CUDA_VISIBLE_DEVICES to local rank

cmake/FindNCCL.cmake

@@ -0,0 +1,54 @@
+# FindNCCL.cmake This module finds the NVIDIA NCCL library and its include
+# directories.
+
+set(NCCL_ROOT_DIR
+    $ENV{NCCL_ROOT_DIR}
+    CACHE PATH "Folder contains NVIDIA NCCL")
+
+find_path(
+  NCCL_INCLUDE_DIRS
+  NAMES nccl.h
+  HINTS ${NCCL_INCLUDE_DIR} ${NCCL_ROOT_DIR} ${NCCL_ROOT_DIR}/include
+        ${CUDA_TOOLKIT_ROOT_DIR}/include)
+
+if($ENV{USE_STATIC_NCCL})
+  message(
+    STATUS "USE_STATIC_NCCL detected. Linking against static NCCL library")
+  set(NCCL_LIBNAME "libnccl_static.a")
+else()
+  set(NCCL_LIBNAME "nccl")
+endif()
+
+find_library(
+  NCCL_LIBRARIES
+  NAMES ${NCCL_LIBNAME}
+  HINTS ${NCCL_LIB_DIR}
+        ${NCCL_ROOT_DIR}
+        ${NCCL_ROOT_DIR}/lib
+        ${NCCL_ROOT_DIR}/lib/x86_64-linux-gnu
+        ${NCCL_ROOT_DIR}/lib64
+        ${CUDA_TOOLKIT_ROOT_DIR}/lib
+        ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIRS
+                                  NCCL_LIBRARIES)
+
+if(NCCL_FOUND)
+  set(NCCL_HEADER_FILE "${NCCL_INCLUDE_DIRS}/nccl.h")
+  message(
+    STATUS "Determining NCCL version from the header file: ${NCCL_HEADER_FILE}")
+  file(
+    STRINGS ${NCCL_HEADER_FILE} NCCL_MAJOR_VERSION_DEFINED
+    REGEX "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+[0-9]+.*$"
+    LIMIT_COUNT 1)
+  if(NCCL_MAJOR_VERSION_DEFINED)
+    string(REGEX REPLACE "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+" ""
+                         NCCL_MAJOR_VERSION ${NCCL_MAJOR_VERSION_DEFINED})
+    message(STATUS "NCCL_MAJOR_VERSION: ${NCCL_MAJOR_VERSION}")
+  endif()
+  message(
+    STATUS
+      "Found NCCL (include: ${NCCL_INCLUDE_DIRS}, library: ${NCCL_LIBRARIES})")
+  mark_as_advanced(NCCL_ROOT_DIR NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
+endif()

docs/src/install.rst

@@ -271,7 +271,7 @@ and the CUDA toolkit. For example on Ubuntu, run the following:
    dpkg -i cuda-keyring_1.1-1_all.deb
    apt-get update -y
    apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev -y
+   apt-get install libblas-dev liblapack-dev liblapacke-dev libcudnn9-dev-cuda-12 -y
 
 
 When building either the Python or C++ APIs make sure to pass the cmake flag

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)
+   state = tree_flatten(optimizer.state, destination={})
-   mx.save_safetensors("optimizer.safetensors", dict(state))
+   mx.save_safetensors("optimizer.safetensors", 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(list(mx.load("optimizer.safetensors").items()))
+   state = tree_unflatten(mx.load("optimizer.safetensors"))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++). 
+front-end (e.g. C++).
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting 
+Basics of Exporting
 -------------------
 
 Let's start with a simple example:
- 
+
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-   
+
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-  
+
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
- 
+
-   params = dict(tree_flatten(model.parameters()))
+   params = tree_flatten(model.parameters(), destination={})
    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/cuda/CMakeLists.txt

@@ -19,6 +19,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/distributed.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
@@ -39,6 +40,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu

mlx/backend/cuda/distributed.cu

@@ -0,0 +1,51 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/distributed/primitives.h"
+#include "mlx/primitives.h"
+
+#include <cassert>
+
+namespace mlx::core {
+namespace distributed {
+void AllReduce::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  assert(inputs.size() == 1);
+  assert(outputs.size() == 1);
+
+  auto& input = inputs[0];
+  auto& output = outputs[0];
+
+  auto& encoder = cu::get_command_encoder(stream());
+
+  if (input.is_donatable()) {
+    output.copy_shared_buffer(input);
+  } else {
+    output.set_data(allocator::malloc(output.nbytes()));
+  }
+
+  encoder.set_input_array(input);
+  encoder.set_output_array(output);
+
+  auto capture = encoder.capture_context();
+  auto& s = stream();
+
+  switch (reduce_type_) {
+    case Sum:
+      distributed::detail::all_sum(group(), input, output, s);
+      break;
+    case Max:
+      distributed::detail::all_max(group(), input, output, s);
+      break;
+    case Min:
+      distributed::detail::all_min(group(), input, output, s);
+      break;
+    default:
+      throw std::runtime_error(
+          "Only all reduce sum, max, and min are supported.");
+  }
+}
+} // namespace distributed
+} // namespace mlx::core

mlx/backend/cuda/primitives.cpp

@@ -6,17 +6,6 @@
 
 namespace mlx::core {
 
-bool fast::ScaledDotProductAttention::use_fallback(
-    const array& q,
-    const array& k,
-    const array& v,
-    bool has_mask,
-    bool has_arr_mask,
-    bool do_causal,
-    Stream s) {
-  return true;
-}
-
 #define NO_GPU_MULTI(func)                                             \
   void func::eval_gpu(                                                 \
       const std::vector<array>& inputs, std::vector<array>& outputs) { \
@@ -53,12 +42,10 @@ NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
 namespace fast {
-NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(CustomKernel)
 } // namespace fast
 
 namespace distributed {
-NO_GPU_MULTI(AllReduce)
 NO_GPU_MULTI(AllGather)
 NO_GPU_MULTI(Send)
 NO_GPU_MULTI(Recv)

