Merge branch 'main' into cuda-sort

.circleci/config.yml

@@ -16,9 +16,6 @@ parameters:
   linux_release:
     type: boolean
     default: false
-  cuda_release:
-    type: boolean
-    default: false
 
 jobs:
   build_documentation:
@@ -41,7 +38,7 @@ jobs:
             pip install --upgrade pip
             pip install --upgrade cmake
             pip install -r docs/requirements.txt
-            pip install . -v
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` pip install . -v
       - when:
           condition:
             not: << parameters.upload-docs >>
@@ -97,15 +94,17 @@ jobs:
           name: Install Python package
           command: |
             CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
               python3 setup.py build_ext --inplace
             CMAKE_ARGS="-DMLX_BUILD_METAL=OFF" \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
               python3 setup.py develop
       - run:
           name: Generate package stubs
           command: |
             echo "stubs"
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
@@ -155,14 +154,15 @@ jobs:
           name: Install Python package
           command: |
             source env/bin/activate
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
+            DEBUG=1 CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+            CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
               pip install -e . -v
       - run:
           name: Generate package stubs
           command: |
             source env/bin/activate
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Run Python tests
           command: |
@@ -205,34 +205,13 @@ jobs:
           name: Run Python tests with JIT
           command: |
             source env/bin/activate
-            CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
+            CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
               pip install -e . -v
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
               METAL_DEBUG_ERROR_MODE=0 \
               python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
 
-  cuda_build_and_test:
-    machine:
-      image: linux-cuda-12:default
-      resource_class: gpu.nvidia.small.gen2
-    steps:
-      - checkout
-      - run:
-          name: Install Python package
-          command: |
-            sudo apt-get update
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            python -m venv env
-            source env/bin/activate
-            CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              pip install -e ".[dev]"
-      - run:
-          name: Run Python tests
-          command: |
-            source env/bin/activate
-            LOW_MEMORY=1 DEVICE=cpu python -m unittest discover python/tests -v
-            LOW_MEMORY=1 DEVICE=gpu python -m tests discover python/tests -v
-
   build_release:
     parameters:
       python_version:
@@ -273,18 +252,21 @@ jobs:
           command: |
             source env/bin/activate
             env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
               pip install . -v
       - run:
           name: Generate package stubs
           command: |
             source env/bin/activate
             pip install typing_extensions
-            python setup.py generate_stubs
+            python setup.py generate_stubs 
       - run:
           name: Build Python package
           command: |
             source env/bin/activate
-            << parameters.build_env >> python -m build -w
+            << parameters.build_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`sysctl -n hw.ncpu` \
+              python -m build -w
       - when:
           condition: << parameters.build_env >>
           steps:
@@ -332,10 +314,14 @@ jobs:
             pip install patchelf
             pip install build
             pip install twine
-            << parameters.extra_env >> pip install . -v
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              pip install . -v
             pip install typing_extensions
-            python setup.py generate_stubs
-            << parameters.extra_env >> python -m build --wheel
+            python setup.py generate_stubs 
+            << parameters.extra_env >> \
+              CMAKE_BUILD_PARALLEL_LEVEL=`nproc` \
+              python -m build --wheel
             auditwheel show dist/*
             auditwheel repair dist/* --plat manylinux_2_31_x86_64
       - run:
@@ -346,46 +332,6 @@ jobs:
       - store_artifacts:
           path: wheelhouse/
 
-  build_cuda_release:
-    parameters:
-      python_version:
-        type: string
-        default: "3.9"
-      extra_env:
-        type: string
-        default: "DEV_RELEASE=1"
-    machine:
-      image: linux-cuda-12:default
-      resource_class: gpu.nvidia.small.gen2
-    steps:
-      - checkout
-      - run:
-          name: Build wheel
-          command: |
-            sudo apt-get update
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            python -m venv env
-            source env/bin/activate
-            pip install auditwheel
-            pip install patchelf
-            pip install build
-            pip install twine
-            << parameters.extra_env >> \
-              CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              pip install ".[dev]" -v
-            python setup.py generate_stubs
-            << parameters.extra_env >> \
-              CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              python -m build --wheel
-            bash python/scripts/repair_cuda.sh
-      - run:
-          name: Upload package
-          command: |
-            source env/bin/activate
-            twine upload wheelhouse/*.whl
-      - store_artifacts:
-          path: wheelhouse/
-
 workflows:
   build_and_test:
     when:
@@ -402,7 +348,6 @@ workflows:
             parameters:
               macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
-      - cuda_build_and_test 
       - build_documentation 
 
   build_pypi_release:
@@ -492,16 +437,6 @@ workflows:
             branches:
               ignore: /.*/
           upload-docs: true
-      - build_linux_release:
-          filters:
-            tags:
-              only: /^v.*/
-            branches:
-              ignore: /.*/
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              extra_env: ["PYPI_RELEASE=1"]
 
   prb:
     when:
@@ -520,8 +455,6 @@ workflows:
               macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test:
           requires: [ hold ]
-      - cuda_build_and_test:
-          requires: [ hold ]
   nightly_build:
     when:
       and:
@@ -665,14 +598,3 @@ workflows:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
               extra_env: ["PYPI_RELEASE=1"]
-  cuda_test_release:
-    when:
-      and:
-        - equal: [ main, << pipeline.git.branch >> ]
-        - << pipeline.parameters.cuda_release >>
-    jobs:
-      - build_cuda_release:
-          matrix:
-            parameters:
-              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
-              extra_env: ["PYPI_RELEASE=1"]

benchmarks/cpp/irregular_strides.cpp

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

benchmarks/python/comparative/bench_torch.py

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

benchmarks/python/conv_unaligned_bench.py

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

docs/src/install.rst

@@ -30,16 +30,6 @@ MLX is also available on conda-forge. To install MLX with conda do:
 
    conda install conda-forge::mlx
 
-CUDA
-^^^^
-
-MLX has a CUDA backend which you can use on any Linux platform with CUDA 12
-and SM 7.0 (Volta) and up. To install MLX with CUDA support, run:
-
-.. code-block:: shell
-
-    pip install mlx-cuda
-
 
 Troubleshooting
 ^^^^^^^^^^^^^^^
@@ -75,8 +65,6 @@ Build Requirements
 Python API
 ^^^^^^^^^^
 
-.. _python install:
-
 To build and install the MLX python library from source, first, clone MLX from
 `its GitHub repo <https://github.com/ml-explore/mlx>`_:
 
@@ -88,20 +76,20 @@ Then simply build and install MLX using pip:
 
 .. code-block:: shell
 
-  pip install .
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install .
 
 For developing, install the package with development dependencies, and use an
 editable install:
 
 .. code-block:: shell
 
-  pip install -e ".[dev]"
+  CMAKE_BUILD_PARALLEL_LEVEL=8 pip install -e ".[dev]"
 
 Once the development dependencies are installed, you can build faster with:
 
 .. code-block:: shell
 
- python setup.py build_ext --inplace
+ CMAKE_BUILD_PARALLEL_LEVEL=8 python setup.py build_ext --inplace
 
 Run the tests with:
 
@@ -119,8 +107,6 @@ IDE:
 C++ API
 ^^^^^^^
 
-.. _cpp install:
-
 Currently, MLX must be built and installed from source.
 
 Similarly to the python library, to build and install the MLX C++ library start
@@ -199,7 +185,6 @@ should point to the path to the built metal library.
 
       xcrun -sdk macosx --show-sdk-version
 
-
 Binary Size Minimization
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -228,50 +213,6 @@ be anwywhere from a few hundred millisecond to a few seconds depending on the
 application. Once a kernel is compiled, it will be cached by the system. The
 Metal kernel cache persists across reboots.
 
-Linux
-^^^^^
-
-To build from source on Linux (CPU only), install the BLAS and LAPACK headers.
-For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   apt-get update -y
-   apt-get install libblas-dev liblapack-dev liblapacke-dev -y
-
-From here follow the instructions to install either the :ref:`Python <python
-install>` or :ref:`C++ <cpp install>` APIs.
-
-CUDA
-^^^^
-
-To build from source on Linux with CUDA, install the BLAS and LAPACK headers
-and the CUDA toolkit. For example on Ubuntu, run the following:
-
-.. code-block:: shell
-
-   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
-   dpkg -i cuda-keyring_1.1-1_all.deb
-   apt-get update -y
-   apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev -y
-
-
-When building either the Python or C++ APIs make sure to pass the cmake flag
-``MLX_BUILD_CUDA=ON``. For example, to build the Python API run:
-
-.. code-block:: shell
-
-  CMAKE_ARGS="-DMLX_BUILD_CUDA=ON" pip install -e ".[dev]"
-
-To build the C++ package run:
-
-.. code-block:: shell
-
-   mkdir -p build && cd build
-   cmake .. -DMLX_BUILD_CUDA=ON && make -j
-
-
 Troubleshooting
 ^^^^^^^^^^^^^^^
 

docs/src/usage/indexing.rst

@@ -107,16 +107,6 @@ same array:
   >>> a
   array([1, 2, 0], dtype=int32)
 
-
-Note, unlike NumPy, updates to the same location are nondeterministic:
-
-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> a[[0, 0]] = mx.array([4, 5])
-
-The first element of ``a`` could be ``4`` or ``5``.
-
 Transformations of functions which use in-place updates are allowed and work as
 expected. For example:
 

mlx/CMakeLists.txt

@@ -55,9 +55,6 @@ endif()
 
 if(MLX_BUILD_CUDA)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda)
-else()
-  target_sources(mlx
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda/no_cuda.cpp)
 endif()
 
 if(MLX_BUILD_METAL OR MLX_BUILD_CUDA)

mlx/backend/common/compiled.cpp

@@ -14,8 +14,6 @@ void print_constant(std::ostream& os, const array& x) {
       return print_float_constant<float16_t>(os, x);
     case bfloat16:
       return print_float_constant<bfloat16_t>(os, x);
-    case float64:
-      return print_float_constant<double>(os, x);
     case complex64:
       return print_complex_constant<complex64_t>(os, x);
     case int8:
@@ -52,8 +50,6 @@ std::string get_type_string(Dtype d) {
       return "float16_t";
     case bfloat16:
       return "bfloat16_t";
-    case float64:
-      return "double";
     case complex64:
       return "complex64_t";
     case bool_:

mlx/backend/common/compiled.h

@@ -18,12 +18,8 @@ std::string get_type_string(Dtype d);
 template <typename T>
 void print_float_constant(std::ostream& os, const array& x) {
   auto old_precision = os.precision();
-  if constexpr (std::is_same_v<T, double>) {
-    os << std::setprecision(std::numeric_limits<double>::digits10 + 1);
-  } else {
-    os << std::setprecision(std::numeric_limits<float>::digits10 + 1);
-  }
-  os << x.item<T>() << std::setprecision(old_precision);
+  os << std::setprecision(std::numeric_limits<float>::digits10 + 1)
+     << x.item<T>() << std::setprecision(old_precision);
 }
 
 template <typename T>

mlx/backend/common/matmul.h

@@ -12,11 +12,16 @@ namespace mlx::core {
 inline std::tuple<Shape, Strides, Strides> collapse_batches(
     const array& a,
     const array& b) {
-  if (a.ndim() == 2) {
-    return {{1}, {0}, {0}};
+  // Get and check the shape for the batched dims
+  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
+  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
+  if (A_bshape != B_bshape) {
+    std::ostringstream msg;
+    msg << "[matmul] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ".";
+    throw std::runtime_error(msg.str());
   }
 
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
   Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
   Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
 
@@ -37,11 +42,17 @@ inline std::tuple<Shape, Strides, Strides> collapse_batches(
 
 inline std::tuple<Shape, Strides, Strides, Strides>
 collapse_batches(const array& a, const array& b, const array& c) {
-  if (a.ndim() == 2) {
-    return {{1}, {0}, {0}, {0}};
+  // Get and check the shape for the batched dims
+  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
+  Shape B_bshape{b.shape().begin(), b.shape().end() - 2};
+  Shape C_bshape{c.shape().begin(), c.shape().end() - 2};
+  if (A_bshape != B_bshape || A_bshape != C_bshape) {
+    std::ostringstream msg;
+    msg << "[addmm] Got matrices with incorrectly broadcasted shapes: " << "A "
+        << a.shape() << ", B " << b.shape() << ", B " << c.shape() << ".";
+    throw std::runtime_error(msg.str());
   }
 
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
   Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
   Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
   Strides C_bstride{c.strides().begin(), c.strides().end() - 2};

mlx/backend/common/reduce.cpp

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

mlx/backend/common/reduce.h

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

mlx/backend/common/utils.cpp

@@ -199,27 +199,14 @@ Dims get_2d_grid_dims_common(
       }
     }
   }
-  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX) {
+  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX || divisor > 1) {
     throw std::runtime_error("Unable to safely factor shape.");
   }
   if (grid_y > grid_x) {
     std::swap(grid_x, grid_y);
   }
-  if (divisor > 1) {
-    grid_x = ((grid_x + divisor - 1) / divisor) * divisor;
-  }
   return std::make_tuple(
       static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y), 1);
 }
 
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2) {
-  auto [bx, by, bz] = get_block_dims_common(dim0, dim1, dim2);
-  auto gx = (dim0 + bx - 1) / bx;
-  auto gy = (dim1 + by - 1) / by;
-  auto gz = (dim2 + bz - 1) / bz;
-
-  return std::make_pair(
-      std::make_tuple(gx, gy, gz), std::make_tuple(bx, by, bz));
-}
-
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -95,9 +95,6 @@ Dims get_2d_grid_dims_common(
     const Strides& strides,
     size_t divisor);
 
-// Get both the block and a grid of blocks that covers dim0, dim1 and dim2.
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2);
-
 struct ContiguousIterator {
   inline void step() {
     int dims = shape_.size();

mlx/backend/cpu/masked_mm.cpp

@@ -6,7 +6,6 @@
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
-#include "mlx/backend/cpu/gemm.h"
 #include "mlx/backend/cpu/lapack.h"
 #include "mlx/primitives.h"
 
@@ -53,58 +52,6 @@ inline void mask_matrix(
   }
 }
 
-template <typename T>
-inline void segmented_mm(
-    const T* a,
-    const T* b,
-    const uint32_t* segments,
-    T* out,
-    bool a_transposed,
-    bool b_transposed,
-    size_t lda,
-    size_t ldb,
-    const Shape& a_shape,
-    const Strides& a_strides,
-    const Shape& b_shape,
-    const Strides& b_strides,
-    size_t num_segments,
-    const Shape& segments_shape,
-    const Strides& segments_strides) {
-  int ndim = a_shape.size();
-  Shape a_copy = a_shape;
-  Shape b_copy = b_shape;
-  int32_t M = a_copy[ndim - 2];
-  int32_t N = b_copy[ndim - 1];
-  for (int i = 0; i < num_segments; i++) {
-    uint32_t k_start =
-        segments[elem_to_loc(2 * i, segments_shape, segments_strides)];
-    uint32_t k_end =
-        segments[elem_to_loc(2 * i + 1, segments_shape, segments_strides)];
-    if (k_end <= k_start) {
-      std::fill_n(out + i * M * N, M * N, T(0));
-      continue;
-    }
-    a_copy[ndim - 1] = k_end - k_start;
-    b_copy[ndim - 2] = k_end - k_start;
-    matmul<T>(
-        a + k_start * a_strides[ndim - 1],
-        b + k_start * b_strides[ndim - 2],
-        out + i * M * N,
-        a_transposed,
-        b_transposed,
-        lda,
-        ldb,
-        N,
-        1.0,
-        0.0,
-        1,
-        a_copy,
-        a_strides,
-        b_copy,
-        b_strides);
-  }
-}
-
 } // namespace
 
 void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
@@ -490,121 +437,4 @@ void GatherMM::eval_cpu(const std::vector<array>& inputs, array& out) {
   encoder.add_temporaries(std::move(temps));
 }
 
-void SegmentedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto& s = stream();
-  auto& encoder = cpu::get_command_encoder(stream());
-  auto check_transpose = [&s, &encoder](const array& x) {
-    auto stx = x.strides()[x.ndim() - 2];
-    auto sty = x.strides()[x.ndim() - 1];
-    if (stx == x.shape(-1) && sty == 1) {
-      return std::make_tuple(false, stx, x);
-    } else if (stx == 1 && sty == x.shape(-2)) {
-      return std::make_tuple(true, sty, x);
-    } else {
-      array xc(x.shape(), x.dtype(), nullptr, {});
-      copy(x, xc, CopyType::General, s);
-      encoder.add_temporary(xc);
-      int64_t stx = x.shape(-1);
-      return std::make_tuple(false, stx, xc);
-    }
-  };
-
-  auto [a_transposed, lda, a] = check_transpose(inputs[0]);
-  auto [b_transposed, ldb, b] = check_transpose(inputs[1]);
-  auto& segments = inputs[2];
-
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(segments);
-  encoder.set_output_array(out);
-  encoder.dispatch([a = array::unsafe_weak_copy(a),
-                    b = array::unsafe_weak_copy(b),
-                    segments = array::unsafe_weak_copy(segments),
-                    out_ptr = out.data<void>(),
-                    a_transposed = a_transposed,
-                    b_transposed = b_transposed,
-                    lda = lda,
-                    ldb = ldb]() {
-    switch (a.dtype()) {
-      case float64:
-        segmented_mm<double>(
-            a.data<double>(),
-            b.data<double>(),
-            segments.data<uint32_t>(),
-            static_cast<double*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case float32:
-        segmented_mm<float>(
-            a.data<float>(),
-            b.data<float>(),
-            segments.data<uint32_t>(),
-            static_cast<float*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case float16:
-        segmented_mm<float16_t>(
-            a.data<float16_t>(),
-            b.data<float16_t>(),
-            segments.data<uint32_t>(),
-            static_cast<float16_t*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      case bfloat16:
-        segmented_mm<bfloat16_t>(
-            a.data<bfloat16_t>(),
-            b.data<bfloat16_t>(),
-            segments.data<uint32_t>(),
-            static_cast<bfloat16_t*>(out_ptr),
-            a_transposed,
-            b_transposed,
-            lda,
-            ldb,
-            a.shape(),
-            a.strides(),
-            b.shape(),
-            b.strides(),
-            segments.size() / 2,
-            segments.shape(),
-            segments.strides());
-        break;
-      default:
-        throw std::invalid_argument(
-            "Segmented mm supports only real float types.");
-    }
-  });
-}
-
 } // namespace mlx::core

mlx/backend/cuda/CMakeLists.txt

@@ -1,85 +1,32 @@
 # Filename rules in cuda backend:
 #
 # * Use .cu/.cuh if code contains device code, and .cpp/.h if not.
-# * Device-only code should be put in device/ subdir.
-# * Files in device/ subdir should not include files outside.
+# * Device-only kernel code should be put in kernels/ subdir.
+# * Files in kernels/ subdir should not include files outside.
 target_sources(
   mlx
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/binary_two.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_contiguous.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel_utils.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/layer_norm.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/random.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/all_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/col_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/init_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
 
-# Embed kernel sources in binary for JIT compilation.
-file(
-  GLOB MLX_JIT_SOURCES
-  RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
-  "${CMAKE_CURRENT_SOURCE_DIR}/device/*.h"
-  "${CMAKE_CURRENT_SOURCE_DIR}/device/*.cuh")
-string(JOIN ":" MLX_JIT_SOURCES_ARG ${MLX_JIT_SOURCES})
-add_custom_command(
-  OUTPUT gen/cuda_jit_sources.h
-  COMMAND
-    ${CMAKE_COMMAND} -DMLX_SOURCE_ROOT=${CMAKE_CURRENT_SOURCE_DIR}
-    -DMLX_JIT_SOURCES=${MLX_JIT_SOURCES_ARG} -P
-    "${CMAKE_CURRENT_SOURCE_DIR}/bin2h.cmake"
-  DEPENDS bin2h.cmake ${MLX_JIT_SOURCES})
-add_custom_target(cuda_jit_sources DEPENDS gen/cuda_jit_sources.h)
-add_dependencies(mlx cuda_jit_sources)
-target_include_directories(mlx PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/gen")
-
 # Enable defining device lambda functions.
 target_compile_options(mlx
                        PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
 
-# CUDA 12.8 emits warning #20280-D for copy kernels which is a false positive.
-# Explicitly pass this flag to suppress the warning, it is safe to set it to
-# true but the warning wouldn't be suppressed.
-if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
-  target_compile_options(
-    mlx
-    PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--static-global-template-stub=false>")
-endif()
-
-# Suppress warning when building for compute capability 7 used by V100.
-target_compile_options(
-  mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--Wno-deprecated-gpu-targets>")
-
 # Compute capability 7 is required for synchronization between CPU/GPU with
 # managed memory. TODO: Add more architectures for potential performance gain.
 set(MLX_CUDA_ARCHITECTURES
@@ -113,9 +60,6 @@ target_include_directories(mlx PRIVATE ${CUDAToolkit_INCLUDE_DIRS})
 # Use cublasLt.
 target_link_libraries(mlx PRIVATE CUDA::cublasLt)
 
-# Use NVRTC and driver APIs.
-target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
-
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)

mlx/backend/cuda/allocator.cpp

@@ -3,7 +3,6 @@
 #include "mlx/backend/cuda/allocator.h"
 #include "mlx/backend/cuda/utils.h"
 #include "mlx/backend/cuda/worker.h"
-#include "mlx/utils.h"
 
 #include <cuda_runtime.h>
 #include <fmt/format.h>
@@ -15,11 +14,9 @@ namespace mlx::core {
 
 namespace cu {
 
-constexpr int page_size = 16384;
-
 CudaAllocator::CudaAllocator()
     : buffer_cache_(
-          page_size,
+          getpagesize(),
           [](CudaBuffer* buf) { return buf->size; },
           [this](CudaBuffer* buf) {
             cuda_free(buf->data);
@@ -34,14 +31,7 @@ CudaAllocator::CudaAllocator()
 
 Buffer CudaAllocator::malloc(size_t size) {
   // Find available buffer from cache.
-  auto orig_size = size;
   std::unique_lock lock(mutex_);
-  if (size < page_size) {
-    size = next_power_of_2(size);
-  } else {
-    size = page_size * ((size + page_size - 1) / page_size);
-  }
-
   CudaBuffer* buf = buffer_cache_.reuse_from_cache(size);
   if (!buf) {
     // If we have a lot of memory pressure or are over the maximum cache size,
@@ -116,6 +106,7 @@ void CudaAllocator::cuda_free(void* buf) {
       return;
     }
   }
+
   cudaFree(buf);
 }
 

mlx/backend/cuda/arg_reduce.cu

@@ -1,182 +0,0 @@
-// Copyright © 2025 Apple Inc.
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T>
-struct IndexValPair {
-  uint32_t index;
-  T val;
-};
-
-template <typename T>
-struct ArgMin {
-  constexpr __device__ T init() {
-    return Limits<T>::max();
-  }
-
-  __device__ IndexValPair<T> operator()(
-      const IndexValPair<T>& best,
-      const IndexValPair<T>& current) {
-    if (best.val > current.val ||
-        (best.val == current.val && best.index > current.index)) {
-      return current;
-    } else {
-      return best;
-    }
-  }
-
-  template <int N>
-  __device__ IndexValPair<T>
-  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
-    for (int i = 0; i < N; i++) {
-      if (vals[i] < best.val) {
-        best.val = vals[i];
-        best.index = offset + i;
-      }
-    }
-    return best;
-  }
-};
-
-template <typename T>
-struct ArgMax {
-  constexpr __device__ T init() {
-    return Limits<T>::min();
-  }
-
-  __device__ IndexValPair<T> operator()(
-      const IndexValPair<T>& best,
-      const IndexValPair<T>& current) {
-    if (best.val < current.val ||
-        (best.val == current.val && best.index > current.index)) {
-      return current;
-    } else {
-      return best;
-    }
-  }
-
-  template <int N>
-  __device__ IndexValPair<T>
-  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
-    for (int i = 0; i < N; i++) {
-      if (vals[i] > best.val) {
-        best.val = vals[i];
-        best.index = offset + i;
-      }
-    }
-    return best;
-  }
-};
-
-template <typename T, typename Op, int BLOCK_DIM, int N_READS = 4>
-__global__ void arg_reduce_general(
-    const T* in,
-    uint32_t* out,
-    size_t size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides in_strides,
-    const __grid_constant__ Strides out_strides,
-    int32_t ndim,
-    int64_t axis_stride,
-    int32_t axis_size) {
-  auto block = cg::this_thread_block();
-
-  int64_t index = cg::this_grid().block_rank();
-  if (index >= size) {
-    return;
-  }
-
-  int64_t in_idx = elem_to_loc(index, shape.data(), in_strides.data(), ndim);
-  int64_t out_idx = elem_to_loc(index, shape.data(), out_strides.data(), ndim);
-
-  Op op;
-  T init = op.init();
-  IndexValPair<T> best{0, init};
-
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    T vals[N_READS];
-    auto tid = r * BLOCK_DIM + block.thread_index().x;
-    cub::LoadDirectBlocked(
-        tid, strided_iterator(in + in_idx, axis_stride), vals, axis_size, init);
-    best = op.reduce_many(best, vals, tid * N_READS);
-  }
-
-  typedef cub::BlockReduce<IndexValPair<T>, BLOCK_DIM> BlockReduceT;
-  __shared__ typename BlockReduceT::TempStorage temp;
-
-  best = BlockReduceT(temp).Reduce(best, op);
-
-  if (block.thread_rank() == 0) {
-    out[out_idx] = best.index;
-  }
-}
-
-} // namespace cu
-
-void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("ArgReduce::eval_gpu");
-  assert(inputs.size() == 1);
-  auto& in = inputs[0];
-  out.set_data(allocator::malloc(out.nbytes()));
-  auto& s = stream();
-
-  // Prepare the shapes, strides and axis arguments.
-  Shape shape = remove_index(in.shape(), axis_);
-  Strides in_strides = remove_index(in.strides(), axis_);
-  Strides out_strides = out.ndim() == in.ndim()
-      ? remove_index(out.strides(), axis_)
-      : out.strides();
-  int64_t axis_stride = in.strides()[axis_];
-  int32_t axis_size = in.shape()[axis_];
-  int32_t ndim = shape.size();
-
-  // ArgReduce.
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_real_types(in.dtype(), "ArgReduce", [&](auto type_tag) {
-    using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    constexpr uint32_t N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      dim3 num_blocks = get_2d_grid_dims(out.shape(), out.strides());
-      auto kernel =
-          cu::arg_reduce_general<T, cu::ArgMax<T>, block_dim(), N_READS>;
-      if (reduce_type_ == ArgReduce::ArgMin) {
-        kernel = cu::arg_reduce_general<T, cu::ArgMin<T>, block_dim(), N_READS>;
-      }
-      encoder.add_kernel_node(
-          kernel,
-          num_blocks,
-          block_dim(),
-          in.data<T>(),
-          out.data<uint32_t>(),
-          out.size(),
-          const_param(shape),
-          const_param(in_strides),
-          const_param(out_strides),
-          ndim,
-          axis_stride,
-          axis_size);
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/bin2h.cmake

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

mlx/backend/cuda/binary.cu

@@ -2,9 +2,9 @@
 
 #include "mlx/backend/common/binary.h"
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/binary_ops.cuh"
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/binary_ops.cuh"
+#include "mlx/backend/cuda/kernels/cucomplex_math.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
 
@@ -17,106 +17,35 @@ namespace cu {
 
 namespace cg = cooperative_groups;
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
   IdxT index = cg::this_grid().thread_rank();
-  int remaining = size - index * N_READS;
-  if (remaining <= 0) {
-    return;
-  }
-
-  if (remaining < N_READS) {
-    for (int i = 0; i < remaining; ++i) {
-      IdxT offset = index * N_READS + i;
-      out[offset] = Op{}(a[0], b[0]);
-    }
-  } else {
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a[0], b[0]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
+  if (index < size) {
+    out[index] = Op{}(a[0], b[0]);
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
   IdxT index = cg::this_grid().thread_rank();
-  int remaining = size - index * N_READS;
-  if (remaining <= 0) {
-    return;
-  }
-
-  if (remaining < N_READS) {
-    for (int i = 0; i < remaining; ++i) {
-      IdxT offset = index * N_READS + i;
-      out[offset] = Op{}(a[0], b[offset]);
-    }
-  } else {
-    auto b_vec = load_vector<N_READS>(b, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a[0], b_vec.val[i]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
+  if (index < size) {
+    out[index] = Op{}(a[0], b[index]);
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
   IdxT index = cg::this_grid().thread_rank();
-  int remaining = size - index * N_READS;
-  if (remaining <= 0) {
-    return;
-  }
-
-  if (remaining < N_READS) {
-    for (int i = 0; i < remaining; ++i) {
-      IdxT offset = index * N_READS + i;
-      out[offset] = Op{}(a[offset], b[0]);
-    }
-  } else {
-    auto a_vec = load_vector<N_READS>(a, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a_vec.val[i], b[0]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
+  if (index < size) {
+    out[index] = Op{}(a[index], b[0]);
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
   IdxT index = cg::this_grid().thread_rank();
-  int remaining = size - index * N_READS;
-  if (remaining <= 0) {
-    return;
-  }
-
-  if (remaining < N_READS) {
-    for (int i = 0; i < remaining; ++i) {
-      IdxT offset = index * N_READS + i;
-      out[offset] = Op{}(a[offset], b[offset]);
-    }
-  } else {
-    auto a_vec = load_vector<N_READS>(a, index);
-    auto b_vec = load_vector<N_READS>(b, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a_vec.val[i], b_vec.val[i]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
+  if (index < size) {
+    out[index] = Op{}(a[index], b[index]);
   }
 }
 
@@ -172,12 +101,10 @@ constexpr bool supports_binary_op() {
     return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
   }
   if (std::is_same_v<Op, NaNEqual>) {
-    return std::is_same_v<Out, bool> && is_inexact_v<In>;
+    return std::is_same_v<Out, bool> &&
+        (is_floating_v<In> || std::is_same_v<In, complex64_t>);
   }
-  if (std::is_same_v<Op, LogAddExp>) {
-    return std::is_same_v<In, Out> && is_inexact_v<In>;
-  }
-  if (std::is_same_v<Op, ArcTan2>) {
+  if (std::is_same_v<Op, LogAddExp> || std::is_same_v<Op, ArcTan2>) {
     return std::is_same_v<In, Out> && is_floating_v<In>;
   }
   if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
@@ -196,12 +123,13 @@ constexpr bool supports_binary_op() {
 template <typename Op>
 void binary_op_gpu_inplace(
     const std::vector<array>& inputs,
-    array& out,
+    std::vector<array>& outputs,
     std::string_view op,
     const Stream& s) {
   assert(inputs.size() > 1);
   const auto& a = inputs[0];
   const auto& b = inputs[1];
+  auto& out = outputs[0];
   if (out.size() == 0) {
     return;
   }
@@ -210,103 +138,99 @@ void binary_op_gpu_inplace(
   encoder.set_input_array(a);
   encoder.set_input_array(b);
   encoder.set_output_array(out);
-  dispatch_all_types(a.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        using InType = cuda_type_t<CTYPE_IN>;
-        using OutType = cuda_type_t<CTYPE_OUT>;
-        auto bopt = get_binary_op_type(a, b);
-        if (bopt == BinaryOpType::General) {
-          dispatch_bool(
-              a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
-                  out.data_size() > INT32_MAX,
-              [&](auto large) {
-                using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-                Shape shape;
-                std::vector<Strides> strides;
-                std::tie(shape, strides) = collapse_contiguous_dims(a, b, out);
-                auto& a_strides = strides[0];
-                auto& b_strides = strides[1];
-                int ndim = shape.size();
-                if (ndim <= 3) {
-                  dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::
-                        binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(kernel, out, large());
-                    encoder.add_kernel_node(
-                        kernel,
-                        num_blocks,
-                        block_dims,
-                        a.data<InType>(),
-                        b.data<InType>(),
-                        out.data<OutType>(),
-                        out.size(),
-                        const_param<dims_constant()>(shape),
-                        const_param<dims_constant()>(a_strides),
-                        const_param<dims_constant()>(b_strides));
-                  });
-                } else {
-                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(a.dtype(), CTYPE_IN, {
+      MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {
+        if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
+          using InType = cuda_type_t<CTYPE_IN>;
+          using OutType = cuda_type_t<CTYPE_OUT>;
+
+          auto bopt = get_binary_op_type(a, b);
+          if (bopt == BinaryOpType::General) {
+            auto [shape, strides] = collapse_contiguous_dims(a, b, out);
+            auto& a_strides = strides[0];
+            auto& b_strides = strides[1];
+            bool large = a.data_size() > UINT32_MAX ||
+                b.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+            MLX_SWITCH_BOOL(large, LARGE, {
+              using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+              int ndim = shape.size();
+              if (ndim <= 3) {
+                MLX_SWITCH_1_2_3(ndim, NDIM, {
+                  auto kernel =
+                      &cu::binary_g_nd<Op, InType, OutType, IdxT, NDIM>;
                   auto [num_blocks, block_dims] =
-                      get_launch_args(kernel, out, large());
-                  encoder.add_kernel_node(
-                      kernel,
-                      num_blocks,
-                      block_dims,
+                      get_launch_args(kernel, out, large);
+                  kernel<<<num_blocks, block_dims, 0, stream>>>(
                       a.data<InType>(),
                       b.data<InType>(),
                       out.data<OutType>(),
-                      out.size(),
-                      const_param(shape),
-                      const_param(a_strides),
-                      const_param(b_strides),
-                      ndim);
-                }
-              });
+                      out.data_size(),
+                      const_param<NDIM>(shape),
+                      const_param<NDIM>(a_strides),
+                      const_param<NDIM>(b_strides));
+                });
+              } else {
+                auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
+                auto [num_blocks, block_dims] =
+                    get_launch_args(kernel, out, large);
+                kernel<<<num_blocks, block_dims, 0, stream>>>(
+                    a.data<InType>(),
+                    b.data<InType>(),
+                    out.data<OutType>(),
+                    out.data_size(),
+                    const_param(shape),
+                    const_param(a_strides),
+                    const_param(b_strides),
+                    ndim);
+              }
+            });
+          } else {
+            MLX_SWITCH_BOOL(out.data_size() > UINT32_MAX, LARGE, {
+              using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+              auto kernel = cu::binary_ss<Op, InType, OutType, IdxT>;
+              if (bopt == BinaryOpType::ScalarVector) {
+                kernel = cu::binary_sv<Op, InType, OutType, IdxT>;
+              } else if (bopt == BinaryOpType::VectorScalar) {
+                kernel = cu::binary_vs<Op, InType, OutType, IdxT>;
+              } else if (bopt == BinaryOpType::VectorVector) {
+                kernel = cu::binary_vv<Op, InType, OutType, IdxT>;
+              }
+              auto [num_blocks, block_dims] =
+                  get_launch_args(kernel, out, LARGE);
+              kernel<<<num_blocks, block_dims, 0, stream>>>(
+                  a.data<InType>(),
+                  b.data<InType>(),
+                  out.data<OutType>(),
+                  out.data_size());
+            });
+          }
         } else {
-          dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
-            using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            // TODO: Choose optimized value based on type size.
-            constexpr int N_READS = 4;
-            auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
-            if (bopt == BinaryOpType::ScalarVector) {
-              kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
-            } else if (bopt == BinaryOpType::VectorScalar) {
-              kernel = cu::binary_vs<Op, InType, OutType, IdxT, N_READS>;
-            } else if (bopt == BinaryOpType::VectorVector) {
-              kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
-            }
-            auto [num_blocks, block_dims] = get_launch_args(
-                kernel,
-                out.data_size(),
-                out.shape(),
-                out.strides(),
-                large(),
-                N_READS);
-            encoder.add_kernel_node(
-                kernel,
-                num_blocks,
-                block_dims,
-                a.data<InType>(),
-                b.data<InType>(),
-                out.data<OutType>(),
-                out.data_size());
-          });
+          throw std::runtime_error(fmt::format(
+              "Can not do binary op {} on inputs of {} with result of {}.",
+              op,
+              dtype_to_string(a.dtype()),
+              dtype_to_string(out.dtype())));
         }
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do binary op {} on inputs of {} with result of {}.",
-            op,
-            dtype_to_string(a.dtype()),
-            dtype_to_string(out.dtype())));
-      }
+      });
     });
   });
 }
 
+template <typename Op>
+void binary_op_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs,
+    std::string_view op,
+    const Stream& s) {
+  auto& a = inputs[0];
+  auto& b = inputs[1];
+  auto bopt = get_binary_op_type(a, b);
+  set_binary_op_output_data(a, b, outputs[0], bopt);
+  set_binary_op_output_data(a, b, outputs[1], bopt);
+  binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
+}
+
 template <typename Op>
 void binary_op_gpu(
     const std::vector<array>& inputs,
@@ -317,7 +241,8 @@ void binary_op_gpu(
   auto& b = inputs[1];
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
-  binary_op_gpu_inplace<Op>(inputs, out, op, s);
+  std::vector<array> outputs{out};
+  binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
 }
 
 #define BINARY_GPU(func)                                                 \
@@ -327,10 +252,19 @@ void binary_op_gpu(
     binary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
   }
 
+#define BINARY_GPU_MULTI(func)                                               \
+  void func::eval_gpu(                                                       \
+      const std::vector<array>& inputs, std::vector<array>& outputs) {       \
+    nvtx3::scoped_range r(#func "::eval_gpu");                               \
+    auto& s = outputs[0].primitive().stream();                               \
+    binary_op_gpu<cu::func>(inputs, outputs, get_primitive_string(this), s); \
+  }
+
 BINARY_GPU(Add)
 BINARY_GPU(ArcTan2)
 BINARY_GPU(Divide)
 BINARY_GPU(Remainder)
+BINARY_GPU(Equal)
 BINARY_GPU(Greater)
 BINARY_GPU(GreaterEqual)
 BINARY_GPU(Less)
@@ -345,17 +279,6 @@ BINARY_GPU(NotEqual)
 BINARY_GPU(Power)
 BINARY_GPU(Subtract)
 
