suppress gcc 10.1 warnings (#2679)

* suppress gcc 10.1 warnings

* suppress gcc 10.1 warnings

README.md

@@ -2,7 +2,7 @@
 
 [**Quickstart**](#quickstart) | [**Installation**](#installation) |
 [**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
-[**Examples**](#examples) 
+[**Examples**](#examples)
 
 [![CircleCI](https://circleci.com/gh/ml-explore/mlx.svg?style=svg)](https://circleci.com/gh/ml-explore/mlx)
 
@@ -11,37 +11,37 @@ brought to you by Apple machine learning research.
 
 Some key features of MLX include:
 
- - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
+- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
    also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
    [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
    the Python API. MLX has higher-level packages like `mlx.nn` and
    `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
    more complex models.
 
- - **Composable function transformations**: MLX supports composable function
-   transformations for automatic differentiation, automatic vectorization,
-   and computation graph optimization.
+- **Composable function transformations**: MLX supports composable function
+  transformations for automatic differentiation, automatic vectorization,
+  and computation graph optimization.
 
- - **Lazy computation**: Computations in MLX are lazy. Arrays are only
-   materialized when needed.
+- **Lazy computation**: Computations in MLX are lazy. Arrays are only
+  materialized when needed.
 
- - **Dynamic graph construction**: Computation graphs in MLX are constructed
-   dynamically. Changing the shapes of function arguments does not trigger
-   slow compilations, and debugging is simple and intuitive.
+- **Dynamic graph construction**: Computation graphs in MLX are constructed
+  dynamically. Changing the shapes of function arguments does not trigger
+  slow compilations, and debugging is simple and intuitive.
 
- - **Multi-device**: Operations can run on any of the supported devices
-   (currently the CPU and the GPU).
+- **Multi-device**: Operations can run on any of the supported devices
+  (currently the CPU and the GPU).
 
- - **Unified memory**: A notable difference from MLX and other frameworks
-   is the *unified memory model*. Arrays in MLX live in shared memory.
-   Operations on MLX arrays can be performed on any of the supported
-   device types without transferring data.
+- **Unified memory**: A notable difference from MLX and other frameworks
+  is the *unified memory model*. Arrays in MLX live in shared memory.
+  Operations on MLX arrays can be performed on any of the supported
+  device types without transferring data.
 
 MLX is designed by machine learning researchers for machine learning
 researchers. The framework is intended to be user-friendly, but still efficient
 to train and deploy models. The design of the framework itself is also
 conceptually simple. We intend to make it easy for researchers to extend and
-improve MLX with the goal of quickly exploring new ideas. 
+improve MLX with the goal of quickly exploring new ideas.
 
 The design of MLX is inspired by frameworks like
 [NumPy](https://numpy.org/doc/stable/index.html),
@@ -91,7 +91,7 @@ Checkout the
 [documentation](https://ml-explore.github.io/mlx/build/html/install.html#)
 for more information on building the C++ and Python APIs from source.
 
-## Contributing 
+## Contributing
 
 Check out the [contribution guidelines](https://github.com/ml-explore/mlx/tree/main/CONTRIBUTING.md) for more information
 on contributing to MLX. See the
@@ -110,7 +110,7 @@ Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
 MLX useful in your research and wish to cite it, please use the following
 BibTex entry:
 
-```
+```text
 @software{mlx2023,
   author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
   title = {{MLX}: Efficient and flexible machine learning on Apple silicon},

mlx/backend/common/matmul.h

@@ -13,7 +13,7 @@ inline std::tuple<Shape, Strides, Strides> collapse_batches(
     const array& a,
     const array& b) {
   if (a.ndim() == 2) {
-    return {{1}, {0}, {0}};
+    return {Shape{1}, Strides{0}, Strides{0}};
   }
 
