Merge 688e421184 into bc53f8293f

* more bug fixes

* more bug fixes

* format

mlx/backend/cuda/binary.cu

@@ -101,10 +101,12 @@ constexpr bool supports_binary_op() {
     return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
   }
   if (std::is_same_v<Op, NaNEqual>) {
-    return std::is_same_v<Out, bool> &&
-        (is_floating_v<In> || std::is_same_v<In, complex64_t>);
+    return std::is_same_v<Out, bool> && is_inexact_v<In>;
   }
-  if (std::is_same_v<Op, LogAddExp> || std::is_same_v<Op, ArcTan2>) {
+  if (std::is_same_v<Op, LogAddExp>) {
+    return std::is_same_v<In, Out> && is_inexact_v<In>;
+  }
+  if (std::is_same_v<Op, ArcTan2>) {
     return std::is_same_v<In, Out> && is_floating_v<In>;
   }
   if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
@@ -150,10 +152,10 @@ void binary_op_gpu_inplace(
             auto [shape, strides] = collapse_contiguous_dims(a, b, out);
             auto& a_strides = strides[0];
             auto& b_strides = strides[1];
-            bool large = a.data_size() > UINT32_MAX ||
-                b.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+            bool large = a.data_size() > INT32_MAX ||
+                b.data_size() > INT32_MAX || out.data_size() > INT32_MAX;
             MLX_SWITCH_BOOL(large, LARGE, {
-              using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+              using IdxT = std::conditional_t<LARGE, int64_t, int32_t>;
               int ndim = shape.size();
               if (ndim <= 3) {
                 MLX_SWITCH_1_2_3(ndim, NDIM, {

mlx/backend/cuda/compiled.cpp

@@ -130,11 +130,13 @@ struct FusedKernelBuilder {
 
 constexpr const char* g_jit_includes = R"(
 #include "mlx/backend/cuda/device/binary_ops.cuh"
+#include "mlx/backend/cuda/device/ternary_ops.cuh"
 #include "mlx/backend/cuda/device/unary_ops.cuh"
 #include "mlx/backend/cuda/device/utils.cuh"
 
 #include <cooperative_groups.h>
 
+#define inf cuda::std::numeric_limits<float>::infinity()
 )";
 
 void Compiled::eval_gpu(

mlx/backend/cuda/device/binary_ops.cuh

@@ -1,6 +1,8 @@
 // Copyright © 2025 Apple Inc.
 
+#include "mlx/backend/cuda/device/cucomplex_math.cuh"
 #include "mlx/backend/cuda/device/fp16_math.cuh"
+#include "mlx/backend/cuda/device/utils.cuh"
 
 #include <cuComplex.h>
 #include <cuda/std/array>
@@ -122,6 +124,26 @@ struct LogAddExp {
         ? maxval
         : T(float(maxval) + log1p(expf(minval - maxval)));
   };
+
+  __device__ cuComplex operator()(cuComplex x, cuComplex y) {
+    if (isnan(cuCrealf(x)) || isnan(cuCimagf(x)) || isnan(cuCrealf(y)) ||
+        isnan(cuCimagf(y))) {
+      return {
+          cuda::std::numeric_limits<float>::quiet_NaN(),
+          cuda::std::numeric_limits<float>::quiet_NaN()};
+    }
+    constexpr float inf = cuda::std::numeric_limits<float>::infinity();
+    auto maxval = x > y ? x : y;
+    auto minval = x < y ? x : y;
+    if (cuCrealf(minval) == -inf || cuCrealf(maxval) == inf)
+      return maxval;
+    float m = exp(cuCrealf(minval) - cuCrealf(maxval));
+    cuComplex dexp{
+        m * cos(cuCimagf(minval) - cuCimagf(maxval)),
+        m * sin(cuCimagf(minval) - cuCimagf(maxval)),
+    };
+    return maxval + log1p(dexp);
+  }
 };
 
