-inline constexpr __device__ short get_pack_factor() {
-  return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
-}
-
-template <int bits, int wsize = 8>
-inline constexpr __device__ short get_bytes_per_pack() {
-  constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
-  return power_of_2_bits ? (wsize / 8) : (bits == 5 ? 5 : 3);
-}
-
-template <typename T, int group_size, int bits>
-__global__ void
-affine_quantize(const T* w, uint8_t* out, T* scales, T* biases, size_t size) {
-  auto block_size = cg::this_thread_block().dim_threads();
-  auto block_idx = cg::this_thread_block().group_index();
-  auto idx_in_block = cg::this_thread_block().thread_index();
-
-  auto tidx = block_idx.x * block_size.x + idx_in_block.x;
-  auto tidy = block_idx.y * block_size.y + idx_in_block.y;
-
-  auto grid_dim_x =
-      cg::this_grid().dim_blocks().x * cg::this_grid().block_index().x;
-  constexpr float eps = 1e-7;
-  constexpr int simd_size = WARP_SIZE;
-  constexpr float n_bins = (1 << bits) - 1;
-  constexpr int pack_factor = get_pack_factor<bits, 8>();
-  constexpr int bytes_per_pack = get_bytes_per_pack<bits>();
-  constexpr int values_per_reduce = group_size / simd_size;
-  constexpr int writes_per_reduce = pack_factor / values_per_reduce;
-  constexpr int writes_per_pack =
-      writes_per_reduce > 1 ? 1 : values_per_reduce / pack_factor;
-  constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
-
-  size_t offset = tidx + grid_dim_x * size_t(tidy);
-  size_t in_index = offset * values_per_reduce;
-  if (in_index >= size) {
-    return;
-  }
-  size_t out_index = power_of_2_bits
-      ? offset * writes_per_pack
-      : offset * bytes_per_pack / writes_per_reduce;
-
-  float w_thread[values_per_reduce];
-  float w_min = Limits<float>::max();
-  float w_max = 0;
-
-#pragma clang loop unroll(full)
-  for (int i = 0; i < values_per_reduce; i++) {
-    float val = w[in_index + i];
-    w_thread[i] = val;
-    w_min = min(w_min, val);
-    w_max = max(w_max, val);
-  }
-
-  cg::greater<float> max_op;
-  cg::less<float> min_op;
-  auto warp = cg::tiled_partition<WARP_SIZE>(cg::this_thread_block());
-
-  w_min = cg::reduce(warp, w_min, min_op);
-  w_max = cg::reduce(warp, w_max, max_op);
-
-  float scale = max((w_max - w_min) / n_bins, eps);
-  bool side = abs(w_min) > abs(w_max);
-  scale = side ? scale : -scale;
-  float edge = side ? w_min : w_max;
-  float q0 = round(edge / scale);
-  bool at_zero = q0 == 0.0f;
-  scale = at_zero ? scale : edge / q0;
-  float bias = at_zero ? 0 : edge;
-
-  // Write out the scales and biases
-  size_t gindex = in_index / group_size;
-  if (in_index % group_size == 0) {
-    scales[gindex] = static_cast<T>(scale);
-    biases[gindex] = static_cast<T>(bias);
-  }
-
-  using OutType = std::conditional_t<bits == 5, uint64_t, uint32_t>;
-  OutType output = 0;
-
-#pragma clang loop unroll(full)
-  for (int i = 0; i < values_per_reduce; i++) {
-    uint8_t val = min(round((w_thread[i] - bias) / scale), n_bins);
-    if (bits == 8) {
-      output = val;
-    } else {
-      output |= val << (bits * (i % pack_factor));
-    }
-
-    if (pack_factor < values_per_reduce && i % pack_factor == pack_factor - 1) {
-      out[out_index + i / pack_factor] = output;
-      output = 0;
-    } else {
-#pragma clang loop unroll(full)
-      for (int j = 1; j < writes_per_reduce; j++) {
-        uint8_t sval = warp.shfl_down(val, j);
-        output |= static_cast<OutType>(sval)
-            << (bits * (j * values_per_reduce + i));
-      }
-    }
-  }
-  if constexpr (bits == 3 || bits == 6) {
-    if (in_index % pack_factor == 0 && out_index % bytes_per_pack == 0) {
-      out[out_index] = output & 0xff;
-      out[out_index + 1] = (output & 0xff00) >> 8;
-      out[out_index + 2] = (output & 0xff0000) >> 16;
-    }
-  } else if constexpr (bits == 5) {
-    if (in_index % pack_factor == 0 && out_index % bytes_per_pack == 0) {
-      out[out_index] = output & 0xff;
-      out[out_index + 1] = (output & 0xff00) >> 8;
-      out[out_index + 2] = (output & 0xff0000) >> 16;
-      out[out_index + 3] = (output & 0xff000000) >> 24;
-      out[out_index + 4] = (output & 0xff00000000) >> 32;
-    }
-  } else {
-    if constexpr (writes_per_reduce > 0) {
-      if (out_index % writes_per_reduce == 0) {
-        out[out_index / writes_per_reduce] = output;
-      }
-    }
-  }
-}
-
-template <typename T, int group_size, int bits>
-__global__ void affine_dequantize(
-    const uint8_t* w,
-    const T* scales,
-    const T* biases,
-    T* out,
-    size_t size) {
-  auto block_size = cg::this_thread_block().dim_threads();
-  auto block_idx = cg::this_thread_block().group_index();
-  auto idx_in_block = cg::this_thread_block().thread_index();
-
-  auto tidx = block_idx.x * block_size.x + idx_in_block.x;
-  auto tidy = block_idx.y * block_size.y + idx_in_block.y;
-
-  auto grid_dim_x =
-      cg::this_grid().dim_blocks().x * cg::this_grid().block_index().x;
-
-  constexpr int pack_factor = get_pack_factor<bits, 8>();
-  constexpr int bytes_per_pack = get_bytes_per_pack<bits>();
-
-  size_t offset = tidx + grid_dim_x * size_t(tidy);
-  size_t oindex = offset * pack_factor;
-
-  if (oindex >= size) {
-    return;
-  }
-
-  size_t gindex = oindex / group_size;
-  T scale = scales[gindex];
-  T bias = biases[gindex];
-  out += oindex;
-
-  if constexpr (bits == 3) {
-    w += offset * bytes_per_pack;
-    out[0] = static_cast<T>(w[0] & 0x7) * scale + bias;
-    out[1] = static_cast<T>((w[0] & 0x38) >> 3) * scale + bias;
-    out[2] = (static_cast<T>((w[0] & 0xc0) >> 6) +
-              static_cast<T>((w[1] & 0x1) << 2)) *
-            scale +
-        bias;
-    out[3] = static_cast<T>((w[1] & 0xe) >> 1) * scale + bias;
-    out[4] = static_cast<T>((w[1] & 0x70) >> 4) * scale + bias;
-    out[5] = (static_cast<T>((w[1] & 0x80) >> 7) +
-              static_cast<T>((w[2] & 0x3) << 1)) *
-            scale +
