split logsumexp

.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"]

CMakeLists.txt

@@ -34,7 +34,6 @@ option(MLX_BUILD_BENCHMARKS "Build benchmarks for mlx" OFF)
 option(MLX_BUILD_PYTHON_BINDINGS "Build python bindings for mlx" OFF)
 option(MLX_BUILD_METAL "Build metal backend" ON)
 option(MLX_BUILD_CPU "Build cpu backend" ON)
-option(MLX_BUILD_CUDA "Build cuda backend" OFF)
 option(MLX_METAL_DEBUG "Enhance metal debug workflow" OFF)
 option(MLX_ENABLE_X64_MAC "Enable building for x64 macOS" OFF)
 option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
@@ -84,10 +83,6 @@ if(MLX_BUILD_METAL)
   set(QUARTZ_LIB "-framework QuartzCore")
 endif()
 
-if(MLX_BUILD_CUDA)
-  enable_language(CUDA)
-endif()
-
 if(MLX_BUILD_METAL AND NOT METAL_LIB)
   message(STATUS "Metal not found. Unable to build GPU")
   set(MLX_BUILD_METAL OFF)
@@ -231,9 +226,6 @@ target_include_directories(
   mlx PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}>
              $<INSTALL_INTERFACE:include>)
 
-# Do not add mlx_EXPORTS define for shared library.
-set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
-
 FetchContent_Declare(
   fmt
   GIT_REPOSITORY https://github.com/fmtlib/fmt.git

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 ^^^^^^^")

benchmarks/python/layer_norm_bench.py

@@ -1,7 +1,5 @@
 # Copyright © 2023-2024 Apple Inc.
 
-from functools import partial
-
 import mlx.core as mx
 import mlx.nn as nn
 from time_utils import time_fn
@@ -20,63 +18,51 @@ def layer_norm(x, w, b, eps):
     return y
 
 
-def time_layer_norm(N, dt):
-    L = 1024
+def time_layer_norm():
     f1 = lambda x, w, b, y: (layer_norm(x, w, b, 1e-5) * y).sum()
     f2 = lambda x, w, b, y: (mx.fast.layer_norm(x, w, b, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0, 1, 2))
     g2 = mx.grad(f2, argnums=(0, 1, 2))
 
-    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
-    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
     mx.eval(x, w, b, y)
 
-    def layer_norm_loop(f, x, w, b):
-        for _ in range(32):
-            x = f(x, w, b)
-        return x
-
-    time_fn(layer_norm_loop, partial(layer_norm, eps=1e-5), x, w, b)
-    time_fn(layer_norm_loop, partial(mx.fast.layer_norm, eps=1e-5), x, w, b)
-
-    def layer_norm_grad_loop(g, x, w, b):
+    def layer_norm_loop(g, x, w, b):
         gx, gw, gb = x, w, b
         for _ in range(32):
             gx, gw, gb = g(gx, gw, gb, y)
         return gx, gw, gb
 
-    time_fn(layer_norm_grad_loop, g1, x, w, b)
-    time_fn(layer_norm_grad_loop, g2, x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g1), x, w, b)
-    time_fn(layer_norm_grad_loop, mx.compile(g2), x, w, b)
+    time_fn(layer_norm_loop, g1, x, w, b)
+    time_fn(layer_norm_loop, g2, x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g1), x, w, b)
+    time_fn(layer_norm_loop, mx.compile(g2), x, w, b)
 
     f1 = lambda x, y: (layer_norm(x, None, None, 1e-5) * y).sum()
     f2 = lambda x, y: (mx.fast.layer_norm(x, None, None, 1e-5) * y).sum()
     g1 = mx.grad(f1, argnums=(0,))
     g2 = mx.grad(f2, argnums=(0,))
 
-    x = mx.random.uniform(shape=(8, L, N)).astype(dt)
-    w = mx.random.uniform(shape=(N,)).astype(dt)
-    b = mx.random.uniform(shape=(N,)).astype(dt)
-    y = mx.random.uniform(shape=(8, L, N)).astype(dt)
+    x = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
+    w = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    b = mx.random.uniform(shape=(4096,)).astype(mx.float16)
+    y = mx.random.uniform(shape=(8, 1024, 4096)).astype(mx.float16)
     mx.eval(x, w, b, y)
 
-    def layer_norm_grad_x_loop(g, x):
+    def layer_norm_loop(g, x):
         gx = x
         for _ in range(32):
             gx = g(gx, y)
         return gx
 
-    time_fn(layer_norm_grad_x_loop, g1, x)
-    time_fn(layer_norm_grad_x_loop, g2, x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g1), x)
-    time_fn(layer_norm_grad_x_loop, mx.compile(g2), x)
+    time_fn(layer_norm_loop, g1, x)
+    time_fn(layer_norm_loop, g2, x)
+    time_fn(layer_norm_loop, mx.compile(g1), x)
+    time_fn(layer_norm_loop, mx.compile(g2), x)
 
 
 if __name__ == "__main__":
-    for dt in [mx.float32, mx.float16, mx.bfloat16]:
-        for n in [1024, 2048, 4096, 8192, 8192 + 1024]:
-            print(dt, n)
-            time_layer_norm(n, dt)
+    time_layer_norm()

cmake/extension.cmake

@@ -11,14 +11,13 @@ include(CMakeParseArguments)
 # Args: TARGET: Custom target to be added for the metal library TITLE: Name of
 # the .metallib OUTPUT_DIRECTORY: Where to place ${TITLE}.metallib SOURCES: List
 # of source files INCLUDE_DIRS: List of include dirs DEPS: List of dependency
-# files (like headers) DEBUG: Boolean, if true, enables debug compile options
-# for this specific library. If not provided, uses global MLX_METAL_DEBUG.
+# files (like headers)
 #
 # clang format on
 
 macro(mlx_build_metallib)
   # Parse args
-  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY DEBUG)
+  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY)
   set(multiValueArgs SOURCES INCLUDE_DIRS DEPS)
   cmake_parse_arguments(MTLLIB "" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -27,10 +26,6 @@ macro(mlx_build_metallib)
 
   # Collect compile options
   set(MTLLIB_COMPILE_OPTIONS -Wall -Wextra -fno-fast-math -Wno-c++17-extensions)
-  if(MLX_METAL_DEBUG OR MTLLIB_DEBUG)
-    set(MTLLIB_COMPILE_OPTIONS ${MTLLIB_COMPILE_OPTIONS} -gline-tables-only
-                               -frecord-sources)
-  endif()
 
   # Prepare metallib build command
   add_custom_command(

docs/src/conf.py

@@ -10,7 +10,7 @@ import mlx.core as mx
 # -- Project information -----------------------------------------------------
 
 project = "MLX"
-copyright = "2023, Apple"
+copyright = "2023, MLX Contributors"
 author = "MLX Contributors"
 version = ".".join(mx.__version__.split(".")[:3])
 release = version

docs/src/dev/custom_metal_kernels.rst

@@ -8,26 +8,23 @@ MLX supports writing custom Metal kernels through the Python and C++ APIs.
 Simple Example
 --------------
 
-.. currentmodule:: mlx.core
-
 Let's write a custom kernel that computes ``exp`` elementwise:
 
 .. code-block:: python
 
-  source = """
-      uint elem = thread_position_in_grid.x;
-      T tmp = inp[elem];
-      out[elem] = metal::exp(tmp);
-  """
-
-  kernel = mx.fast.metal_kernel(
-      name="myexp",
-      input_names=["inp"],
-      output_names=["out"],
-      source=source,
-  )
-
   def exp_elementwise(a: mx.array):
+      source = """
+          uint elem = thread_position_in_grid.x;
+          T tmp = inp[elem];
+          out[elem] = metal::exp(tmp);
+      """
+
+      kernel = mx.fast.metal_kernel(
+          name="myexp",
+          input_names=["inp"],
+          output_names=["out"],
+          source=source,
+      )
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -42,13 +39,8 @@ Let's write a custom kernel that computes ``exp`` elementwise:
   b = exp_elementwise(a)
   assert mx.allclose(b, mx.exp(a))
 
-Every time you make a kernel, a new Metal library is created and possibly
-JIT compiled. To reduce the overhead from that, build the kernel once with
-:func:`fast.metal_kernel` and then use it many times.
-
 .. note::
-   Only pass the body of the Metal kernel in ``source``. The function
-   signature is generated automatically.
+    We are only required to pass the body of the Metal kernel in ``source``.
 
 The full function signature will be generated using:
 
@@ -86,51 +78,44 @@ Putting this all together, the generated function signature for ``myexp`` is as
 
   template [[host_name("custom_kernel_myexp_float")]] [[kernel]] decltype(custom_kernel_myexp_float<float>) custom_kernel_myexp_float<float>;
 
-Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads
-<https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_
-function. This means we will launch ``mx.prod(grid)`` threads, subdivided into
-``threadgroup`` size threadgroups.  For optimal performance, each thread group
-dimension should be less than or equal to the corresponding grid dimension.
+Note: ``grid`` and ``threadgroup`` are parameters to the Metal `dispatchThreads <https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/2866532-dispatchthreads>`_ function.
+This means we will launch ``mx.prod(grid)`` threads, subdivided into ``threadgroup`` size threadgroups.
+For optimal performance, each thread group dimension should be less than or equal to the corresponding grid dimension.
 
-Passing ``verbose=True`` to :func:`ast.metal_kernel.__call__` will print the
-generated code for debugging purposes.
+Passing ``verbose=True`` to ``mx.fast.metal_kernel.__call__`` will print the generated code for debugging purposes.
 
 Using Shape/Strides
 -------------------
 
-:func:`fast.metal_kernel` supports an argument ``ensure_row_contiguous`` which
-is ``True`` by default. This will copy the array inputs if needed
-before the kernel is launched to ensure that the memory layout is row
-contiguous.  Generally this makes writing the kernel easier, since we don't
-have to worry about gaps or the ordering of the dims when indexing.
+``mx.fast.metal_kernel`` supports an argument ``ensure_row_contiguous`` which is ``True`` by default.
+This will copy the ``mx.array`` inputs if needed before the kernel is launched to ensure that the memory layout is row contiguous.
+Generally this makes writing the kernel easier, since we don't have to worry about gaps or the ordering of the dims
+when indexing.
 
-If we want to avoid this copy, :func:`fast.metal_kernel` automatically passes
-``a_shape``, ``a_strides`` and ``a_ndim`` for each input array ``a`` if any are
-present in ``source``. We can then use MLX's built in indexing utils to fetch
-the right elements for each thread.
+If we want to avoid this copy, ``metal_kernel`` automatically passes ``a_shape``, ``a_strides`` and ``a_ndim`` for each
+input array ``a`` if any are present in ``source``.
+We can then use MLX's built in indexing utils to fetch the right elements for each thread.
 
-Let's convert ``myexp`` above to support arbitrarily strided arrays without
-relying on a copy from ``ensure_row_contiguous``:
+Let's convert ``myexp`` above to support arbitrarily strided arrays without relying on a copy from ``ensure_row_contiguous``:
 
 .. code-block:: python
-   
-  source = """
-      uint elem = thread_position_in_grid.x;
-      // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
-      uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
-      T tmp = inp[loc];
-      // Output arrays are always row contiguous
-      out[elem] = metal::exp(tmp);
-  """
-
-  kernel = mx.fast.metal_kernel(
-      name="myexp_strided",
-      input_names=["inp"],
-      output_names=["out"],
-      source=source
-  )
 
   def exp_elementwise(a: mx.array):
+      source = """
+          uint elem = thread_position_in_grid.x;
+          // Utils from `mlx/backend/metal/kernels/utils.h` are automatically included
+          uint loc = elem_to_loc(elem, inp_shape, inp_strides, inp_ndim);
+          T tmp = inp[loc];
+          // Output arrays are always row contiguous
+          out[elem] = metal::exp(tmp);
+      """
+
+      kernel = mx.fast.metal_kernel(
+          name="myexp_strided",
+          input_names=["inp"],
+          output_names=["out"],
+          source=source
+      )
       outputs = kernel(
           inputs=[a],
           template=[("T", mx.float32)],
@@ -157,139 +142,137 @@ We'll start with the following MLX implementation using standard ops:
 
 .. code-block:: python
 
-  def grid_sample_ref(x, grid):
-      N, H_in, W_in, _ = x.shape
-      ix = ((grid[..., 0] + 1) * W_in - 1) / 2
-      iy = ((grid[..., 1] + 1) * H_in - 1) / 2
+    def grid_sample_ref(x, grid):
+        N, H_in, W_in, _ = x.shape
+        ix = ((grid[..., 0] + 1) * W_in - 1) / 2
+        iy = ((grid[..., 1] + 1) * H_in - 1) / 2
 
-      ix_nw = mx.floor(ix).astype(mx.int32)
-      iy_nw = mx.floor(iy).astype(mx.int32)
+        ix_nw = mx.floor(ix).astype(mx.int32)
+        iy_nw = mx.floor(iy).astype(mx.int32)
 
-      ix_ne = ix_nw + 1
-      iy_ne = iy_nw
+        ix_ne = ix_nw + 1
+        iy_ne = iy_nw
 
-      ix_sw = ix_nw
-      iy_sw = iy_nw + 1
+        ix_sw = ix_nw
+        iy_sw = iy_nw + 1
 
-      ix_se = ix_nw + 1
-      iy_se = iy_nw + 1
+        ix_se = ix_nw + 1
+        iy_se = iy_nw + 1
 
-      nw = (ix_se - ix)    * (iy_se - iy)
-      ne = (ix    - ix_sw) * (iy_sw - iy)
-      sw = (ix_ne - ix)    * (iy    - iy_ne)
-      se = (ix    - ix_nw) * (iy    - iy_nw)
+        nw = (ix_se - ix)    * (iy_se - iy)
+        ne = (ix    - ix_sw) * (iy_sw - iy)
+        sw = (ix_ne - ix)    * (iy    - iy_ne)
+        se = (ix    - ix_nw) * (iy    - iy_nw)
 
-      I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
-      I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
-      I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
-      I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
+        I_nw = x[mx.arange(N)[:, None, None], iy_nw, ix_nw, :]
+        I_ne = x[mx.arange(N)[:, None, None], iy_ne, ix_ne, :]
+        I_sw = x[mx.arange(N)[:, None, None], iy_sw, ix_sw, :]
+        I_se = x[mx.arange(N)[:, None, None], iy_se, ix_se, :]
 
-      mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
-      mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
-      mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
-      mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
+        mask_nw = (iy_nw >= 0) & (iy_nw <= H_in - 1) & (ix_nw >= 0) & (ix_nw <= W_in - 1)
+        mask_ne = (iy_ne >= 0) & (iy_ne <= H_in - 1) & (ix_ne >= 0) & (ix_ne <= W_in - 1)
+        mask_sw = (iy_sw >= 0) & (iy_sw <= H_in - 1) & (ix_sw >= 0) & (ix_sw <= W_in - 1)
+        mask_se = (iy_se >= 0) & (iy_se <= H_in - 1) & (ix_se >= 0) & (ix_se <= W_in - 1)
 
-      I_nw *= mask_nw[..., None]
-      I_ne *= mask_ne[..., None]
-      I_sw *= mask_sw[..., None]
-      I_se *= mask_se[..., None]
+        I_nw *= mask_nw[..., None]
+        I_ne *= mask_ne[..., None]
+        I_sw *= mask_sw[..., None]
+        I_se *= mask_se[..., None]
 
-      output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
+        output = nw[..., None] * I_nw + ne[..., None] * I_ne + sw[..., None] * I_sw + se[..., None] * I_se
 
-      return output
+        return output
 
-Now let's use :func:`custom_function` together with :func:`fast.metal_kernel`
+Now let's use ``mx.custom_function`` together with ``mx.fast.metal_kernel``
 to write a fast GPU kernel for both the forward and backward passes.
 
 First we'll implement the forward pass as a fused kernel:
 
 .. code-block:: python
 
-  source = """
-      uint elem = thread_position_in_grid.x;
-      int H = x_shape[1];
-      int W = x_shape[2];
-      int C = x_shape[3];
-      int gH = grid_shape[1];
-      int gW = grid_shape[2];
+    @mx.custom_function
+    def grid_sample(x, grid):
 
-      int w_stride = C;
-      int h_stride = W * w_stride;
-      int b_stride = H * h_stride;
+        assert x.ndim == 4, "`x` must be 4D."
+        assert grid.ndim == 4, "`grid` must be 4D."
 
-      uint grid_idx = elem / C * 2;
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
+        out_shape = (B, gN, gM, C)
 
-      int ix_nw = floor(ix);
-      int iy_nw = floor(iy);
+        assert D == 2, "Last dim of `grid` must be size 2."
 
-      int ix_ne = ix_nw + 1;
-      int iy_ne = iy_nw;
+        source = """
+            uint elem = thread_position_in_grid.x;
+            int H = x_shape[1];
+            int W = x_shape[2];
+            int C = x_shape[3];
+            int gH = grid_shape[1];
+            int gW = grid_shape[2];
 
-      int ix_sw = ix_nw;
-      int iy_sw = iy_nw + 1;
+            int w_stride = C;
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_se = ix_nw + 1;
-      int iy_se = iy_nw + 1;
+            uint grid_idx = elem / C * 2;
+            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-      T nw = (ix_se - ix)    * (iy_se - iy);
-      T ne = (ix    - ix_sw) * (iy_sw - iy);
-      T sw = (ix_ne - ix)    * (iy    - iy_ne);
-      T se = (ix    - ix_nw) * (iy    - iy_nw);
+            int ix_nw = floor(ix);
+            int iy_nw = floor(iy);
 
-      int batch_idx = elem / C / gH / gW * b_stride;
-      int channel_idx = elem % C;
-      int base_idx = batch_idx + channel_idx;
+            int ix_ne = ix_nw + 1;
+            int iy_ne = iy_nw;
 
-      T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
-      T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
-      T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
-      T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
+            int ix_sw = ix_nw;
+            int iy_sw = iy_nw + 1;
 
-      I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
-      I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
-      I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
-      I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
+            int ix_se = ix_nw + 1;
+            int iy_se = iy_nw + 1;
 
-      out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
-  """
+            T nw = (ix_se - ix)    * (iy_se - iy);
+            T ne = (ix    - ix_sw) * (iy_sw - iy);
+            T sw = (ix_ne - ix)    * (iy    - iy_ne);
+            T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-  kernel = mx.fast.metal_kernel(
-      name="grid_sample",
-      input_names=["x", "grid"],
-      output_names=["out"],
-      source=source,
-  )
+            int batch_idx = elem / C / gH / gW * b_stride;
+            int channel_idx = elem % C;
+            int base_idx = batch_idx + channel_idx;
 
-  @mx.custom_function
-  def grid_sample(x, grid):
+            T I_nw = x[base_idx + iy_nw * h_stride + ix_nw * w_stride];
+            T I_ne = x[base_idx + iy_ne * h_stride + ix_ne * w_stride];
+            T I_sw = x[base_idx + iy_sw * h_stride + ix_sw * w_stride];
+            T I_se = x[base_idx + iy_se * h_stride + ix_se * w_stride];
 
-      assert x.ndim == 4, "`x` must be 4D."
-      assert grid.ndim == 4, "`grid` must be 4D."
+            I_nw = iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1 ? I_nw : 0;
+            I_ne = iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1 ? I_ne : 0;
+            I_sw = iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1 ? I_sw : 0;
+            I_se = iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1 ? I_se : 0;
 
-      B, _, _, C = x.shape
-      _, gN, gM, D = grid.shape
-      out_shape = (B, gN, gM, C)
-
-      assert D == 2, "Last dim of `grid` must be size 2."
-
-      outputs = kernel(
-          inputs=[x, grid],
-          template=[("T", x.dtype)],
-          output_shapes=[out_shape],
-          output_dtypes=[x.dtype],
-          grid=(np.prod(out_shape), 1, 1),
-          threadgroup=(256, 1, 1),
-      )
-      return outputs[0]
+            out[elem] = nw * I_nw + ne * I_ne + sw * I_sw + se * I_se;
+        """
+        kernel = mx.fast.metal_kernel(
+            name="grid_sample",
+            input_names=["x", "grid"],
+            output_names=["out"],
+            source=source,
+        )
+        outputs = kernel(
+            inputs=[x, grid],
+            template=[("T", x.dtype)],
+            output_shapes=[out_shape],
+            output_dtypes=[x.dtype],
+            grid=(np.prod(out_shape), 1, 1),
+            threadgroup=(256, 1, 1),
+        )
+        return outputs[0]
 
 For a reasonably sized input such as:
 
 .. code-block:: python
 
-  x.shape = (8, 1024, 1024, 64)
-  grid.shape = (8, 256, 256, 2)
+    x.shape = (8, 1024, 1024, 64)
+    grid.shape = (8, 256, 256, 2)
 
 On an M1 Max, we see a big performance improvement:
 
@@ -298,11 +281,11 @@ On an M1 Max, we see a big performance improvement:
 Grid Sample VJP
 ---------------
 
-Since we decorated ``grid_sample`` with :func:`custom_function`, we can now
-define its custom vjp transform so MLX can differentiate it.
+Since we decorated ``grid_sample`` with ``mx.custom_function``, we can now define
+its custom vjp transform so MLX can differentiate it.
 
 The backwards pass requires atomically updating ``x_grad``/``grid_grad`` and so
-requires a few extra :func:`fast.metal_kernel` features:
+requires a few extra ``mx.fast.metal_kernel`` features:
 
 * ``init_value=0``
     Initialize all of the kernel's outputs to this value before it runs. This allows us to update only part of the output arrays with the kernel.
@@ -316,129 +299,128 @@ We can then implement the backwards pass as follows:
 
 .. code-block:: python
 
-  source = """
-      uint elem = thread_position_in_grid.x;
-      int H = x_shape[1];
-      int W = x_shape[2];
-      int C = x_shape[3];
-      // Pad C to the nearest larger simdgroup size multiple
-      int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
+    @grid_sample.vjp
+    def grid_sample_vjp(primals, cotangent, _):
+        x, grid = primals
+        B, _, _, C = x.shape
+        _, gN, gM, D = grid.shape
 
-      int gH = grid_shape[1];
-      int gW = grid_shape[2];
+        assert D == 2, "Last dim of `grid` must be size 2."
 
