register in task submission

.circleci/config.yml

@@ -85,7 +85,7 @@ jobs:
           name: Install dependencies
           command: |
             pip install --upgrade cmake
-            pip install nanobind==2.4.0
+            pip install nanobind==2.2.0
             pip install numpy
             sudo apt-get update
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
@@ -137,7 +137,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.4.0
+            pip install nanobind==2.2.0
             pip install numpy
             pip install torch
             pip install tensorflow
@@ -226,7 +226,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.4.0
+            pip install nanobind==2.2.0
             pip install --upgrade setuptools
             pip install numpy
             pip install twine
@@ -291,7 +291,7 @@ jobs:
             source env/bin/activate
             pip install --upgrade pip
             pip install --upgrade cmake
-            pip install nanobind==2.4.0
+            pip install nanobind==2.2.0
             pip install --upgrade setuptools
             pip install numpy
             pip install auditwheel

.gitignore

@@ -76,9 +76,6 @@ build/
 *.out
 *.app
 
-# Debug symbols
-*.pdb
-
 # VSCode 
 .vscode/
 .DS_Store

.pre-commit-config.yaml

@@ -1,14 +1,13 @@
 repos:
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v19.1.4
+    rev: v18.1.8
     hooks:
     -   id: clang-format
 # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.10.0
+    rev: 24.8.0
     hooks:
     -   id: black
-    
 -   repo: https://github.com/pycqa/isort
     rev: 5.13.2
     hooks:

CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.25)
+cmake_minimum_required(VERSION 3.24)
 
 project(mlx LANGUAGES C CXX)
 
@@ -20,14 +20,12 @@ option(MLX_METAL_DEBUG "Enhance metal debug workflow" OFF)
 option(MLX_ENABLE_X64_MAC "Enable building for x64 macOS" OFF)
 option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
 option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
-option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
 
 if(NOT MLX_VERSION)
-  set(MLX_VERSION 0.21.1)
+  set(MLX_VERSION 0.19.3)
 endif()
-add_compile_definitions("MLX_VERSION=${MLX_VERSION}")
 
 # --------------------- Processor tests -------------------------
 
@@ -36,6 +34,8 @@ message(
     "Building MLX for ${CMAKE_SYSTEM_PROCESSOR} processor on ${CMAKE_SYSTEM_NAME}"
 )
 
+set(MLX_BUILD_ARM OFF)
+
 if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
   if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
     if(NOT MLX_ENABLE_X64_MAC)
@@ -57,6 +57,10 @@ else()
   message(WARNING "MLX is prioritised for Apple silicon systems using macOS.")
 endif()
 
+if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm64")
+  set(MLX_BUILD_ARM ON)
+endif()
+
 # ----------------------------- Lib -----------------------------
 
 include(FetchContent)
@@ -85,26 +89,25 @@ elseif(MLX_BUILD_METAL)
   # Throw an error if xcrun not found
   execute_process(
     COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_VERSION COMMAND_ERROR_IS_FATAL ANY)
 
-  if(${MACOS_SDK_VERSION} LESS 14.0)
+  if(${MACOS_VERSION} LESS 14.0)
     message(
       FATAL_ERROR
         "MLX requires macOS SDK >= 14.0 to be built with MLX_BUILD_METAL=ON")
   endif()
-  message(STATUS "Building with macOS SDK version ${MACOS_SDK_VERSION}")
+  message(STATUS "Building with SDK for macOS version ${MACOS_VERSION}")
 
   set(METAL_CPP_URL
-      https://developer.apple.com/metal/cpp/files/metal-cpp_macOS15_iOS18.zip)
-
-  if(NOT CMAKE_OSX_DEPLOYMENT_TARGET STREQUAL "")
-    set(XCRUN_FLAGS "-mmacosx-version-min=${CMAKE_OSX_DEPLOYMENT_TARGET}")
-  endif()
+      https://developer.apple.com/metal/cpp/files/metal-cpp_macOS15_iOS18-beta.zip
+  )
+  # Get the metal version
   execute_process(
     COMMAND
       zsh "-c"
-      "echo \"__METAL_VERSION__\" | xcrun -sdk macosx metal ${XCRUN_FLAGS} -E -x metal -P - | tail -1 | tr -d '\n'"
+      "echo \"__METAL_VERSION__\" | xcrun -sdk macosx metal -E -x metal -P - | tail -1 | tr -d '\n'"
     OUTPUT_VARIABLE MLX_METAL_VERSION COMMAND_ERROR_IS_FATAL ANY)
+
   FetchContent_Declare(metal_cpp URL ${METAL_CPP_URL})
 
   FetchContent_MakeAvailable(metal_cpp)
@@ -112,57 +115,20 @@ elseif(MLX_BUILD_METAL)
     mlx PUBLIC $<BUILD_INTERFACE:${metal_cpp_SOURCE_DIR}>
                $<INSTALL_INTERFACE:include/metal_cpp>)
   target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
-endif()
 
-if(WIN32)
-  if(MSVC)
-    # GGUF does not build with MSVC.
-    set(MLX_BUILD_GGUF OFF)
-    # There is no prebuilt OpenBLAS distribution for MSVC.
-    set(MLX_BUILD_BLAS_FROM_SOURCE ON)
-  endif()
-  # Windows implementation of dlfcn.h APIs.
-  FetchContent_Declare(
-    dlfcn-win32
-    GIT_REPOSITORY https://github.com/dlfcn-win32/dlfcn-win32.git
-    GIT_TAG v1.4.1
-    EXCLUDE_FROM_ALL)
-  block()
-  set(BUILD_SHARED_LIBS OFF)
-  FetchContent_MakeAvailable(dlfcn-win32)
-  endblock()
-  target_include_directories(mlx PRIVATE "${dlfcn-win32_SOURCE_DIR}/src")
-  target_link_libraries(mlx PRIVATE dl)
+  add_compile_definitions("MLX_METAL_VERSION=${MLX_METAL_VERSION}")
 endif()
 
 if(MLX_BUILD_CPU)
   find_library(ACCELERATE_LIBRARY Accelerate)
-  if(ACCELERATE_LIBRARY)
+  if(MLX_BUILD_ARM AND ACCELERATE_LIBRARY)
     message(STATUS "Accelerate found ${ACCELERATE_LIBRARY}")
     set(MLX_BUILD_ACCELERATE ON)
+    target_link_libraries(mlx PUBLIC ${ACCELERATE_LIBRARY})
+    add_compile_definitions(ACCELERATE_NEW_LAPACK)
   else()
     message(STATUS "Accelerate or arm neon not found, using default backend.")
     set(MLX_BUILD_ACCELERATE OFF)
-  endif()
-
-  if(MLX_BUILD_ACCELERATE)
-    target_link_libraries(mlx PUBLIC ${ACCELERATE_LIBRARY})
-    add_compile_definitions(ACCELERATE_NEW_LAPACK)
-  elseif(MLX_BUILD_BLAS_FROM_SOURCE)
-    # Download and build OpenBLAS from source code.
-    FetchContent_Declare(
-      openblas
-      GIT_REPOSITORY https://github.com/OpenMathLib/OpenBLAS.git
-      GIT_TAG v0.3.28
-      EXCLUDE_FROM_ALL)
-    set(BUILD_STATIC_LIBS ON) # link statically
-    set(NOFORTRAN ON) # msvc has no fortran compiler
-    FetchContent_MakeAvailable(openblas)
-    target_link_libraries(mlx PRIVATE openblas)
-    target_include_directories(
-      mlx PRIVATE "${openblas_SOURCE_DIR}/lapack-netlib/LAPACKE/include"
-                  "${CMAKE_BINARY_DIR}/generated" "${CMAKE_BINARY_DIR}")
-  else()
     if(${CMAKE_HOST_APPLE})
       # The blas shipped in macOS SDK is not supported, search homebrew for
       # openblas instead.
@@ -180,7 +146,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Lapack lib " ${LAPACK_LIBRARIES})
     message(STATUS "Lapack include " ${LAPACK_INCLUDE_DIRS})
     target_include_directories(mlx PRIVATE ${LAPACK_INCLUDE_DIRS})
-    target_link_libraries(mlx PRIVATE ${LAPACK_LIBRARIES})
+    target_link_libraries(mlx PUBLIC ${LAPACK_LIBRARIES})
     # List blas after lapack otherwise we may accidentally incldue an old
     # version of lapack.h from the include dirs of blas.
     find_package(BLAS REQUIRED)
@@ -193,7 +159,7 @@ if(MLX_BUILD_CPU)
     message(STATUS "Blas lib " ${BLAS_LIBRARIES})
     message(STATUS "Blas include " ${BLAS_INCLUDE_DIRS})
     target_include_directories(mlx PRIVATE ${BLAS_INCLUDE_DIRS})
-    target_link_libraries(mlx PRIVATE ${BLAS_LIBRARIES})
+    target_link_libraries(mlx PUBLIC ${BLAS_LIBRARIES})
   endif()
 else()
   set(MLX_BUILD_ACCELERATE OFF)
@@ -240,7 +206,8 @@ if(MLX_BUILD_PYTHON_BINDINGS)
   execute_process(
     COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir
     OUTPUT_STRIP_TRAILING_WHITESPACE
-    OUTPUT_VARIABLE nanobind_ROOT)
+    OUTPUT_VARIABLE NB_DIR)
+  list(APPEND CMAKE_PREFIX_PATH "${NB_DIR}")
   find_package(nanobind CONFIG REQUIRED)
   add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/python/src)
 endif()

benchmarks/cpp/autograd.cpp

@@ -5,35 +5,35 @@
 #include "mlx/mlx.h"
 #include "time_utils.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 void time_value_and_grad() {
-  auto x = mx::ones({200, 1000});
-  mx::eval(x);
-  auto fn = [](mx::array x) {
+  auto x = ones({200, 1000});
+  eval(x);
+  auto fn = [](array x) {
     for (int i = 0; i < 20; ++i) {
-      x = mx::log(mx::exp(x));
+      x = log(exp(x));
     }
-    return mx::sum(x);
+    return sum(x);
   };
 
-  auto grad_fn = mx::grad(fn);
+  auto grad_fn = grad(fn);
   auto independent_value_and_grad = [&]() {
     auto value = fn(x);
     auto dfdx = grad_fn(x);
-    return std::vector<mx::array>{value, dfdx};
+    return std::vector<array>{value, dfdx};
   };
   TIME(independent_value_and_grad);
 
-  auto value_and_grad_fn = mx::value_and_grad(fn);
+  auto value_and_grad_fn = value_and_grad(fn);
   auto combined_value_and_grad = [&]() {
     auto [value, dfdx] = value_and_grad_fn(x);
-    return std::vector<mx::array>{value, dfdx};
+    return std::vector<array>{value, dfdx};
   };
   TIME(combined_value_and_grad);
 }
 
 int main() {
-  std::cout << "Benchmarks for " << mx::default_device() << std::endl;
+  std::cout << "Benchmarks for " << default_device() << std::endl;
   time_value_and_grad();
 }

benchmarks/cpp/compare_devices.cpp

@@ -4,21 +4,21 @@
 #include "mlx/mlx.h"
 #include "time_utils.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 void time_add_op() {
   std::vector<int> sizes(1, 1);
   for (int i = 0; i < 9; ++i) {
     sizes.push_back(10 * sizes.back());
   }
-  set_default_device(mx::Device::cpu);
+  set_default_device(Device::cpu);
   for (auto size : sizes) {
-    auto a = mx::random::uniform({size});
-    auto b = mx::random::uniform({size});
-    mx::eval(a, b);
+    auto a = random::uniform({size});
+    auto b = random::uniform({size});
+    eval(a, b);
     std::cout << "Size " << size << std::endl;
-    TIMEM("cpu", mx::add, a, b, mx::Device::cpu);
-    TIMEM("gpu", mx::add, a, b, mx::Device::gpu);
+    TIMEM("cpu", add, a, b, Device::cpu);
+    TIMEM("gpu", add, a, b, Device::gpu);
   }
 }
 

benchmarks/cpp/irregular_strides.cpp

@@ -6,105 +6,105 @@
 #include "mlx/mlx.h"
 #include "time_utils.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 void time_irregular_binary_ops_1D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   int size = 1000000;
   int step = 2;
-  auto a = mx::random::uniform({size});
-  auto b = mx::random::uniform({size});
-  mx::eval(a, b);
+  auto a = random::uniform({size});
+  auto b = random::uniform({size});
+  eval(a, b);
   a = slice(a, {0}, {size}, {step});
   b = slice(b, {0}, {size}, {step});
-  TIMEM("1D strided", mx::add, a, b, device);
+  TIMEM("1D strided", add, a, b, device);
 }
 
 void time_irregular_binary_ops_2D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   int size = 2048;
-  auto a = mx::random::uniform({size, size});
-  auto b = mx::random::uniform({size, size});
-  mx::eval(a, b);
-  TIMEM("2D regular", mx::add, a, b, device);
+  auto a = random::uniform({size, size});
+  auto b = random::uniform({size, size});
+  eval(a, b);
+  TIMEM("2D regular", add, a, b, device);
 
-  b = mx::transpose(b);
-  mx::eval(b);
-  TIMEM("2D mx::transpose", mx::add, a, b, device);
+  b = transpose(b);
+  eval(b);
+  TIMEM("2D transpose", add, a, b, device);
 
-  b = mx::random::uniform({size});
-  mx::eval(b);
-  TIMEM("2D broadcast dim 0", mx::add, a, b, device);
+  b = random::uniform({size});
+  eval(b);
+  TIMEM("2D broadcast dim 0", add, a, b, device);
 
-  b = mx::reshape(b, {size, 1});
-  mx::eval(b);
-  TIMEM("2D broadcast dim 1", mx::add, a, b, device);
+  b = reshape(b, {size, 1});
+  eval(b);
+  TIMEM("2D broadcast dim 1", add, a, b, device);
 }
 
 void time_irregular_binary_ops_3D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   int d0 = 32;
   int d1 = 512;
   int d2 = 512;
-  auto a = mx::random::uniform({d0, d1, d2});
-  auto b = mx::random::uniform({d0, d1, d2});
-  TIMEM("3D regular", mx::add, a, b, device);
+  auto a = random::uniform({d0, d1, d2});
+  auto b = random::uniform({d0, d1, d2});
+  TIMEM("3D regular", add, a, b, device);
 
-  b = mx::transpose(b, {0, 2, 1});
-  TIMEM("3D mx::transpose", mx::add, a, b, device);
+  b = transpose(b, {0, 2, 1});
+  TIMEM("3D transpose", add, a, b, device);
 
-  b = mx::random::uniform({d1, d2});
-  TIMEM("3D broadcast dim 0", mx::add, a, b, device);
+  b = random::uniform({d1, d2});
+  TIMEM("3D broadcast dim 0", add, a, b, device);
 
-  b = mx::random::uniform({d0, 1, d2});
-  TIMEM("3D broadcast dim 1", mx::add, a, b, device);
+  b = random::uniform({d0, 1, d2});
+  TIMEM("3D broadcast dim 1", add, a, b, device);
 
-  b = mx::random::uniform({d0, d1, 1});
-  TIMEM("3D broadcast dim 2", mx::add, a, b, device);
+  b = random::uniform({d0, d1, 1});
+  TIMEM("3D broadcast dim 2", add, a, b, device);
 
-  b = mx::random::uniform({d2});
-  TIMEM("3D broadcast dims 0, 1", mx::add, a, b, device);
+  b = random::uniform({d2});
+  TIMEM("3D broadcast dims 0, 1", add, a, b, device);
 
-  b = mx::random::uniform({d1, 1});
-  TIMEM("3D broadcast dims 0, 2", mx::add, a, b, device);
+  b = random::uniform({d1, 1});
+  TIMEM("3D broadcast dims 0, 2", add, a, b, device);
 
-  b = mx::random::uniform({d0, 1, 1});
-  TIMEM("3D broadcast dims 1, 2", mx::add, a, b, device);
+  b = random::uniform({d0, 1, 1});
+  TIMEM("3D broadcast dims 1, 2", add, a, b, device);
 }
 
 void time_irregular_binary_ops_4D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   std::vector<int> shape = {8, 8, 512, 512};
-  auto a = mx::random::uniform(shape);
-  auto b = mx::random::uniform(shape);
+  auto a = random::uniform(shape);
+  auto b = random::uniform(shape);
 
-  TIMEM("4D regular", mx::add, a, b, device);
+  TIMEM("4D regular", add, a, b, device);
 
-  b = mx::transpose(b, {0, 1, 3, 2});
-  TIMEM("4D mx::transpose", mx::add, a, b, device);
+  b = transpose(b, {0, 1, 3, 2});
+  TIMEM("4D transpose", add, a, b, device);
 
   std::string om = "4D broadcast dims ";
   for (int i = 0; i < shape.size(); ++i) {
     shape[i] = 1;
-    b = mx::random::uniform(shape);
+    b = random::uniform(shape);
     std::ostringstream msg;
     msg << om << i;
-    TIMEM(msg.str(), mx::add, a, b, device);
+    TIMEM(msg.str(), add, a, b, device);
 
     for (int j = i + 1; j < shape.size(); ++j) {
       shape[j] = 1;
       std::ostringstream msg;
       msg << om << i << ", " << j;
-      b = mx::random::uniform(shape);
-      TIMEM(msg.str(), mx::add, a, b, device);
+      b = random::uniform(shape);
+      TIMEM(msg.str(), add, a, b, device);
       shape[j] = a.shape(j);
 
       for (int k = j + 1; k < shape.size(); ++k) {
         shape[k] = 1;
         std::ostringstream msg;
         msg << om << i << ", " << j << ", " << k;
-        b = mx::random::uniform(shape);
-        TIMEM(msg.str(), mx::add, a, b, device);
+        b = random::uniform(shape);
+        TIMEM(msg.str(), add, a, b, device);
         shape[k] = a.shape(k);
       }
     }
@@ -113,83 +113,83 @@ void time_irregular_binary_ops_4D() {
 }
 
 void time_irregular_reshape() {
-  auto device = mx::default_device();
+  auto device = default_device();
   std::vector<int> shape;
-  auto reshape_fn = [&shape, device](const mx::array& a) {
-    return mx::reshape(a, shape, device);
+  auto reshape_fn = [&shape, device](const array& a) {
+    return reshape(a, shape, device);
   };
 
   int size = 64;
   int d = 2 * size;
 
-  auto a = mx::random::uniform({d, d, d});
+  auto a = random::uniform({d, d, d});
 
   shape = {8 * size, size, size};
   TIMEM("3D contiguous", reshape_fn, a);
 
-  a = mx::transpose(a);
+  a = transpose(a);
   shape = {8 * size, size, size};
-  TIMEM("3D mx::transpose", reshape_fn, a);
+  TIMEM("3D transpose", reshape_fn, a);
 
-  a = mx::transpose(a, {1, 2, 0});
+  a = transpose(a, {1, 2, 0});
   shape = {8 * size, size, size};
-  TIMEM("3D mx::transpose dims 1 2", reshape_fn, a);
+  TIMEM("3D transpose dims 1 2", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d, d}), {d, d, d});
+  a = broadcast_to(random::uniform({d, d}), {d, d, d});
   TIMEM("3D broadcast dim 0", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d, 1, d}), {d, d, d});
+  a = broadcast_to(random::uniform({d, 1, d}), {d, d, d});
   TIMEM("3D broadcast dim 1", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d, d, 1}), {d, d, d});
+  a = broadcast_to(random::uniform({d, d, 1}), {d, d, d});
   TIMEM("3D broadcast dim 2", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d}), {d, d, d});
+  a = broadcast_to(random::uniform({d}), {d, d, d});
   TIMEM("3D broadcast dims 0, 1", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d, 1}), {d, d, d});
+  a = broadcast_to(random::uniform({d, 1}), {d, d, d});
   TIMEM("3D broadcast dims 0, 2", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({d, 1, 1}), {d, d, d});
+  a = broadcast_to(random::uniform({d, 1, 1}), {d, d, d});
   TIMEM("3D broadcast dims 1, 2", reshape_fn, a);
 
-  a = mx::broadcast_to(mx::random::uniform({1, 1, 1}), {d, d, d});
+  a = broadcast_to(random::uniform({1, 1, 1}), {d, d, d});
   TIMEM("3D broadcast dims 1, 2, 3", reshape_fn, a);
 }
 
 void time_irregular_astype_1D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   int size = 1000000;
   int step = 2;
-  auto a = mx::random::uniform({size});
+  auto a = random::uniform({size});
   a = slice(a, {0}, {size}, {step});
-  TIMEM("1D strided", mx::astype, a, mx::int32, device);
+  TIMEM("1D strided", astype, a, int32, device);
 }
 
 void time_irregular_astype_2D() {
-  auto device = mx::default_device();
+  auto device = default_device();
   int size = 2048;
   std::vector<int> shape = {size, size};
 
-  auto a = mx::random::uniform(shape);
-  TIMEM("2D regular", mx::astype, a, mx::int32, device);
+  auto a = random::uniform(shape);
+  TIMEM("2D regular", astype, a, int32, device);
 
-  a = mx::transpose(a);
-  TIMEM("2D mx::transpose", mx::astype, a, mx::int32, device);
+  a = transpose(a);
+  TIMEM("2D transpose", astype, a, int32, device);
 
-  a = mx::broadcast_to(mx::random::uniform({size}), shape);
-  TIMEM("2D broadcast dim 0", mx::astype, a, mx::int32, device);
+  a = broadcast_to(random::uniform({size}), shape);
+  TIMEM("2D broadcast dim 0", astype, a, int32, device);
 
-  a = mx::broadcast_to(mx::random::uniform({size, 1}), shape);
-  TIMEM("2D broadcast dim 1", mx::astype, a, mx::int32, device);
+  a = broadcast_to(random::uniform({size, 1}), shape);
+  TIMEM("2D broadcast dim 1", astype, a, int32, device);
 }
 
 int main(int argc, char** argv) {
   if (argc > 1) {
     bool use_gpu = !strcmp(argv[1], "gpu");
-    set_default_device(use_gpu ? mx::Device::gpu : mx::Device::cpu);
+    set_default_device(use_gpu ? Device::gpu : Device::cpu);
   }
-  std::cout << "Benchmarks for " << mx::default_device() << std::endl;
+  std::cout << "Benchmarks for " << default_device() << std::endl;
   time_irregular_binary_ops_1D();
   time_irregular_binary_ops_2D();
   time_irregular_binary_ops_3D();

benchmarks/cpp/single_ops.cpp

@@ -3,20 +3,20 @@
 #include "mlx/mlx.h"
 #include "time_utils.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 void time_creation_ops() {
   int M = 2000;
   int N = 500;
   auto shape = {M, N};
-  auto full_fp32 = [&]() { return mx::full(shape, 3.3f); };
+  auto full_fp32 = [&]() { return full(shape, 3.3f); };
   TIME(full_fp32);
-  auto zeros_fp32 = [&]() { return mx::zeros(shape, mx::float32); };
+  auto zeros_fp32 = [&]() { return zeros(shape, float32); };
   TIME(zeros_fp32);
-  auto ones_fp32 = [&]() { return mx::ones(shape, mx::float32); };
+  auto ones_fp32 = [&]() { return ones(shape, float32); };
   TIME(ones_fp32);
 
-  auto arange_fp32 = [&]() { return mx::arange(0.0, 10.0, 1e-4); };
+  auto arange_fp32 = [&]() { return arange(0.0, 10.0, 1e-4); };
   TIME(arange_fp32);
 }
 
@@ -24,196 +24,194 @@ void time_type_conversions() {
   int M = 2000;
   int N = 500;
   auto shape = {M, N};
-  auto device = mx::default_device();
+  auto device = default_device();
 
-  auto a = mx::zeros(shape, mx::float32);
-  mx::eval(a);
-  TIMEM("mx::float32 to mx::int32", mx::astype, a, mx::int32, device);
-  TIMEM("mx::float32 to mx::uint32", mx::astype, a, mx::uint32, device);
+  auto a = zeros(shape, float32);
+  eval(a);
+  TIMEM("float32 to int32", astype, a, int32, device);
+  TIMEM("float32 to uint32", astype, a, uint32, device);
 
-  a = mx::zeros(shape, mx::int32);
-  mx::eval(a);
-  TIMEM("mx::int32 to mx::float32", mx::astype, a, mx::float32, device);
+  a = zeros(shape, int32);
+  eval(a);
+  TIMEM("int32 to float32", astype, a, float32, device);
 
-  a = mx::zeros(shape, mx::bool_);
-  mx::eval(a);
-  TIMEM("bool to mx::float32", mx::astype, a, mx::float32, device);
-  TIMEM("bool to mx::int32", mx::astype, a, mx::int32, device);
-  TIMEM("bool to mx::uint32", mx::astype, a, mx::uint32, device);
+  a = zeros(shape, bool_);
+  eval(a);
+  TIMEM("bool to float32", astype, a, float32, device);
+  TIMEM("bool to int32", astype, a, int32, device);
+  TIMEM("bool to uint32", astype, a, uint32, device);
 }
 
 void time_random_generation() {
   int M = 2000;
   int N = 500;
 
-  auto uniform = [&]() { return mx::random::uniform({M, N}, mx::float32); };
+  auto uniform = [&]() { return random::uniform({M, N}, float32); };
   TIME(uniform);
-  auto normal = [&]() { return mx::random::normal({M, N}, mx::float32); };
+  auto normal = [&]() { return random::normal({M, N}, float32); };
   TIME(normal);
 }
 
 void time_unary_ops() {
   int M = 2000;
   int N = 500;
-  auto device = mx::default_device();
+  auto device = default_device();
 
-  auto a = mx::random::normal({M, N});
-  mx::eval(a);
+  auto a = random::normal({M, N});
+  eval(a);
   TIME(mlx::core::abs, a, device);
-  TIME(mx::negative, a, device);
-  TIME(mx::sign, a, device);
-  TIME(mx::square, a, device);
+  TIME(negative, a, device);
+  TIME(sign, a, device);
+  TIME(square, a, device);
   TIME(mlx::core::sqrt, a, device);
-  TIME(mx::rsqrt, a, device);
+  TIME(rsqrt, a, device);
   TIME(mlx::core::exp, a, device);
 
-  a = mx::random::uniform({M, N});
+  a = random::uniform({M, N});
   TIME(mlx::core::log, a, device);
 }
 
 void time_binary_ops() {
   int M = 1000, N = 100, K = 10;
-  auto condition = mx::random::randint(0, 2, {M, N, K});
-  auto a = mx::random::uniform({M, N, K});
-  auto b = mx::random::uniform({M, N, K});
-  auto device = mx::default_device();
-  mx::eval(a, b);
+  auto condition = random::randint(0, 2, {M, N, K});
+  auto a = random::uniform({M, N, K});
+  auto b = random::uniform({M, N, K});
+  auto device = default_device();
+  eval(a, b);
 
-  TIME(mx::add, a, b, device);
-  TIME(mx::subtract, a, b, device);
-  TIME(mx::multiply, a, b, device);
-  TIME(mx::divide, a, b, device);
-  TIME(mx::maximum, a, b, device);
-  TIME(mx::minimum, a, b, device);
-  TIME(mx::where, condition, a, b, device);
+  TIME(add, a, b, device);
+  TIME(subtract, a, b, device);
+  TIME(multiply, a, b, device);
+  TIME(divide, a, b, device);
+  TIME(maximum, a, b, device);
+  TIME(minimum, a, b, device);
+  TIME(where, condition, a, b, device);
 
-  condition = mx::array({true});
-  b = mx::random::uniform({1});
-  mx::eval(b);
-  TIMEM("scalar", mx::add, a, b, device);
-  TIMEM("vector-scalar", mx::subtract, a, b, device);
-  TIMEM("scalar-vector", mx::subtract, b, a, device);
-  TIMEM("scalar", mx::multiply, a, b, device);
-  TIMEM("vector-scalar", mx::divide, a, b, device);
-  TIMEM("scalar-vector", mx::divide, b, a, device);
-  TIMEM("scalar-vector", mx::where, condition, a, b, device);
+  condition = array({true});
+  b = random::uniform({1});
+  eval(b);
+  TIMEM("scalar", add, a, b, device);
+  TIMEM("vector-scalar", subtract, a, b, device);
+  TIMEM("scalar-vector", subtract, b, a, device);
+  TIMEM("scalar", multiply, a, b, device);
+  TIMEM("vector-scalar", divide, a, b, device);
+  TIMEM("scalar-vector", divide, b, a, device);
+  TIMEM("scalar-vector", where, condition, a, b, device);
 
-  condition = mx::broadcast_to(mx::array({true}), {1000, 100});
-  a = mx::broadcast_to(mx::random::uniform({1}), {1000, 100});
-  b = mx::broadcast_to(mx::random::uniform({1}), {1000, 100});
-  mx::eval(a, b);
-  TIMEM("scalar-scalar broadcast", mx::add, a, b, device);
-  TIMEM("scalar-scalar broadcast", mx::subtract, a, b, device);
-  TIMEM("scalar-scalar broadcast", mx::multiply, a, b, device);
-  TIMEM("scalar-scalar broadcast", mx::divide, a, b, device);
-  TIMEM("scalar-scalar broadcast", mx::where, condition, a, b, device);
+  condition = broadcast_to(array({true}), {1000, 100});
+  a = broadcast_to(random::uniform({1}), {1000, 100});
+  b = broadcast_to(random::uniform({1}), {1000, 100});
+  eval(a, b);
+  TIMEM("scalar-scalar broadcast", add, a, b, device);
+  TIMEM("scalar-scalar broadcast", subtract, a, b, device);
+  TIMEM("scalar-scalar broadcast", multiply, a, b, device);
+  TIMEM("scalar-scalar broadcast", divide, a, b, device);
+  TIMEM("scalar-scalar broadcast", where, condition, a, b, device);
 }
 
 void time_strided_ops() {
   int M = 50, N = 50, O = 50, P = 50;
-  auto a = mx::random::uniform({M, N, O, P});
-  auto b = mx::random::uniform({M, N, O, P});
-  auto device = mx::default_device();
-  mx::eval(a, b);
-  TIMEM("non-strided", mx::add, a, b, device);
-  a = mx::transpose(a, {1, 0, 2, 3});
-  b = mx::transpose(b, {3, 2, 0, 1});
-  mx::eval(a, b);
-  TIMEM("strided", mx::add, a, b, device);
+  auto a = random::uniform({M, N, O, P});
+  auto b = random::uniform({M, N, O, P});
+  auto device = default_device();
+  eval(a, b);
+  TIMEM("non-strided", add, a, b, device);
+  a = transpose(a, {1, 0, 2, 3});
+  b = transpose(b, {3, 2, 0, 1});
+  eval(a, b);
+  TIMEM("strided", add, a, b, device);
 }
 
 void time_comparisons() {
   int M = 1000, N = 100, K = 10;
-  auto a = mx::random::uniform({M, N, K});
-  auto b = mx::random::uniform({M, N, K});
-  auto device = mx::default_device();
-  mx::eval(a, b);
-  TIME(mx::equal, a, b, device);
-  TIME(mx::greater, a, b, device);
-  TIME(mx::greater_equal, a, b, device);
-  TIME(mx::less, a, b, device);
-  TIME(mx::less_equal, a, b, device);
+  auto a = random::uniform({M, N, K});
+  auto b = random::uniform({M, N, K});
+  auto device = default_device();
+  eval(a, b);
+  TIME(equal, a, b, device);
+  TIME(greater, a, b, device);
+  TIME(greater_equal, a, b, device);
+  TIME(less, a, b, device);
+  TIME(less_equal, a, b, device);
 }
 
 void time_matvec() {
   int M = 2000, N = 200;
-  auto a = mx::random::uniform({M, N});
-  auto b = mx::random::uniform({N});
-  auto c = mx::random::uniform({M});
-  mx::eval(a, b, c);
-  auto matvec = [&]() { return mx::matmul(a, b); };
+  auto a = random::uniform({M, N});
+  auto b = random::uniform({N});
+  auto c = random::uniform({M});
+  eval(a, b, c);
+  auto matvec = [&]() { return matmul(a, b); };
   TIME(matvec);
 
-  auto matvec_transpose = [&]() { return mx::matmul(mx::transpose(a), c); };
+  auto matvec_transpose = [&]() { return matmul(transpose(a), c); };
   TIME(matvec_transpose);
 }
 
 void time_matmul() {
   int M = 1000, N = 1000, K = 1000;
-  auto a = mx::random::uniform({M, K});
-  auto b = mx::random::uniform({K, N});
-  auto device = mx::default_device();
-  mx::eval(a, b);
-  TIME(mx::matmul, a, b, device);
+  auto a = random::uniform({M, K});
+  auto b = random::uniform({K, N});
+  auto device = default_device();
+  eval(a, b);
+  TIME(matmul, a, b, device);
 
-  auto transpose_matmul = [&]() { return mx::matmul(mx::transpose(a), b); };
+  auto transpose_matmul = [&]() { return matmul(transpose(a), b); };
   TIME(transpose_matmul);
 }
 
 void time_reductions() {
-  auto a = mx::random::normal({10000, 1000});
-  mx::eval(a);
-  auto sum_all = [&a]() { return mx::sum(a, false); };
+  auto a = random::normal({10000, 1000});
+  eval(a);
+  auto sum_all = [&a]() { return sum(a, false); };
   TIME(sum_all);
 
-  auto sum_along_0 = [&a]() { return mx::sum(a, 0, false); };
+  auto sum_along_0 = [&a]() { return sum(a, 0, false); };
   TIME(sum_along_0);
 
-  auto sum_along_1 = [&a]() { return mx::sum(a, 1, false); };
+  auto sum_along_1 = [&a]() { return sum(a, 1, false); };
   TIME(sum_along_1);
 
-  auto prod_all = [&a]() { return mx::prod(a, false); };
+  auto prod_all = [&a]() { return prod(a, false); };
   TIME(prod_all);
 
-  auto all_true = [&a]() { return mx::all(a, false); };
+  auto all_true = [&a]() { return all(a, false); };
   TIME(all_true);
 
-  auto all_along_0 = [&a]() { return mx::all(a, 0, false); };
+  auto all_along_0 = [&a]() { return all(a, 0, false); };
   TIME(all_along_0);
 
-  auto all_along_1 = [&a]() { return mx::all(a, 1, false); };
+  auto all_along_1 = [&a]() { return all(a, 1, false); };
   TIME(all_along_1);
 
-  auto any_true = [&a]() { return mx::any(a, false); };
+  auto any_true = [&a]() { return any(a, false); };
   TIME(any_true);
 
-  auto argmin_along_0 = [&a]() { return mx::argmin(a, 0, false); };
+  auto argmin_along_0 = [&a]() { return argmin(a, 0, false); };
   TIME(argmin_along_0);
 
-  auto argmin_along_1 = [&a]() { return mx::argmin(a, 1, false); };
+  auto argmin_along_1 = [&a]() { return argmin(a, 1, false); };
   TIME(argmin_along_1);
 }
 
 void time_gather_scatter() {
-  auto a = mx::random::normal({1000, 768});
-  mx::eval(a);
-  auto indices = mx::random::randint(0, 1000, {256});
-  mx::eval(indices);
+  auto a = random::normal({1000, 768});
+  eval(a);
+  auto indices = random::randint(0, 1000, {256});
+  eval(indices);
 
-  auto embedding_lookup = [&a, &indices]() { return mx::take(a, indices, 0); };
+  auto embedding_lookup = [&a, &indices]() { return take(a, indices, 0); };
   TIME(embedding_lookup);
 
-  indices = mx::random::randint(0, 768 * 1000, {256 * 768});
-  mx::eval(indices);
+  indices = random::randint(0, 768 * 1000, {256 * 768});
+  eval(indices);
 
-  auto single_element_lookup = [&a, &indices]() {
-    return mx::take(a, indices);
-  };
+  auto single_element_lookup = [&a, &indices]() { return take(a, indices); };
   TIME(single_element_lookup);
 
-  indices = mx::random::randint(0, 1000, {256});
-  auto updates = mx::random::normal({256, 1, 768});
-  mx::eval(indices, updates);
+  indices = random::randint(0, 1000, {256});
+  auto updates = random::normal({256, 1, 768});
+  eval(indices, updates);
 
   auto embedding_update = [&a, &indices, &updates]() {
     return scatter(a, indices, updates, 0);
@@ -225,10 +223,10 @@ void time_gather_scatter() {
   };
   TIME(embedding_add);
 
-  a = mx::reshape(a, {-1});
-  indices = mx::random::randint(0, 768 * 1000, {768 * 256});
-  updates = mx::random::normal({256 * 768, 1});
-  mx::eval(a, indices, updates);
+  a = reshape(a, {-1});
+  indices = random::randint(0, 768 * 1000, {768 * 256});
+  updates = random::normal({256 * 768, 1});
+  eval(a, indices, updates);
 
   auto single_element_update = [&a, &indices, &updates]() {
     return scatter(a, indices, updates, 0);
@@ -242,21 +240,21 @@ void time_gather_scatter() {
 }
 
 void time_divmod() {
-  auto a = mx::random::normal({1000});
-  auto b = mx::random::normal({1000});
-  mx::eval({a, b});
+  auto a = random::normal({1000});
+  auto b = random::normal({1000});
+  eval({a, b});
 
-  auto divmod_fused = [&a, &b]() { return mx::divmod(a, b); };
+  auto divmod_fused = [&a, &b]() { return divmod(a, b); };
   TIME(divmod_fused);
 
   auto divmod_separate = [&a, &b]() {
-    return std::vector<mx::array>{mx::floor_divide(a, b), mx::remainder(a, b)};
+    return std::vector<array>{floor_divide(a, b), remainder(a, b)};
   };
   TIME(divmod_separate);
 }
 
 int main() {
-  std::cout << "Benchmarks for " << mx::default_device() << std::endl;
+  std::cout << "Benchmarks for " << default_device() << std::endl;
   time_creation_ops();
   time_type_conversions();
   time_unary_ops();

benchmarks/python/comparative/bench_mlx.py

@@ -144,13 +144,6 @@ def reduction(op, axis, x):
     mx.eval(ys)
 
 
-def sum_and_add(axis, x, y):
-    z = x.sum(axis=axis, keepdims=True)
-    for i in range(50):
-        z = (z + y).sum(axis=axis, keepdims=True)
-    mx.eval(z)
-
-
 def softmax(axis, x):
     ys = []
     for i in range(100):
@@ -512,8 +505,5 @@ if __name__ == "__main__":
     elif args.benchmark == "selu":
         print(bench(selu, x))
 
-    elif args.benchmark == "sum_and_add":
-        print(bench(sum_and_add, axis, *xs))
-
     else:
         raise ValueError("Unknown benchmark")

benchmarks/python/sdpa_bench.py

@@ -1,189 +1,62 @@
-# Copyright © 2024 Apple Inc.
-
 import argparse
 import math
-import os
-import subprocess
-import time
 
 import mlx.core as mx
-import numpy as np
+from time_utils import time_fn
 
-device_name = subprocess.check_output(["sysctl", "-n", "machdep.cpu.brand_string"])
-device_name = device_name.decode("utf-8").strip("\n")
-
-N_warmup = 5
-N_iter_bench = 40
-N_iter_func = 8
+MAX_SEQ = 300
+START_SEQ = 100
+SEQ_INCREMENT = 50
 