-        bias;
-    out[6] = static_cast<T>((w[2] & 0x1c) >> 2) * scale + bias;
-    out[7] = static_cast<T>((w[2] & 0xe0) >> 5) * scale + bias;
-  } else if constexpr (bits == 5) {
-    w += offset * bytes_per_pack;
-    out[0] = static_cast<T>(w[0] & 0x1f) * scale + bias;
-    out[1] = (static_cast<T>((w[0] & 0xe0) >> 5) +
-              static_cast<T>((w[1] & 0x3) << 3)) *
-            scale +
-        bias;
-    out[2] = static_cast<T>((w[1] & 0x7c) >> 2) * scale + bias;
-    out[3] = (static_cast<T>((w[1] & 0x80) >> 7) +
-              static_cast<T>((w[2] & 0xf) << 1)) *
-            scale +
-        bias;
-    out[4] = (static_cast<T>((w[2] & 0xf0) >> 4) +
-              static_cast<T>((w[3] & 0x1) << 4)) *
-            scale +
-        bias;
-    out[5] = static_cast<T>((w[3] & 0x3e) >> 1) * scale + bias;
-    out[6] = (static_cast<T>((w[3] & 0xc0) >> 6) +
-              static_cast<T>((w[4] & 0x7) << 2)) *
-            scale +
-        bias;
-    out[7] = static_cast<T>((w[4] & 0xf8) >> 3) * scale + bias;
-  } else if constexpr (bits == 6) {
-    w += offset * bytes_per_pack;
-    out[0] = static_cast<T>(w[0] & 0x3f) * scale + bias;
-    out[1] = (static_cast<T>((w[0] >> 6) & 0x03) +
-              static_cast<T>((w[1] & 0x0f) << 2)) *
-            scale +
-        bias;
-    out[2] = (static_cast<T>((w[1] >> 4) & 0x0f) +
-              static_cast<T>((w[2] & 0x03) << 4)) *
-            scale +
-        bias;
-    out[3] = static_cast<T>((w[2] >> 2) & 0x3f) * scale + bias;
-  } else {
-    uint val = w[offset];
-#pragma clang loop unroll(full)
-    for (int i = 0; i < pack_factor; i++) {
-      uint8_t d;
-      if (bits == 2) {
-        d = (val >> (bits * i)) & 0x03;
-      } else if (bits == 4) {
-        d = (val >> (bits * i)) & 0x0f;
-      } else if (bits == 8) {
-        d = val;
-      }
-      out[i] = scale * static_cast<T>(d) + bias;
-    }
-  }
-}
-
-} // namespace cu
-namespace {
-
-inline array ensure_row_contiguous(
-    const array& x,
-    cu::CommandEncoder& enc,
-    const Stream& s) {
-  if (!x.flags().row_contiguous) {
-    array x_copy = contiguous_copy_gpu(x, s);
-    enc.add_temporary(x_copy);
-    return x_copy;
-  } else {
-    return x;
-  }
-}
-
-} // namespace
-
-template <typename F>
-void dispatch_groups(int group_size, F&& f) {
-  switch (group_size) {
-    case 32:
-      f(std::integral_constant<int, 32>{});
-      break;
-    case 64:
-      f(std::integral_constant<int, 64>{});
-      break;
-    case 128:
-      f(std::integral_constant<int, 128>{});
-      break;
-  }
-}
-
-template <typename F>
-void dispatch_bits(int bits, F&& f) {
-  switch (bits) {
-    case 2:
-      f(std::integral_constant<int, 2>{});
-      break;
-    case 3:
-      f(std::integral_constant<int, 3>{});
-      break;
-    case 4:
-      f(std::integral_constant<int, 4>{});
-      break;
-    case 5:
-      f(std::integral_constant<int, 5>{});
-      break;
-    case 6:
-      f(std::integral_constant<int, 6>{});
-      break;
-    case 8:
-      f(std::integral_constant<int, 8>{});
-      break;
-  }
-}
-
-void fast::AffineQuantize::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  auto& w_pre = inputs[0];
-  auto& out = outputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& s = stream();
-  auto& d = cu::device(s.device);
-  auto& enc = d.get_command_encoder(s);
-
-  auto w = ensure_row_contiguous(w_pre, enc, s);
-  enc.set_input_array(w);
-  if (dequantize_) {
-    auto scales = ensure_row_contiguous(inputs[1], enc, s);
-    auto biases = ensure_row_contiguous(inputs[2], enc, s);
-    enc.set_input_array(scales);
-    enc.set_input_array(biases);
-    enc.set_output_array(out);
-  } else {
-    auto& scales = outputs[1];
-    auto& biases = outputs[2];
-    scales.set_data(allocator::malloc(scales.nbytes()));
-    biases.set_data(allocator::malloc(biases.nbytes()));
-    enc.set_output_array(out);
-    enc.set_output_array(scales);
-    enc.set_output_array(biases);
-  }
-
-  auto dtype = dequantize_ ? outputs[0].dtype() : inputs[0].dtype();
-
-  // Treat uint32 as uint8 in kernel
-  int uint8_per_uint32 = 4;
-  int packs_per_int = (bits_ == 3 || bits_ == 5) ? 8
-      : bits_ == 6                               ? 4
-                                                 : 8 / bits_;
-  int per_thread = dequantize_ ? packs_per_int : group_size_ / WARP_SIZE;
-  size_t size =
-      dequantize_ ? out.size() / packs_per_int : w.size() / per_thread;
-
-  bool large = size > UINT_MAX;
-  auto grid_shape = w.shape();
-
-  if (dequantize_) {
-    grid_shape.back() *= uint8_per_uint32;
-  } else {
-    grid_shape.back() /= per_thread;
-  }
-
-  dispatch_float_types(dtype, "affine_quantize", [&](auto type_tag) {
-    dispatch_groups(group_size_, [&](auto group_size) {
-      dispatch_bits(bits_, [&](auto bits) {
-        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        if (dequantize_) {
-          auto [num_blocks, block_dims] =
-              get_launch_args(size, grid_shape, w.strides(), large);
-          enc.add_kernel_node(
-              cu::affine_dequantize<DataType, group_size.value, bits.value>,
-              num_blocks,
-              block_dims,
-              w.data<uint8_t>(),
-              inputs[1].data<DataType>(),
-              inputs[2].data<DataType>(),
-              out.data<DataType>(),
-              out.size());
-        } else {
-          auto [num_blocks, block_dims] =
-              get_launch_args(size, grid_shape, w.strides(), large);
-          enc.add_kernel_node(
-              cu::affine_quantize<DataType, group_size.value, bits.value>,
-              num_blocks,
-              block_dims,
-              w.data<DataType>(),
-              out.data<uint8_t>(),
-              outputs[1].data<DataType>(),
-              outputs[2].data<DataType>(),
-              w.size());
-        }
-      });
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce.cu

