Fix reduce sum/prod overflow (#2477)

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)
-   mx.save_safetensors("optimizer.safetensors", dict(state))
+   state = tree_flatten(optimizer.state, destination={})
+   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

@@ -151,7 +151,7 @@ parameters, pass them as inputs to the ``call`` wrapper:
      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)
 
 

mlx/backend/cpu/reduce.cpp

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

mlx/backend/cuda/gemms/cublas_batched_gemm_12_9.cu

@@ -10,7 +10,34 @@ namespace mlx::core::cu {
 
 namespace cg = cooperative_groups;
 
-__global__ void set_mm_device_pointers(
+template <int NDIM>
+__global__ void set_mm_device_pointers_nd(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
+    int64_t batch_stride,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset] = elem_to_loc_nd<NDIM>(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data());
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+__global__ void set_mm_device_pointers_g(
     int8_t** pointers,
     int8_t* a_start,
     int8_t* b_start,
@@ -38,7 +65,38 @@ __global__ void set_mm_device_pointers(
       out_start + item_size * index * batch_stride;
 }
 
-__global__ void set_addmm_device_pointers(
+template <int NDIM>
+__global__ void set_addmm_device_pointers_nd(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* c_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> c_batch_strides,
+    int64_t batch_stride,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset, c_offset] = elem_to_loc_nd<NDIM>(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      c_batch_strides.data());
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
+  pointers[index + 3 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+__global__ void set_addmm_device_pointers_g(
     int8_t** pointers,
     int8_t* a_start,
     int8_t* b_start,
@@ -89,37 +147,62 @@ void Matmul::run_batched(
     const mlx::core::Shape& batch_shape,
     const mlx::core::Strides& a_batch_strides,
     const mlx::core::Strides& b_batch_strides) {
-  auto batch_count = out.size() / (M_ * N_);
+  int batch_count = out.size() / (M_ * N_);
   set_pointer_mode(a_desc_, batch_count);
   set_pointer_mode(b_desc_, batch_count);
   set_pointer_mode(out_desc_, batch_count);
 
   // Launch kernel to set device offsets
   auto pointers = array(
-      allocator::malloc(batch_count * sizeof(uint64_t) * 3),
-      {static_cast<int>(batch_count * 3)},
+      allocator::malloc(batch_count * sizeof(void*) * 3),
+      {batch_count * 3},
       uint64);
 
   encoder.add_temporary(pointers);
-  int block_size = 512;
   encoder.set_output_array(pointers);
 
-  encoder.add_kernel_node(
-      cu::set_mm_device_pointers,
-      cuda::ceil_div(pointers.size(), block_size),
-      block_size,
-      0,
-      pointers.data<int8_t*>(),
-      a.data<int8_t>(),
-      b.data<int8_t>(),
-      out.data<int8_t>(),
-      static_cast<int>(out.dtype().size()),
-      const_param(batch_shape),
-      const_param(a_batch_strides),
-      const_param(b_batch_strides),
-      static_cast<int64_t>(M_) * N_,
-      static_cast<int>(batch_shape.size()),
-      batch_count);
+  int block_dims = std::min(batch_count, 256);
+  int num_blocks = cuda::ceil_div(batch_count, block_dims);
+  int64_t batch_stride = M_ * N_;
+  int item_size = out.itemsize();
+
+  int ndim = batch_shape.size();
+  if (ndim <= 3) {
+    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
+      encoder.add_kernel_node(
+          cu::set_mm_device_pointers_nd<ndim_constant()>,
+          num_blocks,
+          block_dims,
+          0,
+          pointers.data<int8_t*>(),
+          a.data<int8_t>(),
+          b.data<int8_t>(),
+          out.data<int8_t>(),
+          item_size,
+          const_param<ndim_constant()>(batch_shape),
+          const_param<ndim_constant()>(a_batch_strides),
+          const_param<ndim_constant()>(b_batch_strides),
+          batch_stride,
+          batch_count);
+    });
+  } else {
+    encoder.add_kernel_node(
+        cu::set_mm_device_pointers_g,
+        num_blocks,
+        block_dims,
+        0,
+        pointers.data<int8_t*>(),
+        a.data<int8_t>(),
+        b.data<int8_t>(),
+        out.data<int8_t>(),
+        item_size,
+        const_param(batch_shape),
+        const_param(a_batch_strides),
+        const_param(b_batch_strides),
+        batch_stride,
+        ndim,
+        batch_count);
+  }
 