 
-def bench(f, *args):
-    for i in range(N_warmup):
-        f(*args)
+def time_self_attention_primitives():
+    mx.random.seed(3)
+    B = 2
+    H = 38
+    D = 64
+    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
+        q = mx.random.uniform(shape=(B, H, R, D))
+        k = mx.random.uniform(shape=(B, H, R, D))
+        v = mx.random.uniform(shape=(B, H, R, D))
+        scale = 1.0 / math.sqrt(float(D))
+        mx.eval(q, k, v)
 
-    s = time.perf_counter_ns()
-    for i in range(N_iter_bench):
-        f(*args)
-    e = time.perf_counter_ns()
-    return (e - s) * 1e-9
+        def sdpa_primitives(qs, ks, vs, alpha):
+            s = (alpha * qs) @ ks.transpose(0, 1, 3, 2)
+            p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
+            o = p @ vs
+            return o
+
+        time_fn(sdpa_primitives, q, k, v, scale)
 
 
-def mlx_sdpa_fused_inner(q, k, v, scale):
-    return mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask=None)
+def time_self_attention_sdpa():
+    mx.random.seed(3)
+    B = 2
+    H = 38
+    D = 64
+    for R in range(START_SEQ, MAX_SEQ, SEQ_INCREMENT):
+        q = mx.random.uniform(shape=(B, H, R, D))
+        k = mx.random.uniform(shape=(B, H, R, D))
+        v = mx.random.uniform(shape=(B, H, R, D))
+        scale = 1.0 / math.sqrt(float(D))
+        mx.eval(q, k, v)
 
+        def sdpa_fused(qs, ks, vs, alpha):
+            o = mx.fast.scaled_dot_product_attention(qs, ks, vs, scale=alpha)
+            return o
 
-def mlx_sdpa_unfused_inner(q, k, v, scale, f32softmax=False):
-    q_dtype = q.dtype
-    q = q * mx.array(scale, q_dtype)
-    n_q_heads = q.shape[-3]
-    n_kv_heads = k.shape[-3]
-    n_repeats = n_q_heads // n_kv_heads
-
-    B = q.shape[0]
-    L = q.shape[2]
-
-    if n_repeats > 1:
-        q = mx.reshape(q, [B, n_kv_heads, n_repeats, L, -1])
-        k = mx.expand_dims(k, 2)
-        v = mx.expand_dims(v, 2)
-
-    scores = q @ mx.swapaxes(k, -1, -2)
-    if f32softmax:
-        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q_dtype)
-    else:
-        scores = mx.softmax(scores, axis=-1)
-
-    out = scores @ v
-    if n_repeats > 1:
-        out = mx.reshape(out, [B, n_q_heads, L, -1])
-
-    return out
-
-
-def mlx_spda_unfused(q, k, v, scale, transpose):
-    q_out = q
-    if transpose:
-        k = mx.transpose(k, (0, 2, 1, 3))
-        v = mx.transpose(v, (0, 2, 1, 3))
-
-    for i in range(N_iter_func):
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-        q_out = mlx_sdpa_unfused_inner(q_out, k, v, scale)
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-
-    mx.eval(q_out)
-    return q_out
-
-
-def mlx_spda_fused(q, k, v, scale, transpose):
-    q_out = q
-    if transpose:
-        k = mx.transpose(k, (0, 2, 1, 3))
-        v = mx.transpose(v, (0, 2, 1, 3))
-
-    for i in range(N_iter_func):
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-        q_out = mlx_sdpa_fused_inner(q_out, k, v, scale)
-        if transpose:
-            q_out = mx.transpose(q_out, (0, 2, 1, 3))
-
-    mx.eval(q_out)
-    return q_out
-
-
-def bench_shape(B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose=True):
-    shape_q = (
-        (B, qsl, n_q_heads, head_dim) if transpose else (B, n_q_heads, qsl, head_dim)
-    )
-    shape_kv = (
-        (B, ksl, n_kv_heads, head_dim) if transpose else (B, n_kv_heads, ksl, head_dim)
-    )
-
-    q_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_q).astype(np_dtype)
-    k_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
-    v_np = np.random.normal(0.0, 1.0 / math.sqrt(head_dim), shape_kv).astype(np_dtype)
-
-    scale = math.sqrt(1.0 / head_dim)
-
-    q_mx = mx.array(q_np)
-    k_mx = mx.array(k_np)
-    v_mx = mx.array(v_np)
-
-    time_mlx_unfused = bench(mlx_spda_unfused, q_mx, k_mx, v_mx, scale, transpose)
-    time_mlx_fused = bench(mlx_spda_fused, q_mx, k_mx, v_mx, scale, transpose)
-
-    if transpose:
-        q_mx = mx.transpose(q_mx, (0, 2, 1, 3))
-        k_mx = mx.transpose(k_mx, (0, 2, 1, 3))
-        v_mx = mx.transpose(v_mx, (0, 2, 1, 3))
-
-    o_mlx_fused = mlx_sdpa_fused_inner(q_mx, k_mx, v_mx, scale)
-    o_mlx_unfused = mlx_sdpa_unfused_inner(q_mx, k_mx, v_mx, scale, f32softmax=True)
-
-    atol = 1e-5 if np_dtype == np.float32 else 1e-4
-
-    if not mx.allclose(o_mlx_fused, o_mlx_unfused, atol=atol):
-        print(
-            f"Failed at (B: {B}, qsl: {qsl}, ksl: {ksl}, head_dim: {head_dim}, n_qh: {n_q_heads}, n_kvh: {n_kv_heads}) [tpose = {transpose}] with max(|a - b|) = {mx.max(mx.abs(o_mlx_unfused - o_mlx_fused)):3.2e}"
-        )
-
-    return time_mlx_fused, time_mlx_unfused
-
-
-def get_gflop_count(B, M, N, K):
-    return float(2.0 * N_iter_bench * N_iter_func * B * M * N * K) / float(1024.0**3)
+        time_fn(sdpa_fused, q, k, v, scale)
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run gemm benchmarks")
+    parser = argparse.ArgumentParser("MLX benchmarks.")
+    parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
+    args = parser.parse_args()
+    if args.gpu:
+        mx.set_default_device(mx.gpu)
+    else:
+        mx.set_default_device(mx.cpu)
 
-    dtypes = ("float16", "float32")[:1]
-    transposes = (False,)
-
-    # fmt: off
-    shapes_64 = (
-        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,    32,    32,       64,   32,    32),
-          (  1,    64,    64,       64,   32,    32),
-          (  1,   128,   128,       64,   32,    32),
-          (  1,   256,   256,       64,   32,    32),
-          (  1,   512,   512,       64,   32,    32),
-          (  1,  1024,  1024,       64,   32,    32),
-          (  1,  2048,  2048,       64,   32,    32),
-          (  1,  4096,  4096,       64,   32,    32),
-    )
-
-    shapes_80 = (
-        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,       80,   32,    32),
-          (  1,  2048,  2048,       80,   32,    32),
-          (  1,  4096,  4096,       80,   32,    32),
-    )
-
-    shapes_128 = (
-        # (  B,   qsl,   ksl, head_dim, n_qh, n_kvh)
-          (  1,  1024,  1024,      128,   32,    32),
-          (  1,  2048,  2048,      128,   32,    32),
-          (  1,  4096,  4096,      128,   32,    32),
-    )
-    # fmt: on
-
-    shapes = shapes_64 + shapes_80 + shapes_128
-
-    print("  B,   qsl,   ksl, hdim, n_qh, n_kvh, tpose,   dtype, t_unfs, t_fuse, diff%")
-
-    for dtype in dtypes:
-        for transpose in transposes:
-            for B, qsl, ksl, head_dim, n_q_heads, n_kv_heads in shapes:
-                np_dtype = getattr(np, dtype)
-                time_mlx_fused, time_mlx_unfused = bench_shape(
-                    B, qsl, ksl, head_dim, n_q_heads, n_kv_heads, np_dtype, transpose
-                )
-                diff = time_mlx_unfused / time_mlx_fused - 1.0
-                t_str = 1 if transpose else 0
-                print(
-                    f"{B:3d}, {qsl:5d}, {ksl:5d}, {head_dim:4d}, {n_q_heads:4d}, {n_kv_heads:5d}, {t_str:5d}, {dtype}, {time_mlx_unfused: 2.3f}, {time_mlx_fused: 2.3f}, {100. * diff:+5.2f}%"
-                )
+    time_self_attention_sdpa()
+    time_self_attention_primitives()

benchmarks/python/sdpa_vector_bench.py

@@ -4,51 +4,42 @@ import math
 import mlx.core as mx
 from time_utils import time_fn
 
-L = 16384
+L = 1024
 H = 32
-H_k = H // 4
+H_k = 32 // 4
 D = 128
-dtype = mx.float16
-loops = 10
 
 
 def attention(q, k, v):
-    def _sdpa(q, k, v):
-        B, Hq, L, D = q.shape
-        _, Hk, S, _ = k.shape
-        q = q.reshape(B, Hk, Hq // Hk, L, D)
-        k = k[:, :, None, :, :]
-        v = v[:, :, None, :, :]
-        s = q @ k.transpose(0, 1, 2, 4, 3)
-        p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
-        o = p @ v
-        return o.reshape(B, Hq, L, D)
-
-    for i in range(loops):
-        q = _sdpa(q, k, v)
-    return q
+    B, Hq, L, D = q.shape
+    _, Hk, S, _ = k.shape
+    q = q.reshape(B, Hk, Hq // Hk, L, D)
+    k = k[:, :, None, :, :]
+    v = v[:, :, None, :, :]
+    s = q @ k.transpose(0, 1, 2, 4, 3)
+    p = mx.softmax(s.astype(mx.float32), axis=-1).astype(s.dtype)
+    o = p @ v
+    return o.reshape(B, Hq, L, D)
 
 
 def sdpa(q, k, v):
-    for i in range(loops):
-        q = mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
-    return q
+    return mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0)
 
 
 def time_self_attention_primitives():
     mx.random.seed(3)
-    q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
-    k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
-    v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    q = mx.random.uniform(shape=(1, H, 1, D))
+    k = mx.random.uniform(shape=(1, H_k, L, D))
+    v = mx.random.uniform(shape=(1, H_k, L, D))
     mx.eval(q, k, v)
     time_fn(attention, q, k, v)
 
 
 def time_self_attention_sdpa():
     mx.random.seed(3)
-    q = mx.random.uniform(shape=(1, H, 1, D)).astype(dtype)
-    k = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
-    v = mx.random.uniform(shape=(1, H_k, L, D)).astype(dtype)
+    q = mx.random.uniform(shape=(1, H, 1, D))
+    k = mx.random.uniform(shape=(1, H_k, L, D))
+    v = mx.random.uniform(shape=(1, H_k, L, D))
     mx.eval(q, k, v)
     time_fn(sdpa, q, k, v)
 

docs/src/dev/extensions.rst

@@ -420,8 +420,8 @@ element in the output.
             constant const float& alpha [[buffer(3)]],
             constant const float& beta [[buffer(4)]],
             constant const int* shape [[buffer(5)]],
-            constant const int64_t* x_strides [[buffer(6)]],
-            constant const int64_t* y_strides [[buffer(7)]],
+            constant const size_t* x_strides [[buffer(6)]],
+            constant const size_t* y_strides [[buffer(7)]],
             constant const int& ndim [[buffer(8)]],
             uint index [[thread_position_in_grid]]) {
         // Convert linear indices to offsets in array
@@ -438,10 +438,24 @@ each instantiation a unique host name so we can identify it.
 
 .. code-block:: C++
 
-    instantiate_kernel("axpby_general_float32", axpby_general, float)
-    instantiate_kernel("axpby_general_float16", axpby_general, float16_t)
-    instantiate_kernel("axpby_general_bfloat16", axpby_general, bfloat16_t)
-    instantiate_kernel("axpby_general_complex64", axpby_general, complex64_t)
+    #define instantiate_axpby(type_name, type)              \
+        template [[host_name("axpby_general_" #type_name)]] \
+        [[kernel]] void axpby_general<type>(                \
+            device const type* x [[buffer(0)]],             \
+            device const type* y [[buffer(1)]],             \
+            device type* out [[buffer(2)]],                 \
+            constant const float& alpha [[buffer(3)]],      \
+            constant const float& beta [[buffer(4)]],       \
+            constant const int* shape [[buffer(5)]],        \
+            constant const size_t* x_strides [[buffer(6)]], \
+            constant const size_t* y_strides [[buffer(7)]], \
+            constant const int& ndim [[buffer(8)]],         \
+            uint index [[thread_position_in_grid]]);
+
+    instantiate_axpby(float32, float);
+    instantiate_axpby(float16, half);
+    instantiate_axpby(bfloat16, bfloat16_t);
+    instantiate_axpby(complex64, complex64_t);
 
 The logic to determine the kernel, set the inputs, resolve the grid dimensions,
 and dispatch to the GPU are contained in :meth:`Axpby::eval_gpu` as shown
@@ -480,7 +494,7 @@ below.
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);
-        compute_encoder.set_compute_pipeline_state(kernel);
+        compute_encoder->setComputePipelineState(kernel);
 
         // Kernel parameters are registered with buffer indices corresponding to
         // those in the kernel declaration at axpby.metal
@@ -495,14 +509,14 @@ below.
         compute_encoder.set_output_array(out, 2);
 
         // Encode alpha and beta
-        compute_encoder.set_bytes(alpha_, 3);
-        compute_encoder.set_bytes(beta_, 4);
+        compute_encoder->setBytes(&alpha_, sizeof(float), 3);
+        compute_encoder->setBytes(&beta_, sizeof(float), 4);
 
         // Encode shape, strides and ndim
-        compute_encoder.set_vector_bytes(x.shape(), 5);
-        compute_encoder.set_vector_bytes(x.strides(), 6);
-        compute_encoder.set_bytes(y.strides(), 7);
-        compute_encoder.set_bytes(ndim, 8);
+        compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
+        compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
+        compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
+        compute_encoder->setBytes(&ndim, sizeof(int), 8);
 
         // We launch 1 thread for each input and make sure that the number of
         // threads in any given threadgroup is not higher than the max allowed
@@ -516,7 +530,7 @@ below.
 
         // Launch the grid with the given number of threads divided among
         // the given threadgroups
-        compute_encoder.dispatch_threads(grid_dims, group_dims);
+        compute_encoder.dispatchThreads(grid_dims, group_dims);
     }
 
 We can now call the :meth:`axpby` operation on both the CPU and the GPU!

docs/src/dev/mlx_in_cpp.rst

@@ -1,121 +0,0 @@
-.. _mlx_in_cpp:
-
-Using MLX in C++
-================
-
-You can use MLX in a C++ project with CMake.
-
-.. note::
-
-  This guide is based one the following `example using MLX in C++ 
-  <https://github.com/ml-explore/mlx/tree/main/examples/cmake_project>`_
-
-First install MLX:
-
-.. code-block:: bash
-
-  pip install -U mlx
-
-You can also install the MLX Python package from source or just the C++
-library. For more information see the :ref:`documentation on installing MLX
-<build_and_install>`.
-
-Next make an example program in ``example.cpp``: 
-
-.. code-block:: C++
-
-  #include <iostream>
-
-  #include "mlx/mlx.h"
-
-  namespace mx = mlx::core;
-
-  int main() {
-    auto x = mx::array({1, 2, 3});
-    auto y = mx::array({1, 2, 3});
-    std::cout << x + y << std::endl;
-    return 0;
-  }
-
-The next step is to setup a CMake file in ``CMakeLists.txt``:
-
-.. code-block:: cmake
-
-  cmake_minimum_required(VERSION 3.27)
-
-  project(example LANGUAGES CXX)
-
-  set(CMAKE_CXX_STANDARD 17)
-  set(CMAKE_CXX_STANDARD_REQUIRED ON)
-
-
-Depending on how you installed MLX, you may need to tell CMake where to
-find it. 
-
-If you installed MLX with Python, then add the following to the CMake file:
-
-.. code-block:: cmake
-
-  find_package(
-    Python 3.9
-    COMPONENTS Interpreter Development.Module
-    REQUIRED)
-  execute_process(
-    COMMAND "${Python_EXECUTABLE}" -m mlx --cmake-dir
-    OUTPUT_STRIP_TRAILING_WHITESPACE
-    OUTPUT_VARIABLE MLX_ROOT)
-
-If you installed the MLX C++ package to a system path, then CMake should be
-able to find it. If you installed it to a non-standard location or CMake can't
-find MLX then set ``MLX_ROOT`` to the location where MLX is installed:
-
-.. code-block:: cmake
-
-  set(MLX_ROOT "/path/to/mlx/")
-
-Next, instruct CMake to find MLX:
-
-.. code-block:: cmake
-
-  find_package(MLX CONFIG REQUIRED)
-
-Finally, add the ``example.cpp`` program as an executable and link MLX.
-
-.. code-block:: cmake
-
-  add_executable(example example.cpp)
-  target_link_libraries(example PRIVATE mlx)
-
-You can build the example with:
-
-.. code-block:: bash
-
-  cmake -B build -DCMAKE_BUILD_TYPE=Release
-  cmake --build build
-
-And run it with:
-
-.. code-block:: bash
-
-  ./build/example
-
-Note ``find_package(MLX CONFIG REQUIRED)`` sets the following variables:
-
-.. list-table:: Package Variables
-   :widths: 20 20 
-   :header-rows: 1
-
-   * - Variable 
-     - Description 
-   * - MLX_FOUND
-     - ``True`` if MLX is found
-   * - MLX_INCLUDE_DIRS
-     - Include directory
-   * - MLX_LIBRARIES
-     - Libraries to link against
-   * - MLX_CXX_FLAGS
-     - Additional compiler flags
-   * - MLX_BUILD_ACCELERATE
-     - ``True`` if MLX was built with Accelerate 
-   * - MLX_BUILD_METAL
-     - ``True`` if MLX was built with Metal

docs/src/index.rst

@@ -45,7 +45,6 @@ are the CPU and GPU.
    usage/numpy
    usage/distributed
    usage/using_streams
-   usage/export
 
 .. toctree::
    :caption: Examples
@@ -62,7 +61,6 @@ are the CPU and GPU.
    python/array
    python/data_types
    python/devices_and_streams
-   python/export
    python/ops
    python/random
    python/transforms
@@ -88,4 +86,3 @@ are the CPU and GPU.
    dev/extensions
    dev/metal_debugger
    dev/custom_metal_kernels
-   dev/mlx_in_cpp

docs/src/install.rst

@@ -1,5 +1,3 @@
-.. _build_and_install:
-
 Build and Install
 =================
 
@@ -55,7 +53,7 @@ Build Requirements
 ^^^^^^^^^^^^^^^^^^
 
 - A C++ compiler with C++17 support (e.g. Clang >= 5.0)
-- `cmake <https://cmake.org/>`_ -- version 3.25 or later, and ``make``
+- `cmake <https://cmake.org/>`_ -- version 3.24 or later, and ``make``
 - Xcode >= 15.0 and macOS SDK >= 14.0
 
 .. note::
@@ -211,7 +209,7 @@ Metal library by run-time compiling kernels the first time they are used in MLX
 on a given machine. Note run-time compilation incurs a cold-start cost which can
 be anwywhere from a few hundred millisecond to a few seconds depending on the
 application. Once a kernel is compiled, it will be cached by the system. The
-Metal kernel cache persists across reboots.
+Metal kernel cache persists accross reboots.
 
 Troubleshooting
 ^^^^^^^^^^^^^^^

docs/src/python/data_types.rst

@@ -66,4 +66,3 @@ documentation for more information. Use :func:`issubdtype` to determine if one
    Dtype
    DtypeCategory
    issubdtype
-   finfo

docs/src/python/export.rst

@@ -1,14 +0,0 @@
-.. _export:
-
-Export Functions
-================
-
-.. currentmodule:: mlx.core
-
-.. autosummary::
-  :toctree: _autosummary
-
-   export_function
-   import_function
-   exporter
-   export_to_dot

docs/src/python/fast.rst

@@ -12,4 +12,5 @@ Fast
   layer_norm
   rope
   scaled_dot_product_attention
+  affine_quantize
   metal_kernel

docs/src/python/nn/layers.rst

@@ -12,7 +12,6 @@ Layers
    ALiBi
    AvgPool1d
    AvgPool2d
-   AvgPool3d
    BatchNorm
    CELU
    Conv1d
@@ -42,7 +41,6 @@ Layers
    LSTM
    MaxPool1d
    MaxPool2d
-   MaxPool3d
    Mish
    MultiHeadAttention
    PReLU

docs/src/python/ops.rst

@@ -89,7 +89,6 @@ Operations
    isneginf
    isposinf
    issubdtype
-   kron
    left_shift
    less
    less_equal
@@ -169,7 +168,6 @@ Operations
    tri
    tril
    triu
-   unflatten
    var
    view
    where

docs/src/usage/compile.rst

@@ -421,77 +421,3 @@ the most opportunity to optimize the computation graph:
   # Compiling the outer function is good to do as it will likely
   # be faster even though the inner functions are compiled
   fun = mx.compile(outer)
-
-
-
-.. _shapeless_compile:
-
-Shapeless Compilation
----------------------
-
-When the shape of an input to a compiled function changes, the function is
-recompiled. You can compile a function once and run it on inputs with
-variable shapes by specifying ``shapeless=True`` to :func:`compile`. In this
-case changes to the shapes of the inputs do not cause the function to be
-recompiled.
-
-.. code-block:: python
-
-  def fun(x, y):
-      return mx.abs(x + y)
-
-  compiled_fun = mx.compile(fun, shapeless=True)
-
-  x = mx.array(1.0)
-  y = mx.array(-2.0)
-
-  # Firt call compiles the function
-  print(compiled_fun(x, y))
-
-  # Second call with different shapes
-  # does not recompile the function
-  x = mx.array([1.0, -6.0])
-  y = mx.array([-2.0, 3.0])
-  print(compiled_fun(x, y))
-
-
-Use shapeless compilations carefully. Since compilation is not triggered when
-shapes change, any graphs which are conditional on the input shapes will not
-work as expected. Shape-dependent computations are common and sometimes subtle
-to detect. For example:
-
-.. code-block:: python
-
-  def fun(x):
-      return x.reshape(x.shape[0] * x.shape[1], -1)
-
-  compiled_fun = mx.compile(fun, shapeless=True)
-
-  x = mx.random.uniform(shape=(2, 3, 4))
-
-  out = compiled_fun(x)
-
-  x = mx.random.uniform(shape=(5, 5, 3))
-
-  # Error, can't reshape (5, 5, 3) to (6, -1)
-  out = compiled_fun(x)
-
-The second call to the ``compiled_fun`` fails because of the call to
-:func:`reshape` which uses the static shape of ``x`` in the first call. We can
-fix this by using :func:`flatten` to avoid hardcoding the shape of ``x``:
-
-.. code-block:: python
-
-  def fun(x):
-      return x.flatten(0, 1)
-
-  compiled_fun = mx.compile(fun, shapeless=True)
-
-  x = mx.random.uniform(shape=(2, 3, 4))
-
-  out = compiled_fun(x)
-
-  x = mx.random.uniform(shape=(5, 5, 3))
-
-  # Ok
-  out = compiled_fun(x)

docs/src/usage/distributed.rst

@@ -141,13 +141,12 @@ everything else remaining the same.
     from mlx.utils import tree_map
 
     def all_reduce_grads(grads):
-        N = mx.distributed.init().size()
+        N = mx.distributed.init()
         if N == 1:
             return grads
         return tree_map(
-            lambda x: mx.distributed.all_sum(x) / N,
-            grads
-        )
+                lambda x: mx.distributed.all_sum(x) / N,
+                grads)
 
     def step(model, x, y):
         loss, grads = loss_grad_fn(model, x, y)

docs/src/usage/export.rst

@@ -1,288 +0,0 @@
-.. _export_usage:
-
-Exporting Functions
-===================
-
-.. currentmodule:: mlx.core
-
-MLX has an API to export and import functions to and from a file. This lets you
-run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++). 
-
-This guide walks through the basics of the MLX export API with some examples.
-To see the full list of functions check-out the :ref:`API documentation
-<export>`.
-
-Basics of Exporting 
--------------------
-
-Let's start with a simple example:
- 
-.. code-block:: python
-
-  def fun(x, y):
-    return x + y
-
-  x = mx.array(1.0)
-  y = mx.array(1.0)
-  mx.export_function("add.mlxfn", fun, x, y)
-
-To export a function, provide sample input arrays that the function
-can be called with. The data doesn't matter, but the shapes and types of the
-arrays do. In the above example we exported ``fun`` with two ``float32``
-scalar arrays. We can then import the function and run it:
-
-.. code-block:: python
-
-  add_fun = mx.import_function("add.mlxfn")
-
-  out, = add_fun(mx.array(1.0), mx.array(2.0))
-  # Prints: array(3, dtype=float32)
-  print(out)
-
-  out, = add_fun(mx.array(1.0), mx.array(3.0))
-  # Prints: array(4, dtype=float32)
-  print(out)
-
-  # Raises an exception
-  add_fun(mx.array(1), mx.array(3.0))
-
-  # Raises an exception
-  add_fun(mx.array([1.0, 2.0]), mx.array(3.0))
-
-Notice the third and fourth calls to ``add_fun`` raise exceptions because the
-shapes and types of the inputs are different than the shapes and types of the
-example inputs we exported the function with.
-
-Also notice that even though the original ``fun`` returns a single output
-array, the imported function always returns a tuple of one or more arrays.
-
-The inputs to :func:`export_function` and to an imported function can be
-specified as variable positional arguments or as a tuple of arrays:
-
-.. code-block:: python
-
-  def fun(x, y):
-    return x + y
-
-  x = mx.array(1.0)
-  y = mx.array(1.0)
-   
-  # Both arguments to fun are positional
-  mx.export_function("add.mlxfn", fun, x, y)
-
-  # Same as above
-  mx.export_function("add.mlxfn", fun, (x, y))
-
-  imported_fun = mx.import_function("add.mlxfn")
-
-  # Ok
-  out, = imported_fun(x, y)
-
-  # Also ok
-  out, = imported_fun((x, y))
-
-You can pass example inputs to functions as positional or keyword arguments. If
-you use keyword arguments to export the function, then you have to use the same
-keyword arguments when calling the imported function.
-
-.. code-block:: python
-
-  def fun(x, y):
-    return x + y
-
-  # One argument to fun is positional, the other is a kwarg
-  mx.export_function("add.mlxfn", fun, x, y=y)
-
-  imported_fun = mx.import_function("add.mlxfn")
-
-  # Ok
-  out, = imported_fun(x, y=y)
-
-  # Also ok
-  out, = imported_fun((x,), {"y": y})
-
-  # Raises since the keyword argument is missing
-  out, = imported_fun(x, y)
-
-  # Raises since the keyword argument has the wrong key
-  out, = imported_fun(x, z=y)
-
-
-Exporting Modules
------------------
-
-An :obj:`mlx.nn.Module` can be exported with or without the parameters included
-in the exported function. Here's an example:
-
-.. code-block:: python
-
-   model = nn.Linear(4, 4)
-   mx.eval(model.parameters())
-
-   def call(x):
-      return model(x)
-
-   mx.export_function("model.mlxfn", call, mx.zeros(4))
-
-In the above example, the :obj:`mlx.nn.Linear` module is exported. Its
-parameters are also saved to the ``model.mlxfn`` file.
-
-.. note::
-
-   For enclosed arrays inside an exported function, be extra careful to ensure
-   they are evaluated. The computation graph that gets exported will include
-   the computation that produces enclosed inputs.
-  
-   If the above example was missing ``mx.eval(model.parameters()``, the
-   exported function would include the random initialization of the
-   :obj:`mlx.nn.Module` parameters.
-
-If you only want to export the ``Module.__call__`` function without the
-parameters, pass them as inputs to the ``call`` wrapper:
-
-.. code-block:: python
-
-   model = nn.Linear(4, 4)
-   mx.eval(model.parameters())
-
-   def call(x, **params):
-     # Set the model's parameters to the input parameters
-     model.update(tree_unflatten(list(params.items())))
-     return model(x)
- 
-   params = dict(tree_flatten(model.parameters()))
-   mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
-
-
-Shapeless Exports
------------------
-
-Just like :func:`compile`, functions can also be exported for dynamically shaped
-inputs. Pass ``shapeless=True`` to :func:`export_function` or :func:`exporter`
-to export a function which can be used for inputs with variable shapes:
-
-.. code-block:: python
-
-  mx.export_function("fun.mlxfn", mx.abs, mx.array(0.0), shapeless=True)
-  imported_abs = mx.import_function("fun.mlxfn")
-
-  # Ok
-  out, = imported_abs(mx.array(-1.0))
-  
-  # Also ok 
-  out, = imported_abs(mx.array([-1.0, -2.0]))
-
-With ``shapeless=False`` (which is the default), the second call to
-``imported_abs`` would raise an exception with a shape mismatch.
-
-Shapeless exporting works the same as shapeless compilation and should be
-used carefully. See the :ref:`documentation on shapeless compilation
-<shapeless_compile>` for more information.
-
-Exporting Multiple Traces
--------------------------
-
-In some cases, functions build different computation graphs for different
-input arguments. A simple way to manage this is to export to a new file with
-each set of inputs. This is a fine option in many cases. But it can be
-suboptimal if the exported functions have a large amount of duplicate constant
-data (for example the parameters of a :obj:`mlx.nn.Module`).
-
-The export API in MLX lets you export multiple traces of the same function to
-a single file by creating an exporting context manager with :func:`exporter`:
-
-.. code-block:: python
-
-  def fun(x, y=None):
-      constant = mx.array(3.0)
-      if y is not None:
-        x += y 
-      return x + constant
-
-  with mx.exporter("fun.mlxfn", fun) as exporter:
-      exporter(mx.array(1.0))
-      exporter(mx.array(1.0), y=mx.array(0.0))
-
-  imported_function = mx.import_function("fun.mlxfn")
-
-  # Call the function with y=None
-  out, = imported_function(mx.array(1.0))
-  print(out)
-
-  # Call the function with y specified
-  out, = imported_function(mx.array(1.0), y=mx.array(1.0))
-  print(out)
-
-In the above example the function constant data, (i.e. ``constant``), is only
-saved once. 
-
-Transformations with Imported Functions
----------------------------------------
-
-Function transformations like :func:`grad`, :func:`vmap`, and :func:`compile` work
-on imported functions just like regular Python functions:
-
-.. code-block:: python
-
-  def fun(x):
-      return mx.sin(x)
-
-  x = mx.array(0.0)
-  mx.export_function("sine.mlxfn", fun, x)
-
-  imported_fun = mx.import_function("sine.mlxfn")
-
-  # Take the derivative of the imported function
-  dfdx = mx.grad(lambda x: imported_fun(x)[0])
-  # Prints: array(1, dtype=float32)
-  print(dfdx(x))
-
-  # Compile the imported function 
-  mx.compile(imported_fun)
-  # Prints: array(0, dtype=float32)
-  print(compiled_fun(x)[0])
-
-
-Importing Functions in C++
---------------------------
-
-Importing and running functions in C++ is basically the same as importing and
-running them in Python. First, follow the :ref:`instructions <mlx_in_cpp>` to
-setup a simple C++ project that uses MLX as a library.
-
-Next, export a simple function from Python:
-
-.. code-block:: python
-
-  def fun(x, y):
-      return mx.exp(x + y)
-
-  x = mx.array(1.0)
-  y = mx.array(1.0)
-  mx.export_function("fun.mlxfn", fun, x, y)
-
-
-Import and run the function in C++ with only a few lines of code:
-
-.. code-block:: c++
-
-  auto fun = mx::import_function("fun.mlxfn");
-
-  auto inputs = {mx::array(1.0), mx::array(1.0)};
-  auto outputs = fun(inputs);
-
-  // Prints: array(2, dtype=float32)
-  std::cout << outputs[0] << std::endl;
-
-Imported functions can be transformed in C++ just like in Python. Use 
-``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
-mx::array>`` for keyword arguments when calling imported functions in C++.
-
-More Examples
--------------
-
-Here are a few more complete examples exporting more complex functions from
-Python and importing and running them in C++:
-
-* `Inference and training a multi-layer perceptron <https://github.com/ml-explore/mlx/tree/main/examples/export>`_

docs/src/usage/function_transforms.rst

@@ -184,8 +184,8 @@ Let's time these two different versions:
   print(timeit.timeit(lambda: mx.eval(naive_add(xs, ys)), number=100))
   print(timeit.timeit(lambda: mx.eval(vmap_add(xs, ys)), number=100))
 
-On an M1 Max the naive version takes in total ``5.639`` seconds whereas the
-vectorized version takes only ``0.024`` seconds, more than 200 times faster.
+On an M1 Max the naive version takes in total ``0.390`` seconds whereas the
+vectorized version takes only ``0.025`` seconds, more than ten times faster.
 
 Of course, this operation is quite contrived. A better approach is to simply do
 ``xs + ys.T``, but for more complex functions :func:`vmap` can be quite handy.

examples/cmake_project/CMakeLists.txt

@@ -1,22 +0,0 @@
-cmake_minimum_required(VERSION 3.27)
-
-project(example LANGUAGES CXX)
-
-set(CMAKE_CXX_STANDARD 17)
-set(CMAKE_CXX_STANDARD_REQUIRED ON)
-
-# Comment the following two commands only the MLX C++ library is installed and
-# set(MLX_ROOT "/path/to/mlx") directly if needed.
-find_package(
-  Python 3.9
-  COMPONENTS Interpreter Development.Module
-  REQUIRED)
-execute_process(
-  COMMAND "${Python_EXECUTABLE}" -m mlx --cmake-dir
-  OUTPUT_STRIP_TRAILING_WHITESPACE
-  OUTPUT_VARIABLE MLX_ROOT)
-
-find_package(MLX CONFIG REQUIRED)
-
-add_executable(example example.cpp)
-target_link_libraries(example PRIVATE mlx)

examples/cmake_project/README.md

@@ -1,26 +0,0 @@
-## Build and Run 
-
-Install MLX with Python:
-
-```bash
-pip install mlx>=0.22
-```
-
-Build the C++ example:
-
-```bash
-cmake -B build -DCMAKE_BUILD_TYPE=Release
-cmake --build build
-```
-
-Run the C++ example:
-
-```
-./build/example
-```
-
-which should output:
-
-```
-array([2, 4, 6], dtype=int32)
-```

examples/cmake_project/example.cpp

@@ -1,14 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include <iostream>
-
-#include "mlx/mlx.h"
-
-namespace mx = mlx::core;
-
-int main() {
-  auto x = mx::array({1, 2, 3});
-  auto y = mx::array({1, 2, 3});
-  std::cout << x + y << std::endl;
-  return 0;
-}

examples/cpp/distributed.cpp

@@ -4,19 +4,19 @@
 
 #include "mlx/mlx.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 int main() {
-  if (!mx::distributed::is_available()) {
+  if (!distributed::is_available()) {
     std::cout << "No communication backend found" << std::endl;
     return 1;
   }
 
-  auto global_group = mx::distributed::init();
+  auto global_group = distributed::init();
   std::cout << global_group.rank() << " / " << global_group.size() << std::endl;
 
-  mx::array x = mx::ones({10});
-  mx::array out = mx::distributed::all_sum(x, global_group);
+  array x = ones({10});
+  array out = distributed::all_sum(x, global_group);
 
   std::cout << out << std::endl;
 }

examples/cpp/linear_regression.cpp

@@ -10,7 +10,7 @@
 /**
  * An example of linear regression with MLX.
  */
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 int main() {
   int num_features = 100;
@@ -19,35 +19,35 @@ int main() {
   float learning_rate = 0.01;
 
   // True parameters
-  auto w_star = mx::random::normal({num_features});
+  auto w_star = random::normal({num_features});
 
   // The input examples (design matrix)
-  auto X = mx::random::normal({num_examples, num_features});
+  auto X = random::normal({num_examples, num_features});
 
   // Noisy labels
-  auto eps = 1e-2 * mx::random::normal({num_examples});
-  auto y = mx::matmul(X, w_star) + eps;
+  auto eps = 1e-2 * random::normal({num_examples});
+  auto y = matmul(X, w_star) + eps;
 
   // Initialize random parameters
-  mx::array w = 1e-2 * mx::random::normal({num_features});
+  array w = 1e-2 * random::normal({num_features});
 
-  auto loss_fn = [&](mx::array w) {
-    auto yhat = mx::matmul(X, w);
-    return (0.5f / num_examples) * mx::sum(mx::square(yhat - y));
+  auto loss_fn = [&](array w) {
+    auto yhat = matmul(X, w);
+    return (0.5f / num_examples) * sum(square(yhat - y));
   };
 
-  auto grad_fn = mx::grad(loss_fn);
+  auto grad_fn = grad(loss_fn);
 
   auto tic = timer::time();
   for (int it = 0; it < num_iters; ++it) {
-    auto grads = grad_fn(w);
-    w = w - learning_rate * grads;
-    mx::eval(w);
+    auto grad = grad_fn(w);
+    w = w - learning_rate * grad;
+    eval(w);
   }
   auto toc = timer::time();
 
   auto loss = loss_fn(w);
-  auto error_norm = std::sqrt(mx::sum(mx::square(w - w_star)).item<float>());
+  auto error_norm = std::sqrt(sum(square(w - w_star)).item<float>());
   auto throughput = num_iters / timer::seconds(toc - tic);
   std::cout << "Loss " << loss << ", |w - w*| = " << error_norm
             << ", Throughput " << throughput << " (it/s)." << std::endl;

examples/cpp/logistic_regression.cpp

@@ -10,7 +10,7 @@
 /**
  * An example of logistic regression with MLX.
  */
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 int main() {
   int num_features = 100;
@@ -19,35 +19,35 @@ int main() {
   float learning_rate = 0.1;
 
   // True parameters
-  auto w_star = mx::random::normal({num_features});
+  auto w_star = random::normal({num_features});
 
   // The input examples
-  auto X = mx::random::normal({num_examples, num_features});
+  auto X = random::normal({num_examples, num_features});
 
   // Labels
-  auto y = mx::matmul(X, w_star) > 0;
+  auto y = matmul(X, w_star) > 0;
 
   // Initialize random parameters
-  mx::array w = 1e-2 * mx::random::normal({num_features});
+  array w = 1e-2 * random::normal({num_features});
 
-  auto loss_fn = [&](mx::array w) {
-    auto logits = mx::matmul(X, w);
+  auto loss_fn = [&](array w) {
+    auto logits = matmul(X, w);
     auto scale = (1.0f / num_examples);
-    return scale * mx::sum(mx::logaddexp(mx::array(0.0f), logits) - y * logits);
+    return scale * sum(logaddexp(array(0.0f), logits) - y * logits);
   };
 
-  auto grad_fn = mx::grad(loss_fn);
+  auto grad_fn = grad(loss_fn);
 
   auto tic = timer::time();
   for (int it = 0; it < num_iters; ++it) {
-    auto grads = grad_fn(w);
-    w = w - learning_rate * grads;
-    mx::eval(w);
+    auto grad = grad_fn(w);
+    w = w - learning_rate * grad;
+    eval(w);
   }
   auto toc = timer::time();
 
   auto loss = loss_fn(w);
-  auto acc = mx::sum((mx::matmul(X, w) > 0) == y) / num_examples;
+  auto acc = sum((matmul(X, w) > 0) == y) / num_examples;
   auto throughput = num_iters / timer::seconds(toc - tic);
   std::cout << "Loss " << loss << ", Accuracy, " << acc << ", Throughput "
             << throughput << " (it/s)." << std::endl;

examples/cpp/metal_capture.cpp

@@ -5,27 +5,27 @@
 
 #include "mlx/mlx.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 int main() {
   // To use Metal debugging and profiling:
   // 1. Build with the MLX_METAL_DEBUG CMake option (i.e. -DMLX_METAL_DEBUG=ON).
   // 2. Run with MTL_CAPTURE_ENABLED=1.
-  mx::metal::start_capture("mlx_trace.gputrace");
+  metal::start_capture("mlx_trace.gputrace");
 
   // Start at index two because the default GPU and CPU streams have indices
   // zero and one, respectively. This naming matches the label assigned to each
   // stream's command queue.
-  auto s2 = new_stream(mx::Device::gpu);
-  auto s3 = new_stream(mx::Device::gpu);
+  auto s2 = new_stream(Device::gpu);
+  auto s3 = new_stream(Device::gpu);
 
-  auto a = mx::arange(1.f, 10.f, 1.f, mx::float32, s2);
-  auto b = mx::arange(1.f, 10.f, 1.f, mx::float32, s3);
-  auto x = mx::add(a, a, s2);
-  auto y = mx::add(b, b, s3);
+  auto a = arange(1.f, 10.f, 1.f, float32, s2);
+  auto b = arange(1.f, 10.f, 1.f, float32, s3);
+  auto x = add(a, a, s2);
+  auto y = add(b, b, s3);
 
   // The multiply will happen on the default stream.
-  std::cout << mx::multiply(x, y) << std::endl;
+  std::cout << multiply(x, y) << std::endl;
 
-  mx::metal::stop_capture();
+  metal::stop_capture();
 }

examples/cpp/tutorial.cpp

@@ -5,11 +5,11 @@
 
 #include "mlx/mlx.h"
 
-namespace mx = mlx::core;
+using namespace mlx::core;
 
 void array_basics() {
   // Make a scalar array:
-  mx::array x(1.0);
+  array x(1.0);
 
   // Get the value out of it:
   auto s = x.item<float>();
@@ -29,31 +29,31 @@ void array_basics() {
 
   // The datatype should be float32:
   auto dtype = x.dtype();
-  assert(dtype == mx::float32);
+  assert(dtype == float32);
 
   // Specify the dtype when constructing the array:
-  x = mx::array(1, mx::int32);
-  assert(x.dtype() == mx::int32);
+  x = array(1, int32);
+  assert(x.dtype() == int32);
   x.item<int>(); // OK
   // x.item<float>();  // Undefined!
 