@@ -5,6 +5,8 @@
 #include "mlx/backend/gpu/copy.h"
 
 #include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/fill.h>
 
 #include <cassert>
 
@@ -45,7 +47,8 @@ void Reduce::eval_gpu(const std::vector<array>& inputs, array& out) {
     }
   }
   if (plan.type == GeneralReduce || broadcasted || !in.flags().contiguous) {
-    array in_copy = contiguous_copy_gpu(in, s);
+    array in_copy(in.shape(), in.dtype(), nullptr, {});
+    copy_gpu(in, in_copy, CopyType::General, s);
     encoder.add_temporary(in_copy);
     in = in_copy;
     plan = get_reduction_plan(in, axes_);

mlx/backend/cuda/reduce/reduce.cuh

@@ -3,6 +3,7 @@
 #include <type_traits>
 
 #include "mlx/backend/common/reduce.h"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/reduce/reduce_ops.cuh"
 #include "mlx/dtype_utils.h"

mlx/backend/cuda/reduce/reduce_ops.cuh

@@ -69,18 +69,6 @@ struct Prod {
 struct Min {
   template <typename T>
   __device__ __forceinline__ T operator()(T a, T b) {
-    if constexpr (is_complex_v<T>) {
-      if (isnan(a.real()) || isnan(a.imag())) {
-        return a;
-      }
-      if (isnan(b.real()) || isnan(b.imag())) {
-        return b;
-      }
-    } else if constexpr (!cuda::std::is_integral_v<T>) {
-      if (isnan(a) || isnan(b)) {
-        return cuda::std::numeric_limits<float>::quiet_NaN();
-      }
-    }
     return a < b ? a : b;
   }
 
@@ -93,18 +81,6 @@ struct Min {
 struct Max {
   template <typename T>
   __device__ __forceinline__ T operator()(T a, T b) {
-    if constexpr (is_complex_v<T>) {
-      if (isnan(a.real()) || isnan(a.imag())) {
-        return a;
-      }
-      if (isnan(b.real()) || isnan(b.imag())) {
-        return b;
-      }
-    } else if constexpr (!cuda::std::is_integral_v<T>) {
-      if (isnan(a) || isnan(b)) {
-        return cuda::std::numeric_limits<float>::quiet_NaN();
-      }
-    }
     return a > b ? a : b;
   }
 
@@ -175,7 +151,7 @@ struct ReduceInit<Or, T> {
 template <typename T>
 struct ReduceInit<Sum, T> {
   static constexpr __host__ __device__ auto value() {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
       return T{0, 0};
     } else {
       return cast_to<typename ReduceResult<Sum, T>::type>(0);
@@ -186,7 +162,7 @@ struct ReduceInit<Sum, T> {
 template <typename T>
 struct ReduceInit<Prod, T> {
   static constexpr __host__ __device__ auto value() {
-    if constexpr (is_complex_v<T>) {
+    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
       return T{1, 0};
     } else {
       return cast_to<typename ReduceResult<Prod, T>::type>(1);

mlx/backend/cuda/rms_norm.cu

@@ -1,6 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/reduce/reduce.cuh"
 #include "mlx/backend/gpu/copy.h"
@@ -10,6 +11,8 @@
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 #include <nvtx3/nvtx3.hpp>
+#include <cub/block/block_load.cuh>
+#include <cub/block/block_reduce.cuh>
 
 namespace mlx::core {
 
@@ -55,7 +58,7 @@ __global__ void rms_norm(
     const T* w,
     T* out,
     float eps,
-    uint32_t axis_size,
+    int32_t axis_size,
     int64_t w_stride) {
   auto grid = cg::this_grid();
   auto block = cg::this_thread_block();
@@ -70,8 +73,8 @@ __global__ void rms_norm(
   float normalizer = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS];
-#pragma unroll
+    cub::LoadDirectBlocked(index, x, xn, axis_size, cast_to<T>(0));
     for (int i = 0; i < N_READS; ++i) {
       float t = static_cast<float>(xn[i]);
       normalizer += t * t;
@@ -83,14 +86,15 @@ __global__ void rms_norm(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS];
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    T wn[N_READS];
-#pragma unroll
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
     for (int i = 0; i < N_READS; ++i) {
-      float y = static_cast<float>(xn[i]) * normalizer;
+      float norm = static_cast<float>(xn[i]) * normalizer;
-      xn[i] = wn[i] * static_cast<T>(y);
+      xn[i] = wn[i] * static_cast<T>(norm);
     }
-    store_vector<N_READS>(out, index, xn, axis_size);
+    cub::StoreDirectBlocked(index, out, xn, axis_size);
   }
 }
 