   // Run matmul
   encoder.set_input_array(pointers);
@@ -150,7 +233,7 @@ void Matmul::run_batched(
     const mlx::core::Strides& c_batch_strides,
     float alpha,
     float beta) {
-  auto batch_count = out.size() / (M_ * N_);
+  int batch_count = out.size() / (M_ * N_);
   set_pointer_mode(a_desc_, batch_count);
   set_pointer_mode(b_desc_, batch_count);
   set_pointer_mode(c_desc_, batch_count);
@@ -159,30 +242,58 @@ void Matmul::run_batched(
   // Launch kernel to set device offsets
   auto pointers = array(
       allocator::malloc(batch_count * sizeof(uint64_t) * 4),
-      {static_cast<int>(batch_count * 4)},
+      {batch_count * 4},
       uint64);
 
   encoder.add_temporary(pointers);
-  int block_size = 512;
   encoder.set_output_array(pointers);
-  encoder.add_kernel_node(
-      cu::set_addmm_device_pointers,
-      cuda::ceil_div(pointers.size(), block_size),
-      block_size,
-      0,
-      pointers.data<int8_t*>(),
-      a.data<int8_t>(),
-      b.data<int8_t>(),
-      c.data<int8_t>(),
-      out.data<int8_t>(),
-      static_cast<int>(out.dtype().size()),
-      const_param(batch_shape),
-      const_param(a_batch_strides),
-      const_param(b_batch_strides),
-      const_param(c_batch_strides),
-      static_cast<int64_t>(M_) * N_,
-      static_cast<int>(batch_shape.size()),
-      batch_count);
+
+  int block_dims = std::min(batch_count, 256);
+  int num_blocks = cuda::ceil_div(batch_count, block_dims);
+  int64_t batch_stride = M_ * N_;
+  int item_size = out.itemsize();
+
+  int ndim = batch_shape.size();
+  if (ndim <= 3) {
+    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
+      encoder.add_kernel_node(
+          cu::set_addmm_device_pointers_nd<ndim_constant()>,
+          num_blocks,
+          block_dims,
+          0,
+          pointers.data<int8_t*>(),
+          a.data<int8_t>(),
+          b.data<int8_t>(),
+          c.data<int8_t>(),
+          out.data<int8_t>(),
+          item_size,
+          const_param<ndim_constant()>(batch_shape),
+          const_param<ndim_constant()>(a_batch_strides),
+          const_param<ndim_constant()>(b_batch_strides),
+          const_param<ndim_constant()>(c_batch_strides),
+          batch_stride,
+          batch_count);
+    });
+  } else {
+    encoder.add_kernel_node(
+        cu::set_addmm_device_pointers_g,
+        num_blocks,
+        block_dims,
+        0,
+        pointers.data<int8_t*>(),
+        a.data<int8_t>(),
+        b.data<int8_t>(),
+        c.data<int8_t>(),
+        out.data<int8_t>(),
+        item_size,
+        const_param(batch_shape),
+        const_param(a_batch_strides),
+        const_param(b_batch_strides),
+        const_param(c_batch_strides),
+        batch_stride,
+        ndim,
+        batch_count);
+  }
 
   // Run matmul
   encoder.set_input_array(pointers);

mlx/backend/cuda/scaled_dot_product_attention.cu

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

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/backend/metal/kernels/reduce.metal

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

mlx/backend/metal/reduce.cpp

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

mlx/ops.cpp

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

mlx/version.h

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

pyproject.toml

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

python/mlx/nn/layers/base.py

@@ -178,7 +178,7 @@ class Module(dict):
 
         if strict:
             new_weights = dict(weights)
-            curr_weights = 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/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
-) -> Any:
+    tree: Any,
+    prefix: str = "",
+    is_leaf: Optional[Callable] = None,
+    destination: Optional[Union[List[Tuple[str, Any]], Dict[str, Any]]] = None,
+) -> Union[List[Tuple[str, Any]], Dict[str, Any]]:
     """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):
-        if isinstance(tree, (list, tuple)):
-            for i, t in enumerate(tree):
-                flat_tree.extend(tree_flatten(t, f"{prefix}.{i}", is_leaf))
-            return flat_tree
-        if isinstance(tree, dict):
-            for k, t in tree.items():
-                flat_tree.extend(tree_flatten(t, f"{prefix}.{k}", is_leaf))
-            return flat_tree
+    # Create the function to update the destination. We are taking advantage of
+    # the fact that list.extend and dict.update have the same API to simplify
+    # the code a bit.
+    if isinstance(destination, list):
+        _add_to_destination = destination.extend
+    elif isinstance(destination, dict):
+        _add_to_destination = destination.update
+    else:
+        raise ValueError("Destination should be either a list or a dictionary or None")
 
-    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] == "":
-        return tree[0][1]
+    items = tree.items() if isinstance(tree, dict) else tree
 
-    try:
-        int(tree[0][0].split(".", maxsplit=1)[0])
-        is_list = True
-    except ValueError:
-        is_list = False
+    # Special case when we have just one element in the tree ie not a tree
+    if len(items) == 1:
+        key, value = next(iter(items))
+        if key == "":
+            return value
 
     # 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
-    if is_list:
+    # Assume they are a list and fail to dict if the keys are not all integers
+    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/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))))

tests/gpu_tests.cpp

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

tests/ops_tests.cpp

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