Add stricter condition to matrix sdpa

docs/requirements.txt

@@ -1,5 +1,4 @@
 sphinx
 breathe
 sphinx-book-theme
-sphinx-copybutton
 mlx

docs/src/conf.py

@@ -18,7 +18,6 @@ release = version
 # -- General configuration ---------------------------------------------------
 
 extensions = [
-    "sphinx_copybutton",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",

docs/src/python/optimizers.rst

@@ -51,14 +51,14 @@ the saved state. Here's a simple example:
    optimizer.update(model, grads)
 
    # Save the state
-   state = tree_flatten(optimizer.state, destination={})
-   mx.save_safetensors("optimizer.safetensors", state)
+   state = tree_flatten(optimizer.state)
+   mx.save_safetensors("optimizer.safetensors", dict(state))
 
    # Later on, for example when loading from a checkpoint,
    # recreate the optimizer and load the state
    optimizer = optim.Adam(learning_rate=1e-2)
 
-   state = tree_unflatten(mx.load("optimizer.safetensors"))
+   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++).
+front-end (e.g. C++). 
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting
+Basics of Exporting 
 -------------------
 
 Let's start with a simple example:
-
+ 
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-
+   
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-
+  
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
-
-   params = tree_flatten(model.parameters(), destination={})
+ 
+   params = dict(tree_flatten(model.parameters()))
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -169,8 +169,8 @@ to export a function which can be used for inputs with variable shapes:
 
   # Ok
   out, = imported_abs(mx.array(-1.0))
-
-  # Also ok
+  
+  # Also ok 
   out, = imported_abs(mx.array([-1.0, -2.0]))
 
 With ``shapeless=False`` (which is the default), the second call to
@@ -197,7 +197,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   def fun(x, y=None):
       constant = mx.array(3.0)
       if y is not None:
-        x += y
+        x += y 
       return x + constant
 
   with mx.exporter("fun.mlxfn", fun) as exporter:
@@ -215,7 +215,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   print(out)
 
 In the above example the function constant data, (i.e. ``constant``), is only
-saved once.
+saved once. 
 
 Transformations with Imported Functions
 ---------------------------------------
@@ -238,7 +238,7 @@ on imported functions just like regular Python functions:
   # Prints: array(1, dtype=float32)
   print(dfdx(x))
 
-  # Compile the imported function
+  # Compile the imported function 
   mx.compile(imported_fun)
   # Prints: array(0, dtype=float32)
   print(compiled_fun(x)[0])
@@ -275,7 +275,7 @@ Import and run the function in C++ with only a few lines of code:
   // Prints: array(2, dtype=float32)
   std::cout << outputs[0] << std::endl;
 
-Imported functions can be transformed in C++ just like in Python. Use
+Imported functions can be transformed in C++ just like in Python. Use 
 ``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
 mx::array>`` for keyword arguments when calling imported functions in C++.
 

mlx/backend/common/utils.cpp

@@ -228,4 +228,31 @@ std::pair<Dims, Dims> get_grid_and_block_common(int dim0, int dim1, int dim2) {
       std::make_tuple(gx, gy, gz), std::make_tuple(bx, by, bz));
 }
 
+array swapaxes_in_eval(const array& x, int axis1, int axis2) {
+  int ndim = x.ndim();
+  if (axis1 < 0) {
+    axis1 += ndim;
+  }
+  if (axis2 < 0) {
+    axis2 += ndim;
+  }
+
+  auto shape = x.shape();
+  std::swap(shape[axis1], shape[axis2]);
+  auto strides = x.strides();
+  std::swap(strides[axis1], strides[axis2]);
+
+  auto [data_size, row_contiguous, col_contiguous] =
+      check_contiguity(shape, strides);
+  bool contiguous = data_size == x.data_size();
+
+  array out(std::move(shape), x.dtype(), nullptr, {});
+  out.copy_shared_buffer(
+      x,
+      std::move(strides),
+      {contiguous, row_contiguous, col_contiguous},
+      x.data_size());
+  return out;
+}
+
 } // namespace mlx::core

mlx/backend/common/utils.h

@@ -196,6 +196,9 @@ void shared_buffer_reshape(
     const Strides& out_strides,
     array& out);
 
+// Like the swapaxes op but safe to call in eval_gpu.
+array swapaxes_in_eval(const array& x, int axis1, int axis2);
+
 template <typename T>
 inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
   vec.erase(std::next(vec.begin(), index));

mlx/backend/cpu/compiled.cpp

@@ -157,12 +157,10 @@ inline void build_kernel(
 #endif
 
   // Start the kernel
-  os << "void " << kernel_name
-     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
+  os << "void " << kernel_name << "(void** args) {" << std::endl;
 
   // Add the input arguments
   int cnt = 0;
-  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     // Skip constants from the input list
     if (is_constant(i)) {
@@ -177,8 +175,8 @@ inline void build_kernel(
        << "];" << std::endl;
     // Scalars and contiguous need no strides
     if (!is_scalar(x) && !contiguous) {
-      os << "  const int64_t* " << xname << "_strides = strides["
-         << strides_index++ << "];" << std::endl;
+      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
+         << "];" << std::endl;
     }
   }
 
@@ -188,8 +186,10 @@ inline void build_kernel(
     os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
        << "*)args[" << cnt++ << "];" << std::endl;
   }
-  // Add output size
-  if (contiguous) {
+  // Add output strides and shape to extract the indices.
+  if (!contiguous) {
+    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
+  } else {
     os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
   }
 
@@ -288,8 +288,17 @@ void Compiled::eval_cpu(
   auto [contiguous, shape, strides] =
       compiled_collapse_contiguous_dims(inputs, outputs[0], is_constant_);
 
+  // Force allocating shape/strides on heap so we can take their data() first
+  // and then std::move them.
+  // TODO: Refactor code to avoid heap allocation.
+  shape.grow();
+  for (auto& s : strides) {
+    s.grow();
+  }
+
   // Collect function input arguments.
   std::vector<void*> args;
+  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     if (is_constant_(i)) {
       continue;
@@ -297,6 +306,9 @@ void Compiled::eval_cpu(
     const auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
+    if (!contiguous && !is_scalar(x)) {
+      args.push_back(strides[strides_index++].data());
+    }
   }
 
   // Get the kernel name from the lib
@@ -331,20 +343,16 @@ void Compiled::eval_cpu(
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
-  if (contiguous) {
+  if (!contiguous) {
+    args.push_back((void*)shape.data());
+  } else {
     args.push_back((void*)outputs[0].data_size());
   }
-  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
+  auto fun = (void (*)(void**))fn_ptr;
   encoder.dispatch([fun,
                     args = std::move(args),
                     strides = std::move(strides),
-                    shape = std::move(shape)]() mutable {
-    SmallVector<int64_t*> strides_ptrs;
-    for (auto& s : strides) {
-      strides_ptrs.push_back(s.data());
-    }
-    fun(shape.data(), strides_ptrs.data(), args.data());
-  });
+                    shape = std::move(shape)]() mutable { fun(args.data()); });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/reduce.cpp

@@ -491,27 +491,19 @@ void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
         switch (in.dtype()) {
           case bool_:
           case uint8:
-            reduce_dispatch_sum_prod<uint8_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint16:
-            reduce_dispatch_sum_prod<uint16_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint32:
-            reduce_dispatch_sum_prod<uint32_t>(in, out, reduce_type_, axes_);
-            break;
-          case uint64:
-            reduce_dispatch_sum_prod<uint64_t>(in, out, reduce_type_, axes_);
-            break;
           case int8:
             reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
             break;
           case int16:
+          case uint16:
             reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
             break;
           case int32:
+          case uint32:
             reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
             break;
           case int64:
+          case uint64:
             reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
             break;
           case float16:

mlx/backend/cuda/CMakeLists.txt

@@ -8,6 +8,7 @@ target_sources(
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/arange.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/binary_two.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
@@ -44,20 +45,18 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/quantized/affine_quantize.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/binary)
-add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/unary)
-
 if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9.0)
   target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_9.cu)
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_9.cu)
 else()
   target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_0.cpp)
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_0.cpp)
 endif()
 
 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
@@ -149,7 +148,7 @@ target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 FetchContent_Declare(
   cudnn
   GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
-  GIT_TAG v1.14.0
+  GIT_TAG v1.12.1
   GIT_SHALLOW TRUE
   EXCLUDE_FROM_ALL)
 set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)

mlx/backend/cuda/binary.cu

@@ -99,89 +99,39 @@ __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
   }
 }
 
-template <
-    typename Op,
-    typename In,
-    typename Out,
-    typename IdxT,
-    int NDIM,
-    int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
 __global__ void binary_g_nd(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), a_strides.data(), b_strides.data());
+    out[index] = Op{}(a[a_idx], b[b_idx]);
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto a_stride_x = a_strides[NDIM - 1];
-  auto b_stride_x = b_strides[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_g(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc(
+        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
+    out[index] = Op{}(a[a_idx], b[b_idx]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto a_stride_x = a_strides[ndim - 1];
-  auto b_stride_x = b_strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc(
-      index_rest * shape_x,
-      shape.data(),
-      a_strides.data(),
-      b_strides.data(),
-      ndim);
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -259,61 +209,39 @@ void binary_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
-                int work_per_thread = 1;
-                auto dim0 = ndim > 0 ? shape.back() : 1;
-                auto rest = out.size() / dim0;
-                if (dim0 >= 4) {
-                  work_per_thread = 4;
-                }
-                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-                auto block_dims = get_block_dims(dim0, rest, 1);
-                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::binary_g_nd<
-                        Op,
-                        InType,
-                        OutType,
-                        IdxT,
-                        dims_constant(),
-                        1>;
-                    if (work_per_thread == 4) {
-                      kernel = cu::binary_g_nd<
-                          Op,
-                          InType,
-                          OutType,
-                          IdxT,
-                          dims_constant(),
-                          4>;
-                    }
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(out, large());
                     encoder.add_kernel_node(
-                        kernel,
-                        {num_blocks_x, num_blocks_y},
+                        cu::binary_g_nd<
+                            Op,
+                            InType,
+                            OutType,
+                            IdxT,
+                            dims_constant()>,
+                        num_blocks,
                         block_dims,
                         0,
                         a.data<InType>(),
                         b.data<InType>(),
                         out.data<OutType>(),
-                        rest,
+                        out.size(),
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT, 1>;
-                  if (work_per_thread == 4) {
-                    kernel = cu::binary_g<Op, InType, OutType, IdxT, 4>;
-                  }
+                  auto [num_blocks, block_dims] = get_launch_args(out, large());
                   encoder.add_kernel_node(
-                      kernel,
-                      {num_blocks_x, num_blocks_y},
+                      cu::binary_g<Op, InType, OutType, IdxT>,
+                      num_blocks,
                       block_dims,
                       0,
                       a.data<InType>(),
                       b.data<InType>(),
                       out.data<OutType>(),
-                      rest,
+                      out.size(),
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -376,4 +304,54 @@ void binary_op_gpu(
     binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
   }
 
+BINARY_GPU(Add)
+BINARY_GPU(ArcTan2)
+BINARY_GPU(Divide)
+BINARY_GPU(Remainder)
+BINARY_GPU(Greater)
+BINARY_GPU(GreaterEqual)
+BINARY_GPU(Less)
+BINARY_GPU(LessEqual)
+BINARY_GPU(LogicalAnd)
+BINARY_GPU(LogicalOr)
+BINARY_GPU(LogAddExp)
+BINARY_GPU(Maximum)
+BINARY_GPU(Minimum)
+BINARY_GPU(Multiply)
+BINARY_GPU(NotEqual)
+BINARY_GPU(Power)
+BINARY_GPU(Subtract)
+
+void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Equal::eval_gpu");
+  auto& s = out.primitive().stream();
+  if (equal_nan_) {
+    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
+  } else {
+    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
+  }
+}
+
+void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
+  auto& s = out.primitive().stream();
+  switch (op_) {
+    case BitwiseBinary::And:
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Or:
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Xor:
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::LeftShift:
+      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::RightShift:
+      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
+      break;
+  }
+}
+
 } // namespace mlx::core

mlx/backend/cuda/binary/CMakeLists.txt

@@ -1,21 +0,0 @@
-target_sources(
-  mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/add.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arctan2.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bitwise_binary.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/divide.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/equal.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater_equal.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less_equal.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_and.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_or.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/log_add_exp.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/minimum.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/maximum.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/multiply.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/power.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/remainder.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/not_equal.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/subtract.cu)

mlx/backend/cuda/binary/add.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Add)
-} // namespace mlx::core

mlx/backend/cuda/binary/arctan2.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(ArcTan2)
-} // namespace mlx::core

mlx/backend/cuda/binary/bitwise_binary.cu

@@ -1,27 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
-  auto& s = out.primitive().stream();
-  switch (op_) {
-    case BitwiseBinary::And:
-      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Or:
-      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Xor:
-      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::LeftShift:
-      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::RightShift:
-      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
-      break;
-  }
-}
-} // namespace mlx::core

mlx/backend/cuda/binary/divide.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Divide)
-} // namespace mlx::core

mlx/backend/cuda/binary/equal.cu

@@ -1,15 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Equal::eval_gpu");
-  auto& s = out.primitive().stream();
-  if (equal_nan_) {
-    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
-  } else {
-    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
-  }
-}
-} // namespace mlx::core

mlx/backend/cuda/binary/greater.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Greater)
-} // namespace mlx::core

mlx/backend/cuda/binary/greater_equal.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(GreaterEqual)
-} // namespace mlx::core

mlx/backend/cuda/binary/less.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Less)
-} // namespace mlx::core

mlx/backend/cuda/binary/less_equal.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LessEqual)
-} // namespace mlx::core

mlx/backend/cuda/binary/log_add_exp.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogAddExp)
-} // namespace mlx::core

mlx/backend/cuda/binary/logical_and.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogicalAnd)
-} // namespace mlx::core

mlx/backend/cuda/binary/logical_or.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogicalOr)
-} // namespace mlx::core

mlx/backend/cuda/binary/maximum.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Maximum)
-} // namespace mlx::core

mlx/backend/cuda/binary/minimum.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Minimum)
-} // namespace mlx::core

mlx/backend/cuda/binary/multiply.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Multiply)
-} // namespace mlx::core

mlx/backend/cuda/binary/not_equal.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(NotEqual)
-} // namespace mlx::core

mlx/backend/cuda/binary/power.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Power)
-} // namespace mlx::core

mlx/backend/cuda/binary/remainder.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Remainder)
-} // namespace mlx::core

mlx/backend/cuda/binary/subtract.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Subtract)
-} // namespace mlx::core

mlx/backend/cuda/binary_two.cu

@@ -127,99 +127,45 @@ binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
   }
 }
 