-void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Equal::eval_gpu");
-  auto& s = out.primitive().stream();
-  auto op = get_primitive_string(this);
-  if (equal_nan_) {
-    binary_op_gpu<cu::NaNEqual>(inputs, out, op, s);
-  } else {
-    binary_op_gpu<cu::Equal>(inputs, out, op, s);
-  }
-}
-
 void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
   nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
   auto& s = out.primitive().stream();

mlx/backend/cuda/binary_two.cu

@@ -1,261 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/binary.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/binary_ops.cuh"
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void
-binary_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto out = Op{}(a[0], b[0]);
-    out_a[0] = out[0];
-    out_b[0] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void
-binary_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto out = Op{}(a[0], b[index]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void
-binary_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto out = Op{}(a[index], b[0]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void
-binary_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto out = Op{}(a[index], b[index]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
-__global__ void binary_g_nd(
-    const In* a,
-    const In* b,
-    Out* out_a,
-    Out* out_b,
-    IdxT size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), a_strides.data(), b_strides.data());
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void binary_g(
-    const In* a,
-    const In* b,
-    Out* out_a,
-    Out* out_b,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides a_strides,
-    const __grid_constant__ Strides b_strides,
-    int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_4d(
-        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
-  }
-}
-
-template <typename Op, typename In, typename Out>
-constexpr bool supports_binary_op() {
-  if (std::is_same_v<Op, DivMod>) {
-    return std::is_same_v<In, Out> &&
-        (std::is_integral_v<Out> || is_floating_v<Out>);
-  }
-  return false;
-}
-
-} // namespace cu
-
-template <typename Op>
-void binary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs,
-    std::string_view op,
-    const Stream& s) {
-  assert(inputs.size() > 1);
-  const auto& a = inputs[0];
-  const auto& b = inputs[1];
-  auto& out_a = outputs[0];
-  auto& out_b = outputs[1];
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, out_a, bopt);
-  set_binary_op_output_data(a, b, out_b, bopt);
-
-  if (out_a.size() == 0) {
-    return;
-  }
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out_a);
-  encoder.set_output_array(out_b);
-  dispatch_all_types(a.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out_a.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        using InType = cuda_type_t<CTYPE_IN>;
-        using OutType = cuda_type_t<CTYPE_OUT>;
-
-        auto bopt = get_binary_op_type(a, b);
-        if (bopt == BinaryOpType::General) {
-          dispatch_bool(
-              a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
-                  out_a.data_size() > INT32_MAX,
-              [&](auto large) {
-                using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-                Shape shape;
-                std::vector<Strides> strides;
-                std::tie(shape, strides) =
-                    collapse_contiguous_dims(a, b, out_a);
-                auto& a_strides = strides[0];
-                auto& b_strides = strides[1];
-                int ndim = shape.size();
-                if (ndim <= 3) {
-                  dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::
-                        binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(kernel, out_a, large());
-                    encoder.add_kernel_node(
-                        kernel,
-                        num_blocks,
-                        block_dims,
-                        a.data<InType>(),
-                        b.data<InType>(),
-                        out_a.data<OutType>(),
-                        out_b.data<OutType>(),
-                        out_a.size(),
-                        const_param<dims_constant()>(shape),
-                        const_param<dims_constant()>(a_strides),
-                        const_param<dims_constant()>(b_strides));
-                  });
-                } else {
-                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
-                  auto [num_blocks, block_dims] =
-                      get_launch_args(kernel, out_a, large());
-                  encoder.add_kernel_node(
-                      kernel,
-                      num_blocks,
-                      block_dims,
-                      a.data<InType>(),
-                      b.data<InType>(),
-                      out_a.data<OutType>(),
-                      out_b.data<OutType>(),
-                      out_a.size(),
-                      const_param(shape),
-                      const_param(a_strides),
-                      const_param(b_strides),
-                      ndim);
-                }
-              });
-        } else {
-          dispatch_bool(out_a.data_size() > INT32_MAX, [&](auto large) {
-            using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            auto kernel = cu::binary_ss<Op, InType, OutType, IdxT>;
-            if (bopt == BinaryOpType::ScalarVector) {
-              kernel = cu::binary_sv<Op, InType, OutType, IdxT>;
-            } else if (bopt == BinaryOpType::VectorScalar) {
-              kernel = cu::binary_vs<Op, InType, OutType, IdxT>;
-            } else if (bopt == BinaryOpType::VectorVector) {
-              kernel = cu::binary_vv<Op, InType, OutType, IdxT>;
-            }
-            auto [num_blocks, block_dims] = get_launch_args(
-                kernel,
-                out_a.data_size(),
-                out_a.shape(),
-                out_a.strides(),
-                large());
-            encoder.add_kernel_node(
-                kernel,
-                num_blocks,
-                block_dims,
-                a.data<InType>(),
-                b.data<InType>(),
-                out_a.data<OutType>(),
-                out_b.data<OutType>(),
-                out_a.data_size());
-          });
-        }
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do binary op {} on inputs of {} with result of {}.",
-            op,
-            dtype_to_string(a.dtype()),
-            dtype_to_string(out_a.dtype())));
-      }
-    });
-  });
-}
-
-template <typename Op>
-void binary_op_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs,
-    std::string_view op,
-    const Stream& s) {
-  auto& a = inputs[0];
-  auto& b = inputs[1];
-  auto bopt = get_binary_op_type(a, b);
-  set_binary_op_output_data(a, b, outputs[0], bopt);
-  set_binary_op_output_data(a, b, outputs[1], bopt);
-  binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
-}
-
-void DivMod::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("DivMod::eval_gpu");
-  auto& s = outputs[0].primitive().stream();
-  binary_op_gpu<cu::DivMod>(inputs, outputs, get_primitive_string(this), s);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/compiled.cpp

@@ -1,233 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/compiled.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/jit_module.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/graph_utils.h"
-#include "mlx/primitives.h"
-
-#include <fmt/format.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-struct FusedKernelBuilder {
-  std::string os;
-  const std::string& kernel_name;
-  const std::vector<array>& inputs;
-  const std::vector<array>& outputs;
-  const std::vector<array>& tape;
-  const std::function<bool(size_t)>& is_constant;
-
-  void build(const char* name, bool contiguous) {
-    NodeNamer namer;
-
-    // Function parameters.
-    std::vector<std::string> params;
-    for (size_t i = 0; i < inputs.size(); ++i) {
-      if (is_constant(i)) {
-        continue;
-      }
-      const auto& x = inputs[i];
-      const std::string& xname = namer.get_name(x);
-      params.push_back(
-          fmt::format("const {}* {}", dtype_to_cuda_type(x.dtype()), xname));
-      if (!is_scalar(x) && !contiguous) {
-        params.push_back(fmt::format(
-            "const __grid_constant__ cuda::std::array<int64_t, NDIM> {}_strides",
-            xname));
-      }
-    }
-    for (const auto& x : outputs) {
-      params.push_back(fmt::format(
-          "{}* {}", dtype_to_cuda_type(x.dtype()), namer.get_name(x)));
-    }
-    if (!contiguous) {
-      params.push_back(
-          "const __grid_constant__ cuda::std::array<int32_t, NDIM> shape");
-    }
-    params.push_back("IdxT size");
-
-    // Build function signature.
-    if (contiguous) {
-      os += "template <typename IdxT = uint32_t>\n";
-    } else {
-      os += "template <int NDIM, typename IdxT = uint32_t>\n";
-    }
-    os += fmt::format("__global__ void {}(\n", kernel_name + name);
-    for (size_t i = 0; i < params.size(); ++i) {
-      os += "    ";
-      os += params[i];
-      if (i != params.size() - 1) {
-        os += ",\n";
-      }
-    }
-    os += ") {\n";
-
-    // Index.
-    os +=
-        "  IdxT index = cg::this_grid().thread_rank();\n"
-        "  if (index >= size) {\n"
-        "    return;\n"
-        "  }\n";
-
-    // Read inputs.
-    for (size_t i = 0; i < inputs.size(); ++i) {
-      const auto& x = inputs[i];
-      const std::string& xname = namer.get_name(x);
-      std::string type = dtype_to_cuda_type(x.dtype());
-      std::string value;
-      if (is_constant(i)) {
-        std::ostringstream ss;
-        print_constant(ss, x);
-        value = fmt::format("static_cast<{}>({})", type, ss.str());
-      } else if (is_scalar(x)) {
-        value = fmt::format("{}[0]", xname);
-      } else if (contiguous) {
-        value = fmt::format("{}[index]", xname);
-      } else {
-        std::string index = fmt::format(
-            "elem_to_loc_nd<NDIM>(index, shape.data(), {}_strides.data())",
-            xname);
-        value = fmt::format("{}[{}]", xname, index);
-      }
-      os += fmt::format("  {} tmp_{} = {};\n", type, xname, value);
-    }
-
-    // Write tape.
-    for (const auto& x : tape) {
-      const std::string& xname = namer.get_name(x);
-      std::string type = dtype_to_cuda_type(x.dtype());
-      std::string value;
-      if (is_static_cast(x.primitive())) {
-        value = fmt::format(
-            "static_cast<{}>(tmp_{})", type, namer.get_name(x.inputs()[0]));
-      } else {
-        std::ostringstream ss;
-        x.primitive().print(ss);
-        value = ss.str();
-        value += "{}(";
-        for (size_t i = 0; i < x.inputs().size() - 1; ++i) {
-          value += fmt::format("tmp_{}, ", namer.get_name(x.inputs()[i]));
-        }
-        value += fmt::format("tmp_{})", namer.get_name(x.inputs().back()));
-      }
-      os += fmt::format("  {} tmp_{} = {};\n", type, xname, value);
-    }
-
-    // Write output.
-    for (const auto& x : outputs) {
-      os += fmt::format("  {0}[index] = tmp_{0};\n", namer.get_name(x));
-    }
-
-    os += "}\n";
-  }
-};
-
-} // namespace cu
-
-constexpr const char* g_jit_includes = R"(
-#include "mlx/backend/cuda/device/binary_ops.cuh"
-#include "mlx/backend/cuda/device/ternary_ops.cuh"
-#include "mlx/backend/cuda/device/unary_ops.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-#define inf cuda::std::numeric_limits<float>::infinity()
-)";
-
-void Compiled::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("Compiled::eval_gpu");
-  auto& s = stream();
-
-  cu::JitModule& mod = cu::get_jit_module(s.device, lib_name(), [&]() {
-    // Build source code.
-    cu::FusedKernelBuilder builder{
-        g_jit_includes, lib_name(), inputs_, outputs_, tape_, is_constant_};
-    builder.os +=
-        "namespace mlx::core::cu {\n\n"
-        "namespace cg = cooperative_groups;\n\n";
-    builder.build("_contiguous", true);
-    builder.os += "\n";
-    builder.build("_strided", false);
-    builder.os += "\n} // namespace mlx::core::cu\n";
-    // Build kernel names.
-    std::vector<std::string> kernel_names = {
-        fmt::format("mlx::core::cu::{}_contiguous<uint32_t>", lib_name()),
-        fmt::format("mlx::core::cu::{}_contiguous<int64_t>", lib_name()),
-    };
-    for (int i = 1; i <= MAX_NDIM; ++i) {
-      kernel_names.push_back(fmt::format(
-          "mlx::core::cu::{}_strided<{}, uint32_t>", lib_name(), i));
-      kernel_names.push_back(
-          fmt::format("mlx::core::cu::{}_strided<{}, int64_t>", lib_name(), i));
-    }
-    return std::make_pair(std::move(builder.os), std::move(kernel_names));
-  });
-
-  // Collapse contiguous dims to route to a faster kernel if possible. Also
-  // handle all broadcasting.
-  auto [contiguous, shape, strides_vec] =
-      compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
-
-  // Whether to use large index.
-  bool large = compiled_use_large_index(inputs, outputs, contiguous);
-
-  cu::KernelArgs args;
-  // Put inputs.
-  int strides_index = 1;
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    if (is_constant_(i)) {
-      continue;
-    }
-    const auto& x = inputs[i];
-    args.append(x);
-    if (!contiguous && !is_scalar(x)) {
-      args.append_ptr(strides_vec[strides_index++].data());
-    }
-  }
-
-  // Put outputs.
-  compiled_allocate_outputs(inputs, outputs, is_constant_, contiguous);
-  for (auto& x : outputs) {
-    args.append(x);
-  }
-
-  // Put shape and size.
-  if (!contiguous) {
-    args.append_ptr(shape.data());
-  }
-  if (large) {
-    args.append<int64_t>(outputs[0].data_size());
-  } else {
-    args.append<uint32_t>(outputs[0].data_size());
-  }
-
-  // Launch kernel.
-  const char* index_type = large ? "int64_t" : "uint32_t";
-  std::string kernel_name = fmt::format("mlx::core::cu::{}", lib_name());
-  if (contiguous) {
-    kernel_name += fmt::format("_contiguous<{}>", index_type);
-  } else {
-    kernel_name += fmt::format("_strided<{}, {}>", shape.size(), index_type);
-  }
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : inputs) {
-    encoder.set_input_array(in);
-  }
-  for (const auto& out : outputs) {
-    encoder.set_output_array(out);
-  }
-
-  auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, outputs[0], large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy.cpp

@@ -0,0 +1,26 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/gpu/copy.h"
+
+namespace mlx::core {
+
+void copy_gpu_inplace(
+    const array& in,
+    array& out,
+    const Shape& data_shape,
+    const Strides& strides_in_pre,
+    const Strides& strides_out_pre,
+    int64_t inp_offset,
+    int64_t out_offset,
+    CopyType ctype,
+    const Stream& s,
+    const std::optional<array>& dynamic_i_offset /* = std::nullopt */,
+    const std::optional<array>& dynamic_o_offset /* = std::nullopt */) {
+  throw std::runtime_error("copy_gpu_inplace not implemented in CUDA backend.");
+}
+
+void fill_gpu(const array& val, array& out, const Stream& s) {
+  throw std::runtime_error("fill_gpu not implemented in CUDA backend.");
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy.cu

@@ -1,87 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/copy/copy.cuh"
-
-namespace mlx::core {
-
-void copy_gpu_inplace(
-    const array& in,
-    array& out,
-    const Shape& shape,
-    const Strides& strides_in,
-    const Strides& strides_out,
-    int64_t offset_in,
-    int64_t offset_out,
-    CopyType ctype,
-    const Stream& s,
-    const std::optional<array>& dynamic_offset_in,
-    const std::optional<array>& dynamic_offset_out) {
-  if (out.size() == 0) {
-    return;
-  }
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  if (ctype == CopyType::Scalar || ctype == CopyType::Vector) {
-    copy_contiguous(encoder, ctype, in, out, offset_in, offset_out);
-    return;
-  }
-
-  if (ctype == CopyType::General || ctype == CopyType::GeneralGeneral) {
-    auto [shape_collapsed, strides_vec] = collapse_contiguous_dims(
-        shape, std::vector{strides_in, strides_out}, INT32_MAX);
-    if (ctype == CopyType::General) {
-      copy_general_input(
-          encoder,
-          ctype,
-          in,
-          out,
-          offset_in,
-          offset_out,
-          shape_collapsed,
-          strides_vec[0]);
-    } else {
-      if (dynamic_offset_in || dynamic_offset_out) {
-        copy_general_dynamic(
-            encoder,
-            ctype,
-            in,
-            out,
-            offset_in,
-            offset_out,
-            shape_collapsed,
-            strides_vec[0],
-            strides_vec[1],
-            dynamic_offset_in ? *dynamic_offset_in : array(0, int64),
-            dynamic_offset_out ? *dynamic_offset_out : array(0, int64));
-      } else {
-        copy_general(
-            encoder,
-            ctype,
-            in,
-            out,
-            offset_in,
-            offset_out,
-            shape_collapsed,
-            strides_vec[0],
-            strides_vec[1]);
-      }
-    }
-    return;
-  }
-}
-
-void fill_gpu(const array& in, array& out, const Stream& s) {
-  if (out.size() == 0) {
-    return;
-  }
-  out.set_data(allocator::malloc(out.nbytes()));
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  copy_contiguous(encoder, CopyType::Scalar, in, out, 0, 0);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy.cuh

@@ -1,55 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/cast_op.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-
-namespace mlx::core {
-
-void copy_contiguous(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out);
-
-void copy_general(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in,
-    const Strides& strides_out);
-
-void copy_general_dynamic(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in,
-    const Strides& strides_out,
-    const array& dynamic_offset_in,
-    const array& dynamic_offset_out);
-
-void copy_general_input(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in);
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy_contiguous.cu

@@ -1,62 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/copy/copy.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename In, typename Out, typename IdxT>
-__global__ void copy_s(const In* in, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    out[index] = CastOp<In, Out>{}(in[0]);
-  }
-}
-
-template <typename In, typename Out, typename IdxT>
-__global__ void copy_v(const In* in, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    out[index] = CastOp<In, Out>{}(in[index]);
-  }
-}
-
-} // namespace cu
-
-void copy_contiguous(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t in_offset,
-    int64_t out_offset) {
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      dispatch_bool(out.data_size() > UINT32_MAX, [&](auto large) {
-        using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
-        using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
-        using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-        auto kernel = cu::copy_s<InType, OutType, IdxT>;
-        if (ctype == CopyType::Vector) {
-          kernel = cu::copy_v<InType, OutType, IdxT>;
-        }
-        auto [num_blocks, block_dims] = get_launch_args(
-            kernel, out.data_size(), out.shape(), out.strides(), large());
-        encoder.add_kernel_node(
-            kernel,
-            num_blocks,
-            block_dims,
-            in.data<InType>() + in_offset,
-            out.data<OutType>() + out_offset,
-            out.data_size());
-      });
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy_general.cu

@@ -1,110 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/copy/copy.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename In, typename Out, typename IdxT, int NDIM>
-__global__ void copy_gg_nd(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), strides_in.data(), strides_out.data());
-    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
-  }
-}
-
-template <typename In, typename Out, typename IdxT>
-__global__ void copy_gg(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides_in,
-    const __grid_constant__ Strides strides_out,
-    int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_4d(
-        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
-    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
-  }
-}
-
-} // namespace cu
-
-void copy_general(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in,
-    const Strides& strides_out) {
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      dispatch_bool(
-          in.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
-          [&](auto large) {
-            using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
-            using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            const InType* in_ptr = in.data<InType>() + offset_in;
-            OutType* out_ptr = out.data<OutType>() + offset_out;
-            int ndim = shape.size();
-            size_t data_size = 1;
-            for (auto& s : shape)
-              data_size *= s;
-            if (ndim <= 3) {
-              dispatch_1_2_3(ndim, [&](auto ndim_constant) {
-                auto kernel =
-                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>;
-                auto [num_blocks, block_dims] = get_launch_args(
-                    kernel, data_size, shape, out.strides(), large());
-                encoder.add_kernel_node(
-                    kernel,
-                    num_blocks,
-                    block_dims,
-                    in_ptr,
-                    out_ptr,
-                    data_size,
-                    const_param<ndim_constant()>(shape),
-                    const_param<ndim_constant()>(strides_in),
-                    const_param<ndim_constant()>(strides_out));
-              });
-            } else { // ndim >= 4
-              auto kernel = cu::copy_gg<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] = get_launch_args(
-                  kernel, data_size, shape, out.strides(), large());
-              encoder.add_kernel_node(
-                  kernel,
-                  num_blocks,
-                  block_dims,
-                  in_ptr,
-                  out_ptr,
-                  data_size,
-                  const_param(shape),
-                  const_param(strides_in),
-                  const_param(strides_out),
-                  ndim);
-            }
-          });
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy_general_dynamic.cu

@@ -1,117 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/copy/copy.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename In, typename Out, typename IdxT, int NDIM>
-__global__ void copy_gg_dynamic_nd(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out,
-    const int64_t* offset_in,
-    const int64_t* offset_out) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), strides_in.data(), strides_out.data());
-    out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
-  }
-}
-
-template <typename In, typename Out, typename IdxT>
-__global__ void copy_gg_dynamic(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides_in,
-    const __grid_constant__ Strides strides_out,
-    int ndim,
-    const int64_t* offset_in,
-    const int64_t* offset_out) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_4d(
-        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
-    out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
-  }
-}
-
-} // namespace cu
-
-void copy_general_dynamic(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in,
-    const Strides& strides_out,
-    const array& dynamic_offset_in,
-    const array& dynamic_offset_out) {
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      dispatch_bool(
-          in.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
-          [&](auto large) {
-            using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
-            using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            const InType* in_ptr = in.data<InType>() + offset_in;
-            OutType* out_ptr = out.data<OutType>() + offset_out;
-            int ndim = shape.size();
-            if (ndim <= 3) {
-              dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel = cu::
-                    copy_gg_dynamic_nd<InType, OutType, IdxT, dims_constant()>;
-                auto [num_blocks, block_dims] =
-                    get_launch_args(kernel, out, large());
-                encoder.add_kernel_node(
-                    kernel,
-                    num_blocks,
-                    block_dims,
-                    in_ptr,
-                    out_ptr,
-                    out.size(),
-                    const_param<dims_constant()>(shape),
-                    const_param<dims_constant()>(strides_in),
-                    const_param<dims_constant()>(strides_out),
-                    dynamic_offset_in.data<int64_t>(),
-                    dynamic_offset_out.data<int64_t>());
-              });
-            } else { // ndim >= 4
-              auto kernel = cu::copy_gg_dynamic<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] =
-                  get_launch_args(kernel, out, large());
-              encoder.add_kernel_node(
-                  kernel,
-                  num_blocks,
-                  block_dims,
-                  in_ptr,
-                  out_ptr,
-                  out.size(),
-                  const_param(shape),
-                  const_param(strides_in),
-                  const_param(strides_out),
-                  ndim,
-                  dynamic_offset_in.data<int64_t>(),
-                  dynamic_offset_out.data<int64_t>());
-            }
-          });
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/copy/copy_general_input.cu

@@ -1,100 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/copy/copy.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename In, typename Out, typename IdxT, int NDIM>
-__global__ void copy_g_nd(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    IdxT idx_in = elem_to_loc_nd<NDIM>(index, shape.data(), strides_in.data());
-    out[index] = CastOp<In, Out>{}(in[idx_in]);
-  }
-}
-
-template <typename In, typename Out, typename IdxT>
-__global__ void copy_g(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides_in,
-    int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    IdxT idx_in = elem_to_loc_4d(index, shape.data(), strides_in.data(), ndim);
-    out[index] = CastOp<In, Out>{}(in[idx_in]);
-  }
-}
-
-} // namespace cu
-
-void copy_general_input(
-    cu::CommandEncoder& encoder,
-    CopyType ctype,
-    const array& in,
-    array& out,
-    int64_t offset_in,
-    int64_t offset_out,
-    const Shape& shape,
-    const Strides& strides_in) {
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      dispatch_bool(
-          in.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
-          [&](auto large) {
-            using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
-            using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            const InType* in_ptr = in.data<InType>() + offset_in;
-            OutType* out_ptr = out.data<OutType>() + offset_out;
-            int ndim = shape.size();
-            if (ndim <= 3) {
-              dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel =
-                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>;
-                auto [num_blocks, block_dims] =
-                    get_launch_args(kernel, out, large());
-                encoder.add_kernel_node(
-                    kernel,
-                    num_blocks,
-                    block_dims,
-                    in_ptr,
-                    out_ptr,
-                    out.size(),
-                    const_param<dims_constant()>(shape),
-                    const_param<dims_constant()>(strides_in));
-              });
-            } else { // ndim >= 4
-              auto kernel = cu::copy_g<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] =
-                  get_launch_args(kernel, out, large());
-              encoder.add_kernel_node(
-                  kernel,
-                  num_blocks,
-                  block_dims,
-                  in_ptr,
-                  out_ptr,
-                  out.size(),
-                  const_param(shape),
-                  const_param(strides_in),
-                  ndim);
-            }
-          });
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/cuda.cpp

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

mlx/backend/cuda/cuda.h

@@ -1,10 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-namespace mlx::core::cu {
-
-/* Check if the CUDA backend is available. */
-bool is_available();
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device.cpp

@@ -2,27 +2,36 @@
 
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/worker.h"
-#include "mlx/utils.h"
+#include "mlx/backend/metal/metal.h"
 
 #include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>
-#include <future>
-#include <unordered_set>
 
 namespace mlx::core {
 
-// Can be tuned with MLX_MAX_OPS_PER_BUFFER
-// This should be less than 255
-constexpr int default_max_nodes_per_graph = 20;
+namespace cu {
 
-int cuda_graph_cache_size() {
-  static int cache_size = []() {
-    return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
-  }();
-  return cache_size;
+DeviceStream::DeviceStream(Device& device) : device_(device), stream_(device) {}
+
+void DeviceStream::synchronize() {
+  cudaStreamSynchronize(stream_);
 }
 
-namespace cu {
+cudaStream_t DeviceStream::schedule_cuda_stream() {
+  // TODO: Return a stream that maximizes parallelism.
+  return stream_;
+}
+
+cudaStream_t DeviceStream::last_cuda_stream() {
+  return stream_;
+}
+
+CommandEncoder& DeviceStream::get_encoder() {
+  if (!encoder_) {
+    encoder_ = std::make_unique<CommandEncoder>(*this);
+  }
+  return *encoder_;
+}
 
 Device::Device(int device) : device_(device) {
   CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
@@ -57,267 +66,45 @@ void Device::make_current() {
   }
 }
 
-CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
-  CHECK_CUDA_ERROR(cudaGraphCreate(&graph, 0));
-  CHECK_CUDA_ERROR(
-      cudaStreamBeginCapture(enc.stream(), cudaStreamCaptureModeGlobal));
-}
-
-CommandEncoder::CaptureContext::~CaptureContext() {
-  CHECK_CUDA_ERROR(cudaStreamEndCapture(enc.stream(), &graph));
-  size_t num_nodes;
-  CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, NULL, &num_nodes));
-  if (num_nodes == 1) {
-    cudaGraphNode_t captured_node;
-    CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, &captured_node, &num_nodes));
-    CUDA_KERNEL_NODE_PARAMS params;
-    CHECK_CUDA_ERROR(cuGraphKernelNodeGetParams(captured_node, &params));
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, enc.graph_, NULL, 0, &params));
-    enc.insert_graph_dependencies(GraphNode{node, 'K'});
-  } else {
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(
-        cudaGraphAddChildGraphNode(&node, enc.graph_, NULL, 0, graph));
-    enc.insert_graph_dependencies(GraphNode{node, 'G'});
-  }
-  CHECK_CUDA_ERROR(cudaGraphDestroy(graph));
-}
-
-CommandEncoder::ConcurrentContext::ConcurrentContext(CommandEncoder& enc)
-    : enc(enc) {
-  enc.in_concurrent_ = true;
-}
-
-CommandEncoder::ConcurrentContext::~ConcurrentContext() {
-  enc.in_concurrent_ = false;
-
-  // Use an empty graph node for synchronization
-  CommandEncoder::GraphNode empty{NULL, 'E', std::to_string(enc.node_count_++)};
-  enc.empty_node_count_++;
-  CHECK_CUDA_ERROR(cudaGraphAddEmptyNode(&empty.node, enc.graph_, NULL, 0));
-
-  // Insert the concurrent -> empty node dependencies
-  for (auto& from : enc.concurrent_nodes_) {
-    enc.from_nodes_.push_back(from.node);
-    enc.to_nodes_.push_back(empty.node);
-    enc.graph_key_ += from.id;
-    enc.graph_key_ += from.node_type;
-    enc.graph_key_ += empty.id;
-    enc.graph_key_ += empty.node_type;
-  }
-
-  // Insert the input -> concurrent node dependencies without updating output
-  // nodes
-  auto outputs = std::move(enc.active_outputs_);
-  enc.insert_graph_dependencies(std::move(enc.concurrent_nodes_));
-
-  // Update output node to be the empty node
-  for (auto o : outputs) {
-    enc.node_map_.emplace(o, empty).first->second = empty;
-  }
-}
-
-void CommandEncoder::insert_graph_dependencies(GraphNode node) {
-  if (node.node_type == 'G') {
-    graph_node_count_++;
-  }
-  node.id = std::to_string(node_count_++);
-  if (in_concurrent_) {
-    concurrent_nodes_.push_back(std::move(node));
-  } else {
-    std::vector<GraphNode> nodes;
-    nodes.push_back(std::move(node));
-    insert_graph_dependencies(std::move(nodes));
-  }
-}
-
-void CommandEncoder::insert_graph_dependencies(std::vector<GraphNode> nodes) {
-  std::vector<GraphNode> deps;
-  {
-    // Dependencies must be added in the same order to produce a consistent
-    // topology
-    std::unordered_set<cudaGraphNode_t> set_deps;
-    for (auto d : active_deps_) {
-      if (auto it = node_map_.find(d); it != node_map_.end()) {
-        auto [_, inserted] = set_deps.insert(it->second.node);
-        if (inserted) {
-          deps.push_back(it->second);
-        }
-      }
-    }
-  }
-  active_deps_.clear();
-
-  for (auto o : active_outputs_) {
-    for (auto& node : nodes) {
-      node_map_.emplace(o, node).first->second = node;
-    }
-  }
-  active_outputs_.clear();
-
-  for (auto& from : deps) {
-    for (auto& to : nodes) {
-      from_nodes_.push_back(from.node);
-      to_nodes_.push_back(to.node);
-      graph_key_ += from.id;
-      graph_key_ += from.node_type;
-      graph_key_ += to.id;
-      graph_key_ += to.node_type;
-    }
-  }
-}
-
-CommandEncoder& Device::get_command_encoder(Stream s) {
-  auto it = encoders_.find(s.index);
-  if (it == encoders_.end()) {
-    it = encoders_.try_emplace(s.index, *this).first;
+DeviceStream& Device::get_stream(Stream s) {
+  auto it = streams_.find(s.index);
+  if (it == streams_.end()) {
+    it = streams_.try_emplace(s.index, *this).first;
   }
   return it->second;
 }
 
-CommandEncoder::CommandEncoder(Device& d) : stream_(d) {
-  CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
-}
-
-void clear_graphs(std::unordered_map<std::string, cudaGraphExec_t>& graphs) {
-  for (auto& [_, graph_exec] : graphs) {
-    CHECK_CUDA_ERROR(cudaGraphExecDestroy(graph_exec));
-  }
-  graphs.clear();
-}
-
-CommandEncoder::~CommandEncoder() {
-  clear_graphs(graph_cache_);
-}
+CommandEncoder::CommandEncoder(DeviceStream& s)
+    : device_(s.device()), stream_(s) {}
 
 void CommandEncoder::add_completed_handler(std::function<void()> task) {
   worker_.add_task(std::move(task));
 }
 
-void CommandEncoder::set_input_array(const array& arr) {
-  auto id = reinterpret_cast<std::uintptr_t>(arr.buffer().ptr());
-  active_deps_.push_back(id);
-}
+void CommandEncoder::end_encoding() {
+  if (!temporaries_.empty()) {
+    add_completed_handler([temporaries = std::move(temporaries_)]() {});
+  }
 
-void CommandEncoder::set_output_array(const array& arr) {
-  auto id = reinterpret_cast<std::uintptr_t>(arr.buffer().ptr());
-  active_deps_.push_back(id);
-  active_outputs_.push_back(id);
-}
+  // There is no kernel running, run completion handlers immediately.
+  if (!has_gpu_work_) {
+    worker_.consume_in_this_thread();
+    return;
+  }
+  has_gpu_work_ = false;
 
-void CommandEncoder::maybe_commit() {
-  if (node_count_ >= env::max_ops_per_buffer(default_max_nodes_per_graph)) {
+  // Put completion handlers in a batch.
+  worker_.end_batch();
+
+  // Signaling kernel completion is expensive, delay until enough batches.
+  // TODO: This number is arbitrarily picked, profile for a better stragety.
+  if (worker_.uncommited_batches() > 8) {
     commit();
   }
 }
 
-void CommandEncoder::add_kernel_node(
-    void* func,
-    dim3 grid_dim,
-    dim3 block_dim,
-    void** params) {
-  cudaKernelNodeParams kernel_params = {0};
-  kernel_params.func = func;
-  kernel_params.gridDim = grid_dim;
-  kernel_params.blockDim = block_dim;
-  kernel_params.kernelParams = params;
-  cudaGraphNode_t node;
-  CHECK_CUDA_ERROR(
-      cudaGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
-  insert_graph_dependencies(GraphNode{node, 'K'});
-}
-
-void CommandEncoder::add_kernel_node(
-    CUfunction func,
-    dim3 grid_dim,
-    dim3 block_dim,
-    void** params) {
-  CUDA_KERNEL_NODE_PARAMS kernel_params = {0};
-  kernel_params.func = func;
-  kernel_params.gridDimX = grid_dim.x;
-  kernel_params.gridDimY = grid_dim.y;
-  kernel_params.gridDimZ = grid_dim.z;
-  kernel_params.blockDimX = block_dim.x;
-  kernel_params.blockDimY = block_dim.y;
-  kernel_params.blockDimZ = block_dim.z;
-  kernel_params.kernelParams = params;
-  CUgraphNode node;
-  CHECK_CUDA_ERROR(
-      cuGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
-  insert_graph_dependencies(GraphNode{node, 'K'});
-}
-
 void CommandEncoder::commit() {
-  if (!temporaries_.empty()) {
-    add_completed_handler([temporaries = std::move(temporaries_)]() {});
-  }
-  if (node_count_ > 0) {
-    if (!from_nodes_.empty()) {
-      CHECK_CUDA_ERROR(cudaGraphAddDependencies(
-          graph_, from_nodes_.data(), to_nodes_.data(), from_nodes_.size()));
-    }
-
-    graph_key_ += ".";
-    graph_key_ += std::to_string(node_count_);
-    graph_key_ += ".";
-    graph_key_ += std::to_string(graph_node_count_);
-    graph_key_ += ".";
-    graph_key_ += std::to_string(empty_node_count_);
-
-    cudaGraphExec_t& graph_exec = graph_cache_[graph_key_];
-
-    if (graph_exec != nullptr) {
-      cudaGraphExecUpdateResult update_result;
-#if CUDART_VERSION >= 12000
-      cudaGraphExecUpdateResultInfo info;
-      cudaGraphExecUpdate(graph_exec, graph_, &info);
-      update_result = info.result;
-#else
-      cudaGraphNode_t error_node;
-      cudaGraphExecUpdate(graph_exec, graph_, &error_node, &update_result);
-#endif // CUDART_VERSION >= 12000
-      if (update_result != cudaGraphExecUpdateSuccess) {
-        cudaGetLastError(); // reset error
-        CHECK_CUDA_ERROR(cudaGraphExecDestroy(graph_exec));
-        graph_exec = nullptr;
-      }
-    }
-    if (graph_exec == nullptr) {
-      CHECK_CUDA_ERROR(
-          cudaGraphInstantiate(&graph_exec, graph_, NULL, NULL, 0));
-    }
-    CHECK_CUDA_ERROR(cudaGraphLaunch(graph_exec, stream_));
-
-    // TODO smarter cache policy
-    if (graph_cache_.size() > cuda_graph_cache_size()) {
-      clear_graphs(graph_cache_);
-    }
-
-    // Reset state
-    node_count_ = 0;
-    graph_node_count_ = 0;
-    from_nodes_.clear();
-    to_nodes_.clear();
-    graph_key_.clear();
-    node_map_.clear();
-    CHECK_CUDA_ERROR(cudaGraphDestroy(graph_));
-    CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
-  }
-
-  // Put completion handlers in a batch.
-  worker_.end_batch();
-  worker_.commit(stream_);
-}
-
-void CommandEncoder::synchronize() {
-  cudaStreamSynchronize(stream_);
-  auto p = std::make_shared<std::promise<void>>();
-  std::future<void> f = p->get_future();
-  add_completed_handler([p = std::move(p)]() { p->set_value(); });
-  worker_.end_batch();
-  commit();
-  f.wait();
+  worker_.commit(stream_.last_cuda_stream());
 }
 
 Device& device(mlx::core::Device device) {
@@ -329,8 +116,12 @@ Device& device(mlx::core::Device device) {
   return it->second;
 }
 
+DeviceStream& get_stream(Stream s) {
+  return device(s.device).get_stream(s);
+}
+
 CommandEncoder& get_command_encoder(Stream s) {
-  return device(s.device).get_command_encoder(s);
+  return get_stream(s).get_encoder();
 }
 
 } // namespace cu

mlx/backend/cuda/device.h

@@ -7,108 +7,41 @@
 #include "mlx/stream.h"
 
 #include <cublasLt.h>
-#include <cuda.h>
 #include <thrust/execution_policy.h>
 
 #include <unordered_map>
 
 namespace mlx::core::cu {
 
-class CommandEncoder {
+class Device;
+class CommandEncoder;
+
+class DeviceStream {
  public:
-  struct CaptureContext {
-    CaptureContext(CommandEncoder& enc);
-    ~CaptureContext();
-    cudaGraph_t graph;
-    CommandEncoder& enc;
-  };
-  struct ConcurrentContext {
-    ConcurrentContext(CommandEncoder& enc);
-    ~ConcurrentContext();
-    CommandEncoder& enc;
-  };
+  explicit DeviceStream(Device& device);
 
-  explicit CommandEncoder(Device& d);
-  ~CommandEncoder();
+  DeviceStream(const DeviceStream&) = delete;
+  DeviceStream& operator=(const DeviceStream&) = delete;
 
-  CommandEncoder(const CommandEncoder&) = delete;
-  CommandEncoder& operator=(const CommandEncoder&) = delete;
-
-  CaptureContext capture_context() {
-    return CaptureContext{*this};
-  }
-  ConcurrentContext concurrent_context() {
-    return ConcurrentContext{*this};
-  }
-
-  void set_input_array(const array& arr);
-  void set_output_array(const array& arr);
-
-  template <typename F, typename... Params>
-  void
-  add_kernel_node(F* func, dim3 grid_dim, dim3 block_dim, Params&&... params) {
-    constexpr size_t num = sizeof...(Params);
-    void* ptrs[num];
-    size_t i = 0;
-    ([&](auto&& p) { ptrs[i++] = static_cast<void*>(&p); }(
-         std::forward<Params>(params)),
-     ...);
-    add_kernel_node((void*)func, grid_dim, block_dim, ptrs);
-  }
-
-  void add_kernel_node(
-      CUfunction func,
-      dim3 grid_dim,
-      dim3 block_dim,
-      void** params);
-
-  void
-  add_kernel_node(void* func, dim3 grid_dim, dim3 block_dim, void** params);
-
-  void add_temporary(const array& arr) {
-    temporaries_.push_back(arr.data_shared_ptr());
-  }
-
-  void add_completed_handler(std::function<void()> task);
-  void maybe_commit();
-  void commit();
-
-  CudaStream& stream() {
-    return stream_;
-  }
-
-  // Wait until kernels and completion handlers are finished
+  // Wait until kernels in the stream complete.
   void synchronize();
 
