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/dev/custom_metal_kernels.rst

@@ -128,7 +128,6 @@ relying on a copy from ``ensure_row_contiguous``:
       input_names=["inp"],
       output_names=["out"],
       source=source
-      ensure_row_contiguous=False,
   )
 
   def exp_elementwise(a: mx.array):
@@ -139,6 +138,7 @@ relying on a copy from ``ensure_row_contiguous``:
           threadgroup=(256, 1, 1),
           output_shapes=[a.shape],
           output_dtypes=[a.dtype],
+          ensure_row_contiguous=False,
       )
       return outputs[0]
 

docs/src/index.rst

@@ -70,7 +70,6 @@ are the CPU and GPU.
    python/fft
    python/linalg
    python/metal
-   python/cuda
    python/memory_management
    python/nn
    python/optimizers

docs/src/python/cuda.rst

@@ -1,9 +0,0 @@
-CUDA
-=====
-
-.. currentmodule:: mlx.core.cuda
-
-.. autosummary::
-  :toctree: _autosummary
-
-  is_available

docs/src/python/fast.rst

@@ -13,4 +13,3 @@ Fast
   rope
   scaled_dot_product_attention
   metal_kernel
-  cuda_kernel

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/compile.rst

@@ -225,7 +225,7 @@ In some cases returning updated state can be pretty inconvenient. Hence,
   def fun(x, y):
       z = x + y
       state.append(z)
-      return mx.exp(z)
+      return mx.exp(z), state
 
   fun(mx.array(1.0), mx.array(2.0))
   # Prints [array(3, dtype=float32)]

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/lapack.h

@@ -47,7 +47,7 @@ INSTANTIATE_LAPACK_REAL(orgqr)
 INSTANTIATE_LAPACK_REAL(syevd)
 INSTANTIATE_LAPACK_REAL(geev)
 INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesdd)
+INSTANTIATE_LAPACK_REAL(gesvdx)
 INSTANTIATE_LAPACK_REAL(getrf)
 INSTANTIATE_LAPACK_REAL(getri)
 INSTANTIATE_LAPACK_REAL(trtri)

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/cpu/svd.cpp

@@ -81,7 +81,9 @@ void svd_impl(
     // Vᵀ of shape N x N. (M x M in lapack).
     const int ldvt = M;
 
-    auto jobz = (u_ptr) ? "A" : "N";
+    auto job_u = (u_ptr) ? "V" : "N";
+    auto job_vt = (u_ptr) ? "V" : "N";
+    static constexpr auto range = "A";
 
     // Will contain the number of singular values after the call has returned.
     int ns = 0;
@@ -89,20 +91,30 @@ void svd_impl(
 
     // Will contain the indices of eigenvectors that failed to converge (not
     // used here but required by lapack).
-    auto iwork = array::Data{allocator::malloc(sizeof(int) * 8 * K)};
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
 
     static const int lwork_query = -1;
 
+    static const int ignored_int = 0;
+    static const T ignored_float = 0;
+
     int info;
 
     // Compute workspace size.
-    gesdd<T>(
-        /* jobz = */ jobz,
+    gesvdx<T>(
+        /* jobu = */ job_u,
+        /* jobvt = */ job_vt,
+        /* range = */ range,
         // M and N are swapped since lapack expects column-major.
         /* m = */ &N,
         /* n = */ &M,
         /* a = */ nullptr,
         /* lda = */ &lda,
+        /* vl = */ &ignored_float,
+        /* vu = */ &ignored_float,
+        /* il = */ &ignored_int,
+        /* iu = */ &ignored_int,
+        /* ns = */ &ns,
         /* s = */ nullptr,
         /* u = */ nullptr,
         /* ldu = */ &ldu,
@@ -124,13 +136,20 @@ void svd_impl(
 
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {
-      gesdd<T>(
-          /* jobz = */ jobz,
+      gesvdx<T>(
+          /* jobu = */ job_u,
+          /* jobvt = */ job_vt,
+          /* range = */ range,
           // M and N are swapped since lapack expects column-major.
           /* m = */ &N,
           /* n = */ &M,
           /* a = */ in_ptr + M * N * i,
           /* lda = */ &lda,
+          /* vl = */ &ignored_float,
+          /* vu = */ &ignored_float,
+          /* il = */ &ignored_int,
+          /* iu = */ &ignored_int,
+          /* ns = */ &ns,
           /* s = */ s_ptr + K * i,
           // According to the identity above, lapack will write Vᵀᵀ as U.
           /* u = */ vt_ptr ? vt_ptr + N * N * i : nullptr,
@@ -148,6 +167,13 @@ void svd_impl(
         ss << "svd_impl: sgesvdx_ failed with code " << info;
         throw std::runtime_error(ss.str());
       }
+
+      if (ns != K) {
+        std::stringstream ss;
+        ss << "svd_impl: expected " << K << " singular values, but " << ns
+           << " were computed.";
+        throw std::runtime_error(ss.str());
+      }
     }
   });
   encoder.add_temporary(in);

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
@@ -16,18 +17,12 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_conv.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_grouped_conv.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/cudnn_utils.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/custom_kernel.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/gemv.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cutlass_gemm.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simple_gemm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
@@ -50,20 +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)
@@ -90,9 +83,6 @@ target_include_directories(mlx PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/gen")
 target_compile_options(mlx
                        PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
 
-# Keep ptx around for inspection
-target_compile_options(mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--keep>")
-
 # Enable calling host constexpr functions from device. This is needed because
 # the constexpr version of isnan is host only.
 target_compile_options(
@@ -158,7 +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)
@@ -178,12 +168,3 @@ target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
         DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)
-
-# Fetch and make available cutlass
-FetchContent_Declare(
-  cutlass
-  GIT_REPOSITORY https://github.com/NVIDIA/cutlass.git
-  GIT_TAG v4.1.0)
-FetchContent_Populate(cutlass)
-target_include_directories(
-  mlx PRIVATE $<BUILD_INTERFACE:${cutlass_SOURCE_DIR}/include>)

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/compiled.cpp

@@ -267,8 +267,7 @@ void Compiled::eval_gpu(
       }
     }
 
-    return std::make_tuple(
-        false, std::move(builder.os), std::move(kernel_names));
+    return std::make_pair(std::move(builder.os), std::move(kernel_names));
   });
 
   // Collapse contiguous dims to route to a faster kernel if possible. Also

mlx/backend/cuda/conv.cpp

@@ -1,12 +1,18 @@
 // Copyright © 2025 Apple Inc.
 
-#include "mlx/backend/cuda/conv/conv.h"
-#include "mlx/backend/cuda/cudnn_utils.h"
 #include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/device/config.h"
 #include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/gpu/copy.h"
+#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>
 #include <nvtx3/nvtx3.hpp>
 
 #include <cassert>
@@ -15,6 +21,9 @@ namespace mlx::core {
 
 namespace {
 
+// Not all engines support it so can not use this API now.
+#define MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API 0
+
 // Alias for better readability.
 #define CONV_FORWARD CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR
 #define CONV_BACKWARD_INPUT \
@@ -22,9 +31,6 @@ namespace {
 #define CONV_BACKWARD_WEIGHT \
   CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR
 
-// Custom placeholder representing fallback kernel.
-#define CONV_FALLBACK static_cast<cudnnBackendDescriptorType_t>(-1)
-
 struct ConvCacheKey {
   int device_id;
   cudnnDataType_t cudnn_dtype;
@@ -44,13 +50,203 @@ struct ConvCacheKey {
 auto& conv_cache() {
   static LRUBytesKeyCache<
       ConvCacheKey,
-      std::pair<
-          cudnnBackendDescriptorType_t,
-          std::optional<cudnn_frontend::ExecutionPlan>>>
+      std::pair<cudnnBackendDescriptorType_t, cudnn_frontend::ExecutionPlan>>
       cache(/* capacity */ 128);
   return cache;
 }
 
+template <typename T, typename Vec>
+inline SmallVector<T> convert_vector(const Vec& vec) {
+  return SmallVector<T>(vec.begin(), vec.end());
+}
+
+template <typename T, template <typename U> class Vec>
+inline std::array<T, MAX_NDIM> fixed_vector(const Vec<T>& vec) {
+  if (vec.size() > MAX_NDIM) {
+    throw std::runtime_error(
+        fmt::format("ndim can not be larger than {}.", MAX_NDIM));
+  }
+  std::array<T, MAX_NDIM> result = {};
+  std::copy_n(vec.begin(), vec.size(), result.begin());
+  return result;
+}
+
+auto nhwc_to_nchw(const array& x) {
+  auto shape = convert_vector<int64_t>(x.shape());
+  shape.insert(shape.begin() + 1, shape.back());
+  shape.erase(shape.end() - 1);
+  auto strides = convert_vector<int64_t>(x.strides());
+  strides.insert(strides.begin() + 1, strides.back());
+  strides.erase(strides.end() - 1);
+  return std::make_tuple(std::move(shape), std::move(strides));
+}
+
+inline cudnnDataType_t dtype_to_cudnn_type(Dtype dtype) {
+  switch (dtype) {
+    case int8:
+      return CUDNN_DATA_INT8;
+    case int32:
+      return CUDNN_DATA_INT32;
+    case uint8:
+      return CUDNN_DATA_UINT8;
+    case float16:
+      return CUDNN_DATA_HALF;
+    case bfloat16:
+      return CUDNN_DATA_BFLOAT16;
+    case float32:
+      return CUDNN_DATA_FLOAT;
+    case float64:
+      return CUDNN_DATA_DOUBLE;
+    default:
+      throw std::runtime_error(fmt::format(
+          "Unsupported dtype in Convolution: {}.", dtype_to_string(dtype)));
+  }
+}
+
+inline uint8_t get_alignment(const array& x) {
+  uint8_t alignment = 1;
+  uintptr_t address = reinterpret_cast<uintptr_t>(x.data<void>());
+  for (; alignment < 32; alignment *= 2) {
+    if (address % (alignment * 2)) {
+      return alignment;
+    }
+  }
+  return alignment;
+}
+
+inline cudnn_frontend::Tensor build_tensor(int64_t id, const array& x) {
+  auto [shape, strides] = nhwc_to_nchw(x);
+  return cudnn_frontend::TensorBuilder()
+      .setDim(shape.size(), shape.data())
+      .setStrides(strides.size(), strides.data())
+      .setId(id)
+      .setAlignment(get_alignment(x))
+      .setDataType(dtype_to_cudnn_type(x.dtype()))
+      .build();
+}
+
+cudnn_frontend::EngineConfigList get_engine_configs(
+    cudnnBackendDescriptorType_t backend_type,
+    Dtype dtype,
+    cudnn_frontend::OperationGraph& op_graph,
+    bool use_fallback = false) {
+  cudnn_frontend::GeneratorSource source;
+  if (use_fallback) {
+    source = [&backend_type](cudnn_frontend::OperationGraph& op_graph) {
+      auto fallback = cudnn_frontend::EngineFallbackListBuilder()
+                          .setOperationGraph(op_graph)
+                          .setOperation(backend_type)
+                          .build();
+      return fallback.getFallbackList();
+    };
+  } else {
+    source = [](cudnn_frontend::OperationGraph& op_graph) {
+      auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
+                            .setOperationGraph(op_graph)
+                            .setHeurMode(CUDNN_HEUR_MODE_A)
+                            .build();
+      return heuristics.getEngineConfig(heuristics.getEngineConfigCount());
+    };
+  }
+
+  cudnn_frontend::EngineConfigGenerator generator(1, &source);
+  auto configs = generator.generate_engine_config(op_graph);
+
+  cudnn_frontend::EngineConfigList filtered_configs;
+  cudnn_frontend::filter(configs, filtered_configs, [dtype](auto c) {
+    if (cudnn_frontend::hasNumericalNote<
+            CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(c)) {
+      return true;
+    }
+    if (cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c) &&
+        dtype == float32 && !env::enable_tf32()) {
+      return true;
+    }
+    return false;
+  });
+  return filtered_configs;
+}
+
+bool execute_plan(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    array& x,
+    array& w,
+    array& y) {
+  int workspace_size = plan.getWorkspaceSize();
+  array workspace(allocator::malloc(workspace_size), {workspace_size}, uint8);
+
+  int64_t uids[3] = {'x', 'w', 'y'};
+  void* data_ptrs[3] = {
+      x.data<void>(),
+      w.data<void>(),
+      y.data<void>(),
+  };
+
+  auto variantPack = cudnn_frontend::VariantPackBuilder()
+                         .setWorkspacePointer(workspace.data<void>())
+                         .setDataPointers(3, data_ptrs)
+                         .setUids(3, uids)
+                         .build();
+
+  auto handle = encoder.device().cudnn_handle();
+  cudnnSetStream(handle, encoder.stream());
+
+#if CUDNN_VERSION >= 90500 && MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API
+  cudaGraph_t graph;
+  cudaGraphCreate(&graph, 0);
+  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+      &graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
+  if (cudnnBackendPopulateCudaGraph(
+          handle, plan.get_raw_desc(), variantPack.get_raw_desc(), graph) !=
+      CUDNN_STATUS_SUCCESS) {
+    return false;
+  }
+  encoder.add_graph_node(graph);
+#else
+  auto capture = encoder.capture_context();
+  if (cudnnBackendExecute(
+          handle, plan.get_raw_desc(), variantPack.get_raw_desc()) !=
+      CUDNN_STATUS_SUCCESS) {
+    // Discard the captured graph when failed.
+    capture.discard = true;
+    return false;
+  }
+#endif
+
+  encoder.add_temporary(workspace);
+  return true;
+}
+
+bool try_engines(
+    cu::CommandEncoder& encoder,
+    const ConvCacheKey& cache_key,
+    cudnnBackendDescriptorType_t backend_type,
+    cudnn_frontend::EngineConfigList& configs,
+    const std::string& op_graph_tag,
+    array& x,
+    array& w,
+    array& y) {
+  for (auto& config : configs) {
+    try {
+      auto plan = cudnn_frontend::ExecutionPlanBuilder()
+                      .setHandle(encoder.device().cudnn_handle())
+                      .setEngineConfig(config, op_graph_tag)
+                      .build();
+      if (execute_plan(encoder, plan, x, w, y)) {
+        conv_cache().emplace(
+            cache_key, std::make_pair(backend_type, std::move(plan)));
+        return true;
+      }
+    } catch (cudnn_frontend::cudnnException& error) {
+      if (error.getCudnnStatus() != CUDNN_STATUS_NOT_SUPPORTED) {
+        throw;
+      }
+    }
+  }
+  return false;
+}
+
 auto get_conv_op_settings(
     cudnnBackendDescriptorType_t backend_type,
     array& x,
@@ -95,7 +291,7 @@ auto get_conv_op_settings(
   }
 }
 