-template <
-    typename Op,
-    typename In,
-    typename Out,
-    typename IdxT,
-    int NDIM,
-    int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
 __global__ void binary_two_g_nd(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), a_strides.data(), b_strides.data());
+    auto out = Op{}(a[a_idx], b[b_idx]);
+    out_a[index] = out[0];
+    out_b[index] = out[1];
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto a_stride_x = a_strides[NDIM - 1];
-  auto b_stride_x = b_strides[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec_a;
-  AlignedVector<Out, N_READS> out_vec_b;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    auto out = Op{}(a_vec[i], b_vec[i]);
-    out_vec_a[i] = out[0];
-    out_vec_b[i] = out[1];
-  }
-  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
-  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_two_g(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx] = elem_to_loc(
+        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
+    auto out = Op{}(a[a_idx], b[b_idx]);
+    out_a[index] = out[0];
+    out_b[index] = out[1];
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto a_stride_x = a_strides[ndim - 1];
-  auto b_stride_x = b_strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx] = elem_to_loc(
-      index_rest * shape_x,
-      shape.data(),
-      a_strides.data(),
-      b_strides.data(),
-      ndim);
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
-
-  AlignedVector<Out, N_READS> out_vec_a;
-  AlignedVector<Out, N_READS> out_vec_b;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    auto out = Op{}(a_vec[i], b_vec[i]);
-    out_vec_a[i] = out[0];
-    out_vec_b[i] = out[1];
-  }
-  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
-  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -279,64 +225,42 @@ void binary_two_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
-                int work_per_thread = 1;
-                auto dim0 = ndim > 0 ? shape.back() : 1;
-                auto rest = out_a.size() / dim0;
-                if (dim0 >= 4) {
-                  work_per_thread = 4;
-                }
-                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-                auto block_dims = get_block_dims(dim0, rest, 1);
-                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::binary_two_g_nd<
-                        Op,
-                        InType,
-                        OutType,
-                        IdxT,
-                        dims_constant(),
-                        1>;
-                    if (work_per_thread == 4) {
-                      kernel = cu::binary_two_g_nd<
-                          Op,
-                          InType,
-                          OutType,
-                          IdxT,
-                          dims_constant(),
-                          4>;
-                    }
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(out_a, large());
                     encoder.add_kernel_node(
-                        kernel,
-                        {num_blocks_x, num_blocks_y},
+                        cu::binary_two_g_nd<
+                            Op,
+                            InType,
+                            OutType,
+                            IdxT,
+                            dims_constant()>,
+                        num_blocks,
                         block_dims,
                         0,
                         a.data<InType>(),
                         b.data<InType>(),
                         out_a.data<OutType>(),
                         out_b.data<OutType>(),
-                        rest,
+                        out_a.size(),
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 1>;
-                  if (work_per_thread == 4) {
-                    kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 4>;
-                  }
+                  auto [num_blocks, block_dims] =
+                      get_launch_args(out_a, large());
                   encoder.add_kernel_node(
-                      kernel,
-                      {num_blocks_x, num_blocks_y},
+                      cu::binary_two_g<Op, InType, OutType, IdxT>,
+                      num_blocks,
                       block_dims,
                       0,
                       a.data<InType>(),
                       b.data<InType>(),
                       out_a.data<OutType>(),
                       out_b.data<OutType>(),
-                      rest,
+                      out_a.size(),
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),

mlx/backend/cuda/conv.cpp

@@ -7,6 +7,9 @@
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
 
+// cudnn_frontend.h redefines this macro.
+#undef CHECK_CUDA_ERROR
+
 #include <cudnn_frontend.h>
 #include <cudnn_frontend_find_plan.h>
 #include <fmt/format.h>
@@ -333,42 +336,6 @@ std::optional<cudnn_frontend::OperationGraph> build_op_graph(
   }
 }
 
-// Transpose from (C_out, H, W, C_in / groups) to (C_in, H, W, C_out / groups).
-array group_transpose(
-    const array& x,
-    int groups,
-    int group_dim,
-    int axis1,
-    int axis2,
-    Stream s) {
-  if (groups == 1) {
-    return swapaxes_in_eval(x, axis1, axis2);
-  }
-  int ndim = x.ndim();
-  if (group_dim < 0) {
-    group_dim += ndim;
-  }
-  if (axis1 < 0) {
-    axis1 += ndim;
-  }
-  if (axis2 < 0) {
-    axis2 += ndim;
-  }
-  if (group_dim <= axis1) {
-    axis1 += 1;
-  }
-  if (group_dim <= axis2) {
-    axis2 += 1;
-  }
-  auto shape = x.shape();
-  shape.insert(shape.begin() + group_dim, groups);
-  shape[group_dim + 1] = shape[group_dim + 1] / groups;
-  array x_trans = reshape_in_eval(x, std::move(shape), s);
-  x_trans = swapaxes_in_eval(x_trans, axis1, axis2);
-  x_trans = flatten_in_eval(x_trans, group_dim, group_dim + 1, s);
-  return x_trans;
-}
-
 // Do necessary transposes and copies to prepare the inputs and outputs for
 // building the cuDNN conv op. It is safe to be called multiple times in one
 // eval_gpu, with cost of possible redundant copies.
@@ -378,14 +345,13 @@ std::tuple<array, array, array> prepare_args(
     array in,
     array wt,
     array out,
-    int groups,
     Stream s) {
   // Transpose the args depending on the backend type.
   // TODO: Handle groups.
   if (backend_type == CONV_BACKWARD_INPUT) {
-    wt = group_transpose(wt, groups, 0, 0, -1, s);
+    wt = swapaxes_in_eval(wt, 0, -1);
   } else if (backend_type == CONV_BACKWARD_WEIGHT) {
-    in = group_transpose(in, groups, -1, 0, -1, s);
+    in = swapaxes_in_eval(in, 0, -1);
     wt = swapaxes_in_eval(wt, 0, -1);
     // Create a contiguous array that shares the data with |out|, but with dim
     // C_in and C_out swapped.
@@ -491,8 +457,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
       get_alignment(out)};
   if (auto it = conv_cache().find(cache_key); it != conv_cache().end()) {
     auto& [backend_type, plan] = it->second;
-    std::tie(in, wt, out) =
-        prepare_args(encoder, backend_type, in, wt, out, groups_, s);
+    std::tie(in, wt, out) = prepare_args(encoder, backend_type, in, wt, out, s);
     register_args(encoder, backend_type, in, wt, out, out_);
     auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
     if (!execute_plan(encoder, plan, x, w, y)) {
@@ -525,7 +490,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
   std::optional<cudnn_frontend::OperationGraph> op_graph;
   for (auto try_backend : try_backends) {
     auto [in_copy, wt_copy, out_copy] =
-        prepare_args(encoder, try_backend, in, wt, out, groups_, s);
+        prepare_args(encoder, try_backend, in, wt, out, s);
     auto [x, w, y] = dispatch_args(try_backend, in_copy, wt_copy, out_copy);
     auto [stride, padding_lo, padding_hi, dilation] = get_conv_op_settings(
         try_backend,

mlx/backend/cuda/copy/copy_general.cu

@@ -10,80 +10,37 @@ namespace cu {
 
 namespace cg = cooperative_groups;
 
-template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
+template <typename In, typename Out, typename IdxT, int NDIM>
 __global__ void copy_gg_nd(
     const In* in,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
+        index, shape.data(), strides_in.data(), strides_out.data());
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto in_stride_x = strides_in[NDIM - 1];
-  auto out_stride_x = strides_out[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
-      index_rest * shape_x,
-      shape.data(),
-      strides_in.data(),
-      strides_out.data());
-
-  auto in_vec =
-      load_vector<N_READS>(in + idx_in, index_x, shape_x, in_stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
-  }
-  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }
 
-template <typename In, typename Out, typename IdxT, int N_READS>
+template <typename In, typename Out, typename IdxT>
 __global__ void copy_gg(
     const In* in,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides strides_in,
     const __grid_constant__ Strides strides_out,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [idx_in, idx_out] = elem_to_loc(
+        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto in_stride_x = strides_in[ndim - 1];
-  auto out_stride_x = strides_out[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [idx_in, idx_out] = elem_to_loc(
-      index_rest * shape_x,
-      shape.data(),
-      strides_in.data(),
-      strides_out.data(),
-      ndim);
-
-  auto in_vec =
-      load_vector<N_READS>(in + idx_in, index_x, shape_x, in_stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
-  }
-  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }
 
 } // namespace cu
@@ -112,52 +69,33 @@ void copy_general(
             size_t data_size = 1;
             for (auto& s : shape)
               data_size *= s;
-
-            int work_per_thread = 1;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = data_size / dim0;
-            if (dim0 >= 4) {
-              work_per_thread = 4;
-            }
-
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto ndim_constant) {
-                auto kernel =
-                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant(), 1>;
-                if (work_per_thread == 4) {
-                  kernel =
-                      cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant(), 4>;
-                }
+                auto [num_blocks, block_dims] =
+                    get_launch_args(data_size, shape, out.strides(), large());
                 encoder.add_kernel_node(
-                    kernel,
-                    {num_blocks_x, num_blocks_y},
+                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>,
+                    num_blocks,
                     block_dims,
                     0,
                     in_ptr,
                     out_ptr,
-                    rest,
+                    data_size,
                     const_param<ndim_constant()>(shape),
                     const_param<ndim_constant()>(strides_in),
                     const_param<ndim_constant()>(strides_out));
               });
             } else { // ndim >= 4
-              auto kernel = cu::copy_gg<InType, OutType, IdxT, 1>;
-              if (work_per_thread == 4) {
-                kernel = cu::copy_gg<InType, OutType, IdxT, 4>;
-              }
+              auto [num_blocks, block_dims] =
+                  get_launch_args(data_size, shape, out.strides(), large());
               encoder.add_kernel_node(
-                  kernel,
-                  {num_blocks_x, num_blocks_y},
+                  cu::copy_gg<InType, OutType, IdxT>,
+                  num_blocks,
                   block_dims,
                   0,
                   in_ptr,
                   out_ptr,
-                  rest,
+                  data_size,
                   const_param(shape),
                   const_param(strides_in),
                   const_param(strides_out),

mlx/backend/cuda/copy/copy_general_input.cu

@@ -10,67 +10,33 @@ namespace cu {
 
 namespace cg = cooperative_groups;
 
-template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
+template <typename In, typename Out, typename IdxT, int NDIM>
 __global__ void copy_g_nd(
     const In* in,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in) {
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    IdxT idx_in = elem_to_loc_nd<NDIM>(index, shape.data(), strides_in.data());
+    out[index] = CastOp<In, Out>{}(in[idx_in]);
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto stride_x = strides[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto idx =
-      elem_to_loc_nd<NDIM>(index_rest * shape_x, shape.data(), strides.data());
-  auto in_vec =
-      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename In, typename Out, typename IdxT, int N_READS>
+template <typename In, typename Out, typename IdxT>
 __global__ void copy_g(
     const In* in,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides,
+    const __grid_constant__ Strides strides_in,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    IdxT idx_in = elem_to_loc(index, shape.data(), strides_in.data(), ndim);
+    out[index] = CastOp<In, Out>{}(in[idx_in]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto stride_x = strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto idx =
-      elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
-  auto in_vec =
-      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 } // namespace cu
@@ -95,49 +61,30 @@ void copy_general_input(
             const InType* in_ptr = in.data<InType>() + offset_in;
             OutType* out_ptr = out.data<OutType>() + offset_out;
             int ndim = shape.size();
-            int work_per_thread = 1;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = out.size() / dim0;
-            if (dim0 >= 4) {
-              work_per_thread = 4;
-            }
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel =
-                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant(), 1>;
-                if (work_per_thread == 4) {
-                  kernel =
-                      cu::copy_g_nd<InType, OutType, IdxT, dims_constant(), 4>;
-                }
+                auto [num_blocks, block_dims] = get_launch_args(out, large());
                 encoder.add_kernel_node(
-                    kernel,
-                    {num_blocks_x, num_blocks_y},
+                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>,
+                    num_blocks,
                     block_dims,
                     0,
                     in_ptr,
                     out_ptr,
-                    rest,
+                    out.size(),
                     const_param<dims_constant()>(shape),
                     const_param<dims_constant()>(strides_in));
               });
             } else { // ndim >= 4
-              auto kernel = cu::copy_g<InType, OutType, IdxT, 1>;
-              if (work_per_thread == 4) {
-                kernel = cu::copy_g<InType, OutType, IdxT, 4>;
-              }
+              auto [num_blocks, block_dims] = get_launch_args(out, large());
               encoder.add_kernel_node(
-                  kernel,
-                  {num_blocks_x, num_blocks_y},
+                  cu::copy_g<InType, OutType, IdxT>,
+                  num_blocks,
                   block_dims,
                   0,
                   in_ptr,
                   out_ptr,
-                  rest,
+                  out.size(),
                   const_param(shape),
                   const_param(strides_in),
                   ndim);

mlx/backend/cuda/device/utils.cuh

@@ -146,23 +146,6 @@ inline __device__ void store_vector(
   }
 }
 
-template <int N, typename T, typename SizeT>
-inline __device__ void store_vector(
-    T* ptr,
-    uint32_t offset,
-    const AlignedVector<T, N>& vec,
-    SizeT size,
-    int64_t stride) {
-  if (is_aligned<N>(ptr) && (offset + 1) * N <= size && stride == 1) {
-    auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
-    to[offset] = vec;
-  } else {
-    for (int i = 0; (offset * N + i) < size && i < N; ++i) {
-      ptr[stride * (offset * N + i)] = vec[i];
-    }
-  }
-}
-
 ///////////////////////////////////////////////////////////////////////////////
 // Type limits utils
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/cuda/gemms/cublas_batched_gemm_12_0.cpp

@@ -4,16 +4,16 @@
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/gemms/cublas_gemm.h"
 