-      int w_stride = C;
-      int h_stride = W * w_stride;
-      int b_stride = H * h_stride;
+        source = """
+            uint elem = thread_position_in_grid.x;
+            int H = x_shape[1];
+            int W = x_shape[2];
+            int C = x_shape[3];
+            // Pad C to the nearest larger simdgroup size multiple
+            int C_padded = ceildiv(C, threads_per_simdgroup) * threads_per_simdgroup;
 
-      uint grid_idx = elem / C_padded * 2;
-      float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
-      float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
+            int gH = grid_shape[1];
+            int gW = grid_shape[2];
 
-      int ix_nw = floor(ix);
-      int iy_nw = floor(iy);
+            int w_stride = C;
+            int h_stride = W * w_stride;
+            int b_stride = H * h_stride;
 
-      int ix_ne = ix_nw + 1;
-      int iy_ne = iy_nw;
+            uint grid_idx = elem / C_padded * 2;
+            float ix = ((grid[grid_idx] + 1) * W - 1) / 2;
+            float iy = ((grid[grid_idx + 1] + 1) * H - 1) / 2;
 
-      int ix_sw = ix_nw;
-      int iy_sw = iy_nw + 1;
+            int ix_nw = floor(ix);
+            int iy_nw = floor(iy);
 
-      int ix_se = ix_nw + 1;
-      int iy_se = iy_nw + 1;
+            int ix_ne = ix_nw + 1;
+            int iy_ne = iy_nw;
 
-      T nw = (ix_se - ix)    * (iy_se - iy);
-      T ne = (ix    - ix_sw) * (iy_sw - iy);
-      T sw = (ix_ne - ix)    * (iy    - iy_ne);
-      T se = (ix    - ix_nw) * (iy    - iy_nw);
+            int ix_sw = ix_nw;
+            int iy_sw = iy_nw + 1;
 
-      int batch_idx = elem / C_padded / gH / gW * b_stride;
-      int channel_idx = elem % C_padded;
-      int base_idx = batch_idx + channel_idx;
+            int ix_se = ix_nw + 1;
+            int iy_se = iy_nw + 1;
 
-      T gix = T(0);
-      T giy = T(0);
-      if (channel_idx < C) {
-          int cot_index = elem / C_padded * C + channel_idx;
-          T cot = cotangent[cot_index];
-          if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
-              int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
+            T nw = (ix_se - ix)    * (iy_se - iy);
+            T ne = (ix    - ix_sw) * (iy_sw - iy);
+            T sw = (ix_ne - ix)    * (iy    - iy_ne);
+            T se = (ix    - ix_nw) * (iy    - iy_nw);
 
-              T I_nw = x[offset];
-              gix -= I_nw * (iy_se - iy) * cot;
-              giy -= I_nw * (ix_se - ix) * cot;
-          }
-          if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
-              int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
+            int batch_idx = elem / C_padded / gH / gW * b_stride;
+            int channel_idx = elem % C_padded;
+            int base_idx = batch_idx + channel_idx;
 
-              T I_ne = x[offset];
-              gix += I_ne * (iy_sw - iy) * cot;
-              giy -= I_ne * (ix - ix_sw) * cot;
-          }
-          if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
-              int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
+            T gix = T(0);
+            T giy = T(0);
+            if (channel_idx < C) {
+                int cot_index = elem / C_padded * C + channel_idx;
+                T cot = cotangent[cot_index];
+                if (iy_nw >= 0 && iy_nw <= H - 1 && ix_nw >= 0 && ix_nw <= W - 1) {
+                    int offset = base_idx + iy_nw * h_stride + ix_nw * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], nw * cot, memory_order_relaxed);
 
-              T I_sw = x[offset];
-              gix -= I_sw * (iy - iy_ne) * cot;
-              giy += I_sw * (ix_ne - ix) * cot;
-          }
-          if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
-              int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
-              atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
+                    T I_nw = x[offset];
+                    gix -= I_nw * (iy_se - iy) * cot;
+                    giy -= I_nw * (ix_se - ix) * cot;
+                }
+                if (iy_ne >= 0 && iy_ne <= H - 1 && ix_ne >= 0 && ix_ne <= W - 1) {
+                    int offset = base_idx + iy_ne * h_stride + ix_ne * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], ne * cot, memory_order_relaxed);
 
-              T I_se = x[offset];
-              gix += I_se * (iy - iy_nw) * cot;
-              giy += I_se * (ix - ix_nw) * cot;
-          }
-      }
+                    T I_ne = x[offset];
+                    gix += I_ne * (iy_sw - iy) * cot;
+                    giy -= I_ne * (ix - ix_sw) * cot;
+                }
+                if (iy_sw >= 0 && iy_sw <= H - 1 && ix_sw >= 0 && ix_sw <= W - 1) {
+                    int offset = base_idx + iy_sw * h_stride + ix_sw * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], sw * cot, memory_order_relaxed);
 
-      T gix_mult = W / 2;
-      T giy_mult = H / 2;
+                    T I_sw = x[offset];
+                    gix -= I_sw * (iy - iy_ne) * cot;
+                    giy += I_sw * (ix_ne - ix) * cot;
+                }
+                if (iy_se >= 0 && iy_se <= H - 1 && ix_se >= 0 && ix_se <= W - 1) {
+                    int offset = base_idx + iy_se * h_stride + ix_se * w_stride;
+                    atomic_fetch_add_explicit(&x_grad[offset], se * cot, memory_order_relaxed);
 
-      // Reduce across each simdgroup first.
-      // This is much faster than relying purely on atomics.
-      gix = simd_sum(gix);
-      giy = simd_sum(giy);
+                    T I_se = x[offset];
+                    gix += I_se * (iy - iy_nw) * cot;
+                    giy += I_se * (ix - ix_nw) * cot;
+                }
+            }
 
-      if (thread_index_in_simdgroup == 0) {
-          atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
-          atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
-      }
-  """
-  kernel = mx.fast.metal_kernel(
-      name="grid_sample_grad",
-      input_names=["x", "grid", "cotangent"],
-      output_names=["x_grad", "grid_grad"],
-      source=source,
-      atomic_outputs=True,
-  )
+            T gix_mult = W / 2;
+            T giy_mult = H / 2;
 
-  @grid_sample.vjp
-  def grid_sample_vjp(primals, cotangent, _):
-      x, grid = primals
-      B, _, _, C = x.shape
-      _, gN, gM, D = grid.shape
+            // Reduce across each simdgroup first.
+            // This is much faster than relying purely on atomics.
+            gix = simd_sum(gix);
+            giy = simd_sum(giy);
 
-      assert D == 2, "Last dim of `grid` must be size 2."
-
-      # pad the output channels to simd group size
-      # so that our `simd_sum`s don't overlap.
-      simdgroup_size = 32
-      C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
-      grid_size = B * gN * gM * C_padded
-      outputs = kernel(
-          inputs=[x, grid, cotangent],
-          template=[("T", x.dtype)],
-          output_shapes=[x.shape, grid.shape],
-          output_dtypes=[x.dtype, x.dtype],
-          grid=(grid_size, 1, 1),
-          threadgroup=(256, 1, 1),
-          init_value=0,
-      )
-      return outputs[0], outputs[1]
+            if (thread_index_in_simdgroup == 0) {
+                atomic_fetch_add_explicit(&grid_grad[grid_idx], gix * gix_mult, memory_order_relaxed);
+                atomic_fetch_add_explicit(&grid_grad[grid_idx + 1], giy * giy_mult, memory_order_relaxed);
+            }
+        """
+        kernel = mx.fast.metal_kernel(
+            name="grid_sample_grad",
+            input_names=["x", "grid", "cotangent"],
+            output_names=["x_grad", "grid_grad"],
+            source=source,
+            atomic_outputs=True,
+        )
+        # pad the output channels to simd group size
+        # so that our `simd_sum`s don't overlap.
+        simdgroup_size = 32
+        C_padded = (C + simdgroup_size - 1) // simdgroup_size * simdgroup_size
+        grid_size = B * gN * gM * C_padded
+        outputs = kernel(
+            inputs=[x, grid, cotangent],
+            template=[("T", x.dtype)],
+            output_shapes=[x.shape, grid.shape],
+            output_dtypes=[x.dtype, x.dtype],
+            grid=(grid_size, 1, 1),
+            threadgroup=(256, 1, 1),
+            init_value=0,
+        )
+        return outputs[0], outputs[1]
 
 There's an even larger speed up for the vjp:
 

docs/src/dev/extensions.rst

@@ -397,11 +397,11 @@ below.
         std::ostringstream kname;
         kname << "axpby_" << "general_" << type_to_name(out);
 
-        // Load the metal library
-        auto lib = d.get_library("mlx_ext");
+        // Make sure the metal library is available
+        d.register_library("mlx_ext");
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname.str(), lib);
+        auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);

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/python/array.rst

@@ -19,8 +19,6 @@ Array
     array.ndim
     array.shape
     array.size
-    array.real
-    array.imag
     array.abs
     array.all
     array.any

docs/src/python/linalg.rst

@@ -16,8 +16,6 @@ Linear Algebra
     cross
     qr
     svd
-    eigvals
-    eig
     eigvalsh
     eigh
     lu

docs/src/usage/indexing.rst

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

examples/extensions/axpby/axpby.cpp

@@ -172,11 +172,11 @@ void Axpby::eval_gpu(
   kname << (contiguous_kernel ? "contiguous_" : "general_");
   kname << type_to_name(out);
 
-  // Load the metal library
-  auto lib = d.get_library("mlx_ext");
+  // Make sure the metal library is available
+  d.register_library("mlx_ext");
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname.str(), lib);
+  auto kernel = d.get_kernel(kname.str(), "mlx_ext");
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);

mlx/CMakeLists.txt

@@ -21,7 +21,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/metal.h)
 
 # Define MLX_VERSION only in the version.cpp file.
-add_library(mlx_version OBJECT ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
+add_library(mlx_version STATIC ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
 target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
 
@@ -47,21 +47,10 @@ add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/distributed)
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/io)
 
 if(MLX_BUILD_METAL)
+  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/gpu)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/metal)
 else()
   target_sources(mlx
                  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/metal/no_metal.cpp)
-endif()
-
-if(MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda)
-else()
-  target_sources(mlx
-                 PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/backend/cuda/no_cuda.cpp)
-endif()
-
-if(MLX_BUILD_METAL OR MLX_BUILD_CUDA)
-  add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/gpu)
-else()
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/backend/no_gpu)
 endif()

mlx/array.h

@@ -224,10 +224,6 @@ class array {
     // Not copyable
     Data(const Data& d) = delete;
     Data& operator=(const Data& d) = delete;
-    Data(Data&& o) : buffer(o.buffer), d(o.d) {
-      o.buffer = allocator::Buffer(nullptr);
-      o.d = [](allocator::Buffer) {};
-    }
     ~Data() {
       d(buffer);
     }

mlx/backend/common/buffer_cache.h

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

mlx/backend/common/compiled.cpp

@@ -1,7 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/common/utils.h"
+#include "mlx/graph_utils.h"
+#include "mlx/primitives.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
@@ -14,8 +15,6 @@ void print_constant(std::ostream& os, const array& x) {
       return print_float_constant<float16_t>(os, x);
     case bfloat16:
       return print_float_constant<bfloat16_t>(os, x);
-    case float64:
-      return print_float_constant<double>(os, x);
     case complex64:
       return print_complex_constant<complex64_t>(os, x);
     case int8:
@@ -52,8 +51,6 @@ std::string get_type_string(Dtype d) {
       return "float16_t";
     case bfloat16:
       return "bfloat16_t";
-    case float64:
-      return "double";
     case complex64:
       return "complex64_t";
     case bool_:
@@ -82,6 +79,55 @@ std::string get_type_string(Dtype d) {
   }
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids) {
+  NodeNamer namer;
+  std::ostringstream os;
+  std::ostringstream constant_hasher;
+
+  // Fill the input names. This is not really necessary, I just like having A,
+  // B, C, ... as the inputs.
+  for (auto& x : inputs) {
+    namer.get_name(x);
+  }
+
+  // The primitives describing the tape. For unary and binary primitives this
+  // must be enough to describe the full computation.
+  for (auto& a : tape) {
+    // name and type of output
+    os << namer.get_name(a) << kindof(a.dtype()) << a.itemsize();
+    // computation performed
+    a.primitive().print(os);
+    // name of inputs to the function
+    for (auto& inp : a.inputs()) {
+      os << namer.get_name(inp);
+    }
+  }
+  os << "_";
+
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      os << "C";
+      print_constant(constant_hasher, x);
+    } else {
+      os << (is_scalar(x) ? "S" : "V");
+    }
+  }
+  os << "_";
+  for (auto& x : inputs) {
+    if (constant_ids.find(x.id()) != constant_ids.end()) {
+      continue;
+    }
+    os << kindof(x.dtype()) << x.itemsize();
+  }
+  os << "_" << std::hash<std::string>{}(constant_hasher.str());
+
+  return os.str();
+}
+
 bool compiled_check_contiguity(
     const std::vector<array>& inputs,
     const Shape& shape) {
@@ -113,7 +159,8 @@ bool compiled_check_contiguity(
 void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const std::function<bool(size_t)>& is_constant,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
     bool contiguous) {
   if (contiguous) {
     int o = 0;
@@ -128,7 +175,8 @@ void compiled_allocate_outputs(
       // - Donatable
       // - Not a constant
       if (in.itemsize() == outputs[o].itemsize() && !is_scalar(in) &&
-          in.is_donatable() && is_constant(i)) {
+          in.is_donatable() &&
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o++].copy_shared_buffer(in);
       }
       // Get representative input flags to properly set non-donated outputs
@@ -156,7 +204,7 @@ void compiled_allocate_outputs(
       // - Not a constant
       if (in.flags().row_contiguous && in.size() == outputs[o].size() &&
           in.itemsize() == outputs[o].itemsize() && in.is_donatable() &&
-          is_constant(i)) {
+          constant_ids_.find(inputs_[i].id()) == constant_ids_.end()) {
         outputs[o].copy_shared_buffer(
             in, outputs[o].strides(), in.flags(), in.data_size());
         o++;
@@ -168,74 +216,4 @@ void compiled_allocate_outputs(
   }
 }
 
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant) {
-  const Shape& shape = out.shape();
-  bool contiguous = compiled_check_contiguity(inputs, shape);
-  if (contiguous) {
-    return {true, shape, {}};
-  }
-
-  std::vector<Strides> strides_vec{out.strides()};
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    // Skip constants.
-    if (is_constant(i)) {
-      continue;
-    }
-
-    // Skip scalar inputs.
-    const auto& x = inputs[i];
-    if (is_scalar(x)) {
-      continue;
-    }
-
-    // Broadcast the inputs to the output shape.
-    Strides xstrides;
-    size_t j = 0;
-    for (; j < shape.size() - x.ndim(); ++j) {
-      if (shape[j] == 1) {
-        xstrides.push_back(out.strides()[j]);
-      } else {
-        xstrides.push_back(0);
-      }
-    }
-    for (size_t i = 0; i < x.ndim(); ++i, ++j) {
-      if (x.shape(i) == 1) {
-        if (shape[j] == 1) {
-          xstrides.push_back(out.strides()[j]);
-        } else {
-          xstrides.push_back(0);
-        }
-      } else {
-        xstrides.push_back(x.strides()[i]);
-      }
-    }
-    strides_vec.push_back(std::move(xstrides));
-  }
-
-  auto tup = collapse_contiguous_dims(shape, strides_vec, INT32_MAX);
-  return {false, std::move(std::get<0>(tup)), std::move(std::get<1>(tup))};
-}
-
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
-    bool contiguous) {
-  if (contiguous) {
-    size_t max_size = 0;
-    for (const auto& in : inputs) {
-      max_size = std::max(max_size, in.data_size());
-    }
-    return max_size > UINT32_MAX;
-  } else {
-    size_t max_size = 0;
-    for (const auto& o : outputs) {
-      max_size = std::max(max_size, o.size());
-    }
-    return max_size > UINT32_MAX;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/common/compiled.h

@@ -1,8 +1,9 @@
 // Copyright © 2023-2024 Apple Inc.
 #pragma once
 
-#include <functional>
 #include <iomanip>
+#include <sstream>
+#include <unordered_set>
 
 #include "mlx/array.h"
 #include "mlx/primitives.h"
@@ -13,17 +14,19 @@ inline bool is_static_cast(const Primitive& p) {
   return (typeid(p) == typeid(Broadcast) || typeid(p) == typeid(AsType));
 }
 
+std::string build_lib_name(
+    const std::vector<array>& inputs,
+    const std::vector<array>& outputs,
+    const std::vector<array>& tape,
+    const std::unordered_set<uintptr_t>& constant_ids);
+
 std::string get_type_string(Dtype d);
 
 template <typename T>
 void print_float_constant(std::ostream& os, const array& x) {
   auto old_precision = os.precision();
-  if constexpr (std::is_same_v<T, double>) {
-    os << std::setprecision(std::numeric_limits<double>::digits10 + 1);
-  } else {
-    os << std::setprecision(std::numeric_limits<float>::digits10 + 1);
-  }
-  os << x.item<T>() << std::setprecision(old_precision);
+  os << std::setprecision(std::numeric_limits<float>::digits10 + 1)
+     << x.item<T>() << std::setprecision(old_precision);
 }
 
 template <typename T>
@@ -57,19 +60,8 @@ bool compiled_check_contiguity(
 void compiled_allocate_outputs(
     const std::vector<array>& inputs,
     std::vector<array>& outputs,
-    const std::function<bool(size_t)>& is_constant,
-    bool contiguous);
-
-// Collapse contiguous dims ignoring scalars and constants.
-std::tuple<bool, Shape, std::vector<Strides>> compiled_collapse_contiguous_dims(
-    const std::vector<array>& inputs,
-    const array& out,
-    const std::function<bool(size_t)>& is_constant);
-
-// Return whether the kernel should use large index.
-bool compiled_use_large_index(
-    const std::vector<array>& inputs,
-    const std::vector<array>& outputs,
+    const std::vector<array>& inputs_,
+    const std::unordered_set<uintptr_t>& constant_ids_,
     bool contiguous);
 
 } // namespace mlx::core

mlx/backend/common/copy.h

@@ -2,7 +2,7 @@
 
 #pragma once
 
-#include "mlx/backend/common/utils.h"
+#include "mlx/array.h"
 
 namespace mlx::core {
 
@@ -26,7 +26,7 @@ inline bool set_copy_output_data(const array& in, array& out, CopyType ctype) {
   if (ctype == CopyType::Vector) {
     // If the input is donateable, we are doing a vector copy and the types
     // have the same size, then the input buffer can hold the output.
-    if (is_donatable(in, out)) {
+    if (in.is_donatable() && in.itemsize() == out.itemsize()) {
       out.copy_shared_buffer(in);
       return true;
     } else {

mlx/backend/common/matmul.h

@@ -1,67 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/utils.h"
-
-#include <sstream>
-
-namespace mlx::core {
-
-inline std::tuple<Shape, Strides, Strides> collapse_batches(
-    const array& a,
-    const array& b) {
-  if (a.ndim() == 2) {
-    return {{1}, {0}, {0}};
-  }
-
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] =
-      collapse_contiguous_dims(A_bshape, std::vector{A_bstride, B_bstride});
-
-  auto a_batch_strides = batch_strides[0];
-  auto b_batch_strides = batch_strides[1];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    a_batch_strides.push_back(0);
-    b_batch_strides.push_back(0);
-  }
-
-  return std::make_tuple(batch_shape, a_batch_strides, b_batch_strides);
-}
-
-inline std::tuple<Shape, Strides, Strides, Strides>
-collapse_batches(const array& a, const array& b, const array& c) {
-  if (a.ndim() == 2) {
-    return {{1}, {0}, {0}, {0}};
-  }
-
-  Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
-  Strides A_bstride{a.strides().begin(), a.strides().end() - 2};
-  Strides B_bstride{b.strides().begin(), b.strides().end() - 2};
-  Strides C_bstride{c.strides().begin(), c.strides().end() - 2};
-
-  auto [batch_shape, batch_strides] = collapse_contiguous_dims(
-      A_bshape, std::vector{A_bstride, B_bstride, C_bstride});
-
-  auto A_batch_stride = batch_strides[0];
-  auto B_batch_stride = batch_strides[1];
-  auto C_batch_stride = batch_strides[2];
-
-  if (batch_shape.empty()) {
-    batch_shape.push_back(1);
-    A_batch_stride.push_back(0);
-    B_batch_stride.push_back(0);
-    C_batch_stride.push_back(0);
-  }
-
-  return std::make_tuple(
-      batch_shape, A_batch_stride, B_batch_stride, C_batch_stride);
-}
-
-} // namespace mlx::core

mlx/backend/common/reduce.cpp

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

mlx/backend/common/reduce.h

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

mlx/backend/common/unary.h

@@ -1,26 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/allocator.h"
-#include "mlx/backend/common/utils.h"
-
-namespace mlx::core {
-
-inline void set_unary_output_data(const array& in, array& out) {
-  if (in.flags().contiguous) {
-    if (is_donatable(in, out)) {
-      out.copy_shared_buffer(in);
-    } else {
-      out.set_data(
-          allocator::malloc(in.data_size() * out.itemsize()),
-          in.data_size(),
-          in.strides(),
-          in.flags());
-    }
-  } else {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
-}
-
-} // namespace mlx::core

mlx/backend/common/utils.cpp

@@ -1,16 +1,9 @@
 // Copyright © 2023-2024 Apple Inc.
 