+  // Return a cuda stream for launching kernels.
+  cudaStream_t schedule_cuda_stream();
+
+  // Return the last cuda stream used.
+  cudaStream_t last_cuda_stream();
+
+  CommandEncoder& get_encoder();
+
+  Device& device() {
+    return device_;
+  }
+
  private:
-  struct GraphNode {
-    cudaGraphNode_t node;
-    // K = kernel
-    // E = empty
-    // G = subgraph
-    char node_type;
-    std::string id;
-  };
-
-  void insert_graph_dependencies(GraphNode node);
-  void insert_graph_dependencies(std::vector<GraphNode> nodes);
-
+  Device& device_;
   CudaStream stream_;
-  cudaGraph_t graph_;
-  Worker worker_;
-  char node_count_{0};
-  char graph_node_count_{0};
-  char empty_node_count_{0};
-  bool in_concurrent_{false};
-  std::vector<cudaGraphNode_t> from_nodes_;
-  std::vector<cudaGraphNode_t> to_nodes_;
-  std::string graph_key_;
-  std::vector<GraphNode> concurrent_nodes_;
-  std::vector<std::shared_ptr<array::Data>> temporaries_;
-  std::unordered_map<std::string, cudaGraphExec_t> graph_cache_;
-  std::vector<std::uintptr_t> active_deps_;
-  std::vector<std::uintptr_t> active_outputs_;
-  std::unordered_map<std::uintptr_t, GraphNode> node_map_;
+  std::unique_ptr<CommandEncoder> encoder_;
 };
 
 class Device {
@@ -122,7 +55,7 @@ class Device {
   // Make this device the current cuda device, required by some cuda calls.
   void make_current();
 
-  CommandEncoder& get_command_encoder(Stream s);
+  DeviceStream& get_stream(Stream s);
 
   int cuda_device() const {
     return device_;
@@ -142,10 +75,64 @@ class Device {
   int compute_capability_major_;
   int compute_capability_minor_;
   cublasLtHandle_t lt_;
-  std::unordered_map<int, CommandEncoder> encoders_;
+  std::unordered_map<int, DeviceStream> streams_;
+};
+
+class CommandEncoder {
+ public:
+  explicit CommandEncoder(DeviceStream& stream);
+
+  CommandEncoder(const CommandEncoder&) = delete;
+  CommandEncoder& operator=(const CommandEncoder&) = delete;
+
+  void set_input_array(const array& arr) {}
+  void set_output_array(const array& arr) {}
+
+  void add_temporary(const array& arr) {
+    temporaries_.push_back(arr.data_shared_ptr());
+  }
+
+  void add_completed_handler(std::function<void()> task);
+  void end_encoding();
+  void commit();
+
+  // Schedule a cuda stream for |fun| to launch kernels, and check error
+  // afterwards.
+  template <typename F>
+  void launch_kernel(F&& fun) {
+    launch_kernel(stream_.schedule_cuda_stream(), std::forward<F>(fun));
+  }
+
+  template <typename F>
+  void launch_kernel(cudaStream_t stream, F&& fun) {
+    device_.make_current();
+    fun(stream);
+    check_cuda_error("kernel launch", cudaGetLastError());
+    has_gpu_work_ = true;
+  }
+
+  Device& device() {
+    return device_;
+  }
+
+  DeviceStream& stream() {
+    return stream_;
+  }
+
+  bool has_gpu_work() const {
+    return has_gpu_work_;
+  }
+
+ private:
+  Device& device_;
+  DeviceStream& stream_;
+  Worker worker_;
+  bool has_gpu_work_{false};
+  std::vector<std::shared_ptr<array::Data>> temporaries_;
 };
 
 Device& device(mlx::core::Device device);
+DeviceStream& get_stream(Stream s);
 CommandEncoder& get_command_encoder(Stream s);
 
 // Return an execution policy that does not sync for result.

mlx/backend/cuda/device/atomic_ops.cuh

@@ -1,72 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-
-#include <cuda/atomic>
-
-namespace mlx::core::cu {
-
-template <typename T>
-inline __device__ void atomic_add(T* out, T val) {
-  cuda::atomic_ref<T, cuda::thread_scope_device> ref(*out);
-  ref += val;
-}
-
-template <typename T>
-inline __device__ void atomic_prod(T* out, T val) {
-  cuda::atomic_ref<T, cuda::thread_scope_device> ref(*out);
-  T old = ref.load();
-  while (!ref.compare_exchange_strong(old, old * val)) {
-  }
-}
-
-template <typename T>
-inline __device__ void atomic_max(T* out, T val) {
-  cuda::atomic_ref<T, cuda::thread_scope_device> ref(*out);
-  ref.fetch_max(val);
-}
-
-template <typename T>
-inline __device__ void atomic_min(T* out, T val) {
-  cuda::atomic_ref<T, cuda::thread_scope_device> ref(*out);
-  ref.fetch_min(val);
-}
-
-// Somehow cuda::atomic_ref does not provide atomic add for following types.
-template <typename T>
-inline __device__ void atomic_add_general(T* out, T val) {
-  cuda::atomic_ref<T, cuda::thread_scope_device> ref(*out);
-  T old = ref.load();
-  while (!ref.compare_exchange_strong(old, old + val)) {
-  }
-}
-
-inline __device__ void atomic_add(__half* out, __half val) {
-  atomicAdd(out, val);
-}
-
-inline __device__ void atomic_add(cuComplex* out, cuComplex val) {
-#if __CUDA_ARCH__ < 900
-  atomic_add_general(out, val);
-#else
-  atomicAdd(out, val);
-#endif
-}
-
-inline __device__ void atomic_add(__nv_bfloat16* out, __nv_bfloat16 val) {
-#if __CUDA_ARCH__ < 800
-#if CCCL_VERSION >= 2008000
-  atomic_add_general(out, val);
-#else
-  bool cccl_version_too_old_for_bfloat16_atomic_add = false;
-  assert(cccl_version_too_old_for_bfloat16_atomic_add);
-#endif
-#else
-  atomicAdd(out, val);
-#endif
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/cast_op.cuh

@@ -1,138 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <cuComplex.h>
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-#include <thrust/iterator/transform_iterator.h>
-
-namespace mlx::core::cu {
-
-// An op that does static_cast, with custom conversions for some types.
-template <typename SrcT, typename DstT, typename = void>
-struct CastOp {
-  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, DstT>;
-
-  __device__ DstT operator()(SrcT x) {
-    return static_cast<DstT>(x);
-  }
-};
-
-// Castings between complex and boolean.
-// TODO: Should make a custom complex type.
-template <>
-struct CastOp<cuComplex, bool> {
-  static constexpr bool is_castable = true;
-
-  __device__ bool operator()(cuComplex x) {
-    return x.x != 0 && x.y != 0;
-  }
-};
-
-template <>
-struct CastOp<bool, cuComplex> {
-  static constexpr bool is_castable = true;
-
-  __device__ cuComplex operator()(bool x) {
-    return x ? make_cuFloatComplex(1, 1) : make_cuFloatComplex(0, 0);
-  }
-};
-
-// Converting a complex number to real number discards the imaginary part.
-template <typename DstT>
-struct CastOp<
-    cuComplex,
-    DstT,
-    cuda::std::enable_if_t<!cuda::std::is_same_v<cuComplex, DstT>>> {
-  static constexpr bool is_castable = cuda::std::is_convertible_v<float, DstT>;
-
-  __device__ DstT operator()(cuComplex x) {
-    static_assert(!cuda::std::is_same_v<cuComplex, DstT>);
-    return static_cast<DstT>(cuCrealf(x));
-  }
-};
-
-// Allow converting a real number to complex number.
-template <typename SrcT>
-struct CastOp<
-    SrcT,
-    cuComplex,
-    cuda::std::enable_if_t<!cuda::std::is_same_v<SrcT, cuComplex>>> {
-  static constexpr bool is_castable = cuda::std::is_convertible_v<SrcT, float>;
-
-  __device__ cuComplex operator()(SrcT x) {
-    static_assert(!cuda::std::is_same_v<SrcT, cuComplex>);
-    return cuComplex{static_cast<float>(x), 0};
-  }
-};
-
-// Do nothing when no casting is needed.
-template <typename SrcT, typename DstT>
-struct CastOp<
-    SrcT,
-    DstT,
-    cuda::std::enable_if_t<cuda::std::is_same_v<SrcT, DstT>>> {
-  static constexpr bool is_castable = true;
-
-  __device__ SrcT operator()(SrcT x) {
-    return x;
-  }
-};
-
-// In CUDA 11 the half types do not define conversions between some types,
-// provide fallbacks here.
-#if CUDART_VERSION < 12000
-template <typename SrcT, typename DstT>
-struct CastOp<
-    SrcT,
-    DstT,
-    cuda::std::enable_if_t<
-        !cuda::std::is_convertible_v<SrcT, DstT> &&
-        !cuda::std::is_same_v<SrcT, cuComplex> &&
-        (cuda::std::is_same_v<DstT, __half> ||
-         cuda::std::is_same_v<DstT, __nv_bfloat16>)>> {
-  static constexpr bool is_castable = true;
-
-  __device__ DstT operator()(SrcT x) {
-    return DstT(static_cast<float>(x));
-  }
-};
-
-template <typename SrcT, typename DstT>
-struct CastOp<
-    SrcT,
-    DstT,
-    cuda::std::enable_if_t<
-        !cuda::std::is_convertible_v<SrcT, DstT> &&
-        !cuda::std::is_same_v<DstT, cuComplex> &&
-        !cuda::std::is_same_v<DstT, __half> &&
-        !cuda::std::is_same_v<DstT, __nv_bfloat16> &&
-        (cuda::std::is_same_v<SrcT, __half> ||
-         cuda::std::is_same_v<SrcT, __nv_bfloat16>)>> {
-  static constexpr bool is_castable = true;
-
-  __device__ DstT operator()(SrcT x) {
-    return DstT(static_cast<float>(x));
-  }
-};
-#endif // CUDART_VERSION < 12000
-
-// Helper to deduce the SrcT.
-template <typename DstT, typename SrcT>
-inline __host__ __device__ auto cast_to(SrcT x) {
-  return CastOp<SrcT, DstT>{}(x);
-}
-
-// Return an iterator that cast the value to DstT using CastOp.
-template <typename DstT, typename Iterator>
-inline __host__ __device__ auto make_cast_iterator(Iterator it) {
-  using SrcT = typename cuda::std::iterator_traits<Iterator>::value_type;
-  if constexpr (std::is_same_v<SrcT, DstT>) {
-    return it;
-  } else {
-    return thrust::make_transform_iterator(it, CastOp<SrcT, DstT>{});
-  }
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/config.h

@@ -1,12 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-// This file is used by both CUDA kernel code and host-only C++ code.
-
-#pragma once
-
-// The maximum dimensions of shape/strides passed as kernel parameters.
-#define MAX_NDIM 10
-
-// All existing NVIDIA hardware has a fixed 32 warp size. Though a built-in
-// warpSize variable exists, using it would prevent compile-time optimizations.
-#define WARP_SIZE 32

mlx/backend/cuda/device/gather.cuh

@@ -1,53 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device/indexing.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, typename IdxT, int NIDX, int IDX_NDIM, typename LocT>
-__global__ void gather(
-    const T* src,
-    T* out,
-    LocT size,
-    const __grid_constant__ Shape src_shape,
-    const __grid_constant__ Strides src_strides,
-    int32_t src_ndim,
-    const __grid_constant__ Shape slice_sizes,
-    uint32_t slice_size,
-    const __grid_constant__ cuda::std::array<int32_t, NIDX> axes,
-    const __grid_constant__ cuda::std::array<IdxT*, NIDX> indices,
-    const __grid_constant__ cuda::std::array<int32_t, NIDX * IDX_NDIM>
-        indices_shape,
-    const __grid_constant__ cuda::std::array<int64_t, NIDX * IDX_NDIM>
-        indices_strides) {
-  LocT out_idx = cg::this_grid().thread_rank();
-  if (out_idx >= size) {
-    return;
-  }
-
-  LocT src_elem = out_idx % slice_size;
-  LocT idx_elem = out_idx / slice_size;
-
-  LocT src_loc =
-      elem_to_loc(src_elem, slice_sizes.data(), src_strides.data(), src_ndim);
-
-#pragma unroll
-  for (int i = 0; i < NIDX; ++i) {
-    LocT idx_loc = elem_to_loc_nd<IDX_NDIM>(
-        idx_elem,
-        indices_shape.data() + i * IDX_NDIM,
-        indices_strides.data() + i * IDX_NDIM);
-    int32_t axis = axes[i];
-    LocT idx_val = absolute_index(indices[i][idx_loc], src_shape[axis]);
-    src_loc += idx_val * src_strides[axis];
-  }
-
-  out[out_idx] = src[src_loc];
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/gather_axis.cuh

@@ -1,65 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device/indexing.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-template <
-    typename T,
-    typename IdxT,
-    int NDIM,
-    bool SrcC,
-    bool IdxC,
-    typename LocT>
-__global__ void gather_axis(
-    const T* src,
-    const IdxT* indices,
-    T* out,
-    LocT idx_size_pre,
-    LocT idx_size_axis,
-    LocT idx_size_post,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> src_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> idx_strides,
-    int32_t axis,
-    int32_t axis_size,
-    int64_t src_stride_axis,
-    int64_t idx_stride_axis) {
-  LocT index = cg::this_grid().thread_rank();
-  if (index >= idx_size_pre * idx_size_axis * idx_size_post) {
-    return;
-  }
-
-  auto [x, y, z] = index_to_dims(index, idx_size_axis, idx_size_pre);
-
-  LocT elem_idx = z * idx_size_post;
-
-  LocT idx_loc = y * idx_stride_axis;
-  if constexpr (IdxC) {
-    idx_loc += elem_idx * idx_size_axis + x;
-  } else {
-    idx_loc +=
-        elem_to_loc_nd<NDIM>(elem_idx + x, shape.data(), idx_strides.data());
-  }
-
-  auto idx_val = absolute_index(indices[idx_loc], axis_size);
-
-  LocT src_loc = idx_val * src_stride_axis;
-  if constexpr (SrcC) {
-    src_loc += elem_idx * axis_size + x;
-  } else {
-    src_loc +=
-        elem_to_loc_nd<NDIM>(elem_idx + x, shape.data(), src_strides.data());
-  }
-
-  LocT out_idx = y * idx_size_post + elem_idx * idx_size_axis + x;
-
-  out[out_idx] = src[src_loc];
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/indexing.cuh

@@ -1,30 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include <cuda/std/tuple>
-#include <cuda/std/type_traits>
-
-namespace mlx::core::cu {
-
-// Convert an absolute index to positions in a 3d grid, assuming the index is
-// calculated with:
-// index = x * dim1 * dim2 + y * dim2 + z
-template <typename T>
-inline __host__ __device__ cuda::std::tuple<T, T, T>
-index_to_dims(T index, T dim1, T dim2) {
-  T x = index / (dim1 * dim2);
-  T y = (index % (dim1 * dim2)) / dim2;
-  T z = index % dim2;
-  return cuda::std::make_tuple(x, y, z);
-}
-
-// Get absolute index from possible negative index.
-template <typename IdxT>
-inline __host__ __device__ auto absolute_index(IdxT idx, int32_t size) {
-  if constexpr (cuda::std::is_unsigned_v<IdxT>) {
-    return idx;
-  } else {
-    return static_cast<int32_t>(idx < 0 ? idx + size : idx);
-  }
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/scatter.cuh

@@ -1,68 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device/indexing.cuh"
-#include "mlx/backend/cuda/device/scatter_ops.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-template <
-    typename T,
-    typename IdxT,
-    typename Op,
-    int NIDX,
-    int IDX_NDIM,
-    typename LocT>
-__global__ void scatter(
-    const T* upd,
-    T* out,
-    LocT size,
-    const __grid_constant__ Shape upd_shape,
-    const __grid_constant__ Strides upd_strides,
-    int32_t upd_ndim,
-    LocT upd_post_idx_size,
-    const __grid_constant__ Shape out_shape,
-    const __grid_constant__ Strides out_strides,
-    int32_t out_ndim,
-    const __grid_constant__ cuda::std::array<int32_t, NIDX> axes,
-    const __grid_constant__ cuda::std::array<IdxT*, NIDX> indices,
-    const __grid_constant__ cuda::std::array<int32_t, NIDX * IDX_NDIM>
-        indices_shape,
-    const __grid_constant__ cuda::std::array<int64_t, NIDX * IDX_NDIM>
-        indices_strides) {
-  LocT upd_idx = cg::this_grid().thread_rank();
-  if (upd_idx >= size) {
-    return;
-  }
-
-  LocT out_elem = upd_idx % upd_post_idx_size;
-  LocT idx_elem = upd_idx / upd_post_idx_size;
-
-  LocT out_idx = elem_to_loc(
-      out_elem, upd_shape.data() + IDX_NDIM, out_strides.data(), out_ndim);
-
-#pragma unroll
-  for (int i = 0; i < NIDX; ++i) {
-    LocT idx_loc = elem_to_loc_nd<IDX_NDIM>(
-        idx_elem,
-        indices_shape.data() + i * IDX_NDIM,
-        indices_strides.data() + i * IDX_NDIM);
-    int32_t axis = axes[i];
-    LocT idx_val = absolute_index(indices[i][idx_loc], out_shape[axis]);
-    out_idx += idx_val * out_strides[axis];
-  }
-
-  LocT upd_loc = elem_to_loc(
-      out_elem + idx_elem * upd_post_idx_size,
-      upd_shape.data(),
-      upd_strides.data(),
-      upd_ndim);
-
-  Op{}(out + out_idx, upd[upd_loc]);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/scatter_axis.cuh

@@ -1,67 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device/indexing.cuh"
-#include "mlx/backend/cuda/device/scatter_ops.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core::cu {
-
-namespace cg = cooperative_groups;
-
-template <
-    typename T,
-    typename IdxT,
-    typename Op,
-    int NDIM,
-    bool UpdC,
-    bool IdxC,
-    typename LocT>
-__global__ void scatter_axis(
-    const T* upd,
-    const IdxT* indices,
-    T* out,
-    LocT idx_size_pre,
-    LocT idx_size_axis,
-    LocT idx_size_post,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> upd_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> idx_strides,
-    int32_t axis,
-    int32_t axis_size,
-    int64_t upd_stride_axis,
-    int64_t idx_stride_axis) {
-  LocT index = cg::this_grid().thread_rank();
-  if (index >= idx_size_pre * idx_size_axis * idx_size_post) {
-    return;
-  }
-
-  auto [x, y, z] = index_to_dims(index, idx_size_axis, idx_size_pre);
-
-  LocT elem_idx = z * idx_size_post;
-
-  LocT idx_loc = y * idx_stride_axis;
-  if constexpr (IdxC) {
-    idx_loc += elem_idx * idx_size_axis + x;
-  } else {
-    idx_loc +=
-        elem_to_loc_nd<NDIM>(elem_idx + x, shape.data(), idx_strides.data());
-  }
-
-  auto idx_val = absolute_index(indices[idx_loc], axis_size);
-
-  LocT upd_loc = y * upd_stride_axis;
-  if constexpr (UpdC) {
-    upd_loc += elem_idx * idx_size_axis + x;
-  } else {
-    upd_loc +=
-        elem_to_loc_nd<NDIM>(elem_idx + x, shape.data(), upd_strides.data());
-  }
-
-  LocT out_idx = idx_val * idx_size_post + elem_idx * axis_size + x;
-
-  Op{}(out + out_idx, upd[upd_loc]);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/scatter_ops.cuh

@@ -1,44 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device/atomic_ops.cuh"
-
-namespace mlx::core::cu {
-
-struct ScatterAssign {
-  template <typename T>
-  __device__ void operator()(T* out, T val) const {
-    *out = val;
-  }
-};
-
-struct ScatterSum {
-  template <typename T>
-  __device__ void operator()(T* out, T val) const {
-    atomic_add(out, val);
-  }
-};
-
-struct ScatterProd {
-  template <typename T>
-  __device__ void operator()(T* out, T val) const {
-    atomic_prod(out, val);
-  }
-};
-
-struct ScatterMax {
-  template <typename T>
-  __device__ void operator()(T* out, T val) const {
-    atomic_max(out, val);
-  }
-};
-
-struct ScatterMin {
-  template <typename T>
-  __device__ void operator()(T* out, T val) const {
-    atomic_min(out, val);
-  }
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/ternary_ops.cuh

@@ -1,13 +0,0 @@
-// Copyright © 2025 Apple Inc.
-#pragma once
-
-namespace mlx::core::cu {
-
-struct Select {
-  template <typename T>
-  __device__ T operator()(bool condition, T x, T y) {
-    return condition ? x : y;
-  }
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/utils.cuh

@@ -1,379 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-// This file must not include any host-only code, utilies that work under both
-// host and device can be put here.
-//
-// See more about the requirements at:
-// https://docs.nvidia.com/cuda/nvrtc/#language
-
-#pragma once
-
-#include "mlx/backend/cuda/device/config.h"
-
-#include <cuComplex.h>
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-#include <cuda/std/array>
-#include <cuda/std/limits>
-#include <cuda/std/tuple>
-
-namespace mlx::core::cu {
-
-///////////////////////////////////////////////////////////////////////////////
-// CUDA kernel utils
-///////////////////////////////////////////////////////////////////////////////
-
-// To pass shape/strides to kernels via constant memory, their size must be
-// known at compile time.
-using Shape = cuda::std::array<int32_t, MAX_NDIM>;
-using Strides = cuda::std::array<int64_t, MAX_NDIM>;
-
-// Vectorized load/store.
-template <typename T, int N>
-struct alignas(sizeof(T) * N) AlignedVector {
-  T val[N];
-};
-
-template <int N, typename T>
-inline __device__ AlignedVector<T, N> load_vector(
-    const T* ptr,
-    uint32_t offset) {
-  auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
-  return from[offset];
-}
-
-template <int N, typename T>
-inline __device__ void
-store_vector(T* ptr, uint32_t offset, const AlignedVector<T, N>& vec) {
-  auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
-  to[offset] = vec;
-}
-
-///////////////////////////////////////////////////////////////////////////////
-// Type limits utils
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename T, typename = void>
-struct Limits {
-  static constexpr __host__ __device__ T max() {
-    return cuda::std::numeric_limits<T>::max();
-  }
-  static constexpr __host__ __device__ T min() {
-    return cuda::std::numeric_limits<T>::min();
-  }
-  static constexpr __host__ __device__ T finite_max() {
-    return cuda::std::numeric_limits<T>::max();
-  }
-  static constexpr __host__ __device__ T finite_min() {
-    return cuda::std::numeric_limits<T>::min();
-  }
-};
-
-template <typename T>
-struct Limits<
-    T,
-    cuda::std::enable_if_t<
-        cuda::std::is_same_v<T, float> || cuda::std::is_same_v<T, double>>> {
-  static constexpr __host__ __device__ T max() {
-    return cuda::std::numeric_limits<T>::infinity();
-  }
-  static constexpr __host__ __device__ T min() {
-    return -cuda::std::numeric_limits<T>::infinity();
-  }
-  static constexpr __host__ __device__ T finite_max() {
-    return cuda::std::numeric_limits<T>::max();
-  }
-  static constexpr __host__ __device__ T finite_min() {
-    return cuda::std::numeric_limits<T>::lowest();
-  }
-};
-
-// CUDA 11 does not have host side arithmatic operators for half types.
-template <typename T>
-struct Limits<
-    T,
-    cuda::std::enable_if_t<
-        cuda::std::is_same_v<T, __half> ||
-        cuda::std::is_same_v<T, __nv_bfloat16>>> {
-  static constexpr __host__ __device__ T max() {
-    return cuda::std::numeric_limits<T>::infinity();
-  }
-  static constexpr __host__ __device__ T min() {
-#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
-    return -cuda::std::numeric_limits<float>::infinity();
-#else
-    return -cuda::std::numeric_limits<T>::infinity();
-#endif
-  }
-  static constexpr __host__ __device__ T finite_max() {
-    return cuda::std::numeric_limits<T>::max();
-  }
-  static constexpr __host__ __device__ T finite_min() {
-#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
-    return cuda::std::numeric_limits<float>::lowest();
-#else
-    return cuda::std::numeric_limits<T>::lowest();
-#endif
-  }
-};
-
-template <>
-struct Limits<bool> {
-  static constexpr __host__ __device__ bool max() {
-    return true;
-  }
-  static constexpr __host__ __device__ bool min() {
-    return false;
-  }
-};
-
-template <>
-struct Limits<cuComplex> {
-  static constexpr __host__ __device__ cuComplex max() {
-    return {Limits<float>::max(), Limits<float>::max()};
-  }
-  static constexpr __host__ __device__ cuComplex min() {
-    return {Limits<float>::min(), Limits<float>::min()};
-  }
-};
-
-///////////////////////////////////////////////////////////////////////////////
-// Indexing utils
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename IdxT = int64_t>
-inline __host__ __device__ IdxT
-elem_to_loc(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
-  IdxT loc = 0;
-  for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
-    loc += (elem % shape[i]) * IdxT(strides[i]);
-    elem /= shape[i];
-  }
-  return loc;
-}
-
-// Optimize when the ndim is known at compile time.
-template <int NDIM, typename IdxT = int64_t>
-inline __host__ __device__ IdxT
-elem_to_loc_nd(IdxT elem, const int* shape, const int64_t* strides) {
-  IdxT loc = 0;
-#pragma unroll
-  for (int i = NDIM - 1; i >= 0; --i) {
-    loc += (elem % shape[i]) * IdxT(strides[i]);
-    elem /= shape[i];
-  }
-  return loc;
-}
-
-template <int NDIM, typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_nd(
-    IdxT elem,
-    const int* shape,
-    const int64_t* a_strides,
-    const int64_t* b_strides) {
-  IdxT a_loc = 0;
-  IdxT b_loc = 0;
-#pragma unroll
-  for (int i = NDIM - 1; i >= 0; --i) {
-    int dim_idx = elem % shape[i];
-    a_loc += dim_idx * IdxT(a_strides[i]);
-    b_loc += dim_idx * IdxT(b_strides[i]);
-    elem /= shape[i];
-  }
-  return cuda::std::make_tuple(a_loc, b_loc);
-}
-
-template <int NDIM, typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_nd(
-    IdxT elem,
-    const int* shape,
-    const int64_t* a_strides,
-    const int64_t* b_strides,
-    const int64_t* c_strides) {
-  IdxT a_loc = 0;
-  IdxT b_loc = 0;
-  IdxT c_loc = 0;
-#pragma unroll
-  for (int i = NDIM - 1; i >= 0; --i) {
-    int dim_idx = elem % shape[i];
-    a_loc += dim_idx * IdxT(a_strides[i]);
-    b_loc += dim_idx * IdxT(b_strides[i]);
-    c_loc += dim_idx * IdxT(c_strides[i]);
-    elem /= shape[i];
-  }
-  return cuda::std::make_tuple(a_loc, b_loc, c_loc);
-}
-
-// Optimized version when ndim is larger than 4.
-template <typename IdxT = int64_t>
-inline __host__ __device__ IdxT
-elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
-  IdxT loc = 0;
-  for (int i = ndim - 1; i >= 0; --i) {
-    loc += (elem % shape[i]) * IdxT(strides[i]);
-    elem /= shape[i];
-  }
-  return loc;
-}
-
-template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
-    IdxT elem,
-    const int* shape,
-    const int64_t* a_strides,
-    const int64_t* b_strides,
-    int ndim) {
-  IdxT a_loc = 0;
-  IdxT b_loc = 0;
-  for (int i = ndim - 1; i >= 0; --i) {
-    int dim_idx = elem % shape[i];
-    a_loc += dim_idx * IdxT(a_strides[i]);
-    b_loc += dim_idx * IdxT(b_strides[i]);
-    elem /= shape[i];
-  }
-  return cuda::std::make_tuple(a_loc, b_loc);
-}
-
-template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_4d(
-    IdxT elem,
-    const int* shape,
-    const int64_t* a_strides,
-    const int64_t* b_strides,
-    const int64_t* c_strides,
-    int ndim) {
-  IdxT a_loc = 0;
-  IdxT b_loc = 0;
-  IdxT c_loc = 0;
-  for (int i = ndim - 1; i >= 0; --i) {
-    int dim_idx = elem % shape[i];
-    a_loc += dim_idx * IdxT(a_strides[i]);
-    b_loc += dim_idx * IdxT(b_strides[i]);
-    c_loc += dim_idx * IdxT(c_strides[i]);
-    elem /= shape[i];
-  }
-  return cuda::std::make_tuple(a_loc, b_loc, c_loc);
-}
-
-///////////////////////////////////////////////////////////////////////////////
-// Elem to loc in a loop utils
-///////////////////////////////////////////////////////////////////////////////
-
-template <int DIM, bool General = true, typename OffsetT = size_t>
-struct LoopedElemToLoc {
-  int dim;
-  LoopedElemToLoc<DIM - 1, General, OffsetT> inner_looper;
-  OffsetT offset{0};
-  int index{0};
-
-  __device__ LoopedElemToLoc(int dim) : dim(dim), inner_looper(dim - 1) {}
-
-  __device__ void next(const int* shape, const int64_t* strides) {
-    if (dim == 0) {
-      return;
-    }
-    index++;
-    offset += OffsetT(strides[dim - 1]);
-    if (index >= shape[dim - 1]) {
-      index = 0;
-      inner_looper.next(shape, strides);
-      offset = inner_looper.offset;
-    }
-  }
-
-  __device__ void next(int n, const int* shape, const int64_t* strides) {
-    if (dim == 0) {
-      return;
-    }
-    index += n;
-    offset += n * OffsetT(strides[dim - 1]);
-
-    if (index >= shape[dim - 1]) {
-      int extra = index - shape[dim - 1];
-      if (extra >= shape[dim - 1]) {
-        inner_looper.next(1 + extra / shape[dim - 1], shape, strides);
-        extra = extra % shape[dim - 1];
-      } else {
-        inner_looper.next(shape, strides);
-      }
-      index = 0;
-      offset = inner_looper.offset;
-      if (extra > 0) {
-        next(extra, shape, strides);
-      }
-    }
-  }
-
-  __device__ OffsetT location() {
-    return offset;
-  }
-};
-
-template <typename OffsetT>
-struct LoopedElemToLoc<1, true, OffsetT> {
-  int dim;
-  OffsetT offset{0};
-  int index{0};
-
-  __device__ LoopedElemToLoc(int dim) : dim(dim) {}
-
-  __device__ void next(const int* shape, const int64_t* strides) {
-    index++;
-    if (dim > 1) {
-      offset = elem_to_loc<OffsetT>(index, shape, strides, dim);
-    } else {
-      offset += OffsetT(strides[0]);
-    }
-  }
-
-  __device__ void next(int n, const int* shape, const int64_t* strides) {
-    index += n;
-    if (dim > 1) {
-      offset = elem_to_loc<OffsetT>(index, shape, strides, dim);
-    } else {
-      offset = index * OffsetT(strides[0]);
-    }
-  }
-
-  __device__ OffsetT location() {
-    return offset;
-  }
-};
-
-template <typename OffsetT>
-struct LoopedElemToLoc<1, false, OffsetT> {
-  OffsetT offset{0};
-
-  __device__ LoopedElemToLoc(int) {}
-
-  __device__ void next(const int*, const int64_t* strides) {
-    offset += OffsetT(strides[0]);
-  }
-
-  __device__ void next(int n, const int*, const int64_t* strides) {
-    offset += n * OffsetT(strides[0]);
-  }
-
-  __device__ OffsetT location() {
-    return offset;
-  }
-};
-
-inline __device__ cuComplex log1p(cuComplex in) {
-  float x = cuCrealf(in);
-  float y = cuCimagf(in);
-  float zabs = sqrt(x * x + y * y);
-  float theta = atan2f(y, x + 1);
-  if (zabs < 0.5f) {
-    float r = x * (2 + x) + y * y;
-    if (r == 0) { // handle underflow
-      return {x, theta};
-    }
-    return {0.5f * log1pf(r), theta};
-  } else {
-    auto z0 = sqrt((x + 1) * (x + 1) + y * y);
-    return {log(z0), theta};
-  }
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/eval.cpp

@@ -37,20 +37,22 @@ void eval(array& arr) {
   }
 
   auto& encoder = cu::get_command_encoder(arr.primitive().stream());
-  // Keep used buffers alive until kernel finishes running.
-  std::unordered_set<std::shared_ptr<array::Data>> buffers;
-  for (auto& in : arr.inputs()) {
-    buffers.insert(in.data_shared_ptr());
+  if (encoder.has_gpu_work()) {
+    // Keep used buffers alive until kernel finishes running.
+    std::unordered_set<std::shared_ptr<array::Data>> buffers;
+    for (auto& in : arr.inputs()) {
+      buffers.insert(in.data_shared_ptr());
+    }
+    for (auto& s : arr.siblings()) {
+      buffers.insert(s.data_shared_ptr());
+    }
+    // Remove the output if it was donated to by an input.
+    if (auto it = buffers.find(arr.data_shared_ptr()); it != buffers.end()) {
+      buffers.erase(it);
+    }
+    encoder.add_completed_handler([buffers = std::move(buffers)]() {});
   }
-  for (auto& s : arr.siblings()) {
-    buffers.insert(s.data_shared_ptr());
-  }
-  // Remove the output if it was donated to by an input.
-  if (auto it = buffers.find(arr.data_shared_ptr()); it != buffers.end()) {
-    buffers.erase(it);
-  }
-  encoder.add_completed_handler([buffers = std::move(buffers)]() {});
-  encoder.maybe_commit();
+  encoder.end_encoding();
 }
 
 void finalize(Stream s) {
@@ -60,7 +62,7 @@ void finalize(Stream s) {
 
 void synchronize(Stream s) {
   nvtx3::scoped_range r("gpu::synchronize");
-  cu::get_command_encoder(s).synchronize();
+  cu::get_stream(s).synchronize();
 }
 
 } // namespace mlx::core::gpu

mlx/backend/cuda/event.cu

@@ -61,9 +61,7 @@ void CudaEvent::wait(Stream s) {
   if (s.device == mlx::core::Device::cpu) {
     scheduler::enqueue(s, [*this]() mutable { wait(); });
   } else {
-    auto& enc = cu::get_command_encoder(s);
-    enc.commit();
-    wait(enc.stream());
+    wait(cu::get_stream(s).last_cuda_stream());
   }
 }
 
@@ -76,9 +74,7 @@ void CudaEvent::record(Stream s) {
   if (s.device == mlx::core::Device::cpu) {
     throw std::runtime_error("CudaEvent can not wait on cpu stream.");
   } else {
-    auto& enc = cu::get_command_encoder(s);
-    enc.commit();
-    record(enc.stream());
+    record(cu::get_stream(s).last_cuda_stream());
   }
 }
 
@@ -140,9 +136,11 @@ void SharedEvent::wait(Stream s, uint64_t value) {
     scheduler::enqueue(s, [*this, value]() mutable { wait(value); });
   } else {
     auto& encoder = get_command_encoder(s);
-    encoder.commit();
-    wait(encoder.stream(), value);
+    encoder.launch_kernel(
+        encoder.stream().last_cuda_stream(),
+        [this, value](cudaStream_t stream) { wait(stream, value); });
     encoder.add_completed_handler([ac = ac_]() {});
+    encoder.end_encoding();
   }
 }
 