-namespace mlx::core {
+namespace mlx::core::cu {
 
-void CublasGemm::run_batched(
+void Matmul::run_batched(
     cu::CommandEncoder& encoder,
     array& out,
     const array& a,
     const array& b,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides) {
   encoder.set_input_array(a);
   encoder.set_input_array(b);
   encoder.set_output_array(out);
@@ -22,7 +22,7 @@ void CublasGemm::run_batched(
   ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
   auto concurrent = encoder.concurrent_context();
   for (size_t i = 0; i < nbatch; ++i) {
-    execute(
+    run_impl(
         encoder,
         out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
         a.data<int8_t>() + a.itemsize() * a_it.loc,
@@ -33,16 +33,16 @@ void CublasGemm::run_batched(
   }
 }
 
-void CublasGemm::run_batched(
+void Matmul::run_batched(
     cu::CommandEncoder& encoder,
     array& out,
     const array& a,
     const array& b,
     const array& c,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides,
-    const Strides& c_batch_strides,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    const mlx::core::Strides& c_batch_strides,
     float alpha,
     float beta) {
   encoder.set_input_array(a);
@@ -56,7 +56,7 @@ void CublasGemm::run_batched(
   ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
   auto concurrent = encoder.concurrent_context();
   for (size_t i = 0; i < nbatch; ++i) {
-    execute(
+    run_impl(
         encoder,
         out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
         a.data<int8_t>() + a.itemsize() * a_it.loc,
@@ -70,4 +70,4 @@ void CublasGemm::run_batched(
   }
 }
 
-} // namespace mlx::core
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_batched_gemm_12_9.cu

@@ -0,0 +1,208 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core::cu {
+
+namespace cg = cooperative_groups;
+
+__global__ void set_mm_device_pointers(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ Shape batch_shape,
+    const __grid_constant__ Strides a_batch_strides,
+    const __grid_constant__ Strides b_batch_strides,
+    int64_t batch_stride,
+    int batch_ndim,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset] = elem_to_loc(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      batch_ndim);
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+__global__ void set_addmm_device_pointers(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* c_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ Shape batch_shape,
+    const __grid_constant__ Strides a_batch_strides,
+    const __grid_constant__ Strides b_batch_strides,
+    const __grid_constant__ Strides c_batch_strides,
+    int64_t batch_stride,
+    int batch_ndim,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset, c_offset] = elem_to_loc(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      c_batch_strides.data(),
+      batch_ndim);
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
+  pointers[index + 3 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+void set_pointer_mode(cublasLtMatrixLayout_t desc, int batch_count) {
+  auto batch_mode = CUBLASLT_BATCH_MODE_POINTER_ARRAY;
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+      desc,
+      CUBLASLT_MATRIX_LAYOUT_BATCH_MODE,
+      &batch_mode,
+      sizeof(batch_mode)));
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+      desc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(int32_t)));
+}
+
+void Matmul::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides) {
+  auto batch_count = out.size() / (M_ * N_);
+  set_pointer_mode(a_desc_, batch_count);
+  set_pointer_mode(b_desc_, batch_count);
+  set_pointer_mode(out_desc_, batch_count);
+
+  // Launch kernel to set device offsets
+  auto pointers = array(
+      allocator::malloc(batch_count * sizeof(uint64_t) * 3),
+      {static_cast<int>(batch_count * 3)},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  int block_size = 512;
+  encoder.set_output_array(pointers);
+
+  encoder.add_kernel_node(
+      cu::set_mm_device_pointers,
+      cuda::ceil_div(pointers.size(), block_size),
+      block_size,
+      0,
+      pointers.data<int8_t*>(),
+      a.data<int8_t>(),
+      b.data<int8_t>(),
+      out.data<int8_t>(),
+      static_cast<int>(out.dtype().size()),
+      const_param(batch_shape),
+      const_param(a_batch_strides),
+      const_param(b_batch_strides),
+      static_cast<int64_t>(M_) * N_,
+      static_cast<int>(batch_shape.size()),
+      batch_count);
+
+  // Run matmul
+  encoder.set_input_array(pointers);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+
+  auto a_pointers = pointers.data<int8_t*>();
+  auto b_pointers = a_pointers + batch_count;
+  auto out_pointers = b_pointers + batch_count;
+  run_impl(
+      encoder,
+      reinterpret_cast<void*>(out_pointers),
+      reinterpret_cast<void*>(a_pointers),
+      reinterpret_cast<void*>(b_pointers),
+      nullptr);
+}
+
+void Matmul::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const array& c,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    const mlx::core::Strides& c_batch_strides,
+    float alpha,
+    float beta) {
+  auto batch_count = out.size() / (M_ * N_);
+  set_pointer_mode(a_desc_, batch_count);
+  set_pointer_mode(b_desc_, batch_count);
+  set_pointer_mode(c_desc_, batch_count);
+  set_pointer_mode(out_desc_, batch_count);
+
+  // Launch kernel to set device offsets
+  auto pointers = array(
+      allocator::malloc(batch_count * sizeof(uint64_t) * 4),
+      {static_cast<int>(batch_count * 4)},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  int block_size = 512;
+  encoder.set_output_array(pointers);
+  encoder.add_kernel_node(
+      cu::set_addmm_device_pointers,
+      cuda::ceil_div(pointers.size(), block_size),
+      block_size,
+      0,
+      pointers.data<int8_t*>(),
+      a.data<int8_t>(),
+      b.data<int8_t>(),
+      c.data<int8_t>(),
+      out.data<int8_t>(),
+      static_cast<int>(out.dtype().size()),
+      const_param(batch_shape),
+      const_param(a_batch_strides),
+      const_param(b_batch_strides),
+      const_param(c_batch_strides),
+      static_cast<int64_t>(M_) * N_,
+      static_cast<int>(batch_shape.size()),
+      batch_count);
+
+  // Run matmul
+  encoder.set_input_array(pointers);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(c);
+  encoder.set_output_array(out);
+
+  auto a_pointers = pointers.data<int8_t*>();
+  auto b_pointers = a_pointers + batch_count;
+  auto c_pointers = b_pointers + batch_count;
+  auto out_pointers = c_pointers + batch_count;
+  run_impl(
+      encoder,
+      reinterpret_cast<void*>(out_pointers),
+      reinterpret_cast<void*>(a_pointers),
+      reinterpret_cast<void*>(b_pointers),
+      reinterpret_cast<void*>(c_pointers),
+      alpha,
+      beta);
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm.cpp

@@ -7,12 +7,10 @@
 
 #include <fmt/format.h>
 
-namespace mlx::core {
-
-namespace {
+namespace mlx::core::cu {
 
 struct CublasPreference {
-  CublasPreference(cu::Device& device) {
+  CublasPreference(Device& device) {
     // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
     // for Hopper+:
     // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
@@ -35,7 +33,7 @@ struct CublasPreference {
   cublasLtMatmulPreference_t pref_{nullptr};
 };
 
-cublasLtMatmulPreference_t cublas_preference(cu::Device& device) {
+cublasLtMatmulPreference_t cublas_preference(Device& device) {
   static CublasPreference pref(device);
   return pref.pref_;
 }
@@ -54,7 +52,7 @@ cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
       return CUBLAS_COMPUTE_64F;
     default:
       throw std::runtime_error(fmt::format(
-          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
   }
 }
 
@@ -72,7 +70,7 @@ cudaDataType_t dtype_to_cublas_type(Dtype dtype) {
       return CUDA_C_32F;
     default:
       throw std::runtime_error(fmt::format(
-          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
   }
 }
 
@@ -104,10 +102,8 @@ cublasLtMatrixLayout_t create_matrix_layout(
   return desc;
 }
 
-} // namespace
-
-CublasGemm::CublasGemm(
-    cu::Device& device,
+Matmul::Matmul(
+    Device& device,
     Dtype dtype,
     bool a_transposed,
     uint64_t a_rows,
@@ -159,8 +155,8 @@ CublasGemm::CublasGemm(
       type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
 }
 
-CublasGemm::CublasGemm(
-    cu::Device& device,
+Matmul::Matmul(
+    Device& device,
     Dtype dtype,
     bool a_transposed,
     uint64_t a_rows,
@@ -175,7 +171,7 @@ CublasGemm::CublasGemm(
     int64_t a_batch_stride,
     int64_t b_batch_stride,
     int64_t c_batch_stride)
-    : CublasGemm(
+    : Matmul(
           device,
           dtype,
           a_transposed,
@@ -194,7 +190,7 @@ CublasGemm::CublasGemm(
       type, a_rows, b_cols, false, ldc, batch_count, c_batch_stride);
 }
 
-CublasGemm::~CublasGemm() {
+Matmul::~Matmul() {
   CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(a_desc_));
   CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(b_desc_));
   CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(c_desc_));
@@ -202,73 +198,7 @@ CublasGemm::~CublasGemm() {
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescDestroy(matmul_desc_));
 }
 
-void CublasGemm::run(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
-  int batch_count = out.size() / (M_ * N_);
-  if (batch_count / batch_shape.back() > 1) {
-    run_batched(
-        encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
-    return;
-  }
-
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-
-  execute(encoder, out.data<void>(), a.data<void>(), b.data<void>(), nullptr);
-}
-
-void CublasGemm::run(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const array& c,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides,
-    const Strides& c_batch_strides,
-    float alpha,
-    float beta) {
-  int batch_count = out.size() / (M_ * N_);
-  if (batch_count / batch_shape.back() > 1) {
-    run_batched(
-        encoder,
-        out,
-        a,
-        b,
-        c,
-        batch_shape,
-        a_batch_strides,
-        b_batch_strides,
-        c_batch_strides,
-        alpha,
-        beta);
-    return;
-  }
-
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-
-  execute(
-      encoder,
-      out.data<void>(),
-      a.data<void>(),
-      b.data<void>(),
-      c.data<void>(),
-      alpha,
-      beta);
-}
-
-void CublasGemm::execute(
+void Matmul::run_impl(
     cu::CommandEncoder& encoder,
     void* out,
     const void* a,
@@ -326,4 +256,29 @@ void CublasGemm::execute(
       encoder.stream()));
 }
 
-} // namespace mlx::core
+void Matmul::run(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const std::optional<array>& c /* = std::nullopt */,
+    float alpha /* = 1 */,
+    float beta /* = 0 */) {
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  if (c) {
+    encoder.set_input_array(*c);
+  }
+  encoder.set_output_array(out);
+
+  run_impl(
+      encoder,
+      out.data<void>(),
+      a.data<void>(),
+      b.data<void>(),
+      c ? c->data<void>() : nullptr,
+      alpha,
+      beta);
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm.h

@@ -5,13 +5,13 @@
 #include "mlx/backend/cuda/device.h"
 
 #include <cublasLt.h>
+#include <optional>
 
-namespace mlx::core {
-
-class CublasGemm {
+namespace mlx::core::cu {
+class Matmul {
  public:
-  CublasGemm(
-      cu::Device& device,
+  Matmul(
+      Device& device,
       Dtype dtype,
       bool a_transposed,
       uint64_t a_rows,
@@ -25,8 +25,8 @@ class CublasGemm {
       int64_t a_batch_stride,
       int64_t b_batch_stride);
 
-  CublasGemm(
-      cu::Device& device,
+  Matmul(
+      Device& device,
       Dtype dtype,
       bool a_transposed,
       uint64_t a_rows,
@@ -42,54 +42,41 @@ class CublasGemm {
       int64_t b_batch_stride,
       int64_t c_batch_stride);
 
-  ~CublasGemm();
+  ~Matmul();
 
   void run(
       cu::CommandEncoder& encoder,
       array& out,
       const array& a,
       const array& b,
-      const Shape& batch_shape,
-      const Strides& a_batch_strides,
-      const Strides& b_batch_strides);
+      const std::optional<array>& c = std::nullopt,
+      float alpha = 1,
+      float beta = 0);
 
-  void run(
+  void run_batched(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const mlx::core::Shape& batch_shape,
+      const mlx::core::Strides& a_batch_strides,
+      const mlx::core::Strides& b_batch_strides);
+
+  void run_batched(
       cu::CommandEncoder& encoder,
       array& out,
       const array& a,
       const array& b,
       const array& c,
-      const Shape& batch_shape,
-      const Strides& a_batch_strides,
-      const Strides& b_batch_strides,
-      const Strides& c_batch_strides,
+      const mlx::core::Shape& batch_shape,
+      const mlx::core::Strides& a_batch_strides,
+      const mlx::core::Strides& b_batch_strides,
+      const mlx::core::Strides& c_batch_strides,
       float alpha,
       float beta);
 
  private:
-  void run_batched(
-      cu::CommandEncoder& encoder,
-      array& out,
-      const array& a,
-      const array& b,
-      const Shape& batch_shape,
-      const Strides& a_batch_strides,
-      const Strides& b_batch_strides);
-
-  void run_batched(
-      cu::CommandEncoder& encoder,
-      array& out,
-      const array& a,
-      const array& b,
-      const array& c,
-      const Shape& batch_shape,
-      const Strides& a_batch_strides,
-      const Strides& b_batch_strides,
-      const Strides& c_batch_strides,
-      float alpha,
-      float beta);
-
-  void execute(
+  void run_impl(
       cu::CommandEncoder& encoder,
       void* out,
       const void* a,
@@ -110,4 +97,4 @@ class CublasGemm {
   cublasLtMatmulHeuristicResult_t heuristic_;
 };
 
-} // namespace mlx::core
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm_batched_12_9.cu