-std::optional<cudnn_frontend::OperationGraph> build_conv_op_graph(
+std::optional<cudnn_frontend::OperationGraph> build_op_graph(
     cu::CommandEncoder& encoder,
     cudnnBackendDescriptorType_t backend_type,
     Dtype dtype,
@@ -121,9 +317,9 @@ std::optional<cudnn_frontend::OperationGraph> build_conv_op_graph(
                          .build();
 
     auto op = cudnn_frontend::OperationBuilder(backend_type)
-                  .setxDesc(build_cudnn_tensor_nchw('x', x))
-                  .setwDesc(build_cudnn_tensor_nchw('w', w))
-                  .setyDesc(build_cudnn_tensor_nchw('y', y))
+                  .setxDesc(build_tensor('x', x))
+                  .setwDesc(build_tensor('w', w))
+                  .setyDesc(build_tensor('y', y))
                   .setcDesc(conv_desc)
                   .build();
 
@@ -140,42 +336,6 @@ std::optional<cudnn_frontend::OperationGraph> build_conv_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.
@@ -185,14 +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.
@@ -285,12 +444,12 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
   ConvCacheKey cache_key{
       encoder.device().cuda_device(),
       dtype_to_cudnn_type(dtype),
-      vector_key(in.shape()),
-      vector_key(wt.shape()),
-      vector_key(kernel_strides_),
-      vector_key(padding_lo_),
-      vector_key(padding_hi_),
-      vector_key(kernel_dilation_),
+      fixed_vector(in.shape()),
+      fixed_vector(wt.shape()),
+      fixed_vector(kernel_strides_),
+      fixed_vector(padding_lo_),
+      fixed_vector(padding_hi_),
+      fixed_vector(kernel_dilation_),
       groups_,
       flip_,
       get_alignment(in),
@@ -298,29 +457,11 @@ 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;
-    if (plan) {
-      // Run cached plan.
-      std::tie(in, wt, out) =
-          prepare_args(encoder, backend_type, in, wt, out, groups_, s);
-      register_args(encoder, backend_type, in, wt, out, out_);
-      auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
-      if (!encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
-        throw std::runtime_error("[conv] Cached plan failed to execute.");
-      }
-    } else {
-      // Run fallback kernel.
-      gemm_conv(
-          encoder,
-          in,
-          wt,
-          out,
-          kernel_strides_,
-          padding_lo_,
-          kernel_dilation_,
-          input_dilation_,
-          groups_,
-          flip_,
-          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)) {
+      throw std::runtime_error("[conv] Cached plan failed to execute.");
     }
     return;
   }
@@ -349,7 +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,
@@ -361,7 +502,7 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
         padding_hi_,
         kernel_dilation_,
         input_dilation_);
-    op_graph = build_conv_op_graph(
+    op_graph = build_op_graph(
         encoder,
         try_backend,
         dtype,
@@ -380,39 +521,26 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
       break;
     }
   }
-
-  if (op_graph) {
-    // Setup inputs and outputs.
-    register_args(encoder, backend_type, in, wt, out, out_);
-
-    // Find a plan for the graph and execute it.
-    auto plan = find_cudnn_plan_from_op_graph(
-        encoder.device().cudnn_handle(), backend_type, dtype, *op_graph);
-    if (!plan) {
-      throw std::runtime_error("[conv] Unable to find an execution plan.");
-    }
-    auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
-    if (encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
-      conv_cache().emplace(
-          cache_key, std::make_pair(backend_type, std::move(*plan)));
-      return;
-    }
+  if (!op_graph) {
+    throw std::runtime_error("[conv] Can not build op graph.");
   }
 
-  // Use fallback kernel for settings not supported by cuDNN.
-  gemm_conv(
-      encoder,
-      in,
-      wt,
-      out,
-      kernel_strides_,
-      padding_lo_,
-      kernel_dilation_,
-      input_dilation_,
-      groups_,
-      flip_,
-      s);
-  conv_cache().emplace(cache_key, std::make_pair(CONV_FALLBACK, std::nullopt));
+  // Get ready to execute the graph.
+  register_args(encoder, backend_type, in, wt, out, out_);
+
+  // Try to run plans based on heuristics.
+  auto configs = get_engine_configs(backend_type, dtype, *op_graph);
+  auto tag = op_graph->getTag();
+  auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
+  if (try_engines(encoder, cache_key, backend_type, configs, tag, x, w, y)) {
+    return;
+  }
+  // Then try fallback plans.
+  configs = get_engine_configs(backend_type, dtype, *op_graph);
+  if (try_engines(encoder, cache_key, backend_type, configs, tag, x, w, y)) {
+    return;
+  }
+  throw std::runtime_error("[conv] Unable to find a working engine.");
 }
 
 } // namespace mlx::core

mlx/backend/cuda/conv/conv.h

@@ -1,126 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/gpu/copy.h"
-
-namespace mlx::core {
-
-template <int NDIM>
-struct ConvParams {
-  int N; // Batch size
-  int C; // In channels
-  int O; // Out channels
-  int strides[NDIM];
-  int padding[NDIM];
-  int kernel_dilation[NDIM];
-  int input_dilation[NDIM];
-  int groups;
-  bool flip;
-  int in_spatial_dims[NDIM];
-  int wt_spatial_dims[NDIM];
-  int out_spatial_dims[NDIM];
-  int64_t in_strides[NDIM + 2];
-
-  ConvParams(
-      const array& in,
-      const array& wt,
-      const array& out,
-      const std::vector<int>& strides,
-      const std::vector<int>& padding,
-      const std::vector<int>& kernel_dilation,
-      const std::vector<int>& input_dilation,
-      int groups,
-      bool flip)
-      : N(in.shape(0)),
-        C(in.shape(-1)),
-        O(wt.shape(0)),
-        groups(groups),
-        flip(flip) {
-    std::copy_n(strides.begin(), NDIM, this->strides);
-    std::copy_n(padding.begin(), NDIM, this->padding);
-    std::copy_n(kernel_dilation.begin(), NDIM, this->kernel_dilation);
-    std::copy_n(input_dilation.begin(), NDIM, this->input_dilation);
-    std::copy_n(in.shape().begin() + 1, NDIM, this->in_spatial_dims);
-    std::copy_n(wt.shape().begin() + 1, NDIM, this->wt_spatial_dims);
-    std::copy_n(out.shape().begin() + 1, NDIM, this->out_spatial_dims);
-    std::copy_n(in.strides().begin(), NDIM + 2, this->in_strides);
-  }
-};
-
-void gemm_grouped_conv(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    const std::vector<int>& strides,
-    const std::vector<int>& padding,
-    const std::vector<int>& kernel_dilation,
-    const std::vector<int>& input_dilation,
-    int groups,
-    bool flip,
-    Stream s);
-
-void gemm_conv(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    const std::vector<int>& strides,
-    const std::vector<int>& padding,
-    const std::vector<int>& kernel_dilation,
-    const std::vector<int>& input_dilation,
-    bool flip,
-    Stream s);
-
-inline void gemm_conv(
-    cu::CommandEncoder& encoder,
-    array in,
-    array wt,
-    array& out,
-    const std::vector<int>& strides,
-    const std::vector<int>& padding,
-    const std::vector<int>& kernel_dilation,
-    const std::vector<int>& input_dilation,
-    int groups,
-    bool flip,
-    Stream s) {
-  if (!in.flags().row_contiguous) {
-    in = contiguous_copy_gpu(in, s);
-    encoder.add_temporary(in);
-  }
-  if (!wt.flags().row_contiguous) {
-    wt = contiguous_copy_gpu(wt, s);
-    encoder.add_temporary(wt);
-  }
-
-  if (groups == 1) {
-    gemm_conv(
-        encoder,
-        in,
-        wt,
-        out,
-        strides,
-        padding,
-        kernel_dilation,
-        input_dilation,
-        flip,
-        s);
-  } else {
-    gemm_grouped_conv(
-        encoder,
-        in,
-        wt,
-        out,
-        strides,
-        padding,
-        kernel_dilation,
-        input_dilation,
-        groups,
-        flip,
-        s);
-  }
-}
-
-} // namespace mlx::core

mlx/backend/cuda/conv/gemm_conv.cu

@@ -1,217 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/conv/conv.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, int NDIM>
-__global__ void naive_unfold_nd(
-    const T* in,
-    T* out,
-    int filter_size,
-    int out_pixels,
-    const __grid_constant__ ConvParams<NDIM> params) {
-  auto block = cg::this_thread_block();
-  auto tid = block.group_index();
-  auto lid = block.thread_index();
-
-  int index_batch = tid.z / out_pixels; // [0, N)
-  int index_out_spatial = tid.z % out_pixels; // [0, H_out * W_out)
-  int index_wt_spatial =
-      tid.x * block.dim_threads().x + lid.x; // [0, H_wt * W_wt)
-
-  if (index_wt_spatial >= filter_size / params.C) {
-    return;
-  }
-
-  in += tid.y; // [0, C)
-  out += tid.z * filter_size + index_wt_spatial * params.C + tid.y;
-
-  bool valid = index_batch < params.N;
-
-  // Get the coordinates in input.
-  int index_in[NDIM] = {};
-#pragma unroll
-  for (int i = NDIM - 1; i >= 0; --i) {
-    int index_out = index_out_spatial % params.out_spatial_dims[i];
-    int index_wt = index_wt_spatial % params.wt_spatial_dims[i];
-
-    if (params.flip) {
-      index_wt = params.wt_spatial_dims[i] - index_wt - 1;
-    }
-
-    int index = index_out * params.strides[i] - params.padding[i] +
-        index_wt * params.kernel_dilation[i];
-    int index_max =
-        1 + params.input_dilation[i] * (params.in_spatial_dims[i] - 1);
-
-    valid &= (index >= 0) && (index < index_max) &&
-        (index % params.input_dilation[i] == 0);
-
-    index_in[i] = index / params.input_dilation[i];
-
-    index_out_spatial /= params.out_spatial_dims[i];
-    index_wt_spatial /= params.wt_spatial_dims[i];
-  }
-
-  if (valid) {
-    int in_offset = index_batch * params.in_strides[0];
-#pragma unroll
-    for (int i = 0; i < NDIM; ++i) {
-      in_offset += index_in[i] * params.in_strides[i + 1];
-    }
-    *out = in[in_offset];
-  } else {
-    *out = T{0};
-  }
-}
-
-} // namespace cu
-
-template <int NDIM>
-array unfold_inputs_nd(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    int mat_M,
-    int mat_K,
-    int mat_N,
-    ConvParams<NDIM>& params) {
-  array unfolded({mat_M, mat_K}, in.dtype(), nullptr, {});
-  unfolded.set_data(allocator::malloc(unfolded.nbytes()));
-  encoder.add_temporary(unfolded);
-
-  int filter_size = params.C;
-#pragma unroll
-  for (int i = 0; i < NDIM; ++i) {
-    filter_size *= params.wt_spatial_dims[i];
-  }
-
-  int out_pixels = 1;
-#pragma unroll
-  for (int i = 0; i < NDIM; ++i) {
-    out_pixels *= params.out_spatial_dims[i];
-  }
-
-  int wt_spatial_size = mat_K / params.C;
-  dim3 block_dims;
-  block_dims.x = std::min(std::max(wt_spatial_size, 32), 1024);
-  dim3 num_blocks;
-  num_blocks.x = cuda::ceil_div(wt_spatial_size, block_dims.x);
-  num_blocks.y = params.C;
-  num_blocks.z = mat_M;
-
-  encoder.set_input_array(in);
-  encoder.set_output_array(unfolded);
-  dispatch_float_types(in.dtype(), "unfold", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    encoder.add_kernel_node(
-        cu::naive_unfold_nd<DataType, NDIM>,
-        num_blocks,
-        block_dims,
-        0,
-        in.data<DataType>(),
-        unfolded.data<DataType>(),
-        filter_size,
-        out_pixels,
-        params);
-  });
-
-  return unfolded;
-}
-
-template <int NDIM>
-void gemm_conv_nd(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    ConvParams<NDIM>& params,
-    Stream s) {
-  // Get gemm shapes.
-  int mat_M = out.size() / params.O; // N * H_out * W_out
-  int mat_K = wt.size() / params.O; // C * H_wt * W_wt
-  int mat_N = params.O; // O
-
-  // Unfold input to (N * H_out * W_out, C * H_wt * W_wt) for gemm.
-  array in_unfolded =
-      unfold_inputs_nd<NDIM>(encoder, in, mat_M, mat_K, mat_N, params);
-
-  // Reshape weight to (C * H_wt * W_wt, O) for gemm.
-  array wt_reshaped({mat_K, mat_N}, wt.dtype(), nullptr, {});
-  wt_reshaped.copy_shared_buffer(
-      wt,
-      {1, mat_K},
-      {false, false, /* col_contiguous */ true},
-      wt.data_size());
-
-  // Single batch.
-  Shape batch_shape{1};
-  Strides a_batch_strides{0};
-  Strides b_batch_strides{0};
-
-  // Run matmul.
-  CublasGemm gemm(
-      encoder.device(),
-      in.dtype(),
-      false, // a_transposed
-      mat_M, // a_rows
-      mat_K, // a_cols
-      mat_K, // lda
-      true, // b_transposed
-      mat_K, // b_rows
-      mat_N, // b_cols
-      mat_K, // ldb
-      batch_shape.back(),
-      a_batch_strides.back(),
-      b_batch_strides.back());
-  gemm.run(
-      encoder,
-      out,
-      in_unfolded,
-      wt_reshaped,
-      batch_shape,
-      a_batch_strides,
-      b_batch_strides);
-}
-
-void gemm_conv(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    const std::vector<int>& strides,
-    const std::vector<int>& padding,
-    const std::vector<int>& kernel_dilation,
-    const std::vector<int>& input_dilation,
-    bool flip,
-    Stream s) {
-  int conv_ndim = in.ndim() - 2;
-  if (conv_ndim < 1 || conv_ndim > 3) {
-    throw std::runtime_error(
-        fmt::format("[conv] Unsupported gemm_conv for {}D conv.", conv_ndim));
-  }
-  dispatch_1_2_3(conv_ndim, [&](auto ndim_constant) {
-    ConvParams<ndim_constant()> params(
-        in,
-        wt,
-        out,
-        strides,
-        padding,
-        kernel_dilation,
-        input_dilation,
-        1, // groups
-        flip);
-    gemm_conv_nd<ndim_constant()>(encoder, in, wt, out, params, s);
-  });
-}
-
-} // namespace mlx::core