@@ -122,10 +126,13 @@ __global__ void rms_norm_vjp(
   // Normalizer.
   float2 factors = {};
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    T xn[N_READS];
+    T wn[N_READS] = {};
+    T gn[N_READS] = {};
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    cub::LoadDirectBlocked(index, x, xn, axis_size, cast_to<T>(0));
-    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
     for (int i = 0; i < N_READS; i++) {
       float t = static_cast<float>(xn[i]);
       float wi = wn[i];
@@ -142,9 +149,12 @@ __global__ void rms_norm_vjp(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    T xn[N_READS];
-    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    T wn[N_READS];
-    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    T gn[N_READS];
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
     for (int i = 0; i < N_READS; i++) {
       float xi = xn[i];
       float wi = wn[i];
@@ -154,9 +164,9 @@ __global__ void rms_norm_vjp(
         wn[i] = static_cast<T>(gi * xi * normalizer);
       }
     }
-    store_vector<N_READS>(gx, index, xn, axis_size);
+    cub::StoreDirectBlocked(index, gx, xn, axis_size);
     if constexpr (HAS_W) {
-      store_vector<N_READS>(gw, index, wn, axis_size);
+      cub::StoreDirectBlocked(index, gw, wn, axis_size);
     }
   }
 }
@@ -196,7 +206,8 @@ void RMSNorm::eval_gpu(
       }
       return x;
     } else {
-      array x_copy = contiguous_copy_gpu(x, s);
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }
@@ -214,9 +225,9 @@ void RMSNorm::eval_gpu(
   encoder.set_input_array(w);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "rms_norm", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr uint32_t N_READS = 4;
-    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::rms_norm<DataType, block_dim(), N_READS>;
       encoder.add_kernel_node(
           kernel,
@@ -248,7 +259,9 @@ void RMSNormVJP::eval_gpu(
       return x;
     }
     copied = true;
-    return contiguous_copy_gpu(x, s);
+    array x_copy(x.shape(), x.dtype(), nullptr, {});
+    copy_gpu(x, x_copy, CopyType::General, s);
+    return x_copy;
   };
   bool donate_x = inputs[0].is_donatable();
   bool donate_g = inputs[2].is_donatable();
@@ -303,10 +316,11 @@ void RMSNormVJP::eval_gpu(
   encoder.set_output_array(gw_temp);
   dispatch_float_types(gx.dtype(), "rms_norm_vjp", [&](auto type_tag) {
     dispatch_bool(has_w, [&](auto has_w_constant) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+      constexpr int N_READS = 4;
-      constexpr int N_READS = 16 / sizeof(DataType);
       dispatch_block_dim(
           cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+            using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+            constexpr int N_READS = 4;
             auto kernel = cu::rms_norm_vjp<
                 DataType,
                 has_w_constant.value,

mlx/backend/cuda/scan.cu

@@ -379,7 +379,9 @@ void Scan::eval_gpu(const std::vector<array>& inputs, array& out) {
           in.flags());
     }
   } else {
-    in = contiguous_copy_gpu(in, s);
+    array arr_copy(in.shape(), in.dtype(), nullptr, {});
+    copy_gpu(in, arr_copy, CopyType::General, s);
+    in = std::move(arr_copy);
     out.copy_shared_buffer(in);
   }
 

mlx/backend/cuda/softmax.cu

@@ -11,6 +11,7 @@
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 #include <nvtx3/nvtx3.hpp>
+#include <cub/block/block_load.cuh>
 
 #include <cassert>
 
@@ -44,21 +45,20 @@ __global__ void softmax(const T* in, T* out, int axis_size) {
   AccT maxval = Limits<AccT>::finite_min();
   AccT normalizer = cast_to<AccT>(0);
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
+    AccT vals[N_READS];
-    auto vals = load_vector<N_READS>(in, index, axis_size, Limits<T>::min());
+    cub::LoadDirectBlocked(
+        r * BLOCK_DIM + block.thread_rank(),
+        make_cast_iterator<AccT>(in),
+        vals,
+        axis_size,
+        Limits<AccT>::min());
     prevmax = maxval;
-#pragma unroll
+    maxval = max_op(maxval, cub::ThreadReduce(vals, max_op));
-    for (int i = 0; i < N_READS; ++i) {
-      maxval = max_op(maxval, static_cast<AccT>(vals[i]));
-    }
-
     // Online normalizer calculation for softmax:
     // https://github.com/NVIDIA/online-softmax
     normalizer = normalizer * softmax_exp(prevmax - maxval);
-#pragma unroll
     for (int i = 0; i < N_READS; i++) {
-      normalizer =
+      normalizer = normalizer + softmax_exp(vals[i] - maxval);
-          normalizer + softmax_exp(static_cast<AccT>(vals[i]) - maxval);
     }
   }
 
@@ -95,11 +95,12 @@ __global__ void softmax(const T* in, T* out, int axis_size) {
   // Write output.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    auto vals = load_vector<N_READS>(in, index, axis_size, T(0));
+    T vals[N_READS];
+    cub::LoadDirectBlocked(index, in, vals, axis_size);
     for (int i = 0; i < N_READS; i++) {
       vals[i] = softmax_exp(static_cast<AccT>(vals[i]) - maxval) * normalizer;
     }
-    store_vector<N_READS>(out, index, vals, axis_size);
+    cub::StoreDirectBlocked(index, out, vals, axis_size);
   }
 }
 