   Shape A_bshape{a.shape().begin(), a.shape().end() - 2};
@@ -38,7 +38,7 @@ inline std::tuple<Shape, Strides, Strides> collapse_batches(
 inline std::tuple<Shape, Strides, Strides, Strides>
 collapse_batches(const array& a, const array& b, const array& c) {
   if (a.ndim() == 2) {
-    return {{1}, {0}, {0}, {0}};
+    return {Shape{1}, Strides{0}, Strides{0}, Strides{0}};
   }
 
   Shape A_bshape{a.shape().begin(), a.shape().end() - 2};

mlx/backend/cpu/matmul.cpp

@@ -131,10 +131,6 @@ void Matmul::eval_cpu(const std::vector<array>& inputs, array& out) {
 }
 
 void AddMM::eval_cpu(const std::vector<array>& inputs, array& out) {
-  if (out.dtype() != float32) {
-    throw std::runtime_error(
-        "[AddMM::eval_cpu] Currently only supports float32.");
-  }
   if (out.size() == 0) {
     out.set_data(allocator::malloc(out.nbytes()));
     return;

mlx/backend/cpu/sort.cpp

@@ -15,6 +15,18 @@ namespace mlx::core {
 
 namespace {
 
+// NaN-aware comparator that places NaNs at the end
+template <typename T>
+bool nan_aware_less(T a, T b) {
+  if constexpr (std::is_floating_point_v<T> || std::is_same_v<T, complex64_t>) {
+    if (std::isnan(a))
+      return false;
+    if (std::isnan(b))
+      return true;
+  }
+  return a < b;
+}
+
 template <typename T>
 struct StridedIterator {
   using iterator_category = std::random_access_iterator_tag;
@@ -130,7 +142,7 @@ void sort(array& out, int axis) {
     StridedIterator st(data_ptr, axis_stride, 0);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::stable_sort(st, ed);
+    std::stable_sort(st, ed, nan_aware_less<T>);
     src_it.step();
   }
 }
@@ -184,6 +196,15 @@ void argsort(const array& in, array& out, int axis) {
     std::stable_sort(st, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
+
+      // Handle NaNs (place them at the end)
+      if (std::is_floating_point<T>::value) {
+        if (std::isnan(v1))
+          return false;
+        if (std::isnan(v2))
+          return true;
+      }
+
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }
@@ -219,7 +240,7 @@ void partition(array& out, int axis, int kth) {
     StridedIterator md(data_ptr, axis_stride, kth);
     StridedIterator ed(data_ptr, axis_stride, axis_size);
 
-    std::nth_element(st, md, ed);
+    std::nth_element(st, md, ed, nan_aware_less<T>);
   }
 }
 
@@ -276,6 +297,15 @@ void argpartition(const array& in, array& out, int axis, int kth) {
     std::nth_element(st, md, ed, [data_ptr, in_stride](IdxT a, IdxT b) {
       auto v1 = data_ptr[a * in_stride];
       auto v2 = data_ptr[b * in_stride];
+
+      // Handle NaNs (place them at the end)
+      if (std::is_floating_point<T>::value) {
+        if (std::isnan(v1))
+          return false;
+        if (std::isnan(v2))
+          return true;
+      }
+
       return v1 < v2 || (v1 == v2 && a < b);
     });
   }

mlx/backend/cpu/unary_ops.h

@@ -77,7 +77,8 @@ struct Real {
 struct Sigmoid {
   template <int N, typename T>
   Simd<T, N> operator()(Simd<T, N> x) {
-    return 1.0f / (1.0f + simd::exp(-x));
+    auto y = 1.0f / (1.0f + simd::exp(simd::abs(x)));
+    return simd::select(x < Simd<T, N>{0}, y, Simd<T, N>{1} - y);
   }
   SINGLE()
 };

mlx/backend/cuda/CMakeLists.txt

@@ -170,6 +170,10 @@ target_link_libraries(mlx PRIVATE CUDNN::cudnn_all)
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)
+# Supress warnings: note: parameter passing for argument of type
+# ‘std::pair<float, float>’ when C++17 is enabled changed to match C++14 in GCC
+# 10.1
+target_compile_options(mlx PRIVATE -Wno-psabi)
 