mlx/backend/cuda/conv/gemm_grouped_conv.cu

@@ -1,231 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/conv/conv.h"
-#include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-
-#include <cooperative_groups.h>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename T, int NDIM>
-__global__ void naive_grouped_unfold_transpose_nd(
-    const T* in,
-    T* out,
-    int filter_size,
-    int out_pixels,
-    const __grid_constant__ ConvParams<NDIM> params) {
-  auto block = cg::this_thread_block();
-  auto tid = block.group_index();
-  auto lid = block.thread_index();
-
-  int index_batch = tid.z / out_pixels; // [0, N)
-  int index_out_spatial = tid.z % out_pixels; // [0, H_out * W_out)
-  int index_wt_spatial =
-      tid.x * block.dim_threads().x + lid.x; // [0, H_wt * W_wt)
-
-  if (index_wt_spatial >= filter_size / params.C) {
-    return;
-  }
-
-  in += tid.y; // [0, C)
-  out += tid.z * filter_size + tid.y * (filter_size / params.C);
-
-  bool valid = index_batch < params.N;
-
-  // Get the coordinates in input.
-  int index_in[NDIM] = {};
-  int wt_stride = 1;
-#pragma unroll
-  for (int i = NDIM - 1; i >= 0; --i) {
-    int index_out = index_out_spatial % params.out_spatial_dims[i];
-    int index_wt = index_wt_spatial % params.wt_spatial_dims[i];
-    out += index_wt * wt_stride;
-
-    if (params.flip) {
-      index_wt = params.wt_spatial_dims[i] - index_wt - 1;
-    }
-
-    int index = index_out * params.strides[i] - params.padding[i] +
-        index_wt * params.kernel_dilation[i];
-    int index_max =
-        1 + params.input_dilation[i] * (params.in_spatial_dims[i] - 1);
-
-    valid &= (index >= 0) && (index < index_max) &&
-        (index % params.input_dilation[i] == 0);
-
-    index_in[i] = index / params.input_dilation[i];
-
-    index_out_spatial /= params.out_spatial_dims[i];
-    index_wt_spatial /= params.wt_spatial_dims[i];
-    wt_stride *= params.wt_spatial_dims[i];
-  }
-
-  if (valid) {
-    int in_offset = index_batch * params.in_strides[0];
-#pragma unroll
-    for (int i = 0; i < NDIM; ++i) {
-      in_offset += index_in[i] * params.in_strides[i + 1];
-    }
-    *out = in[in_offset];
-  } else {
-    *out = T{0};
-  }
-}
-
-} // namespace cu
-
-template <int NDIM>
-array grouped_unfold_transpose_inputs_nd(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    int mat_M,
-    int mat_K,
-    int mat_N,
-    ConvParams<NDIM>& params) {
-  array unfolded({mat_M, mat_K * params.groups}, in.dtype(), nullptr, {});
-  unfolded.set_data(allocator::malloc(unfolded.nbytes()));
-  encoder.add_temporary(unfolded);
-
-  int filter_size = params.C;
-#pragma unroll
-  for (int i = 0; i < NDIM; ++i) {
-    filter_size *= params.wt_spatial_dims[i];
-  }
-
-  int out_pixels = 1;
-#pragma unroll
-  for (int i = 0; i < NDIM; ++i) {
-    out_pixels *= params.out_spatial_dims[i];
-  }
-
-  int wt_spatial_size = (mat_K * params.groups) / params.C;
-  dim3 block_dims;
-  block_dims.x = std::min(std::max(wt_spatial_size, 32), 1024);
-  dim3 num_blocks;
-  num_blocks.x = cuda::ceil_div(wt_spatial_size, block_dims.x);
-  num_blocks.y = params.C;
-  num_blocks.z = mat_M;
-
-  encoder.set_input_array(in);
-  encoder.set_output_array(unfolded);
-  dispatch_float_types(in.dtype(), "unfold", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    encoder.add_kernel_node(
-        cu::naive_grouped_unfold_transpose_nd<DataType, NDIM>,
-        num_blocks,
-        block_dims,
-        0,
-        in.data<DataType>(),
-        unfolded.data<DataType>(),
-        filter_size,
-        out_pixels,
-        params);
-  });
-
-  return unfolded;
-}
-
-template <int NDIM>
-void gemm_grouped_conv_nd(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    ConvParams<NDIM>& params,
-    Stream s) {
-  // Get gemm shapes.
-  int C_per_group = params.C / params.groups;
-  int O_per_group = params.O / params.groups;
-  int mat_M = out.size() / params.O; // N * H_out * W_out
-  int mat_K = wt.size() / params.O; // C_per_group * H_wt * W_wt
-  int mat_N = O_per_group; // O_per_group
-
-  // Unfold input to (N * H_out * W_out, C * H_wt * W_wt) for gemm.
-  array in_unfolded = grouped_unfold_transpose_inputs_nd<NDIM>(
-      encoder, in, mat_M, mat_K, mat_N, params);
-
-  // Reshape weight to (O, C_per_group, H_wt * W_wt) for gemm.
-  int wt_spatial_size = (wt.size() / wt.shape(0)) / wt.shape(-1);
-  array wt_view(
-      {params.O, C_per_group, wt_spatial_size}, wt.dtype(), nullptr, {});
-  wt_view.copy_shared_buffer(
-      wt, {wt.strides(0), 1, C_per_group}, wt.flags(), wt.size());
-  array wt_reshaped = contiguous_copy_gpu(wt_view, s);
-
-  // Batch with size of groups.
-  Shape batch_shape{params.groups};
-  Strides a_batch_strides{mat_K};
-  Strides b_batch_strides{mat_N * mat_K};
-
-  // Run matmul.
-  CublasGemm gemm(
-      encoder.device(),
-      in.dtype(),
-      false, // a_transposed
-      mat_M, // a_rows
-      mat_K, // a_cols
-      mat_K * params.groups, // lda
-      true, // b_transposed
-      mat_K, // b_rows
-      mat_N, // b_cols
-      mat_K, // ldb
-      batch_shape.back(),
-      a_batch_strides.back(),
-      b_batch_strides.back());
-  gemm.set_out(
-      out.dtype(),
-      false, // out_transposed
-      mat_M, // out_rows
-      mat_N, // out_cols
-      mat_N * params.groups, // out_ld
-      params.groups, // batch_count
-      mat_N); // batch_stride
-  gemm.run(
-      encoder,
-      out,
-      in_unfolded,
-      wt_reshaped,
-      batch_shape,
-      a_batch_strides,
-      b_batch_strides);
-}
-
-void gemm_grouped_conv(
-    cu::CommandEncoder& encoder,
-    const array& in,
-    const array& wt,
-    array& out,
-    const std::vector<int>& strides,
-    const std::vector<int>& padding,
-    const std::vector<int>& kernel_dilation,
-    const std::vector<int>& input_dilation,
-    int groups,
-    bool flip,
-    Stream s) {
-  int conv_ndim = in.ndim() - 2;
-  if (conv_ndim < 1 || conv_ndim > 3) {
-    throw std::runtime_error(
-        fmt::format("[conv] Unsupported gemm_conv for {}D conv.", conv_ndim));
-  }
-  dispatch_1_2_3(conv_ndim, [&](auto ndim_constant) {
-    ConvParams<ndim_constant()> params(
-        in,
-        wt,
-        out,
-        strides,
-        padding,
-        kernel_dilation,
-        input_dilation,
-        groups,
-        flip);
-    gemm_grouped_conv_nd<ndim_constant()>(encoder, in, wt, out, params, s);
-  });
-}
-
-} // namespace mlx::core

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/cudnn_utils.cpp

@@ -1,272 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/cudnn_utils.h"
-#include "mlx/backend/cuda/device.h"
-
-namespace mlx::core {
-
-namespace {
-
-// Create a cudnn tensor descriptor.
-template <typename Vec>
-inline cudnn_frontend::Tensor build_cudnn_tensor(
-    int64_t id,
-    const array& x,
-    const Vec& shape,
-    const Vec& strides) {
-  return cudnn_frontend::TensorBuilder()
-      .setDim(shape.size(), shape.data())
-      .setStrides(strides.size(), strides.data())
-      .setId(id)
-      .setAlignment(get_alignment(x))
-      .setDataType(dtype_to_cudnn_type(x.dtype()))
-      .build();
-}
-
-// In MLX a singleton dim (shape[dim] == 1) can have any stride, but in cuDNN
-// whether a tensor is contiguous is determined with:
-// shape[dim] == shape[dim + 1] * strides[dim + 1]
-// So a contiguous array with singleton dims in MLX may be mistakenly treated
-// as strided in cuDNN, and we work around it by normalizing the strides.
-Strides normalized_strides(const array& x) {
-  if (!x.flags().row_contiguous || x.ndim() < 2) {
-    return x.strides();
-  }
-  Strides strides = x.strides();
-  for (int i = x.ndim() - 2; i >= 0; --i) {
-    if (x.shape(i) == 1) {
-      strides[i] = x.shape(i + 1) * strides[i + 1];
-    }
-  }
-  return strides;
-}
-
-// Return the shape and strides after transposing from NHWC to NCHW.
-auto nhwc_to_nchw(SmallVector<int64_t> shape, SmallVector<int64_t> strides) {
-  assert(shape.size() >= 3);
-  shape.insert(shape.begin() + 1, shape.back());
-  shape.erase(shape.end() - 1);
-  strides.insert(strides.begin() + 1, strides.back());
-  strides.erase(strides.end() - 1);
-  return std::make_tuple(std::move(shape), std::move(strides));
-}
-
-inline auto nhwc_to_nchw(const array& x) {
-  return nhwc_to_nchw(
-      convert_vector<int64_t>(x.shape()), normalized_strides(x));
-}
-
-// Return available engines for a |op_graph|.
-cudnn_frontend::EngineConfigList get_cudnn_engine_configs(
-    cudnnBackendDescriptorType_t backend_type,
-    Dtype dtype,
-    cudnn_frontend::OperationGraph& op_graph,
-    bool use_fallback = true) {
-  SmallVector<cudnn_frontend::GeneratorSource, 2> sources;
-  sources.push_back([](auto& op_graph) {
-    auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
-                          .setOperationGraph(op_graph)
-                          .setHeurMode(CUDNN_HEUR_MODE_A)
-                          .build();
-    return heuristics.getEngineConfig(heuristics.getEngineConfigCount());
-  });
-  if (use_fallback) {
-    sources.push_back([&backend_type](auto& op_graph) {
-      auto fallback = cudnn_frontend::EngineFallbackListBuilder()
-                          .setOperationGraph(op_graph)
-                          .setOperation(backend_type)
-                          .build();
-      return fallback.getFallbackList();
-    });
-  }
-
-  auto configs =
-      cudnn_frontend::EngineConfigGenerator(sources.size(), sources.data())
-          .generate_engine_config(op_graph);
-
-  cudnn_frontend::EngineConfigList filtered_configs;
-  cudnn_frontend::filter(configs, filtered_configs, [dtype](auto c) {
-    if (cudnn_frontend::hasNumericalNote<
-            CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(c)) {
-      return true;
-    }
-    if (cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c) &&
-        dtype == float32 && !env::enable_tf32()) {
-      return true;
-    }
-    return false;
-  });
-  return filtered_configs;
-}
-
-// Take |engine_configs| and |op_graph| and find a working execution plans
-// from them.
-std::optional<cudnn_frontend::ExecutionPlan>
-find_cudnn_plan_from_engine_configs(
-    cudnnHandle_t handle,
-    const cudnn_frontend::EngineConfigList& engine_configs,
-    const cudnn_frontend::OperationGraph& op_graph) {
-  auto op_graph_tag = op_graph.getTag();
-  for (const auto& config : engine_configs) {
-    try {
-      return cudnn_frontend::ExecutionPlanBuilder()
-          .setHandle(handle)
-          .setEngineConfig(config, op_graph_tag)
-          .build();
-    } catch (cudnn_frontend::cudnnException& error) {
-      if (error.getCudnnStatus() != CUDNN_STATUS_NOT_SUPPORTED) {
-        throw;
-      }
-    }
-  }
-  return std::nullopt;
-}
-
-// Prepare workspace and args to execute plan.
-template <typename F>
-bool prepare_cudnn_plan(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    int num_args,
-    const int64_t* uids,
-    void** data_ptrs,
-    F&& execute) {
-  int workspace_size = plan.getWorkspaceSize();
-  array workspace(
-      workspace_size > 0 ? allocator::malloc(workspace_size)
-                         : allocator::Buffer(nullptr),
-      {workspace_size},
-      uint8);
-
-  auto args = cudnn_frontend::VariantPackBuilder()
-                  .setWorkspacePointer(workspace.data<void>())
-                  .setDataPointers(num_args, data_ptrs)
-                  .setUids(num_args, uids)
-                  .build();
-
-  auto handle = encoder.device().cudnn_handle();
-  cudnnSetStream(handle, encoder.stream());
-
-  if (!execute(handle, plan.get_raw_desc(), args.get_raw_desc())) {
-    return false;
-  }
-
-  encoder.add_temporary(workspace);
-  return true;
-}
-
-} // namespace
-
-cudnn_frontend::Tensor build_cudnn_tensor(int64_t id, const array& x) {
-  auto shape = convert_vector<int64_t>(x.shape());
-  return build_cudnn_tensor(id, x, shape, normalized_strides(x));
-}
-
-cudnn_frontend::Tensor build_cudnn_tensor_nchw(int64_t id, const array& x) {
-  auto [shape, strides] = nhwc_to_nchw(x);
-  return build_cudnn_tensor(id, x, shape, strides);
-}
-
-cudnn_frontend::Tensor build_cudnn_tensor_4d_nchw(int64_t id, const array& x) {
-  if (x.ndim() == 0) {
-    SmallVector<int64_t, 4> scalar_dims = {1, 1, 1, 1};
-    return build_cudnn_tensor(id, x, scalar_dims, scalar_dims);
-  }
-  if (x.ndim() == 1) {
-    int64_t s = x.shape(0);
-    SmallVector<int64_t, 4> shape = {1, x.shape(0), 1, 1};
-    SmallVector<int64_t, 4> strides = {s, 1, s, s};
-    return build_cudnn_tensor(id, x, shape, strides);
-  }
-  if (x.ndim() == 2) {
-    int64_t s =
-        x.flags().row_contiguous ? x.shape(1) * x.strides(1) : x.strides(0);
-    SmallVector<int64_t, 4> shape = {x.shape(0), x.shape(1), 1, 1};
-    SmallVector<int64_t, 4> strides = {s, x.strides(1), s, s};
-    return build_cudnn_tensor(id, x, shape, strides);
-  }
-  if (x.ndim() == 3 || x.ndim() == 4) {
-    return build_cudnn_tensor_nchw(id, x);
-  }
-  throw std::runtime_error(
-      fmt::format("Unsupported array with {} dims.", x.ndim()));
-}
-
-cudnn_frontend::Tensor build_cudnn_scalar_4d(int64_t id, Dtype dtype) {
-  SmallVector<int64_t, 4> scalar_dims = {1, 1, 1, 1};
-  return cudnn_frontend::TensorBuilder()
-      .setDim(scalar_dims.size(), scalar_dims.data())
-      .setStrides(scalar_dims.size(), scalar_dims.data())
-      .setId(id)
-      .setAlignment(16)
-      .setDataType(dtype_to_cudnn_type(dtype))
-      .setByValue(true)
-      .build();
-}
-
-std::optional<cudnn_frontend::ExecutionPlan> find_cudnn_plan_from_op_graph(
-    cudnnHandle_t handle,
-    cudnnBackendDescriptorType_t backend_type,
-    Dtype dtype,
-    cudnn_frontend::OperationGraph& op_graph) {
-  auto engine_configs = get_cudnn_engine_configs(backend_type, dtype, op_graph);
-  return find_cudnn_plan_from_engine_configs(handle, engine_configs, op_graph);
-}
-
-bool encode_cudnn_plan_with_capturing(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    int num_args,
-    const int64_t* uids,
-    void** data_ptrs) {
-  return prepare_cudnn_plan(
-      encoder,
-      plan,
-      num_args,
-      uids,
-      data_ptrs,
-      [&](auto handle, auto plan, auto args) {
-        auto capture = encoder.capture_context();
-        if (cudnnBackendExecute(handle, plan, args) != CUDNN_STATUS_SUCCESS) {
-          // Discard the captured graph when failed.
-          capture.discard = true;
-          return false;
-        }
-        return true;
-      });
-}
-
-#if CUDNN_VERSION >= 90500
-bool encode_cudnn_plan_with_graph_api(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    CudaGraph& graph,
-    int num_args,
-    const int64_t* uids,
-    void** data_ptrs) {
-  return prepare_cudnn_plan(
-      encoder,
-      plan,
-      num_args,
-      uids,
-      data_ptrs,
-      [&](auto handle, auto plan, auto args) {
-        if (!graph) {
-          graph = CudaGraph(encoder.device());
-          if (cudnnBackendPopulateCudaGraph(handle, plan, args, graph) !=
-              CUDNN_STATUS_SUCCESS) {
-            return false;
-          }
-        } else {
-          if (cudnnBackendUpdateCudaGraph(handle, plan, args, graph) !=
-              CUDNN_STATUS_SUCCESS) {
-            return false;
-          }
-        }
-        encoder.add_graph_node(graph);
-        return true;
-      });
-}
-#endif
-
-} // namespace mlx::core

