[CUDA] Initial implementation of Convolution with cuDNN (#2385)

* Link with cuDNN

* Initial implementation

* Remove backend apis

* Fix recording cudnn conv

* More unused backend apis

* Fix C++ conv tests

* include cudnn as python dep

* Install libcudnn9-dev-cuda-12 in CI

* cudnn only accepts contiguous inputs

* Switch to backend apis

* Plan needs to be kept alive

* Turn off tf32

* Add cache

* Test the native cuda graph api

* Set cudnn stream before execution

* Make LRUCache more like a normal container

* Do error check for cublas handle

* Zero-initilizing array

* Use tf32 for conv

* Skip TestConv.test_torch_conv_2D test

---------

Co-authored-by: Awni Hannun <awni@apple.com>

.circleci/config.yml

@@ -216,6 +216,7 @@ jobs:
           name: Install Python package
           command: |
             sudo apt-get update
+            sudo apt-get install libcudnn9-dev-cuda-12
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
             python3 -m venv env
             source env/bin/activate
@@ -385,7 +386,7 @@ jobs:
             wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
             sudo dpkg -i cuda-keyring_1.1-1_all.deb
             sudo apt-get update
-            sudo apt install cuda-toolkit-12-9
+            sudo apt-get install cuda-toolkit-12-9 libcudnn9-dev-cuda-12
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
             sudo apt-get install zip
             pip install auditwheel

mlx/backend/cuda/CMakeLists.txt

@@ -15,6 +15,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
@@ -131,6 +132,23 @@ target_link_libraries(mlx PRIVATE CUDA::cublasLt)
 # Use NVRTC and driver APIs.
 target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 
+# Use the frontend APIs of cuDNN.
+FetchContent_Declare(
+  cudnn
+  GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
+  GIT_TAG v1.12.1
+  GIT_SHALLOW TRUE
+  EXCLUDE_FROM_ALL)
+set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
+set(CUDNN_FRONTEND_BUILD_SAMPLES OFF)
+set(CUDNN_FRONTEND_BUILD_TESTS OFF)
+set(CUDNN_FRONTEND_BUILD_PYTHON_BINDINGS OFF)
+FetchContent_MakeAvailable(cudnn)
+target_link_libraries(mlx PRIVATE cudnn_frontend)
+# Link with the actual cuDNN libraries.
+include(${cudnn_frontend_SOURCE_DIR}/cmake/cuDNN.cmake)
+target_link_libraries(mlx PRIVATE CUDNN::cudnn_all)
+
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)

