Merge branch 'main' into metal-thread-safe

CMakeLists.txt

@@ -34,6 +34,7 @@ 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)
@@ -83,6 +84,10 @@ 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)
@@ -226,6 +231,9 @@ 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

cmake/extension.cmake

@@ -11,13 +11,14 @@ 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)
+# files (like headers) DEBUG: Boolean, if true, enables debug compile options
+# for this specific library. If not provided, uses global MLX_METAL_DEBUG.
 #
 # clang format on
 
 macro(mlx_build_metallib)
   # Parse args
-  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY)
+  set(oneValueArgs TARGET TITLE OUTPUT_DIRECTORY DEBUG)
   set(multiValueArgs SOURCES INCLUDE_DIRS DEPS)
   cmake_parse_arguments(MTLLIB "" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -26,6 +27,10 @@ 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, MLX Contributors"
+copyright = "2023, Apple"
 author = "MLX Contributors"
 version = ".".join(mx.__version__.split(".")[:3])
 release = version

docs/src/python/array.rst

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

docs/src/python/linalg.rst

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

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 STATIC ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
+add_library(mlx_version OBJECT ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp)
 target_compile_definitions(mlx_version PRIVATE MLX_VERSION="${MLX_VERSION}")
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:mlx_version>)
 
@@ -51,5 +51,14 @@ if(MLX_BUILD_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)
+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

@@ -224,6 +224,10 @@ 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/hadamard.h

@@ -99,7 +99,11 @@ inline std::pair<int, int> decompose_hadamard(int n) {
           "[hadamard] Only supports n = m*2^k where m in (1, 12, 20, 28).");
     }
   }
+  if (n > (1 << 26)) {
+    throw std::invalid_argument(
+        "[hadamard] Only supports n = m*2^k where k <= 26");
+  }
   return {n, m};
 }
 
-} // namespace mlx::core
+} // namespace mlx::core

mlx/backend/common/utils.h

@@ -165,4 +165,11 @@ 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

@@ -46,6 +46,7 @@ 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

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

mlx/backend/cpu/conv.cpp

@@ -22,7 +22,8 @@ void slow_conv_1D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -60,7 +61,8 @@ void slow_conv_1D(
                     out_stride_O = out.strides()[2],
 
                     flip,
-                    padding = padding[0],
+                    padding_lo = padding_lo[0],
+                    padding_hi = padding_hi[0],
                     wt_stride = wt_strides[0],
                     wt_dilation = wt_dilation[0],
                     in_dilation = in_dilation[0]]() mutable {
@@ -77,7 +79,7 @@ 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 + wh_flip * wt_dilation;
+              int ih = oh * wt_stride - padding_lo + wh_flip * wt_dilation;
 
               auto ih_div = std::div(ih, in_dilation);
 
@@ -109,7 +111,8 @@ void slow_conv_2D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -120,230 +123,235 @@ 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,
-                    wt_strides,
-                    wt_dilation,
-                    in_dilation,
-                    flip]() mutable {
-    bool is_idil_one = in_dilation[0] == 1 && in_dilation[1] == 1;
+       padding_lo,
+       padding_hi,
+       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[0];
-      int iw_base = ow * wt_strides[1] - padding[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];
 
-      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[0] + init_h;
+        for (int i = 0; i < f_out_jump_h; ++i) {
+          int ih_loop = i * wt_strides[0] - padding_lo[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[1] + init_w;
+        for (int j = 0; j < f_out_jump_w; ++j) {
+          int iw_loop = j * wt_strides[1] - padding_lo[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[0];
-          int iw_base = ow * wt_strides[1] - padding[1];
+              int ih_base = oh * wt_strides[0] - padding_lo[0];
+              int iw_base = ow * wt_strides[1] - padding_lo[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[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 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 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;
+        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;
 
-    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>
@@ -351,7 +359,8 @@ void slow_conv_3D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -400,7 +409,8 @@ void slow_conv_3D(
                     out_stride_H = out.strides()[2],
                     out_stride_W = out.strides()[3],
                     out_stride_O = out.strides()[4],
-                    padding,
+                    padding_lo,
+                    padding_hi,
                     wt_strides,
                     wt_dilation,
                     in_dilation,
@@ -415,9 +425,9 @@ 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[0];
-      int ih_base = oh * wt_strides[1] - padding[1];
-      int iw_base = ow * wt_strides[2] - padding[2];
+      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];
 
       for (int o = 0; o < O; ++o) {
         float r = 0.;
@@ -478,7 +488,7 @@ 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[0] + init_d;
+      int id_loop = i * wt_strides[0] - padding_lo[0] + init_d;
 
       int wd_base = 0;
       while (wd_base < wD && id_loop % in_dilation[0] != 0) {
@@ -490,7 +500,7 @@ void slow_conv_3D(
     }
 
     for (int i = 0; i < f_out_jump_h; ++i) {
-      int ih_loop = i * wt_strides[1] - padding[1] + init_h;
+      int ih_loop = i * wt_strides[1] - padding_lo[1] + init_h;
 
       int wh_base = 0;
       while (wh_base < wH && ih_loop % in_dilation[1] != 0) {
@@ -502,7 +512,7 @@ void slow_conv_3D(
     }
 
     for (int j = 0; j < f_out_jump_w; ++j) {
-      int iw_loop = j * wt_strides[2] - padding[2] + init_w;
+      int iw_loop = j * wt_strides[2] - padding_lo[2] + init_w;
 
       int ww_base = 0;
       while (ww_base < wW && iw_loop % in_dilation[2] != 0) {
@@ -521,9 +531,9 @@ 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[0];
-      int ih_base = oh * wt_strides[1] - padding[1];
-      int iw_base = ow * wt_strides[2] - padding[2];
+      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 wd_base = base_d[od % f_out_jump_d];
       int wh_base = base_h[oh % f_out_jump_h];
@@ -573,24 +583,30 @@ void slow_conv_3D(
     };
 
     int oD_border_0 = 0;
-    int oD_border_1 =
-        is_idil_one ? ((padding[0] + wt_strides[0] - 1) / wt_strides[0]) : oD;
+    int oD_border_1 = is_idil_one
+        ? ((padding_lo[0] + wt_strides[0] - 1) / wt_strides[0])
+        : oD;
     int oD_border_2 = std::max(
-        oD_border_1, (iD + padding[0] - wD * wt_dilation[0]) / wt_strides[0]);
+        oD_border_1,
+        (iD + padding_lo[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[1] + wt_strides[1] - 1) / wt_strides[1]) : oH;
+    int oH_border_1 = is_idil_one
+        ? ((padding_lo[1] + wt_strides[1] - 1) / wt_strides[1])
+        : oH;
     int oH_border_2 = std::max(
-        oH_border_1, (iH + padding[1] - wH * wt_dilation[1]) / wt_strides[1]);
+        oH_border_1,
+        (iH + padding_lo[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[2] + wt_strides[2] - 1) / wt_strides[2]) : oW;
+    int oW_border_1 = is_idil_one
+        ? ((padding_lo[2] + wt_strides[2] - 1) / wt_strides[2])
+        : oW;
     int oW_border_2 = std::max(
-        oW_border_1, (iW + padding[2] - wW * wt_dilation[2]) / wt_strides[2]);
+        oW_border_1,
+        (iW + padding_lo[2] - wW * wt_dilation[2]) / wt_strides[2]);
     int oW_border_3 = oW;
 
     for (int n = 0; n < N; ++n) {
@@ -658,7 +674,8 @@ void dispatch_slow_conv_1D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -669,7 +686,8 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -680,7 +698,8 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -691,7 +710,8 @@ void dispatch_slow_conv_1D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -707,7 +727,8 @@ void dispatch_slow_conv_2D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -718,7 +739,8 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -729,7 +751,8 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -740,7 +763,8 @@ void dispatch_slow_conv_2D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -756,7 +780,8 @@ void dispatch_slow_conv_3D(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -767,7 +792,8 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -778,7 +804,8 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -789,7 +816,8 @@ void dispatch_slow_conv_3D(
         in,
         wt,
         out,
-        padding,
+        padding_lo,
+        padding_hi,
         wt_strides,
         wt_dilation,
         in_dilation,
@@ -829,7 +857,8 @@ void explicit_gemm_conv_1D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     Stream stream) {
@@ -848,7 +877,7 @@ void explicit_gemm_conv_1D_cpu(
   auto& encoder = cpu::get_command_encoder(stream);
 
   // Pad input
-  Shape padded_shape = {N, iH + 2 * padding[0], C};
+  Shape padded_shape = {N, iH + padding_lo[0] + padding_hi[0], C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -857,7 +886,7 @@ void explicit_gemm_conv_1D_cpu(
   copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
-  size_t data_offset = padding[0] * in_padded.strides()[1];
+  size_t data_offset = padding_lo[0] * in_padded.strides()[1];
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -971,7 +1000,8 @@ void explicit_gemm_conv_2D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     Stream stream) {
@@ -989,7 +1019,11 @@ void explicit_gemm_conv_2D_cpu(
   auto& encoder = cpu::get_command_encoder(stream);
 
   // Pad input
-  Shape padded_shape = {N, iH + 2 * padding[0], iW + 2 * padding[1], C};
+  Shape padded_shape = {
+      N,
+      iH + padding_lo[0] + padding_hi[0],
+      iW + padding_lo[1] + padding_hi[1],
+      C};
   array in_padded(padded_shape, conv_dtype, nullptr, {});
 
   // Fill with zeros
@@ -998,8 +1032,8 @@ void explicit_gemm_conv_2D_cpu(
   copy(temps.back(), in_padded, CopyType::Scalar, stream);
 
   // Pick input slice from padded
-  size_t data_offset =
-      padding[0] * in_padded.strides()[1] + padding[1] * in_padded.strides()[2];
+  size_t data_offset = padding_lo[0] * in_padded.strides()[1] +
+      padding_lo[1] * in_padded.strides()[2];
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -1091,7 +1125,8 @@ void explicit_gemm_conv_ND_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const bool flip,
@@ -1114,7 +1149,7 @@ 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] + 2 * padding[i];
+    padded_shape[i + 1] = iDim[i] + padding_lo[i] + padding_hi[i];
   }
   padded_shape.back() = C;
   array in_padded(padded_shape, conv_dtype, nullptr, {});
@@ -1125,9 +1160,10 @@ void explicit_gemm_conv_ND_cpu(
 
   // Pick input slice from padded
   size_t data_offset = 0;
-  for (size_t i = 0; i < padding.size(); i++) {
-    data_offset += padding[i] * in_padded.strides()[i + 1];
+  for (size_t i = 0; i < padding_lo.size(); i++) {
+    data_offset += padding_lo[i] * in_padded.strides()[i + 1];
   }
+
   array in_padded_slice(in.shape(), in_padded.dtype(), nullptr, {});
   in_padded_slice.copy_shared_buffer(
       in_padded,
@@ -1261,7 +1297,8 @@ void conv_1D_cpu(
     const array& in,
     const array& wt,
     array out,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1270,22 +1307,40 @@ 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, wt_strides, wt_dilation, stream);
+        in, wt, out, padding_lo, padding_hi, 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, wt_strides, wt_dilation, flip, stream);
+        in,
+        wt,
+        out,
+        padding_lo,
+        padding_hi,
+        wt_strides,
+        wt_dilation,
+        flip,
+        stream);
   }
 
   return dispatch_slow_conv_1D(
-      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
+      in,
+      wt,
+      out,
+      padding_lo,
+      padding_hi,
+      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,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1295,18 +1350,35 @@ 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, wt_strides, wt_dilation, flip, stream);
+        in,
+        wt,
+        out,
+        padding_lo,
+        padding_hi,
+        wt_strides,
+        wt_dilation,
+        flip,
+        stream);
   }
-
   return dispatch_slow_conv_2D(
-      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
+      in,
+      wt,
+      out,
+      padding_lo,
+      padding_hi,
+      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,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
     const std::vector<int>& wt_dilation,
     const std::vector<int>& in_dilation,
@@ -1317,11 +1389,28 @@ 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, wt_strides, wt_dilation, flip, stream);
+        in,
+        wt,
+        out,
+        padding_lo,
+        padding_hi,
+        wt_strides,
+        wt_dilation,
+        flip,
+        stream);
   }
 
   return dispatch_slow_conv_3D(
-      in, wt, out, padding, wt_strides, wt_dilation, in_dilation, flip, stream);
+      in,
+      wt,
+      out,
+      padding_lo,
+      padding_hi,
+      wt_strides,
+      wt_dilation,
+      in_dilation,
+      flip,
+      stream);
 }
 
 } // namespace
@@ -1338,7 +1427,8 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
+        padding_hi_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -1351,7 +1441,8 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
+        padding_hi_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -1364,7 +1455,8 @@ void Convolution::eval_cpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
+        padding_hi_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,

mlx/backend/cpu/eig.cpp

@@ -0,0 +1,174 @@
+// 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

@@ -12,6 +12,133 @@ 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,
@@ -19,8 +146,10 @@ 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<T>();
+  auto eig_ptr = values.data<R>();
   char jobz = compute_eigenvectors ? 'V' : 'N';
 
   auto& encoder = cpu::get_command_encoder(stream);
@@ -33,49 +162,17 @@ void eigh_impl(
                     N = vectors.shape(-1),
                     size = vectors.size()]() mutable {
     // Work query
-    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;
-    }
+    EighWork<T> work(jobz, uplo, N);
 
-    auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
-    auto iwork_buf = array::Data{allocator::malloc(sizeof(int) * liwork)};
+    // Work loop
     for (size_t i = 0; i < size / (N * N); ++i) {
-      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);
+      work.run(vec_ptr, eig_ptr);
       vec_ptr += N * N;
       eig_ptr += N;
-      if (info != 0) {
+      if (work.info != 0) {
         std::stringstream msg;
         msg << "[Eigh::eval_cpu] Eigenvalue decomposition failed with error code "
-            << info;
+            << work.info;
         throw std::runtime_error(msg.str());
       }
     }
@@ -131,6 +228,10 @@ 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

@@ -257,15 +257,11 @@ void gather_axis(
     const array& ind,
     array& out,
     const int axis) {
-  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 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 ind_ptr = ind.data<IdxT>();
   auto src_ptr = src.data<T>();
@@ -585,15 +581,11 @@ 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 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 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 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>
-#endif
+#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())
 
 #ifdef MLX_USE_ACCELERATE
 #include <Accelerate/Accelerate.h>
@@ -32,7 +32,7 @@
 
 #endif
 
-#define INSTANTIATE_LAPACK_TYPES(FUNC)                       \
+#define INSTANTIATE_LAPACK_REAL(FUNC)                        \
   template <typename T, typename... Args>                    \
   void FUNC(Args... args) {                                  \
     if constexpr (std::is_same_v<T, float>) {                \
@@ -42,11 +42,24 @@
     }                                                        \
   }
 
-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)
+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)

mlx/backend/cpu/matmul.cpp

@@ -132,6 +132,10 @@ 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];
@@ -139,7 +143,9 @@ 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/cuda/CMakeLists.txt

@@ -0,0 +1,55 @@
+# Filename rules in cuda backend:
+#
+# * Use .cu/.cuh if code contains device code, and .cpp/.h if not.
+# * Device-only kernel code should be put in kernels/ subdir.
+# * Files in kernels/ subdir should not include files outside.
+target_sources(
+  mlx
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/fence.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
+
+# Enable defining device lambda functions.
+target_compile_options(mlx
+                       PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
+
+# 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})
+
+# Suppress nvcc warnings on MLX headers.
+target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
+                                   --diag_suppress=997>)

mlx/backend/cuda/allocator.cpp

@@ -0,0 +1,154 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/allocator.h"
+#include "mlx/backend/cuda/utils.h"
+#include "mlx/backend/cuda/worker.h"
+
+#include <cuda_runtime.h>
+#include <fmt/format.h>
+
+#include <cassert>
+
+namespace mlx::core {
+
+namespace cu {
+
+CudaAllocator::CudaAllocator() {
+  // TODO: Set memory limit for multi-device.
+  size_t free, total;
+  CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
+  memory_limit_ = total * 0.8;
+}
+
+Buffer CudaAllocator::malloc(size_t size) {
+  // TODO: Check memory limit.
+  auto* 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)));
+  }
+  std::lock_guard lock(mutex_);
+  active_memory_ += size;
+  peak_memory_ = std::max(active_memory_, peak_memory_);
+  return Buffer{buf};
+}
+
+void CudaAllocator::free(Buffer buffer) {
+  auto* buf = static_cast<CudaBuffer*>(buffer.ptr());
+  if (!buf) {
+    return;
+  }
+
+  // If 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([buffer]() { allocator().free(buffer); });
+      worker_->end_batch();
+      worker_->commit();
+      return;
+    }
+  }
+
+  size_t size = buf->size;
+  cudaFree(buf->data);
+  delete buf;
+  std::lock_guard lock(mutex_);
+  active_memory_ -= size;
+}
+
+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());
+}
+
+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;
+}
+
+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();
+}
+
+// TODO: Implement buffer cache.
+size_t get_cache_memory() {
+  return 0;
+}
+size_t set_cache_limit(size_t) {
+  return 0;
+}
+size_t set_wired_limit(size_t) {
+  return 0;
+}
+void clear_cache() {}
+
+} // namespace mlx::core

mlx/backend/cuda/allocator.h

@@ -0,0 +1,58 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/allocator.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();
+
+  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);
+
+ 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 active_memory_{0};
+  size_t peak_memory_{0};
+};
+
+CudaAllocator& allocator();
+
+} // namespace mlx::core::cu

mlx/backend/cuda/copy.cpp

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

mlx/backend/cuda/device.cpp

@@ -0,0 +1,117 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/worker.h"
+#include "mlx/backend/metal/metal.h"
+
+#include <fmt/format.h>
+#include <nvtx3/nvtx3.hpp>
+
+namespace mlx::core {
+
+namespace cu {
+
+DeviceStream::DeviceStream(Device& device) : device_(device), stream_(device) {}
+
+void DeviceStream::synchronize() {
+  cudaStreamSynchronize(stream_);
+}
+
+cudaStream_t DeviceStream::schedule_cuda_stream() {
+  // TODO: Return a stream that maximizes parallelism.
+  return stream_;
+}
+
+cudaStream_t DeviceStream::last_cuda_stream() {
+  return stream_;
+}
+
+CommandEncoder& DeviceStream::get_encoder() {
+  if (!encoder_) {
+    encoder_ = std::make_unique<CommandEncoder>(*this);
+  }
+  return *encoder_;
+}
+
+Device::Device(int device) : device_(device) {
+  // Validate the requirements of device.
+  int attr = 0;
+  cudaDeviceGetAttribute(&attr, cudaDevAttrConcurrentManagedAccess, device_);
+  if (attr != 1) {
+    throw std::runtime_error(fmt::format(
+        "Device {} does not support synchronization in managed memory.",
+        device_));
+  }
+}
+
+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_;
+  }
+}
+
+DeviceStream& Device::get_stream(Stream s) {
+  auto it = streams_.find(s.index);
+  if (it == streams_.end()) {
+    it = streams_.try_emplace(s.index, *this).first;
+  }
+  return it->second;
+}
+
+CommandEncoder::CommandEncoder(DeviceStream& s)
+    : device_(s.device()), stream_(s) {}
+
+void CommandEncoder::add_completed_handler(std::function<void()> task) {
+  worker_.add_task(std::move(task));
+}
+
+void CommandEncoder::end_encoding() {
+  if (!temporaries_.empty()) {
+    add_completed_handler([temporaries = std::move(temporaries_)]() {});
+  }
+
+  // There is no kernel running, run completion handlers immediately.
+  if (!has_gpu_work_) {
+    worker_.consume_in_this_thread();
+    return;
+  }
+  has_gpu_work_ = false;
+
+  // Put completion handlers in a batch.
+  worker_.end_batch();
+
+  // Signaling kernel completion is expensive, delay until enough batches.
+  // TODO: This number is arbitrarily picked, profile for a better stragety.
+  if (worker_.uncommited_batches() > 8) {
+    commit();
+  }
+}
+
+void CommandEncoder::commit() {
+  worker_.commit(stream_.last_cuda_stream());
+}
+
+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;
+}
+
+DeviceStream& get_stream(Stream s) {
+  return device(s.device).get_stream(s);
+}
+
+CommandEncoder& get_command_encoder(Stream s) {
+  return get_stream(s).get_encoder();
+}
+
+} // namespace cu
+
+} // namespace mlx::core

mlx/backend/cuda/device.h

@@ -0,0 +1,131 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/array.h"
+#include "mlx/backend/cuda/worker.h"
+#include "mlx/stream.h"
+
+#include <thrust/execution_policy.h>
+
+#include <unordered_map>
+
+namespace mlx::core::cu {
+
+class Device;
+class CommandEncoder;
+
+class DeviceStream {
+ public:
+  explicit DeviceStream(Device& device);
+
+  DeviceStream(const DeviceStream&) = delete;
+  DeviceStream& operator=(const DeviceStream&) = delete;
+
+  // Wait until kernels in the stream complete.
+  void synchronize();
+
+  // Return a cuda stream for launching kernels.
+  cudaStream_t schedule_cuda_stream();
+
+  // Return the last cuda stream used.
+  cudaStream_t last_cuda_stream();
+
+  CommandEncoder& get_encoder();
+
+  Device& device() {
+    return device_;
+  }
+
+ private:
+  Device& device_;
+  CudaStream stream_;
+  std::unique_ptr<CommandEncoder> encoder_;
+};
+
+class Device {
+ public:
+  explicit Device(int 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();
+
+  DeviceStream& get_stream(Stream s);
+
+  int cuda_device() const {
+    return device_;
+  }
+
+ private:
+  int device_;
+  std::unordered_map<int, DeviceStream> streams_;
+};
+
+class CommandEncoder {
+ public:
+  explicit CommandEncoder(DeviceStream& stream);
+
+  CommandEncoder(const CommandEncoder&) = delete;
+  CommandEncoder& operator=(const CommandEncoder&) = delete;
+
+  void set_input_array(const array& arr) {}
+  void set_output_array(const array& arr) {}
+
+  void add_temporary(const array& arr) {
+    temporaries_.push_back(arr.data_shared_ptr());
+  }
+
+  void add_completed_handler(std::function<void()> task);
+  void end_encoding();
+  void commit();
+
+  // Schedule a cuda stream for |fun| to launch kernels, and check error
+  // afterwards.
+  template <typename F>
+  void launch_kernel(F&& fun) {
+    launch_kernel(stream_.schedule_cuda_stream(), std::forward<F>(fun));
+  }
+
+  template <typename F>
+  void launch_kernel(cudaStream_t stream, F&& fun) {
+    device_.make_current();
+    fun(stream);
+    check_cuda_error("kernel launch", cudaGetLastError());
+    has_gpu_work_ = true;
+  }
+
+  Device& device() {
+    return device_;
+  }
+
+  DeviceStream& stream() {
+    return stream_;
+  }
+
+  bool has_gpu_work() const {
+    return has_gpu_work_;
+  }
+
+ private:
+  Device& device_;
+  DeviceStream& stream_;
+  Worker worker_;
+  bool has_gpu_work_{false};
+  std::vector<std::shared_ptr<array::Data>> temporaries_;
+};
+
+Device& device(mlx::core::Device device);
+DeviceStream& get_stream(Stream s);
+CommandEncoder& get_command_encoder(Stream s);
+
+// Return an execution policy that does not sync for result.
+// 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/dtype_utils.cuh