mlx/backend/cuda/scaled_dot_product_attention.cu

@@ -0,0 +1,781 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/device/config.h"
+#include "mlx/backend/cuda/device/utils.cuh"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/lru_cache.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/fast_primitives.h"
+#include "mlx/transforms_impl.h"
+
+#include <nvtx3/nvtx3.hpp>
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+#define PRAGMA_LOOP_UNROLL #pragma unroll
+
+struct AttnParams {
+  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];
+};
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_1pass(
+    const T* Q,
+    const T* K,
+    const T* V,
+    T* O,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 32;
+  constexpr int BD = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  const int inner_k_stride = BN * int(params.K_strides[2]);
+  const int inner_v_stride = BN * int(params.V_strides[2]);
+
+  typedef float U;
+
+  U q[v_per_thread];
+  U k[v_per_thread];
+  U o[v_per_thread];
+
+  __shared__ U outputs[BN][BD + 1];
+  __shared__ U max_scores[BN];
+  __shared__ U sum_exp_scores[BN];
+
+  const U scale_log2 = params.scale * 1.44269504089f;
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = blockIdx.z;
+  const int head_idx = blockIdx.x;
+  const int kv_head_idx = head_idx / params.gqa_factor;
+
+  const int q_seq_idx = blockIdx.y;
+  const int kv_seq_idx = warp_idx;
+
+  Q += batch_idx * params.Q_strides[0] + // Batch
+      head_idx * params.Q_strides[1] + // Head
+      q_seq_idx * params.Q_strides[2]; // Sequence
+
+  K += batch_idx * params.K_strides[0] + // Batch
+      kv_head_idx * params.K_strides[1] + // Head
+      kv_seq_idx * params.K_strides[2]; // Sequence
+
+  V += batch_idx * params.V_strides[0] + // Batch
+      kv_head_idx * params.V_strides[1] + // Head
+      kv_seq_idx * params.V_strides[2]; // Sequence
+
+  O += batch_idx * params.O_strides[0] + // Batch
+      head_idx * params.O_strides[1] + // Head
+      q_seq_idx * params.O_strides[2]; // Sequence
+
+  // Read the query and 0 the output accumulator
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    q[i] = scale_log2 * static_cast<U>(Q[v_per_thread * lane_idx + i]);
+  }
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = 0.f;
+  }
+
+  U max_score = -INFINITY;
+  U sum_exp_score = 0.f;
+
+  // For each key
+  for (int i = kv_seq_idx; i < params.kL; i += BN) {
+    bool use_key = true;
+    if constexpr (do_causal) {
+      use_key = i <= (params.kL - params.qL + q_seq_idx);
+    }
+
+    if (use_key) {
+      // Read the key
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        k[j] = K[v_per_thread * lane_idx + j];
+      }
+
+      // Compute the i-th score
+      U score = 0.f;
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        score += q[j] * k[j];
+      }
+
+      // Warp sum
+      score = cg::reduce(warp, score, cg::plus<U>());
+
+      // Update the accumulators
+      U new_max = max(max_score, score);
+      U factor = exp2f(max_score - new_max);
+      U exp_score = exp2f(score - new_max);
+
+      max_score = new_max;
+      sum_exp_score = sum_exp_score * factor + exp_score;
+
+      // Update the output accumulator
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        o[j] = o[j] * factor +
+            exp_score * static_cast<U>(V[v_per_thread * lane_idx + j]);
+      }
+    }
+
+    // Move the pointers to the next kv
+    K += inner_k_stride;
+    V += inner_v_stride;
+  }
+
+  if (lane_idx == 0) {
+    max_scores[warp_idx] = max_score;
+    sum_exp_scores[warp_idx] = sum_exp_score;
+  }
+  block.sync();
+
+  max_score = max_scores[lane_idx];
+  U new_max = cg::reduce(warp, max_score, cg::greater<U>());
+  U factor = exp2f(max_score - new_max);
+  sum_exp_score =
+      cg::reduce(warp, sum_exp_scores[lane_idx] * factor, cg::plus<U>());
+  sum_exp_score = __frcp_rn(sum_exp_score);
+
+  // Now we need to aggregate all the outputs
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    outputs[lane_idx][warp_idx] = o[i];
+    block.sync();
+    U ot = outputs[warp_idx][lane_idx] * factor;
+    o[i] = cg::reduce(warp, ot, cg::plus<U>()) * sum_exp_score;
+    block.sync();
+  }
+
+  // And write the output
+  if (lane_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      O[v_per_thread * warp_idx + i] = static_cast<T>(o[i]);
+    }
+  }
+}
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_2pass_1(
+    const T* Q,
+    const T* K,
+    const T* V,
+    float* partials,
+    float* sums,
+    float* maxs,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 8;
+  constexpr int BD = 32;
+  constexpr int blocks = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  const int inner_k_stride = blocks * BN * int(params.K_strides[2]);
+  const int inner_v_stride = blocks * BN * int(params.V_strides[2]);
+
+  typedef float U;
+
+  U q[v_per_thread];
+  U k[v_per_thread];
+  U o[v_per_thread];
+
+  __shared__ U outputs[BN][BD + 1];
+  __shared__ U max_scores[BN];
+  __shared__ U sum_exp_scores[BN];
+
+  const U scale_log2 = params.scale * 1.44269504089f;
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = blockIdx.z / blocks;
+  const int block_idx = blockIdx.z % blocks;
+  const int head_idx = blockIdx.x;
+  const int kv_head_idx = head_idx / params.gqa_factor;
+
+  const int q_seq_idx = blockIdx.y;
+  const int kv_seq_idx = block_idx * BN + warp_idx;
+
+  Q += batch_idx * params.Q_strides[0] + // Batch
+      head_idx * params.Q_strides[1] + // Head
+      q_seq_idx * params.Q_strides[2]; // Sequence
+
+  K += batch_idx * params.K_strides[0] + // Batch
+      kv_head_idx * params.K_strides[1] + // Head
+      kv_seq_idx * params.K_strides[2]; // Sequence
+
+  V += batch_idx * params.V_strides[0] + // Batch
+      kv_head_idx * params.V_strides[1] + // Head
+      kv_seq_idx * params.V_strides[2]; // Sequence
+
+  const int p_stride_s = blocks;
+  const int p_stride_h = params.qL * p_stride_s;
+  const int p_stride_b = params.H * p_stride_h;
+  const int p_offset = batch_idx * p_stride_b + // Batch
+      head_idx * p_stride_h + // Head
+      q_seq_idx * p_stride_s + // Sequence
+      block_idx; // Block
+
+  partials += p_offset * D;
+  sums += p_offset;
+  maxs += p_offset;
+
+  // Read the query and 0 the output accumulator
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    q[i] = scale_log2 * static_cast<U>(Q[v_per_thread * lane_idx + i]);
+  }
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = 0.f;
+  }
+
+  U max_score = -1e9;
+  U sum_exp_score = 0.f;
+
+  // For each key
+  for (int i = kv_seq_idx; i < params.kL; i += blocks * BN) {
+    bool use_key = true;