mlx/backend/cuda/conv.cpp

@@ -0,0 +1,340 @@
+// Copyright © 2025 Apple Inc.
+
+#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>
+#include <numeric>
+
+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
+
+struct ConvCacheKey {
+  int device_id;
+  cudnnBackendDescriptorType_t backend_type;
+  cudnnDataType_t cudnn_type;
+  std::array<int, MAX_NDIM> input_shape;
+  std::array<int, MAX_NDIM> filter_shape;
+  std::array<int, MAX_NDIM> padding_lo;
+  std::array<int, MAX_NDIM> padding_hi;
+  std::array<int, MAX_NDIM> stride;
+  std::array<int, MAX_NDIM> dilation;
+  int groups;
+  uint8_t input_alignment;
+  uint8_t filter_alignment;
+  uint8_t output_alignment;
+};
+
+auto& conv_cache() {
+  static LRUBytesKeyCache<ConvCacheKey, cudnn_frontend::ExecutionPlan> cache(
+      /* capacity */ 128);
+  return cache;
+}
+
+template <typename T, typename U>
+inline std::vector<T> convert_vector(const std::vector<U>& vec) {
+  return std::vector<T>(vec.begin(), vec.end());
+}
+
+template <typename T>
+inline std::array<T, MAX_NDIM> fixed_vector(const std::vector<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(shape, 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,
+    const array& in,
+    const array& wt,
+    array& out) {
+  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] = {
+      const_cast<void*>(in.data<void>()),
+      const_cast<void*>(wt.data<void>()),
+      out.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,
+    cudnn_frontend::EngineConfigList& configs,
+    const ConvCacheKey& cache_key,
+    const std::string& op_graph_tag,
+    const array& in,
+    const array& wt,
+    array& out) {
+  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, in, wt, out)) {
+        conv_cache().emplace(cache_key, std::move(plan));
+        return true;
+      }
+    } catch (cudnn_frontend::cudnnException&) {
+    }
+  }
+  return false;
+}
+
+} // namespace
+
+void Convolution::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Convolution::eval_gpu");
+  if (out.size() == 0) {
+    return;
+  }
+
+  assert(inputs.size() == 2);
+  array in = inputs[0];
+  array wt = inputs[1];
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  // cuDNN requires contiguous input.
+  // TODO: Handle NCHW format specially.
+  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);
+  }
+
+  encoder.set_input_array(in);
+  encoder.set_input_array(wt);
+  encoder.set_output_array(out);
+
+  auto backend_type = CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR;
+  auto cudnn_type = dtype_to_cudnn_type(in.dtype());
+
+  // Search cache.
+  ConvCacheKey cache_key{
+      encoder.device().cuda_device(),
+      backend_type,
+      cudnn_type,
+      fixed_vector(in.shape()),
+      fixed_vector(wt.shape()),
+      fixed_vector(padding_lo_),
+      fixed_vector(padding_hi_),
+      fixed_vector(kernel_strides_),
+      fixed_vector(kernel_dilation_),
+      groups_,
+      get_alignment(in),
+      get_alignment(wt),
+      get_alignment(out)};
+  if (auto it = conv_cache().find(cache_key); it != conv_cache().end()) {
+    if (!execute_plan(encoder, it->second, in, wt, out)) {
+      throw std::runtime_error("Cached convolution plan failed to execute.");
+    }
+    return;
+  }
+
+  // Build operation graph.
+  auto compute_data_type = (in.dtype() == float16 || in.dtype() == bfloat16)
+      ? CUDNN_DATA_FLOAT
+      : cudnn_type;
+
+  auto stride = convert_vector<int64_t>(kernel_strides_);
+  auto padding_lo = convert_vector<int64_t>(padding_lo_);
+  auto padding_hi = convert_vector<int64_t>(padding_hi_);
+  auto dilation = convert_vector<int64_t>(kernel_dilation_);
+
+  auto conv_desc = cudnn_frontend::ConvDescBuilder()
+                       .setDataType(compute_data_type)
+                       .setMathMode(CUDNN_CROSS_CORRELATION)
+                       .setNDims(stride.size())
+                       .setStrides(stride.size(), stride.data())
+                       .setPrePadding(padding_lo.size(), padding_lo.data())
+                       .setPostPadding(padding_hi.size(), padding_hi.data())
+                       .setDilation(dilation.size(), dilation.data())
+                       .build();
+
+  auto op = cudnn_frontend::OperationBuilder(backend_type)
+                .setxDesc(build_tensor('x', in))
+                .setwDesc(build_tensor('w', wt))
+                .setyDesc(build_tensor('y', out))
+                .setcDesc(conv_desc)
+                .build();
+
+  std::array<cudnn_frontend::Operation const*, 1> ops = {&op};
+  auto op_graph = cudnn_frontend::OperationGraphBuilder()
+                      .setHandle(encoder.device().cudnn_handle())
+                      .setOperationGraph(ops.size(), ops.data())
+                      .build();
+
+  // Try to run plans based on heuristics.
+  auto configs = get_engine_configs(backend_type, in.dtype(), op_graph);
+  auto op_graph_tag = op_graph.getTag();
+  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
+    return;
+  }
+  // Then try fallback plans.
+  configs = get_engine_configs(backend_type, in.dtype(), op_graph);
+  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
+    return;
+  }
+  throw std::runtime_error("Unable to find an engine for convolution.");
+}
+
+} // namespace mlx::core

mlx/backend/cuda/device.cpp

@@ -9,12 +9,23 @@
 #include <future>
 #include <unordered_set>
 
-namespace mlx::core {
+namespace mlx::core::cu {
+
+namespace {
 
 // Can be tuned with MLX_MAX_OPS_PER_BUFFER
 // This should be less than 255
 constexpr int default_max_nodes_per_graph = 20;
 