@@ -0,0 +1,35 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <cuComplex.h>
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
+
+namespace mlx::core {
+
+// 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;
+
+} // namespace mlx::core

mlx/backend/cuda/eval.cpp

@@ -0,0 +1,68 @@
+// 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());
+  if (encoder.has_gpu_work()) {
+    // Keep used buffers alive until kernel finishes running.
+    std::unordered_set<std::shared_ptr<array::Data>> buffers;
+    for (auto& in : arr.inputs()) {
+      buffers.insert(in.data_shared_ptr());
+    }
+    for (auto& s : arr.siblings()) {
+      buffers.insert(s.data_shared_ptr());
+    }
+    // Remove the output if it was donated to by an input.
+    if (auto it = buffers.find(arr.data_shared_ptr()); it != buffers.end()) {
+      buffers.erase(it);
+    }
+    encoder.add_completed_handler([buffers = std::move(buffers)]() {});
+  }
+  encoder.end_encoding();
+}
+
+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_stream(s).synchronize();
+}
+
+} // namespace mlx::core::gpu

mlx/backend/cuda/event.cu

@@ -0,0 +1,265 @@
+// Copyright © 2024 Apple Inc.
+
+#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 {
+    wait(cu::get_stream(s).last_cuda_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 {
+    record(cu::get_stream(s).last_cuda_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.
+  allocator::Buffer buffer = allocator::malloc(sizeof(Atomic));
+  Atomic* ac = static_cast<Atomic*>(buffer.raw_ptr());
+  new (ac) Atomic(0);
+  ac_ = std::shared_ptr<Atomic>(ac, [buffer](Atomic* ptr) {
+    ptr->~Atomic();
+    allocator::free(buffer);
+  });
+}
+
+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.launch_kernel(
+        encoder.stream().last_cuda_stream(),
+        [this, value](cudaStream_t stream) { wait(stream, value); });
+    encoder.add_completed_handler([ac = ac_]() {});
+    encoder.end_encoding();
+  }
+}
+
+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) {
+    scheduler::enqueue(s, [*this, value]() mutable { signal(value); });
+  } else {
+    auto& encoder = get_command_encoder(s);
+    encoder.launch_kernel(
+        encoder.stream().last_cuda_stream(),
+        [this, value](cudaStream_t stream) { signal(stream, value); });
+    encoder.add_completed_handler([ac = ac_]() {});
+    encoder.end_encoding();
+  }
+}
+
+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

@@ -0,0 +1,66 @@
+// 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.cu

@@ -0,0 +1,70 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/event.h"
+#include "mlx/fence.h"
+#include "mlx/scheduler.h"
+
+#include <nvtx3/nvtx3.hpp>
+
+namespace mlx::core {
+
+namespace {
+
+__host__ __device__ void busy_wait(cuda::atomic<uint64_t>* ac, uint64_t value) {
+  while (true) {
+    // In theory the atomic_thread_fence is not needed, but for CUDA 11 without
+    // it the load() may never return new value.
+    cuda::atomic_thread_fence(cuda::memory_order_seq_cst);
+    uint64_t current = ac->load();
+    if (current >= value) {
+      break;
+    }
+  }
+}
+
+__global__ void busy_wait_kernel(cuda::atomic<uint64_t>* ac, uint64_t value) {
+  busy_wait(ac, value);
+}
+
+} // namespace
+
+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());
+  // We can't use SharedEvent::wait because it could hang in CUDA 11, see also:
+  // https://github.com/ml-explore/mlx/issues/2137
+  const auto& ac = fence->event.atomic();
+  if (s.device == mlx::core::Device::cpu) {
+    scheduler::enqueue(s, [ac, count = fence->count]() {
+      nvtx3::scoped_range r("Fence::wait()");
+      busy_wait(ac.get(), count);
+    });
+  } else {
+    nvtx3::scoped_range r("Fence::wait(s)");
+    auto& encoder = cu::get_command_encoder(s);
+    encoder.launch_kernel(
+        encoder.stream().last_cuda_stream(), [&](cudaStream_t stream) {
+          busy_wait_kernel<<<1, 1, 0>>>(ac.get(), fence->count);
+        });
+    encoder.add_completed_handler([ac]() {});
+    encoder.end_encoding();
+  }
+}
+
+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/kernels/arange.cuh

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

mlx/backend/cuda/kernels/fp16_math.cuh

@@ -0,0 +1,76 @@
+// 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 {
+
+///////////////////////////////////////////////////////////////////////////////
+// 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/primitives.cu

@@ -0,0 +1,164 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/dtype_utils.cuh"
+#include "mlx/backend/cuda/kernels/arange.cuh"
+#include "mlx/backend/cuda/kernels/fp16_math.cuh"
+#include "mlx/distributed/primitives.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/fast_primitives.h"
+#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& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_output_array(out);
+  encoder.launch_kernel([&, this](cudaStream_t stream) {
+    MLX_SWITCH_INT_FLOAT_TYPES_CHECKED(out.dtype(), "Arange", CTYPE, {
+      using OutType = cuda_type_t<CTYPE>;
+      CTYPE step =
+          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
+      thrust::transform(
+          cu::thrust_policy(stream),
+          thrust::counting_iterator<uint32_t>(0),
+          thrust::counting_iterator<uint32_t>(out.data_size()),
+          thrust::device_pointer_cast(out.data<OutType>()),
+          cu::Arange<OutType>{
+              static_cast<OutType>(start_), static_cast<OutType>(step)});
+    });
+  });
+}
+
+#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(func)                                                  \
+  void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
+    throw std::runtime_error(#func " has no CUDA implementation.");   \
+  }
+
+NO_GPU(Abs)
+NO_GPU(Add)
+NO_GPU(AddMM)
+NO_GPU(ArcCos)
+NO_GPU(ArcCosh)
+NO_GPU(ArcSin)
+NO_GPU(ArcSinh)
+NO_GPU(ArcTan)
+NO_GPU(ArcTan2)
+NO_GPU(ArcTanh)
+NO_GPU(ArgPartition)
+NO_GPU(ArgReduce)
+NO_GPU(ArgSort)
+NO_GPU(BitwiseBinary)
+NO_GPU(BitwiseInvert)
+NO_GPU(BlockMaskedMM)
+NO_GPU(Ceil)
+NO_GPU_MULTI(Compiled)
+NO_GPU(Conjugate)
+NO_GPU(Convolution)
+NO_GPU(Cos)
+NO_GPU(Cosh)
+NO_GPU(Divide)
+NO_GPU_MULTI(DivMod)
+NO_GPU(DynamicSlice)
+NO_GPU(DynamicSliceUpdate)
+NO_GPU(Remainder)
+NO_GPU(Equal)
+NO_GPU(Erf)
+NO_GPU(ErfInv)
+NO_GPU(Exp)
+NO_GPU(Expm1)
+NO_GPU(FFT)
+NO_GPU(Floor)
+NO_GPU(Gather)
+NO_GPU(GatherAxis)
+NO_GPU(GatherMM)
+NO_GPU(GatherQMM)
+NO_GPU(Greater)
+NO_GPU(GreaterEqual)
+NO_GPU(Hadamard)
+NO_GPU(Imag)
+NO_GPU(Less)
+NO_GPU(LessEqual)
+NO_GPU(Load)
+NO_GPU(Log)
+NO_GPU(Log1p)
+NO_GPU(LogicalNot)
+NO_GPU(LogicalAnd)
+NO_GPU(LogicalOr)
+NO_GPU(LogAddExp)
+NO_GPU(LogSumExp)
+NO_GPU_MULTI(LUF)
+NO_GPU(Matmul)
+NO_GPU(Maximum)
+NO_GPU(Minimum)
+NO_GPU(Multiply)
+NO_GPU(Negative)
+NO_GPU(NotEqual)
+NO_GPU(Partition)
+NO_GPU(Power)
+NO_GPU_MULTI(QRF)
+NO_GPU(QuantizedMatmul)
+NO_GPU(RandomBits)
+NO_GPU(Real)
+NO_GPU(Reduce)
+NO_GPU(Round)
+NO_GPU(Scan)
+NO_GPU(Scatter)
+NO_GPU(ScatterAxis)
+NO_GPU(Select)
+NO_GPU(Sigmoid)
+NO_GPU(Sign)
+NO_GPU(Sin)
+NO_GPU(Sinh)
+NO_GPU(SliceUpdate)
+NO_GPU(Softmax)
+NO_GPU(Sort)
+NO_GPU(Square)
+NO_GPU(Sqrt)
+NO_GPU(Subtract)
+NO_GPU_MULTI(SVD)
+NO_GPU(Tan)
+NO_GPU(Tanh)
+NO_GPU(Inverse)
+NO_GPU(Cholesky)
+NO_GPU_MULTI(Eig)
+NO_GPU_MULTI(Eigh)
+
+namespace fast {
+NO_GPU_MULTI(LayerNorm)
+NO_GPU_MULTI(LayerNormVJP)
+NO_GPU_MULTI(RMSNorm)
+NO_GPU_MULTI(RMSNormVJP)
+NO_GPU_MULTI(RoPE)
+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/slicing.cpp

@@ -0,0 +1,15 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/gpu/slicing.h"
+
+namespace mlx::core {
+
+void concatenate_gpu(
+    const std::vector<array>& inputs,
+    array& out,
+    int axis,
+    const Stream& s) {
+  throw std::runtime_error("concatenate_gpu not implemented in CUDA backend.");
+}
+
+} // namespace mlx::core

mlx/backend/cuda/utils.cpp

@@ -0,0 +1,26 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/utils.h"
+#include "mlx/backend/cuda/device.h"
+
+#include <fmt/format.h>
+
+namespace mlx::core {
+
+CudaStream::CudaStream(cu::Device& device) {
+  device.make_current();
+  CHECK_CUDA_ERROR(cudaStreamCreateWithFlags(&stream_, cudaStreamNonBlocking));
+}
+
+CudaStream::~CudaStream() {
+  CHECK_CUDA_ERROR(cudaStreamDestroy(stream_));
+}
+
+void check_cuda_error(const char* name, cudaError_t err) {
+  if (err != cudaSuccess) {
+    throw std::runtime_error(
+        fmt::format("{} failed: {}", name, cudaGetErrorString(err)));
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cuda/utils.h

@@ -0,0 +1,36 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <cuda_runtime.h>
+
+namespace mlx::core {
+
+namespace cu {
+class Device;
+}
+
+// Cuda stream managed with RAII.
+class CudaStream {
+ public:
+  explicit CudaStream(cu::Device& device);
+  ~CudaStream();
+
+  CudaStream(const CudaStream&) = delete;
+  CudaStream& operator=(const CudaStream&) = delete;
+
+  operator cudaStream_t() const {
+    return stream_;
+  }
+
+ private:
+  cudaStream_t stream_;
+};
+
+// Throw exception if the cuda API does not succeed.
+void check_cuda_error(const char* name, cudaError_t err);
+
+// The macro version that prints the command that failed.
+#define CHECK_CUDA_ERROR(cmd) check_cuda_error(#cmd, (cmd))
+
+} // namespace mlx::core

mlx/backend/cuda/worker.cpp

@@ -0,0 +1,90 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/worker.h"
+#include "mlx/backend/cuda/allocator.h"
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+Worker::Worker()
+    : signal_stream_(device(mlx::core::Device::gpu)),
+      worker_(&Worker::thread_fn, this) {}
+
+Worker::~Worker() {
+  {
+    std::lock_guard lock(worker_mutex_);
+    stop_ = true;
+  }
+  worker_event_.signal(batch_ + 1);
+  worker_.join();
+}
+
+void Worker::add_task(std::function<void()> task) {
+  pending_tasks_.push_back(std::move(task));
+}
+
+void Worker::consume_in_this_thread() {
+  for (auto& task : pending_tasks_) {
+    task();
+  }
+  pending_tasks_.clear();
+}
+
+void Worker::end_batch() {
+  batch_++;
+  {
+    std::lock_guard lock(worker_mutex_);
+    worker_tasks_[batch_] = std::move(pending_tasks_);
+  }
+  uncommited_batches_++;
+}
+
+void Worker::commit() {
+  if (uncommited_batches_ == 0) {
+    return;
+  }
+  uncommited_batches_ = 0;
+  worker_event_.signal(batch_);
+}
+
+void Worker::commit(cudaStream_t stream) {
+  if (uncommited_batches_ == 0) {
+    return;
+  }
+  uncommited_batches_ = 0;
+  // Signal the |worker_event_| in |signal_stream_| after the kernels in
+  // |stream_| finish running.
+  signal_event_.record(stream);
+  signal_event_.wait(signal_stream_);
+  worker_event_.signal(signal_stream_, batch_);
+}
+
+void Worker::thread_fn() {
+  // The worker thread is safe to free buffers.
+  allocator().register_this_thread();
+
+  while (!stop_) {
+    uint64_t batch = worker_event_.value();
+    Tasks tasks;
+    {
+      std::lock_guard lock(worker_mutex_);
+      // Move tasks in signaled batches.
+      auto end = worker_tasks_.upper_bound(batch);
+      for (auto it = worker_tasks_.begin(); it != end; ++it) {
+        if (tasks.empty()) {
+          tasks = std::move(it->second);
+        } else {
+          std::move(
+              it->second.begin(), it->second.end(), std::back_inserter(tasks));
+        }
+      }
+      worker_tasks_.erase(worker_tasks_.begin(), end);
+    }
+    for (auto& task : tasks) {
+      task();
+    }
+    worker_event_.wait(batch + 1);
+  }
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/worker.h

@@ -0,0 +1,68 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/event.h"
+#include "mlx/backend/cuda/utils.h"
+
+#include <functional>
+#include <map>
+#include <mutex>
+#include <thread>
+
+namespace mlx::core::cu {
+
+// Run tasks in worker thread, synchronized with cuda stream.
+class Worker {
+ public:
+  Worker();
+  ~Worker();
+
+  Worker(const Worker&) = delete;
+  Worker& operator=(const Worker&) = delete;
+
+  // Add a pending |task| that will run when consumed or commited.
+  void add_task(std::function<void()> task);
+
+  // Run pending tasks immediately in current thread.
+  void consume_in_this_thread();
+
+  // Put pending tasks in a batch.
+  void end_batch();
+
+  // Inform worker thread to run current batches now.
+  void commit();
+
+  // Inform worker thread to run current batches after kernels in |stream|
+  // finish running.
+  void commit(cudaStream_t stream);
+
+  // Return how many batches have been added but not committed yet.
+  size_t uncommited_batches() const {
+    return uncommited_batches_;
+  }
+
+ private:
+  void thread_fn();
+
+  uint64_t batch_{0};
+  size_t uncommited_batches_{0};
+
+  // Cuda stream and event for signaling kernel completion.
+  CudaStream signal_stream_;
+  CudaEvent signal_event_;
+
+  // Worker thread.
+  SharedEvent worker_event_;
+  std::thread worker_;
+  std::mutex worker_mutex_;
+  bool stop_{false};
+
+  // Tasks are put in |pending_tasks_| first, and then moved to
+  // |worker_tasks_| when end_batch() is called.
+  using Tasks = std::vector<std::function<void()>>;
+  Tasks pending_tasks_;
+  std::map<uint64_t, Tasks> worker_tasks_;
+};
+
+} // namespace mlx::core::cu

mlx/backend/gpu/CMakeLists.txt

@@ -0,0 +1,5 @@
+target_sources(
+  mlx
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/copy.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp)

mlx/backend/gpu/copy.cpp

@@ -0,0 +1,49 @@
+// Copyright © 2023-2024 Apple Inc.
+
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/primitives.h"
+
+#include <cassert>
+
+namespace mlx::core {
+
+void copy_gpu(const array& in, array& out, CopyType ctype, const Stream& s) {
+  bool donated = set_copy_output_data(in, out, ctype);
+  if (donated && in.dtype() == out.dtype()) {
+    // If the output has the same type as the input then there is nothing to
+    // copy, just use the buffer.
+    return;
+  }
+  if (ctype == CopyType::GeneralGeneral) {
+    ctype = CopyType::General;
+  }
+  copy_gpu_inplace(in, out, ctype, s);
+}
+
+void copy_gpu(const array& in, array& out, CopyType ctype) {
+  copy_gpu(in, out, ctype, out.primitive().stream());
+}
+
+void copy_gpu_inplace(
+    const array& in,
+    array& out,
+    CopyType ctype,
+    const Stream& s) {
+  assert(in.shape() == out.shape());
+  return copy_gpu_inplace(
+      in, out, in.shape(), in.strides(), out.strides(), 0, 0, ctype, s);
+}
+
+void copy_gpu_inplace(
+    const array& in,
+    array& out,
+    const Strides& i_strides,
+    int64_t i_offset,
+    CopyType ctype,
+    const Stream& s) {
+  assert(in.shape() == out.shape());
+  return copy_gpu_inplace(
+      in, out, in.shape(), i_strides, out.strides(), i_offset, 0, ctype, s);
+}
+
+} // namespace mlx::core

mlx/backend/gpu/copy.h

@@ -5,6 +5,8 @@
 #include "mlx/backend/common/copy.h"
 #include "mlx/stream.h"
 