 struct Maximum {

mlx/backend/cuda/device/ternary_ops.cuh

@@ -1,4 +1,5 @@
 // Copyright © 2025 Apple Inc.
+#pragma once
 
 namespace mlx::core::cu {
 

mlx/backend/cuda/device/utils.cuh

@@ -187,8 +187,8 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_nd(
 template <typename IdxT = int64_t>
 inline __host__ __device__ IdxT
 elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
-  IdxT loc = elem_to_loc_nd<3>(elem, shape, strides);
-  for (int i = ndim - 1; i >= 3; --i) {
+  IdxT loc = 0;
+  for (int i = ndim - 1; i >= 0; --i) {
     loc += (elem % shape[i]) * IdxT(strides[i]);
     elem /= shape[i];
   }
@@ -202,8 +202,9 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
     const int64_t* a_strides,
     const int64_t* b_strides,
     int ndim) {
-  auto [a_loc, b_loc] = elem_to_loc_nd<3>(elem, shape, a_strides, b_strides);
-  for (int i = ndim - 1; i >= 3; --i) {
+  IdxT a_loc = 0;
+  IdxT b_loc = 0;
+  for (int i = ndim - 1; i >= 0; --i) {
     int dim_idx = elem % shape[i];
     a_loc += dim_idx * a_strides[i];
     b_loc += dim_idx * b_strides[i];
@@ -220,9 +221,10 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_4d(
     const int64_t* b_strides,
     const int64_t* c_strides,
     int ndim) {
-  auto [a_loc, b_loc, c_loc] =
-      elem_to_loc_nd<3>(elem, shape, a_strides, b_strides, c_strides);
-  for (int i = ndim - 1; i >= 3; --i) {
+  IdxT a_loc = 0;
+  IdxT b_loc = 0;
+  IdxT c_loc = 0;
+  for (int i = ndim - 1; i >= 0; --i) {
     int dim_idx = elem % shape[i];
     a_loc += dim_idx * a_strides[i];
     b_loc += dim_idx * b_strides[i];
@@ -336,4 +338,21 @@ struct LoopedElemToLoc<1, false, OffsetT> {
   }
 };
 
+inline __device__ cuComplex log1p(cuComplex in) {
+  float x = cuCrealf(in);
+  float y = cuCimagf(in);
+  float zabs = sqrt(x * x + y * y);
+  float theta = atan2f(y, x + 1);
+  if (zabs < 0.5f) {
+    float r = x * (2 + x) + y * y;
+    if (r == 0) { // handle underflow
+      return {x, theta};
+    }
+    return {0.5f * log1pf(r), theta};
+  } else {
+    auto z0 = sqrt((x + 1) * (x + 1) + y * y);
+    return {log(z0), theta};
+  }
+}
+
 } // namespace mlx::core::cu

mlx/backend/cuda/indexing.cpp

@@ -65,8 +65,8 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
   int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
 
-  bool large = (nidx > 0 && inputs[1].size() > UINT32_MAX) ||
-      (src.size() > UINT32_MAX) || (out.size() > UINT32_MAX);
+  bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
+      (src.size() > INT32_MAX) || (out.size() > INT32_MAX);
 
   uint32_t slice_size = std::accumulate(
       slice_sizes_.begin(), slice_sizes_.end(), 1, std::multiplies<uint32_t>());
@@ -88,7 +88,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
             dtype_to_cuda_type(idx_dtype),
             nidx,
             ndim,
-            large ? "int64_t" : "uint32_t"));
+            large ? "int64_t" : "int32_t"));
       }
     }
     return std::make_pair(jit_source_gather, std::move(kernel_names));
@@ -99,7 +99,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (large) {
     mod.append_arg<int64_t>(out.size());
   } else {
-    mod.append_arg<uint32_t>(out.size());
+    mod.append_arg<int32_t>(out.size());
   }
   mod.append_ndim_arg(src.shape());
   mod.append_ndim_arg(src.strides());
@@ -115,7 +115,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
       dtype_to_cuda_type(idx_dtype),
       nidx,
       idx_ndim,
-      large ? "int64_t" : "uint32_t");
+      large ? "int64_t" : "int32_t");
 
   auto& encoder = cu::get_command_encoder(s);
   for (const auto& in : inputs) {
@@ -152,14 +152,14 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   Dtype idx_dtype = nidx > 0 ? inputs[1].dtype() : int32;
   int32_t idx_ndim = nidx > 0 ? inputs[1].ndim() : 0;
 
-  bool large = (nidx > 0 && inputs[1].size() > UINT32_MAX) ||
-      (upd.size() > UINT32_MAX) || (out.size() > UINT32_MAX);
+  bool large = (nidx > 0 && inputs[1].size() > INT32_MAX) ||
+      (upd.size() > INT32_MAX) || (out.size() > INT32_MAX);
 