+#define CHECK_CUDNN_ERROR(cmd) check_cudnn_error(#cmd, (cmd))
+
+void check_cudnn_error(const char* name, cudnnStatus_t err) {
+  if (err != CUDNN_STATUS_SUCCESS) {
+    throw std::runtime_error(
+        fmt::format("{} failed: {}.", name, cudnnGetErrorString(err)));
+  }
+}
+
 int cuda_graph_cache_size() {
   static int cache_size = []() {
     return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
@@ -22,7 +33,7 @@ int cuda_graph_cache_size() {
   return cache_size;
 }
 
-namespace cu {
+} // namespace
 
 Device::Device(int device) : device_(device) {
   CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
@@ -40,11 +51,14 @@ Device::Device(int device) : device_(device) {
   }
   // The cublasLt handle is used by matmul.
   make_current();
-  cublasLtCreate(&lt_);
+  CHECK_CUBLAS_ERROR(cublasLtCreate(&lt_));
+  // The cudnn handle is used by Convolution.
+  CHECK_CUDNN_ERROR(cudnnCreate(&cudnn_));
 }
 
 Device::~Device() {
-  cublasLtDestroy(lt_);
+  CHECK_CUDNN_ERROR(cudnnDestroy(cudnn_));
+  CHECK_CUBLAS_ERROR(cublasLtDestroy(lt_));
 }
 
 void Device::make_current() {
@@ -66,29 +80,36 @@ CommandEncoder& Device::get_command_encoder(Stream s) {
 }
 
 CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
+  enc.device().make_current();
   CHECK_CUDA_ERROR(
       cudaStreamBeginCapture(enc.stream(), cudaStreamCaptureModeGlobal));
 }
 
 CommandEncoder::CaptureContext::~CaptureContext() {
   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;
+  }
+
+  // Extract and add as single kernel node when possible.
   size_t num_nodes;
   CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, NULL, &num_nodes));
   if (num_nodes == 1) {
     cudaGraphNode_t captured_node;
     CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, &captured_node, &num_nodes));
-    CUDA_KERNEL_NODE_PARAMS params;
-    CHECK_CUDA_ERROR(cuGraphKernelNodeGetParams(captured_node, &params));
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, enc.graph_, NULL, 0, &params));
-    enc.insert_graph_dependencies(GraphNode{node, 'K'});
-  } else {
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(
-        cudaGraphAddChildGraphNode(&node, enc.graph_, NULL, 0, graph));
-    enc.insert_graph_dependencies(GraphNode{node, 'G'});
+    cudaGraphNodeType type;
+    CHECK_CUDA_ERROR(cudaGraphNodeGetType(captured_node, &type));
+    if (type == cudaGraphNodeTypeKernel) {
+      CUDA_KERNEL_NODE_PARAMS params;
+      CHECK_CUDA_ERROR(cuGraphKernelNodeGetParams(captured_node, &params));
+      enc.add_kernel_node(params);
+      return;
+    }
   }
-  CHECK_CUDA_ERROR(cudaGraphDestroy(graph));
+  // Otherwise add the captured graph as subgraph.
+  enc.add_graph_node(graph);
 }
 
 CommandEncoder::ConcurrentContext::ConcurrentContext(CommandEncoder& enc)
@@ -221,10 +242,7 @@ void CommandEncoder::add_kernel_node(
   kernel_params.gridDim = grid_dim;
   kernel_params.blockDim = block_dim;
   kernel_params.kernelParams = params;
-  cudaGraphNode_t node;
-  CHECK_CUDA_ERROR(
-      cudaGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
-  insert_graph_dependencies(GraphNode{node, 'K'});
+  add_kernel_node(kernel_params);
 }
 
 void CommandEncoder::add_kernel_node(
@@ -241,12 +259,27 @@ void CommandEncoder::add_kernel_node(
   kernel_params.blockDimY = block_dim.y;
   kernel_params.blockDimZ = block_dim.z;
   kernel_params.kernelParams = params;
-  CUgraphNode node;
-  CHECK_CUDA_ERROR(
-      cuGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
+  add_kernel_node(kernel_params);
+}
+
+void CommandEncoder::add_kernel_node(const cudaKernelNodeParams& params) {
+  cudaGraphNode_t node;
+  CHECK_CUDA_ERROR(cudaGraphAddKernelNode(&node, graph_, NULL, 0, &params));
   insert_graph_dependencies(GraphNode{node, 'K'});
 }
 