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda

mlx/backend/cuda/allocator.cpp

@@ -30,15 +30,20 @@ SmallSizePool::SmallSizePool() {
   next_free_ = buffer_;
 
   CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
+
+  int device_count = 0;
+  CHECK_CUDA_ERROR(cudaGetDeviceCount(&device_count));
+  for (int i = 0; i < device_count; ++i) {
 #if CUDART_VERSION >= 13000
-  cudaMemLocation loc;
-  loc.type = cudaMemLocationTypeDevice;
-  loc.id = 0;
+    cudaMemLocation loc;
+    loc.type = cudaMemLocationTypeDevice;
+    loc.id = i;
 #else
-  int loc = 0;
+    int loc = i;
 #endif // CUDART_VERSION >= 13000
-  CHECK_CUDA_ERROR(
-      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, loc));
+    CHECK_CUDA_ERROR(
+        cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetAccessedBy, loc));
+  }
 
   auto curr = next_free_;
   for (size_t i = 1; i < num_blocks; ++i) {

mlx/backend/cuda/conv.cpp

@@ -382,20 +382,19 @@ void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
   }
 
   if (op_graph) {
-    // Setup inputs and outputs.
-    register_args(encoder, backend_type, in, wt, out, out_);
-
     // Find a plan for the graph and execute it.
     auto plan = find_cudnn_plan_from_op_graph(
         encoder.device().cudnn_handle(), backend_type, dtype, *op_graph);
-    if (!plan) {
-      throw std::runtime_error("[conv] Unable to find an execution plan.");
-    }
-    auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
-    if (encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
-      conv_cache().emplace(
-          cache_key, std::make_pair(backend_type, std::move(*plan)));
-      return;
+    if (plan) {
+      // Setup inputs and outputs.
+      register_args(encoder, backend_type, in, wt, out, out_);
+
+      auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
+      if (encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
+        conv_cache().emplace(
+            cache_key, std::make_pair(backend_type, std::move(*plan)));
+        return;
+      }
     }
   }
 

mlx/backend/cuda/cudnn_utils.cpp

@@ -210,6 +210,9 @@ std::optional<cudnn_frontend::ExecutionPlan> find_cudnn_plan_from_op_graph(
     Dtype dtype,
     cudnn_frontend::OperationGraph& op_graph) {
   auto engine_configs = get_cudnn_engine_configs(backend_type, dtype, op_graph);
+  if (engine_configs.empty()) {
+    return std::nullopt;
+  }
   return find_cudnn_plan_from_engine_configs(handle, engine_configs, op_graph);
 }
 

mlx/backend/cuda/device/unary_ops.cuh

@@ -257,8 +257,8 @@ struct Round {
 struct Sigmoid {
   template <typename T>
   __device__ T operator()(T x) {
-    T y = 1 / (1 + exp(-abs(x)));
-    return (x < 0) ? 1 - y : y;
+    T y = 1 / (1 + exp(abs(x)));
+    return (x < 0) ? y : 1 - y;
   }
 };
 