mlx/backend/cuda/cudnn_utils.h

@@ -1,164 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/array.h"
-#include "mlx/backend/cuda/device/config.h"
-#include "mlx/backend/cuda/utils.h"
-#include "mlx/dtype_utils.h"
-
-#include <cudnn_frontend.h>
-#include <cudnn_frontend_find_plan.h>
-#include <fmt/format.h>
-
-#include <algorithm>
-#include <array>
-
-namespace mlx::core {
-
-namespace cu {
-class CommandEncoder;
-}
-
-// Return pointer alignment of |x|'s data.
-inline uint8_t get_alignment(const array& x) {
-  uint8_t alignment = 1;
-  uintptr_t address = reinterpret_cast<uintptr_t>(x.data<void>());
-  for (; alignment < 32; alignment *= 2) {
-    if (address % (alignment * 2)) {
-      return alignment;
-    }
-  }
-  return alignment;
-}
-
-// Convert the type of elements in |vec| to |T|.
-template <typename T, typename Vec>
-inline SmallVector<T> convert_vector(const Vec& vec) {
-  return SmallVector<T>(vec.begin(), vec.end());
-}
-
-// Return an array that can be used as map key for |vec| with size <= MAX_NDIM.
-//
-// There are 2 differences from the const_param util from kernel_utils.cuh:
-// 1. The rest of array is filled with 0.
-// 2. This util can be used in .cpp files.
-template <typename T, template <typename U> class Vec>
-inline std::array<T, MAX_NDIM> vector_key(const Vec<T>& vec) {
-  if (vec.size() > MAX_NDIM) {
-    throw std::runtime_error(
-        fmt::format("ndim can not be larger than {}.", MAX_NDIM));
-  }
-  std::array<T, MAX_NDIM> result = {};
-  std::copy_n(vec.begin(), vec.size(), result.begin());
-  return result;
-}
-
-// Helpers used by get_data_ptrs to get pointers.
-inline void* get_data_ptr(const array& arr) {
-  return const_cast<void*>(arr.data<void>());
-}
-
-template <typename T, typename = std::enable_if_t<std::is_scalar_v<T>>>
-inline void* get_data_ptr(T& scalar) {
-  return &scalar;
-}
-
-// Return an array filled with data pointers of args.
-template <typename... Args>
-inline std::array<void*, sizeof...(Args)> get_data_ptrs(Args&... args) {
-  return {get_data_ptr(args)...};
-}
-
-// Map dtype to cudnn data type.
-inline cudnnDataType_t dtype_to_cudnn_type(Dtype dtype) {
-  switch (dtype) {
-    case int8:
-      return CUDNN_DATA_INT8;
-    case int32:
-      return CUDNN_DATA_INT32;
-    case uint8:
-      return CUDNN_DATA_UINT8;
-    case float16:
-      return CUDNN_DATA_HALF;
-    case bfloat16:
-      return CUDNN_DATA_BFLOAT16;
-    case float32:
-      return CUDNN_DATA_FLOAT;
-    case float64:
-      return CUDNN_DATA_DOUBLE;
-    default:
-      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in Convolution: {}.", dtype_to_string(dtype)));
-  }
-}
-
-// Create a tensor descriptor from |x|.
-cudnn_frontend::Tensor build_cudnn_tensor(int64_t id, const array& x);
-
-// Create a tensor descriptor from |x|, and transpose from NHWC to NCHW.
-cudnn_frontend::Tensor build_cudnn_tensor_nchw(int64_t id, const array& x);
-
-// Create a tensor descriptor from |x|, make sure it is 4D, and transpose it
-// from NHWC to NCHW.
-cudnn_frontend::Tensor build_cudnn_tensor_4d_nchw(int64_t id, const array& x);
-
-// Create a 4D scalar tensor descriptor, which is passed by value.
-cudnn_frontend::Tensor build_cudnn_scalar_4d(int64_t id, Dtype dtype);
-
-// Find a working plan for |op_graph|.
-std::optional<cudnn_frontend::ExecutionPlan> find_cudnn_plan_from_op_graph(
-    cudnnHandle_t handle,
-    cudnnBackendDescriptorType_t backend_type,
-    Dtype dtype,
-    cudnn_frontend::OperationGraph& op_graph);
-
-// Encode the plan to command buffer by capturing.
-bool encode_cudnn_plan_with_capturing(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    int num_args,
-    const int64_t* uids,
-    void** data_ptrs);
-
-#if CUDNN_VERSION >= 90500
-// Encode the plan to command buffer by using native graph api of cudnn. If the
-// |graph| is empty it will be populated, otherwise it will be updated.
-bool encode_cudnn_plan_with_graph_api(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    CudaGraph& graph,
-    int num_args,
-    const int64_t* uids,
-    void** data_ptrs);
-#endif
-
-// Helpers to make calls like encode_cudnn_plan(..., {'x', 'y', 'z'}, x, y, z).
-template <typename... Args>
-bool encode_cudnn_plan(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    std::initializer_list<int64_t> uids,
-    Args&... args) {
-  assert(uids.size() == sizeof...(args));
-  auto data_ptrs = get_data_ptrs(args...);
-  return encode_cudnn_plan_with_capturing(
-      encoder, plan, uids.size(), uids.begin(), data_ptrs.data());
-}
-
-#if CUDNN_VERSION >= 90500
-template <typename... Args>
-bool encode_cudnn_plan(
-    cu::CommandEncoder& encoder,
-    cudnn_frontend::ExecutionPlan& plan,
-    CudaGraph& graph,
-    std::initializer_list<int64_t> uids,
-    Args&... args) {
-  assert(uids.size() == sizeof...(args));
-  auto data_ptrs = get_data_ptrs(args...);
-  return encode_cudnn_plan_with_graph_api(
-      encoder, plan, graph, uids.size(), uids.begin(), data_ptrs.data());
-}
-#endif
-
-} // namespace mlx::core

mlx/backend/cuda/custom_kernel.cpp

@@ -1,379 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include <iostream>
-
-#include "mlx/backend/common/compiled.h"
-#include "mlx/backend/cuda/jit_module.h"
-#include "mlx/backend/cuda/utils.h"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/fast.h"
-#include "mlx/fast_primitives.h"
-
-#include <fmt/format.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core::fast {
-
-namespace {
-
-constexpr const char* default_header = R"(
-#include "mlx/backend/cuda/device/utils.cuh"
-
-#include <cooperative_groups.h>
-
-#define inf cuda::std::numeric_limits<float>::infinity()
-
-)";
-
-std::string template_arguments_hash(
-    const std::vector<std::pair<std::string, TemplateArg>>& template_args) {
-  if (template_args.empty()) {
-    return "";
-  }
-
-  std::string hash;
-  hash.reserve(512);
-
-  for (const auto& [name, arg] : template_args) {
-    if (std::holds_alternative<int>(arg)) {
-      hash += fmt::format("_{}", std::get<int>(arg));
-    } else if (std::holds_alternative<bool>(arg)) {
-      hash += (std::get<bool>(arg)) ? "_t" : "_f";
-    } else if (std::holds_alternative<Dtype>(arg)) {
-      hash += "_";
-      hash += get_type_string(std::get<Dtype>(arg));
-    }
-  }
-
-  return hash;
-}
-
-std::string build_kernel(
-    const std::string& func_name,
-    const std::string& header,
-    const std::string& source,
-    const std::vector<std::string>& input_names,
-    const std::vector<array>& inputs,
-    const std::vector<std::string>& output_names,
-    const std::vector<Dtype>& output_dtypes,
-    const std::vector<std::pair<std::string, TemplateArg>>& template_args,
-    const std::vector<CustomKernelShapeInfo>& shape_infos) {
-  std::string kernel_source;
-  kernel_source.reserve(header.size() + source.size() + 8192);
-  kernel_source += default_header;
-  kernel_source += header;
-  kernel_source +=
-      "namespace mlx::core::cu {\n\n"
-      "namespace cg = cooperative_groups;\n\n";
-
-  kernel_source += "__global__ void ";
-  kernel_source += func_name;
-  kernel_source += "(\n";
-
-  // Add inputs
-  for (int i = 0; i < inputs.size(); ++i) {
-    const auto& name = input_names[i];
-    const auto& arr = inputs[i];
-    kernel_source += "    const ";
-    kernel_source += dtype_to_cuda_type(arr.dtype());
-    kernel_source += "* ";
-    kernel_source += name;
-    kernel_source += ",\n";
-    // Add input shape, strides and ndim if present in the source
-    if (arr.ndim() > 0) {
-      if (shape_infos[i].shape) {
-        kernel_source += "    const __grid_constant__ Shape ";
-        kernel_source += name;
-        kernel_source += "_shape,\n";
-      }
-      if (shape_infos[i].strides) {
-        kernel_source += "    const __grid_constant__ Strides ";
-        kernel_source += name;
-        kernel_source += "_strides,\n";
-      }
-      if (shape_infos[i].ndim) {
-        kernel_source += "    const __grid_constant__ int ";
-        kernel_source += name;
-        kernel_source += "_ndim,\n";
-      }
-    }
-  }
-
-  // Add outputs
-  for (int i = 0; i < output_names.size(); ++i) {
-    const auto& name = output_names[i];
-    const auto& dtype = output_dtypes[i];
-    kernel_source += "    ";
-    kernel_source += dtype_to_cuda_type(dtype);
-    kernel_source += "* ";
-    kernel_source += name;
-    if (i < output_names.size() - 1) {
-      kernel_source += ",\n";
-    } else {
-      kernel_source += ") {\n";
-    }
-  }
-
-  // Set compile time constants
-  if (!template_args.empty()) {
-    for (const auto& [name, arg] : template_args) {
-      if (std::holds_alternative<int>(arg)) {
-        kernel_source +=
-            fmt::format("  constexpr int {} = {};\n", name, std::get<int>(arg));
-      } else if (std::holds_alternative<bool>(arg)) {
-        kernel_source += fmt::format(
-            "  constexpr bool {} = {};\n", name, std::get<bool>(arg));
-      } else {
-        kernel_source += fmt::format(
-            "  using {} = {};\n",
-            name,
-            dtype_to_cuda_type(std::get<Dtype>(arg)));
-      }
-    }
-    kernel_source += "\n";
-  }
-
-  kernel_source += source;
-  kernel_source += "\n}\n\n} // namespace mlx::core::cu\n";
-
-  return kernel_source;
-}
-
-} // namespace
-
-CustomKernelFunction cuda_kernel(
-    const std::string& name,
-    const std::vector<std::string>& input_names,
-    const std::vector<std::string>& output_names,
-    const std::string& source,
-    const std::string& header,
-    bool ensure_row_contiguous,
-    int shared_memory) {
-  if (output_names.empty()) {
-    throw std::invalid_argument(
-        "[custom_kernel] Must specify at least one output.");
-  }
-
-  std::vector<CustomKernelShapeInfo> shape_infos;
-  for (auto& n : input_names) {
-    CustomKernelShapeInfo shape_info;
-    shape_info.shape = source.find(n + "_shape") != std::string::npos;
-    shape_info.strides = source.find(n + "_strides") != std::string::npos;
-    shape_info.ndim = source.find(n + "_ndim") != std::string::npos;
-    shape_infos.push_back(shape_info);
-  }
-
-  return [=, shape_infos = std::move(shape_infos)](
-             const std::vector<array>& inputs,
-             const std::vector<Shape>& output_shapes,
-             const std::vector<Dtype>& output_dtypes,
-             std::tuple<int, int, int> grid,
-             std::tuple<int, int, int> threadgroup,
-             const std::vector<std::pair<std::string, TemplateArg>>&
-                 template_args = {},
-             std::optional<float> init_value = std::nullopt,
-             bool verbose = false,
-             StreamOrDevice s_ = {}) {
-    if (inputs.size() != input_names.size()) {
-      std::ostringstream msg;
-      msg << "[custom_kernel] Expected `inputs` to have size "
-          << input_names.size() << " but got size " << inputs.size() << "."
-          << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-    if (output_shapes.size() != output_names.size()) {
-      std::ostringstream msg;
-      msg << "[custom_kernel] Expected `output_shapes` to have size "
-          << output_names.size() << " but got size " << output_shapes.size()
-          << "." << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-    if (output_dtypes.size() != output_names.size()) {
-      std::ostringstream msg;
-      msg << "[custom_kernel] Expected `output_dtypes` to have size "
-          << output_names.size() << " but got size " << output_dtypes.size()
-          << "." << std::endl;
-      throw std::invalid_argument(msg.str());
-    }
-
-    auto s = to_stream(s_);
-    if (s.device != Device::gpu) {
-      throw std::invalid_argument("[custom_kernel] Only supports the GPU.");
-    }
-
-    std::string kernel_name =
-        "custom_kernel_" + name + template_arguments_hash(template_args);
-    std::string kernel_source = build_kernel(
-        kernel_name,
-        header,
-        source,
-        input_names,
-        inputs,
-        output_names,
-        output_dtypes,
-        template_args,
-        shape_infos);
-
-    if (verbose) {
-      std::cout << "Generated source code for `" << kernel_name
-                << "`:" << std::endl
-                << "```" << std::endl
-                << kernel_source << std::endl
-                << "```" << std::endl;
-    }
-
-    return array::make_arrays(
-        std::move(output_shapes),
-        std::move(output_dtypes),
-        std::make_shared<CustomKernel>(
-            s,
-            std::move(kernel_name),
-            std::move(kernel_source),
-            grid,
-            threadgroup,
-            shape_infos,
-            ensure_row_contiguous,
-            init_value,
-            std::vector<ScalarArg>{},
-            false,
-            shared_memory),
-        std::move(inputs));
-  };
-}
-
-std::vector<array> precompiled_cuda_kernel(
-    const std::string& name,
-    const std::string& compiled_source,
-    const std::vector<array>& inputs,
-    const std::vector<Shape>& output_shapes,
-    const std::vector<Dtype>& output_dtypes,
-    const std::vector<ScalarArg>& scalars,
-    std::tuple<int, int, int> grid,
-    std::tuple<int, int, int> threadgroup,
-    int shared_memory,
-    std::optional<float> init_value,
-    bool ensure_row_contiguous,
-    StreamOrDevice s) {
-  std::vector<CustomKernelShapeInfo> shape_infos(
-      inputs.size(), CustomKernelShapeInfo{false, false, false});
-  return array::make_arrays(
-      output_shapes,
-      output_dtypes,
-      std::make_shared<CustomKernel>(
-          to_stream(s),
-          name,
-          compiled_source,
-          grid,
-          threadgroup,
-          shape_infos,
-          ensure_row_contiguous,
-          init_value,
-          scalars,
-          true,
-          shared_memory),
-      inputs);
-}
-
-void CustomKernel::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("CustomKernel::eval_gpu");
-  auto& s = stream();
-
-  std::vector<array> copies;
-
-  // Allocate and initialize the output arrays
-  for (auto& out : outputs) {
-    if (init_value_) {
-      copies.emplace_back(init_value_.value(), out.dtype());
-      fill_gpu(copies.back(), out, s);
-    } else {
-      out.set_data(allocator::malloc(out.nbytes()));
-    }
-  }
-
-  // Create the input arrays and copy if needed
-  auto check_input = [&copies, &s, this](const array& x) -> const array {
-    bool no_copy = x.flags().row_contiguous;
-    if (!ensure_row_contiguous_ || no_copy) {
-      return x;
-    } else {
-      copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
-      copy_gpu(x, copies.back(), CopyType::General, s);
-      return copies.back();
-    }
-  };
-  std::vector<array> checked_inputs;
-  for (const array& in : inputs) {
-    checked_inputs.push_back(check_input(in));
-  }
-
-  // Compile the custom kernel
-  std::string kernel_name =
-      (is_precompiled_) ? name_ : "mlx::core::cu::" + name_;
-  cu::JitModule& mod = cu::get_jit_module(
-      s.device,
-      name_,
-      [&]() {
-        return std::make_tuple(
-            is_precompiled_, source_, std::vector{kernel_name});
-      },
-      false);
-
-  // Make the arguments
-  cu::KernelArgs args;
-  for (int i = 0; i < checked_inputs.size(); i++) {
-    const array& in = checked_inputs[i];
-    auto& shape_info = shape_infos_[i];
-    args.append(in);
-    if (shape_info.shape) {
-      args.append_ndim(in.shape());
-    }
-    if (shape_info.strides) {
-      args.append_ndim(in.strides());
-    }
-    if (shape_info.ndim) {
-      args.append<int32_t>(in.ndim());
-    }
-  }
-  for (auto& out : outputs) {
-    args.append(out);
-  }
-  for (auto& s : scalar_arguments_) {
-    if (std::holds_alternative<bool>(s)) {
-      args.append(std::get<bool>(s));
-    } else if (std::holds_alternative<int>(s)) {
-      args.append(std::get<int>(s));
-    } else if (std::holds_alternative<float>(s)) {
-      args.append(std::get<float>(s));
-    }
-  }
-
-  // Make the grid
-  const auto [tx, ty, tz] = threadgroup_;
-  const auto [gx, gy, gz] = grid_;
-  dim3 block(std::min(tx, gx), std::min(ty, gy), std::min(tz, gz));
-  dim3 grid((gx + tx - 1) / tx, (gy + ty - 1) / ty, (gz + tz - 1) / tz);
-
-  // Call the kernel
-  auto& encoder = cu::get_command_encoder(s);
-  for (const auto& in : checked_inputs) {
-    encoder.set_input_array(in);
-  }
-  for (const auto& out : outputs) {
-    encoder.set_output_array(out);
-  }
-  for (const auto& t : copies) {
-    encoder.add_temporary(t);
-  }
-  auto kernel =
-      mod.get_kernel(kernel_name, [smem = shared_memory_](CUfunction kernel) {
-        if (smem > 0 && smem > 48000) {
-          cuFuncSetAttribute(
-              kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, smem);
-        }
-      });
-  encoder.add_kernel_node(kernel, grid, block, shared_memory_, args.args());
-}
-
-} // namespace mlx::core::fast