+void CommandEncoder::add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params) {
+  CUgraphNode node;
+  CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, graph_, NULL, 0, &params));
+  insert_graph_dependencies(GraphNode{node, 'K'});
+}
+
+void CommandEncoder::add_graph_node(cudaGraph_t child) {
+  cudaGraphNode_t node;
+  CHECK_CUDA_ERROR(cudaGraphAddChildGraphNode(&node, graph_, NULL, 0, child));
+  insert_graph_dependencies(GraphNode{node, 'G'});
+}
+
 void CommandEncoder::commit() {
   if (!temporaries_.empty()) {
     add_completed_handler([temporaries = std::move(temporaries_)]() {});
@@ -331,6 +364,4 @@ CommandEncoder& get_command_encoder(Stream s) {
   return device(s.device).get_command_encoder(s);
 }
 
-} // namespace cu
-
-} // namespace mlx::core
+} // namespace mlx::core::cu

mlx/backend/cuda/device.h

@@ -8,6 +8,7 @@
 
 #include <cublasLt.h>
 #include <cuda.h>
+#include <cudnn.h>
 #include <thrust/execution_policy.h>
 
 #include <unordered_map>
@@ -21,6 +22,7 @@ class CommandEncoder {
     ~CaptureContext();
     cudaGraph_t graph;
     CommandEncoder& enc;
+    bool discard{false};
   };
   struct ConcurrentContext {
     ConcurrentContext(CommandEncoder& enc);
@@ -65,6 +67,11 @@ class CommandEncoder {
   void
   add_kernel_node(void* func, dim3 grid_dim, dim3 block_dim, void** params);
 
+  // Low-level graph helpers.
+  void add_kernel_node(const cudaKernelNodeParams& params);
+  void add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params);
+  void add_graph_node(cudaGraph_t child);
+
   void add_temporary(const array& arr) {
     temporaries_.push_back(arr.data_shared_ptr());
   }
@@ -73,6 +80,10 @@ class CommandEncoder {
   void maybe_commit();
   void commit();
 
+  Device& device() {
+    return device_;
+  }
+
   CudaStream& stream() {
     return stream_;
   }
@@ -137,12 +148,16 @@ class Device {
   cublasLtHandle_t lt_handle() const {
     return lt_;
   }
+  cudnnHandle_t cudnn_handle() const {
+    return cudnn_;
+  }
 
  private:
   int device_;
   int compute_capability_major_;
   int compute_capability_minor_;
   cublasLtHandle_t lt_;
+  cudnnHandle_t cudnn_;
   std::unordered_map<int, CommandEncoder> encoders_;
 };
 