+    if constexpr (do_causal) {
+      use_key = i <= (params.kL - params.qL + q_seq_idx);
+    }
+
+    if (use_key) {
+      // Read the key
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        k[j] = K[v_per_thread * lane_idx + j];
+      }
+
+      // Compute the i-th score
+      U score = 0.f;
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        score += q[j] * k[j];
+      }
+
+      // Warp sum
+      score = cg::reduce(warp, score, cg::plus<U>());
+
+      // Update the accumulators
+      U new_max = max(max_score, score);
+      U factor = exp2f(max_score - new_max);
+      U exp_score = exp2f(score - new_max);
+
+      max_score = new_max;
+      sum_exp_score = sum_exp_score * factor + exp_score;
+
+      // Update the output accumulator
+      PRAGMA_LOOP_UNROLL
+      for (int j = 0; j < v_per_thread; j++) {
+        o[j] = o[j] * factor +
+            exp_score * static_cast<U>(V[v_per_thread * lane_idx + j]);
+      }
+    }
+
+    // Move the pointers to the next kv
+    K += inner_k_stride;
+    V += inner_v_stride;
+  }
+
+  if (lane_idx == 0) {
+    max_scores[warp_idx] = max_score;
+    sum_exp_scores[warp_idx] = sum_exp_score;
+  }
+
+  block.sync();
+
+  max_score = (lane_idx < BN) ? max_scores[lane_idx] : -1e9;
+  U new_max = cg::reduce(warp, max_score, cg::greater<U>());
+  U factor = exp2f(max_score - new_max);
+  sum_exp_score = (lane_idx < BN) ? sum_exp_scores[lane_idx] : 0.f;
+  sum_exp_score = cg::reduce(warp, sum_exp_score * factor, cg::plus<U>());
+
+  // Write the sum and new max
+  if (warp_idx == 0) {
+    sums[0] = sum_exp_score;
+    maxs[0] = new_max;
+  }
+
+  // Now we need to aggregate all the outputs
+  auto ff = exp2f(max_scores[warp_idx] - new_max);
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    outputs[warp_idx][lane_idx] = o[i] * ff;
+    block.sync();
+
+    if (warp_idx == 0) {
+      U ot = outputs[0][lane_idx];
+      PRAGMA_LOOP_UNROLL
+      for (int j = 1; j < BN; j++) {
+        ot += outputs[j][lane_idx];
+        warp.sync();
+      }
+      o[i] = ot;
+    }
+    block.sync();
+  }
+
+  if (warp_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      partials[v_per_thread * lane_idx + i] = o[i];
+    }
+  }
+}
+
+template <typename T, bool do_causal, int D>
+__global__ void kernel_sdpav_2pass_2(
+    const float* partials,
+    const float* sums,
+    const float* maxs,
+    T* O,
+    __grid_constant__ const AttnParams params) {
+  constexpr int BN = 32;
+  constexpr int BD = 32;
+  constexpr int blocks = 32;
+
+  constexpr int v_per_thread = D / BD;
+
+  typedef float U;
+
+  U o[v_per_thread];
+  __shared__ U outputs[BN][BD + 1];
+
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<32>(block);
+
+  const int lane_idx = warp.thread_rank();
+  const int warp_idx = warp.meta_group_rank();
+
+  // Adjust to thread block and thread
+  const int batch_idx = blockIdx.z;
+  const int head_idx = blockIdx.x;
+  const int q_seq_idx = blockIdx.y;
+
+  const int p_stride_s = blocks;
+  const int p_stride_h = params.qL * p_stride_s;
+  const int p_stride_b = params.H * p_stride_h;
+  const int p_offset = batch_idx * p_stride_b + // Batch
+      head_idx * p_stride_h + // Head
+      q_seq_idx * p_stride_s; // Sequence
+
+  partials += p_offset * D + warp_idx * D;
+  sums += p_offset;
+  maxs += p_offset;
+
+  O += batch_idx * params.O_strides[0] + // Batch
+      head_idx * params.O_strides[1] + // Head
+      q_seq_idx * params.O_strides[2]; // Sequence
+
+  U max_score = maxs[lane_idx];
+  U new_max = cg::reduce(warp, max_score, cg::greater<U>());
+  U factor = exp2f(max_score - new_max);
+  U sum_exp_score = cg::reduce(warp, sums[lane_idx] * factor, cg::plus<U>());
+  sum_exp_score = __frcp_rn(sum_exp_score);
+
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    o[i] = partials[v_per_thread * lane_idx + i];
+  }
+
+  // Now we need to aggregate all the outputs
+  PRAGMA_LOOP_UNROLL
+  for (int i = 0; i < v_per_thread; i++) {
+    outputs[lane_idx][warp_idx] = o[i];
+    block.sync();
+    U ot = outputs[warp_idx][lane_idx] * factor;
+    o[i] = cg::reduce(warp, ot, cg::plus<U>()) * sum_exp_score;
+    block.sync();
+  }
+
+  // And write the output
+  if (lane_idx == 0) {
+    PRAGMA_LOOP_UNROLL
+    for (int i = 0; i < v_per_thread; i++) {
+      O[v_per_thread * warp_idx + i] = static_cast<T>(o[i]);
+    }
+  }
+}
+
+} // namespace cu
+
+namespace {
+
+template <typename F>
+void dispatch_headdim(int n, F&& f) {
+  switch (n) {
+    case 64:
+      f(std::integral_constant<int, 64>{});
+      break;
+    case 96:
+      f(std::integral_constant<int, 96>{});
+      break;
+    case 128:
+      f(std::integral_constant<int, 128>{});
+      break;
+  }
+}
+
+void sdpa_vector_1pass_fallback(
+    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);
+
+  cu::AttnParams params{
+      /* int B = */ q.shape(0),
+      /* int H = */ q.shape(1),
+      /* int D = */ q.shape(3),
+
+      /* int qL = */ q.shape(2),
+      /* int kL = */ k.shape(2),
+
+      /* int gqa_factor = */ q.shape(1) / k.shape(1),
+      /* 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)}};
+
+  dim3 grid_dim(params.H, params.qL, params.B);
+  dim3 block_dim(1024, 1, 1);
+
+  dispatch_float_types(o.dtype(), "kernel_sdpav_1pass", [&](auto type_tag) {
+    dispatch_bool(do_causal_, [&](auto do_causal) {
+      dispatch_headdim(params.D, [&](auto headdim) {
+        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+
+        auto kernel =
+            cu::kernel_sdpav_1pass<DataType, do_causal.value, headdim.value>;
+        encoder.add_kernel_node(
+            kernel,
+            grid_dim,
+            block_dim,
+            0,
+            q.data<DataType>(),
+            k.data<DataType>(),
+            v.data<DataType>(),
+            o.data<DataType>(),
+            params);
+      });
+    });
+  });
+}
+
+void sdpa_vector_2pass_fallback(
+    const Stream& s,
+    cu::CommandEncoder& encoder,
+    const array& q,
+    const array& k,
+    const array& v,
+    const float scale,
+    array& o,
+    bool do_causal_ = false) {
+  cu::AttnParams params{
+      /* int B = */ q.shape(0),
+      /* int H = */ q.shape(1),
+      /* int D = */ q.shape(3),
+
+      /* int qL = */ q.shape(2),
+      /* int kL = */ k.shape(2),
+
+      /* int gqa_factor = */ q.shape(1) / k.shape(1),
+      /* 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)}};
+
+  // Allocate the intermediates
+  int blocks = 32;
+
+  Shape intermediate_shape;
+  intermediate_shape.reserve(o.ndim() + 1);
+  intermediate_shape.insert(
+      intermediate_shape.end(), o.shape().begin(), o.shape().end() - 1);