 #include "mlx/backend/common/utils.h"
-#include "mlx/primitives.h"
 
 namespace mlx::core {
 
-std::string get_primitive_string(Primitive* primitive) {
-  std::ostringstream op_t;
-  primitive->print(op_t);
-  return op_t.str();
-}
-
 std::tuple<Shape, std::vector<Strides>> collapse_contiguous_dims(
     const Shape& shape,
     const std::vector<Strides>& strides,
@@ -108,118 +101,4 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
   return collapse_contiguous_dims(a.shape(), a.strides(), size_cap);
 }
 
-Dims get_block_dims_common(int dim0, int dim1, int dim2, int pow2 /* = 10 */) {
-  int pows[3] = {0, 0, 0};
-  int sum = 0;
-  while (true) {
-    int presum = sum;
-    // Check all the pows
-    if (dim0 >= (1 << (pows[0] + 1))) {
-      pows[0]++;
-      sum++;
-    }
-    if (sum == 10) {
-      break;
-    }
-    if (dim1 >= (1 << (pows[1] + 1))) {
-      pows[1]++;
-      sum++;
-    }
-    if (sum == 10) {
-      break;
-    }
-    if (dim2 >= (1 << (pows[2] + 1))) {
-      pows[2]++;
-      sum++;
-    }
-    if (sum == presum || sum == pow2) {
-      break;
-    }
-  }
-  return std::make_tuple(1ul << pows[0], 1ul << pows[1], 1ul << pows[2]);
-}
-
-Dims get_2d_grid_dims_common(const Shape& shape, const Strides& strides) {
-  // Dims with strides of 0 are ignored as they
-  // correspond to broadcasted dimensions
-  size_t grid_x = 1;
-  size_t grid_y = 1;
-  for (int i = 0; i < shape.size(); ++i) {
-    if (strides[i] == 0) {
-      continue;
-    }
-    if (grid_x * shape[i] < UINT32_MAX) {
-      grid_x *= shape[i];
-    } else {
-      grid_y *= shape[i];
-    }
-  }
-  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX) {
-    throw std::runtime_error("Unable to safely factor shape.");
-  }
-  if (grid_y > grid_x) {
-    std::swap(grid_x, grid_y);
-  }
-  return std::make_tuple(
-      static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y), 1);
-}
-
-Dims get_2d_grid_dims_common(
-    const Shape& shape,
-    const Strides& strides,
-    size_t divisor) {
-  // Compute the 2d grid dimensions such that the total size of the grid is
-  // divided by divisor.
-  size_t grid_x = 1;
-  size_t grid_y = 1;
-  for (int i = 0; i < shape.size(); ++i) {
-    if (strides[i] == 0) {
-      continue;
-    }
-
-    // No need to add this shape we can just remove it from the divisor.
-    if (divisor % shape[i] == 0) {
-      divisor /= shape[i];
-      continue;
-    }
-
-    if (grid_x * shape[i] < UINT32_MAX) {
-      grid_x *= shape[i];
-    } else {
-      grid_y *= shape[i];
-    }
-
-    if (divisor > 1) {
-      if (grid_x % divisor == 0) {
-        grid_x /= divisor;
-        divisor = 1;
-      } else if (grid_y % divisor == 0) {
-        grid_y /= divisor;
-        divisor = 1;
-      }
-    }
-  }
-  if (grid_y > UINT32_MAX || grid_x > UINT32_MAX) {
-    throw std::runtime_error("Unable to safely factor shape.");
-  }
-  if (grid_y > grid_x) {
-    std::swap(grid_x, grid_y);
-  }
-  if (divisor > 1) {
-    grid_x = ((grid_x + divisor - 1) / divisor) * divisor;
-  }
-  return std::make_tuple(
-      static_cast<uint32_t>(grid_x), static_cast<uint32_t>(grid_y), 1);
-}
-
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2) {
-  auto [bx, by, bz] = get_block_dims_common(dim0, dim1, dim2);
-  auto gx = (dim0 + bx - 1) / bx;
-  auto gy = (dim1 + by - 1) / by;
-  auto gz = (dim2 + bz - 1) / bz;
-
-  return std::make_pair(
-      std::make_tuple(gx, gy, gz), std::make_tuple(bx, by, bz));
-}
-
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -2,15 +2,12 @@
 
 #pragma once
 
-#include <tuple>
 #include <vector>
 
 #include "mlx/array.h"
 
 namespace mlx::core {
 
-std::string get_primitive_string(Primitive* primitive);
-
 inline int64_t
 elem_to_loc(int elem, const Shape& shape, const Strides& strides) {
   int64_t loc = 0;
@@ -73,31 +70,6 @@ std::pair<Shape, Strides> collapse_contiguous_dims(
     const array& a,
     int64_t size_cap = std::numeric_limits<int32_t>::max());
 
-// Compute the thread block dimensions which fit the given
-// input dimensions.
-// - The thread block dimensions will be powers of two
-// - The thread block size will be less than 2^pow2
-using Dims = std::tuple<uint32_t, uint32_t, uint32_t>;
-Dims get_block_dims_common(int dim0, int dim1, int dim2, int pow2 = 10);
-
-// Computes a 2D grid where each element is < UINT_MAX
-// Assumes:
-// - overall size (product of non-broadcasted dimensions) is < UINT_MAX^2
-// - shape and strides correspond to a contiguous (no holes) but
-//   possibly broadcasted array
-Dims get_2d_grid_dims_common(const Shape& shape, const Strides& strides);
-
-// Same as above but we do an implicit division with divisor.
-// Basically, equivalent to factorizing
-//    Prod(s \forall s in shape if strides[s] > 0) / divisor.
-Dims get_2d_grid_dims_common(
-    const Shape& shape,
-    const Strides& strides,
-    size_t divisor);
-
-// Get both the block and a grid of blocks that covers dim0, dim1 and dim2.
-std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2);
-
 struct ContiguousIterator {
   inline void step() {
     int dims = shape_.size();
@@ -193,11 +165,4 @@ void shared_buffer_reshape(
     const array& in,
     const Strides& out_strides,
     array& out);
-
-template <typename T>
-inline std::vector<T> remove_index(std::vector<T> vec, size_t index) {
-  vec.erase(std::next(vec.begin(), index));
-  return vec;
-}
-
 } // namespace mlx::core

mlx/backend/cpu/CMakeLists.txt

@@ -46,7 +46,6 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/eig.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eigh.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/encoder.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/fft.cpp

mlx/backend/cpu/arg_reduce.cpp

@@ -14,8 +14,10 @@ template <typename InT, typename OpT>
 void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   auto axis_size = in.shape()[axis];
   auto axis_stride = in.strides()[axis];
-  Strides strides = remove_index(in.strides(), axis);
-  Shape shape = remove_index(in.shape(), axis);
+  Strides strides = in.strides();
+  Shape shape = in.shape();
+  strides.erase(strides.begin() + axis);
+  shape.erase(shape.begin() + axis);
   auto in_ptr = in.data<InT>();
   auto out_ptr = out.data<uint32_t>();
 

mlx/backend/cpu/compiled.cpp

@@ -146,9 +146,18 @@ inline void build_kernel(
     const std::vector<array>& inputs,
     const std::vector<array>& outputs,
     const std::vector<array>& tape,
-    const std::function<bool(size_t)>& is_constant,
+    const std::unordered_set<uintptr_t>& constant_ids,
     bool contiguous,
     int ndim) {
+  // All outputs should have the exact same shape and will be row contiguous
+  auto output_shape = outputs[0].shape();
+  auto output_strides = outputs[0].strides();
+
+  // Constants are scalars that are captured by value and cannot change
+  auto is_constant = [&constant_ids](const array& x) {
+    return constant_ids.find(x.id()) != constant_ids.end();
+  };
+
   NodeNamer namer;
 
 #ifdef _MSC_VER
@@ -161,15 +170,14 @@ inline void build_kernel(
 
   // Add the input arguments
   int cnt = 0;
-  for (size_t i = 0; i < inputs.size(); ++i) {
+  for (auto& x : inputs) {
+    auto& xname = namer.get_name(x);
+
     // Skip constants from the input list
-    if (is_constant(i)) {
+    if (is_constant(x)) {
       continue;
     }
 
-    const auto& x = inputs[i];
-    auto& xname = namer.get_name(x);
-
     auto tstr = get_type_string(x.dtype());
     os << "  " << tstr << "* " << xname << " = (" << tstr << "*)args[" << cnt++
        << "];" << std::endl;
@@ -203,11 +211,10 @@ inline void build_kernel(
   }
 
   // Read the inputs in tmps
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    const auto& x = inputs[i];
+  for (auto& x : inputs) {
     auto& xname = namer.get_name(x);
 
-    if (is_constant(i)) {
+    if (is_constant(x)) {
       os << "  " << get_type_string(x.dtype()) << " tmp_" << xname << " = ";
       print_constant(os, x);
       os << ";" << std::endl;
@@ -257,9 +264,8 @@ inline void build_kernel(
   } else {
     for (int d = ndim - 1; d >= 0; --d) {
       // Update pointers
-      for (size_t i = 0; i < inputs.size(); ++i) {
-        const auto& x = inputs[i];
-        if (is_constant(i) || is_scalar(x)) {
+      for (auto& x : inputs) {
+        if (is_constant(x) || is_scalar(x)) {
           continue;
         }
         auto& xname = namer.get_name(x);
@@ -281,37 +287,65 @@ inline void build_kernel(
 void Compiled::eval_cpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
+  if (kernel_lib_.empty()) {
+    kernel_lib_ = build_lib_name(inputs_, outputs_, tape_, constant_ids_);
+  }
+
+  // Figure out which kernel we are using
+  auto& shape = outputs[0].shape();
+  auto contiguous = compiled_check_contiguity(inputs, shape);
   auto& encoder = cpu::get_command_encoder(stream());
 
-  // Collapse contiguous dims to route to a faster kernel if possible. Also
-  // handle all broadcasting.
-  auto [contiguous, shape, strides] =
-      compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
-
-  // Collect function input arguments.
+  // Handle all broadcasting and collect function input arguments
   std::vector<void*> args;
-  int strides_index = 1;
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    if (is_constant_(i)) {
+  std::vector<std::vector<size_t>> strides;
+  for (int i = 0; i < inputs.size(); i++) {
+    // Skip constants.
+    if (constant_ids_.find(inputs_[i].id()) != constant_ids_.end()) {
       continue;
     }
-    const auto& x = inputs[i];
+    auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
-    if (!contiguous && !is_scalar(x)) {
-      args.push_back(strides[strides_index++].data());
+
+    if (contiguous || is_scalar(x)) {
+      continue;
     }
+
+    // Broadcast the input to the output shape.
+    std::vector<size_t> xstrides;
+    int j = 0;
+    for (; j < shape.size() - x.ndim(); j++) {
+      if (shape[j] == 1) {
+        xstrides.push_back(outputs[0].strides()[j]);
+      } else {
+        xstrides.push_back(0);
+      }
+    }
+    for (int i = 0; i < x.ndim(); i++, j++) {
+      if (x.shape(i) == 1) {
+        if (shape[j] == 1) {
+          xstrides.push_back(outputs[0].strides()[j]);
+        } else {
+          xstrides.push_back(0);
+        }
+      } else {
+        xstrides.push_back(x.strides()[i]);
+      }
+    }
+    strides.push_back(std::move(xstrides));
+    args.push_back(strides.back().data());
   }
 
   // Get the kernel name from the lib
   int ndim = shape.size();
   auto kernel_name = kernel_lib_ + (contiguous ? "_contiguous" : "_strided_");
   if (!contiguous) {
-    kernel_name += std::to_string(ndim);
+    kernel_name += std::to_string(shape.size());
   }
 
   // Get the function
-  auto fn_ptr = compile(kernel_name, [&, contiguous = contiguous]() {
+  auto fn_ptr = compile(kernel_name, [&]() {
     std::ostringstream kernel;
     kernel << get_kernel_preamble() << std::endl;
     kernel << "extern \"C\"  {" << std::endl;
@@ -321,7 +355,7 @@ void Compiled::eval_cpu(
         inputs_,
         outputs_,
         tape_,
-        is_constant_,
+        constant_ids_,
         contiguous,
         ndim);
     // Close extern "C"
@@ -329,22 +363,26 @@ void Compiled::eval_cpu(
     return kernel.str();
   });
 
-  compiled_allocate_outputs(inputs, outputs, is_constant_, contiguous);
+  compiled_allocate_outputs(
+      inputs, outputs, inputs_, constant_ids_, contiguous);
 
   for (auto& x : outputs) {
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
+  Shape out_shape;
   if (!contiguous) {
-    args.push_back((void*)shape.data());
+    out_shape = outputs[0].shape();
+    args.push_back((void*)out_shape.data());
   } else {
     args.push_back((void*)outputs[0].data_size());
   }
   auto fun = (void (*)(void**))fn_ptr;
-  encoder.dispatch([fun,
-                    args = std::move(args),
-                    strides = std::move(strides),
-                    shape = std::move(shape)]() mutable { fun(args.data()); });
+  encoder.dispatch(
+      [fun,
+       args = std::move(args),
+       strides = std::move(strides),
+       out_shape = std::move(out_shape)]() mutable { fun(args.data()); });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/conv.cpp

@@ -22,8 +22,7 @@ void slow_conv_1D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -61,8 +60,7 @@ void slow_conv_1D(
                     out_stride_O = out.strides()[2],
 
                     flip,
-                    padding_lo = padding_lo[0],
-                    padding_hi = padding_hi[0],
+                    padding = padding[0],
                     wt_stride = wt_strides[0],
                     wt_dilation = wt_dilation[0],
                     in_dilation = in_dilation[0]]() mutable {
@@ -79,7 +77,7 @@ void slow_conv_1D(
               const T* wt_ptr = filter_wt_ptr + wh * wt_stride_H;
 
               int wh_flip = flip ? (wH - wh - 1) : wh;
-              int ih = oh * wt_stride - padding_lo + wh_flip * wt_dilation;
+              int ih = oh * wt_stride - padding + wh_flip * wt_dilation;
 
               auto ih_div = std::div(ih, in_dilation);
 
@@ -111,8 +109,7 @@ void slow_conv_2D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -123,235 +120,230 @@ void slow_conv_2D(
   encoder.set_input_array(wt);
   encoder.set_output_array(out);
 
-  encoder.dispatch(
-      [st_wt_ptr = wt.data<T>(),
-       st_in_ptr = in.data<T>(),
-       st_out_ptr = out.data<T>(),
+  encoder.dispatch([st_wt_ptr = wt.data<T>(),
+                    st_in_ptr = in.data<T>(),
+                    st_out_ptr = out.data<T>(),
 
-       N = in.shape(0), // Batch size, should be the same as out.shape(0)
-       iH = 1 + in_dilation[0] * (in.shape(1) - 1), // Input spatial dim
-       iW = 1 + in_dilation[1] * (in.shape(2) - 1), // Input spatial dim
-       C = in.shape(3), // In channels
-       oH = out.shape(1), // Output spatial dim
-       oW = out.shape(2), // Output spatial dim
-       O = wt.shape(0), // Out channels
-       wH = wt.shape(1), // Weight spatial dim
-       wW = wt.shape(2), // Weight spatial dim
+                    N = in.shape(
+                        0), // Batch size, should be the same as out.shape(0)
+                    iH = 1 +
+                        in_dilation[0] * (in.shape(1) - 1), // Input spatial dim
+                    iW = 1 +
+                        in_dilation[1] * (in.shape(2) - 1), // Input spatial dim
+                    C = in.shape(3), // In channels
+                    oH = out.shape(1), // Output spatial dim
+                    oW = out.shape(2), // Output spatial dim
+                    O = wt.shape(0), // Out channels
+                    wH = wt.shape(1), // Weight spatial dim
+                    wW = wt.shape(2), // Weight spatial dim
 
-       groups = in.shape(3) / wt.shape(3),
-       C_per_group = wt.shape(3),
+                    groups = in.shape(3) / wt.shape(3),
+                    C_per_group = wt.shape(3),
 
-       in_stride_N = in.strides()[0],
-       in_stride_H = in.strides()[1],
-       in_stride_W = in.strides()[2],
-       in_stride_C = in.strides()[3],
+                    in_stride_N = in.strides()[0],
+                    in_stride_H = in.strides()[1],
+                    in_stride_W = in.strides()[2],
+                    in_stride_C = in.strides()[3],
 
-       wt_stride_O = wt.strides()[0],
-       wt_stride_H = wt.strides()[1],
-       wt_stride_W = wt.strides()[2],
-       wt_stride_C = wt.strides()[3],
+                    wt_stride_O = wt.strides()[0],
+                    wt_stride_H = wt.strides()[1],
+                    wt_stride_W = wt.strides()[2],
+                    wt_stride_C = wt.strides()[3],
 
-       out_stride_N = out.strides()[0],
-       out_stride_H = out.strides()[1],
-       out_stride_W = out.strides()[2],
-       out_stride_O = out.strides()[3],
+                    out_stride_N = out.strides()[0],
+                    out_stride_H = out.strides()[1],
+                    out_stride_W = out.strides()[2],
+                    out_stride_O = out.strides()[3],
 
-       padding_lo,
-       padding_hi,
-       wt_strides,
-       wt_dilation,
-       in_dilation,
-       flip]() mutable {
-        bool is_idil_one = in_dilation[0] == 1 && in_dilation[1] == 1;
+                    padding,
+                    wt_strides,
+                    wt_dilation,
+                    in_dilation,
+                    flip]() mutable {
+    bool is_idil_one = in_dilation[0] == 1 && in_dilation[1] == 1;
 
-        const int O_per_group = O / groups;
-        auto pt_conv_no_checks =
-            [&](const T* in_ptr, const T* wt_ptr, T* out_ptr, int oh, int ow) {
-              out_ptr += oh * out_stride_H + ow * out_stride_W;
-              int ih_base = oh * wt_strides[0] - padding_lo[0];
-              int iw_base = ow * wt_strides[1] - padding_lo[1];
+    const int O_per_group = O / groups;
+    auto pt_conv_no_checks = [&](const T* in_ptr,
+                                 const T* wt_ptr,
+                                 T* out_ptr,
+                                 int oh,
+                                 int ow) {
+      out_ptr += oh * out_stride_H + ow * out_stride_W;
+      int ih_base = oh * wt_strides[0] - padding[0];
+      int iw_base = ow * wt_strides[1] - padding[1];
 
-              for (int g = 0; g < groups; ++g) {
-                for (int o = g * O_per_group; o < (g + 1) * O_per_group; ++o) {
-                  float r = 0.;
+      for (int g = 0; g < groups; ++g) {
+        for (int o = g * O_per_group; o < (g + 1) * O_per_group; ++o) {
+          float r = 0.;
 
-                  for (int wh = 0; wh < wH; ++wh) {
-                    for (int ww = 0; ww < wW; ++ww) {
-                      int wh_flip = flip ? wH - wh - 1 : wh;
-                      int ww_flip = flip ? wW - ww - 1 : ww;
-                      int ih = ih_base + wh_flip * wt_dilation[0];
-                      int iw = iw_base + ww_flip * wt_dilation[1];
+          for (int wh = 0; wh < wH; ++wh) {
+            for (int ww = 0; ww < wW; ++ww) {
+              int wh_flip = flip ? wH - wh - 1 : wh;
+              int ww_flip = flip ? wW - ww - 1 : ww;
+              int ih = ih_base + wh_flip * wt_dilation[0];
+              int iw = iw_base + ww_flip * wt_dilation[1];
 
-                      const T* wt_ptr_pt =
-                          wt_ptr + wh * wt_stride_H + ww * wt_stride_W;
-                      const T* in_ptr_pt =
-                          in_ptr + ih * in_stride_H + iw * in_stride_W;
+              const T* wt_ptr_pt = wt_ptr + wh * wt_stride_H + ww * wt_stride_W;
+              const T* in_ptr_pt = in_ptr + ih * in_stride_H + iw * in_stride_W;
 
-                      for (int c = g * C_per_group; c < (g + 1) * C_per_group;
-                           ++c) {
-                        r += static_cast<float>(in_ptr_pt[c * in_stride_C]) *
-                            static_cast<float>(
-                                 wt_ptr_pt[(c % C_per_group) * wt_stride_C]);
-                      } // c
-                    } // ww
-                  } // wh
+              for (int c = g * C_per_group; c < (g + 1) * C_per_group; ++c) {
+                r += static_cast<float>(in_ptr_pt[c * in_stride_C]) *
+                    static_cast<float>(
+                         wt_ptr_pt[(c % C_per_group) * wt_stride_C]);
+              } // c
+            } // ww
+          } // wh
 
-                  out_ptr[0] = static_cast<T>(r);
-                  out_ptr += out_stride_O;
-                  wt_ptr += wt_stride_O;
-                } // o
-              } // g
-            };
+          out_ptr[0] = static_cast<T>(r);
+          out_ptr += out_stride_O;
+          wt_ptr += wt_stride_O;
+        } // o
+      } // g
+    };
 
-        int jump_h = flip ? -wt_dilation[0] : wt_dilation[0];
-        int jump_w = flip ? -wt_dilation[1] : wt_dilation[1];
+    int jump_h = flip ? -wt_dilation[0] : wt_dilation[0];
+    int jump_w = flip ? -wt_dilation[1] : wt_dilation[1];
 
-        int init_h = (flip ? (wH - 1) * wt_dilation[0] : 0);
-        int init_w = (flip ? (wW - 1) * wt_dilation[1] : 0);
+    int init_h = (flip ? (wH - 1) * wt_dilation[0] : 0);
+    int init_w = (flip ? (wW - 1) * wt_dilation[1] : 0);
 
-        int f_wgt_jump_h =
-            std::lcm(in_dilation[0], wt_dilation[0]) / wt_dilation[0];
-        int f_wgt_jump_w =
-            std::lcm(in_dilation[1], wt_dilation[1]) / wt_dilation[1];
+    int f_wgt_jump_h =
+        std::lcm(in_dilation[0], wt_dilation[0]) / wt_dilation[0];
+    int f_wgt_jump_w =
+        std::lcm(in_dilation[1], wt_dilation[1]) / wt_dilation[1];
 
-        int f_out_jump_h =
-            std::lcm(in_dilation[0], wt_strides[0]) / wt_strides[0];
-        int f_out_jump_w =
-            std::lcm(in_dilation[1], wt_strides[1]) / wt_strides[1];
+    int f_out_jump_h = std::lcm(in_dilation[0], wt_strides[0]) / wt_strides[0];
+    int f_out_jump_w = std::lcm(in_dilation[1], wt_strides[1]) / wt_strides[1];
 
-        std::vector<int> base_h(f_out_jump_h);
-        std::vector<int> base_w(f_out_jump_w);
+    std::vector<int> base_h(f_out_jump_h);
+    std::vector<int> base_w(f_out_jump_w);
 
-        for (int i = 0; i < f_out_jump_h; ++i) {
-          int ih_loop = i * wt_strides[0] - padding_lo[0] + init_h;
+    for (int i = 0; i < f_out_jump_h; ++i) {
+      int ih_loop = i * wt_strides[0] - padding[0] + init_h;
 