mlx/backend/cuda/device.cpp

@@ -91,7 +91,9 @@ CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
 }
 
 CommandEncoder::CaptureContext::~CaptureContext() {
-  graph.end_capture(enc.stream());
+  CHECK_CUDA_ERROR(cudaStreamEndCapture(enc.stream(), &graph));
+  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+      &graph, [](cudaGraph_t* p) { CHECK_CUDA_ERROR(cudaGraphDestroy(*p)); });
   if (discard) {
     return;
   }
@@ -183,10 +185,9 @@ void CommandEncoder::insert_graph_dependencies(std::vector<GraphNode> nodes) {
 }
 
 CommandEncoder::CommandEncoder(Device& d)
-    : device_(d),
-      stream_(d),
-      graph_(d),
-      graph_cache_(cuda_graph_cache_size()) {}
+    : device_(d), stream_(d), graph_cache_(cuda_graph_cache_size()) {
+  CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
+}
 
 void CommandEncoder::add_completed_handler(std::function<void()> task) {
   worker_.add_task(std::move(task));
@@ -310,7 +311,8 @@ void CommandEncoder::commit() {
     to_nodes_.clear();
     graph_key_.clear();
     node_map_.clear();
-    graph_ = CudaGraph(device_);
+    CHECK_CUDA_ERROR(cudaGraphDestroy(graph_));
+    CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
   }
 
   // Put completion handlers in a batch.

mlx/backend/cuda/device.h

@@ -21,7 +21,7 @@ class CommandEncoder {
   struct CaptureContext {
     CaptureContext(CommandEncoder& enc);
     ~CaptureContext();
-    CudaGraph graph;
+    cudaGraph_t graph;
     CommandEncoder& enc;
     bool discard{false};
   };
@@ -115,7 +115,7 @@ class CommandEncoder {
 
   Device& device_;
   CudaStream stream_;
-  CudaGraph graph_;
+  cudaGraph_t graph_;
   Worker worker_;
   char node_count_{0};
   char graph_node_count_{0};

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_));
@@ -202,92 +198,7 @@ CublasGemm::~CublasGemm() {
   CHECK_CUBLAS_ERROR(cublasLtMatmulDescDestroy(matmul_desc_));
 }
 
-void CublasGemm::set_out(
-    Dtype dtype,
-    bool transposed,
-    uint64_t rows,
-    uint64_t cols,
-    int64_t ld,
-    int32_t batch_count,
-    int64_t batch_stride) {
-  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(out_desc_));
-  out_desc_ = create_matrix_layout(
-      dtype_to_cublas_type(dtype),
-      rows,
-      cols,
-      transposed,
-      ld,
-      batch_count,
-      batch_stride);
-}
-
-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,
@@ -345,4 +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,
@@ -42,65 +42,41 @@ class CublasGemm {
       int64_t b_batch_stride,
       int64_t c_batch_stride);
 
-  ~CublasGemm();
-
-  // The output's descriptor is inferred from inputs by default, use this method
-  // for unusual output.
-  void set_out(
-      Dtype dtype,
-      bool transposed,
-      uint64_t rows,
-      uint64_t cols,
-      int64_t ld,
-      int32_t batch_count,
-      int64_t batch_stride);
+  ~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,
@@ -121,4 +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/gemms/cutlass_gemm.cu

@@ -1,396 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-
-#include <cute/tensor.hpp>
-#include <cutlass/arch/arch.h>
-#include <cutlass/cutlass.h>
-#include <cutlass/gemm/device/gemm.h>
-#include <cutlass/layout/matrix.h>
-#include <cutlass/numeric_types.h>
-
-#include <iostream>
-
-namespace mlx::core::cu {
-
-namespace {
-
-using namespace cute;
-using bf16 = cute::bfloat16_t;
-
-template <typename Kernel>
-void configure_matmul(Kernel kernel, int smem_size) {
-  static bool initialized = false;
-  if (!initialized) {
-    initialized = true;
-    cudaFuncSetAttribute(
-        kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
-  }
-}
-
-template <bool transpose, typename Tiler>
-constexpr int get_feature_size(Tiler smem) {
-  int feature_size = (transpose) ? size<0>(smem) : size<1>(smem);
-  return (feature_size >= 64) ? 64 : feature_size;
-}
-
-constexpr int constexpr_log2(int x) {
-  return (x > 0) ? 1 + constexpr_log2(x >> 1) : -1;
-}
-
-template <int feature_size, int itemsize, int copy_bits>
-constexpr int get_swizzle_bits() {
-  constexpr int swizzle_bits =
-      constexpr_log2(feature_size * itemsize / copy_bits);
-  return (swizzle_bits > 3) ? 3 : swizzle_bits;
-}
-
-template <int itemsize, bool transpose, int copy_bits, typename Tiler>
-constexpr auto make_smem_layout(Tiler smem) {
-  constexpr int feature_size = get_feature_size<transpose>(smem);
-  constexpr int swizzle_bits =
-      get_swizzle_bits<feature_size, itemsize, copy_bits>();
-
-  using F = Int<feature_size>;
-  using BaseLayout = std::conditional_t<
-      transpose,
-      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
-      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
-
-  auto swizzled =
-      make_composed_layout(Swizzle<swizzle_bits, 3, 3>{}, 0, BaseLayout{});
-
-  return tile_to_shape(swizzled, smem);
-}
-
-template <int itemsize, bool transpose, int copy_bits, typename Tiler>
-constexpr auto make_result_smem_layout(Tiler smem) {
-  constexpr int feature_size = get_feature_size<transpose>(smem);
-  constexpr int swizzle_bits =
-      get_swizzle_bits<feature_size, itemsize, copy_bits>();
-
-  using F = Int<feature_size>;
-  using BaseLayout = std::conditional_t<
-      transpose,
-      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
-      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
-
-  auto swizzled = make_composed_layout(
-      Swizzle<transpose ? 0 : swizzle_bits, 3, 4>{}, 0, BaseLayout{});
-
-  return tile_to_shape(swizzled, smem);
-}
-
-template <
-    int num_threads,
-    int itemsize,
-    bool transpose,
-    int copy_bits,
-    typename Copier,
-    typename Tiler>
-constexpr auto make_tiled_copy(Copier copy_op, Tiler smem) {
-  constexpr int num_elements = copy_bits / itemsize;
-  constexpr int feature_size = transpose ? size<0>(smem) : size<1>(smem);
-  constexpr int copies_per_feature = feature_size / num_elements;
-
-  using E = Int<num_elements>;
-  using C = Int<copies_per_feature>;
-  using R = Int<num_threads / copies_per_feature>;
-
-  using ThreadLayout = std::conditional_t<
-      transpose,
-      Layout<cute::Shape<C, R>, cute::Stride<_1, C>>,
-      Layout<cute::Shape<R, C>, cute::Stride<C, _1>>>;
-  using ValueLayout = std::conditional_t<
-      transpose,
-      Layout<cute::Shape<E, _1>>,
-      Layout<cute::Shape<_1, E>>>;
-
-  return make_tiled_copy(copy_op, ThreadLayout{}, ValueLayout{});
-}
-
-template <int rasterization_factor>
-__device__ inline int2 raster_tile(int x, int y) {
-  return {
-      x / rasterization_factor,
-      (x % rasterization_factor) + y * rasterization_factor};
-}
-
-template <
-    typename T,
-    typename SLayoutA,
-    typename SLayoutB,
-    typename SLayoutC,
-    typename CopyA,
-    typename CopyB,
-    typename CopyC,
-    typename MMA,
-    int rasterization_factor>
-__global__ static __launch_bounds__(decltype(size(MMA{}))::value) void matmul_kernel(
-    const T* __restrict__ A,
-    const T* __restrict__ B,
-    T* __restrict__ C,
-    SLayoutA SA,
-    SLayoutB SB,
-    SLayoutC SC,
-    CopyA copy_a,
-    CopyB copy_b,
-    CopyC copy_c,
-    MMA mma,
-    int M,
-    int N,
-    int K) {
-  constexpr auto BM = size<0>(SA);
-  constexpr auto BN = size<0>(SB);
-  constexpr auto BK = size<1>(SA);
-  constexpr auto PIPE = size<2>(SA);
-
-  const int2 tile = raster_tile<rasterization_factor>(blockIdx.x, blockIdx.y);
-  const int blocks_m = ceil_div(M, BM);
-  const int blocks_n = ceil_div(N, BN);
-
-  // Exit early if the tile is OOB
-  if (tile.x >= blocks_m || tile.y >= blocks_n) {
-    return;
-  }
-
-  // Make the full tensors
-  Tensor full_A =
-      make_tensor(make_gmem_ptr(A), make_shape(M, K), make_stride(K, _1{}));
-  Tensor full_B =
-      make_tensor(make_gmem_ptr(B), make_shape(N, K), make_stride(K, _1{}));
-  Tensor full_C =
-      make_tensor(make_gmem_ptr(C), make_shape(M, N), make_stride(N, _1{}));
-
-  // Partition the tensors into tiles and select the ones for this threadblock
-  Tensor local_A =
-      local_tile(full_A, make_shape(BM, BK), make_coord(tile.x, _));
-  Tensor local_B =
-      local_tile(full_B, make_shape(BN, BK), make_coord(tile.y, _));
-  Tensor local_C =
-      local_tile(full_C, make_shape(BM, BN), make_coord(tile.x, tile.y));
-
-  // Make shared memory tensors
-  extern __shared__ char shared_memory[];
-  T* shared_A_ptr = reinterpret_cast<T*>(shared_memory);
-  T* shared_B_ptr =
-      reinterpret_cast<T*>(shared_memory + cosize(SA) * sizeof(T));
-  T* shared_C_ptr = reinterpret_cast<T*>(shared_memory);
-  Tensor shared_A = make_tensor(make_smem_ptr(shared_A_ptr), SA);
-  Tensor shared_B = make_tensor(make_smem_ptr(shared_B_ptr), SB);
-  Tensor shared_C = make_tensor(make_smem_ptr(shared_C_ptr), SC);
-
-  // Get the copies that correspond to this thread
-  auto thread_copy_a = copy_a.get_slice(threadIdx.x);
-  Tensor local_A_src = thread_copy_a.partition_S(local_A);
-  Tensor local_A_dst = thread_copy_a.partition_D(shared_A);
-  auto thread_copy_b = copy_b.get_slice(threadIdx.x);
-  Tensor local_B_src = thread_copy_a.partition_S(local_B);
-  Tensor local_B_dst = thread_copy_a.partition_D(shared_B);
-  auto thread_copy_c = copy_c.get_slice(threadIdx.x);
-  Tensor local_C_src = thread_copy_c.partition_S(shared_C);
-  Tensor local_C_dst = thread_copy_c.partition_D(local_C);
-
-  // Start fetches
-  int k_tile_count = size<2>(local_A);
-  int k_tile_next = 0;
-  CUTE_UNROLL
-  for (int k = 0; k < PIPE - 1; k++) {
-    copy(copy_a, local_A_src(_, _, _, k_tile_next), local_A_dst(_, _, _, k));
-    copy(copy_b, local_B_src(_, _, _, k_tile_next), local_B_dst(_, _, _, k));
-    cp_async_fence();
-    k_tile_count--;
-    k_tile_next += (k_tile_count > 0);
-  }
-
-  // Get the MMA that corresponds to this thread and allocate registers
-  auto thread_mma = mma.get_slice(threadIdx.x);
-  Tensor mma_shared_A = thread_mma.partition_A(shared_A);
-  Tensor mma_shared_B = thread_mma.partition_B(shared_B);
-  Tensor mma_shared_C = thread_mma.partition_C(shared_C);
-  Tensor mma_global_C = thread_mma.partition_C(local_C);
-  Tensor mma_frag_A = mma.make_fragment_A(mma_shared_A(_, _, _, 0));
-  Tensor mma_frag_B = mma.make_fragment_B(mma_shared_B(_, _, _, 0));
-  Tensor mma_frag_C = mma.make_fragment_C(mma_global_C);
-  clear(mma_frag_C);
-
-  // Make shared to register copies
-  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_a;
-  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_b;
-  auto s2r_copy_a = make_tiled_copy_A(s2r_atom_a, mma);
-  auto s2r_thread_copy_a = s2r_copy_a.get_slice(threadIdx.x);
-  auto s2r_copy_b = make_tiled_copy_B(s2r_atom_b, mma);
-  auto s2r_thread_copy_b = s2r_copy_b.get_slice(threadIdx.x);
-  Tensor mma_A_src = s2r_thread_copy_a.partition_S(shared_A);
-  Tensor mma_A_dst = s2r_thread_copy_a.retile_D(mma_frag_A);
-  Tensor mma_B_src = s2r_thread_copy_b.partition_S(shared_B);
-  Tensor mma_B_dst = s2r_thread_copy_b.retile_D(mma_frag_B);
-
-  constexpr auto RPIPE = size<2>(mma_shared_A);
-  int smem_read = 0;
-  int smem_write = PIPE - 1;
-  Tensor mma_A_src_p = mma_A_src(_, _, _, smem_read);
-  Tensor mma_B_src_p = mma_B_src(_, _, _, smem_read);
-
-  // Start the register pipeline
-  if constexpr (RPIPE > 1) {
-    cp_async_wait<PIPE - 2>();
-    __syncthreads();
-    copy(s2r_copy_a, mma_A_src_p(_, _, Int<0>{}), mma_A_dst(_, _, Int<0>{}));
-    copy(s2r_copy_b, mma_B_src_p(_, _, Int<0>{}), mma_B_dst(_, _, Int<0>{}));
-  }
-
-  CUTE_NO_UNROLL
-  while (k_tile_count > -(PIPE - 1)) {
-    CUTE_UNROLL
-    for (int k_block = 0; k_block < RPIPE; k_block++) {
-      if (k_block == RPIPE - 1) {
-        mma_A_src_p = mma_A_src(_, _, _, smem_read);
-        mma_B_src_p = mma_B_src(_, _, _, smem_read);
-        cp_async_wait<PIPE - 2>();
-        __syncthreads();
-      }
-
-      // Load the next register tile
-      auto k_block_next = (k_block + 1) % RPIPE;
-      copy(
-          s2r_copy_a,
-          mma_A_src_p(_, _, k_block_next),
-          mma_A_dst(_, _, k_block_next));
-      copy(
-          s2r_copy_b,
-          mma_B_src_p(_, _, k_block_next),
-          mma_B_dst(_, _, k_block_next));
-
-      if (k_block == 0) {
-        copy(
-            copy_a,
-            local_A_src(_, _, _, k_tile_next),
-            local_A_dst(_, _, _, smem_write));
-        copy(
-            copy_b,
-            local_B_src(_, _, _, k_tile_next),
-            local_B_dst(_, _, _, smem_write));
-        cp_async_fence();
-        k_tile_count--;
-        k_tile_next += (k_tile_count > 0);
-        smem_write = smem_read;
-        smem_read = (smem_read == PIPE - 1) ? 0 : (smem_read + 1);
-      }
-
-      gemm(
-          mma,
-          mma_frag_A(_, _, k_block),
-          mma_frag_B(_, _, k_block),
-          mma_frag_C);
-    }
-  }
-
-  copy(mma_frag_C, mma_shared_C);
-  __syncthreads();
-  copy(copy_c, local_C_src, local_C_dst);
-
-  // if (threadIdx.x == 0) {
-  //   print("fC: "); print(mma_frag_C); print("\n");
-  //   print("sC: "); print(mma_shared_C); print("\n");
-  //   print("dC: "); print(local_C_dst); print("\n");
-  //
-  //   print(s2r_atom_a); print("\n");
-  // }
-}
-
-} // namespace
-
-void cutlass_gemm(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    cu::CommandEncoder& enc) {
-  enc.set_input_array(a);
-  enc.set_input_array(b);
-  enc.set_output_array(out);
-  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    if constexpr (std::is_same_v<DataType, __nv_bfloat16>) {
-      using namespace cute;
-
-      // Tile definitions
-      auto BM = Int<128>{};
-      auto BN = Int<128>{};
-      auto BK = Int<64>{};
-      auto BP = Int<3>{};
-      auto GM = Int<8>{};
-
-      // Thread definitions
-      using TM = Int<2>;
-      using TN = Int<2>;
-      using TK = Int<1>;
-      constexpr int num_threads = TM::value * TN::value * 32;
-
-      auto SA = make_smem_layout<16, false, 128>(make_shape(BM, BK, BP));
-      auto SB = make_smem_layout<16, false, 128>(make_shape(BN, BK, BP));
-      auto SC = make_result_smem_layout<16, false, 128>(make_shape(BM, BN));
-
-      constexpr auto smem_size = (cosize(SA) + cosize(SB)) * sizeof(bf16);
-
-      auto async_copy_op =
-          Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<uint128_t>, bf16>{};
-      auto tiled_copy_a = make_tiled_copy<num_threads, 16, false, 128>(
-          async_copy_op, make_shape(BM, BK));
-      auto tiled_copy_b = make_tiled_copy<num_threads, 16, false, 128>(
-          async_copy_op, make_shape(BN, BK));
-
-      auto sync_copy_op = Copy_Atom<UniversalCopy<uint128_t>, bf16>{};
-      auto tiled_copy_c = make_tiled_copy<num_threads, 16, false, 128>(
-          sync_copy_op, make_shape(BM, BN));
-
-      auto mma_op = SM80_16x8x16_F32BF16BF16F32_TN{};
-      auto tiled_mma = make_tiled_mma(
-          mma_op, Layout<cute::Shape<TM, TN, TK>>{}, Tile<_32, _32, _16>{});
-
-      auto kernel = matmul_kernel<
-          bf16,
-          decltype(SA),
-          decltype(SB),
-          decltype(SC),
-          decltype(tiled_copy_a),
-          decltype(tiled_copy_b),
-          decltype(tiled_copy_c),
-          decltype(tiled_mma),
-          GM.value>;
-      configure_matmul(kernel, smem_size);
-
-      dim3 block(size(tiled_mma));
-      dim3 grid(
-          size(ceil_div(M, BM) * GM), size(ceil_div(ceil_div(N, BN), GM)));
-
-      enc.add_kernel_node(
-          kernel,
-          grid,
-          block,
-          smem_size,
-          a.data<bf16>(),
-          b.data<bf16>(),
-          out.data<bf16>(),
-          SA,
-          SB,
-          SC,
-          tiled_copy_a,
-          tiled_copy_b,
-          tiled_copy_c,
-          tiled_mma,
-          M,
-          N,
-          K);
-    } else {
-      throw std::runtime_error("Only bfloat16 supported");
-    }
-  });
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cutlass_gemm.h