@@ -1,327 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <int NDIM>
-__global__ void set_mm_device_pointers_nd(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
-    int64_t batch_stride,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset] = elem_to_loc_nd<NDIM>(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data());
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-__global__ void set_mm_device_pointers_g(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ Shape batch_shape,
-    const __grid_constant__ Strides a_batch_strides,
-    const __grid_constant__ Strides b_batch_strides,
-    int64_t batch_stride,
-    int batch_ndim,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset] = elem_to_loc(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data(),
-      batch_ndim);
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-template <int NDIM>
-__global__ void set_addmm_device_pointers_nd(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* c_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> c_batch_strides,
-    int64_t batch_stride,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset, c_offset] = elem_to_loc_nd<NDIM>(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data(),
-      c_batch_strides.data());
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
-  pointers[index + 3 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-__global__ void set_addmm_device_pointers_g(
-    int8_t** pointers,
-    int8_t* a_start,
-    int8_t* b_start,
-    int8_t* c_start,
-    int8_t* out_start,
-    int item_size,
-    const __grid_constant__ Shape batch_shape,
-    const __grid_constant__ Strides a_batch_strides,
-    const __grid_constant__ Strides b_batch_strides,
-    const __grid_constant__ Strides c_batch_strides,
-    int64_t batch_stride,
-    int batch_ndim,
-    int batch_count) {
-  auto index = cg::this_grid().thread_rank();
-  if (index >= batch_count) {
-    return;
-  }
-  auto [a_offset, b_offset, c_offset] = elem_to_loc(
-      index,
-      batch_shape.data(),
-      a_batch_strides.data(),
-      b_batch_strides.data(),
-      c_batch_strides.data(),
-      batch_ndim);
-  pointers[index] = a_start + item_size * a_offset;
-  pointers[index + batch_count] = b_start + item_size * b_offset;
-  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
-  pointers[index + 3 * batch_count] =
-      out_start + item_size * index * batch_stride;
-}
-
-} // namespace cu
-
-namespace {
-
-void set_pointer_mode(cublasLtMatrixLayout_t desc, int batch_count) {
-  auto batch_mode = CUBLASLT_BATCH_MODE_POINTER_ARRAY;
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc,
-      CUBLASLT_MATRIX_LAYOUT_BATCH_MODE,
-      &batch_mode,
-      sizeof(batch_mode)));
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
-      desc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(int32_t)));
-}
-
-} // namespace
-
-void CublasGemm::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides) {
-  int batch_count = out.size() / (M_ * N_);
-  set_pointer_mode(a_desc_, batch_count);
-  set_pointer_mode(b_desc_, batch_count);
-  set_pointer_mode(out_desc_, batch_count);
-
-  // Launch kernel to set device offsets
-  auto pointers = array(
-      allocator::malloc(batch_count * sizeof(void*) * 3),
-      {batch_count * 3},
-      uint64);
-
-  encoder.add_temporary(pointers);
-  encoder.set_output_array(pointers);
-
-  int block_dims = std::min(batch_count, 256);
-  int num_blocks = cuda::ceil_div(batch_count, block_dims);
-  int64_t batch_stride = M_ * N_;
-  int item_size = out.itemsize();
-
-  int ndim = batch_shape.size();
-  if (ndim <= 3) {
-    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
-      encoder.add_kernel_node(
-          cu::set_mm_device_pointers_nd<ndim_constant()>,
-          num_blocks,
-          block_dims,
-          0,
-          pointers.data<int8_t*>(),
-          a.data<int8_t>(),
-          b.data<int8_t>(),
-          out.data<int8_t>(),
-          item_size,
-          const_param<ndim_constant()>(batch_shape),
-          const_param<ndim_constant()>(a_batch_strides),
-          const_param<ndim_constant()>(b_batch_strides),
-          batch_stride,
-          batch_count);
-    });
-  } else {
-    encoder.add_kernel_node(
-        cu::set_mm_device_pointers_g,
-        num_blocks,
-        block_dims,
-        0,
-        pointers.data<int8_t*>(),
-        a.data<int8_t>(),
-        b.data<int8_t>(),
-        out.data<int8_t>(),
-        item_size,
-        const_param(batch_shape),
-        const_param(a_batch_strides),
-        const_param(b_batch_strides),
-        batch_stride,
-        ndim,
-        batch_count);
-  }
-
-  // Run matmul
-  encoder.set_input_array(pointers);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_output_array(out);
-
-  auto a_pointers = pointers.data<int8_t*>();
-  auto b_pointers = a_pointers + batch_count;
-  auto out_pointers = b_pointers + batch_count;
-  execute(
-      encoder,
-      reinterpret_cast<void*>(out_pointers),
-      reinterpret_cast<void*>(a_pointers),
-      reinterpret_cast<void*>(b_pointers),
-      nullptr);
-}
-
-void CublasGemm::run_batched(
-    cu::CommandEncoder& encoder,
-    array& out,
-    const array& a,
-    const array& b,
-    const array& c,
-    const Shape& batch_shape,
-    const Strides& a_batch_strides,
-    const Strides& b_batch_strides,
-    const Strides& c_batch_strides,
-    float alpha,
-    float beta) {
-  int batch_count = out.size() / (M_ * N_);
-  set_pointer_mode(a_desc_, batch_count);
-  set_pointer_mode(b_desc_, batch_count);
-  set_pointer_mode(c_desc_, batch_count);
-  set_pointer_mode(out_desc_, batch_count);
-
-  // Launch kernel to set device offsets
-  auto pointers = array(
-      allocator::malloc(batch_count * sizeof(uint64_t) * 4),
-      {batch_count * 4},
-      uint64);
-
-  encoder.add_temporary(pointers);
-  encoder.set_output_array(pointers);
-
-  int block_dims = std::min(batch_count, 256);
-  int num_blocks = cuda::ceil_div(batch_count, block_dims);
-  int64_t batch_stride = M_ * N_;
-  int item_size = out.itemsize();
-
-  int ndim = batch_shape.size();
-  if (ndim <= 3) {
-    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
-      encoder.add_kernel_node(
-          cu::set_addmm_device_pointers_nd<ndim_constant()>,
-          num_blocks,
-          block_dims,
-          0,
-          pointers.data<int8_t*>(),
-          a.data<int8_t>(),
-          b.data<int8_t>(),
-          c.data<int8_t>(),
-          out.data<int8_t>(),
-          item_size,
-          const_param<ndim_constant()>(batch_shape),
-          const_param<ndim_constant()>(a_batch_strides),
-          const_param<ndim_constant()>(b_batch_strides),
-          const_param<ndim_constant()>(c_batch_strides),
-          batch_stride,
-          batch_count);
-    });
-  } else {
-    encoder.add_kernel_node(
-        cu::set_addmm_device_pointers_g,
-        num_blocks,
-        block_dims,
-        0,
-        pointers.data<int8_t*>(),
-        a.data<int8_t>(),
-        b.data<int8_t>(),
-        c.data<int8_t>(),
-        out.data<int8_t>(),
-        item_size,
-        const_param(batch_shape),
-        const_param(a_batch_strides),
-        const_param(b_batch_strides),
-        const_param(c_batch_strides),
-        batch_stride,
-        ndim,
-        batch_count);
-  }
-
-  // Run matmul
-  encoder.set_input_array(pointers);
-  encoder.set_input_array(a);
-  encoder.set_input_array(b);
-  encoder.set_input_array(c);
-  encoder.set_output_array(out);
-
-  auto a_pointers = pointers.data<int8_t*>();
-  auto b_pointers = a_pointers + batch_count;
-  auto c_pointers = b_pointers + batch_count;
-  auto out_pointers = c_pointers + batch_count;
-  execute(
-      encoder,
-      reinterpret_cast<void*>(out_pointers),
-      reinterpret_cast<void*>(a_pointers),
-      reinterpret_cast<void*>(b_pointers),
-      reinterpret_cast<void*>(c_pointers),
-      alpha,
-      beta);
-}
-
-} // namespace mlx::core

mlx/backend/cuda/matmul.cpp

@@ -97,7 +97,7 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
-  CublasGemm gemm(
+  cu::Matmul matmul(
       cu::device(s.device),
       a.dtype(),
       a_transposed,
@@ -111,7 +111,14 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
       batch_shape.back(),
       a_batch_strides.back(),
       b_batch_strides.back());
-  gemm.run(encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
+
+  if ((batch_count / batch_shape.back()) == 1) {
+    matmul.run(encoder, out, a, b);
+    return;
+  }
+
+  matmul.run_batched(
+      encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
 }
 
 void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -179,7 +186,7 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
 
-  CublasGemm gemm(
+  cu::Matmul matmul(
       cu::device(s.device),
       a.dtype(),
       a_transposed,
@@ -195,7 +202,12 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
       a_batch_strides.back(),
       b_batch_strides.back(),
       c_batch_strides.back());
-  gemm.run(
+
+  if ((batch_count / batch_shape.back()) == 1) {
+    matmul.run(encoder, out, a, b, c, alpha_, beta_);
+    return;
+  }
+  matmul.run_batched(
       encoder,
       out,
       a,

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -8,13 +8,19 @@
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/dtype_utils.h"
 #include "mlx/fast_primitives.h"
-#include "mlx/transforms_impl.h"
 
+// cudnn_frontend.h redefines this macro.
+#undef CHECK_CUDA_ERROR
+
+#include <cudnn_frontend.h>
+#include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>
 
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 
+namespace fe = cudnn_frontend;
+
 namespace mlx::core {
 
 namespace cu {
@@ -639,6 +645,294 @@ void sdpa_vector_fallback(
   }
 }
 
+struct SDPACacheKey {
+  int device_id;
+  fe::DataType_t cudnn_type;
+
+  int B;
+  int H;
+  int D;
+
+  int qL;
+  int kL;
+
+  int gqa_factor;
+  float scale;
+
+  int64_t Q_strides[3];
+  int64_t K_strides[3];
+  int64_t V_strides[3];
+  int64_t O_strides[3];
+
+  bool generate_stats;
+  bool causal_mask;
+};
+
+auto& sdpa_cache() {
+  static LRUBytesKeyCache<SDPACacheKey, std::shared_ptr<fe::graph::Graph>>
+      cache(
+          /* capacity */ 128);
+  return cache;
+}
+
+#define Q_UID 1
+#define K_UID 2
+#define V_UID 3
+#define O_UID 4
+#define STATS_UID 5
+
+std::shared_ptr<fe::graph::Graph> get_sdpa_forward_graph(
+    cu::CommandEncoder& encoder,
+    const SDPACacheKey& cache_key) {
+  // Check if graph has already been fully built
+  if (auto it = sdpa_cache().find(cache_key); it != sdpa_cache().end()) {
+    return it->second;
+  }
+
+  // Set up new graph
+  auto graph = std::make_shared<fe::graph::Graph>();
+
+  graph->set_io_data_type(cache_key.cudnn_type)
+      .set_intermediate_data_type(fe::DataType_t::FLOAT)
+      .set_compute_data_type(fe::DataType_t::FLOAT);
+
+  auto Q = graph->tensor(
+      fe::graph::Tensor_attributes()
+          .set_name("Q")
+          .set_uid(Q_UID)
+          .set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
+          .set_stride(
+              {cache_key.Q_strides[0],
+               cache_key.Q_strides[1],
+               cache_key.Q_strides[2],
+               1}));
+
+  int h_kv = cache_key.H / cache_key.gqa_factor;
+  auto K =
+      graph->tensor(fe::graph::Tensor_attributes()
+                        .set_name("K")
+                        .set_uid(K_UID)
+                        .set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
+                        .set_stride(
+                            {cache_key.K_strides[0],
+                             cache_key.K_strides[1],
+                             cache_key.V_strides[2],
+                             1}));
+
+  auto V =
+      graph->tensor(fe::graph::Tensor_attributes()
+                        .set_name("V")
+                        .set_uid(V_UID)
+                        .set_dim({cache_key.B, h_kv, cache_key.kL, cache_key.D})
+                        .set_stride(
+                            {cache_key.V_strides[0],
+                             cache_key.V_strides[1],
+                             cache_key.V_strides[2],
+                             1}));
+
+  auto sdpa_options = fe::graph::SDPA_attributes()
+                          .set_name("flash_attention")
+                          .set_is_inference(!cache_key.generate_stats)
+                          .set_attn_scale(cache_key.scale);
+
+  if (cache_key.causal_mask && cache_key.qL > 1) {
+    sdpa_options.set_diagonal_alignment(fe::DiagonalAlignment_t::TOP_LEFT)
+        .set_diagonal_band_right_bound(0);
+  }
+
+  auto [O, Stats] = graph->sdpa(Q, K, V, sdpa_options);
+
+  O->set_output(true)
+      .set_uid(O_UID)
+      .set_dim({cache_key.B, cache_key.H, cache_key.qL, cache_key.D})
+      .set_stride(
+          {cache_key.O_strides[0],
+           cache_key.O_strides[1],
+           cache_key.O_strides[2],
+           1});
+
+  if (cache_key.generate_stats) {
+    Stats->set_output(true)
+        .set_data_type(fe::DataType_t::FLOAT)
+        .set_uid(STATS_UID);
+  }
+
+  // Build and Validate cudnn graph
+
+  auto handle = encoder.device().cudnn_handle();
+
+  // cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
+  if (cudnnGetVersion() < 90600) {
+    auto build_status = graph->build(handle, {fe::HeurMode_t::A});
+    if (!build_status.is_good()) {
+      throw std::runtime_error(
+          "Unable to build cudnn graph for attention."
+          " Failed with message: " +
+          build_status.get_message());
+    }
+
+  } else {
+    auto val_status = graph->validate();
+    auto op_status = graph->build_operation_graph(handle);
+
+    auto plan_stauts =
+        graph->create_execution_plans({cudnn_frontend::HeurMode_t::A});
+    if (!plan_stauts.is_good()) {
+      throw std::runtime_error(
+          "Unable to create exec plan for cudnn attention."
+          " Failed with message: " +
+          plan_stauts.get_message());
+    }
+
+    graph->select_behavior_notes(
+        {cudnn_frontend::BehaviorNote_t::SUPPORTS_CUDA_GRAPH_NATIVE_API});
+
+    auto support_status = graph->check_support(handle);
+    if (!support_status.is_good()) {
+      throw std::runtime_error(
+          "No cuda graph support for cudnn attention."
+          " Failed with message: " +
+          support_status.get_message());
+    }
+
+    auto build_status = graph->build_plans(handle);
+    if (!build_status.is_good()) {
+      throw std::runtime_error(
+          "Unable to build cudnn graph for attention."
+          " Failed with message: " +
+          build_status.get_message());
+    }
+  }
+
+  auto [it, _] = sdpa_cache().emplace(cache_key, graph);
+
+  return it->second;
+}
+
+inline fe::DataType_t dtype_to_cudnn_type(Dtype dtype) {
+  switch (dtype) {
+    case int8:
+      return fe::DataType_t::INT8;
+    case int32:
+      return fe::DataType_t::INT32;
+    case uint8:
+      return fe::DataType_t::UINT8;
+    case float16:
+      return fe::DataType_t::HALF;
+    case bfloat16:
+      return fe::DataType_t::BFLOAT16;
+    case float32:
+      return fe::DataType_t::FLOAT;
+    case float64:
+      return fe::DataType_t::DOUBLE;
+    default:
+      throw std::runtime_error(fmt::format(
+          "Unsupported dtype in SDPA: {}.", dtype_to_string(dtype)));
+  }
+}
+
+void sdpa_cudnn(
+    const Stream& s,
+    cu::CommandEncoder& encoder,
+    const array& q,
+    const array& k,
+    const array& v,
+    const float scale,
+    array& o,
+    bool do_causal_ = false) {
+  encoder.set_input_array(q);
+  encoder.set_input_array(k);
+  encoder.set_input_array(v);
+  encoder.set_output_array(o);
+
+  auto cudnn_type = dtype_to_cudnn_type(q.dtype());
+
+  int B = q.shape(0);
+  int H = q.shape(1);
+  int D = q.shape(3);
+  int gqa_factor = q.shape(1) / k.shape(1);
+
+  int qL = q.shape(2);
+  int kL = k.shape(2);
+
+  SDPACacheKey cache_key{
+      /* int device_id = */ encoder.device().cuda_device(),
+      /* fe::DataType_t cudnn_type = */ cudnn_type,
+
+      /* int B = */ B,
+      /* int H = */ H,
+      /* int D = */ D,
+
+      /* int qL = */ qL,
+      /* int kL = */ kL,
+
+      /* int gqa_factor = */ gqa_factor,
+      /* float scale = */ scale,
+
+      /* int64_t Q_strides[3] = */ {q.strides(0), q.strides(1), q.strides(2)},
+      /* int64_t K_strides[3] = */ {k.strides(0), k.strides(1), k.strides(2)},
+      /* int64_t V_strides[3] = */ {v.strides(0), v.strides(1), v.strides(2)},
+      /* int64_t O_strides[3] = */ {o.strides(0), o.strides(1), o.strides(2)},
+
+      /* bool generate_stats = */ false,
+      /* bool causal_mask = */ do_causal_};
+
+  auto graph = get_sdpa_forward_graph(encoder, cache_key);
+
+  int64_t workspace_size = 0;
+  auto workspace_status = graph->get_workspace_size(workspace_size);
+  if (!workspace_status.is_good()) {
+    throw std::runtime_error("Unable to get workspace for cudnn attention.");
+  }
+
+  array workspace(
+      allocator::malloc(workspace_size), {int(workspace_size)}, uint8);
+  auto workspace_ptr = workspace.data<void>();
+
+  std::unordered_map<int64_t, void*> variant_pack = {
+      {Q_UID, const_cast<void*>(q.data<void>())},
+      {K_UID, const_cast<void*>(k.data<void>())},
+      {V_UID, const_cast<void*>(v.data<void>())},
+      {O_UID, o.data<void>()}};
+
+  auto handle = encoder.device().cudnn_handle();
+  cudnnSetStream(handle, encoder.stream());
+
+  // cuDNN only supports native CUDA graphs for sdpa in 9.6 or above.
+  if (cudnnGetVersion() < 90600) {
+    auto capture = encoder.capture_context();
+    auto exec_status = graph->execute(handle, variant_pack, workspace_ptr);
+
+    if (!exec_status.is_good()) {
+      capture.discard = true;
+      throw std::runtime_error(
+          "Unable to execute cudnn attention."
+          " Failed with message: " +
+          exec_status.get_message());
+    }
+  } else {
+    cudaGraph_t cu_graph;
+    cudaGraphCreate(&cu_graph, 0);
+
+    std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+        &cu_graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
+
+    auto cu_graph_status = graph->populate_cuda_graph(
+        handle, variant_pack, workspace_ptr, cu_graph);
+
+    if (!cu_graph_status.is_good()) {
+      throw std::runtime_error(
+          "Unable to add cuda graph for cudnn attention."
+          " Failed with message: " +
+          cu_graph_status.get_message());
+    }
+
+    encoder.add_graph_node(cu_graph);
+  }
+
+  encoder.add_temporary(workspace);
+}
+
 } // namespace
 