mlx/backend/cuda/lru_cache.h

@@ -0,0 +1,146 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <list>
+#include <unordered_map>
+#include <utility>
+
+namespace mlx::core {
+
+template <
+    typename K,
+    typename V,
+    template <typename...> typename M = std::unordered_map>
+class LRUCache {
+ public:
+  using value_type = std::pair<K, V>;
+  using list_type = std::list<value_type>;
+  using iterator = typename list_type::iterator;
+  using const_iterator = typename list_type::const_iterator;
+  using map_type = M<K, iterator>;
+
+  explicit LRUCache(size_t capacity) : capacity_(capacity) {}
+
+  size_t size() const {
+    return map_.size();
+  }
+  size_t capacity() const {
+    return capacity_;
+  }
+  bool empty() const {
+    return vlist_.empty();
+  }
+
+  void resize(size_t new_capacity) {
+    capacity_ = new_capacity;
+    trim();
+  }
+
+  iterator begin() {
+    return vlist_.begin();
+  }
+  const_iterator begin() const {
+    return vlist_.begin();
+  }
+  iterator end() {
+    return vlist_.end();
+  }
+  const_iterator end() const {
+    return vlist_.end();
+  }
+
+  void clear() {
+    map_.clear();
+    vlist_.clear();
+  }
+
+  iterator find(const K& key) {
+    auto it = map_.find(key);
+    if (it == map_.end())
+      return end();
+    vlist_.splice(vlist_.begin(), vlist_, it->second);
+    return it->second;
+  }
+
+  template <typename U>
+  std::pair<iterator, bool> emplace(const K& key, U&& value) {
+    auto it = map_.find(key);
+    if (it != map_.end()) {
+      vlist_.splice(vlist_.begin(), vlist_, it->second);
+      return {it->second, false};
+    }
+
+    vlist_.emplace_front(key, std::forward<U>(value));
+    map_[key] = vlist_.begin();
+
+    trim();
+
+    return {vlist_.begin(), true};
+  }
+
+  iterator erase(iterator pos) {
+    map_.erase(pos->first);
+    return vlist_.erase(pos);
+  }
+
+ private:
+  void trim() {
+    while (map_.size() > capacity_) {
+      auto last = std::prev(vlist_.end());
+      map_.erase(last->first);
+      vlist_.pop_back();
+    }
+  }
+
+  list_type vlist_;
+  map_type map_;
+  size_t capacity_;
+};
+
+// Turn a POD struct into a container key by doing bytes compare.
+template <typename T>
+struct BytesKey {
+  T pod;
+  static_assert(std::is_standard_layout_v<T>, "T is not POD");
+
+  BytesKey(T pod) : pod(std::move(pod)) {}
+
+  BytesKey(const BytesKey& other) {
+    memcpy(&pod, &other.pod, sizeof(T));
+  }
+
+  BytesKey(BytesKey&& other) {
+    memcpy(&pod, &other.pod, sizeof(T));
+  }
+
+  bool operator==(const BytesKey& other) const {
+    auto* ptr1 = reinterpret_cast<const uint8_t*>(&pod);
+    auto* ptr2 = reinterpret_cast<const uint8_t*>(&other.pod);
+    return memcmp(ptr1, ptr2, sizeof(T)) == 0;
+  }
+};
+
+// Compute hash according to the bytes value of T.
+template <typename T>
+struct BytesHash {
+  static_assert(std::is_standard_layout_v<T>, "T is not POD");
+
+  size_t operator()(const T& pod) const {
+    auto* ptr = reinterpret_cast<const uint8_t*>(&pod);
+    uint32_t value = 0x811C9DC5;
+    for (int i = 0; i < sizeof(T); ++i) {
+      value ^= ptr[i];
+      value *= 0x01000193;
+    }
+    return value;
+  }
+};
+
+template <typename K, typename V>
+using BytesKeyHashMap = std::unordered_map<K, V, BytesHash<K>>;
+
+template <typename K, typename V>
+using LRUBytesKeyCache = LRUCache<BytesKey<K>, V, BytesKeyHashMap>;
+
+} // namespace mlx::core

mlx/backend/cuda/matmul.cpp

@@ -8,7 +8,6 @@
 #include "mlx/primitives.h"
 #include "mlx/utils.h"
 
-#include <cublasLt.h>
 #include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>
 
@@ -18,16 +17,6 @@ namespace mlx::core {
 
 namespace cu {
 
-#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
-
-void check_cublas_error(const char* name, cublasStatus_t err) {
-  if (err != CUBLAS_STATUS_SUCCESS) {
-    // TODO: Use cublasGetStatusString when it is widely available.
-    throw std::runtime_error(
-        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
-  }
-}
-
 struct CublasPreference {
   CublasPreference(Device& device) {
     // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB

mlx/backend/cuda/primitives.cu

@@ -71,7 +71,6 @@ bool fast::ScaledDotProductAttention::use_fallback(
   }
 
 NO_GPU(BlockMaskedMM)
-NO_GPU(Convolution)
 NO_GPU(DynamicSlice)
 NO_GPU(DynamicSliceUpdate)
 NO_GPU(FFT)

mlx/backend/cuda/utils.cpp

@@ -17,6 +17,14 @@ CudaStream::~CudaStream() {
   CHECK_CUDA_ERROR(cudaStreamDestroy(stream_));
 }
 