@@ -1,18 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device.h"
-
-namespace mlx::core::cu {
-
-void cutlass_gemm(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    cu::CommandEncoder& enc);
-
-}

mlx/backend/cuda/gemms/simple_gemm.cu

@@ -1,69 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/cuda/steel/gemm.cuh"
-#include "mlx/dtype_utils.h"
-
-#include <iostream>
-
-namespace mlx::core::cu {
-
-namespace {
-
-template <typename Kernel>
-static void configure_smem(Kernel kernel, int SM) {
-  static bool done = false;
-  if (done) {
-    return;
-  }
-  std::cout << "configuring" << std::endl;
-  cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, SM);
-  cudaFuncSetAttribute(
-      kernel,
-      cudaFuncAttributePreferredSharedMemoryCarveout,
-      cudaSharedmemCarveoutMaxShared);
-  done = true;
-}
-
-} // namespace
-
-void simple_gemm(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    cu::CommandEncoder& enc) {
-  enc.set_input_array(a);
-  enc.set_input_array(b);
-  enc.set_output_array(out);
-  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
-    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-    constexpr int BM = 128;
-    constexpr int BN = 128;
-    constexpr int BK = 32;
-    constexpr int PIPE = 3;
-    constexpr int SM = PIPE * sizeof(DataType) * (BM * BK + BN * BK);
-    constexpr int WM = 2;
-    constexpr int WN = 4;
-
-    auto kernel = ab_t_aligned<DataType, BM, BN, BK, WM, WN, PIPE>;
-    configure_smem(kernel, SM);
-
-    dim3 grid(N / BN, M / BM);
-    enc.add_kernel_node(
-        kernel,
-        grid,
-        WM * WN * WARP_SIZE,
-        SM,
-        a.data<DataType>(),
-        b.data<DataType>(),
-        out.data<DataType>(),
-        N,
-        K);
-  });
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/gemms/simple_gemm.h

@@ -1,18 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include "mlx/backend/cuda/device.h"
-
-namespace mlx::core::cu {
-
-void simple_gemm(
-    const array& a,
-    const array& b,
-    array& out,
-    int M,
-    int N,
-    int K,
-    cu::CommandEncoder& enc);
-
-}

mlx/backend/cuda/indexing.cpp

@@ -94,7 +94,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
             large ? "int64_t" : "int32_t"));
       }
     }
-    return std::make_tuple(false, jit_source_gather, std::move(kernel_names));
+    return std::make_pair(jit_source_gather, std::move(kernel_names));
   });
 
   cu::KernelArgs args;
@@ -189,7 +189,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
             large ? "int64_t" : "int32_t"));
       }
     }
-    return std::make_tuple(false, jit_source_scatter, std::move(kernel_names));
+    return std::make_pair(jit_source_scatter, std::move(kernel_names));
   });
 
   cu::KernelArgs args;
@@ -268,8 +268,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_tuple(
-        false, jit_source_gather_axis, std::move(kernel_names));
+    return std::make_pair(jit_source_gather_axis, std::move(kernel_names));
   });
 
   size_t idx_size_pre = 1;
@@ -372,8 +371,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_tuple(
-        false, jit_source_scatter_axis, std::move(kernel_names));
+    return std::make_pair(jit_source_scatter_axis, std::move(kernel_names));
   });
 
   size_t idx_size_pre = 1;

mlx/backend/cuda/jit_module.cpp

@@ -101,8 +101,8 @@ const std::filesystem::path& ptx_cache_dir() {
 bool read_cached_ptx(
     const std::filesystem::path& cache_dir,
     const std::string& module_name,
-    std::string& ptx,
-    std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
+    std::vector<char>* ptx,
+    std::vector<std::pair<std::string, std::string>>* ptx_kernels) {
   if (cache_dir.empty()) {
     return false;
   }
@@ -117,15 +117,15 @@ bool read_cached_ptx(
   if (!ptx_file.good()) {
     return false;
   }
-  ptx.resize(ptx_size);
-  ptx_file.read(ptx.data(), ptx_size);
+  ptx->resize(ptx_size);
+  ptx_file.read(ptx->data(), ptx_size);
 
   std::ifstream txt_file(cache_dir / (module_name + ".txt"), std::ios::binary);
   std::string line;
   while (std::getline(txt_file, line)) {
     auto tab = line.find('\t');
     if (tab != std::string::npos) {
-      ptx_kernels.emplace_back(line.substr(0, tab), line.substr(tab + 1));
+      ptx_kernels->emplace_back(line.substr(0, tab), line.substr(tab + 1));
     }
   }
   return true;
@@ -135,7 +135,7 @@ bool read_cached_ptx(
 void write_cached_ptx(
     const std::filesystem::path& cache_dir,
     const std::string& module_name,
-    const std::string& ptx,
+    const std::vector<char>& ptx,
     const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
     const std::string& source_code) {
   if (cache_dir.empty()) {
@@ -217,85 +217,85 @@ constexpr const char* g_headers[] = {
     jit_source_utils,
 };
 
-void compile(
+} // namespace
+
+JitModule::JitModule(
     Device& device,
     const std::string& module_name,
-    const std::string& source,
-    const std::vector<std::string>& kernel_names,
-    std::string& ptx,
-    std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
-  // Create the program
-  nvrtcProgram prog;
-  CHECK_NVRTC_ERROR(nvrtcCreateProgram(
-      &prog,
-      source.c_str(),
-      (module_name + ".cu").c_str(),
-      std::size(g_headers),
-      g_headers,
-      g_include_names));
-  std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
-      &prog,
-      [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
-  for (const auto& name : kernel_names) {
-    CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
+    const KernelBuilder& builder) {
+  // Check cache.
+  std::vector<char> ptx;
+  std::vector<std::pair<std::string, std::string>> ptx_kernels;
+  if (!read_cached_ptx(ptx_cache_dir(), module_name, &ptx, &ptx_kernels)) {
+    // Create program.
+    auto [source_code, kernel_names] = builder();
+    nvrtcProgram prog;
+    CHECK_NVRTC_ERROR(nvrtcCreateProgram(
+        &prog,
+        source_code.c_str(),
+        (module_name + ".cu").c_str(),
+        std::size(g_headers),
+        g_headers,
+        g_include_names));
+    std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
+        &prog,
+        [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
+    for (const auto& name : kernel_names) {
+      CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
+    }
+
+    // Compile program.
+    std::vector<const char*> args;
+    bool use_sass = compiler_supports_device_sass(device);
+    std::string compute = fmt::format(
+        "--gpu-architecture={}_{}{}",
+        use_sass ? "sm" : "compute",
+        device.compute_capability_major(),
+        device.compute_capability_minor());
+    args.push_back(compute.c_str());
+    std::string cccl_include = cccl_dir();
+    if (!cccl_include.empty()) {
+      cccl_include = fmt::format("--include-path={}", cccl_include);
+      args.push_back(cccl_include.c_str());
+    }
+    std::string cuda_include =
+        fmt::format("--include-path={}/include", cuda_home());
+    args.push_back(cuda_include.c_str());
+    nvrtcResult compile_result =
+        nvrtcCompileProgram(prog, args.size(), args.data());
+    if (compile_result != NVRTC_SUCCESS) {
+      size_t log_size;
+      CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
+      std::vector<char> log(log_size + 1, 0);
+      CHECK_NVRTC_ERROR(nvrtcGetProgramLog(prog, log.data()));
+      throw std::runtime_error(
+          fmt::format("Failed to compile kernel: {}.", log.data()));
+    }
+
+    // Get mangled names of kernel names.
+    for (const auto& name : kernel_names) {
+      const char* mangled;
+      CHECK_NVRTC_ERROR(nvrtcGetLoweredName(prog, name.c_str(), &mangled));
+      ptx_kernels.emplace_back(name, mangled);
+    }
+
+    // Get ptx data.
+    size_t ptx_size;
+    if (use_sass) {
+      CHECK_NVRTC_ERROR(nvrtcGetCUBINSize(prog, &ptx_size));
+    } else {
+      CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
+    }
+    ptx.resize(ptx_size, 0);
+    if (use_sass) {
+      CHECK_NVRTC_ERROR(nvrtcGetCUBIN(prog, ptx.data()));
+    } else {
+      CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
+    }
+    write_cached_ptx(
+        ptx_cache_dir(), module_name, ptx, ptx_kernels, source_code);
   }
 