 namespace fast {
@@ -651,9 +945,6 @@ bool ScaledDotProductAttention::use_fallback(
     bool has_arr_mask,
     bool do_causal,
     Stream s) {
-  if (detail::in_grad_tracing()) {
-    return true;
-  }
   if (s.device == Device::cpu) {
     return true;
   }
@@ -669,7 +960,15 @@ bool ScaledDotProductAttention::use_fallback(
   const bool supported_vector_config =
       sdpa_supported_head_dim && query_sequence_length < 4;
 
-  const bool supported_config = supported_vector_config;
+  auto& cu_device = cu::device(s.device);
+
+  const bool supported_matrix_config = query_sequence_length > 4 &&
+      cu_device.compute_capability_major() >= 8 &&
+      query_sequence_length == key_sequence_length &&
+      (q.dtype() == float16 || q.dtype() == bfloat16);
+
+  const bool supported_config =
+      (supported_matrix_config || supported_vector_config);
 
   return has_arr_mask || !supported_config;
 }
@@ -703,6 +1002,10 @@ void ScaledDotProductAttention::eval_gpu(
     }
   };
 
+  auto is_matrix_contiguous = [](const array& arr) {
+    return arr.strides(-1) == 1;
+  };
+
   // We are in vector mode ie single query
   if (q_pre.shape(2) < 4) {
     auto q_copy_unless = [](const array& arr) {
@@ -756,7 +1059,7 @@ void ScaledDotProductAttention::eval_gpu(
 
       array::Flags flags{
           /* bool contiguous = */ 1,
-          /* bool row_contiguous = */ o.shape(2) == 1,
+          /* bool row_contiguous = */ 0,
           /* bool col_contiguous = */ 0,
       };
 
@@ -770,9 +1073,35 @@ void ScaledDotProductAttention::eval_gpu(
     return sdpa_vector_fallback(s, encoder, q, k, v, scale_, o, do_causal_);
   }
 
-  // Full attention mode should never reach here
+  // Full attention mode
   else {
-    throw std::runtime_error("Doesn't support matrix yet.");
+    const auto& q = copy_unless(is_matrix_contiguous, q_pre);
+    const auto& k = copy_unless(is_matrix_contiguous, k_pre);
+    const auto& v = copy_unless(is_matrix_contiguous, v_pre);
+
+    for (const auto& cp : copies) {
+      encoder.add_temporary(cp);
+    }
+
+    int64_t str_oD = 1;
+    int64_t str_oH = o.shape(3);
+    int64_t str_oL = o.shape(1) * str_oH;
+    int64_t str_oB = o.shape(2) * str_oL;
+    size_t data_size = o.shape(0) * str_oB;
+
+    array::Flags flags{
+        /* bool contiguous = */ 1,
+        /* bool row_contiguous = */ 0,
+        /* bool col_contiguous = */ 0,
+    };
+
+    o.set_data(
+        allocator::malloc(o.nbytes()),
+        data_size,
+        {str_oB, str_oH, str_oL, str_oD},
+        flags);
+
+    return sdpa_cudnn(s, encoder, q, k, v, scale_, o, do_causal_);
   }
 }
 

mlx/backend/cuda/sort.cu

@@ -1,5 +1,6 @@
 // Copyright © 2025 Apple Inc.
 
+#include "mlx/backend/common/utils.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/gpu/copy.h"

mlx/backend/cuda/ternary.cu

@@ -39,98 +39,52 @@ ternary_v(const bool* a, const T* b, const T* c, T* out, IdxT size) {
   }
 }
 
-template <typename Op, typename T, typename IdxT, int NDIM, int N_READS>
+template <typename Op, typename T, typename IdxT, int NDIM>
 __global__ void ternary_g_nd(
     const bool* a,
     const T* b,
     const T* c,
     T* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> c_strides) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx, c_idx] = elem_to_loc_nd<NDIM>(
+        index,
+        shape.data(),
+        a_strides.data(),
+        b_strides.data(),
+        c_strides.data());
+    out[index] = Op{}(a[a_idx], b[b_idx], c[c_idx]);
   }
-
-  auto shape_x = shape[NDIM - 1];
-  auto a_stride_x = a_strides[NDIM - 1];
-  auto b_stride_x = b_strides[NDIM - 1];
-  auto c_stride_x = c_strides[NDIM - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx, c_idx] = elem_to_loc_nd<NDIM>(
-      index_rest * shape_x,
-      shape.data(),
-      a_strides.data(),
-      b_strides.data(),
-      c_strides.data());
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, false);
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, T(0));
-  auto c_vec =
-      load_vector<N_READS>(c + c_idx, index_x, shape_x, c_stride_x, T(0));
-
-  AlignedVector<T, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i], c_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename Op, typename T, typename IdxT, int N_READS>
+template <typename Op, typename T, typename IdxT>
 __global__ void ternary_g(
     const bool* a,
     const T* b,
     const T* c,
     T* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     const __grid_constant__ Strides c_strides,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto [a_idx, b_idx, c_idx] = elem_to_loc(
+        index,
+        shape.data(),
+        a_strides.data(),
+        b_strides.data(),
+        c_strides.data(),
+        ndim);
+    out[index] = Op{}(a[a_idx], b[b_idx], c[c_idx]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto a_stride_x = a_strides[ndim - 1];
-  auto b_stride_x = b_strides[ndim - 1];
-  auto c_stride_x = c_strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto [a_idx, b_idx, c_idx] = elem_to_loc(
-      index_rest * shape_x,
-      shape.data(),
-      a_strides.data(),
-      b_strides.data(),
-      c_strides.data(),
-      ndim);
-  auto a_vec =
-      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, false);
-  auto b_vec =
-      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, T(0));
-  auto c_vec =
-      load_vector<N_READS>(c + c_idx, index_x, shape_x, c_stride_x, T(0));
-
-  AlignedVector<T, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(a_vec[i], b_vec[i], c_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 } // namespace cu
@@ -169,55 +123,36 @@ void ternary_op_gpu_inplace(
             auto& b_strides = strides[1];
             auto& c_strides = strides[2];
             int ndim = shape.size();
-            int work_per_thread = 1;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = out.size() / dim0;
-            if (dim0 >= 4) {
-              work_per_thread = 4;
-            }
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel =
-                    cu::ternary_g_nd<Op, DType, IdxT, dims_constant(), 1>;
-                if (work_per_thread == 4) {
-                  kernel =
-                      cu::ternary_g_nd<Op, DType, IdxT, dims_constant(), 4>;
-                }
+                auto [num_blocks, block_dims] = get_launch_args(out, large());
                 encoder.add_kernel_node(
-                    kernel,
-                    {num_blocks_x, num_blocks_y},
+                    cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>,
+                    num_blocks,
                     block_dims,
                     0,
                     a.data<bool>(),
                     b.data<DType>(),
                     c.data<DType>(),
                     out.data<DType>(),
-                    rest,
+                    out.size(),
                     const_param<dims_constant()>(shape),
                     const_param<dims_constant()>(a_strides),
                     const_param<dims_constant()>(b_strides),
                     const_param<dims_constant()>(c_strides));
               });
             } else {
-              auto kernel = cu::ternary_g<Op, DType, IdxT, 1>;
-              if (work_per_thread == 4) {
-                kernel = cu::ternary_g<Op, DType, IdxT, 4>;
-              }
+              auto [num_blocks, block_dims] = get_launch_args(out, large());
               encoder.add_kernel_node(
-                  kernel,
-                  {num_blocks_x, num_blocks_y},
+                  cu::ternary_g<Op, DType, IdxT>,
+                  num_blocks,
                   block_dims,
                   0,
                   a.data<bool>(),
                   b.data<DType>(),
                   c.data<DType>(),
                   out.data<DType>(),
-                  rest,
+                  out.data_size(),
                   const_param(shape),
                   const_param(a_strides),
                   const_param(b_strides),

mlx/backend/cuda/unary.cu

@@ -37,36 +37,19 @@ __global__ void unary_v(const In* in, Out* out, IdxT size) {
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void unary_g(
     const In* in,
     Out* out,
-    IdxT size_rest,
+    IdxT size,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides strides,
     int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    auto idx = elem_to_loc(index, shape.data(), strides.data(), ndim);
+    out[index] = Op{}(in[idx]);
   }
-
-  auto shape_x = shape[ndim - 1];
-  auto stride_x = strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto idx =
-      elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
-  auto in_vec =
-      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(in_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -144,7 +127,8 @@ void unary_op_gpu_inplace(
           using OutType = cuda_type_t<CTYPE_OUT>;
           if (contig) {
             using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            constexpr int N_READS = 16 / sizeof(OutType);
+            // TODO: Choose optimized value based on type size.
+            constexpr int N_READS = 4;
             auto [num_blocks, block_dims] = get_launch_args(
                 out.data_size(), out.shape(), out.strides(), large, N_READS);
             encoder.add_kernel_node(
@@ -158,30 +142,18 @@ void unary_op_gpu_inplace(
           } else {
             using IdxT = std::conditional_t<large(), int64_t, int32_t>;
             auto [shape, strides] = collapse_contiguous_dims(in);
-            auto ndim = shape.size();
-            int work_per_thread = 1;
-            auto kernel = cu::unary_g<Op, InType, OutType, IdxT, 1>;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = out.size() / dim0;
-            if (dim0 >= 4) {
-              kernel = cu::unary_g<Op, InType, OutType, IdxT, 4>;
-              work_per_thread = 4;
-            }
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
+            auto [num_blocks, block_dims] = get_launch_args(out, large);
             encoder.add_kernel_node(
-                kernel,
-                {num_blocks_x, num_blocks_y},
+                cu::unary_g<Op, InType, OutType, IdxT>,
+                num_blocks,
                 block_dims,
                 0,
                 in.data<InType>(),
                 out.data<OutType>(),
-                rest,
+                out.data_size(),
                 const_param(shape),
                 const_param(strides),
-                ndim);
+                shape.size());
           }
         });
       } else {

mlx/backend/cuda/unary/CMakeLists.txt

@@ -1,34 +0,0 @@
-target_sources(
-  mlx
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/abs.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arccos.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arccosh.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arcsin.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arcsinh.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arctan.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arctanh.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bitwise_invert.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/ceil.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/conjugate.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/cos.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/cosh.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/erf.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/erf_inv.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/exp.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/expm1.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/floor.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/imag.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/log.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/log1p.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_not.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/negative.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/real.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/round.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/sigmoid.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/sign.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/sin.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/sinh.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/sqrt.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/square.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/tan.cu
-  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/tanh.cu)

mlx/backend/cuda/unary/abs.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Abs)
-} // namespace mlx::core

mlx/backend/cuda/unary/arccos.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcCos)
-} // namespace mlx::core

mlx/backend/cuda/unary/arccosh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcCosh)
-} // namespace mlx::core

mlx/backend/cuda/unary/arcsin.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcSin)
-} // namespace mlx::core

mlx/backend/cuda/unary/arcsinh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcSinh)
-} // namespace mlx::core

mlx/backend/cuda/unary/arctan.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcTan)
-} // namespace mlx::core