@@ -124,7 +125,8 @@ void Softmax::eval_gpu(const std::vector<array>& inputs, array& out) {
       }
       return x;
     } else {
-      array x_copy = contiguous_copy_gpu(x, s);
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }
@@ -140,9 +142,9 @@ void Softmax::eval_gpu(const std::vector<array>& inputs, array& out) {
   encoder.set_input_array(in);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "softmax", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int N_READS = 4;
-    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
+      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::softmax<DataType, DataType, block_dim(), N_READS>;
       if (precise) {
         kernel = cu::softmax<DataType, float, block_dim(), N_READS>;

mlx/backend/cuda/sort.cu

@@ -72,7 +72,8 @@ void gpu_sort(const Stream& s, array in, array& out_, int axis, bool argsort) {
   bool is_segmented_sort = in.flags().contiguous && in.strides()[axis] == 1;
   if (!is_segmented_sort) {
     array trans = swapaxes_in_eval(in, axis, last_dim);
-    in = contiguous_copy_gpu(trans, s);
+    in = array(trans.shape(), trans.dtype(), nullptr, {});
+    copy_gpu(trans, in, CopyType::General, s);
     encoder.add_temporary(in);
     out = array(allocator::malloc(out.nbytes()), in.shape(), out.dtype());
     encoder.add_temporary(out);

mlx/backend/cuda/ternary.cu

@@ -32,7 +32,7 @@ ternary_v(const bool* a, const T* b, const T* c, T* out, IdxT size) {
     AlignedVector<T, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(a_vec[i], b_vec[i], c_vec[i]);
+      out_vec.val[i] = Op{}(a_vec.val[i], b_vec.val[i], c_vec.val[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -76,7 +76,7 @@ __global__ void ternary_g(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [a_idx, b_idx, c_idx] = elem_to_loc(
+    auto [a_idx, b_idx, c_idx] = elem_to_loc_4d(
         index,
         shape.data(),
         a_strides.data(),
@@ -125,9 +125,12 @@ void ternary_op_gpu_inplace(
             int ndim = shape.size();
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto [num_blocks, block_dims] = get_launch_args(out, large());
+                auto kernel =
+                    cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>;
+                auto [num_blocks, block_dims] =
+                    get_launch_args(kernel, out, large());
                 encoder.add_kernel_node(
-                    cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>,
+                    kernel,
                     num_blocks,
                     block_dims,
                     a.data<bool>(),
@@ -141,9 +144,11 @@ void ternary_op_gpu_inplace(
                     const_param<dims_constant()>(c_strides));
               });
             } else {
-              auto [num_blocks, block_dims] = get_launch_args(out, large());
+              auto kernel = cu::ternary_g<Op, DType, IdxT>;
+              auto [num_blocks, block_dims] =
+                  get_launch_args(kernel, out, large());
               encoder.add_kernel_node(
-                  cu::ternary_g<Op, DType, IdxT>,
+                  kernel,
                   num_blocks,
                   block_dims,
                   a.data<bool>(),
@@ -161,11 +166,18 @@ void ternary_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(DType);
+        // TODO: Choose optimized value based on type size.
+        constexpr int N_READS = 4;
+        auto kernel = cu::ternary_v<Op, DType, 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(
-            cu::ternary_v<Op, DType, IdxT, N_READS>,
+            kernel,
             num_blocks,
             block_dims,
             a.data<bool>(),

mlx/backend/cuda/unary.cu

@@ -2,7 +2,9 @@
 
 #include "mlx/backend/common/unary.h"
 #include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/device/unary_ops.cuh"
+#include "mlx/backend/cuda/iterators/general_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
@@ -30,7 +32,7 @@ __global__ void unary_v(const In* in, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(in_vec[i]);
+      out_vec.val[i] = Op{}(in_vec.val[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -47,7 +49,7 @@ __global__ void unary_g(
     int ndim) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto idx = elem_to_loc(index, shape.data(), strides.data(), ndim);
+    auto idx = elem_to_loc_4d(index, shape.data(), strides.data(), ndim);
     out[index] = Op{}(in[idx]);
   }
 }
@@ -69,10 +71,10 @@ constexpr bool supports_unary_op() {
         !std::is_same_v<In, bool>;
   }
   if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor>) {
-    return std::is_same_v<In, Out> && !mlx::core::is_complex_v<In>;
+    return std::is_same_v<In, Out> && !std::is_same_v<In, complex64_t>;
   }
   if (std::is_same_v<Op, Conjugate>) {
-    return std::is_same_v<In, Out> && mlx::core::is_complex_v<In>;
+    return std::is_same_v<In, Out> && std::is_same_v<In, complex64_t>;
   }
   if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcSin> ||
       std::is_same_v<Op, ArcTan> || std::is_same_v<Op, Cos> ||
@@ -86,7 +88,7 @@ constexpr bool supports_unary_op() {
     return std::is_same_v<In, Out> && is_inexact_v<In>;
   }
   if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
-    return mlx::core::is_complex_v<In> && std::is_same_v<Out, float>;
+    return std::is_same_v<In, complex64_t> && std::is_same_v<Out, float>;
   }
   if (std::is_same_v<Op, LogicalNot>) {
     return std::is_same_v<In, Out> && std::is_same_v<In, bool>;
@@ -100,7 +102,7 @@ template <typename Op>
 void unary_op_gpu_inplace(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   auto& in = inputs[0];
   if (in.size() == 0) {
@@ -129,10 +131,16 @@ void unary_op_gpu_inplace(
             using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
             // TODO: Choose optimized value based on type size.
             constexpr int N_READS = 4;
+            auto kernel = cu::unary_v<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(
-                cu::unary_v<Op, InType, OutType, IdxT, N_READS>,
+                kernel,
                 num_blocks,
                 block_dims,
                 in.data<InType>(),
@@ -141,9 +149,10 @@ void unary_op_gpu_inplace(
           } else {
             using IdxT = std::conditional_t<large(), int64_t, int32_t>;
             auto [shape, strides] = collapse_contiguous_dims(in);
-            auto [num_blocks, block_dims] = get_launch_args(out, large);
+            auto kernel = cu::unary_g<Op, InType, OutType, IdxT>;
+            auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
             encoder.add_kernel_node(
-                cu::unary_g<Op, InType, OutType, IdxT>,
+                kernel,
                 num_blocks,
                 block_dims,
                 in.data<InType>(),
@@ -169,7 +178,7 @@ template <typename Op>
 void unary_op_gpu(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   set_unary_output_data(inputs[0], out);
   unary_op_gpu_inplace<Op>(inputs, out, op, s);
@@ -179,7 +188,7 @@ void unary_op_gpu(
   void func::eval_gpu(const std::vector<array>& inputs, array& out) {   \
     nvtx3::scoped_range r(#func "::eval_gpu");                          \
     auto& s = out.primitive().stream();                                 \
-    unary_op_gpu<cu::func>(inputs, out, name(), s);                   \
+    unary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
   }
 