   // Make a multidimensional array:
-  x = mx::array({1.0f, 2.0f, 3.0f, 4.0f}, {2, 2});
+  x = array({1.0f, 2.0f, 3.0f, 4.0f}, {2, 2});
   // mlx is row-major by default so the first row of this array
   // is [1.0, 2.0] and the second row is [3.0, 4.0]
 
   // Make an array of shape {2, 2} filled with ones:
-  auto y = mx::ones({2, 2});
+  auto y = ones({2, 2});
 
   // Pointwise add x and y:
-  auto z = mx::add(x, y);
+  auto z = add(x, y);
 
   // Same thing:
   z = x + y;
 
   // mlx is lazy by default. At this point `z` only
   // has a shape and a type but no actual data:
-  assert(z.dtype() == mx::float32);
+  assert(z.dtype() == float32);
   assert(z.shape(0) == 2);
   assert(z.shape(1) == 2);
 
@@ -63,33 +63,33 @@ void array_basics() {
   // and inputs. When `eval` is called on an array (or arrays), the array and
   // all of its dependencies are recursively evaluated to produce the result.
   // Once an array is evaluated, it has data and is detached from its inputs.
-  mx::eval(z);
+  eval(z);
 
-  // Of course the array can still be an input to other operations. You can
-  // even call eval on the array again, this will just be a no-op:
-  mx::eval(z); // no-op
+  // Of course the array can still be an input to other operations. You can even
+  // call eval on the array again, this will just be a no-op:
+  eval(z); // no-op
 
   // Some functions or methods on arrays implicitly evaluate them. For example
   // accessing a value in an array or printing the array implicitly evaluate it:
-  z = mx::ones({1});
+  z = ones({1});
   z.item<float>(); // implicit evaluation
 
-  z = mx::ones({2, 2});
+  z = ones({2, 2});
   std::cout << z << std::endl; // implicit evaluation
 }
 
 void automatic_differentiation() {
-  auto fn = [](mx::array x) { return mx::square(x); };
+  auto fn = [](array x) { return square(x); };
 
   // Computing the derivative function of a function
-  auto grad_fn = mx::grad(fn);
+  auto grad_fn = grad(fn);
   // Call grad_fn on the input to get the derivative
-  auto x = mx::array(1.5);
+  auto x = array(1.5);
   auto dfdx = grad_fn(x);
   // dfdx is 2 * x
 
   // Get the second derivative by composing grad with grad
-  auto d2fdx2 = mx::grad(mx::grad(fn))(x);
+  auto d2fdx2 = grad(grad(fn))(x);
   // d2fdx2 is 2
 }
 

examples/export/CMakeLists.txt

@@ -1,22 +0,0 @@
-cmake_minimum_required(VERSION 3.27)
-
-project(import_mlx LANGUAGES CXX)
-
-set(CMAKE_CXX_STANDARD 17)
-set(CMAKE_CXX_STANDARD_REQUIRED ON)
-
-find_package(
-  Python 3.9
-  COMPONENTS Interpreter Development.Module
-  REQUIRED)
-execute_process(
-  COMMAND "${Python_EXECUTABLE}" -m mlx --cmake-dir
-  OUTPUT_STRIP_TRAILING_WHITESPACE
-  OUTPUT_VARIABLE MLX_ROOT)
-find_package(MLX CONFIG REQUIRED)
-
-add_executable(eval_mlp eval_mlp.cpp)
-target_link_libraries(eval_mlp PRIVATE mlx)
-
-add_executable(train_mlp train_mlp.cpp)
-target_link_libraries(train_mlp PRIVATE mlx)

examples/export/README.md

@@ -1,49 +0,0 @@
-## Setup
-
-Install MLX:
-
-```bash
-pip install mlx>=0.22
-```
-
-Build the C++ examples:
-
-```bash
-cmake -B build -DCMAKE_BUILD_TYPE=Release
-cmake --build build
-```
-
-## Run
-
-### Eval MLP
-
-Run the Python script to export the eval function:
-
-```bash
-python eval_mlp.py
-```
-
-Then run the C++ program to import and run the function:
-
-```
-./build/eval_mlp
-```
-
-The Python and C++ programs should output the same result.
-
-### Train MLP
-
-Run the Python script to export the model initialization and training
-functions:
-
-```bash
-python train_mlp.py
-```
-
-Then run the C++ program to import and run the functions:
-
-```
-./build/train_mlp
-```
-
-The Python and C++ programs should output the same results.

examples/export/eval_mlp.cpp

@@ -1,25 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include <mlx/mlx.h>
-#include <iostream>
-
-namespace mx = mlx::core;
-
-int main() {
-  int batch_size = 8;
-  int input_dim = 32;
-
-  // Make the input
-  mx::random::seed(42);
-  auto example_x = mx::random::uniform({batch_size, input_dim});
-
-  // Import the function
-  auto forward = mx::import_function("eval_mlp.mlxfn");
-
-  // Call the imported function
-  auto out = forward({example_x})[0];
-
-  std::cout << out << std::endl;
-
-  return 0;
-}

examples/export/eval_mlp.py

@@ -1,52 +0,0 @@
-# Copyright © 2024 Apple Inc.
-
-import mlx.core as mx
-import mlx.nn as nn
-import mlx.utils
-
-
-class MLP(nn.Module):
-    """A simple MLP."""
-
-    def __init__(
-        self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int
-    ):
-        super().__init__()
-        layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim]
-        self.layers = [
-            nn.Linear(idim, odim)
-            for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:])
-        ]
-
-    def __call__(self, x):
-        for l in self.layers[:-1]:
-            x = nn.relu(l(x))
-        return self.layers[-1](x)
-
-
-if __name__ == "__main__":
-
-    batch_size = 8
-    input_dim = 32
-    output_dim = 10
-
-    # Load the model
-    mx.random.seed(0)  # Seed for params
-    model = MLP(num_layers=5, input_dim=input_dim, hidden_dim=64, output_dim=output_dim)
-    mx.eval(model)
-
-    # Note, the model parameters are saved in the export function
-    def forward(x):
-        return model(x)
-
-    mx.random.seed(42)  # Seed for input
-    example_x = mx.random.uniform(shape=(batch_size, input_dim))
-
-    mx.export_function("eval_mlp.mlxfn", forward, example_x)
-
-    # Import in Python
-    imported_forward = mx.import_function("eval_mlp.mlxfn")
-    expected = forward(example_x)
-    (out,) = imported_forward(example_x)
-    assert mx.allclose(expected, out)
-    print(out)

examples/export/train_mlp.cpp

@@ -1,35 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include <mlx/mlx.h>
-#include <iostream>
-
-namespace mx = mlx::core;
-
-int main() {
-  int batch_size = 8;
-  int input_dim = 32;
-  int output_dim = 10;
-
-  auto state = mx::import_function("init_mlp.mlxfn")({});
-
-  // Make the input
-  mx::random::seed(42);
-  auto example_X = mx::random::normal({batch_size, input_dim});
-  auto example_y = mx::random::randint(0, output_dim, {batch_size});
-
-  // Import the function
-  auto step = mx::import_function("train_mlp.mlxfn");
-
-  // Call the imported function
-  for (int it = 0; it < 100; ++it) {
-    state.insert(state.end(), {example_X, example_y});
-    state = step(state);
-    eval(state);
-    auto loss = state.back();
-    state.pop_back();
-    if (it % 10 == 0) {
-      std::cout << "Loss " << loss.item<float>() << std::endl;
-    }
-  }
-  return 0;
-}

examples/export/train_mlp.py

@@ -1,76 +0,0 @@
-# Copyright © 2024 Apple Inc.
-
-import mlx.core as mx
-import mlx.nn as nn
-import mlx.optimizers as optim
-import mlx.utils
-
-
-class MLP(nn.Module):
-    """A simple MLP."""
-
-    def __init__(
-        self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int
-    ):
-        super().__init__()
-        layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim]
-        self.layers = [
-            nn.Linear(idim, odim)
-            for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:])
-        ]
-
-    def __call__(self, x):
-        for l in self.layers[:-1]:
-            x = nn.relu(l(x))
-        return self.layers[-1](x)
-
-
-if __name__ == "__main__":
-
-    batch_size = 8
-    input_dim = 32
-    output_dim = 10
-
-    def init():
-        # Seed for the parameter initialization
-        mx.random.seed(0)
-        model = MLP(
-            num_layers=3, input_dim=input_dim, hidden_dim=64, output_dim=output_dim
-        )
-        optimizer = optim.SGD(learning_rate=1e-1)
-        optimizer.init(model.parameters())
-        state = [model.parameters(), optimizer.state]
-        tree_structure, state = zip(*mlx.utils.tree_flatten(state))
-        return model, optimizer, tree_structure, state
-
-    # Export the model parameter initialization
-    model, optimizer, tree_structure, state = init()
-    mx.eval(state)
-    mx.export_function("init_mlp.mlxfn", lambda: init()[-1])
-
-    def loss_fn(params, X, y):
-        model.update(params)
-        return nn.losses.cross_entropy(model(X), y, reduction="mean")
-
-    def step(*inputs):
-        *state, X, y = inputs
-        params, opt_state = mlx.utils.tree_unflatten(list(zip(tree_structure, state)))
-        optimizer.state = opt_state
-        loss, grads = mx.value_and_grad(loss_fn)(params, X, y)
-        params = optimizer.apply_gradients(grads, params)
-        _, state = zip(*mlx.utils.tree_flatten([params, optimizer.state]))
-        return *state, loss
-
-    # Make some random data
-    mx.random.seed(42)
-    example_X = mx.random.normal(shape=(batch_size, input_dim))
-    example_y = mx.random.randint(low=0, high=output_dim, shape=(batch_size,))
-    mx.export_function("train_mlp.mlxfn", step, *state, example_X, example_y)
-
-    # Export one step of SGD
-    imported_step = mx.import_function("train_mlp.mlxfn")
-
-    for it in range(100):
-        *state, loss = imported_step(*state, example_X, example_y)
-        if it % 10 == 0:
-            print(f"Loss {loss.item():.6}")

examples/extensions/CMakeLists.txt

@@ -18,7 +18,8 @@ find_package(
 execute_process(
   COMMAND "${Python_EXECUTABLE}" -m nanobind --cmake_dir
   OUTPUT_STRIP_TRAILING_WHITESPACE
-  OUTPUT_VARIABLE nanobind_ROOT)
+  OUTPUT_VARIABLE NB_DIR)
+list(APPEND CMAKE_PREFIX_PATH "${NB_DIR}")
 find_package(nanobind CONFIG REQUIRED)
 
 # ----------------------------- Extensions -----------------------------

examples/extensions/axpby/axpby.cpp

@@ -19,7 +19,7 @@
 #include "mlx/backend/metal/utils.h"
 #endif
 
-namespace my_ext {
+namespace mlx::core {
 
 ///////////////////////////////////////////////////////////////////////////////
 // Operation Implementation
@@ -32,24 +32,24 @@ namespace my_ext {
  *  Follow numpy style broadcasting between x and y
  *  Inputs are upcasted to floats if needed
  **/
-mx::array axpby(
-    const mx::array& x, // Input mx::array x
-    const mx::array& y, // Input mx::array y
+array axpby(
+    const array& x, // Input array x
+    const array& y, // Input array y
     const float alpha, // Scaling factor for x
     const float beta, // Scaling factor for y
-    mx::StreamOrDevice s /* = {} */ // Stream on which to schedule the operation
+    StreamOrDevice s /* = {} */ // Stream on which to schedule the operation
 ) {
   // Promote dtypes between x and y as needed
   auto promoted_dtype = promote_types(x.dtype(), y.dtype());
 
   // Upcast to float32 for non-floating point inputs x and y
-  auto out_dtype = mx::issubdtype(promoted_dtype, mx::float32)
+  auto out_dtype = issubdtype(promoted_dtype, float32)
       ? promoted_dtype
-      : promote_types(promoted_dtype, mx::float32);
+      : promote_types(promoted_dtype, float32);
 
   // Cast x and y up to the determined dtype (on the same stream s)
-  auto x_casted = mx::astype(x, out_dtype, s);
-  auto y_casted = mx::astype(y, out_dtype, s);
+  auto x_casted = astype(x, out_dtype, s);
+  auto y_casted = astype(y, out_dtype, s);
 
   // Broadcast the shapes of x and y (on the same stream s)
   auto broadcasted_inputs = broadcast_arrays({x_casted, y_casted}, s);
@@ -57,12 +57,12 @@ mx::array axpby(
 
   // Construct the array as the output of the Axpby primitive
   // with the broadcasted and upcasted arrays as inputs
-  return mx::array(
-      /* const mx::Shape& shape = */ out_shape,
-      /* mx::Dtype dtype = */ out_dtype,
-      /* std::shared_ptr<mx::Primitive> primitive = */
+  return array(
+      /* const std::vector<int>& shape = */ out_shape,
+      /* Dtype dtype = */ out_dtype,
+      /* std::unique_ptr<Primitive> primitive = */
       std::make_shared<Axpby>(to_stream(s), alpha, beta),
-      /* const std::vector<mx::array>& inputs = */ broadcasted_inputs);
+      /* const std::vector<array>& inputs = */ broadcasted_inputs);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -71,16 +71,16 @@ mx::array axpby(
 
 template <typename T>
 void axpby_impl(
-    const mx::array& x,
-    const mx::array& y,
-    mx::array& out,
+    const array& x,
+    const array& y,
+    array& out,
     float alpha_,
     float beta_) {
   // We only allocate memory when we are ready to fill the output
   // malloc_or_wait synchronously allocates available memory
   // There may be a wait executed here if the allocation is requested
   // under memory-pressured conditions
-  out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   // Collect input and output data pointers
   const T* x_ptr = x.data<T>();
@@ -94,8 +94,8 @@ void axpby_impl(
   // Do the element-wise operation for each output
   for (size_t out_idx = 0; out_idx < out.size(); out_idx++) {
     // Map linear indices to offsets in x and y
-    auto x_offset = mx::elem_to_loc(out_idx, x.shape(), x.strides());
-    auto y_offset = mx::elem_to_loc(out_idx, y.shape(), y.strides());
+    auto x_offset = elem_to_loc(out_idx, x.shape(), x.strides());
+    auto y_offset = elem_to_loc(out_idx, y.shape(), y.strides());
 
     // We allocate the output to be contiguous and regularly strided
     // (defaults to row major) and hence it doesn't need additional mapping
@@ -105,8 +105,8 @@ void axpby_impl(
 
 /** Fall back implementation for evaluation on CPU */
 void Axpby::eval(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
   // Check the inputs (registered in the op while constructing the out array)
   assert(inputs.size() == 2);
   auto& x = inputs[0];
@@ -114,14 +114,14 @@ void Axpby::eval(
   auto& out = outputs[0];
 
   // Dispatch to the correct dtype
-  if (out.dtype() == mx::float32) {
+  if (out.dtype() == float32) {
     return axpby_impl<float>(x, y, out, alpha_, beta_);
-  } else if (out.dtype() == mx::float16) {
-    return axpby_impl<mx::float16_t>(x, y, out, alpha_, beta_);
-  } else if (out.dtype() == mx::bfloat16) {
-    return axpby_impl<mx::bfloat16_t>(x, y, out, alpha_, beta_);
-  } else if (out.dtype() == mx::complex64) {
-    return axpby_impl<mx::complex64_t>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == float16) {
+    return axpby_impl<float16_t>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == bfloat16) {
+    return axpby_impl<bfloat16_t>(x, y, out, alpha_, beta_);
+  } else if (out.dtype() == complex64) {
+    return axpby_impl<complex64_t>(x, y, out, alpha_, beta_);
   } else {
     throw std::runtime_error(
         "Axpby is only supported for floating point types.");
@@ -136,9 +136,9 @@ void Axpby::eval(
 
 template <typename T>
 void axpby_impl_accelerate(
-    const mx::array& x,
-    const mx::array& y,
-    mx::array& out,
+    const array& x,
+    const array& y,
+    array& out,
     float alpha_,
     float beta_) {
   // Accelerate library provides catlas_saxpby which does
@@ -150,10 +150,10 @@ void axpby_impl_accelerate(
   // The data in the output array is allocated to match the strides in y
   // such that x, y, and out are contiguous in the same mode and
   // no transposition is needed
-  out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+  out.set_data(allocator::malloc_or_wait(out.nbytes()));
 
   // We then copy over the elements using the contiguous vector specialization
-  copy_inplace(y, out, mx::CopyType::Vector);
+  copy_inplace(y, out, CopyType::Vector);
 
   // Get x and y pointers for catlas_saxpby
   const T* x_ptr = x.data<T>();
@@ -175,15 +175,15 @@ void axpby_impl_accelerate(
 
 /** Evaluate primitive on CPU using accelerate specializations */
 void Axpby::eval_cpu(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
   assert(inputs.size() == 2);
   auto& x = inputs[0];
   auto& y = inputs[1];
   auto& out = outputs[0];
 
   // Accelerate specialization for contiguous single precision float arrays
-  if (out.dtype() == mx::float32 &&
+  if (out.dtype() == float32 &&
       ((x.flags().row_contiguous && y.flags().row_contiguous) ||
        (x.flags().col_contiguous && y.flags().col_contiguous))) {
     axpby_impl_accelerate<float>(x, y, out, alpha_, beta_);
@@ -198,8 +198,8 @@ void Axpby::eval_cpu(
 
 /** Evaluate primitive on CPU falling back to common backend */
 void Axpby::eval_cpu(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs) {
   eval(inputs, outputs);
 }
 
@@ -213,8 +213,8 @@ void Axpby::eval_cpu(
 
 /** Evaluate primitive on GPU */
 void Axpby::eval_gpu(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& outputs) {
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
   // Prepare inputs
   assert(inputs.size() == 2);
   auto& x = inputs[0];
@@ -225,7 +225,7 @@ void Axpby::eval_gpu(
   // and each stream carries its device identifiers
   auto& s = stream();
   // We get the needed metal device using the stream
-  auto& d = mx::metal::device(s.device);
+  auto& d = metal::device(s.device);
 
   // Prepare to specialize based on contiguity
   bool contiguous_kernel =
@@ -235,12 +235,12 @@ void Axpby::eval_gpu(
   // Allocate output memory with strides based on specialization
   if (contiguous_kernel) {
     out.set_data(
-        mx::allocator::malloc_or_wait(x.data_size() * out.itemsize()),
+        allocator::malloc_or_wait(x.data_size() * out.itemsize()),
         x.data_size(),
         x.strides(),
         x.flags());
   } else {
-    out.set_data(mx::allocator::malloc_or_wait(out.nbytes()));
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)
@@ -257,7 +257,7 @@ void Axpby::eval_gpu(
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // Kernel parameters are registered with buffer indices corresponding to
   // those in the kernel declaration at axpby.metal
@@ -272,15 +272,15 @@ void Axpby::eval_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode alpha and beta
-  compute_encoder.set_bytes(alpha_, 3);
-  compute_encoder.set_bytes(beta_, 4);
+  compute_encoder->setBytes(&alpha_, sizeof(float), 3);
+  compute_encoder->setBytes(&beta_, sizeof(float), 4);
 
   // Encode shape, strides and ndim if needed
   if (!contiguous_kernel) {
-    compute_encoder.set_vector_bytes(x.shape(), 5);
-    compute_encoder.set_vector_bytes(x.strides(), 6);
-    compute_encoder.set_vector_bytes(y.strides(), 7);
-    compute_encoder.set_bytes(ndim, 8);
+    compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
+    compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
+    compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
+    compute_encoder->setBytes(&ndim, sizeof(int), 8);
   }
 
   // We launch 1 thread for each input and make sure that the number of
@@ -295,15 +295,15 @@ void Axpby::eval_gpu(
 
   // Launch the grid with the given number of threads divided among
   // the given threadgroups
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 }
 
 #else // Metal is not available
 
 /** Fail evaluation on GPU */
 void Axpby::eval_gpu(
-    const std::vector<mx::array>& inputs,
-    std::vector<mx::array>& out) {
+    const std::vector<array>& inputs,
+    std::vector<array>& out) {
   throw std::runtime_error("Axpby has no GPU implementation.");
 }
 
@@ -314,9 +314,9 @@ void Axpby::eval_gpu(
 ///////////////////////////////////////////////////////////////////////////////
 
 /** The Jacobian-vector product. */
-std::vector<mx::array> Axpby::jvp(
-    const std::vector<mx::array>& primals,
-    const std::vector<mx::array>& tangents,
+std::vector<array> Axpby::jvp(
+    const std::vector<array>& primals,
+    const std::vector<array>& tangents,
     const std::vector<int>& argnums) {
   // Forward mode diff that pushes along the tangents
   // The jvp transform on the primitive can built with ops
@@ -328,8 +328,8 @@ std::vector<mx::array> Axpby::jvp(
   // scaled by beta
   if (argnums.size() > 1) {
     auto scale = argnums[0] == 0 ? alpha_ : beta_;
-    auto scale_arr = mx::array(scale, tangents[0].dtype());
-    return {mx::multiply(scale_arr, tangents[0], stream())};
+    auto scale_arr = array(scale, tangents[0].dtype());
+    return {multiply(scale_arr, tangents[0], stream())};
   }
   // If, argnums = {0, 1}, we take contributions from both
   // which gives us jvp = tangent_x * alpha + tangent_y * beta
@@ -339,24 +339,24 @@ std::vector<mx::array> Axpby::jvp(
 }
 
 /** The vector-Jacobian product. */
-std::vector<mx::array> Axpby::vjp(
-    const std::vector<mx::array>& primals,
-    const std::vector<mx::array>& cotangents,
+std::vector<array> Axpby::vjp(
+    const std::vector<array>& primals,
+    const std::vector<array>& cotangents,
     const std::vector<int>& argnums,
-    const std::vector<mx::array>&) {
+    const std::vector<array>&) {
   // Reverse mode diff
-  std::vector<mx::array> vjps;
+  std::vector<array> vjps;
   for (auto arg : argnums) {
     auto scale = arg == 0 ? alpha_ : beta_;
-    auto scale_arr = mx::array(scale, cotangents[0].dtype());
-    vjps.push_back(mx::multiply(scale_arr, cotangents[0], stream()));
+    auto scale_arr = array(scale, cotangents[0].dtype());
+    vjps.push_back(multiply(scale_arr, cotangents[0], stream()));
   }
   return vjps;
 }
 
 /** Vectorize primitive along given axis */
-std::pair<std::vector<mx::array>, std::vector<int>> Axpby::vmap(
-    const std::vector<mx::array>& inputs,
+std::pair<std::vector<array>, std::vector<int>> Axpby::vmap(
+    const std::vector<array>& inputs,
     const std::vector<int>& axes) {
   throw std::runtime_error("Axpby has no vmap implementation.");
 }
@@ -367,4 +367,4 @@ bool Axpby::is_equivalent(const Primitive& other) const {
   return alpha_ == r_other.alpha_ && beta_ == r_other.beta_;
 }
 
-} // namespace my_ext
+} // namespace mlx::core

examples/extensions/axpby/axpby.h

@@ -5,9 +5,7 @@
 #include "mlx/ops.h"
 #include "mlx/primitives.h"
 
-namespace mx = mlx::core;
-
-namespace my_ext {
+namespace mlx::core {
 
 ///////////////////////////////////////////////////////////////////////////////
 // Operation
@@ -20,22 +18,22 @@ namespace my_ext {
  *  Follow numpy style broadcasting between x and y
  *  Inputs are upcasted to floats if needed
  **/
-mx::array axpby(
-    const mx::array& x, // Input array x
-    const mx::array& y, // Input array y
+array axpby(
+    const array& x, // Input array x
+    const array& y, // Input array y
     const float alpha, // Scaling factor for x
     const float beta, // Scaling factor for y
-    mx::StreamOrDevice s = {} // Stream on which to schedule the operation
+    StreamOrDevice s = {} // Stream on which to schedule the operation
 );
 
 ///////////////////////////////////////////////////////////////////////////////
 // Primitive
 ///////////////////////////////////////////////////////////////////////////////
 
-class Axpby : public mx::Primitive {
+class Axpby : public Primitive {
  public:
-  explicit Axpby(mx::Stream stream, float alpha, float beta)
-      : mx::Primitive(stream), alpha_(alpha), beta_(beta) {};
+  explicit Axpby(Stream stream, float alpha, float beta)
+      : Primitive(stream), alpha_(alpha), beta_(beta) {};
 
   /**
    * A primitive must know how to evaluate itself on the CPU/GPU
@@ -44,25 +42,23 @@ class Axpby : public mx::Primitive {
    * To avoid unnecessary allocations, the evaluation function
    * is responsible for allocating space for the array.
    */
-  void eval_cpu(
-      const std::vector<mx::array>& inputs,
-      std::vector<mx::array>& outputs) override;
-  void eval_gpu(
-      const std::vector<mx::array>& inputs,
-      std::vector<mx::array>& outputs) override;
+  void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override;
+  void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override;
 
   /** The Jacobian-vector product. */
-  std::vector<mx::array> jvp(
-      const std::vector<mx::array>& primals,
-      const std::vector<mx::array>& tangents,
+  std::vector<array> jvp(
+      const std::vector<array>& primals,
+      const std::vector<array>& tangents,
       const std::vector<int>& argnums) override;
 
   /** The vector-Jacobian product. */
-  std::vector<mx::array> vjp(
-      const std::vector<mx::array>& primals,
-      const std::vector<mx::array>& cotangents,
+  std::vector<array> vjp(
+      const std::vector<array>& primals,
+      const std::vector<array>& cotangents,
       const std::vector<int>& argnums,
-      const std::vector<mx::array>& outputs) override;
+      const std::vector<array>& outputs) override;
 
   /**
    * The primitive must know how to vectorize itself across
@@ -70,8 +66,8 @@ class Axpby : public mx::Primitive {
    * representing the vectorized computation and the axis which
    * corresponds to the output vectorized dimension.
    */
-  std::pair<std::vector<mx::array>, std::vector<int>> vmap(
-      const std::vector<mx::array>& inputs,
+  std::pair<std::vector<array>, std::vector<int>> vmap(
+      const std::vector<array>& inputs,
       const std::vector<int>& axes) override;
 
   /** Print the primitive. */
@@ -80,16 +76,14 @@ class Axpby : public mx::Primitive {
   }
 
   /** Equivalence check **/
-  bool is_equivalent(const mx::Primitive& other) const override;
+  bool is_equivalent(const Primitive& other) const override;
 
  private:
   float alpha_;
   float beta_;
 
   /** Fall back implementation for evaluation on CPU */
-  void eval(
-      const std::vector<mx::array>& inputs,
-      std::vector<mx::array>& outputs);
+  void eval(const std::vector<array>& inputs, std::vector<array>& outputs);
 };
 
-} // namespace my_ext
+} // namespace mlx::core

examples/extensions/axpby/axpby.metal

@@ -2,6 +2,7 @@
 
 #include <metal_stdlib>
 
+#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/utils.h"
 
 template <typename T>
@@ -12,8 +13,8 @@ template <typename T>
     constant const float& alpha [[buffer(3)]],
     constant const float& beta [[buffer(4)]],
     constant const int* shape [[buffer(5)]],
-    constant const int64_t* x_strides [[buffer(6)]],
-    constant const int64_t* y_strides [[buffer(7)]],
+    constant const size_t* x_strides [[buffer(6)]],
+    constant const size_t* y_strides [[buffer(7)]],
     constant const int& ndim [[buffer(8)]],
     uint index [[thread_position_in_grid]]) {
   auto x_offset = elem_to_loc(index, shape, x_strides, ndim);
@@ -34,14 +35,29 @@ template <typename T>
       static_cast<T>(alpha) * x[index] + static_cast<T>(beta) * y[index];
 }
 
-// clang-format off
-#define instantiate_axpby(type_name, type)                             \
-  instantiate_kernel("axpby_general_" #type_name, axpby_general, type) \
-  instantiate_kernel(                                                  \
-          "axpby_contiguous_" #type_name, axpby_contiguous, type)
+#define instantiate_axpby(type_name, type)                               \
+  template [[host_name("axpby_general_" #type_name)]] [[kernel]] void    \
+  axpby_general<type>(                                                   \
+      device const type* x [[buffer(0)]],                                \
+      device const type* y [[buffer(1)]],                                \
+      device type* out [[buffer(2)]],                                    \
+      constant const float& alpha [[buffer(3)]],                         \
+      constant const float& beta [[buffer(4)]],                          \
+      constant const int* shape [[buffer(5)]],                           \
+      constant const size_t* x_strides [[buffer(6)]],                    \
+      constant const size_t* y_strides [[buffer(7)]],                    \
+      constant const int& ndim [[buffer(8)]],                            \
+      uint index [[thread_position_in_grid]]);                           \
+  template [[host_name("axpby_contiguous_" #type_name)]] [[kernel]] void \
+  axpby_contiguous<type>(                                                \
+      device const type* x [[buffer(0)]],                                \
+      device const type* y [[buffer(1)]],                                \
+      device type* out [[buffer(2)]],                                    \
+      constant const float& alpha [[buffer(3)]],                         \
+      constant const float& beta [[buffer(4)]],                          \
+      uint index [[thread_position_in_grid]]);
 
 instantiate_axpby(float32, float);
 instantiate_axpby(float16, half);
 instantiate_axpby(bfloat16, bfloat16_t);
-instantiate_axpby(complex64, complex64_t);
-// clang-format on
+instantiate_axpby(complex64, complex64_t);

examples/extensions/bindings.cpp

@@ -8,12 +8,14 @@
 namespace nb = nanobind;
 using namespace nb::literals;
 
+using namespace mlx::core;
+
 NB_MODULE(_ext, m) {
   m.doc() = "Sample extension for MLX";
 
   m.def(
       "axpby",
-      &my_ext::axpby,
+      &axpby,
       "x"_a,
       "y"_a,
       "alpha"_a,

examples/extensions/pyproject.toml

@@ -1,8 +1,8 @@
 [build-system]
 requires = [
   "setuptools>=42",
-  "cmake>=3.25",
+  "cmake>=3.24",
   "mlx>=0.18.0",
-  "nanobind==2.4.0",
+  "nanobind==2.2.0",
 ]
 build-backend = "setuptools.build_meta"

examples/extensions/requirements.txt

@@ -1,4 +1,4 @@
 setuptools>=42
-cmake>=3.25
-mlx>=0.21.0
+cmake>=3.24
+mlx>=0.18.1
 nanobind==2.2.0

mlx.pc.in

@@ -28,19 +28,10 @@ endif()
 if (@MLX_BUILD_METAL@)
     set(MLX_BUILD_METAL @MLX_BUILD_METAL@)
     set(MLX_CXX_FLAGS ${MLX_CXX_FLAGS} -D_METAL_)
-    set(MLX_INCLUDE_DIRS 
-        "${MLX_INCLUDE_DIRS};"
+    set_and_check(MLX_INCLUDE_DIRS 
+        ${MLX_INCLUDE_DIRS} 
         @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/metal_cpp
     )
-    if(@MLX_METAL_VERSION@ GREATER_EQUAL 310)
-      set(MLX_INCLUDE_DIRS
-        "${MLX_INCLUDE_DIRS};"
-        @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/mlx/backend/metal/kernels/metal_3_1)
-    else()
-      set(MLX_INCLUDE_DIRS
-        "${MLX_INCLUDE_DIRS};"
-        @PACKAGE_CMAKE_INSTALL_INCLUDEDIR@/mlx/backend/metal/kernels/metal_3_0)
-    endif()
 endif()
 
 set_target_properties(mlx PROPERTIES
@@ -49,4 +40,4 @@ set_target_properties(mlx PROPERTIES
 )
 
 include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(MLX DEFAULT_MSG MLX_LIBRARY MLX_INCLUDE_DIRS)
+find_package_handle_standard_args(MLX DEFAULT_MSG MLX_LIBRARY MLX_INCLUDE_DIRS)

mlx/CMakeLists.txt

@@ -5,7 +5,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/compile.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/dtype.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/export.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/einsum.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fast.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp
@@ -19,16 +18,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/linalg.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
-if(MSVC)
-  # Disable some MSVC warnings to speed up compilation.
-  target_compile_options(mlx PUBLIC /wd4068 /wd4244 /wd4267 /wd4804)
-endif()
-
-if(WIN32)
-  # Export symbols by default to behave like macOS/linux.
-  set_target_properties(mlx PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS TRUE)
-endif()
-
 if(MLX_BUILD_CPU)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/common)
 else()

mlx/allocator.cpp

@@ -19,7 +19,7 @@ Buffer malloc(size_t size) {
 }
 
 void free(Buffer buffer) {
-  allocator().free(buffer);
+  return allocator().free(buffer);
 }
 
 Buffer CommonAllocator::malloc(size_t size, bool) {

mlx/array.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023-2024 Apple Inc.
 #include <functional>
-#include <unordered_map>
 
 #include "mlx/array.h"
 #include "mlx/ops.h"
@@ -25,13 +24,13 @@ bool retain_graph() {
 } // namespace
 
 array::array(const std::complex<float>& val, Dtype dtype /* = complex64 */)
-    : array_desc_(std::make_shared<ArrayDesc>(Shape{}, dtype)) {
+    : array_desc_(std::make_shared<ArrayDesc>(std::vector<int>{}, dtype)) {
   auto cval = static_cast<complex64_t>(val);
   init(&cval);
 }
 
 array::array(
-    Shape shape,
+    std::vector<int> shape,
     Dtype dtype,
     std::shared_ptr<Primitive> primitive,
     std::vector<array> inputs)
@@ -42,7 +41,7 @@ array::array(
           std::move(inputs))) {}
 
 std::vector<array> array::make_arrays(
-    std::vector<Shape> shapes,
+    std::vector<std::vector<int>> shapes,
     const std::vector<Dtype>& dtypes,
     const std::shared_ptr<Primitive>& primitive,
     const std::vector<array>& inputs) {
@@ -61,20 +60,24 @@ std::vector<array> array::make_arrays(
 
 array::array(std::initializer_list<float> data)
     : array_desc_(std::make_shared<ArrayDesc>(
-          Shape{static_cast<ShapeElem>(data.size())},
+          std::vector<int>{static_cast<int>(data.size())},
           float32)) {
   init(data.begin());
 }
 
 array::array(std::initializer_list<int> data, Dtype dtype)
     : array_desc_(std::make_shared<ArrayDesc>(
-          Shape{static_cast<ShapeElem>(data.size())},
+          std::vector<int>{static_cast<int>(data.size())},
           dtype)) {
   init(data.begin());
 }
 
 /* Build an array from a shared buffer */
-array::array(allocator::Buffer data, Shape shape, Dtype dtype, Deleter deleter)
+array::array(
+    allocator::Buffer data,
+    std::vector<int> shape,
+    Dtype dtype,
+    deleter_t deleter)
     : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
   set_data(data, deleter);
 }
@@ -119,10 +122,10 @@ void array::eval() {
 }
 
 bool array::is_tracer() const {
-  return (array_desc_->is_tracer && in_tracing()) || retain_graph();
+  return array_desc_->is_tracer && in_tracing() || retain_graph();
 }
 
-void array::set_data(allocator::Buffer buffer, Deleter d) {
+void array::set_data(allocator::Buffer buffer, deleter_t d) {
   array_desc_->data = std::make_shared<Data>(buffer, d);
   array_desc_->data_ptr = buffer.raw_ptr();
   array_desc_->data_size = size();
@@ -135,9 +138,9 @@ void array::set_data(allocator::Buffer buffer, Deleter d) {
 void array::set_data(
     allocator::Buffer buffer,
     size_t data_size,
-    Strides strides,
+    std::vector<size_t> strides,
     Flags flags,
-    Deleter d) {
+    deleter_t d) {
   array_desc_->data = std::make_shared<Data>(buffer, d);
   array_desc_->data_ptr = buffer.raw_ptr();
   array_desc_->data_size = data_size;
@@ -147,7 +150,7 @@ void array::set_data(
 
 void array::copy_shared_buffer(
     const array& other,
-    const Strides& strides,
+    const std::vector<size_t>& strides,
     Flags flags,
     size_t data_size,
     size_t offset /* = 0 */) {
@@ -166,7 +169,7 @@ void array::copy_shared_buffer(const array& other) {
 
 void array::move_shared_buffer(
     array other,
-    const Strides& strides,
+    const std::vector<size_t>& strides,
     Flags flags,
     size_t data_size,
     size_t offset /* = 0 */) {
@@ -211,8 +214,6 @@ array::~array() {
     if (do_detach) {
       for (auto& s : siblings()) {
         for (auto& ss : s.siblings()) {
-          // Set to null here to avoid descending into array destructor
-          // for siblings
           ss.array_desc_ = nullptr;
         }
         s.array_desc_->siblings.clear();
@@ -233,13 +234,13 @@ void array::ArrayDesc::init() {
   }
 }
 