-          int wh_base = 0;
-          while (wh_base < wH && ih_loop % in_dilation[0] != 0) {
-            wh_base++;
-            ih_loop += jump_h;
-          }
+      int wh_base = 0;
+      while (wh_base < wH && ih_loop % in_dilation[0] != 0) {
+        wh_base++;
+        ih_loop += jump_h;
+      }
 
-          base_h[i] = wh_base;
-        }
+      base_h[i] = wh_base;
+    }
 
-        for (int j = 0; j < f_out_jump_w; ++j) {
-          int iw_loop = j * wt_strides[1] - padding_lo[1] + init_w;
+    for (int j = 0; j < f_out_jump_w; ++j) {
+      int iw_loop = j * wt_strides[1] - padding[1] + init_w;
 
-          int ww_base = 0;
-          while (ww_base < wW && iw_loop % in_dilation[1] != 0) {
-            ww_base++;
-            iw_loop += jump_w;
-          }
+      int ww_base = 0;
+      while (ww_base < wW && iw_loop % in_dilation[1] != 0) {
+        ww_base++;
+        iw_loop += jump_w;
+      }
 
-          base_w[j] = ww_base;
-        }
+      base_w[j] = ww_base;
+    }
 
-        auto pt_conv_all_checks =
-            [&](const T* in_ptr, const T* wt_ptr, T* out_ptr, int oh, int ow) {
-              out_ptr += oh * out_stride_H + ow * out_stride_W;
+    auto pt_conv_all_checks =
+        [&](const T* in_ptr, const T* wt_ptr, T* out_ptr, int oh, int ow) {
+          out_ptr += oh * out_stride_H + ow * out_stride_W;
 
-              int ih_base = oh * wt_strides[0] - padding_lo[0];
-              int iw_base = ow * wt_strides[1] - padding_lo[1];
+          int ih_base = oh * wt_strides[0] - padding[0];
+          int iw_base = ow * wt_strides[1] - padding[1];
 
-              int wh_base = base_h[oh % f_out_jump_h];
-              int ww_base = base_w[ow % f_out_jump_w];
+          int wh_base = base_h[oh % f_out_jump_h];
+          int ww_base = base_w[ow % f_out_jump_w];
 
-              for (int g = 0; g < groups; ++g) {
-                for (int o = g * O_per_group; o < (g + 1) * O_per_group; ++o) {
-                  float r = 0.;
+          for (int g = 0; g < groups; ++g) {
+            for (int o = g * O_per_group; o < (g + 1) * O_per_group; ++o) {
+              float r = 0.;
 
-                  for (int wh = wh_base; wh < wH; wh += f_wgt_jump_h) {
-                    for (int ww = ww_base; ww < wW; ww += f_wgt_jump_w) {
-                      int wh_flip = flip ? wH - wh - 1 : wh;
-                      int ww_flip = flip ? wW - ww - 1 : ww;
-                      int ih = ih_base + wh_flip * wt_dilation[0];
-                      int iw = iw_base + ww_flip * wt_dilation[1];
+              for (int wh = wh_base; wh < wH; wh += f_wgt_jump_h) {
+                for (int ww = ww_base; ww < wW; ww += f_wgt_jump_w) {
+                  int wh_flip = flip ? wH - wh - 1 : wh;
+                  int ww_flip = flip ? wW - ww - 1 : ww;
+                  int ih = ih_base + wh_flip * wt_dilation[0];
+                  int iw = iw_base + ww_flip * wt_dilation[1];
 
-                      if (ih >= 0 && ih < iH && iw >= 0 && iw < iW) {
-                        const T* wt_ptr_pt =
-                            wt_ptr + wh * wt_stride_H + ww * wt_stride_W;
+                  if (ih >= 0 && ih < iH && iw >= 0 && iw < iW) {
+                    const T* wt_ptr_pt =
+                        wt_ptr + wh * wt_stride_H + ww * wt_stride_W;
 
-                        int ih_dil = !is_idil_one ? (ih / in_dilation[0]) : ih;
-                        int iw_dil = !is_idil_one ? (iw / in_dilation[1]) : iw;
+                    int ih_dil = !is_idil_one ? (ih / in_dilation[0]) : ih;
+                    int iw_dil = !is_idil_one ? (iw / in_dilation[1]) : iw;
 
-                        const T* in_ptr_pt = in_ptr + ih_dil * in_stride_H +
-                            iw_dil * in_stride_W;
+                    const T* in_ptr_pt =
+                        in_ptr + ih_dil * in_stride_H + iw_dil * in_stride_W;
 
-                        for (int c = g * C_per_group; c < (g + 1) * C_per_group;
-                             ++c) {
-                          r += static_cast<float>(in_ptr_pt[c * in_stride_C]) *
-                              static_cast<float>(
-                                   wt_ptr_pt[(c % C_per_group) * wt_stride_C]);
-                        } // c
+                    for (int c = g * C_per_group; c < (g + 1) * C_per_group;
+                         ++c) {
+                      r += static_cast<float>(in_ptr_pt[c * in_stride_C]) *
+                          static_cast<float>(
+                               wt_ptr_pt[(c % C_per_group) * wt_stride_C]);
+                    } // c
 
-                      } // ih, iw check
-                    } // ww
-                  } // wh
+                  } // ih, iw check
+                } // ww
+              } // wh
 
-                  out_ptr[0] = static_cast<T>(r);
-                  out_ptr += out_stride_O;
-                  wt_ptr += wt_stride_O;
-                } // o
-              } // g
-            };
+              out_ptr[0] = static_cast<T>(r);
+              out_ptr += out_stride_O;
+              wt_ptr += wt_stride_O;
+            } // o
+          } // g
+        };
 
-        int oH_border_0 = 0;
-        int oH_border_1 = is_idil_one
-            ? ((padding_lo[0] + wt_strides[0] - 1) / wt_strides[0])
-            : oH;
-        int oH_border_2 = std::max(
-            oH_border_1,
-            (iH + padding_lo[0] - wH * wt_dilation[0]) / wt_strides[0]);
-        int oH_border_3 = oH;
+    int oH_border_0 = 0;
+    int oH_border_1 =
+        is_idil_one ? ((padding[0] + wt_strides[0] - 1) / wt_strides[0]) : oH;
+    int oH_border_2 = std::max(
+        oH_border_1, (iH + padding[0] - wH * wt_dilation[0]) / wt_strides[0]);
+    int oH_border_3 = oH;
 
-        int oW_border_0 = 0;
-        int oW_border_1 = is_idil_one
-            ? ((padding_lo[1] + wt_strides[1] - 1) / wt_strides[1])
-            : oW;
-        int oW_border_2 = std::max(
-            oW_border_1,
-            (iW + padding_lo[1] - wW * wt_dilation[1]) / wt_strides[1]);
-        int oW_border_3 = oW;
+    int oW_border_0 = 0;
+    int oW_border_1 =
+        is_idil_one ? ((padding[1] + wt_strides[1] - 1) / wt_strides[1]) : oW;
+    int oW_border_2 = std::max(
+        oW_border_1, (iW + padding[1] - wW * wt_dilation[1]) / wt_strides[1]);
+    int oW_border_3 = oW;
 
-        for (int n = 0; n < N; ++n) {
-          // Case 1: oh might put us out of bounds
-          for (int oh = oH_border_0; oh < oH_border_1; ++oh) {
-            for (int ow = 0; ow < oW; ++ow) {
-              pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
-            } // ow
-          } // oh
+    for (int n = 0; n < N; ++n) {
+      // Case 1: oh might put us out of bounds
+      for (int oh = oH_border_0; oh < oH_border_1; ++oh) {
+        for (int ow = 0; ow < oW; ++ow) {
+          pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
+        } // ow
+      } // oh
 
-          // Case 2: oh in bounds
-          for (int oh = oH_border_1; oh < oH_border_2; ++oh) {
-            // Case a: ow might put us out of bounds
-            for (int ow = oW_border_0; ow < oW_border_1; ++ow) {
-              pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
-            } // ow
+      // Case 2: oh in bounds
+      for (int oh = oH_border_1; oh < oH_border_2; ++oh) {
+        // Case a: ow might put us out of bounds
+        for (int ow = oW_border_0; ow < oW_border_1; ++ow) {
+          pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
+        } // ow
 
-            // Case b: ow in bounds
-            for (int ow = oW_border_1; ow < oW_border_2; ++ow) {
-              pt_conv_no_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
-            } // ow
+        // Case b: ow in bounds
+        for (int ow = oW_border_1; ow < oW_border_2; ++ow) {
+          pt_conv_no_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
+        } // ow
 
-            // Case c: ow might put us out of bounds
-            for (int ow = oW_border_2; ow < oW_border_3; ++ow) {
-              pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
-            } // ow
+        // Case c: ow might put us out of bounds
+        for (int ow = oW_border_2; ow < oW_border_3; ++ow) {
+          pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
+        } // ow
 
-          } // oh
+      } // oh
 
-          // Case 3: oh might put us out of bounds
-          for (int oh = oH_border_2; oh < oH_border_3; ++oh) {
-            for (int ow = 0; ow < oW; ++ow) {
-              pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
-            } // ow
-          } // oh
+      // Case 3: oh might put us out of bounds
+      for (int oh = oH_border_2; oh < oH_border_3; ++oh) {
+        for (int ow = 0; ow < oW; ++ow) {
+          pt_conv_all_checks(st_in_ptr, st_wt_ptr, st_out_ptr, oh, ow);
+        } // ow
+      } // oh
 
-          st_in_ptr += in_stride_N;
-          st_out_ptr += out_stride_N;
+      st_in_ptr += in_stride_N;
+      st_out_ptr += out_stride_N;
 
-        } // n
-      });
+    } // n
+  });
 }
 
 template <typename T>
@@ -359,8 +351,7 @@ void slow_conv_3D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -409,8 +400,7 @@ void slow_conv_3D(
                     out_stride_H = out.strides()[2],
                     out_stride_W = out.strides()[3],
                     out_stride_O = out.strides()[4],
-                    padding_lo,
-                    padding_hi,
+                    padding,
                     wt_strides,
                     wt_dilation,
                     in_dilation,
@@ -425,9 +415,9 @@ void slow_conv_3D(
                                  int oh,
                                  int ow) {
       out_ptr += od * out_stride_D + oh * out_stride_H + ow * out_stride_W;
-      int id_base = od * wt_strides[0] - padding_lo[0];
-      int ih_base = oh * wt_strides[1] - padding_lo[1];
-      int iw_base = ow * wt_strides[2] - padding_lo[2];
+      int id_base = od * wt_strides[0] - padding[0];
+      int ih_base = oh * wt_strides[1] - padding[1];
+      int iw_base = ow * wt_strides[2] - padding[2];
 
       for (int o = 0; o < O; ++o) {
         float r = 0.;
@@ -488,7 +478,7 @@ void slow_conv_3D(
     std::vector<int> base_w(f_out_jump_w);
 
     for (int i = 0; i < f_out_jump_d; ++i) {
-      int id_loop = i * wt_strides[0] - padding_lo[0] + init_d;
+      int id_loop = i * wt_strides[0] - padding[0] + init_d;
 
       int wd_base = 0;
       while (wd_base < wD && id_loop % in_dilation[0] != 0) {
@@ -500,7 +490,7 @@ void slow_conv_3D(
     }
 
     for (int i = 0; i < f_out_jump_h; ++i) {
-      int ih_loop = i * wt_strides[1] - padding_lo[1] + init_h;
+      int ih_loop = i * wt_strides[1] - padding[1] + init_h;
 
       int wh_base = 0;
       while (wh_base < wH && ih_loop % in_dilation[1] != 0) {
@@ -512,7 +502,7 @@ void slow_conv_3D(
     }
 
     for (int j = 0; j < f_out_jump_w; ++j) {
-      int iw_loop = j * wt_strides[2] - padding_lo[2] + init_w;
+      int iw_loop = j * wt_strides[2] - padding[2] + init_w;
 
       int ww_base = 0;
       while (ww_base < wW && iw_loop % in_dilation[2] != 0) {
@@ -531,9 +521,9 @@ void slow_conv_3D(
                                   int ow) {
       out_ptr += od * out_stride_D + oh * out_stride_H + ow * out_stride_W;
 
-      int id_base = od * wt_strides[0] - padding_lo[0];
-      int ih_base = oh * wt_strides[1] - padding_lo[1];
-      int iw_base = ow * wt_strides[2] - padding_lo[2];
+      int id_base = od * wt_strides[0] - padding[0];
+      int ih_base = oh * wt_strides[1] - padding[1];
+      int iw_base = ow * wt_strides[2] - padding[2];
 
       int wd_base = base_d[od % f_out_jump_d];
       int wh_base = base_h[oh % f_out_jump_h];
@@ -583,30 +573,24 @@ void slow_conv_3D(
     };
 
     int oD_border_0 = 0;
-    int oD_border_1 = is_idil_one
-        ? ((padding_lo[0] + wt_strides[0] - 1) / wt_strides[0])
-        : oD;
+    int oD_border_1 =
+        is_idil_one ? ((padding[0] + wt_strides[0] - 1) / wt_strides[0]) : oD;
     int oD_border_2 = std::max(
-        oD_border_1,
-        (iD + padding_lo[0] - wD * wt_dilation[0]) / wt_strides[0]);
+        oD_border_1, (iD + padding[0] - wD * wt_dilation[0]) / wt_strides[0]);
     int oD_border_3 = oD;
 
     int oH_border_0 = 0;
-    int oH_border_1 = is_idil_one
-        ? ((padding_lo[1] + wt_strides[1] - 1) / wt_strides[1])
-        : oH;
+    int oH_border_1 =
+        is_idil_one ? ((padding[1] + wt_strides[1] - 1) / wt_strides[1]) : oH;
     int oH_border_2 = std::max(
-        oH_border_1,
-        (iH + padding_lo[1] - wH * wt_dilation[1]) / wt_strides[1]);
+        oH_border_1, (iH + padding[1] - wH * wt_dilation[1]) / wt_strides[1]);
     int oH_border_3 = oH;
 
     int oW_border_0 = 0;
-    int oW_border_1 = is_idil_one
-        ? ((padding_lo[2] + wt_strides[2] - 1) / wt_strides[2])
-        : oW;
+    int oW_border_1 =
+        is_idil_one ? ((padding[2] + wt_strides[2] - 1) / wt_strides[2]) : oW;
     int oW_border_2 = std::max(
-        oW_border_1,
-        (iW + padding_lo[2] - wW * wt_dilation[2]) / wt_strides[2]);
+        oW_border_1, (iW + padding[2] - wW * wt_dilation[2]) / wt_strides[2]);
     int oW_border_3 = oW;
 
     for (int n = 0; n < N; ++n) {
@@ -674,8 +658,7 @@ void dispatch_slow_conv_1D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -686,8 +669,7 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -698,8 +680,7 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -710,8 +691,7 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -727,8 +707,7 @@ void dispatch_slow_conv_2D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -739,8 +718,7 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -751,8 +729,7 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -763,8 +740,7 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -780,8 +756,7 @@ void dispatch_slow_conv_3D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -792,8 +767,7 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -804,8 +778,7 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -816,8 +789,7 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding_lo,
-        padding_hi,
+        padding,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -857,8 +829,7 @@ void explicit_gemm_conv_1D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     Stream stream) {
@@ -877,7 +848,7 @@ void explicit_gemm_conv_1D_cpu(
   auto& encoder = cpu::get_command_encoder(stream);
 
   // Pad input
-  Shape padded_shape = {N, iH + padding_lo[0] + padding_hi[0], C};
+  Shape padded_shape = {N, iH + 2 * padding[0], C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -886,7 +857,7 @@ void explicit_gemm_conv_1D_cpu(
   copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
-  size_t data_offset = padding_lo[0] * in_padded.strides()[1];
+  size_t data_offset = padding[0] * in_padded.strides()[1];
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -1000,8 +971,7 @@ void explicit_gemm_conv_2D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     Stream stream) {
@@ -1019,11 +989,7 @@ void explicit_gemm_conv_2D_cpu(
   auto& encoder = cpu::get_command_encoder(stream);
 
   // Pad input
-  Shape padded_shape = {
-      N,
-      iH + padding_lo[0] + padding_hi[0],
-      iW + padding_lo[1] + padding_hi[1],
-      C};
+  Shape padded_shape = {N, iH + 2 * padding[0], iW + 2 * padding[1], C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -1032,8 +998,8 @@ void explicit_gemm_conv_2D_cpu(
   copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
-  size_t data_offset = padding_lo[0] * in_padded.strides()[1] +
-      padding_lo[1] * in_padded.strides()[2];
+  size_t data_offset =
+      padding[0] * in_padded.strides()[1] + padding[1] * in_padded.strides()[2];
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -1125,8 +1091,7 @@ void explicit_gemm_conv_ND_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const bool flip,
@@ -1149,7 +1114,7 @@ void explicit_gemm_conv_ND_cpu(
   Shape padded_shape(in.shape().size());
   padded_shape.front() = N;
   for (size_t i = 0; i < iDim.size(); i++) {
-    padded_shape[i + 1] = iDim[i] + padding_lo[i] + padding_hi[i];
+    padded_shape[i + 1] = iDim[i] + 2 * padding[i];
   }
   padded_shape.back() = C;
   array in_padded(padded_shape, conv_dtype, nullptr, {});
@@ -1160,10 +1125,9 @@ void explicit_gemm_conv_ND_cpu(
 
   // Pick input slice from padded
   size_t data_offset = 0;
-  for (size_t i = 0; i < padding_lo.size(); i++) {
-    data_offset += padding_lo[i] * in_padded.strides()[i + 1];
+  for (size_t i = 0; i < padding.size(); i++) {
+    data_offset += padding[i] * in_padded.strides()[i + 1];
   }
-
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -1297,8 +1261,7 @@ void conv_1D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1307,40 +1270,22 @@ void conv_1D_cpu(
   const int groups = in.shape().back() / wt.shape().back();
   if (wt_dilation[0] == 1 && in_dilation[0] == 1 && !flip) {
     return explicit_gemm_conv_1D_cpu(
-        in, wt, out, padding_lo, padding_hi, wt_strides, wt_dilation, stream);
+        in, wt, out, padding, wt_strides, wt_dilation, stream);
   }
   if (wt_dilation[0] == 1 && in_dilation[0] == 1 && groups == 1) {
     return explicit_gemm_conv_ND_cpu(
-        in,
-        wt,
-        out,
-        padding_lo,
-        padding_hi,
-        wt_strides,
-        wt_dilation,
-        flip,
-        stream);
+        in, wt, out, padding, wt_strides, wt_dilation, flip, stream);
   }
 
   return dispatch_slow_conv_1D(
-      in,
-      wt,
-      out,
-      padding_lo,
-      padding_hi,
-      wt_strides,
-      wt_dilation,
-      in_dilation,
-      flip,
-      stream);
+      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
 }
 
 void conv_2D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1350,35 +1295,18 @@ void conv_2D_cpu(
   if (wt_dilation[0] == 1 && wt_dilation[1] == 1 && in_dilation[0] == 1 &&
       in_dilation[1] == 1 && groups == 1) {
     return explicit_gemm_conv_ND_cpu(
-        in,
-        wt,
-        out,
-        padding_lo,
-        padding_hi,
-        wt_strides,
-        wt_dilation,
-        flip,
-        stream);
+        in, wt, out, padding, wt_strides, wt_dilation, flip, stream);
   }
+
   return dispatch_slow_conv_2D(
-      in,
-      wt,
-      out,
-      padding_lo,
-      padding_hi,
-      wt_strides,
-      wt_dilation,
-      in_dilation,
-      flip,
-      stream);
+      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
 }
 
 void conv_3D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding_lo,
-    const std::vector<int>& padding_hi,
+    const std::vector<int>& padding,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1389,28 +1317,11 @@ void conv_3D_cpu(
       in_dilation[0] == 1 && in_dilation[1] == 1 && in_dilation[2] == 1 &&
       groups == 1) {
     return explicit_gemm_conv_ND_cpu(
-        in,
-        wt,
-        out,
-        padding_lo,
-        padding_hi,
-        wt_strides,
-        wt_dilation,
-        flip,
-        stream);
+        in, wt, out, padding, wt_strides, wt_dilation, flip, stream);
   }
 
   return dispatch_slow_conv_3D(
-      in,
-      wt,
-      out,
-      padding_lo,
-      padding_hi,
-      wt_strides,
-      wt_dilation,
-      in_dilation,
-      flip,
-      stream);
+      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
 }
 