@@ -164,9 +162,11 @@ void SharedEvent::signal(Stream s, uint64_t value) {
     scheduler::enqueue(s, [*this, value]() mutable { signal(stream, value); });
   } else {
     auto& encoder = get_command_encoder(s);
-    encoder.commit();
-    signal(encoder.stream(), value);
+    encoder.launch_kernel(
+        encoder.stream().last_cuda_stream(),
+        [this, value](cudaStream_t stream) { signal(stream, value); });
     encoder.add_completed_handler([ac = ac_]() {});
+    encoder.end_encoding();
   }
 }
 

mlx/backend/cuda/indexing.cpp

@@ -1,428 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/compiled.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/jit_module.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include "cuda_jit_sources.h"
-
-#include <cuda.h>
-#include <fmt/format.h>
-#include <nvrtc.h>
-#include <nvtx3/nvtx3.hpp>
-
-#include <cassert>
-#include <numeric>
-
-namespace mlx::core {
-
-namespace {
-
-constexpr const char* g_scatter_ops[] = {"Max", "Min", "Sum", "Prod", "Assign"};
-
-void append_indices_arg(
-    cu::KernelArgs& args,
-    const std::vector<array>& inputs,
-    int nidx,
-    int idx_ndim) {
-  std::vector<const void*> indices(nidx);
-  for (int i = 0; i < nidx; ++i) {
-    indices[i] = inputs[i + 1].data<void>();
-  }
-  args.append(std::move(indices));
-  std::vector<int32_t> indices_shape(nidx * idx_ndim);
-  for (int i = 0; i < nidx; ++i) {
-    std::copy_n(
-        inputs[i + 1].shape().begin(),
-        idx_ndim,
-        indices_shape.data() + i * idx_ndim);
-  }
-  args.append(std::move(indices_shape));
-  std::vector<int64_t> indices_strides(nidx * idx_ndim);
-  for (int i = 0; i < nidx; ++i) {
-    std::copy_n(
-        inputs[i + 1].strides().begin(),
-        idx_ndim,
-        indices_strides.data() + i * idx_ndim);
-  }
-  args.append(std::move(indices_strides));
-}
-
-} // namespace
-
-void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Gather::eval_gpu");
-  assert(inputs.size() > 0);
-  const auto& src = inputs[0];
-
-  out.set_data(allocator::malloc(out.nbytes()));
-  if (out.size() == 0) {
-    return;
-  }
-
-  int nidx = inputs.size() - 1;
-  Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
-  int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
-
-  bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
-      (src.size() > INT32_MAX) || (out.size() > INT32_MAX);
-
-  uint32_t slice_size = std::accumulate(
-      slice_sizes_.begin(), slice_sizes_.end(), 1, std::multiplies<uint32_t>());
-
-  std::string module_name = fmt::format(
-      "gather_{}_{}_{}",
-      dtype_to_string(out.dtype()),
-      dtype_to_string(idx_dtype),
-      nidx);
-
-  auto& s = stream();
-  cu::JitModule& mod = cu::get_jit_module(s.device, module_name, [&]() {
-    std::vector<std::string> kernel_names;
-    for (int ndim = 0; ndim <= MAX_NDIM; ++ndim) {
-      for (int large = 0; large <= 1; ++large) {
-        kernel_names.push_back(fmt::format(
-            "mlx::core::cu::gather<{}, {}, {}, {}, {}>",
-            dtype_to_cuda_type(out.dtype()),
-            dtype_to_cuda_type(idx_dtype),
-            nidx,
-            ndim,
-            large ? "int64_t" : "int32_t"));
-      }
-    }
-    return std::make_pair(jit_source_gather, std::move(kernel_names));
-  });
-
-  cu::KernelArgs args;
-  args.append(src);
-  args.append(out);
-  if (large) {
-    args.append<int64_t>(out.size());
-  } else {
-    args.append<int32_t>(out.size());
-  }
-  args.append_ndim(src.shape());
-  args.append_ndim(src.strides());
-  args.append<int32_t>(src.ndim());
-  args.append_ndim(slice_sizes_);
-  args.append(slice_size);
-  args.append(axes_);
-  append_indices_arg(args, inputs, nidx, idx_ndim);
-
-  std::string kernel_name = fmt::format(
-      "mlx::core::cu::gather<{}, {}, {}, {}, {}>",
-      dtype_to_cuda_type(out.dtype()),
-      dtype_to_cuda_type(idx_dtype),
-      nidx,
-      idx_ndim,
-      large ? "int64_t" : "int32_t");
-
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : inputs) {
-    encoder.set_input_array(in);
-  }
-  encoder.set_output_array(out);
-
-  auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
-}
-
-void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Gather::eval_gpu");
-  assert(inputs.size() > 1);
-  auto& upd = inputs.back();
-
-  // Copy src into out.
-  CopyType copy_type;
-  if (inputs[0].data_size() == 1) {
-    copy_type = CopyType::Scalar;
-  } else if (inputs[0].flags().row_contiguous) {
-    copy_type = CopyType::Vector;
-  } else {
-    copy_type = CopyType::General;
-  }
-  copy_gpu(inputs[0], out, copy_type);
-
-  // Empty update.
-  if (upd.size() == 0) {
-    return;
-  }
-
-  int nidx = axes_.size();
-  Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
-  int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
-
-  bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
-      (upd.size() > INT32_MAX) || (out.size() > INT32_MAX);
-
-  int32_t upd_post_idx_size = std::accumulate(
-      upd.shape().begin() + idx_ndim,
-      upd.shape().end(),
-      1,
-      std::multiplies<int32_t>());
-
-  const char* op = g_scatter_ops[reduce_type_];
-  std::string module_name = fmt::format(
-      "scatter_{}_{}_{}_{}",
-      dtype_to_string(out.dtype()),
-      dtype_to_string(idx_dtype),
-      op,
-      nidx);
-
-  auto& s = stream();
-  cu::JitModule& mod = cu::get_jit_module(s.device, module_name, [&]() {
-    std::vector<std::string> kernel_names;
-    for (int ndim = 0; ndim <= MAX_NDIM; ++ndim) {
-      for (int large = 0; large <= 1; ++large) {
-        kernel_names.push_back(fmt::format(
-            "mlx::core::cu::scatter<{}, {}, mlx::core::cu::Scatter{}, {}, {}, {}>",
-            dtype_to_cuda_type(out.dtype()),
-            dtype_to_cuda_type(idx_dtype),
-            op,
-            nidx,
-            ndim,
-            large ? "int64_t" : "int32_t"));
-      }
-    }
-    return std::make_pair(jit_source_scatter, std::move(kernel_names));
-  });
-
-  cu::KernelArgs args;
-  args.append(upd);
-  args.append(out);
-  if (large) {
-    args.append<int64_t>(upd.size());
-  } else {
-    args.append<int32_t>(upd.size());
-  }
-  args.append_ndim(upd.shape());
-  args.append_ndim(upd.strides());
-  args.append<int32_t>(upd.ndim());
-  if (large) {
-    args.append<int64_t>(upd_post_idx_size);
-  } else {
-    args.append<int32_t>(upd_post_idx_size);
-  }
-  args.append_ndim(out.shape());
-  args.append_ndim(out.strides());
-  args.append<int32_t>(out.ndim());
-  args.append(axes_);
-  append_indices_arg(args, inputs, nidx, idx_ndim);
-
-  std::string kernel_name = fmt::format(
-      "mlx::core::cu::scatter<{}, {}, mlx::core::cu::Scatter{}, {}, {}, {}>",
-      dtype_to_cuda_type(out.dtype()),
-      dtype_to_cuda_type(idx_dtype),
-      op,
-      nidx,
-      idx_ndim,
-      large ? "int64_t" : "int32_t");
-
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : inputs) {
-    encoder.set_input_array(in);
-  }
-  encoder.set_output_array(out);
-  auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, upd, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
-}
-
-void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("GatherAxis::eval_gpu");
-  assert(inputs.size() > 1);
-  const auto& src = inputs[0];
-  const auto& idx = inputs[1];
-
-  out.set_data(allocator::malloc(out.nbytes()));
-  if (out.size() == 0) {
-    return;
-  }
-
-  bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
-
-  std::string module_name = fmt::format(
-      "gather_axis_{}_{}",
-      dtype_to_string(out.dtype()),
-      dtype_to_string(idx.dtype()));
-
-  auto& s = stream();
-  cu::JitModule& mod = cu::get_jit_module(s.device, module_name, [&]() {
-    std::vector<std::string> kernel_names;
-    for (int ndim = 0; ndim <= MAX_NDIM; ++ndim) {
-      for (int contiguous = 0; contiguous < 4; ++contiguous) {
-        for (int large = 0; large <= 1; ++large) {
-          kernel_names.push_back(fmt::format(
-              "mlx::core::cu::gather_axis<{}, {}, {}, {}, {}, {}>",
-              dtype_to_cuda_type(out.dtype()),
-              dtype_to_cuda_type(idx.dtype()),
-              ndim,
-              contiguous & 1 ? true : false,
-              contiguous & 2 ? true : false,
-              large ? "int64_t" : "int32_t"));
-        }
-      }
-    }
-    return std::make_pair(jit_source_gather_axis, std::move(kernel_names));
-  });
-
-  size_t idx_size_pre = 1;
-  size_t idx_size_post = 1;
-  for (int i = 0; i < axis_; ++i) {
-    idx_size_pre *= idx.shape(i);
-  }
-  for (int i = axis_ + 1; i < idx.ndim(); ++i) {
-    idx_size_post *= idx.shape(i);
-  }
-  size_t idx_size_axis = idx.shape(axis_);
-
-  cu::KernelArgs args;
-  args.append(src);
-  args.append(idx);
-  args.append(out);
-  if (large) {
-    args.append<int64_t>(idx_size_pre);
-    args.append<int64_t>(idx_size_axis);
-    args.append<int64_t>(idx_size_post);
-  } else {
-    args.append<int32_t>(idx_size_pre);
-    args.append<int32_t>(idx_size_axis);
-    args.append<int32_t>(idx_size_post);
-  }
-  args.append(remove_index(idx.shape(), axis_));
-  args.append(remove_index(src.strides(), axis_));
-  args.append(remove_index(idx.strides(), axis_));
-  args.append<int32_t>(axis_);
-  args.append(src.shape(axis_));
-  args.append(src.strides(axis_));
-  args.append(idx.strides(axis_));
-
-  std::string kernel_name = fmt::format(
-      "mlx::core::cu::gather_axis<{}, {}, {}, {}, {}, {}>",
-      dtype_to_cuda_type(out.dtype()),
-      dtype_to_cuda_type(idx.dtype()),
-      src.ndim() - 1,
-      src.flags().row_contiguous,
-      idx.flags().row_contiguous,
-      large ? "int64_t" : "int32_t");
-
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : inputs) {
-    encoder.set_input_array(in);
-  }
-  encoder.set_output_array(out);
-  auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
-}
-
-void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("ScatterAxis::eval_gpu");
-  assert(inputs.size() > 2);
-  const auto& src = inputs[0];
-  const auto& idx = inputs[1];
-  const auto& upd = inputs[2];
-
-  // Copy src into out.
-  CopyType copy_type;
-  if (src.data_size() == 1) {
-    copy_type = CopyType::Scalar;
-  } else if (src.flags().row_contiguous) {
-    copy_type = CopyType::Vector;
-  } else {
-    copy_type = CopyType::General;
-  }
-  copy_gpu(src, out, copy_type);
-
-  // Empty update.
-  if (upd.size() == 0) {
-    return;
-  }
-
-  bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
-
-  const char* op = reduce_type_ == ScatterAxis::Sum ? "Sum" : "Assign";
-  std::string module_name = fmt::format(
-      "scatter_axis_{}_{}_{}",
-      dtype_to_string(out.dtype()),
-      dtype_to_string(idx.dtype()),
-      op);
-
-  auto& s = stream();
-  cu::JitModule& mod = cu::get_jit_module(s.device, module_name, [&]() {
-    std::vector<std::string> kernel_names;
-    for (int ndim = 0; ndim <= MAX_NDIM; ++ndim) {
-      for (int contiguous = 0; contiguous < 4; ++contiguous) {
-        for (int large = 0; large <= 1; ++large) {
-          kernel_names.push_back(fmt::format(
-              "mlx::core::cu::scatter_axis<{}, {}, mlx::core::cu::Scatter{}, {}, {}, {}, {}>",
-              dtype_to_cuda_type(out.dtype()),
-              dtype_to_cuda_type(idx.dtype()),
-              op,
-              ndim,
-              contiguous & 1 ? true : false,
-              contiguous & 2 ? true : false,
-              large ? "int64_t" : "int32_t"));
-        }
-      }
-    }
-    return std::make_pair(jit_source_scatter_axis, std::move(kernel_names));
-  });
-
-  size_t idx_size_pre = 1;
-  size_t idx_size_post = 1;
-  for (int i = 0; i < axis_; ++i) {
-    idx_size_pre *= idx.shape(i);
-  }
-  for (int i = axis_ + 1; i < idx.ndim(); ++i) {
-    idx_size_post *= idx.shape(i);
-  }
-  size_t idx_size_axis = idx.shape(axis_);
-
-  cu::KernelArgs args;
-  args.append(upd);
-  args.append(idx);
-  args.append(out);
-  if (large) {
-    args.append<int64_t>(idx_size_pre);
-    args.append<int64_t>(idx_size_axis);
-    args.append<int64_t>(idx_size_post);
-  } else {
-    args.append<int32_t>(idx_size_pre);
-    args.append<int32_t>(idx_size_axis);
-    args.append<int32_t>(idx_size_post);
-  }
-  args.append(remove_index(idx.shape(), axis_));
-  args.append(remove_index(upd.strides(), axis_));
-  args.append(remove_index(idx.strides(), axis_));
-  args.append<int32_t>(axis_);
-  args.append(out.shape(axis_));
-  args.append(upd.strides(axis_));
-  args.append(idx.strides(axis_));
-
-  std::string kernel_name = fmt::format(
-      "mlx::core::cu::scatter_axis<{}, {}, mlx::core::cu::Scatter{}, {}, {}, {}, {}>",
-      dtype_to_cuda_type(out.dtype()),
-      dtype_to_cuda_type(idx.dtype()),
-      op,
-      idx.ndim() - 1,
-      upd.flags().row_contiguous,
-      idx.flags().row_contiguous,
-      large ? "int64_t" : "int32_t");
-
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : inputs) {
-    encoder.set_input_array(in);
-  }
-  encoder.set_output_array(out);
-  auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
-}
-
-} // namespace mlx::core

mlx/backend/cuda/iterators/strided_iterator.cuh

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

mlx/backend/cuda/jit_module.cpp

@@ -1,303 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/jit_module.h"
-#include "mlx/backend/cuda/device.h"
-
-#include "cuda_jit_sources.h"
-
-#include <cstdlib>
-#include <filesystem>
-#include <fstream>
-#include <unordered_map>
-
-#include <fmt/format.h>
-#include <nvrtc.h>
-
-namespace mlx::core::cu {
-
-namespace {
-
-#define CHECK_NVRTC_ERROR(cmd) check_nvrtc_error(#cmd, (cmd))
-
-void check_nvrtc_error(const char* name, nvrtcResult err) {
-  if (err != NVRTC_SUCCESS) {
-    throw std::runtime_error(
-        fmt::format("{} failed: {}", name, nvrtcGetErrorString(err)));
-  }
-}
-
-// Return the location of the CUDA toolkit.
-const std::string& cuda_home() {
-  static std::string home = []() -> std::string {
-    const char* home = std::getenv("CUDA_HOME");
-    if (home) {
-      return home;
-    }
-    home = std::getenv("CUDA_PATH");
-    if (home) {
-      return home;
-    }
-#if defined(__linux__)
-    home = "/usr/local/cuda";
-    if (std::filesystem::exists(home)) {
-      return home;
-    }
-#endif
-    throw std::runtime_error(
-        "Environment variable CUDA_HOME or CUDA_PATH is not set.");
-  }();
-  return home;
-}
-
-// Get the cache directory for storing compiled results.
-const std::filesystem::path& ptx_cache_dir() {
-  static std::filesystem::path cache = []() -> std::filesystem::path {
-    std::filesystem::path cache;
-    if (auto c = std::getenv("MLX_PTX_CACHE"); c) {
-      cache = c;
-    } else {
-      cache = std::filesystem::temp_directory_path() / "mlx" / "ptx";
-    }
-    if (!std::filesystem::exists(cache)) {
-      std::error_code error;
-      if (!std::filesystem::create_directories(cache, error)) {
-        return std::filesystem::path();
-      }
-    }
-    return cache;
-  }();
-  return cache;
-}
-
-// Try to read the cached |ptx| and |ptx_kernels| from |cache_dir|.
-bool read_cached_ptx(
-    const std::filesystem::path& cache_dir,
-    const std::string& module_name,
-    std::vector<char>* ptx,
-    std::vector<std::pair<std::string, std::string>>* ptx_kernels) {
-  if (cache_dir.empty()) {
-    return false;
-  }
-
-  auto ptx_path = cache_dir / (module_name + ".ptx");
-  std::error_code error;
-  auto ptx_size = std::filesystem::file_size(ptx_path, error);
-  if (error) {
-    return false;
-  }
-  std::ifstream ptx_file(ptx_path, std::ios::binary);
-  if (!ptx_file.good()) {
-    return false;
-  }
-  ptx->resize(ptx_size);
-  ptx_file.read(ptx->data(), ptx_size);
-
-  std::ifstream txt_file(cache_dir / (module_name + ".txt"), std::ios::binary);
-  std::string line;
-  while (std::getline(txt_file, line)) {
-    auto tab = line.find('\t');
-    if (tab != std::string::npos) {
-      ptx_kernels->emplace_back(line.substr(0, tab), line.substr(tab + 1));
-    }
-  }
-  return true;
-}
-
-// Write the |ptx| and |ptx_kernels| to |cache_dir| with |name|.
-void write_cached_ptx(
-    const std::filesystem::path& cache_dir,
-    const std::string& module_name,
-    const std::vector<char>& ptx,
-    const std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
-  if (cache_dir.empty()) {
-    return;
-  }
-
-  std::ofstream ptx_file(cache_dir / (module_name + ".ptx"), std::ios::binary);
-  if (!ptx.empty()) {
-    ptx_file.write(&ptx.front(), ptx.size());
-  }
-  std::ofstream txt_file(cache_dir / (module_name + ".txt"), std::ios::binary);
-  for (const auto& [name, mangled] : ptx_kernels) {
-    txt_file << name << "\t" << mangled << std::endl;
-  }
-}
-
-// Return if |device|'s version is not newer than |major|.|minor| version.
-inline bool version_lower_equal(Device& device, int major, int minor) {
-  if (device.compute_capability_major() < major) {
-    return true;
-  } else if (device.compute_capability_major() == major) {
-    return device.compute_capability_minor() <= minor;
-  } else {
-    return false;
-  }
-}
-
-// Return whether NVRTC supports compiling to |device|'s SASS code.
-bool compiler_supports_device_sass(Device& device) {
-  int nvrtc_major, nvrtc_minor;
-  CHECK_NVRTC_ERROR(nvrtcVersion(&nvrtc_major, &nvrtc_minor));
-  if (nvrtc_major < 9) {
-    return false;
-  } else if (nvrtc_major == 9) {
-    return version_lower_equal(device, 7, 2);
-  } else if (nvrtc_major == 10) {
-    return version_lower_equal(device, 7, 5);
-  } else if (nvrtc_major == 11 && nvrtc_minor == 0) {
-    return version_lower_equal(device, 8, 0);
-  } else if (nvrtc_major == 11 && nvrtc_minor < 8) {
-    return version_lower_equal(device, 8, 6);
-  } else {
-    return true;
-  }
-}
-
-#define INCLUDE_PREFIX "mlx/backend/cuda/device/"
-
-constexpr const char* g_include_names[] = {
-    INCLUDE_PREFIX "atomic_ops.cuh",
-    INCLUDE_PREFIX "binary_ops.cuh",
-    INCLUDE_PREFIX "cast_op.cuh",
-    INCLUDE_PREFIX "config.h",
-    INCLUDE_PREFIX "cucomplex_math.cuh",
-    INCLUDE_PREFIX "fp16_math.cuh",
-    INCLUDE_PREFIX "indexing.cuh",
-    INCLUDE_PREFIX "scatter_ops.cuh",
-    INCLUDE_PREFIX "unary_ops.cuh",
-    INCLUDE_PREFIX "ternary_ops.cuh",
-    INCLUDE_PREFIX "utils.cuh",
-};
-
-#undef INCLUDE_PREFIX
-
-constexpr const char* g_headers[] = {
-    jit_source_atomic_ops,
-    jit_source_binary_ops,
-    jit_source_cast_op,
-    jit_source_config,
-    jit_source_cucomplex_math,
-    jit_source_fp16_math,
-    jit_source_indexing,
-    jit_source_scatter_ops,
-    jit_source_unary_ops,
-    jit_source_ternary_ops,
-    jit_source_utils,
-};
-
-} // namespace
-
-JitModule::JitModule(
-    Device& device,
-    const std::string& module_name,
-    const KernelBuilder& builder) {
-  // Check cache.
-  std::vector<char> ptx;
-  std::vector<std::pair<std::string, std::string>> ptx_kernels;
-  if (!read_cached_ptx(ptx_cache_dir(), module_name, &ptx, &ptx_kernels)) {
-    // Create program.
-    auto [source_code, kernel_names] = builder();
-    nvrtcProgram prog;
-    CHECK_NVRTC_ERROR(nvrtcCreateProgram(
-        &prog,
-        source_code.c_str(),
-        (module_name + ".cu").c_str(),
-        std::size(g_headers),
-        g_headers,
-        g_include_names));
-    std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
-        &prog,
-        [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
-    for (const auto& name : kernel_names) {
-      CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
-    }
-
-    // Compile program.
-    bool use_sass = compiler_supports_device_sass(device);
-    std::string compute = fmt::format(
-        "--gpu-architecture={}_{}{}",
-        use_sass ? "sm" : "compute",
-        device.compute_capability_major(),
-        device.compute_capability_minor());
-    std::string include = fmt::format("--include-path={}/include", cuda_home());
-    const char* args[] = {compute.c_str(), include.c_str()};
-    nvrtcResult compile_result =
-        nvrtcCompileProgram(prog, std::size(args), args);
-    if (compile_result != NVRTC_SUCCESS) {
-      size_t log_size;
-      CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
-      std::vector<char> log(log_size + 1, 0);
-      CHECK_NVRTC_ERROR(nvrtcGetProgramLog(prog, log.data()));
-      throw std::runtime_error(
-          fmt::format("Failed to compile kernel: {}.", log.data()));
-    }
-
-    // Get mangled names of kernel names.
-    for (const auto& name : kernel_names) {
-      const char* mangled;
-      CHECK_NVRTC_ERROR(nvrtcGetLoweredName(prog, name.c_str(), &mangled));
-      ptx_kernels.emplace_back(name, mangled);
-    }
-
-    // Get ptx data.
-    size_t ptx_size;
-    if (use_sass) {
-      CHECK_NVRTC_ERROR(nvrtcGetCUBINSize(prog, &ptx_size));
-    } else {
-      CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
-    }
-    ptx.resize(ptx_size, 0);
-    if (use_sass) {
-      CHECK_NVRTC_ERROR(nvrtcGetCUBIN(prog, ptx.data()));
-    } else {
-      CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
-    }
-    write_cached_ptx(ptx_cache_dir(), module_name, ptx, ptx_kernels);
-  }
-
-  // Load module.
-  char jit_log[4089] = {};
-  CUjit_option options[] = {
-      CU_JIT_ERROR_LOG_BUFFER, CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES};
-  void* values[] = {jit_log, reinterpret_cast<void*>(std::size(jit_log) - 1)};
-  CUresult jit_result = cuModuleLoadDataEx(
-      &module_, ptx.data(), std::size(options), options, values);
-  if (jit_result != CUDA_SUCCESS) {
-    throw std::runtime_error(fmt::format(
-        "Failed to load compiled {} kernel: {}.", module_name, jit_log));
-  }
-
-  // Load kernels.
-  for (const auto& [name, mangled] : ptx_kernels) {
-    CUfunction kernel;
-    CHECK_CUDA_ERROR(cuModuleGetFunction(&kernel, module_, mangled.c_str()));
-    kernels_[name] = kernel;
-  }
-}
-
-JitModule::~JitModule() {
-  CHECK_CUDA_ERROR(cuModuleUnload(module_));
-}
-
-CUfunction JitModule::get_kernel(const std::string& kernel_name) {
-  auto it = kernels_.find(kernel_name);
-  if (it == kernels_.end()) {
-    throw std::runtime_error(
-        fmt::format("There is no kernel named {}.", kernel_name));
-  }
-  return it->second;
-}
-
-JitModule& get_jit_module(
-    const mlx::core::Device& device,
-    const std::string& name,
-    const KernelBuilder& builder) {
-  static std::unordered_map<std::string, JitModule> map;
-  auto it = map.find(name);
-  if (it == map.end()) {
-    it = map.try_emplace(name, cu::device(device), name, builder).first;
-  }
-  return it->second;
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/jit_module.h

@@ -1,107 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/array.h"
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/config.h"
-
-#include <deque>
-#include <unordered_map>
-#include <utility>
-#include <variant>
-
-#include <cuda.h>
-#include <fmt/format.h>
-
-namespace mlx::core::cu {
-
-class Device;
-
-using KernelBuilderResult = std::pair<
-    /* source code */ std::string,
-    /* kernel names */ std::vector<std::string>>;
-using KernelBuilder = std::function<KernelBuilderResult()>;
-
-struct KernelArgs {
-  void** args() {
-    return args_.data();
-  }
-
-  void append(const array& a) {
-    append(reinterpret_cast<CUdeviceptr>(a.data<void>()));
-  }
-
-  template <typename T>
-  void append(T val) {
-    storage_.emplace_back(val);
-    append_ptr(&storage_.back());
-  }
-
-  template <typename T>
-  void append(std::vector<T> vec) {
-    if (vec.empty()) {
-      // The nullptr can not be used as arg, pass something not null.
-      append(std::monostate{});
-    } else {
-      append_ptr(vec.data());
-      storage_.emplace_back(std::move(vec));
-    }
-  }
-
-  // Make sure the arg is copied to an array with size of NDIM.
-  template <size_t NDIM = MAX_NDIM, typename T>
-  void append_ndim(std::vector<T> vec) {
-    if (vec.size() > NDIM) {
-      throw std::runtime_error(
-          fmt::format("ndim can not be larger than {}.", NDIM));
-    }
-    vec.resize(NDIM);
-    append(std::move(vec));
-  }
-
-  void append_ptr(const void* v) {
-    args_.push_back(const_cast<void*>(v));
-  }
-
- private:
-  std::vector<void*> args_;
-
-  // The cuLaunchKernel API requires passing pointers to arguments so store
-  // temporary values untill kernel is launched.
-  using Arg = std::variant<
-      std::monostate,
-      CUdeviceptr,
-      int32_t,
-      uint32_t,
-      int64_t,
-      std::vector<const void*>,
-      std::vector<int32_t>,
-      std::vector<int64_t>>;
-  std::deque<Arg> storage_;
-};
-
-class JitModule {
- public:
-  JitModule(
-      Device& device,
-      const std::string& module_name,
-      const KernelBuilder& builder);
-  ~JitModule();
-
-  JitModule(const JitModule&) = delete;
-  JitModule& operator=(const JitModule&) = delete;
-  CUfunction get_kernel(const std::string& kernel_name);
-
- private:
-  CUmodule module_{nullptr};
-  std::unordered_map<std::string, CUfunction> kernels_;
-};
-
-JitModule& get_jit_module(
-    const mlx::core::Device& device,
-    const std::string& name,
-    const KernelBuilder& builder);
-
-} // namespace mlx::core::cu

mlx/backend/cuda/kernel_utils.cu

@@ -23,11 +23,4 @@ dim3 get_2d_grid_dims(
   return dim3(std::get<0>(dims), std::get<1>(dims), std::get<2>(dims));
 }
 
-std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2) {
-  auto [grid, block] = get_grid_and_block_common(dim0, dim1, dim2);
-  auto [gx, gy, gz] = grid;
-  auto [bx, by, bz] = block;
-  return std::make_pair(dim3(gx, gy, gz), dim3(bx, by, bz));
-}
-
 } // namespace mlx::core

mlx/backend/cuda/kernel_utils.cuh

@@ -1,18 +1,15 @@
 // Copyright © 2025 Apple Inc.
 
 // This file includes host-only utilies for writing CUDA kernels, the difference
-// from backend/cuda/device/utils.cuh is that the latter file only include
+// from backend/cuda/kernels/utils.cuh is that the latter file only include
 // device-only code.
 
 #pragma once
 
-#include <type_traits>
-
 #include "mlx/array.h"
-#include "mlx/backend/cuda/device/utils.cuh"
+#include "mlx/backend/cuda/kernels/utils.cuh"
 
 #include <cuComplex.h>
-#include <cuda.h>
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 #include <fmt/format.h>
@@ -20,46 +17,35 @@
 
 namespace mlx::core {
 
-template <typename F>
-void dispatch_1_2_3(int n, F&& f) {
-  switch (n) {
-    case 1:
-      f(std::integral_constant<int, 1>{});
-      break;
-    case 2:
-      f(std::integral_constant<int, 2>{});
-      break;
-    case 3:
-      f(std::integral_constant<int, 3>{});
-      break;
+// Convert a number between 1~3 to constexpr.
+#define MLX_SWITCH_1_2_3(N, NDIM, ...) \
+  switch (N) {                         \
+    case 1: {                          \
+      constexpr int NDIM = 1;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
+    case 2: {                          \
+      constexpr int NDIM = 2;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
+    case 3: {                          \
+      constexpr int NDIM = 3;          \
+      __VA_ARGS__;                     \
+      break;                           \
+    }                                  \
   }
-}
 
-template <typename F>
-void dispatch_bool(bool v, F&& f) {
-  if (v) {
-    f(std::true_type{});
-  } else {
-    f(std::false_type{});
+// Like MLX_SWITCH_ALL_TYPES but for booleans.
+#define MLX_SWITCH_BOOL(BOOL, BOOL_ALIAS, ...) \
+  if (BOOL) {                                  \
+    constexpr bool BOOL_ALIAS = true;          \
+    __VA_ARGS__;                               \
+  } else {                                     \
+    constexpr bool BOOL_ALIAS = false;         \
+    __VA_ARGS__;                               \
   }
-}
-
-template <typename F>
-void dispatch_block_dim(int threads, F&& f) {
-  if (threads <= WARP_SIZE) {
-    f(std::integral_constant<int, WARP_SIZE>{});
-  } else if (threads <= WARP_SIZE * 2) {
-    f(std::integral_constant<int, WARP_SIZE * 2>{});
-  } else if (threads <= WARP_SIZE * 4) {
-    f(std::integral_constant<int, WARP_SIZE * 4>{});
-  } else if (threads <= WARP_SIZE * 8) {
-    f(std::integral_constant<int, WARP_SIZE * 8>{});
-  } else if (threads <= WARP_SIZE * 16) {
-    f(std::integral_constant<int, WARP_SIZE * 16>{});
-  } else {
-    f(std::integral_constant<int, WARP_SIZE * 32>{});
-  }
-}
 
 // Maps CPU types to CUDA types.
 template <typename T>
@@ -91,11 +77,6 @@ inline constexpr bool is_floating_v =
     cuda::std::is_same_v<T, float> || cuda::std::is_same_v<T, double> ||
     cuda::std::is_same_v<T, float16_t> || cuda::std::is_same_v<T, bfloat16_t>;
 
-// Type traits for detecting complex or real floating point numbers.
-template <typename T>
-inline constexpr bool is_inexact_v =
-    is_floating_v<T> || cuda::std::is_same_v<T, complex64_t>;
-
 // Utility to copy data from vector to array in host.
 template <int NDIM = MAX_NDIM, typename T = int32_t>
 inline cuda::std::array<T, NDIM> const_param(const std::vector<T>& vec) {
@@ -115,19 +96,12 @@ dim3 get_2d_grid_dims(
     const Shape& shape,
     const Strides& strides,
     size_t divisor);
-std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2);
 
 // Return a block size that achieves maximum potential occupancy for kernel.
 template <typename T>
 inline uint max_occupancy_block_dim(T kernel) {
   int _, block_dim;
-  if constexpr (std::is_same_v<T, CUfunction>) {
-    CHECK_CUDA_ERROR(
-        cuOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel, 0, 0, 0));
-  } else {
-    CHECK_CUDA_ERROR(
-        cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
-  }
+  CHECK_CUDA_ERROR(cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
   return block_dim;
 }
 
@@ -136,19 +110,17 @@ inline uint max_occupancy_block_dim(T kernel) {
 template <typename T>
 inline std::tuple<dim3, uint> get_launch_args(
     T kernel,
-    size_t size,
-    const Shape& shape,
-    const Strides& strides,
+    const array& arr,
     bool large,
     int work_per_thread = 1) {
-  size_t nthreads = cuda::ceil_div(size, work_per_thread);
+  size_t nthreads = cuda::ceil_div(arr.size(), work_per_thread);
   uint block_dim = max_occupancy_block_dim(kernel);
   if (block_dim > nthreads) {
     block_dim = nthreads;
   }
   dim3 num_blocks;
   if (large) {
-    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
+    num_blocks = get_2d_grid_dims(arr.shape(), arr.strides(), work_per_thread);
     num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
   } else {
     num_blocks.x = cuda::ceil_div(nthreads, block_dim);
@@ -156,14 +128,4 @@ inline std::tuple<dim3, uint> get_launch_args(
   return std::make_tuple(num_blocks, block_dim);
 }
 
-template <typename T>
-inline std::tuple<dim3, uint> get_launch_args(
-    T kernel,
-    const array& arr,
-    bool large,
-    int work_per_thread = 1) {
-  return get_launch_args(
-      kernel, arr.size(), arr.shape(), arr.strides(), large, work_per_thread);
-}
-
 } // namespace mlx::core

mlx/backend/cuda/kernels/binary_ops.cuh

@@ -1,8 +1,6 @@
 // Copyright © 2025 Apple Inc.
 
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
 
 #include <cuComplex.h>
 #include <cuda/std/array>
@@ -22,7 +20,7 @@ struct FloorDivide {
     if constexpr (cuda::std::is_integral_v<T>) {
       return x / y;
     } else {
-      return truncf(x / y);
+      return trunc(x / y);
     }
   }
 };
@@ -124,26 +122,6 @@ struct LogAddExp {
         ? maxval
         : T(float(maxval) + log1p(expf(minval - maxval)));
   };
-
-  __device__ cuComplex operator()(cuComplex x, cuComplex y) {
-    if (isnan(cuCrealf(x)) || isnan(cuCimagf(x)) || isnan(cuCrealf(y)) ||
-        isnan(cuCimagf(y))) {
-      return {
-          cuda::std::numeric_limits<float>::quiet_NaN(),
-          cuda::std::numeric_limits<float>::quiet_NaN()};
-    }
-    float inf = cuda::std::numeric_limits<float>::infinity();
-    auto maxval = x > y ? x : y;
-    auto minval = x < y ? x : y;
-    if (cuCrealf(minval) == -inf || cuCrealf(maxval) == inf)
-      return maxval;
-    float m = exp(cuCrealf(minval) - cuCrealf(maxval));
-    cuComplex dexp{
-        m * cos(cuCimagf(minval) - cuCimagf(maxval)),
-        m * sin(cuCimagf(minval) - cuCimagf(maxval)),
-    };
-    return maxval + log1p(dexp);
-  }
 };
 
 struct Maximum {
@@ -216,13 +194,6 @@ struct Power {
       }
       return res;
     } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (base.y == 0 && base.x == 0) {
-        if (isnan(exp.x) || isnan(exp.y)) {
-          auto nan = cuda::std::numeric_limits<float>::quiet_NaN();
-          return make_cuFloatComplex(nan, nan);
-        }
-        return make_cuFloatComplex(0.0, 0.0);
-      }
       auto x_theta = atan2f(base.y, base.x);
       auto x_ln_r = 0.5 * logf(base.x * base.x + base.y * base.y);
       auto mag = expf(exp.x * x_ln_r - exp.y * x_theta);

mlx/backend/cuda/kernels/unary_ops.cuh

@@ -2,10 +2,8 @@
 
 #pragma once
 
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <math_constants.h>
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
+#include "mlx/backend/cuda/kernels/utils.cuh"
 
 namespace mlx::core::cu {
 
@@ -27,8 +25,6 @@ struct ArcCos {
   __device__ T operator()(T x) {
     return acos(x);
   }
-
-  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcCosh {
@@ -43,8 +39,6 @@ struct ArcSin {
   __device__ T operator()(T x) {
     return asin(x);
   }
-
-  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcSinh {
@@ -59,8 +53,6 @@ struct ArcTan {
   __device__ T operator()(T x) {
     return atan(x);
   }
-
-  __device__ cuComplex operator()(cuComplex x);
 };
 
 struct ArcTanh {
@@ -191,38 +183,21 @@ struct Imag {
 struct Log {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto r = log(cuCrealf(Abs{}(x)));
-      auto i = atan2f(cuCimagf(x), cuCrealf(x));
-      return {r, i};
-    } else {
-      return log(x);
-    }
+    return log(x);
   }
 };
 
 struct Log2 {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto y = Log{}(x);
-      return {cuCrealf(y) / CUDART_LN2_F, cuCimagf(y) / CUDART_LN2_F};
-    } else {
-      return log2(x);
-    }
+    return log2(x);
   }
 };
 
 struct Log10 {
   template <typename T>
   __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto y = Log{}(x);
-      return {cuCrealf(y) / CUDART_LNT_F, cuCimagf(y) / CUDART_LNT_F};
-      return y;
-    } else {
-      return log10(x);
-    }
+    return log10(x);
   }
 };
 
@@ -267,6 +242,13 @@ struct Round {
   }
 };
 
+struct Rsqrt {
+  template <typename T>
+  __device__ T operator()(T x) {
+    return rsqrt(x);
+  }
+};
+
 struct Sigmoid {
   template <typename T>
   __device__ T operator()(T x) {
@@ -332,29 +314,6 @@ struct Sqrt {
   __device__ T operator()(T x) {
     return sqrt(x);
   }
-
-  __device__ cuComplex operator()(cuComplex x) {
-    auto xr = cuCrealf(x);
-    auto xi = cuCimagf(x);
-    if (xr == 0.0f && xi == 0.0f) {
-      return {0.0f, 0.0f};
-    }
-    auto r = cuCrealf(Abs{}(x));
-    auto a = sqrt((r + xr) / 2.0f);
-    auto b_abs = sqrt((r - xr) / 2.0f);
-    auto b = copysign(b_abs, xi);
-    return {a, b};
-  }
-};
-
-struct Rsqrt {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return rsqrt(x);
-  }
-  __device__ cuComplex operator()(cuComplex x) {
-    return 1.0f / Sqrt{}(x);
-  }
 };
 
 struct Tan {
@@ -387,22 +346,4 @@ struct Tanh {
   }
 };
 
-__device__ cuComplex ArcCos::operator()(cuComplex x) {
-  auto i = cuComplex{0.0, 1.0};
-  auto y = Log{}(x + i * Sqrt{}(1.0 - x * x));
-  return {cuCimagf(y), -cuCrealf(y)};
-};
-
-__device__ cuComplex ArcSin::operator()(cuComplex x) {
-  auto i = cuComplex{0.0f, 1.0f};
-  auto y = Log{}(i * x + Sqrt{}(1.0f - x * x));
-  return {cuCimagf(y), -cuCrealf(y)};
-};
-
-__device__ cuComplex ArcTan::operator()(cuComplex x) {
-  auto i = cuComplex{0.0f, 1.0f};
-  auto ix = i * x;
-  return (1.0f / cuComplex{0.0f, 2.0f}) * Log{}((1.0f + ix) / (1.0f - ix));
-};
-
 } // namespace mlx::core::cu

mlx/backend/cuda/kernels/utils.cuh

@@ -0,0 +1,104 @@
+// Copyright © 2025 Apple Inc.
+
+// This file must not include any host-only code, utilies that work under both
+// host and device can be put here.
+//
+// See more about the requirements at:
+// https://docs.nvidia.com/cuda/nvrtc/#language
+
+#pragma once
+
+#include <cuComplex.h>
+#include <cuda/std/array>
+#include <cuda/std/limits>
+#include <cuda/std/tuple>
+
+namespace mlx::core::cu {
+
+///////////////////////////////////////////////////////////////////////////////
+// CUDA kernel utils
+///////////////////////////////////////////////////////////////////////////////
+
+// To pass shape/strides to kernels via constant memory, their size must be
+// known at compile time.
+#define MAX_NDIM 8
+
+using Shape = cuda::std::array<int32_t, MAX_NDIM>;
+using Strides = cuda::std::array<int64_t, MAX_NDIM>;
+
+///////////////////////////////////////////////////////////////////////////////
+// Indexing utils
+///////////////////////////////////////////////////////////////////////////////
+
+template <typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
+  IdxT loc = 0;
+  for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+// Optimize when the ndim is known at compile time.
+template <int NDIM, typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc_nd(IdxT elem, const int* shape, const int64_t* strides) {
+  IdxT loc = 0;
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+template <int NDIM, typename IdxT = int64_t>
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_nd(
+    IdxT elem,
+    const int* shape,
+    const int64_t* a_strides,
+    const int64_t* b_strides) {
+  IdxT a_loc = 0;
+  IdxT b_loc = 0;
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    int dim_idx = elem % shape[i];
+    a_loc += dim_idx * a_strides[i];
+    b_loc += dim_idx * b_strides[i];
+    elem /= shape[i];
+  }
+  return cuda::std::make_tuple(a_loc, b_loc);
+}
+
+// Optimized version when ndim is larger than 4.
+template <typename IdxT = int64_t>
+inline __host__ __device__ IdxT
+elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
+  IdxT loc = elem_to_loc_nd<3>(elem, shape, strides);
+  for (int i = ndim - 1; i >= 3; --i) {
+    loc += (elem % shape[i]) * IdxT(strides[i]);
+    elem /= shape[i];
+  }
+  return loc;
+}
+
+template <typename IdxT = int64_t>
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
+    IdxT elem,
+    const int* shape,
+    const int64_t* a_strides,
+    const int64_t* b_strides,
+    int ndim) {
+  auto [a_loc, b_loc] = elem_to_loc_nd<3>(elem, shape, a_strides, b_strides);
+  for (int i = ndim - 1; i >= 3; --i) {
+    int dim_idx = elem % shape[i];
+    a_loc += dim_idx * a_strides[i];
+    b_loc += dim_idx * b_strides[i];
+    elem /= shape[i];
+  }
+  return cuda::std::make_tuple(a_loc, b_loc);
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/layer_norm.cu