-array::ArrayDesc::ArrayDesc(Shape shape, Dtype dtype)
+array::ArrayDesc::ArrayDesc(std::vector<int> shape, Dtype dtype)
     : shape(std::move(shape)), dtype(dtype), status(Status::available) {
   init();
 }
 
 array::ArrayDesc::ArrayDesc(
-    Shape shape,
+    std::vector<int> shape,
     Dtype dtype,
     std::shared_ptr<Primitive> primitive,
     std::vector<array> inputs)
@@ -277,19 +278,7 @@ array::ArrayDesc::~ArrayDesc() {
     }
     ad.inputs.clear();
     for (auto& [_, a] : input_map) {
-      bool is_deletable =
-          (a.array_desc_.use_count() <= a.siblings().size() + 1);
-      // An array with siblings is deletable only if all of its siblings
-      // are deletable
-      for (auto& s : a.siblings()) {
-        if (!is_deletable) {
-          break;
-        }
-        int is_input = (input_map.find(s.id()) != input_map.end());
-        is_deletable &=
-            s.array_desc_.use_count() <= a.siblings().size() + is_input;
-      }
-      if (is_deletable) {
+      if (a.array_desc_.use_count() <= a.siblings().size() + 1) {
         for_deletion.push_back(std::move(a.array_desc_));
       }
     }
@@ -303,14 +292,6 @@ array::ArrayDesc::~ArrayDesc() {
     auto top = std::move(for_deletion.back());
     for_deletion.pop_back();
     append_deletable_inputs(*top);
-
-    // Clear out possible siblings to break circular references
-    for (auto& s : top->siblings) {
-      // Set to null here to avoid descending into top-level
-      // array destructor for siblings
-      s.array_desc_ = nullptr;
-    }
-    top->siblings.clear();
   }
 }
 
@@ -322,7 +303,7 @@ array::ArrayIterator::ArrayIterator(const array& arr, int idx)
 }
 
 array::ArrayIterator::reference array::ArrayIterator::operator*() const {
-  auto start = Shape(arr.ndim(), 0);
+  auto start = std::vector<int>(arr.ndim(), 0);
   auto end = arr.shape();
   auto shape = arr.shape();
   shape.erase(shape.begin());

mlx/array.h

@@ -15,11 +15,7 @@ namespace mlx::core {
 
 // Forward declaration
 class Primitive;
-
-using Deleter = std::function<void(allocator::Buffer)>;
-using ShapeElem = int32_t;
-using Shape = std::vector<ShapeElem>;
-using Strides = std::vector<int64_t>;
+using deleter_t = std::function<void(allocator::Buffer)>;
 
 class array {
   /* An array is really a node in a graph. It contains a shared ArrayDesc
@@ -37,7 +33,7 @@ class array {
   template <typename It>
   array(
       It data,
-      Shape shape,
+      std::vector<int> shape,
       Dtype dtype =
           TypeToDtype<typename std::iterator_traits<It>::value_type>());
 
@@ -53,15 +49,15 @@ class array {
   template <typename T>
   array(
       std::initializer_list<T> data,
-      Shape shape,
+      std::vector<int> shape,
       Dtype dtype = TypeToDtype<T>());
 
   /* Build an array from a buffer */
   array(
       allocator::Buffer data,
-      Shape shape,
+      std::vector<int> shape,
       Dtype dtype,
-      Deleter deleter = allocator::free);
+      deleter_t deleter = allocator::free);
 
   /** Assignment to rvalue does not compile. */
   array& operator=(const array& other) && = delete;
@@ -100,7 +96,7 @@ class array {
   }
 
   /** The shape of the array as a vector of integers. */
-  const Shape& shape() const {
+  const std::vector<int>& shape() const {
     return array_desc_->shape;
   }
 
@@ -109,12 +105,12 @@ class array {
    *
    *  This function supports negative indexing and provides
    *  bounds checking. */
-  auto shape(int dim) const {
+  int shape(int dim) const {
     return shape().at(dim < 0 ? dim + ndim() : dim);
   }
 
   /** The strides of the array. */
-  const Strides& strides() const {
+  const std::vector<size_t>& strides() const {
     return array_desc_->strides;
   }
 
@@ -123,7 +119,7 @@ class array {
    *
    *  This function supports negative indexing and provides
    *  bounds checking. */
-  auto strides(int dim) const {
+  size_t strides(int dim) const {
     return strides().at(dim < 0 ? dim + ndim() : dim);
   }
 
@@ -188,13 +184,13 @@ class array {
    */
 
   array(
-      Shape shape,
+      std::vector<int> shape,
       Dtype dtype,
       std::shared_ptr<Primitive> primitive,
       std::vector<array> inputs);
 
   static std::vector<array> make_arrays(
-      std::vector<Shape> shapes,
+      std::vector<std::vector<int>> shapes,
       const std::vector<Dtype>& dtypes,
       const std::shared_ptr<Primitive>& primitive,
       const std::vector<array>& inputs);
@@ -211,8 +207,8 @@ class array {
 
   struct Data {
     allocator::Buffer buffer;
-    Deleter d;
-    Data(allocator::Buffer buffer, Deleter d = allocator::free)
+    deleter_t d;
+    Data(allocator::Buffer buffer, deleter_t d = allocator::free)
         : buffer(buffer), d(d) {}
     // Not copyable
     Data(const Data& d) = delete;
@@ -401,18 +397,18 @@ class array {
   // Check if the array is a tracer array
   bool is_tracer() const;
 
-  void set_data(allocator::Buffer buffer, Deleter d = allocator::free);
+  void set_data(allocator::Buffer buffer, deleter_t d = allocator::free);
 
   void set_data(
       allocator::Buffer buffer,
       size_t data_size,
-      Strides strides,
+      std::vector<size_t> strides,
       Flags flags,
-      Deleter d = allocator::free);
+      deleter_t d = allocator::free);
 
   void copy_shared_buffer(
       const array& other,
-      const Strides& strides,
+      const std::vector<size_t>& strides,
       Flags flags,
       size_t data_size,
       size_t offset = 0);
@@ -421,7 +417,7 @@ class array {
 
   void move_shared_buffer(
       array other,
-      const Strides& strides,
+      const std::vector<size_t>& strides,
       Flags flags,
       size_t data_size,
       size_t offset = 0);
@@ -440,8 +436,8 @@ class array {
   void init(const It src);
 
   struct ArrayDesc {
-    Shape shape;
-    Strides strides;
+    std::vector<int> shape;
+    std::vector<size_t> strides;
     size_t size;
     Dtype dtype;
     std::shared_ptr<Primitive> primitive;
@@ -475,10 +471,10 @@ class array {
     // The arrays position in the output list
     uint32_t position{0};
 
-    explicit ArrayDesc(Shape shape, Dtype dtype);
+    explicit ArrayDesc(std::vector<int> shape, Dtype dtype);
 
     explicit ArrayDesc(
-        Shape shape,
+        std::vector<int> shape,
         Dtype dtype,
         std::shared_ptr<Primitive> primitive,
         std::vector<array> inputs);
@@ -499,14 +495,14 @@ class array {
 
 template <typename T>
 array::array(T val, Dtype dtype /* = TypeToDtype<T>() */)
-    : array_desc_(std::make_shared<ArrayDesc>(Shape{}, dtype)) {
+    : array_desc_(std::make_shared<ArrayDesc>(std::vector<int>{}, dtype)) {
   init(&val);
 }
 
 template <typename It>
 array::array(
   It data,
-  Shape shape,
+  std::vector<int> shape,
   Dtype dtype /* = TypeToDtype<typename std::iterator_traits<It>::value_type>() */) :
     array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
   init(data);
@@ -517,7 +513,7 @@ array::array(
     std::initializer_list<T> data,
     Dtype dtype /* = TypeToDtype<T>() */)
     : array_desc_(std::make_shared<ArrayDesc>(
-          Shape{static_cast<ShapeElem>(data.size())},
+          std::vector<int>{static_cast<int>(data.size())},
           dtype)) {
   init(data.begin());
 }
@@ -525,7 +521,7 @@ array::array(
 template <typename T>
 array::array(
     std::initializer_list<T> data,
-    Shape shape,
+    std::vector<int> shape,
     Dtype dtype /* = TypeToDtype<T>() */)
     : array_desc_(std::make_shared<ArrayDesc>(std::move(shape), dtype)) {
   if (data.size() != size()) {

mlx/backend/accelerate/primitives.cpp

@@ -43,7 +43,6 @@ DEFAULT(NumberOfElements)
 DEFAULT(Equal)
 DEFAULT(Erf)
 DEFAULT(ErfInv)
-DEFAULT(ExpandDims)
 DEFAULT(FFT)
 DEFAULT(Floor)
 DEFAULT(Gather)
@@ -66,6 +65,7 @@ DEFAULT(Pad)
 DEFAULT(Partition)
 DEFAULT_MULTI(QRF)
 DEFAULT(RandomBits)
+DEFAULT(Reshape)
 DEFAULT(Remainder)
 DEFAULT(Round)
 DEFAULT(Scatter)
@@ -76,7 +76,6 @@ DEFAULT(Slice)
 DEFAULT(SliceUpdate)
 DEFAULT_MULTI(Split)
 DEFAULT(Sort)
-DEFAULT(Squeeze)
 DEFAULT(StopGradient)
 DEFAULT_MULTI(SVD)
 DEFAULT(Transpose)

mlx/backend/common/CMakeLists.txt

@@ -5,21 +5,13 @@ else()
   set(COMPILER ${CMAKE_CXX_COMPILER})
 endif()
 
-if(MSVC)
-  set(SHELL_EXT ps1)
-  set(SHELL_CMD powershell -ExecutionPolicy Bypass -File)
-else()
-  set(SHELL_EXT sh)
-  set(SHELL_CMD /bin/bash)
-endif()
-
 add_custom_command(
   OUTPUT compiled_preamble.cpp
   COMMAND
-    ${SHELL_CMD} ${CMAKE_CURRENT_SOURCE_DIR}/make_compiled_preamble.${SHELL_EXT}
+    /bin/bash ${CMAKE_CURRENT_SOURCE_DIR}/make_compiled_preamble.sh
     ${CMAKE_CURRENT_BINARY_DIR}/compiled_preamble.cpp ${COMPILER}
-    ${PROJECT_SOURCE_DIR} ${CLANG} ${CMAKE_SYSTEM_PROCESSOR}
-  DEPENDS make_compiled_preamble.${SHELL_EXT}
+    ${PROJECT_SOURCE_DIR} ${CLANG}
+  DEPENDS make_compiled_preamble.sh
           compiled_preamble.h
           ${PROJECT_SOURCE_DIR}/mlx/types/half_types.h
           ${PROJECT_SOURCE_DIR}/mlx/types/fp16.h
@@ -66,6 +58,5 @@ target_sources(
 if(IOS)
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled_nocpu.cpp)
 else()
-  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled_cpu.cpp
-                             ${CMAKE_CURRENT_SOURCE_DIR}/jit_compiler.cpp)
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/compiled_cpu.cpp)
 endif()

mlx/backend/common/arg_reduce.cpp

@@ -13,8 +13,8 @@ template <typename InT, typename OpT>
 void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   auto axis_size = in.shape()[axis];
   auto axis_stride = in.strides()[axis];
-  Strides strides = in.strides();
-  Shape shape = in.shape();
+  std::vector<size_t> strides = in.strides();
+  std::vector<int> shape = in.shape();
   strides.erase(strides.begin() + axis);
   shape.erase(shape.begin() + axis);
   for (uint32_t i = 0; i < out.size(); ++i) {

mlx/backend/common/binary.h

@@ -28,8 +28,8 @@ BinaryOpType get_binary_op_type(const array& a, const array& b) {
   } else if (b.data_size() == 1 && a.flags().contiguous) {
     bopt = BinaryOpType::VectorScalar;
   } else if (
-      (a.flags().row_contiguous && b.flags().row_contiguous) ||
-      (a.flags().col_contiguous && b.flags().col_contiguous)) {
+      a.flags().row_contiguous && b.flags().row_contiguous ||
+      a.flags().col_contiguous && b.flags().col_contiguous) {
     bopt = BinaryOpType::VectorVector;
   } else {
     bopt = BinaryOpType::General;
@@ -178,10 +178,10 @@ void binary_op_dims(
     const T* b,
     U* out,
     Op op,
-    const Shape& shape,
-    const Strides& a_strides,
-    const Strides& b_strides,
-    const Strides& out_strides,
+    const std::vector<int>& shape,
+    const std::vector<size_t>& a_strides,
+    const std::vector<size_t>& b_strides,
+    const std::vector<size_t>& out_strides,
     int axis) {
   auto stride_a = a_strides[axis];
   auto stride_b = b_strides[axis];
@@ -212,10 +212,10 @@ void binary_op_dispatch_dims(
     array& out,
     Op op,
     int dim,
-    const Shape& shape,
-    const Strides& a_strides,
-    const Strides& b_strides,
-    const Strides& out_strides) {
+    const std::vector<int>& shape,
+    const std::vector<size_t>& a_strides,
+    const std::vector<size_t>& b_strides,
+    const std::vector<size_t>& out_strides) {
   const T* a_ptr = a.data<T>();
   const T* b_ptr = b.data<T>();
   U* out_ptr = out.data<U>();
@@ -258,10 +258,10 @@ void binary_op_dispatch_dims(
       return;
   }
 
-  ContiguousIterator a_it(shape, a_strides, dim - 3);
-  ContiguousIterator b_it(shape, b_strides, dim - 3);
-  auto stride = out_strides[dim - 4];
-  for (int64_t elem = 0; elem < a.size(); elem += stride) {
+  ContiguousIterator<size_t> a_it(shape, a_strides, dim - 3);
+  ContiguousIterator<size_t> b_it(shape, b_strides, dim - 3);
+  size_t stride = out_strides[dim - 4];
+  for (size_t elem = 0; elem < a.size(); elem += stride) {
     binary_op_dims<T, U, Op, 3, Strided>(
         a_ptr + a_it.loc,
         b_ptr + b_it.loc,
@@ -327,7 +327,7 @@ void binary_op(
   const auto& strides = new_strides[2];
 
   // Get the left-most dim such that the array is row contiguous after
-  auto leftmost_rc_dim = [&strides](const auto& arr_strides) {
+  auto leftmost_rc_dim = [&strides](const std::vector<size_t>& arr_strides) {
     int d = arr_strides.size() - 1;
     for (; d >= 0 && arr_strides[d] == strides[d]; d--) {
     }
@@ -337,7 +337,7 @@ void binary_op(
   auto b_rc_dim = leftmost_rc_dim(b_strides);
 
   // Get the left-most dim such that the array is a broadcasted "scalar" after
-  auto leftmost_s_dim = [](const auto& arr_strides) {
+  auto leftmost_s_dim = [](const std::vector<size_t>& arr_strides) {
     int d = arr_strides.size() - 1;
     for (; d >= 0 && arr_strides[d] == 0; d--) {
     }

mlx/backend/common/binary_two.h

@@ -16,10 +16,10 @@ void binary_op_dims(
     U* out_a,
     U* out_b,
     Op op,
-    const Shape& shape,
-    const Strides& a_strides,
-    const Strides& b_strides,
-    const Strides& out_strides,
+    const std::vector<int>& shape,
+    const std::vector<size_t>& a_strides,
+    const std::vector<size_t>& b_strides,
+    const std::vector<size_t>& out_strides,
     int axis) {
   auto stride_a = a_strides[axis];
   auto stride_b = b_strides[axis];
@@ -96,9 +96,9 @@ void binary_op_dispatch_dims(
       return;
   }
 
-  ContiguousIterator a_it(shape, a_strides, ndim - 2);
-  ContiguousIterator b_it(shape, b_strides, ndim - 2);
-  auto stride = out_strides[ndim - 3];
+  ContiguousIterator<size_t> a_it(shape, a_strides, ndim - 2);
+  ContiguousIterator<size_t> b_it(shape, b_strides, ndim - 2);
+  size_t stride = out_strides[ndim - 3];
   for (size_t elem = 0; elem < a.size(); elem += stride) {
     binary_op_dims<T, U, Op, 2>(
         a_ptr + a_it.loc,

mlx/backend/common/common.cpp

@@ -39,7 +39,7 @@ void AsStrided::eval(const std::vector<array>& inputs, array& out) {
   // rely on data_size anyway.
   size_t data_size = out.size();
 
-  return move_or_copy(in, out, strides_, flags, data_size, offset_);
+  return out.copy_shared_buffer(in, strides_, flags, data_size, offset_);
 }
 
 void Broadcast::eval(const std::vector<array>& inputs, array& out) {
@@ -49,7 +49,7 @@ void Broadcast::eval(const std::vector<array>& inputs, array& out) {
     out.set_data(nullptr);
     return;
   }
-  Strides strides(out.ndim(), 0);
+  std::vector<size_t> strides(out.ndim(), 0);
   int diff = out.ndim() - in.ndim();
   for (int i = in.ndim() - 1; i >= 0; --i) {
     strides[i + diff] = (in.shape()[i] == 1) ? 0 : in.strides()[i];
@@ -58,12 +58,12 @@ void Broadcast::eval(const std::vector<array>& inputs, array& out) {
   if (out.size() > in.size()) {
     flags.row_contiguous = flags.col_contiguous = false;
   }
-  move_or_copy(in, out, strides, flags, in.data_size());
+  out.copy_shared_buffer(in, strides, flags, in.data_size());
 }
 
 void Copy::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  move_or_copy(inputs[0], out);
+  out.copy_shared_buffer(inputs[0]);
 }
 
 void CustomTransforms::eval(
@@ -72,7 +72,7 @@ void CustomTransforms::eval(
   assert(inputs.size() > outputs.size());
   for (int i = 0, j = inputs.size() - outputs.size(); i < outputs.size();
        i++, j++) {
-    move_or_copy(inputs[j], outputs[i]);
+    outputs[i].copy_shared_buffer(inputs[j]);
   }
 }
 
@@ -81,20 +81,10 @@ void Depends::eval(
     std::vector<array>& outputs) {
   assert(inputs.size() > outputs.size());
   for (int i = 0; i < outputs.size(); i++) {
-    move_or_copy(inputs[i], outputs[i]);
+    outputs[i].copy_shared_buffer(inputs[i]);
   }
 }
 
-void ExpandDims::eval(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-  const auto& in = inputs[0];
-  auto strides = in.strides();
-  for (auto ax : axes_) {
-    strides.insert(strides.begin() + ax, 1);
-  }
-  move_or_copy(in, out, strides, in.flags(), in.data_size());
-}
-
 void NumberOfElements::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
@@ -151,7 +141,9 @@ void NumberOfElements::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
-std::pair<bool, Strides> prepare_reshape(const array& in, const array& out) {
+std::pair<bool, std::vector<size_t>> Reshape::prepare_reshape(
+    const array& in,
+    const array& out) {
   // Special case for empty arrays or row contiguous arrays
   if (in.size() == 0 || in.flags().row_contiguous) {
     return {false, out.strides()};
@@ -159,7 +151,8 @@ std::pair<bool, Strides> prepare_reshape(const array& in, const array& out) {
 
   // Special case for scalars
   if (in.ndim() == 0) {
-    return {false, Strides(out.ndim(), 0)};
+    std::vector<size_t> out_strides(out.ndim(), 0);
+    return {false, out_strides};
   }
 
   // Firstly let's collapse all the contiguous dimensions of the input
@@ -167,7 +160,7 @@ std::pair<bool, Strides> prepare_reshape(const array& in, const array& out) {
 
   // If shapes fit exactly in the contiguous dims then no copy is necessary so
   // let's check.
-  Strides out_strides;
+  std::vector<size_t> out_strides;
   bool copy_necessary = false;
   int j = 0;
   for (int i = 0; i < out.ndim(); i++) {
@@ -188,9 +181,9 @@ std::pair<bool, Strides> prepare_reshape(const array& in, const array& out) {
   return {copy_necessary, out_strides};
 }
 
-void shared_buffer_reshape(
+void Reshape::shared_buffer_reshape(
     const array& in,
-    const Strides& out_strides,
+    const std::vector<size_t>& out_strides,
     array& out) {
   auto flags = in.flags();
   if (flags.row_contiguous) {
@@ -201,7 +194,7 @@ void shared_buffer_reshape(
     auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
     flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
   }
-  move_or_copy(in, out, out_strides, flags, in.data_size());
+  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
 }
 
 void Split::eval(
@@ -256,28 +249,26 @@ void Split::eval(
   }
 }
 
-void Squeeze::eval(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-  const auto& in = inputs[0];
-  Strides strides;
-  for (int i = 0, j = 0; i < in.ndim(); ++i) {
-    if (j < axes_.size() && i == axes_[j]) {
-      j++;
-    } else {
-      strides.push_back(in.strides(i));
-    }
+std::tuple<int64_t, std::vector<int64_t>> SliceUpdate::prepare_slice(
+    const array& in) {
+  int64_t data_offset = 0;
+  std::vector<int64_t> inp_strides(in.ndim(), 0);
+  for (int i = 0; i < in.ndim(); ++i) {
+    data_offset += start_indices_[i] * in.strides()[i];
+    inp_strides[i] = in.strides()[i] * strides_[i];
   }
-  move_or_copy(in, out, strides, in.flags(), in.data_size());
+
+  return std::make_tuple(data_offset, inp_strides);
 }
 
 void StopGradient::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  move_or_copy(inputs[0], out);
+  out.copy_shared_buffer(inputs[0]);
 }
 
 void Transpose::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
-  Strides out_strides(out.ndim());
+  std::vector<size_t> out_strides(out.ndim());
   auto& in = inputs[0];
   for (int ax = 0; ax < axes_.size(); ++ax) {
     out_strides[ax] = in.strides()[axes_[ax]];
@@ -294,8 +285,8 @@ void Transpose::eval(const std::vector<array>& inputs, array& out) {
   //   true, they stay true)
   auto flags = in.flags();
   if (flags.contiguous && in.data_size() == in.size()) {
-    int64_t f_stride = 1;
-    int64_t b_stride = 1;
+    size_t f_stride = 1;
+    size_t b_stride = 1;
     flags.col_contiguous = true;
     flags.row_contiguous = true;
     for (int i = 0, ri = out.ndim() - 1; i < out.ndim(); ++i, --ri) {
@@ -306,7 +297,7 @@ void Transpose::eval(const std::vector<array>& inputs, array& out) {
       b_stride *= out.shape(ri);
     }
   }
-  move_or_copy(in, out, out_strides, flags, in.data_size());
+  out.copy_shared_buffer(in, out_strides, flags, in.data_size());
 }
 
 } // namespace mlx::core

mlx/backend/common/compiled.cpp

@@ -130,7 +130,7 @@ std::string build_lib_name(
 
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
-    const Shape& shape) {
+    const std::vector<int>& shape) {
   bool contiguous = true;
   bool all_contig = true;
   bool all_row_contig = true;
@@ -165,7 +165,7 @@ void compiled_allocate_outputs(
     bool move_buffers /* = false */) {
   if (contiguous) {
     int o = 0;
-    Strides strides;
+    std::vector<size_t> strides;
     size_t data_size;
     array::Flags flags;
     for (int i = 0; i < inputs.size() && o < outputs.size(); ++i) {

mlx/backend/common/compiled.h

@@ -56,7 +56,7 @@ inline bool is_scalar(const array& x) {
 // Check if we can use a contiguous operation given inputs and the output shape
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
-    const Shape& shape);
+    const std::vector<int>& shape);
 
 // Allocate space for the outputs possibly with input donation
 void compiled_allocate_outputs(

mlx/backend/common/compiled_cpu.cpp

@@ -9,7 +9,6 @@
 
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/common/compiled_preamble.h"
-#include "mlx/backend/common/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
 
@@ -45,9 +44,12 @@ namespace detail {
 bool compile_available_for_device(const Device& device) {
   return true;
 }
-
 } // namespace detail
 
+std::string get_temp_file(const std::string& name) {
+  return std::filesystem::temp_directory_path().append(name);
+}
+
 // Return a pointer to a compiled function
 void* compile(
     const std::string& kernel_name,
@@ -66,30 +68,24 @@ void* compile(
   std::string source_code = source_builder();
   std::string kernel_file_name;
 
-  // Deal with long kernel names. Maximum length for filename on macOS is 255
-  // characters, and on Windows the maximum length for whole path is 260. Clip
-  // file name with a little extra room and append a 16 character hash.
-#ifdef _WIN32
-  constexpr int max_file_name_length = 140;
-#else
+  // Deal with long kernel names. Maximum length for files on macOS is 255
+  // characters. Clip file name with a little extra room and append a 16
+  // character hash.
   constexpr int max_file_name_length = 245;
-#endif
   if (kernel_name.size() > max_file_name_length) {
     std::ostringstream file_name;
     file_name
         << std::string_view(kernel_name).substr(0, max_file_name_length - 16);
-    auto file_id =
-        std::hash<std::string>{}(kernel_name.substr(max_file_name_length - 16));
+    auto file_id = std::hash<std::string>{}(kernel_name);
     file_name << "_" << std::hex << std::setw(16) << file_id << std::dec;
     kernel_file_name = file_name.str();
   } else {
     kernel_file_name = kernel_name;
   }
 
-  auto output_dir = std::filesystem::temp_directory_path();
-
-  std::string shared_lib_name = "lib" + kernel_file_name + ".so";
-  auto shared_lib_path = (output_dir / shared_lib_name).string();
+  std::ostringstream shared_lib_name;
+  shared_lib_name << "lib" << kernel_file_name << ".so";
+  auto shared_lib_path = get_temp_file(shared_lib_name.str());
   bool lib_exists = false;
   {
     std::ifstream f(shared_lib_path.c_str());
@@ -98,16 +94,19 @@ void* compile(
 
   if (!lib_exists) {
     // Open source file and write source code to it
-    std::string source_file_name = kernel_file_name + ".cpp";
-    auto source_file_path = (output_dir / source_file_name).string();
+    std::ostringstream source_file_name;
+    source_file_name << kernel_file_name << ".cpp";
+    auto source_file_path = get_temp_file(source_file_name.str());
 
     std::ofstream source_file(source_file_path);
     source_file << source_code;
     source_file.close();
 
-    std::string command = JitCompiler::build_command(
-        output_dir, source_file_name, shared_lib_name);
-    auto return_code = system(command.c_str());
+    std::ostringstream build_command;
+    build_command << "g++ -std=c++17 -O3 -Wall -fPIC -shared '"
+                  << source_file_path << "' -o '" << shared_lib_path << "'";
+    std::string build_command_str = build_command.str();
+    auto return_code = system(build_command_str.c_str());
     if (return_code) {
       std::ostringstream msg;
       msg << "[Compile::eval_cpu] Failed to compile function " << kernel_name
@@ -152,11 +151,6 @@ inline void build_kernel(
 
   NodeNamer namer;
 
-#ifdef _MSC_VER
-  // Export the symbol
-  os << "__declspec(dllexport) ";
-#endif
-
   // Start the kernel
   os << "void " << kernel_name << "(void** args) {" << std::endl;
 
@@ -285,7 +279,7 @@ void Compiled::eval_cpu(
 
   // Figure out which kernel we are using
   auto& shape = outputs[0].shape();
-  auto contiguous = compiled_check_contiguity(inputs, shape);
+  bool contiguous = compiled_check_contiguity(inputs, shape);
 
   // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;

mlx/backend/common/conv.cpp

@@ -726,7 +726,7 @@ void explicit_gemm_conv_1D_cpu(
   auto conv_dtype = float32;
 
   // Pad input
-  Shape padded_shape = {N, iH + 2 * padding[0], C};
+  std::vector<int> padded_shape = {N, iH + 2 * padding[0], C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -746,9 +746,9 @@ void explicit_gemm_conv_1D_cpu(
   copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral);
 
   // Make strided view
-  Shape strided_shape = {N, oH, wH, C};
+  std::vector<int> strided_shape = {N, oH, wH, C};
 
-  Strides strided_strides = {
+  std::vector<size_t> strided_strides = {
       in_padded.strides()[0],
       in_padded.strides()[1] * wt_strides[0],
       in_padded.strides()[1],
@@ -765,7 +765,7 @@ void explicit_gemm_conv_1D_cpu(
       in_padded, strided_strides, flags, in_strided_view.size(), 0);
 
   // Materialize strided view
-  Shape strided_reshape = {N * oH, wH * C};
+  std::vector<int> strided_reshape = {N * oH, wH * C};
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
   copy(in_strided_view, in_strided, CopyType::General);
 
@@ -843,7 +843,8 @@ void explicit_gemm_conv_2D_cpu(
   auto conv_dtype = out.dtype();
 
   // Pad input
-  Shape padded_shape = {N, iH + 2 * padding[0], iW + 2 * padding[1], C};
+  std::vector<int> padded_shape = {
+      N, iH + 2 * padding[0], iW + 2 * padding[1], C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -864,9 +865,9 @@ void explicit_gemm_conv_2D_cpu(
   copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral);
 
   // Make strided view
-  Shape strided_shape = {N, oH, oW, wH, wW, C};
+  std::vector<int> strided_shape = {N, oH, oW, wH, wW, C};
 
-  Strides strided_strides = {
+  std::vector<size_t> strided_strides = {
       in_padded.strides()[0],
       in_padded.strides()[1] * wt_strides[0],
       in_padded.strides()[2] * wt_strides[1],
@@ -880,7 +881,7 @@ void explicit_gemm_conv_2D_cpu(
       in_padded, strided_strides, flags, in_strided_view.size(), 0);
 
   // Materialize strided view
-  Shape strided_reshape = {N * oH * oW, wH * wW * C};
+  std::vector<int> strided_reshape = {N * oH * oW, wH * wW * C};
   array in_strided(strided_reshape, in_strided_view.dtype(), nullptr, {});
   copy(in_strided_view, in_strided, CopyType::General);
 
@@ -933,19 +934,19 @@ void explicit_gemm_conv_ND_cpu(
     const std::vector<int>& wt_dilation,
     const bool flip) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
-  const auto iDim =
-      Shape(in.shape().begin() + 1, in.shape().end() - 1); // Input spatial dim
-  const auto oDim = Shape(
+  const auto iDim = std::vector<int>(
+      in.shape().begin() + 1, in.shape().end() - 1); // Input spatial dim
+  const auto oDim = std::vector<int>(
       out.shape().begin() + 1, out.shape().end() - 1); // Output spatial dim
   const int O = wt.shape(0); // Out channels
   const int C = wt.shape(-1); // In channels
-  const auto wDim =
-      Shape(wt.shape().begin() + 1, wt.shape().end() - 1); // Weight spatial dim
+  const auto wDim = std::vector<int>(
+      wt.shape().begin() + 1, wt.shape().end() - 1); // Weight spatial dim
 
   auto conv_dtype = float32;
 
   // Pad input
-  Shape padded_shape(in.shape().size());
+  std::vector<int> padded_shape(in.shape().size());
   padded_shape.front() = N;
   for (size_t i = 0; i < iDim.size(); i++) {
     padded_shape[i + 1] = iDim[i] + 2 * padding[i];
@@ -973,7 +974,7 @@ void explicit_gemm_conv_ND_cpu(
   copy_inplace(in, in_padded_slice, CopyType::GeneralGeneral);
 
   // Make strided view
-  Shape strided_shape(oDim.size() + wDim.size() + 2);
+  std::vector<int> strided_shape(oDim.size() + wDim.size() + 2);
   strided_shape.front() = N;
   for (size_t i = 0; i < oDim.size(); i++) {
     strided_shape[i + 1] = oDim[i];
@@ -983,7 +984,7 @@ void explicit_gemm_conv_ND_cpu(
   }
   strided_shape.back() = C;
 
-  Strides strided_strides(in.shape().size() * 2 - 2);
+  std::vector<size_t> strided_strides(in.shape().size() * 2 - 2);
   strided_strides[0] = in_padded.strides()[0];
   for (size_t i = 0; i < wt_strides.size(); i++) {
     strided_strides[i + 1] = in_padded.strides()[i + 1] * wt_strides[i];
@@ -999,7 +1000,7 @@ void explicit_gemm_conv_ND_cpu(
       in_padded, strided_strides, flags, in_strided_view.size(), 0);
 
   // Materialize strided view
-  Shape strided_reshape = {N, C};
+  std::vector<int> strided_reshape = {N, C};
   for (const auto& o : oDim) {
     strided_reshape[0] *= o;
   }

mlx/backend/common/copy.cpp

@@ -26,13 +26,13 @@ void copy_vector(const array& src, array& dst) {
   std::copy(src_ptr, src_ptr + src.data_size(), dst_ptr);
 }
 
-template <typename SrcT, typename DstT, int D>
+template <typename SrcT, typename DstT, typename StrideT, int D>
 inline void copy_dims(
     const SrcT* src,
     DstT* dst,
-    const Shape& shape,
-    const Strides& i_strides,
-    const Strides& o_strides,
+    const std::vector<int>& shape,
+    const std::vector<StrideT>& i_strides,
+    const std::vector<StrideT>& o_strides,
     int axis) {
   auto stride_src = i_strides[axis];
   auto stride_dst = o_strides[axis];
@@ -40,7 +40,7 @@ inline void copy_dims(
 
   for (int i = 0; i < N; i++) {
     if constexpr (D > 1) {
-      copy_dims<SrcT, DstT, D - 1>(
+      copy_dims<SrcT, DstT, StrideT, D - 1>(
           src, dst, shape, i_strides, o_strides, axis + 1);
     } else {
       *dst = static_cast<DstT>(*src);
@@ -50,13 +50,13 @@ inline void copy_dims(
   }
 }
 
-template <typename SrcT, typename DstT>
+template <typename SrcT, typename DstT, typename StrideT>
 void copy_general_general(
     const array& src,
     array& dst,
-    const Shape& data_shape,
-    const Strides& i_strides,
-    const Strides& o_strides,
+    const std::vector<int>& data_shape,
+    const std::vector<StrideT>& i_strides,
+    const std::vector<StrideT>& o_strides,
     int64_t i_offset,
     int64_t o_offset) {
   if (data_shape.empty()) {
@@ -65,30 +65,30 @@ void copy_general_general(
     *dst_ptr = val;
     return;
   }
-  auto [shape, strides] =
-      collapse_contiguous_dims(data_shape, {i_strides, o_strides});
+  auto [shape, strides] = collapse_contiguous_dims(
+      data_shape, std::vector<std::vector<StrideT>>{i_strides, o_strides});
   auto src_ptr = src.data<SrcT>() + i_offset;
   auto dst_ptr = dst.data<DstT>() + o_offset;
   int ndim = shape.size();
   if (ndim == 1) {
-    copy_dims<SrcT, DstT, 1>(
+    copy_dims<SrcT, DstT, StrideT, 1>(
         src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
     return;
   } else if (ndim == 2) {
-    copy_dims<SrcT, DstT, 2>(
+    copy_dims<SrcT, DstT, StrideT, 2>(
         src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
     return;
   } else if (ndim == 3) {
-    copy_dims<SrcT, DstT, 3>(
+    copy_dims<SrcT, DstT, StrideT, 3>(
         src_ptr, dst_ptr, shape, strides[0], strides[1], 0);
     return;
   }
-  ContiguousIterator in(shape, strides[0], ndim - 3);
-  ContiguousIterator out(shape, strides[1], ndim - 3);
-  auto stride = std::accumulate(
-      shape.end() - 3, shape.end(), 1, std::multiplies<int64_t>());
-  for (int64_t elem = 0; elem < src.size(); elem += stride) {
-    copy_dims<SrcT, DstT, 3>(
+  ContiguousIterator<StrideT> in(shape, strides[0], ndim - 3);
+  ContiguousIterator<StrideT> out(shape, strides[1], ndim - 3);
+  StrideT stride = std::accumulate(
+      shape.end() - 3, shape.end(), 1, std::multiplies<StrideT>());
+  for (StrideT elem = 0; elem < src.size(); elem += stride) {
+    copy_dims<SrcT, DstT, StrideT, 3>(
         src_ptr + in.loc,
         dst_ptr + out.loc,
         shape,
@@ -102,37 +102,37 @@ void copy_general_general(
 
 template <typename SrcT, typename DstT>
 inline void copy_general_general(const array& src, array& dst) {
-  copy_general_general<SrcT, DstT>(
+  copy_general_general<SrcT, DstT, size_t>(
       src, dst, src.shape(), src.strides(), dst.strides(), 0, 0);
 }
 
-template <typename SrcT, typename DstT>
+template <typename SrcT, typename DstT, typename StrideT>
 void copy_general(
     const array& src,
     array& dst,
-    const Shape& data_shape,
-    const Strides& i_strides,
-    const Strides&,
+    const std::vector<int>& data_shape,
+    const std::vector<StrideT>& i_strides,
+    const std::vector<StrideT>&,
     int64_t i_offset,
     int64_t o_offset) {
-  copy_general_general<SrcT, DstT>(
+  copy_general_general<SrcT, DstT, StrideT>(
       src,
       dst,
       data_shape,
       i_strides,
-      make_contiguous_strides(data_shape),
+      make_contiguous_strides<StrideT>(data_shape),
       i_offset,
       o_offset);
 }
 
 template <typename SrcT, typename DstT>
 inline void copy_general(const array& src, array& dst) {
-  copy_general_general<SrcT, DstT>(
+  copy_general_general<SrcT, DstT, size_t>(
       src,
       dst,
       src.shape(),
       src.strides(),
-      make_contiguous_strides(src.shape()),
+      make_contiguous_strides<size_t>(src.shape()),
       0,
       0);
 }
@@ -282,12 +282,13 @@ void copy(const array& src, array& dst, CopyType ctype) {
   copy_inplace(src, dst, ctype);
 }
 
+template <typename StrideT>
 void copy_inplace(
     const array& src,
     array& dst,
-    const Shape& data_shape,
-    const Strides& i_strides,
-    const Strides& o_strides,
+    const std::vector<int>& data_shape,
+    const std::vector<StrideT>& i_strides,
+    const std::vector<StrideT>& o_strides,
     int64_t i_offset,
     int64_t o_offset,
     CopyType ctype) {
@@ -310,4 +311,24 @@ void copy_inplace(
   }
 }
 
+template void copy_inplace<size_t>(
+    const array& src,
+    array& dst,
+    const std::vector<int>& data_shape,
+    const std::vector<size_t>& i_strides,
+    const std::vector<size_t>& o_strides,
+    int64_t i_offset,
+    int64_t o_offset,
+    CopyType ctype);
+
+template void copy_inplace<int64_t>(
+    const array& src,
+    array& dst,
+    const std::vector<int>& data_shape,
+    const std::vector<int64_t>& i_strides,
+    const std::vector<int64_t>& o_strides,
+    int64_t i_offset,
+    int64_t o_offset,
+    CopyType ctype);
+
 } // namespace mlx::core

mlx/backend/common/copy.h

@@ -26,12 +26,13 @@ enum class CopyType {
 void copy(const array& src, array& dst, CopyType ctype);
 void copy_inplace(const array& src, array& dst, CopyType ctype);
 