 } // namespace
@@ -1427,8 +1338,7 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_lo_,
-        padding_hi_,
+        padding_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -1441,8 +1351,7 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_lo_,
-        padding_hi_,
+        padding_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -1455,8 +1364,7 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_lo_,
-        padding_hi_,
+        padding_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,

mlx/backend/cpu/eig.cpp

@@ -1,174 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/allocator.h"
-#include "mlx/array.h"
-#include "mlx/backend/cpu/copy.h"
-#include "mlx/backend/cpu/encoder.h"
-#include "mlx/backend/cpu/lapack.h"
-#include "mlx/linalg.h"
-#include "mlx/primitives.h"
-
-namespace mlx::core {
-
-namespace {
-
-template <typename T>
-void eig_impl(
-    array& a,
-    array& vectors,
-    array& values,
-    bool compute_eigenvectors,
-    Stream stream) {
-  using OT = std::complex<T>;
-  auto a_ptr = a.data<T>();
-  auto eig_ptr = values.data<OT>();
-
-  auto& encoder = cpu::get_command_encoder(stream);
-  encoder.set_input_array(a);
-  encoder.set_output_array(values);
-  OT* vec_ptr = nullptr;
-  if (compute_eigenvectors) {
-    encoder.set_output_array(vectors);
-    vec_ptr = vectors.data<OT>();
-  }
-  encoder.dispatch([a_ptr,
-                    vec_ptr,
-                    eig_ptr,
-                    compute_eigenvectors,
-                    N = vectors.shape(-1),
-                    size = vectors.size()]() mutable {
-    // Work query
-    char jobr = 'N';
-    char jobl = compute_eigenvectors ? 'V' : 'N';
-    int n_vecs_r = 1;
-    int n_vecs_l = compute_eigenvectors ? N : 1;
-    int lwork = -1;
-    int info;
-    {
-      T work;
-      int iwork;
-      geev<T>(
-          &jobl,
-          &jobr,
-          &N,
-          nullptr,
-          &N,
-          nullptr,
-          nullptr,
-          nullptr,
-          &n_vecs_l,
-          nullptr,
-          &n_vecs_r,
-          &work,
-          &lwork,
-          &info);
-      lwork = static_cast<int>(work);
-    }
-
-    auto eig_tmp_data = array::Data{allocator::malloc(sizeof(T) * N * 2)};
-    auto vec_tmp_data =
-        array::Data{allocator::malloc(vec_ptr ? sizeof(T) * N * N * 2 : 0)};
-    auto eig_tmp = static_cast<T*>(eig_tmp_data.buffer.raw_ptr());
-    auto vec_tmp = static_cast<T*>(vec_tmp_data.buffer.raw_ptr());
-    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
-    for (size_t i = 0; i < size / (N * N); ++i) {
-      geev<T>(
-          &jobl,
-          &jobr,
-          &N,
-          a_ptr,
-          &N,
-          eig_tmp,
-          eig_tmp + N,
-          vec_tmp,
-          &n_vecs_l,
-          nullptr,
-          &n_vecs_r,
-          static_cast<T*>(work_buf.buffer.raw_ptr()),
-          &lwork,
-          &info);
-      for (int i = 0; i < N; ++i) {
-        eig_ptr[i] = {eig_tmp[i], eig_tmp[N + i]};
-      }
-      if (vec_ptr) {
-        for (int i = 0; i < N; ++i) {
-          if (eig_ptr[i].imag() != 0) {
-            // This vector and the next are a pair
-            for (int j = 0; j < N; ++j) {
-              vec_ptr[i * N + j] = {
-                  vec_tmp[i * N + j], -vec_tmp[(i + 1) * N + j]};
-              vec_ptr[(i + 1) * N + j] = {
-                  vec_tmp[i * N + j], vec_tmp[(i + 1) * N + j]};
-            }
-            i += 1;
-          } else {
-            for (int j = 0; j < N; ++j) {
-              vec_ptr[i * N + j] = {vec_tmp[i * N + j], 0};
-            }
-          }
-        }
-        vec_ptr += N * N;
-      }
-      a_ptr += N * N;
-      eig_ptr += N;
-      if (info != 0) {
-        std::stringstream msg;
-        msg << "[Eig::eval_cpu] Eigenvalue decomposition failed with error code "
-            << info;
-        throw std::runtime_error(msg.str());
-      }
-    }
-  });
-  encoder.add_temporary(a);
-}
-
-} // namespace
-
-void Eig::eval_cpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  const auto& a = inputs[0];
-  auto& values = outputs[0];
-
-  auto vectors = compute_eigenvectors_
-      ? outputs[1]
-      : array(a.shape(), complex64, nullptr, {});
-
-  auto a_copy = array(a.shape(), a.dtype(), nullptr, {});
-  copy(
-      a,
-      a_copy,
-      a.flags().row_contiguous ? CopyType::Vector : CopyType::General,
-      stream());
-
-  values.set_data(allocator::malloc(values.nbytes()));
-
-  if (compute_eigenvectors_) {
-    // Set the strides and flags so the eigenvectors
-    // are in the columns of the output
-    auto flags = vectors.flags();
-    auto strides = vectors.strides();
-    auto ndim = a.ndim();
-    std::swap(strides[ndim - 1], strides[ndim - 2]);
-
-    if (a.size() > 1) {
-      flags.row_contiguous = false;
-      if (ndim > 2) {
-        flags.col_contiguous = false;
-      } else {
-        flags.col_contiguous = true;
-      }
-    }
-    vectors.set_data(
-        allocator::malloc(vectors.nbytes()), vectors.size(), strides, flags);
-  }
-  switch (a.dtype()) {
-    case float32:
-      eig_impl<float>(a_copy, vectors, values, compute_eigenvectors_, stream());
-      break;
-    default:
-      throw std::runtime_error("[Eig::eval_cpu] only supports float32.");
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cpu/eigh.cpp

@@ -12,133 +12,6 @@ namespace mlx::core {
 
 namespace {
 
-template <typename T, class Enable = void>
-struct EighWork {};
-
-template <typename T>
-struct EighWork<
-    T,
-    typename std::enable_if<std::is_floating_point<T>::value>::type> {
-  using R = T;
-
-  char jobz;
-  char uplo;
-  int N;
-  int lwork;
-  int liwork;
-  int info;
-  std::vector<array::Data> buffers;
-
-  EighWork(char jobz_, char uplo_, int N_)
-      : jobz(jobz_), uplo(uplo_), N(N_), lwork(-1), liwork(-1) {
-    T work;
-    int iwork;
-    syevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        nullptr,
-        &N,
-        nullptr,
-        &work,
-        &lwork,
-        &iwork,
-        &liwork,
-        &info);
-    lwork = static_cast<int>(work);
-    liwork = iwork;
-    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
-    buffers.emplace_back(allocator::malloc(sizeof(int) * liwork));
-  }
-
-  void run(T* vectors, T* values) {
-    syevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        vectors,
-        &N,
-        values,
-        static_cast<T*>(buffers[0].buffer.raw_ptr()),
-        &lwork,
-        static_cast<int*>(buffers[1].buffer.raw_ptr()),
-        &liwork,
-        &info);
-  }
-};
-
-template <>
-struct EighWork<std::complex<float>> {
-  using T = std::complex<float>;
-  using R = float;
-
-  char jobz;
-  char uplo;
-  int N;
-  int lwork;
-  int lrwork;
-  int liwork;
-  int info;
-  std::vector<array::Data> buffers;
-
-  EighWork(char jobz_, char uplo_, int N_)
-      : jobz(jobz_), uplo(uplo_), N(N_), lwork(-1), lrwork(-1), liwork(-1) {
-    T work;
-    R rwork;
-    int iwork;
-    heevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        nullptr,
-        &N,
-        nullptr,
-        &work,
-        &lwork,
-        &rwork,
-        &lrwork,
-        &iwork,
-        &liwork,
-        &info);
-    lwork = static_cast<int>(work.real());
-    lrwork = static_cast<int>(rwork);
-    liwork = iwork;
-    buffers.emplace_back(allocator::malloc(sizeof(T) * lwork));
-    buffers.emplace_back(allocator::malloc(sizeof(R) * lrwork));
-    buffers.emplace_back(allocator::malloc(sizeof(int) * liwork));
-  }
-
-  void run(T* vectors, R* values) {
-    heevd<T>(
-        &jobz,
-        &uplo,
-        &N,
-        vectors,
-        &N,
-        values,
-        static_cast<T*>(buffers[0].buffer.raw_ptr()),
-        &lwork,
-        static_cast<R*>(buffers[1].buffer.raw_ptr()),
-        &lrwork,
-        static_cast<int*>(buffers[2].buffer.raw_ptr()),
-        &liwork,
-        &info);
-    if (jobz == 'V') {
-      // We have pre-transposed the vectors but we also must conjugate them
-      // when they are complex.
-      //
-      // We could vectorize this but it is so fast in comparison to heevd that
-      // it doesn't really matter.
-      for (int i = 0; i < N; i++) {
-        for (int j = 0; j < N; j++) {
-          *vectors = std::conj(*vectors);
-          vectors++;
-        }
-      }
-    }
-  }
-};
-
 template <typename T>
 void eigh_impl(
     array& vectors,
@@ -146,10 +19,8 @@ void eigh_impl(
     const std::string& uplo,
     bool compute_eigenvectors,
     Stream stream) {
-  using R = typename EighWork<T>::R;
-
   auto vec_ptr = vectors.data<T>();
-  auto eig_ptr = values.data<R>();
+  auto eig_ptr = values.data<T>();
   char jobz = compute_eigenvectors ? 'V' : 'N';
 
   auto& encoder = cpu::get_command_encoder(stream);
@@ -162,17 +33,49 @@ void eigh_impl(
                     N = vectors.shape(-1),
                     size = vectors.size()]() mutable {
     // Work query
-    EighWork<T> work(jobz, uplo, N);
+    int lwork = -1;
+    int liwork = -1;
+    int info;
+    {
+      T work;
+      int iwork;
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          nullptr,
+          &N,
+          nullptr,
+          &work,
+          &lwork,
+          &iwork,
+          &liwork,
+          &info);
+      lwork = static_cast<int>(work);
+      liwork = iwork;
+    }
 
-    // Work loop
+    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
+    auto iwork_buf = array::Data{allocator::malloc(sizeof(int) * liwork)};
     for (size_t i = 0; i < size / (N * N); ++i) {
-      work.run(vec_ptr, eig_ptr);
+      syevd<T>(
+          &jobz,
+          &uplo,
+          &N,
+          vec_ptr,
+          &N,
+          eig_ptr,
+          static_cast<T*>(work_buf.buffer.raw_ptr()),
+          &lwork,
+          static_cast<int*>(iwork_buf.buffer.raw_ptr()),
+          &liwork,
+          &info);
       vec_ptr += N * N;
       eig_ptr += N;
-      if (work.info != 0) {
+      if (info != 0) {
         std::stringstream msg;
         msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
-            << work.info;
+            << info;
         throw std::runtime_error(msg.str());
       }
     }
@@ -228,10 +131,6 @@ void Eigh::eval_cpu(
       eigh_impl<double>(
           vectors, values, uplo_, compute_eigenvectors_, stream());
       break;
-    case complex64:
-      eigh_impl<std::complex<float>>(
-          vectors, values, uplo_, compute_eigenvectors_, stream());
-      break;
     default:
       throw std::runtime_error(
           "[Eigh::eval_cpu] only supports float32 or float64.");

mlx/backend/cpu/indexing.cpp

@@ -257,11 +257,15 @@ void gather_axis(
     const array& ind,
     array& out,
     const int axis) {
-  auto shape = remove_index(ind.shape(), axis);
-  ContiguousIterator ind_it(
-      shape, remove_index(ind.strides(), axis), src.ndim() - 1);
-  ContiguousIterator src_it(
-      shape, remove_index(src.strides(), axis), src.ndim() - 1);
+  auto strides = ind.strides();
+  strides.erase(strides.begin() + axis);
+  auto shape = ind.shape();
+  shape.erase(shape.begin() + axis);
+  ContiguousIterator ind_it(shape, strides, src.ndim() - 1);
+
+  strides = src.strides();
+  strides.erase(strides.begin() + axis);
+  ContiguousIterator src_it(shape, strides, src.ndim() - 1);
 
   auto ind_ptr = ind.data<IdxT>();
   auto src_ptr = src.data<T>();
@@ -581,11 +585,15 @@ void Scatter::eval_cpu(const std::vector<array>& inputs, array& out) {
 
 template <typename T, typename IdxT, typename OpT>
 void scatter_axis(array& out, const array idx, const array& upd, int axis) {
-  auto shape = remove_index(idx.shape(), axis);
-  ContiguousIterator idx_it(
-      shape, remove_index(idx.strides(), axis), upd.ndim() - 1);
-  ContiguousIterator upd_it(
-      shape, remove_index(upd.strides(), axis), upd.ndim() - 1);
+  auto strides = idx.strides();
+  strides.erase(strides.begin() + axis);
+  auto shape = idx.shape();
+  shape.erase(shape.begin() + axis);
+  ContiguousIterator idx_it(shape, strides, upd.ndim() - 1);
+
+  strides = upd.strides();
+  strides.erase(strides.begin() + axis);
+  ContiguousIterator upd_it(shape, strides, upd.ndim() - 1);
 
   auto idx_ptr = idx.data<IdxT>();
   auto upd_ptr = upd.data<T>();

mlx/backend/cpu/lapack.h

@@ -2,14 +2,14 @@
 
 #pragma once
 
+// Required for Visual Studio.
+// https://github.com/OpenMathLib/OpenBLAS/blob/develop/docs/install.md
+#ifdef _MSC_VER
 #include <complex>
 #define LAPACK_COMPLEX_CUSTOM
 #define lapack_complex_float std::complex<float>
 #define lapack_complex_double std::complex<double>
-#define lapack_complex_float_real(z) ((z).real())
-#define lapack_complex_float_imag(z) ((z).imag())
-#define lapack_complex_double_real(z) ((z).real())
-#define lapack_complex_double_imag(z) ((z).imag())
+#endif
 
 #ifdef MLX_USE_ACCELERATE
 #include <Accelerate/Accelerate.h>
@@ -32,7 +32,7 @@
 
 #endif
 
-#define INSTANTIATE_LAPACK_REAL(FUNC)                        \
+#define INSTANTIATE_LAPACK_TYPES(FUNC)                       \
   template <typename T, typename... Args>                    \
   void FUNC(Args... args) {                                  \
     if constexpr (std::is_same_v<T, float>) {                \
@@ -42,24 +42,11 @@
     }                                                        \
   }
 
-INSTANTIATE_LAPACK_REAL(geqrf)
-INSTANTIATE_LAPACK_REAL(orgqr)
-INSTANTIATE_LAPACK_REAL(syevd)
-INSTANTIATE_LAPACK_REAL(geev)
-INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesvdx)
-INSTANTIATE_LAPACK_REAL(getrf)
-INSTANTIATE_LAPACK_REAL(getri)
-INSTANTIATE_LAPACK_REAL(trtri)
-
-#define INSTANTIATE_LAPACK_COMPLEX(FUNC)                            \
-  template <typename T, typename... Args>                           \
-  void FUNC(Args... args) {                                         \
-    if constexpr (std::is_same_v<T, std::complex<float>>) {         \
-      MLX_LAPACK_FUNC(c##FUNC)(std::forward<Args>(args)...);        \
-    } else if constexpr (std::is_same_v<T, std::complex<double>>) { \
-      MLX_LAPACK_FUNC(z##FUNC)(std::forward<Args>(args)...);        \
-    }                                                               \
-  }
-
-INSTANTIATE_LAPACK_COMPLEX(heevd)
+INSTANTIATE_LAPACK_TYPES(geqrf)
+INSTANTIATE_LAPACK_TYPES(orgqr)
+INSTANTIATE_LAPACK_TYPES(syevd)
+INSTANTIATE_LAPACK_TYPES(potrf)
+INSTANTIATE_LAPACK_TYPES(gesvdx)
+INSTANTIATE_LAPACK_TYPES(getrf)
+INSTANTIATE_LAPACK_TYPES(getri)
+INSTANTIATE_LAPACK_TYPES(trtri)

mlx/backend/cpu/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/cpu/matmul.cpp

@@ -132,10 +132,6 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[AddMM::eval_cpu] Currently only supports float32.");
   }
-  if (out.size() == 0) {
-    out.set_data(allocator::malloc(out.nbytes()));
-    return;
-  }
 
   // Fill output with C
   auto& c = inputs[2];
@@ -143,9 +139,7 @@ void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
       ? CopyType::Scalar
       : (c.flags().row_contiguous ? CopyType::Vector : CopyType::General);
   copy(c, out, ctype, stream());
-  if (inputs[0].shape(-1) == 0) {
-    return;
-  }
+
   matmul_general(inputs[0], inputs[1], out, stream(), alpha_, beta_);
 }
 

mlx/backend/cpu/quantized.cpp

@@ -13,18 +13,9 @@ namespace mlx::core {
 
 namespace {
 
-inline constexpr short get_pack_factor(int bits, int wsize = 8) {
-  return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
-}
-
-inline constexpr short get_bytes_per_pack(int bits, int wsize = 8) {
-  auto power_of_2_bits = (bits & (bits - 1)) == 0;
-  return power_of_2_bits ? (wsize / 8) : (bits == 5 ? 5 : 3);
-}
-
 template <typename T, int bits>
 void extract_bits(const uint8_t* w_in, T* w_out) {
-  static_assert(bits == 3 || bits == 5 || bits == 6);
+  assert(bits == 3 || bits == 6);
   if (bits == 3) {
     w_out[0] = static_cast<T>(w_in[0] & 0x7);
     w_out[1] = static_cast<T>((w_in[0] & 0x38) >> 3);
@@ -34,16 +25,6 @@ void extract_bits(const uint8_t* w_in, T* w_out) {
     w_out[5] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0x3) << 1));
     w_out[6] = static_cast<T>((w_in[2] & 0x1c) >> 2);
     w_out[7] = static_cast<T>((w_in[2] & 0xe0) >> 5);
-  } else if (bits == 5) {
-    w_out[0] = static_cast<T>(w_in[0] & 0x1f);
-    w_out[1] = static_cast<T>(((w_in[0] & 0xe0) >> 5) + ((w_in[1] & 0x3) << 3));
-    w_out[2] = static_cast<T>((w_in[1] & 0x7c) >> 2);
-    w_out[3] = static_cast<T>(((w_in[1] & 0x80) >> 7) + ((w_in[2] & 0xf) << 1));
-    w_out[4] = static_cast<T>(((w_in[2] & 0xf0) >> 4) + ((w_in[3] & 0x1) << 4));
-    w_out[5] = static_cast<T>((w_in[3] & 0x3e) >> 1);
-    w_out[6] = static_cast<T>(((w_in[3] & 0xc0) >> 6) + ((w_in[4] & 0x7) << 2));
-    w_out[7] = static_cast<T>((w_in[4] & 0xf8) >> 3);
-
   } else if (bits == 6) {
     w_out[0] = static_cast<T>(w_in[0] & 0x3f);
     w_out[1] =
@@ -65,8 +46,8 @@ void _qmm(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-  constexpr int pack_factor = get_pack_factor(bits, 8);
-  constexpr int bytes_per_pack = get_bytes_per_pack(bits);
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
   constexpr int packs_in_group = group_size / pack_factor;
 
   for (int m = 0; m < M; m++) {
@@ -84,7 +65,7 @@ void _qmm(
         T scale = *scales_local++;
         T bias = *biases_local++;
         for (int ng = 0; ng < packs_in_group; ng++) {
-          if constexpr (bits == 3 || bits == 5 || bits == 6) {
+          if (bits == 3 || bits == 6) {
             T wl[pack_factor];
             extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
@@ -123,9 +104,8 @@ void _qmm_t(
     int N,
     int K) {
   constexpr int bitmask = (1 << bits) - 1;
-
-  constexpr int pack_factor = get_pack_factor(bits, 8);
-  constexpr int bytes_per_pack = get_bytes_per_pack(bits);
+  constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
+  constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
   constexpr int packs_in_group = group_size / pack_factor;
 
   for (int m = 0; m < M; m++) {
@@ -141,7 +121,7 @@ void _qmm_t(
         T bias = *biases_local++;
 
         for (int kw = 0; kw < packs_in_group; kw++) {
-          if constexpr (bits == 3 || bits == 5 || bits == 6) {
+          if (bits == 3 || bits == 6) {
             T wl[pack_factor];
             extract_bits<T, bits>(w_local, wl);
 #pragma clang loop unroll(full)
@@ -324,10 +304,6 @@ void _qmm_dispatch_typed(
       _qmm_dispatch_group<T, 4>(
           result, x, w, scales, biases, M, N, K, group_size, transposed_w);
       break;
-    case 5:
-      _qmm_dispatch_group<T, 5>(
-          result, x, w, scales, biases, M, N, K, group_size, transposed_w);
-      break;
     case 6:
       _qmm_dispatch_group<T, 6>(
           result, x, w, scales, biases, M, N, K, group_size, transposed_w);
@@ -637,8 +613,9 @@ void quantize(
   float eps = 1e-7;
 
   bool power_of_2_bits = is_power_of_2(bits);
-  int el_per_int = get_pack_factor(bits, 32);
-  int bytes_per_pack = get_bytes_per_pack(bits);
+  int el_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
+  // For 3/6 bits we read 3 uint8s at a time instead of 1 uint32
+  int bytes_per_pack = power_of_2_bits ? 1 : 3;
   int int_per_group = group_size * bytes_per_pack / el_per_int;
   size_t n_groups = w_size / group_size;
 
@@ -663,21 +640,15 @@ void quantize(
     }
     size_t out_idx = i * int_per_group;
     for (int j = 0; j < int_per_group / bytes_per_pack; ++j) {
-      uint64_t out_el = 0;
+      uint32_t out_el = 0;
       for (int k = 0; k < el_per_int; ++k) {
         float w_el = w[w_idx + j * el_per_int + k];
         w_el = std::rint((w_el - bias) / scale);
         w_el = std::min(std::max(w_el, 0.0f), n_bins);
-        out_el |= static_cast<uint64_t>(w_el) << (k * bits);
+        out_el |= static_cast<uint32_t>(w_el) << (k * bits);
       }
       if (power_of_2_bits) {
         out[out_idx + j] = out_el;
-      } else if (bits == 5) {
-        out[out_idx + bytes_per_pack * j] = out_el & 0xff;
-        out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;
-        out[out_idx + bytes_per_pack * j + 2] = (out_el & 0xff0000) >> 16;
-        out[out_idx + bytes_per_pack * j + 3] = (out_el & 0xff000000) >> 24;
-        out[out_idx + bytes_per_pack * j + 4] = (out_el & 0xff00000000) >> 32;
       } else {
         out[out_idx + bytes_per_pack * j] = out_el & 0xff;
         out[out_idx + bytes_per_pack * j + 1] = (out_el & 0xff00) >> 8;

mlx/backend/cpu/unary.h

@@ -2,13 +2,32 @@
 
 #pragma once
 
-#include "mlx/backend/common/unary.h"
+#include "mlx/allocator.h"
+#include "mlx/array.h"
+#include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
 #include "mlx/utils.h"
 
 namespace mlx::core {
 
+void set_unary_output_data(const array& in, array& out) {
+  if (in.flags().contiguous) {
+    if (is_donatable(in, out)) {
+      out.copy_shared_buffer(in);
+    } else {
+      auto size = in.data_size();
+      out.set_data(
+          allocator::malloc(size * out.itemsize()),
+          size,
+          in.strides(),
+          in.flags());
+    }
+  } else {
+    out.set_data(allocator::malloc(out.nbytes()));
+  }
+}
+
 template <typename T, typename U = T, typename Op>
 void unary_op(const T* a, U* out, size_t shape, size_t stride) {
   for (size_t i = 0; i < shape; i += 1) {

mlx/backend/cuda/CMakeLists.txt

@@ -1,121 +0,0 @@
-# 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.
-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}/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
-    "70;80"
-    CACHE STRING "CUDA architectures")
-message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
-set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
-                                     "${MLX_CUDA_ARCHITECTURES}")
-
-# Use fixed version of CCCL.
-FetchContent_Declare(
-  cccl
-  URL "https://github.com/NVIDIA/cccl/releases/download/v2.8.1/cccl-v2.8.1.zip")
-FetchContent_MakeAvailable(cccl)
-target_include_directories(mlx BEFORE PRIVATE "${cccl_SOURCE_DIR}/include")
-
-# Use fixed version of NVTX.
-FetchContent_Declare(
-  nvtx3
-  GIT_REPOSITORY https://github.com/NVIDIA/NVTX.git
-  GIT_TAG v3.1.1
-  GIT_SHALLOW TRUE
-  SOURCE_SUBDIR c EXCLUDE_FROM_ALL)
-FetchContent_MakeAvailable(nvtx3)
-target_link_libraries(mlx PUBLIC $<BUILD_INTERFACE:nvtx3-cpp>)
-
-# Make cuda runtime APIs available in non-cuda files.
-find_package(CUDAToolkit REQUIRED)
-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