@@ -1,405 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/fast_primitives.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-inline __device__ float3 plus_f3(const float3& a, const float3& b) {
-  return {a.x + b.x, a.y + b.y, a.z + b.z};
-}
-
-// Similar to cub::BlockReduce, but result is broadcasted to every thread.
-template <typename T, int BLOCK_DIM>
-struct BlockBroadcastReduce {
-  static_assert(WARP_SIZE <= BLOCK_DIM && BLOCK_DIM <= WARP_SIZE * WARP_SIZE);
-  static_assert(BLOCK_DIM % WARP_SIZE == 0);
-  using TempStorage = T[BLOCK_DIM / WARP_SIZE];
-
-  cg::thread_block& block;
-  TempStorage& temp;
-
-  template <typename Op>
-  __device__ T Reduce(const T& input, const Op& op, const T& init_value) {
-    auto warp = cg::tiled_partition<WARP_SIZE>(block);
-    T x = cg::reduce(warp, input, op);
-    if (warp.thread_rank() == 0) {
-      temp[warp.meta_group_rank()] = x;
-    }
-    block.sync();
-    x = warp.thread_rank() < warp.meta_group_size() ? temp[warp.thread_rank()]
-                                                    : init_value;
-    return cg::reduce(warp, x, op);
-  }
-
-  __device__ T Sum(const T& input) {
-    return Reduce(input, cg::plus<T>{}, T{});
-  }
-};
-
-template <typename T, int BLOCK_DIM, int N_READS = 4>
-__global__ void layer_norm(
-    const T* x,
-    const T* w,
-    const T* b,
-    T* out,
-    float eps,
-    int32_t axis_size,
-    int64_t w_stride,
-    int64_t b_stride) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-
-  using BlockReduceT = BlockBroadcastReduce<float, BLOCK_DIM>;
-  __shared__ typename BlockReduceT::TempStorage temp;
-
-  x += grid.block_rank() * axis_size;
-  out += grid.block_rank() * axis_size;
-
-  // Sum.
-  float sum = 0;
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS] = {};
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
-  }
-  sum = BlockReduceT{block, temp}.Sum(sum);
-
-  // Mean.
-  float mean = sum / axis_size;
-
-  // Normalizer.
-  float normalizer = 0;
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-    for (int i = 0; i < N_READS; ++i) {
-      float t = static_cast<float>(xn[i]) - mean;
-      normalizer += t * t;
-    }
-  }
-  normalizer = BlockReduceT{block, temp}.Sum(normalizer);
-  normalizer = rsqrt(normalizer / axis_size + eps);
-
-  // Outputs.
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    T bn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(b, b_stride), bn, axis_size);
-    for (int i = 0; i < N_READS; ++i) {
-      float norm = (static_cast<float>(xn[i]) - mean) * normalizer;
-      xn[i] = wn[i] * static_cast<T>(norm) + bn[i];
-    }
-    cub::StoreDirectBlocked(index, out, xn, axis_size);
-  }
-}
-
-template <typename T, bool HAS_W, int BLOCK_DIM, int N_READS = 4>
-__global__ void layer_norm_vjp(
-    const T* x,
-    const T* w,
-    const T* g,
-    T* gx,
-    T* gw,
-    float eps,
-    int32_t axis_size,
-    int64_t w_stride) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-
-  using BlockReduceF = BlockBroadcastReduce<float, BLOCK_DIM>;
-  using BlockReduceF3 = BlockBroadcastReduce<float3, BLOCK_DIM>;
-  __shared__ union {
-    typename BlockReduceF::TempStorage f;
-    typename BlockReduceF3::TempStorage f3;
-  } temp;
-
-  x += grid.block_rank() * axis_size;
-  g += grid.block_rank() * axis_size;
-  gx += grid.block_rank() * axis_size;
-  gw += grid.block_rank() * axis_size;
-
-  // Sum.
-  float sum = 0;
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS] = {};
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
-  }
-  sum = BlockReduceF{block, temp.f}.Sum(sum);
-
-  // Mean.
-  float mean = sum / axis_size;
-
-  // Normalizer.
-  float3 factors = {};
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    T xn[N_READS];
-    T wn[N_READS] = {};
-    T gn[N_READS] = {};
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      float t = static_cast<float>(xn[i]) - mean;
-      float wi = wn[i];
-      float gi = gn[i];
-      float wg = wi * gi;
-      factors = plus_f3(factors, {wg, wg * t, t * t});
-    }
-  }
-  factors = BlockReduceF3{block, temp.f3}.Reduce(factors, plus_f3, {});
-  float meanwg = factors.x / axis_size;
-  float meanwgxc = factors.y / axis_size;
-  float normalizer2 = 1 / (factors.z / axis_size + eps);
-  float normalizer = sqrt(normalizer2);
-
-  // Outputs.
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    T gn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      float xi = (static_cast<float>(xn[i]) - mean) * normalizer;
-      float wi = wn[i];
-      float gi = gn[i];
-      xn[i] = normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2;
-      if constexpr (HAS_W) {
-        wn[i] = gi * xi;
-      }
-    }
-    cub::StoreDirectBlocked(index, gx, xn, axis_size);
-    if constexpr (HAS_W) {
-      cub::StoreDirectBlocked(index, gw, wn, axis_size);
-    }
-  }
-}
-
-} // namespace cu
-
-namespace fast {
-
-bool LayerNorm::use_fallback(Stream s) {
-  return s.device == Device::cpu;
-}
-
-// TODO: There are duplicate code with backend/metal/normalization.cpp
-void LayerNorm::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("LayerNorm::eval_gpu");
-  auto& s = stream();
-  auto& out = outputs[0];
-
-  // Make sure that the last dimension is contiguous.
-  auto set_output = [&s, &out](const array& x) {
-    bool no_copy = x.flags().contiguous && x.strides()[x.ndim() - 1] == 1;
-    if (no_copy && x.ndim() > 1) {
-      auto s = x.strides()[x.ndim() - 2];
-      no_copy &= (s == 0 || s == x.shape().back());
-    }
-    if (no_copy) {
-      if (x.is_donatable()) {
-        out.copy_shared_buffer(x);
-      } else {
-        out.set_data(
-            allocator::malloc(x.data_size() * x.itemsize()),
-            x.data_size(),
-            x.strides(),
-            x.flags());
-      }
-      return x;
-    } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_gpu(x, x_copy, CopyType::General, s);
-      out.copy_shared_buffer(x_copy);
-      return x_copy;
-    }
-  };
-
-  const array x = set_output(inputs[0]);
-  const array& w = inputs[1];
-  const array& b = inputs[2];
-
-  int32_t axis_size = x.shape().back();
-  int32_t n_rows = x.data_size() / axis_size;
-  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
-  int64_t b_stride = (b.ndim() == 1) ? b.strides()[0] : 0;
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "layernorm", [&](auto type_tag) {
-    constexpr uint32_t N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      auto kernel = cu::layer_norm<DataType, block_dim(), N_READS>;
-      encoder.add_kernel_node(
-          kernel,
-          n_rows,
-          block_dim(),
-          x.data<DataType>(),
-          w.data<DataType>(),
-          b.data<DataType>(),
-          out.data<DataType>(),
-          eps_,
-          axis_size,
-          w_stride,
-          b_stride);
-    });
-  });
-}
-
-void LayerNormVJP::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("LayerNormVJP::eval_gpu");
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  // Ensure row contiguity. We could relax this step by checking that the array
-  // is contiguous (no broadcasts or holes) and that the input strides are the
-  // same as the cotangent strides but for now this is simpler.
-  auto check_input = [&s](const array& x, bool& copied) {
-    if (x.flags().row_contiguous) {
-      copied = false;
-      return x;
-    }
-    copied = true;
-    array x_copy(x.shape(), x.dtype(), nullptr, {});
-    copy_gpu(x, x_copy, CopyType::General, s);
-    return x_copy;
-  };
-  bool donate_x = inputs[0].is_donatable();
-  bool donate_g = inputs[3].is_donatable();
-  bool copied;
-  auto x = check_input(inputs[0], copied);
-  donate_x |= copied;
-  const array& w = inputs[1];
-  const array& b = inputs[2];
-  bool g_copied;
-  auto g = check_input(inputs[3], g_copied);
-  donate_g |= g_copied;
-  array& gx = outputs[0];
-  array& gw = outputs[1];
-  array& gb = outputs[2];
-
-  // Check whether we had a weight.
-  bool has_w = w.ndim() != 0;
-
-  // Allocate space for the outputs.
-  bool g_in_gx = false;
-  if (donate_x) {
-    gx.copy_shared_buffer(x);
-  } else if (donate_g) {
-    gx.copy_shared_buffer(g);
-    g_in_gx = true;
-  } else {
-    gx.set_data(allocator::malloc(gx.nbytes()));
-  }
-  if (g_copied && !g_in_gx) {
-    encoder.add_temporary(g);
-  }
-
-  int32_t axis_size = x.shape().back();
-  int32_t n_rows = x.data_size() / axis_size;
-  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
-
-  // Allocate a temporary to store the gradients for w and allocate the output
-  // gradient accumulators.
-  array gw_temp =
-      (has_w) ? array({n_rows, x.shape().back()}, gw.dtype(), nullptr, {}) : w;
-  bool g_in_gw = false;
-  if (has_w) {
-    if (!g_in_gx && donate_g) {
-      g_in_gw = true;
-      gw_temp.copy_shared_buffer(g);
-    } else {
-      gw_temp.set_data(allocator::malloc(gw_temp.nbytes()));
-      encoder.add_temporary(gw_temp);
-    }
-  }
-
-  // The gradient for b in case we had a b.
-  bool has_gb = (gb.ndim() == 1 && gb.size() == axis_size);
-  if (has_gb) {
-    ReductionPlan plan(
-        ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
-    col_reduce(encoder, g, gb, Reduce::ReduceType::Sum, {0}, plan);
-  }
-
-  // Insert dependency if `g` was donated
-  if ((g_in_gx || g_in_gw) && has_gb) {
-    encoder.set_input_array(gb);
-  }
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_input_array(g);
-  encoder.set_output_array(gx);
-  encoder.set_output_array(gw_temp);
-  dispatch_float_types(gx.dtype(), "layernorm_vjp", [&](auto type_tag) {
-    dispatch_bool(has_w, [&](auto has_w_constant) {
-      constexpr int N_READS = 4;
-      dispatch_block_dim(
-          cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-            using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-            auto kernel = cu::layer_norm_vjp<
-                DataType,
-                has_w_constant.value,
-                block_dim(),
-                N_READS>;
-            encoder.add_kernel_node(
-                kernel,
-                n_rows,
-                block_dim(),
-                x.data<DataType>(),
-                w.data<DataType>(),
-                g.data<DataType>(),
-                gx.data<DataType>(),
-                gw_temp.data<DataType>(),
-                eps_,
-                axis_size,
-                w_stride);
-          });
-    });
-  });
-
-  if (has_w) {
-    ReductionPlan plan(
-        ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
-    col_reduce(encoder, gw_temp, gw, Reduce::ReduceType::Sum, {0}, plan);
-  }
-}
-
-} // namespace fast
-
-} // namespace mlx::core

mlx/backend/cuda/logsumexp.cu

@@ -1,162 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/cast_op.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T>
-inline __device__ T softmax_exp(T x) {
-  // Softmax doesn't need high precision exponential cause x is gonna be in
-  // (-oo, 0] anyway and subsequently it will be divided by sum(exp(x_i)).
-  return __expf(x);
-}
-
-template <typename T, typename AccT, int BLOCK_DIM, int N_READS = 4>
-__global__ void logsumexp(const T* in, T* out, int axis_size) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  in += grid.block_rank() * axis_size;
-
-  cg::greater<AccT> max_op;
-  cg::plus<AccT> plus_op;
-
-  // Thread reduce.
-  AccT prevmax;
-  AccT maxval = Limits<AccT>::finite_min();
-  AccT normalizer = 0;
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    AccT vals[N_READS];
-    cub::LoadDirectBlocked(
-        r * BLOCK_DIM + block.thread_rank(),
-        make_cast_iterator<AccT>(in),
-        vals,
-        axis_size,
-        Limits<AccT>::min());
-    prevmax = maxval;
-    maxval = max_op(maxval, cub::ThreadReduce(vals, max_op));
-    // Online normalizer calculation for softmax:
-    // https://github.com/NVIDIA/online-softmax
-    normalizer = normalizer * softmax_exp(prevmax - maxval);
-    for (int i = 0; i < N_READS; i++) {
-      normalizer = normalizer + softmax_exp(vals[i] - maxval);
-    }
-  }
-
-  // First warp reduce.
-  prevmax = maxval;
-  maxval = cg::reduce(warp, maxval, max_op);
-  normalizer = normalizer * softmax_exp(prevmax - maxval);
-  normalizer = cg::reduce(warp, normalizer, plus_op);
-
-  __shared__ AccT local_max[WARP_SIZE];
-  __shared__ AccT local_normalizer[WARP_SIZE];
-
-  // Write to shared memory and do second warp reduce.
-  prevmax = maxval;
-  if (warp.thread_rank() == 0) {
-    local_max[warp.meta_group_rank()] = maxval;
-  }
-  block.sync();
-  maxval = warp.thread_rank() < warp.meta_group_size()
-      ? local_max[warp.thread_rank()]
-      : Limits<AccT>::finite_min();
-  maxval = cg::reduce(warp, maxval, max_op);
-  normalizer = normalizer * softmax_exp(prevmax - maxval);
-  if (warp.thread_rank() == 0) {
-    local_normalizer[warp.meta_group_rank()] = normalizer;
-  }
-  block.sync();
-  normalizer = warp.thread_rank() < warp.meta_group_size()
-      ? local_normalizer[warp.thread_rank()]
-      : AccT{};
-  normalizer = cg::reduce(warp, normalizer, plus_op);
-
-  // Write output.
-  if (block.thread_rank() == 0) {
-    out[grid.block_rank()] = isinf(maxval) ? maxval : log(normalizer) + maxval;
-  }
-}
-
-} // namespace cu
-
-void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("LogSumExp::eval_gpu");
-  assert(inputs.size() == 1);
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  // Make sure that the last dimension is contiguous.
-  auto ensure_contiguous = [&s, &encoder](const array& x) {
-    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
-      return x;
-    } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_gpu(x, x_copy, CopyType::General, s);
-      encoder.add_temporary(x_copy);
-      return x_copy;
-    }
-  };
-
-  auto in = ensure_contiguous(inputs[0]);
-  if (in.flags().row_contiguous) {
-    out.set_data(allocator::malloc(out.nbytes()));
-  } else {
-    auto n = in.shape(-1);
-    auto flags = in.flags();
-    auto strides = in.strides();
-    for (auto& s : strides) {
-      s /= n;
-    }
-    bool col_contig = strides[0] == 1;
-    for (int i = 1; col_contig && i < strides.size(); ++i) {
-      col_contig &=
-          (out.shape(i) == 1 || strides[i - 1] == out.shape(i) * strides[i]);
-    }
-    flags.col_contiguous = col_contig;
-    out.set_data(
-        allocator::malloc(in.nbytes() / n),
-        in.data_size() / n,
-        std::move(strides),
-        flags);
-  }
-
-  int axis_size = in.shape().back();
-  int n_rows = in.data_size() / axis_size;
-
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "logsumexp", [&](auto type_tag) {
-    constexpr int N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      auto kernel = cu::logsumexp<DataType, float, block_dim(), N_READS>;
-      encoder.add_kernel_node(
-          kernel,
-          n_rows,
-          block_dim(),
-          in.data<DataType>(),
-          out.data<DataType>(),
-          axis_size);
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/matmul.cpp

@@ -5,7 +5,6 @@
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
-#include "mlx/utils.h"
 
 #include <cublasLt.h>
 #include <fmt/format.h>
@@ -42,16 +41,12 @@ class MatMul {
       int64_t ldb,
       int32_t batch_count,
       int64_t a_batch_stride,
-      int64_t b_batch_stride)
-      : handle_(device.lt_handle()) {
+      int64_t b_batch_stride) {
     heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
 
-    auto scale_type = dtype_to_cuda_type(dtype);
-    if (dtype == bfloat16 || dtype == float16) {
-      scale_type = CUDA_R_32F;
-    }
+    auto type = dtype_to_cuda_type(dtype);
     CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
-        &matmul_desc_, dtype_to_compute_type(dtype), scale_type));
+        &matmul_desc_, dtype_to_compute_type(dtype), type));
     int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
     CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
         matmul_desc_,
@@ -70,7 +65,6 @@ class MatMul {
         &op,
         sizeof(cublasOperation_t)));
 
-    auto type = dtype_to_cuda_type(dtype);
     a_desc_ = create_matrix_layout(
         type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
     b_desc_ = create_matrix_layout(
@@ -148,7 +142,7 @@ class MatMul {
     if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
       int ret = 0;
       CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
-          handle_,
+          encoder.device().lt_handle(),
           matmul_desc_,
           a_desc_,
           b_desc_,
@@ -163,46 +157,47 @@ class MatMul {
       }
     }
 
-    void* workspace_ptr = nullptr;
-    if (heuristic_.workspaceSize > 0) {
-      array workspace(
-          allocator::malloc(heuristic_.workspaceSize),
-          {static_cast<int>(heuristic_.workspaceSize)},
-          int8);
-      encoder.add_temporary(workspace);
-      workspace_ptr = workspace.data<void>();
-    }
+    array workspace(
+        allocator::malloc(heuristic_.workspaceSize),
+        {static_cast<int>(heuristic_.workspaceSize)},
+        int8);
+    encoder.add_temporary(workspace);
 
-    auto capture = encoder.capture_context();
-    CHECK_CUBLAS_ERROR(cublasLtMatmul(
-        handle_,
-        matmul_desc_,
-        &alpha,
-        a,
-        a_desc_,
-        b,
-        b_desc_,
-        &beta,
-        c ? c : out,
-        c ? c_desc_ : out_desc_,
-        out,
-        out_desc_,
-        &heuristic_.algo,
-        workspace_ptr,
-        heuristic_.workspaceSize,
-        encoder.stream()));
+    encoder.launch_kernel([&](cudaStream_t stream) {
+      CHECK_CUBLAS_ERROR(cublasLtMatmul(
+          encoder.device().lt_handle(),
+          matmul_desc_,
+          &alpha,
+          a,
+          a_desc_,
+          b,
+          b_desc_,
+          &beta,
+          c ? c : out,
+          c ? c_desc_ : out_desc_,
+          out,
+          out_desc_,
+          &heuristic_.algo,
+          workspace.data<void>(),
+          workspace.nbytes(),
+          stream));
+    });
   }
 
  private:
   cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
     switch (dtype) {
+      case uint8:
+      case uint16:
+      case int8:
+      case int16:
+      case int32:
+        return CUBLAS_COMPUTE_32I;
       case float16:
-        return CUBLAS_COMPUTE_32F;
       case bfloat16:
-        return CUBLAS_COMPUTE_32F;
+        return CUBLAS_COMPUTE_16F;
       case float32:
-        return mlx::core::env::enable_tf32() ? CUBLAS_COMPUTE_32F_FAST_TF32
-                                             : CUBLAS_COMPUTE_32F;
+        return CUBLAS_COMPUTE_32F;
       case float64:
       case complex64:
         return CUBLAS_COMPUTE_64F;
@@ -214,6 +209,16 @@ class MatMul {
 
   cudaDataType_t dtype_to_cuda_type(Dtype dtype) {
     switch (dtype) {
+      case uint8:
+        return CUDA_R_8U;
+      case uint16:
+        return CUDA_R_16U;
+      case int8:
+        return CUDA_R_8I;
+      case int16:
+        return CUDA_R_16I;
+      case int32:
+        return CUDA_R_32I;
       case float16:
         return CUDA_R_16F;
       case bfloat16:
@@ -259,7 +264,6 @@ class MatMul {
     return desc;
   }
 
-  cublasLtHandle_t handle_{nullptr};
   cublasLtMatmulDesc_t matmul_desc_{nullptr};
   cublasLtMatmulPreference_t pref_{nullptr};
   cublasLtMatrixLayout_t a_desc_{nullptr};
@@ -274,7 +278,7 @@ class MatMul {
 namespace {
 
 std::tuple<bool, int64_t, array>
-check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
+check_transpose(std::vector<array>& copies, const Stream& s, const array& arr) {
   auto stx = arr.strides()[arr.ndim() - 2];
   auto sty = arr.strides()[arr.ndim() - 1];
   if (sty == 1 && stx == arr.shape(-1)) {
@@ -284,7 +288,7 @@ check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
   } else {
     array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
     copy_gpu(arr, arr_copy, CopyType::General, s);
-    enc.add_temporary(arr_copy);
+    copies.push_back(arr_copy);
     return std::make_tuple(false, arr.shape(-1), arr_copy);
   }
 }
@@ -318,8 +322,13 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   // Keep a vector with copies to be cleared in the completed buffer to release
   // the arrays
-  auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
-  auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);
+  std::vector<array> copies;
+  auto [a_transposed, lda, a] = check_transpose(copies, s, a_pre);
+  auto [b_transposed, ldb, b] = check_transpose(copies, s, b_pre);
+
+  for (auto& temp : copies) {
+    encoder.add_temporary(temp);
+  }
 
   /////////////////////////////////////////////////////////////////////////////
   // Check and collapse batch dimensions
@@ -344,7 +353,7 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
   // Invoke cublasLt
 
   cu::MatMul matmul(
-      cu::device(s.device),
+      encoder.device(),
       a.dtype(),
       a_transposed,
       M,
@@ -358,19 +367,9 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
       a_batch_strides.back(),
       b_batch_strides.back());
 
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-  auto nbatch = batch_count / batch_shape.back();
-  if (nbatch == 1) {
-    matmul.run(encoder, out.data<int8_t>(), a.data<int8_t>(), b.data<int8_t>());
-    return;
-  }
-
   ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
   ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
-  auto concurrent = encoder.concurrent_context();
-  for (size_t i = 0; i < nbatch; ++i) {
+  for (size_t i = 0; i < batch_count / batch_shape.back(); ++i) {
     matmul.run(
         encoder,
         out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,
@@ -402,9 +401,14 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   // Keep a vector with copies to be cleared in the completed buffer to release
   // the arrays
-  auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
-  auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);
-  auto [c_transposed, ldc, c] = check_transpose(encoder, s, c_pre);
+  std::vector<array> copies;
+  auto [a_transposed, lda, a] = check_transpose(copies, s, a_pre);
+  auto [b_transposed, ldb, b] = check_transpose(copies, s, b_pre);
+  auto [c_transposed, ldc, c] = check_transpose(copies, s, c_pre);
+
+  for (auto& temp : copies) {
+    encoder.add_temporary(temp);
+  }
 
   /////////////////////////////////////////////////////////////////////////////
   // Check and collapse batch dimensions
@@ -432,7 +436,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   // Invoke cublasLt
 
   cu::MatMul matmul(
-      cu::device(s.device),
+      encoder.device(),
       a.dtype(),
       a_transposed,
       M,
@@ -449,29 +453,10 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       b_batch_strides.back(),
       c_batch_strides.back());
 
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-
-  auto nbatch = batch_count / batch_shape.back();
-  if (nbatch == 1) {
-    matmul.run(
-        encoder,
-        out.data<int8_t>(),
-        a.data<int8_t>(),
-        b.data<int8_t>(),
-        c.data<int8_t>(),
-        alpha_,
-        beta_);
-    return;
-  }
-
   ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
   ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
   ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
-  auto concurrent = encoder.concurrent_context();
-  for (size_t i = 0; i < nbatch; ++i) {
+  for (size_t i = 0; i < batch_count / batch_shape.back(); ++i) {
     matmul.run(
         encoder,
         out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,

mlx/backend/cuda/no_cuda.cpp

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

mlx/backend/cuda/primitives.cu

@@ -1,9 +1,9 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/arange.cuh"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/arange.cuh"
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
 #include "mlx/distributed/primitives.h"
 #include "mlx/dtype_utils.h"
 #include "mlx/fast_primitives.h"
@@ -24,21 +24,22 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (out.size() == 0) {
     return;
   }
-  auto& encoder = cu::get_command_encoder(stream());
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
   encoder.set_output_array(out);
-  auto capture = encoder.capture_context();
-  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    using OutType = cuda_type_t<CTYPE>;
-    CTYPE step =
-        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
-    thrust::transform(
-        cu::thrust_policy(encoder.stream()),
-        thrust::counting_iterator<uint32_t>(0),
-        thrust::counting_iterator<uint32_t>(out.data_size()),
-        thrust::device_pointer_cast(out.data<OutType>()),
-        cu::Arange<OutType>{
-            static_cast<OutType>(start_), static_cast<OutType>(step)});
+  encoder.launch_kernel([&, this](cudaStream_t stream) {
+    MLX_SWITCH_INT_FLOAT_TYPES_CHECKED(out.dtype(), "Arange", CTYPE, {
+      using OutType = cuda_type_t<CTYPE>;
+      CTYPE step =
+          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
+      thrust::transform(
+          cu::thrust_policy(stream),
+          thrust::counting_iterator<uint32_t>(0),
+          thrust::counting_iterator<uint32_t>(out.data_size()),
+          thrust::device_pointer_cast(out.data<OutType>()),
+          cu::Arange<OutType>{
+              static_cast<OutType>(start_), static_cast<OutType>(step)});
+    });
   });
 }
 
@@ -70,20 +71,34 @@ bool fast::ScaledDotProductAttention::use_fallback(
     throw std::runtime_error(#func " has no CUDA implementation.");   \
   }
 
+NO_GPU(ArgPartition)
+NO_GPU(ArgReduce)
 NO_GPU(BlockMaskedMM)
+NO_GPU_MULTI(Compiled)
 NO_GPU(Convolution)
+NO_GPU_MULTI(DivMod)
 NO_GPU(DynamicSlice)
 NO_GPU(DynamicSliceUpdate)
 NO_GPU(FFT)
+NO_GPU(Gather)
+NO_GPU(GatherAxis)
 NO_GPU(GatherMM)
 NO_GPU(GatherQMM)
 NO_GPU(Hadamard)
 NO_GPU(Load)
+NO_GPU(LogSumExp)
 NO_GPU_MULTI(LUF)
+NO_GPU(Partition)
 NO_GPU_MULTI(QRF)
 NO_GPU(QuantizedMatmul)
+NO_GPU(RandomBits)
+NO_GPU(Reduce)
 NO_GPU(Scan)
-NO_GPU(SegmentedMM)
+NO_GPU(Scatter)
+NO_GPU(ScatterAxis)
+NO_GPU(Select)
+NO_GPU(SliceUpdate)
+NO_GPU(Softmax)
 NO_GPU_MULTI(SVD)
 NO_GPU(Inverse)
 NO_GPU(Cholesky)
@@ -91,6 +106,11 @@ NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
 namespace fast {
+NO_GPU_USE_FALLBACK(LayerNorm)
+NO_GPU_MULTI(LayerNormVJP)
+NO_GPU_USE_FALLBACK(RMSNorm)
+NO_GPU_MULTI(RMSNormVJP)
+NO_GPU_USE_FALLBACK(RoPE)
 NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(AffineQuantize)
 NO_GPU_MULTI(CustomKernel)

mlx/backend/cuda/random.cu

@@ -1,194 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-__constant__ constexpr uint32_t rotations[2][4] = {
-    {13, 15, 26, 6},
-    {17, 29, 16, 24}};
-
-union rbits {
-  uint2 val;
-  uint8_t bytes[2][4];
-};
-
-__device__ rbits threefry2x32_hash(uint2 key, uint2 count) {
-  uint32_t ks[] = {key.x, key.y, key.x ^ key.y ^ 0x1BD11BDA};
-
-  rbits v;
-  v.val.x = count.x + ks[0];
-  v.val.y = count.y + ks[1];
-
-  for (int i = 0; i < 5; ++i) {
-    for (auto r : rotations[i % 2]) {
-      v.val.x += v.val.y;
-      v.val.y = (v.val.y << r) | (v.val.y >> (32 - r));
-      v.val.y ^= v.val.x;
-    }
-    v.val.x += ks[(i + 1) % 3];
-    v.val.y += ks[(i + 2) % 3] + i + 1;
-  }
-
-  return v;
-}
-
-__global__ void rbitsc(
-    const uint32_t* keys,
-    uint8_t* out,
-    dim3 grid_dims,
-    bool odd,
-    uint32_t bytes_per_key) {
-  auto grid = cg::this_grid();
-  uint thread_index = grid.thread_rank();
-  uint index_x = thread_index % grid_dims.x;
-  uint index_y = thread_index / grid_dims.x;
-  if (index_x >= grid_dims.x || index_y >= grid_dims.y) {
-    return;
-  }
-
-  auto kidx = 2 * index_x;
-  auto key = uint2{keys[kidx], keys[kidx + 1]};
-  auto half_size = grid_dims.y - odd;
-  out += index_x * bytes_per_key;
-  bool drop_last = odd && (index_y == half_size);
-  auto bits = threefry2x32_hash(
-      key, uint2{index_y, drop_last ? 0 : index_y + grid_dims.y});
-  size_t idx = size_t(index_y) << 2;
-  for (int i = 0; i < 4; ++i) {
-    out[idx + i] = bits.bytes[0][i];
-  }
-  if (!drop_last) {
-    idx = (drop_last ? 0 : size_t(index_y) + grid_dims.y) << 2;
-    if ((index_y + 1) == half_size && (bytes_per_key % 4) > 0) {
-      int edge_bytes = (bytes_per_key % 4);
-      for (int i = 0; i < edge_bytes; ++i) {
-        out[idx + i] = bits.bytes[1][i];
-      }
-    } else {
-      for (int i = 0; i < 4; ++i) {
-        out[idx + i] = bits.bytes[1][i];
-      }
-    }
-  }
-}
-
-__global__ void rbits(
-    const uint32_t* keys,
-    uint8_t* out,
-    dim3 grid_dims,
-    bool odd,
-    uint32_t bytes_per_key,
-    int32_t ndim,
-    const __grid_constant__ Shape key_shape,
-    const __grid_constant__ Strides key_strides) {
-  auto grid = cg::this_grid();
-  uint thread_index = grid.thread_rank();
-  uint index_x = thread_index % grid_dims.x;
-  uint index_y = thread_index / grid_dims.x;
-  if (index_x >= grid_dims.x || index_y >= grid_dims.y) {
-    return;
-  }
-
-  auto kidx = 2 * index_x;
-  auto k1_elem = elem_to_loc(kidx, key_shape.data(), key_strides.data(), ndim);
-  auto k2_elem =
-      elem_to_loc(kidx + 1, key_shape.data(), key_strides.data(), ndim);
-  auto key = uint2{keys[k1_elem], keys[k2_elem]};
-  auto half_size = grid_dims.y - odd;
-  out += size_t(index_x) * bytes_per_key;
-  bool drop_last = odd && (index_y == half_size);
-  auto bits = threefry2x32_hash(
-      key, uint2{index_y, drop_last ? 0 : index_y + grid_dims.y});
-  size_t idx = size_t(index_y) << 2;
-  for (int i = 0; i < 4; ++i) {
-    out[idx + i] = bits.bytes[0][i];
-  }
-  if (!drop_last) {
-    idx = (drop_last ? 0 : size_t(index_y) + grid_dims.y) << 2;
-    if ((index_y + 1) == half_size && (bytes_per_key % 4) > 0) {
-      int edge_bytes = (bytes_per_key % 4);
-      for (int i = 0; i < edge_bytes; ++i) {
-        out[idx + i] = bits.bytes[1][i];
-      }
-    } else {
-      for (int i = 0; i < 4; ++i) {
-        out[idx + i] = bits.bytes[1][i];
-      }
-    }
-  }
-}
-
-} // namespace cu
-
-void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("RandomBits::eval_gpu");
-  assert(inputs.size() == 1);
-
-  // keys has shape (N1, ..., NK, 2)
-  // out has shape (N1, ..., NK, M1, M2, ...)
-  auto& keys = inputs[0];
-  uint32_t num_keys = keys.size() / 2;
-
-  uint32_t elems_per_key = out.size() / num_keys;
-  uint32_t bytes_per_key = out.itemsize() * elems_per_key;
-  out.set_data(allocator::malloc(out.nbytes()));
-  if (out.size() == 0) {
-    return;
-  }
-
-  uint32_t out_per_key = (bytes_per_key + 4 - 1) / 4;
-  uint32_t half_size = out_per_key / 2;
-  bool odd = out_per_key % 2;
-
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(keys);
-  encoder.set_output_array(out);
-  dim3 grid_dims{num_keys, half_size + odd};
-  int64_t total = grid_dims.x * grid_dims.y;
-  int32_t threads_y = 1;
-  while ((total / threads_y) >= (1U << 31)) {
-    threads_y *= 2;
-  }
-  int32_t threads_x = cuda::ceil_div(total, threads_y);
-  auto [grid, block] = get_grid_and_block(threads_x, threads_y, 1);
-  auto& stream = encoder.stream();
-  if (keys.flags().row_contiguous) {
-    encoder.add_kernel_node(
-        cu::rbitsc,
-        grid,
-        block,
-        keys.data<uint32_t>(),
-        out.data<uint8_t>(),
-        grid_dims,
-        odd,
-        bytes_per_key);
-  } else {
-    encoder.add_kernel_node(
-        cu::rbits,
-        grid,
-        block,
-        keys.data<uint32_t>(),
-        out.data<uint8_t>(),
-        grid_dims,
-        odd,
-        bytes_per_key,
-        keys.ndim(),
-        const_param(keys.shape()),
-        const_param(keys.strides()));
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce.cu

@@ -1,76 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-#include "mlx/backend/gpu/copy.h"
-
-#include <nvtx3/nvtx3.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/fill.h>
-
-#include <cassert>
-
-namespace mlx::core {
-
-void Reduce::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Reduce::eval_gpu");
-  assert(inputs.size() == 1);
-  array in = inputs[0];
-
-  // Make sure no identity reductions trickle down here.
-  assert(!axes_.empty());
-  assert(out.size() != in.size());
-
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  if (in.size() == 0) {
-    init_reduce(encoder, in, out, reduce_type_);
-    return;
-  }
-
-  // Reduce.
-  ReductionPlan plan = get_reduction_plan(in, axes_);
-
-  // If it is a general reduce then copy the input to a contiguous array and
-  // recompute the plan.
-  //
-  // TODO: Instead of copying we can use elem-to-loc to deal with broadcasting
-  //       like we do in Metal. When it comes to broadcasted reduction axes
-  //       some can be ignored eg for min/max.
-  bool broadcasted = false;
-  for (int i = 0, j = 0; i < in.ndim() && !broadcasted; i++) {
-    if (j < axes_.size() && axes_[j] == i) {
-      j++;
-    } else {
-      broadcasted = in.strides(i) == 0;
-    }
-  }
-  if (plan.type == GeneralReduce || broadcasted || !in.flags().contiguous) {
-    array in_copy(in.shape(), in.dtype(), nullptr, {});
-    copy_gpu(in, in_copy, CopyType::General, s);
-    encoder.add_temporary(in_copy);
-    in = in_copy;
-    plan = get_reduction_plan(in, axes_);
-  }
-
-  if (plan.type == ContiguousAllReduce) {
-    all_reduce(encoder, in, out, reduce_type_);
-    return;
-  }
-
-  if (plan.type == ContiguousReduce || plan.type == GeneralContiguousReduce) {
-    row_reduce(encoder, in, out, reduce_type_, axes_, plan);
-    return;
-  }
-
-  if (plan.type == ContiguousStridedReduce ||
-      plan.type == GeneralStridedReduce) {
-    col_reduce(encoder, in, out, reduce_type_, axes_, plan);
-    return;
-  }
-
-  throw std::runtime_error("No plan reached in reduce.");
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/all_reduce.cu