-  // Compile program.
-  std::vector<const char*> args;
-  bool use_sass = compiler_supports_device_sass(device);
-  std::string compute = fmt::format(
-      "--gpu-architecture={}_{}{}",
-      use_sass ? "sm" : "compute",
-      device.compute_capability_major(),
-      device.compute_capability_minor());
-  args.push_back(compute.c_str());
-  std::string cccl_include = cccl_dir();
-  if (!cccl_include.empty()) {
-    cccl_include = fmt::format("--include-path={}", cccl_include);
-    args.push_back(cccl_include.c_str());
-  }
-  std::string cuda_include =
-      fmt::format("--include-path={}/include", cuda_home());
-  args.push_back(cuda_include.c_str());
-  nvrtcResult compile_result =
-      nvrtcCompileProgram(prog, args.size(), args.data());
-  if (compile_result != NVRTC_SUCCESS) {
-    size_t log_size;
-    CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
-    std::vector<char> log(log_size + 1, 0);
-    CHECK_NVRTC_ERROR(nvrtcGetProgramLog(prog, log.data()));
-    throw std::runtime_error(
-        fmt::format("Failed to compile kernel: {}.", log.data()));
-  }
-
-  // Get mangled names of kernel names.
-  for (const auto& name : kernel_names) {
-    const char* mangled;
-    CHECK_NVRTC_ERROR(nvrtcGetLoweredName(prog, name.c_str(), &mangled));
-    ptx_kernels.emplace_back(name, mangled);
-  }
-
-  // Get ptx data.
-  size_t ptx_size;
-  if (use_sass) {
-    CHECK_NVRTC_ERROR(nvrtcGetCUBINSize(prog, &ptx_size));
-  } else {
-    CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
-  }
-  ptx.resize(ptx_size);
-  if (use_sass) {
-    CHECK_NVRTC_ERROR(nvrtcGetCUBIN(prog, ptx.data()));
-  } else {
-    CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
-  }
-}
-
-void load_module(
-    const std::string& module_name,
-    const std::string& ptx,
-    const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
-    CUmodule& module_,
-    std::unordered_map<std::string, std::pair<CUfunction, bool>>& kernels) {
   // Load module.
   char jit_log[4089] = {};
   CUjit_option options[] = {
@@ -312,69 +312,21 @@ void load_module(
   for (const auto& [name, mangled] : ptx_kernels) {
     CUfunction kernel;
     CHECK_CUDA_ERROR(cuModuleGetFunction(&kernel, module_, mangled.c_str()));
-    kernels[name] = std::make_pair(kernel, false);
+    kernels_[name] = kernel;
   }
 }
 
-} // namespace
-
-JitModule::JitModule(
-    Device& device,
-    const std::string& module_name,
-    const KernelBuilder& builder,
-    bool use_disk_cache) {
-  // Will hold the actual device executable source code and kernel names
-  std::string ptx;
-  std::vector<std::pair<std::string, std::string>> ptx_kernels;
-
-  // Try to load them from the file cache
-  if (!read_cached_ptx(ptx_cache_dir(), module_name, ptx, ptx_kernels)) {
-    auto [precompiled, source_code, kernel_names] = builder();
-
-    // Get the PTX or cubin
-    if (precompiled) {
-      ptx = std::move(source_code);
-      for (auto& name : kernel_names) {
-        ptx_kernels.emplace_back(name, name);
-      }
-    } else {
-      compile(device, module_name, source_code, kernel_names, ptx, ptx_kernels);
-    }
-
-    // If requested save them in the file cache for the next launch
-    if (use_disk_cache) {
-      write_cached_ptx(
-          ptx_cache_dir(), module_name, ptx, ptx_kernels, source_code);
-    }
-  }
-
-  // Load the module
-  load_module(module_name, ptx, ptx_kernels, module_, kernels_);
-}
-
 JitModule::~JitModule() {
   CHECK_CUDA_ERROR(cuModuleUnload(module_));
 }
 
-CUfunction JitModule::get_kernel(
-    const std::string& kernel_name,
-    std::function<void(CUfunction)> configure_kernel) {
+CUfunction JitModule::get_kernel(const std::string& kernel_name) {
   auto it = kernels_.find(kernel_name);
   if (it == kernels_.end()) {
     throw std::runtime_error(
         fmt::format("There is no kernel named {}.", kernel_name));
   }
-
-  // If it is the first time we run this kernel then configure it. Do it only
-  // once!
-  if (!it->second.second) {
-    if (configure_kernel) {
-      configure_kernel(it->second.first);
-    }
-    it->second.second = true;
-  }
-
-  return it->second.first;
+  return it->second;
 }
 
 std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
@@ -385,12 +337,11 @@ std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,
-    const KernelBuilder& builder,
-    bool cache) {
+    const KernelBuilder& builder) {
   auto& map = get_jit_module_cache();
   auto it = map.find(name);
   if (it == map.end()) {
-    it = map.try_emplace(name, cu::device(device), name, builder, cache).first;
+    it = map.try_emplace(name, cu::device(device), name, builder).first;
   }
   return it->second;
 }

mlx/backend/cuda/jit_module.h

@@ -19,8 +19,7 @@ namespace mlx::core::cu {
 
 class Device;
 
-using KernelBuilderResult = std::tuple<
-    /* precompiled */ bool,
+using KernelBuilderResult = std::pair<
     /* source code */ std::string,
     /* kernel names */ std::vector<std::string>>;
 using KernelBuilder = std::function<KernelBuilderResult()>;
@@ -64,16 +63,14 @@ struct KernelArgs {
  private:
   std::vector<void*> args_;
 
-  // The cuGraphAddKernelNode API requires passing pointers to arguments so
-  // store temporary values until the node is created.
+  // The cuLaunchKernel API requires passing pointers to arguments so store
+  // temporary values untill kernel is launched.
   using Arg = std::variant<
       std::monostate,
       CUdeviceptr,
-      bool,
       int32_t,
       uint32_t,
       int64_t,
-      float,
       SmallVector<const void*>,
       SmallVector<int32_t>,
       SmallVector<int64_t>>;
@@ -85,19 +82,16 @@ class JitModule {
   JitModule(
       Device& device,
       const std::string& module_name,
-      const KernelBuilder& builder,
-      bool cache);
+      const KernelBuilder& builder);
   ~JitModule();
 
   JitModule(const JitModule&) = delete;
   JitModule& operator=(const JitModule&) = delete;
-  CUfunction get_kernel(
-      const std::string& kernel_name,
-      std::function<void(CUfunction)> configure_kernel = nullptr);
+  CUfunction get_kernel(const std::string& kernel_name);
 
  private:
   CUmodule module_{nullptr};
-  std::unordered_map<std::string, std::pair<CUfunction, bool>> kernels_;
+  std::unordered_map<std::string, CUfunction> kernels_;
 };
 
 std::unordered_map<std::string, JitModule>& get_jit_module_cache();
@@ -105,7 +99,6 @@ std::unordered_map<std::string, JitModule>& get_jit_module_cache();
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,
-    const KernelBuilder& builder,
-    bool use_disk_cache = true);
+    const KernelBuilder& builder);
 
 } // namespace mlx::core::cu

mlx/backend/cuda/matmul.cpp

@@ -3,9 +3,7 @@
 #include "mlx/backend/common/matmul.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/gemms/cublas_gemm.h"
-#include "mlx/backend/cuda/gemms/cutlass_gemm.h"
 #include "mlx/backend/cuda/gemms/gemv.h"
-#include "mlx/backend/cuda/gemms/simple_gemm.h"
 #include "mlx/backend/gpu/copy.h"
 #include "mlx/primitives.h"
 
@@ -13,14 +11,8 @@
 #include <numeric>
 
 namespace mlx::core {
-
 namespace {
 
-int get_test_gemm() {
-  static int t = env::get_var("MLX_ENABLE_TEST_GEMM", 0);
-  return t;
-}
-
 std::tuple<bool, int64_t, array>
 check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
   auto stx = arr.strides()[arr.ndim() - 2];
@@ -103,21 +95,9 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  if (M % 512 == 0 && N % 512 == 0 && K % 512 == 0 && !a_transposed &&
-      b_transposed && batch_count == 1 && get_test_gemm() == 1) {
-    cu::simple_gemm(a, b, out, M, N, K, encoder);
-    return;
-  }
-
-  if (M % 512 == 0 && N % 512 == 0 && K % 512 == 0 && !a_transposed &&
-      b_transposed && batch_count == 1 && get_test_gemm() == 2) {
-    cu::cutlass_gemm(a, b, out, M, N, K, encoder);
-    return;
-  }
-
   /////////////////////////////////////////////////////////////////////////////
   // Invoke cublasLt
-  CublasGemm gemm(
+  cu::Matmul matmul(
       cu::device(s.device),
       a.dtype(),
       a_transposed,
@@ -131,7 +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) {
@@ -199,7 +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,
@@ -215,7 +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/no_cuda.cpp

@@ -1,47 +1,11 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/cuda.h"
-#include "mlx/fast.h"
 
-namespace mlx::core {
-
-namespace cu {
+namespace mlx::core::cu {
 
 bool is_available() {
   return false;
 }
 
-} // namespace cu
-
-namespace fast {
-
-CustomKernelFunction cuda_kernel(
-    const std::string&,
-    const std::vector<std::string>&,
-    const std::vector<std::string>&,
-    const std::string&,
-    const std::string&,
-    bool,
-    int) {
-  throw std::runtime_error("[cuda_kernel] No CUDA back-end.");
-}
-
-std::vector<array> precompiled_cuda_kernel(
-    const std::string&,
-    const std::string&,
-    const std::vector<array>&,
-    const std::vector<Shape>&,
-    const std::vector<Dtype>&,
-    const std::vector<ScalarArg>&,
-    std::tuple<int, int, int>,
-    std::tuple<int, int, int>,
-    int shared_memory,
-    std::optional<float> init_value,
-    bool ensure_row_contiguous,
-    StreamOrDevice) {
-  throw std::runtime_error("[cuda_kernel] No CUDA back-end.");
-}
-
-} // namespace fast
-
-} // namespace mlx::core
+} // namespace mlx::core::cu

mlx/backend/cuda/primitives.cpp

@@ -41,6 +41,10 @@ NO_GPU(Cholesky)
 NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
+namespace fast {
+NO_GPU_MULTI(CustomKernel)
+} // namespace fast
+
 namespace distributed {
 NO_GPU_MULTI(AllReduce)
 NO_GPU_MULTI(AllGather)

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/steel/gemm.cuh

@@ -4,189 +4,95 @@
 
 namespace mlx::core::cu {
 
-template <typename T, int BM, int BN, int BK, int WM, int WN>
-__device__ inline void gemm_ab_t(
-    RegisterTile<float, BM / WM, BN / WN>& C,
-    SharedTile<T, BM, BK>& As,
-    SharedTile<T, BN, BK>& Bs,
-    RegisterTileLoader<SharedTile<T, BM, BK>>& rloader_a,
-    RegisterTileLoader<SharedTile<T, BN, BK>>& rloader_b) {
-  RegisterTile<T, BM / WM, 16> A[2];
-  RegisterTile<T, BN / WN, 16> B[2];
-
-  rloader_a.load(A[0], As.base_addr(), 0);
-  rloader_b.load(B[0], Bs.base_addr(), 0);
-
-  MLX_UNROLL
-  for (int k = 1; k < BK / 16; k++) {
-    rloader_a.load(A[k & 1], As.base_addr(), k);
-    rloader_b.load(B[k & 1], Bs.base_addr(), k);
-
-    mma_t(C, A[(k - 1) & 1], B[(k - 1) & 1]);
-  }
-  mma_t(C, A[(BK / 16 - 1) & 1], B[(BK / 16 - 1) & 1]);
-}
-
 /**
  * An example gemm written with the utils.
  *
  * Computes A @ B.T when A and B are all aligned with the block sizes.
  */
-// template <typename T, int BM, int BN, int BK, int WM, int WN, int PIPE>
-//__global__ __launch_bounds__(WM * WN * WARP_SIZE, 1)
-// void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
-//   constexpr int NUM_WARPS = WM * WN;
-//   constexpr int WARP_STEP_M = BM / WM;
-//   constexpr int WARP_STEP_N = BN / WN;
-//
-//   // Precompute some offsets for each thread
-//   const int warpid = threadIdx.x / 32;
-//   const int laneid = threadIdx.x % 32;
-//   const int wm = warpid / WN;
-//   const int wn = warpid % WN;
-//   const int offset_m = wm * WARP_STEP_M;
-//   const int offset_n = wn * WARP_STEP_N;
-//
-//   // Allocate shared memory
-//   extern __shared__ char shmem[];
-//   SharedTile<T, BM, BK>(&as)[PIPE] =
-//       *(SharedTile<T, BM, BK>(*)[PIPE])(&shmem[0]);
-//   SharedTile<T, BN, BK>(&bs)[PIPE] =
-//       *(SharedTile<T, BN, BK>(*)[PIPE])(&shmem[sizeof(T) * PIPE * BM * BK]);
-//
-//   // Move the global pointers to the tile
-//   a += blockIdx.y * BM * K;
-//   b += blockIdx.x * BN * K;
-//   y += blockIdx.y * BM * N + blockIdx.x * BN;
-//
-//   // Make the loaders to/from SMEM
-//   SharedTileLoader<NUM_WARPS, SharedTile<T, BM, BK>> sloader_a(a, K);
-//   SharedTileLoader<NUM_WARPS, SharedTile<T, BN, BK>> sloader_b(b, K);
-//   RegisterTileLoader<SharedTile<T, BM, BK>> rloader_a(offset_m, laneid);
-//   RegisterTileLoader<SharedTile<T, BN, BK>> rloader_b(offset_n, laneid);
-//
-//   // Start the SM pipeline
-//   MLX_UNROLL
-//   for (int i = 0; i < PIPE - 1; i++) {
-//     sloader_a.load_async(as[i].base_addr());
-//     sloader_b.load_async(bs[i].base_addr());
-//     cp_async_commit();
-//     sloader_a.next();
-//     sloader_b.next();
-//   }
-//
-//   // Allocate and zero the MMA accumulator
-//   RegisterTile<float, BM / WM, BN / WN> C;
-//   C.fill(0);
-//
-//   // Matmul loop
-//   int num_blocks = K / BK;
-//   int sread = 0;
-//   int swrite = PIPE - 1;
-//   for (int i = 0; i < num_blocks; i++) {
-//     cp_async_wait<PIPE - 1>();
-//
-//     gemm_ab_t<T, BM, BN, BK, WM, WN>(
-//         C, as[sread], bs[sread], rloader_a, rloader_b);
-//
-//     sloader_a.load_async(as[swrite].base_addr());
-//     sloader_b.load_async(bs[swrite].base_addr());
-//     cp_async_commit();
-//     sloader_a.next(i + PIPE < num_blocks);
-//     sloader_b.next(i + PIPE < num_blocks);
-//
-//     swrite = sread;
-//     sread = (sread + 1) % PIPE;
-//   }
-//
-//   C.store_global(y, N, offset_m, offset_n);
-// }
-
-template <typename T, int BM, int BN, int BK, int WM, int WN, int PIPE>
-__global__ __launch_bounds__(
-    WM* WN* WARP_SIZE,
-    1) void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
-  constexpr int NUM_WARPS = WM * WN;
-  constexpr int WARP_STEP_M = BM / WM;
-  constexpr int WARP_STEP_N = BN / WN;
+template <typename T, int BM, int BN, int BK>
+__global__ void ab_t_aligned(const T* a, const T* b, T* y, int N, int K) {
+  constexpr int WARPS_M = 2;
+  constexpr int WARPS_N = 2;
+  constexpr int NUM_WARPS = WARPS_M * WARPS_N;
+  constexpr int WARP_STEP_M = BM / WARPS_M;
+  constexpr int WARP_STEP_N = BN / WARPS_N;
 