mlx/backend/cuda/unary/arctanh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ArcTanh)
-} // namespace mlx::core

mlx/backend/cuda/unary/bitwise_invert.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(BitwiseInvert)
-} // namespace mlx::core

mlx/backend/cuda/unary/ceil.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Ceil)
-} // namespace mlx::core

mlx/backend/cuda/unary/conjugate.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Conjugate)
-} // namespace mlx::core

mlx/backend/cuda/unary/cos.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Cos)
-} // namespace mlx::core

mlx/backend/cuda/unary/cosh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Cosh)
-} // namespace mlx::core

mlx/backend/cuda/unary/erf.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Erf)
-} // namespace mlx::core

mlx/backend/cuda/unary/erf_inv.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(ErfInv)
-} // namespace mlx::core

mlx/backend/cuda/unary/exp.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Exp)
-} // namespace mlx::core

mlx/backend/cuda/unary/expm1.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Expm1)
-} // namespace mlx::core

mlx/backend/cuda/unary/floor.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Floor)
-} // namespace mlx::core

mlx/backend/cuda/unary/imag.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Imag)
-} // namespace mlx::core

mlx/backend/cuda/unary/log.cu

@@ -1,21 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-void Log::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Log::eval_gpu");
-  auto& s = out.primitive().stream();
-  switch (base_) {
-    case Base::e:
-      unary_op_gpu<cu::Log>(inputs, out, name(), s);
-      break;
-    case Base::two:
-      unary_op_gpu<cu::Log2>(inputs, out, name(), s);
-      break;
-    case Base::ten:
-      unary_op_gpu<cu::Log10>(inputs, out, name(), s);
-      break;
-  }
-}
-} // namespace mlx::core

mlx/backend/cuda/unary/log1p.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Log1p)
-} // namespace mlx::core

mlx/backend/cuda/unary/logical_not.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(LogicalNot)
-} // namespace mlx::core

mlx/backend/cuda/unary/negative.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Negative)
-} // namespace mlx::core

mlx/backend/cuda/unary/real.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Real)
-} // namespace mlx::core

mlx/backend/cuda/unary/round.cu

@@ -1,18 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Round::eval_gpu");
-  assert(inputs.size() == 1);
-  const auto& in = inputs[0];
-  auto& s = out.primitive().stream();
-  if (issubdtype(in.dtype(), inexact)) {
-    unary_op_gpu<cu::Round>(inputs, out, name(), s);
-  } else {
-    // No-op integer types
-    out.copy_shared_buffer(in);
-  }
-}
-} // namespace mlx::core

mlx/backend/cuda/unary/sigmoid.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Sigmoid)
-} // namespace mlx::core

mlx/backend/cuda/unary/sign.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Sign)
-} // namespace mlx::core

mlx/backend/cuda/unary/sin.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Sin)
-} // namespace mlx::core

mlx/backend/cuda/unary/sinh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Sinh)
-} // namespace mlx::core

mlx/backend/cuda/unary/sqrt.cu

@@ -1,15 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-void Sqrt::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Sqrt::eval_gpu");
-  auto& s = out.primitive().stream();
-  if (recip_) {
-    unary_op_gpu<cu::Rsqrt>(inputs, out, "Rsqrt", s);
-  } else {
-    unary_op_gpu<cu::Sqrt>(inputs, out, "Sqrt", s);
-  }
-}
-} // namespace mlx::core

mlx/backend/cuda/unary/square.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Square)
-} // namespace mlx::core

mlx/backend/cuda/unary/tan.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Tan)
-} // namespace mlx::core

mlx/backend/cuda/unary/tanh.cu

@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/unary/unary.cuh"
-
-namespace mlx::core {
-UNARY_GPU(Tanh)
-} // namespace mlx::core

mlx/backend/cuda/unary/unary.cuh

@@ -1,215 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/unary.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/unary_ops.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void unary_v(const In* in, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (IdxT i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(in[i]);
-    }
-  } else {
-    auto in_vec = load_vector<N_READS>(in, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(in_vec[i]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void unary_g(
-    const In* in,
-    Out* out,
-    IdxT size_rest,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides,
-    int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
-  }
-
-  auto shape_x = shape[ndim - 1];
-  auto stride_x = strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto idx =
-      elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
-  auto in_vec =
-      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(in_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
-}
-
-template <typename Op, typename In, typename Out>
-constexpr bool supports_unary_op() {
-  if (std::is_same_v<Op, Abs> || std::is_same_v<Op, Negative> ||
-      std::is_same_v<Op, Sign> || std::is_same_v<Op, Square>) {
-    return std::is_same_v<In, Out>;
-  }
-  if (std::is_same_v<Op, ArcCosh> || std::is_same_v<Op, ArcSinh> ||
-      std::is_same_v<Op, ArcTanh> || std::is_same_v<Op, Erf> ||
-      std::is_same_v<Op, ErfInv> || std::is_same_v<Op, Expm1> ||
-      std::is_same_v<Op, Sigmoid>) {
-    return std::is_same_v<In, Out> && is_floating_v<In>;
-  }
-  if (std::is_same_v<Op, BitwiseInvert>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
-        !std::is_same_v<In, bool>;
-  }
-  if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor>) {
-    return std::is_same_v<In, Out> && !mlx::core::is_complex_v<In>;
-  }
-  if (std::is_same_v<Op, Conjugate>) {
-    return std::is_same_v<In, Out> && mlx::core::is_complex_v<In>;
-  }
-  if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcSin> ||
-      std::is_same_v<Op, ArcTan> || std::is_same_v<Op, Cos> ||
-      std::is_same_v<Op, Cosh> || std::is_same_v<Op, Exp> ||
-      std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
-      std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p> ||
-      std::is_same_v<Op, Round> || std::is_same_v<Op, Rsqrt> ||
-      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Sin> ||
-      std::is_same_v<Op, Sinh> || std::is_same_v<Op, Tan> ||
-      std::is_same_v<Op, Tanh>) {
-    return std::is_same_v<In, Out> && is_inexact_v<In>;
-  }
-  if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
-    return mlx::core::is_complex_v<In> && std::is_same_v<Out, float>;
-  }
-  if (std::is_same_v<Op, LogicalNot>) {
-    return std::is_same_v<In, Out> && std::is_same_v<In, bool>;
-  }
-  return false;
-}
-
-} // namespace cu
-
-template <typename Op>
-void unary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  auto& in = inputs[0];
-  if (in.size() == 0) {
-    return;
-  }
-  bool contig = in.flags().contiguous;
-  bool large;
-  if (!contig) {
-    large = in.data_size() > INT32_MAX || out.size() > INT32_MAX;
-  } else {
-    large = in.data_size() > UINT32_MAX;
-  }
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        dispatch_bool(large, [&](auto large) {
-          using InType = cuda_type_t<CTYPE_IN>;
-          using OutType = cuda_type_t<CTYPE_OUT>;
-          if (contig) {
-            using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            constexpr int N_READS = 16 / sizeof(OutType);
-            auto [num_blocks, block_dims] = get_launch_args(
-                out.data_size(), out.shape(), out.strides(), large, N_READS);
-            encoder.add_kernel_node(
-                cu::unary_v<Op, InType, OutType, IdxT, N_READS>,
-                num_blocks,
-                block_dims,
-                0,
-                in.data<InType>(),
-                out.data<OutType>(),
-                out.data_size());
-          } else {
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            auto [shape, strides] = collapse_contiguous_dims(in);
-            auto ndim = shape.size();
-            int work_per_thread = 1;
-            auto kernel = cu::unary_g<Op, InType, OutType, IdxT, 1>;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = out.size() / dim0;
-            if (dim0 >= 4) {
-              kernel = cu::unary_g<Op, InType, OutType, IdxT, 4>;
-              work_per_thread = 4;
-            }
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-            encoder.add_kernel_node(
-                kernel,
-                {num_blocks_x, num_blocks_y},
-                block_dims,
-                0,
-                in.data<InType>(),
-                out.data<OutType>(),
-                rest,
-                const_param(shape),
-                const_param(strides),
-                ndim);
-          }
-        });
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do unary op {} on input of {} with output of {}.",
-            op,
-            dtype_to_string(in.dtype()),
-            dtype_to_string(out.dtype())));
-      }
-    });
-  });
-}
-
-template <typename Op>
-void unary_op_gpu(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  set_unary_output_data(inputs[0], out);
-  unary_op_gpu_inplace<Op>(inputs, out, op, s);
-}
-
-#define UNARY_GPU(func)                                               \
-  void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
-    nvtx3::scoped_range r(#func "::eval_gpu");                        \
-    auto& s = out.primitive().stream();                               \
-    unary_op_gpu<cu::func>(inputs, out, name(), s);                   \
-  }
-
-} // namespace mlx::core

mlx/backend/gpu/copy.cpp

@@ -52,70 +52,4 @@ array contiguous_copy_gpu(const array& arr, const Stream& s) {
   return arr_copy;
 }
 
-void reshape_gpu(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);
-  }
-}
-
-array flatten_in_eval(const array& x, int start_axis, int end_axis, Stream s) {
-  int ndim = x.ndim();
-  if (start_axis < 0) {
-    start_axis += ndim;
-  }
-  if (end_axis < 0) {
-    end_axis += ndim;
-  }
-  start_axis = std::max(0, start_axis);
-  end_axis = std::min(ndim - 1, end_axis);
-
-  return reshape_in_eval(x, Flatten::output_shape(x, start_axis, end_axis), s);
-}
-
-array reshape_in_eval(const array& x, Shape shape, Stream s) {
-  array out(std::move(shape), x.dtype(), nullptr, {});
-  reshape_gpu(x, out, s);
-  return out;
-}
-
-array swapaxes_in_eval(const array& x, int axis1, int axis2) {
-  int ndim = x.ndim();
-  if (axis1 < 0) {
-    axis1 += ndim;
-  }
-  if (axis2 < 0) {
-    axis2 += ndim;
-  }
-
-  auto shape = x.shape();
-  std::swap(shape[axis1], shape[axis2]);
-  auto strides = x.strides();
-  std::swap(strides[axis1], strides[axis2]);
-
-  auto [data_size, row_contiguous, col_contiguous] =
-      check_contiguity(shape, strides);
-  bool contiguous = data_size == x.data_size();
-
-  array out(std::move(shape), x.dtype(), nullptr, {});
-  out.copy_shared_buffer(
-      x,
-      std::move(strides),
-      {contiguous, row_contiguous, col_contiguous},
-      x.data_size());
-  return out;
-}
-
 } // namespace mlx::core

mlx/backend/gpu/copy.h

@@ -46,12 +46,4 @@ void fill_gpu(const array& val, array& out, const Stream& s);
 // Return a contiguous array with same shape that copies the data of |arr|.
 array contiguous_copy_gpu(const array& arr, const Stream& s);
 
-// Copy data from |in| and transpose to |out|'s shape.
-void reshape_gpu(const array& in, array& out, Stream s);
-
-// Like the normal ops but safe to call in eval_gpu.
-array flatten_in_eval(const array& x, int start_axis, int end_axis, Stream s);
-array reshape_in_eval(const array& x, Shape shape, Stream s);
-array swapaxes_in_eval(const array& x, int axis1, int axis2);
-
 } // namespace mlx::core

mlx/backend/gpu/primitives.cpp

@@ -20,6 +20,29 @@
 
 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);
@@ -101,7 +124,7 @@ void Full::eval_gpu(const std::vector<array>& inputs, array& out) {
 
 void Flatten::eval_gpu(const std::vector<array>& inputs, array& out) {
   MLX_PROFILER_RANGE("Flatten::eval_gpu");
-  reshape_gpu(inputs[0], out, stream());
+  reshape(inputs[0], out, stream());
 }
 
 void NumberOfElements::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -127,7 +150,7 @@ void Pad::eval_gpu(const std::vector<array>& inputs, array& out) {
 
 void Reshape::eval_gpu(const std::vector<array>& inputs, array& out) {
   MLX_PROFILER_RANGE("Reshape::eval_gpu");
-  reshape_gpu(inputs[0], out, stream());
+  reshape(inputs[0], out, stream());
 }
 
 void Split::eval_gpu(
@@ -201,7 +224,7 @@ void Transpose::eval_gpu(const std::vector<array>& inputs, array& out) {
 
 void Unflatten::eval_gpu(const std::vector<array>& inputs, array& out) {
   MLX_PROFILER_RANGE("Unflatten::eval_gpu");
-  reshape_gpu(inputs[0], out, stream());
+  reshape(inputs[0], out, stream());
 }
 
 void View::eval_gpu(const std::vector<array>& inputs, array& out) {

mlx/backend/metal/device.h

@@ -60,12 +60,22 @@ struct CommandEncoder {
     enc_->updateFence(fence);
   }
 
-  template <typename Vec, typename = std::enable_if_t<is_vector_v<Vec>>>
-  void set_vector_bytes(const Vec& vec, size_t nelems, int idx) {
-    enc_->setBytes(vec.data(), nelems * sizeof(typename Vec::value_type), idx);
+  template <typename T>
+  void set_vector_bytes(const SmallVector<T>& vec, size_t nelems, int idx) {
+    enc_->setBytes(vec.data(), nelems * sizeof(T), idx);
   }
-  template <typename Vec, typename = std::enable_if_t<is_vector_v<Vec>>>
-  void set_vector_bytes(const Vec& vec, int idx) {
+  template <typename T>
+  void set_vector_bytes(const SmallVector<T>& vec, int idx) {
+    return set_vector_bytes(vec, vec.size(), idx);
+  }
+
+  // TODO: Code is duplicated but they should be deleted soon.
+  template <typename T>
+  void set_vector_bytes(const std::vector<T>& vec, size_t nelems, int idx) {
+    enc_->setBytes(vec.data(), nelems * sizeof(T), idx);
+  }
+  template <typename T>
+  void set_vector_bytes(const std::vector<T>& vec, int idx) {
     return set_vector_bytes(vec, vec.size(), idx);
   }
 
@@ -94,7 +104,7 @@ struct CommandEncoder {
   };
 
   // Outputs of all kernels in the encoder including temporaries
-  std::unordered_set<const void*>& outputs() {
+  std::unordered_set<const void*> outputs() {
     return all_outputs_;
   };
 

mlx/backend/metal/kernels/conv.metal

@@ -166,6 +166,115 @@ instantiate_naive_unfold_nd_dims(float32, float);
 instantiate_naive_unfold_nd_dims(float16, half);
 instantiate_naive_unfold_nd_dims(bfloat16, bfloat16_t);
 