-  uint32_t upd_post_idx_size = std::accumulate(
+  int32_t upd_post_idx_size = std::accumulate(
       upd.shape().begin() + idx_ndim,
       upd.shape().end(),
       1,
-      std::multiplies<uint32_t>());
+      std::multiplies<int32_t>());
 
   const char* op = g_scatter_ops[reduce_type_];
   std::string module_name = fmt::format(
@@ -181,7 +181,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
             op,
             nidx,
             ndim,
-            large ? "int64_t" : "uint32_t"));
+            large ? "int64_t" : "int32_t"));
       }
     }
     return std::make_pair(jit_source_scatter, std::move(kernel_names));
@@ -192,7 +192,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (large) {
     mod.append_arg<int64_t>(upd.size());
   } else {
-    mod.append_arg<uint32_t>(upd.size());
+    mod.append_arg<int32_t>(upd.size());
   }
   mod.append_ndim_arg(upd.shape());
   mod.append_ndim_arg(upd.strides());
@@ -200,7 +200,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   if (large) {
     mod.append_arg<int64_t>(upd_post_idx_size);
   } else {
-    mod.append_arg<uint32_t>(upd_post_idx_size);
+    mod.append_arg<int32_t>(upd_post_idx_size);
   }
   mod.append_ndim_arg(out.shape());
   mod.append_ndim_arg(out.strides());
@@ -215,7 +215,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
       op,
       nidx,
       idx_ndim,
-      large ? "int64_t" : "uint32_t");
+      large ? "int64_t" : "int32_t");
 
   auto& encoder = cu::get_command_encoder(s);
   for (const auto& in : inputs) {
@@ -238,7 +238,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  bool large = idx.size() > UINT32_MAX || src.size() > UINT32_MAX;
+  bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
 
   std::string module_name = fmt::format(
       "gather_axis_{}_{}",
@@ -258,7 +258,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
               ndim,
               contiguous & 1 ? true : false,
               contiguous & 2 ? true : false,
-              large ? "int64_t" : "uint32_t"));
+              large ? "int64_t" : "int32_t"));
         }
       }
     }
@@ -283,9 +283,9 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
     mod.append_arg<int64_t>(idx_size_axis);
     mod.append_arg<int64_t>(idx_size_post);
   } else {
-    mod.append_arg<uint32_t>(idx_size_pre);
-    mod.append_arg<uint32_t>(idx_size_axis);
-    mod.append_arg<uint32_t>(idx_size_post);
+    mod.append_arg<int32_t>(idx_size_pre);
+    mod.append_arg<int32_t>(idx_size_axis);
+    mod.append_arg<int32_t>(idx_size_post);
   }
   mod.append_arg(remove_index(idx.shape(), axis_));
   mod.append_arg(remove_index(src.strides(), axis_));
@@ -302,7 +302,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
       src.ndim() - 1,
       src.flags().row_contiguous,
       idx.flags().row_contiguous,
-      large ? "int64_t" : "uint32_t");
+      large ? "int64_t" : "int32_t");
 
   auto& encoder = cu::get_command_encoder(s);
   for (const auto& in : inputs) {
@@ -337,7 +337,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
     return;
   }
 
-  bool large = idx.size() > UINT32_MAX || src.size() > UINT32_MAX;
+  bool large = idx.size() > INT32_MAX || src.size() > INT32_MAX;
 
   const char* op = reduce_type_ == ScatterAxis::Sum ? "Sum" : "Assign";
   std::string module_name = fmt::format(
@@ -360,7 +360,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
               ndim,
               contiguous & 1 ? true : false,
               contiguous & 2 ? true : false,
-              large ? "int64_t" : "uint32_t"));
+              large ? "int64_t" : "int32_t"));
         }
       }
     }
@@ -385,9 +385,9 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
     mod.append_arg<int64_t>(idx_size_axis);
     mod.append_arg<int64_t>(idx_size_post);
   } else {
-    mod.append_arg<uint32_t>(idx_size_pre);
-    mod.append_arg<uint32_t>(idx_size_axis);
-    mod.append_arg<uint32_t>(idx_size_post);
+    mod.append_arg<int32_t>(idx_size_pre);
+    mod.append_arg<int32_t>(idx_size_axis);
+    mod.append_arg<int32_t>(idx_size_post);
   }
   mod.append_arg(remove_index(idx.shape(), axis_));
   mod.append_arg(remove_index(upd.strides(), axis_));
@@ -405,7 +405,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
       idx.ndim() - 1,
       upd.flags().row_contiguous,
       idx.flags().row_contiguous,
-      large ? "int64_t" : "uint32_t");
+      large ? "int64_t" : "int32_t");
 