+template <typename stride_t>
 void copy_inplace(
     const array& src,
     array& dst,
-    const Shape& data_shape,
-    const Strides& i_strides,
-    const Strides& o_strides,
+    const std::vector<int>& data_shape,
+    const std::vector<stride_t>& i_strides,
+    const std::vector<stride_t>& o_strides,
     int64_t i_offset,
     int64_t o_offset,
     CopyType ctype);

mlx/backend/common/default_primitives.cpp

@@ -57,7 +57,6 @@ DEFAULT(Equal)
 DEFAULT(Erf)
 DEFAULT(ErfInv)
 DEFAULT(Exp)
-DEFAULT(ExpandDims)
 DEFAULT(Expm1)
 DEFAULT(FFT)
 DEFAULT(Floor)
@@ -87,6 +86,7 @@ DEFAULT_MULTI(QRF)
 DEFAULT(QuantizedMatmul)
 DEFAULT(RandomBits)
 DEFAULT(Reduce)
+DEFAULT(Reshape)
 DEFAULT(Round)
 DEFAULT(Scan)
 DEFAULT(Scatter)
@@ -101,7 +101,6 @@ DEFAULT(Softmax)
 DEFAULT(Sort)
 DEFAULT_MULTI(Split)
 DEFAULT(Square)
-DEFAULT(Squeeze)
 DEFAULT(Sqrt)
 DEFAULT(StopGradient)
 DEFAULT(Subtract)
@@ -131,7 +130,7 @@ inline void matmul_common_general(
     } else {
       array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
       copy(arr, arr_copy, CopyType::General);
-      stx = arr.shape(-1);
+      size_t stx = arr.shape(-1);
       return std::make_tuple(false, stx, arr_copy);
     }
   };

mlx/backend/common/indexing.cpp

@@ -32,7 +32,7 @@ void gather(
     const std::vector<array>& inds,
     array& out,
     const std::vector<int>& axes,
-    const Shape& slice_sizes) {
+    const std::vector<int>& slice_sizes) {
   // If the array is row contiguous then we can do a contiguous copy given
   // two conditions on the slice size:
   // - Any number of leading ones in the slice sizes are allowed
@@ -80,10 +80,11 @@ void gather(
   T* dst_ptr = out.data<T>();
   size_t out_idx = 0;
 
-  std::vector<ContiguousIterator> its(inds.begin(), inds.end());
-  ContiguousIterator src_it;
+  std::vector<ContiguousIterator<size_t>> its(inds.begin(), inds.end());
+  ContiguousIterator<size_t> src_it;
   if (!can_copy && src.ndim() > 0) {
-    src_it = ContiguousIterator(slice_sizes, src.strides(), src.ndim());
+    src_it = std::move(
+        ContiguousIterator<size_t>(slice_sizes, src.strides(), src.ndim()));
   }
   for (int idx = 0; idx < ind_size; idx++) {
     size_t src_idx = 0;
@@ -118,7 +119,7 @@ void dispatch_gather(
     const std::vector<array>& inds,
     array& out,
     const std::vector<int>& axes,
-    const Shape& size) {
+    const std::vector<int>& size) {
   switch (out.dtype()) {
     case bool_:
       gather<bool, IdxT>(src, inds, out, axes, size);
@@ -222,16 +223,16 @@ void scatter(
   auto inds_ndim = updates.ndim() - out.ndim();
   size_t n_updates = nind ? inds[0].size() : 1;
 
-  Shape update_shape(
+  std::vector<int> update_shape(
       updates.shape().begin() + inds_ndim, updates.shape().end());
   size_t update_size = 1;
   for (auto us : update_shape) {
     update_size *= us;
   }
 
-  std::vector<ContiguousIterator> its(inds.begin(), inds.end());
-  ContiguousIterator update_it(updates);
-  ContiguousIterator out_it(update_shape, out.strides(), out.ndim());
+  std::vector<ContiguousIterator<size_t>> its(inds.begin(), inds.end());
+  ContiguousIterator<size_t> update_it(updates);
+  ContiguousIterator<size_t> out_it(update_shape, out.strides(), out.ndim());
 
   for (int i = 0; i < n_updates; ++i) {
     size_t out_offset = 0;

mlx/backend/common/jit_compiler.cpp

@@ -1,128 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include "mlx/backend/common/jit_compiler.h"
-
-#include <sstream>
-#include <vector>
-
-#include <fmt/format.h>
-
-namespace mlx::core {
-
-#ifdef _MSC_VER
-
-namespace {
-
-// Split string into array.
-std::vector<std::string> str_split(const std::string& str, char delimiter) {
-  std::vector<std::string> tokens;
-  std::string token;
-  std::istringstream tokenStream(str);
-  while (std::getline(tokenStream, token, delimiter)) {
-    tokens.push_back(token);
-  }
-  return tokens;
-}
-
-// Run a command and get its output.
-std::string exec(const std::string& cmd) {
-  std::unique_ptr<FILE, decltype(&_pclose)> pipe(
-      _popen(cmd.c_str(), "r"), _pclose);
-  if (!pipe) {
-    throw std::runtime_error("popen() failed.");
-  }
-  char buffer[128];
-  std::string ret;
-  while (fgets(buffer, sizeof(buffer), pipe.get())) {
-    ret += buffer;
-  }
-  // Trim trailing spaces.
-  ret.erase(
-      std::find_if(
-          ret.rbegin(),
-          ret.rend(),
-          [](unsigned char ch) { return !std::isspace(ch); })
-          .base(),
-      ret.end());
-  return ret;
-}
-
-// Get path information about MSVC.
-struct VisualStudioInfo {
-  VisualStudioInfo() {
-#ifdef _M_ARM64
-    arch = "arm64";
-#else
-    arch = "x64";
-#endif
-    // Get path of Visual Studio.
-    std::string vs_path = exec(fmt::format(
-        "\"{0}\\Microsoft Visual Studio\\Installer\\vswhere.exe\""
-        " -property installationPath",
-        std::getenv("ProgramFiles(x86)")));
-    if (vs_path.empty()) {
-      throw std::runtime_error("Can not find Visual Studio.");
-    }
-    // Read the envs from vcvarsall.
-    std::string envs = exec(fmt::format(
-        "\"{0}\\VC\\Auxiliary\\Build\\vcvarsall.bat\" {1} >NUL && set",
-        vs_path,
-        arch));
-    for (const std::string& line : str_split(envs, '\n')) {
-      // Each line is in the format "ENV_NAME=values".
-      auto pos = line.find_first_of('=');
-      if (pos == std::string::npos || pos == 0 || pos == line.size() - 1)
-        continue;
-      std::string name = line.substr(0, pos);
-      std::string value = line.substr(pos + 1);
-      if (name == "LIB") {
-        libpaths = str_split(value, ';');
-      } else if (name == "VCToolsInstallDir") {
-        cl_exe = fmt::format("{0}\\bin\\Host{1}\\{1}\\cl.exe", value, arch);
-      }
-    }
-  }
-  std::string arch;
-  std::string cl_exe;
-  std::vector<std::string> libpaths;
-};
-
-const VisualStudioInfo& GetVisualStudioInfo() {
-  static VisualStudioInfo info;
-  return info;
-}
-
-} // namespace
-
-#endif // _MSC_VER
-
-std::string JitCompiler::build_command(
-    const std::filesystem::path& dir,
-    const std::string& source_file_name,
-    const std::string& shared_lib_name) {
-#ifdef _MSC_VER
-  const VisualStudioInfo& info = GetVisualStudioInfo();
-  std::string libpaths;
-  for (const std::string& lib : info.libpaths) {
-    libpaths += fmt::format(" /libpath:\"{0}\"", lib);
-  }
-  return fmt::format(
-      "\""
-      "cd /D \"{0}\" && "
-      "\"{1}\" /LD /EHsc /MD /Ox /nologo /std:c++17 \"{2}\" "
-      "/link /out:\"{3}\" {4} >nul"
-      "\"",
-      dir.string(),
-      info.cl_exe,
-      source_file_name,
-      shared_lib_name,
-      libpaths);
-#else
-  return fmt::format(
-      "g++ -std=c++17 -O3 -Wall -fPIC -shared '{0}' -o '{1}'",
-      (dir / source_file_name).string(),
-      (dir / shared_lib_name).string());
-#endif
-}
-
-} // namespace mlx::core

mlx/backend/common/jit_compiler.h

@@ -1,17 +0,0 @@
-// Copyright © 2024 Apple Inc.
-#pragma once
-
-#include <filesystem>
-
-namespace mlx::core {
-
-class JitCompiler {
- public:
-  // Build a shell command that compiles a source code file to a shared library.
-  static std::string build_command(
-      const std::filesystem::path& dir,
-      const std::string& source_file_name,
-      const std::string& shared_lib_name);
-};
-
-} // namespace mlx::core

mlx/backend/common/lapack.h

@@ -2,15 +2,6 @@
 
 #pragma once
 
-// Required for Visual Studio.
-// https://github.com/OpenMathLib/OpenBLAS/blob/develop/docs/install.md
-#ifdef _MSC_VER
-#include <complex>
-#define LAPACK_COMPLEX_CUSTOM
-#define lapack_complex_float std::complex<float>
-#define lapack_complex_double std::complex<double>
-#endif
-
 #ifdef ACCELERATE_NEW_LAPACK
 #include <Accelerate/Accelerate.h>
 #else

mlx/backend/common/make_compiled_preamble.ps1

@@ -1,38 +0,0 @@
-# This script generates a C++ function that provides the CPU
-# code for use with kernel generation.
-#
-# Copyright © 2024 Apple Inc.
-
-$OUTPUT_FILE = $args[0]
-$CL = $args[1]
-$SRCDIR = $args[2]
-
-# Get command result as array.
-$CONTENT = & $CL /std:c++17 /EP "/I$SRCDIR" /Tp "$SRCDIR/mlx/backend/common/compiled_preamble.h"
-# Remove empty lines.
-# Otherwise there will be too much empty lines making the result unreadable.
-$CONTENT = $CONTENT | Where-Object { $_.Trim() -ne '' }
-# Concatenate to string.
-$CONTENT = $CONTENT -join "`n"
-
-# Append extra content.
-$CONTENT = @"
-$($CONTENT)
-using namespace mlx::core;
-using namespace mlx::core::detail;
-"@
-
-# Convert each char to ASCII code.
-# Unlike the unix script that outputs string literal directly, the output from
-# MSVC is way too large to be embedded as string and compilation will fail, so
-# we store it as static array instead.
-$CHARCODES = ([System.Text.Encoding]::ASCII.GetBytes($CONTENT) -join ', ') + ', 0'
-
-$OUTPUT = @"
-const char* get_kernel_preamble() {
-  static char preamble[] = { $CHARCODES };
-  return preamble;
-}
-"@
-
-Set-Content -Path $OUTPUT_FILE -Value $OUTPUT

mlx/backend/common/make_compiled_preamble.sh

@@ -10,16 +10,15 @@ OUTPUT_FILE=$1
 GCC=$2
 SRCDIR=$3
 CLANG=$4
-ARCH=$5
 
 if [ "$CLANG" = "TRUE" ]; then
   read -r -d '' INCLUDES <<- EOM
-#include <cmath>
-#include <complex>
-#include <cstdint>
-#include <vector>
+  #include <cmath>
+  #include <complex>
+  #include <cstdint>
+  #include <vector>
 EOM
-CC_FLAGS="-arch ${ARCH}"
+CC_FLAGS=""
 else
 CC_FLAGS="-std=c++17"
 fi

mlx/backend/common/masked_mm.cpp

@@ -19,10 +19,10 @@ inline void mask_matrix(
     int block_size,
     const int X,
     const int Y,
-    const int64_t X_data_str,
-    const int64_t Y_data_str,
-    const int64_t X_mask_str,
-    const int64_t Y_mask_str,
+    const size_t X_data_str,
+    const size_t Y_data_str,
+    const size_t X_mask_str,
+    const size_t Y_mask_str,
     const size_t mask_offset) {
   int tX = (X + block_size - 1) / block_size;
   int tY = (Y + block_size - 1) / block_size;
@@ -84,7 +84,7 @@ void BlockMaskedMM::eval(const std::vector<array>& inputs, array& out) {
         } else {
           array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
           copy(arr, arr_copy, CopyType::General);
-          int64_t stx = arr.shape(-1);
+          size_t stx = arr.shape(-1);
           return std::make_tuple(false, stx, arr_copy);
         }
       };
@@ -117,13 +117,13 @@ void BlockMaskedMM::eval(const std::vector<array>& inputs, array& out) {
                        int Y,
                        size_t X_data_str,
                        size_t Y_data_str) {
-    auto mask_offset = elem_to_loc(
+    size_t mask_offset = elem_to_loc(
         mask.shape(-1) * mask.shape(-2) * batch_idx,
         mask.shape(),
         mask.strides());
 
-    auto X_mask_str = mask.strides()[mask.ndim() - 2];
-    auto Y_mask_str = mask.strides()[mask.ndim() - 1];
+    size_t X_mask_str = mask.strides()[mask.ndim() - 2];
+    size_t Y_mask_str = mask.strides()[mask.ndim() - 1];
 
     if (mask.dtype() == bool_) {
       return mask_matrix(
@@ -230,7 +230,7 @@ void GatherMM::eval(const std::vector<array>& inputs, array& out) {
     } else {
       array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
       copy(arr, arr_copy, CopyType::General);
-      int64_t stx = arr.shape(-1);
+      size_t stx = arr.shape(-1);
       return std::make_tuple(false, stx, arr_copy);
     }
   };
@@ -262,13 +262,13 @@ void GatherMM::eval(const std::vector<array>& inputs, array& out) {
   auto& lhs_indices = inputs[2];
   auto& rhs_indices = inputs[3];
 
-  auto batch_shape = get_batch_dims(out.shape());
+  std::vector<int> batch_shape = get_batch_dims(out.shape());
   int batch_ndim = batch_shape.size();
 
-  auto batch_shape_A = get_batch_dims(a.shape());
-  auto batch_strides_A = get_batch_dims(a.strides());
-  auto batch_shape_B = get_batch_dims(b.shape());
-  auto batch_strides_B = get_batch_dims(b.strides());
+  std::vector<int> batch_shape_A = get_batch_dims(a.shape());
+  std::vector<size_t> batch_strides_A = get_batch_dims(a.strides());
+  std::vector<int> batch_shape_B = get_batch_dims(b.shape());
+  std::vector<size_t> batch_strides_B = get_batch_dims(b.strides());
 
   const uint32_t* lhs_indices_ptr = lhs_indices.data<uint32_t>();
   const uint32_t* rhs_indices_ptr = rhs_indices.data<uint32_t>();

mlx/backend/common/ops.h

@@ -500,12 +500,7 @@ struct Equal {
 struct NaNEqual {
   template <typename T>
   bool operator()(T x, T y) {
-    if constexpr (std::is_integral_v<T>) {
-      // isnan always returns false for integers, and MSVC refuses to compile.
-      return x == y;
-    } else {
-      return x == y || (std::isnan(x) && std::isnan(y));
-    }
+    return x == y || (std::isnan(x) && std::isnan(y));
   }
 };
 

mlx/backend/common/primitives.cpp

@@ -19,16 +19,6 @@
 
 namespace mlx::core {
 
-void reshape(const array& in, array& out) {
-  auto [copy_necessary, out_strides] = prepare_reshape(in, out);
-  if (copy_necessary) {
-    out.set_data(allocator::malloc_or_wait(out.nbytes()));
-    copy_inplace(in, out, CopyType::General);
-  } else {
-    shared_buffer_reshape(in, out_strides, out);
-  }
-}
-
 void Abs::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
@@ -169,17 +159,6 @@ void Conjugate::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
-void Contiguous::eval_cpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-  auto& in = inputs[0];
-  if (in.flags().row_contiguous ||
-      (allow_col_major_ && in.flags().col_contiguous)) {
-    out.copy_shared_buffer(in);
-  } else {
-    copy(in, out, CopyType::General);
-  }
-}
-
 void Cos::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   const auto& in = inputs[0];
@@ -268,14 +247,6 @@ void Expm1::eval(const std::vector<array>& inputs, array& out) {
   }
 }
 
-void Flatten::eval_cpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out);
-}
-
-void Unflatten::eval_cpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out);
-}
-
 void Floor::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
@@ -435,8 +406,18 @@ void Real::eval_cpu(const std::vector<array>& inputs, array& out) {
   unary_op<complex64_t, float>(inputs[0], out, detail::Real());
 }
 
-void Reshape::eval_cpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out);
+void Reshape::eval(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 1);
+  const auto& in = inputs[0];
+
+  auto [copy_necessary, out_strides] = prepare_reshape(in, out);
+
+  if (copy_necessary) {
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    copy_inplace(in, out, CopyType::General);
+  } else {
+    shared_buffer_reshape(in, out_strides, out);
+  }
 }
 
 void Round::eval(const std::vector<array>& inputs, array& out) {
@@ -506,17 +487,34 @@ void Slice::eval(const std::vector<array>& inputs, array& out) {
   auto& in = inputs[0];
 
   // Calculate out strides, initial offset and if copy needs to be made
-  auto [data_offset, inp_strides] = prepare_slice(in, start_indices_, strides_);
-  size_t data_end = 1;
-  for (int i = 0; i < end_indices_.size(); ++i) {
-    if (in.shape()[i] > 1) {
-      auto end_idx = start_indices_[i] + out.shape()[i] * strides_[i] - 1;
-      data_end += end_idx * in.strides()[i];
+  auto [copy_needed, data_offset, inp_strides] =
+      prepare_slice(in, start_indices_, strides_);
+
+  // Do copy if needed
+  if (copy_needed) {
+    out.set_data(allocator::malloc_or_wait(out.nbytes()));
+    std::vector<int64_t> ostrides{out.strides().begin(), out.strides().end()};
+    copy_inplace<int64_t>(
+        /* const array& src = */ in,
+        /* array& dst = */ out,
+        /* const std::vector<int>& data_shape = */ out.shape(),
+        /* const std::vector<stride_t>& i_strides = */ inp_strides,
+        /* const std::vector<stride_t>& o_strides = */ ostrides,
+        /* int64_t i_offset = */ data_offset,
+        /* int64_t o_offset = */ 0,
+        /* CopyType ctype = */ CopyType::General);
+  } else {
+    size_t data_end = 1;
+    for (int i = 0; i < end_indices_.size(); ++i) {
+      if (in.shape()[i] > 1) {
+        auto end_idx = start_indices_[i] + out.shape()[i] * strides_[i] - 1;
+        data_end += end_idx * in.strides()[i];
+      }
     }
+    size_t data_size = data_end - data_offset;
+    std::vector<size_t> ostrides{inp_strides.begin(), inp_strides.end()};
+    shared_buffer_slice(in, ostrides, data_offset, data_size, out);
   }
-  size_t data_size = data_end - data_offset;
-  Strides ostrides{inp_strides.begin(), inp_strides.end()};
-  shared_buffer_slice(in, ostrides, data_offset, data_size, out);
 }
 
 void SliceUpdate::eval(const std::vector<array>& inputs, array& out) {
@@ -541,11 +539,11 @@ void SliceUpdate::eval(const std::vector<array>& inputs, array& out) {
   copy(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype);
 
   // Calculate out strides, initial offset and if copy needs to be made
-  auto [data_offset, out_strides] = prepare_slice(in, start_indices_, strides_);
+  auto [data_offset, out_strides] = prepare_slice(out);
 
   // Do copy
-  Strides upd_strides{upd.strides().begin(), upd.strides().end()};
-  copy_inplace(
+  std::vector<int64_t> upd_strides{upd.strides().begin(), upd.strides().end()};
+  copy_inplace<int64_t>(
       /* const array& src = */ upd,
       /* array& dst = */ out,
       /* const std::vector<int>& data_shape = */ upd.shape(),
@@ -605,10 +603,10 @@ void View::eval_cpu(const std::vector<array>& inputs, array& out) {
   // - type size is the same
   // - type size is smaller and the last axis is contiguous
   // - the entire array is row contiguous
-  if (ibytes == obytes || (obytes < ibytes && in.strides().back() == 1) ||
+  if (ibytes == obytes || obytes < ibytes && in.strides().back() == 1 ||
       in.flags().row_contiguous) {
     auto strides = in.strides();
-    for (int i = 0; i < static_cast<int>(strides.size()) - 1; ++i) {
+    for (int i = 0; i < strides.size() - 1; ++i) {
       strides[i] *= ibytes;
       strides[i] /= obytes;
     }

mlx/backend/common/qrf.cpp

@@ -54,7 +54,7 @@ void qrf_impl(const array& a, array& q, array& r) {
   // Copy the input to be column contiguous
   flags.col_contiguous = num_matrices == 1;
   flags.row_contiguous = false;
-  auto strides = in.strides();
+  std::vector<size_t> strides = in.strides();
   strides[in.ndim() - 2] = 1;
   strides[in.ndim() - 1] = M;
   in.set_data(

mlx/backend/common/quantized.cpp

@@ -2,38 +2,13 @@
 
 #include <cassert>
 
-#include "mlx/backend/common/copy.h"
-#include "mlx/backend/common/ops.h"
-#include "mlx/fast_primitives.h"
+#include "mlx/backend/metal/copy.h"
 #include "mlx/primitives.h"
-#include "mlx/utils.h"
 
 namespace mlx::core {
 
 namespace {
 
-template <typename T, int bits>
-void extract_bits(const uint8_t* w_in, T* w_out) {
-  assert(bits == 3 || bits == 6);
-  if (bits == 3) {
-    w_out[0] = static_cast<T>(w_in[0] & 0x7);
-    w_out[1] = static_cast<T>((w_in[0] & 0x38) >> 3);
-    w_out[2] = static_cast<T>(((w_in[0] & 0xc0) >> 6) + ((w_in[1] & 0x1) << 2));
-    w_out[3] = static_cast<T>((w_in[1] & 0xe) >> 1);
-    w_out[4] = static_cast<T>((w_in[1] & 0x70) >> 4);
-    w_out[5] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0x3) << 1));
-    w_out[6] = static_cast<T>((w_in[2] & 0x1c) >> 2);
-    w_out[7] = static_cast<T>((w_in[2] & 0xe0) >> 5);
-  } else if (bits == 6) {
-    w_out[0] = static_cast<T>(w_in[0] & 0x3f);
-    w_out[1] =
-        static_cast<T>(((w_in[0] >> 6) & 0x03) + ((w_in[1] & 0x0f) << 2));
-    w_out[2] =
-        static_cast<T>(((w_in[1] >> 4) & 0x0f) + ((w_in[2] & 0x03) << 4));
-    w_out[3] = static_cast<T>((w_in[2] >> 2) & 0x3f);
-  }
-}
-
 template <typename T, int bits, int group_size>
 void _qmm(
     T* result,
@@ -45,12 +20,13 @@ void _qmm(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
-  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
+  constexpr int pack_factor = 32 / bits;
   constexpr int packs_in_group = group_size / pack_factor;
+  const int Ng = N / group_size;
+  const int Nw = N / pack_factor;
 
   for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
+    const uint32_t* w_local = w;
     const T* scales_local = scales;
     const T* biases_local = biases;
 
@@ -64,25 +40,13 @@ void _qmm(
         T scale = *scales_local++;
         T bias = *biases_local++;
         for (int ng = 0; ng < packs_in_group; ng++) {
-          if (bits == 3 || bits == 6) {
-            T wl[pack_factor];
-            extract_bits<T, bits>(w_local, wl);
-#pragma clang loop unroll(full)
-            for (int p = 0; p < pack_factor; p++) {
-              (*result_local++) += xi * (scale * wl[p] + bias);
-            }
-            w_local += bytes_per_pack;
+          uint32_t wi = *w_local++;
 
-          } else {
-            uint8_t wi = *w_local++;
 #pragma clang loop unroll(full)
-            for (int p = 0; p < pack_factor; p++) {
-              (*result_local++) +=
-                  xi * (scale * static_cast<T>(wi & bitmask) + bias);
-              if (bits != 8) {
-                wi >>= bits;
-              }
-            }
+          for (int p = 0; p < pack_factor; p++) {
+            (*result_local++) +=
+                xi * (scale * static_cast<T>(wi & bitmask) + bias);
+            wi >>= bits;
           }
         }
       }
@@ -103,12 +67,13 @@ void _qmm_t(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
-  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
+  constexpr int pack_factor = 32 / bits;
   constexpr int packs_in_group = group_size / pack_factor;
+  const int Kg = K / group_size;
+  const int Kw = K / pack_factor;
 
   for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
+    const uint32_t* w_local = w;
     const T* scales_local = scales;
     const T* biases_local = biases;
 
@@ -120,26 +85,12 @@ void _qmm_t(
         T bias = *biases_local++;
 
         for (int kw = 0; kw < packs_in_group; kw++) {
-          if (bits == 3 || bits == 6) {
-            T wl[pack_factor];
-            extract_bits<T, bits>(w_local, wl);
-#pragma clang loop unroll(full)
-            for (int p = 0; p < pack_factor; p++) {
-              sum += x_local[p] * (scale * wl[p] + bias);
-            }
-            w_local += bytes_per_pack;
-            x_local += pack_factor;
+          uint32_t wi = *w_local++;
 
-          } else {
-            uint8_t wi = *w_local++;
 #pragma clang loop unroll(full)
-            for (int p = 0; p < pack_factor; p++) {
-              sum +=
-                  (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
-              if (bits != 8) {
-                wi >>= bits;
-              }
-            }
+          for (int p = 0; p < pack_factor; p++) {
+            sum += (*x_local++) * (scale * static_cast<T>(wi & bitmask) + bias);
+            wi >>= bits;
           }
         }
       }
@@ -151,55 +102,6 @@ void _qmm_t(
   }
 }
 
-template <typename T, int bits, int group_size>
-void _qmm_dispatch_transpose(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const T* scales,
-    const T* biases,
-    int M,
-    int N,
-    int K,
-    bool transposed_w) {
-  if (transposed_w) {
-    return _qmm_t<T, bits, group_size>(result, x, w, scales, biases, M, N, K);
-  } else {
-    return _qmm<T, bits, group_size>(result, x, w, scales, biases, M, N, K);
-  }
-}
-
-template <typename T, int bits>
-void _qmm_dispatch_group(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const T* scales,
-    const T* biases,
-    int M,
-    int N,
-    int K,
-    int group_size,
-    bool transposed_w) {
-  switch (group_size) {
-    case 32:
-      _qmm_dispatch_transpose<T, bits, 32>(
-          result, x, w, scales, biases, M, N, K, transposed_w);
-      break;
-    case 64:
-      _qmm_dispatch_transpose<T, bits, 64>(
-          result, x, w, scales, biases, M, N, K, transposed_w);
-      break;
-    case 128:
-      _qmm_dispatch_transpose<T, bits, 128>(
-          result, x, w, scales, biases, M, N, K, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "Quantization group size must be 32, 64 or 128.");
-  }
-}
-
 template <typename T>
 void _qmm_dispatch_typed(
     T* result,
@@ -214,29 +116,79 @@ void _qmm_dispatch_typed(
     int bits,
     bool transposed_w) {
   switch (bits) {
-    case 2:
-      _qmm_dispatch_group<T, 2>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
-    case 3:
-      _qmm_dispatch_group<T, 3>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
-    case 4:
-      _qmm_dispatch_group<T, 4>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
-    case 6:
-      _qmm_dispatch_group<T, 6>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
-    case 8:
-      _qmm_dispatch_group<T, 8>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument("Quantization bits must be 2, 3, 4, 6 or 8.");
+    case 2: {
+      switch (group_size) {
+        case 32:
+          if (transposed_w) {
+            return _qmm_t<T, 2, 32>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 2, 32>(result, x, w, scales, biases, M, N, K);
+          }
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 2, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 2, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 2, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 2, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
+    case 4: {
+      switch (group_size) {
+        case 32:
+          if (transposed_w) {
+            return _qmm_t<T, 4, 32>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 4, 32>(result, x, w, scales, biases, M, N, K);
+          }
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 4, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 4, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 4, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 4, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
+    case 8: {
+      switch (group_size) {
+        case 32:
+          if (transposed_w) {
+            return _qmm_t<T, 8, 32>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 8, 32>(result, x, w, scales, biases, M, N, K);
+          }
+        case 64:
+          if (transposed_w) {
+            return _qmm_t<T, 8, 64>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 8, 64>(result, x, w, scales, biases, M, N, K);
+          }
+        case 128:
+          if (transposed_w) {
+            return _qmm_t<T, 8, 128>(result, x, w, scales, biases, M, N, K);
+          } else {
+            return _qmm<T, 8, 128>(result, x, w, scales, biases, M, N, K);
+          }
+      }
+    }
   }
+  std::ostringstream msg;
+  msg << "Quantization type not supported. Provided bits=" << bits
+      << " and group_size=" << group_size
+      << ". The supported options are bits in "
+      << "{2, 4, 8} and group_size in {64, 128}.";
+  throw std::invalid_argument(msg.str());
 }
 
 void _qmm_dispatch(
@@ -452,114 +404,4 @@ void GatherQMM::eval(const std::vector<array>& inputs, array& out) {
       transpose_);
 }
 
-template <typename T, typename U>
-void quantize(
-    const array& w_,
-    array& out_,
-    array& scales_,
-    array& biases_,
-    int bits,
-    int group_size) {
-  const T* w = w_.data<T>();
-
-  auto out = out_.data<U>();
-  T* scales = scales_.data<T>();
-  T* biases = biases_.data<T>();
-
-  T n_bins = (1 << bits) - 1;
-  T eps = 1e-7;
-  bool power_of_2_bits = is_power_of_2(bits);
-  int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
-  // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
-  int bytes_per_pack = power_of_2_bits ? 1 : 3;
-  int int_per_group = group_size * bytes_per_pack / el_per_int;
-  size_t n_groups = w_.size() / group_size;
-
-  for (size_t i = 0; i < n_groups; ++i) {
-    size_t w_idx = i * group_size;
-    T w_min = std::numeric_limits<float>::infinity();
-    T w_max = -w_min;
-    for (int j = 0; j < group_size; ++j) {
-      w_max = std::max(w_max, w[w_idx + j]);
-      w_min = std::min(w_min, w[w_idx + j]);
-    }
-    bool mask = std::abs(w_min) > std::abs(w_max);
-    T scale = std::max(T((w_max - w_min) / n_bins), eps);
-    scale = mask ? scale : -scale;
-
-    auto edge = mask ? w_min : w_max;
-    auto q0 = std::rint(edge / scale);
-    if (q0 == 0) {
-      scales[i] = scale;
-      biases[i] = 0;
-    } else {
-      scales[i] = edge / q0;
-      biases[i] = edge;
-    }
-    size_t out_idx = i * int_per_group;
-    for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
-      uint32_t out_el = 0;
-      for (int k = 0; k < el_per_int; ++k) {
-        T w_el = w[w_idx + j * el_per_int + k];
-        w_el = std::rint((w_el - biases[i]) / scales[i]);
-        w_el = std::min(std::max(w_el, T(0)), n_bins);
-        out_el |= static_cast<uint32_t>(w_el) << (k * bits);
-      }
-      if (power_of_2_bits) {
-        out[out_idx + j] = out_el;
-      } else {
-        out[out_idx + bytes_per_pack * j] = out_el & 0xff;
-        out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;
-        out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
-      }
-    }
-  }
-}
-
-void fast::AffineQuantize::eval_cpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  auto ensure_row_contiguous = [](const array& arr) {
-    if (arr.flags().row_contiguous) {
-      return arr;
-    } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy(arr, arr_copy, CopyType::General);
-      return arr_copy;
-    }
-  };
-  auto w = ensure_row_contiguous(inputs[0]);
-
-  auto& out = outputs[0];
-  out.set_data(allocator::malloc_or_wait(out.nbytes()));
-
-  auto& scales = outputs[1];
-  auto& biases = outputs[2];
-  scales.set_data(allocator::malloc_or_wait(scales.nbytes()));
-  biases.set_data(allocator::malloc_or_wait(biases.nbytes()));
-  if (w.dtype() == float16) {
-    if (is_power_of_2(bits_)) {
-      quantize<float16_t, uint32_t>(w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<float16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else if (w.dtype() == bfloat16) {
-    if (is_power_of_2(bits_)) {
-      quantize<bfloat16_t, uint32_t>(
-          w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<bfloat16_t, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else if (w.dtype() == float32) {
-    if (is_power_of_2(bits_)) {
-      quantize<float, uint32_t>(w, out, scales, biases, bits_, group_size_);
-    } else {
-      quantize<float, uint8_t>(w, out, scales, biases, bits_, group_size_);
-    }
-  } else {
-    throw std::runtime_error(
-        "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
-  }
-}
-
 } // namespace mlx::core

mlx/backend/common/reduce.cpp

@@ -120,53 +120,45 @@ struct MinReduce {
 };
 
 template <typename InT>
-void reduce_dispatch_and_or(
+void reduce_dispatch_out(
     const array& in,
     array& out,
     Reduce::ReduceType rtype,
     const std::vector<int>& axes) {
-  if (rtype == Reduce::And) {
-    reduction_op<InT, bool>(in, out, axes, true, AndReduce());
-  } else {
-    reduction_op<InT, bool>(in, out, axes, false, OrReduce());
-  }
-}
-
-template <typename InT>
-void reduce_dispatch_sum_prod(
-    const array& in,
-    array& out,
-    Reduce::ReduceType rtype,
-    const std::vector<int>& axes) {
-  if (rtype == Reduce::Sum) {
-    auto op = [](auto y, auto x) { (*y) = (*y) + x; };
-    if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t>(in, out, axes, 0, op);
-    } else {
-      reduction_op<InT, InT>(in, out, axes, 0, op);
+  switch (rtype) {
+    case Reduce::And: {
+      reduction_op<InT, bool>(in, out, axes, true, AndReduce());
+      break;
     }
-  } else {
-    auto op = [](auto y, auto x) { (*y) *= x; };
-    if constexpr (std::is_integral_v<InT> && sizeof(InT) <= 4) {
-      reduction_op<InT, int32_t>(in, out, axes, 1, op);
-    } else {
+    case Reduce::Or: {
+      reduction_op<InT, bool>(in, out, axes, false, OrReduce());
+      break;
+    }
+    case Reduce::Sum: {
+      auto op = [](auto y, auto x) { (*y) = (*y) + x; };
+      if (out.dtype() == int32) {
+        // special case since the input type can be bool
+        reduction_op<InT, int32_t>(in, out, axes, 0, op);
+      } else {
+        reduction_op<InT, InT>(in, out, axes, 0, op);
+      }
+      break;
+    }
+    case Reduce::Prod: {
+      auto op = [](auto y, auto x) { (*y) *= x; };
       reduction_op<InT, InT>(in, out, axes, 1, op);
+      break;
+    }
+    case Reduce::Max: {
+      auto init = Limits<InT>::min;
+      reduction_op<InT, InT>(in, out, axes, init, MaxReduce());
+      break;
+    }
+    case Reduce::Min: {
+      auto init = Limits<InT>::max;
+      reduction_op<InT, InT>(in, out, axes, init, MinReduce());
+      break;
     }
-  }
-}
-
-template <typename InT>
-void reduce_dispatch_min_max(
-    const array& in,
-    array& out,
-    Reduce::ReduceType rtype,
-    const std::vector<int>& axes) {
-  if (rtype == Reduce::Max) {
-    auto init = Limits<InT>::min;
-    reduction_op<InT, InT>(in, out, axes, init, MaxReduce());
-  } else {
-    auto init = Limits<InT>::max;
-    reduction_op<InT, InT>(in, out, axes, init, MinReduce());
   }
 }
 
@@ -174,19 +166,19 @@ void reduce_dispatch_min_max(
 
 void nd_loop(
     std::function<void(int)> callback,
-    const Shape& shape,
-    const Strides& strides) {
+    const std::vector<int>& shape,
+    const std::vector<size_t>& strides) {
   std::function<void(int, int)> loop_inner;
   loop_inner = [&](int dim, int offset) {
     if (dim < shape.size() - 1) {
-      auto size = shape[dim];
-      auto stride = strides[dim];
+      int size = shape[dim];
+      size_t stride = strides[dim];
       for (int i = 0; i < size; i++) {
         loop_inner(dim + 1, offset + i * stride);
       }
     } else {
-      auto size = shape[dim];
-      auto stride = strides[dim];
+      int size = shape[dim];
+      size_t stride = strides[dim];
       for (int i = 0; i < size; i++) {
         callback(offset + i * stride);
       }
@@ -198,114 +190,46 @@ void nd_loop(
 void Reduce::eval(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
-  switch (reduce_type_) {
-    case Reduce::And:
-    case Reduce::Or: {
-      switch (in.dtype()) {
-        case bool_:
-        case uint8:
-        case int8:
-          reduce_dispatch_and_or<int8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-        case uint16:
-        case float16:
-        case bfloat16:
-          reduce_dispatch_and_or<int16_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint32:
-        case int32:
-        case float32:
-          reduce_dispatch_and_or<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint64:
-        case int64:
-        case complex64:
-          reduce_dispatch_and_or<int64_t>(in, out, reduce_type_, axes_);
-          break;
-      }
+  switch (in.dtype()) {
+    case bool_:
+      reduce_dispatch_out<bool>(in, out, reduce_type_, axes_);
       break;
-    }
-    case Reduce::Sum:
-    case Reduce::Prod: {
-      switch (in.dtype()) {
-        case bool_:
-        case uint8:
-        case int8:
-          reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-        case uint16:
-          reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
-          break;
-        case int32:
-        case uint32:
-          reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case int64:
-        case uint64:
-          reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
-          break;
-        case float16:
-          reduce_dispatch_sum_prod<float16_t>(in, out, reduce_type_, axes_);
-          break;
-        case bfloat16:
-          reduce_dispatch_sum_prod<bfloat16_t>(in, out, reduce_type_, axes_);
-          break;
-        case float32:
-          reduce_dispatch_sum_prod<float>(in, out, reduce_type_, axes_);
-          break;
-        case complex64:
-          reduce_dispatch_sum_prod<complex64_t>(in, out, reduce_type_, axes_);
-          break;
-      }
+    case uint8:
+      reduce_dispatch_out<uint8_t>(in, out, reduce_type_, axes_);
       break;
-    }
-    case Reduce::Max:
-    case Reduce::Min: {
-      switch (in.dtype()) {
-        case bool_:
-          reduce_dispatch_min_max<bool>(in, out, reduce_type_, axes_);
-          break;
-        case uint8:
-          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint16:
-          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint32:
-          reduce_dispatch_min_max<uint32_t>(in, out, reduce_type_, axes_);
-          break;
-        case uint64:
-          reduce_dispatch_min_max<uint64_t>(in, out, reduce_type_, axes_);
-          break;
-        case int8:
-          reduce_dispatch_min_max<uint8_t>(in, out, reduce_type_, axes_);
-          break;
-        case int16:
-          reduce_dispatch_min_max<uint16_t>(in, out, reduce_type_, axes_);
-          break;
-        case int32:
-          reduce_dispatch_min_max<int32_t>(in, out, reduce_type_, axes_);
-          break;
-        case int64:
-          reduce_dispatch_min_max<int64_t>(in, out, reduce_type_, axes_);
-          break;
-        case float16:
-          reduce_dispatch_min_max<float16_t>(in, out, reduce_type_, axes_);
-          break;
-        case float32:
-          reduce_dispatch_min_max<float>(in, out, reduce_type_, axes_);
-          break;
-        case bfloat16:
-          reduce_dispatch_min_max<bfloat16_t>(in, out, reduce_type_, axes_);
-          break;
-        case complex64:
-          reduce_dispatch_min_max<complex64_t>(in, out, reduce_type_, axes_);
-          break;
-      }
+    case uint16:
+      reduce_dispatch_out<uint16_t>(in, out, reduce_type_, axes_);
+      break;
+    case uint32:
+      reduce_dispatch_out<uint32_t>(in, out, reduce_type_, axes_);
+      break;
+    case uint64:
+      reduce_dispatch_out<uint64_t>(in, out, reduce_type_, axes_);
+      break;
+    case int8:
+      reduce_dispatch_out<uint8_t>(in, out, reduce_type_, axes_);
+      break;
+    case int16:
+      reduce_dispatch_out<uint16_t>(in, out, reduce_type_, axes_);
+      break;
+    case int32:
+      reduce_dispatch_out<int32_t>(in, out, reduce_type_, axes_);
+      break;
+    case int64:
+      reduce_dispatch_out<int64_t>(in, out, reduce_type_, axes_);
+      break;
+    case float16:
+      reduce_dispatch_out<float16_t>(in, out, reduce_type_, axes_);
+      break;
+    case float32:
+      reduce_dispatch_out<float>(in, out, reduce_type_, axes_);
+      break;
+    case bfloat16:
+      reduce_dispatch_out<bfloat16_t>(in, out, reduce_type_, axes_);
+      break;
+    case complex64:
+      reduce_dispatch_out<complex64_t>(in, out, reduce_type_, axes_);
       break;
-    }
   }
 }
 

mlx/backend/common/reduce.h

@@ -38,10 +38,13 @@ enum ReductionOpType {
 
 struct ReductionPlan {
   ReductionOpType type;
-  Shape shape;
-  Strides strides;
+  std::vector<int> shape;
+  std::vector<size_t> strides;
 