+  intermediate_shape.push_back(blocks);
+  intermediate_shape.push_back(o.shape().back());
+
+  array intermediate(intermediate_shape, float32, nullptr, {});
+  intermediate_shape.pop_back();
+  array sums(intermediate_shape, float32, nullptr, {});
+  array maxs(std::move(intermediate_shape), float32, nullptr, {});
+
+  intermediate.set_data(allocator::malloc(intermediate.nbytes()));
+  sums.set_data(allocator::malloc(sums.nbytes()));
+  maxs.set_data(allocator::malloc(maxs.nbytes()));
+
+  encoder.add_temporary(intermediate);
+  encoder.add_temporary(sums);
+  encoder.add_temporary(maxs);
+
+  dispatch_float_types(o.dtype(), "kernel_sdpav_2pass", [&](auto type_tag) {
+    dispatch_bool(do_causal_, [&](auto do_causal) {
+      dispatch_headdim(params.D, [&](auto headdim) {
+        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+
+        {
+          auto kernel = cu::
+              kernel_sdpav_2pass_1<DataType, do_causal.value, headdim.value>;
+
+          encoder.set_input_array(q);
+          encoder.set_input_array(k);
+          encoder.set_input_array(v);
+          encoder.set_output_array(intermediate);
+          encoder.set_output_array(sums);
+          encoder.set_output_array(maxs);
+
+          dim3 grid_dim(params.H, params.qL, params.B * 32);
+          dim3 block_dim(8 * 32, 1, 1);
+
+          encoder.add_kernel_node(
+              kernel,
+              grid_dim,
+              block_dim,
+              0,
+              q.data<DataType>(),
+              k.data<DataType>(),
+              v.data<DataType>(),
+              intermediate.data<float>(),
+              sums.data<float>(),
+              maxs.data<float>(),
+              params);
+        }
+
+        {
+          auto kernel = cu::
+              kernel_sdpav_2pass_2<DataType, do_causal.value, headdim.value>;
+
+          encoder.set_input_array(intermediate);
+          encoder.set_input_array(sums);
+          encoder.set_input_array(maxs);
+          encoder.set_output_array(o);
+
+          dim3 grid_dim(params.H, params.qL, params.B);
+          dim3 block_dim(1024, 1, 1);
+
+          encoder.add_kernel_node(
+              kernel,
+              grid_dim,
+              block_dim,
+              0,
+              intermediate.data<float>(),
+              sums.data<float>(),
+              maxs.data<float>(),
+              o.data<DataType>(),
+              params);
+        }
+      });
+    });
+  });
+}
+
+void sdpa_vector_fallback(
+    const Stream& s,
+    cu::CommandEncoder& encoder,
+    const array& q,
+    const array& k,
+    const array& v,
+    const float scale,
+    array& o,
+    bool do_causal_ = false) {
+  int kL = k.shape(2);
+
+  if (kL > 1024) {
+    return sdpa_vector_2pass_fallback(
+        s, encoder, q, k, v, scale, o, do_causal_);
+  } else {
+    return sdpa_vector_1pass_fallback(
+        s, encoder, q, k, v, scale, o, do_causal_);
+  }
+}
+
+} // namespace
+
+namespace fast {
+
+bool ScaledDotProductAttention::use_fallback(
+    const array& q,
+    const array& k,
+    const array& v,
+    bool has_mask,
+    bool has_arr_mask,
+    bool do_causal,
+    Stream s) {
+  if (detail::in_grad_tracing()) {
+    return true;
+  }
+  if (s.device == Device::cpu) {
+    return true;
+  }
+
+  const int value_head_dim = v.shape(-1);
+  const int query_head_dim = q.shape(-1);
+  const int query_sequence_length = q.shape(2);
+  const int key_sequence_length = k.shape(2);
+
+  const bool sdpa_supported_head_dim = query_head_dim == value_head_dim &&
+      (query_head_dim == 64 || query_head_dim == 96 || query_head_dim == 128);
+
+  const bool supported_vector_config =
+      sdpa_supported_head_dim && query_sequence_length < 4;
+
+  const bool supported_config = supported_vector_config;
+
+  return has_arr_mask || !supported_config;
+}
+
+void ScaledDotProductAttention::eval_gpu(
+    const std::vector<array>& inputs,
+    array& out) {
+  nvtx3::scoped_range r("ScaledDotProductAttention::eval_gpu");
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  auto& q_pre = inputs[0];
+  auto& k_pre = inputs[1];
+  auto& v_pre = inputs[2];
+  auto& o = out;
+
+  std::vector<array> copies;
+
+  // Define some copy functions to ensure the layout of the inputs is as
+  // expected.
+  copies.reserve(3);
+  auto copy_unless = [&copies, &s](
+                         auto predicate, const array& arr) -> const array& {
+    if (!predicate(arr)) {
+      array arr_copy = contiguous_copy_gpu(arr, s);
+      copies.push_back(std::move(arr_copy));
+      return copies.back();
+    } else {
+      return arr;
+    }
+  };
+
+  // We are in vector mode ie single query
+  if (q_pre.shape(2) < 4) {
+    auto q_copy_unless = [](const array& arr) {
+      if (arr.flags().row_contiguous) {
+        return true;
+      }
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (shape[0] == 1 || shape[1] == 1) {
+        // If either the batch or head dimension is a singleton, the other can
+        // be transposed with the sequence dimension
+        auto bidx = shape[0] == 1 ? 1 : 0;
+        return (strides[3] == 1) && (strides[2] == shape[3] * shape[bidx]) &&
+            (strides[bidx] == shape[3]);
+      }
+      return false;
+    };
+
+    auto kv_copy_unless = [](const array& arr) {
+      // keys and values should be copied if:
+      // - the last dimension is not contiguous
+      // - the batch and head dim are not contiguous
+      auto& strides = arr.strides();
+      auto& shape = arr.shape();
+      if (strides.back() != 1) {
+        return false;
+      }
+      if (shape[0] == 1 || shape[1] == 1) {
+        return true;
+      }
+      return (strides[0] == strides[1] * shape[1]);
+    };
+
+    const auto& q = copy_unless(q_copy_unless, q_pre);
+    const auto& k = copy_unless(kv_copy_unless, k_pre);
+    const auto& v = copy_unless(kv_copy_unless, v_pre);
+
+    for (const auto& cp : copies) {
+      encoder.add_temporary(cp);
+    }
+
+    // Donate the query if possible
+    if (q.is_donatable() && q.flags().row_contiguous && q.size() == o.size()) {
+      o.copy_shared_buffer(q);
+    } else {
+      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 = */ o.shape(2) == 1,
+          /* bool col_contiguous = */ 0,
+      };
+
+      o.set_data(
+          allocator::malloc(o.nbytes()),
+          data_size,
+          {str_oB, str_oH, str_oL, str_oD},
+          flags);
+    }
+
+    return sdpa_vector_fallback(s, encoder, q, k, v, scale_, o, do_causal_);
+  }
+
+  // Full attention mode should never reach here
+  else {
+    throw std::runtime_error("Doesn't support matrix yet.");
+  }
+}
+
+} // namespace fast
+
+} // namespace mlx::core