   auto& encoder = cu::get_command_encoder(s);
   for (const auto& in : inputs) {

mlx/backend/cuda/ternary.cu

@@ -101,10 +101,10 @@ void ternary_op_gpu_inplace(
         auto& a_strides = strides[0];
         auto& b_strides = strides[1];
         auto& c_strides = strides[2];
-        bool large = a.data_size() > UINT32_MAX || b.data_size() > UINT32_MAX ||
-            c.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
+        bool large = a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
+            c.data_size() > INT32_MAX || out.data_size() > INT32_MAX;
         MLX_SWITCH_BOOL(large, LARGE, {
-          using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
+          using IdxT = std::conditional_t<LARGE, int64_t, int32_t>;
           int ndim = shape.size();
           if (ndim <= 3) {
             MLX_SWITCH_1_2_3(ndim, NDIM, {

mlx/backend/cuda/unary.cu

@@ -27,13 +27,12 @@ constexpr bool supports_unary_op() {
       std::is_same_v<Op, ArcSin> || std::is_same_v<Op, ArcSinh> ||
       std::is_same_v<Op, ArcTan> || 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, Log1p> ||
-      std::is_same_v<Op, Sigmoid> || std::is_same_v<Op, Sqrt> ||
-      std::is_same_v<Op, Rsqrt>) {
+      std::is_same_v<Op, Expm1> || std::is_same_v<Op, Sigmoid> ||
+      std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Rsqrt>) {
     return std::is_same_v<In, Out> && is_floating_v<In>;
   }
   if (std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
-      std::is_same_v<Op, Log10>) {
+      std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p>) {
     return std::is_same_v<In, Out> && is_inexact_v<In>;
   }
   if (std::is_same_v<Op, BitwiseInvert>) {

mlx/backend/cuda/utils.cpp

@@ -31,6 +31,9 @@ const char* dtype_to_cuda_type(const Dtype& dtype) {
   if (dtype == bfloat16) {
     return "__nv_bfloat16";
   }
+  if (dtype == complex64) {
+    return "cuComplex";
+  }
 #define SPECIALIZE_DtypeToString(CPP_TYPE, DTYPE) \
   if (dtype == DTYPE) {                           \
     return #CPP_TYPE;                             \

mlx/transforms.cpp

@@ -2,6 +2,7 @@
 #include <algorithm>
 #include <deque>
 #include <future>
+#include <mutex>
 #include <numeric>
 #include <set>
 #include <sstream>
@@ -36,6 +37,42 @@ class Synchronizer : public Primitive {
   DEFINE_PRINT(Synchronize);
 };
 
+class Interrupt {
+ private:
+  static std::mutex mutex_;
+  static bool eval_running_;
+  static bool interrupt_;
+
+ public:
+  Interrupt() {
+    std::unique_lock lk(mutex_);
+    eval_running_ = true;
+  }
+
+  static bool interrupt() {
+    std::unique_lock lk(mutex_);
+    if (eval_running_) {
+      interrupt_ = true;
+      return true;
+    }
+    return false;
+  }
+
+  static bool interrupted() {
+    std::unique_lock lk(mutex_);
+    return interrupt_;
+  }
+
+  ~Interrupt() {
+    std::unique_lock lk(mutex_);
+    eval_running_ = false;
+    interrupt_ = false;
+  }
+};
+std::mutex Interrupt::mutex_{};
+bool Interrupt::eval_running_ = false;
+bool Interrupt::interrupt_ = false;
+
 // Initialize the static tracing members from transforms_impl.h
 //
 // These are used to implement the in_tracing() function the returns true if we
@@ -50,6 +87,8 @@ int detail::InTracing::grad_counter{0};
 int detail::RetainGraph::tracing_counter{0};
 
 array eval_impl(std::vector<array> outputs, bool async) {
+  Interrupt interrupt;
+
   std::deque<array> tape;
 
   // Make an effort to choose a good output stream
@@ -260,6 +299,11 @@ array eval_impl(std::vector<array> outputs, bool async) {
     if (!arr.is_tracer()) {
       arr.detach();
     }
+
+    if (Interrupt::interrupted()) {
+      synchronizer.attach_event(Event{stream});
+      break;
+    }
   }
 
   // Signal the event in its stream
@@ -274,6 +318,10 @@ array eval_impl(std::vector<array> outputs, bool async) {
   return synchronizer;
 }
 
+bool interrupt_eval() {
+  return Interrupt::interrupt();
+}
+
 void async_eval(std::vector<array> outputs) {
   if (outputs.empty()) {
     return;

mlx/transforms.h

@@ -22,6 +22,14 @@ void eval(Arrays&&... outputs) {
   eval(std::vector<array>{std::forward<Arrays>(outputs)...});
 }
 