-  ReductionPlan(ReductionOpType type_, Shape shape_, Strides strides_)
+  ReductionPlan(
+      ReductionOpType type_,
+      std::vector<int> shape_,
+      std::vector<size_t> strides_)
       : type(type_), shape(std::move(shape_)), strides(std::move(strides_)) {}
   ReductionPlan(ReductionOpType type_) : type(type_) {}
 };
@@ -52,10 +55,10 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes);
 // Should this be in utils?
 void nd_loop(
     std::function<void(int)> callback,
-    const Shape& shape,
-    const Strides& strides);
+    const std::vector<int>& shape,
+    const std::vector<size_t>& strides);
 
-std::pair<Shape, Strides> shapes_without_reduction_axes(
+std::pair<std::vector<int>, std::vector<size_t>> shapes_without_reduction_axes(
     const array& x,
     const std::vector<int>& axes);
 
@@ -110,6 +113,9 @@ void reduction_op(
     return;
   }
 
+  std::vector<int> shape;
+  std::vector<size_t> strides;
+
   if (plan.type == ContiguousReduce && plan.shape.size() == 1) {
     int reduction_size = plan.shape[0];
     const T* x_ptr = x.data<T>();
@@ -129,7 +135,7 @@ void reduction_op(
     U* out_ptr = out.data<U>();
     // Unrolling the following loop (and implementing it in order for
     // ContiguousReduce) should hold extra performance boost.
-    auto [shape, strides] = shapes_without_reduction_axes(x, axes);
+    std::tie(shape, strides) = shapes_without_reduction_axes(x, axes);
     if (plan.shape.size() == 0) {
       for (int i = 0; i < out.size(); i++, out_ptr++) {
         int offset = elem_to_loc(i, shape, strides);
@@ -175,7 +181,7 @@ void reduction_op(
     plan.strides.pop_back();
     const T* x_ptr = x.data<T>();
     U* out_ptr = out.data<U>();
-    auto [shape, strides] = shapes_without_reduction_axes(x, axes);
+    std::tie(shape, strides) = shapes_without_reduction_axes(x, axes);
     if (plan.shape.size() == 0) {
       for (int i = 0; i < out.size(); i += reduction_stride) {
         int offset = elem_to_loc(i, shape, strides);
@@ -205,7 +211,7 @@ void reduction_op(
   if (plan.type == GeneralReduce) {
     const T* x_ptr = x.data<T>();
     U* out_ptr = out.data<U>();
-    auto [shape, strides] = shapes_without_reduction_axes(x, axes);
+    std::tie(shape, strides) = shapes_without_reduction_axes(x, axes);
     for (int i = 0; i < out.size(); i++, out_ptr++) {
       int offset = elem_to_loc(i, shape, strides);
       U val = init;

mlx/backend/common/reduce_utils.cpp

@@ -4,11 +4,11 @@
 
 namespace mlx::core {
 
-std::pair<Shape, Strides> shapes_without_reduction_axes(
+std::pair<std::vector<int>, std::vector<size_t>> shapes_without_reduction_axes(
     const array& x,
     const std::vector<int>& axes) {
-  auto shape = x.shape();
-  auto strides = x.strides();
+  std::vector<int> shape = x.shape();
+  std::vector<size_t> strides = x.strides();
 
   for (int i = axes.size() - 1; i >= 0; i--) {
     int a = axes[i];
@@ -29,8 +29,8 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
   // Row contiguous input so the output is row contiguous
   if (x.flags().row_contiguous) {
     // Merge consecutive axes
-    Shape shape = {x.shape(axes[0])};
-    Strides strides = {x.strides()[axes[0]]};
+    std::vector<int> shape = {x.shape(axes[0])};
+    std::vector<size_t> strides = {x.strides()[axes[0]]};
     for (int i = 1; i < axes.size(); i++) {
       if (axes[i] - 1 == axes[i - 1] && x.shape(axes[i]) > 1) {
         shape.back() *= x.shape(axes[i]);
@@ -69,7 +69,7 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
 
   // Sort reduction axes by stride in order to merge them and figure out if we
   // have a contiguous reduction.
-  std::vector<std::pair<int, int64_t>> reductions;
+  std::vector<std::pair<int, size_t>> reductions;
   for (auto a : axes) {
     if (x.shape(a) > 1) {
       reductions.push_back(std::make_pair(x.shape(a), x.strides()[a]));
@@ -93,8 +93,8 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
     }
   }
 
-  Shape shape;
-  Strides strides;
+  std::vector<int> shape;
+  std::vector<size_t> strides;
   for (auto r : reductions) {
     shape.push_back(r.first);
     strides.push_back(r.second);
@@ -109,15 +109,15 @@ ReductionPlan get_reduction_plan(const array& x, const std::vector<int>& axes) {
   // Delegate to the general strided reduction op if the axes after
   // strides.back() are contiguous.
   if (strides.back() > 1) {
-    int64_t size = 1;
+    int size = 1;
     bool have_expand = false;
     for (int i = x.ndim() - 1; i >= 0; i--) {
       if (axes.back() == i) {
         continue;
       }
 
-      auto stride_i = x.strides()[i];
-      auto shape_i = x.shape(i);
+      size_t stride_i = x.strides()[i];
+      int shape_i = x.shape(i);
       if (stride_i == 0) {
         if (shape_i == 1) {
           continue;

mlx/backend/common/slicing.cpp

@@ -4,22 +4,24 @@
 
 namespace mlx::core {
 
-std::tuple<int64_t, Strides> prepare_slice(
+std::tuple<bool, int64_t, std::vector<int64_t>> prepare_slice(
     const array& in,
-    const Shape& start_indices,
-    const Shape& strides) {
+    const std::vector<int>& start_indices,
+    const std::vector<int>& strides) {
   int64_t data_offset = 0;
-  Strides inp_strides(in.ndim(), 0);
+  bool copy_needed = false;
+  std::vector<int64_t> inp_strides(in.ndim(), 0);
   for (int i = 0; i < in.ndim(); ++i) {
     data_offset += start_indices[i] * in.strides()[i];
     inp_strides[i] = in.strides()[i] * strides[i];
+    copy_needed |= strides[i] < 0;
   }
-  return std::make_tuple(data_offset, inp_strides);
+  return std::make_tuple(copy_needed, data_offset, inp_strides);
 }
 
 void shared_buffer_slice(
     const array& in,
-    const Strides& out_strides,
+    const std::vector<size_t>& out_strides,
     size_t data_offset,
     size_t data_size,
     array& out) {
@@ -32,7 +34,7 @@ void shared_buffer_slice(
   flags.col_contiguous = is_col_contiguous;
   flags.contiguous = (no_bsx_size == data_size);
 
-  move_or_copy(in, out, out_strides, flags, data_size, data_offset);
+  out.copy_shared_buffer(in, out_strides, flags, data_size, data_offset);
 }
 
 } // namespace mlx::core

mlx/backend/common/slicing.h

@@ -6,14 +6,14 @@
 
 namespace mlx::core {
 
-std::tuple<int64_t, Strides> prepare_slice(
+std::tuple<bool, int64_t, std::vector<int64_t>> prepare_slice(
     const array& in,
-    const Shape& start_indices,
-    const Shape& strides);
+    const std::vector<int>& start_indices,
+    const std::vector<int>& strides);
 
 void shared_buffer_slice(
     const array& in,
-    const Strides& out_strides,
+    const std::vector<size_t>& out_strides,
     size_t data_offset,
     size_t data_size,
     array& out);

mlx/backend/common/sort.cpp

@@ -14,10 +14,10 @@ namespace mlx::core {
 
 namespace {
 
-template <typename T>
+template <typename T, typename IdxT = int32_t>
 struct StridedIterator {
   using iterator_category = std::random_access_iterator_tag;
-  using difference_type = int32_t;
+  using difference_type = IdxT;
   using value_type = T;
   using reference = value_type&;
   using pointer = value_type*;
@@ -25,7 +25,7 @@ struct StridedIterator {
   // Constructors
   StridedIterator() = default;
 
-  explicit StridedIterator(T* ptr, int64_t stride, difference_type offset = 0)
+  explicit StridedIterator(T* ptr, size_t stride, difference_type offset = 0)
       : ptr_(ptr + offset * stride), stride_(stride) {}
 
   explicit StridedIterator(array& arr, int axis, difference_type offset = 0)
@@ -99,7 +99,7 @@ struct StridedIterator {
   }
 
  private:
-  int64_t stride_;
+  size_t stride_;
   T* ptr_;
 };
 
@@ -120,11 +120,11 @@ void sort(const array& in, array& out, int axis) {
   auto remaining_strides = out.strides();
   remaining_strides.erase(remaining_strides.begin() + axis);
 
-  auto axis_stride = out.strides()[axis];
-  auto axis_size = out.shape(axis);
+  size_t axis_stride = out.strides()[axis];
+  int axis_size = out.shape(axis);
 
   // Perform sorting in place
-  ContiguousIterator src_it(
+  ContiguousIterator<size_t> src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
     T* data_ptr = out.data<T>() + src_it.loc;
@@ -158,14 +158,14 @@ void argsort(const array& in, array& out, int axis) {
   auto out_remaining_strides = out.strides();
   out_remaining_strides.erase(out_remaining_strides.begin() + axis);
 
-  auto in_stride = in.strides()[axis];
-  auto out_stride = out.strides()[axis];
-  auto axis_size = in.shape(axis);
+  size_t in_stride = in.strides()[axis];
+  size_t out_stride = out.strides()[axis];
+  int axis_size = in.shape(axis);
 
   // Perform sorting
-  ContiguousIterator in_it(
+  ContiguousIterator<size_t> in_it(
       in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
-  ContiguousIterator out_it(
+  ContiguousIterator<size_t> out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
     const T* data_ptr = in.data<T>() + in_it.loc;
@@ -208,13 +208,13 @@ void partition(const array& in, array& out, int axis, int kth) {
   auto remaining_strides = in.strides();
   remaining_strides.erase(remaining_strides.begin() + axis);
 
-  auto axis_stride = in.strides()[axis];
+  size_t axis_stride = in.strides()[axis];
   int axis_size = in.shape(axis);
 
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition in place
-  ContiguousIterator src_it(
+  ContiguousIterator<size_t> src_it(
       remaining_shape, remaining_strides, remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
     T* data_ptr = out.data<T>() + src_it.loc;
@@ -249,16 +249,16 @@ void argpartition(const array& in, array& out, int axis, int kth) {
   auto out_remaining_strides = out.strides();
   out_remaining_strides.erase(out_remaining_strides.begin() + axis);
 
-  auto in_stride = in.strides()[axis];
-  auto out_stride = out.strides()[axis];
-  auto axis_size = in.shape(axis);
+  size_t in_stride = in.strides()[axis];
+  size_t out_stride = out.strides()[axis];
+  int axis_size = in.shape(axis);
 
   kth = kth < 0 ? kth + axis_size : kth;
 
   // Perform partition
-  ContiguousIterator in_it(
+  ContiguousIterator<size_t> in_it(
       in_remaining_shape, in_remaining_strides, in_remaining_shape.size());
-  ContiguousIterator out_it(
+  ContiguousIterator<size_t> out_it(
       out_remaining_shape, out_remaining_strides, out_remaining_shape.size());
   for (int i = 0; i < n_rows; i++) {
     const T* data_ptr = in.data<T>() + in_it.loc;

mlx/backend/common/ternary.h

@@ -78,11 +78,11 @@ void ternary_op_dims(
     const T3* c,
     U* out,
     Op op,
-    const Shape& shape,
-    const Strides& a_strides,
-    const Strides& b_strides,
-    const Strides& c_strides,
-    const Strides& out_strides,
+    const std::vector<int>& shape,
+    const std::vector<size_t>& a_strides,
+    const std::vector<size_t>& b_strides,
+    const std::vector<size_t>& c_strides,
+    const std::vector<size_t>& out_strides,
     int axis) {
   auto stride_a = a_strides[axis];
   auto stride_b = b_strides[axis];
@@ -164,10 +164,10 @@ void ternary_op_dispatch_dims(
       return;
   }
 
-  ContiguousIterator a_it(shape, a_strides, ndim - 2);
-  ContiguousIterator b_it(shape, b_strides, ndim - 2);
-  ContiguousIterator c_it(shape, c_strides, ndim - 2);
-  auto stride = out_strides[ndim - 3];
+  ContiguousIterator<size_t> a_it(shape, a_strides, ndim - 2);
+  ContiguousIterator<size_t> b_it(shape, b_strides, ndim - 2);
+  ContiguousIterator<size_t> c_it(shape, c_strides, ndim - 2);
+  size_t stride = out_strides[ndim - 3];
   for (size_t elem = 0; elem < a.size(); elem += stride) {
     ternary_op_dims<T1, T2, T3, U, Op, 2>(
         a_ptr + a_it.loc,

mlx/backend/common/utils.cpp

@@ -4,35 +4,15 @@
 
 namespace mlx::core {
 
-void move_or_copy(const array& in, array& out) {
-  if (in.is_donatable()) {
-    out.move_shared_buffer(in);
-  } else {
-    out.copy_shared_buffer(in);
-  }
-}
-
-void move_or_copy(
-    const array& in,
-    array& out,
-    const Strides& strides,
-    array::Flags flags,
-    size_t data_size,
-    size_t offset /* = 0 */) {
-  if (in.is_donatable()) {
-    out.move_shared_buffer(in, strides, flags, data_size, offset);
-  } else {
-    out.copy_shared_buffer(in, strides, flags, data_size, offset);
-  }
-}
-
-std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
-    const Shape& shape,
-    const std::vector<Strides>& strides,
-    int64_t size_cap) {
+template <typename StrideT>
+std::tuple<std::vector<int>, std::vector<std::vector<StrideT>>>
+collapse_contiguous_dims_impl(
+    const std::vector<int>& shape,
+    const std::vector<std::vector<StrideT>>& strides,
+    StrideT size_cap) {
   // Make a vector that has axes separated with -1. Collapse all axes between
   // -1.
-  Shape to_collapse;
+  std::vector<int> to_collapse;
   if (shape.size() > 0) {
     if (shape[0] != 1) {
       to_collapse.push_back(0);
@@ -41,7 +21,7 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     for (int i = 1; i < shape.size(); i++) {
       bool contiguous = true;
       size *= shape[i];
-      for (const auto& st : strides) {
+      for (const std::vector<StrideT>& st : strides) {
         if (st[i] * shape[i] != st[i - 1] || size > size_cap) {
           contiguous = false;
           size = shape[i];
@@ -58,8 +38,8 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     to_collapse.push_back(-1);
   }
 
-  Shape out_shape;
-  std::vector<Strides> out_strides(strides.size());
+  std::vector<int> out_shape;
+  std::vector<std::vector<StrideT>> out_strides(strides.size());
   for (int i = 0;;) {
     while (i < to_collapse.size() && to_collapse[i] == -1) {
       ++i;
@@ -74,7 +54,7 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     }
     out_shape.push_back(current_shape);
     for (int j = 0; j < strides.size(); j++) {
-      const auto& st = strides[j];
+      const std::vector<StrideT>& st = strides[j];
       out_strides[j].push_back(st[to_collapse[k - 1]]);
     }
     i = k + 1;
@@ -89,12 +69,29 @@ std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
   return std::make_tuple(out_shape, out_strides);
 }
 
-std::pair<Shape, Strides> collapse_contiguous_dims(
-    const Shape& shape,
-    const Strides& strides,
-    int64_t size_cap) {
-  Shape collapsed_shape;
-  Strides collapsed_strides;
+std::tuple<std::vector<int>, std::vector<std::vector<int64_t>>>
+collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<std::vector<int64_t>>& strides,
+    int64_t size_cap /* = std::numeric_limits<int32_t>::max() */) {
+  return collapse_contiguous_dims_impl(shape, strides, size_cap);
+}
+
+std::tuple<std::vector<int>, std::vector<std::vector<size_t>>>
+collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<std::vector<size_t>>& strides,
+    size_t size_cap /* = std::numeric_limits<int32>::max() */) {
+  return collapse_contiguous_dims_impl(shape, strides, size_cap);
+}
+
+template <typename StrideT>
+std::pair<std::vector<int>, std::vector<StrideT>> collapse_contiguous_dims_impl(
+    const std::vector<int>& shape,
+    const std::vector<StrideT>& strides,
+    StrideT size_cap) {
+  std::vector<int> collapsed_shape;
+  std::vector<StrideT> collapsed_strides;
 
   if (shape.size() > 0) {
     collapsed_shape.push_back(shape[0]);
@@ -104,7 +101,7 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
         continue;
       } else if (
           strides[i] * shape[i] != collapsed_strides.back() ||
-          collapsed_shape.back() * static_cast<int64_t>(shape[i]) > size_cap) {
+          collapsed_shape.back() * static_cast<StrideT>(shape[i]) > size_cap) {
         collapsed_shape.push_back(shape[i]);
         collapsed_strides.push_back(strides[i]);
       } else {
@@ -117,10 +114,25 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
   return std::make_pair(collapsed_shape, collapsed_strides);
 }
 
-std::pair<Shape, Strides> collapse_contiguous_dims(
+std::pair<std::vector<int>, std::vector<int64_t>> collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<int64_t>& strides,
+    int64_t size_cap /* = std::numeric_limits<int32_t>::max() */) {
+  return collapse_contiguous_dims_impl<int64_t>(shape, strides, size_cap);
+}
+
+std::pair<std::vector<int>, std::vector<size_t>> collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<size_t>& strides,
+    size_t size_cap /* = std::numeric_limits<int32_t>::max() */) {
+  return collapse_contiguous_dims_impl<size_t>(shape, strides, size_cap);
+}
+
+std::pair<std::vector<int>, std::vector<size_t>> collapse_contiguous_dims(
     const array& a,
-    int64_t size_cap /* = std::numeric_limits<int32_t>::max()*/) {
-  return collapse_contiguous_dims(a.shape(), a.strides(), size_cap);
+    size_t size_cap /* = std::numeric_limits<int32_t>::max()*/) {
+  return collapse_contiguous_dims_impl<size_t>(
+      a.shape(), a.strides(), size_cap);
 }
 
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -8,9 +8,12 @@
 
 namespace mlx::core {
 
-inline int64_t
-elem_to_loc(int elem, const Shape& shape, const Strides& strides) {
-  int64_t loc = 0;
+template <typename StrideT>
+inline StrideT elem_to_loc(
+    int elem,
+    const std::vector<int>& shape,
+    const std::vector<StrideT>& strides) {
+  StrideT loc = 0;
   for (int i = shape.size() - 1; i >= 0; --i) {
     auto q_and_r = ldiv(elem, shape[i]);
     loc += q_and_r.rem * strides[i];
@@ -19,15 +22,16 @@ elem_to_loc(int elem, const Shape& shape, const Strides& strides) {
   return loc;
 }
 
-inline int64_t elem_to_loc(int elem, const array& a) {
+inline size_t elem_to_loc(int elem, const array& a) {
   if (a.flags().row_contiguous) {
     return elem;
   }
   return elem_to_loc(elem, a.shape(), a.strides());
 }
 
-inline Strides make_contiguous_strides(const Shape& shape) {
-  Strides strides(shape.size(), 1);
+template <typename StrideT>
+std::vector<StrideT> make_contiguous_strides(const std::vector<int>& shape) {
+  std::vector<StrideT> strides(shape.size(), 1);
   for (int i = shape.size() - 1; i > 0; i--) {
     strides[i - 1] = strides[i] * shape[i];
   }
@@ -40,15 +44,22 @@ inline Strides make_contiguous_strides(const Shape& shape) {
 //
 // When multiple arrays are passed they should all have the same shape. The
 // collapsed axes are also the same so one shape is returned.
-std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
-    const Shape& shape,
-    const std::vector<Strides>& strides,
+std::tuple<std::vector<int>, std::vector<std::vector<int64_t>>>
+collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<std::vector<int64_t>>& strides,
     int64_t size_cap = std::numeric_limits<int32_t>::max());
+std::tuple<std::vector<int>, std::vector<std::vector<size_t>>>
+collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<std::vector<size_t>>& strides,
+    size_t size_cap = std::numeric_limits<int32_t>::max());
 
-inline std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
+inline std::tuple<std::vector<int>, std::vector<std::vector<size_t>>>
+collapse_contiguous_dims(
     const std::vector<array>& xs,
     size_t size_cap = std::numeric_limits<int32_t>::max()) {
-  std::vector<Strides> strides;
+  std::vector<std::vector<size_t>> strides;
   for (auto& x : xs) {
     strides.emplace_back(x.strides());
   }
@@ -62,14 +73,19 @@ inline auto collapse_contiguous_dims(Arrays&&... xs) {
 }
 
 // The single array version of the above.
-std::pair<Shape, Strides> collapse_contiguous_dims(
-    const Shape& shape,
-    const Strides& strides,
+std::pair<std::vector<int>, std::vector<int64_t>> collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<int64_t>& strides,
     int64_t size_cap = std::numeric_limits<int32_t>::max());
-std::pair<Shape, Strides> collapse_contiguous_dims(
+std::pair<std::vector<int>, std::vector<size_t>> collapse_contiguous_dims(
+    const std::vector<int>& shape,
+    const std::vector<size_t>& strides,
+    size_t size_cap = std::numeric_limits<int32_t>::max());
+std::pair<std::vector<int>, std::vector<size_t>> collapse_contiguous_dims(
     const array& a,
-    int64_t size_cap = std::numeric_limits<int32_t>::max());
+    size_t size_cap = std::numeric_limits<int32_t>::max());
 
+template <typename StrideT>
 struct ContiguousIterator {
   inline void step() {
     int dims = shape_.size();
@@ -86,7 +102,7 @@ struct ContiguousIterator {
     loc += strides_[i];
   }
 
-  void seek(int64_t n) {
+  void seek(StrideT n) {
     loc = 0;
     for (int i = shape_.size() - 1; i >= 0; --i) {
       auto q_and_r = ldiv(n, shape_[i]);
@@ -107,34 +123,37 @@ struct ContiguousIterator {
       : shape_(a.shape()), strides_(a.strides()) {
     if (!shape_.empty()) {
       std::tie(shape_, strides_) = collapse_contiguous_dims(shape_, strides_);
-      pos_ = Shape(shape_.size(), 0);
+      pos_ = std::vector<int>(shape_.size(), 0);
     }
   }
 
   explicit ContiguousIterator(
-      const Shape& shape,
-      const Strides& strides,
+      const std::vector<int>& shape,
+      const std::vector<StrideT>& strides,
       int dims)
       : shape_(shape.begin(), shape.begin() + dims),
         strides_(strides.begin(), strides.begin() + dims) {
     if (!shape_.empty()) {
       std::tie(shape_, strides_) = collapse_contiguous_dims(shape_, strides_);
-      pos_ = Shape(shape_.size(), 0);
+      pos_ = std::vector<int>(shape_.size(), 0);
     }
   }
 
-  int64_t loc{0};
+  StrideT loc{0};
 
  private:
-  Shape shape_;
-  Strides strides_;
-  Shape pos_;
+  std::vector<int> shape_;
+  std::vector<StrideT> strides_;
+  std::vector<int> pos_;
 };
 
-inline auto check_contiguity(const Shape& shape, const Strides& strides) {
+template <typename StrideT>
+inline auto check_contiguity(
+    const std::vector<int>& shape,
+    const std::vector<StrideT>& strides) {
   size_t no_broadcast_data_size = 1;
-  int64_t f_stride = 1;
-  int64_t b_stride = 1;
+  size_t f_stride = 1;
+  size_t b_stride = 1;
   bool is_row_contiguous = true;
   bool is_col_contiguous = true;
 
@@ -159,19 +178,4 @@ inline bool is_donatable(const array& in, const array& out) {
       in.buffer_size() <= out.nbytes() + donation_extra;
 }
 
-void move_or_copy(const array& in, array& out);
-void move_or_copy(
-    const array& in,
-    array& out,
-    const Strides& strides,
-    array::Flags flags,
-    size_t data_size,
-    size_t offset = 0);
-
-std::pair<bool, Strides> prepare_reshape(const array& in, const array& out);
-
-void shared_buffer_reshape(
-    const array& in,
-    const Strides& out_strides,
-    array& out);
 } // namespace mlx::core

mlx/backend/metal/CMakeLists.txt

@@ -14,21 +14,14 @@ function(make_jit_source SRC_FILE)
     COMMAND
       /bin/bash ${CMAKE_CURRENT_SOURCE_DIR}/make_compiled_preamble.sh
       ${CMAKE_CURRENT_BINARY_DIR}/jit ${CMAKE_C_COMPILER} ${PROJECT_SOURCE_DIR}
-      ${SRC_FILE}
+      ${SRC_FILE} "-DMLX_METAL_VERSION=${MLX_METAL_VERSION}"
     DEPENDS make_compiled_preamble.sh kernels/${SRC_FILE}.h ${ARGN})
   add_custom_target(${SRC_NAME} DEPENDS jit/${SRC_NAME}.cpp)
   add_dependencies(mlx ${SRC_NAME})
   target_sources(mlx PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/jit/${SRC_NAME}.cpp)
 endfunction(make_jit_source)
 
-make_jit_source(
-  utils
-  kernels/jit/bf16.h
-  kernels/metal_3_0/bf16.h
-  kernels/metal_3_1/bf16.h
-  kernels/bf16_math.h
-  kernels/complex.h
-  kernels/defines.h)
+make_jit_source(utils kernels/bf16.h kernels/complex.h kernels/defines.h)
 make_jit_source(unary_ops kernels/erf.h kernels/expm1f.h)
 make_jit_source(binary_ops)
 make_jit_source(ternary_ops)

mlx/backend/metal/allocator.cpp

@@ -30,24 +30,20 @@ BufferCache::BufferCache(MTL::Device* device)
     : device_(device), head_(nullptr), tail_(nullptr), pool_size_(0) {}
 
 BufferCache::~BufferCache() {
-  auto pool = metal::new_scoped_memory_pool();
+  auto thread_pool = metal::new_scoped_memory_pool();
   clear();
 }
 
-int BufferCache::clear() {
-  int n_release = 0;
+void BufferCache::clear() {
   for (auto& [size, holder] : buffer_pool_) {
-    if (holder->buf) {
+    if (holder->buf)
       holder->buf->release();
-      n_release++;
-    }
     delete holder;
   }
   buffer_pool_.clear();
   pool_size_ = 0;
   head_ = nullptr;
   tail_ = nullptr;
-  return n_release;
 }
 
 MTL::Buffer* BufferCache::reuse_from_cache(size_t size) {
@@ -85,11 +81,10 @@ void BufferCache::recycle_to_cache(MTL::Buffer* buf) {
   }
 }
 
-int BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
+void BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
   if (min_bytes_to_free >= 0.9 * pool_size_) {
-    return clear();
+    clear();
   } else {
-    int n_release = 0;
     size_t total_bytes_freed = 0;
 
     while (tail_ && (total_bytes_freed < min_bytes_to_free)) {
@@ -97,12 +92,10 @@ int BufferCache::release_cached_buffers(size_t min_bytes_to_free) {
         total_bytes_freed += tail_->buf->length();
         tail_->buf->release();
         tail_->buf = nullptr;
-        n_release++;
       }
       remove_from_list(tail_);
     }
     pool_size_ -= total_bytes_freed;
-    return n_release;
   }
 }
 
@@ -151,24 +144,22 @@ MetalAllocator::MetalAllocator()
       residency_set_(device_),
       buffer_cache_(device_) {
   auto memsize = std::get<size_t>(device_info()["memory_size"]);
-  auto max_rec_size =
-      std::get<size_t>(device_info()["max_recommended_working_set_size"]);
-  resource_limit_ = std::get<size_t>(device_info()["resource_limit"]);
-  block_limit_ = std::min(1.5 * max_rec_size, 0.95 * memsize);
-  gc_limit_ = std::min(static_cast<size_t>(0.95 * max_rec_size), block_limit_);
+  block_limit_ =
+      std::min(1.5 * device_->recommendedMaxWorkingSetSize(), 0.95 * memsize);
+  gc_limit_ = std::min(
+      static_cast<size_t>(0.95 * device_->recommendedMaxWorkingSetSize()),
+      block_limit_);
   max_pool_size_ = block_limit_;
   device(mlx::core::Device::gpu)
       .set_residency_set(residency_set_.mtl_residency_set());
 }
 
 size_t MetalAllocator::set_cache_limit(size_t limit) {
-  std::unique_lock lk(mutex_);
   std::swap(limit, max_pool_size_);
   return limit;
 };
 
 size_t MetalAllocator::set_memory_limit(size_t limit, bool relaxed) {
-  std::unique_lock lk(mutex_);
   std::swap(limit, block_limit_);
   relaxed_ = relaxed;
   gc_limit_ = std::min(
@@ -178,7 +169,6 @@ size_t MetalAllocator::set_memory_limit(size_t limit, bool relaxed) {
 };
 
 size_t MetalAllocator::set_wired_limit(size_t limit) {
-  std::unique_lock lk(mutex_);
   std::swap(limit, wired_limit_);
   residency_set_.resize(wired_limit_);
   return limit;
@@ -193,8 +183,7 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
   // More helpful message if maximum buffer length is exceeded
   if (size > device_->maxBufferLength()) {
     std::ostringstream msg;
-    msg << "[metal::malloc] Attempting to allocate " << size
-        << " bytes which is greater than"
+    msg << "Attempting to allocate " << size << " bytes which is greater than"
         << " the maximum allowed buffer size of " << device_->maxBufferLength()
         << " bytes.";
     throw std::runtime_error(msg.str());
@@ -216,30 +205,20 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
       return Buffer{nullptr};
     }
 
-    auto pool = metal::new_scoped_memory_pool();
+    auto thread_pool = metal::new_scoped_memory_pool();
 
     // 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_);
+    if (mem_required >= gc_limit_) {
+      buffer_cache_.release_cached_buffers(mem_required - gc_limit_);
     }
 
     // Allocate new buffer if needed
     size_t res_opt = MTL::ResourceStorageModeShared;
     res_opt |= MTL::ResourceHazardTrackingModeUntracked;
-    if (num_resources_ >= resource_limit_) {
-      std::ostringstream msg;
-      msg << "[metal::malloc] Resource limit (" << resource_limit_
-          << ") exceeded.";
-      throw std::runtime_error(msg.str());
-    }
     lk.unlock();
     buf = device_->newBuffer(size, res_opt);
     lk.lock();
-    if (buf) {
-      num_resources_++;
-    }
   }
 
   active_memory_ += buf->length();
@@ -247,9 +226,8 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
 
   // Maintain the cache below the requested limit
   if (get_cache_memory() >= max_pool_size_) {
-    auto pool = metal::new_scoped_memory_pool();
-    num_resources_ -= buffer_cache_.release_cached_buffers(
-        get_cache_memory() - max_pool_size_);
+    auto thread_pool = metal::new_scoped_memory_pool();
+    buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
 
   residency_set_.insert(buf);
@@ -259,24 +237,19 @@ Buffer MetalAllocator::malloc(size_t size, bool allow_swap /* = false */) {
 
 void MetalAllocator::clear_cache() {
   std::unique_lock lk(mutex_);
-  auto pool = metal::new_scoped_memory_pool();
-  num_resources_ -= buffer_cache_.clear();
+  buffer_cache_.clear();
 }
 
 void MetalAllocator::free(Buffer buffer) {
   auto buf = static_cast<MTL::Buffer*>(buffer.ptr());
-  if (buf == nullptr) {
-    return;
-  }
   std::unique_lock lk(mutex_);
   residency_set_.erase(buf);
   active_memory_ -= buf->length();
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
   } else {
-    num_resources_--;
     lk.unlock();
-    auto pool = metal::new_scoped_memory_pool();
+    auto thread_pool = metal::new_scoped_memory_pool();
     buf->release();
   }
 }

mlx/backend/metal/allocator.h

@@ -23,11 +23,11 @@ class BufferCache {
 
   MTL::Buffer* reuse_from_cache(size_t size);
   void recycle_to_cache(MTL::Buffer* buf);
-  int release_cached_buffers(size_t min_bytes_to_free);
+  void release_cached_buffers(size_t min_bytes_to_free);
   size_t cache_size() {
     return pool_size_;
   }
-  int clear();
+  void clear();
 
  private:
   struct BufferHolder {
@@ -94,8 +94,6 @@ class MetalAllocator : public allocator::Allocator {
   size_t max_pool_size_;
   size_t wired_limit_{0};
   bool relaxed_{true};
-  size_t num_resources_{0};
-  size_t resource_limit_{0};
 
   std::mutex mutex_;
 };

mlx/backend/metal/binary.cpp

@@ -22,37 +22,37 @@ std::string get_kernel_name(
     BinaryOpType bopt,
     const std::string& op,
     const array& a,
-    bool large,
+    bool use_2d,
     int ndim,
     int work_per_thread) {
-  std::string kname;
+  std::ostringstream kname;
   switch (bopt) {
     case BinaryOpType::ScalarScalar:
-      kname = "ss";
+      kname << "ss";
       break;
     case BinaryOpType::ScalarVector:
-      kname = (large ? "sv2" : "sv");
+      kname << (use_2d ? "sv2" : "sv");
       break;
     case BinaryOpType::VectorScalar:
-      kname = (large ? "vs2" : "vs");
+      kname << (use_2d ? "vs2" : "vs");
       break;
     case BinaryOpType::VectorVector:
-      kname = (large ? "vv2" : "vv");
+      kname << (use_2d ? "vv2" : "vv");
       break;
     case BinaryOpType::General:
-      kname = "g";
+      kname << "g";
       if (ndim <= 3) {
-        kname += std::to_string(ndim);
+        kname << ndim;
       } else {
-        concatenate(kname, "n", std::to_string(work_per_thread));
-      }
-      if (large) {
-        kname += "large";
+        kname << "n";
+        if (work_per_thread > 1) {
+          kname << work_per_thread;
+        }
       }
       break;
   }
-  concatenate(kname, "_", op, type_to_name(a));
-  return kname;
+  kname << "_" << op << type_to_name(a);
+  return kname.str();
 }
 
 void binary_op_gpu_inplace(
@@ -75,32 +75,24 @@ void binary_op_gpu_inplace(
       auto [shape, strides] = collapse_contiguous_dims(a, b, out);
       return std::make_tuple(shape, strides[0], strides[1], strides[2]);
     } else {
-      decltype(a.strides()) e{};
-      return std::make_tuple(decltype(a.shape()){}, e, e, e);
+      std::vector<size_t> e;
+      return std::make_tuple(std::vector<int>{}, e, e, e);
     }
   };
   auto [shape, strides_a, strides_b, strides_out] = maybe_collapse();
 
-  bool large;
+  bool use_2d = out.data_size() > UINT32_MAX;
   auto ndim = shape.size();
-  int work_per_thread;
-  if (bopt == BinaryOpType::General) {
-    large = a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
-        out.size() > INT32_MAX;
-    work_per_thread = large ? 4 : 2;
-  } else {
-    large = out.data_size() > UINT32_MAX;
-    work_per_thread = 1;
-  }
+  int work_per_thread = (bopt == BinaryOpType::General) ? 4 : 1;
   std::string kernel_name =
-      get_kernel_name(bopt, op, a, large, shape.size(), work_per_thread);
+      get_kernel_name(bopt, op, a, use_2d, shape.size(), work_per_thread);
   auto& d = metal::device(s.device);
 
   auto kernel = outputs.size() == 2
       ? get_binary_two_kernel(d, kernel_name, a.dtype(), out.dtype(), op)
       : get_binary_kernel(d, kernel_name, a.dtype(), out.dtype(), op);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // - If a is donated it goes to the first output
   // - If b is donated it goes to the first output if a was not donated
@@ -125,15 +117,19 @@ void binary_op_gpu_inplace(
     size_t rest = out.size() / (dim0 * dim1);
 
     if (ndim > 3) {
-      compute_encoder.set_vector_bytes(shape, arg_idx++);
-      compute_encoder.set_vector_bytes(strides_a, arg_idx++);
-      compute_encoder.set_vector_bytes(strides_b, arg_idx++);
-      compute_encoder.set_bytes<int>(ndim, arg_idx++);
+      compute_encoder->setBytes(shape.data(), ndim * sizeof(int), arg_idx++);
+      compute_encoder->setBytes(
+          strides_a.data(), ndim * sizeof(size_t), arg_idx++);
+      compute_encoder->setBytes(
+          strides_b.data(), ndim * sizeof(size_t), arg_idx++);
+      compute_encoder->setBytes(&ndim, sizeof(int), arg_idx++);
       dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     } else {
       // The shape is implicit in the grid for <= 3D
-      compute_encoder.set_vector_bytes(strides_a, arg_idx++);
-      compute_encoder.set_vector_bytes(strides_b, arg_idx++);
+      compute_encoder->setBytes(
+          strides_a.data(), ndim * sizeof(size_t), arg_idx++);
+      compute_encoder->setBytes(
+          strides_b.data(), ndim * sizeof(size_t), arg_idx++);
     }
 
     if (thread_group_size != 1024) {
@@ -141,7 +137,7 @@ void binary_op_gpu_inplace(
     }
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   } else {
     // Launch a 1D or 2D grid of threads
     size_t nthreads = out.data_size();
@@ -149,9 +145,9 @@ void binary_op_gpu_inplace(
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
+                                 : MTL::Size(nthreads, 1, 1);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/compiled.cpp

@@ -1,6 +1,5 @@
 // Copyright © 2023-2024 Apple Inc.
-#include <fmt/format.h>
-#include <iostream> //TODO
+
 #include <sstream>
 
 #include "mlx/backend/common/compiled.h"
@@ -12,12 +11,12 @@
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
-using namespace fmt::literals;
-
 namespace mlx::core {
 
+constexpr int WORK_PER_THREAD = 4;
+
 inline void build_kernel(
-    std::string& os,
+    std::ostream& os,
     const std::string& kernel_name,
     const std::vector<array>& inputs,
     const std::vector<array>& outputs,
@@ -42,8 +41,8 @@ inline void build_kernel(
   int cnt = 0;
 