+#include <optional>
+
 namespace mlx::core {
 
 // Generic copy inplace

mlx/backend/gpu/primitives.cpp

@@ -0,0 +1,217 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/primitives.h"
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/backend/gpu/slicing.h"
+
+#include <cassert>
+
+#define MLX_PROFILER_RANGE(message)
+
+namespace mlx::core {
+
+namespace {
+
+void reshape(const array& in, array& out, Stream s) {
+  auto [copy_necessary, out_strides] = prepare_reshape(in, out);
+  if (copy_necessary) {
+    out.set_data(allocator::malloc(out.nbytes()));
+    copy_gpu_inplace(
+        in,
+        out,
+        in.shape(),
+        in.strides(),
+        make_contiguous_strides(in.shape()),
+        0,
+        0,
+        CopyType::General,
+        s);
+  } else {
+    shared_buffer_reshape(in, out_strides, out);
+  }
+}
+
+} // namespace
+
+void AsStrided::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("AsStrided::eval_gpu");
+  eval(inputs, out);
+}
+
+void AsType::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("AsType::eval_gpu");
+  CopyType ctype =
+      inputs[0].flags().contiguous ? CopyType::Vector : CopyType::General;
+  copy_gpu(inputs[0], out, ctype);
+}
+
+void Broadcast::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Broadcast::eval_gpu");
+  eval(inputs, out);
+}
+
+void BroadcastAxes::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("BroadcastAxes::eval_gpu");
+  eval(inputs, out);
+}
+
+void Concatenate::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Concatenate::eval_gpu");
+  concatenate_gpu(inputs, out, axis_, stream());
+}
+
+void Contiguous::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Contiguous::eval_gpu");
+  assert(inputs.size() == 1);
+  auto& in = inputs[0];
+  constexpr size_t extra_bytes = 16384;
+  if (in.buffer_size() <= out.nbytes() + extra_bytes &&
+      (in.flags().row_contiguous ||
+       (allow_col_major_ && in.flags().col_contiguous))) {
+    out.copy_shared_buffer(in);
+  } else {
+    copy_gpu(in, out, CopyType::General);
+  }
+}
+
+void Copy::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Copy::eval_gpu");
+  eval(inputs, out);
+}
+
+void CustomTransforms::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  MLX_PROFILER_RANGE("CustomTransforms::eval_gpu");
+  eval(inputs, outputs);
+}
+
+void Depends::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  MLX_PROFILER_RANGE("Depends::eval_gpu");
+  eval(inputs, outputs);
+}
+
+void ExpandDims::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("ExpandDims::eval_gpu");
+  eval(inputs, out);
+}
+
+void Full::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Full::eval_gpu");
+  auto in = inputs[0];
+  CopyType ctype;
+  if (in.data_size() == 1) {
+    ctype = CopyType::Scalar;
+  } else if (in.flags().contiguous) {
+    ctype = CopyType::Vector;
+  } else {
+    ctype = CopyType::General;
+  }
+  copy_gpu(in, out, ctype);
+}
+
+void Flatten::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Flatten::eval_gpu");
+  reshape(inputs[0], out, stream());
+}
+
+void NumberOfElements::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("NumberOfElements::eval_gpu");
+  eval(inputs, out);
+}
+
+void Pad::eval_gpu(const std::vector<array>& inputs, array& out) {
+  // Inputs must be base input array and scalar val array
+  assert(inputs.size() == 2);
+  auto& in = inputs[0];
+  auto& val = inputs[1];
+
+  // Padding value must be a scalar
+  assert(val.size() == 1);
+
+  // Padding value, input and output must be of the same type
+  assert(val.dtype() == in.dtype() && in.dtype() == out.dtype());
+
+  pad_gpu(in, val, out, axes_, low_pad_size_, stream());
+}
+
+void Reshape::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Reshape::eval_gpu");
+  reshape(inputs[0], out, stream());
+}
+
+void Split::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  MLX_PROFILER_RANGE("Split::eval_gpu");
+  eval(inputs, outputs);
+}
+
+void Slice::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Slice::eval_gpu");
+  assert(inputs.size() == 1);
+  if (out.size() == 0) {
+    out.set_data(nullptr);
+    return;
+  }
+
+  auto& in = inputs[0];
+  slice_gpu(in, out, start_indices_, strides_, stream());
+}
+
+void Squeeze::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Squeeze::eval_gpu");
+  eval(inputs, out);
+}
+
+void StopGradient::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("StopGradient::eval_gpu");
+  eval(inputs, out);
+}
+
+void Transpose::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Transpose::eval_gpu");
+  eval(inputs, out);
+}
+
+void Unflatten::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("Unflatten::eval_gpu");
+  reshape(inputs[0], out, stream());
+}
+
+void View::eval_gpu(const std::vector<array>& inputs, array& out) {
+  MLX_PROFILER_RANGE("View::eval_gpu");
+  auto& in = inputs[0];
+  auto ibytes = size_of(in.dtype());
+  auto obytes = size_of(out.dtype());
+  // Conditions for buffer copying (disjunction):
+  // - type size is the same
+  // - type size is smaller and the last axis is contiguous
+  // - the entire array is row contiguous
+  if (ibytes == obytes || (obytes < ibytes && in.strides().back() == 1) ||
+      in.flags().row_contiguous) {
+    auto strides = in.strides();
+    for (int i = 0; i < static_cast<int>(strides.size()) - 1; ++i) {
+      strides[i] *= ibytes;
+      strides[i] /= obytes;
+    }
+    out.copy_shared_buffer(
+        in, strides, in.flags(), in.data_size() * ibytes / obytes);
+  } else {
+    auto tmp = array(in.shape(), in.dtype(), nullptr, {});
+    tmp.set_data(allocator::malloc(tmp.nbytes()));
+    copy_gpu_inplace(in, tmp, CopyType::General, stream());
+
+    auto flags = out.flags();
+    flags.contiguous = true;
+    flags.row_contiguous = true;
+    auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
+    flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
+    out.copy_shared_buffer(tmp, out.strides(), flags, out.size());
+  }
+}
+
+} // namespace mlx::core

mlx/backend/gpu/slicing.cpp

@@ -0,0 +1,44 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/slicing.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/backend/gpu/slicing.h"
+
+namespace mlx::core {
+
+void slice_gpu(
+    const array& in,
+    array& out,
+    const Shape& start_indices,
+    const Shape& strides,
+    const Stream& s) {
+  slice(in, out, start_indices, strides);
+}
+
+void pad_gpu(
+    const array& in,
+    const array& val,
+    array& out,
+    const std::vector<int>& axes,
+    const Shape& low_pad_size,
+    const Stream& s) {
+  // Fill output with val
+  fill_gpu(val, out, s);
+
+  // Find offset for start of input values
+  size_t data_offset = 0;
+  for (int i = 0; i < axes.size(); i++) {
+    auto ax = axes[i] < 0 ? out.ndim() + axes[i] : axes[i];
+    data_offset += out.strides()[ax] * low_pad_size[i];
+  }
+
+  // Extract slice from output where input will be pasted
+  array out_slice(in.shape(), out.dtype(), nullptr, {});
+  out_slice.copy_shared_buffer(
+      out, out.strides(), out.flags(), out_slice.size(), data_offset);
+
+  // Copy input values into the slice
+  copy_gpu_inplace(in, out_slice, CopyType::GeneralGeneral, s);
+}
+
+} // namespace mlx::core

mlx/backend/metal/binary.cpp

@@ -90,7 +90,7 @@ void binary_op_gpu_inplace(
     work_per_thread = large ? 4 : 2;
   } else {
     large = out.data_size() > UINT32_MAX;
-    work_per_thread = 1;
+    work_per_thread = get_work_per_thread(a.dtype());
   }
   std::string kernel_name =
       get_kernel_name(bopt, op, a, large, shape.size(), work_per_thread);
@@ -137,13 +137,20 @@ void binary_op_gpu_inplace(
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     // Launch a 1D or 2D grid of threads
-    size_t nthreads = out.data_size();
+    size_t nthreads = ceildiv(out.data_size(), work_per_thread);
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
+
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
+    MTL::Size grid_dims;
+    if (large) {
+      compute_encoder.set_bytes<int64_t>(out.data_size(), arg_idx++);
+      grid_dims = get_2d_grid_dims(out.shape(), out.strides(), work_per_thread);
+    } else {
+      compute_encoder.set_bytes<int>(out.data_size(), arg_idx++);
+      grid_dims = MTL::Size(nthreads, 1, 1);
+    }
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }

mlx/backend/metal/compiled.cpp

@@ -64,6 +64,7 @@ inline void build_kernel(
         cnt++);
   }
 
+  std::string idx_type = use_big_index ? "int64_t" : "uint";
   if (add_indices) {
     os += fmt::format(
         "    constant const int64_t* in_strides [[buffer({0})]],\n", cnt++);
@@ -83,6 +84,9 @@ inline void build_kernel(
         "    constant const int64_t* output_strides [[buffer({0})]],\n", cnt++);
     os += fmt::format(
         "    constant const int* output_shape [[buffer({0})]],\n", cnt++);
+  } else {
+    os += fmt::format(
+        "    constant const {0}& size [[buffer({1})]],\n", idx_type, cnt++);
   }
   if (dynamic_dims) {
     os += fmt::format("    constant const int& ndim [[buffer({0})]],\n", cnt++);
@@ -92,13 +96,14 @@ inline void build_kernel(
   os += "    uint3 pos [[thread_position_in_grid]],\n";
   os += "    uint3 grid [[threads_per_grid]]) {\n";
 
-  std::string idx_type = use_big_index ? "int64_t" : "uint";
+  os += fmt::format("  constexpr int N_ = {0};\n", work_per_thread);
   if (contiguous && use_big_index) {
     // This is only used for contiguous kernels which don't have
     // a third grid dimension
-    os += "  int64_t index = pos.x + grid.x * int64_t(pos.y);\n";
+    os += "  int64_t index = N_ * (pos.x + grid.x * int64_t(pos.y));\n";
+  } else if (contiguous) {
+    os += "  uint index = N_ * pos.x;\n";
   } else if (work_per_thread > 1) {
-    os += fmt::format("  constexpr int N_ = {0};\n", work_per_thread);
     os += fmt::format(
         "  int xshape = output_shape[{0}];\n",
         dynamic_dims ? "ndim - 1" : std::to_string(ndim - 1));
@@ -110,6 +115,9 @@ inline void build_kernel(
         "  {0} index = pos.x + grid.x * (pos.y + {0}(grid.y) * pos.z);\n",
         idx_type);
   }
+  if (work_per_thread > 1 && contiguous) {
+    os += "  for (int i = 0; i < N_ && index < size; ++i) {\n";
+  }
 
   // Read constant / contiguous inputs in tmps
   std::vector<array> nc_inputs;
@@ -193,7 +201,7 @@ inline void build_kernel(
   }
 
   // Open per-thread loop
-  if (work_per_thread > 1) {
+  if (work_per_thread > 1 && !contiguous) {
     os +=
         "  for (int i = 0; i < N_ && (int(N_ * pos.x) + i) < xshape; ++i) {\n";
   }
@@ -272,6 +280,7 @@ void Compiled::eval_gpu(
   auto& s = stream();
   auto& d = metal::device(s.device);
   auto lib = d.get_library(kernel_lib_, [&]() {
+    int work_per_thread = get_work_per_thread(outputs_[0].dtype());
     std::string kernel = metal::utils();
     concatenate(
         kernel, metal::unary_ops(), metal::binary_ops(), metal::ternary_ops());
@@ -284,7 +293,9 @@ void Compiled::eval_gpu(
         constant_ids_,
         /* contiguous = */ true,
         /* ndim = */ 0,
-        /* dynamic_dims = */ false);
+        /* dynamic_dims = */ false,
+        /* use_big_index = */ false,
+        /* work_per_thread = */ work_per_thread);
     build_kernel(
         kernel,
         kernel_lib_ + "_contiguous_large",
@@ -295,7 +306,8 @@ void Compiled::eval_gpu(
         /* contiguous = */ true,
         /* ndim = */ 0,
         /* dynamic_dims = */ false,
-        /* use_big_index = */ true);
+        /* use_big_index = */ true,
+        /* work_per_thread = */ work_per_thread);
     for (int i = 1; i < 8; i++) {
       build_kernel(
           kernel,
@@ -468,6 +480,13 @@ void Compiled::eval_gpu(
   if (!contiguous) {
     compute_encoder.set_vector_bytes(strides[0], cnt++);
     compute_encoder.set_vector_bytes(shape, cnt++);
+  } else {
+    auto size = outputs[0].data_size();
+    if (large) {
+      compute_encoder.set_bytes<int64_t>(size, cnt++);
+    } else {
+      compute_encoder.set_bytes<int>(size, cnt++);
+    }
   }
 
   // Put the number of dims in if it is dynamic
@@ -477,12 +496,13 @@ void Compiled::eval_gpu(
 
   // Launch the kernel
   if (contiguous) {
-    size_t nthreads = outputs[0].data_size();
+    int work_per_thread = get_work_per_thread(outputs[0].dtype());
+    size_t nthreads = ceildiv(outputs[0].data_size(), work_per_thread);
     MTL::Size group_dims(
         std::min(nthreads, kernel->maxTotalThreadsPerThreadgroup()), 1, 1);
-
     MTL::Size grid_dims = large
-        ? get_2d_grid_dims(outputs[0].shape(), outputs[0].strides())
+        ? get_2d_grid_dims(
+              outputs[0].shape(), outputs[0].strides(), work_per_thread)
         : MTL::Size(nthreads, 1, 1);
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {

mlx/backend/metal/conv.cpp

@@ -1,11 +1,10 @@
 // Copyright © 2023-2024 Apple Inc.
-
 #include <algorithm>
 #include <cassert>
 #include <numeric>
 #include <sstream>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/kernels/defines.h"
@@ -178,83 +177,6 @@ void explicit_gemm_conv_group_ND_gpu(
       /*copies = */ copies);
 }
 
-void conv_1D_gpu(
-    const Stream& s,
-    metal::Device& d,
-    const array& in,
-    const array& wt,
-    array out,
-    const std::vector<int>& padding,
-    const std::vector<int>& wt_strides,
-    const std::vector<int>& wt_dilation,
-    const std::vector<int>& in_dilation,
-    int groups,
-    bool flip) {
-  // Make conv params
-  MLXConvParams<1> conv_params{
-      /* const int  N = */ static_cast<int>(in.shape(0)),
-      /* const int  C = */ static_cast<int>(in.shape(2)),
-      /* const int  O = */ static_cast<int>(wt.shape(0)),
-      /* const int iS[NDIM] = */ {static_cast<int>(in.shape(1))},
-      /* const int wS[NDIM] = */ {static_cast<int>(wt.shape(1))},
-      /* const int oS[NDIM] = */ {static_cast<int>(out.shape(1))},
-      /* const int str[NDIM] = */ {wt_strides[0]},
-      /* const int pad[NDIM] = */ {padding[0]},
-      /* const int kdil[NDIM] = */ {wt_dilation[0]},
-      /* const int idil[NDIM] = */ {in_dilation[0]},
-      /* const size_t in_strides[NDIM + 2] = */
-      {in.strides()[0], in.strides()[1], in.strides()[2]},
-      /* const size_t wt_strides[NDIM + 2] = */
-      {wt.strides()[0], wt.strides()[1], wt.strides()[2]},
-      /* const size_t out_strides[NDIM + 2] = */
-      {out.strides()[0], out.strides()[1], out.strides()[2]},
-      /* const int groups = */ groups,
-      /* const bool flip = */ flip};
-
-  // Direct to explicit gemm conv
-  if (groups > 1) {
-    return explicit_gemm_conv_group_ND_gpu(s, d, in, wt, out, conv_params);
-  } else {
-    return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
-  }
-}
-
-void slow_conv_2D_gpu(
-    const Stream& s,
-    metal::Device& d,
-    const array& in,
-    const array& wt,
-    array out,
-    const MLXConvParams<2>& conv_params) {
-  int bm = 16, bn = 8;
-  int tm = 4, tn = 4;
-
-  std::ostringstream kname;
-  kname << "naive_conv_2d_" << type_to_name(out) << "_bm" << bm << "_bn" << bn
-        << "_tm" << tm << "_tn" << tn;
-
-  // Encode and dispatch kernel
-  auto& compute_encoder = d.get_command_encoder(s.index);
-  auto kernel = d.get_kernel(kname.str());
-  compute_encoder.set_compute_pipeline_state(kernel);
-
-  size_t n_pixels = conv_params.oS[0] * conv_params.oS[1];
-
-  size_t grid_dim_x = (n_pixels + (tm * bm) - 1) / (tm * bm);
-  size_t grid_dim_y = (conv_params.O + (tn * bn) - 1) / (tn * bn);
-  size_t grid_dim_z = conv_params.N;
-
-  MTL::Size group_dims = MTL::Size(bm, bn, 1);
-  MTL::Size grid_dims = MTL::Size(grid_dim_x, grid_dim_y, grid_dim_z);
-
-  compute_encoder.set_input_array(in, 0);
-  compute_encoder.set_input_array(wt, 1);
-  compute_encoder.set_output_array(out, 2);
-
-  compute_encoder.set_bytes(conv_params, 3);
-  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
-}
-
 void implicit_gemm_conv_2D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -771,6 +693,141 @@ void depthwise_conv_2D_gpu(
   compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 }
 
+void dispatch_conv_2D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const MLXConvParams<2>& conv_params,
+    std::vector<array>& copies) {
+  bool is_stride_one = conv_params.str[0] == 1 && conv_params.str[1] == 1;
+  bool is_kdil_one = conv_params.kdil[0] == 1 && conv_params.kdil[1] == 1;
+  bool is_idil_one = conv_params.idil[0] == 1 && conv_params.idil[1] == 1;
+
+  if (is_idil_one && conv_params.groups > 1) {
+    const int C_per_group = conv_params.C / conv_params.groups;
+    const int O_per_group = conv_params.O / conv_params.groups;
+
+    if (C_per_group == 1 && O_per_group == 1 && is_kdil_one &&
+        conv_params.wS[0] <= 7 && conv_params.wS[1] <= 7 &&
+        conv_params.str[0] <= 2 && conv_params.str[1] <= 2 &&
+        conv_params.oS[0] % 8 == 0 && conv_params.oS[1] % 8 == 0 &&
+        conv_params.wt_strides[1] == conv_params.wS[1] &&
+        conv_params.C % 16 == 0 && conv_params.C == conv_params.O) {
+      return depthwise_conv_2D_gpu(s, d, in, wt, out, conv_params);
+    }
+
+    if ((C_per_group <= 4 || C_per_group % 16 == 0) &&
+        (O_per_group <= 16 || O_per_group % 16 == 0)) {
+      return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
+    } else {
+      return explicit_gemm_conv_group_ND_gpu(s, d, in, wt, out, conv_params);
+    }
+  }
+
+  // Direct to winograd conv
+  bool inp_large =
+      (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
+  bool channels_large = (conv_params.C + conv_params.O) >= 256;
+  if (!conv_params.flip && is_stride_one && is_kdil_one && is_idil_one &&
+      conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
+      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
+      channels_large) {
+    return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
+  }
+
+  // Direct to implicit gemm conv
+  if (is_idil_one && (conv_params.C <= 4 || conv_params.C % 16 == 0) &&
+      (conv_params.O <= 16 || conv_params.O % 16 == 0)) {
+    return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
+  }
+
+  else if (conv_params.C % 16 == 0 && conv_params.O % 16 == 0) {
+    return implicit_gemm_conv_2D_general_gpu(s, d, in, wt, out, conv_params);
+  }
+
+  // Direct to explicit gemm conv
+  else {
+    return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
+  }
+}
+
+void conv_1D_gpu(
+    const Stream& s,
+    metal::Device& d,
+    const array& in,
+    const array& wt,
+    array out,
+    const std::vector<int>& padding,
+    const std::vector<int>& wt_strides,
+    const std::vector<int>& wt_dilation,
+    const std::vector<int>& in_dilation,
+    int groups,
+    bool flip,
+    std::vector<array>& copies) {
+  bool is_idil_one = in_dilation[0] == 1;
+  int C = in.shape(2);
+  int O = wt.shape(0);
+  const int C_per_group = in.shape(2) / groups;
+  const int O_per_group = wt.shape(0) / groups;
+
+  // Direct to implicit gemm conv
+  if (is_idil_one && (C_per_group <= 4 || C_per_group % 16 == 0) &&
+      (O_per_group <= 16 || O_per_group % 16 == 0)) {
+    MLXConvParams<2> conv_params{
+        /* const int  N = */ static_cast<int>(in.shape(0)),
+        /* const int  C = */ C,
+        /* const int  O = */ O,
+        /* const int iS[NDIM] = */ {static_cast<int>(in.shape(1)), 1},
+        /* const int wS[NDIM] = */ {static_cast<int>(wt.shape(1)), 1},
+        /* const int oS[NDIM] = */ {static_cast<int>(out.shape(1)), 1},
+        /* const int str[NDIM] = */ {wt_strides[0], 1},
+        /* const int pad[NDIM] = */ {padding[0], 0},
+        /* const int kdil[NDIM] = */ {wt_dilation[0], 1},
+        /* const int idil[NDIM] = */ {in_dilation[0], 1},
+        /* const size_t in_strides[NDIM + 2] = */
+        {in.strides()[0], in.strides()[1], 0, in.strides()[2]},
+        /* const size_t wt_strides[NDIM + 2] = */
+        {wt.strides()[0], wt.strides()[1], 0, wt.strides()[2]},
+        /* const size_t out_strides[NDIM + 2] = */
+        {out.strides()[0], out.strides()[1], 0, out.strides()[2]},
+        /* const int groups = */ groups,
+        /* const bool flip = */ flip};
+
+    dispatch_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
+    return;
+  }
+
+  // Make conv params
+  MLXConvParams<1> conv_params{
+      /* const int  N = */ static_cast<int>(in.shape(0)),
+      /* const int  C = */ static_cast<int>(in.shape(2)),
+      /* const int  O = */ static_cast<int>(wt.shape(0)),
+      /* const int iS[NDIM] = */ {static_cast<int>(in.shape(1))},
+      /* const int wS[NDIM] = */ {static_cast<int>(wt.shape(1))},
+      /* const int oS[NDIM] = */ {static_cast<int>(out.shape(1))},
+      /* const int str[NDIM] = */ {wt_strides[0]},
+      /* const int pad[NDIM] = */ {padding[0]},
+      /* const int kdil[NDIM] = */ {wt_dilation[0]},
+      /* const int idil[NDIM] = */ {in_dilation[0]},
+      /* const size_t in_strides[NDIM + 2] = */
+      {in.strides()[0], in.strides()[1], in.strides()[2]},
+      /* const size_t wt_strides[NDIM + 2] = */
+      {wt.strides()[0], wt.strides()[1], wt.strides()[2]},
+      /* const size_t out_strides[NDIM + 2] = */
+      {out.strides()[0], out.strides()[1], out.strides()[2]},
+      /* const int groups = */ groups,
+      /* const bool flip = */ flip};
+
+  // Direct to explicit gemm conv
+  if (groups > 1) {
+    return explicit_gemm_conv_group_ND_gpu(s, d, in, wt, out, conv_params);
+  } else {
+    return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
+  }
+}
+
 void conv_2D_gpu(
     const Stream& s,
     metal::Device& d,
@@ -808,57 +865,7 @@ void conv_2D_gpu(
       /* const int groups = */ groups,
       /* const bool flip = */ flip,
   };
-
-  bool is_stride_one = conv_params.str[0] == 1 && conv_params.str[1] == 1;
-  bool is_kdil_one = conv_params.kdil[0] == 1 && conv_params.kdil[1] == 1;
-  bool is_idil_one = conv_params.idil[0] == 1 && conv_params.idil[1] == 1;
-
-  if (is_idil_one && groups > 1) {
-    const int C_per_group = conv_params.C / groups;
-    const int O_per_group = conv_params.O / groups;
-
-    if (C_per_group == 1 && O_per_group == 1 && is_kdil_one &&
-        conv_params.wS[0] <= 7 && conv_params.wS[1] <= 7 &&
-        conv_params.str[0] <= 2 && conv_params.str[1] <= 2 &&
-        conv_params.oS[0] % 8 == 0 && conv_params.oS[1] % 8 == 0 &&
-        conv_params.wt_strides[1] == conv_params.wS[1] &&
-        conv_params.C % 16 == 0 && conv_params.C == conv_params.O) {
-      return depthwise_conv_2D_gpu(s, d, in, wt, out, conv_params);
-    }
-
-    if ((C_per_group <= 4 || C_per_group % 16 == 0) &&
-        (O_per_group <= 16 || O_per_group % 16 == 0)) {
-      return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
-    } else {
-      return explicit_gemm_conv_group_ND_gpu(s, d, in, wt, out, conv_params);
-    }
-  }
-
-  // Direct to winograd conv
-  bool inp_large =
-      (conv_params.N * conv_params.iS[0] * conv_params.iS[1]) >= 1ul << 12;
-  bool channels_large = (conv_params.C + conv_params.O) >= 256;
-  if (!flip && is_stride_one && is_kdil_one && is_idil_one &&
-      conv_params.wS[0] == 3 && conv_params.wS[1] == 3 &&
-      conv_params.C % 32 == 0 && conv_params.O % 32 == 0 && inp_large &&
-      channels_large) {
-    return winograd_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
-  }
-
-  // Direct to implicit gemm conv
-  if (is_idil_one && (conv_params.C <= 4 || conv_params.C % 16 == 0) &&
-      (conv_params.O <= 16 || conv_params.O % 16 == 0)) {
-    return implicit_gemm_conv_2D_gpu(s, d, in, wt, out, conv_params);
-  }
-
-  else if (conv_params.C % 16 == 0 && conv_params.O % 16 == 0) {
-    return implicit_gemm_conv_2D_general_gpu(s, d, in, wt, out, conv_params);
-  }
-
-  // Direct to explicit gemm conv
-  else {
-    return explicit_gemm_conv_ND_gpu(s, d, in, wt, out, conv_params);
-  }
+  dispatch_conv_2D_gpu(s, d, in, wt, out, conv_params, copies);
 }
 