@@ -1,215 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#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>
-#include <unistd.h>
-
-#include <cassert>
-
-namespace mlx::core {
-
-namespace cu {
-
-constexpr int page_size = 16384;
-
-CudaAllocator::CudaAllocator()
-    : buffer_cache_(
-          page_size,
-          [](CudaBuffer* buf) { return buf->size; },
-          [this](CudaBuffer* buf) {
-            cuda_free(buf->data);
-            delete buf;
-          }) {
-  // TODO: Set memory limit for multi-device.
-  size_t free, total;
-  CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
-  memory_limit_ = total * 0.8;
-  max_pool_size_ = memory_limit_;
-}
-
-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,
-    // try to reclaim memory from the cache.
-    size_t mem_required = get_active_memory() + get_cache_memory() + size;
-    if (mem_required >= memory_limit_) {
-      buffer_cache_.release_cached_buffers(mem_required - memory_limit_);
-    }
-
-    lock.unlock();
-    buf = new CudaBuffer{nullptr, size};
-    cudaError_t err = cudaMallocManaged(&buf->data, size);
-    if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
-      throw std::runtime_error(fmt::format(
-          "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
-    }
-    lock.lock();
-  }
-  active_memory_ += size;
-  peak_memory_ = std::max(active_memory_, peak_memory_);
-
-  // Maintain the cache below the requested limit.
-  if (get_cache_memory() > max_pool_size_) {
-    buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
-  }
-
-  return Buffer{buf};
-}
-
-void CudaAllocator::free(Buffer buffer) {
-  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
-  if (!buf) {
-    return;
-  }
-
-  std::unique_lock lock(mutex_);
-  active_memory_ -= buf->size;
-  if (get_cache_memory() < max_pool_size_) {
-    buffer_cache_.recycle_to_cache(buf);
-  } else {
-    lock.unlock();
-    cuda_free(buf->data);
-    delete buf;
-  }
-}
-
-size_t CudaAllocator::size(Buffer buffer) const {
-  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
-  if (!buf) {
-    return 0;
-  }
-  return buf->size;
-}
-
-void CudaAllocator::register_this_thread() {
-  std::lock_guard lock(worker_mutex_);
-  allowed_threads_.insert(std::this_thread::get_id());
-}
-
-void CudaAllocator::cuda_free(void* buf) {
-  // If cuda_free() is called from a unregistered thread, reschedule the call to
-  // worker.
-  {
-    std::lock_guard lock(worker_mutex_);
-    if (allowed_threads_.count(std::this_thread::get_id()) == 0) {
-      if (!worker_) {
-        worker_.reset(new Worker);
-      }
-      worker_->add_task([this, buf]() { this->cuda_free(buf); });
-      worker_->end_batch();
-      worker_->commit();
-      return;
-    }
-  }
-  cudaFree(buf);
-}
-
-size_t CudaAllocator::get_active_memory() const {
-  return active_memory_;
-}
-
-size_t CudaAllocator::get_peak_memory() const {
-  return peak_memory_;
-}
-
-void CudaAllocator::reset_peak_memory() {
-  std::lock_guard lock(mutex_);
-  peak_memory_ = 0;
-}
-
-size_t CudaAllocator::get_memory_limit() {
-  return memory_limit_;
-}
-
-size_t CudaAllocator::set_memory_limit(size_t limit) {
-  std::lock_guard lock(mutex_);
-  std::swap(limit, memory_limit_);
-  return limit;
-}
-
-size_t CudaAllocator::get_cache_memory() const {
-  return buffer_cache_.cache_size();
-}
-
-size_t CudaAllocator::set_cache_limit(size_t limit) {
-  std::lock_guard lk(mutex_);
-  std::swap(limit, max_pool_size_);
-  return limit;
-}
-
-void CudaAllocator::clear_cache() {
-  std::lock_guard lk(mutex_);
-  buffer_cache_.clear();
-}
-
-CudaAllocator& allocator() {
-  // By creating the |allocator_| on heap, the destructor of CudaAllocator
-  // will not be called on exit and buffers in the cache will be leaked. This
-  // can save some time at program exit.
-  static CudaAllocator* allocator_ = new CudaAllocator;
-  return *allocator_;
-}
-
-} // namespace cu
-
-namespace allocator {
-
-Allocator& allocator() {
-  return cu::allocator();
-}
-
-void* Buffer::raw_ptr() {
-  if (!ptr_) {
-    return nullptr;
-  }
-  return static_cast<cu::CudaBuffer*>(ptr_)->data;
-}
-
-} // namespace allocator
-
-size_t get_active_memory() {
-  return cu::allocator().get_active_memory();
-}
-size_t get_peak_memory() {
-  return cu::allocator().get_peak_memory();
-}
-void reset_peak_memory() {
-  return cu::allocator().reset_peak_memory();
-}
-size_t set_memory_limit(size_t limit) {
-  return cu::allocator().set_memory_limit(limit);
-}
-size_t get_memory_limit() {
-  return cu::allocator().get_memory_limit();
-}
-size_t get_cache_memory() {
-  return cu::allocator().get_cache_memory();
-}
-size_t set_cache_limit(size_t limit) {
-  return cu::allocator().set_cache_limit(limit);
-}
-void clear_cache() {
-  cu::allocator().clear_cache();
-}
-
-// Not supported in CUDA.
-size_t set_wired_limit(size_t) {
-  return 0;
-}
-
-} // namespace mlx::core

mlx/backend/cuda/allocator.h

@@ -1,67 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/allocator.h"
-#include "mlx/backend/common/buffer_cache.h"
-
-#include <mutex>
-#include <set>
-#include <thread>
-#include <utility>
-
-namespace mlx::core::cu {
-
-class Worker;
-
-using allocator::Buffer;
-
-// Stores cuda-managed unified memory.
-struct CudaBuffer {
-  void* data;
-  size_t size;
-};
-
-class CudaAllocator : public allocator::Allocator {
- public:
-  Buffer malloc(size_t size) override;
-  void free(Buffer buffer) override;
-  size_t size(Buffer buffer) const override;
-
-  // Register current thread as safe to free buffers.
-  // In cuda freeing a buffer implicitly synchronizes stream, and for threads
-  // that may be waited by gpu stream (for example cpu stream threads), freeing
-  // buffers there would result in dead lock.
-  void register_this_thread();
-
-  // Call cudaFree in the safe thread.
-  void cuda_free(void* buf);
-
-  size_t get_active_memory() const;
-  size_t get_peak_memory() const;
-  void reset_peak_memory();
-  size_t get_memory_limit();
-  size_t set_memory_limit(size_t limit);
-  size_t get_cache_memory() const;
-  size_t set_cache_limit(size_t limit);
-  void clear_cache();
-
- private:
-  CudaAllocator();
-  friend CudaAllocator& allocator();
-
-  std::mutex worker_mutex_;
-  std::unique_ptr<Worker> worker_;
-  std::set<std::thread::id> allowed_threads_;
-
-  std::mutex mutex_;
-  size_t memory_limit_;
-  size_t max_pool_size_;
-  BufferCache<CudaBuffer> buffer_cache_;
-  size_t active_memory_{0};
-  size_t peak_memory_{0};
-};
-
-CudaAllocator& allocator();
-
-} // namespace mlx::core::cu

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

@@ -1,382 +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, int N_READS>
-__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);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__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);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__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);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__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);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
-__global__ void binary_g_nd(
-    const In* a,
-    const In* b,
-    Out* out,
-    IdxT size,
-    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());
-    out[index] = Op{}(a[a_idx], b[b_idx]);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT>
-__global__ void binary_g(
-    const In* a,
-    const In* b,
-    Out* out,
-    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);
-    out[index] = Op{}(a[a_idx], b[b_idx]);
-  }
-}
-
-template <typename Op, typename In, typename Out>
-constexpr bool supports_binary_op() {
-  if (std::is_same_v<Op, Add> || std::is_same_v<Op, Divide> ||
-      std::is_same_v<Op, Maximum> || std::is_same_v<Op, Minimum> ||
-      std::is_same_v<Op, Multiply> || std::is_same_v<Op, Subtract> ||
-      std::is_same_v<Op, Power> || std::is_same_v<Op, Remainder>) {
-    return std::is_same_v<In, Out>;
-  }
-  if (std::is_same_v<Op, Equal> || std::is_same_v<Op, Greater> ||
-      std::is_same_v<Op, GreaterEqual> || std::is_same_v<Op, Less> ||
-      std::is_same_v<Op, LessEqual> || std::is_same_v<Op, NotEqual>) {
-    return std::is_same_v<Out, bool>;
-  }
-  if (std::is_same_v<Op, LogicalAnd> || std::is_same_v<Op, LogicalOr>) {
-    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>;
-  }
-  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>) {
-    return std::is_same_v<In, Out> && is_floating_v<In>;
-  }
-  if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
-      std::is_same_v<Op, BitwiseXor>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In>;
-  }
-  if (std::is_same_v<Op, LeftShift> || std::is_same_v<Op, RightShift>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
-        !std::is_same_v<In, bool>;
-  }
-  return false;
-}
-
-} // namespace cu
-
-template <typename Op>
-void binary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    array& out,
-    std::string_view op,
-    const Stream& s) {
-  assert(inputs.size() > 1);
-  const auto& a = inputs[0];
-  const auto& b = inputs[1];
-  if (out.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);
-  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>;
-                  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(shape),
-                      const_param(a_strides),
-                      const_param(b_strides),
-                      ndim);
-                }
-              });
-        } 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());
-          });
-        }
-      } 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,
-    array& out,
-    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, out, bopt);
-  binary_op_gpu_inplace<Op>(inputs, out, op, s);
-}
-
-#define BINARY_GPU(func)                                                 \
-  void func::eval_gpu(const std::vector<array>& inputs, array& out) {    \
-    nvtx3::scoped_range r(#func "::eval_gpu");                           \
-    auto& s = out.primitive().stream();                                  \
-    binary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
-  }
-
-BINARY_GPU(Add)
-BINARY_GPU(ArcTan2)
-BINARY_GPU(Divide)
-BINARY_GPU(Remainder)
-BINARY_GPU(Greater)
-BINARY_GPU(GreaterEqual)
-BINARY_GPU(Less)
-BINARY_GPU(LessEqual)
-BINARY_GPU(LogicalAnd)
-BINARY_GPU(LogicalOr)
-BINARY_GPU(LogAddExp)
-BINARY_GPU(Maximum)
-BINARY_GPU(Minimum)
-BINARY_GPU(Multiply)
-BINARY_GPU(NotEqual)
-BINARY_GPU(Power)
-BINARY_GPU(Subtract)
-
-void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Equal::eval_gpu");
-  auto& s = out.primitive().stream();
-  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();
-  auto op = get_primitive_string(this);
-  switch (op_) {
-    case BitwiseBinary::And:
-      binary_op_gpu<cu::BitwiseAnd>(inputs, out, op, s);
-      break;
-    case BitwiseBinary::Or:
-      binary_op_gpu<cu::BitwiseOr>(inputs, out, op, s);
-      break;
-    case BitwiseBinary::Xor:
-      binary_op_gpu<cu::BitwiseXor>(inputs, out, op, s);
-      break;
-    case BitwiseBinary::LeftShift:
-      binary_op_gpu<cu::LeftShift>(inputs, out, op, s);
-      break;
-    case BitwiseBinary::RightShift:
-      binary_op_gpu<cu::RightShift>(inputs, out, op, s);
-      break;
-  }
-}
-
-} // namespace mlx::core

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.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

@@ -1,338 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/worker.h"
-#include "mlx/utils.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;
-
-int cuda_graph_cache_size() {
-  static int cache_size = []() {
-    return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
-  }();
-  return cache_size;
-}
-
-namespace cu {
-
-Device::Device(int device) : device_(device) {
-  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
-      &compute_capability_major_, cudaDevAttrComputeCapabilityMajor, device_));
-  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
-      &compute_capability_minor_, cudaDevAttrComputeCapabilityMinor, device_));
-  // Validate the requirements of device.
-  int attr = 0;
-  CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
-      &attr, cudaDevAttrConcurrentManagedAccess, device_));
-  if (attr != 1) {
-    throw std::runtime_error(fmt::format(
-        "Device {} does not support synchronization in managed memory.",
-        device_));
-  }
-  // The cublasLt handle is used by matmul.
-  make_current();
-  cublasLtCreate(&lt_);
-}
-
-Device::~Device() {
-  cublasLtDestroy(lt_);
-}
-
-void Device::make_current() {
-  // We need to set/get current CUDA device very frequently, cache it to reduce
-  // actual calls of CUDA APIs. This function assumes single-thread in host.
-  static int current = 0;
-  if (current != device_) {
-    CHECK_CUDA_ERROR(cudaSetDevice(device_));
-    current = device_;
-  }
-}
-
-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;
-  }
-  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_);
-}
-
-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::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);
-}
-
-void CommandEncoder::maybe_commit() {
-  if (node_count_ >= env::max_ops_per_buffer(default_max_nodes_per_graph)) {
-    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();
-}
-
-Device& device(mlx::core::Device device) {
-  static std::unordered_map<int, Device> devices;
-  auto it = devices.find(device.index);
-  if (it == devices.end()) {
-    it = devices.try_emplace(device.index, device.index).first;
-  }
-  return it->second;
-}
-
-CommandEncoder& get_command_encoder(Stream s) {
-  return device(s.device).get_command_encoder(s);
-}
-
-} // namespace cu
-
-} // namespace mlx::core

mlx/backend/cuda/device.h

@@ -1,158 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/array.h"
-#include "mlx/backend/cuda/worker.h"
-#include "mlx/stream.h"
-
-#include <cublasLt.h>
-#include <cuda.h>
-#include <thrust/execution_policy.h>
-
-#include <unordered_map>
-
-namespace mlx::core::cu {
-
-class CommandEncoder {
- public:
-  struct CaptureContext {
-    CaptureContext(CommandEncoder& enc);
-    ~CaptureContext();
-    cudaGraph_t graph;
-    CommandEncoder& enc;
-  };
-  struct ConcurrentContext {
-    ConcurrentContext(CommandEncoder& enc);
-    ~ConcurrentContext();
-    CommandEncoder& enc;
-  };
-
-  explicit CommandEncoder(Device& d);
-  ~CommandEncoder();
-
-  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
-  void synchronize();
-
- 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);
-
-  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_;
-};
-
-class Device {
- public:
-  explicit Device(int device);
-  ~Device();
-
-  Device(const Device&) = delete;
-  Device& operator=(const Device&) = delete;
-
-  // Make this device the current cuda device, required by some cuda calls.
-  void make_current();
-
-  CommandEncoder& get_command_encoder(Stream s);
-
-  int cuda_device() const {
-    return device_;
-  }
-  int compute_capability_major() const {
-    return compute_capability_major_;
-  }
-  int compute_capability_minor() const {
-    return compute_capability_minor_;
-  }
-  cublasLtHandle_t lt_handle() const {
-    return lt_;
-  }
-
- private:
-  int device_;
-  int compute_capability_major_;
-  int compute_capability_minor_;
-  cublasLtHandle_t lt_;
-  std::unordered_map<int, CommandEncoder> encoders_;
-};
-
-Device& device(mlx::core::Device device);
-CommandEncoder& get_command_encoder(Stream s);
-
-// Return an execution policy that does not sync for result.
-// Note that not all thrust APIs support async policy, confirm before using.
-inline auto thrust_policy(cudaStream_t stream) {
-  // TODO: Connect thrust's custom allocator with mlx's allocator.
-  return thrust::cuda::par_nosync.on(stream);
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/arange.cuh

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

mlx/backend/cuda/device/atomic_ops.cuh

@@ -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/binary_ops.cuh

@@ -1,307 +0,0 @@
-// 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 <cuComplex.h>
-#include <cuda/std/array>
-
-namespace mlx::core::cu {
-
-struct Add {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x + y;
-  }
-};
-
-struct FloorDivide {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      return x / y;
-    } else {
-      return truncf(x / y);
-    }
-  }
-};
-
-struct Divide {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x / y;
-  }
-};
-
-struct Remainder {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      if constexpr (cuda::std::is_signed_v<T>) {
-        auto r = x % y;
-        if (r != 0 && (r < 0 != y < 0)) {
-          r += y;
-        }
-        return r;
-      } else {
-        return x % y;
-      }
-    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return x % y;
-    } else {
-      T r = fmod(x, y);
-      if (r != 0 && (r < 0 != y < 0)) {
-        r = r + y;
-      }
-      return r;
-    }
-  }
-};
-
-struct Equal {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    return x == y;
-  }
-};
-
-struct NaNEqual {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    if constexpr (std::is_same_v<T, cuComplex>) {
-      return x == y ||
-          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) && isnan(cuCimagf(x)) &&
-           isnan(cuCimagf(y))) ||
-          (cuCrealf(x) == cuCrealf(y) && isnan(cuCimagf(x)) &&
-           isnan(cuCimagf(y))) ||
-          (isnan(cuCrealf(x)) && isnan(cuCrealf(y)) &&
-           cuCimagf(x) == cuCimagf(y));
-    } else {
-      return x == y || (isnan(x) && isnan(y));
-    }
-  }
-};
-
-struct Greater {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    return x > y;
-  }
-};
-
-struct GreaterEqual {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    return x >= y;
-  }
-};
-
-struct Less {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    return x < y;
-  }
-};
-
-struct LessEqual {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    return x <= y;
-  }
-};
-
-struct LogAddExp {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    if (isnan(x) || isnan(y)) {
-      return cuda::std::numeric_limits<T>::quiet_NaN();
-    }
-    T maxval = max(x, y);
-    T minval = min(x, y);
-    return (minval == -cuda::std::numeric_limits<T>::infinity() ||
-            maxval == cuda::std::numeric_limits<T>::infinity())
-        ? 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 {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      return max(x, y);
-    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
-        return x;
-      }
-      return x > y ? x : y;
-    } else {
-      if (isnan(x)) {
-        return x;
-      }
-      return x > y ? x : y;
-    }
-  }
-};
-
-struct Minimum {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      return min(x, y);
-    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (isnan(cuCrealf(x)) || isnan(cuCimagf(x))) {
-        return x;
-      }
-      return x < y ? x : y;
-    } else {
-      if (isnan(x)) {
-        return x;
-      }
-      return x < y ? x : y;
-    }
-  }
-};
-
-struct Multiply {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x * y;
-  }
-};
-
-struct NotEqual {
-  template <typename T>
-  __device__ bool operator()(T x, T y) {
-    if constexpr (std::is_same_v<T, cuComplex>) {
-      return cuCrealf(x) != cuCrealf(y) || cuCimagf(x) != cuCimagf(y);
-    } else {
-      return x != y;
-    }
-  }
-};
-
-struct Power {
-  template <typename T>
-  __device__ T operator()(T base, T exp) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      T res = 1;
-      while (exp) {
-        if (exp & 1) {
-          res *= base;
-        }
-        exp >>= 1;
-        base *= base;
-      }
-      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);
-      auto phase = exp.y * x_ln_r + exp.x * x_theta;
-      return make_cuFloatComplex(mag * cosf(phase), mag * sinf(phase));
-    } else {
-      return powf(base, exp);
-    }
-  }
-};
-
-struct Subtract {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x - y;
-  }
-};
-
-struct LogicalAnd {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x && y;
-  };
-};
-
-struct LogicalOr {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x || y;
-  };
-};
-
-struct BitwiseAnd {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x & y;
-  };
-};
-
-struct BitwiseOr {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x | y;
-  };
-};
-
-struct BitwiseXor {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x ^ y;
-  };
-};
-
-struct LeftShift {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x << y;
-  };
-};
-
-struct RightShift {
-  template <typename T>
-  __device__ T operator()(T x, T y) {
-    return x >> y;
-  };
-};
-
-struct ArcTan2 {
-  template <typename T>
-  __device__ T operator()(T y, T x) {
-    return atan2f(y, x);
-  }
-};
-
-struct DivMod {
-  template <typename T>
-  __device__ cuda::std::array<T, 2> operator()(T x, T y) {
-    return {FloorDivide{}(x, y), Remainder{}(x, y)};
-  };
-};
-
-} // 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/cucomplex_math.cuh