 UNARY_GPU(Abs)
@@ -215,15 +224,16 @@ UNARY_GPU(Tanh)
 void Log::eval_gpu(const std::vector<array>& inputs, array& out) {
   nvtx3::scoped_range r("Log::eval_gpu");
   auto& s = out.primitive().stream();
+  auto op = get_primitive_string(this);
   switch (base_) {
     case Base::e:
-      unary_op_gpu<cu::Log>(inputs, out, name(), s);
+      unary_op_gpu<cu::Log>(inputs, out, op, s);
       break;
     case Base::two:
-      unary_op_gpu<cu::Log2>(inputs, out, name(), s);
+      unary_op_gpu<cu::Log2>(inputs, out, op, s);
       break;
     case Base::ten:
-      unary_op_gpu<cu::Log10>(inputs, out, name(), s);
+      unary_op_gpu<cu::Log10>(inputs, out, op, s);
       break;
   }
 }
@@ -234,7 +244,7 @@ void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
   const auto& in = inputs[0];
   auto& s = out.primitive().stream();
   if (issubdtype(in.dtype(), inexact)) {
-    unary_op_gpu<cu::Round>(inputs, out, name(), s);
+    unary_op_gpu<cu::Round>(inputs, out, get_primitive_string(this), s);
   } else {
     // No-op integer types
     out.copy_shared_buffer(in);

mlx/backend/cuda/utils.cpp

@@ -17,14 +17,6 @@ CudaStream::~CudaStream() {
   CHECK_CUDA_ERROR(cudaStreamDestroy(stream_));
 }
 
-void check_cublas_error(const char* name, cublasStatus_t err) {
-  if (err != CUBLAS_STATUS_SUCCESS) {
-    // TODO: Use cublasGetStatusString when it is widely available.
-    throw std::runtime_error(
-        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
-  }
-}
-
 void check_cuda_error(const char* name, cudaError_t err) {
   if (err != cudaSuccess) {
     throw std::runtime_error(
@@ -69,7 +61,7 @@ const char* dtype_to_cuda_type(const Dtype& dtype) {
     case float64:
       return "double";
     case complex64:
-      return "complex64_t";
+      return "cuComplex";
     default:
       return "unknown";
   }

mlx/backend/cuda/utils.h

@@ -4,7 +4,6 @@
 
 #pragma once
 
-#include <cublasLt.h>
 #include <cuda.h>
 #include <cuda_runtime.h>
 
@@ -34,12 +33,10 @@ class CudaStream {
 };
 
 // Throw exception if the cuda API does not succeed.
-void check_cublas_error(const char* name, cublasStatus_t err);
 void check_cuda_error(const char* name, cudaError_t err);
 void check_cuda_error(const char* name, CUresult err);
 
 // The macro version that prints the command that failed.
-#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
 #define CHECK_CUDA_ERROR(cmd) check_cuda_error(#cmd, (cmd))
 
 // Convert Dtype to CUDA C++ types.

mlx/backend/cuda/worker.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/worker.h"
+#include "mlx/backend/cuda/allocator.h"
 #include "mlx/backend/cuda/device.h"
 
 namespace mlx::core::cu {
@@ -11,10 +12,10 @@ Worker::Worker()
 
 Worker::~Worker() {
   {
-    std::lock_guard lock(mtx_);
+    std::lock_guard lock(worker_mutex_);
     stop_ = true;
   }
-  cond_.notify_one();
+  worker_event_.signal(batch_ + 1);
   worker_.join();
 }
 
@@ -22,41 +23,53 @@ void Worker::add_task(std::function<void()> task) {
   pending_tasks_.push_back(std::move(task));
 }
 
-void Worker::signal(void* data) {
+void Worker::consume_in_this_thread() {
-  auto w = static_cast<Worker*>(data);
+  for (auto& task : pending_tasks_) {
-  {
+    task();
-    std::lock_guard lock(w->mtx_);
-    w->signaled_batch_++;
   }
-  w->cond_.notify_one();
+  pending_tasks_.clear();
+}
+
+void Worker::end_batch() {
+  batch_++;
+  {
+    std::lock_guard lock(worker_mutex_);
+    worker_tasks_[batch_] = std::move(pending_tasks_);
+  }
+  uncommited_batches_++;
+}
+
+void Worker::commit() {
+  if (uncommited_batches_ == 0) {
+    return;
+  }
+  uncommited_batches_ = 0;
+  worker_event_.signal(batch_);
 }
 