mlx/backend/cuda/unary.cu

@@ -1,284 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/common/unary.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/device/unary_ops.cuh"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/dtype_utils.h"
-#include "mlx/primitives.h"
-
-#include <cooperative_groups.h>
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace cu {
-
-namespace cg = cooperative_groups;
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void unary_v(const In* in, Out* out, IdxT size) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_READS > size) {
-    for (IdxT i = index * N_READS; i < size; ++i) {
-      out[i] = Op{}(in[i]);
-    }
-  } else {
-    auto in_vec = load_vector<N_READS>(in, index);
-
-    AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_READS; ++i) {
-      out_vec[i] = Op{}(in_vec[i]);
-    }
-
-    store_vector<N_READS>(out, index, out_vec);
-  }
-}
-
-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
-__global__ void unary_g(
-    const In* in,
-    Out* out,
-    IdxT size_rest,
-    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides,
-    int ndim) {
-  auto block = cg::this_thread_block();
-  auto grid = cg::this_grid();
-  IdxT index_rest =
-      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
-  if (index_rest >= size_rest) {
-    return;
-  }
-
-  auto shape_x = shape[ndim - 1];
-  auto stride_x = strides[ndim - 1];
-  IdxT index_x =
-      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
-  auto idx =
-      elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
-  auto in_vec =
-      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
-  AlignedVector<Out, N_READS> out_vec;
-#pragma unroll
-  for (int i = 0; i < N_READS; ++i) {
-    out_vec[i] = Op{}(in_vec[i]);
-  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
-}
-
-template <typename Op, typename In, typename Out>
-constexpr bool supports_unary_op() {
-  if (std::is_same_v<Op, Abs> || std::is_same_v<Op, Negative> ||
-      std::is_same_v<Op, Sign> || std::is_same_v<Op, Square>) {
-    return std::is_same_v<In, Out>;
-  }
-  if (std::is_same_v<Op, ArcCosh> || std::is_same_v<Op, ArcSinh> ||
-      std::is_same_v<Op, ArcTanh> || std::is_same_v<Op, Erf> ||
-      std::is_same_v<Op, ErfInv> || std::is_same_v<Op, Expm1> ||
-      std::is_same_v<Op, Sigmoid>) {
-    return std::is_same_v<In, Out> && is_floating_v<In>;
-  }
-  if (std::is_same_v<Op, BitwiseInvert>) {
-    return std::is_same_v<In, Out> && std::is_integral_v<In> &&
-        !std::is_same_v<In, bool>;
-  }
-  if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor>) {
-    return std::is_same_v<In, Out> && !mlx::core::is_complex_v<In>;
-  }
-  if (std::is_same_v<Op, Conjugate>) {
-    return std::is_same_v<In, Out> && mlx::core::is_complex_v<In>;
-  }
-  if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcSin> ||
-      std::is_same_v<Op, ArcTan> || std::is_same_v<Op, Cos> ||
-      std::is_same_v<Op, Cosh> || std::is_same_v<Op, Exp> ||
-      std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
-      std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p> ||
-      std::is_same_v<Op, Round> || std::is_same_v<Op, Rsqrt> ||
-      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Sin> ||
-      std::is_same_v<Op, Sinh> || std::is_same_v<Op, Tan> ||
-      std::is_same_v<Op, Tanh>) {
-    return std::is_same_v<In, Out> && is_inexact_v<In>;
-  }
-  if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
-    return mlx::core::is_complex_v<In> && std::is_same_v<Out, float>;
-  }
-  if (std::is_same_v<Op, LogicalNot>) {
-    return std::is_same_v<In, Out> && std::is_same_v<In, bool>;
-  }
-  return false;
-}
-
-} // namespace cu
-
-template <typename Op>
-void unary_op_gpu_inplace(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  auto& in = inputs[0];
-  if (in.size() == 0) {
-    return;
-  }
-  bool contig = in.flags().contiguous;
-  bool large;
-  if (!contig) {
-    large = in.data_size() > INT32_MAX || out.size() > INT32_MAX;
-  } else {
-    large = in.data_size() > UINT32_MAX;
-  }
-
-  auto& encoder = cu::get_command_encoder(s);
-  encoder.set_input_array(in);
-  encoder.set_output_array(out);
-  dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
-    dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
-      using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
-      using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