mlx/backend/metal/device.h

@@ -104,7 +104,7 @@ struct CommandEncoder {
   };
 
   // Outputs of all kernels in the encoder including temporaries
-  std::unordered_set<const void*> outputs() {
+  std::unordered_set<const void*>& outputs() {
     return all_outputs_;
   };
 

mlx/distributed/CMakeLists.txt

@@ -6,3 +6,4 @@ target_sources(
 
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/mpi)
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ring)
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/nccl)

mlx/distributed/distributed.cpp

@@ -2,9 +2,11 @@
 
 #include <unordered_map>
 
+#include <iostream>
 #include "mlx/distributed/distributed.h"
 #include "mlx/distributed/distributed_impl.h"
 #include "mlx/distributed/mpi/mpi.h"
+#include "mlx/distributed/nccl/nccl.h"
 #include "mlx/distributed/ring/ring.h"
 
 namespace mlx::core::distributed {
@@ -80,7 +82,7 @@ class EmptyGroup : public GroupImpl {
 } // namespace detail
 
 bool is_available() {
-  return mpi::is_available() || ring::is_available();
+  return mpi::is_available() || ring::is_available() || nccl::is_available();
 }
 
 int Group::rank() const {
@@ -111,6 +113,8 @@ Group init(bool strict /* = false */, const std::string& bk /* = "any" */) {
     group = mpi::init(strict);
   } else if (bk == "ring") {
     group = ring::init(strict);
+  } else if (bk == "nccl") {
+    group = nccl::init(strict);
   } else if (bk == "any") {
     group = ring::init(false);
     bk_ = "ring";

mlx/distributed/distributed.h

@@ -3,7 +3,6 @@
 #pragma once
 
 #include <memory>
-
 #include "mlx/array.h"
 
 namespace mlx::core::distributed {

mlx/distributed/nccl/CMakeLists.txt

@@ -0,0 +1,8 @@
+if(MLX_BUILD_CUDA)
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/nccl.cpp)
+  find_package(NCCL REQUIRED)
+  target_link_libraries(mlx PRIVATE ${NCCL_LIBRARIES})
+  target_include_directories(mlx PRIVATE ${NCCL_INCLUDE_DIRS})
+else()
+  target_sources(mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/no_nccl.cpp)
+endif()