 void conv_3D_gpu(
@@ -952,7 +959,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -967,7 +974,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -983,12 +990,13 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
         in,
         wt,
         out,
-        padding_,
+        padding_lo_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
         groups_,
-        flip_);
+        flip_,
+        copies);
   }
   // Throw error
   else {

mlx/backend/metal/copy.cpp

@@ -1,35 +1,15 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <sstream>
-
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"
-#include "mlx/primitives.h"
 
 namespace mlx::core {
 
 constexpr int MAX_COPY_SPECIALIZED_DIMS = 3;
 
-void copy_gpu(const array& in, array& out, CopyType ctype, const Stream& s) {
-  bool donated = set_copy_output_data(in, out, ctype);
-  if (donated && in.dtype() == out.dtype()) {
-    // If the output has the same type as the input then there is nothing to
-    // copy, just use the buffer.
-    return;
-  }
-  if (ctype == CopyType::GeneralGeneral) {
-    ctype = CopyType::General;
-  }
-  copy_gpu_inplace(in, out, ctype, s);
-}
-
-void copy_gpu(const array& in, array& out, CopyType ctype) {
-  copy_gpu(in, out, ctype, out.primitive().stream());
-}
-
 void copy_gpu_inplace(
     const array& in,
     array& out,
@@ -104,6 +84,8 @@ void copy_gpu_inplace(
             "[Copy::eval_gpu] Dynamic output offset requires GeneralGeneral copy");
       }
     }
+  } else {
+    work_per_thread = get_work_per_thread(in.dtype());
   }
   concatenate(kernel_name, "_copy", type_to_name(in), type_to_name(out));
   auto kernel = dynamic ? get_dynamic_copy_kernel(d, kernel_name, in, out)
@@ -165,39 +147,23 @@ void copy_gpu_inplace(
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
-    size_t nthreads = out.data_size();
+    size_t nthreads = ceildiv(out.data_size(), work_per_thread);
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
+    MTL::Size grid_dims;
+    if (large) {
+      compute_encoder.set_bytes<int64_t>(out.data_size(), 2);
+      grid_dims = get_2d_grid_dims(out.shape(), out.strides(), work_per_thread);
+    } else {
+      compute_encoder.set_bytes<int>(out.data_size(), 2);
+      grid_dims = MTL::Size(nthreads, 1, 1);
+    }
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }
 
-void copy_gpu_inplace(
-    const array& in,
-    array& out,
-    CopyType ctype,
-    const Stream& s) {
-  assert(in.shape() == out.shape());
-  return copy_gpu_inplace(
-      in, out, in.shape(), in.strides(), out.strides(), 0, 0, ctype, s);
-}
-
-void copy_gpu_inplace(
-    const array& in,
-    array& out,
-    const Strides& i_strides,
-    int64_t i_offset,
-    CopyType ctype,
-    const Stream& s) {
-  assert(in.shape() == out.shape());
-  return copy_gpu_inplace(
-      in, out, in.shape(), i_strides, out.strides(), i_offset, 0, ctype, s);
-}
-
 void fill_gpu(const array& val, array& out, const Stream& s) {
   if (out.size() == 0) {
     return;
@@ -214,14 +180,21 @@ void fill_gpu(const array& val, array& out, const Stream& s) {
   compute_encoder.set_input_array(val, 0);
   compute_encoder.set_output_array(out, 1);
 
+  int work_per_thread = get_work_per_thread(val.dtype());
   auto thread_group_size = kernel->maxTotalThreadsPerThreadgroup();
-  size_t nthreads = out.data_size();
+  size_t nthreads = ceildiv(out.data_size(), work_per_thread);
   if (thread_group_size > nthreads) {
     thread_group_size = nthreads;
   }
   MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-  MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                              : MTL::Size(nthreads, 1, 1);
+  MTL::Size grid_dims;
+  if (large) {
+    compute_encoder.set_bytes<int64_t>(out.data_size(), 2);
+    grid_dims = get_2d_grid_dims(out.shape(), out.strides(), work_per_thread);
+  } else {
+    compute_encoder.set_bytes<int>(out.data_size(), 2);
+    grid_dims = MTL::Size(nthreads, 1, 1);
+  }
   compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 

mlx/backend/metal/custom_kernel.cpp

@@ -1,6 +1,6 @@
 // Copyright © 2024 Apple Inc.
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/jit/includes.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/fast_primitives.h"

mlx/backend/metal/device.h

@@ -95,6 +95,10 @@ struct CommandEncoder {
     return enc_->setBytes(&v, sizeof(T), idx);
   }
 
+  void set_threadgroup_memory_length(size_t length, int idx) {
+    enc_->setThreadgroupMemoryLength(length, idx);
+  }
+
   ConcurrentContext start_concurrent() {
     return ConcurrentContext(*this);
   }

mlx/backend/metal/distributed.cpp

@@ -4,7 +4,7 @@
 
 #include "mlx/allocator.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/distributed/ops.h"

mlx/backend/metal/fence.cpp

@@ -141,7 +141,7 @@ void Fence::update(Stream stream, const array& x) {
   compute_encoder.set_compute_pipeline_state(kernel);
   compute_encoder.set_input_array(x, 0);
   compute_encoder.set_bytes(nthreads, 1);
-  compute_encoder.dispatch_threadgroups(group_dims, grid_dims);
+  compute_encoder.dispatch_threadgroups(grid_dims, group_dims);
 
   // Barrier on previous kernels
   compute_encoder.barrier();

mlx/backend/metal/fft.cpp

@@ -7,10 +7,10 @@
 
 #include "mlx/3rdparty/pocketfft.h"
 #include "mlx/backend/common/utils.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/backend/gpu/slicing.h"
 #include "mlx/backend/metal/binary.h"
-#include "mlx/backend/metal/copy.h"
 #include "mlx/backend/metal/kernels.h"
-#include "mlx/backend/metal/slicing.h"
 #include "mlx/backend/metal/unary.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/utils.h"
@@ -632,7 +632,7 @@ void fft_op(
   func_consts.push_back(make_int(&rader_m, 3));
 
   // The overall number of FFTs we're going to compute for this input
-  int size = out.dtype() == float32 ? out.size() : in.size();
+  size_t size = out.dtype() == float32 ? out.size() : in.size();
   if (real && inverse && four_step_params.required) {
     size = out.size();
   }
@@ -659,8 +659,6 @@ void fft_op(
     // We can perform 2 RFFTs at once so the batch size is halved.
     batch_size = (batch_size + 2 - 1) / 2;
   }
-  int out_buffer_size = out.size();
-
   auto& compute_encoder = d.get_command_encoder(s.index);
   auto in_type_str = in.dtype() == float32 ? "float" : "float2";
   auto out_type_str = out.dtype() == float32 ? "float" : "float2";

mlx/backend/metal/hadamard.cpp

@@ -1,11 +1,9 @@
 // Copyright © 2024 Apple Inc.
 
-#include <map>
-
-#include "mlx/backend/common/compiled.h"
 #include "mlx/backend/common/hadamard.h"
+#include "mlx/backend/common/compiled.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/jit/includes.h"
 #include "mlx/backend/metal/kernels.h"
@@ -15,7 +13,6 @@
 namespace mlx::core {
 
 constexpr int MAX_HADAMARD_THREADS_PER_GROUP = 256;
-constexpr int MAX_HADAMARD_BYTES = 32768; // 32KB
 
 std::string gen_hadamard_codelet(int m) {
   // Generate a O(m^2) hadamard codelet for a given M
@@ -60,121 +57,142 @@ std::string gen_hadamard_codelet(int m) {
   return source.str();
 }
 
-void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
-  auto& s = stream();
+void hadamard_mn_contiguous(
+    const array& x,
+    array& y,
+    int m,
+    int n1,
+    int n2,
+    float scale,
+    metal::Device& d,
+    const Stream& s) {
+  int n = n1 * n2;
+  int read_width_n1 = n1 == 2 ? 2 : 4;
+  int read_width_n2 = n2 == 2 ? 2 : 4;
+  int read_width_m = (n == 2 || m == 28) ? 2 : 4;
+  int max_radix_1 = std::min(n1, 16);
+  int max_radix_2 = std::min(n2, 16);
+  float scale_n1 = 1.0;
+  float scale_n2 = (m == 1) ? scale : 1.0;
+  float scale_m = scale;
 
-  auto& in = inputs[0];
+  // n2 is a row contiguous power of 2 hadamard transform
+  MTL::Size group_dims_n2(n2 / max_radix_2, 1, 1);
+  MTL::Size grid_dims_n2(n2 / max_radix_2, x.size() / n2, 1);
 
-  std::vector<array> copies;
-  // Only support the last axis for now
-  int axis = in.ndim() - 1;
-  auto check_input = [&copies, &s](const array& x) {
-    // TODO(alexbarron) pass strides to kernel to relax this constraint
-    bool no_copy = x.flags().row_contiguous;
-    if (no_copy) {
-      return x;
-    } else {
-      copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
-      copy_gpu(x, copies.back(), CopyType::General, s);
-      return copies.back();
+  // n1 is a strided power of 2 hadamard transform with stride n2
+  MTL::Size group_dims_n1(n1 / max_radix_1, 1, 1);
+  MTL::Size grid_dims_n1(n1 / max_radix_1, x.size() / n, n2);
+
+  // m is a strided hadamard transform with stride n = n1 * n2
+  MTL::Size group_dims_m(
+      std::min(n / read_width_m, MAX_HADAMARD_THREADS_PER_GROUP), 1, 1);
+  MTL::Size grid_dims_m(
+      group_dims_m.width, x.size() / m / read_width_m / group_dims_m.width, 1);
+
+  // Make the kernel
+  std::string kname;
+  kname.reserve(32);
+  concatenate(kname, "hadamard_", n * m, "_", type_to_name(x));
+  auto lib = d.get_library(kname, [&]() {
+    std::string kernel;
+    concatenate(
+        kernel,
+        metal::utils(),
+        gen_hadamard_codelet(m),
+        metal::hadamard(),
+        get_template_definition(
+            "n2" + kname,
+            "hadamard_n",
+            get_type_string(x.dtype()),
+            n2,
+            max_radix_2,
+            read_width_n2));
+    if (n1 > 1) {
+      kernel += get_template_definition(
+          "n1" + kname,
+          "hadamard_n",
+          get_type_string(x.dtype()),
+          n1,
+          max_radix_1,
+          read_width_n1,
+          n2);
     }
-  };
-  const array& in_contiguous = check_input(in);
-
-  if (in_contiguous.is_donatable()) {
-    out.copy_shared_buffer(in_contiguous);
-  } else {
-    out.set_data(allocator::malloc(out.nbytes()));
-  }
-
-  int n, m;
-  std::tie(n, m) = decompose_hadamard(in.shape(axis));
-
-  if (n * (int)size_of(in.dtype()) > MAX_HADAMARD_BYTES) {
-    throw std::invalid_argument(
-        "[hadamard] For n = m*2^k, 2^k > 8192 for FP32 or 2^k > 16384 for FP16/BF16 NYI");
-  }
-
-  int max_radix = std::min(n, 16);
-  // Use read_width 2 for m = 28 to avoid register spilling
-  int read_width = (n == 2 || m == 28) ? 2 : 4;
-
-  std::ostringstream kname;
-  kname << "hadamard_" << n * m << "_" << type_to_name(out);
-  auto kernel_name = kname.str();
-  auto& d = metal::device(s.device);
-  const auto& lib_name = kernel_name;
-  auto lib = d.get_library(lib_name, [&]() {
-    std::ostringstream kernel_source;
-    auto codelet = gen_hadamard_codelet(m);
-    kernel_source << metal::utils() << codelet << metal::hadamard();
-    kernel_source << get_template_definition(
-        "n" + kernel_name,
-        "hadamard_n",
-        get_type_string(in.dtype()),
-        n,
-        max_radix,
-        read_width);
-    kernel_source << get_template_definition(
-        "m" + kernel_name,
-        "hadamard_m",
-        get_type_string(in.dtype()),
-        n,
-        m,
-        read_width);
-    return kernel_source.str();
+    if (m > 1) {
+      kernel += get_template_definition(
+          "m" + kname,
+          "hadamard_m",
+          get_type_string(x.dtype()),
+          n,
+          m,
+          read_width_m);
+    }
+    return kernel;
   });
 
-  int batch_size = in.size() / n;
-  int threads_per = n / max_radix;
-
-  auto& compute_encoder = d.get_command_encoder(s.index);
-
-  auto launch_hadamard = [&](const array& in,
-                             array& out,
-                             const std::string& kernel_name,
-                             float scale) {
-    auto kernel = d.get_kernel(kernel_name, lib);
-    assert(threads_per <= kernel->maxTotalThreadsPerThreadgroup());
-
+  // Launch the strided transform for n1
+  if (n1 > 1) {
+    auto& compute_encoder = d.get_command_encoder(s.index);
+    auto kernel = d.get_kernel("n1" + kname, lib);
     compute_encoder.set_compute_pipeline_state(kernel);
-    compute_encoder.set_input_array(in, 0);
-    compute_encoder.set_output_array(out, 1);
-    compute_encoder.set_bytes(scale, 2);
-
-    MTL::Size group_dims = MTL::Size(1, threads_per, 1);
-    MTL::Size grid_dims = MTL::Size(batch_size, threads_per, 1);
-    compute_encoder.dispatch_threads(grid_dims, group_dims);
-  };
-
-  if (m > 1) {
-    // When m is greater than 1, we decompose the
-    // computation into two uploads to the GPU:
-    //
-    // e.g. len(x) = 12*4 = 48, m = 12, n = 4
-    //
-    // y = h48 @ x
-    //
-    // Upload 1:
-    // tmp = a.reshape(12, 4) @ h4
-    //
-    // Upload 2:
-    // y = h12 @ tmp
-    array temp(in.shape(), in.dtype(), nullptr, {});
-    temp.set_data(allocator::malloc(temp.nbytes()));
-    copies.push_back(temp);
-
-    launch_hadamard(in_contiguous, temp, "n" + kernel_name, 1.0);
-
-    // Metal sometimes reports 256 max threads per group for hadamard_m kernel
-    threads_per = std::min(n / read_width, MAX_HADAMARD_THREADS_PER_GROUP);
-    batch_size = in.size() / m / read_width / threads_per;
-    launch_hadamard(temp, out, "m" + kernel_name, scale_);
-  } else {
-    launch_hadamard(in_contiguous, out, "n" + kernel_name, scale_);
+    compute_encoder.set_input_array(x, 0);
+    compute_encoder.set_output_array(y, 1);
+    compute_encoder.set_bytes(scale_n1, 2);
+    compute_encoder.dispatch_threads(grid_dims_n1, group_dims_n1);
   }
 
-  d.add_temporaries(std::move(copies), s.index);
+  // Launch the transform for n2
+  auto& compute_encoder = d.get_command_encoder(s.index);
+  auto kernel = d.get_kernel("n2" + kname, lib);
+  compute_encoder.set_compute_pipeline_state(kernel);
+  compute_encoder.set_input_array(n1 > 1 ? y : x, 0);
+  compute_encoder.set_output_array(y, 1);
+  compute_encoder.set_bytes(scale_n2, 2);
+  compute_encoder.dispatch_threads(grid_dims_n2, group_dims_n2);
+
+  // Launch the strided transform for m
+  if (m > 1) {
+    auto kernel = d.get_kernel("m" + kname, lib);
+    compute_encoder.set_compute_pipeline_state(kernel);
+    compute_encoder.set_input_array(y, 0);
+    compute_encoder.set_output_array(y, 1);
+    compute_encoder.set_bytes(scale_m, 2);
+    compute_encoder.dispatch_threads(grid_dims_m, group_dims_m);
+  }
+}
+
+void Hadamard::eval_gpu(const std::vector<array>& inputs, array& out) {
+  auto& s = stream();
+  auto& d = metal::device(s.device);
+  auto& in = inputs[0];
+
+  // Split the hadamard transform so that all of them work on vectors smaller
+  // than 8192 elements.
+  //
+  // We decompose it in the following way:
+  //
+  // n = m * n1 * n2 = m * 2^k1 * 2^k2
+  //
+  // where m is in (1, 12, 20, 28) and n1 and n2 <= 8192
+  auto [n, m] = decompose_hadamard(in.shape().back());
+  int n1 = 1, n2 = n;
+  if (n > 8192) {
+    for (n2 = 2; n2 * n2 < n; n2 *= 2) {
+    }
+    n1 = n / n2;
+  }
+
+  if (in.flags().row_contiguous) {
+    if (in.is_donatable()) {
+      out.copy_shared_buffer(in);
+    } else {
+      out.set_data(allocator::malloc(out.nbytes()));
+    }
+    hadamard_mn_contiguous(in, out, m, n1, n2, scale_, d, s);
+  } else {
+    copy_gpu(in, out, CopyType::General, s);
+    hadamard_mn_contiguous(out, out, m, n1, n2, scale_, d, s);
+  }
 }
 