+/**
+ * Interrupt an ongoing eval.
+ *
+ * Leaves the graph in a valid state. Returns true if an ongoing eval was
+ * interrupted and false otherwise.
+ */
+bool interrupt_eval();
+
 /**
  *  Computes the output and vector-Jacobian product (VJP) of a function.
  *

python/tests/cuda_skip.py

@@ -1,24 +1,50 @@
 cuda_skip = {
     "TestArray.test_api",
-    "TestAutograd.test_cumprod_grad",
-    "TestAutograd.test_slice_grads",
-    "TestAutograd.test_split_against_slice",
-    "TestAutograd.test_stop_gradient",
-    "TestAutograd.test_topk_grad",
     "TestAutograd.test_update_state",
-    "TestAutograd.test_vjp",
     "TestBF16.test_arg_reduction_ops",
-    "TestBF16.test_binary_ops",
     "TestBF16.test_reduction_ops",
-    "TestBlas.test_block_masked_matmul",
     "TestBlas.test_complex_gemm",
+    "TestCompile.test_compile_dynamic_dims",
+    "TestEinsum.test_ellipses",
+    "TestEinsum.test_opt_einsum_test_cases",
+    "TestLoad.test_load_f8_e4m3",
+    "TestMemory.test_memory_info",
+    "TestLayers.test_group_norm",
+    "TestLayers.test_pooling",
+    "TestLayers.test_quantized_embedding",
+    "TestLayers.test_sin_pe",
+    "TestLayers.test_upsample",
+    "TestOps.test_array_equal",
+    "TestOps.test_complex_ops",
+    "TestOps.test_dynamic_slicing",
+    "TestOps.test_softmax",
+    "TestOps.test_sort",
+    "TestOps.test_tile",
+    "TestReduce.test_axis_permutation_sums",
+    "TestReduce.test_dtypes",
+    "TestReduce.test_expand_sums",
+    "TestReduce.test_many_reduction_axes",
+    "TestUpsample.test_torch_upsample",
+    # DivMod NYI
+    "TestOps.test_divmod",
+    "TestEval.test_multi_output_eval_during_transform",
+    # Partition NYI
+    "TestAutograd.test_topk_grad",
+    "TestOps.test_argpartition",
+    "TestOps.test_partition",
+    # Block masked matmul NYI
+    "TestBlas.test_block_masked_matmul",
+    # Gather matmul NYI
     "TestBlas.test_gather_matmul",
     "TestBlas.test_gather_matmul_grad",
-    "TestBlas.test_matmul_batched",
-    "TestBlas.test_matrix_vector_attn",
-    "TestCompile.test_compile_dynamic_dims",
-    "TestCompile.test_compile_inf",
-    "TestCompile.test_inf_constant",
+    # Scan NYI
+    "TestAutograd.test_cumprod_grad",
+    "TestOps.test_scans",
+    "TestOps.test_logcumsumexp",
+    # Hadamard NYI
+    "TestOps.test_hadamard",
+    "TestOps.test_hadamard_grad_vmap",
+    # Convolutions NYI
     "TestConv.test_1d_conv_with_2d",
     "TestConv.test_asymmetric_padding",
     "TestConv.test_basic_grad_shapes",
@@ -45,11 +71,11 @@ cuda_skip = {
     "TestConvTranspose.test_torch_conv_transpose_3D",
     "TestConvTranspose.test_torch_conv_transpose_3D_grad",
     "TestConvTranspose.test_torch_conv_transpose_3d_output_padding",
-    "TestEinsum.test_attention",
-    "TestEinsum.test_ellipses",
-    "TestEinsum.test_opt_einsum_test_cases",
-    "TestEval.test_multi_output_eval_during_transform",
     "TestExportImport.test_export_conv",
+    "TestLayers.test_conv1d",
+    "TestLayers.test_conv2d",
+    "TestVmap.test_vmap_conv",
+    # FFTs NYI
     "TestFFT.test_fft",
     "TestFFT.test_fft_big_powers_of_two",
     "TestFFT.test_fft_contiguity",
@@ -59,52 +85,22 @@ cuda_skip = {
     "TestFFT.test_fft_large_numbers",
     "TestFFT.test_fft_shared_mem",
     "TestFFT.test_fftn",
-    "TestInit.test_orthogonal",
+    # Lapack ops NYI
     "TestLinalg.test_cholesky",
     "TestLinalg.test_cholesky_inv",
     "TestLinalg.test_eig",
     "TestLinalg.test_eigh",
     "TestLinalg.test_inverse",
+    "TestVmap.test_vmap_inverse",
     "TestLinalg.test_lu",
     "TestLinalg.test_lu_factor",
     "TestLinalg.test_pseudo_inverse",
     "TestLinalg.test_qr_factorization",
+    "TestInit.test_orthogonal",
     "TestLinalg.test_svd_decomposition",
+    "TestVmap.test_vmap_svd",
     "TestLinalg.test_tri_inverse",
-    "TestLoad.test_load_f8_e4m3",
-    "TestLosses.test_binary_cross_entropy",
-    "TestMemory.test_memory_info",
-    "TestLayers.test_conv1d",
-    "TestLayers.test_conv2d",
-    "TestLayers.test_elu",
-    "TestLayers.test_group_norm",
-    "TestLayers.test_hard_shrink",
-    "TestLayers.test_pooling",
-    "TestLayers.test_quantized_embedding",
-    "TestLayers.test_sin_pe",
-    "TestLayers.test_softshrink",
-    "TestLayers.test_upsample",
-    "TestOps.test_argpartition",
-    "TestOps.test_array_equal",
-    "TestOps.test_as_strided",
-    "TestOps.test_binary_ops",
-    "TestOps.test_bitwise_grad",
-    "TestOps.test_complex_ops",
-    "TestOps.test_divmod",
-    "TestOps.test_dynamic_slicing",
-    "TestOps.test_hadamard",
-    "TestOps.test_hadamard_grad_vmap",
-    "TestOps.test_irregular_binary_ops",
-    "TestOps.test_kron",
-    "TestOps.test_log1p",
-    "TestOps.test_logaddexp",
-    "TestOps.test_logcumsumexp",
-    "TestOps.test_partition",
-    "TestOps.test_scans",
-    "TestOps.test_softmax",
-    "TestOps.test_sort",
-    "TestOps.test_tensordot",
-    "TestOps.test_tile",
+    # Quantization NYI
     "TestQuantized.test_gather_matmul_grad",
     "TestQuantized.test_gather_qmm",
     "TestQuantized.test_gather_qmm_sorted",
@@ -120,12 +116,4 @@ cuda_skip = {
     "TestQuantized.test_small_matrix",
     "TestQuantized.test_throw",
     "TestQuantized.test_vjp_scales_biases",
-    "TestReduce.test_axis_permutation_sums",
-    "TestReduce.test_dtypes",
-    "TestReduce.test_expand_sums",
-    "TestReduce.test_many_reduction_axes",
-    "TestUpsample.test_torch_upsample",
-    "TestVmap.test_vmap_conv",
-    "TestVmap.test_vmap_inverse",
-    "TestVmap.test_vmap_svd",
 }