   // Start the kernel
-  os += fmt::format(
-      "[[host_name(\"{0}\")]]\n[[kernel]] void {0}(\n", kernel_name);
+  os << "[[host_name(\"" << kernel_name << "\")]]\n"
+     << "[[kernel]] void " << kernel_name << "(\n";
 
   // Add the input arguments
   for (auto& x : inputs) {
@@ -55,61 +54,51 @@ inline void build_kernel(
     }
 
     // Scalars and contiguous need no strides
-    if (!is_scalar(x) && !contiguous) {
+    if (is_scalar(x) || contiguous) {
+      os << "    device const " << get_type_string(x.dtype()) << "* " << xname
+         << " [[buffer(" << cnt++ << ")]],\n";
+    } else {
       add_indices = true;
+      os << "    device const " << get_type_string(x.dtype()) << "* " << xname
+         << " [[buffer(" << cnt++ << ")]],\n";
     }
-    os += fmt::format(
-        "    device const {0}* {1} [[buffer({2})]],\n",
-        get_type_string(x.dtype()),
-        xname,
-        cnt++);
   }
 
   if (add_indices) {
-    os += fmt::format(
-        "    constant const int64_t* in_strides [[buffer({0})]],\n", cnt++);
+    os << "    constant const size_t* in_strides [[buffer(" << cnt++
+       << ")]],\n";
   }
 
   // Add the output arguments
   for (auto& x : outputs) {
-    os += fmt::format(
-        "    device {0}* {1} [[buffer({2})]],\n",
-        get_type_string(x.dtype()),
-        namer.get_name(x),
-        cnt++);
+    os << "    device " << get_type_string(x.dtype()) << "* "
+       << namer.get_name(x) << " [[buffer(" << cnt++ << ")]],\n";
   }
   // Add output strides and shape to extract the indices.
   if (!contiguous) {
-    os += fmt::format(
-        "    constant const int64_t* output_strides [[buffer({0})]],\n", cnt++);
-    os += fmt::format(
-        "    constant const int* output_shape [[buffer({0})]],\n", cnt++);
+    os << "    constant const size_t* output_strides [[buffer(" << cnt++
+       << ")]],\n"
+       << "    constant const int* output_shape [[buffer(" << cnt++ << ")]],\n";
   }
   if (dynamic_dims) {
-    os += fmt::format("    constant const int& ndim [[buffer({0})]],\n", cnt++);
+    os << "    constant const int& ndim [[buffer(" << cnt++ << ")]],\n";
   }
 
   // The thread index in the whole grid
-  os += "    uint3 pos [[thread_position_in_grid]],\n";
-  os += "    uint3 grid [[threads_per_grid]]) {\n";
+  os << "    uint3 pos [[thread_position_in_grid]],\n"
+     << "    uint3 grid [[threads_per_grid]]) {\n";
 
-  std::string idx_type = use_big_index ? "int64_t" : "uint";
-  if (contiguous && use_big_index) {
+  if (use_big_index) {
     // This is only used for contiguous kernels which don't have
     // a third grid dimension
-    os += "  int64_t index = pos.x + grid.x * int64_t(pos.y);\n";
+    os << "  size_t index = pos.x + grid.x * size_t(pos.y);\n";
   } else if (work_per_thread > 1) {
-    os += fmt::format("  constexpr int N_ = {0};\n", work_per_thread);
-    os += fmt::format(
-        "  int xshape = output_shape[{0}];\n",
-        dynamic_dims ? "ndim - 1" : std::to_string(ndim - 1));
-    os += fmt::format(
-        "  {0} index = N_ * pos.x + xshape * (pos.y + {0}(grid.y) * pos.z);\n",
-        idx_type);
+    os << "  constexpr int N_ = " << std::to_string(work_per_thread) << ";\n"
+       << "  int xshape = output_shape["
+       << (dynamic_dims ? "ndim - 1" : std::to_string(ndim - 1)) << "];\n"
+       << "  size_t index = N_ * pos.x + xshape * (pos.y + size_t(grid.y) * pos.z);\n";
   } else {
-    os += fmt::format(
-        "  {0} index = pos.x + grid.x * (pos.y + {0}(grid.y) * pos.z);\n",
-        idx_type);
+    os << " size_t index = pos.x + grid.x * (pos.y + size_t(grid.y) * pos.z);\n";
   }
 
   // Read constant / contiguous inputs in tmps
@@ -120,19 +109,16 @@ inline void build_kernel(
 
     if (is_constant(x)) {
       auto type_str = get_type_string(x.dtype());
-      std::ostringstream ss;
-      print_constant(ss, x);
-      os += fmt::format(
-          "  auto tmp_{0} = static_cast<{1}>({2});\n",
-          xname,
-          get_type_string(x.dtype()),
-          ss.str());
+      os << "  auto tmp_" << xname << " = static_cast<"
+         << get_type_string(x.dtype()) << ">(";
+      print_constant(os, x);
+      os << ");\n";
     } else if (is_scalar(x)) {
-      os += fmt::format(
-          "  {0} tmp_{1} = {1}[0];\n", get_type_string(x.dtype()), xname);
+      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
+         << xname << "[0];\n";
     } else if (contiguous) {
-      os += fmt::format(
-          "  {0} tmp_{1} = {1}[index];\n", get_type_string(x.dtype()), xname);
+      os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
+         << xname << "[index];\n";
     } else {
       nc_inputs.push_back(x);
     }
@@ -141,96 +127,83 @@ inline void build_kernel(
   // Initialize the indices for non-contiguous inputs
   for (int i = 0; i < nc_inputs.size(); ++i) {
     auto& xname = namer.get_name(nc_inputs[i]);
-    os += fmt::format("  {0} index_{1} = ", idx_type, xname);
     if (ndim == 1) {
       int offset = i * ndim;
-      os +=
-          fmt::format("elem_to_loc_1<uint>(pos.x, in_strides[{0}]);\n", offset);
+      os << "  size_t index_" << xname << " = elem_to_loc_1(pos.x, "
+         << "in_strides[" << offset << "]);\n";
     } else if (ndim == 2) {
       int offset = i * ndim;
-      os += fmt::format(
-          "elem_to_loc_2<{0}>({{pos.x, pos.y}}, in_strides + {1});\n",
-          idx_type,
-          offset);
+      os << "  size_t index_" << xname << " = elem_to_loc_2({pos.x, pos.y}, "
+         << "in_strides + " << offset << ");\n";
     } else if (ndim == 3) {
       int offset = i * ndim;
-      os += fmt::format(
-          "elem_to_loc_3<{0}>(pos, in_strides + {1});\n", idx_type, offset);
+      os << "  size_t index_" << xname << " = elem_to_loc_3(pos, "
+         << "in_strides + " << offset << ");\n";
     } else if (!dynamic_dims) {
-      int offset = (i + 1) * ndim;
-      os += fmt::format(
-          "N_ * pos.x * {0}(in_strides[{1}]) + pos.y * {0}(in_strides[{2}]);\n",
-          idx_type,
-          offset - 1,
-          offset - 2);
+      int offset = i * ndim;
+      os << "  size_t index_" << xname << " = N_ * pos.x * in_strides["
+         << offset + ndim - 1 << "]"
+         << " + pos.y * in_strides[" << offset + ndim - 2 << "];\n";
     } else {
-      os += fmt::format(
-          "N_ * pos.x * {0}(in_strides[ndim * {1} + ndim - 1]) + pos.y * {0}(in_strides[ndim * {1} + ndim - 2]);\n",
-          idx_type,
-          i);
+      os << "  size_t index_" << xname << " = N_ * pos.x * in_strides[ndim * "
+         << i << " + ndim - 1]"
+         << " + pos.y * in_strides[ndim * " << i << " + ndim - 2];\n";
     }
   }
-
   if (!nc_inputs.empty() && (ndim > 3 || dynamic_dims)) {
-    os += "  uint zpos = pos.z;\n";
+    os << "  uint zpos = pos.z;\n";
     if (dynamic_dims) {
-      os += "  for (int d = ndim - 3; d >= 0; --d) {\n";
+      os << "  for (int d = ndim - 3; d >= 0; --d) {\n";
     } else {
-      os += fmt::format("  for (int d = {0}; d >= 0; --d) {{\n", ndim - 3);
+      os << "  for (int d = " << ndim - 3 << "; d >= 0; --d) {\n";
     }
-    os += "    uint l = zpos % output_shape[d];\n";
+    os << "    uint l = zpos % output_shape[d];\n";
     for (int i = 0; i < nc_inputs.size(); ++i) {
       auto& xname = namer.get_name(nc_inputs[i]);
-      os += fmt::format("    index_{0} += ", xname);
+      os << "    index_" << xname << " += ";
       if (dynamic_dims) {
-        os +=
-            fmt::format("l * {0}(in_strides[{1} * ndim + d]);\n", idx_type, i);
+        os << "l * in_strides[" << i << " * ndim + d];\n";
       } else {
-        os +=
-            fmt::format("l * {0}(in_strides[{1} + d]);\n", idx_type, i * ndim);
+        os << "l * in_strides[" << i * ndim << " + d];\n";
       }
     }
-    os += "    zpos /= output_shape[d];\n  }\n";
+    os << "    zpos /= output_shape[d];\n  }\n";
   }
 
   // Open per-thread loop
   if (work_per_thread > 1) {
-    os +=
-        "  for (int i = 0; i < N_ && (int(N_ * pos.x) + i) < xshape; ++i) {\n";
+    os << "  for (int i = 0; i < N_ && (int(N_ * pos.x) + i) < xshape; ++i) {\n";
   }
 
   // Read non-contiguous inputs into tmps
   for (int i = 0; i < nc_inputs.size(); ++i) {
     auto& x = nc_inputs[i];
     auto& xname = namer.get_name(x);
-    os += fmt::format(
-        "  {0} tmp_{1} = {1}[index_{1}];\n", get_type_string(x.dtype()), xname);
+    os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = "
+       << xname << "[index_" << xname << "];\n";
   }
 
   // Actually write the computation
   for (auto& x : tape) {
-    os += fmt::format(
-        "  {0} tmp_{1} = ", get_type_string(x.dtype()), namer.get_name(x));
+    os << "  " << get_type_string(x.dtype()) << " tmp_" << namer.get_name(x)
+       << " = ";
     if (is_static_cast(x.primitive())) {
-      os += fmt::format(
-          "static_cast<{0}>(tmp_{1});\n",
-          get_type_string(x.dtype()),
-          namer.get_name(x.inputs()[0]));
+      os << "static_cast<" << get_type_string(x.dtype()) << ">(tmp_"
+         << namer.get_name(x.inputs()[0]) << ");\n";
     } else {
-      std::ostringstream ss;
-      x.primitive().print(ss);
-      os += ss.str();
-      os += "()(";
+      x.primitive().print(os);
+      os << "()(";
       for (int i = 0; i < x.inputs().size() - 1; i++) {
-        os += fmt::format("tmp_{0}, ", namer.get_name(x.inputs()[i]));
+        os << "tmp_" << namer.get_name(x.inputs()[i]) << ", ";
       }
-      os += fmt::format("tmp_{0});\n", namer.get_name(x.inputs().back()));
+      os << "tmp_" << namer.get_name(x.inputs().back()) << ");\n";
     }
   }
 
   // Write the outputs from tmps
   for (auto& x : outputs) {
-    os += fmt::format("  {0}[index] = tmp_{0};\n", namer.get_name(x));
+    os << "  " << namer.get_name(x) << "[index] = tmp_" << namer.get_name(x)
+       << ";\n";
   }
   // Increment indices and close per thread loop
   if (work_per_thread > 1) {
@@ -238,18 +211,18 @@ inline void build_kernel(
       auto& x = nc_inputs[i];
       auto& xname = namer.get_name(x);
       if (!dynamic_dims) {
-        os += fmt::format(
-            "  index_{0} += in_strides[{1}];\n", xname, i * ndim + ndim - 1);
+        os << "  index_" << xname << " += "
+           << "in_strides[" << i * ndim + ndim - 1 << "];\n";
       } else {
-        os += fmt::format(
-            "  index_{0} += in_strides[{1} * ndim + ndim - 1];\n", xname, i);
+        os << "  index_" << xname << " += "
+           << "in_strides[" << i << " * ndim + ndim - 1];\n";
       }
     }
-    os += "  index++;\n  }\n";
+    os << "  index++;\n  }\n";
   }
 
   // Finish the kernel
-  os += "}\n";
+  os << "}\n";
 
   if (cnt > 31) {
     std::ostringstream msg;
@@ -273,9 +246,9 @@ void Compiled::eval_gpu(
   auto& s = stream();
   auto& d = metal::device(s.device);
   auto lib = d.get_library(kernel_lib_, [&]() {
-    std::string kernel = metal::utils();
-    concatenate(
-        kernel, metal::unary_ops(), metal::binary_ops(), metal::ternary_ops());
+    std::ostringstream kernel;
+    kernel << metal::utils() << metal::unary_ops() << metal::binary_ops()
+           << metal::ternary_ops();
     build_kernel(
         kernel,
         kernel_lib_ + "_contiguous",
@@ -288,7 +261,7 @@ void Compiled::eval_gpu(
         /* dynamic_dims = */ false);
     build_kernel(
         kernel,
-        kernel_lib_ + "_contiguous_large",
+        kernel_lib_ + "_contiguous_big",
         inputs_,
         outputs_,
         tape_,
@@ -309,21 +282,7 @@ void Compiled::eval_gpu(
           /* ndim = */ i,
           /* dynamic_dims = */ false,
           /* use_big_index = */ false,
-          /* work_per_thread = */ i > 3 ? 2 : 1);
-      if (i > 1) {
-        build_kernel(
-            kernel,
-            kernel_lib_ + "_strided_" + std::to_string(i) + "_large",
-            inputs_,
-            outputs_,
-            tape_,
-            constant_ids_,
-            /* contiguous = */ false,
-            /* ndim = */ i,
-            /* dynamic_dims = */ false,
-            /* use_big_index = */ true,
-            /* work_per_thread = */ i > 3 ? 4 : 1);
-      }
+          /* work_per_thread = */ i > 3 ? WORK_PER_THREAD : 1);
     }
     build_kernel(
         kernel,
@@ -336,32 +295,20 @@ void Compiled::eval_gpu(
         /* ndim = */ 0,
         /* dynamic_dims = */ true,
         /* use_big_index = */ false,
-        /* work_per_thread = */ 2);
-    build_kernel(
-        kernel,
-        kernel_lib_ + "_strided_dynamic_large",
-        inputs_,
-        outputs_,
-        tape_,
-        constant_ids_,
-        /* contiguous = */ false,
-        /* ndim = */ 0,
-        /* dynamic_dims = */ true,
-        /* use_big_index = */ true,
-        /* work_per_thread = */ 4);
-    return kernel;
+        /* work_per_thread = */ WORK_PER_THREAD);
+    return kernel.str();
   });
 
   // Figure out which kernel we are using
   auto& output_shape = outputs[0].shape();
-  auto contiguous = compiled_check_contiguity(inputs, output_shape);
+  bool contiguous = compiled_check_contiguity(inputs, output_shape);
 
   // Collapse contiguous dims to route to a faster kernel if possible. Also
   // handle all broadcasting.
-  std::vector<Strides> initial_strides;
+  std::vector<std::vector<size_t>> initial_strides;
   initial_strides.push_back(outputs[0].strides());
-  Shape shape;
-  std::vector<Strides> strides;
+  std::vector<int> shape;
+  std::vector<std::vector<size_t>> strides;
   if (!contiguous) {
     for (int i = 0; i < inputs.size(); i++) {
       // Skip constants.
@@ -376,7 +323,7 @@ void Compiled::eval_gpu(
       }
 
       // Broadcast the inputs to the output shape.
-      Strides xstrides;
+      std::vector<size_t> xstrides;
       int j = 0;
       for (; j < output_shape.size() - x.ndim(); j++) {
         if (output_shape[j] == 1) {
@@ -402,19 +349,13 @@ void Compiled::eval_gpu(
         collapse_contiguous_dims(output_shape, initial_strides, INT32_MAX);
   }
 
-  bool large;
+  bool use_2d = false;
   if (contiguous) {
     size_t max_size = 0;
     for (auto& in : inputs) {
       max_size = std::max(max_size, in.data_size());
     }
-    large = (max_size > UINT32_MAX);
-  } else {
-    size_t max_size = 0;
-    for (auto& o : outputs) {
-      max_size = std::max(max_size, o.size());
-    }
-    large = (max_size > UINT32_MAX);
+    use_2d = (max_size > UINT32_MAX);
   }
 
   // Get the kernel from the lib
@@ -427,18 +368,17 @@ void Compiled::eval_gpu(
     } else {
       kernel_name += std::to_string(shape.size());
     }
-  }
-  if (large) {
-    kernel_name += "_large";
+  } else if (use_2d) {
+    kernel_name += "_big";
   }
   auto kernel = d.get_kernel(kernel_name, lib);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // Put the inputs in
   int cnt = 0;
   int stride_idx = 1; // idx 0 is the output strides
-  Strides in_strides;
+  std::vector<size_t> in_strides;
   for (int i = 0; i < inputs.size(); i++) {
     if (constant_ids_.find(inputs_[i].id()) != constant_ids_.end()) {
       continue;
@@ -454,7 +394,8 @@ void Compiled::eval_gpu(
     }
   }
   if (!in_strides.empty()) {
-    compute_encoder.set_vector_bytes(in_strides, cnt++);
+    compute_encoder->setBytes(
+        in_strides.data(), in_strides.size() * sizeof(size_t), cnt++);
   }
 
   compiled_allocate_outputs(
@@ -467,13 +408,14 @@ void Compiled::eval_gpu(
 
   // Put the output shape and strides in
   if (!contiguous) {
-    compute_encoder.set_vector_bytes(strides[0], cnt++);
-    compute_encoder.set_vector_bytes(shape, cnt++);
+    compute_encoder->setBytes(
+        strides[0].data(), strides[0].size() * sizeof(size_t), cnt++);
+    compute_encoder->setBytes(shape.data(), shape.size() * sizeof(int), cnt++);
   }
 
   // Put the number of dims in if it is dynamic
   if (dynamic) {
-    compute_encoder.set_bytes(ndim, cnt++);
+    compute_encoder->setBytes(&ndim, sizeof(int), cnt++);
   }
 
   // Launch the kernel
@@ -482,15 +424,15 @@ void Compiled::eval_gpu(
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
 
-    MTL::Size grid_dims = large
+    MTL::Size grid_dims = use_2d
         ? get_2d_grid_dims(outputs[0].shape(), outputs[0].strides())
         : MTL::Size(nthreads, 1, 1);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   } else {
     size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
     size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
     size_t rest = outputs[0].size() / (dim0 * dim1);
-    int work_per_thread = ndim > 3 ? (large ? 4 : 2) : 1;
+    int work_per_thread = ndim > 3 ? WORK_PER_THREAD : 1;
     dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     NS::UInteger thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
     int pow2;
@@ -503,7 +445,7 @@ void Compiled::eval_gpu(
     }
     auto group_dims = get_block_dims(dim0, dim1, rest, pow2);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 

mlx/backend/metal/conv.cpp

@@ -34,7 +34,7 @@ void explicit_gemm_conv_ND_gpu(
   int implicit_K = wt.size() / conv_params.O;
   int implicit_N = conv_params.O;
   // Prepare unfolding array
-  Shape unfolded_shape{implicit_M, implicit_K};
+  std::vector<int> unfolded_shape{implicit_M, implicit_K};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
 
   in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
@@ -44,28 +44,27 @@ void explicit_gemm_conv_ND_gpu(
   kname << "naive_unfold_nd_" << type_to_name(in_unfolded) << "_" << N;
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(in_unfolded, 1);
 
-  compute_encoder.set_bytes(conv_params, 2);
+  compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
 
   // Launch unfolding kernel
-  size_t tgp_x = std::min(conv_params.C, 64);
+  int tgp_x = std::min(conv_params.C, 64);
   tgp_x = 32 * ((tgp_x + 32 - 1) / 32);
-  size_t tgp_y = 256 / tgp_x;
+  int tgp_y = 256 / tgp_x;
 
+  MTL::Size group_dims = MTL::Size(tgp_x, tgp_y, 1);
   MTL::Size grid_dims = MTL::Size(
       conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
-  MTL::Size group_dims = MTL::Size(
-      std::min(tgp_x, grid_dims.width), std::min(tgp_y, grid_dims.height), 1);
 
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 
   // Reshape weight
-  Shape wt_reshape{implicit_K, implicit_N};
-  Strides wt_restride{1, implicit_K};
+  std::vector<int> wt_reshape{implicit_K, implicit_N};
+  std::vector<size_t> wt_restride{1, static_cast<size_t>(implicit_K)};
   array wt_reshaped(wt_reshape, wt.dtype(), nullptr, {});
   auto wt_flags = wt.flags();
   wt_flags.row_contiguous = false;
@@ -113,7 +112,7 @@ void explicit_gemm_conv_group_ND_gpu(
   }
 
   // Prepare unfolding array
-  Shape unfolded_shape{implicit_M, implicit_K * groups};
+  std::vector<int> unfolded_shape{implicit_M, implicit_K * groups};
   array in_unfolded(unfolded_shape, in.dtype(), nullptr, {});
   in_unfolded.set_data(allocator::malloc_or_wait(in_unfolded.nbytes()));
 
@@ -123,31 +122,33 @@ void explicit_gemm_conv_group_ND_gpu(
         << N;
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   compute_encoder.set_input_array(in, 0);
   compute_encoder.set_output_array(in_unfolded, 1);
 
-  compute_encoder.set_bytes(conv_params, 2);
+  compute_encoder->setBytes(&conv_params, sizeof(conv_params), 2);
 
   // Launch unfolding kernel
-  size_t tgp_x = std::min(conv_params.C, 64);
+  int tgp_x = std::min(conv_params.C, 64);
   tgp_x = 32 * ((tgp_x + 32 - 1) / 32);
-  size_t tgp_y = 256 / tgp_x;
+  int tgp_y = 256 / tgp_x;
 
+  MTL::Size group_dims = MTL::Size(tgp_x, tgp_y, 1);
   MTL::Size grid_dims = MTL::Size(
       conv_params.C, unfolded_shape[1] / conv_params.C, unfolded_shape[0]);
-  MTL::Size group_dims = MTL::Size(
-      std::min(tgp_x, grid_dims.width), std::min(tgp_y, grid_dims.height), 1);
 
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 
   // Transpose kernel weights so that we can slice them by contiguous chunks
   // of channel groups.
   array wt_view(
       {wt.shape(0), C_per_group, kernel_size}, wt.dtype(), nullptr, {});
   wt_view.copy_shared_buffer(
-      wt, {wt.strides(0), 1, C_per_group}, wt.flags(), wt.size());
+      wt,
+      {wt.strides(0), 1, static_cast<size_t>(C_per_group)},
+      wt.flags(),
+      wt.size());
 
   // Materialize
   auto wt_transpose = array(wt_view.shape(), wt_view.dtype(), nullptr, {});
@@ -192,12 +193,12 @@ void conv_1D_gpu(
     bool flip) {
   // Make conv params
   MLXConvParams<1> conv_params{
-      /* const int  N = */ static_cast<int>(in.shape(0)),
-      /* const int  C = */ static_cast<int>(in.shape(2)),
-      /* const int  O = */ static_cast<int>(wt.shape(0)),
-      /* const int iS[NDIM] = */ {static_cast<int>(in.shape(1))},
-      /* const int wS[NDIM] = */ {static_cast<int>(wt.shape(1))},
-      /* const int oS[NDIM] = */ {static_cast<int>(out.shape(1))},
+      /* const int  N = */ in.shape(0),
+      /* const int  C = */ in.shape(2),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in.shape(1)},
+      /* const int wS[NDIM] = */ {wt.shape(1)},
+      /* const int oS[NDIM] = */ {out.shape(1)},
       /* const int str[NDIM] = */ {wt_strides[0]},
       /* const int pad[NDIM] = */ {padding[0]},
       /* const int kdil[NDIM] = */ {wt_dilation[0]},
@@ -236,7 +237,7 @@ void slow_conv_2D_gpu(
   // Encode and dispatch kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kname.str());
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   size_t n_pixels = conv_params.oS[0] * conv_params.oS[1];
 
@@ -251,8 +252,8 @@ void slow_conv_2D_gpu(
   compute_encoder.set_input_array(wt, 1);
   compute_encoder.set_output_array(out, 2);
 
-  compute_encoder.set_bytes(conv_params, 3);
-  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_gpu(
@@ -351,7 +352,7 @@ void implicit_gemm_conv_2D_gpu(
       wn,
       n_channel_specialization,
       small_filter);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // Deduce grid launch dimensions
   int tile = 1 << swizzle_log;
@@ -367,11 +368,11 @@ void implicit_gemm_conv_2D_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode params
-  compute_encoder.set_bytes(conv_params, 3);
-  compute_encoder.set_bytes(gemm_params, 4);
+  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
+  compute_encoder->setBytes(&gemm_params, sizeof(ImplicitGemmConv2DParams), 4);
 
   // Launch kernel
-  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void implicit_gemm_conv_2D_general_gpu(
@@ -505,7 +506,7 @@ void implicit_gemm_conv_2D_general_gpu(
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel =
       get_steel_conv_general_kernel(d, kname.str(), out, bm, bn, bk, wm, wn);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // Deduce grid launch dimensions
   int tile = 1 << swizzle_log;
@@ -522,15 +523,17 @@ void implicit_gemm_conv_2D_general_gpu(
   compute_encoder.set_output_array(out, 2);
 
   // Encode params
-  compute_encoder.set_bytes(conv_params, 3);
-  compute_encoder.set_bytes(gemm_params, 4);
-  compute_encoder.set_bytes(jump_params, 5);
+  compute_encoder->setBytes(&conv_params, sizeof(MLXConvParams<2>), 3);
+  compute_encoder->setBytes(&gemm_params, sizeof(ImplicitGemmConv2DParams), 4);
+  compute_encoder->setBytes(&jump_params, sizeof(Conv2DGeneralJumpParams), 5);
 
-  compute_encoder.set_vector_bytes(base_h, 6);
-  compute_encoder.set_vector_bytes(base_w, 7);
+  compute_encoder->setBytes(
+      base_h.data(), sizeof(Conv2DGeneralBaseInfo) * base_h.size(), 6);
+  compute_encoder->setBytes(
+      base_w.data(), sizeof(Conv2DGeneralBaseInfo) * base_w.size(), 7);
 
   // Launch kernel
-  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+  compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
 }
 
 void winograd_conv_2D_gpu(
@@ -541,6 +544,63 @@ void winograd_conv_2D_gpu(
     array out,
     const MLXConvParams<2>& conv_params,
     std::vector<array>& copies_w) {
+  std::vector<int> padded_shape = {
+      conv_params.N,
+      conv_params.iS[0] + 2 * conv_params.pad[0],
+      conv_params.iS[1] + 2 * conv_params.pad[1],
+      conv_params.C};
+
+  padded_shape[1] = 6 * ((padded_shape[1] - 2 + 5) / 6) + 2;
+  padded_shape[2] = 6 * ((padded_shape[2] - 2 + 5) / 6) + 2;
+
+  array in_padded(padded_shape, in.dtype(), nullptr, {});
+
+  // Fill with zeros
+  array zero_arr = array(0, in.dtype());
+  fill_gpu(zero_arr, in_padded, s);
+  copies_w.push_back(zero_arr);
+
+  // Pick input slice from padded
+  size_t data_offset = conv_params.pad[0] * in_padded.strides()[1] +
+      conv_params.pad[1] * in_padded.strides()[2];
+  array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
+  in_padded_slice.copy_shared_buffer(
+      in_padded,
+      in_padded.strides(),
+      in_padded.flags(),
+      in_padded_slice.size(),
+      data_offset);
+
+  // Copy input values into the slice
+  copy_gpu_inplace(in, in_padded_slice, CopyType::GeneralGeneral, s);
+
+  copies_w.push_back(in_padded_slice);
+  copies_w.push_back(in_padded);
+
+  MLXConvParams<2> conv_params_updated{
+      /* const int  N = */ in_padded.shape(0),
+      /* const int  C = */ in_padded.shape(3),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in_padded.shape(1), in_padded.shape(2)},
+      /* const int wS[NDIM] = */ {wt.shape(1), wt.shape(2)},
+      /* const int oS[NDIM] = */ {out.shape(1), out.shape(2)},
+      /* const int str[NDIM] = */ {1, 1},
+      /* const int pad[NDIM] = */ {0, 0},
+      /* const int kdil[NDIM] = */ {1, 1},
+      /* const int idil[NDIM] = */ {1, 1},
+      /* const size_t in_strides[NDIM + 2] = */
+      {in_padded.strides()[0],
+       in_padded.strides()[1],
+       in_padded.strides()[2],
+       in_padded.strides()[3]},
+      /* const size_t wt_strides[NDIM + 2] = */
+      {wt.strides()[0], wt.strides()[1], wt.strides()[2], wt.strides()[3]},
+      /* const size_t out_strides[NDIM + 2] = */
+      {out.strides()[0], out.strides()[1], out.strides()[2], out.strides()[3]},
+      /* const int groups = */ 1,
+      /* const bool flip = */ false,
+  };
+
   int O_c = conv_params.O;
   int C_c = conv_params.C;
 
@@ -550,8 +610,8 @@ void winograd_conv_2D_gpu(
   int N_tiles = N_tiles_n * N_tiles_h * N_tiles_w;
 
   // Do filter transform
-  Shape filt_wg_shape = {8 * 8, conv_params.C, conv_params.O};
-  array filt_wg(std::move(filt_wg_shape), wt.dtype(), nullptr, {});
+  std::vector<int> filt_wg_shape = {8 * 8, conv_params.C, conv_params.O};
+  array filt_wg(filt_wg_shape, wt.dtype(), nullptr, {});
   filt_wg.set_data(allocator::malloc_or_wait(filt_wg.nbytes()));
   copies_w.push_back(filt_wg);
   {
@@ -562,23 +622,23 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder->setComputePipelineState(kernel);
 
     compute_encoder.set_input_array(wt, 0);
     compute_encoder.set_output_array(filt_wg, 1);
 
-    compute_encoder.set_bytes(C_c, 2);
-    compute_encoder.set_bytes(O_c, 3);
+    compute_encoder->setBytes(&C_c, sizeof(int), 2);
+    compute_encoder->setBytes(&O_c, sizeof(int), 3);
 
     MTL::Size group_dims = MTL::Size(32, bo, 1);
     MTL::Size grid_dims = MTL::Size(O_c / bo, 1, 1);
 
-    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 
   // Do input transform
-  Shape inp_wg_shape = {8 * 8, N_tiles, conv_params.C};
-  array inp_wg(std::move(inp_wg_shape), in.dtype(), nullptr, {});
+  std::vector<int> inp_wg_shape = {8 * 8, N_tiles, conv_params.C};
+  array inp_wg(inp_wg_shape, in.dtype(), nullptr, {});
   inp_wg.set_data(allocator::malloc_or_wait(inp_wg.nbytes()));
   copies_w.push_back(inp_wg);
   {
@@ -590,22 +650,23 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder->setComputePipelineState(kernel);
 
-    compute_encoder.set_input_array(in, 0);
+    compute_encoder.set_input_array(in_padded, 0);
     compute_encoder.set_output_array(inp_wg, 1);
 
-    compute_encoder.set_bytes(conv_params, 2);
+    compute_encoder->setBytes(
+        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
 
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 
   // Do batched gemm
-  Shape out_wg_shape = {8 * 8, N_tiles, conv_params.O};
-  array out_wg(std::move(out_wg_shape), in.dtype(), nullptr, {});
+  std::vector<int> out_wg_shape = {8 * 8, N_tiles, conv_params.O};
+  array out_wg(out_wg_shape, in.dtype(), nullptr, {});
   out_wg.set_data(allocator::malloc_or_wait(out_wg.nbytes()));
   copies_w.push_back(out_wg);
   {
@@ -637,17 +698,18 @@ void winograd_conv_2D_gpu(
           << bc;
     auto& compute_encoder = d.get_command_encoder(s.index);
     auto kernel = d.get_kernel(kname.str());
-    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder->setComputePipelineState(kernel);
 
     compute_encoder.set_input_array(out_wg, 0);
     compute_encoder.set_output_array(out, 1);
 
-    compute_encoder.set_bytes(conv_params, 2);
+    compute_encoder->setBytes(
+        &conv_params_updated, sizeof(MLXConvParams<2>), 2);
 
     MTL::Size group_dims = MTL::Size(32, wn, wm);
     MTL::Size grid_dims = MTL::Size(N_tiles_w, N_tiles_h, N_tiles_n);
 
-    compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
+    compute_encoder.dispatchThreadgroups(grid_dims, group_dims);
   }
 }
 
@@ -666,15 +728,12 @@ void conv_2D_gpu(
     std::vector<array>& copies) {
   // Make conv params
   MLXConvParams<2> conv_params{
-      /* const int  N = */ static_cast<int>(in.shape(0)),
-      /* const int  C = */ static_cast<int>(in.shape(3)),
-      /* const int  O = */ static_cast<int>(wt.shape(0)),
-      /* const int iS[NDIM] = */
-      {static_cast<int>(in.shape(1)), static_cast<int>(in.shape(2))},
-      /* const int wS[NDIM] = */
-      {static_cast<int>(wt.shape(1)), static_cast<int>(wt.shape(2))},
-      /* const int oS[NDIM] = */
-      {static_cast<int>(out.shape(1)), static_cast<int>(out.shape(2))},
+      /* const int  N = */ in.shape(0),
+      /* const int  C = */ in.shape(3),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in.shape(1), in.shape(2)},
+      /* const int wS[NDIM] = */ {wt.shape(1), wt.shape(2)},
+      /* const int oS[NDIM] = */ {out.shape(1), out.shape(2)},
       /* const int str[NDIM] = */ {wt_strides[0], wt_strides[1]},
       /* const int pad[NDIM] = */ {padding[0], padding[1]},
       /* const int kdil[NDIM] = */ {wt_dilation[0], wt_dilation[1]},
@@ -746,21 +805,12 @@ void conv_3D_gpu(
     std::vector<array>& copies) {
   // Make conv params
   MLXConvParams<3> conv_params{
-      /* const int  N = */ static_cast<int>(in.shape(0)),
-      /* const int  C = */ static_cast<int>(in.shape(4)),
-      /* const int  O = */ static_cast<int>(wt.shape(0)),
-      /* const int iS[NDIM] = */
-      {static_cast<int>(in.shape(1)),
-       static_cast<int>(in.shape(2)),
-       static_cast<int>(in.shape(3))},
-      /* const int wS[NDIM] = */
-      {static_cast<int>(wt.shape(1)),
-       static_cast<int>(wt.shape(2)),
-       static_cast<int>(wt.shape(3))},
-      /* const int oS[NDIM] = */
-      {static_cast<int>(out.shape(1)),
-       static_cast<int>(out.shape(2)),
-       static_cast<int>(out.shape(3))},
+      /* const int  N = */ in.shape(0),
+      /* const int  C = */ in.shape(4),
+      /* const int  O = */ wt.shape(0),
+      /* const int iS[NDIM] = */ {in.shape(1), in.shape(2), in.shape(3)},
+      /* const int wS[NDIM] = */ {wt.shape(1), wt.shape(2), wt.shape(3)},
+      /* const int oS[NDIM] = */ {out.shape(1), out.shape(2), out.shape(3)},
       /* const int str[NDIM] = */ {wt_strides[0], wt_strides[1], wt_strides[2]},
       /* const int pad[NDIM] = */ {padding[0], padding[1], padding[2]},
       /* const int kdil[NDIM] = */

mlx/backend/metal/copy.cpp

@@ -43,12 +43,13 @@ void copy_gpu(const array& in, array& out, CopyType ctype) {
   copy_gpu(in, out, ctype, out.primitive().stream());
 }
 
+template <typename stride_t>
 void copy_gpu_inplace(
     const array& in,
     array& out,
-    const Shape& data_shape,
-    const Strides& strides_in_pre,
-    const Strides& strides_out_pre,
+    const std::vector<int>& data_shape,
+    const std::vector<stride_t>& strides_in_pre,
+    const std::vector<stride_t>& strides_out_pre,
     int64_t inp_offset,
     int64_t out_offset,
     CopyType ctype,
@@ -67,52 +68,50 @@ void copy_gpu_inplace(
               /* size_cap = */ INT32_MAX);
           return std::make_tuple(shape, strides[0], strides[1]);
         } else {
-          Strides e{};
-          return std::make_tuple(Shape{}, e, e);
+          std::vector<stride_t> e;
+          return std::make_tuple(std::vector<int>{}, e, e);
         }
       };
   auto [shape, strides_in_, strides_out_] = maybe_collapse();
   int ndim = shape.size();
-  bool large;
-  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
-    // Allow for negative strides
-    large = in.data_size() > INT32_MAX || out.data_size() > INT32_MAX;
-  } else {
-    large = out.data_size() > UINT32_MAX;
-  }
+
+  bool use_2d = out.data_size() > UINT32_MAX;
   auto& d = metal::device(s.device);
   int work_per_thread = 1;
   std::string kernel_name;
-  switch (ctype) {
-    case CopyType::Scalar:
-      kernel_name = (large ? "s2" : "s");
-      break;
-    case CopyType::Vector:
-      kernel_name = (large ? "v2" : "v");
-      break;
-    case CopyType::General:
-      kernel_name = "g";
-      break;
-    case CopyType::GeneralGeneral:
-      kernel_name = "gg";
-      break;
-  }
-  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
-    if (shape.size() <= MAX_COPY_SPECIALIZED_DIMS) {
-      kernel_name += std::to_string(shape.size());
-    } else {
-      work_per_thread = large ? 4 : 2;
-      concatenate(kernel_name, "n", std::to_string(work_per_thread));
+  {
+    std::ostringstream kname;
+    switch (ctype) {
+      case CopyType::Scalar:
+        kname << (use_2d ? "s2" : "s");
+        break;
+      case CopyType::Vector:
+        kname << (use_2d ? "v2" : "v");
+        break;
+      case CopyType::General:
+        kname << "g";
+        break;
+      case CopyType::GeneralGeneral:
+        kname << "gg";
+        break;
     }
-    if (large) {
-      kernel_name += "large";
+    if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
+      if (shape.size() <= MAX_COPY_SPECIALIZED_DIMS) {
+        kname << shape.size();
+      } else {
+        work_per_thread = 4;
+        kname << "n4";
+      }
     }
+    kname << "_copy";
+    kname << type_to_name(in) << type_to_name(out);
+    kernel_name = kname.str();
   }
-  concatenate(kernel_name, "_copy", type_to_name(in), type_to_name(out));
+
   auto kernel = get_copy_kernel(d, kernel_name, in, out);
 
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
   bool donate_in = in.data_shared_ptr() == nullptr;
 
   inp_offset *= size_of(in.dtype());
@@ -123,26 +122,26 @@ void copy_gpu_inplace(
 
   auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
   if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
-    Strides strides_in{strides_in_.begin(), strides_in_.end()};
-    Strides strides_out{strides_out_.begin(), strides_out_.end()};
+    std::vector<int64_t> strides_in{strides_in_.begin(), strides_in_.end()};
+    std::vector<int64_t> strides_out{strides_out_.begin(), strides_out_.end()};
     if (ndim > 3) {
-      compute_encoder.set_vector_bytes(shape, ndim, 2);
+      set_vector_bytes(compute_encoder, shape, ndim, 2);
     }
-    compute_encoder.set_vector_bytes(strides_in, ndim, 3);
+    set_vector_bytes(compute_encoder, strides_in, ndim, 3);
     if (ctype == CopyType::GeneralGeneral) {
-      compute_encoder.set_vector_bytes(strides_out, ndim, 4);
+      set_vector_bytes(compute_encoder, strides_out, ndim, 4);
     }
 