mlx/distributed/nccl/nccl.cpp

@@ -0,0 +1,359 @@
+#include <arpa/inet.h>
+#include <cuda_runtime.h>
+#include <nccl.h>
+#include <netdb.h>
+#include <sys/socket.h>
+#include <unistd.h>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <iostream>
+#include <mutex>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/distributed/distributed.h"
+#include "mlx/distributed/distributed_impl.h"
+
+namespace mlx::core::distributed::nccl {
+
+#define CHECK_CUDA(cmd)              \
+  do {                               \
+    cudaError_t e = cmd;             \
+    if (e != cudaSuccess) {          \
+      fprintf(                       \
+          stderr,                    \
+          "CUDA error %s:%d '%s'\n", \
+          __FILE__,                  \
+          __LINE__,                  \
+          cudaGetErrorString(e));    \
+      exit(1);                       \
+    }                                \
+  } while (0)
+
+#define CHECK_NCCL(cmd)              \
+  do {                               \
+    ncclResult_t r = cmd;            \
+    if (r != ncclSuccess) {          \
+      fprintf(                       \
+          stderr,                    \
+          "NCCL error %s:%d '%s'\n", \
+          __FILE__,                  \
+          __LINE__,                  \
+          ncclGetErrorString(r));    \
+      exit(1);                       \
+    }                                \
+  } while (0)
+
+namespace detail {
+
+inline void sendAll(int sock, const void* buf, size_t len) {
+  const char* ptr = reinterpret_cast<const char*>(buf);
+  while (len > 0) {
+    ssize_t sent = send(sock, ptr, len, 0);
+    if (sent <= 0) {
+      perror("send");
+      exit(1);
+    }
+    ptr += sent;
+    len -= sent;
+  }
+}
+
+inline void recvAll(int sock, void* buf, size_t len) {
+  char* ptr = reinterpret_cast<char*>(buf);
+  while (len > 0) {
+    ssize_t rec = recv(sock, ptr, len, 0);
+    if (rec <= 0) {
+      perror("recv");
+      exit(1);
+    }
+    ptr += rec;
+    len -= rec;
+  }
+}
+
+inline void bootstrap_unique_id(
+    ncclUniqueId& id,
+    int rank,
+    int size,
+    const std::string& initMethod) {
+  // Parse the init method to extract the host and port
+  if (initMethod.rfind("tcp://", 0) != 0)
+    throw;
+  auto hostport = initMethod.substr(6);
+  auto colon = hostport.find(':');
+  std::string host = hostport.substr(0, colon);
+  int port = std::stoi(hostport.substr(colon + 1));
+
+  if (rank == 0) {
+    // create a unique id on the rank 0
+    CHECK_NCCL(ncclGetUniqueId(&id));
+
+    // create a socket to send the unique id to all other ranks
+    int sock = socket(AF_INET, SOCK_STREAM, 0);
+
+    if (sock < 0) {
+      std::ostringstream msg;
+      msg << "[nccl] Couldn't create socket (error: " << errno << ")";
+      throw std::runtime_error(msg.str());
+    }
+
+    sockaddr_in serv = {};
+    serv.sin_family = AF_INET;
+    serv.sin_addr.s_addr = htonl(INADDR_ANY);
+    serv.sin_port = htons(port);
+
+    int reuse = 1;
+    // Without this, if rank-0 crashes or restarts process quickly,
+    // the OS might refuse to let binding to the same port, so reuse
+
+    if (setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, &reuse, sizeof(reuse)) < 0) {
+      std::ostringstream msg;
+      msg << "[nccl] setsockopt() failed: " << strerror(errno);
+      throw std::runtime_error(msg.str());
+    }
+
+    if (bind(sock, reinterpret_cast<sockaddr*>(&serv), sizeof(serv)) < 0) {
+      std::ostringstream msg;
+      msg << "[nccl] bind() failed: " << strerror(errno);
+      throw std::runtime_error(msg.str());
+    }
+    if (listen(sock, size - 1) < 0) {
+      std::ostringstream msg;
+      msg << "[nccl] listen() failed: " << strerror(errno);
+      throw std::runtime_error(msg.str());
+    }
+
+    for (int peer = 1; peer < size; ++peer) {
+      int conn = accept(sock, nullptr, nullptr);
+      if (conn < 0) {
+        std::ostringstream msg;
+        msg << "[nccl] accept() failed: " << strerror(errno);
+        throw std::runtime_error(msg.str());
+      }
+      sendAll(conn, &id, sizeof(id));
+      close(conn);
+    }
+    close(sock);
+
+  } else {
+    // Here just wanted to make show that rank 0 has enough time to bind
+    // so we will retry to connect until max attempts
+
+    int sock = socket(AF_INET, SOCK_STREAM, 0);
+    if (sock < 0) {
+      std::ostringstream msg;
+      msg << "[nccl] socket() failed: " << strerror(errno);
+      throw std::runtime_error(msg.str());
+    }
+
+    hostent* he = gethostbyname(host.c_str());
+    if (!he) {
+      throw std::runtime_error("[nccl] lookup failed for host: " + host);
+    }
+    sockaddr_in serv = {};
+    serv.sin_family = AF_INET;
+    memcpy(&serv.sin_addr, he->h_addr_list[0], he->h_length);
+    serv.sin_port = htons(port);
+
+    const int max_retries = 30;
+    int attempt = 0;
+    bool connected = false;
+
+    for (attempt = 0; attempt < max_retries; ++attempt) {
+      if (connect(sock, reinterpret_cast<sockaddr*>(&serv), sizeof(serv)) ==
+          0) {
+        connected = true;
+        std::cout << "[Rank " << rank << "] Connected successfully on attempt "
+                  << attempt + 1 << std::endl;
+        break;
+      }
+      if (errno != ECONNREFUSED) {
+        break;
+      }
+      std::this_thread::sleep_for(std::chrono::milliseconds(500));
+    }
+
+    if (!connected) {
+      std::ostringstream msg;
+      msg << "[Rank " << rank << "] connect() failed after " << attempt
+          << " retries: " << strerror(errno);
+      close(sock);
+      throw std::runtime_error(msg.str());
+    }
+    recvAll(sock, &id, sizeof(id));
+    close(sock);
+  }
+}
+
+template <typename T>
+struct type_identity {
+  using type = T;
+};
+
+template <typename F>
+void dispatch_dtype(const array& arr, F&& f) {
+  switch (arr.dtype()) {
+    case bool_:
+      throw std::invalid_argument("[nccl] Boolean arrays not supported");
+    case int8:
+      f(type_identity<int8_t>{}, ncclChar);
+      break;
+    case uint8:
+      f(type_identity<uint8_t>{}, ncclUint8);
+      break;
+    case int32:
+      f(type_identity<int32_t>{}, ncclInt);
+      break;
+    case uint32:
+      f(type_identity<uint32_t>{}, ncclUint32);
+      break;
+    case int64:
+      f(type_identity<int64_t>{}, ncclInt64);
+      break;
+    case uint64:
+      f(type_identity<uint64_t>{}, ncclUint64);
+      break;
+    case float16:
+      f(type_identity<float16_t>{}, ncclHalf);
+      break;
+    case bfloat16:
+      f(type_identity<bfloat16_t>{}, ncclBfloat16);
+      break;
+    case float32:
+      f(type_identity<float>{}, ncclFloat);
+      break;
+    case float64:
+      f(type_identity<double>{}, ncclDouble);
+      break;
+    default:
+      throw std::invalid_argument("[nccl] Unknown or unsupported dtype");
+  }
+}
+
+} // namespace detail
+
+using GroupImpl = mlx::core::distributed::detail::GroupImpl;
+class NCCLGroup : public GroupImpl {
+ public:
+  NCCLGroup(int worldRank, int worldSize, const std::string initMethod)
+      : rank_(worldRank),
+        size_(worldSize),
+        comm_(nullptr),
+        initMethod_(initMethod) {
+    if (initialized_)
+      return;
+    int ndev;
+    CHECK_CUDA(cudaGetDeviceCount(&ndev));
+    CHECK_CUDA(cudaSetDevice(rank_ % ndev));
+    detail::bootstrap_unique_id(uniqueId_, rank_, size_, initMethod_);
+    CHECK_NCCL(ncclCommInitRank(&comm_, size_, uniqueId_, rank_));
+    initialized_ = true;
+  }
+
+  ~NCCLGroup() {
+    ncclCommDestroy(comm_);
+    ncclGroupEnd();
+    initialized_ = false;
+  }
+
+  int rank() override {
+    return rank_;
+  }
+
+  int size() override {
+    return size_;
+  }
+
+  void all_sum(const array& input, array& output, Stream stream) override {
+    detail::dispatch_dtype(input, [&](auto type_tag, ncclDataType_t dt) {
+      using T = typename decltype(type_tag)::type;
+      all_reduce_impl<T>(input, output, stream, dt, ncclSum);
+    });
+  }
+
+  virtual std::shared_ptr<GroupImpl> split(int color, int key = -1) override {
+    throw std::runtime_error("[nccl] Group split not supported.");
+  }
+
+  void all_gather(const array& input, array& output, Stream stream) override {
+    throw std::runtime_error(
+        "[nccl] All gather not supported in NCCL backend.");
+  }
+
+  void send(const array& input, int dst, Stream stream) override {
+    throw std::runtime_error("[nccl] Send not supported in NCCL backend.");
+  }
+
+  void recv(array& output, int src, Stream stream) override {
+    throw std::runtime_error("[nccl] Recv not supported in NCCL backend.");
+  }
+
+  void all_max(const array& input, array& output, Stream stream) override {
+    throw std::runtime_error("[nccl] All max not supported in NCCL backend.");
+  }
+
+  void all_min(const array& input, array& output, Stream stream) override {
+    throw std::runtime_error("[nccl] All min not supported in NCCL backend.");
+  }
+
+  template <typename T>
+  void all_reduce_impl(
+      const array& input,
+      array& output,
+      Stream stream,
+      ncclDataType_t dt,
+      ncclRedOp_t op) {
+    auto& encoder = cu::get_command_encoder(stream);
+
+    CHECK_NCCL(ncclAllReduce(
+        input.data<T>(),
+        output.data<T>(),
+        input.size(),
+        dt,
+        op,
+        comm_,
+        encoder.stream()));
+  }
+
+  int rank_, size_;
+  std::string initMethod_;
+  ncclUniqueId uniqueId_;
+  ncclComm_t comm_;
+  bool initialized_ = false;
+};
+
+bool is_available() {
+  return true;
+}
+
+namespace detail {
+static std::string get_env_var_or_throw(const char* env_var_name) {
+  const char* value = std::getenv(env_var_name);
+  if (value == nullptr) {
+    std::ostringstream msg;
+    msg << "[nccl] Required environment variable '" << env_var_name
+        << "' is not set. "
+        << "Please set it before initializing the distributed backend.";
+    throw std::runtime_error(msg.str());
+  }
+  return std::string(value);
+}
+} // namespace detail
+
+std::shared_ptr<GroupImpl> init(bool strict /* = false */) {
+  std::string host = detail::get_env_var_or_throw("NCCL_HOST_IP");
+  std::string port = detail::get_env_var_or_throw("NCCL_PORT");
+  std::string rank_str = detail::get_env_var_or_throw("MLX_RANK");
+  std::string n_nodes_str = detail::get_env_var_or_throw("MLX_WORLD_SIZE");
+
+  int rank = std::stoi(rank_str);
+  int n_nodes = std::stoi(n_nodes_str);
+  std::string init_method = "tcp://" + host + ":" + port;
+
+  return std::make_shared<NCCLGroup>(rank, n_nodes, init_method);
+}
+} // namespace mlx::core::distributed::nccl

mlx/distributed/nccl/nccl.h

@@ -0,0 +1,12 @@
+// Copyright © 2024 Apple Inc.
+
+#include "mlx/distributed/distributed.h"
+
+namespace mlx::core::distributed::nccl {
+
+using GroupImpl = mlx::core::distributed::detail::GroupImpl;
+
+bool is_available();
+std::shared_ptr<GroupImpl> init(bool strict = false);
+
+} // namespace mlx::core::distributed::nccl

mlx/distributed/nccl/no_nccl.cpp

@@ -0,0 +1,20 @@
+// Copyright © 2024 Apple Inc.
+
+#include "mlx/distributed/nccl/nccl.h"
+
+namespace mlx::core::distributed::nccl {
+
+using GroupImpl = mlx::core::distributed::detail::GroupImpl;
+
+bool is_available() {
+  return false;
+}
+
+std::shared_ptr<GroupImpl> init(bool strict /* = false */) {
+  if (strict) {
+    throw std::runtime_error("Cannot initialize nccl distributed backend.");
+  }
+  return nullptr;
+}
+
+} // namespace mlx::core::distributed::nccl

mlx/distributed/ops.cpp

@@ -31,8 +31,7 @@ array all_sum(
   return array(
       x.shape(),
       x.dtype(),
-      std::make_shared<AllReduce>(
+      std::make_shared<AllReduce>(to_stream(s), group, AllReduce::Sum),
-          to_stream(s, Device::cpu), group, AllReduce::Sum),
       {x});
 }
 

mlx/distributed/ring/ring.cpp

@@ -975,7 +975,6 @@ class RingGroup : public GroupImpl {
 
   int rank_;
   int size_;
-
   bool verbose_;
 