 } // namespace mlx::core

mlx/backend/metal/indexing.cpp

@@ -2,7 +2,8 @@
 #include <fmt/format.h>
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/jit/includes.h"
 #include "mlx/backend/metal/jit/indexing.h"
@@ -458,17 +459,9 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 2);
 
   // Set source info
-  auto shape = idx.shape();
-  shape.erase(shape.begin() + axis_);
-  compute_encoder.set_vector_bytes(shape, 3);
-
-  auto strides = src.strides();
-  strides.erase(strides.begin() + axis_);
-  compute_encoder.set_vector_bytes(strides, 4);
-
-  strides = idx.strides();
-  strides.erase(strides.begin() + axis_);
-  compute_encoder.set_vector_bytes(strides, 5);
+  compute_encoder.set_vector_bytes(remove_index(idx.shape(), axis_), 3);
+  compute_encoder.set_vector_bytes(remove_index(src.strides(), axis_), 4);
+  compute_encoder.set_vector_bytes(remove_index(idx.strides(), axis_), 5);
   compute_encoder.set_bytes(ndim - 1, 6);
   compute_encoder.set_bytes(axis_, 7);
   compute_encoder.set_bytes(src.shape(axis_), 8);
@@ -582,17 +575,9 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.set_output_array(out, 2);
 
   // Set source info
-  auto shape = idx.shape();
-  shape.erase(shape.begin() + axis_);
-  compute_encoder.set_vector_bytes(shape, 3);
-
-  auto strides = upd.strides();
-  strides.erase(strides.begin() + axis_);
-  compute_encoder.set_vector_bytes(strides, 4);
-
-  strides = idx.strides();
-  strides.erase(strides.begin() + axis_);
-  compute_encoder.set_vector_bytes(strides, 5);
+  compute_encoder.set_vector_bytes(remove_index(idx.shape(), axis_), 3);
+  compute_encoder.set_vector_bytes(remove_index(upd.strides(), axis_), 4);
+  compute_encoder.set_vector_bytes(remove_index(idx.strides(), axis_), 5);
   compute_encoder.set_bytes(ndim - 1, 6);
   compute_encoder.set_bytes(axis_, 7);
   compute_encoder.set_bytes(out.shape(axis_), 8);

mlx/backend/metal/kernels/arg_reduce.metal

@@ -80,9 +80,10 @@ template <typename T, typename Op, int N_READS = 4>
     const constant size_t& ndim [[buffer(5)]],
     const constant int64_t& axis_stride [[buffer(6)]],
     const constant size_t& axis_size [[buffer(7)]],
-    uint gid [[thread_position_in_grid]],
-    uint lid [[thread_position_in_threadgroup]],
-    uint lsize [[threads_per_threadgroup]],
+    uint3 gid [[thread_position_in_grid]],
+    uint3 gsize [[threads_per_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint3 lsize [[threads_per_threadgroup]],
     uint simd_size [[threads_per_simdgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
@@ -104,17 +105,18 @@ template <typename T, typename Op, int N_READS = 4>
 
   // Compute the input/output index. There is one beginning and one output for
   // the whole threadgroup.
-  auto in_idx = elem_to_loc(gid / lsize, shape, in_strides, ndim);
-  auto out_idx = elem_to_loc(gid / lsize, shape, out_strides, ndim);
+  int64_t row_idx = gid.y + static_cast<int64_t>(gsize.y) * gid.z;
+  auto in_idx = elem_to_loc(row_idx, shape, in_strides, ndim);
+  auto out_idx = elem_to_loc(row_idx, shape, out_strides, ndim);
 
   IndexValPair<T> best{0, Op::init};
 
   threadgroup IndexValPair<T> local_data[32];
 
   // Loop over the reduction axis in lsize*N_READS buckets
-  for (uint r = 0; r < ceildiv(axis_size, N_READS * lsize); r++) {
+  for (uint r = 0; r < ceildiv(axis_size, N_READS * lsize.x); r++) {
     // Read the current value
-    uint32_t current_index = r * lsize * N_READS + lid * N_READS;
+    uint32_t current_index = r * lsize.x * N_READS + lid.x * N_READS;
     uint32_t offset = current_index;
     const device T* current_in = in + in_idx + current_index * axis_stride;
     T vals[N_READS];
@@ -144,7 +146,7 @@ template <typename T, typename Op, int N_READS = 4>
   }
 
   // Read the appropriate value from local data and perform one simd reduction
-  uint simd_groups = ceildiv(lsize, simd_size);
+  uint simd_groups = ceildiv(lsize.x, simd_size);
   if (simd_lane_id < simd_groups) {
     best = local_data[simd_lane_id];
   }
@@ -154,7 +156,7 @@ template <typename T, typename Op, int N_READS = 4>
   }
 
   // Finally write the output
-  if (lid == 0) {
+  if (lid.x == 0) {
     out[out_idx] = best.index;
   }
 }

mlx/backend/metal/kernels/binary.h

@@ -9,64 +9,85 @@ template <typename T, typename U, typename Op>
   c[index] = Op()(a[0], b[0]);
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_sv(
     device const T* a,
     device const T* b,
     device U* c,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  c[index] = Op()(a[0], b[index]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    c[index + i] = Op()(a[0], b[index + i]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vs(
     device const T* a,
     device const T* b,
     device U* c,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  c[index] = Op()(a[index], b[0]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    c[index + i] = Op()(a[index + i], b[0]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vv(
     device const T* a,
     device const T* b,
     device U* c,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  c[index] = Op()(a[index], b[index]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    c[index + i] = Op()(a[index + i], b[index + i]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_sv2(
     device const T* a,
     device const T* b,
     device U* c,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  int64_t offset = index.x + grid_dim.x * int64_t(index.y);
-  c[offset] = Op()(a[0], b[offset]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    c[offset + i] = Op()(a[0], b[offset + i]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vs2(
     device const T* a,
     device const T* b,
     device U* c,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  int64_t offset = index.x + grid_dim.x * int64_t(index.y);
-  c[offset] = Op()(a[offset], b[0]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    c[offset + i] = Op()(a[offset + i], b[0]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vv2(
     device const T* a,
     device const T* b,
     device U* c,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  int64_t offset = index.x + grid_dim.x * int64_t(index.y);
-  c[offset] = Op()(a[offset], b[offset]);
+  int64_t offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    c[offset + i] = Op()(a[offset + i], b[offset + i]);
+  }
 }
 
 template <typename T, typename U, typename Op, typename IdxT = int64_t>

mlx/backend/metal/kernels/binary_two.h

@@ -12,82 +12,103 @@ template <typename T, typename U, typename Op>
   d[index] = out[1];
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_sv(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  auto out = Op()(a[0], b[index]);
-  c[index] = out[0];
-  d[index] = out[1];
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    auto out = Op()(a[0], b[index + i]);
+    c[index + i] = out[0];
+    d[index + i] = out[1];
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vs(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  auto out = Op()(a[index], b[0]);
-  c[index] = out[0];
-  d[index] = out[1];
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    auto out = Op()(a[index + i], b[0]);
+    c[index + i] = out[0];
+    d[index + i] = out[1];
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vv(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  auto out = Op()(a[index], b[index]);
-  c[index] = out[0];
-  d[index] = out[1];
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    auto out = Op()(a[index + i], b[index + i]);
+    c[index + i] = out[0];
+    d[index + i] = out[1];
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_sv2(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  auto out = Op()(a[0], b[offset]);
-  c[offset] = out[0];
-  d[offset] = out[1];
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    auto out = Op()(a[0], b[offset + i]);
+    c[offset + i] = out[0];
+    d[offset + i] = out[1];
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vs2(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  auto out = Op()(a[offset], b[0]);
-  c[offset] = out[0];
-  d[offset] = out[1];
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    auto out = Op()(a[offset + i], b[0]);
+    c[offset + i] = out[0];
+    d[offset + i] = out[1];
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void binary_vv2(
     device const T* a,
     device const T* b,
     device U* c,
     device U* d,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  auto out = Op()(a[offset], b[offset]);
-  c[offset] = out[0];
-  d[offset] = out[1];
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    auto out = Op()(a[offset + i], b[offset + i]);
+    c[offset + i] = out[0];
+    d[offset + i] = out[1];
+  }
 }
 
 template <typename T, typename U, typename Op, typename IdxT = int64_t>

mlx/backend/metal/kernels/copy.h

@@ -1,39 +1,53 @@
 // Copyright © 2024 Apple Inc.
 
-template <typename T, typename U>
+template <typename T, typename U, int N = WorkPerThread<T>::n>
 [[kernel]] void copy_s(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  dst[index] = static_cast<U>(src[0]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    dst[index + i] = static_cast<U>(src[0]);
+  }
 }
 
-template <typename T, typename U>
+template <typename T, typename U, int N = WorkPerThread<T>::n>
 [[kernel]] void copy_v(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  dst[index] = static_cast<U>(src[index]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    dst[index + i] = static_cast<U>(src[index + i]);
+  }
 }
 
-template <typename T, typename U>
+template <typename T, typename U, int N = WorkPerThread<T>::n>
 [[kernel]] void copy_s2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  dst[offset] = static_cast<U>(src[0]);
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    dst[offset + i] = static_cast<U>(src[0]);
+  }
 }
 
-template <typename T, typename U>
+template <typename T, typename U, int N = WorkPerThread<T>::n>
 [[kernel]] void copy_v2(
     device const T* src [[buffer(0)]],
     device U* dst [[buffer(1)]],
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  dst[offset] = static_cast<U>(src[offset]);
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    dst[offset + i] = static_cast<U>(src[offset + i]);
+  }
 }
 
 template <typename T, typename U, typename IdxT = int64_t>

mlx/backend/metal/kernels/fft/readwrite.h

@@ -98,7 +98,7 @@ struct ReadWriter {
   }
 
   METAL_FUNC void load() const {
-    int batch_idx = elem.x * grid.y * n;
+    size_t batch_idx = size_t(elem.x * grid.y) * n;
     short tg_idx = elem.y * grid.z + elem.z;
     short max_index = grid.y * n - 2;
 
@@ -121,7 +121,7 @@ struct ReadWriter {
   }
 
   METAL_FUNC void write() const {
-    int batch_idx = elem.x * grid.y * n;
+    size_t batch_idx = size_t(elem.x * grid.y) * n;
     short tg_idx = elem.y * grid.z + elem.z;
     short max_index = grid.y * n - 2;
 
@@ -144,7 +144,7 @@ struct ReadWriter {
 
   // Padded IO for Bluestein's algorithm
   METAL_FUNC void load_padded(int length, const device float2* w_k) const {
-    int batch_idx = elem.x * grid.y * length + elem.y * length;
+    size_t batch_idx = size_t(elem.x * grid.y) * length + elem.y * length;
     int fft_idx = elem.z;
     int m = grid.z;
 
@@ -161,7 +161,7 @@ struct ReadWriter {
   }
 
   METAL_FUNC void write_padded(int length, const device float2* w_k) const {
-    int batch_idx = elem.x * grid.y * length + elem.y * length;
+    size_t batch_idx = size_t(elem.x * grid.y) * length + elem.y * length;
     int fft_idx = elem.z;
     int m = grid.z;
     float2 inv_factor = {1.0f / n, -1.0f / n};
@@ -261,7 +261,7 @@ METAL_FUNC bool ReadWriter<float, float2>::out_of_bounds() const {
 
 template <>
 METAL_FUNC void ReadWriter<float, float2>::load() const {
-  int batch_idx = elem.x * grid.y * n * 2 + elem.y * n * 2;
+  size_t batch_idx = size_t(elem.x * grid.y) * n * 2 + elem.y * n * 2;
   threadgroup float2* seq_buf = buf + elem.y * n;
 
   // No out of bounds accesses on odd batch sizes
@@ -283,7 +283,8 @@ template <>
 METAL_FUNC void ReadWriter<float, float2>::write() const {
   short n_over_2 = (n / 2) + 1;
 
-  int batch_idx = elem.x * grid.y * n_over_2 * 2 + elem.y * n_over_2 * 2;
+  size_t batch_idx =
+      size_t(elem.x * grid.y) * n_over_2 * 2 + elem.y * n_over_2 * 2;
   threadgroup float2* seq_buf = buf + elem.y * n;
 
   int grid_index = elem.x * grid.y + elem.y;
@@ -317,7 +318,7 @@ template <>
 METAL_FUNC void ReadWriter<float, float2>::load_padded(
     int length,
     const device float2* w_k) const {
-  int batch_idx = elem.x * grid.y * length * 2 + elem.y * length * 2;
+  size_t batch_idx = size_t(elem.x * grid.y) * length * 2 + elem.y * length * 2;
   threadgroup float2* seq_buf = buf + elem.y * n;
 
   // No out of bounds accesses on odd batch sizes
@@ -345,8 +346,8 @@ METAL_FUNC void ReadWriter<float, float2>::write_padded(
     int length,
     const device float2* w_k) const {
   int length_over_2 = (length / 2) + 1;
-  int batch_idx =
-      elem.x * grid.y * length_over_2 * 2 + elem.y * length_over_2 * 2;
+  size_t batch_idx =
+      size_t(elem.x * grid.y) * length_over_2 * 2 + elem.y * length_over_2 * 2;
   threadgroup float2* seq_buf = buf + elem.y * n + length - 1;
 
   int grid_index = elem.x * grid.y + elem.y;
@@ -397,7 +398,8 @@ METAL_FUNC bool ReadWriter<float2, float>::out_of_bounds() const {
 template <>
 METAL_FUNC void ReadWriter<float2, float>::load() const {
   short n_over_2 = (n / 2) + 1;
-  int batch_idx = elem.x * grid.y * n_over_2 * 2 + elem.y * n_over_2 * 2;
+  size_t batch_idx =
+      size_t(elem.x * grid.y) * n_over_2 * 2 + elem.y * n_over_2 * 2;
   threadgroup float2* seq_buf = buf + elem.y * n;
 
   // No out of bounds accesses on odd batch sizes
@@ -458,8 +460,8 @@ METAL_FUNC void ReadWriter<float2, float>::load_padded(
   int n_over_2 = (n / 2) + 1;
   int length_over_2 = (length / 2) + 1;
 
-  int batch_idx =
-      elem.x * grid.y * length_over_2 * 2 + elem.y * length_over_2 * 2;
+  size_t batch_idx =
+      size_t(elem.x * grid.y) * length_over_2 * 2 + elem.y * length_over_2 * 2;
   threadgroup float2* seq_buf = buf + elem.y * n;
 
   // No out of bounds accesses on odd batch sizes
@@ -503,7 +505,7 @@ template <>
 METAL_FUNC void ReadWriter<float2, float>::write_padded(
     int length,
     const device float2* w_k) const {
-  int batch_idx = elem.x * grid.y * length * 2 + elem.y * length * 2;
+  size_t batch_idx = size_t(elem.x * grid.y) * length * 2 + elem.y * length * 2;
   threadgroup float2* seq_buf = buf + elem.y * n + length - 1;
 
   int grid_index = elem.x * grid.y + elem.y;

mlx/backend/metal/kernels/hadamard.h

@@ -26,7 +26,7 @@ METAL_FUNC void radix_func(thread float* x) {
   }
 }
 
-template <typename T, int N, int max_radix, int read_width>
+template <typename T, int N, int max_radix, int read_width, int stride = 1>
 [[kernel]] void hadamard_n(
     const device T* in [[buffer(0)]],
     device T* out [[buffer(1)]],
@@ -46,18 +46,25 @@ template <typename T, int N, int max_radix, int read_width>
   constexpr short logFinal = logN % logR;
   constexpr short final_radix = 1 << (logFinal);
 
-  int batch_idx = elem.x * N;
-  short i = elem.y;
+  int batch_idx = elem.y * N * stride + elem.z;
+  short i = elem.x;
 
   threadgroup T buf[N];
 
   // Read values from device
-  STEEL_PRAGMA_UNROLL
-  for (short j = 0; j < max_radix / read_width; j++) {
-    short index = j * read_width * num_threads + i * read_width;
+  if (stride == 1) {
     STEEL_PRAGMA_UNROLL
-    for (short r = 0; r < read_width; r++) {
-      buf[index + r] = in[batch_idx + index + r];
+    for (short j = 0; j < max_radix / read_width; j++) {
+      short index = j * read_width * num_threads + i * read_width;
+      STEEL_PRAGMA_UNROLL
+      for (short r = 0; r < read_width; r++) {
+        buf[index + r] = in[batch_idx + index + r];
+      }
+    }
+  } else {
+    STEEL_PRAGMA_UNROLL
+    for (short j = 0; j < max_radix; j++) {
+      buf[j * num_threads + i] = in[batch_idx + (j * num_threads + i) * stride];
     }
   }
 
@@ -113,12 +120,20 @@ template <typename T, int N, int max_radix, int read_width>
   }
 
   // Write values to device
-  STEEL_PRAGMA_UNROLL
-  for (short j = 0; j < max_radix / read_width; j++) {
-    short index = j * read_width * num_threads + i * read_width;
+  if (stride == 1) {
     STEEL_PRAGMA_UNROLL
-    for (short r = 0; r < read_width; r++) {
-      out[batch_idx + index + r] = T(buf[index + r] * scale);
+    for (short j = 0; j < max_radix / read_width; j++) {
+      short index = j * read_width * num_threads + i * read_width;
+      STEEL_PRAGMA_UNROLL
+      for (short r = 0; r < read_width; r++) {
+        out[batch_idx + index + r] = T(buf[index + r] * scale);
+      }
+    }
+  } else {
+    STEEL_PRAGMA_UNROLL
+    for (short j = 0; j < max_radix; j++) {
+      out[batch_idx + (j * num_threads + i) * stride] =
+          buf[j * num_threads + i];
     }
   }
 }

mlx/backend/metal/kernels/logsumexp.h

@@ -103,8 +103,8 @@ template <typename T, typename AccT = float, int N_READS = 4>
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
-        vals[i] = (offset + i < axis_size) ? AccT(in[offset + i])
-                                           : Limits<AccT>::finite_min;
+        vals[i] =
+            (offset + i < axis_size) ? AccT(in[offset + i]) : Limits<AccT>::min;
       }
     }
     prevmax = maxval;
@@ -134,10 +134,7 @@ template <typename T, typename AccT = float, int N_READS = 4>
   threadgroup_barrier(mem_flags::mem_threadgroup);
   normalizer = simd_sum(local_normalizer[simd_lane_id]);
 
-  if (simd_group_id == 0) {
-    normalizer = simd_sum(local_normalizer[simd_lane_id]);
-    if (simd_lane_id == 0) {
-      out[gid] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
-    }
+  if (lid == 0) {
+    out[gid] = isinf(maxval) ? T(maxval) : T(log(normalizer) + maxval);
   }
 }

mlx/backend/metal/kernels/reduction/reduce_row.h

@@ -224,7 +224,7 @@ template <
 
   if ((non_row_reductions < 32 && row_size <= 8) || non_row_reductions <= 8) {
     // Simple loop over non_row_reductions and reduce the row in the thread.
-    IdxT out_idx = tid.x + tsize.y * IdxT(tid.y);
+    IdxT out_idx = tid.x + tsize.x * IdxT(tid.y);
     in += elem_to_loc<IdxT>(out_idx, shape, strides, ndim);
 
     for (uint r = 0; r < non_row_reductions; r++) {

mlx/backend/metal/kernels/sdpa_vector.h

@@ -56,9 +56,9 @@ template <typename T, int D, int V = D>
   const int head_idx = tid.x;
   const int q_seq_idx = tid.y;
   const int kv_head_idx = head_idx / gqa_factor;
-  const int o_offset = tpg.x * q_seq_idx + head_idx;
+  const int o_offset = head_idx * tpg.y + q_seq_idx;
   const int q_offset =
-      query_transposed ? o_offset : head_idx * tpg.y + q_seq_idx;
+      query_transposed ? tpg.x * q_seq_idx + head_idx : o_offset;
   queries += q_offset * D + simd_lid * qk_per_thread;
   keys += kv_head_idx * k_head_stride + simd_gid * k_seq_stride +
       simd_lid * qk_per_thread;
@@ -213,9 +213,9 @@ template <typename T, int D, int V = D>
   const int block_idx = tid.z;
   const int head_idx = tid.x;
   const int q_seq_idx = tid.y;
-  const int o_offset = tpg.x * q_seq_idx + head_idx;
+  const int o_offset = head_idx * tpg.y + q_seq_idx;
   const int q_offset =
-      query_transposed ? o_offset : head_idx * tpg.y + q_seq_idx;
+      query_transposed ? tpg.x * q_seq_idx + head_idx : o_offset;
   const int kv_head_idx = head_idx / gqa_factor;
 
   queries += q_offset * D + simd_lid * qk_per_thread;
@@ -358,8 +358,8 @@ template <typename T, int D>
   // Adjust positions
   const int head_idx = tid.x;
   const int q_seq_idx = tid.y;
-  const int n_heads = tpg.x;
-  const int q_offset = n_heads * q_seq_idx + head_idx;
+  const int q_offset = head_idx * tpg.y + q_seq_idx;
+  ;
   partials += q_offset * blocks * D + simd_gid * D + simd_lid * elem_per_thread;
   sums += q_offset * blocks;
   maxs += q_offset * blocks;

mlx/backend/metal/kernels/softmax.h

@@ -128,8 +128,8 @@ template <typename T, typename AccT = T, int N_READS = SOFTMAX_N_READS>
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
-        vals[i] = (offset + i < axis_size) ? AccT(in[offset + i])
-                                           : Limits<AccT>::finite_min;
+        vals[i] =
+            (offset + i < axis_size) ? AccT(in[offset + i]) : Limits<AccT>::min;
       }
     }
     prevmax = maxval;

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

@@ -381,6 +381,7 @@ struct Conv2DWeightBlockLoader {
   const constant MLXConvParams<2>* params;
 
   int weight_hw;
+  int weight_step;
 
   const int read_n;
   const bool do_read;
@@ -402,6 +403,7 @@ struct Conv2DWeightBlockLoader {
         src(src_ + bi * src_ld + bj),
         params(params_),
         weight_hw(0),
+        weight_step(params->C / params->groups),
         read_n(offsets.y + bi),
         do_read(read_n + n_rows * TROWS <= gemm_params_->N) {}
 
@@ -435,15 +437,15 @@ struct Conv2DWeightBlockLoader {
   /* Iteration helper */
   METAL_FUNC void next() {
     if (++weight_hw < (params->wS[1] * params->wS[0])) {
-      src += params->wt_strides[2];
+      src += weight_step;
       return;
     }
 
     weight_hw = 0;
 
-    src += BK - (params->wS[1] * params->wS[0] - 1) * params->wt_strides[2];
+    src += BK - (params->wS[1] * params->wS[0] - 1) * weight_step;
   }
 };
 
 } // namespace steel
-} // namespace mlx
+} // namespace mlx

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

@@ -272,7 +272,7 @@ struct Conv2DWeightBlockLoaderSmallChannels {
       return;
     }
 
-    const device T* curr_src = src + weight_hw * params->wt_strides[2];
+    const device T* curr_src = src + weight_hw * (params->C / params->groups);
 
     if (BN != 8 || do_read) {
       STEEL_PRAGMA_UNROLL
@@ -316,4 +316,4 @@ struct Conv2DWeightBlockLoaderSmallChannels {
 };
 
 } // namespace steel
-} // namespace mlx
+} // namespace mlx

mlx/backend/metal/kernels/ternary.h

@@ -1,25 +1,32 @@
 // Copyright © 2024 Apple Inc.
 