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

mlx/backend/cuda/device/fp16_math.cuh

@@ -1,194 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-#include <cuda/std/limits>
-#include <cuda/std/type_traits>
-
-namespace mlx::core::cu {
-
-///////////////////////////////////////////////////////////////////////////////
-// Unary ops for half types.
-///////////////////////////////////////////////////////////////////////////////
-
-#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
-#define MLX_DEFINE_UNARY_OP(NAME, HALF_OP)           \
-  template <typename T>                              \
-  __forceinline__ __device__ auto NAME(T x) {        \
-    if constexpr (cuda::std::is_same_v<T, __half>) { \
-      return HALF_OP(x);                             \
-    } else {                                         \
-      return ::NAME(x);                              \
-    }                                                \
-  }
-#else
-#define MLX_DEFINE_UNARY_OP(NAME, HALF_OP)                         \
-  template <typename T>                                            \
-  __forceinline__ __device__ auto NAME(T x) {                      \
-    if constexpr (cuda::std::is_same_v<T, __half>) {               \
-      return HALF_OP(x);                                           \
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
-      return HALF_OP(x);                                           \
-    } else {                                                       \
-      return ::NAME(x);                                            \
-    }                                                              \
-  }
-#endif
-
-#define MLX_DEFINE_UNARY_OP_FALLBCK(NAME)                          \
-  template <typename T>                                            \
-  __forceinline__ __device__ auto NAME(T x) {                      \
-    if constexpr (cuda::std::is_same_v<T, __half>) {               \
-      return ::NAME(__half2float(x));                              \
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
-      return ::NAME(__bfloat162float(x));                          \
-    } else {                                                       \
-      return ::NAME(x);                                            \
-    }                                                              \
-  }
-
-MLX_DEFINE_UNARY_OP(abs, __habs)
-MLX_DEFINE_UNARY_OP(ceil, hceil)
-MLX_DEFINE_UNARY_OP(cos, hcos)
-MLX_DEFINE_UNARY_OP(exp, hexp)
-MLX_DEFINE_UNARY_OP(floor, hfloor)
-MLX_DEFINE_UNARY_OP(isnan, __hisnan)
-MLX_DEFINE_UNARY_OP(log, hlog)
-MLX_DEFINE_UNARY_OP(log2, hlog2)
-MLX_DEFINE_UNARY_OP(log10, hlog10)
-MLX_DEFINE_UNARY_OP(rint, hrint)
-MLX_DEFINE_UNARY_OP(rsqrt, hrsqrt)
-MLX_DEFINE_UNARY_OP(sin, hsin)
-MLX_DEFINE_UNARY_OP(sqrt, hsqrt)
-MLX_DEFINE_UNARY_OP_FALLBCK(acos)
-MLX_DEFINE_UNARY_OP_FALLBCK(acosh)
-MLX_DEFINE_UNARY_OP_FALLBCK(asin)
-MLX_DEFINE_UNARY_OP_FALLBCK(asinh)
-MLX_DEFINE_UNARY_OP_FALLBCK(atan)
-MLX_DEFINE_UNARY_OP_FALLBCK(atanh)
-MLX_DEFINE_UNARY_OP_FALLBCK(cosh)
-MLX_DEFINE_UNARY_OP_FALLBCK(log1p)
-MLX_DEFINE_UNARY_OP_FALLBCK(sinh)
-MLX_DEFINE_UNARY_OP_FALLBCK(tan)
-#if __CUDA_ARCH__ >= 1280
-MLX_DEFINE_UNARY_OP(tanh, htanh)
-#else
-MLX_DEFINE_UNARY_OP_FALLBCK(tanh)
-#endif
-
-#undef MLX_DEFINE_UNARY_OP
-#undef MLX_DEFINE_UNARY_OP_FALLBCK
-
-///////////////////////////////////////////////////////////////////////////////
-// Binary ops for half types.
-///////////////////////////////////////////////////////////////////////////////
-
-#if CUDART_VERSION < 12000 && __CUDA_ARCH__ < 800
-#define MLX_DEFINE_BINARY_OP(NAME, HALF_OP)          \
-  template <typename T>                              \
-  __forceinline__ __device__ auto NAME(T x, T y) {   \
-    if constexpr (cuda::std::is_same_v<T, __half>) { \
-      return HALF_OP(x, y);                          \
-    } else {                                         \
-      return ::NAME(x, y);                           \
-    }                                                \
-  }
-#else
-#define MLX_DEFINE_BINARY_OP(NAME, HALF_OP)                        \
-  template <typename T>                                            \
-  __forceinline__ __device__ auto NAME(T x, T y) {                 \
-    if constexpr (cuda::std::is_same_v<T, __half>) {               \
-      return HALF_OP(x, y);                                        \
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) { \
-      return HALF_OP(x, y);                                        \
-    } else {                                                       \
-      return ::NAME(x, y);                                         \
-    }                                                              \
-  }
-#endif
-
-MLX_DEFINE_BINARY_OP(max, __hmax)
-MLX_DEFINE_BINARY_OP(min, __hmin)
-
-#undef MLX_DEFINE_BINARY_OP
-
-template <typename T>
-__forceinline__ __device__ T fmod(T x, T y) {
-  if constexpr (cuda::std::is_same_v<T, __half>) {
-    return __float2half(::fmod(__half2float(x), __half2float(y)));
-#if CUDART_VERSION >= 12000 || __CUDA_ARCH__ >= 800
-  } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
-    return __float2bfloat16(::fmod(__bfloat162float(x), __bfloat162float(y)));
-#endif
-  } else {
-    return ::fmod(x, y);
-  }
-}
-
-///////////////////////////////////////////////////////////////////////////////
-// Additional C++ operator overrides between half types and native types.
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename T, typename U>
-constexpr bool is_integral_except =
-    cuda::std::is_integral_v<T> && !cuda::std::is_same_v<T, U>;
-
-template <typename T, typename U>
-constexpr bool is_arithmetic_except =
-    cuda::std::is_arithmetic_v<T> && !cuda::std::is_same_v<T, U>;
-
-#define MLX_DEFINE_HALF_OP(HALF, HALF2FLOAT, FLOAT2HALF, OP)          \
-  template <                                                          \
-      typename T,                                                     \
-      typename = cuda::std::enable_if_t<is_integral_except<T, HALF>>> \
-  __forceinline__ __device__ HALF operator OP(HALF x, T y) {          \
-    return FLOAT2HALF(HALF2FLOAT(x) OP static_cast<float>(y));        \
-  }                                                                   \
-  template <                                                          \
-      typename T,                                                     \
-      typename = cuda::std::enable_if_t<is_integral_except<T, HALF>>> \
-  __forceinline__ __device__ HALF operator OP(T x, HALF y) {          \
-    return FLOAT2HALF(static_cast<float>(x) OP HALF2FLOAT(y));        \
-  }
-
-#define MLX_DEFINE_HALF_CMP(HALF, HALF2FLOAT, OP)                       \
-  template <                                                            \
-      typename T,                                                       \
-      typename = cuda::std::enable_if_t<is_arithmetic_except<T, HALF>>> \
-  __forceinline__ __device__ bool operator OP(HALF x, T y) {            \
-    return HALF2FLOAT(x) OP static_cast<float>(y);                      \
-  }                                                                     \
-  template <                                                            \
-      typename T,                                                       \
-      typename = cuda::std::enable_if_t<is_arithmetic_except<T, HALF>>> \
-  __forceinline__ __device__ bool operator OP(T x, HALF y) {            \
-    return static_cast<float>(y) OP HALF2FLOAT(x);                      \
-  }
-
-MLX_DEFINE_HALF_OP(__half, __half2float, __float2half, +)
-MLX_DEFINE_HALF_OP(__half, __half2float, __float2half, -)
-MLX_DEFINE_HALF_OP(__half, __half2float, __float2half, *)
-MLX_DEFINE_HALF_OP(__half, __half2float, __float2half, /)
-MLX_DEFINE_HALF_OP(__nv_bfloat16, __bfloat162float, __float2bfloat16, +)
-MLX_DEFINE_HALF_OP(__nv_bfloat16, __bfloat162float, __float2bfloat16, -)
-MLX_DEFINE_HALF_OP(__nv_bfloat16, __bfloat162float, __float2bfloat16, *)
-MLX_DEFINE_HALF_OP(__nv_bfloat16, __bfloat162float, __float2bfloat16, /)
-MLX_DEFINE_HALF_CMP(__half, __half2float, <)
-MLX_DEFINE_HALF_CMP(__half, __half2float, >)
-MLX_DEFINE_HALF_CMP(__half, __half2float, <=)
-MLX_DEFINE_HALF_CMP(__half, __half2float, >=)
-MLX_DEFINE_HALF_CMP(__half, __half2float, ==)
-MLX_DEFINE_HALF_CMP(__half, __half2float, !=)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, <)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, >)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, <=)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, >=)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, ==)
-MLX_DEFINE_HALF_CMP(__nv_bfloat16, __bfloat162float, !=)
-
-#undef MLX_DEFINE_HALF_OP
-#undef MLX_DEFINE_HALF_CMP
-
-} // namespace mlx::core::cu

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/unary_ops.cuh

@@ -1,408 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <math_constants.h>
-
-namespace mlx::core::cu {
-
-struct Abs {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_unsigned_v<T>) {
-      return x;
-    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {sqrt(cuCrealf(x) * cuCrealf(x) + cuCimagf(x) * cuCimagf(x)), 0};
-    } else {
-      return abs(x);
-    }
-  }
-};
-
-struct ArcCos {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return acos(x);
-  }
-
-  __device__ cuComplex operator()(cuComplex x);
-};
-
-struct ArcCosh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return acosh(x);
-  }
-};
-
-struct ArcSin {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return asin(x);
-  }
-
-  __device__ cuComplex operator()(cuComplex x);
-};
-
-struct ArcSinh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return asinh(x);
-  }
-};
-
-struct ArcTan {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return atan(x);
-  }
-
-  __device__ cuComplex operator()(cuComplex x);
-};
-
-struct ArcTanh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return atanh(x);
-  }
-};
-
-struct BitwiseInvert {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return ~x;
-  }
-};
-
-struct Ceil {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      return x;
-    } else {
-      return ceil(x);
-    }
-  }
-};
-
-struct Conjugate {
-  __device__ cuComplex operator()(cuComplex x) {
-    return {cuCrealf(x), -cuCimagf(x)};
-  }
-};
-
-struct Cos {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {
-          cos(cuCrealf(x)) * cosh(cuCimagf(x)),
-          -sin(cuCrealf(x)) * sinh(cuCimagf(x))};
-    } else {
-      return cos(x);
-    }
-  }
-};
-
-struct Cosh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {
-          cosh(cuCrealf(x)) * cos(cuCimagf(x)),
-          sinh(cuCrealf(x)) * sin(cuCimagf(x))};
-    } else {
-      return cosh(x);
-    }
-  }
-};
-
-struct Erf {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, __half>) {
-      return erf(__half2float(x));
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
-      return erf(__bfloat162float(x));
-    } else {
-      return erf(x);
-    }
-  }
-};
-
-struct ErfInv {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, __half>) {
-      return erfinv(__half2float(x));
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
-      return erfinv(__bfloat162float(x));
-    } else {
-      return erfinv(x);
-    }
-  }
-};
-
-struct Exp {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto m = exp(cuCrealf(x));
-      return {m * cos(cuCimagf(x)), m * sinh(cuCimagf(x))};
-    } else {
-      return exp(x);
-    }
-  }
-};
-
-struct Expm1 {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, __half>) {
-      return expm1(__half2float(x));
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
-      return expm1(__bfloat162float(x));
-    } else {
-      return expm1(x);
-    }
-  }
-};
-
-struct Floor {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_integral_v<T>) {
-      return x;
-    } else {
-      return floor(x);
-    }
-  }
-};
-
-struct Imag {
-  __device__ float operator()(cuComplex x) {
-    return cuCimagf(x);
-  }
-};
-
-struct Log {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto r = log(cuCrealf(Abs{}(x)));
-      auto i = atan2f(cuCimagf(x), cuCrealf(x));
-      return {r, i};
-    } else {
-      return log(x);
-    }
-  }
-};
-
-struct Log2 {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (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);
-    }
-  }
-};
-
-struct Log10 {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      auto y = Log{}(x);
-      return {cuCrealf(y) / CUDART_LNT_F, cuCimagf(y) / CUDART_LNT_F};
-      return y;
-    } else {
-      return log10(x);
-    }
-  }
-};
-
-struct Log1p {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return log1p(x);
-  }
-};
-
-struct LogicalNot {
-  __device__ bool operator()(bool x) {
-    return !x;
-  }
-};
-
-struct Negative {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return 0 - x;
-    } else {
-      return -x;
-    }
-  }
-};
-
-struct Real {
-  __device__ float operator()(cuComplex x) {
-    return cuCrealf(x);
-  }
-};
-
-struct Round {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {rint(cuCrealf(x)), rint(cuCimagf(x))};
-    } else {
-      return rint(x);
-    }
-  }
-};
-
-struct Sigmoid {
-  template <typename T>
-  __device__ T operator()(T x) {
-    T y = 1 / (1 + exp(-abs(x)));
-    return (x < 0) ? 1 - y : y;
-  }
-};
-
-struct Sign {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_unsigned_v<T>) {
-      return x != 0;
-    } else if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      if (cuCrealf(x) == 0 && cuCimagf(x) == 0) {
-        return x;
-      } else {
-        return x / Abs()(x);
-      }
-    } else if constexpr (cuda::std::is_same_v<T, __nv_bfloat16>) {
-      return static_cast<float>((x > T(0.f)) - (x < T(0.f)));
-    } else {
-      return (x > T(0)) - (x < T(0));
-    }
-  }
-};
-
-struct Sin {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {
-          sin(cuCrealf(x)) * cosh(cuCimagf(x)),
-          cos(cuCrealf(x)) * sinh(cuCimagf(x))};
-    } else {
-      return sin(x);
-    }
-  }
-};
-
-struct Sinh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      return {
-          sinh(cuCrealf(x)) * cos(cuCimagf(x)),
-          cosh(cuCrealf(x)) * sin(cuCimagf(x))};
-    } else {
-      return sinh(x);
-    }
-  }
-};
-
-struct Square {
-  template <typename T>
-  __device__ T operator()(T x) {
-    return x * x;
-  }
-};
-
-struct Sqrt {
-  template <typename T>
-  __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 {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      float tan_a = tan(cuCrealf(x));
-      float tanh_b = tanh(cuCimagf(x));
-      float t1 = tan_a * tanh_b;
-      float denom = 1. + t1 * t1;
-      return {(tan_a - tanh_b * t1) / denom, (tanh_b + tan_a * t1) / denom};
-    } else {
-      return tan(x);
-    }
-  }
-};
-
-struct Tanh {
-  template <typename T>
-  __device__ T operator()(T x) {
-    if constexpr (cuda::std::is_same_v<T, cuComplex>) {
-      float tanh_a = tanh(cuCrealf(x));
-      float tan_b = tan(cuCimagf(x));
-      float t1 = tanh_a * tan_b;
-      float denom = 1. + t1 * t1;
-      return {(tanh_a + tan_b * t1) / denom, (tan_b - tanh_a * t1) / denom};
-    } else {
-      return tanh(x);
-    }
-  }
-};
-
-__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/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

@@ -1,66 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/gpu/eval.h"
-#include "mlx/backend/cuda/allocator.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/gpu/available.h"
-#include "mlx/primitives.h"
-
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core::gpu {
-
-bool is_available() {
-  return true;
-}
-
-void new_stream(Stream s) {
-  // Force initalization of cuda, so cuda runtime get destroyed at last.
-  cudaFree(nullptr);
-  // Ensure the static stream objects get created.
-  cu::get_command_encoder(s);
-  // The main thread is safe to free buffers.
-  cu::allocator().register_this_thread();
-}
-
-void eval(array& arr) {
-  nvtx3::scoped_range r("gpu::eval");
-  auto outputs = arr.outputs();
-  {
-    // If the array is a tracer hold a reference
-    // to its inputs so they don't get donated
-    std::vector<array> inputs;
-    if (arr.is_tracer()) {
-      inputs = arr.inputs();
-    }
-    arr.primitive().eval_gpu(arr.inputs(), outputs);
-  }
-
-  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());
-  }
-  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();
-}
-
-void finalize(Stream s) {
-  nvtx3::scoped_range r("gpu::finalize");
-  cu::get_command_encoder(s).commit();
-}
-
-void synchronize(Stream s) {
-  nvtx3::scoped_range r("gpu::synchronize");
-  cu::get_command_encoder(s).synchronize();
-}
-
-} // namespace mlx::core::gpu

mlx/backend/cuda/event.cu

@@ -1,269 +0,0 @@
-// Copyright © 2024 Apple Inc.
-
-#include "mlx/backend/cuda/allocator.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/event.h"
-#include "mlx/backend/cuda/utils.h"
-#include "mlx/event.h"
-#include "mlx/scheduler.h"
-
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-///////////////////////////////////////////////////////////////////////////////
-// CudaEvent implementations
-///////////////////////////////////////////////////////////////////////////////
-
-// Cuda event managed with RAII.
-class CudaEventHandle {
- public:
-  CudaEventHandle() {
-    CHECK_CUDA_ERROR(cudaEventCreateWithFlags(
-        &event_, cudaEventDisableTiming | cudaEventBlockingSync));
-  }
-
-  ~CudaEventHandle() {
-    CHECK_CUDA_ERROR(cudaEventDestroy(event_));
-  }
-
-  CudaEventHandle(const CudaEventHandle&) = delete;
-  CudaEventHandle& operator=(const CudaEventHandle&) = delete;
-
-  operator cudaEvent_t() const {
-    return event_;
-  }
-
- private:
-  cudaEvent_t event_;
-};
-
-CudaEvent::CudaEvent() : event_(std::make_shared<CudaEventHandle>()) {}
-
-void CudaEvent::wait() {
-  nvtx3::scoped_range r("cu::CudaEvent::wait");
-  if (!recorded_) {
-    throw std::runtime_error("Should not wait on a CudaEvent before record.");
-  }
-  cudaEventSynchronize(*event_);
-}
-
-void CudaEvent::wait(cudaStream_t stream) {
-  if (!recorded_) {
-    throw std::runtime_error("Should not wait on a CudaEvent before record.");
-  }
-  cudaStreamWaitEvent(stream, *event_);
-}
-
-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());
-  }
-}
-
-void CudaEvent::record(cudaStream_t stream) {
-  cudaEventRecord(*event_, stream);
-  recorded_ = true;
-}
-
-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());
-  }
-}
-
-bool CudaEvent::completed() const {
-  return cudaEventQuery(*event_) == cudaSuccess;
-}
-
-///////////////////////////////////////////////////////////////////////////////
-// SharedEvent implementations
-///////////////////////////////////////////////////////////////////////////////
-
-namespace {
-
-__host__ __device__ void event_wait(SharedEvent::Atomic* ac, uint64_t value) {
-  uint64_t current;
-  while ((current = ac->load()) < value) {
-    ac->wait(current);
-  }
-}
-
-__host__ __device__ void event_signal(SharedEvent::Atomic* ac, uint64_t value) {
-  ac->store(value);
-  ac->notify_all();
-}
-
-__global__ void event_wait_kernel(SharedEvent::Atomic* ac, uint64_t value) {
-  event_wait(ac, value);
-}
-
-__global__ void event_signal_kernel(SharedEvent::Atomic* ac, uint64_t value) {
-  event_signal(ac, value);
-}
-
-} // namespace
-
-SharedEvent::SharedEvent() {
-  // Allocate cuda::atomic on managed memory.
-  Atomic* ac;
-  CHECK_CUDA_ERROR(cudaMallocManaged(&ac, sizeof(Atomic)));
-  new (ac) Atomic(0);
-  ac_ = std::shared_ptr<Atomic>(ac, [](Atomic* ptr) {
-    ptr->~Atomic();
-    allocator().cuda_free(ptr);
-  });
-}
-
-void SharedEvent::wait(uint64_t value) {
-  nvtx3::scoped_range r("cu::SharedEvent::wait");
-  event_wait(ac_.get(), value);
-}
-
-void SharedEvent::wait(cudaStream_t stream, uint64_t value) {
-  event_wait_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
-}
-
-void SharedEvent::wait(Stream s, uint64_t value) {
-  nvtx3::scoped_range r("cu::SharedEvent::wait(s)");
-  if (s.device == mlx::core::Device::cpu) {
-    scheduler::enqueue(s, [*this, value]() mutable { wait(value); });
-  } else {
-    auto& encoder = get_command_encoder(s);
-    encoder.commit();
-    wait(encoder.stream(), value);
-    encoder.add_completed_handler([ac = ac_]() {});
-  }
-}
-
-void SharedEvent::signal(uint64_t value) {
-  nvtx3::scoped_range r("cu::SharedEvent::signal");
-  event_signal(ac_.get(), value);
-}
-
-void SharedEvent::signal(cudaStream_t stream, uint64_t value) {
-  event_signal_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
-}
-
-void SharedEvent::signal(Stream s, uint64_t value) {
-  nvtx3::scoped_range r("cu::SharedEvent::signal(s)");
-  if (s.device == mlx::core::Device::cpu) {
-    // Signal through a GPU stream so the atomic is updated in GPU - updating
-    // the atomic in CPU sometimes does not get GPU notified.
-    static CudaStream stream(device(mlx::core::Device::gpu));
-    scheduler::enqueue(s, [*this, value]() mutable { signal(stream, value); });
-  } else {
-    auto& encoder = get_command_encoder(s);
-    encoder.commit();
-    signal(encoder.stream(), value);
-    encoder.add_completed_handler([ac = ac_]() {});
-  }
-}
-
-bool SharedEvent::is_signaled(uint64_t value) const {
-  nvtx3::scoped_range r("cu::SharedEvent::is_signaled");
-  return ac_->load() >= value;
-}
-
-uint64_t SharedEvent::value() const {
-  nvtx3::scoped_range r("cu::SharedEvent::value");
-  return ac_->load();
-}
-
-} // namespace cu
-
-///////////////////////////////////////////////////////////////////////////////
-// Event implementations
-///////////////////////////////////////////////////////////////////////////////
-
-namespace {
-
-struct EventImpl {
-  // CudaEvent is preferred when possible because it is fast, however we have
-  // to fallback to SharedEvent in following cases:
-  // 1. the event is used to wait/signal a cpu stream;
-  // 2. signal value other than 1 has been specified.
-  std::unique_ptr<cu::CudaEvent> cuda;
-  std::unique_ptr<cu::SharedEvent> shared;
-
-  bool is_created() const {
-    return cuda || shared;
-  }
-
-  void ensure_created(Stream s, uint64_t signal_value) {
-    if (is_created()) {
-      return;
-    }
-    if (s.device == mlx::core::Device::cpu || signal_value > 1) {
-      nvtx3::mark("Using slow SharedEvent");
-      shared = std::make_unique<cu::SharedEvent>();
-    } else {
-      cuda = std::make_unique<cu::CudaEvent>();
-    }
-  }
-};
-
-} // namespace
-
-Event::Event(Stream s) : stream_(s) {
-  event_ = std::shared_ptr<void>(
-      new EventImpl(), [](void* ptr) { delete static_cast<EventImpl*>(ptr); });
-}
-
-void Event::wait() {
-  auto* event = static_cast<EventImpl*>(event_.get());
-  assert(event->is_created());
-  if (event->cuda) {
-    assert(value() == 1);
-    event->cuda->wait();
-  } else {
-    event->shared->wait(value());
-  }
-}
-
-void Event::wait(Stream s) {
-  auto* event = static_cast<EventImpl*>(event_.get());
-  assert(event->is_created());
-  if (event->cuda) {
-    assert(value() == 1);
-    event->cuda->wait(s);
-  } else {
-    event->shared->wait(s, value());
-  }
-}
-
-void Event::signal(Stream s) {
-  auto* event = static_cast<EventImpl*>(event_.get());
-  event->ensure_created(s, value());
-  if (event->cuda) {
-    assert(value() == 1);
-    event->cuda->record(s);
-  } else {
-    event->shared->signal(s, value());
-  }
-}
-
-bool Event::is_signaled() const {
-  auto* event = static_cast<EventImpl*>(event_.get());
-  if (!event->is_created()) {
-    return false;
-  }
-  if (event->cuda) {
-    assert(value() == 1);
-    return event->cuda->recorded() && event->cuda->completed();
-  } else {
-    return event->shared->is_signaled(value());
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cuda/event.h