-      if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
-        dispatch_bool(large, [&](auto large) {
-          using InType = cuda_type_t<CTYPE_IN>;
-          using OutType = cuda_type_t<CTYPE_OUT>;
-          if (contig) {
-            using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            constexpr int N_READS = 16 / sizeof(OutType);
-            auto [num_blocks, block_dims] = get_launch_args(
-                out.data_size(), out.shape(), out.strides(), large, N_READS);
-            encoder.add_kernel_node(
-                cu::unary_v<Op, InType, OutType, IdxT, N_READS>,
-                num_blocks,
-                block_dims,
-                0,
-                in.data<InType>(),
-                out.data<OutType>(),
-                out.data_size());
-          } else {
-            using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-            auto [shape, strides] = collapse_contiguous_dims(in);
-            auto ndim = shape.size();
-            int work_per_thread = 1;
-            auto kernel = cu::unary_g<Op, InType, OutType, IdxT, 1>;
-            auto dim0 = ndim > 0 ? shape.back() : 1;
-            auto rest = out.size() / dim0;
-            if (dim0 >= 4) {
-              kernel = cu::unary_g<Op, InType, OutType, IdxT, 4>;
-              work_per_thread = 4;
-            }
-            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
-            auto block_dims = get_block_dims(dim0, rest, 1);
-            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
-            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
-            encoder.add_kernel_node(
-                kernel,
-                {num_blocks_x, num_blocks_y},
-                block_dims,
-                0,
-                in.data<InType>(),
-                out.data<OutType>(),
-                rest,
-                const_param(shape),
-                const_param(strides),
-                ndim);
-          }
-        });
-      } else {
-        throw std::runtime_error(fmt::format(
-            "Can not do unary op {} on input of {} with output of {}.",
-            op,
-            dtype_to_string(in.dtype()),
-            dtype_to_string(out.dtype())));
-      }
-    });
-  });
-}
-
-template <typename Op>
-void unary_op_gpu(
-    const std::vector<array>& inputs,
-    array& out,
-    const char* op,
-    const Stream& s) {
-  set_unary_output_data(inputs[0], out);
-  unary_op_gpu_inplace<Op>(inputs, out, op, s);
-}
-
-#define UNARY_GPU(func)                                               \
-  void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
-    nvtx3::scoped_range r(#func "::eval_gpu");                        \
-    auto& s = out.primitive().stream();                               \
-    unary_op_gpu<cu::func>(inputs, out, name(), s);                   \
-  }
-
-UNARY_GPU(Abs)
-UNARY_GPU(ArcCos)
-UNARY_GPU(ArcCosh)
-UNARY_GPU(ArcSin)
-UNARY_GPU(ArcSinh)
-UNARY_GPU(ArcTan)
-UNARY_GPU(ArcTanh)
-UNARY_GPU(BitwiseInvert)
-UNARY_GPU(Ceil)
-UNARY_GPU(Conjugate)
-UNARY_GPU(Cos)
-UNARY_GPU(Cosh)
-UNARY_GPU(Erf)
-UNARY_GPU(ErfInv)
-UNARY_GPU(Exp)
-UNARY_GPU(Expm1)
-UNARY_GPU(Floor)
-UNARY_GPU(Imag)
-UNARY_GPU(Log1p)
-UNARY_GPU(LogicalNot)
-UNARY_GPU(Negative)
-UNARY_GPU(Real)
-UNARY_GPU(Sigmoid)
-UNARY_GPU(Sign)
-UNARY_GPU(Sin)
-UNARY_GPU(Sinh)
-UNARY_GPU(Square)
-UNARY_GPU(Tan)
-UNARY_GPU(Tanh)
-
-void Log::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Log::eval_gpu");
-  auto& s = out.primitive().stream();
-  switch (base_) {
-    case Base::e:
-      unary_op_gpu<cu::Log>(inputs, out, name(), s);
-      break;
-    case Base::two:
-      unary_op_gpu<cu::Log2>(inputs, out, name(), s);
-      break;
-    case Base::ten:
-      unary_op_gpu<cu::Log10>(inputs, out, name(), s);
-      break;
-  }
-}
-
-void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Round::eval_gpu");
-  assert(inputs.size() == 1);
-  const auto& in = inputs[0];
-  auto& s = out.primitive().stream();
-  if (issubdtype(in.dtype(), inexact)) {
-    unary_op_gpu<cu::Round>(inputs, out, name(), s);
-  } else {
-    // No-op integer types
-    out.copy_shared_buffer(in);
-  }
-}
-
-void Sqrt::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Sort::eval_gpu");
-  auto& s = out.primitive().stream();
-  if (recip_) {
-    unary_op_gpu<cu::Rsqrt>(inputs, out, "Rsqrt", s);
-  } else {
-    unary_op_gpu<cu::Sqrt>(inputs, out, "Sqrt", s);
-  }
-}
-
-} // namespace mlx::core