@@ -1,157 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <cub/block/block_load.cuh>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, typename U, typename ReduceOp, int N = 4>
-__global__ void all_reduce(T* in, U* out, size_t block_step, size_t size) {
-  // TODO: Process multiple "rows" in each thread
-  constexpr int M = 1;
-
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  const U init = cu::ReduceInit<ReduceOp, T>::value();
-  ReduceOp op;
-
-  T vals[N];
-  U accs[M];
-  accs[0] = init;
-
-  size_t start = grid.block_rank() * block_step;
-  size_t end = start + block_step;
-  size_t check = min(end, size);
-
-  size_t i = start;
-  for (; i + block.size() * N <= check; i += block.size() * N) {
-    cub::LoadDirectBlockedVectorized<T, N>(block.thread_rank(), in + i, vals);
-    for (int j = 0; j < N; j++) {
-      accs[0] = op(accs[0], cast_to<U>(vals[j]));
-    }
-  }
-
-  if (i < check) {
-    cub::LoadDirectBlocked(
-        block.thread_rank(), in + i, vals, check - i, cast_to<T>(init));
-    for (int i = 0; i < N; i++) {
-      accs[0] = op(accs[0], cast_to<U>(vals[i]));
-    }
-  }
-
-  __shared__ U shared_accumulators[32];
-  block_reduce(block, warp, accs, shared_accumulators, op, init);
-
-  if (block.thread_rank() == 0) {
-    out[grid.block_rank()] = accs[0];
-  }
-}
-
-} // namespace cu
-
-void all_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type) {
-  constexpr int N_READS = 8;
-
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto get_args = [](size_t size, int N) {
-    int threads = std::min(512UL, (size + N - 1) / N);
-    threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
-    int reductions_per_step = threads * N;
-    size_t steps_needed =
-        (size + reductions_per_step - 1) / reductions_per_step;
-
-    int blocks;
-    if (steps_needed < 32) {
-      blocks = 1;
-    } else if (steps_needed < 128) {
-      blocks = 32;
-    } else if (steps_needed < 512) {
-      blocks = 128;
-    } else if (steps_needed < 1024) {
-      blocks = 512;
-    } else {
-      blocks = 1024;
-    }
-
-    size_t steps_per_block = (steps_needed + blocks - 1) / blocks;
-    size_t block_step = steps_per_block * reductions_per_step;
-
-    return std::make_tuple(blocks, threads, block_step);
-  };
-
-  int blocks, threads;
-  size_t block_step;
-  size_t insize = in.size();
-  Dtype dt = in.dtype();
-
-  // Cub doesn't like const pointers for load (sigh).
-  void* indata = const_cast<void*>(in.data<void>());
-
-  // Large array so allocate an intermediate and accumulate there
-  std::tie(blocks, threads, block_step) = get_args(insize, N_READS);
-  encoder.set_input_array(in);
-  if (blocks > 1) {
-    array intermediate({blocks}, out.dtype(), nullptr, {});
-    intermediate.set_data(allocator::malloc(intermediate.nbytes()));
-    encoder.add_temporary(intermediate);
-    encoder.set_output_array(intermediate);
-    dispatch_all_types(dt, [&](auto type_tag) {
-      dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-        using OP = MLX_GET_TYPE(reduce_type_tag);
-        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        using U = typename cu::ReduceResult<OP, T>::type;
-        auto kernel = cu::all_reduce<T, U, OP, N_READS>;
-        encoder.add_kernel_node(
-            kernel,
-            blocks,
-            threads,
-            static_cast<T*>(indata),
-            intermediate.data<U>(),
-            block_step,
-            insize);
-      });
-    });
-
-    // Set the input for the next step and recalculate the blocks
-    indata = intermediate.data<void>();
-    dt = intermediate.dtype();
-    insize = intermediate.size();
-    std::tie(blocks, threads, block_step) = get_args(insize, N_READS);
-    encoder.set_input_array(intermediate);
-  }
-
-  encoder.set_output_array(out);
-  dispatch_all_types(dt, [&](auto type_tag) {
-    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-      using OP = MLX_GET_TYPE(reduce_type_tag);
-      using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      using U = typename cu::ReduceResult<OP, T>::type;
-      auto kernel = cu::all_reduce<T, U, OP, N_READS>;
-      encoder.add_kernel_node(
-          kernel,
-          blocks,
-          threads,
-          static_cast<T*>(indata),
-          out.data<U>(),
-          block_step,
-          insize);
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/col_reduce.cu

@@ -1,265 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include <numeric>
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <cub/block/block_load.cuh>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-struct ColReduceArgs {
-  // The size of the contiguous column reduction.
-  size_t reduction_size;
-  int64_t reduction_stride;
-
-  // Input shape and strides excluding the reduction axes.
-  Shape shape;
-  Strides strides;
-  int ndim;
-
-  // Input shape and strides of the reduction axes (including last dimension).
-  Shape reduce_shape;
-  Strides reduce_strides;
-  int reduce_ndim;
-
-  // The number of column we are reducing. Namely prod(reduce_shape).
-  size_t non_col_reductions;
-
-  ColReduceArgs(
-      const array& in,
-      const ReductionPlan& plan,
-      const std::vector<int>& axes) {
-    using ShapeVector = decltype(plan.shape);
-    using StridesVector = decltype(plan.strides);
-
-    ShapeVector shape_vec;
-    StridesVector strides_vec;
-
-    assert(!plan.shape.empty());
-    reduction_size = plan.shape.back();
-    reduction_stride = plan.strides.back();
-
-    int64_t stride_back = 1;
-    std::tie(shape_vec, strides_vec) = shapes_without_reduction_axes(in, axes);
-    while (!shape_vec.empty() && stride_back < reduction_stride) {
-      stride_back *= shape_vec.back();
-      shape_vec.pop_back();
-      strides_vec.pop_back();
-    }
-    std::vector<int> indices(shape_vec.size());
-    std::iota(indices.begin(), indices.end(), 0);
-    std::sort(indices.begin(), indices.end(), [&](int left, int right) {
-      return strides_vec[left] > strides_vec[right];
-    });
-    ShapeVector sorted_shape;
-    StridesVector sorted_strides;
-    for (auto idx : indices) {
-      sorted_shape.push_back(shape_vec[idx]);
-      sorted_strides.push_back(strides_vec[idx]);
-    }
-    std::tie(shape_vec, strides_vec) =
-        collapse_contiguous_dims(sorted_shape, sorted_strides);
-    shape = const_param(shape_vec);
-    strides = const_param(strides_vec);
-    ndim = shape_vec.size();
-
-    reduce_shape = const_param(plan.shape);
-    reduce_strides = const_param(plan.strides);
-    reduce_ndim = plan.shape.size();
-
-    non_col_reductions = 1;
-    for (int i = 0; i < reduce_ndim - 1; i++) {
-      non_col_reductions *= reduce_shape[i];
-    }
-  }
-};
-
-template <
-    typename T,
-    typename U,
-    typename Op,
-    int NDIM,
-    int BM,
-    int BN,
-    int N_READS = 4>
-__global__ void
-col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  constexpr int threads_per_row = BN / N_READS;
-
-  // Compute the indices for the tile
-  size_t tile_idx = grid.block_rank();
-  size_t tile_x = tile_idx % ((args.reduction_stride + BN - 1) / BN);
-  size_t tile_y = tile_idx / ((args.reduction_stride + BN - 1) / BN);
-
-  // Compute the indices for the thread within the tile
-  short thread_x = block.thread_rank() % threads_per_row;
-  short thread_y = block.thread_rank() / threads_per_row;
-
-  // Move the input pointer
-  in += elem_to_loc(tile_y, args.shape.data(), args.strides.data(), args.ndim) +
-      tile_x * BN;
-
-  // Initialize the running totals
-  Op op;
-  U totals[N_READS];
-  for (int i = 0; i < N_READS; i++) {
-    totals[i] = ReduceInit<Op, T>::value();
-  }
-
-  LoopedElemToLoc<NDIM, (NDIM > 2)> loop(args.reduce_ndim);
-  loop.next(thread_y, args.reduce_shape.data(), args.reduce_strides.data());
-  size_t total = args.non_col_reductions * args.reduction_size;
-  if (tile_x * BN + BN <= args.reduction_stride) {
-    if (args.reduction_stride % N_READS == 0) {
-      for (size_t r = thread_y; r < total; r += BM) {
-        T vals[N_READS];
-        cub::LoadDirectBlockedVectorized(thread_x, in + loop.location(), vals);
-        for (int i = 0; i < N_READS; i++) {
-          totals[i] = op(totals[i], cast_to<U>(vals[i]));
-        }
-        loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
-      }
-    } else {
-      for (size_t r = thread_y; r < total; r += BM) {
-        T vals[N_READS];
-        cub::LoadDirectBlocked(thread_x, in + loop.location(), vals);
-        for (int i = 0; i < N_READS; i++) {
-          totals[i] = op(totals[i], cast_to<U>(vals[i]));
-        }
-        loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
-      }
-    }
-  } else {
-    for (size_t r = thread_y; r < total; r += BM) {
-      T vals[N_READS];
-      cub::LoadDirectBlocked(
-          thread_x,
-          in + loop.location(),
-          vals,
-          args.reduction_stride - tile_x * BN,
-          cast_to<T>(ReduceInit<Op, T>::value()));
-      for (int i = 0; i < N_READS; i++) {
-        totals[i] = op(totals[i], cast_to<U>(vals[i]));
-      }
-      loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
-    }
-  }
-
-  // Do warp reduce for each output.
-  constexpr int n_outputs = BN / threads_per_row;
-  static_assert(BM == 32 && n_outputs == N_READS);
-  __shared__ U shared_vals[BM * BN];
-  short s_idx = thread_y * BN + thread_x * N_READS;
-  for (int i = 0; i < N_READS; i++) {
-    shared_vals[s_idx + i] = totals[i];
-  }
-  block.sync();
-  s_idx = warp.thread_rank() * BN + warp.meta_group_rank() * n_outputs;
-  for (int i = 0; i < n_outputs; i++) {
-    totals[i] = cg::reduce(warp, shared_vals[s_idx + i], op);
-  }
-
-  // Write result.
-  if (warp.thread_rank() == 0) {
-    cub::StoreDirectBlocked(
-        warp.meta_group_rank(),
-        out + tile_y * args.reduction_stride + tile_x * BN,
-        totals,
-        args.reduction_stride - tile_x * BN);
-  }
-}
-
-} // namespace cu
-
-inline auto output_grid_for_col_reduce(
-    const array& out,
-    const cu::ColReduceArgs& args,
-    int bn) {
-  int gx, gy = 1;
-  size_t n_inner_blocks = cuda::ceil_div(args.reduction_stride, bn);
-  size_t n_outer_blocks = out.size() / args.reduction_stride;
-  size_t n_blocks = n_outer_blocks * n_inner_blocks;
-  while (n_blocks / gy > INT32_MAX) {
-    gy *= 2;
-  }
-  gx = cuda::ceil_div(n_blocks, gy);
-
-  return dim3(gx, gy, 1);
-}
-
-void col_reduce_looped(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan,
-    cu::ColReduceArgs args) {
-  // Allocate data for the output using in's layout to access them as
-  // contiguously as possible.
-  allocate_same_layout(out, in, axes);
-
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto type_tag) {
-    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-      dispatch_reduce_ndim(args.reduce_ndim, [&](auto reduce_ndim) {
-        using OP = MLX_GET_TYPE(reduce_type_tag);
-        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        using U = typename cu::ReduceResult<OP, T>::type;
-        // Cub doesn't like const pointers for vectorized loads. (sigh)
-        T* indata = const_cast<T*>(in.data<T>());
-
-        constexpr int N_READS = 4;
-        constexpr int BM = 32;
-        constexpr int BN = 32;
-        dim3 grid = output_grid_for_col_reduce(out, args, BN);
-        int blocks = BM * BN / N_READS;
-        auto kernel =
-            cu::col_reduce_looped<T, U, OP, reduce_ndim(), BM, BN, N_READS>;
-        encoder.add_kernel_node(
-            kernel, grid, blocks, indata, out.data<U>(), args);
-      });
-    });
-  });
-}
-
-void col_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan) {
-  // Current col reduce options
-  //
-  // - col_reduce_looped
-  //
-  //   It is a general strided reduce. Each threadblock computes the output for
-  //   a subrow of the fast moving axis. For instance 32 elements.
-  //
-  // Notes: As in row reduce we opt to read as much in order as possible and
-  //        leave transpositions as they are (contrary to our Metal backend).
-  //
-  //        Moreover we need different kernels for short rows and tuning
-
-  // Make the args struct to help route to the best kernel
-  cu::ColReduceArgs args(in, plan, axes);
-
-  // Fallback col reduce
-  col_reduce_looped(encoder, in, out, reduce_type, axes, plan, args);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/init_reduce.cu

@@ -1,49 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, typename U, typename Op>
-__global__ void init_reduce(U* out, size_t size) {
-  auto index = cg::this_grid().thread_rank();
-  if (index < size) {
-    out[index] = ReduceInit<Op, T>::value();
-  }
-}
-
-} // namespace cu
-
-void init_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type) {
-  // Allocate if needed
-  if (out.data_shared_ptr() == nullptr) {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
-
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto type_tag) {
-    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-      using OP = MLX_GET_TYPE(reduce_type_tag);
-      using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      using U = typename cu::ReduceResult<OP, T>::type;
-      auto kernel = cu::init_reduce<T, U, OP>;
-      dim3 grid = get_2d_grid_dims(out.shape(), out.strides());
-      dim3 block(grid.x < 1024 ? grid.x : 1024, 1, 1);
-      grid.x = (grid.x + 1023) / 1024;
-      encoder.add_kernel_node(kernel, grid, block, out.data<U>(), out.size());
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/reduce.cuh

@@ -1,72 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include <type_traits>
-
-#include "mlx/backend/common/reduce.h"
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/cuda/reduce/reduce_ops.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-namespace mlx::core {
-
-template <typename F>
-void dispatch_reduce_ndim(int ndim, F&& f) {
-  if (ndim == 1) {
-    f(std::integral_constant<int, 1>{});
-  } else if (ndim == 2) {
-    f(std::integral_constant<int, 2>{});
-  } else {
-    f(std::integral_constant<int, 5>{});
-  }
-}
-
-template <typename F>
-void dispatch_reduce_ops(Reduce::ReduceType reduce_type, F&& f) {
-  if (reduce_type == Reduce::ReduceType::And) {
-    f(type_identity<cu::And>{});
-  } else if (reduce_type == Reduce::ReduceType::Or) {
-    f(type_identity<cu::Or>{});
-  } else if (reduce_type == Reduce::ReduceType::Sum) {
-    f(type_identity<cu::Sum>{});
-  } else if (reduce_type == Reduce::ReduceType::Prod) {
-    f(type_identity<cu::Prod>{});
-  } else if (reduce_type == Reduce::ReduceType::Max) {
-    f(type_identity<cu::Max>{});
-  } else if (reduce_type == Reduce::ReduceType::Min) {
-    f(type_identity<cu::Min>{});
-  } else {
-    throw std::invalid_argument("Unknown reduce type.");
-  }
-}
-
-void all_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type);
-
-void row_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan);
-
-void col_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan);
-
-void init_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type);
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/reduce_ops.cuh

@@ -1,187 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device/atomic_ops.cuh"
-#include "mlx/backend/cuda/device/cast_op.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-#include "mlx/backend/cuda/reduce/reduce_utils.cuh"
-
-namespace mlx::core::cu {
-
-// Reduce ops.
-struct And {
-  __device__ __forceinline__ bool operator()(bool a, bool b) {
-    return a && b;
-  }
-
-  __device__ void atomic_update(bool* x, bool y) {
-    atomic_reduce<bool, And>(x, y);
-  }
-};
-
-struct Or {
-  __device__ __forceinline__ bool operator()(bool a, bool b) {
-    return a || b;
-  }
-
-  __device__ void atomic_update(bool* x, bool y) {
-    atomic_reduce<bool, Or>(x, y);
-  }
-};
-
-struct Sum {
-  template <typename T>
-  __device__ __forceinline__ T operator()(T a, T b) {
-    return a + b;
-  }
-
-  template <typename T>
-  __device__ void atomic_update(T* x, T y) {
-    atomic_reduce<T, Sum>(x, y);
-  }
-
-  __device__ void atomic_update(__nv_bfloat16* x, __nv_bfloat16 y) {
-    atomic_add(x, y);
-  }
-
-  __device__ void atomic_update(int* x, int y) {
-    atomic_add(x, y);
-  }
-
-  __device__ void atomic_update(float* x, float y) {
-    atomic_add(x, y);
-  }
-};
-
-struct Prod {
-  template <typename T>
-  __device__ __forceinline__ T operator()(T a, T b) {
-    return a * b;
-  }
-
-  template <typename T>
-  __device__ void atomic_update(T* x, T y) {
-    atomic_reduce<T, Prod>(x, y);
-  }
-};
-
-struct Min {
-  template <typename T>
-  __device__ __forceinline__ T operator()(T a, T b) {
-    return a < b ? a : b;
-  }
-
-  template <typename T>
-  __device__ void atomic_update(T* x, T y) {
-    atomic_reduce<T, Min>(x, y);
-  }
-};
-
-struct Max {
-  template <typename T>
-  __device__ __forceinline__ T operator()(T a, T b) {
-    return a > b ? a : b;
-  }
-
-  template <typename T>
-  __device__ void atomic_update(T* x, T y) {
-    atomic_reduce<T, Max>(x, y);
-  }
-};
-
-// Traits to get the result type of reduce op.
-template <typename Op, typename T>
-struct ReduceResult;
-
-template <typename T>
-struct ReduceResult<And, T> {
-  using type = bool;
-};
-
-template <typename T>
-struct ReduceResult<Or, T> {
-  using type = bool;
-};
-
-template <typename T>
-struct ReduceResult<Sum, T> {
-  using type = cuda::std::conditional_t<
-      (cuda::std::is_integral_v<T> && sizeof(T) <= 4),
-      int32_t,
-      T>;
-};
-
-template <typename T>
-struct ReduceResult<Prod, T> {
-  using type = cuda::std::conditional_t<
-      (cuda::std::is_integral_v<T> && sizeof(T) <= 4),
-      int32_t,
-      T>;
-};
-
-template <typename T>
-struct ReduceResult<Min, T> {
-  using type = T;
-};
-
-template <typename T>
-struct ReduceResult<Max, T> {
-  using type = T;
-};
-
-// Traits to get the init value of reduce op.
-template <typename Op, typename T>
-struct ReduceInit;
-
-template <typename T>
-struct ReduceInit<And, T> {
-  static constexpr __host__ __device__ bool value() {
-    return true;
-  }
-};
-
-template <typename T>
-struct ReduceInit<Or, T> {
-  static constexpr __host__ __device__ bool value() {
-    return false;
-  }
-};
-
-template <typename T>
-struct ReduceInit<Sum, T> {
-  static constexpr __host__ __device__ auto value() {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return T{0, 0};
-    } else {
-      return cast_to<typename ReduceResult<Sum, T>::type>(0);
-    }
-  }
-};
-
-template <typename T>
-struct ReduceInit<Prod, T> {
-  static constexpr __host__ __device__ auto value() {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return T{1, 0};
-    } else {
-      return cast_to<typename ReduceResult<Prod, T>::type>(1);
-    }
-  }
-};
-
-template <typename T>
-struct ReduceInit<Min, T> {
-  static constexpr __host__ __device__ T value() {
-    return Limits<T>::max();
-  }
-};
-
-template <typename T>
-struct ReduceInit<Max, T> {
-  static constexpr __host__ __device__ T value() {
-    return Limits<T>::min();
-  }
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/reduce/reduce_utils.cuh

@@ -1,142 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <numeric>
-
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <size_t N>
-struct uint_by_size;
-template <>
-struct uint_by_size<2> {
-  using type = uint16_t;
-};
-template <>
-struct uint_by_size<4> {
-  using type = uint32_t;
-};
-template <>
-struct uint_by_size<8> {
-  using type = unsigned long long int;
-};
-
-template <typename T, typename Op>
-__device__ void atomic_reduce(T* x, T y) {
-  if constexpr (sizeof(T) == 1) {
-    using U = uint16_t;
-    U* x_int = (U*)((char*)x - ((size_t)x % 2));
-    int shift = ((char*)x - (char*)x_int) * 8;
-    int mask = 0xff << shift;
-    U old_val, new_val;
-    do {
-      old_val = *x_int;
-      T result = Op{}(static_cast<T>((old_val >> shift) & 0xff), y);
-      new_val = (old_val & ~mask) | (result << shift);
-    } while (atomicCAS(x_int, old_val, new_val) != old_val);
-  } else {
-    using U = typename uint_by_size<sizeof(T)>::type;
-    U* x_int = (U*)(x);
-    U old_val, new_val;
-    do {
-      old_val = *x_int;
-      T result = Op{}(*((T*)&old_val), y);
-      new_val = *((U*)&result);
-    } while (atomicCAS(x_int, old_val, new_val) != old_val);
-  }
-}
-
-template <typename T, int N, typename Block, typename Warp, typename Op>
-inline __device__ void
-block_reduce(Block block, Warp warp, T (&vals)[N], T* smem, Op op, T init) {
-  // First reduce in the current warp
-  for (int i = 0; i < N; i++) {
-    vals[i] = cg::reduce(warp, vals[i], op);
-  }
-
-  // Reduce across warps
-  if (warp.meta_group_size() > 1) {
-    if (warp.thread_rank() == 0) {
-      for (int i = 0; i < N; i++) {
-        smem[warp.meta_group_rank() * N + i] = vals[i];
-      }
-    }
-    block.sync();
-    if (warp.thread_rank() < warp.meta_group_size()) {
-      for (int i = 0; i < N; i++) {
-        vals[i] = smem[warp.thread_rank() * N + i];
-      }
-    } else {
-      for (int i = 0; i < N; i++) {
-        vals[i] = init;
-      }
-    }
-    for (int i = 0; i < N; i++) {
-      vals[i] = cg::reduce(warp, vals[i], op);
-    }
-  }
-}
-
-} // namespace cu
-
-inline void allocate_same_layout(
-    array& out,
-    const array& in,
-    const std::vector<int>& axes) {
-  if (in.flags().row_contiguous) {
-    out.set_data(allocator::malloc(out.nbytes()));
-    return;
-  }
-
-  if (out.ndim() < in.ndim()) {
-    throw std::runtime_error(
-        "Reduction without keepdims only supported for row-contiguous inputs");
-  }
-
-  // Calculate the transpositions applied to in in order to apply them to out.
-  std::vector<int> axis_order(in.ndim());
-  std::iota(axis_order.begin(), axis_order.end(), 0);
-  std::sort(axis_order.begin(), axis_order.end(), [&](int left, int right) {
-    return in.strides(left) > in.strides(right);
-  });
-
-  // Transpose the shape and calculate the strides
-  Shape out_shape(in.ndim());
-  Strides out_strides(in.ndim(), 1);
-  for (int i = 0; i < in.ndim(); i++) {
-    out_shape[i] = out.shape(axis_order[i]);
-  }
-  for (int i = in.ndim() - 2; i >= 0; i--) {
-    out_strides[i] = out_shape[i + 1] * out_strides[i + 1];
-  }
-
-  // Reverse the axis order to get the final strides
-  Strides final_strides(in.ndim());
-  for (int i = 0; i < in.ndim(); i++) {
-    final_strides[axis_order[i]] = out_strides[i];
-  }
-
-  // Calculate the resulting contiguity and do the memory allocation
-  auto [data_size, rc, cc] = check_contiguity(out.shape(), final_strides);
-  auto fl = in.flags();
-  fl.row_contiguous = rc;
-  fl.col_contiguous = cc;
-  fl.contiguous = true;
-  out.set_data(
-      allocator::malloc(out.nbytes()),
-      data_size,
-      final_strides,
-      fl,
-      allocator::free);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/reduce/row_reduce.cu

@@ -1,368 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include <numeric>
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-struct RowReduceArgs {
-  // The size of the row being reduced, i.e. the size of last dimension.
-  int row_size;
-
-  // Input shape and strides excluding the reduction axes.
-  Shape shape;
-  Strides strides;
-  int ndim;
-
-  // Input shape and strides of the reduction axes excluding last dimension.
-  Shape reduce_shape;
-  Strides reduce_strides;
-  int reduce_ndim;
-
-  // The number of rows we are reducing. Namely prod(reduce_shape).
-  size_t non_row_reductions;
-
-  RowReduceArgs(
-      const array& in,
-      const ReductionPlan& plan,
-      const std::vector<int>& axes) {
-    assert(!plan.shape.empty());
-    row_size = plan.shape.back();
-
-    auto [shape_vec, strides_vec] = shapes_without_reduction_axes(in, axes);
-    std::tie(shape_vec, strides_vec) =
-        collapse_contiguous_dims(shape_vec, strides_vec);
-    shape = const_param(shape_vec);
-    strides = const_param(strides_vec);
-    ndim = shape_vec.size();
-
-    reduce_shape = const_param(plan.shape);
-    reduce_strides = const_param(plan.strides);
-    reduce_ndim = plan.shape.size() - 1;
-
-    non_row_reductions = 1;
-    for (int i = 0; i < reduce_ndim; i++) {
-      non_row_reductions *= reduce_shape[i];
-    }
-  }
-
-  // Convert shape and strides as if in was contiguous
-  void sort_access_pattern(const array& in, const std::vector<int>& axes) {
-    auto shape_vec = in.shape();
-    auto strides_vec = in.strides();
-    std::tie(shape_vec, strides_vec) =
-        shapes_without_reduction_axes(shape_vec, strides_vec, axes);
-    std::vector<int> indices(shape_vec.size());
-    std::iota(indices.begin(), indices.end(), 0);
-    std::sort(indices.begin(), indices.end(), [&](int left, int right) {
-      return strides_vec[left] > strides_vec[right];
-    });
-    decltype(shape_vec) sorted_shape;
-    decltype(strides_vec) sorted_strides;
-    for (auto idx : indices) {
-      sorted_shape.push_back(shape_vec[idx]);
-      sorted_strides.push_back(strides_vec[idx]);
-    }
-    std::tie(shape_vec, strides_vec) =
-        collapse_contiguous_dims(sorted_shape, sorted_strides);
-    shape = const_param(shape_vec);
-    strides = const_param(strides_vec);
-    ndim = shape_vec.size();
-  }
-};
-
-template <typename T, typename U, typename ReduceOp, int N = 4, int M = 1>
-__global__ void row_reduce_simple(T* in, U* out, size_t n_rows, int size) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  const U init = cu::ReduceInit<ReduceOp, T>::value();
-  ReduceOp op;
-
-  T vals[M][N];
-  U accs[M];
-  for (int i = 0; i < M; i++) {
-    accs[i] = init;
-  }
-
-  const size_t start_row =
-      min(n_rows - M, static_cast<size_t>(grid.block_rank() * M));
-  const size_t full_blocks = size / (block.size() * N);
-  const size_t final_offset = full_blocks * (block.size() * N);
-  in += start_row * size;
-  out += start_row;
-
-  if (size % N == 0) {
-    for (size_t r = 0; r < full_blocks; r++) {
-      for (int k = 0; k < M; k++) {
-        cub::LoadDirectBlockedVectorized<T, N>(
-            block.thread_rank(),
-            in + k * size + r * (block.size() * N),
-            vals[k]);
-        for (int j = 0; j < N; j++) {
-          accs[k] = op(accs[k], cast_to<U>(vals[k][j]));
-        }
-      }
-    }
-  } else {
-    for (size_t r = 0; r < full_blocks; r++) {
-      for (int k = 0; k < M; k++) {
-        cub::LoadDirectBlocked(
-            block.thread_rank(),
-            in + k * size + r * (block.size() * N),
-            vals[k]);
-        for (int j = 0; j < N; j++) {
-          accs[k] = op(accs[k], cast_to<U>(vals[k][j]));
-        }
-      }
-    }
-  }
-
-  if (final_offset < size) {
-    for (int k = 0; k < M; k++) {
-      cub::LoadDirectBlocked(
-          block.thread_rank(),
-          in + k * size + final_offset,
-          vals[k],
-          size,
-          cast_to<T>(init));
-      for (int j = 0; j < N; j++) {
-        accs[k] = op(accs[k], cast_to<U>(vals[k][j]));
-      }
-    }
-  }
-
-  __shared__ U shared_accumulators[32 * M];
-  block_reduce(block, warp, accs, shared_accumulators, op, init);
-
-  if (block.thread_rank() == 0) {
-    if (grid.block_rank() * M + M <= n_rows) {
-      for (int i = 0; i < M; i++) {
-        out[i] = accs[i];
-      }
-    } else {
-      short offset = grid.block_rank() * M + M - n_rows;
-      for (int i = offset; i < M; i++) {
-        out[i] = accs[i];
-      }
-    }
-  }
-}
-
-template <
-    typename T,
-    typename U,
-    typename Op,
-    int NDIM,
-    int BLOCK_DIM,
-    int N_READS = 4>
-__global__ void row_reduce_looped(
-    T* in,
-    U* out,
-    size_t out_size,
-    const __grid_constant__ RowReduceArgs args) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  size_t out_idx = grid.block_rank();
-
-  Op op;
-
-  U total[1];
-  U init = ReduceInit<Op, T>::value();
-  total[0] = init;
-  LoopedElemToLoc<NDIM, (NDIM > 2)> loop(args.reduce_ndim);
-  size_t full_blocks = args.row_size / (BLOCK_DIM * N_READS);
-  size_t final_offset = full_blocks * BLOCK_DIM * N_READS;
-
-  in += elem_to_loc(out_idx, args.shape.data(), args.strides.data(), args.ndim);
-
-  for (size_t n = 0; n < args.non_row_reductions; n++) {
-    for (size_t r = 0; r < full_blocks; r++) {
-      T vals[N_READS];
-      cub::LoadDirectBlockedVectorized<T, N_READS>(
-          block.thread_rank(),
-          in + loop.location() + r * BLOCK_DIM * N_READS,
-          vals);
-      for (int i = 0; i < N_READS; i++) {
-        total[0] = op(total[0], cast_to<U>(vals[i]));
-      }
-    }
-    if (final_offset < args.row_size) {
-      T vals[N_READS];
-      cub::LoadDirectBlocked(
-          block.thread_rank(),
-          in + loop.location() + final_offset,
-          vals,
-          args.row_size - final_offset,
-          cast_to<T>(init));
-      for (int i = 0; i < N_READS; i++) {
-        total[0] = op(total[0], cast_to<U>(vals[i]));
-      }
-    }
-    // TODO: Maybe block.sync() here?
-    loop.next(args.reduce_shape.data(), args.reduce_strides.data());
-  }
-
-  __shared__ U shared_accumulators[32];
-  block_reduce(block, warp, total, shared_accumulators, op, init);
-
-  if (block.thread_rank() == 0) {
-    out[out_idx] = total[0];
-  }
-}
-
-} // namespace cu
-
-void row_reduce_simple(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan) {
-  constexpr int N_READS = 8;
-
-  // Allocate data for the output using in's layout to avoid elem_to_loc in the
-  // kernel.
-  allocate_same_layout(out, in, axes);
-
-  // TODO: If out.size() < 1024 which will be a common case then write this in
-  //       2 passes. Something like 32 * out.size() and then do a warp reduce.
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto type_tag) {
-    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-      using OP = MLX_GET_TYPE(reduce_type_tag);
-      using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      using U = typename cu::ReduceResult<OP, T>::type;
-
-      // Cub doesn't like const pointers for vectorized loads. (sigh)
-      T* indata = const_cast<T*>(in.data<T>());
-
-      // Calculate the grid and block dims
-      size_t reductions = (plan.shape.back() + N_READS - 1) / N_READS;
-      dim3 grid = get_2d_grid_dims(out.shape(), out.strides());
-      int threads = std::min(1024UL, reductions);
-      threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
-      dim3 block(threads, 1, 1);
-
-      // Pick the kernel
-      auto kernel = cu::row_reduce_simple<T, U, OP, N_READS>;
-      if (grid.x >= 1024) {
-        grid.x = (grid.x + 1) / 2;
-        kernel = cu::row_reduce_simple<T, U, OP, N_READS, 2>;
-      }
-
-      int size = plan.shape.back();
-      encoder.add_kernel_node(
-          kernel, grid, block, indata, out.data<U>(), out.size(), size);
-    });
-  });
-}
-
-void row_reduce_looped(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan,
-    cu::RowReduceArgs args) {
-  constexpr int N_READS = 8;
-
-  // Allocate data for the output using in's layout to access them as
-  // contiguously as possible.
-  allocate_same_layout(out, in, axes);
-
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto type_tag) {
-    dispatch_reduce_ops(reduce_type, [&](auto reduce_type_tag) {
-      using OP = MLX_GET_TYPE(reduce_type_tag);
-      using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      using U = typename cu::ReduceResult<OP, T>::type;
-      // Cub doesn't like const pointers for vectorized loads. (sigh)
-      T* indata = const_cast<T*>(in.data<T>());
-
-      // Calculate the grid and block dims
-      args.sort_access_pattern(in, axes);
-      dim3 grid = get_2d_grid_dims(out.shape(), out.strides());
-      size_t reductions = (args.row_size + N_READS - 1) / N_READS;
-      int threads = std::min(1024UL, reductions);
-      threads = ((threads + WARP_SIZE - 1) / WARP_SIZE) * WARP_SIZE;
-      dim3 block(threads, 1, 1);
-
-      // Pick the kernel
-      auto kernel = cu::row_reduce_looped<T, U, OP, 1, 32, N_READS>;
-      dispatch_reduce_ndim(args.reduce_ndim, [&](auto reduce_ndim) {
-        dispatch_block_dim(threads, [&](auto threads_constant) {
-          kernel = cu::row_reduce_looped<
-              T,
-              U,
-              OP,
-              reduce_ndim.value,
-              threads_constant.value,
-              N_READS>;
-          block.x = threads_constant.value;
-        });
-      });
-
-      encoder.add_kernel_node(
-          kernel, grid, block, indata, out.data<U>(), out.size(), args);
-    });
-  });
-}
-
-void row_reduce(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    array& out,
-    Reduce::ReduceType reduce_type,
-    const std::vector<int>& axes,
-    const ReductionPlan& plan) {
-  // Current row reduction options
-  //
-  // - row_reduce_simple
-  //
-  //   That means that we are simply reducing across the fastest moving axis.
-  //   We are reducing 1 or 2 rows per threadblock depending on the size of
-  //   output.
-  //
-  // - row_reduce_looped
-  //
-  //   It is a general row reduction. We are computing 1 output per
-  //   threadblock. We read the fastest moving axis vectorized and loop over
-  //   the rest of the axes.
-  //
-  // Notes: We opt to read as much in order as possible and leave
-  //        transpositions as they are (contrary to our Metal backend).
-
-  // Simple row reduce means that we have 1 axis that we are reducing over and
-  // it has stride 1.
-  if (plan.shape.size() == 1) {
-    row_reduce_simple(encoder, in, out, reduce_type, axes, plan);
-    return;
-  }
-
-  // Make the args struct to help route to the best kernel
-  cu::RowReduceArgs args(in, plan, axes);
-
-  // Fallback row reduce
-  row_reduce_looped(encoder, in, out, reduce_type, axes, plan, std::move(args));
-}
-
-} // namespace mlx::core