-    size_t dim0 = ndim > 0 ? shape[ndim - 1] : 1;
-    size_t dim1 = ndim > 1 ? shape[ndim - 2] : 1;
+    int dim0 = ndim > 0 ? shape[ndim - 1] : 1;
+    int dim1 = ndim > 1 ? shape[ndim - 2] : 1;
 
     size_t data_size = 1;
     for (auto& s : shape)
       data_size *= s;
-    size_t rest = data_size / (dim0 * dim1);
+    int rest = data_size / (dim0 * dim1);
 
     if (ndim > MAX_COPY_SPECIALIZED_DIMS) {
-      compute_encoder.set_bytes(ndim, 5);
+      compute_encoder->setBytes(&ndim, sizeof(int), 5);
       dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
     }
 
@@ -153,16 +152,16 @@ void copy_gpu_inplace(
 
     auto group_dims = get_block_dims(dim0, dim1, rest);
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   } else {
     size_t nthreads = out.data_size();
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
+                                 : MTL::Size(nthreads, 1, 1);
+    compute_encoder.dispatchThreads(grid_dims, group_dims);
   }
 }
 
@@ -179,13 +178,14 @@ void copy_gpu_inplace(
 void copy_gpu_inplace(
     const array& in,
     array& out,
-    const Strides& istride,
+    const std::vector<int64_t>& istride,
     int64_t ioffset,
     CopyType ctype,
     const Stream& s) {
   assert(in.shape() == out.shape());
+  std::vector<int64_t> ostrides{out.strides().begin(), out.strides().end()};
   return copy_gpu_inplace(
-      in, out, in.shape(), istride, out.strides(), ioffset, 0, ctype, s);
+      in, out, in.shape(), istride, ostrides, ioffset, 0, ctype, s);
 }
 
 void fill_gpu(const array& val, array& out, const Stream& s) {
@@ -193,13 +193,13 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
     return;
   }
   out.set_data(allocator::malloc_or_wait(out.nbytes()));
-  bool large = out.data_size() > UINT32_MAX;
+  bool use_2d = out.data_size() > UINT32_MAX;
   auto& d = metal::device(s.device);
-  std::string kernel_name = std::string(large ? "s2" : "s") + "_copy" +
+  std::string kernel_name = std::string(use_2d ? "s2" : "s") + "_copy" +
       type_to_name(val) + type_to_name(out);
   auto kernel = get_copy_kernel(d, kernel_name, val, out);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   compute_encoder.set_input_array(val, 0);
   compute_encoder.set_output_array(out, 1);
@@ -210,9 +210,9 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
     thread_group_size = nthreads;
   }
   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-  MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                              : MTL::Size(nthreads, 1, 1);
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  MTL::Size grid_dims = use_2d ? get_2d_grid_dims(out.shape(), out.strides())
+                               : MTL::Size(nthreads, 1, 1);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 }
 
 } // namespace mlx::core

mlx/backend/metal/copy.h

@@ -8,12 +8,13 @@
 namespace mlx::core {
 
 // Generic copy inplace
+template <typename stride_t>
 void copy_gpu_inplace(
     const array& in,
     array& out,
-    const Shape& data_shape,
-    const Strides& i_strides,
-    const Strides& o_strides,
+    const std::vector<int>& data_shape,
+    const std::vector<stride_t>& i_strides,
+    const std::vector<stride_t>& o_strides,
     int64_t i_offset,
     int64_t o_offset,
     CopyType ctype,
@@ -31,7 +32,7 @@ void copy_gpu_inplace(
 void copy_gpu_inplace(
     const array& in,
     array& out,
-    const Strides& istride,
+    const std::vector<int64_t>& istride,
     int64_t ioffset,
     CopyType ctype,
     const Stream& s);

mlx/backend/metal/custom_kernel.cpp

@@ -43,7 +43,7 @@ void CustomKernel::eval_gpu(
       d.get_library(lib_name, [this] { return metal::utils() + source_; });
   auto kernel = d.get_kernel(name_, lib);
   auto& compute_encoder = d.get_command_encoder(s.index);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
   int index = 0;
   for (int i = 0; i < checked_inputs.size(); i++) {
     const array& in = checked_inputs[i];
@@ -53,15 +53,15 @@ void CustomKernel::eval_gpu(
     if (in.ndim() > 0) {
       int ndim = in.ndim();
       if (shape_info.shape) {
-        compute_encoder.set_vector_bytes(in.shape(), ndim, index);
+        set_vector_bytes(compute_encoder, in.shape(), ndim, index);
         index++;
       }
       if (shape_info.strides) {
-        compute_encoder.set_vector_bytes(in.strides(), ndim, index);
+        set_vector_bytes(compute_encoder, in.strides(), ndim, index);
         index++;
       }
       if (shape_info.ndim) {
-        compute_encoder.set_bytes(ndim, index);
+        compute_encoder->setBytes(&ndim, sizeof(int), index);
         index++;
       }
     }
@@ -72,11 +72,10 @@ void CustomKernel::eval_gpu(
   }
 
   const auto [tx, ty, tz] = threadgroup_;
+  MTL::Size group_dims = MTL::Size(tx, ty, tz);
   const auto [gx, gy, gz] = grid_;
-  MTL::Size group_dims =
-      MTL::Size(std::min(tx, gx), std::min(ty, gy), std::min(tz, gz));
   MTL::Size grid_dims = MTL::Size(gx, gy, gz);
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder->dispatchThreads(grid_dims, group_dims);
 
   d.add_temporaries(std::move(copies), s.index);
 }

mlx/backend/metal/device.cpp

@@ -23,18 +23,14 @@ constexpr int MAX_BUFFERS_PER_QUEUE = 12;
 
 constexpr const char* default_mtllib_path = METAL_PATH;
 
-auto get_metal_version() {
-  auto get_metal_version_ = []() {
-    if (__builtin_available(macOS 15, iOS 18, tvOS 18, visionOS 2, *)) {
-      return MTL::LanguageVersion3_2;
-    } else if (__builtin_available(macOS 14, iOS 17, tvOS 17, visionOS 1, *)) {
-      return MTL::LanguageVersion3_1;
-    } else {
-      return MTL::LanguageVersion3_0;
-    }
-  };
-  static auto metal_version_ = get_metal_version_();
-  return metal_version_;
+constexpr auto get_metal_version() {
+#if (MLX_METAL_VERSION >= 320)
+  return MTL::LanguageVersion3_2;
+#elif (MLX_METAL_VERSION >= 310)
+  return MTL::LanguageVersion3_1;
+#else
+  return MTL::LanguageVersion3_0;
+#endif
 }
 
 auto load_device() {
@@ -138,7 +134,6 @@ void CommandEncoder::set_input_array(
     const array& a,
     int idx,
     int64_t offset /* = 0 */) {
-  all_inputs_.insert(a.buffer().ptr());
   auto r_buf = static_cast<MTL::Resource*>(const_cast<void*>(a.buffer().ptr()));
   needs_barrier_ =
       needs_barrier_ | (prev_outputs_.find(r_buf) != prev_outputs_.end());
@@ -155,7 +150,6 @@ void CommandEncoder::set_output_array(
     int64_t offset /* = 0 */) {
   // Add barriers before adding the output to the output set
   set_input_array(a, idx, offset);
-  all_outputs_.insert(a.buffer().ptr());
   auto buf = static_cast<MTL::Resource*>(a.buffer().ptr());
   if (concurrent_) {
     concurrent_outputs_.insert(buf);
@@ -175,20 +169,38 @@ void CommandEncoder::maybeInsertBarrier() {
   next_outputs_.clear();
 }
 
-void CommandEncoder::dispatch_threadgroups(
+void CommandEncoder::dispatchThreadgroups(
     MTL::Size grid_dims,
     MTL::Size group_dims) {
   maybeInsertBarrier();
   enc_->dispatchThreadgroups(grid_dims, group_dims);
 }
 
-void CommandEncoder::dispatch_threads(
+void CommandEncoder::dispatchThreads(
     MTL::Size grid_dims,
     MTL::Size group_dims) {
   maybeInsertBarrier();
   enc_->dispatchThreads(grid_dims, group_dims);
 }
 
+void DeviceStream::register_inputs(const std::vector<array>& arrays) {
+  std::lock_guard<std::mutex> lk(fence_mtx);
+  for (auto& a : arrays) {
+    auto buf = a.buffer().ptr();
+    if (auto it = outputs.find(buf); it != outputs.end()) {
+      waiting_on.insert(it->second);
+    }
+  }
+}
+
+void DeviceStream::register_outputs(const std::vector<array>& arrays) {
+  for (auto& a : arrays) {
+    if (a.data<void>() != nullptr) {
+      all_outputs.insert(a.buffer().ptr());
+    }
+  }
+}
+
 Device::Device() {
   auto pool = new_scoped_memory_pool();
   device_ = load_device();
@@ -280,43 +292,27 @@ void Device::end_encoding(int index) {
     // - Update the map of outputs to include this command encoder's outputs.
     // - Always signal this command encoders fence.
     // - Add a completion handler for this command encoder that removes outputs
-    //   from the map to limit the growth of the map and avoid unnecessary waits
+    //   from the map to limit the growth of the map and avoid unecessary waits
     // - Temporaries are a special case as they do not cross command encoder
     //   boundaries. These can be removed early from the encoders inputs and
     //   outputs since they don't need synchronization.
     auto& enc = *stream.encoder;
-    // Remove temporaries from inputs and outputs
-    for (auto& t : stream.temporaries) {
-      if (t.data<void>() != nullptr) {
-        enc.outputs().erase(t.buffer().ptr());
-        enc.inputs().erase(t.buffer().ptr());
-      }
-    }
 
-    // Keep references to the fences we waited on and put them
-    // in the completion handler so they are not prematurely released
-    std::unordered_set<std::shared_ptr<Fence>> waiting_on;
+    for (auto& f : stream.waiting_on) {
+      enc->waitForFence(f->fence);
+    }
     {
       std::lock_guard<std::mutex> lk(stream.fence_mtx);
-      for (auto in : enc.inputs()) {
-        if (auto it = stream.outputs.find(in); it != stream.outputs.end()) {
-          // If we've already waited on a fence, don't wait on it again.
-          if (waiting_on.find(it->second) == waiting_on.end()) {
-            enc.wait_for_fence(it->second->fence);
-            waiting_on.insert(it->second);
-          }
-        }
-      }
-      for (auto out : enc.outputs()) {
+      for (auto out : stream.all_outputs) {
         stream.outputs[out] = stream.fence;
       }
     }
-    enc.update_fence(stream.fence->fence);
+    enc->updateFence(stream.fence->fence);
     stream.buffer->addCompletedHandler(
         [&stream,
-         waiting_on = std::move(waiting_on),
+         waiting_on = std::move(stream.waiting_on),
          fence = std::move(stream.fence),
-         outputs = std::move(enc.outputs()),
+         outputs = std::move(stream.all_outputs),
          temporaries =
              std::move(stream.temporaries)](MTL::CommandBuffer*) mutable {
           temporaries.clear();
@@ -329,6 +325,9 @@ void Device::end_encoding(int index) {
             }
           }
         });
+  } else {
+    stream.all_outputs.clear();
+    stream.waiting_on.clear();
   }
   stream.encoder = nullptr;
 }
@@ -645,32 +644,21 @@ void new_stream(Stream stream) {
 
 std::unordered_map<std::string, std::variant<std::string, size_t>>
 device_info() {
-  auto init_device_info = []()
-      -> std::unordered_map<std::string, std::variant<std::string, size_t>> {
-    auto pool = new_scoped_memory_pool();
-    auto raw_device = device(default_device()).mtl_device();
-    auto arch = std::string(raw_device->architecture()->name()->utf8String());
+  auto raw_device = device(default_device()).mtl_device();
+  auto arch = std::string(raw_device->architecture()->name()->utf8String());
 
-    size_t memsize = 0;
-    size_t length = sizeof(memsize);
-    sysctlbyname("hw.memsize", &memsize, &length, NULL, 0);
+  int mib[] = {CTL_HW, HW_MEMSIZE};
+  size_t memsize = 0;
+  size_t length = sizeof(memsize);
 
-    size_t rsrc_limit = 0;
-    sysctlbyname("iogpu.rsrc_limit", &rsrc_limit, &length, NULL, 0);
-    if (rsrc_limit == 0) {
-      rsrc_limit = 499000;
-    }
+  sysctl(mib, 2, &memsize, &length, NULL, 0);
 
-    return {
-        {"architecture", arch},
-        {"max_buffer_length", raw_device->maxBufferLength()},
-        {"max_recommended_working_set_size",
-         raw_device->recommendedMaxWorkingSetSize()},
-        {"memory_size", memsize},
-        {"resource_limit", rsrc_limit}};
-  };
-  static auto device_info_ = init_device_info();
-  return device_info_;
+  return {
+      {"architecture", arch},
+      {"max_buffer_length", raw_device->maxBufferLength()},
+      {"max_recommended_working_set_size",
+       raw_device->recommendedMaxWorkingSetSize()},
+      {"memory_size", memsize}};
 }
 
 } // namespace mlx::core::metal

mlx/backend/metal/device.h

@@ -58,58 +58,21 @@ struct CommandEncoder {
     CommandEncoder& enc;
   };
 
+  MTL::ComputeCommandEncoder* operator->() {
+    return enc_;
+  }
+
   void set_input_array(const array& a, int idx, int64_t offset = 0);
   void set_output_array(array& a, int idx, int64_t offset = 0);
-  void dispatch_threadgroups(MTL::Size grid_dims, MTL::Size group_dims);
-  void dispatch_threads(MTL::Size grid_dims, MTL::Size group_dims);
+  void dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims);
+  void dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims);
   void maybeInsertBarrier();
 
-  void set_compute_pipeline_state(MTL::ComputePipelineState* kernel) {
-    enc_->setComputePipelineState(kernel);
-  }
-
-  void wait_for_fence(MTL::Fence* fence) {
-    enc_->waitForFence(fence);
-  }
-
-  void update_fence(MTL::Fence* fence) {
-    enc_->updateFence(fence);
-  }
-
-  template <typename T>
-  void set_vector_bytes(const std::vector<T>& vec, size_t nelems, int idx) {
-    enc_->setBytes(vec.data(), nelems * sizeof(T), idx);
-  }
-  template <typename T>
-  void set_vector_bytes(const std::vector<T>& vec, int idx) {
-    return set_vector_bytes(vec, vec.size(), idx);
-  }
-
-  template <typename T>
-  void set_bytes(const T* v, int n, int idx) {
-    return enc_->setBytes(v, n * sizeof(T), idx);
-  }
-
-  template <typename T>
-  void set_bytes(const T& v, int idx) {
-    return enc_->setBytes(&v, sizeof(T), idx);
-  }
-
   ConcurrentContext start_concurrent() {
     return ConcurrentContext(*this);
   }
   ~CommandEncoder();
 
-  // Inputs to all kernels in the encoder including temporaries
-  std::unordered_set<const void*>& inputs() {
-    return all_inputs_;
-  };
-
-  // Outputs of all kernels in the encoder including temporaries
-  std::unordered_set<const void*> outputs() {
-    return all_outputs_;
-  };
-
  private:
   MTL::ComputeCommandEncoder* enc_;
   bool needs_barrier_{false};
@@ -117,8 +80,6 @@ struct CommandEncoder {
   std::unordered_set<MTL::Resource*> prev_outputs_;
   std::unordered_set<MTL::Resource*> next_outputs_;
   std::unordered_set<MTL::Resource*> concurrent_outputs_;
-  std::unordered_set<const void*> all_inputs_;
-  std::unordered_set<const void*> all_outputs_;
 };
 
 struct Fence {
@@ -148,11 +109,15 @@ struct DeviceStream {
   MTL::CommandBuffer* buffer{nullptr};
   int buffer_ops{0};
 
-  // The command encoder, fence, and temporaries are updated between command
-  // encoders
+  void register_inputs(const std::vector<array>& inputs);
+  void register_outputs(const std::vector<array>& inputs);
+
+  // The following variables are all reset between command encoders
   std::unique_ptr<CommandEncoder> encoder{nullptr};
   std::shared_ptr<Fence> fence;
   std::vector<array> temporaries;
+  std::unordered_set<std::shared_ptr<Fence>> waiting_on;
+  std::unordered_set<const void*> all_outputs;
 };
 
 class Device {
@@ -217,10 +182,15 @@ class Device {
 
   void set_residency_set(const MTL::ResidencySet* residency_set);
 
+  DeviceStream& get_stream(int index) {
+    return stream_map_.find(index)->second;
+  }
+
  private:
   DeviceStream& get_stream_(int index) {
     return stream_map_.find(index)->second;
   }
+
   MTL::Library* get_library_cache_(const std::string& name);
 
   MTL::Library* get_library_(const std::string& name);

mlx/backend/metal/distributed.cpp

@@ -3,7 +3,6 @@
 #include <cassert>
 
 #include "mlx/allocator.h"
-#include "mlx/backend/common/utils.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/distributed/ops.h"
 #include "mlx/distributed/primitives.h"
@@ -90,14 +89,13 @@ void Send::eval_gpu(
 
   auto& in = inputs[0];
   auto& out = outputs[0];
-  move_or_copy(in, out);
 
   // Schedule an async send on the comm stream
   auto task = [in = in, out = out, group = group(), dst = dst_]() mutable {
     if (in.event().valid()) {
       in.event().wait();
     }
-    distributed::detail::send(group, out, dst);
+    distributed::detail::send(group, in, dst);
     out.event().signal();
   };
   scheduler::enqueue(detail::communication_stream(), std::move(task));
@@ -135,7 +133,6 @@ void Recv::eval_gpu(
   // Encode a wait event as there is no input for the recv to encode a signal.
   auto& s = stream();
   auto& d = metal::device(s.device);
-  d.end_encoding(s.index);
   auto command_buffer = d.get_command_buffer(s.index);
   command_buffer->encodeWait(
       static_cast<MTL::Event*>(out.event().raw_event().get()),

mlx/backend/metal/fft.cpp

@@ -363,7 +363,7 @@ void multi_upload_bluestein_fft(
   auto [w_k, w_q] = compute_bluestein_constants(n, plan.bluestein_n);
 
   // Broadcast w_q and w_k to the batch size
-  Strides b_strides(in.ndim(), 0);
+  std::vector<size_t> b_strides(in.ndim(), 0);
   b_strides[axis] = 1;
   array w_k_broadcast({}, complex64, nullptr, {});
   array w_q_broadcast({}, complex64, nullptr, {});
@@ -386,8 +386,8 @@ void multi_upload_bluestein_fft(
     copies.push_back(slice_temp);
     copies.push_back(conj_temp);
 
-    Shape rstarts(in.ndim(), 0);
-    Shape rstrides(in.ndim(), 1);
+    std::vector<int> rstarts(in.ndim(), 0);
+    std::vector<int> rstrides(in.ndim(), 1);
     rstarts[axis] = in.shape(axis) - back_offset;
     rstrides[axis] = -1;
     unary_op_gpu({in}, conj_temp, "Conjugate", s);
@@ -431,19 +431,19 @@ void multi_upload_bluestein_fft(
       s);
 
   int offset = plan.bluestein_n - (2 * n - 1);
-  Shape starts(in.ndim(), 0);
-  Shape strides(in.ndim(), 1);
+  std::vector<int> starts(in.ndim(), 0);
+  std::vector<int> strides(in.ndim(), 1);
   starts[axis] = plan.bluestein_n - offset - n;
   slice_gpu(pad_temp1, temp, starts, strides, s);
 
   binary_op_gpu_inplace({temp, w_k_broadcast}, temp1, "Multiply", s);
 
   if (real && !inverse) {
-    Shape rstarts(in.ndim(), 0);
-    Shape rstrides(in.ndim(), 1);
+    std::vector<int> rstarts(in.ndim(), 0);
+    std::vector<int> rstrides(in.ndim(), 1);
     slice_gpu(temp1, out, rstarts, strides, s);
   } else if (real && inverse) {
-    Strides b_strides(in.ndim(), 0);
+    std::vector<size_t> b_strides(in.ndim(), 0);
     auto inv_n = array({1.0f / n}, {1}, float32);
     array temp_float(out.shape(), out.dtype(), nullptr, {});
     copies.push_back(temp_float);
@@ -531,8 +531,8 @@ void fft_op(
       return x;
     } else {
       array x_copy(x.shape(), x.dtype(), nullptr, {});
-      Strides strides;
-      int64_t cur_stride = x.shape(axis);
+      std::vector<size_t> strides;
+      size_t cur_stride = x.shape(axis);
       for (int a = 0; a < x.ndim(); a++) {
         if (a == axis) {
           strides.push_back(1);
@@ -699,7 +699,7 @@ void fft_op(
     auto kernel =
         get_fft_kernel(d, base_name, hash_name, func_consts, template_def);
 
-    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder->setComputePipelineState(kernel);
     compute_encoder.set_input_array(in_contiguous, 0);
     compute_encoder.set_output_array(out, 1);
 
@@ -711,9 +711,9 @@ void fft_op(
 
       compute_encoder.set_input_array(w_q, 2); // w_q
       compute_encoder.set_input_array(w_k, 3); // w_k
-      compute_encoder.set_bytes(n, 4);
-      compute_encoder.set_bytes(plan.bluestein_n, 5);
-      compute_encoder.set_bytes(total_batch_size, 6);
+      compute_encoder->setBytes(&n, sizeof(int), 4);
+      compute_encoder->setBytes(&plan.bluestein_n, sizeof(int), 5);
+      compute_encoder->setBytes(&total_batch_size, sizeof(int), 6);
     } else if (plan.rader_n > 1) {
       auto [b_q, g_q, g_minus_q] = compute_raders_constants(plan.rader_n, s);
       copies.push_back(b_q);
@@ -723,22 +723,22 @@ void fft_op(
       compute_encoder.set_input_array(b_q, 2);
       compute_encoder.set_input_array(g_q, 3);
       compute_encoder.set_input_array(g_minus_q, 4);
-      compute_encoder.set_bytes(n, 5);
-      compute_encoder.set_bytes(total_batch_size, 6);
-      compute_encoder.set_bytes(plan.rader_n, 7);
+      compute_encoder->setBytes(&n, sizeof(int), 5);
+      compute_encoder->setBytes(&total_batch_size, sizeof(int), 6);
+      compute_encoder->setBytes(&plan.rader_n, sizeof(int), 7);
     } else if (four_step_params.required) {
-      compute_encoder.set_bytes(four_step_params.n1, 2);
-      compute_encoder.set_bytes(four_step_params.n2, 3);
-      compute_encoder.set_bytes(total_batch_size, 4);
+      compute_encoder->setBytes(&four_step_params.n1, sizeof(int), 2);
+      compute_encoder->setBytes(&four_step_params.n2, sizeof(int), 3);
+      compute_encoder->setBytes(&total_batch_size, sizeof(int), 4);
     } else {
-      compute_encoder.set_bytes(n, 2);
-      compute_encoder.set_bytes(total_batch_size, 3);
+      compute_encoder->setBytes(&n, sizeof(int), 2);
+      compute_encoder->setBytes(&total_batch_size, sizeof(int), 3);
     }
 
     auto group_dims = MTL::Size(1, threadgroup_batch_size, threads_per_fft);
     auto grid_dims =
         MTL::Size(batch_size, threadgroup_batch_size, threads_per_fft);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder->dispatchThreads(grid_dims, group_dims);
   }
 
   d.add_temporaries(std::move(copies), s.index);
@@ -777,7 +777,7 @@ void nd_fft_op(
     // Mirror np.fft.(i)rfftn and perform a real transform
     // only on the final axis.
     bool step_real = (real && index == axes.size() - 1);
-    auto step_shape = inverse ? out.shape(axis) : in.shape(axis);
+    int step_shape = inverse ? out.shape(axis) : in.shape(axis);
     const array& in_arr = i == axes.size() - 1 ? in : temp_arrs[1 - i % 2];
     array& out_arr = i == 0 ? out : temp_arrs[i % 2];
     fft_op(in_arr, out_arr, axis, inverse, step_real, inplace, s);

mlx/backend/metal/hadamard.cpp

@@ -137,14 +137,14 @@ void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
     auto kernel = d.get_kernel(kernel_name, lib);
     assert(threads_per <= kernel->maxTotalThreadsPerThreadgroup());
 
-    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder->setComputePipelineState(kernel);
     compute_encoder.set_input_array(in, 0);
     compute_encoder.set_output_array(out, 1);
-    compute_encoder.set_bytes(scale, 2);
+    compute_encoder->setBytes(&scale, sizeof(float), 2);
 
     MTL::Size group_dims = MTL::Size(1, threads_per, 1);
     MTL::Size grid_dims = MTL::Size(batch_size, threads_per, 1);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
+    compute_encoder->dispatchThreads(grid_dims, group_dims);
   };
 
   if (m > 1) {

mlx/backend/metal/indexing.cpp

@@ -53,31 +53,27 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   int idx_ndim = nidx ? inputs[1].ndim() : 0;
   size_t ndim = src.ndim();
 
-  bool large_index = nidx && inputs[1].size() > INT32_MAX;
-  bool large_src = src.size() > INT32_MAX;
-  bool large_out = out.size() > INT32_MAX;
-  bool large = large_index || large_src || large_out;
-
+  std::string lib_name;
+  std::string kernel_name;
   std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
-  std::string kernel_name = fmt::format(
-      "gather{0}{1}_{2}_{3}_{4}",
-      type_to_name(out),
-      idx_type_name,
-      nidx,
-      idx_ndim,
-      large ? "int64_t" : "int");
-  std::string lib_name = kernel_name;
+  {
+    std::ostringstream kname;
+    kname << "gather" << type_to_name(out) << idx_type_name << "_" << nidx
+          << "_" << idx_ndim;
+    lib_name = kname.str();
+    kernel_name = lib_name;
+  }
 
   auto lib = d.get_library(lib_name, [&]() {
-    std::string kernel_source = metal::utils();
-    kernel_source += metal::gather();
+    std::ostringstream kernel_source;
+    kernel_source << metal::utils() << metal::gather();
     std::string out_type_str = get_type_string(out.dtype());
     std::string idx_type_str =
         nidx ? get_type_string(inputs[1].dtype()) : "bool";
     auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
 
     // Index dimension specializations
-    kernel_source += fmt::format(
+    kernel_source << fmt::format(
         gather_kernels,
         type_to_name(out) + idx_type_name,
         out_type_str,
@@ -85,14 +81,13 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
         nidx,
         idx_args,
         idx_arr,
-        idx_ndim,
-        large ? "int64_t" : "int");
-    return kernel_source;
+        idx_ndim);
+    return kernel_source.str();
   });
 
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto kernel = d.get_kernel(kernel_name, lib);
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   size_t slice_size = 1;
   for (auto s : slice_sizes_) {
@@ -136,20 +131,20 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 1);
 
   // Set source info
-  compute_encoder.set_vector_bytes(src.shape(), 2);
-  compute_encoder.set_vector_bytes(src.strides(), 3);
-  compute_encoder.set_bytes(ndim, 4);
-  compute_encoder.set_vector_bytes(slice_sizes_, 5);
-  compute_encoder.set_vector_bytes(axes_, 6);
+  set_vector_bytes(compute_encoder, src.shape(), 2);
+  set_vector_bytes(compute_encoder, src.strides(), 3);
+  compute_encoder->setBytes(&ndim, sizeof(size_t), 4);
+  set_vector_bytes(compute_encoder, slice_sizes_, 5);
+  set_vector_bytes(compute_encoder, axes_, 6);
 
   // Set index info
   //
   // We don't need to check for empty idx_shapes because gather has a
   // idx_ndim == 0 specialization
-  compute_encoder.set_vector_bytes(idx_shapes, 7);
-  compute_encoder.set_vector_bytes(idx_strides, 8);
-  compute_encoder.set_vector_bytes(idx_contigs, 9);
-  compute_encoder.set_bytes(idx_ndim, 10);
+  set_vector_bytes(compute_encoder, idx_shapes, 7);
+  set_vector_bytes(compute_encoder, idx_strides, 8);
+  set_vector_bytes(compute_encoder, idx_contigs, 9);
+  compute_encoder->setBytes(&idx_ndim, sizeof(int), 10);
 
   // Set index buffers
   for (int i = 0; i < nidx; ++i) {
@@ -157,7 +152,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 
   // Launch grid
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 }
 
 void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -214,6 +209,8 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     nwork = 32;
   }
 
+  std::string lib_name;
+  std::string kernel_name;
   std::string idx_type_name = nidx ? type_to_name(inputs[1]) : "";
   std::string op_name;
   switch (reduce_type_) {
@@ -234,24 +231,18 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
       break;
   }
   auto upd_contig = upd.flags().row_contiguous;
-  bool large_out = out.size() > INT32_MAX;
-  bool large_idx = nidx && (inputs[1].size() > INT32_MAX);
-  bool large_upd = upd.size() > INT32_MAX;
-  bool large = large_out || large_idx || large_upd;
-  std::string kernel_name = fmt::format(
-      "scatter{0}{1}_{2}_{3}_{4}_nwork{5}_{6}",
-      type_to_name(out),
-      idx_type_name,
-      op_name,
-      nidx,
-      upd_contig ? "updc_true" : "updc_false",
-      nwork,
-      large ? "int64_t" : "int");
-  std::string lib_name = kernel_name;
-
+  {
+    std::ostringstream kname;
+    kname << "scatter" << type_to_name(out) << idx_type_name;
+    kname << "_" << op_name << "_" << nidx << "_"
+          << (upd_contig ? "updc_true" : "updc_false") << "_nwork" << nwork;
+    lib_name = kname.str();
+    kernel_name = kname.str();
+  }
   auto lib = d.get_library(lib_name, [&]() {
-    std::string kernel_source = metal::utils();
-    concatenate(kernel_source, metal::reduce_utils(), metal::scatter());
+    std::ostringstream kernel_source;
+    kernel_source << metal::utils() << metal::reduce_utils()
+                  << metal::scatter();
 
     std::string out_type_str = get_type_string(out.dtype());
     std::string idx_type_str =
@@ -279,7 +270,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     }
     auto [idx_args, idx_arr] = make_index_args(idx_type_str, nidx);
 
-    kernel_source += fmt::format(
+    kernel_source << fmt::format(
         scatter_kernels,
         type_to_name(out) + idx_type_name + "_" + op_name,
         out_type_str,
@@ -289,9 +280,8 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
         idx_args,
         idx_arr,
         upd_contig,
-        nwork,
-        large ? "int64_t" : "int");
-    return kernel_source;
+        nwork);
+    return kernel_source.str();
   });
 
   auto& compute_encoder = d.get_command_encoder(s.index);
@@ -299,7 +289,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   size_t nthreads = upd.size();
 
-  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder->setComputePipelineState(kernel);
 
   // Set all the buffers
   compute_encoder.set_input_array(upd, 1);
@@ -312,8 +302,8 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     upd_size *= upd.shape(i);
   }
   // Collect all idx shapes and strides into one place
-  Shape idx_shapes;
-  Strides idx_strides;
+  std::vector<int> idx_shapes;
+  std::vector<size_t> idx_strides;
   // To access .data() use char instead of bool
   // bool is 1 byte in Metal so this is safe
   std::vector<char> idx_contigs;
@@ -332,30 +322,30 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (upd_ndim == 0) {
     // Need placeholders so Metal doesn't compalain
     int shape_ = 0;
-    int64_t stride_ = 0;
-    compute_encoder.set_bytes(shape_, 3);
-    compute_encoder.set_bytes(stride_, 4);
+    size_t stride_ = 0;
+    compute_encoder->setBytes(&shape_, sizeof(int), 3);
+    compute_encoder->setBytes(&stride_, sizeof(size_t), 4);
   } else {
-    compute_encoder.set_vector_bytes(upd.shape(), 3);
-    compute_encoder.set_vector_bytes(upd.strides(), 4);
+    set_vector_bytes(compute_encoder, upd.shape(), 3);
+    set_vector_bytes(compute_encoder, upd.strides(), 4);
   }
-  compute_encoder.set_bytes(upd_ndim, 5);
-  compute_encoder.set_bytes(upd_size, 6);
+  compute_encoder->setBytes(&upd_ndim, sizeof(size_t), 5);
+  compute_encoder->setBytes(&upd_size, sizeof(size_t), 6);
 
   // Set output info
   size_t out_ndim = out.ndim();
   if (out_ndim == 0) {
     // Need placeholders so Metal doesn't compalain
     int shape_ = 0;
-    int64_t stride_ = 0;
-    compute_encoder.set_bytes(shape_, 7);
-    compute_encoder.set_bytes(stride_, 8);
+    size_t stride_ = 0;
+    compute_encoder->setBytes(&shape_, sizeof(int), 7);
+    compute_encoder->setBytes(&stride_, sizeof(size_t), 8);
   } else {
-    compute_encoder.set_vector_bytes(out.shape(), 7);
-    compute_encoder.set_vector_bytes(out.strides(), 8);
+    set_vector_bytes(compute_encoder, out.shape(), 7);
+    set_vector_bytes(compute_encoder, out.strides(), 8);
   }
-  compute_encoder.set_bytes(out_ndim, 9);
-  compute_encoder.set_vector_bytes(axes_, 10);
+  compute_encoder->setBytes(&out_ndim, sizeof(size_t), 9);
+  compute_encoder->setBytes(axes_.data(), axes_.size() * sizeof(int), 10);
 
   // Set index info
   if (idx_ndim == 0) {
@@ -365,11 +355,11 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     idx_strides.push_back(0);
     idx_contigs.push_back(false);
   }
-  compute_encoder.set_vector_bytes(idx_shapes, 11);
-  compute_encoder.set_vector_bytes(idx_strides, 12);
-  compute_encoder.set_vector_bytes(idx_contigs, 13);
-  compute_encoder.set_bytes(idx_ndim, 14);
-  compute_encoder.set_bytes(idx_size, 15);
+  set_vector_bytes(compute_encoder, idx_shapes, 11);
+  set_vector_bytes(compute_encoder, idx_strides, 12);
+  set_vector_bytes(compute_encoder, idx_contigs, 13);
+  compute_encoder->setBytes(&idx_ndim, sizeof(int), 14);
+  compute_encoder->setBytes(&idx_size, sizeof(size_t), 15);
 
   // Set index buffers
   for (int i = 0; i < nidx; ++i) {
@@ -385,7 +375,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error("[Scatter::eval_gpu] Invalid number of threads");
   }
   MTL::Size group_dims = get_block_dims(upd_size, grid_y, 1);
-  compute_encoder.dispatch_threads(grid_dims, group_dims);
+  compute_encoder.dispatchThreads(grid_dims, group_dims);
 }
 
 } // namespace mlx::core

mlx/backend/metal/jit/gemv_masked.h

@@ -11,13 +11,13 @@ gemv_{trans}masked<{itype}, {outm_t}, {opm_t}, {bm}, {bn}, {sm}, {sn}, {tm}, {tn
     const constant int& marix_ld [[buffer(6)]],
     const constant int& batch_ndim [[buffer(9)]],
     const constant int* batch_shape [[buffer(10)]],
-    const constant int64_t* vector_batch_stride [[buffer(11)]],
-    const constant int64_t* matrix_batch_stride [[buffer(12)]],
+    const constant size_t* vector_batch_stride [[buffer(11)]],
+    const constant size_t* matrix_batch_stride [[buffer(12)]],
     const device {outm_t}* out_mask [[buffer(20)]],
     const device {opm_t}* mat_mask [[buffer(21)]],
     const device {opm_t}* vec_mask [[buffer(22)]],
     const constant int* mask_strides [[buffer(23)]],
-    const constant int64_t* mask_batch_strides [[buffer(24)]],
+    const constant size_t* mask_batch_strides [[buffer(24)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],

mlx/backend/metal/jit/indexing.h

@@ -1,16 +1,16 @@
 // Copyright © 2023-2024 Apple Inc.
 
 constexpr std::string_view gather_kernels = R"(
-[[kernel]] void gather{0}_{3}_{6}_{7}(
+[[kernel]] void gather{0}_{3}_{6}(
     const device {1}* src [[buffer(0)]],
     device {1}* out [[buffer(1)]],
     const constant int* src_shape [[buffer(2)]],
-    const constant int64_t* src_strides [[buffer(3)]],
+    const constant size_t* src_strides [[buffer(3)]],
     const constant size_t& src_ndim [[buffer(4)]],
     const constant int* slice_sizes [[buffer(5)]],
     const constant int* axes [[buffer(6)]],
     const constant int* idx_shapes [[buffer(7)]],
-    const constant int64_t* idx_strides [[buffer(8)]],
+    const constant size_t* idx_strides [[buffer(8)]],
     const constant bool* idx_contigs [[buffer(9)]],
     const constant int& idx_ndim [[buffer(10)]],
     {4}
@@ -19,7 +19,7 @@ constexpr std::string_view gather_kernels = R"(
   Indices<{2}, {3}> idxs{{
     {{ {5} }}, idx_shapes, idx_strides, idx_contigs, idx_ndim}};
 
-  return gather_impl<{1}, {2}, {3}, {6}, {7}>(
+  return gather_impl<{1}, {2}, {3}, {6}>(
       src,
       out,
       src_shape,
@@ -34,19 +34,19 @@ constexpr std::string_view gather_kernels = R"(
 )";
 
 constexpr std::string_view scatter_kernels = R"(
-[[kernel]] void scatter{0}_{4}_updc_{7}_nwork{8}_{9}(
+[[kernel]] void scatter{0}_{4}_updc_{7}_nwork{8}(
     const device {1}* updates [[buffer(1)]],
     device mlx_atomic<{1}>* out [[buffer(2)]],
     const constant int* upd_shape [[buffer(3)]],
-    const constant int64_t* upd_strides [[buffer(4)]],
+    const constant size_t* upd_strides [[buffer(4)]],
     const constant size_t& upd_ndim [[buffer(5)]],
     const constant size_t& upd_size [[buffer(6)]],
     const constant int* out_shape [[buffer(7)]],
-    const constant int64_t* out_strides [[buffer(8)]],
+    const constant size_t* out_strides [[buffer(8)]],
     const constant size_t& out_ndim [[buffer(9)]],
     const constant int* axes [[buffer(10)]],
     const constant int* idx_shapes [[buffer(11)]],
-    const constant int64_t* idx_strides [[buffer(12)]],
+    const constant size_t* idx_strides [[buffer(12)]],
     const constant bool* idx_contigs [[buffer(13)]],
     const constant int& idx_ndim [[buffer(14)]],
     const constant size_t& idx_size [[buffer(15)]],
@@ -54,7 +54,7 @@ constexpr std::string_view scatter_kernels = R"(
     uint2 gid [[thread_position_in_grid]]) {{
   Indices<{2}, {4}> idxs{{ {{ {6} }}, idx_shapes, idx_strides, idx_contigs, idx_ndim}};
 
-  return scatter_impl<{1}, {2}, {3}, {4}, {7}, {8}, {9}>(
+  return scatter_impl<{1}, {2}, {3}, {4}, {7}, {8}>(
       updates,
       out,
       upd_shape,