   // Precompute some offsets for each thread
   const int warpid = threadIdx.x / 32;
   const int laneid = threadIdx.x % 32;
-  const int wm = warpid / WN;
-  const int wn = warpid % WN;
+  const int wm = warpid / WARPS_N;
+  const int wn = warpid % WARPS_N;
   const int offset_m = wm * WARP_STEP_M;
   const int offset_n = wn * WARP_STEP_N;
 
   // Allocate shared memory
   extern __shared__ char shmem[];
-  SharedTile<T, BM, BK>(&as)[PIPE] =
-      *(SharedTile<T, BM, BK>(*)[PIPE])(&shmem[0]);
-  SharedTile<T, BN, BK>(&bs)[PIPE] =
-      *(SharedTile<T, BN, BK>(*)[PIPE])(&shmem[sizeof(T) * PIPE * BM * BK]);
+  SharedTile<T, BM, BK>(&as)[2] = *(SharedTile<T, BM, BK>(*)[2])(&shmem[0]);
+  SharedTile<T, BN, BK>(&bs)[2] =
+      *(SharedTile<T, BN, BK>(*)[2])(&shmem[sizeof(T) * 2 * BM * BK]);
+
+  // Allocate registers for the MMA
+  RegisterTile<float, BM / WARPS_M, BN / WARPS_N> C;
+  RegisterTile<T, BM / WARPS_M, 16> A;
+  RegisterTile<T, BN / WARPS_N, 16> B;
 
   // Move the global pointers to the tile
   a += blockIdx.y * BM * K;
   b += blockIdx.x * BN * K;
   y += blockIdx.y * BM * N + blockIdx.x * BN;
 
-  // Make the loaders to/from SMEM
-  using sloader = SharedTileLoader<NUM_WARPS, SharedTile<T, BM, BK>>;
-  constexpr int SSTEP = sloader::STEP_ROWS * sizeof(T) * BK;
-  const int srow = threadIdx.x / sloader::NUM_LOADS_PER_ROW;
-  const int scol =
-      (threadIdx.x % sloader::NUM_LOADS_PER_ROW) * sloader::ELEMENTS_PER_LOAD;
-  a += srow * K + scol;
-  b += srow * K + scol;
-  uint32_t sm_offsets[PIPE][2];
-  MLX_UNROLL
-  for (int s = 0; s < PIPE; s++) {
-    sm_offsets[s][0] = as[s].loc(as[s].base_addr(), srow, scol);
-    sm_offsets[s][1] = bs[s].loc(bs[s].base_addr(), srow, scol);
-  }
-  RegisterTileLoader<SharedTile<T, BM, BK>> rloader_a(offset_m, laneid);
-  RegisterTileLoader<SharedTile<T, BN, BK>> rloader_b(offset_n, laneid);
-
-  // Start the SM pipeline
-  MLX_UNROLL
-  for (int s = 0; s < PIPE - 1; s++) {
-    MLX_UNROLL
-    for (int l = 0; l < sloader::NUM_LOADS_PER_THREAD; l++) {
-      cp_async<16>(sm_offsets[s][0] + l * SSTEP, a);
-      cp_async<16>(sm_offsets[s][1] + l * SSTEP, b);
-      a += sloader::STEP_ROWS * K;
-      b += sloader::STEP_ROWS * K;
-    }
-    cp_async_commit();
-  }
-
-  // Allocate and zero the MMA accumulator
-  RegisterTile<float, BM / WM, BN / WN> C;
+  // Zero the accumulators
   C.fill(0);
 
-  // Matmul loop
-  int num_blocks = K / BK;
-  int sread = 0;
-  int swrite = PIPE - 1;
-  for (int i = 0; i < num_blocks; i++) {
-    cp_async_wait<PIPE - 1>();
+  // Start the SM pipeline
+  load_async<NUM_WARPS>(as[0], as[0].base_addr(), a, K);
+  load_async<NUM_WARPS>(bs[0], bs[0].base_addr(), b, K);
+  cp_async_commit();
 
-    gemm_ab_t<T, BM, BN, BK, WM, WN>(
-        C, as[sread], bs[sread], rloader_a, rloader_b);
-
-    if (false) {
-      MLX_UNROLL
-      for (int l = 0; l < sloader::NUM_LOADS_PER_THREAD; l++) {
-        cp_async<16>(sm_offsets[swrite][0] + l * SSTEP, a);
-        cp_async<16>(sm_offsets[swrite][1] + l * SSTEP, b);
-        a += sloader::STEP_ROWS * K;
-        b += sloader::STEP_ROWS * K;
-      }
-    }
+  int tic = 0;
+  for (int k_block = BK; k_block < K; k_block += BK) {
+    load_async<NUM_WARPS>(as[tic ^ 1], as[tic ^ 1].base_addr(), a + k_block, K);
+    load_async<NUM_WARPS>(bs[tic ^ 1], bs[tic ^ 1].base_addr(), b + k_block, K);
     cp_async_commit();
+    cp_async_wait<1>();
+    __syncthreads();
 
-    swrite = sread;
-    sread = (sread + 1) % PIPE;
+    MLX_UNROLL
+    for (int k = 0; k < BK / 16; k++) {
+      A.load(
+          as[tic],
+          as[tic].base_addr(),
+          offset_m + laneid % 16,
+          k * 16 + laneid / 16 * 8);
+      B.load(
+          bs[tic],
+          bs[tic].base_addr(),
+          offset_n + laneid % 16,
+          k * 16 + laneid / 16 * 8);
+
+      mma_t(C, A, B);
+    }
+
+    tic ^= 1;
+  }
+
+  // Empty the pipeline
+  cp_async_wait_all();
+  __syncthreads();
+  MLX_UNROLL
+  for (int k = 0; k < BK / 16; k++) {
+    A.load(
+        as[tic],
+        as[tic].base_addr(),
+        offset_m + laneid % 16,
+        k * 16 + laneid / 16 * 8);
+    B.load(
+        bs[tic],
+        bs[tic].base_addr(),
+        offset_n + laneid % 16,
+        k * 16 + laneid / 16 * 8);
+
+    mma_t(C, A, B);
   }
 
   C.store_global(y, N, offset_m, offset_n);

mlx/backend/cuda/steel/tiles.cuh

@@ -223,10 +223,59 @@ struct RegisterTile {
   }
 };
 
+/**
+ * A simple container of multiple Tile16x16.
+ *
+ * Provides utility functions for loading and manipulating collections of basic
+ * tiles.
+ */
+template <typename T, int ROWS_, int COLS_>
+struct RegisterTile {
+  static constexpr int ROWS = ROWS_;
+  static constexpr int COLS = COLS_;
+  static constexpr int TILES_X = COLS / 16;
+  static constexpr int TILES_Y = ROWS / 16;
+
+  Tile16x16<T> data[TILES_X * TILES_Y];
+
+  __device__ inline void fill(T v) {
+    MLX_UNROLL
+    for (int i = 0; i < TILES_Y; i++) {
+      MLX_UNROLL
+      for (int j = 0; j < TILES_X; j++) {
+        data[i * TILES_X + j].fill(v);
+      }
+    }
+  }
+
+  template <typename Tile>
+  __device__ inline void
+  load(Tile& tile, uint32_t base_address, int row, int col) {
+    MLX_UNROLL
+    for (int i = 0; i < TILES_Y; i++) {
+      MLX_UNROLL
+      for (int j = 0; j < TILES_X; j++) {
+        data[i * TILES_X + j].load(
+            tile.loc(base_address, row + i * 16, col + j * 16));
+      }
+    }
+  }
+
+  template <typename U>
+  __device__ inline void store_global(U* x, int N, int row, int col) {
+    MLX_UNROLL
+    for (int i = 0; i < TILES_Y; i++) {
+      MLX_UNROLL
+      for (int j = 0; j < TILES_X; j++) {
+        data[i * TILES_X + j].store_global(
+            x + (row + i * 16) * N + col + j * 16, N);
+      }
+    }
+  }
+};
+
 template <typename T, int ROWS_, int COLS_>
 struct SharedTile {
-  using value_type = T;
-
   static constexpr int ROWS = ROWS_;
   static constexpr int COLS = COLS_;
   static constexpr int TILES_X = COLS / 16;
@@ -268,26 +317,23 @@ struct SharedTile {
     }
   }
 
-  __device__ static inline uint32_t offset(int row, int col) {
+  // Return the location of the element at (row, col) using the swizzle.
+  __device__ static inline uint32_t loc(uint32_t ptr, int row, int col) {
     if constexpr (swizzle_bytes > 0) {
       static constexpr int swizzle_repeat = swizzle_bytes * 8;
       static constexpr int subtile_cols = swizzle_bytes / sizeof(T);
       const int outer_idx = col / subtile_cols;
-      const uint32_t addr = sizeof(T) *
-          (outer_idx * ROWS * subtile_cols + row * subtile_cols +
-           col % subtile_cols);
+      const uint32_t addr = ptr +
+          sizeof(T) *
+              (outer_idx * ROWS * subtile_cols + row * subtile_cols +
+               col % subtile_cols);
       const int swizzle = ((addr % swizzle_repeat) >> 7) << 4;
       return (addr ^ swizzle);
     } else {
-      return sizeof(T) * (row * COLS + col);
+      return ptr + sizeof(T) * (row * COLS + col);
     }
   }
 
-  // Return the location of the element at (row, col) using the swizzle.
-  __device__ static inline uint32_t loc(uint32_t ptr, int row, int col) {
-    return ptr + offset(row, col);
-  }
-
   // Convenience functions to edit elements going through the swizzle.
   __device__ inline T& operator()(int row, int col) {
     return *ptr(data, row, col);
@@ -318,76 +364,6 @@ struct SharedTile {
   }
 };
 
-template <int NUM_WARPS, typename Tile>
-struct SharedTileLoader {
-  using T = typename Tile::value_type;
-
-  static constexpr int NUM_THREADS = NUM_WARPS * 32;
-  static constexpr int ELEMENTS_PER_LOAD = sizeof(float4) / sizeof(T);
-  static constexpr int NUM_LOADS = Tile::NUMEL / ELEMENTS_PER_LOAD;
-  static constexpr int NUM_LOADS_PER_THREAD = NUM_LOADS / NUM_THREADS;
-  static constexpr int NUM_LOADS_PER_ROW = Tile::COLS / ELEMENTS_PER_LOAD;
-  static constexpr int STEP_ROWS = NUM_THREADS / NUM_LOADS_PER_ROW;
-
-  const T* x_;
-  int N_;
-  uint32_t offset_;
-
-  __device__ SharedTileLoader(const T* x, int N) : x_(x), N_(N) {
-    const int row = threadIdx.x / NUM_LOADS_PER_ROW;
-    const int col = threadIdx.x % NUM_LOADS_PER_ROW;
-
-    x_ += row * N + col * ELEMENTS_PER_LOAD;
-    offset_ = Tile::offset(row, col * ELEMENTS_PER_LOAD);
-  }
-
-  __device__ inline void load_async(uint32_t base_address) {
-    MLX_UNROLL
-    for (int i = 0; i < NUM_LOADS_PER_THREAD; i++) {
-      cp_async<16>(
-          base_address + offset_ + i * STEP_ROWS * sizeof(T) * Tile::COLS,
-          x_ + i * STEP_ROWS * N_);
-    }
-  }
-
-  __device__ inline void next() {
-    x_ += Tile::COLS;
-  }
-};
-
-template <typename Tile>
-struct RegisterTileLoader {
-  using T = typename Tile::value_type;
-
-  uint32_t offset_[Tile::COLS / 16];
-
-  __device__ RegisterTileLoader(int offset_row, int laneid) {
-    const int row = offset_row + laneid & 15;
-    const int col = (laneid >> 4) << 3;
-
-    MLX_UNROLL
-    for (int i = 0; i < Tile::COLS / 16; i++) {
-      offset_[i] = Tile::offset(row, col + i * 16);
-    }
-  }
-
-  template <typename T, int ROWS, int COLS>
-  __device__ inline void
-  load(RegisterTile<T, ROWS, COLS>& x, uint32_t base_address, int col) {
-    constexpr int TILES_Y = RegisterTile<T, ROWS, COLS>::TILES_Y;
-    constexpr int TILES_X = RegisterTile<T, ROWS, COLS>::TILES_X;
-
-    MLX_UNROLL
-    for (int i = 0; i < TILES_Y; i++) {
-      MLX_UNROLL
-      for (int j = 0; j < TILES_X; j++) {
-        x.data[i * TILES_X + j].load(
-            base_address + offset_[j + col] + i * 16 * Tile::COLS * sizeof(T));
-      }
-    }
-  }
-};
-
 /**
  * Load the tile from global memory by loading 16 bytes at a time and storing
  * them immediately.

mlx/backend/cuda/steel/utils.cuh

@@ -21,15 +21,15 @@ __device__ inline void cp_async(uint32_t row_address, const T* x) {
 #if defined(MLX_CUDA_SM_80_ENABLED)
   if constexpr (N == 16) {
     asm volatile(
-        "cp.async.cg.shared::cta.global [%0], [%1], 16;\n" ::"r"(row_address),
+        "cp.async.ca.shared::cta.global [%0], [%1], 16;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int4*>(x)));
   } else if constexpr (N == 8) {
     asm volatile(
-        "cp.async.cg.shared::cta.global [%0], [%1], 8;\n" ::"r"(row_address),
+        "cp.async.ca.shared::cta.global [%0], [%1], 8;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int2*>(x)));
   } else if constexpr (N == 4) {
     asm volatile(
-        "cp.async.cg.shared::cta.global [%0], [%1], 4;\n" ::"r"(row_address),
+        "cp.async.ca.shared::cta.global [%0], [%1], 4;\n" ::"r"(row_address),
         "l"(reinterpret_cast<const int*>(x)));
   }
 #endif

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