 void Worker::commit(cudaStream_t stream) {
-  // Move pending tasks into tasks
+  if (uncommited_batches_ == 0) {
-  if (pending_tasks_.empty()) {
     return;
   }
-  {
+  uncommited_batches_ = 0;
-    std::lock_guard lock(mtx_);
+  // Signal the |worker_event_| in |signal_stream_| after the kernels in
-    // Move pending tasks into ready tasks
+  // |stream_| finish running.
-    worker_tasks_[++committed_batch_] = std::move(pending_tasks_);
-  }
   signal_event_.record(stream);
   signal_event_.wait(signal_stream_);
-  cudaLaunchHostFunc(signal_stream_, signal, this);
+  worker_event_.signal(signal_stream_, batch_);
 }
 
 void Worker::thread_fn() {
+  // The worker thread is safe to free buffers.
+  allocator().register_this_thread();
+
   while (!stop_) {
-    uint64_t current_batch = 0;
+    uint64_t batch = worker_event_.value();
     Tasks tasks;
     {
-      std::unique_lock<std::mutex> lk(mtx_);
+      std::lock_guard lock(worker_mutex_);
-      cond_.wait(lk, [this, &current_batch] {
+      // Move tasks in signaled batches.
-        return this->signaled_batch_ > current_batch || this->stop_;
+      auto end = worker_tasks_.upper_bound(batch);
-      });
-      current_batch = signaled_batch_;
-      auto end = worker_tasks_.upper_bound(current_batch);
       for (auto it = worker_tasks_.begin(); it != end; ++it) {
         if (tasks.empty()) {
           tasks = std::move(it->second);
@@ -72,6 +85,7 @@ void Worker::thread_fn() {
       auto task = std::move(tasks[i]);
       task();
     }
+    worker_event_.wait(batch + 1);
   }
 }
 

mlx/backend/cuda/worker.h

@@ -5,7 +5,6 @@
 #include "mlx/backend/cuda/event.h"
 #include "mlx/backend/cuda/utils.h"
 
-#include <condition_variable>
 #include <functional>
 #include <map>
 #include <mutex>
@@ -25,24 +24,38 @@ class Worker {
   // Add a pending |task| that will run when consumed or commited.
   void add_task(std::function<void()> task);
 
+  // Run pending tasks immediately in current thread.
+  void consume_in_this_thread();
+
+  // Put pending tasks in a batch.
+  void end_batch();
+
+  // Inform worker thread to run current batches now.
+  void commit();
+
   // Inform worker thread to run current batches after kernels in |stream|
   // finish running.
   void commit(cudaStream_t stream);
 
+  // Return how many batches have been added but not committed yet.
+  size_t uncommited_batches() const {
+    return uncommited_batches_;
+  }
+
  private:
-  static void signal(void*);
-
   void thread_fn();
-  std::mutex mtx_;
-  std::condition_variable cond_;
 
-  uint64_t committed_batch_{0};
+  uint64_t batch_{0};
-  uint64_t signaled_batch_{0};
+  size_t uncommited_batches_{0};
 
   // Cuda stream and event for signaling kernel completion.
   CudaStream signal_stream_;
   CudaEvent signal_event_;
 
+  // Worker thread.
+  SharedEvent worker_event_;
+  std::thread worker_;
+  std::mutex worker_mutex_;
   bool stop_{false};
 
   // Tasks are put in |pending_tasks_| first, and then moved to
@@ -50,7 +63,6 @@ class Worker {
   using Tasks = std::vector<std::function<void()>>;
   Tasks pending_tasks_;
   std::map<uint64_t, Tasks> worker_tasks_;
-  std::thread worker_;
 };
 
 } // namespace mlx::core::cu

mlx/backend/gpu/copy.cpp

@@ -46,10 +46,4 @@ void copy_gpu_inplace(
       in, out, in.shape(), i_strides, out.strides(), i_offset, 0, ctype, s);
 }
 
-array contiguous_copy_gpu(const array& arr, const Stream& s) {
-  array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-  copy_gpu(arr, arr_copy, CopyType::General, s);
-  return arr_copy;
-}
-
 } // namespace mlx::core

mlx/backend/gpu/copy.h

@@ -43,7 +43,4 @@ void copy_gpu_inplace(
 // Fill the output with the scalar val
 void fill_gpu(const array& val, array& out, const Stream& s);
 
-// Return a contiguous array with same shape that copies the data of |arr|.
-array contiguous_copy_gpu(const array& arr, const Stream& s);
-
 } // namespace mlx::core

mlx/backend/metal/allocator.cpp

@@ -128,7 +128,8 @@ Buffer MetalAllocator::malloc(size_t size) {
 
     auto pool = metal::new_scoped_memory_pool();
 
-    // If we have a lot of memory pressure try to reclaim memory from the cache
+    // If we have a lot of memory pressure or are over the maximum cache size,
+    // try to reclaim memory from the cache
     if (mem_required >= gc_limit_ || num_resources_ >= resource_limit_) {
       num_resources_ -=
           buffer_cache_.release_cached_buffers(mem_required - gc_limit_);

mlx/backend/metal/binary.cpp

@@ -7,20 +7,20 @@
 
 #define BINARY_GPU(func)                                              \
   void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
-    binary_op_gpu(inputs, out, name());                               \
+    binary_op_gpu(inputs, out, get_primitive_string(this));           \
   }
 
 #define BINARY_GPU_MULTI(func)                                         \
   void func::eval_gpu(                                                 \
       const std::vector<array>& inputs, std::vector<array>& outputs) { \
-    binary_op_gpu(inputs, outputs, name());                            \
+    binary_op_gpu(inputs, outputs, get_primitive_string(this));        \
   }
 