@@ -1,66 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/stream.h"
-
-#include <cuda_runtime.h>
-#include <cuda/atomic>
-
-#include <memory>
-
-namespace mlx::core::cu {
-
-class CudaEventHandle;
-
-// Wrapper of native cuda event. It can synchronize between GPU streams, or wait
-// on GPU stream in CPU stream, but can not wait on CPU stream.
-class CudaEvent {
- public:
-  CudaEvent();
-
-  void wait();
-  void wait(cudaStream_t stream);
-  void wait(Stream s);
-  void record(cudaStream_t stream);
-  void record(Stream s);
-
-  // Return whether the recorded kernels have completed. Note that this method
-  // returns true if record() has not been called.
-  bool completed() const;
-
-  bool recorded() const {
-    return recorded_;
-  }
-
- private:
-  bool recorded_{false};
-  std::shared_ptr<CudaEventHandle> event_;
-};
-
-// Event that can synchronize between CPU and GPU. It is much slower than
-// CudaEvent so the latter should always be preferred when possible.
-class SharedEvent {
- public:
-  using Atomic = cuda::atomic<uint64_t>;
-
-  SharedEvent();
-
-  void wait(uint64_t value);
-  void wait(cudaStream_t stream, uint64_t value);
-  void wait(Stream s, uint64_t value);
-  void signal(uint64_t value);
-  void signal(cudaStream_t stream, uint64_t value);
-  void signal(Stream s, uint64_t value);
-  bool is_signaled(uint64_t value) const;
-  uint64_t value() const;
-
-  const std::shared_ptr<Atomic>& atomic() const {
-    return ac_;
-  }
-
- private:
-  std::shared_ptr<Atomic> ac_;
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/fence.cpp

@@ -1,29 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/fence.h"
-#include "mlx/backend/cuda/event.h"
-
-namespace mlx::core {
-
-struct FenceImpl {
-  uint32_t count;
-  cu::SharedEvent event;
-};
-
-Fence::Fence(Stream s) {
-  fence_ = std::shared_ptr<void>(
-      new FenceImpl{0}, [](void* ptr) { delete static_cast<FenceImpl*>(ptr); });
-}
-
-void Fence::wait(Stream s, const array&) {
-  auto* fence = static_cast<FenceImpl*>(fence_.get());
-  fence->event.wait(fence->count);
-}
-
-void Fence::update(Stream s, const array&) {
-  auto* fence = static_cast<FenceImpl*>(fence_.get());
-  fence->count++;
-  fence->event.signal(s, fence->count);
-}
-
-} // namespace mlx::core

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/general_iterator.cuh

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

mlx/backend/cuda/iterators/strided_iterator.cuh

@@ -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

@@ -1,33 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/utils.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-
-namespace mlx::core {
-
-dim3 get_block_dims(int dim0, int dim1, int dim2, int pow2) {
-  Dims dims = get_block_dims_common(dim0, dim1, dim2, pow2);
-  return dim3(std::get<0>(dims), std::get<1>(dims), std::get<2>(dims));
-}
-
-dim3 get_2d_grid_dims(const Shape& shape, const Strides& strides) {
-  Dims dims = get_2d_grid_dims_common(shape, strides);
-  return dim3(std::get<0>(dims), std::get<1>(dims), std::get<2>(dims));
-}
-
-dim3 get_2d_grid_dims(
-    const Shape& shape,
-    const Strides& strides,
-    size_t divisor) {
-  Dims dims = get_2d_grid_dims_common(shape, strides, divisor);
-  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

@@ -1,169 +0,0 @@
-// 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
-// device-only code.
-
-#pragma once
-
-#include <type_traits>
-
-#include "mlx/array.h"
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cuComplex.h>
-#include <cuda.h>
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-#include <fmt/format.h>
-#include <cuda/cmath>
-
-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;
-  }
-}
-
-template <typename F>
-void dispatch_bool(bool v, F&& f) {
-  if (v) {
-    f(std::true_type{});
-  } else {
-    f(std::false_type{});
-  }
-}
-
-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>
-struct CTypeToCudaType {
-  using type = T;
-};
-
-template <>
-struct CTypeToCudaType<float16_t> {
-  using type = __half;
-};
-
-template <>
-struct CTypeToCudaType<bfloat16_t> {
-  using type = __nv_bfloat16;
-};
-
-template <>
-struct CTypeToCudaType<complex64_t> {
-  using type = cuComplex;
-};
-
-template <typename T>
-using cuda_type_t = typename CTypeToCudaType<T>::type;
-
-// Type traits for detecting floating numbers.
-template <typename T>
-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) {
-  if (vec.size() > NDIM) {
-    throw std::runtime_error(
-        fmt::format("ndim can not be larger than {}.", NDIM));
-  }
-  cuda::std::array<T, NDIM> result;
-  std::copy_n(vec.begin(), vec.size(), result.begin());
-  return result;
-}
-
-// Compute the grid and block dimensions, check backend/common/utils.h for docs.
-dim3 get_block_dims(int dim0, int dim1, int dim2, int pow2 = 10);
-dim3 get_2d_grid_dims(const Shape& shape, const Strides& strides);
-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));
-  }
-  return block_dim;
-}
-
-// Get the num_blocks and block_dims that maximize occupancy for |kernel|,
-// assuming each thread handles |work_per_thread| elements of |arr|.
-template <typename T>
-inline std::tuple<dim3, uint> get_launch_args(
-    T kernel,
-    size_t size,
-    const Shape& shape,
-    const Strides& strides,
-    bool large,
-    int work_per_thread = 1) {
-  size_t nthreads = cuda::ceil_div(size, work_per_thread);
-  uint block_dim = max_occupancy_block_dim(kernel);
-  if (block_dim > nthreads) {
-    block_dim = nthreads;
-  }
-  dim3 num_blocks;
-  if (large) {
-    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
-    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
-  } else {
-    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
-  }
-  return std::make_tuple(num_blocks, block_dim);
-}
-
-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/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

@@ -1,489 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/matmul.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-#include "mlx/utils.h"
-
-#include <cublasLt.h>
-#include <fmt/format.h>
-#include <nvtx3/nvtx3.hpp>
-
-#include <numeric>
-
-namespace mlx::core {
-
-namespace cu {
-
-#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
-
-void check_cublas_error(const char* name, cublasStatus_t err) {
-  if (err != CUBLAS_STATUS_SUCCESS) {
-    // TODO: Use cublasGetStatusString when it is widely available.
-    throw std::runtime_error(
-        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
-  }
-}
-
-class MatMul {
- public:
-  MatMul(
-      Device& device,
-      Dtype dtype,
-      bool a_transposed,
-      uint64_t a_rows,
-      uint64_t a_cols,
-      int64_t lda,
-      bool b_transposed,
-      uint64_t b_rows,
-      uint64_t b_cols,
-      int64_t ldb,
-      int32_t batch_count,
-      int64_t a_batch_stride,
-      int64_t b_batch_stride)
-      : handle_(device.lt_handle()) {
-    heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
-
-    auto scale_type = dtype_to_cuda_type(dtype);
-    if (dtype == bfloat16 || dtype == float16) {
-      scale_type = CUDA_R_32F;
-    }
-    CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
-        &matmul_desc_, dtype_to_compute_type(dtype), scale_type));
-    int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
-    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-        matmul_desc_,
-        CUBLASLT_MATMUL_DESC_POINTER_MODE,
-        &pointer_mode,
-        sizeof(int32_t)));
-    cublasOperation_t op = CUBLAS_OP_N;
-    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-        matmul_desc_,
-        CUBLASLT_MATMUL_DESC_TRANSA,
-        &op,
-        sizeof(cublasOperation_t)));
-    CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-        matmul_desc_,
-        CUBLASLT_MATMUL_DESC_TRANSB,
-        &op,
-        sizeof(cublasOperation_t)));
-
-    auto type = dtype_to_cuda_type(dtype);
-    a_desc_ = create_matrix_layout(
-        type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
-    b_desc_ = create_matrix_layout(
-        type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
-    out_desc_ = create_matrix_layout(
-        type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
-
-    // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
-    // for Hopper+:
-    // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
-    uint64_t MiB = 1024 * 1024;
-    uint64_t workspace_size =
-        device.compute_capability_major() >= 9 ? 32 * MiB : 4 * MiB;
-
-    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceCreate(&pref_));
-    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceSetAttribute(
-        pref_,
-        CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
-        &workspace_size,
-        sizeof(uint64_t)));
-  }
-
-  MatMul(
-      Device& device,
-      Dtype dtype,
-      bool a_transposed,
-      uint64_t a_rows,
-      uint64_t a_cols,
-      int64_t lda,
-      bool b_transposed,
-      uint64_t b_rows,
-      uint64_t b_cols,
-      int64_t ldb,
-      bool c_transposed,
-      int64_t ldc,
-      int32_t batch_count,
-      int64_t a_batch_stride,
-      int64_t b_batch_stride,
-      int64_t c_batch_stride)
-      : MatMul(
-            device,
-            dtype,
-            a_transposed,
-            a_rows,
-            a_cols,
-            lda,
-            b_transposed,
-            b_rows,
-            b_cols,
-            ldb,
-            batch_count,
-            a_batch_stride,
-            b_batch_stride) {
-    auto type = dtype_to_cuda_type(dtype);
-    c_desc_ = create_matrix_layout(
-        type, a_rows, b_cols, c_transposed, ldc, batch_count, c_batch_stride);
-  }
-
-  ~MatMul() {
-    cublasLtMatrixLayoutDestroy(a_desc_);
-    cublasLtMatrixLayoutDestroy(b_desc_);
-    cublasLtMatrixLayoutDestroy(c_desc_);
-    cublasLtMatrixLayoutDestroy(out_desc_);
-    cublasLtMatmulDescDestroy(matmul_desc_);
-  }
-
-  void run(
-      cu::CommandEncoder& encoder,
-      void* out,
-      void* a,
-      void* b,
-      void* c = nullptr,
-      float alpha = 1,
-      float beta = 0) {
-    if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
-      int ret = 0;
-      CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
-          handle_,
-          matmul_desc_,
-          a_desc_,
-          b_desc_,
-          out_desc_,
-          out_desc_,
-          pref_,
-          1,
-          &heuristic_,
-          &ret));
-      if (ret == 0) {
-        throw std::runtime_error("Can not find algorithm for matmul.");
-      }
-    }
-
-    void* workspace_ptr = nullptr;
-    if (heuristic_.workspaceSize > 0) {
-      array workspace(
-          allocator::malloc(heuristic_.workspaceSize),
-          {static_cast<int>(heuristic_.workspaceSize)},
-          int8);
-      encoder.add_temporary(workspace);
-      workspace_ptr = workspace.data<void>();
-    }
-
-    auto capture = encoder.capture_context();
-    CHECK_CUBLAS_ERROR(cublasLtMatmul(
-        handle_,
-        matmul_desc_,
-        &alpha,
-        a,
-        a_desc_,
-        b,
-        b_desc_,
-        &beta,
-        c ? c : out,
-        c ? c_desc_ : out_desc_,
-        out,
-        out_desc_,
-        &heuristic_.algo,
-        workspace_ptr,
-        heuristic_.workspaceSize,
-        encoder.stream()));
-  }
-
- private:
-  cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
-    switch (dtype) {
-      case float16:
-        return CUBLAS_COMPUTE_32F;
-      case bfloat16:
-        return CUBLAS_COMPUTE_32F;
-      case float32:
-        return mlx::core::env::enable_tf32() ? CUBLAS_COMPUTE_32F_FAST_TF32
-                                             : CUBLAS_COMPUTE_32F;
-      case float64:
-      case complex64:
-        return CUBLAS_COMPUTE_64F;
-      default:
-        throw std::runtime_error(fmt::format(
-            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
-    }
-  }
-
-  cudaDataType_t dtype_to_cuda_type(Dtype dtype) {
-    switch (dtype) {
-      case float16:
-        return CUDA_R_16F;
-      case bfloat16:
-        return CUDA_R_16BF;
-      case float32:
-        return CUDA_R_32F;
-      case float64:
-        return CUDA_R_64F;
-      case complex64:
-        return CUDA_C_32F;
-      default:
-        throw std::runtime_error(fmt::format(
-            "Unsupported dtype in MatMul: {}.", dtype_to_string(dtype)));
-    }
-  }
-
-  cublasLtMatrixLayout_t create_matrix_layout(
-      cudaDataType_t type,
-      uint64_t rows,
-      uint64_t cols,
-      bool transposed,
-      int64_t ld,
-      int32_t batch_count,
-      int64_t batch_stride) {
-    cublasLtMatrixLayout_t desc;
-    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutCreate(&desc, type, rows, cols, ld));
-    cublasLtOrder_t order =
-        transposed ? CUBLASLT_ORDER_COL : CUBLASLT_ORDER_ROW;
-    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-        desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order, sizeof(cublasLtOrder_t)));
-    if (batch_count > 1) {
-      CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-          desc,
-          CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT,
-          &batch_count,
-          sizeof(int32_t)));
-      CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-          desc,
-          CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET,
-          &batch_stride,
-          sizeof(int64_t)));
-    }
-    return desc;
-  }
-
-  cublasLtHandle_t handle_{nullptr};
-  cublasLtMatmulDesc_t matmul_desc_{nullptr};
-  cublasLtMatmulPreference_t pref_{nullptr};
-  cublasLtMatrixLayout_t a_desc_{nullptr};
-  cublasLtMatrixLayout_t b_desc_{nullptr};
-  cublasLtMatrixLayout_t c_desc_{nullptr};
-  cublasLtMatrixLayout_t out_desc_{nullptr};
-  cublasLtMatmulHeuristicResult_t heuristic_;
-};
-
-} // namespace cu
-
-namespace {
-
-std::tuple<bool, int64_t, array>
-check_transpose(cu::CommandEncoder& enc, 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)) {
-    return std::make_tuple(false, stx, arr);
-  } else if (stx == 1 && sty == arr.shape(-2)) {
-    return std::make_tuple(true, sty, arr);
-  } else {
-    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-    copy_gpu(arr, arr_copy, CopyType::General, s);
-    enc.add_temporary(arr_copy);
-    return std::make_tuple(false, arr.shape(-1), arr_copy);
-  }
-}
-
-} // namespace
-
-void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Matmul::eval_gpu");
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  assert(inputs.size() == 2);
-  auto& a_pre = inputs[0];
-  auto& b_pre = inputs[1];
-  // Return 0s if either input is empty.
-  if (a_pre.size() == 0 || b_pre.size() == 0) {
-    array zero(0, a_pre.dtype());
-    encoder.add_temporary(zero);
-    fill_gpu(zero, out, s);
-    return;
-  }
-
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Init checks and prep
-
-  int M = a_pre.shape(-2);
-  int N = b_pre.shape(-1);
-  int K = a_pre.shape(-1);
-
-  // 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);
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Check and collapse batch dimensions
-
-  auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
-
-  auto batch_count = out.size() / (M * N);
-
-  // Collapse batches into M if needed
-  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
-      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
-      b_batch_strides.back() == 0) {
-    M *= batch_shape.back();
-    batch_count = 1;
-
-    a_batch_strides = {0};
-    b_batch_strides = {0};
-    batch_shape = {1};
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Invoke cublasLt
-
-  cu::MatMul matmul(
-      cu::device(s.device),
-      a.dtype(),
-      a_transposed,
-      M,
-      K,
-      lda,
-      b_transposed,
-      K,
-      N,
-      ldb,
-      batch_shape.back(),
-      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) {
-    matmul.run(
-        encoder,
-        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,
-        a.data<int8_t>() + a.itemsize() * a_it.loc,
-        b.data<int8_t>() + b.itemsize() * b_it.loc);
-    a_it.step();
-    b_it.step();
-  }
-}
-
-void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("AddMM::eval_gpu");
-  auto& s = stream();
-  auto& encoder = cu::get_command_encoder(s);
-
-  assert(inputs.size() == 3);
-  auto& a_pre = inputs[0];
-  auto& b_pre = inputs[1];
-  auto& c_pre = inputs[2];
-
-  out.set_data(allocator::malloc(out.nbytes()));
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Init checks and prep
-
-  int M = a_pre.shape(-2);
-  int N = b_pre.shape(-1);
-  int K = a_pre.shape(-1);
-
-  // 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);
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Check and collapse batch dimensions
-
-  auto [batch_shape, a_batch_strides, b_batch_strides, c_batch_strides] =
-      collapse_batches(a, b, c);
-
-  auto batch_count = out.size() / (M * N);
-
-  // Collapse batches into M if needed
-  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
-      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
-      c_batch_strides.back() == M * c.strides()[c.ndim() - 2] &&
-      b_batch_strides.back() == 0) {
-    M *= batch_shape.back();
-    batch_count = 1;
-
-    a_batch_strides = {0};
-    b_batch_strides = {0};
-    c_batch_strides = {0};
-    batch_shape = {1};
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // Invoke cublasLt
-
-  cu::MatMul matmul(
-      cu::device(s.device),
-      a.dtype(),
-      a_transposed,
-      M,
-      K,
-      lda,
-      b_transposed,
-      K,
-      N,
-      ldb,
-      c_transposed,
-      ldc,
-      batch_shape.back(),
-      a_batch_strides.back(),
-      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) {
-    matmul.run(
-        encoder,
-        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M * N,
-        a.data<int8_t>() + a.itemsize() * a_it.loc,
-        b.data<int8_t>() + b.itemsize() * b_it.loc,
-        c.data<int8_t>() + c.itemsize() * c_it.loc,
-        alpha_,
-        beta_);
-    a_it.step();
-    b_it.step();
-    c_it.step();
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cuda/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

@@ -1,106 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/arange.cuh"
-#include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/distributed/primitives.h"
-#include "mlx/dtype_utils.h"
-#include "mlx/fast_primitives.h"
-#include "mlx/primitives.h"
-
-#include <nvtx3/nvtx3.hpp>
-#include <thrust/device_ptr.h>
-#include <thrust/transform.h>
-
-#include <cassert>
-
-namespace mlx::core {
-
-void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Arange::eval_gpu");
-  assert(inputs.size() == 0);
-  out.set_data(allocator::malloc(out.nbytes()));
-  if (out.size() == 0) {
-    return;
-  }
-  auto& encoder = cu::get_command_encoder(stream());
-  encoder.set_output_array(out);
-  auto capture = encoder.capture_context();
-  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
-    using CTYPE = MLX_GET_TYPE(type_tag);
-    using OutType = cuda_type_t<CTYPE>;
-    CTYPE step =
-        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
-    thrust::transform(
-        cu::thrust_policy(encoder.stream()),
-        thrust::counting_iterator<uint32_t>(0),
-        thrust::counting_iterator<uint32_t>(out.data_size()),
-        thrust::device_pointer_cast(out.data<OutType>()),
-        cu::Arange<OutType>{
-            static_cast<OutType>(start_), static_cast<OutType>(step)});
-  });
-}
-
-bool fast::ScaledDotProductAttention::use_fallback(
-    const array& q,
-    const array& k,
-    const array& v,
-    bool has_mask,
-    bool has_arr_mask,
-    bool do_causal,
-    Stream s) {
-  return true;
-}
-
-#define NO_GPU_MULTI(func)                                             \
-  void func::eval_gpu(                                                 \
-      const std::vector<array>& inputs, std::vector<array>& outputs) { \
-    throw std::runtime_error(#func " has no CUDA implementation.");    \
-  }
-
-#define NO_GPU_USE_FALLBACK(func)     \
-  bool func::use_fallback(Stream s) { \
-    return true;                      \
-  }                                   \
-  NO_GPU_MULTI(func)
-
-#define NO_GPU(func)                                                  \
-  void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
-    throw std::runtime_error(#func " has no CUDA implementation.");   \
-  }
-
-NO_GPU(BlockMaskedMM)
-NO_GPU(Convolution)
-NO_GPU(DynamicSlice)
-NO_GPU(DynamicSliceUpdate)
-NO_GPU(FFT)
-NO_GPU(GatherMM)
-NO_GPU(GatherQMM)
-NO_GPU(Hadamard)
-NO_GPU(Load)
-NO_GPU_MULTI(LUF)
-NO_GPU_MULTI(QRF)
-NO_GPU(QuantizedMatmul)
-NO_GPU(Scan)
-NO_GPU(SegmentedMM)
-NO_GPU_MULTI(SVD)
-NO_GPU(Inverse)
-NO_GPU(Cholesky)
-NO_GPU_MULTI(Eig)
-NO_GPU_MULTI(Eigh)
-
-namespace fast {
-NO_GPU(ScaledDotProductAttention)
-NO_GPU_MULTI(AffineQuantize)
-NO_GPU_MULTI(CustomKernel)
-} // namespace fast
-
-namespace distributed {
-NO_GPU_MULTI(AllReduce)
-NO_GPU_MULTI(AllGather)
-NO_GPU_MULTI(Send)
-NO_GPU_MULTI(Recv)
-} // namespace distributed
-
-} // namespace mlx::core

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