mlx/backend/cuda/rms_norm.cu

@@ -1,354 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/cuda/reduce/reduce.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/fast_primitives.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-inline __device__ float2 plus_f2(const float2& a, const float2& b) {
-  return {a.x + b.x, a.y + b.y};
-}
-
-// Similar to cub::BlockReduce, but result is broadcasted to every thread.
-template <typename T, int BLOCK_DIM>
-struct BlockBroadcastReduce {
-  static_assert(WARP_SIZE <= BLOCK_DIM && BLOCK_DIM <= WARP_SIZE * WARP_SIZE);
-  static_assert(BLOCK_DIM % WARP_SIZE == 0);
-  using TempStorage = T[BLOCK_DIM / WARP_SIZE];
-
-  cg::thread_block& block;
-  TempStorage& temp;
-
-  template <typename Op>
-  __device__ T Reduce(const T& input, const Op& op, const T& init_value) {
-    auto warp = cg::tiled_partition<WARP_SIZE>(block);
-    T x = cg::reduce(warp, input, op);
-    if (warp.thread_rank() == 0) {
-      temp[warp.meta_group_rank()] = x;
-    }
-    block.sync();
-    x = warp.thread_rank() < warp.meta_group_size() ? temp[warp.thread_rank()]
-                                                    : init_value;
-    return cg::reduce(warp, x, op);
-  }
-
-  __device__ T Sum(const T& input) {
-    return Reduce(input, cg::plus<T>{}, T{});
-  }
-};
-
-template <typename T, int BLOCK_DIM, int N_READS = 4>
-__global__ void rms_norm(
-    const T* x,
-    const T* w,
-    T* out,
-    float eps,
-    int32_t axis_size,
-    int64_t w_stride) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-
-  using BlockReduceT = BlockBroadcastReduce<float, BLOCK_DIM>;
-  __shared__ typename BlockReduceT::TempStorage temp;
-
-  x += grid.block_rank() * axis_size;
-  out += grid.block_rank() * axis_size;
-
-  // Normalizer.
-  float normalizer = 0;
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size, cast_to<T>(0));
-    for (int i = 0; i < N_READS; ++i) {
-      float t = static_cast<float>(xn[i]);
-      normalizer += t * t;
-    }
-  }
-  normalizer = BlockReduceT{block, temp}.Sum(normalizer);
-  normalizer = rsqrt(normalizer / axis_size + eps);
-
-  // Outputs.
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; ++i) {
-      float norm = static_cast<float>(xn[i]) * normalizer;
-      xn[i] = wn[i] * static_cast<T>(norm);
-    }
-    cub::StoreDirectBlocked(index, out, xn, axis_size);
-  }
-}
-
-template <typename T, bool HAS_W, int BLOCK_DIM, int N_READS = 4>
-__global__ void rms_norm_vjp(
-    const T* x,
-    const T* w,
-    const T* g,
-    T* gx,
-    T* gw,
-    float eps,
-    int32_t axis_size,
-    int64_t w_stride) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-
-  using BlockReduceF = BlockBroadcastReduce<float, BLOCK_DIM>;
-  using BlockReduceF2 = BlockBroadcastReduce<float2, BLOCK_DIM>;
-  __shared__ union {
-    typename BlockReduceF::TempStorage f;
-    typename BlockReduceF2::TempStorage f2;
-  } temp;
-
-  x += grid.block_rank() * axis_size;
-  g += grid.block_rank() * axis_size;
-  gx += grid.block_rank() * axis_size;
-  gw += grid.block_rank() * axis_size;
-
-  // Normalizer.
-  float2 factors = {};
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    T xn[N_READS];
-    T wn[N_READS] = {};
-    T gn[N_READS] = {};
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    cub::LoadDirectBlocked(index, x, xn, axis_size, cast_to<T>(0));
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      float t = static_cast<float>(xn[i]);
-      float wi = wn[i];
-      float gi = gn[i];
-      float wg = wi * gi;
-      factors = plus_f2(factors, {wg * t, t * t});
-    }
-  }
-  factors = BlockReduceF2{block, temp.f2}.Reduce(factors, plus_f2, {});
-  float meangwx = factors.x / axis_size;
-  float normalizer = rsqrt(factors.y / axis_size + eps);
-  float normalizer3 = normalizer * normalizer * normalizer;
-
-  // Outputs.
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    T gn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      float xi = xn[i];
-      float wi = wn[i];
-      float gi = gn[i];
-      xn[i] = static_cast<T>(normalizer * wi * gi - xi * meangwx * normalizer3);
-      if constexpr (HAS_W) {
-        wn[i] = static_cast<T>(gi * xi * normalizer);
-      }
-    }
-    cub::StoreDirectBlocked(index, gx, xn, axis_size);
-    if constexpr (HAS_W) {
-      cub::StoreDirectBlocked(index, gw, wn, axis_size);
-    }
-  }
-}
-
-} // namespace cu
-
-namespace fast {
-
-bool RMSNorm::use_fallback(Stream s) {
-  return s.device == Device::cpu;
-}
-
-// TODO: There are duplicate code with backend/metal/normalization.cpp
-void RMSNorm::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("RMSNorm::eval_gpu");
-  auto& s = stream();
-  auto& out = outputs[0];
-
-  // Make sure that the last dimension is contiguous.
-  auto set_output = [&s, &out](const array& x) {
-    bool no_copy = x.flags().contiguous && x.strides()[x.ndim() - 1] == 1;
-    if (no_copy && x.ndim() > 1) {
-      auto s = x.strides()[x.ndim() - 2];
-      no_copy &= (s == 0 || s == x.shape().back());
-    }
-    if (no_copy) {
-      if (x.is_donatable()) {
-        out.copy_shared_buffer(x);
-      } else {
-        out.set_data(
-            allocator::malloc(x.data_size() * x.itemsize()),
-            x.data_size(),
-            x.strides(),
-            x.flags());
-      }
-      return x;
-    } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_gpu(x, x_copy, CopyType::General, s);
-      out.copy_shared_buffer(x_copy);
-      return x_copy;
-    }
-  };
-
-  const array x = set_output(inputs[0]);
-  const array& w = inputs[1];
-
-  int32_t axis_size = x.shape().back();
-  int32_t n_rows = x.data_size() / axis_size;
-  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "rms_norm", [&](auto type_tag) {
-    constexpr uint32_t N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      auto kernel = cu::rms_norm<DataType, block_dim(), N_READS>;
-      encoder.add_kernel_node(
-          kernel,
-          n_rows,
-          block_dim(),
-          x.data<DataType>(),
-          w.data<DataType>(),
-          out.data<DataType>(),
-          eps_,
-          axis_size,
-          w_stride);
-    });
-  });
-}
-
-void RMSNormVJP::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("RMSNormVJP::eval_gpu");
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  // Ensure row contiguity. We could relax this step by checking that the array
-  // is contiguous (no broadcasts or holes) and that the input strides are the
-  // same as the cotangent strides but for now this is simpler.
-  auto check_input = [&s](const array& x, bool& copied) {
-    if (x.flags().row_contiguous) {
-      copied = false;
-      return x;
-    }
-    copied = true;
-    array x_copy(x.shape(), x.dtype(), nullptr, {});
-    copy_gpu(x, x_copy, CopyType::General, s);
-    return x_copy;
-  };
-  bool donate_x = inputs[0].is_donatable();
-  bool donate_g = inputs[2].is_donatable();
-  bool copied;
-  auto x = check_input(inputs[0], copied);
-  donate_x |= copied;
-  const array& w = inputs[1];
-  bool g_copied;
-  auto g = check_input(inputs[2], g_copied);
-  donate_g |= g_copied;
-  array& gx = outputs[0];
-  array& gw = outputs[1];
-
-  // Check whether we had a weight.
-  bool has_w = w.ndim() != 0;
-
-  // Allocate space for the outputs.
-  bool g_in_gx = false;
-  if (donate_x) {
-    gx.copy_shared_buffer(x);
-  } else if (donate_g) {
-    gx.copy_shared_buffer(g);
-    g_in_gx = true;
-  } else {
-    gx.set_data(allocator::malloc(gx.nbytes()));
-  }
-  if (g_copied && !g_in_gx) {
-    encoder.add_temporary(g);
-  }
-
-  int32_t axis_size = x.shape().back();
-  int32_t n_rows = x.data_size() / axis_size;
-  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
-
-  // Allocate a temporary to store the gradients for w and allocate the output
-  // gradient accumulators.
-  array gw_temp =
-      (has_w) ? array({n_rows, x.shape().back()}, gw.dtype(), nullptr, {}) : w;
-  if (has_w) {
-    if (!g_in_gx && donate_g) {
-      gw_temp.copy_shared_buffer(g);
-    } else {
-      gw_temp.set_data(allocator::malloc(gw_temp.nbytes()));
-      encoder.add_temporary(gw_temp);
-    }
-  }
-
-  encoder.set_input_array(x);
-  encoder.set_input_array(w);
-  encoder.set_input_array(g);
-  encoder.set_output_array(gx);
-  encoder.set_output_array(gw_temp);
-  dispatch_float_types(gx.dtype(), "rms_norm_vjp", [&](auto type_tag) {
-    dispatch_bool(has_w, [&](auto has_w_constant) {
-      constexpr int N_READS = 4;
-      dispatch_block_dim(
-          cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-            using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-            constexpr int N_READS = 4;
-            auto kernel = cu::rms_norm_vjp<
-                DataType,
-                has_w_constant.value,
-                block_dim(),
-                N_READS>;
-            encoder.add_kernel_node(
-                kernel,
-                n_rows,
-                block_dim(),
-                x.data<DataType>(),
-                w.data<DataType>(),
-                g.data<DataType>(),
-                gx.data<DataType>(),
-                gw_temp.data<DataType>(),
-                eps_,
-                axis_size,
-                w_stride);
-          });
-    });
-  });
-
-  if (has_w) {
-    ReductionPlan plan(
-        ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
-    col_reduce(encoder, gw_temp, gw, Reduce::ReduceType::Sum, {0}, plan);
-  }
-}
-
-} // namespace fast
-
-} // namespace mlx::core

mlx/backend/cuda/rope.cu

@@ -1,401 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/fast_primitives.h"
-
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-template <typename T, bool traditional, bool forward>
-__device__ void rope_single_impl(
-    const T* in,
-    T* out,
-    int32_t offset,
-    float inv_freq,
-    float scale,
-    int64_t stride,
-    uint2 pos,
-    uint2 dims) {
-  float L = scale * static_cast<float>(offset);
-
-  // Compute costheta, sintheta
-  float theta = L * inv_freq;
-  float costheta = cos(theta);
-  float sintheta = sin(theta);
-
-  // Compute the input and output indices
-  uint index_1, index_2;
-  if (traditional) {
-    index_1 = 2 * pos.x + pos.y * stride;
-    index_2 = index_1 + 1;
-  } else {
-    index_1 = pos.x + pos.y * stride;
-    index_2 = index_1 + dims.x;
-  }
-
-  // Read and write the output
-  float x1 = static_cast<float>(in[index_1]);
-  float x2 = static_cast<float>(in[index_2]);
-  float rx1;
-  float rx2;
-  if (forward) {
-    rx1 = x1 * costheta - x2 * sintheta;
-    rx2 = x1 * sintheta + x2 * costheta;
-  } else {
-    rx1 = x2 * sintheta + x1 * costheta;
-    rx2 = x2 * costheta - x1 * sintheta;
-  }
-  out[index_1] = static_cast<T>(rx1);
-  out[index_2] = static_cast<T>(rx2);
-}
-
-template <typename T, bool traditional, bool forward>
-__global__ void rope_single(
-    const T* in,
-    T* out,
-    const int32_t* offset,
-    float scale,
-    float base,
-    int64_t stride,
-    uint2 dims) {
-  uint2 pos = make_uint2(
-      blockIdx.x * blockDim.x + threadIdx.x,
-      blockIdx.y * blockDim.y + threadIdx.y);
-  if (pos.x >= dims.x || pos.y >= dims.y) {
-    return;
-  }
-
-  float d = static_cast<float>(pos.x) / static_cast<float>(dims.x);
-  float inv_freq = exp2(-d * base);
-  rope_single_impl<T, traditional, forward>(
-      in, out, *offset, inv_freq, scale, stride, pos, dims);
-}
-
-template <typename T, bool traditional, bool forward>
-__global__ void rope_single_freqs(
-    const T* in,
-    T* out,
-    const int32_t* offset,
-    const float* freqs,
-    float scale,
-    int64_t stride,
-    uint2 dims,
-    int64_t freq_stride) {
-  uint2 pos = make_uint2(
-      blockIdx.x * blockDim.x + threadIdx.x,
-      blockIdx.y * blockDim.y + threadIdx.y);
-  if (pos.x >= dims.x || pos.y >= dims.y) {
-    return;
-  }
-
-  float inv_freq = 1.0 / freqs[freq_stride * pos.x];
-  rope_single_impl<T, traditional, forward>(
-      in, out, *offset, inv_freq, scale, stride, pos, dims);
-}
-
-template <typename T, bool traditional, bool forward, int N = 4>
-__device__ void rope_impl(
-    const T* in,
-    T* out,
-    int offset,
-    float inv_freq,
-    float scale,
-    const cuda::std::array<int64_t, 3> strides,
-    const cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
-    uint3 pos,
-    uint3 dims) {
-  float L = scale * static_cast<float>(pos.y + offset);
-
-  // Compute costheta, sintheta
-  float theta = L * inv_freq;
-  float costheta = cos(theta);
-  float sintheta = sin(theta);
-
-  // Compute the input and output indices
-  size_t in_index_1, in_index_2;
-  size_t out_index_1, out_index_2;
-  if (traditional) {
-    out_index_1 = 2 * pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
-    out_index_2 = out_index_1 + 1;
-    in_index_1 =
-        2 * pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
-    in_index_2 = in_index_1 + strides[2];
-  } else {
-    out_index_1 = pos.x * out_strides[2] + pos.y * out_strides[1] +
-        N * pos.z * out_strides[0];
-    out_index_2 = out_index_1 + dims.x * out_strides[2];
-    in_index_1 =
-        pos.x * strides[2] + pos.y * strides[1] + N * pos.z * strides[0];
-    in_index_2 = in_index_1 + dims.x * strides[2];
-  }
-  for (int i = 0; i < N && pos.z * N + i < n_batch; ++i) {
-    // Read and write the output
-    float x1 = static_cast<float>(in[in_index_1]);
-    float x2 = static_cast<float>(in[in_index_2]);
-    float rx1;
-    float rx2;
-    if (forward) {
-      rx1 = x1 * costheta - x2 * sintheta;
-      rx2 = x1 * sintheta + x2 * costheta;
-    } else {
-      rx1 = x2 * sintheta + x1 * costheta;
-      rx2 = x2 * costheta - x1 * sintheta;
-    }
-    out[out_index_1] = static_cast<T>(rx1);
-    out[out_index_2] = static_cast<T>(rx2);
-    in_index_1 += strides[0];
-    in_index_2 += strides[0];
-    out_index_1 += out_strides[0];
-    out_index_2 += out_strides[0];
-  }
-}
-
-template <typename T, bool traditional, bool forward>
-__global__ void rope(
-    const T* in,
-    T* out,
-    const int32_t* offset,
-    float scale,
-    float base,
-    const __grid_constant__ cuda::std::array<int64_t, 3> strides,
-    const __grid_constant__ cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
-    uint3 dims) {
-  uint3 pos = make_uint3(
-      blockIdx.x * blockDim.x + threadIdx.x,
-      blockIdx.y * blockDim.y + threadIdx.y,
-      blockIdx.z * blockDim.z + threadIdx.z);
-  if (pos.x >= dims.x || pos.y >= dims.y || pos.z >= dims.z) {
-    return;
-  }
-
-  float d = static_cast<float>(pos.x) / static_cast<float>(dims.x);
-  float inv_freq = exp2(-d * base);
-  rope_impl<T, traditional, forward>(
-      in,
-      out,
-      *offset,
-      inv_freq,
-      scale,
-      strides,
-      out_strides,
-      n_batch,
-      pos,
-      dims);
-}
-
-template <typename T, bool traditional, bool forward>
-__global__ void rope_freqs(
-    const T* in,
-    T* out,
-    const int32_t* offset,
-    const float* freqs,
-    float scale,
-    float base,
-    const __grid_constant__ cuda::std::array<int64_t, 3> strides,
-    const __grid_constant__ cuda::std::array<int64_t, 3> out_strides,
-    int64_t n_batch,
-    uint3 dims,
-    int64_t freq_stride) {
-  uint3 pos = make_uint3(
-      blockIdx.x * blockDim.x + threadIdx.x,
-      blockIdx.y * blockDim.y + threadIdx.y,
-      blockIdx.z * blockDim.z + threadIdx.z);
-  if (pos.x >= dims.x || pos.y >= dims.y || pos.z >= dims.z) {
-    return;
-  }
-
-  float inv_freq = 1.0 / freqs[freq_stride * pos.x];
-  rope_impl<T, traditional, forward>(
-      in,
-      out,
-      *offset,
-      inv_freq,
-      scale,
-      strides,
-      out_strides,
-      n_batch,
-      pos,
-      dims);
-}
-
-} // namespace cu
-
-namespace fast {
-
-bool RoPE::use_fallback(Stream s) {
-  return s.device == Device::cpu;
-}
-
-void RoPE::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("RoPE::eval_gpu");
-
-  auto& s = stream();
-  auto& in = inputs[0];
-  auto& offset = inputs[1];
-  auto& out = outputs[0];
-
-  if (in.ndim() < 3) {
-    throw std::runtime_error("[RoPE] Input must have at least 3 dimensions");
-  }
-
-  cuda::std::array<int64_t, 3> strides;
-  cuda::std::array<int64_t, 3> out_strides;
-  bool donated = false;
-  int ndim = in.ndim();
-  int dispatch_ndim = in.ndim();
-  while (in.shape(-dispatch_ndim) == 1 && dispatch_ndim > 3) {
-    dispatch_ndim--;
-  }
-  size_t mat_size = in.shape(-2) * in.shape(-1);
-
-  // We apply rope to less that the whole vector so copy to output and then
-  // apply in-place.
-  if (dims_ < in.shape(-1)) {
-    donated = true;
-    auto ctype =
-        (in.flags().row_contiguous) ? CopyType::Vector : CopyType::General;
-    copy_gpu(in, out, ctype, s);
-    strides[0] = mat_size;
-    strides[1] = out.strides()[ndim - 2];
-    strides[2] = out.strides()[ndim - 1];
-  }
-
-  // Either copy or apply in-place
-  else if (in.flags().row_contiguous) {
-    if (in.is_donatable()) {
-      donated = true;
-      out.copy_shared_buffer(in);
-    } else {
-      out.set_data(allocator::malloc(out.nbytes()));
-    }
-    strides[0] = mat_size;
-    strides[1] = in.strides()[ndim - 2];
-    strides[2] = in.strides()[ndim - 1];
-  } else if (dispatch_ndim == 3) {
-    // Handle non-contiguous 3D inputs
-    out.set_data(allocator::malloc(out.nbytes()));
-    strides[0] = in.strides()[ndim - 3];
-    strides[1] = in.strides()[ndim - 2];
-    strides[2] = in.strides()[ndim - 1];
-  } else {
-    // Copy non-contiguous > 3D inputs into the output and treat
-    // input as donated
-    donated = true;
-    copy_gpu(in, out, CopyType::General, s);
-    strides[0] = mat_size;
-    strides[1] = out.strides()[ndim - 2];
-    strides[2] = out.strides()[ndim - 1];
-  }
-  out_strides[0] = mat_size;
-  out_strides[1] = out.strides()[ndim - 2];
-  out_strides[2] = out.strides()[ndim - 1];
-
-  // Some flags to help us dispatch below
-  bool single = in.flags().row_contiguous && (mat_size == in.shape(-1));
-  bool with_freqs = inputs.size() == 3;
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(donated ? out : in);
-  encoder.set_input_array(offset);
-  if (with_freqs) {
-    encoder.set_input_array(inputs[2]);
-  }
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "rope", [&](auto type_tag) {
-    dispatch_bool(traditional_, [&](auto traditional) {
-      dispatch_bool(forward_, [&](auto forward) {
-        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        if (single && !with_freqs) {
-          auto kernel =
-              cu::rope_single<DataType, traditional.value, forward.value>;
-          uint2 dims = make_uint2(dims_ / 2, in.size() / mat_size);
-          auto [grid, block] = get_grid_and_block(dims.x, dims.y, 1);
-          encoder.add_kernel_node(
-              kernel,
-              grid,
-              block,
-              (donated ? out : in).data<DataType>(),
-              out.data<DataType>(),
-              offset.data<int32_t>(),
-              scale_,
-              std::log2(base_),
-              mat_size,
-              dims);
-        } else if (single) {
-          auto kernel =
-              cu::rope_single_freqs<DataType, traditional.value, forward.value>;
-          uint2 dims = make_uint2(dims_ / 2, in.size() / mat_size);
-          auto [grid, block] = get_grid_and_block(dims.x, dims.y, 1);
-          encoder.add_kernel_node(
-              kernel,
-              grid,
-              block,
-              (donated ? out : in).data<DataType>(),
-              out.data<DataType>(),
-              offset.data<int32_t>(),
-              inputs[2].data<float>(),
-              scale_,
-              mat_size,
-              dims,
-              inputs[2].strides(0));
-        } else if (with_freqs) {
-          auto kernel =
-              cu::rope_freqs<DataType, traditional.value, forward.value>;
-          uint3 dims =
-              make_uint3(dims_ / 2, in.shape(-2), in.size() / mat_size);
-          dims.z = (dims.z + 3) / 4;
-          auto [grid, block] = get_grid_and_block(dims.x, dims.y, dims.z);
-          encoder.add_kernel_node(
-              kernel,
-              grid,
-              block,
-              (donated ? out : in).data<DataType>(),
-              out.data<DataType>(),
-              offset.data<int32_t>(),
-              inputs[2].data<float>(),
-              scale_,
-              std::log2(base_),
-              strides,
-              out_strides,
-              in.size() / mat_size,
-              dims,
-              inputs[2].strides(0));
-        } else {
-          auto kernel = cu::rope<DataType, traditional.value, forward.value>;
-          uint3 dims =
-              make_uint3(dims_ / 2, in.shape(-2), in.size() / mat_size);
-          dims.z = (dims.z + 3) / 4;
-          auto [grid, block] = get_grid_and_block(dims.x, dims.y, dims.z);
-          encoder.add_kernel_node(
-              kernel,
-              grid,
-              block,
-              (donated ? out : in).data<DataType>(),
-              out.data<DataType>(),
-              offset.data<int32_t>(),
-              scale_,
-              std::log2(base_),
-              strides,
-              out_strides,
-              in.size() / mat_size,
-              dims);
-        }
-      });
-    });
-  });
-}
-
-} // namespace fast
-
-} // namespace mlx::core

mlx/backend/cuda/slicing.cpp

@@ -1,11 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
-#include "mlx/backend/common/slicing.h"
-#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/gpu/slicing.h"
 
-#include <numeric>
-
 namespace mlx::core {
 
 void concatenate_gpu(
@@ -13,29 +9,7 @@ void concatenate_gpu(
     array& out,
     int axis,
     const Stream& s) {
-  std::vector<int> sizes;
-  sizes.push_back(0);
-  for (auto& p : inputs) {
-    sizes.push_back(p.shape(axis));
-  }
-  std::partial_sum(sizes.cbegin(), sizes.cend(), sizes.begin());
-
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  auto strides = out.strides();
-  auto flags = out.flags();
-  flags.row_contiguous = false;
-  flags.col_contiguous = false;
-  flags.contiguous = false;
-  // TODO: Handle concurrent outputs:
-  // https://github.com/ml-explore/mlx/pull/2145#discussion_r2070753816
-  for (int i = 0; i < inputs.size(); i++) {
-    array out_slice(inputs[i].shape(), out.dtype(), nullptr, {});
-    size_t data_offset = strides[axis] * sizes[i];
-    out_slice.copy_shared_buffer(
-        out, strides, flags, out_slice.size(), data_offset);
-    copy_gpu_inplace(inputs[i], out_slice, CopyType::GeneralGeneral, s);
-  }
+  throw std::runtime_error("concatenate_gpu not implemented in CUDA backend.");
 }
 
 } // namespace mlx::core

mlx/backend/cuda/softmax.cu

@@ -1,163 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/cast_op.cuh"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <cooperative_groups/reduce.h>
-#include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T>
-inline __device__ T softmax_exp(T x) {
-  // Softmax doesn't need high precision exponential cause x is gonna be in
-  // (-oo, 0] anyway and subsequently it will be divided by sum(exp(x_i)).
-  return __expf(x);
-}
-
-template <typename T, typename AccT, int BLOCK_DIM, int N_READS = 4>
-__global__ void softmax(const T* in, T* out, int axis_size) {
-  auto grid = cg::this_grid();
-  auto block = cg::this_thread_block();
-  auto warp = cg::tiled_partition<WARP_SIZE>(block);
-
-  in += grid.block_rank() * axis_size;
-  out += grid.block_rank() * axis_size;
-
-  cg::greater<AccT> max_op;
-  cg::plus<AccT> plus_op;
-
-  // Thread reduce.
-  AccT prevmax;
-  AccT maxval = Limits<AccT>::finite_min();
-  AccT normalizer = cast_to<AccT>(0);
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    AccT vals[N_READS];
-    cub::LoadDirectBlocked(
-        r * BLOCK_DIM + block.thread_rank(),
-        make_cast_iterator<AccT>(in),
-        vals,
-        axis_size,
-        Limits<AccT>::min());
-    prevmax = maxval;
-    maxval = max_op(maxval, cub::ThreadReduce(vals, max_op));
-    // Online normalizer calculation for softmax:
-    // https://github.com/NVIDIA/online-softmax
-    normalizer = normalizer * softmax_exp(prevmax - maxval);
-    for (int i = 0; i < N_READS; i++) {
-      normalizer = normalizer + softmax_exp(vals[i] - maxval);
-    }
-  }
-
-  // First warp reduce.
-  prevmax = maxval;
-  maxval = cg::reduce(warp, maxval, max_op);
-  normalizer = normalizer * softmax_exp(prevmax - maxval);
-  normalizer = cg::reduce(warp, normalizer, plus_op);
-
-  __shared__ AccT local_max[WARP_SIZE];
-  __shared__ AccT local_normalizer[WARP_SIZE];
-
-  // Write to shared memory and do second warp reduce.
-  prevmax = maxval;
-  if (warp.thread_rank() == 0) {
-    local_max[warp.meta_group_rank()] = maxval;
-  }
-  block.sync();
-  maxval = warp.thread_rank() < warp.meta_group_size()
-      ? local_max[warp.thread_rank()]
-      : Limits<AccT>::min();
-  maxval = cg::reduce(warp, maxval, max_op);
-  normalizer = normalizer * softmax_exp(prevmax - maxval);
-  if (warp.thread_rank() == 0) {
-    local_normalizer[warp.meta_group_rank()] = normalizer;
-  }
-  block.sync();
-  normalizer = warp.thread_rank() < warp.meta_group_size()
-      ? local_normalizer[warp.thread_rank()]
-      : AccT{};
-  normalizer = cg::reduce(warp, normalizer, plus_op);
-  normalizer = 1 / normalizer;
-
-  // Write output.
-  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    auto index = r * BLOCK_DIM + block.thread_rank();
-    T vals[N_READS];
-    cub::LoadDirectBlocked(index, in, vals, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      vals[i] = softmax_exp(static_cast<AccT>(vals[i]) - maxval) * normalizer;
-    }
-    cub::StoreDirectBlocked(index, out, vals, axis_size);
-  }
-}
-
-} // namespace cu
-
-void Softmax::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Softmax::eval_gpu");
-  assert(inputs.size() == 1);
-  auto& s = stream();
-
-  // Make sure that the last dimension is contiguous.
-  auto set_output = [&s, &out](const array& x) {
-    if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
-      if (x.is_donatable()) {
-        out.copy_shared_buffer(x);
-      } else {
-        out.set_data(
-            allocator::malloc(x.data_size() * x.itemsize()),
-            x.data_size(),
-            x.strides(),
-            x.flags());
-      }
-      return x;
-    } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_gpu(x, x_copy, CopyType::General, s);
-      out.copy_shared_buffer(x_copy);
-      return x_copy;
-    }
-  };
-
-  array in = set_output(inputs[0]);
-  bool precise = in.dtype() != float32 && precise_;
-
-  int axis_size = in.shape().back();
-  int n_rows = in.data_size() / axis_size;
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_float_types(out.dtype(), "softmax", [&](auto type_tag) {
-    constexpr int N_READS = 4;
-    dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-      auto kernel = cu::softmax<DataType, DataType, block_dim(), N_READS>;
-      if (precise) {
-        kernel = cu::softmax<DataType, float, block_dim(), N_READS>;
-      }
-      encoder.add_kernel_node(
-          kernel,
-          n_rows,
-          block_dim(),
-          in.data<DataType>(),
-          out.data<DataType>(),
-          axis_size);
-    });
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/sort.cu

@@ -50,21 +50,43 @@ array swapaxes_in_eval(const array& in, int axis1, int axis2) {
   return out;
 }
 
-struct OffsetTransform {
-  int nsort;
+template <typename... Args>
+void segmented_sort_pairs(cu::CommandEncoder& encoder, Args&&... args) {
+  // Allocate temporary storage.
+  size_t size;
+  CHECK_CUDA_ERROR(
+      cub::DeviceSegmentedSort::StableSortPairs(nullptr, size, args...));
+  array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
+  encoder.add_temporary(temp);
+  // Run op.
+  CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortPairs(
+      temp.data<void>(), size, args...));
+}
 
-  int __device__ operator()(int i) {
-    return i * nsort;
-  }
-};
+template <typename... Args>
+void segmented_sort(cu::CommandEncoder& encoder, Args&&... args) {
+  // Allocate temporary storage.
+  size_t size;
+  CHECK_CUDA_ERROR(
+      cub::DeviceSegmentedSort::StableSortKeys(nullptr, size, args...));
+  array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
+  encoder.add_temporary(temp);
+  // Run op.
+  CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortKeys(
+      temp.data<void>(), size, args...));
+}
 
 void gpu_sort(const Stream& s, array in, array& out_, int axis, bool argsort) {
   array out = out_;
   auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(in);
+  encoder.set_output_array(out);
+
   if (axis < 0) {
     axis += in.ndim();
   }
   int nsort = in.shape(axis);
+  int nsegments = in.data_size() / nsort;
   int last_dim = in.ndim() - 1;
 
   // If we are not sorting the innermost dimension of a contiguous array,
@@ -78,103 +100,60 @@ void gpu_sort(const Stream& s, array in, array& out_, int axis, bool argsort) {
     out = array(allocator::malloc(out.nbytes()), in.shape(), out.dtype());
     encoder.add_temporary(out);
   } else {
-    out.set_data(
-        allocator::malloc(in.data_size() * out.itemsize()),
-        in.data_size(),
-        in.strides(),
-        in.flags());
+    out.set_data(allocator::malloc(out.nbytes()));
   }
 
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    auto& stream = encoder.stream();
-    if constexpr (!std::is_same_v<CTYPE, complex64_t>) {
-      using Type = cuda_type_t<CTYPE>;
-      auto offsets = thrust::make_transform_iterator(
-          thrust::make_counting_iterator(0), OffsetTransform{nsort});
-      if (argsort) {
-        // Indices in the sorted dimension.
-        array indices(allocator::malloc(out.nbytes()), in.shape(), out.dtype());
-        encoder.add_temporary(indices);
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(in.dtype(), CTYPE, {
+      if constexpr (!std::is_same_v<CTYPE, complex64_t>) {
+        using Type = cuda_type_t<CTYPE>;
+        auto offsets = thrust::make_transform_iterator(
+            thrust::make_counting_iterator(0),
+            [nsort] __device__(int i) { return i * nsort; });
+        if (argsort) {
+          // Indices in the sorted dimension.
+          array indices(
+              allocator::malloc(out.nbytes()), in.shape(), out.dtype());
+          encoder.add_temporary(indices);
+          thrust::transform(
+              cu::thrust_policy(stream),
+              thrust::counting_iterator<uint32_t>(0),
+              thrust::counting_iterator<uint32_t>(indices.data_size()),
+              thrust::device_pointer_cast(indices.data<uint32_t>()),
+              ModOp<uint32_t>{static_cast<uint32_t>(nsort)});
 
-        // In argsort though we don't need the result of sorted values, the
-        // API requires us to provide an array to store it.
-        array discard(allocator::malloc(in.nbytes()), in.shape(), in.dtype());
-        encoder.add_temporary(discard);
+          // In argsort though we don't need the result of sorted values, the
+          // API requires us to provide an array to store it.
+          array discard(allocator::malloc(in.nbytes()), in.shape(), in.dtype());
+          encoder.add_temporary(discard);
 
-        size_t size;
-        CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortPairs(
-            nullptr,
-            size,
-            in.data<Type>(),
-            discard.data<Type>(),
-            indices.data<uint32_t>(),
-            out.data<uint32_t>(),
-            in.data_size(),
-            in.data_size() / nsort,
-            offsets,
-            offsets + 1,
-            stream));
-
-        array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
-        encoder.add_temporary(temp);
-
-        // Start capturing after allocations
-        auto capture = encoder.capture_context();
-        thrust::transform(
-            cu::thrust_policy(stream),
-            thrust::counting_iterator<uint32_t>(0),
-            thrust::counting_iterator<uint32_t>(indices.data_size()),
-            thrust::device_pointer_cast(indices.data<uint32_t>()),
-            ModOp<uint32_t>{static_cast<uint32_t>(nsort)});
-
-        CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortPairs(
-            temp.data<void>(),
-            size,
-            in.data<Type>(),
-            discard.data<Type>(),
-            indices.data<uint32_t>(),
-            out.data<uint32_t>(),
-            in.data_size(),
-            in.data_size() / nsort,
-            offsets,
-            offsets + 1,
-            stream));
+          segmented_sort_pairs(
+              encoder,
+              in.data<Type>(),
+              discard.data<Type>(),
+              indices.data<uint32_t>(),
+              out.data<uint32_t>(),
+              in.data_size(),
+              nsegments,
+              offsets,
+              offsets + 1,
+              stream);
+        } else {
+          segmented_sort(
+              encoder,
+              in.data<Type>(),
+              out.data<Type>(),
+              in.data_size(),
+              nsegments,
+              offsets,
+              offsets + 1,
+              stream);
+        }
       } else {
-        size_t size;
-        CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortKeys(
-            nullptr,
-            size,
-            in.data<Type>(),
-            out.data<Type>(),
-            in.data_size(),
-            in.data_size() / nsort,
-            offsets,
-            offsets + 1,
-            stream));
-
-        array temp(allocator::malloc(size), {static_cast<int>(size)}, uint8);
-        encoder.add_temporary(temp);
-
-        // Start capturing after allocations
-        auto capture = encoder.capture_context();
-        CHECK_CUDA_ERROR(cub::DeviceSegmentedSort::StableSortKeys(
-            temp.data<void>(),
-            size,
-            in.data<Type>(),
-            out.data<Type>(),
-            in.data_size(),
-            in.data_size() / nsort,
-            offsets,
-            offsets + 1,
-            stream));
+        throw std::runtime_error(
+            "CUDA backend does not support sorting complex numbers");
       }
-    } else {
-      throw std::runtime_error(
-          "CUDA backend does not support sorting complex numbers");
-    }
+    });
   });
 
   if (!is_segmented_sort) {
@@ -198,14 +177,4 @@ void Sort::eval_gpu(const std::vector<array>& inputs, array& out) {
   gpu_sort(stream(), inputs[0], out, axis_, false);
 }
 
-void ArgPartition::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("ArgPartition::eval_gpu");
-  gpu_sort(stream(), inputs[0], out, axis_, true);
-}
-
-void Partition::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Partition::eval_gpu");
-  gpu_sort(stream(), inputs[0], out, axis_, false);
-}
-
 } // namespace mlx::core

mlx/backend/cuda/ternary.cu

@@ -1,190 +0,0 @@
-// Copyright © 2025 Apple Inc.
-#include "mlx/backend/common/ternary.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/ternary_ops.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename Op, typename T, typename IdxT>
-__global__ void
-ternary_v(const bool* a, const T* b, const T* c, T* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    out[index] = Op{}(a[index], b[index], c[index]);
-  }
-}
-
-template <typename Op, typename T, typename IdxT, int NDIM>
-__global__ void ternary_g_nd(
-    const bool* a,
-    const T* b,
-    const T* c,
-    T* out,
-    IdxT size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> c_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx, c_idx] = elem_to_loc_nd<NDIM>(
-        index,
-        shape.data(),
-        a_strides.data(),
-        b_strides.data(),
-        c_strides.data());
-    out[index] = Op{}(a[a_idx], b[b_idx], c[c_idx]);
-  }
-}
-
-template <typename Op, typename T, typename IdxT>
-__global__ void ternary_g(
-    const bool* a,
-    const T* b,
-    const T* c,
-    T* out,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides a_strides,
-    const __grid_constant__ Strides b_strides,
-    const __grid_constant__ Strides c_strides,
-    int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx, c_idx] = elem_to_loc_4d(
-        index,
-        shape.data(),
-        a_strides.data(),
-        b_strides.data(),
-        c_strides.data(),
-        ndim);
-    out[index] = Op{}(a[a_idx], b[b_idx], c[c_idx]);
-  }
-}
-
-} // namespace cu
-
-template <typename Op>
-void ternary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    array& out,
-    const Stream& s) {
-  const auto& a = inputs[0];
-  const auto& b = inputs[1];
-  const auto& c = inputs[2];
-  if (out.size() == 0) {
-    return;
-  }
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-  dispatch_all_types(out.dtype(), [&](auto type_tag) {
-    using DType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-
-    auto topt = get_ternary_op_type(a, b, c);
-    if (topt == TernaryOpType::General) {
-      dispatch_bool(
-          a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
-              c.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
-          [&](auto large) {
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            Shape shape;
-            std::vector<Strides> strides;
-            std::tie(shape, strides) = collapse_contiguous_dims(a, b, c, out);
-            auto& a_strides = strides[0];
-            auto& b_strides = strides[1];
-            auto& c_strides = strides[2];
-            int ndim = shape.size();
-            if (ndim <= 3) {
-              dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel =
-                    cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>;
-                auto [num_blocks, block_dims] =
-                    get_launch_args(kernel, out, large());
-                encoder.add_kernel_node(
-                    kernel,
-                    num_blocks,
-                    block_dims,
-                    a.data<bool>(),
-                    b.data<DType>(),
-                    c.data<DType>(),
-                    out.data<DType>(),
-                    out.size(),
-                    const_param<dims_constant()>(shape),
-                    const_param<dims_constant()>(a_strides),
-                    const_param<dims_constant()>(b_strides),
-                    const_param<dims_constant()>(c_strides));
-              });
-            } else {
-              auto kernel = cu::ternary_g<Op, DType, IdxT>;
-              auto [num_blocks, block_dims] =
-                  get_launch_args(kernel, out, large());
-              encoder.add_kernel_node(
-                  kernel,
-                  num_blocks,
-                  block_dims,
-                  a.data<bool>(),
-                  b.data<DType>(),
-                  c.data<DType>(),
-                  out.data<DType>(),
-                  out.data_size(),
-                  const_param(shape),
-                  const_param(a_strides),
-                  const_param(b_strides),
-                  const_param(c_strides),
-                  ndim);
-            }
-          });
-    } else {
-      dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
-        using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-        auto kernel = cu::ternary_v<Op, DType, IdxT>;
-        auto [num_blocks, block_dims] = get_launch_args(
-            kernel, out.data_size(), out.shape(), out.strides(), large());
-        encoder.add_kernel_node(
-            kernel,
-            num_blocks,
-            block_dims,
-            a.data<bool>(),
-            b.data<DType>(),
-            c.data<DType>(),
-            out.data<DType>(),
-            out.data_size());
-      });
-    }
-  });
-}
-
-template <typename Op>
-void ternary_op_gpu(
-    const std::vector<array>& inputs,
-    array& out,
-    const Stream& s) {
-  auto& a = inputs[0];
-  auto& b = inputs[1];
-  auto& c = inputs[2];
-  auto topt = get_ternary_op_type(a, b, c);
-  set_ternary_op_output_data(a, b, c, out, topt);
-  ternary_op_gpu_inplace<Op>(inputs, out, s);
-}
-
-void Select::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("select::eval_gpu");
-  auto& s = out.primitive().stream();
-  ternary_op_gpu<cu::Select>(inputs, out, s);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/unary.cu

@@ -2,77 +2,54 @@
 
 #include "mlx/backend/common/unary.h"
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/cucomplex_math.cuh"
-#include "mlx/backend/cuda/device/unary_ops.cuh"
 #include "mlx/backend/cuda/iterators/general_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/kernels/cucomplex_math.cuh"
+#include "mlx/backend/cuda/kernels/unary_ops.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
 