-template <typename T, typename Op>
+template <typename T, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void ternary_v(
     device const bool* a,
     device const T* b,
     device const T* c,
     device T* d,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  d[index] = Op()(a[index], b[index], c[index]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    d[index + i] = Op()(a[index + i], b[index + i], c[index + i]);
+  }
 }
 
-template <typename T, typename Op>
+template <typename T, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void ternary_v2(
     device const bool* a,
     device const T* b,
     device const T* c,
     device T* d,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  d[offset] = Op()(a[offset], b[offset], c[offset]);
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    d[offset + i] = Op()(a[offset + i], b[offset + i], c[offset + i]);
+  }
 }
 
 template <typename T, typename Op, typename IdxT = int64_t>

mlx/backend/metal/kernels/unary.h

@@ -1,21 +1,28 @@
 // Copyright © 2024 Apple Inc.
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void unary_v(
     device const T* in,
     device U* out,
+    constant uint& size,
     uint index [[thread_position_in_grid]]) {
-  out[index] = Op()(in[index]);
+  index *= N;
+  for (int i = 0; i < N && (index + i) < size; ++i) {
+    out[index + i] = Op()(in[index + i]);
+  }
 }
 
-template <typename T, typename U, typename Op>
+template <typename T, typename U, typename Op, int N = WorkPerThread<T>::n>
 [[kernel]] void unary_v2(
     device const T* in,
     device U* out,
+    constant int64_t& size,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-  auto offset = index.x + grid_dim.x * int64_t(index.y);
-  out[offset] = Op()(in[offset]);
+  auto offset = N * (index.x + grid_dim.x * int64_t(index.y));
+  for (int i = 0; i < N && (offset + i) < size; ++i) {
+    out[offset + i] = Op()(in[offset + i]);
+  }
 }
 
 template <

mlx/backend/metal/kernels/utils.h

@@ -15,6 +15,14 @@
 
 typedef half float16_t;
 
+// Work per thread values for different types. The values here are expected to
+// match get_work_per_thread in mlx/backend/metal/utils.h
+template <typename U>
+struct WorkPerThread {
+  static_assert(sizeof(U) <= 8, "Type too large");
+  static constexpr int constant n = 8 / sizeof(U);
+};
+
 ///////////////////////////////////////////////////////////////////////////////
 // Type limits utils
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/metal/logsumexp.cpp

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 #include <algorithm>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"

mlx/backend/metal/matmul.cpp

@@ -7,7 +7,7 @@
 
 #include "mlx/backend/common/broadcasting.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/kernels/defines.h"
@@ -716,6 +716,23 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
     throw std::runtime_error(
         "[matmul] Does not yet support non-floating point types.");
   }
+
+  // Return 0s if either input is empty
+  if (out.size() == 0) {
+    out.set_data(allocator::malloc(out.nbytes()));
+    return;
+  }
+
+  // Copy c into out and return
+  if (inputs[0].shape(-1) == 0) {
+    copy_gpu(
+        inputs[2],
+        out,
+        inputs[2].flags().row_contiguous ? CopyType::Vector : CopyType::General,
+        stream());
+    return;
+  }
+
   out.set_data(allocator::malloc(out.nbytes()));
   auto& s = stream();
   auto& d = metal::device(s.device);

mlx/backend/metal/normalization.cpp

@@ -1,7 +1,7 @@
 // Copyright © 2024 Apple Inc.
 #include <algorithm>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/reduce.h"

mlx/backend/metal/primitives.cpp

@@ -7,10 +7,10 @@
 #include "mlx/backend/common/compiled.h"
 #include "mlx/backend/common/slicing.h"
 #include "mlx/backend/common/utils.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/backend/gpu/slicing.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
-#include "mlx/backend/metal/slicing.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/primitives.h"
 #include "mlx/scheduler.h"
@@ -25,25 +25,6 @@ void arange_set_scalars(T start, T next, metal::CommandEncoder& enc) {
   enc.set_bytes(step, 1);
 }
 
-void reshape(const array& in, array& out, Stream s) {
-  auto [copy_necessary, out_strides] = prepare_reshape(in, out);
-  if (copy_necessary) {
-    out.set_data(allocator::malloc(out.nbytes()));
-    copy_gpu_inplace(
-        in,
-        out,
-        in.shape(),
-        in.strides(),
-        make_contiguous_strides(in.shape()),
-        0,
-        0,
-        CopyType::General,
-        s);
-  } else {
-    shared_buffer_reshape(in, out_strides, out);
-  }
-}
-
 static array compute_dynamic_offset(
     const array& indices,
     const Strides& strides,
@@ -201,8 +182,8 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
         (thread_group_size + simd_size - 1) / simd_size * simd_size;
     assert(thread_group_size <= kernel->maxTotalThreadsPerThreadgroup());
 
-    size_t n_threads = out.size() * thread_group_size;
-    MTL::Size grid_dims = MTL::Size(n_threads, 1, 1);
+    auto gd = get_2d_grid_dims(out.shape(), out.strides());
+    MTL::Size grid_dims = MTL::Size(thread_group_size, gd.width, gd.height);
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
     compute_encoder.set_compute_pipeline_state(kernel);
     compute_encoder.set_input_array(in, 0);
@@ -226,105 +207,10 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
 }
 
-void AsType::eval_gpu(const std::vector<array>& inputs, array& out) {
-  CopyType ctype =
-      inputs[0].flags().contiguous ? CopyType::Vector : CopyType::General;
-  copy_gpu(inputs[0], out, ctype);
-}
-
-void AsStrided::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void Broadcast::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void BroadcastAxes::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void Concatenate::eval_gpu(const std::vector<array>& inputs, array& out) {
-  concatenate_gpu(inputs, out, axis_, stream());
-}
-
-void Contiguous::eval_gpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-  auto& in = inputs[0];
-  constexpr size_t extra_bytes = 16384;
-  if (in.buffer_size() <= out.nbytes() + extra_bytes &&
-      (in.flags().row_contiguous ||
-       (allow_col_major_ && in.flags().col_contiguous))) {
-    out.copy_shared_buffer(in);
-  } else {
-    copy_gpu(in, out, CopyType::General);
-  }
-}
-
-void Copy::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void CustomTransforms::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  eval(inputs, outputs);
-}
-
-void Depends::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  eval(inputs, outputs);
-}
-
-void Full::eval_gpu(const std::vector<array>& inputs, array& out) {
-  auto in = inputs[0];
-  CopyType ctype;
-  if (in.data_size() == 1) {
-    ctype = CopyType::Scalar;
-  } else if (in.flags().contiguous) {
-    ctype = CopyType::Vector;
-  } else {
-    ctype = CopyType::General;
-  }
-  copy_gpu(in, out, ctype);
-}
-
-void ExpandDims::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void Flatten::eval_gpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out, stream());
-}
-
-void Unflatten::eval_gpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out, stream());
-}
-
 void Load::eval_gpu(const std::vector<array>& inputs, array& out) {
   throw std::runtime_error("[Load::eval_gpu] Not implemented.");
 }
 
-void NumberOfElements::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void Pad::eval_gpu(const std::vector<array>& inputs, array& out) {
-  // Inputs must be base input array and scalar val array
-  assert(inputs.size() == 2);
-  auto& in = inputs[0];
-  auto& val = inputs[1];
-
-  // Padding value must be a scalar
-  assert(val.size() == 1);
-
-  // Padding value, input and output must be of the same type
-  assert(val.dtype() == in.dtype() && in.dtype() == out.dtype());
-
-  pad_gpu(in, val, out, axes_, low_pad_size_, stream());
-}
-
 void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
 
@@ -370,27 +256,6 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
   compute_encoder.dispatch_threads(grid_dims, group_dims);
 }
 
-void Reshape::eval_gpu(const std::vector<array>& inputs, array& out) {
-  reshape(inputs[0], out, stream());
-}
-
-void Split::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  eval(inputs, outputs);
-}
-
-void Slice::eval_gpu(const std::vector<array>& inputs, array& out) {
-  assert(inputs.size() == 1);
-  if (out.size() == 0) {
-    out.set_data(nullptr);
-    return;
-  }
-
-  auto& in = inputs[0];
-  slice_gpu(in, out, start_indices_, strides_, stream());
-}
-
 void DynamicSlice::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (out.size() == 0) {
     out.set_data(nullptr);
@@ -492,18 +357,6 @@ void SliceUpdate::eval_gpu(const std::vector<array>& inputs, array& out) {
       /* const Stream& s = */ stream());
 }
 
-void Squeeze::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void StopGradient::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
-void Transpose::eval_gpu(const std::vector<array>& inputs, array& out) {
-  eval(inputs, out);
-}
-
 void QRF::eval_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
@@ -525,10 +378,16 @@ void Cholesky::eval_gpu(const std::vector<array>& inputs, array& out) {
       "[Cholesky::eval_gpu] Metal Cholesky decomposition NYI.");
 }
 
+void Eig::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  throw std::runtime_error("[Eig::eval_gpu] Metal Eig NYI.");
+}
+
 void Eigh::eval_gpu(
     const std::vector<array>& inputs,
     std::vector<array>& outputs) {
-  throw std::runtime_error("[Eigvalsh::eval_gpu] Metal Eigh NYI.");
+  throw std::runtime_error("[Eigh::eval_gpu] Metal Eigh NYI.");
 }
 
 void LUF::eval_gpu(
@@ -537,35 +396,4 @@ void LUF::eval_gpu(
   throw std::runtime_error("[LUF::eval_gpu] Metal LU factorization NYI.");
 }
 
-void View::eval_gpu(const std::vector<array>& inputs, array& out) {
-  auto& in = inputs[0];
-  auto ibytes = size_of(in.dtype());
-  auto obytes = size_of(out.dtype());
-  // Conditions for buffer copying (disjunction):
-  // - type size is the same
-  // - type size is smaller and the last axis is contiguous
-  // - the entire array is row contiguous
-  if (ibytes == obytes || (obytes < ibytes && in.strides().back() == 1) ||
-      in.flags().row_contiguous) {
-    auto strides = in.strides();
-    for (int i = 0; i < static_cast<int>(strides.size()) - 1; ++i) {
-      strides[i] *= ibytes;
-      strides[i] /= obytes;
-    }
-    out.copy_shared_buffer(
-        in, strides, in.flags(), in.data_size() * ibytes / obytes);
-  } else {
-    auto tmp = array(in.shape(), in.dtype(), nullptr, {});
-    tmp.set_data(allocator::malloc(tmp.nbytes()));
-    copy_gpu_inplace(in, tmp, CopyType::General, stream());
-
-    auto flags = out.flags();
-    flags.contiguous = true;
-    flags.row_contiguous = true;
-    auto max_dim = std::max_element(out.shape().begin(), out.shape().end());
-    flags.col_contiguous = out.size() <= 1 || out.size() == *max_dim;
-    out.copy_shared_buffer(tmp, out.strides(), flags, out.size());
-  }
-}
-
 } // namespace mlx::core

mlx/backend/metal/quantized.cpp

@@ -4,7 +4,7 @@
 
 #include "mlx/backend/common/broadcasting.h"
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/reduce.h"

mlx/backend/metal/reduce.cpp

@@ -3,7 +3,7 @@
 #include <algorithm>
 #include <cassert>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/kernels/defines.h"

mlx/backend/metal/rope.cpp

@@ -1,5 +1,5 @@
 // Copyright © 2023-2024 Apple Inc.
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/utils.h"
 #include "mlx/fast_primitives.h"
 

mlx/backend/metal/scaled_dot_product_attention.cpp

@@ -2,7 +2,7 @@
 #include <sstream>
 
 #include "mlx/backend/common/compiled.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 
 #include "mlx/backend/metal/kernels/steel/attn/params.h"
@@ -154,9 +154,9 @@ void sdpa_vector(
   int gqa_factor = q.shape(1) / k.shape(1);
   int N = k.shape(2);
   int B = q.shape(0) * q.shape(1);
-  size_t k_head_stride = k.strides()[1];
+  size_t k_head_stride = k.shape(1) == 1 ? k.strides(0) : k.strides(1);
   size_t k_seq_stride = k.strides()[2];
-  size_t v_head_stride = v.strides()[1];
+  size_t v_head_stride = v.shape(1) == 1 ? v.strides(0) : v.strides(1);
   size_t v_seq_stride = v.strides()[2];
 
   MTL::Size group_dims(1024, 1, 1);
@@ -199,11 +199,10 @@ void sdpa_vector(
   if (has_mask) {
     auto& m = *mask;
     compute_encoder.set_input_array(m, 11 + float_mask);
-    auto nd = m.ndim();
-    int32_t kv_seq_stride =
-        nd >= 1 && m.shape(-1) > 1 ? m.strides()[nd - 1] : 0;
-    int32_t q_seq_stride = nd >= 2 && m.shape(-2) > 1 ? m.strides()[nd - 2] : 0;
-    int32_t head_stride = nd >= 3 && m.shape(-3) > 1 ? m.strides()[nd - 3] : 0;
+    int32_t kv_seq_stride = m.shape(3) > 1 ? m.strides(3) : 0;
+    int32_t q_seq_stride = m.shape(2) > 1 ? m.strides(2) : 0;
+    int32_t head_stride =
+        m.shape(1) > 1 ? m.strides(1) : (m.shape(0) > 1 ? m.strides(0) : 0);
     compute_encoder.set_bytes(kv_seq_stride, 13);
     compute_encoder.set_bytes(q_seq_stride, 14);
     compute_encoder.set_bytes(head_stride, 15);
@@ -238,9 +237,10 @@ void sdpa_vector_2pass(
   int N = k.shape(2);
   int blocks = 32;
   int B = q.shape(0) * q.shape(1);
-  size_t k_head_stride = k.strides()[1];
+
+  size_t k_head_stride = k.shape(1) == 1 ? k.strides(0) : k.strides(1);
   size_t k_seq_stride = k.strides()[2];
-  size_t v_head_stride = v.strides()[1];
+  size_t v_head_stride = v.shape(1) == 1 ? v.strides(0) : v.strides(1);
   size_t v_seq_stride = v.strides()[2];
   MTL::Size group_dims(8 * 32, 1, 1);
   MTL::Size grid_dims(B, q.shape(2), blocks);
@@ -302,11 +302,10 @@ void sdpa_vector_2pass(
   if (has_mask) {
     auto& m = *mask;
     compute_encoder.set_input_array(m, 13 + float_mask);
-    auto nd = m.ndim();
-    int32_t kv_seq_stride =
-        nd >= 1 && m.shape(-1) > 1 ? m.strides()[nd - 1] : 0;
-    int32_t q_seq_stride = nd >= 2 && m.shape(-2) > 1 ? m.strides()[nd - 2] : 0;
-    int32_t head_stride = nd >= 3 && m.shape(-3) > 1 ? m.strides()[nd - 3] : 0;
+    int32_t kv_seq_stride = m.shape(3) > 1 ? m.strides(3) : 0;
+    int32_t q_seq_stride = m.shape(2) > 1 ? m.strides(2) : 0;
+    int32_t head_stride =
+        m.shape(1) > 1 ? m.strides(1) : (m.shape(0) > 1 ? m.strides(0) : 0);
     compute_encoder.set_bytes(kv_seq_stride, 15);
     compute_encoder.set_bytes(q_seq_stride, 16);
     compute_encoder.set_bytes(head_stride, 17);
@@ -368,18 +367,6 @@ void ScaledDotProductAttention::eval_gpu(
     }
   };
 
-  // Checks if arr is row contiguous or the sequence and head dimension are
-  // transposed
-  auto is_contiguous_or_head_seq_transposed = [](const array& arr) {
-    if (arr.flags().row_contiguous) {
-      return true;
-    }
-    auto& strides = arr.strides();
-    auto& shape = arr.shape();
-    return (strides[3] == 1) && (strides[2] == shape[3] * shape[1]) &&
-        (strides[1] == shape[3]) && (strides[0] == strides[2] * shape[2]);
-  };
-
   // Checks that the headdim dimension has stride 1.
   auto is_matrix_contiguous = [](const array& arr) {
     return arr.strides(-1) == 1;
@@ -387,30 +374,58 @@ void ScaledDotProductAttention::eval_gpu(
 
   // We are in vector mode ie single query
   if (q_pre.shape(2) <= 8) {
-    const auto& q = copy_unless(is_contiguous_or_head_seq_transposed, q_pre);
-    const auto& k = copy_unless(is_matrix_contiguous, k_pre);
-    const auto& v = copy_unless(is_matrix_contiguous, v_pre);
+    auto q_copy_unless = [](const array& arr) {
+      if (arr.flags().row_contiguous) {
+        return true;
+      }
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (shape[0] == 1 || shape[1] == 1) {
+        // If either the batch or head dimension is a singleton, the other can
+        // be transposed with the sequence dimension
+        auto bidx = shape[0] == 1 ? 1 : 0;
+        return (strides[3] == 1) && (strides[2] == shape[3] * shape[bidx]) &&
+            (strides[bidx] == shape[3]);
+      }
+      return false;
+    };
+
+    auto kv_copy_unless = [](const array& arr) {
+      // keys and values should be copied if:
+      // - the last dimension is not contiguous
+      // - the batch and head dim are not contiguous
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (strides.back() != 1) {
+        return false;
+      }
+      if (shape[0] == 1 || shape[1] == 1) {
+        return true;
+      }
+      return (strides[0] == strides[1] * shape[1]);
+    };
+
+    const auto& q = copy_unless(q_copy_unless, q_pre);
+    const auto& k = copy_unless(kv_copy_unless, k_pre);
+    const auto& v = copy_unless(kv_copy_unless, v_pre);
 
