Fixes for transpositions and expands

mlx/backend/cuda/reduce.cu

@@ -34,7 +34,19 @@ void Reduce::eval_gpu(const std::vector<array>& inputs, array& out) {
 
   // If it is a general reduce then copy the input to a contiguous array and
   // recompute the plan.
-  if (plan.type == GeneralReduce) {
+  //
+  // TODO: Instead of copying we can use elem-to-loc to deal with broadcasting
+  //       like we do in Metal. When it comes to broadcasted reduction axes
+  //       some can be ignored eg for min/max.
+  bool broadcasted = false;
+  for (int i = 0, j = 0; i < in.ndim() && !broadcasted; i++) {
+    if (j < axes_.size() && axes_[j] == i) {
+      j++;
+    } else {
+      broadcasted = in.strides(i) == 0;
+    }
+  }
+  if (plan.type == GeneralReduce || broadcasted) {
     array in_copy(in.shape(), in.dtype(), nullptr, {});
     copy_gpu(in, in_copy, CopyType::General, s);
     encoder.add_temporary(in_copy);

mlx/backend/cuda/reduce/col_reduce.cu

@@ -104,6 +104,7 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
   loop.next(thread_y, args.reduce_shape.data(), args.reduce_strides.data());
   size_t total = args.non_col_reductions * args.reduction_size;
   if (tile_x * BN + BN <= args.reduction_stride) {
+    if (args.reduction_stride % N_READS == 0) {
       for (size_t r = thread_y; r < total; r += BM) {
         T vals[N_READS];
         cub::LoadDirectBlockedVectorized(thread_x, in + loop.location(), vals);
@@ -112,6 +113,16 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
         }
         loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
       }
+    } else {
+      for (size_t r = thread_y; r < total; r += BM) {
+        T vals[N_READS];
+        cub::LoadDirectBlocked(thread_x, in + loop.location(), vals);
+        for (int i = 0; i < N_READS; i++) {
+          totals[i] = op(totals[i], __cast<U, T>(vals[i]));
+        }
+        loop.next(BM, args.reduce_shape.data(), args.reduce_strides.data());
+      }
+    }
   } else {
     for (size_t r = thread_y; r < total; r += BM) {
       T vals[N_READS];
@@ -157,11 +168,13 @@ col_reduce_looped(T* in, U* out, const __grid_constant__ ColReduceArgs args) {
 inline auto output_grid_for_col_reduce(
     const array& out,
     const cu::ColReduceArgs& args) {
-  auto out_shape = out.shape();
+  Shape out_shape;
-  auto out_strides = out.strides();
+  Strides out_strides;
-  while (!out_shape.empty() && out_strides.back() < args.reduction_stride) {
+  for (int i = 0; i < out.ndim(); i++) {
-    out_shape.pop_back();
+    if (out.strides(i) >= args.reduction_stride) {
-    out_strides.pop_back();
+      out_shape.push_back(out.shape(i));
+      out_strides.push_back(out.strides(i));
+    }
   }
   return get_2d_grid_dims(out_shape, out_strides);
 }

mlx/backend/cuda/reduce/reduce_utils.cuh

@@ -113,7 +113,7 @@ inline void allocate_same_layout(
   auto out_strides = in.strides();
   for (auto ax : axes) {
     for (auto& s : out_strides) {
-      if (s > in.strides(ax)) {
+      if (s > in.strides(ax) && in.strides(ax) > 0) {
         s /= in.shape(ax);
       }
     }

mlx/backend/cuda/reduce/row_reduce.cu

@@ -1,5 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
+#include <numeric>
+
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/cast_op.cuh"
 #include "mlx/backend/cuda/reduce/reduce.cuh"
@@ -57,20 +59,24 @@ struct RowReduceArgs {
   }
 
   // Convert shape and strides as if in was contiguous
-  void convert_shapes_to_contiguous(
+  void sort_access_pattern(const array& in, const std::vector<int>& axes) {
-      const array& in,
-      const std::vector<int>& axes) {
     auto shape_vec = in.shape();
     auto strides_vec = in.strides();
-    size_t s = 1;
-    for (int i = in.ndim() - 1; i >= 0; i--) {
-      strides_vec[i] = s;
-      s *= shape_vec[i];
-    }
     std::tie(shape_vec, strides_vec) =
         shapes_without_reduction_axes(shape_vec, strides_vec, axes);
+    std::vector<int> indices(shape_vec.size());
+    std::iota(indices.begin(), indices.end(), 0);
+    std::sort(indices.begin(), indices.end(), [&](int left, int right) {
+      return strides_vec[left] > strides_vec[right];
+    });
+    decltype(shape_vec) sorted_shape;
+    decltype(strides_vec) sorted_strides;
+    for (auto idx : indices) {
+      sorted_shape.push_back(shape_vec[idx]);
+      sorted_strides.push_back(strides_vec[idx]);
+    }
     std::tie(shape_vec, strides_vec) =
-        collapse_contiguous_dims(shape_vec, strides_vec);
+        collapse_contiguous_dims(sorted_shape, sorted_strides);
     shape = const_param(shape_vec);
     strides = const_param(strides_vec);
     ndim = shape_vec.size();
@@ -282,7 +288,7 @@ void row_reduce_looped(
         using U = cu::ReduceResult<OP, T>::type;
 
         // Calculate the grid and block dims
-        args.convert_shapes_to_contiguous(x, axes);
+        args.sort_access_pattern(x, axes);
         dim3 grid = get_2d_grid_dims(out.shape(), out.strides());
         size_t reductions = (args.row_size + N_READS - 1) / N_READS;
         int threads = std::min(1024UL, reductions);

python/tests/cuda_skip.py

@@ -1,7 +1,6 @@
 cuda_skip = {
     "TestArray.test_api",
     "TestBF16.test_arg_reduction_ops",
-    "TestBF16.test_reduction_ops",
     "TestBlas.test_complex_gemm",
     "TestEinsum.test_ellipses",
     "TestEinsum.test_opt_einsum_test_cases",
@@ -15,8 +14,6 @@ cuda_skip = {
     "TestOps.test_dynamic_slicing",
     "TestReduce.test_axis_permutation_sums",
     "TestReduce.test_dtypes",
-    "TestReduce.test_expand_sums",
-    "TestReduce.test_many_reduction_axes",
     "TestUpsample.test_torch_upsample",
     # Block masked matmul NYI
     "TestBlas.test_block_masked_matmul",