   ThreadPool pool_;

mlx/version.h

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

python/mlx/distributed_run.py

@@ -415,6 +415,45 @@ def launch_mpi(parser, hosts, args, command):
             pass
 
 
+def launch_nccl(parser, hosts, args, command):
+    master_host = hosts[0].ips[0]
+    master_port = args.nccl_port
+    world_size = args.nproc_per_node * len(hosts)
+
+    base_env = os.environ.copy()
+    base_env.update(
+        {
+            "NCCL_DEBUG": "INFO",
+            "NCCL_SOCKET_IFNAME": "lo",  # Use loopback for local communication
+            "NCCL_HOST_IP": master_host,
+            "NCCL_PORT": str(master_port),
+            "MLX_WORLD_SIZE": str(world_size),
+        }
+    )
+    procs = []
+    try:
+        for rank in range(world_size):
+            env = base_env.copy()
+            env["MLX_RANK"] = str(rank)
+            env["CUDA_VISIBLE_DEVICES"] = str(rank % args.nproc_per_node)
+            p = Popen(command, env=env)
+            procs.append(p)
+
+        for p in procs:
+            ret = p.wait()
+            if ret != 0:
+                raise RuntimeError(f"Rank process exited with {ret}")
+
+    except (RuntimeError, KeyboardInterrupt) as err:
+        for p in procs:
+            if p.poll() is None:
+                try:
+                    p.kill()
+                except Exception:
+                    pass
+        raise
+
+
 def check_ssh_connections(hosts):
     results = [False] * len(hosts)
 
@@ -665,7 +704,7 @@ def distributed_config():
     )
     parser.add_argument(
         "--backend",
-        choices=["ring", "mpi"],
+        choices=["ring", "mpi", "nccl"],
         default="ring",
         help="Which distributed backend to configure",
     )
@@ -737,7 +776,7 @@ def main():
     parser.add_argument("--hostfile", help="The file containing the hosts")
     parser.add_argument(
         "--backend",
-        choices=["ring", "mpi"],
+        choices=["ring", "mpi", "nccl"],
         default="ring",
         help="Which distributed backend to launch",
     )
@@ -769,6 +808,19 @@ def main():
     parser.add_argument(
         "--cwd", help="Set the working directory on each node to the provided one"
     )
+    parser.add_argument(
+        "--nccl-port",
+        type=int,
+        default=12345,
+        help="The port to use for the NCCL communication (only for nccl backend)",
+    )
+    parser.add_argument(
+        "--nproc-per-node",
+        type=positive_number,
+        default=1,
+        help="How many processes to run per node (only for nccl backend)",
+    )
+
     args, rest = parser.parse_known_args()
     if rest[0] == "--":
         rest.pop(0)
@@ -799,8 +851,10 @@ def main():
     # Launch
     if args.backend == "ring":
         launch_ring(parser, hosts, args, rest)
-    elif args.backend == "mpi":
+    if args.backend == "mpi":
         launch_mpi(parser, hosts, args, rest)
+    if args.backend == "nccl":
+        launch_nccl(parser, hosts, args, rest)
 
 
 if __name__ == "__main__":

python/mlx/nn/layers/base.py

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

python/mlx/nn/utils.py

@@ -76,6 +76,7 @@ def average_gradients(
     group: Optional[mx.distributed.Group] = None,
     all_reduce_size: int = 32 * 1024**2,
     communication_type: Optional[mx.Dtype] = None,
+    stream: mx.Stream = mx.cpu,
 ):
     """Average the gradients across the distributed processes in the passed group.
 
@@ -94,6 +95,7 @@ def average_gradients(
         communication_type (Optional[mlx.core.Dtype]): If provided cast to this
             type before performing the communication. Typically cast to a
             smaller float to reduce the communication size. Default: ``None``.
+        stream (mlx.core.Stream): The stream to use for the reduction. Default: ``mlx.cpu``.
     """
     group = group or mx.distributed.init()
     N = group.size()
@@ -104,7 +106,7 @@ def average_gradients(
     def _average(x):
         dt = x.dtype
         x = x.astype(communication_type) if communication_type is not None else x
-        return mx.distributed.all_sum(x, stream=mx.cpu).astype(dt) / N
+        return mx.distributed.all_sum(x, stream=stream).astype(dt) / N
 
     if all_reduce_size <= 0:
         return tree_map(_average, gradients)

python/mlx/utils.py

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

python/src/distributed.cpp

@@ -79,7 +79,7 @@ void init_distributed(nb::module_& parent_module) {
             in case ``mx.distributed.is_available()`` returns False otherwise
             it throws a runtime error. Default: ``False``
           backend (str, optional): Which distributed backend to initialize.
-            Possible values ``mpi``, ``ring``, ``any``. If set to ``any`` all
+            Possible values ``mpi``, ``ring``, ``nccl``, ``any``. If set to ``any`` all
             available backends are tried and the first one that succeeds
             becomes the global group which will be returned in subsequent
             calls. Default: ``any``