     // Donate the query if possible
-    if (q.is_donatable() && (q.shape(2) == 1 || !q.flags().row_contiguous) &&
-        q.size() == o.size()) {
+    if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
       o.copy_shared_buffer(q);
     } else {
-      if (o.shape(2) == 1) {
-        o.set_data(allocator::malloc(o.nbytes()));
-      } else {
-        auto strides = o.strides();
-        strides[2] = o.shape(1) * o.shape(3);
-        strides[1] = o.shape(3);
-        auto flags = q.flags();
-        flags.row_contiguous = q.shape(1) == 1;
-        o.set_data(
-            allocator::malloc(o.nbytes()), o.size(), std::move(strides), flags);
-      }
+      o.set_data(allocator::malloc(o.nbytes()));
     }
 
-    auto mask =
-        inputs.size() > 3 ? std::optional<array>{inputs[3]} : std::nullopt;
+    auto mask_copy_unless = [&q](const array& arr) {
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      return arr.flags().row_contiguous || q.shape(0) == 1 || q.shape(1) == 1 ||
+          (strides[0] == strides[1] * shape[1]);
+    };
+
+    auto mask = inputs.size() > 3
+        ? std::optional<array>{copy_unless(mask_copy_unless, inputs[3])}
+        : std::nullopt;
 
     // We route to the 2 pass fused attention if
     // - The device is large and the sequence length long

mlx/backend/metal/scan.cpp

@@ -3,7 +3,7 @@
 #include <cassert>
 #include <sstream>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"

mlx/backend/metal/slicing.cpp

@@ -2,21 +2,12 @@
 
 #include <numeric>
 
-#include "mlx/backend/common/slicing.h"
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/backend/gpu/slicing.h"
 #include "mlx/backend/metal/device.h"
 
 namespace mlx::core {
 
-void slice_gpu(
-    const array& in,
-    array& out,
-    const Shape& start_indices,
-    const Shape& strides,
-    const Stream& s) {
-  slice(in, out, start_indices, strides);
-}
-
 void concatenate_gpu(
     const std::vector<array>& inputs,
     array& out,
@@ -48,30 +39,4 @@ void concatenate_gpu(
   }
 }
 
-void pad_gpu(
-    const array& in,
-    const array& val,
-    array& out,
-    const std::vector<int>& axes,
-    const Shape& low_pad_size,
-    const Stream& s) {
-  // Fill output with val
-  fill_gpu(val, out, s);
-
-  // Find offset for start of input values
-  size_t data_offset = 0;
-  for (int i = 0; i < axes.size(); i++) {
-    auto ax = axes[i] < 0 ? out.ndim() + axes[i] : axes[i];
-    data_offset += out.strides()[ax] * low_pad_size[i];
-  }
-
-  // Extract slice from output where input will be pasted
-  array out_slice(in.shape(), out.dtype(), nullptr, {});
-  out_slice.copy_shared_buffer(
-      out, out.strides(), out.flags(), out_slice.size(), data_offset);
-
-  // Copy input values into the slice
-  copy_gpu_inplace(in, out_slice, CopyType::GeneralGeneral, s);
-}
-
 } // namespace mlx::core

mlx/backend/metal/softmax.cpp

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 #include <algorithm>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/kernels/defines.h"

mlx/backend/metal/sort.cpp

@@ -2,7 +2,7 @@
 
 #include <algorithm>
 
-#include "mlx/backend/metal/copy.h"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/backend/metal/device.h"
 #include "mlx/backend/metal/kernels.h"
 #include "mlx/backend/metal/utils.h"

mlx/backend/metal/ternary.cpp

@@ -45,7 +45,7 @@ void ternary_op_gpu_inplace(
     work_per_thread = large ? 4 : 2;
   } else {
     large = out.data_size() > INT32_MAX;
-    work_per_thread = 1;
+    work_per_thread = get_work_per_thread(b.dtype());
   }
   std::string kernel_name;
   if (topt == TernaryOpType::General) {
@@ -106,13 +106,19 @@ void ternary_op_gpu_inplace(
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
     // Launch a 1D or 2D grid of threads
-    size_t nthreads = out.data_size();
+    size_t nthreads = ceildiv(out.data_size(), work_per_thread);
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
+    MTL::Size grid_dims;
+    if (large) {
+      compute_encoder.set_bytes<int64_t>(out.data_size(), 4);
+      grid_dims = get_2d_grid_dims(out.shape(), out.strides(), work_per_thread);
+    } else {
+      compute_encoder.set_bytes<int>(out.data_size(), 4);
+      grid_dims = MTL::Size(nthreads, 1, 1);
+    }
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }

mlx/backend/metal/unary.cpp

@@ -34,18 +34,19 @@ void unary_op_gpu_inplace(
   };
   auto [shape, strides] = maybe_collapse();
   int ndim = shape.size();
-  size_t nthreads = contig ? in.data_size() : in.size();
   bool large;
   if (!contig) {
     large = in.data_size() > INT32_MAX || out.size() > INT32_MAX;
   } else {
     large = in.data_size() > UINT32_MAX;
   }
-  int work_per_thread = !contig && large ? 4 : 1;
+  int work_per_thread;
   std::string kernel_name;
   if (contig) {
+    work_per_thread = get_work_per_thread(in.dtype());
     kernel_name = (large ? "v2" : "v");
   } else {
+    work_per_thread = large ? 4 : 1;
     kernel_name = "gn" + std::to_string(work_per_thread);
     if (large) {
       kernel_name += "large";
@@ -75,12 +76,20 @@ void unary_op_gpu_inplace(
     MTL::Size grid_dims = MTL::Size(dim0, dim1, rest);
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   } else {
+    size_t nthreads = ceildiv(in.data_size(), work_per_thread);
     if (thread_group_size > nthreads) {
       thread_group_size = nthreads;
     }
+
     MTL::Size group_dims = MTL::Size(thread_group_size, 1, 1);
-    MTL::Size grid_dims = large ? get_2d_grid_dims(out.shape(), out.strides())
-                                : MTL::Size(nthreads, 1, 1);
+    MTL::Size grid_dims;
+    if (large) {
+      compute_encoder.set_bytes<int64_t>(in.data_size(), 2);
+      grid_dims = get_2d_grid_dims(out.shape(), out.strides(), work_per_thread);
+    } else {
+      compute_encoder.set_bytes<int>(in.data_size(), 2);
+      grid_dims = MTL::Size(nthreads, 1, 1);
+    }
     compute_encoder.dispatch_threads(grid_dims, group_dims);
   }
 }

mlx/backend/metal/utils.h

@@ -84,4 +84,12 @@ void concatenate(std::string& acc, T first, Args... args) {
   concatenate(acc, args...);
 }
 
+inline int get_work_per_thread(Dtype dtype) {
+  return std::max(1, 8 / dtype.size());
+}
+
+inline size_t ceildiv(size_t n, size_t m) {
+  return (n + m - 1) / m;
+}
+
 } // namespace mlx::core

mlx/backend/no_cpu/primitives.cpp

@@ -55,6 +55,7 @@ NO_CPU(DynamicSlice)
 NO_CPU(DynamicSliceUpdate)
 NO_CPU(NumberOfElements)
 NO_CPU(Remainder)
+NO_CPU_MULTI(Eig)
 NO_CPU_MULTI(Eigh)
 NO_CPU(Equal)
 NO_CPU(Erf)

mlx/backend/no_gpu/primitives.cpp

@@ -126,6 +126,7 @@ NO_GPU(Unflatten)
 NO_GPU(Inverse)
 NO_GPU(Cholesky)
 NO_GPU_MULTI(Eigh)
+NO_GPU_MULTI(Eig)
 NO_GPU(View)
 
 namespace fast {

mlx/compile.cpp

@@ -168,6 +168,15 @@ void merge_one(array& dst, array& src, ParentsMap& parents_map) {
     parent.first.inputs()[parent.second] = dst;
     pairs.push_back(parent);
   }
+
+  // If src is a parent of dst, remove it from dst's parents
+  for (auto it = pairs.begin(); it != pairs.end();) {
+    if (it->first.id() == src.id()) {
+      it = pairs.erase(it);
+    } else {
+      it++;
+    }
+  }
   // Remove the source from the map to avoid fusing with it again
   parents_map.erase(src_parents);
 }

mlx/export.cpp

@@ -331,6 +331,7 @@ struct PrimitiveFactory {
       SERIALIZE_PRIMITIVE(SVD),
       SERIALIZE_PRIMITIVE(Inverse),
       SERIALIZE_PRIMITIVE(Cholesky),
+      SERIALIZE_PRIMITIVE(Eig),
       SERIALIZE_PRIMITIVE(Eigh),
       SERIALIZE_PRIMITIVE(AffineQuantize),
       SERIALIZE_PRIMITIVE(RMSNorm),
@@ -470,6 +471,9 @@ bool FunctionTable::match(
     if (x.dtype() != y.dtype()) {
       return false;
     }
+    if (x.ndim() != y.ndim()) {
+      return false;
+    }
     if (!shapeless && x.shape() != y.shape()) {
       return false;
     }

mlx/linalg.cpp

@@ -27,6 +27,15 @@ void check_float(Dtype dtype, const std::string& prefix) {
   }
 }
 
+void check_float_or_complex(Dtype dtype, const std::string& prefix) {
+  if (dtype != float32 && dtype != float64 && dtype != complex64) {
+    std::ostringstream msg;
+    msg << prefix << " Arrays must have type float32, float64 or complex64. "
+        << "Received array with type " << dtype << ".";
+    throw std::invalid_argument(msg.str());
+  }
+}
+
 Dtype at_least_float(const Dtype& d) {
   return issubdtype(d, inexact) ? d : promote_types(d, float32);
 }
@@ -488,12 +497,12 @@ array cross(
   return concatenate(outputs, axis, s);
 }
 
-void validate_eigh(
+void validate_eig(
     const array& a,
     const StreamOrDevice& stream,
     const std::string fname) {
   check_cpu_stream(stream, fname);
-  check_float(a.dtype(), fname);
+  check_float_or_complex(a.dtype(), fname);
 
   if (a.ndim() < 2) {
     std::ostringstream msg;
@@ -511,11 +520,12 @@ array eigvalsh(
     const array& a,
     std::string UPLO /* = "L" */,
     StreamOrDevice s /* = {} */) {
-  validate_eigh(a, s, "[linalg::eigvalsh]");
+  validate_eig(a, s, "[linalg::eigvalsh]");
   Shape out_shape(a.shape().begin(), a.shape().end() - 1);
+  Dtype eigval_type = a.dtype() == complex64 ? float32 : a.dtype();
   return array(
       std::move(out_shape),
-      a.dtype(),
+      eigval_type,
       std::make_shared<Eigh>(to_stream(s), UPLO, false),
       {a});
 }
@@ -524,15 +534,36 @@ std::pair<array, array> eigh(
     const array& a,
     std::string UPLO /* = "L" */,
     StreamOrDevice s /* = {} */) {
-  validate_eigh(a, s, "[linalg::eigh]");
+  validate_eig(a, s, "[linalg::eigh]");
+  Dtype eigval_type = a.dtype() == complex64 ? float32 : a.dtype();
   auto out = array::make_arrays(
       {Shape(a.shape().begin(), a.shape().end() - 1), a.shape()},
-      {a.dtype(), a.dtype()},
+      {eigval_type, a.dtype()},
       std::make_shared<Eigh>(to_stream(s), UPLO, true),
       {a});
   return std::make_pair(out[0], out[1]);
 }
 
+array eigvals(const array& a, StreamOrDevice s /* = {} */) {
+  validate_eig(a, s, "[linalg::eigvals]");
+  Shape out_shape(a.shape().begin(), a.shape().end() - 1);
+  return array(
+      std::move(out_shape),
+      complex64,
+      std::make_shared<Eig>(to_stream(s), false),
+      {a});
+}
+
+std::pair<array, array> eig(const array& a, StreamOrDevice s /* = {} */) {
+  validate_eig(a, s, "[linalg::eig]");
+  auto out = array::make_arrays(
+      {Shape(a.shape().begin(), a.shape().end() - 1), a.shape()},
+      {complex64, complex64},
+      std::make_shared<Eig>(to_stream(s), true),
+      {a});
+  return std::make_pair(out[0], out[1]);
+}
+
 void validate_lu(
     const array& a,
     const StreamOrDevice& stream,

mlx/linalg.h

@@ -99,6 +99,10 @@ array cross(
     int axis = -1,
     StreamOrDevice s = {});
 
+std::pair<array, array> eig(const array& a, StreamOrDevice s = {});
+
+array eigvals(const array& a, StreamOrDevice s = {});
+
 array eigvalsh(const array& a, std::string UPLO = "L", StreamOrDevice s = {});
 
 std::pair<array, array>

mlx/ops.cpp

@@ -472,9 +472,24 @@ array hadamard_transform(
     const array& a,
     std::optional<float> scale_ /* = std::nullopt */,
     StreamOrDevice s /* = {} */) {
+  if (a.size() == 0) {
+    throw std::invalid_argument(
+        "[hadamard_transform] Does not support empty arrays.");
+  }
   // Default to an orthonormal Hadamard matrix scaled by 1/sqrt(N)
-  float scale = scale_.has_value() ? *scale_ : 1.0f / std::sqrt(a.shape(-1));
+  int n = a.ndim() > 0 ? a.shape(-1) : 1;
+  float scale = scale_.has_value() ? *scale_ : 1.0f / std::sqrt(n);
   auto dtype = issubdtype(a.dtype(), floating) ? a.dtype() : float32;
+
+  // Nothing to do for a scalar
+  if (n == 1) {
+    if (scale == 1) {
+      return a;
+    }
+
+    return multiply(a, array(scale, dtype), s);
+  }
+
   return array(
       a.shape(),
       dtype,
@@ -3160,6 +3175,10 @@ array scatter_axis(
     throw std::invalid_argument(msg.str());
   }
 
+  if (a.size() == 0) {
+    return a;
+  }
+
   auto upd = astype(values, a.dtype(), s);
 
   // Squeeze leading singletons out of update
@@ -3565,21 +3584,21 @@ Shape conv_out_shape(
 
   if (pads_lo.size() != spatial_dims || pads_hi.size() != spatial_dims) {
     std::ostringstream msg;
-    msg << "[conv] Invalid padding " << pads_lo << " | " << pads_hi << "for "
+    msg << "[conv] Invalid padding " << pads_lo << " | " << pads_hi << " for "
         << spatial_dims << "D convolution.";
     throw std::invalid_argument(msg.str());
   }
 
   if (kernel_dilation.size() != spatial_dims) {
     std::ostringstream msg;
-    msg << "[conv] Invalid kernel dilation " << kernel_dilation << "for "
+    msg << "[conv] Invalid kernel dilation " << kernel_dilation << " for "
         << spatial_dims << "D convolution.";
     throw std::invalid_argument(msg.str());
   }
 
   if (input_dilation.size() != spatial_dims) {
     std::ostringstream msg;
-    msg << "[conv] Invalid input dilation " << input_dilation << "for "
+    msg << "[conv] Invalid input dilation " << input_dilation << " for "
         << spatial_dims << "D convolution.";
     throw std::invalid_argument(msg.str());
   }
@@ -3963,6 +3982,7 @@ array conv_general(
           to_stream(s),
           stride,
           padding_lo,
+          padding_hi,
           kernel_dilation,
           input_dilation,
           groups,
@@ -4314,6 +4334,10 @@ array addmm(
 
     c = reshape(c, c_reshape, s);
   }
+  if (c.shape() != out_shape) {
+    throw std::invalid_argument(
+        "[addmm] input c must broadcast to the output shape");
+  }
 
   auto out = array(
       std::move(out_shape),

mlx/primitives.cpp

@@ -875,6 +875,43 @@ std::pair<std::vector<array>, std::vector<int>> Cholesky::vmap(
   return {{linalg::cholesky(a, upper_, stream())}, {ax}};
 }
 
+std::pair<std::vector<array>, std::vector<int>> Eig::vmap(
+    const std::vector<array>& inputs,
+    const std::vector<int>& axes) {
+  assert(inputs.size() == 1);
+  assert(axes.size() == 1);
+
+  bool needs_move = axes[0] >= (inputs[0].ndim() - 2);
+  auto a = needs_move ? moveaxis(inputs[0], axes[0], 0, stream()) : inputs[0];
+  auto ax = needs_move ? 0 : axes[0];
+
+  std::vector<array> outputs;
+  if (compute_eigenvectors_) {
+    auto [values, vectors] = linalg::eig(a, stream());
+    outputs = {values, vectors};
+  } else {
+    outputs = {linalg::eigvals(a, stream())};
+  }
+
+  return {outputs, std::vector<int>(outputs.size(), ax)};
+}
+
+std::vector<Shape> Eig::output_shapes(const std::vector<array>& inputs) {
+  auto shape = inputs[0].shape();
+  shape.pop_back(); // Remove last dimension for eigenvalues
+  if (compute_eigenvectors_) {
+    return {
+        std::move(shape), inputs[0].shape()}; // Eigenvalues and eigenvectors
+  } else {
+    return {std::move(shape)}; // Only eigenvalues
+  }
+}
+
+bool Eig::is_equivalent(const Primitive& other) const {
+  auto& e_other = static_cast<const Eig&>(other);
+  return compute_eigenvectors_ == e_other.compute_eigenvectors_;
+}
+
 std::pair<std::vector<array>, std::vector<int>> Eigh::vmap(
     const std::vector<array>& inputs,
     const std::vector<int>& axes) {
@@ -1055,7 +1092,8 @@ array conv_weight_backward_patches(
     const array& wt,
     const array& cotan,
     const std::vector<int>& kernel_strides,
-    const std::vector<int>& padding,
+    const std::vector<int>& padding_lo,
+    const std::vector<int>& padding_hi,
     StreamOrDevice s) {
   // Resolve Padded input shapes and strides
   Shape padding_starts(in.ndim(), 0);
@@ -1064,9 +1102,9 @@ array conv_weight_backward_patches(
 
   // padded shape
   for (int i = 1; i < in.ndim() - 1; i++) {
-    in_padded_shape[i] += 2 * padding[i - 1];
-    padding_ends[i] += padding[i - 1];
-    padding_starts[i] += padding[i - 1];
+    in_padded_shape[i] += padding_lo[i - 1] + padding_hi[i - 1];
+    padding_ends[i] += padding_lo[i - 1];
+    padding_starts[i] += padding_lo[i - 1];
   }
 
   // padded strides (contiguous)
@@ -1078,9 +1116,14 @@ array conv_weight_backward_patches(
   // Pad input
   std::vector<int> padded_axes(in.ndim() - 2, 0);
   std::iota(padded_axes.begin(), padded_axes.end(), 1);
-  Shape padding_(padding.begin(), padding.end());
-  auto in_padded = pad(
-      in, padded_axes, padding_, padding_, array(0, in.dtype()), "constant", s);
+  auto in_padded =
+      pad(in,
+          padded_axes,
+          Shape(padding_lo),
+          Shape(padding_hi),
+          array(0, in.dtype()),
+          "constant",
+          s);
 
   // Resolve strided patches
 
@@ -1147,16 +1190,16 @@ std::vector<array> Convolution::vjp(
   for (int a : argnums) {
     // Grads for input
     if (a == 0) {
-      std::vector<int> padding_lo = padding_;
-      std::vector<int> padding_hi = padding_;
+      std::vector<int> padding_lo = padding_lo_;
+      std::vector<int> padding_hi = padding_hi_;
 
       for (int i = 0; i < padding_lo.size(); ++i) {
         int wt_size = 1 + kernel_dilation_[i] * (wt.shape(1 + i) - 1);
-        padding_lo[i] = wt_size - padding_[i] - 1;
+        padding_lo[i] = wt_size - padding_lo_[i] - 1;
 
         int in_size = 1 + input_dilation_[i] * (in.shape(1 + i) - 1);
         int out_size = 1 + kernel_strides_[i] * (cotan.shape(1 + i) - 1);
-        padding_hi[i] = in_size - out_size + padding_[i];
+        padding_hi[i] = in_size - out_size + padding_hi_[i];
       }
 
       // Check for negative padding
@@ -1226,18 +1269,18 @@ std::vector<array> Convolution::vjp(
 
       if (no_dilation && !flip_ && groups_ == 1) {
         auto grad = conv_weight_backward_patches(
-            in, wt, cotan, kernel_strides_, padding_, stream());
+            in, wt, cotan, kernel_strides_, padding_lo_, padding_hi_, stream());
         grads.push_back(grad);
       } else {
-        std::vector<int> padding_lo = padding_;
-        std::vector<int> padding_hi = padding_;
+        auto padding_hi = padding_lo_;
 
         for (int i = 0; i < padding_hi.size(); ++i) {
           int in_size = 1 + input_dilation_[i] * (in.shape(1 + i) - 1);
           int out_size = 1 + kernel_strides_[i] * (cotan.shape(1 + i) - 1);
           int wt_size = 1 + kernel_dilation_[i] * (wt.shape(1 + i) - 1);
-          padding_hi[i] = out_size - in_size + wt_size - padding_[i] - 1;
+          padding_hi[i] = out_size - in_size + wt_size - padding_hi[i] - 1;
         }
+
         auto cotan_trans = swapaxes(cotan, 0, -1, stream());
         auto in_trans = group_transpose(in, -1, 0, -1);
 