 namespace mlx::core {
 
 std::string get_kernel_name(
     BinaryOpType bopt,
-    const char* op,
+    const std::string& op,
     const array& a,
     bool large,
     int ndim,
@@ -65,7 +65,7 @@ std::string get_kernel_name(
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   auto& a = inputs[0];
   auto& b = inputs[1];
@@ -165,7 +165,7 @@ void binary_op_gpu_inplace(
 void binary_op_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
@@ -179,7 +179,7 @@ void binary_op_gpu(
 void binary_op_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op) {
+    const std::string& op) {
   auto& s = outputs[0].primitive().stream();
   binary_op_gpu(inputs, outputs, op, s);
 }
@@ -187,7 +187,7 @@ void binary_op_gpu(
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   std::vector<array> outputs = {out};
   binary_op_gpu_inplace(inputs, outputs, op, s);
@@ -196,7 +196,7 @@ void binary_op_gpu_inplace(
 void binary_op_gpu(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s) {
   assert(inputs.size() == 2);
   auto& a = inputs[0];
@@ -209,7 +209,7 @@ void binary_op_gpu(
 void binary_op_gpu(
     const std::vector<array>& inputs,
     array& out,
-    const char* op) {
+    const std::string& op) {
   auto& s = out.primitive().stream();
   binary_op_gpu(inputs, out, op, s);
 }
@@ -237,19 +237,19 @@ BINARY_GPU(Subtract)
 void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
   switch (op_) {
     case BitwiseBinary::And:
-      binary_op_gpu(inputs, out, name());
+      binary_op_gpu(inputs, out, get_primitive_string(this));
       break;
     case BitwiseBinary::Or:
-      binary_op_gpu(inputs, out, name());
+      binary_op_gpu(inputs, out, get_primitive_string(this));
       break;
     case BitwiseBinary::Xor:
-      binary_op_gpu(inputs, out, name());
+      binary_op_gpu(inputs, out, get_primitive_string(this));
       break;
     case BitwiseBinary::LeftShift:
-      binary_op_gpu(inputs, out, name());
+      binary_op_gpu(inputs, out, get_primitive_string(this));
       break;
     case BitwiseBinary::RightShift:
-      binary_op_gpu(inputs, out, name());
+      binary_op_gpu(inputs, out, get_primitive_string(this));
       break;
   }
 }

mlx/backend/metal/binary.h

@@ -9,25 +9,25 @@ namespace mlx::core {
 void binary_op_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    const std::string& op,
     const Stream& s);
 
 void binary_op_gpu(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s);
 
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const char* op,
+    const std::string& op,
     const Stream& s);
 
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
     array& out,
-    const char* op,
+    const std::string& op,
     const Stream& s);
 
 } // namespace mlx::core

mlx/backend/metal/compiled.cpp

@@ -212,7 +212,9 @@ inline void build_kernel(
           get_type_string(x.dtype()),
           namer.get_name(x.inputs()[0]));
     } else {
-      os += x.primitive().name();
+      std::ostringstream ss;
+      x.primitive().print(ss);
+      os += ss.str();
       os += "()(";
       for (int i = 0; i < x.inputs().size() - 1; i++) {
         os += fmt::format("tmp_{0}, ", namer.get_name(x.inputs()[i]));

mlx/backend/metal/conv.cpp

@@ -149,7 +149,8 @@ void explicit_gemm_conv_group_ND_gpu(
       wt, {wt.strides(0), 1, C_per_group}, wt.flags(), wt.size());
 
   // Materialize
-  array wt_transpose = contiguous_copy_gpu(wt_view, s);
+  auto wt_transpose = array(wt_view.shape(), wt_view.dtype(), nullptr, {});
+  copy_gpu(wt_view, wt_transpose, CopyType::General, s);
 
   // Perform gemm
   std::vector<array> copies = {in_unfolded, wt_transpose};
@@ -960,12 +961,16 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
   auto in = inputs[0];
   auto wt = inputs[1];
   if (!in.flags().row_contiguous) {
-    in = contiguous_copy_gpu(in, s);
+    array arr_copy(in.shape(), in.dtype(), nullptr, {});
-    copies.push_back(in);
+    copy_gpu(in, arr_copy, CopyType::General, s);
+    copies.push_back(arr_copy);
+    in = arr_copy;
   }
   if (!wt.flags().row_contiguous) {
-    wt = contiguous_copy_gpu(wt, s);
+    array arr_copy(wt.shape(), wt.dtype(), nullptr, {});
-    copies.push_back(wt);
+    copy_gpu(wt, arr_copy, CopyType::General, s);
+    copies.push_back(arr_copy);
+    wt = arr_copy;
   }
 
   // 3D conv

mlx/backend/metal/copy.cpp

@@ -86,7 +86,7 @@ void copy_gpu_inplace(
     }
   } else {
     work_per_thread = get_work_per_thread(out.dtype(), out.data_size());
-    if (!large && work_per_thread > 1) {
+    if (work_per_thread > 1) {
       kernel_name += "n";
     }
   }

mlx/backend/metal/device.cpp

@@ -1,18 +1,20 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include <cstdlib>
+#include <filesystem>
 #include <sstream>
 
 #define NS_PRIVATE_IMPLEMENTATION
 #define CA_PRIVATE_IMPLEMENTATION
 #define MTL_PRIVATE_IMPLEMENTATION
 
-#include "mlx/backend/common/utils.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/metal.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/utils.h"
 
+namespace fs = std::filesystem;
+
 namespace mlx::core::metal {
 
 namespace {
@@ -78,7 +80,12 @@ MTL::Library* try_load_bundle(
 std::pair<MTL::Library*, NS::Error*> load_colocated_library(
     MTL::Device* device,
     const std::string& relative_path) {
-  auto path = current_binary_dir() / relative_path;
+  std::string binary_dir = get_binary_directory();
+  if (binary_dir.size() == 0) {
+    return {nullptr, nullptr};
+  }
+
+  auto path = fs::path(binary_dir) / relative_path;
   if (!path.has_extension()) {
     path.replace_extension(".metallib");
   }
@@ -190,7 +197,7 @@ MTL::Library* load_library(
 
   std::ostringstream msg;
   msg << "Failed to load the metallib " << lib_name << ".metallib. "
-      << "We attempted to load it from <" << current_binary_dir() << "/"
+      << "We attempted to load it from <" << get_binary_directory() << "/"
       << lib_name << ".metallib" << ">";
 #ifdef SWIFTPM_BUNDLE
   msg << " and from the Swift PM bundle.";