+void check_cublas_error(const char* name, cublasStatus_t err) {
+  if (err != CUBLAS_STATUS_SUCCESS) {
+    // TODO: Use cublasGetStatusString when it is widely available.
+    throw std::runtime_error(
+        fmt::format("{} failed with code: {}.", name, static_cast<int>(err)));
+  }
+}
+
 void check_cuda_error(const char* name, cudaError_t err) {
   if (err != cudaSuccess) {
     throw std::runtime_error(

mlx/backend/cuda/utils.h

@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include <cublasLt.h>
 #include <cuda.h>
 #include <cuda_runtime.h>
 
@@ -33,10 +34,12 @@ class CudaStream {
 };
 
 // Throw exception if the cuda API does not succeed.
+void check_cublas_error(const char* name, cublasStatus_t err);
 void check_cuda_error(const char* name, cudaError_t err);
 void check_cuda_error(const char* name, CUresult err);
 
 // The macro version that prints the command that failed.
+#define CHECK_CUBLAS_ERROR(cmd) check_cublas_error(#cmd, (cmd))
 #define CHECK_CUDA_ERROR(cmd) check_cuda_error(#cmd, (cmd))
 
 // Convert Dtype to CUDA C++ types.

python/scripts/repair_cuda.sh

@@ -16,7 +16,7 @@ rm "${repaired_wheel}"
 mlx_so="mlx/lib/libmlx.so"
 rpath=$(patchelf --print-rpath "${mlx_so}")
 base="\$ORIGIN/../../nvidia"
-rpath=$rpath:${base}/cublas/lib:${base}/cuda_nvrtc/lib
+rpath=$rpath:${base}/cublas/lib:${base}/cuda_nvrtc/lib:${base}/cudnn/lib
 patchelf --force-rpath --set-rpath "$rpath" "$mlx_so"
 python ../python/scripts/repair_record.py ${mlx_so}
 

python/tests/cuda_skip.py

@@ -15,19 +15,12 @@ cuda_skip = {
     "TestOps.test_hadamard_grad_vmap",
     # Convolutions NYI
     "TestConv.test_1d_conv_with_2d",
-    "TestConv.test_asymmetric_padding",
-    "TestConv.test_basic_grad_shapes",
-    "TestConv.test_conv2d_unaligned_channels",
     "TestConv.test_conv_1d_groups_flipped",
     "TestConv.test_conv_general_flip_grad",
     "TestConv.test_conv_groups_grad",
-    "TestConv.test_numpy_conv",
-    "TestConv.test_repeated_conv",
-    "TestConv.test_torch_conv_1D",
     "TestConv.test_torch_conv_1D_grad",
     "TestConv.test_torch_conv_2D",
     "TestConv.test_torch_conv_2D_grad",
-    "TestConv.test_torch_conv_3D",
     "TestConv.test_torch_conv_3D_grad",
     "TestConv.test_torch_conv_depthwise",
     "TestConv.test_torch_conv_general",
@@ -40,10 +33,6 @@ cuda_skip = {
     "TestConvTranspose.test_torch_conv_transpose_3D",
     "TestConvTranspose.test_torch_conv_transpose_3D_grad",
     "TestConvTranspose.test_torch_conv_transpose_3d_output_padding",
-    "TestExportImport.test_export_conv",
-    "TestLayers.test_conv1d",
-    "TestLayers.test_conv2d",
-    "TestVmap.test_vmap_conv",
     # FFTs NYI
     "TestFFT.test_fft",
     "TestFFT.test_fft_big_powers_of_two",

setup.py

@@ -289,6 +289,7 @@ if __name__ == "__main__":
             install_requires += [
                 "nvidia-cublas-cu12==12.9.*",
                 "nvidia-cuda-nvrtc-cu12==12.9.*",
+                "nvidia-cudnn-cu12==12.9.*",
             ]
         else:
             name = "mlx-cpu"