+///////////////////////////////////////////////////////////////////////////////
+/// Slow and naive conv2d kernels
+///////////////////////////////////////////////////////////////////////////////
+
+template <
+    typename T,
+    const int BM, /* Threadgroup rows (in threads) */
+    const int BN, /* Threadgroup cols (in threads) */
+    const int TM, /* Thread rows (in elements) */
+    const int TN, /* Thread cols (in elements) */
+    const int BC = 16>
+[[kernel]] void naive_conv_2d(
+    const device T* in [[buffer(0)]],
+    const device T* wt [[buffer(1)]],
+    device T* out [[buffer(2)]],
+    const constant MLXConvParams<2>& params [[buffer(3)]],
+    uint3 tid [[threadgroup_position_in_grid]],
+    uint3 lid [[thread_position_in_threadgroup]],
+    uint simd_gid [[simdgroup_index_in_threadgroup]],
+    uint simd_lid [[thread_index_in_simdgroup]]) {
+  (void)simd_gid;
+  (void)simd_lid;
+
+  out += tid.z * params.out_strides[0];
+  in += tid.z * params.in_strides[0];
+
+  int out_o = tid.y * BN * TN + lid.y * TN;
+  int out_hw = tid.x * BM * TM + lid.x * TM;
+
+  int out_h[TM];
+  int out_w[TN];
+
+  for (int m = 0; m < TM; ++m) {
+    int mm = (out_hw + m);
+    out_h[m] = mm / params.oS[1];
+    out_w[m] = mm % params.oS[1];
+  }
+
+  T in_local[TM];
+  T wt_local[TN];
+  T out_local[TM * TN] = {T(0)};
+
+  for (int h = 0; h < params.wS[0]; ++h) {
+    for (int w = 0; w < params.wS[1]; ++w) {
+      for (int c = 0; c < params.C; ++c) {
+        // Local in
+        for (int m = 0; m < TM; m++) {
+          int i = out_h[m] * params.str[0] - params.pad[0] + h * params.kdil[0];
+          int j = out_w[m] * params.str[1] - params.pad[1] + w * params.kdil[1];
+
+          bool valid = i >= 0 && i < params.iS[0] && j >= 0 && j < params.iS[1];
+          in_local[m] = valid
+              ? in[i * params.in_strides[1] + j * params.in_strides[2] + c]
+              : T(0);
+        }
+
+        // Load weight
+        for (int n = 0; n < TN; ++n) {
+          int o = out_o + n;
+          wt_local[n] = o < params.O
+              ? wt[o * params.wt_strides[0] + h * params.wt_strides[1] +
+                   w * params.wt_strides[2] + c]
+              : T(0);
+        }
+
+        // Accumulate
+        for (int m = 0; m < TM; ++m) {
+          for (int n = 0; n < TN; ++n) {
+            out_local[m * TN + n] += in_local[m] * wt_local[n];
+          }
+        }
+      }
+    }
+  }
+
+  for (int m = 0; m < TM; ++m) {
+    for (int n = 0; n < TN; ++n) {
+      if (out_h[m] < params.oS[0] && out_w[m] < params.oS[1] &&
+          (out_o + n) < params.O)
+        out[out_h[m] * params.out_strides[1] +
+            out_w[m] * params.out_strides[2] + out_o + n] =
+            out_local[m * TN + n];
+    }
+  }
+}
+
+// Instantiations
+
+#define instantiate_naive_conv_2d(name, itype, bm, bn, tm, tn)              \
+  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm \
+                       "_tn" #tn)]] [[kernel]] void                         \
+  naive_conv_2d<itype, bm, bn, tm, tn>(                                     \
+      const device itype* in [[buffer(0)]],                                 \
+      const device itype* wt [[buffer(1)]],                                 \
+      device itype* out [[buffer(2)]],                                      \
+      const constant MLXConvParams<2>& params [[buffer(3)]],                \
+      uint3 tid [[threadgroup_position_in_grid]],                           \
+      uint3 lid [[thread_position_in_threadgroup]],                         \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],                     \
+      uint simd_lid [[thread_index_in_simdgroup]]);
+
+#define instantiate_naive_conv_2d_blocks(name, itype) \
+  instantiate_naive_conv_2d(name, itype, 16, 8, 4, 4) \
+      instantiate_naive_conv_2d(name, itype, 16, 8, 2, 4)
+
+instantiate_naive_conv_2d_blocks(float32, float);
+instantiate_naive_conv_2d_blocks(float16, half);
+instantiate_naive_conv_2d_blocks(bfloat16, bfloat16_t);
+
 ///////////////////////////////////////////////////////////////////////////////
 /// Depthwise convolution kernels
 ///////////////////////////////////////////////////////////////////////////////

mlx/backend/metal/kernels/gemv_masked.h

@@ -262,37 +262,36 @@ struct GEMVKernel {
       vec_mask_offset += vec_mask_step;
     }
 
-    if (leftover > 0) {
-      if (!has_operand_mask ||
-          (bool(mat_mask[mat_mask_offset]) &&
-           bool(vec_mask[vec_mask_offset]))) {
-        T block_scale{1};
-        if (has_mul_operand_mask) {
-          block_scale =
-              T(mat_mask[mat_mask_offset]) * T(vec_mask[vec_mask_offset]);
-        }
+    if (leftover > 0 &&
+        (!has_operand_mask ||
+         (bool(mat_mask[mat_mask_offset]) &&
+          bool(vec_mask[vec_mask_offset])))) {
+      T block_scale{1};
+      if (has_mul_operand_mask) {
+        block_scale =
+            T(mat_mask[mat_mask_offset]) * T(vec_mask[vec_mask_offset]);
+      }
 
-        load_safe<AccT>(in_vec, v_coeff, bn, in_size);
+      load_safe<AccT>(in_vec, v_coeff, bn, in_size);
 
-        // Apply scale
-        if (has_mul_operand_mask) {
-          MLX_MTL_PRAGMA_UNROLL
-          for (int tn = 0; tn < TN; tn++) {
-            v_coeff[tn] *= block_scale;
-          }
-        }
-
-        // Per thread work loop
+      // Apply scale
+      if (has_mul_operand_mask) {
         MLX_MTL_PRAGMA_UNROLL
-        for (int tm = 0; tm < TM; tm++) {
-          // Load for the row
-          load_safe(&mat[tm * matrix_ld], inter, bn, in_size);
+        for (int tn = 0; tn < TN; tn++) {
+          v_coeff[tn] *= block_scale;
+        }
+      }
 
-          // Accumulate results
-          MLX_MTL_PRAGMA_UNROLL
-          for (int tn = 0; tn < TN; tn++) {
-            result[tm] += inter[tn] * v_coeff[tn];
-          }
+      // Per thread work loop
+      MLX_MTL_PRAGMA_UNROLL
+      for (int tm = 0; tm < TM; tm++) {
+        // Load for the row
+        load_safe(&mat[tm * matrix_ld], inter, bn, in_size);
+
+        // Accumulate results
+        MLX_MTL_PRAGMA_UNROLL
+        for (int tn = 0; tn < TN; tn++) {
+          result[tm] += inter[tn] * v_coeff[tn];
         }
       }
     }
@@ -545,32 +544,31 @@ struct GEMVTKernel {
         vec_mask_offset += vec_mask_step;
       }
 
-      if (leftover > 0) {
-        if (!has_operand_mask ||
-            (bool(mat_mask[mat_mask_offset]) &&
-             bool(vec_mask[vec_mask_offset]))) {
-          T block_scale{1};
+      if (leftover > 0 &&
+          (!has_operand_mask ||
+           (bool(mat_mask[mat_mask_offset]) &&
+            bool(vec_mask[vec_mask_offset])))) {
+        T block_scale{1};
+        if (has_mul_operand_mask) {
+          block_scale =
+              T(mat_mask[mat_mask_offset]) * T(vec_mask[vec_mask_offset]);
+        }
+
+        for (int tm = 0; tm < TM && bm + tm < in_vec_size; tm++) {
+          v_coeff[tm] = static_cast<AccT>(in_vec[bm + tm]);
+
           if (has_mul_operand_mask) {
-            block_scale =
-                T(mat_mask[mat_mask_offset]) * T(vec_mask[vec_mask_offset]);
+            v_coeff[tm] *= block_scale;
           }
 
-          for (int tm = 0; tm < TM && bm + tm < in_vec_size; tm++) {
-            v_coeff[tm] = static_cast<AccT>(in_vec[bm + tm]);
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
+            inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
+          }
 
-            if (has_mul_operand_mask) {
-              v_coeff[tm] *= block_scale;
-            }
-
-            MLX_MTL_PRAGMA_UNROLL
-            for (int tn = 0; tn < TN; tn++) {
-              inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
-            }
-
-            MLX_MTL_PRAGMA_UNROLL
-            for (int tn = 0; tn < TN; tn++) {
-              result[tn] += v_coeff[tm] * inter[tn];
-            }
+          MLX_MTL_PRAGMA_UNROLL
+          for (int tn = 0; tn < TN; tn++) {
+            result[tn] += v_coeff[tm] * inter[tn];
           }
         }
       }

mlx/backend/metal/kernels/reduce.metal

@@ -134,10 +134,6 @@ instantiate_and_or(and, And)
 instantiate_and_or(or, Or)
 
 #define instantiate_sum_prod(name, op)                                       \
-  instantiate_reduce_functions(name, uint8, uint8_t, int32_t, op)            \
-  instantiate_reduce_functions(name, uint16, uint16_t, uint32_t, op)         \
-  instantiate_reduce_functions(name, uint32, uint32_t, uint32_t, op)         \
-  instantiate_reduce_functions(name, uint64, uint64_t, uint64_t, op)         \
   instantiate_reduce_functions(name, int8, int8_t, int32_t, op)              \
   instantiate_reduce_functions(name, int16, int16_t, int32_t, op)            \
   instantiate_reduce_functions(name, int32, int32_t, int32_t, op)            \

mlx/backend/metal/kernels/sort.h

@@ -45,9 +45,7 @@ struct ThreadSort {
       for (short j = i & 1; j < N_PER_THREAD - 1; j += 2) {
         if (op(vals[j + 1], vals[j])) {
           thread_swap(vals[j + 1], vals[j]);
-          if (ARG_SORT) {
-            thread_swap(idxs[j + 1], idxs[j]);
-          }
+          thread_swap(idxs[j + 1], idxs[j]);
         }
       }
     }
@@ -113,9 +111,7 @@ struct BlockMergeSort {
       bool pred = (b_idx < B_sz) && (a_idx >= A_sz || op(b, a));
 
       vals[i] = pred ? b : a;
-      if (ARG_SORT) {
-        idxs[i] = pred ? Bs_idx[b_idx] : As_idx[a_idx];
-      }
+      idxs[i] = pred ? Bs_idx[b_idx] : As_idx[a_idx];
 
       b_idx += short(pred);
       a_idx += short(!pred);

mlx/backend/metal/reduce.cpp

@@ -247,25 +247,15 @@ std::pair<Dtype, Dtype> remap_reduce_types(
     const std::string& op_name) {
   if (op_name == "sum" || op_name == "prod") {
     if (issubdtype(in.dtype(), integer)) {
-      switch (in.dtype()) {
-        case uint8:
-          return {uint8, uint32};
-        case uint16:
-          return {uint16, uint32};
-        case uint32:
-          return {uint32, uint32};
-        case uint64:
-          return {uint64, uint64};
-        case int8:
+      switch (in.dtype().size()) {
+        case 1:
           return {int8, int32};
-        case int16:
+        case 2:
           return {int16, int32};
-        case int32:
+        case 4:
           return {int32, int32};
-        case int64:
+        case 8:
           return {int64, int64};
-        default:
-          throw std::runtime_error("Unsupported integer type");
       }
     }
     if (in.dtype() == bool_) {

mlx/ops.cpp

@@ -2381,20 +2381,9 @@ array logsumexp(
     throw std::invalid_argument(
         "[logsumexp] Received non-empty axes for array with 0 dimensions.");
   }
-  bool reduce_last_dim =
-      !axes.empty() && (axes.back() == a.ndim() - 1 || axes.back() == -1);
-  if (reduce_last_dim) {
-    // For more than 2 axes check if axes is [0, 1, ..., NDIM - 1] and shape
-    // is [1, 1, ..., N].
-    for (int i = axes.size() - 2; i >= 0; --i) {
-      if ((axes[i] + 1 != axes[i + 1]) || (a.shape(axes[i]) != 1)) {
-        reduce_last_dim = false;
-        break;
-      }
-    }
-  }
   bool is_complex = issubdtype(a.dtype(), complexfloating);
-  if (!is_complex && reduce_last_dim) {
+  if (!is_complex && axes.size() == 1 &&
+      (a.ndim() == axes[0] + 1 || axes[0] == -1)) {
     auto dtype = at_least_float(a.dtype());
     auto out_shape = a.shape();
     out_shape.back() = 1;
@@ -3414,20 +3403,10 @@ array softmax(
     throw std::invalid_argument(
         "[softmax] Received non-empty axes for array with 0 dimensions.");
   }
-  bool reduce_last_dim =
-      !axes.empty() && (axes.back() == a.ndim() - 1 || axes.back() == -1);
-  if (reduce_last_dim) {
-    // For more than 2 axes check if axes is [0, 1, ..., NDIM - 1] and shape
-    // is [1, 1, ..., N].
-    for (int i = axes.size() - 2; i >= 0; --i) {
-      if ((axes[i] + 1 != axes[i + 1]) || (a.shape(axes[i]) != 1)) {
-        reduce_last_dim = false;
-        break;
-      }
-    }
-  }
+
   bool is_complex = issubdtype(a.dtype(), complexfloating);
-  if (!is_complex && reduce_last_dim) {
+  if (!is_complex && axes.size() == 1 &&
+      (a.ndim() == axes[0] + 1 || axes[0] == -1)) {
     auto dtype = at_least_float(a.dtype());
     return array(
         a.shape(),

mlx/small_vector.h

@@ -440,7 +440,6 @@ class SmallVector {
     end_ = begin_;
   }
 
- private:
   // Grows the backing store by a factor of two, and at least to {min_capacity}.
   // TODO: Move to private after removing external code using this method.
   MLX_NOINLINE void grow(size_t min_capacity = 0) {
@@ -470,6 +469,7 @@ class SmallVector {
     end_of_storage_ = new_storage + new_capacity;
   }
 