mlx/backend/metal/kernels/sort.h

@@ -19,11 +19,28 @@ METAL_FUNC void thread_swap(thread T& a, thread T& b) {
   b = w;
 }
 
+template <typename T, typename = void>
+struct Init {
+  static constexpr constant T v = Limits<T>::max;
+};
+
+template <typename T>
+struct Init<T, metal::enable_if_t<metal::is_floating_point_v<T>>> {
+  static constexpr constant T v = metal::numeric_limits<T>::quiet_NaN();
+};
+
 template <typename T>
 struct LessThan {
-  static constexpr constant T init = Limits<T>::max;
-
-  METAL_FUNC bool operator()(T a, T b) {
+  static constexpr constant T init = Init<T>::v;
+  METAL_FUNC bool operator()(T a, T b) const {
+    if constexpr (
+        metal::is_floating_point_v<T> || metal::is_same_v<T, complex64_t>) {
+      bool an = isnan(a);
+      bool bn = isnan(b);
+      if (an | bn) {
+        return (!an) & bn;
+      }
+    }
     return a < b;
   }
 };

mlx/backend/metal/kernels/unary_ops.h

@@ -309,8 +309,8 @@ struct Round {
 struct Sigmoid {
   template <typename T>
   T operator()(T x) {
-    auto y = 1 / (1 + metal::exp(-metal::abs(x)));
-    return (x < 0) ? 1 - y : y;
+    auto y = 1 / (1 + metal::exp(metal::abs(x)));
+    return (x < 0) ? y : 1 - y;
   }
 };
 

mlx/distributed/nccl/nccl.cpp

@@ -21,6 +21,9 @@
 
 namespace mlx::core::distributed::nccl {
 
+// Can be tuned with MLX_NCCL_TIMEOUT
+constexpr int nccl_timeout = 300000; // miliseconds
+
 #define CHECK_CUDA(cmd)              \
   do {                               \
     cudaError_t e = cmd;             \
@@ -181,8 +184,9 @@ inline void bootstrap_unique_id(
     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
+    // Here we want to make sure that rank 0 has enough time to bind
+    // so we will retry to connect until elapsed time exceeds nccl_timeout
+    // this is particularity important for multinode setup
 
     int sock = socket(AF_INET, SOCK_STREAM, 0);
     if (sock < 0) {
@@ -200,32 +204,41 @@ inline void bootstrap_unique_id(
     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;
+    const int timeout_ms = env::nccl_timeout(nccl_timeout);
     bool connected = false;
 
-    bool do_log = std::getenv("NCCL_DEBUG") == "INFO";
-    for (attempt = 0; attempt < max_retries; ++attempt) {
+    const char* dbg = std::getenv("NCCL_DEBUG");
+    bool do_log = (dbg && std::string(dbg) == "INFO");
+
+    auto start = std::chrono::steady_clock::now();
+    int attempt = 0;
+
+    while (true) {
+      auto elapsed_ms = std::chrono::duration_cast<std::chrono::milliseconds>(
+                            std::chrono::steady_clock::now() - start)
+                            .count();
+      if (elapsed_ms > timeout_ms)
+        break;
       if (connect(sock, reinterpret_cast<sockaddr*>(&serv), sizeof(serv)) ==
           0) {
         connected = true;
         if (do_log) {
-          std::cout << "[Rank " << rank
-                    << "] Connected successfully on attempt " << attempt + 1
-                    << std::endl;
+          std::cout << "[Rank " << rank << "] Connected successfully after "
+                    << elapsed_ms << " miliseconds" << std::endl;
           break;
         }
       }
       if (errno != ECONNREFUSED) {
         break;
       }
+      ++attempt;
       std::this_thread::sleep_for(std::chrono::milliseconds(500));
     }
 
     if (!connected) {
       std::ostringstream msg;
-      msg << "[Rank " << rank << "] connect() failed after " << attempt
-          << " retries: " << strerror(errno);
+      msg << "[Rank " << rank << "] connect() failed after " << timeout_ms
+          << " milliseconds and " << attempt << " retries: " << strerror(errno);
       close(sock);
       throw std::runtime_error(msg.str());
     }
@@ -256,7 +269,6 @@ class NCCLGroup : public GroupImpl {
 