python/tests/nccl_test_distributed.py

@@ -0,0 +1,284 @@
+# Copyright © 2024 Apple Inc.
+import mlx.core as mx
+import mlx.nn as nn
+import mlx_tests
+from mlx.nn.layers.distributed import shard_inplace, shard_linear
+from mlx.nn.utils import average_gradients
+
+
+class TestNCCLDistributed(mlx_tests.MLXTestCase):
+    @classmethod
+    def setUpClass(cls):
+        world = mx.distributed.init(strict=True, backend="nccl")
+        rank = world.rank()
+        mx.set_default_device(mx.Device(mx.gpu, rank % 8))
+
+    def test_all_reduce(self):
+        world = mx.distributed.init()
+        dtypes = [
+            (mx.int8, 0),
+            (mx.uint8, 0),
+            (mx.int32, 0),
+            (mx.uint32, 0),
+            (mx.float32, 1e-6),
+            (mx.float16, 5e-3),
+            (mx.bfloat16, 1e-1),
+        ]
+        sizes = [
+            (7,),
+            (10,),
+            (1024,),
+            (1024, 1024),
+        ]
+        key = mx.random.key(0)
+
+        for dt, rtol in dtypes:
+            for sh in sizes:
+                x = (
+                    mx.random.uniform(shape=(world.size(),) + sh, key=key) * 10
+                ).astype(dt)
+
+                # All sum
+                y = mx.distributed.all_sum(x[world.rank()])
+                z = x.sum(0)
+                maxrelerror = (y - z).abs()
+                if rtol > 0:
+                    maxrelerror /= z.abs()
+                maxrelerror = maxrelerror.max()
+                self.assertLessEqual(maxrelerror, rtol)
+
+    def test_average_gradients(self):
+        original_all_sum = mx.distributed.all_sum
+        n_calls = 0
+        xtype = None
+
+        def new_all_sum(x, **kwargs):
+            nonlocal n_calls
+            nonlocal xtype
+
+            n_calls += 1
+            if xtype is not None:
+                self.assertEqual(xtype, x.dtype)
+
+            return original_all_sum(x, **kwargs)
+
+        mx.distributed.all_sum = new_all_sum
+        try:
+            grads = [mx.ones(10) for i in range(10)]
+            new_grads = average_gradients(grads, stream=mx.gpu)
+            mx.eval(new_grads)
+            self.assertEqual(len(new_grads), 10)
+            self.assertTrue(all(mx.all(g == 1) for g in new_grads))
+            self.assertEqual(n_calls, 1)
+
+            n_calls = 0
+            new_grads = average_gradients(grads, all_reduce_size=4 * 50, stream=mx.gpu)
+            mx.eval(new_grads)
+            self.assertEqual(len(new_grads), 10)
+            self.assertTrue(all(mx.all(g == 1) for g in new_grads))
+            self.assertEqual(n_calls, 2)
+
+            n_calls = 0
+            new_grads = average_gradients(grads, all_reduce_size=0, stream=mx.gpu)
+            mx.eval(new_grads)
+            self.assertEqual(len(new_grads), 10)
+            self.assertTrue(all(mx.all(g == 1) for g in new_grads))
+            self.assertEqual(n_calls, 10)
+
+            n_calls = 0
+            xtype = mx.float16
+            new_grads = average_gradients(
+                grads,
+                all_reduce_size=2 * 50,
+                communication_type=mx.float16,
+                stream=mx.gpu,
+            )
+            mx.eval(new_grads)
+            self.assertEqual(len(new_grads), 10)
+            self.assertTrue(all(g.dtype == mx.float32 for g in new_grads))
+            self.assertTrue(all(mx.all(g == 1) for g in new_grads))
+            self.assertEqual(n_calls, 2)
+
+        finally:
+            mx.distributed.all_sum = original_all_sum
+
+    def test_donation(self):
+        x = mx.random.normal((1024,))
+        mx.eval(x)
+        mx.synchronize()
+
+        mx.reset_peak_memory()
+        scale = mx.array(2.0)
+        y = mx.distributed.all_sum(x)
+        mx.eval(y)
+        mx.synchronize()
+        all_sum_only = mx.get_peak_memory()
+        y = mx.distributed.all_sum(x) * scale
+        mx.eval(y)
+        mx.synchronize()
+        all_sum_with_binary = mx.get_peak_memory()
+
+        self.assertEqual(all_sum_only, all_sum_with_binary)
+
+    def test_shard_linear(self):
+        # Seed the prng to have the same inputs and weights generated everywhere
+        mx.random.seed(0xF0F0F0F0)
+
+        # Prepare inputs
+        world = mx.distributed.init()
+        part = (
+            slice(None),
+            slice(
+                world.rank() * 1024 // world.size(),
+                (world.rank() + 1) * 1024 // world.size(),
+            ),
+        )
+        x = mx.random.normal((4, 1024))
+
+        # Create and shard some linear layers
+        lin = nn.Linear(1024, 1024, bias=True)
+        slin1 = shard_linear(lin, "all-to-sharded")
+        slin2 = shard_linear(lin, "sharded-to-all")
+        y = lin(x)
+        y1 = slin1(x)
+        y2 = slin2(x[part])
+        self.assertTrue(mx.allclose(y, y2, atol=1e-4, rtol=1e-4))
+        self.assertTrue(mx.allclose(y[part], y1, atol=1e-4, rtol=1e-4))
+
+        # Check the backward works as expected
+        def dummy_loss(model, x, y):
+            return (model(x) * y).sum()
+
+        mod = nn.Sequential(
+            nn.Linear(128, 128),
+            nn.Linear(128, 128),
+            nn.Linear(128, 128),
+            nn.Linear(128, 128),
+        )
+        smod = nn.Sequential(
+            shard_linear(mod.layers[0], "all-to-sharded"),
+            shard_linear(mod.layers[1], "sharded-to-all"),
+            shard_linear(mod.layers[2], "all-to-sharded"),
+            shard_linear(mod.layers[3], "sharded-to-all"),
+        )
+
+        grad1 = nn.value_and_grad(mod, dummy_loss)
+        grad2 = nn.value_and_grad(smod, dummy_loss)
+
+        x = mx.random.normal((4, 128))
+        y = mx.random.normal((4, 128))
+
+        l1, g1 = grad1(mod, x, y)
+        l2, g2 = grad2(smod, x, y)
+        mx.eval(l1, g1, l2, g2)
+
+        part = slice(
+            world.rank() * 128 // world.size(), (world.rank() + 1) * 128 // world.size()
+        )
+
+        self.assertTrue(mx.allclose(l1, l2))
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][0]["weight"][part],
+                g2["layers"][0]["weight"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][2]["weight"][part],
+                g2["layers"][2]["weight"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][1]["weight"][:, part],
+                g2["layers"][1]["weight"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][3]["weight"][:, part],
+                g2["layers"][3]["weight"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][0]["bias"][part],
+                g2["layers"][0]["bias"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][2]["bias"][part],
+                g2["layers"][2]["bias"],
+                atol=1e-6,
+                rtol=1e-4,
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][1]["bias"], g2["layers"][1]["bias"], atol=1e-6, rtol=1e-4
+            )
+        )
+        self.assertTrue(
+            mx.allclose(
+                g1["layers"][3]["bias"], g2["layers"][3]["bias"], atol=1e-6, rtol=1e-4
+            )
+        )
+
+    def test_shard_predicate(self):
+        mx.random.seed(0xF0F0F0F0)
+
+        class MyConv(nn.Module):
+            def __init__(self, *args, **kwargs):
+                super().__init__()
+                self.aggregate = kwargs.pop("aggregate", False)
+                self.conv = nn.Conv2d(*args, **kwargs)
+
+            def __call__(self, x):
+                x = self.conv(x)
+                if self.aggregate:
+                    x = mx.distributed.all_sum(x)
+                return x
+
+        def sharding(path, weight):
+            parts = path.split(".")
+            even = int(parts[1]) % 2 == 0
+            if even:
+                return 0
+            else:
+                return -1 if parts[-1] != "bias" else None
+
+        mod = nn.Sequential(
+            MyConv(3, 128, kernel_size=3),
+            MyConv(128, 128, kernel_size=3),
+            MyConv(128, 128, kernel_size=3),
+            MyConv(128, 3, kernel_size=3),
+        )
+        smod = nn.Sequential(
+            MyConv(3, 128, kernel_size=3),
+            MyConv(128, 128, kernel_size=3, aggregate=True),
+            MyConv(128, 128, kernel_size=3),
+            MyConv(128, 3, kernel_size=3, aggregate=True),
+        )
+        smod.update(mod.parameters())
+        shard_inplace(smod, sharding)
+
+        x = mx.random.normal((4, 16, 16, 3))
+        y1 = mod(x)
+        y2 = smod(x)
+        self.assertTrue(mx.allclose(y1, y2, atol=1e-6, rtol=1e-4))
+
+
+if __name__ == "__main__":
+    mlx_tests.MLXTestRunner()

python/tests/test_nn.py

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