+ private:
   MLX_NOINLINE void free_storage() {
     std::destroy_n(begin_, end_ - begin_);
     if (is_big()) {
@@ -519,18 +519,6 @@ class SmallVector {
       std::is_trivially_destructible<T>::value;
 };
 
-template <typename>
-struct is_vector : std::false_type {};
-
-template <typename T, size_t Size, typename Allocator>
-struct is_vector<SmallVector<T, Size, Allocator>> : std::true_type {};
-
-template <typename T, typename Allocator>
-struct is_vector<std::vector<T, Allocator>> : std::true_type {};
-
-template <typename Vec>
-inline constexpr bool is_vector_v = is_vector<Vec>::value;
-
 #undef MLX_HAS_BUILTIN
 #undef MLX_HAS_ATTRIBUTE
 #undef MLX_LIKELY

mlx/utils.cpp

@@ -259,6 +259,43 @@ std::ostream& operator<<(std::ostream& os, array a) {
   return os;
 }
 
+std::ostream& operator<<(std::ostream& os, const SmallVector<int>& v) {
+  os << "(";
+  for (int i = 0; i < v.size(); ++i) {
+    os << v[i] << ((i == v.size() - 1) ? "" : ",");
+  }
+  os << ")";
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const SmallVector<int64_t>& v) {
+  os << "(";
+  for (int i = 0; i < v.size(); ++i) {
+    os << v[i] << ((i == v.size() - 1) ? "" : ",");
+  }
+  os << ")";
+  return os;
+}
+
+// TODO: Code is duplicated but they should be deleted soon.
+std::ostream& operator<<(std::ostream& os, const std::vector<int>& v) {
+  os << "(";
+  for (int i = 0; i < v.size(); ++i) {
+    os << v[i] << ((i == v.size() - 1) ? "" : ",");
+  }
+  os << ")";
+  return os;
+}
+
+std::ostream& operator<<(std::ostream& os, const std::vector<int64_t>& v) {
+  os << "(";
+  for (int i = 0; i < v.size(); ++i) {
+    os << v[i] << ((i == v.size() - 1) ? "" : ",");
+  }
+  os << ")";
+  return os;
+}
+
 namespace env {
 
 int get_var(const char* name, int default_value) {

mlx/utils.h

@@ -100,6 +100,10 @@ std::ostream& operator<<(std::ostream& os, const Stream& s);
 std::ostream& operator<<(std::ostream& os, const Dtype& d);
 std::ostream& operator<<(std::ostream& os, const Dtype::Kind& k);
 std::ostream& operator<<(std::ostream& os, array a);
+std::ostream& operator<<(std::ostream& os, const SmallVector<int>& v);
+std::ostream& operator<<(std::ostream& os, const SmallVector<int64_t>& v);
+std::ostream& operator<<(std::ostream& os, const std::vector<int>& v);
+std::ostream& operator<<(std::ostream& os, const std::vector<int64_t>& v);
 inline std::ostream& operator<<(std::ostream& os, const complex64_t& v) {
   return os << v.real() << (v.imag() >= 0 ? "+" : "") << v.imag() << "j";
 }
@@ -110,19 +114,6 @@ inline std::ostream& operator<<(std::ostream& os, const bfloat16_t& v) {
   return os << static_cast<float>(v);
 }
 
-template <typename Vec, typename = std::enable_if_t<is_vector_v<Vec>>>
-inline std::ostream& operator<<(std::ostream& os, const Vec& v) {
-  os << "(";
-  for (auto it = v.begin(); it != v.end(); ++it) {
-    os << *it;
-    if (it != std::prev(v.end())) {
-      os << ",";
-    }
-  }
-  os << ")";
-  return os;
-}
-
 inline bool is_power_of_2(int n) {
   return ((n & (n - 1)) == 0) && n != 0;
 }

mlx/version.h

@@ -3,8 +3,8 @@
 #pragma once
 
 #define MLX_VERSION_MAJOR 0
-#define MLX_VERSION_MINOR 28
-#define MLX_VERSION_PATCH 0
+#define MLX_VERSION_MINOR 27
+#define MLX_VERSION_PATCH 1
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

pyproject.toml

@@ -2,6 +2,6 @@
 requires = [
   "setuptools>=80",
   "nanobind==2.4.0",
-  "cmake>=3.25,<4.1",
+  "cmake>=3.25",
 ]
 build-backend = "setuptools.build_meta"

python/mlx/nn/layers/base.py

@@ -178,7 +178,7 @@ class Module(dict):
 
         if strict:
             new_weights = dict(weights)
-            curr_weights = tree_flatten(self.parameters(), destination={})
+            curr_weights = dict(tree_flatten(self.parameters()))
             if extras := (new_weights.keys() - curr_weights.keys()):
                 num_extra = len(extras)
                 extras = ",\n".join(sorted(extras))
@@ -212,7 +212,7 @@ class Module(dict):
         - ``.npz`` will use :func:`mx.savez`
         - ``.safetensors`` will use :func:`mx.save_safetensors`
         """
-        params_dict = tree_flatten(self.parameters(), destination={})
+        params_dict = dict(tree_flatten(self.parameters()))
 
         if file.endswith(".npz"):
             mx.savez(file, **params_dict)

python/mlx/utils.py

@@ -1,7 +1,7 @@
 # Copyright © 2023 Apple Inc.
 from collections import defaultdict
 from itertools import zip_longest
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional, Tuple
 
 
 def tree_map(
@@ -114,11 +114,8 @@ def tree_map_with_path(
 
 
 def tree_flatten(
-    tree: Any,
-    prefix: str = "",
-    is_leaf: Optional[Callable] = None,
-    destination: Optional[Union[List[Tuple[str, Any]], Dict[str, Any]]] = None,
-) -> Union[List[Tuple[str, Any]], Dict[str, Any]]:
+    tree: Any, prefix: str = "", is_leaf: Optional[Callable] = None
+) -> Any:
     """Flattens a Python tree to a list of key, value tuples.
 
     The keys are using the dot notation to define trees of arbitrary depth and
@@ -131,12 +128,9 @@ def tree_flatten(
         print(tree_flatten([[[0]]]))
         # [("0.0.0", 0)]
 
-        print(tree_flatten([[[0]]], prefix=".hello"))
+        print(tree_flatten([[[0]]], ".hello"))
         # [("hello.0.0.0", 0)]
 
-        tree_flatten({"a": {"b": 1}}, destination={})
-        {"a.b": 1}
-
     .. note::
        Dictionaries should have keys that are valid Python identifiers.
 
@@ -146,50 +140,26 @@ def tree_flatten(
             always discarded.
         is_leaf (callable): An optional callable that returns True if the
             passed object is considered a leaf or False otherwise.
-        destination (list or dict, optional): A list or dictionary to store the
-            flattened tree. If None an empty list will be used. Default: ``None``.
 
     Returns:
-        Union[List[Tuple[str, Any]], Dict[str, Any]]: The flat representation of
-            the Python tree.
+        List[Tuple[str, Any]]: The flat representation of the Python tree.
     """
-    if destination is None:
-        destination = []
+    flat_tree = []
 
-    # Create the function to update the destination. We are taking advantage of
-    # the fact that list.extend and dict.update have the same API to simplify
-    # the code a bit.
-    if isinstance(destination, list):
-        _add_to_destination = destination.extend
-    elif isinstance(destination, dict):
-        _add_to_destination = destination.update
-    else:
-        raise ValueError("Destination should be either a list or a dictionary or None")
+    if is_leaf is None or not is_leaf(tree):
+        if isinstance(tree, (list, tuple)):
+            for i, t in enumerate(tree):
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{i}", is_leaf))
+            return flat_tree
+        if isinstance(tree, dict):
+            for k, t in tree.items():
+                flat_tree.extend(tree_flatten(t, f"{prefix}.{k}", is_leaf))
+            return flat_tree
 
-    # Leaf identified by is_leaf so add it and return
-    if is_leaf is not None and is_leaf(tree):
-        _add_to_destination([(prefix[1:], tree)])
-        return destination
-
-    # List or tuple so recursively add each subtree
-    if isinstance(tree, (list, tuple)):
-        for i, item in enumerate(tree):
-            tree_flatten(item, f"{prefix}.{i}", is_leaf, destination)
-        return destination
-
-    # Dictionary so recursively add each subtree
-    if isinstance(tree, dict):
-        for key, value in tree.items():
-            tree_flatten(value, f"{prefix}.{key}", is_leaf, destination)
-        return destination
-
-    # Leaf so add it and return
-    _add_to_destination([(prefix[1:], tree)])
-
-    return destination
+    return [(prefix[1:], tree)]
 
 
-def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
+def tree_unflatten(tree: List[Tuple[str, Any]]) -> Any:
     """Recreate a Python tree from its flat representation.
 
     .. code-block:: python
@@ -200,34 +170,31 @@ def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
         print(d)
         # {"hello": {"world": 42}}
 
-        d = tree_unflatten({"hello.world": 42})
-        print(d)
-        # {"hello": {"world": 42}}
-
     Args:
-        tree (list[tuple[str, Any]] or dict[str, Any]): The flat representation of a Python tree.
+        tree (list[tuple[str, Any]]): The flat representation of a Python tree.
            For instance as returned by :meth:`tree_flatten`.
 
     Returns:
         A Python tree.
     """
-    items = tree.items() if isinstance(tree, dict) else tree
+    if len(tree) == 1 and tree[0][0] == "":
+        return tree[0][1]
 
-    # Special case when we have just one element in the tree ie not a tree
-    if len(items) == 1:
-        key, value = next(iter(items))
-        if key == "":
-            return value
+    try:
+        int(tree[0][0].split(".", maxsplit=1)[0])
+        is_list = True
+    except ValueError:
+        is_list = False
 
     # collect children
     children = defaultdict(list)
-    for key, value in items:
+    for key, value in tree:
         current_idx, *next_idx = key.split(".", maxsplit=1)
         next_idx = "" if not next_idx else next_idx[0]
         children[current_idx].append((next_idx, value))
 
-    # Assume they are a list and fail to dict if the keys are not all integers
-    try:
+    # recursively map them to the original container
+    if is_list:
         keys = sorted((int(idx), idx) for idx in children.keys())
         l = []
         for i, k in keys:
@@ -235,7 +202,7 @@ def tree_unflatten(tree: Union[List[Tuple[str, Any]], Dict[str, Any]]) -> Any:
             l.extend([{} for _ in range(i - len(l))])
             l.append(tree_unflatten(children[k]))
         return l
-    except ValueError:
+    else:
         return {k: tree_unflatten(v) for k, v in children.items()}
 
 

python/tests/cuda_skip.py

@@ -17,6 +17,7 @@ cuda_skip = {
     "TestConv.test_1d_conv_with_2d",
     "TestConv.test_conv_1d_groups_flipped",
     "TestConv.test_conv_general_flip_grad",
+    "TestConv.test_conv_groups_grad",
     "TestConv.test_torch_conv_2D",
     "TestConv.test_torch_conv_depthwise",
     "TestConv.test_torch_conv_general",

python/tests/test_nn.py

@@ -80,7 +80,7 @@ class TestBase(mlx_tests.MLXTestCase):
                 self.weights = {"w1": mx.zeros((2, 2)), "w2": mx.ones((2, 2))}
 
         model = DictModule()
-        params = tree_flatten(model.parameters(), destination={})
+        params = dict(tree_flatten(model.parameters()))
         self.assertEqual(len(params), 2)
         self.assertTrue(mx.array_equal(params["weights.w1"], mx.zeros((2, 2))))
         self.assertTrue(mx.array_equal(params["weights.w2"], mx.ones((2, 2))))

tests/gpu_tests.cpp

@@ -155,19 +155,6 @@ TEST_CASE("test gpu reduce") {
     CHECK_EQ(prod(a, Device::gpu).item<int32_t>(), 1);
   }
 
-  // sum and prod overflow
-  {
-    auto a = full({256, 2, 2}, 1u, uint8);
-    CHECK_EQ(sum(a, Device::gpu).item<uint32_t>(), 256 * 4);
-    CHECK_EQ(prod(a, Device::gpu).item<uint32_t>(), 1);
-
-    a = full({65535, 2, 2}, 1u, uint16);
-    CHECK_EQ(sum(a, Device::gpu).item<uint32_t>(), 65535 * 4);
-    CHECK_EQ(prod(a, Device::gpu).item<uint32_t>(), 1);
-  }
-}
-
-TEST_CASE("test gpu reduce with axes") {
   // reducing only some axes and irregular layouts
   {
     array a(1.0f);

tests/ops_tests.cpp

@@ -915,23 +915,6 @@ TEST_CASE("test reduction ops") {
     CHECK(array_equal(sum(x, 1), array({3.0f, 6.0f}, {2})).item<bool>());
   }
 
-  // Test unsigned sum
-  {
-    const int num_elems = 1000;
-
-    auto x = astype(full({num_elems}, 255), uint8);
-    CHECK_EQ(sum(x, Device::cpu).item<uint32_t>(), 255 * num_elems);
-
-    x = astype(full({num_elems}, 65535), uint16);
-    CHECK_EQ(sum(x, Device::cpu).item<uint32_t>(), 65535 * num_elems);
-
-    x = full({3, 3, 3}, 10000, uint32);
-    CHECK_EQ(sum(x, Device::cpu).item<uint32_t>(), 270000);
-
-    x = full({3, 3, 3}, 10000, uint64);
-    CHECK_EQ(sum(x, Device::cpu).item<uint64_t>(), 270000);
-  }
-
   // Test prod
   {
     auto x = array({});
@@ -964,21 +947,6 @@ TEST_CASE("test reduction ops") {
     CHECK(array_equal(prod(x, 1), array({true, false})).item<bool>());
   }
 
-  // Test unsigned prod
-  {
-    auto x = array({255, 255}, {2}, uint8);
-    CHECK_EQ(prod(x, Device::cpu).item<uint32_t>(), 65025);
-
-    x = array({65535, 2}, {2}, uint16);
-    CHECK_EQ(prod(x, Device::cpu).item<uint32_t>(), 131070);
-
-    x = array({100000, 2}, {2}, uint32);
-    CHECK_EQ(prod(x, Device::cpu).item<uint32_t>(), 200000);
-
-    x = array({100000, 2}, {2}, uint64);
-    CHECK_EQ(prod(x, Device::cpu).item<uint64_t>(), 200000);
-  }
-
   // Test all
   {
     auto x = array({});