@@ -1245,7 +1288,7 @@ std::vector<array> Convolution::vjp(
             /* const array& input = */ in_trans,
             /* const array& weight = */ cotan_trans,
             /* std::vector<int> stride = */ kernel_dilation_,
-            /* std::vector<int> padding_lo = */ padding_lo,
+            /* std::vector<int> padding_lo = */ padding_lo_,
             /* std::vector<int> padding_hi = */ padding_hi,
             /* std::vector<int> kernel_dilation = */ kernel_strides_,
             /* std::vector<int> input_dilation = */ input_dilation_,
@@ -1283,7 +1326,8 @@ std::pair<std::vector<array>, std::vector<int>> Convolution::vmap(
         in,
         w,
         kernel_strides_,
-        padding_,
+        padding_lo_,
+        padding_hi_,
         kernel_dilation_,
         input_dilation_,
         groups,
@@ -1332,7 +1376,8 @@ std::pair<std::vector<array>, std::vector<int>> Convolution::vmap(
 
 bool Convolution::is_equivalent(const Primitive& other) const {
   const Convolution& c_other = static_cast<const Convolution&>(other);
-  return padding_ == c_other.padding_ &&
+  return padding_lo_ == c_other.padding_lo_ &&
+      padding_hi_ == c_other.padding_hi_ &&
       kernel_strides_ == c_other.kernel_strides_ &&
       kernel_dilation_ == c_other.kernel_dilation_ &&
       input_dilation_ == c_other.input_dilation_ &&
@@ -1484,14 +1529,16 @@ std::vector<array> Divide::vjp(
     const std::vector<int>& argnums,
     const std::vector<array>&) {
   std::vector<array> vjps;
+  array denominator_bar = conjugate(primals[1], stream());
   for (auto arg : argnums) {
     if (arg == 0) {
-      vjps.push_back(divide(cotangents[0], primals[1], stream()));
+      vjps.push_back(divide(cotangents[0], denominator_bar, stream()));
     } else {
       vjps.push_back(negative(
           divide(
-              multiply(cotangents[0], primals[0], stream()),
-              square(primals[1], stream()),
+              multiply(
+                  cotangents[0], conjugate(primals[0], stream()), stream()),
+              square(denominator_bar, stream()),
               stream()),
           stream()));
     }
@@ -1946,30 +1993,74 @@ std::vector<array> FFT::vjp(
   assert(argnums.size() == 1);
   auto& in = primals[0];
   std::vector<int> axes(axes_.begin(), axes_.end());
+
+  // TODO: Add it as an option to do an unnormalized or scaled fft so that this
+  //       isn't part of the graph.
+  double n_elements = 1;
+  for (auto ax : axes) {
+    n_elements *= inverse_ ? cotangents[0].shape(ax) : primals[0].shape(ax);
+  }
+
   if (real_ && inverse_) {
-    auto out = fft::fftn(cotangents[0], axes, stream());
-    auto start = Shape(out.ndim(), 0);
-    auto stop = in.shape();
-    out = slice(out, start, stop, stream());
-    auto mask_shape = out.shape();
-    mask_shape[axes_.back()] -= 2;
-    auto mask = full(mask_shape, 2.0f, stream());
-    auto pad_shape = out.shape();
-    pad_shape[axes_.back()] = 1;
-    auto pad = full(pad_shape, 1.0f, stream());
-    mask = concatenate({pad, mask, pad}, axes_.back(), stream());
-    return {multiply(mask, out, stream())};
+    // Make a mask to account for the double use in the forward pass.
+    // Everything except the DC and nyquist frequencies gets doubled.
+    int N = in.shape(axes_.back());
+    bool odd = cotangents[0].shape(axes_.back()) % 2;
+    Shape c(in.ndim(), 1);
+    c[axes_.back()] = N;
+    array indices = reshape(arange(N, stream()), std::move(c), stream());
+    array first(0, indices.dtype());
+    array last(N - 1 + odd, indices.dtype());
+    array one(1 / n_elements, in.dtype());
+    array two(2 / n_elements, in.dtype());
+    array mask = where(
+        logical_and(
+            greater(indices, first, stream()),
+            less(indices, last, stream()),
+            stream()),
+        two,
+        one,
+        stream());
+    return {
+        multiply(fft::rfftn(cotangents[0], axes, stream()), mask, stream())};
   } else if (real_) {
     Shape n;
     for (auto ax : axes_) {
-      n.push_back(in.shape()[ax]);
+      n.push_back(in.shape(ax));
     }
-    return {astype(
-        fft::fftn(cotangents[0], n, axes, stream()), in.dtype(), stream())};
+    // Make a mask to account for the double use in the forward pass.
+    // Everything except the DC and nyquist frequencies gets halved.
+    int N = cotangents[0].shape(axes_.back());
+    bool odd = in.shape(axes_.back()) % 2;
+    Shape c(in.ndim(), 1);
+    c[axes_.back()] = N;
+    array indices = reshape(arange(N, stream()), std::move(c), stream());
+    array first(0, indices.dtype());
+    array last(N - 1 + odd, indices.dtype());
+    array one(1, complex64);
+    array half(0.5, complex64);
+    array mask = where(
+        logical_and(
+            greater(indices, first, stream()),
+            less(indices, last, stream()),
+            stream()),
+        half,
+        one,
+        stream());
+    return {multiply(
+        fft::irfftn(multiply(cotangents[0], mask, stream()), n, axes, stream()),
+        array(n_elements, in.dtype()),
+        stream())};
   } else if (inverse_) {
-    return {fft::ifftn(cotangents[0], axes, stream())};
+    return {multiply(
+        fft::fftn(cotangents[0], axes, stream()),
+        array(1 / n_elements, complex64),
+        stream())};
   } else {
-    return {fft::fftn(cotangents[0], axes, stream())};
+    return {multiply(
+        fft::ifftn(cotangents[0], axes, stream()),
+        array(n_elements, complex64),
+        stream())};
   }
 }
 
@@ -2772,7 +2863,8 @@ std::vector<array> Multiply::vjp(
     const std::vector<array>&) {
   std::vector<array> vjps;
   for (auto arg : argnums) {
-    vjps.push_back(multiply(primals[1 - arg], cotangents[0], stream()));
+    vjps.push_back(multiply(
+        conjugate(primals[1 - arg], stream()), cotangents[0], stream()));
   }
   return vjps;
 }
@@ -3456,7 +3548,7 @@ std::vector<array> Reduce::vjp(
   }
 
   else {
-    throw std::runtime_error("Reduce type VJP not yet implemented.");
+    return {zeros_like(in, stream())};
   }
 }
 

mlx/primitives.h

@@ -689,13 +689,15 @@ class Convolution : public UnaryPrimitive {
   explicit Convolution(
       Stream stream,
       const std::vector<int>& kernel_strides,
-      const std::vector<int>& padding,
+      const std::vector<int>& padding_lo,
+      const std::vector<int>& padding_hi,
       const std::vector<int>& kernel_dilation,
       const std::vector<int>& input_dilation,
       const int groups = 1,
       const bool flip = false)
       : UnaryPrimitive(stream),
-        padding_(padding),
+        padding_lo_(padding_lo),
+        padding_hi_(padding_hi),
         kernel_strides_(kernel_strides),
         kernel_dilation_(kernel_dilation),
         input_dilation_(input_dilation),
@@ -716,7 +718,8 @@ class Convolution : public UnaryPrimitive {
   bool is_equivalent(const Primitive& other) const override;
   auto state() const {
     return std::make_tuple(
-        padding_,
+        padding_lo_,
+        padding_hi_,
         kernel_strides_,
         kernel_dilation_,
         input_dilation_,
@@ -725,7 +728,8 @@ class Convolution : public UnaryPrimitive {
   }
 
  private:
-  std::vector<int> padding_;
+  std::vector<int> padding_lo_;
+  std::vector<int> padding_hi_;
   std::vector<int> kernel_strides_;
   std::vector<int> kernel_dilation_;
   std::vector<int> input_dilation_;
@@ -2377,6 +2381,29 @@ class Cholesky : public UnaryPrimitive {
   bool upper_;
 };
 
+class Eig : public Primitive {
+ public:
+  explicit Eig(Stream stream, bool compute_eigenvectors)
+      : Primitive(stream), compute_eigenvectors_(compute_eigenvectors) {}
+  void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override;
+  void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
+      override;
+
+  DEFINE_VMAP()
+  DEFINE_PRINT(Eig)
+
+  std::vector<Shape> output_shapes(const std::vector<array>& inputs) override;
+
+  bool is_equivalent(const Primitive& other) const override;
+  auto state() const {
+    return compute_eigenvectors_;
+  }
+
+ private:
+  bool compute_eigenvectors_;
+};
+
 class Eigh : public Primitive {
  public:
   explicit Eigh(Stream stream, std::string uplo, bool compute_eigenvectors)

mlx/random.cpp

@@ -176,24 +176,51 @@ array uniform(
       array(0.0, dtype), array(1.0, dtype), shape, dtype, key, to_stream(s));
 }
 
+inline array complex_normal(
+    Shape shape,
+    const std::optional<array>& loc,
+    const std::optional<array>& scale,
+    const std::optional<array>& key,
+    StreamOrDevice s) {
+  auto stream = to_stream(s);
+  auto low = above_minus_one_with_default(float32);
+  auto high = array(1.0f, float32);
+  shape.push_back(2);
+  auto samples =
+      erfinv(uniform(low, high, shape, float32, key, stream), stream);
+  samples = squeeze(view(samples, complex64, stream), -1, stream);
+  if (scale.has_value()) {
+    samples = multiply(*scale, samples, stream);
+  }
+  if (loc.has_value()) {
+    samples = add(*loc, samples, stream);
+  }
+  return samples;
+}
+
 array normal(
     const Shape& shape,
     Dtype dtype,
-    const float loc /* = 0.0 */,
-    const float scale /* = 1.0 */,
-    const std::optional<array>& key /*= nullopt */,
+    const std::optional<array>& loc,
+    const std::optional<array>& scale,
+    const std::optional<array>& key,
     StreamOrDevice s /* = {} */) {
+  if (dtype == complex64) {
+    return complex_normal(shape, loc, scale, key, s);
+  }
+
   auto stream = to_stream(s);
   auto low = above_minus_one_with_default(dtype);
   auto high = array(1.0f, dtype);
   auto samples = uniform(low, high, shape, dtype, key, stream);
-  samples =
-      multiply(array(std::sqrt(2.0), dtype), erfinv(samples, stream), stream);
-  if (scale != 1.0) {
-    samples = multiply(array(scale, dtype), samples, stream);
+  auto applied_scale = array(std::sqrt(2.0), dtype);
+  if (scale.has_value()) {
+    applied_scale =
+        multiply(applied_scale, astype(*scale, dtype, stream), stream);
   }
-  if (loc != 0.0) {
-    samples = add(array(loc, dtype), samples, stream);
+  samples = multiply(applied_scale, erfinv(samples, stream), stream);
+  if (loc.has_value()) {
+    samples = add(astype(*loc, dtype, stream), samples, stream);
   }
   return samples;
 }

mlx/random.h

@@ -94,12 +94,24 @@ inline array uniform(
 
 /** Generate samples from the standard normal distribution. */
 array normal(
+    const Shape& shape,
+    Dtype dtype,
+    const std::optional<array>& loc,
+    const std::optional<array>& scale,
+    const std::optional<array>& key,
+    StreamOrDevice s = {});
+inline array normal(
     const Shape& shape,
     Dtype dtype,
     const float loc,
     const float scale,
     const std::optional<array>& key = std::nullopt,
-    StreamOrDevice s = {});
+    StreamOrDevice s = {}) {
+  auto loc_ = loc == 0 ? std::nullopt : std::make_optional(array(loc, dtype));
+  auto scale_ =
+      scale == 1 ? std::nullopt : std::make_optional(array(scale, dtype));
+  return normal(shape, dtype, loc_, scale_, key, s);
+}
 inline array normal(
     const Shape& shape,
     const float loc,
@@ -113,13 +125,13 @@ inline array normal(
     const Dtype dtype,
     const std::optional<array>& key = std::nullopt,
     StreamOrDevice s = {}) {
-  return normal(shape, dtype, 0.0, 1.0, key, s);
+  return normal(shape, dtype, std::nullopt, std::nullopt, key, s);
 }
 inline array normal(
     const Shape& shape,
     const std::optional<array>& key = std::nullopt,
     StreamOrDevice s = {}) {
-  return normal(shape, float32, 0.0, 1.0, key, s);
+  return normal(shape, float32, std::nullopt, std::nullopt, key, s);
 }
 
 /** Generate samples from a multivariate normal distribution. **/

mlx/version.h

@@ -4,7 +4,7 @@
 
 #define MLX_VERSION_MAJOR 0
 #define MLX_VERSION_MINOR 25
-#define MLX_VERSION_PATCH 1
+#define MLX_VERSION_PATCH 2
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/mlx/distributed_run.py

@@ -270,9 +270,11 @@ def launch_ring(parser, hosts, args, command):
 
         # Repeat the stdout and stderr to the local machine
         to_read = [p.stdout.fileno(), p.stderr.fileno()]
-        to_write = [p.stdin.fileno()]
+        to_write = [p.stdin.fileno(), sys.stdout.fileno(), sys.stderr.fileno()]
         pidfile = ""
         stdin_buffer = b""
+        stdout_buffer = b""
+        stderr_buffer = b""
         while p.poll() is None:
             try:
                 stdin_buffer += input_queue.get_nowait()
@@ -280,8 +282,6 @@ def launch_ring(parser, hosts, args, command):
                 pass
             rlist, wlist, _ = select(to_read, to_write, [], 1.0)
             for fd in rlist:
-                is_stdout = fd == p.stdout.fileno()
-                outfile = sys.stdout if is_stdout else sys.stderr
                 msg = os.read(fd, 8192).decode(errors="ignore")
 
                 # Fetch the PID file first if we haven't already
@@ -289,12 +289,21 @@ def launch_ring(parser, hosts, args, command):
                     pidfile, *msg = msg.split("\n", maxsplit=1)
                     msg = msg[0] if msg else ""
 
-                outfile.write(msg)
-                outfile.flush()
+                is_stdout = fd == p.stdout.fileno()
+                if is_stdout:
+                    stdout_buffer += msg.encode()
+                else:
+                    stderr_buffer += msg.encode()
             for fd in wlist:
-                if len(stdin_buffer) > 0:
+                if fd == p.stdin.fileno() and len(stdin_buffer) > 0:
                     n = os.write(fd, stdin_buffer)
                     stdin_buffer = stdin_buffer[n:]
+                elif fd == sys.stdout.fileno() and len(stdout_buffer) > 0:
+                    n = os.write(fd, stdout_buffer)
+                    stdout_buffer = stdout_buffer[n:]
+                elif fd == sys.stderr.fileno() and len(stderr_buffer) > 0:
+                    n = os.write(fd, stderr_buffer)
+                    stderr_buffer = stderr_buffer[n:]
             if stop:
                 p.terminate()
                 break

python/mlx/nn/layers/upsample.py

@@ -25,7 +25,16 @@ def _scaled_indices(N, scale, align_corners, dim, ndims):
 
 
 def _nearest_indices(N, scale, dim, ndims):
-    return _scaled_indices(N, scale, True, dim, ndims).astype(mx.uint32)
+    M = int(scale * N)
+    indices = mx.arange(M, dtype=mx.float32)
+    if M > N:
+        indices = (indices + 0.5) * (N / M) - 0.5
+        indices = indices.round()
+    else:
+        indices = indices * (N / M)
+    shape = [1] * ndims
+    shape[dim] = -1
+    return indices.astype(mx.uint32).reshape(shape)
 
 
 def _linear_indices(N, scale, align_corners, dim, ndims):

python/src/array.cpp

@@ -319,6 +319,18 @@ void init_array(nb::module_& m) {
           R"pbdoc(
             The array's :class:`Dtype`.
           )pbdoc")
+      .def_prop_ro(
+          "real",
+          [](const mx::array& a) { return mx::real(a); },
+          R"pbdoc(
+            The real part of a complex array.
+          )pbdoc")
+      .def_prop_ro(
+          "imag",
+          [](const mx::array& a) { return mx::imag(a); },
+          R"pbdoc(
+            The imaginary part of a complex array.
+          )pbdoc")
       .def(
           "item",
           &to_scalar,

python/src/linalg.cpp

@@ -236,7 +236,7 @@ void init_linalg(nb::module_& parent_module) {
 
         Returns:
             Union[tuple(array, ...), array]:
-              If compute_uv is ``True`` returns the ``U``, ``S``, and ``Vt`` matrices, such that 
+              If compute_uv is ``True`` returns the ``U``, ``S``, and ``Vt`` matrices, such that
               ``A = U @ diag(S) @ Vt``. If compute_uv is ``False`` returns singular values array ``S``.
       )pbdoc");
   m.def(
@@ -407,6 +407,76 @@ void init_linalg(nb::module_& parent_module) {
         Returns:
             array: The cross product of ``a`` and ``b`` along the specified axis.
       )pbdoc");
+  m.def(
+      "eigvals",
+      &mx::linalg::eigvals,
+      "a"_a,
+      nb::kw_only(),
+      "stream"_a = nb::none(),
+      R"pbdoc(
+        Compute the eigenvalues of a square matrix.
+
+        This function differs from :func:`numpy.linalg.eigvals` in that the
+        return type is always complex even if the eigenvalues are all real.
+
+        This function supports arrays with at least 2 dimensions. When the
+        input has more than two dimensions, the eigenvalues are computed for
+        each matrix in the last two dimensions.
+
+        Args:
+            a (array): The input array.
+            stream (Stream, optional): Stream or device. Defaults to ``None``
+              in which case the default stream of the default device is used.
+
+        Returns:
+            array: The eigenvalues (not necessarily in order).
+
+        Example:
+            >>> A = mx.array([[1., -2.], [-2., 1.]])
+            >>> eigenvalues = mx.linalg.eigvals(A, stream=mx.cpu)
+            >>> eigenvalues
+            array([3+0j, -1+0j], dtype=complex64)
+      )pbdoc");
+  m.def(
+      "eig",
+      [](const mx::array& a, mx::StreamOrDevice s) {
+        auto result = mx::linalg::eig(a, s);
+        return nb::make_tuple(result.first, result.second);
+      },
+      "a"_a,
+      nb::kw_only(),
+      "stream"_a = nb::none(),
+      R"pbdoc(
+        Compute the eigenvalues and eigenvectors of a square matrix.
+
+        This function differs from :func:`numpy.linalg.eig` in that the
+        return type is always complex even if the eigenvalues are all real.
+
+        This function supports arrays with at least 2 dimensions. When the input
+        has more than two dimensions, the eigenvalues and eigenvectors are
+        computed for each matrix in the last two dimensions.
+
+        Args:
+            a (array): The input array.
+            stream (Stream, optional): Stream or device. Defaults to ``None``
+              in which case the default stream of the default device is used.
+
+        Returns:
+            Tuple[array, array]:
+              A tuple containing the eigenvalues and the normalized right
+              eigenvectors. The column ``v[:, i]`` is the eigenvector
+              corresponding to the i-th eigenvalue.
+
+        Example:
+            >>> A = mx.array([[1., -2.], [-2., 1.]])
+            >>> w, v = mx.linalg.eig(A, stream=mx.cpu)
+            >>> w
+            array([3+0j, -1+0j], dtype=complex64)
+            >>> v
+            array([[0.707107+0j, 0.707107+0j],
+                   [-0.707107+0j, 0.707107+0j]], dtype=complex64)
+      )pbdoc");
+
   m.def(
       "eigvalsh",
       &mx::linalg::eigvalsh,

python/src/metal.cpp

@@ -49,21 +49,21 @@ void init_metal(nb::module_& m) {
   metal.def(
       "set_memory_limit",
       [](size_t limit) {
-        DEPRECATE("mx.metal.set_memory_limt", "mx.set_memory_limit");
+        DEPRECATE("mx.metal.set_memory_limit", "mx.set_memory_limit");
         return mx::set_memory_limit(limit);
       },
       "limit"_a);
   metal.def(
       "set_cache_limit",
       [](size_t limit) {
-        DEPRECATE("mx.metal.set_cache_limt", "mx.set_cache_limit");
+        DEPRECATE("mx.metal.set_cache_limit", "mx.set_cache_limit");
         return mx::set_cache_limit(limit);
       },
       "limit"_a);
   metal.def(
       "set_wired_limit",
       [](size_t limit) {
-        DEPRECATE("mx.metal.set_wired_limt", "mx.set_wired_limit");
+        DEPRECATE("mx.metal.set_wired_limit", "mx.set_wired_limit");
         return mx::set_wired_limit(limit);
       },
       "limit"_a);