python/tests/test_losses.py

@@ -83,14 +83,14 @@ class TestLosses(mlx_tests.MLXTestCase):
                 logits, targets, reduction="mean"
             )
             expected_mean = mx.mean(expected_none)
-            self.assertEqual(losses_mean, expected_mean)
+            self.assertTrue(mx.allclose(losses_mean, expected_mean))
 
             # Test with reduction 'sum'
             losses_sum = nn.losses.binary_cross_entropy(
                 logits, targets, reduction="sum"
             )
             expected_sum = mx.sum(expected_none)
-            self.assertEqual(losses_sum, expected_sum)
+            self.assertTrue(mx.allclose(losses_sum, expected_sum))
 
             # With weights, no label smoothing
             weights = mx.array([1.0, 2.0, 1.0, 2.0])

tests/eval_tests.cpp

@@ -1,5 +1,4 @@
 // Copyright © 2023 Apple Inc.
-
 #include "doctest/doctest.h"
 
 #include "mlx/mlx.h"
@@ -91,3 +90,17 @@ TEST_CASE("test eval graph retention when not tracing") {
   CHECK(!a.has_primitive());
   CHECK(a.is_available());
 }
+
+TEST_CASE("test interrupt eval") {
+  auto x = zeros({1024}, int32);
+  for (int i = 0; i < 1000; ++i) {
+    x = x + 1;
+  }
+  std::thread t([x]() { eval(x); });
+  while (!interrupt_eval()) {
+  }
+  t.join();
+  // Check that x is not evaluated
+  CHECK(!x.is_available());
+  CHECK(array_equal(x, full({1024}, 1000, int32)).item<bool>());
+}