Custom cuda kernel (#2517)

mlx/backend/cuda/CMakeLists.txt

@@ -20,6 +20,7 @@ target_sources(
           ${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

mlx/backend/cuda/compiled.cpp

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

mlx/backend/cuda/custom_kernel.cpp

@@ -0,0 +1,379 @@
+// 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/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_pair(jit_source_gather, std::move(kernel_names));
+    return std::make_tuple(false, 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_pair(jit_source_scatter, std::move(kernel_names));
+    return std::make_tuple(false, jit_source_scatter, std::move(kernel_names));
   });
 
   cu::KernelArgs args;
@@ -268,7 +268,8 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_pair(jit_source_gather_axis, std::move(kernel_names));
+    return std::make_tuple(
+        false, jit_source_gather_axis, std::move(kernel_names));
   });
 
   size_t idx_size_pre = 1;
@@ -371,7 +372,8 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_pair(jit_source_scatter_axis, std::move(kernel_names));
+    return std::make_tuple(
+        false, 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::vector<char>* ptx,
-    std::vector<std::pair<std::string, std::string>>* ptx_kernels) {
+    std::string& 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::vector<char>& ptx,
+    const std::string& 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,
 };
 
-} // namespace
-
-JitModule::JitModule(
+void compile(
     Device& device,
     const std::string& module_name,
-    const KernelBuilder& builder) {
-  // Check cache.
-  std::vector<char> ptx;
-  std::vector<std::pair<std::string, std::string>> ptx_kernels;
-  if (!read_cached_ptx(ptx_cache_dir(), module_name, &ptx, &ptx_kernels)) {
-    // Create program.
-    auto [source_code, kernel_names] = builder();
-    nvrtcProgram prog;
-    CHECK_NVRTC_ERROR(nvrtcCreateProgram(
-        &prog,
-        source_code.c_str(),
-        (module_name + ".cu").c_str(),
-        std::size(g_headers),
-        g_headers,
-        g_include_names));
-    std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
-        &prog,
-        [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
-    for (const auto& name : kernel_names) {
-      CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
-    }
-
-    // Compile program.
-    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);
+    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()));
   }
 
+  // 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[] = {
@@ -312,21 +312,69 @@ JitModule::JitModule(
   for (const auto& [name, mangled] : ptx_kernels) {
     CUfunction kernel;
     CHECK_CUDA_ERROR(cuModuleGetFunction(&kernel, module_, mangled.c_str()));
-    kernels_[name] = kernel;
+    kernels[name] = std::make_pair(kernel, false);
   }
 }
 
+} // 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) {
+CUfunction JitModule::get_kernel(
+    const std::string& kernel_name,
+    std::function<void(CUfunction)> configure_kernel) {
   auto it = kernels_.find(kernel_name);
   if (it == kernels_.end()) {
     throw std::runtime_error(
         fmt::format("There is no kernel named {}.", kernel_name));
   }
-  return it->second;
+
+  // 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;
 }
 
 std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
@@ -337,11 +385,12 @@ 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) {
+    const KernelBuilder& builder,
+    bool cache) {
   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).first;
+    it = map.try_emplace(name, cu::device(device), name, builder, cache).first;
   }
   return it->second;
 }

mlx/backend/cuda/jit_module.h

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

mlx/backend/cuda/no_cuda.cpp

@@ -1,11 +1,47 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/cuda.h"
+#include "mlx/fast.h"
 
-namespace mlx::core::cu {
+namespace mlx::core {
+
+namespace cu {
 
 bool is_available() {
   return false;
 }
 
-} // namespace mlx::core::cu
+} // 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

mlx/backend/cuda/primitives.cpp

@@ -41,10 +41,6 @@ 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)