   ~NCCLGroup() {
     ncclCommDestroy(comm_);
-    ncclGroupEnd();
     initialized_ = false;
   }
 

mlx/utils.h

@@ -165,6 +165,11 @@ inline bool enable_tf32() {
   return enable_tf32_;
 }
 
+inline int nccl_timeout(int default_value) {
+  static int nccl_timeout = get_var("MLX_NCCL_TIMEOUT", default_value);
+  return nccl_timeout;
+}
+
 } // namespace env
 
 } // namespace mlx::core

mlx/version.h

@@ -4,7 +4,7 @@
 
 #define MLX_VERSION_MAJOR 0
 #define MLX_VERSION_MINOR 29
-#define MLX_VERSION_PATCH 2
+#define MLX_VERSION_PATCH 3
 #define MLX_VERSION_NUMERIC \
   (100000 * MLX_VERSION_MAJOR + 1000 * MLX_VERSION_MINOR + MLX_VERSION_PATCH)
 

python/tests/test_blas.py

@@ -712,6 +712,15 @@ class TestBlas(mlx_tests.MLXTestCase):
             expected = beta * c + alpha * (a @ b)
             self.assertTrue(mx.allclose(expected, out))
 
+        # Test half precision
+        for t, tol in [(mx.float16, 1e-3), (mx.bfloat16, 1e-2)]:
+            c = mx.ones((32, 32)).astype(t)
+            a = mx.random.uniform(shape=(32, 32)).astype(t)
+            b = mx.random.uniform(shape=(32, 32)).astype(t)
+            out = mx.addmm(c, a, b)
+            expected = a @ b + c
+            self.assertTrue(mx.allclose(out, expected, rtol=tol, atol=tol))
+
     def test_addmm_grad(self):
         def make_ref_addmm(alpha, beta):
             return lambda c, a, b: alpha * (a @ b) + beta * c

python/tests/test_ops.py

@@ -1041,6 +1041,12 @@ class TestOps(mlx_tests.MLXTestCase):
         expected = 1 / (1 + np.exp(-a, dtype=np.float32))
         self.assertTrue(np.allclose(result, expected))
 
+        # Low precision
+        a = mx.array(-8.0).astype(mx.float16)
+        self.assertNotEqual(mx.sigmoid(a).item(), 0.0)
+        a = mx.array(8.0).astype(mx.float16)
+        self.assertNotEqual(mx.sigmoid(a).item(), 1.0)
+
     def test_allclose(self):
         a = mx.array(1.0)
         b = mx.array(1.0)
@@ -3094,8 +3100,6 @@ class TestOps(mlx_tests.MLXTestCase):
         out = mx.depends(b, c)
         self.assertTrue(mx.array_equal(out, b))
 
-
-class TestBroadcast(mlx_tests.MLXTestCase):
     def test_broadcast_shapes(self):
         # Basic broadcasting
         self.assertEqual(mx.broadcast_shapes((1, 2, 3), (3,)), (1, 2, 3))
@@ -3134,6 +3138,12 @@ class TestBroadcast(mlx_tests.MLXTestCase):
         with self.assertRaises(ValueError):
             mx.broadcast_shapes()
 
+    def test_sort_nan(self):
+        x = mx.array([3.0, mx.nan, 2.0, 0.0])
+        expected = mx.array([0.0, 2.0, 3.0, mx.nan])
+        self.assertTrue(mx.array_equal(mx.sort(x), expected, equal_nan=True))
+        x = mx.array([3.0, mx.nan, 2.0, 0.0]) + 1j * mx.array([1.0] * 4)
+
 
 if __name__ == "__main__":
     mlx_tests.MLXTestRunner()