-#include <cooperative_groups.h>
 #include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>
 
 namespace mlx::core {
 
 namespace cu {
 
-namespace cg = cooperative_groups;
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void unary_v(const In* in, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    out[index] = Op{}(in[index]);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void unary_g(
-    const In* in,
-    Out* out,
-    IdxT size,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides,
-    int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto idx = elem_to_loc_4d(index, shape.data(), strides.data(), ndim);
-    out[index] = Op{}(in[idx]);
-  }
-}
-
 template <typename Op, typename In, typename Out>
 constexpr bool supports_unary_op() {
   if (std::is_same_v<Op, Abs> || std::is_same_v<Op, Negative> ||
-      std::is_same_v<Op, Sign> || std::is_same_v<Op, Square>) {
+      std::is_same_v<Op, Sign>) {
     return std::is_same_v<In, Out>;
   }
-  if (std::is_same_v<Op, ArcCosh> || std::is_same_v<Op, ArcSinh> ||
-      std::is_same_v<Op, ArcTanh> || std::is_same_v<Op, Erf> ||
-      std::is_same_v<Op, ErfInv> || std::is_same_v<Op, Expm1> ||
-      std::is_same_v<Op, Sigmoid>) {
+  if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcCosh> ||
+      std::is_same_v<Op, ArcSin> || std::is_same_v<Op, ArcSinh> ||
+      std::is_same_v<Op, ArcTan> || std::is_same_v<Op, ArcTanh> ||
+      std::is_same_v<Op, Erf> || std::is_same_v<Op, ErfInv> ||
+      std::is_same_v<Op, Expm1> || std::is_same_v<Op, Log1p> ||
+      std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
+      std::is_same_v<Op, Log10> || std::is_same_v<Op, Sigmoid> ||
+      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Rsqrt>) {
     return std::is_same_v<In, Out> && is_floating_v<In>;
   }
   if (std::is_same_v<Op, BitwiseInvert>) {
     return std::is_same_v<In, Out> && std::is_integral_v<In> &&
         !std::is_same_v<In, bool>;
   }
-  if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor>) {
+  if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor> ||
+      std::is_same_v<Op, Square>) {
     return std::is_same_v<In, Out> && !std::is_same_v<In, complex64_t>;
   }
   if (std::is_same_v<Op, Conjugate>) {
     return std::is_same_v<In, Out> && std::is_same_v<In, complex64_t>;
   }
-  if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcSin> ||
-      std::is_same_v<Op, ArcTan> || std::is_same_v<Op, Cos> ||
-      std::is_same_v<Op, Cosh> || std::is_same_v<Op, Exp> ||
-      std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
-      std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p> ||
-      std::is_same_v<Op, Round> || std::is_same_v<Op, Rsqrt> ||
-      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Sin> ||
-      std::is_same_v<Op, Sinh> || std::is_same_v<Op, Tan> ||
-      std::is_same_v<Op, Tanh>) {
-    return std::is_same_v<In, Out> && is_inexact_v<In>;
+  if (std::is_same_v<Op, Cos> || std::is_same_v<Op, Cosh> ||
+      std::is_same_v<Op, Exp> || std::is_same_v<Op, Round> ||
+      std::is_same_v<Op, Sin> || std::is_same_v<Op, Sinh> ||
+      std::is_same_v<Op, Tan> || std::is_same_v<Op, Tanh>) {
+    return std::is_same_v<In, Out> &&
+        (is_floating_v<In> || std::is_same_v<In, complex64_t>);
   }
   if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
     return std::is_same_v<In, complex64_t> && std::is_same_v<Out, float>;
@@ -95,61 +72,36 @@ void unary_op_gpu_inplace(
   if (in.size() == 0) {
     return;
   }
-  bool contig = in.flags().contiguous;
-  bool large;
-  if (!contig) {
-    large = in.data_size() > INT32_MAX || out.size() > INT32_MAX;
-  } else {
-    large = in.data_size() > UINT32_MAX;
-  }
 
   auto& encoder = cu::get_command_encoder(s);
   encoder.set_input_array(in);
   encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        dispatch_bool(large, [&](auto large) {
-          using IdxT = std::conditional_t<large(), int64_t, int32_t>;
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_ALL_TYPES(in.dtype(), CTYPE_IN, {
+      MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {
+        if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
           using InType = cuda_type_t<CTYPE_IN>;
           using OutType = cuda_type_t<CTYPE_OUT>;
-          using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-          if (contig) {
-            auto kernel = cu::unary_v<Op, InType, OutType, IdxT>;
-            auto [num_blocks, block_dims] = get_launch_args(
-                kernel, out.data_size(), out.shape(), out.strides(), large);
-            encoder.add_kernel_node(
-                kernel,
-                num_blocks,
-                block_dims,
-                in.data<InType>(),
-                out.data<OutType>(),
-                out.data_size());
+          auto policy = cu::thrust_policy(stream);
+          auto in_ptr = thrust::device_pointer_cast(in.data<InType>());
+          auto out_ptr = thrust::device_pointer_cast(out.data<OutType>());
+          if (in.flags().contiguous) {
+            thrust::transform(
+                policy, in_ptr, in_ptr + in.data_size(), out_ptr, Op());
           } else {
             auto [shape, strides] = collapse_contiguous_dims(in);
-            auto kernel = cu::unary_g<Op, InType, OutType, IdxT>;
-            auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
-            encoder.add_kernel_node(
-                kernel,
-                num_blocks,
-                block_dims,
-                in.data<InType>(),
-                out.data<OutType>(),
-                out.data_size(),
-                const_param(shape),
-                const_param(strides),
-                shape.size());
+            auto [in_begin, in_end] = cu::make_general_iterators<int64_t>(
+                in_ptr, in.data_size(), shape, strides);
+            thrust::transform(policy, in_begin, in_end, out_ptr, Op());
           }
-        });
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do unary op {} on input of {} with output of {}.",
-            op,
-            dtype_to_string(in.dtype()),
-            dtype_to_string(out.dtype())));
-      }
+        } else {
+          throw std::runtime_error(fmt::format(
+              "Can not do unary op {} on input of {} with output of {}.",
+              op,
+              dtype_to_string(in.dtype()),
+              dtype_to_string(out.dtype())));
+        }
+      });
     });
   });
 }

mlx/backend/cuda/utils.cpp

@@ -2,7 +2,6 @@
 
 #include "mlx/backend/cuda/utils.h"
 #include "mlx/backend/cuda/device.h"
-#include "mlx/dtype_utils.h"
 
 #include <fmt/format.h>
 
@@ -24,47 +23,4 @@ void check_cuda_error(const char* name, cudaError_t err) {
   }
 }
 
-void check_cuda_error(const char* name, CUresult err) {
-  if (err != CUDA_SUCCESS) {
-    const char* err_str = "Unknown error";
-    cuGetErrorString(err, &err_str);
-    throw std::runtime_error(fmt::format("{} failed: {}", name, err_str));
-  }
-}
-
-const char* dtype_to_cuda_type(const Dtype& dtype) {
-  switch (dtype) {
-    case bool_:
-      return "bool";
-    case int8:
-      return "int8_t";
-    case int16:
-      return "int16_t";
-    case int32:
-      return "int32_t";
-    case int64:
-      return "int64_t";
-    case uint8:
-      return "uint8_t";
-    case uint16:
-      return "uint16_t";
-    case uint32:
-      return "uint32_t";
-    case uint64:
-      return "uint64_t";
-    case float16:
-      return "__half";
-    case bfloat16:
-      return "__nv_bfloat16";
-    case float32:
-      return "float";
-    case float64:
-      return "double";
-    case complex64:
-      return "cuComplex";
-    default:
-      return "unknown";
-  }
-}
-
 } // namespace mlx::core

mlx/backend/cuda/utils.h

@@ -4,7 +4,6 @@
 
 #pragma once
 
-#include <cuda.h>
 #include <cuda_runtime.h>
 
 namespace mlx::core {
@@ -13,8 +12,6 @@ namespace cu {
 class Device;
 }
 
-struct Dtype;
-
 // Cuda stream managed with RAII.
 class CudaStream {
  public:
@@ -34,12 +31,8 @@ class CudaStream {
 
 // Throw exception if the cuda API does not succeed.
 void check_cuda_error(const char* name, cudaError_t err);
-void check_cuda_error(const char* name, CUresult err);
 
 // The macro version that prints the command that failed.
 #define CHECK_CUDA_ERROR(cmd) check_cuda_error(#cmd, (cmd))
 
-// Convert Dtype to CUDA C++ types.
-const char* dtype_to_cuda_type(const Dtype& dtype);
-
 } // namespace mlx::core

mlx/backend/cuda/worker.cpp

@@ -80,9 +80,7 @@ void Worker::thread_fn() {
       }
       worker_tasks_.erase(worker_tasks_.begin(), end);
     }
-    // Make sure tasks are cleared before the next wait
-    for (int i = 0; i < tasks.size(); ++i) {
-      auto task = std::move(tasks[i]);
+    for (auto& task : tasks) {
       task();
     }
     worker_event_.wait(batch + 1);

mlx/backend/gpu/primitives.cpp

@@ -1,7 +1,6 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/primitives.h"
-#include "mlx/backend/common/slicing.h"
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/gpu/slicing.h"
@@ -171,41 +170,6 @@ void Slice::eval_gpu(const std::vector<array>& inputs, array& out) {
   slice_gpu(in, out, start_indices_, strides_, stream());
 }
 
-void SliceUpdate::eval_gpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 2);
-  if (out.size() == 0) {
-    out.set_data(nullptr);
-    return;
-  }
-
-  auto& in = inputs[0];
-  auto& upd = inputs[1];
-
-  if (upd.size() == 0) {
-    out.copy_shared_buffer(in);
-    return;
-  }
-
-  auto ctype = in.flags().contiguous && in.size() == in.data_size()
-      ? CopyType::Vector
-      : CopyType::General;
-  copy_gpu(in, out, in.data_size() == 1 ? CopyType::Scalar : ctype, stream());
-  auto [data_offset, out_strides] =
-      prepare_slice(out, start_indices_, strides_);
-
-  // Do copy
-  copy_gpu_inplace(
-      /* const array& src = */ upd,
-      /* array& dst = */ out,
-      /* const Shape& data_shape = */ upd.shape(),
-      /* const Strides& i_strides = */ upd.strides(),
-      /* const Strides& o_strides = */ out_strides,
-      /* int64_t i_offset = */ 0,
-      /* int64_t o_offset = */ data_offset,
-      /* CopyType ctype = */ CopyType::GeneralGeneral,
-      /* const Stream& s = */ stream());
-}
-
 void Squeeze::eval_gpu(const std::vector<array>& inputs, array& out) {
   MLX_PROFILER_RANGE("Squeeze::eval_gpu");
   eval(inputs, out);

mlx/backend/metal/CMakeLists.txt

@@ -63,7 +63,6 @@ if(MLX_METAL_JIT)
   make_jit_source(steel/gemm/kernels/steel_gemm_masked kernels/steel/defines.h)
   make_jit_source(steel/gemm/kernels/steel_gemm_gather)
   make_jit_source(steel/gemm/kernels/steel_gemm_splitk)
-  make_jit_source(steel/gemm/kernels/steel_gemm_segmented)
   make_jit_source(
     steel/conv/conv
     kernels/steel/utils.h

mlx/backend/metal/conv.cpp

@@ -155,26 +155,26 @@ void explicit_gemm_conv_group_ND_gpu(
   // Perform gemm
   std::vector<array> copies = {in_unfolded, wt_transpose};
   return steel_matmul_regular(
-      /* const Stream& s = */ s,
-      /* Device& d = */ d,
-      /* const array& a = */ in_unfolded,
-      /* const array& b = */ wt_transpose,
-      /* array& c = */ out,
-      /* int M = */ implicit_M,
-      /* int N = */ implicit_N,
-      /* int K = */ implicit_K,
-      /* int batch_size_out = */ groups,
-      /* int lda = */ implicit_K * groups,
-      /* int ldb = */ implicit_K,
-      /* int ldd = */ implicit_N * groups,
-      /* bool transpose_a = */ false,
-      /* bool transpose_b = */ true,
-      /* std::vector<array>& copies = */ copies,
-      /* Shape batch_shape = */ {1},
-      /* Strides batch_strides = */ {0},
-      /* int64_t A_batch_strides = */ int64_t(implicit_K),
-      /* int64_t B_batch_strides = */ int64_t(implicit_N) * implicit_K,
-      /* int64_t matrix_stride_out = */ int64_t(implicit_N));
+      s,
+      d,
+      /* a = */ in_unfolded,
+      /* b = */ wt_transpose,
+      /* c = */ out,
+      /* M = */ implicit_M,
+      /* N = */ implicit_N,
+      /* K = */ implicit_K,
+      /* batch_size_out = */ groups,
+      /* a_cols = */ implicit_K * groups,
+      /* b_cols = */ implicit_K,
+      /* out_cols = */ implicit_N * groups,
+      /* a_transposed = */ false,
+      /* b_transposed = */ true,
+      /* batch_shape = */ {1},
+      /* batch_strides = */ {0},
+      /* A_batch_strides = */ size_t(implicit_K),
+      /* B_batch_strides = */ size_t(implicit_N) * implicit_K,
+      /* matrix_stride_out = */ size_t(implicit_N),
+      /*copies = */ copies);
 }
 
 void implicit_gemm_conv_2D_gpu(
@@ -391,7 +391,6 @@ void implicit_gemm_conv_2D_general_gpu(
   // Get channel iteration info
   int channel_k_iters = ((conv_params.C + bk - 1) / bk);
   int gemm_k_iters = channel_k_iters;
-  bool align_C = conv_params.C % bk == 0;
 
   // Fix host side helper params
   int sign = (conv_params.flip ? -1 : 1);
@@ -420,33 +419,14 @@ void implicit_gemm_conv_2D_general_gpu(
       /* const int swizzle_log = */ swizzle_log};
 
   // Determine kernel
-  std::string kname;
-  kname.reserve(64);
-  concatenate(
-      kname,
-      "implicit_gemm_conv_2d_general_",
-      type_to_name(out),
-      "_bm",
-      bm,
-      "_bn",
-      bn,
-      "_bk",
-      bk,
-      "_wm",
-      wm,
-      "_wn",
-      wn);
-  std::string hash_name;
-  hash_name.reserve(64);
-  concatenate(hash_name, kname, "_alC_", align_C);
-  metal::MTLFCList func_consts = {
-      {&align_C, MTL::DataType::DataTypeBool, 200},
-  };
+  std::ostringstream kname;
+  kname << "implicit_gemm_conv_2d_general_" << type_to_name(out) << "_bm" << bm
+        << "_bn" << bn << "_bk" << bk << "_wm" << wm << "_wn" << wn;
 
   // Encode and dispatch kernel
   auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = get_steel_conv_general_kernel(
-      d, kname, hash_name, func_consts, out, bm, bn, bk, wm, wn);
+  auto kernel =
+      get_steel_conv_general_kernel(d, kname.str(), out, bm, bn, bk, wm, wn);
   compute_encoder.set_compute_pipeline_state(kernel);
 
   // Deduce grid launch dimensions
@@ -748,10 +728,8 @@ void dispatch_conv_2D_gpu(
 
   // Direct to winograd conv
   bool inp_large =
-      (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 4096;
+      (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
   bool channels_large = (conv_params.C + conv_params.O) >= 256;
-  bool out_large =
-      (conv_params.N * conv_params.oS[0] * conv_params.oS[1]) >= 256;
   if (!conv_params.flip && is_stride_one && is_kdil_one && is_idil_one &&
       conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
       conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
@@ -765,7 +743,7 @@ void dispatch_conv_2D_gpu(
     return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
   }
 
-  else if ((conv_params.C % 16 == 0 && conv_params.O % 16 == 0) || out_large) {
+  else if (conv_params.C % 16 == 0 && conv_params.O % 16 == 0) {
     return implicit_gemm_conv_2D_general_gpu(s, d, in, wt, out, conv_params);
   }
 

mlx/backend/metal/device.cpp

@@ -297,9 +297,6 @@ Device::Device() {
   device_ = load_device();
   default_library_ = load_default_library(device_);
   arch_ = std::string(device_->architecture()->name()->utf8String());
-  int ag_tens = arch_[arch_.size() - 3] - '0';
-  int ag_ones = arch_[arch_.size() - 2] - '0';
-  arch_gen_ = ag_tens * 10 + ag_ones;
   auto arch = arch_.back();
   switch (arch) {
     case 'p': // phone

mlx/backend/metal/device.h

@@ -177,10 +177,6 @@ class Device {
     return arch_;
   }
 
-  int get_architecture_gen() const {
-    return arch_gen_;
-  }
-
   void new_queue(int index);
 
   MTL::CommandQueue* get_queue(Stream stream);
@@ -272,7 +268,6 @@ class Device {
       library_kernels_;
   const MTL::ResidencySet* residency_set_{nullptr};
   std::string arch_;
-  int arch_gen_;
   int max_ops_per_buffer_;
   int max_mb_per_buffer_;
 };

mlx/backend/metal/indexing.cpp

@@ -575,17 +575,9 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 2);
 
   // Set source info
-  if (ndim > 1) {
-    compute_encoder.set_vector_bytes(remove_index(idx.shape(), axis_), 3);
-    compute_encoder.set_vector_bytes(remove_index(upd.strides(), axis_), 4);
-    compute_encoder.set_vector_bytes(remove_index(idx.strides(), axis_), 5);
-  } else {
-    // The following will be ignored in the kernel but we still have to set
-    // some value so that metal validation passes.
-    compute_encoder.set_vector_bytes(idx.shape(), 3);
-    compute_encoder.set_vector_bytes(upd.strides(), 4);
-    compute_encoder.set_vector_bytes(idx.strides(), 5);
-  }
+  compute_encoder.set_vector_bytes(remove_index(idx.shape(), axis_), 3);
+  compute_encoder.set_vector_bytes(remove_index(upd.strides(), axis_), 4);
+  compute_encoder.set_vector_bytes(remove_index(idx.strides(), axis_), 5);
   compute_encoder.set_bytes(ndim - 1, 6);
   compute_encoder.set_bytes(axis_, 7);
   compute_encoder.set_bytes(out.shape(axis_), 8);

mlx/backend/metal/jit/includes.h

@@ -34,7 +34,6 @@ const char* steel_gemm_fused();
 const char* steel_gemm_masked();
 const char* steel_gemm_splitk();
 const char* steel_gemm_gather();
-const char* steel_gemm_segmented();
 const char* conv();
 const char* steel_conv();
 const char* steel_conv_general();

mlx/backend/metal/jit_kernels.cpp

@@ -652,43 +652,6 @@ MTL::ComputePipelineState* get_steel_gemm_gather_kernel(
   return d.get_kernel(kernel_name, lib, hash_name, func_consts);
 }
 
-MTL::ComputePipelineState* get_steel_gemm_segmented_kernel(
-    metal::Device& d,
-    const std::string& kernel_name,
-    const std::string& hash_name,
-    const metal::MTLFCList& func_consts,
-    const array& out,
-    bool transpose_a,
-    bool transpose_b,
-    int bm,
-    int bn,
-    int bk,
-    int wm,
-    int wn) {
-  const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name, [&]() {
-    std::string kernel_source;
-    concatenate(
-        kernel_source,
-        metal::utils(),
-        metal::gemm(),
-        metal::steel_gemm_segmented(),
-        get_template_definition(
-            lib_name,
-            "segmented_mm",
-            get_type_string(out.dtype()),
-            bm,
-            bn,
-            bk,
-            wm,
-            wn,
-            transpose_a,
-            transpose_b));
-    return kernel_source;
-  });
-  return d.get_kernel(kernel_name, lib, hash_name, func_consts);
-}
-
 MTL::ComputePipelineState* get_gemv_masked_kernel(
     metal::Device& d,
     const std::string& kernel_name,
@@ -764,8 +727,6 @@ MTL::ComputePipelineState* get_steel_conv_kernel(
 MTL::ComputePipelineState* get_steel_conv_general_kernel(
     metal::Device& d,
     const std::string& kernel_name,
-    const std::string& hash_name,
-    const metal::MTLFCList& func_consts,
     const array& out,
     int bm,
     int bn,
@@ -788,7 +749,7 @@ MTL::ComputePipelineState* get_steel_conv_general_kernel(
                          wn);
     return kernel_source.str();
   });
-  return d.get_kernel(kernel_name, lib, hash_name, func_consts);
+  return d.get_kernel(kernel_name, lib);
 }
 
 MTL::ComputePipelineState* get_fft_kernel(

mlx/backend/metal/kernels.h

@@ -175,20 +175,6 @@ MTL::ComputePipelineState* get_steel_gemm_gather_kernel(
     int wn,
     bool rhs);
 
-MTL::ComputePipelineState* get_steel_gemm_segmented_kernel(
-    metal::Device& d,
-    const std::string& kernel_name,
-    const std::string& hash_name,
-    const metal::MTLFCList& func_consts,
-    const array& out,
-    bool transpose_a,
-    bool transpose_b,
-    int bm,
-    int bn,
-    int bk,
-    int wm,
-    int wn);
-
 MTL::ComputePipelineState* get_steel_conv_kernel(
     metal::Device& d,
     const std::string& kernel_name,
@@ -219,8 +205,6 @@ MTL::ComputePipelineState* get_gemv_masked_kernel(
 MTL::ComputePipelineState* get_steel_conv_general_kernel(
     metal::Device& d,
     const std::string& kernel_name,
-    const std::string& hash_name,
-    const metal::MTLFCList& func_consts,
     const array& out,
     int bm,
     int bn,

mlx/backend/metal/kernels/CMakeLists.txt

@@ -71,7 +71,6 @@ set(STEEL_HEADERS
     steel/gemm/kernels/steel_gemm_fused.h
     steel/gemm/kernels/steel_gemm_gather.h
     steel/gemm/kernels/steel_gemm_masked.h
-    steel/gemm/kernels/steel_gemm_segmented.h
     steel/gemm/kernels/steel_gemm_splitk.h
     steel/utils/type_traits.h
     steel/utils/integral_constant.h)
@@ -121,7 +120,6 @@ if(NOT MLX_METAL_JIT)
   build_kernel(steel/gemm/kernels/steel_gemm_gather ${STEEL_HEADERS})
   build_kernel(steel/gemm/kernels/steel_gemm_masked ${STEEL_HEADERS})
   build_kernel(steel/gemm/kernels/steel_gemm_splitk ${STEEL_HEADERS})
-  build_kernel(steel/gemm/kernels/steel_gemm_segmented ${STEEL_HEADERS})
   build_kernel(gemv_masked steel/utils.h)
 endif()
 

mlx/backend/metal/kernels/binary_ops.h

@@ -235,13 +235,6 @@ struct Power {
 
   template <>
   complex64_t operator()(complex64_t x, complex64_t y) {
-    if (x.real == 0 && x.imag == 0) {
-      if (metal::isnan(y.real) || metal::isnan(y.imag)) {
-        auto nan = metal::numeric_limits<float>::quiet_NaN();
-        return {nan, nan};
-      }
-      return {0.0, 0.0};
-    }
     auto x_theta = metal::atan2(x.imag, x.real);
     auto x_ln_r = 0.5 * metal::log(x.real * x.real + x.imag * x.imag);
     auto mag = metal::exp(y.real * x_ln_r - y.imag * x_theta);

mlx/backend/metal/kernels/layer_norm.metal

@@ -31,7 +31,6 @@ inline void threadgroup_sum(
   for (int i = 0; i < N; i++) {
     x[i] = simd_sum(x[i]);
   }
-  threadgroup_barrier(mem_flags::mem_threadgroup);
   if (simd_lane_id == 0) {
     for (int i = 0; i < N; i++) {
       xs[N * simd_group_id + i] = x[i];

mlx/backend/metal/kernels/steel/conv/kernels/steel_conv_general.h

@@ -2,8 +2,6 @@
 
 #include "mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h"
 
-constant bool align_C [[function_constant(200)]];
-
 template <
     typename T,
     int BM,
@@ -120,58 +118,23 @@ implicit_gemm_conv_2d_general(
   // Prepare threadgroup mma operation
   mma_t mma_op(simd_gid, simd_lid);
 
-  if (align_C) {
-    int gemm_k_iterations =
-        base_wh_size * base_ww_size * gemm_params->gemm_k_iterations;
+  int gemm_k_iterations =
+      base_wh_size * base_ww_size * gemm_params->gemm_k_iterations;
 
-    for (int k = 0; k < gemm_k_iterations; k++) {
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-      // Load elements into threadgroup
-      loader_a.load_unsafe();
-      loader_b.load_unsafe();
+  for (int k = 0; k < gemm_k_iterations; k++) {
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+    // Load elements into threadgroup
+    loader_a.load_unsafe();
+    loader_b.load_unsafe();
 
-      threadgroup_barrier(mem_flags::mem_threadgroup);
+    threadgroup_barrier(mem_flags::mem_threadgroup);
 
-      // Multiply and accumulate threadgroup elements
-      mma_op.mma(As, Bs);
+    // Multiply and accumulate threadgroup elements
+    mma_op.mma(As, Bs);
 
-      // Prepare for next iteration
-      loader_a.next();
-      loader_b.next();
-    }
-  }
-
-  else {
-    for (int k = 1; k < gemm_params->gemm_k_iterations; k++) {
-      for (int j = 0; j < base_wh_size * base_ww_size; j++) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        // Load elements into threadgroup
-        loader_a.load_unsafe();
-        loader_b.load_unsafe();
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Multiply and accumulate threadgroup elements
-        mma_op.mma(As, Bs);
-
-        // Prepare for next iteration
-        loader_a.next();
-        loader_b.next();
-      }
-    }
-    const short remaining_k = params->C % BK;
-    for (int j = 0; j < base_wh_size * base_ww_size; j++) {
-      // Load elements into threadgroup
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-      loader_a.load_safe(remaining_k);
-      loader_b.load_safe(remaining_k);
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-      // Multiply and accumulate threadgroup elements
-      mma_op.mma(As, Bs);
-      // Prepare for next iteration
-      loader_a.next();
-      loader_b.next();
-    }
+    // Prepare for next iteration
+    loader_a.next();
+    loader_b.next();
   }
 
   threadgroup_barrier(mem_flags::mem_none);

mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h

@@ -137,52 +137,6 @@ struct Conv2DInputBlockLoaderGeneral {
     }
   }
 
-  METAL_FUNC void load_safe(const short remaining_k) const {
-    STEEL_PRAGMA_UNROLL
-    for (short i = 0, is = 0; i < n_rows; ++i, is += TROWS) {
-      // Find bounds
-      int n = read_n[i];
-
-      int h_flip = params->flip ? params->wS[0] - weight_h - 1 : weight_h;
-      int w_flip = params->flip ? params->wS[1] - weight_w - 1 : weight_w;
-
-      int ih_dil = read_ih[i] + h_flip * params->kdil[0];
-      int iw_dil = read_iw[i] + w_flip * params->kdil[1];
-
-      int ih = ih_dil / params->idil[0];
-      int iw = iw_dil / params->idil[1];
-
-      size_t offset = ih * params->in_strides[1] + iw * params->in_strides[2];
-
-      // Read from input if in bounds
-      if ((n < params->N) && (ih_dil >= 0 && ih < params->iS[0]) &&
-          (iw_dil >= 0 && iw < params->iS[1])) {
-        if (bj + vec_size <= remaining_k) {
-          STEEL_PRAGMA_UNROLL
-          for (short j = 0; j < vec_size; ++j) {
-            dst[is * dst_ld + j] = (src[i])[offset + j];
-          }
-        } else {
-          for (short j = 0; j < vec_size; ++j) {
-            if (bj + j < remaining_k) {
-              dst[is * dst_ld + j] = (src[i])[offset + j];
-            } else {
-              dst[is * dst_ld + j] = T(0);
-            }
-          }
-        }
-      }
-
-      // Zero pad otherwise
-      else {
-        STEEL_PRAGMA_UNROLL
-        for (short j = 0; j < vec_size; ++j) {
-          dst[is * dst_ld + j] = T(0);
-        }
-      }
-    }
-  }
-
   /* Iteration helper */
   METAL_FUNC void next() {
     weight_w += jump_params->f_wgt_jump_w;
@@ -308,55 +262,6 @@ struct Conv2DWeightBlockLoaderGeneral {
     }
   }
 
-  METAL_FUNC void load_safe(const short remaining_k) const {
-    const device T* curr_src = src + weight_h * params->wt_strides[1] +
-        weight_w * params->wt_strides[2];
-
-    if ((start_row + BN <= params->O)) {
-      STEEL_PRAGMA_UNROLL
-      for (short i = 0; i < BN; i += TROWS) {
-        if (bj + vec_size <= remaining_k) {
-          STEEL_PRAGMA_UNROLL
-          for (short j = 0; j < vec_size; j++) {
-            dst[i * dst_ld + j] = curr_src[i * src_ld + j];
-          }
-        } else {
-          for (short j = 0; j < vec_size; j++) {
-            if (bj + j < remaining_k) {
-              dst[i * dst_ld + j] = curr_src[i * src_ld + j];
-            } else {
-              dst[i * dst_ld + j] = T(0);
-            }
-          }
-        }
-      }
-    } else {
-      for (short i = 0; i < BN; i += TROWS) {
-        if ((start_row + i) < params->O) {
-          if (bj + vec_size <= remaining_k) {
-            STEEL_PRAGMA_UNROLL
-            for (short j = 0; j < vec_size; j++) {
-              dst[i * dst_ld + j] = curr_src[i * src_ld + j];
-            }
-          } else {
-            for (short j = 0; j < vec_size; j++) {
-              if (bj + j < remaining_k) {
-                dst[i * dst_ld + j] = curr_src[i * src_ld + j];
-              } else {
-                dst[i * dst_ld + j] = T(0);
-              }
-            }
-          }
-        } else {
-          STEEL_PRAGMA_UNROLL
-          for (short j = 0; j < vec_size; j++) {
-            dst[i * dst_ld + j] = T(0);
-          }
-        }
-      }
-    }
-  }
-
   /* Iteration helper */
   METAL_FUNC void next() {
     weight_w += jump_params->f_wgt_jump_w;

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_fused.h

@@ -33,8 +33,8 @@ template <
     device T* D [[buffer(3)]],
     const constant GEMMParams* params [[buffer(4)]],
     const constant GEMMAddMMParams* addmm_params [[buffer(5), function_constant(use_out_source)]],
-    const constant int* batch_shape [[buffer(6), function_constant(has_batch)]],
-    const constant int64_t* batch_strides [[buffer(7), function_constant(has_batch)]],
+    const constant int* batch_shape [[buffer(6)]],
+    const constant int64_t* batch_strides [[buffer(7)]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_segmented.h

@@ -1,266 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-using namespace mlx::steel;
-
-constant bool segments_contiguous [[function_constant(199)]];
-constant bool align_M [[function_constant(200)]];
-constant bool align_N [[function_constant(201)]];
-
-template <
-    typename T,
-    int BM,
-    int BN,
-    int BK,
-    int WM,
-    int WN,
-    bool transpose_a,
-    bool transpose_b,
-    typename AccumType = float>
-[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void segmented_mm(
-    const device T* A [[buffer(0)]],
-    const device T* B [[buffer(1)]],
-    const device uint32_t* segments [[buffer(2)]],
-    device T* C [[buffer(3)]],
-    const constant GEMMParams* params [[buffer(4)]],
-    uint simd_lane_id [[thread_index_in_simdgroup]],
-    uint simd_group_id [[simdgroup_index_in_threadgroup]],
-    uint3 tid [[threadgroup_position_in_grid]]) {
-  using gemm_kernel = GEMMKernel<
-      T,
-      T,
-      BM,
-      BN,
-      BK,
-      WM,
-      WN,
-      transpose_a,
-      transpose_b,
-      true,
-      true,
-      AccumType>;
-
-  using loader_a_t = typename gemm_kernel::loader_a_t;
-  using loader_b_t = typename gemm_kernel::loader_b_t;
-  using mma_t = typename gemm_kernel::mma_t;
-
-  if (params->tiles_n <= static_cast<int>(tid.x) ||
-      params->tiles_m <= static_cast<int>(tid.y)) {
-    return;
-  }
-
-  // Prepare threadgroup memory
-  threadgroup T As[gemm_kernel::tgp_mem_size_a];
-  threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
-
-  // Find the block in A, B, C
-  const int c_row = tid.y * BM;
-  const int c_col = tid.x * BN;
-  const size_t c_row_long = size_t(c_row);
-  const size_t c_col_long = size_t(c_col);
-
-  // Prepare threadgroup bounds
-  const short tgp_bm = align_M ? BM : short(min(BM, params->M - c_row));
-  const short tgp_bn = align_N ? BN : short(min(BN, params->N - c_col));
-
-  // Move the pointers to the output tile
-  A += transpose_a ? c_row_long : c_row_long * params->lda;
-  B += transpose_b ? c_col_long * params->ldb : c_col_long;
-  C += c_row_long * params->ldd + c_col_long;
-
-  // Move the pointers to the start of the segment
-  uint32_t k_start, k_end;
-  if (segments_contiguous) {
-    k_start = segments[2 * tid.z];
-    k_end = segments[2 * tid.z + 1];
-  } else {
-    // We accept either contiguous (above) or weird strides where the beginning
-    // of the next one is the previous one. Basically the last two strides are
-    // both 1!
-    k_start = segments[tid.z];
-    k_end = segments[tid.z + 1];
-  }
-  A += transpose_a ? k_start * params->lda : k_start;
-  B += transpose_b ? k_start : k_start * params->ldb;
-  C += tid.z * params->batch_stride_d;
-
-  // Prepare threadgroup mma operation
-  thread mma_t mma_op(simd_group_id, simd_lane_id);
-
-  // Prepare threadgroup loading operations
-  thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
-  thread loader_b_t loader_b(B, params->ldb, Bs, simd_group_id, simd_lane_id);
-
-  // Matrix level alignment so only check K
-  if (align_M && align_N) {
-    uint32_t k = k_start + BK;
-    for (; k <= k_end; k += BK) {
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      // Load elements into threadgroup
-      loader_a.load_unsafe();
-      loader_b.load_unsafe();
-
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      // Multiply and accumulate threadgroup elements
-      mma_op.mma(As, Bs);
-
-      // Prepare for next iteration
-      loader_a.next();
-      loader_b.next();
-    }
-    short k_remain = BK - short(k - k_end);
-    const short2 tile_dims_A =
-        transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
-    const short2 tile_dims_B =
-        transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
-    if (k_remain > 0) {
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-      loader_a.load_safe(tile_dims_A);
-      loader_b.load_safe(tile_dims_B);
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-      mma_op.mma(As, Bs);
-    }
-    mma_op.store_result(C, params->ldd);
-  } else {
-    // Tile aligned do the same as above
-    if ((align_M || tgp_bm == BM) && (align_N || tgp_bn == BN)) {
-      uint32_t k = k_start + BK;
-      for (; k <= k_end; k += BK) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Load elements into threadgroup
-        loader_a.load_unsafe();
-        loader_b.load_unsafe();
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Multiply and accumulate threadgroup elements
-        mma_op.mma(As, Bs);
-
-        // Prepare for next iteration
-        loader_a.next();
-        loader_b.next();
-      }
-      short k_remain = BK - short(k - k_end);
-      const short2 tile_dims_A =
-          transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
-      const short2 tile_dims_B =
-          transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
-      if (k_remain > 0) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        loader_a.load_safe(tile_dims_A);
-        loader_b.load_safe(tile_dims_B);
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        mma_op.mma(As, Bs);
-      }
-      mma_op.store_result(C, params->ldd);
-    }
-
-    // Tile partially aligned check rows
-    else if (align_N || tgp_bn == BN) {
-      uint32_t k = k_start + BK;
-      for (; k <= k_end; k += BK) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Load elements into threadgroup
-        loader_a.load_safe(
-            transpose_a ? short2(tgp_bm, BK) : short2(BK, tgp_bm));
-        loader_b.load_unsafe();
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Multiply and accumulate threadgroup elements
-        mma_op.mma(As, Bs);
-
-        // Prepare for next iteration
-        loader_a.next();
-        loader_b.next();
-      }
-      short k_remain = BK - short(k - k_end);
-      const short2 tile_dims_A =
-          transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
-      const short2 tile_dims_B =
-          transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
-      if (k_remain > 0) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        loader_a.load_safe(tile_dims_A);
-        loader_b.load_safe(tile_dims_B);
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        mma_op.mma(As, Bs);
-      }
-      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
-    }
-
-    // Tile partially aligned check cols
-    else if (align_M || tgp_bm == BM) {
-      uint32_t k = k_start + BK;
-      for (; k <= k_end; k += BK) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Load elements into threadgroup
-        loader_a.load_unsafe();
-        loader_b.load_safe(
-            transpose_b ? short2(BK, tgp_bn) : short2(tgp_bn, BK));
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Multiply and accumulate threadgroup elements
-        mma_op.mma(As, Bs);
-
-        // Prepare for next iteration
-        loader_a.next();
-        loader_b.next();
-      }
-      short k_remain = BK - short(k - k_end);
-      const short2 tile_dims_A =
-          transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
-      const short2 tile_dims_B =
-          transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
-      if (k_remain > 0) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        loader_a.load_safe(tile_dims_A);
-        loader_b.load_safe(tile_dims_B);
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        mma_op.mma(As, Bs);
-      }
-      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
-    }
-
-    // Nothing aligned so check both rows and cols
-    else {
-      uint32_t k = k_start + BK;
-      for (; k <= k_end; k += BK) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Load elements into threadgroup
-        loader_a.load_safe(
-            transpose_a ? short2(tgp_bm, BK) : short2(BK, tgp_bm));
-        loader_b.load_safe(
-            transpose_b ? short2(BK, tgp_bn) : short2(tgp_bn, BK));
-
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        // Multiply and accumulate threadgroup elements
-        mma_op.mma(As, Bs);
-
-        // Prepare for next iteration
-        loader_a.next();
-        loader_b.next();
-      }
-      short k_remain = BK - short(k - k_end);
-      const short2 tile_dims_A =
-          transpose_a ? short2(tgp_bm, k_remain) : short2(k_remain, tgp_bm);
-      const short2 tile_dims_B =
-          transpose_b ? short2(k_remain, tgp_bn) : short2(tgp_bn, k_remain);
-      if (k_remain > 0) {
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        loader_a.load_safe(tile_dims_A);
-        loader_b.load_safe(tile_dims_B);
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-        mma_op.mma(As, Bs);
-      }
-      mma_op.store_result_safe(C, params->ldd, short2(tgp_bn, tgp_bm));
-    }
-  }
-}

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_segmented.metal

@@ -1,43 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-// clang-format off
-#include "mlx/backend/metal/kernels/utils.h"
-#include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
-#include "mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_segmented.h"
-
-#define instantiate_segmented_mm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-  instantiate_kernel(                                                         \
-      "steel_segmented_mm_" #tname "_" #iname "_" #oname "_bm" #bm "_bn" #bn  \
-      "_bk" #bk "_wm" #wm "_wn" #wn,                                          \
-      segmented_mm,                                                           \
-      itype,                                                                  \
-      bm,                                                                     \
-      bn,                                                                     \
-      bk,                                                                     \
-      wm,                                                                     \
-      wn,                                                                     \
-      trans_a,                                                                \
-      trans_b,                                                                \
-      float)
-
-#define instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-  instantiate_segmented_mm(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
-  instantiate_segmented_mm(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
-  instantiate_segmented_mm(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn)      \
-  instantiate_segmented_mm(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
-
-#define instantiate_segmented_mm_shapes_helper(iname, itype, oname, otype)                 \
-  instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2)  \
-  instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 1, 2)  \
-  instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, 64, 32, 32, 2, 2)  \
-  instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 1, 2)  \
-  instantiate_segmented_mm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2)
-// clang-format on
-
-instantiate_segmented_mm_shapes_helper(float16, half, float16, half);
-instantiate_segmented_mm_shapes_helper(
-    bfloat16,
-    bfloat16_t,
-    bfloat16,
-    bfloat16_t);
-instantiate_segmented_mm_shapes_helper(float32, float, float32, float);